Sc. College Journal Res.72

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Journal of Patna Science College ISSN 2347 - 9604

IMPACT OF TWO PESTICIDES ON SERUM FREE AMINO ACID POOL OF MICE:A COMPARATIVE STUDY USING THIN LAYER CHROMATOGRAPHY.

#Bipin Bihari Mishra, *Prakriti verma , #S.R.Padmadeo and #Kumud Ranjan Thakur#Post Graduate Department of Biochemistry Patna University, Patna- 800005

*Post graduate Department of Zoology, Patna University, Patna- [email protected], [email protected]

Abstract : An attempt has been made to identify and quantify the serum free amino acids in normal andpesticide exposed mice. The laboratory mice (Mus musculus) were exposed to Dimethoate (o,o-dimethyl,S-methyl-carbamoyl-methyl phosphorodithioate),an organophosphate commonly known as rogor,with a dose of 20 mg/kg body weight and an organochlorine, Endosulfan (6,7,8,9,10,10hexachloro-1,5,5a,6,9,9-hexahydro-6,9-Methano-2,4,3- benzodixathiepin-3-oxide) with a dose of 2 mg/kg bodyweight for 21 days. Every week blood were collected, centrifuged and serum were separated to estimatethe free amino acids by Thin layer chromatography. Amino acids were located on the chromatogram witha 0.01% ninhydrin solution in acetone. Each chromatogram reveals 4-5 fractions with a wide range ofamino acid such as aspartic acid, glutamic acid, serine, tyrosine, alanine, valine. The quantity and presenceof each amino acid depends on the doses and the exposure of pesticides. Other amino acids were presentin lower concentration and quantitative estimation was not possible. Finding indicates that the exposure ofpesticide brings aiteration of free amino acids content in blood.

Key words : Rogor , Endosulfan, amino acid, Thin Layer Chromatography, Mus musculus

Introduction : It is well known that pesticides are being widely used in agriculture, which has manyharmful effects on living organism. Animals in the natural environment are regularly exposed to lowconcentration of these xenobiotics, which are sub-lethal. Residual amounts of organochlorines andorganophosphate pesticides have been detected in the soil, water reservoirs, vegetables, grains and otherfood products (John et al., 2003). Man is the ultimate consumer of pesticide residues. These pesticideresidues in animal products and other food items ultimately get accumulated in the man especially in theadipose tissue, blood and. lymphoid organs. When fed to man or animals at very low doses daily formonths or years, these accumulated pesticides in body, may harm the normal functions causing variousdiseases in man and animals.The widespread use of pesticides in public health and agriculture has causedsevere environmental pollution and health hazards including cases of severe, sub- chronic and chronichuman poisoning. . Endosulfan (6,7,8,9,10,10 hexachloro-1,5,5a,6,9,9a-hexahydro-6,9-Methano-2,4,3-benzodixathiepin-3-oxide) is one of the organochlorine compound used extensively for the control ofagricultural pests. Its metabolites have strong tendencies to get accumulated in different organ and tissue ofthe body e.g. Adipose tissue and liver (Winter and Street,1992; Thao et al.,1993).Deleterious effect ofendosulfan have been studied by many researchers (Sinha et al., 1995;Choudhary et al.,2002) but itseffect on serum amino acid pool has not been studied. Similarly, Dimethoate (O,O-dimethyl S–methylcarbomylmethyl phosphodithioate),also known as Rogor , is one of the most important

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organophosphorus insecticides used frequently in agriculture. Many studies have been carried on thetoxicity of dimethoate on non-target animal like mice (Betrsian et al., 1995; Kamath et al., 2008). Hassanet al. (1994) have reported several changes in serum parameter and amino acid content in rats afterchronic sub lethal dose of dimethoate. In the present investigation, an attempt has been made to examineand compare the effect of Rogor and Endosulfan on serum free amino acid pool at different oral dose.

Materials and Methods : Test animal - Mus musculus L.(Swiss albino mice):- For the presentinvestigation adult Swiss albino mice Mus musculus were selected. Thirty female albino mice of sameage group and average weight of 23 gm were procured from research laboratory; MAHAVIR CANCERSANSTHAN, Phulwarisharif, Patna. The albino mice were housed in poly propylene cages and maintainedin controlled temperature (27degree centigrade), humidity (0.5-10%) and light cycle. They were fedwith cereal made bread and gold mohar brand animal feed manufactured by Lipton India limited companyDelhi and water ad libitum. All the experimental mice were categorized into following groups:-

Group I – Normal, Group II – Endosulfan(E) treatment(E7= treated for 7days,E14 = treatedfor 14 days and E21= treated for 21 days), and Group III – Rogor (R) treatement (R7= treated for7days, R14 = treated for 14 days and R21= treated for 21 days).

Selection of pesticides : Two pesticides of analytical grade were used, one Endosulfan or “Endocel(EC 35%)” manufactured by “Excel Industries Ltd., Ruvapari Road, Bhawanagar (Gujarat)”, andsecond Dimethoate or “Rogor (EC 30%)” manufactured by “ANU products, old Faridabad (Haryana)”.

The oral LD50 value of endosulfan for mice was calculated by standard interpolation methodwhich was 7.36 mg/kg body weight/day(EXTOXNET, 1996). The oral LD50 value of Rogor for micewas calculated by standard interpolation method which was 160 mg/kg body weight (EXTOXNET,1996). After calculating the LD50 value of endosulfan and rogor, single sublethal dose of endosulfan (2mg/kg body weight/day ) and rogor (20 mg/kg body weight/day ) were considered ,their stock solutionwere prepared in distilled water and administrated orally by gavages method for the interval of 7, 14 &21 days (3 weeks respectively). The controlled group of mice was given only normal saline.

After scheduled interval of exposure of 7th, 14th, and 21th day; the test animal were anesthesized,blood sample were collected in different vials, by puncturing ocular vein with the help of sterile syringe.Blood were extracted and refrigerated at -20oC in sterilized paraffin covered tubes for amino acid andbiochemical analysis. Serum was analyzed for quantitative estimation of Total protein respectively.

Methods for Amino Acids Estimation : Amino acid profile in the blood serum of Mus musculuswere assayed using Thin layer chromatography to arrive at Rf (Retardation factor) values of standardamino acid calculated thus:-

R = (Wilson and Walker, 2005)

Free amino acid were determined by the method of Moore and Stein (1954) using Thin LayerChromatographic and quantifie d on Systronic UV-Spe ctrophotome ter (UV-U75-Spectrophotometer) at 570 nm.

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Amino acid standard preparation : Stock solution of lysine, glutamine, methionine and other aminoacid were prepared by dissolving the proper amount of each amino acid in deionised water. Each stocksolution was then used to prepare working solution from which a calibration curve was constructed.

Preparation of serum for TLC : A collected blood sample were centrifuged at 3000 rpm for 10 minat 4°C to obtain serum that was then deproteinized with sulphosalicylic acid and centrifuged .Thesupernatant was stored at low temperature pending analysis of amino acid.

Qualitative Analysis of free amino acids of the blood serum of normal and pesticide treatedMus musculus was done using Thin Layer chromatography (TLC).

Process : The TLC sheets were activiated by heating in an oven for 30 minute at 1000- 1200 C. A lineis drawn three centimeter above the bottom and then 8-10 approximately equal small spots applied at2cm intervals on silica gel coated 6 of TLC aluminium sheets No-1.05554.0007 purchased fromMerck KGa A 64271 Darmstadt, Germany Tel= 49(0) 615172-2440,www.merck.de. Using amicropipette, the prepared test solution and standard is taken and lightly dotted a small amount on eachpencil marks. The sheet is then left for sometime or wafted swiftly over a blue flame to speed evaporation.This process is repeated 25 to 30 times, applying at the same area, to build up a concentration.

Using n - butanol : acetic acid : water in the ratio of (4 : 1 : 5 ) as elutant, a 0.1% ninhydrinin acetone as spraying reagent. The process has been done after approximate 3-6 hrs. Each amino acidwas detected through purple colour spots by heating the sheets at 1100 C for 15 minutes and then theRf value were calculated. Each Chromatogram reveals 4-5 fractions with a wide range of amino acidsuch as aspartic acid, glutamic acid, serine, tyrosine, alanine, methionine, valine. The spots were identifiedby comparing it with the Rf value of standard amino acids.

To quantify the amount of amino acid in each spot after chromatography, the sheets weresprayed with ninhydrin to identify the spots. The position of corresponding spot in the sheet werescrapped off and then taken in a test tube adding 5 ml of acetone to it. Then 2 ml. of 1% ninhydrinsolution were added. The tube were placed on a water bath for 20 minutes, full colour were developedat the end of this period. The coloured solution was transferred to measuring cylinder (10 ml) made tovolume and read on Systronic UV Spectrophotometer (UV-U75-Spectrophotometer) at 570 nm withthe help of cuvette.. Reading was compared with reading for known solution of amino acids treated insimilar manner. Using lysine (1.13mg./ml) run for comparison.Spectrophotometric reading of aminoacid present in each spot of chromatogram were taken to know their quantity. The quantity and presenceof each amino acids depends on the doses and the exposure of pesticides. Other amino acids werepresent in lower concentration and quantitative estimation was not possible.

Methods For Biochemical Analysis : All the biochemical assessments have been done for normal/control, Rogor and Endosulfan treated mice Mus musculus, 6 independent observation have beentaken in each groups.

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Total protein level was estimated according to the method of Lowery et al (1951) using bovine serumalbumin as standard .Blood was centrifuged to separate serum at 3000 rpm.

Proteins are the polymer of amino acids. Protein are constituents of muscle , enzyme ,hormonesand several other key functional and structural entites in the body. They are involved in the maintenanceof the normal distribution of water between blood and the tissues. Consisting mainly of albumin andglobulin the fractions vary independently and widely in disease. Increased levels are found mainly indehydration. Decreased levels are found mainly in malnutrition, impaired synthesis, and protein lossesas in hemorrhage or excessive protein catabolism. Estimation of total protein was done to know theimpact of alteration of serum amino acid pool on serum total protein.

Biochemical analysis was done in BT- 260 plus Semi-Automatic Chemistry Analyzer,manufactured by Nanchang Biotech A&C Biotechnical Industry Co. China.

Six observations have been taken in each case, then Mean and Standard deviation is calculated by theformula. The ‘t’ test have been applied through standard biostatistical formula by considering mean ofnormal Mus musculus as standard mean and comparing individual mean of different doses and durationof Endosulfan and Rogor exposures to their respective control mean. After applying ‘t’ test the calculatedvalues were referred to fisher’s table to see level of significance at (P<0.05) and (P<0.01).

Observation

Amino Acid Analysis : For biochemical impact of pesticide, that is, Endosulphan and Rogor onanimal amino acid analysis have been done through TLC and spectrophotometer were done andobservation was done.

Mice

Rogor Treated (dose 20 mg/kg body weight) : Valine increase more than normal in R7 and thendisappear from serum same is the case with glutamic acid. Tyrosine was detected in control, R7 andR21 except R14.Aspartic acid appear in control and R7 only .Methionine, alanine and serine abruptlyappear only in R21only. (Text graph 1, 2, 3)

Endosulphan Treated (dose of 2 mg/kg body weight) : Amino acid Valine, serine and Glutamic acidwhich was present in control in high quantity was absent in all the treated case thus loss of amino acidin serum. Aspartic acid was detected in high concentration in control but it decreased from E7 TO E21.(Text graph1, 2, 3).

There is a few serum free amino acid in case of endosulfan treated group as compared to rogortreated group.(Table II,III,IV)

Blood Serumanalysis For biochemical impact of Endosulfan and Rogor treatment on Mus musculus,Total protein test of blood have been done. (Table I)

Total protein test have been done for control, Endosulfan and Rogor treated (dose and durationdependent) Mus musculus independently. After applying‘t’ test the calculated values were referred to

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fisher’s table to see level of significance at (P<0.05) and (P<0.01).Where * = significant at (P<0.05)and ** = significant at (P<0.01).

The observed serum Total protein of control, Endosulfan and Rogor treated Mus musculushave been shown in the following next tables –

TABLE – IShowing Total Protein in blood of control, Endosulfan and Rogor treated Mus musculus.

TABLE -IIShowing amino acids in blood serum of control, Rogor and endosulfan treated Mus musculus after

7 days, [taking lysine as standard.]

O.D = Optical density at 570 nm, Rf = Retardation factor

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TABLE -III

Showing amino acids in blood serum of control, Rogor and endosulfan treated Mus musculus after14 days, [taking lysine as standard.]


TABLE -IV

Showing amino acids in blood serum of control, Rogor and endosulfan treated Mus musculus after 21days, [taking lysine as standard.]

S .No

AMINO AC ID

C ONT ROL R OGER ENDOSU LPH AN R f VALUE

OD Mean±

S .D.

R f VALUE

OD

C oMean± S.D.

R f VALUE

OD Mean±

S .D.

1 Alanine - - - - - - - - - 2 Asparti

c a cid 0.23 0.2

18 5.955±0.013 - - 0.23 0.1

293.515±0.011

3 Glutamic a cid

0.32 0.150

4.108±0.004 - - - - - -

4 Glycine - - - - - - - - -

5 Methionine

- - - - - - - - -

6 Serine - - - - - - - - -

7 Tyros ine 0.47 0.101

2.752±0.009 - - - - - -

8 Val ine 0.60 0.094

2.565±0.009 - - - - - -

S.No

AMINO ACID

CONTROL ROGER ENDOSULPHANR f VALUE

OD Mean±

S.D.

R f VALUE

OD Mean±

S.D.

R f VALUE

OD Mean±

S.D.

1 Alanine

- - - 0.39 0.069

1.861±0.020 - - -

2 Aspartic acid

0.23

0.218

5.955±0.013 - - - 0.25 0.035

2.167±0.017

3 Glutamic acid

0.32

0.150

4.108±0.004 - - - - - -

4 Glycine - - - - - - - - - 5 Methio

nine - - - 0.58 0.0

802.174±0.013 - - -

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6 Serine

- - - 0.27 0.154

4.211±0.005 - - -

7 Tyrosine 0.47

0.101

2.752±0.009 0.45 0.085

2.315±0.008 - - -

8 Valine

0.60

0.094

2.565±0.009 - - - - - -


TEXT GRAPH 1

TEXT GRAPH 2

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TEXT GRAPH 3

Discussion & Conclusion : Mus musculus is the animal model which has been included in this studyto know the effect of same pesticide in mammal as well as in human who consume fishes as well asother pesticide affected agro-products because mice indirectly represent human. The central theme ofpresent investigation is to evaluate the exact comparative toxicity of endosulfan and rogor on Musmusculus based on their serum biochemical test exclusively serum amino acid analysis through TLC&Total protein of serum. Various parameters have witnessed significance changes upon different dosesand duration of pesticides treatment.

In case of mice, 4 amino acid (Val,Tyr,Asp,Glu) were detected in good concentration in serumwhich goes on decreasing on 7th day and 14th day while on 21 day there were increase in concentrationof amino acid in all treatment, but there is no rise of additional amino acid. The decrease is more in caseof endosulfan than rogor treated mice. The decrease in Free Amino Acid (FAA) is due to their utilizationfor new protein synthesis or for production of energy to cope up with prevailing toxic condition due tointoxicant induced stress (Wilson and Poe,1974;James et al,1979) while increase in the concentrationof free amino acid, as seen in rogor treated group, can result from an acceleration of protein catabolismor an inhibition of protein synthesis, or both (Chung et al.,1954) thereby indicating that pesticide caninterfere in translation process. It is clearly observed that amino acid such as valine and glutamic acidappear in control and short day exposure but on long exposure it was absent. Aspartic acid level alsodecreases significantly on long exposure to pesticide. Some amino acid which was absent in controlappear abruptly in serum on long exposure. It was also seen that endosulfan dose is more harmful. (Text graph 1,2,3)

In case of mice Total protein start shooting up gradually from 7th day of treatment and reachesits peak upto 12 days treatment on endosulphan treatment. . However, on Rogor treatment, after 7days Total protein shows slight elevation but it is not as sharp as in endosulphan. But in all cases, it ishigher than the control, signifying toxic status of mice. The decrease trend of protein content may bedue to metabolic utilization of ketoacids to gluconeogenesis pathway for synthesis of glucose or due to

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directing FAA for synthesis of necessary protein for maintenance of osmotic and ionic regulation instress condition (Schmidt 1975) while increase in protein content could stimulate protein synthesis ordetoxification enzymes at the expense of glycogen to meet additional requirement in synthesis activity torevive the body from toxic stress(Susan et al.,2010),(Table I), utilizing serum free amino acids.

Regarding serum free amino acid (FAA), similar finding in increase in FAA in fish and albinorat during fenvalerate intoxication has been reported by Radhaiah(1988) and Lakshmi(1989).

A perfect correlation among biochemical findings have clearly shown that Endosulfan and Rogorcauses biochemical abnormalities in concerned tissues but dose of Endosulfan produce more seriousbiochemical abnormalities in body thus Endosulfan produce far more toxic effect as compared to rogor.

Therefore, present investigations strongly supports the reports revealing the toxicities induceddue to pesticides and helps us to be aware of the fact that we are definitely getting exposed to thedeadly chemicals and letting them persist in our body.

Acknowledgement : Authors are thankful to Department of Biochemistry and Department of Zoologyfor providing infrastructural facilities.

References:

Betrosian,A.,Balla,M.,Kafiri,G.,Kofinas,G.,Makri,R.and Kakouri,A. (1995) : Multiple systems organfailure from organophosphate poisoning.,J.Toxicol.Clin.Toxicol.,33(3),257-260 .

Choudhary,N. and Joshi, S.C (2002) : Effect of short term endosulfan on hematology and serumanalysis of male rat. Ind. J. Toxicol., 9(2), 83-87.

Hassan,A.A.,Minatogawa,Y.,Hirai,T. and Kido,R. (1994): Changes of some serum parametersand amino acids content in rats after chronic sublethal doses of dimethoate.,Arch.Environ.Contam.

Toxicol.,27(2),256-259.

James, J.H, Ziparo ,V., Jeppsson ,B.and Fischer, J.E. (1979) : Hyperammonemia, plasma amino acidimbalance and blood brain aminoacid transport: A unified theory of portal systemicencephalopathy. Lancet, 2, 772-775.

John,S., Kale,M., Rathore,N. and Bhatnagar,D. (2001): Protective effect of vitamin E in dimethoateand malathion induced oxidative stress in rat erythrocytes.,J.Nutr.Biochem.,12,500-504.

Kamath,V.Joshi,A.K.R.and Rajini,.S. (2008) : Dimethoate induced biochemical perturbation in ratpancreas and its attenuation by cashew nut skin extract.,J.Nutr.Biochem.,90,58-65.

Lakshmi Rajyam C. (1989) : Evaluation of toxic effects of a synthetic pyrethroid fenvalerate in albinorat. Ph.D. thesis, S.V. University,Tirupati, India. .

Lowry, Ï. H.,Rosebrough, Í.J., Farr, A. L. and Randall,R.J. (1951) : Protein measurement with folin-phenol reagent. J. Biol. Chem., 193, 265.

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Moore, S., Stein, W.H. (1954) : A modified ninhydrin reagent for the photometric determination ofamino acids and related compounds. The Journal of Biological Chemistry., 211, 907 913.

Radhaiah, V. (1988) : Studies on the toxic impact of a pyrethroid insecticide fenvalerate on somemetabolic aspects and histopathology of a fresh water teleost Tilapia mossambica (Peters).Ph.D. thesis, S.V. University, Tirupati, India.

Schmidt,E., Schmidt, F.W. (1973) : Gamma glutamyl transpeptidase. Dtsch. Med. Wschr., 98, 1572-1577.

Sinha,N.,Narayan,R.and Saxena, D.K. (1995) : Endosulfan induced biochemical changes in the testisof rat.,Vet. Hum. Toxicol.,(37),547-549.

Susan,T.A., Sobha, K., Veeraiah, K. and Tilak, K.S (2010) : Studies on biochemical changes in thetssue of Labeo rohita and Cirrhinus mrigala exposed to fenvalerate technical grade.,Journalof Toxicology and Environmental Health science.,2(5),53-62.

Thao,V.U.D., Kawano M. and Tatsukawa, R. (1993) : Persistant organochlorine residue in soils fromtropical and sub-tropical Asian countries.,Environ.Pollut.,81,61-71.

Wilson,K. and Walker,J. (2005) : Principle and Techniques of Biochemistry and Molecular biology,Cambridge University Press, New York,6,546

Wilson RP, Poe, W.E. (1974) : Nitrogen metabolism in channel catfish Ictalurus punctatus: Relativepool sizes of free aminoacids and related compounds in tissues of cat fish. Comp. Biochem.Physiol., 48, 545-556.

Winter,s.and Street, B. (1992) : Organochlorine compounds in the three steps terrestrial foodchain.,Chemosphere.,24,1706-1774.

Wu, C. and Bollman, J.L. (1954) : Effect of ethionine on the free amino acids in the rat. J.Biol.Chem.,673-680.

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MICROBIAL PRODUCTION OF CITRIC ACIDKumar Pranay and S.R.Padmadeo

Department of Biochemistry, Patna University, Patna-800 005

Abstract : The selected fungus was cultured for the quantitative production of citric acid by using standardand modified Doegler and Prescott broth for 7 days. In the normal Doeglers and Prescott broth the fungusproduced highest yield of citric acid (3.252g/ml) after 6 days of incubation with corresponding biomass(3.303g/ml). It was observed that increase in the biomass was proportional to the citric acid produced. Itwas observed that culture containing xylose as carbon source produced highest yield of citric acid (2.650g/ml) with biomass 0.126g/ml .The lowest production was observed in culture containing sugar beet (0.396g/ml) as carbon source having biomass 0.788g/ml. It was observed that the culture containing yeast extractas nitrogen source produced highest yield of citric acid (2.760g/ml) with biomass 2.210g/ml.Culturecontaining casein gave the lowest yield (0.215g/ml) of citric acid with biomass 4.169g/ml. The productionof citric acid reduced drastically on increasing the concentration of MgSO4.7H2O in the broth.

Key words : Microbial production, citric acid

Introduction : Citric acid (2-hydroxy-propane-1, 2, 3-tricarboxylic acid) derives its name from the Latinword citrus, a tree whose fruit is like the lemon. Citric acid is a tricarboxylic acid with a molecular weightof 210.14 g/mol, which contains three carboxylic functional groups with three different values of pKa (3.1,4.7, and 6.4). It is a primary metabolic product formed in the tricarboxylic acid (or Krebs) cycle and isfound in small quantities in virtually all plants and animals, being isolated from lemon juice in 1784.

Wehmer was the first to demonstrate that Citromyces (now Penicillium) accumulated citricacid in a medium containing sugar and inorganic salts (Wehmer, 1893). Since then, many organismshave been found to accumulate citric acid: A. niger, Aspergillus awamori, Aspergillus nidulans,Aspergillus fonsecaeus, Aspergillus luchensis, Aspergillus phoenicus, Aspergillus wentii, Aspergillussait oi, Aspergillus flav us, Absidiasp., Acremonium sp., Botrytis sp., Eupenicillium sp.,Mucorpiriformis, Penicillium janthinellum, Penicillium restrictum , Talaromyces sp., Trichodermaviride and Ustulina vulgaris (Papagianni et al.,2007).

About 99% of world production of citric acid occurs via microbial processes, which can becarried out using surface or submerged cultures. The product is sold as an anhydrous or monohydrateacid and about 70% of total production of 1.5 million tons per year (Lancini,2008) is used in food andbeverage industry as an acidifier or antioxidant to preserve or enhance the flavors and aromas of fruitjuices, ice cream, and marmalades. 20% is used, as such, in the pharmaceutical industry as antioxidantto preserve vitamins, effervescent, pH corrector, blood preservative, or in the form of iron citrate as asource of iron for the body as well as in tablets, ointments and cosmetic preparations.

The accumulation of citric acid is strongly influenced by the composition of the medium, especiallyin submerged fermentation processes. It was shown that the factors mainly affecting the citric fermentationare the type and concentration of carbon source, nitrogen and phosphate limitation, pH, aeration, oligoelements concentration, and morphology of the producing microorganism. Certain nutrients have to be

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in excess (such as sugars, protons or oxygen), other at limiting levels (such as nitrogen and phosphate)and others below well-established threshold values (such as trace metals, particularly manganese).

The carbon source for citric fermentation has been the subject of many studies, especially regardingthe use ofpolysaccharides. In general, only the sugars that are quickly assimilated by the microorganismallow high final yield of citric acid (Mattey, 1999)). In general, sucrose is preferable to glucose (Gupta etal., 1976, Hossain et al., 1984, Kubeic et al., 1989), as A.niger has an extracellular mycelium-boundinvertase that is active at low pH. The most widely used carbon sources in industrial fermentations areglucose syrups from starch hydrolysis, sugar beet molasses and low quality-sugarcane byproducts that, ingeneral, are contaminated by high levels of cations from previous processes. Cations usually come frominsoluble residues formed by precipitation with potassium ferrocyanide. Due to the complexity of thesepretreatments, a lot of research has been conducted using refined sugars, mainly glucose or sucrose.

The concentration of carbon source is also crucial for citric fermentation. The final yield of citricacid increases with initial sugar concentration in batch processes or glucose feeding rate in chemostat,while the specific growth rate has an opposite behaviour (Honecker et al., 1989, Papagianni et al.,1999, Shu et al., 1948).

Some complex media (such as molasses) are rich in nitrogen and rarely need to be supplementedwith a nitrogen source. The highly-pure media used in laboratory scale research are usually supplementedwith ammonium salts, particularly ammonium nitrate and sulfate, which in turn leads to a decrease in pHthat favors fermentation (Mattey,1999). Other sources of nitrogen such as urea and yeast/malt extracthave also been employed successfully.

The pH of the medium is important in two stages of the process. All fermentations start from sporesand their germination requires pH > 5. The absorption of ammonia by germinating spores causes release ofprotons, thus lowering the pH and improving the production of citric acid. The low pH value during theproduction phase (pH > 2) reduces the risk of contamination by other microorganisms and inhibits theproduction of unwanted organic acids (gluconic and oxalic acids), which makes the product recovery easier.

A. niger requires certain trace metals for growth (Mattey, 1999). However, a limitation byother trace elements is necessary for citric acid production (Shu et al., 1948), especially in the submergedfermentation. The metals that should be in limiting concentrations are Zn, Mn, Fe, Cu and heavy metals

Methods: The media for isolation of fungi included potato dextrose agar and Sabourouad agar

obtained from different commercial sources (Himedia and E.Merck)10g of soil obtained from 4 differentherbal drug companies of India and one prepared with locally available raw was suspended in 90ml ofsterile normal saline (0.87% w/v). The samples were shaken vigorously using orbital shaker (CertomatWR, B.Barun) for one hour followed by serial dilution to 10-6, 0.1ml of selected spreaded on differentagar media (in triplicate) by spread plate technique for assessment of microbial load. The plates wereincubated at 35±2ºC for 48hrs. Colony forming units (log10 CFU)/g of each sample was calculated forassessing the microbial load (average of three plates).

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Identification of microorganisms: The parameter for identification of predominantly obtainedmorphotypes of fungi in soil samples were as described in Manual of soil fungi (Gilman, 1975).

Quantitative estimation of citric acid : For citric acid production the Doegler & Prescott brothswere inoculated with fresh culture of A.foetidus and incubated at 35°C for 24, 48, 72, 96, and 120hrs.After incubation the culture containing the citric acid was filtered with the help of filter paper. Thefiltrate was collected in a flask and known volume was used estimate the citric acid quantitatively. Thebiomass was collected in filter paper and was dried at 37°C to measure the dry mass. 50ml of thefiltrate was mixed with equal volume of Ca (OH)2 and kept at room temperature for 2 to3 hours.Precipitate of calcium citrate was collected and equal volume of 1N H2SO4 was added in it and againkept at room temperature for 2 to 3 hours. The supernatant, containing the citric acid was collected inanother plate and dried in hot air oven at 60° to 70° C to form the citric acid crystals. Lastly the weightof crystal was calculated (Fig 1).

Effect of carbon sources on production: To see the effect, six different flasks of Doeglers andPrescott broth containing lactose, dextrose, and xylose, juice of sugar beat, sugar cane and grape asthe carbon source were prepared. Fresh cultures of the isolates were inoculated in the medium andincubated at 35°C for 144 hours.After incubation the samples were collected to estimate the citric acidand biomass production (Fig 2).

Effect of nitrogen sources on production: In Doeglers and Prescott broths, urea, yeast extract,casein and peptone were added in separate flasks instead of ammonium nitrate. The modified mediumwas inoculated with 48 hrs old culture and incubated for 144 hrs at 35°C.Then the samples werecollected to estimate the amount of citric acid and biomass produced (Fig 3).

Effect of trace elements on production: This was done by preparing three different flasks of Doeglers andPrescott broth containing no magnesium sulphate, 4 times of original amount and 8 times of original amount.

The medium was inoculated with 144 hours old culture and incubated at 35°C for 48 hours.Then the samples were collected to estimate the amount of citric acid and biomass produce (Fig.4).

Results: The isolated fungus was cultured for the quantitative production of citric acid by using standardand modified Doegler and Prescott broth for 7 days. In the normal Doeglers and Prescott broth thefungus produced highest yield of citric acid (3.252g/ml) after 6 days of incubation with correspondingbiomass (3.303g/ml). It was observed that increase in the biomass was proportional to the citric acidproduced. The isolated fungus was cultured in modified medium containing lactose, dextrose, andxylose and fruit juices like sugar cane, sugar beat and grape. It was observed that culture containingxylose as carbon source produced highest yield of citric acid (2.650g/ml) with biomass 0.126g/ml .Thelowest production was observed in culture containing sugar beet (0.396g/ml) as carbon source havingbiomass 0.788g/ml. The effect of nitrogen source was monitored on the production of citric acid. It wasobserved that the culture containing yeast extract as nitrogen source produced highest yield of citric acid(2.760g/ml) with biomass 2.210g/ml. culture containing casein gave the lowest yield (0.215g/ml) of citric

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acid with biomass 4.169g/ml. The effect of MgSO4.7H2O was monitored it was observed that productionof citric acid reduced drastically on increasing the concentration of MgSO4.7H2O in the broth.

Discussion: In this work, fungus was isolated from soil and agro- waste. The isolated fungus wasidentified on the basis of morphological characteristics. The isolate produced highest amount of citricacid after 144 hrs of incubation. Reduction was observed in the citric acid production as the incubationtime increased, this may be due to the over growth of mycelium which resulted in increased viscosity ofthe medium (Mattey and Allan, 1990). Here, the influence of carbon and nitrogen sources on the citricacid by the isolates was investigated. It was found that citric acid production varies greatly amongdifferent carbon and nitrogen sources (Ikram- ulhaq et al, 2002).The accumulation of large amounts ofcitrate by the filamentous fungus A.foetidus is known to depend on Mn2+ion(Rohr et al., 1993).

Our work also supports the hypothesis that relief to citrate inhibition of phosphofructokinase isa major event related to manganese deficiency stimulation of acidogenesis in A.foetidus. The citric acidcontent of commercially available lemonade and other juice products vary widely (Kristina l.Pennistonet al., 2009) and similar results were obtained.

Conclusion: Citric acid production shows great variation under different growth conditions with varyingcarbon and nitrogen sources. Biomass production is also variable and the production is influenced bytrace elements.

Fig.1. Citric Acid and Biomass produced at different time intervals

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Fig.2. Effect of different carbon sources on citric acid and biomass production

Fig.3. Effect of different nitrogen sources on citric acid and biomass production

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Fig.4. Effect of Magnes ium Sulphate on citric acid and biomass production

References:

Gupta, JK, Heding LG and Jorgensen OB (1976). Effect of sugars, hydrogen ion concentration andammonium nitrate on the formation of citric acid by Aspergillus niger.ActaMicrobiologicaAcademiaeHungaricae23: 63–67.

Haq I Kram, Ali Sikander, Qadeer MA and Iqbal Javed(2002).Citric acid fermentation by mutantstrain of Aspergillus niger GCHC-7 using Molasses based medium.Electronic Journal ofBiotechnology5:121-125.

Honecker, S, Bisping B, Yang Z and Rehm HJ (1989). Influence of sucrose concentration andphosphate limitation on citric acid production by immobilized cells of Aspergillus niger. AppliedMicrobiology and Biotechnology31: 17–24.

Hossain M, Brooks JD and Maddox IS (1984). The effect of the sugar source on citric acid productionby Aspergillus niger. Applied Microbiology and Biotechnology19:393 397.

Kubicek C.P, Röhr M (1989).Citric acid fermentation. Critical Reviews in Biotechnology4: 331–373.

Lancini, G (2008). Parte I – L’usoindustrialed eimicrorganismi. Storia e campi di applicazione. In:Donadio, S.; Marino, G. (eds.). Biotecnologie Microbiche, Casa Editrice Ambrosiana Milan,P: 5-35.

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Mattey M and Allan A (1990).Glycogen accumulation in Aspergillus niger. Transient BiochemicalSolicitis 8:1020-22.

Mattey M (1992). The production of organic acids.Critical Reviews in Biotechnology12: 87–132.

Papagianni M (2007). Advances in citric acid fermentation by Aspergillus niger: Biochemical aspects,membrane transport and modelling. Biotechnology Advances25: 244-263.

Papagianni M, Mattey M, Berovic M and Kristiansen B (1999). Aspergillus nigermorphology andcitric acid production in submerged batch fermentation: effects of culture pH, phosphate andmanganese levels. Food Technology and Biotechnology37: 165–171.

Papagianni M, Mattey M, Kristiansen B (1999). Hyphal vacuolation and fragmentation in batch andfed-batch culture of Aspergillus niger and its relation to citric acid production. ProcessBiochemistry35: 359–366.

Papagianni M, Mattey M, Kristiansen B (1999). The influence ofglucose concentration on citric acidproduction and morphology of Aspergillus niger in batch and fed-batch culture. EnzymeandMicrobial Technology25: 710–717.

Penniston L Kristine, Nakada Y Stephen, Holmes P Assimo and G Dean(2009). Quantitative Assessmentof Lemon Juice, Lime Juice and Commercially available Fruit Juice Products.Journal ofEndurology22:567-570.

RöhrM, Kubicek CP, Zehentgruber O and Orthofer R (1993). Accumulation and partial reconsumptionof polyols during citric acid fermentation by Aspergillus niger. Applied Microbiology andBiotechnology27:235–239.

Shu, P and Johnson MJ (1948). Citric acid production by submerged fermentation with Aspergillusniger. Industrial & Engineering Chemistry 40:1202–1205.

Shu, P and Johnson MJ (1948). The interdependence of medium constituents in citric acid productionby submerged fermentation. Journal of Bacteriology54: 161–167.

Wehmer C (1893). Note sur la fermentation.9:728.

ATCC 9142 from a treated ethanol fermentation co-product using solidstatefermentation. Letters inApplied Microbiology48: 639-644.

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Journal of Patna Science College ISSN 2347 - 9604Vol. 1, 19 - 36 [2013]

BIOM AS S AN D NUTRIEN T DYNAM ICS OF FREE FLOATIN GMACROPHYTES OF BARAILA WETLAND, VAISHALI

Shardendu*1, D. Sayantan2 and S. R. Padmadeo3

*Corresponding Author1. Associate Professor, Laboratory of Environment and Biotechnology, Department of Botany, Patna

Science College, Patna University, Patna.Email: [email protected]; Contact No. 9473240391

2. Junior Research Fellow (U.G.C.), Laboratory of Environment and Biotechnology, Department ofBotany, Patna University, Patna.Email: [email protected]

3. Professor and Head, Department of Biochemistry, Patna University, Patna.

Abstract : The present study reports the variation of biomass and nutrient concentration offree floating aquatic macrophytes like Eichornia crassipes, Lemna minor, Azolla pinnata andUtricularia flex uosa at monthly intervals for one year (February 2010 – February 2011), andvariation their seasonal and annual net primary productivity in Baraila wetland (25°76’N and85°56’E), Vaishali district, Bihar, India. Since, E. crassipes was present in the wetland throughoutthe year, it constituted 51.8% of total annual biomass. L. minor was also reported throughoutthe year with maximum biomass of 2.36±0.5 g m-2 . Among the above species, E. crassipes wasrecorded with the highes t net primary productivity (662.07 g m-2 season -1 ) in the rainy monthsof 2010, followed by U. flexuosa (112.8 g m-2 season -1 ) in the same month. The higher amountof N and K were determined in L. minor and A. pinnata and the order of fall of nutrientconcentration was N >K>Ca>P>Mg>Na. There was little change in the order of nutrientconcentration of E. crassipes , and higher amount of K and Ca was measured, which graduallydecreased in the order of K>Ca>N>Mg>Na>P. In U. flexuosa , the order of decrease in thenutrient concentration was followed as N>K>Na>Ca>Mg>P. Variation in the change of nutrientsand biomass were jus tified statistically by ANOVA and the relationship between accumulationof nutrients in plant tis sues with water nutrients and biomass was analysed by the two factorRegression Analysis.

Key Words: biomass, primary productivity, nutrients, free floating aquatic macrophytes.

1. Introduction : Plants harvest solar energy in turn, involved in production of plant biomass. Aquaticecosystems are the best sites for studying the energy flow through primary producers (Shardendu andAmbasht, 1991). Among primary producers, macrophytic communities are more productive per unitarea when compared with the phytoplankton communities (Westlake, 1963) but aquatic macrophytes

20


may be insignificant in the deep water lakes. They show luxuriant growth in water bodies of shallowbasin (Westlake, 1965) and belong to most productive biotopes on earth (Wetzel, 1975a). The aquaticmacrophytes may contribute to primary production by detritus formation (Adams and McCracken,1974), acting as nutrient source (Carignan and Kalff, 1980) and serving as substrate for other organismssuch as periphytic algae, bacteria and macrofauna.

The productivity of aquatic macrophytes in turn depends on the available nutrients ofgrowing medium (Irfan and Shardendu, 2009; Shardendu and Ambasht, 1991). Different nutrientelements and heavy metals often become the limiting factors affecting the aquatic ecosystemfunctioning (Shardendu et al., 2003; Azaizeh et al., 2006; Sayantan and Shardendu, 2013). Theemergents differ from other aquatic macrophytes by obtaining their nutrients almost completelyfrom the soil. Nutrient dynamics of emergent macrophytes have been well studied by variousresearchers (Westlake, 1965; Sahai and Sinha, 1970; Shardendu and Ambasht, 1991; Irfan andShardendu, 2009). Variation in the tissue nutrient levels have been well attributed to additiveeffects of seasonal trends.

This study has been performed to assess the effect of season on the biomass accumulation,primary production and nutrient element composition of the floating aquatic macrophytes Eichorniacrassipes, Lemna minor, Azolla pinnata and Utricularia flexuosa , growing in the Baraila wetland,situated in Vaishali district of Bihar, India.

2. Materials and Methods

2.1 Site Description : Sampling was done from Baraila Chaur (wetland) of Vaishali district of Bihar,India, situated on 25°76’N and 85°56’E. Temperature varies between 30.81°C and 5°C with averageannual rainfall of 1168 mm. The wetland spreads over about 24 to 26 miles which remains covered withwater in the rainy season. Climatically this region comes into the ‘Sub-humid transition climate belt’.(Misra, 2007).

2.2 Plant Sampling, Biomass Estimation and Calculation of Primary Productivity : The wetlandwas divided into 5 segments and from each segment triplicates of 4 free floating aquatic macrophytespecies namely Eichhornia crassipes, Lemna minor, Azolla pinnata and Utricularia flexuosawere sampled to obtain a total of each 15 individual species at monthly intervals between February2009 and February 2010.

Standing crop biomass was measured by harvest method, as described by Odum (1956).Samples were collected from 25 X 25 cm area and brought to the laboratory in acid rinsed polyethylenebags. Plants were thoroughly washed under running tap water to remove dirt and dried in oven at 80°C

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for 48 h to a constant weight. The biomass was calculated on dry weight basis. The biomass data wereutilized for the calculation of primary production. Seasonal positive monthly changes of standing cropbiomass were added to calculate seasonal net productivity.

2.3 Analys is of Plant Nutrients : The harvested plant samples were dried as described in Section2.2, powdered in stainless steel grinder and sieved through 2 mm stainless steel mesh to obtain sub-samples. The sub-samples (0.5 – 1.0 g) were divided into 3 portions for analysis of various nutrients.First portion was used for analysis of potassium, calcium, and sodium. The sub-samples were convertedinto ash in muffle furnace at 480°C for 12 h after adding 2 ml saturated solution of magnesium nitrate (toprevent phosphorus volatilization) and dissolved in 1 N HCl. The solution was then used to estimate K,Ca and Na using Flame Photometer (Systronics).

The second portion of ground sample was utilized for analysis of total nitrogen and totalphosphorus. The powdered samples were subjected to Persulfate Digestion (Langer and Hendrix,1982; Ebina et al., 1983), to convert all forms of phosphorus and nitrogen into Dissolved InorganicPhosphorus (DIP) and nitrate-nitrogen respectively. DIP was quantified by the Stannous Chloridemethod, described by Walinga et al., (1995) and nitrate-nitrogen was determined by Phenol-di-sulfonicAcid method (APHA, 2005).

The third portion of sub-sample was estimated for magnesium by wet ashing method describedby Misra (1968).

2.4 Statistical Analysis : Statistical analyses were performed using ANOVA and Multiple RegressionAnalysis. ANOVA was done to test for between-months variation in plant nutrients of Eichorniacrassipes, Lemna minor, Azolla pinnata and Utricularia flexuosa and Multiple Regression Analysiswas done to test the dependency of plant nutrient (PN) on nutrients in water (NW) and plant biomass(PB). Statistics was performed using software STATISTICA v5.

3. Results

3.1 Estimation of Biomass : Free floating zone consists of four species i.e. Eichhornia crassipes,Lemna minor, Azolla pinnata and Utricularia flexuosa. The first two were present throughout theyear, whereas Azolla pinnata was absent in April, May and June and Utricularia flexuosa was notrecorded in May. Eichhornia crassipes constituted more than half (51.2%) of the total biomass ofthe pond, and its maximum biomass was 1190.70±89.04 g m-2 in October and minimum of531.4±43.07 g m-2 in May (Fig 1). Lemna minor was another species of this zone which had2.36±0.50 g m-2 in September, afterwards there was a decrease in biomass and minimum of

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0.56±0.13 was recorded in May (Fig 3). Azolla pinnata showed 0.91±0.143 g m-2 in August(absent in summer) and after that there was gradual increase in biomass of the species and in Februaryit had 4.59±0.679 g m-2 (Fig 2).

Figure 1. Mean monthly variation of biomass of Eichhornia crassipes in Baraila wetland.

Figure 2. . Mean monthly variation of biomass of Azolla pinnata in Baraila wetland.

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The fourth species of this zone was Utricularia flexuosa which was absent in May. It startedgerminating in June (4.33±0.56 g m-2). There was a gradual increase in the biomass to reach a maximumvalue of 144.33±24.37 g m-2 in November (Fig 4).

Total biomass of free – floating zone was 241A.38 g m-2. The fluctuation during different monthsshowed that June (319.49 g m-2) and October (315.76 g m-2) were the most favourable period forgrowth while April (173.29 g m-2) and January (173.66 g m-2) were the least. The percentage contributionof the constituent species to this zone was 91.38 for Eichhornia crassipes, 0.14 for Lemna minor,0.21 for Azolla pinnata and 8.27 for Utricularia flexuosa .

The annual average biomass for Eichhornia crassipes was 882.32 g m-2, while Lemna minor,Azolla pinnata and Utricularia flexuosa added only 0.08%, 0.12% and 4.63% to the total biomassrespectively, having their respective annual average biomass of 1/31 g m-2, 2.04 g m-2 and 79.85 g m-2.

Figure 3. Mean monthly variation of biomass of Lemna minor in Baraila wetland.

Figure 4. Mean monthly variation of biomass of Utricularia flexuosa in Baraila wetland.

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Winter season was most suitable for the growth of all the four species of this zone while summer was theleast. So the total addition in biomass to this zone in winter was 283.14 g m-2, in rainy was 266.12 g m-2 and in summer 174.88 g m-2 which was 39.1%, 36.75% and 24.15% respectively (Table 1).

Table 1. Variation in standing crop biomass in plants of Baraila wetland between 2010 – 2011.

3.2 Primary production

Primary Productivity : Free floating zone was represented by four members in the wetland; theywere Eichhornia crassipes, Lemna minor, Azolla pinnata and Utricularia flexuosa . Among themE. crassipes was the dominant member whose rate of productivity in rainy season was 5.427 g m-2day-1 which was the maximum rate of production in any zone or any member of the pond. This wasfollowed by winter season where the rate was 0.412 g m-2 day-1 (Table 2). There was no increase inbiomass in summer season. This species was responsible for approximately fifty percent of totalproduction in the pond. This was followed by L. minor, which was among the few species whichproduced in all the three seasons of the year, though the rate of production was very low. Highest rateof production was 0.014 g m-2 day-1 in rainy season followed by 0.0028 g m-2 day-1 in winterseason and 0.0006 g m-2 day-1 in summer season. A. pinnata was absent in summer season. Itshighest rate of production of 0.023 g m-2 day-1 was obtained in winter season followed by 0.016 g m-2 day-1 in rainy season. The fourth species of free floating zone was Utricularia flexuosa which wasnot only perennial but showed positive biomass differences in all the three seasons of the year. Themaximum rate of production of 0.925 g m-2 day-1was noted in summer season. The value (0.659 g m-2 day-1) for winter season was intermediate between rainy and summer.

The free floating zone produced with rapid rate of 6.382 g m-2 day-1 in rainy season whichwas maximum for any zone and any season in the wetland. This was followed by winter 0.659 g m-2day-1and the lowest rate of 0.037 g m-2 day-1was in summer season (Table 2).

The annual production of members of free floating zone was calculated by two methods in bothof them similar results were observed. The annual production of E. crassipes was 719.80 g m-2 day-1which was highest addition for a species to this zone and to wetland as well. This was followed by U.

Fa cto rs Spec ies Sum mer Ra iny W int er % co ntrib utio n To tal Sta ndin g

% c ont ributio n o f

o f species to zo ne

cro p biom ass o f species /zo ne

Sta nding cro p biom a ss

Total stand ing

1 . Eichh orn ia crassipe s 6 74 .13 9 63 .56 1 00 9.2 8 9 1.8 8 8 8 2.3 2 5 1.2

b iom ass 2 . Lem na mino r 0 .67 8 1 .61 1 .63 0 .14 1 .3 1 0 .08

3 . Azo lla p in na ta 1 .29 1 .31 3 .53 0 .21 2 .0 4 0 .12

4 . Utr icula ria f le xu osa 2 3.4 4 9 8 118 .1 1 8 .27 7 9.8 5 4 .63

% con trib utio n o f th e se aso n to 2 4.1 5 3 6.7 5 3 9.1 th is z on e

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flexuosa 144.33 g m-2 day-1, A. pinnata 4.78 g m-2 day-1and lowest 2.14 g m-2 day-1was for L.minor.

Table 2. Seasonal productivity of plants of Baraila wetland from 2010-2011 to 2010 by positive changemethod

3.3 Nutrient Composition in the Tissue of Eleocharis plantaginea : There were four species infree – floating zone, Eichhornia crassipes, Lemna minor, Azolla pinnata and Utricularia flexuosa,in which first two were present through out the year, U. flexuosa was absent in May and A. pinnata inApril, May and June. In E. crassipes nitrogen ranged from 9.82 to 17.72 mg g-1, phosphorus 0.78 to1.68 mg g-1, potassium 20.85 to 35.51 mg g-1, calcium 8.10 to 16.08 mg g-1, magnesium 4.35 to 8.75mg g-1 and sodium 1.73 to 3.28 mg g-1. The sequence falls in the order of potassium > calcium >nitrogen > magnesium > sodium > phosphorus. Range of elemental content in L. minor was 18.57 to29.57 mg g-1 for potassium, 10.43 to 19.55 mg g-1 for calcium 2.75 to 4.11 mg g-1 for magnesium and1.82 to 2.93 mg g-1 for sodium. The decrease in the mean annual content of elements were nitrogen >potassium > calcium > phosphorus > magnesium > sodium. In A. pinnata the elemental concentrationvaried from 26.41 to 37.91 mg g-1 for nitrogen, 1.22 to 2.57 mg g-1 for phosphorus, 9.35 to 14.63 mgg-1 for potassium, 7.58 to 11.41 mg g-1 for calcium, 3.59 to 6.08 mg g-1 for magnesium and 6.58 to 9.88mg g-1 for sodium. The order of fall of different elements was the same as in L. minor. U. flexuosa is theonly insectivorous species present in the study ponds in which the nitrogen ranged from 14.64 to 22.19mg g-1, phosphorus from 0.71 to 1.25 mg g-1, potassium 11.55 to 18.21 mg g-1, calcium from 1.51 to2.78 mg g-1, magnesium 0.98 to 2.67 mg g-1 and sodium 7.41 to 13.75 mg g-1. The order of fall ofdifferent elements were nitrogen > potassium > sodium > calcium > magnesium > phosphorus. Themean annual concentration of different elements in all the four species followed variable trends. Themean annual nitrogen content were maximum 30.57mg g-1 for Azolla pinnata, 22.54 mg g-1 for L.minor, 18.24 mg g-1 for U. flexuosa and 13.64 mg g-1 for E. crassipes. The mean annual phosphoruscontent decreased in the order of 6.55 mg g-1 for L. minor, 1.78 mg g-1 for A. pinnata , 1.11 mg g-1 forE. crassipes and 9.58 mg g-1 for U. flexuosa . The potassium content decreased in order 27.15 mg g-

Summer Rainy Winter 16 Fe b. - 15 June 16 June - 15 Oct . 16 O ct. - 15 Feb.

SPECIES Productivity Production Product ivity Production Productivity Produc tion

g m-2

season-1 g m-2

season -1 g m -2 season -1 g m-2 season -1

g m-2

sea son-1g m-2

season-1

1. Eichhornia crassipes - - 662.07 5.43 50.73 0.412 2. Lemna minor 0.07 0.0006 1.73 0.014 0.34 0.003 3. Azolla pinnata - - 2 .004 0.016 2.78 0.023 4. Utr icularia flexuosa 4.33 0.037 112.8 0.925 27.2 0.221

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1 for E. crassipes, 17.67 mg g-1 for L. minor, 14.65 mg g-1 for U. flexuosa and 11.76 mg g-1 for A.pinnata. The maximum annual mean for calcium was 15.58 mg g-1 for L. minor followed by 11.66 mgg-1 for E. crassipes, 9.79 mg g-1 for A. pinnata and minimum 2.15 mg g-1 for U. flexuosa. The contentof magnesium which is also the index of production was maximum 6.21 mg g-1 for E. crassipes followedby 5.32 mg g-1 for A. pinnata, 3.37 mg g-1 for L. minor and minimum 1.73 mg g-1 for U. flexuosa.Sodium content varied from 10.28 mg g-1 in U. flexuosa, 7.88 mg g-1 for A. pinnata, 2.60 for E.crassipes and 2.43 mg g-1 for L. minor.

Figure 5. Mean monthly variation of Mg, Na and P in Eichhornia crassipes of Barailawetland.

Figure 6. Mean monthly variation of N, K and Ca in Eichhornia crassipes of Baraila wetland.

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Monthly changes in the amounts of elements of E. crassipes had a different trend.The maximum nitrogen was a fall in the concentration and the minimum of 9.82±1.14 mg g-

1 was noted in November. Seasonally, summer months April (15.59±1.24 mg g-1 ), May(16.29±1.27 mg g-1 ) and June (17.71±1.41 mg g -1) had higher values than the months ofrainy season, July (16.19±2.02 mg g-1 ), August (14.62 mg g-1) and September (13.56±1.75mg g-1). Winter months October (12.12±1.87 mg g -1 ), December (11.24±0.71 mg g-1 ) andJanuary (11.95±0.99 mg g-1 ), had lower values than both the summer and rainy months(Fig. 6). Monthly variation in phosphorus content ranged from 1.68±0.12 mg g-1 in June to0.78±0.15 mg g-1 in October, though the very close to minimum value (0.79±0.15 mg g-1 )was observed in January 2011. Concentration of phosphorus in other months were 1.21±0.12mg g-1 for March, 1.32±0.18 mg g -1 for April, 1.44±0.25 mg g-1 for May and these werehigher than the contents of 1.25±0.14 mg g-1 in July, 1.02±0.13 mg g-1 in August and0.82±0.11 mg g-1 in Septe mber. In February of 1982 (1.07±0.12 mg g-1) and 2011(1.05±0.09 mg g-1 ) the concentration values were very close followed by December(1.05±0.15 mg g-1) and November (0.92±0.06 mg g -1 ) (F ig. 5). Maximum potassium content(35.51±2.98 mg g-1) of E. crassipes was recorded in June followed by fall in the contentwhich reached the minimum values 20.85±2.54 mg g-1 in November and 20.85±4.40 mg g-

1 in December. Rainy season months of July (32.35±1.96 mg g-1 ), August (30.15±2.11 mgg-1) and September (26.67±2.33 mg g -1 ) had s lightly lower concentration of potassium thanthe summer months March (27.85±2.35 mg g-1), April (30.63±1.64 mg g-1 ) and May(33.75±1.94 mg g-1 ). February of 1982 (24.02±2.02 mg g-1 ) and of 1983 (24.02±1.60 mgg-1) had the same content followed by January (21.69±1.75 mg g-1 ), (F ig. 6). Regardingcalcium content November (14.52±1.70 mg g-1 ), December (16.08±1.56 mg g-1 ) andJanuary (14.29±1.85 mg g-1 ) together constituted higher content than the July (10.82±1.49mg g-1 ), August (11.46±0.93 mg g-1 ) and September (12.48±1.24 mg g-1 ). The minimumcalcium content of 8.10±0.81 mg g-1 was in May followed 9.32±1.17 mg g-1 in June (Fig.6). Magnesium content of E. crassipes was quite low (4.62±0.84 mg g -1) in May, afterwhich it increased to October (8.75±0.97 mg g-1 ), then again decreased. Magnesium contentreached the minimum (4.35±0.67 mg g-1 ) in January 2011. The values of winter season,6.98±0.87 mg g-1 in November, 5.68±0.77 mg g-1 in December and 5.38±0.55 mg g -1 inFebruary 2011 were lower than in the months of rainy season i.e. 6.37±0.60 mg g-1 in July,7.75±0.87 mg g-1 in August and 7.92±0.68 mg g -1 in September as well as of the twosummer months, April (5.12±0.56 mg g-1 ) and June (5.51±0.56 mg g-1) (Fig. 5). Sodiumcontent of E. crassipes had almost two equal peaks, one (3.25±0.38 mg g-1 ) in June andanother (3.28±0.23 mg g-1 ) in January. After the first peak, there was gradual fall in thecontent, which reached to the minimum (1.73±0.20 mg g-1 ) in September, which thenenhanced. Seasonally, winter had higher content than the rainy and summer months (Fig.

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5). Monthly variation of nitrogen, phosphorus , potass ium, magnesium and sodium werehighly significant (p<0.005) whereas calcium changed at p<0.025 (Table 3).

Figure 9 Mean monthly variation of Mg, Na and P in Lemna minor of Baraila wetland.

Figure10. Mean monthly variation of N, K and Ca in Azolla pinnata of Baraila wetland.

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L. minor was the another member of the free-floating zone. The maximum nitrogen content of29.57±2.19 mg g-1 was found in June and the minimum of 18.57±2.77 mg g-1 in October. April (21.02±2.38mg g-1), May (24.63±1.88 mg g-1) and July (25.50±2.20 mg g-1) together constituted the months in whichhigher nitrogen values were obtained than in November (19.47±2.26 mg g-1), December (20.63±1.95mg g-1) and January (21.07±1.98 mg g-1) (Fig. 10). Phosphorus content also varied in a similar way. Thehighest of 10.52±1.10 mg g-1 was recorded in May and the minimum of 4.52±0.93 mg g-1 in October andanother lower value close to this of 4.59±0.75 mg g-1 was observed in January. Seasonally, higherconcentration was recorded in the summer than the rainy season (Fig. 9). In contrast to nitrogen andphosphorus, the high contents in potassium of 21.02±1.84 mg g-1 were recorded in February 2011 and inthe summer months of May (19.49±2.17 mg g-1) and June (18.46±2.70 mg g-1). The minimum 14.50±1.62mg g-1) of potassium was recorded for September followed by July (17.45±1.80 mg g-1) and June(16.38±1.81 mg g-1). Calcium content of L. minor was minimum (10.43±1.15 mg g-1) in May, whichenhanced thereafter upto December (19.55±1.84 mg g-1). In winter calcium was higher in L. minor thanin other seasons (Fig. 10). The trend of the monthly variation of magnesium was different than of otherelements. Maximum of 4.11±0.38 mg g-1 was in September, which got reduced upto January 2011

-1). In April (2.85±0.34 mg g-1), May (2.95±0.41 mg g-1 -

1) values were significantly lower than in October (3.75±0.38 mg g-1), November (3.31±0.28 mg g-1) andDecember(3.12±0.31 mg g-1). Variation of sodium content of L. minor ranged from 2.93±0.32 mg g-1 inJuly to 1.82±0.30 mg g-1 in October. Concentration in other months were 2.73±0.38 mg g-1 in May,2.43±0.26 mg g-1 in June, 2.67±0.44 mg g-1 in August and 2.22±0.24 mg g-1 in September (Fig. 9).Phosphorus, calcium changed significantly p<0.005 between months whereas magnesium at p<0.025,potassium p<0.100 and nitrogen and sodium p<0.250 (Table 3).

Figure 7 Mean monthly variation of Mg, Na and P in Azolla pinnata of Baraila wetland.

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Figure8. Mean monthly variation of N, K and Ca in Azolla pinnata of Baraila wetland.

The other species of this zone was Azolla pinnata, containing an endophyte of Anabaenaazollae, a nitrogen fixing blue green algae. The nitrogen content ranged from maximum 37.91±1.93 mgg-1 in July to 26.41±1.36 mg g-1 in December. Among other months in August (35.28±2.37 mg g-1),September (32.55±1.33 mg g-1) and October (30.15±2.62 mg g-1) a comparatively higher nitrogencontent was recorded than in January (27.01±1.91 mg g-1) and February 2010 (29.63±2.12 mg g-1)(Fig. 8). Phosphorus content was also highest (2.57±0.21 mg g-1) in July and the same was true forpotassium (14.63±1.79 mg g-1) in July. The lowest values were 1.22±0.09 mg g-1 of phosphorus inDecember (Fig. 7), and 9.35±1.24 mg g-1 for potassium in October (Fig. 8). For both these elementsthe rainy season had higher values than winter. In contrast to N, P and K and the minimum calciumconcentration of 7.58±0.81 mg g-1 was in August, followed by 8.21±0.63 mg g-1 in September and9.69±0.93 mg g-1 in October. The maximum content 11.41±0.73 mg g-1 was noted in January 2011followed by February 2010 and 2011 (11.29±0.67 mg g-1 and 11.02±1.44 mg g-1) (Fig. 7). Theamount of magnesium concentration was 5.78±0.55 mg g-1 in November, 5.92±0.79 mg g-1 in Decemberand 5.99±0.8 mg g-1 in January which was higher than 3.59±0.46 mg g-1 in July, 4.11±0.34 mg g-1 inAugust and 4.51±0.49 mg g-1 in September. The maximum of 9.88±0.75 mg g-1 sodium content of A.pinnata was in July, after which it decreased to the minimum of 6.58±0.80 mg g-1 in December, and itfluctuated in January and February. In March very close (6.78±0.37 mg g-1) to the lowest value wasnoted (Fig. 7). Statistically, variation of elemental concentration in A. pinnata between the monthswere significant for nitrogen at p<0.025, phosphorus at p<0.005, calcium and sodium at p<0.1 and formagnesium at p<0.25 level, whereas the variation of potassium was not significant (Table 3).

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Figure 11 Mean monthly variation of Mg, Na and P in Utricularia flexuosa of Baraila wetland.

Figure12. Mean monthly variation of N, K and Ca in Utricularia flexuosa of Baraila wetland.

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The fourth and last species of this zone Utricularia flexuosa was present throughout the yearexcept in May. The variation of nitrogen of this insectivorous species was between 22.19±0.89 mg g-

1 in July to 14.64±1.53 mg g-1 in concentration of nitrogen than the rainy season months while in wintermonths concentrations were the least (Fig. 11). The maximum amount of phosphorus (1.25±0.14 mg g-

1) was in July, after which the concentration fell down to the minimum of 0.71±0.17 mg g-1 in Novemberand then there were ups and downs (Fig. 12). In contrast to nitrogen and phosphorus, the potassiumcontent was least (11.55±0.88 mg g-1) in June followed by the enhancement in the content in rainy andwinter months and maximum of 18.21±1.13 mg g-1 was noted in January 2011. Again it decreased. Thetrend of calcium content of U. flexuosa was almost similar to that of potassium having highest 2.78±0.24mg g-1 in January 2011 and lowest 1.51±0.16 mg g-1 in September. In the winter months amount ofcalcium was higher than in the other two seasons (Fig. 11). Magnesium and sodium content had almostan opposite pattern. The minimum value of 0.98±0.14 mg g-1 of magnesium and maximum value ofmagnesium (2.67±0.22 mg g-1) was recorded in November while lowest value of sodium (7.41±0.99mg g-1) was found in October (Fig. 12). Statistically, calcium magnesium and sodium changed betweenthe months highly significantly (p<0.005), whereas variation of nitrogen was significant at p<0.025,phosphorus p<0.25 and potassium p<0.1 (Table 3).

Table 3. ANOVA for plant - nutrients plants of of free floating zone.

Nutrients Sources of Variation d.f. S.S. M.S. F P< E. crassip es N Mo nth 12 1.949 0.1624 4.89 0.005 Error 24 0.796 0.0332 P Mo nth 12 0.026 0.0022 3.67 0.005 Error 24 0.015 3 0.006 K Mo nth 12 8.925 0.7438 5.48 0.005 Error 24 3.256 0.1357 Ca Mo nth 12 2.254 0.1878 3.41 0.025 Error 24 1.321 0.055 Mg Mo nth 12 0.649 0.054 4.50 0.005 Error 24 0.296 0.012 Na Mo nth 12 0.081 0.007 3.50 0.005 Error 24 0.053 0.002 L. minor N Mo nth 12 3.087 0.2573 1.75 0.25 Error 24 3.533 0.1472 P Mo nth 12 1.093 0.0911 10.47 0.005 Error 24 0.209 0.0087 K Mo nth 12 1.029 0.0858 2.03 0.1 Error 24 1.014 0.0423 Ca Mo nth 12 2.571 0.2143 3.86 0.005 Error 24 1.332 0.0555 Mg Mo nth 12 0.084 0.007 3.18 0.025 Error 24 0.053 0.0022 Na Mo nth 12 0.034 0.0028 1.57 0.25 Error 24 0.042 0.0018

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A. pinnata N Month 9 3.637 0.4041 4.12 0.025 Error 18 1.768 0.0982

P Month 9 0.051 0.0057 5.18 0.0025 Error 18 0.0194 0.0011 K Month 9 0.547 0.0608 1.24 NS Error 18 0.883 0.0491

Ca Month 9 0.533 0.0592 2.01 0.1 Error 18 0.53 0.0294 Mg Month 9 0.227 0.0252 1.75 0.25 Error 18 0.259 0.0144

Na Month 9 0.351 0.039 2.57 0.1 Error 18 0.274 0.0152U. flexuosa N Month 11 1.737 0.1579 2.79 0.025

Error 22 1.244 0.0565 P Month 11 0.0088 0.0008 1.60 0.25 Error 22 0.0113 0.0005 K Month 11 1.552 0.1411 2.40 0.1

Error 22 1.297 0.589 Ca Month 11 0.051 0.0046 4.60 0.005 Error 22 0.021 0.001 Mg Month 11 0.073 0.0066 4.40 0.005

Error 22 0.034 0.0015 Na Month 11 1.144 0.104 3.77 0.005 Error 22 0.608 0.0276

Table 4 Multiple regression equations relating the plant nutrient (PN) in E. plantaginea, nutrient in water(NW) and plant biomass (PB) of different species from Baraila wetland; the squared multiple correlationcoefficient (R2) and F statistics are also shown/

Nutrients Regression Equation R2 FE. crassipes

N PN = 0.27 + 0.005NW + 0.001PB 0.78** 7.88***

P PN = 0.02 + 0.007NW + 0.00004PB 0.89*** 5.00*

K PN = 1.69 - 0.681NW + 0.002PB 0.10 n.s.

0.99 n.s.

Ca PN = -0.01 + 0.014NW - 0.001PB 0.43 n.s.

25.45**

Mg PN = 0.04 + 0.065NW + 0.0002PB 0.86*** 66.25*

Na PN = 0.003+ 0.054NW + 0.0001PB 0.92*** 17.50***

L. minor

N PN = 0.73 + 0.010NW + 0.511PB 0.91*** 8.24**

P PN = 0.08 + 0.051NW + 0.117PB 0.96*** 20.33***

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K PN = 0.83 + 0.998NW - 0.105PB 0.79**. 4.81*

Ca PN = -0.01 + 0.014NW - 0.001PB 0.96*** 68.09***

Mg PN = 0.04 + 0.065NW + 0.0002PB 0.91*** 35.19***

Na PN = 0.003+ 0.054NW + 0.0001PB 0.96*** 25.00***

A. pinnata

N PN = 1.76 + 0.011NW + 0.154PB 0.76** 2.57 n.s.

P PN = 0.07 + 0.012NW + 0.005PB 0.87*** 5.00***

K PN = 0.45 + 0.664NW - 0.014PB 0.85**. 3.021*

Ca PN = -0.01 + 0.014NW - 0.001PB 0.96*** 68.09***

Mg PN = 0.04 + 0.065NW + 0.0002PB 0.91*** 35.19***

Na PN = 0.003+ 0.054NW + 0.0001PB 0.96*** 25.00***

R2 values are significant; *P<0.05; **P<0.01 with d.f. 11.

F values are significant; *P<0.05; **P<0.01; ***P<0.005; with d.f. (2,10).

n.s. Non Significant.

1. Discussion : Free-floating zone had four constituents but Eichhornia crassipes was the mostdominant species which had maximum biomass 11.70 g m-2 in the month of October and contributedmore than 50% to the total biomass of the pond and more than 90% to the biomass of this zone. Herealso maximum biomass in October showed that these post-monsoonal months have equilibiuium ofenvironmental factors, like heat, light, nutrients and other biological and geological factors, when themaximum growth and development of aquatic plants occur. The standing crop biomass of this zone washigher than other reports except from Westlake (1963) (Table 1).

The free-floating zone macrophytes were intermediate in production as well as in situationbetween emergent and submerged zone. A maximum rate of 5.43 g m-2 day-1 was recorded for E.crassipes whereas maximum rate of production this zone was 3.19 g m-2 day-1. These macrophytesare less productive than the emergents (Westlake 1965) was true on zonal basis but rate of E.crassipes was higher than E. plantaginea. Higher rate of net roduction was noted in rainy seasonbecause this was the flowering and fruiting period of species. However, data of productivity onfloating macrophytes are very few. Sahai and Sinha (1970) and Srivastava (1973) have reported amaximum rate of 3.8 g m-2 day-1 organic matter net production for E. crassipes which is lower thanthe rate of production in present investigation while Verma (1979) observed maximum rate of 15 gm-2 day-1 dry matter production for free floating zone which was quite higher than the rate of netproduction in present inves tigation.

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In species of free-floating zone the variation in nutrients with season was well marked. Insummer, nitrogen, phosphorus and potassium were maximum and calcium minimum in Eichhorniacrassipes and Lemna minor while role of summer in Azolla pinnata and Utricularia flexuosa didnot arise because former was completely absent in summer season whereas latter was not noted inMay. In winter season, nitrogen, phosphorus and potassium were significantly lower in most of thecases and calcium was quite high. Effect of rainy season on variation of magnesium and sodium wasquite distinct, in most of the cases concentration of magnesium was higher whereas opposite was thetrend for sodium. Nitrogen, phosphorus and potassium are the protoplasmic constituents, whichwere highly needed in the early development of plants, when these are ontogenetically young andyoung and also metabolically very active whereas the calcium, the cell wall constituent was enrichedwith ageing of the species. Higher concentration of magnesium in rainy season was due to higherbiomass in the same season whereas low concentration of sodium in the rainy season was due to thedilution effect of sodium in water. The mean annual of nutrient concentration of this zone decreasedin the order of N>K>Ca>Na>Mg>P. The result of regression analysis showed that all plant nutrientsin Eichhornia crassipes and Utricularia flexuosa s trongly depended on the level of nutrientconcentration in water (Table 4). This was similar to the report of Gosset and Noris (1971) whichdemonstrated positive correlation between the N and P contents of the tissues of E. crassipes andthose of the environment but opposed to this report, Boyd and Vickers (1971) were unable tocorrelate the two. Concentrations of N and P in tissues of U. flexuosa were not high to justify thethird source of this elements due to its insectivorous habit as reported by Collar, Coleman and Boyd(1971). Regression analysis showed that in Lemna minor, potassium and sodium were waterdependent whereas other four nutrients were related to variations in biomass, this might be attributedto highly productive and relatively short life cycle of duckweed (Rajmankova 1978). In Azollapinnata increased in N, Ca and Mg depended on biomass whereas P, K and Na on water nutrients.Nitrogen dependence on biomass might be assigned to occurrence of nitrogen fixing blue green algaAnabaena azollae in Azolla. This difference may not be due to season. It may also be due tovariation in age.

References :

Adams M.S. and McCracken M.D. 1974. Seasonal production of the Myriophyllum component ofthe littoral lake Wingra, Wisconsin. J. Ecol. 62: 457-65.

Boyd C.E. 1971. Further studies on productivity, nutrient and pigment relationships in Typha latifoliapopulations. Bull. Torrey Bot. Club. 98: 144-50.

Boyd C.E. and Vickers D.E. 1971.Variation in elemental content of Eichhornia crassipes .Hydrobiologia. 38: 409-14.

Carignan R and Kalff S. 1980. Phosphorus sources of aquatic weeds water or sediments? Science.207: 987-89.

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Gosset D.R. and Norris W.E. 1971. Relationship between nutrient availability and content of nitrogenand phosphorus in tissues of aquatic macrophytes Eichhornia crassipes Mart. Solms.Hydrobiologia.38: 15-28.

Irfan S. and Shardendu. 2009. Dynamics of nitrogen in subtropical wetland and its uptake and storageby Pistia stratiotes. J. Environ. Biol. 30: 977-81

Rajmankova E. 1978. Growth, production and nutrient uptake of Duckweedsin fish ponds and uinexperimental cultures. In: Pond Littoral Ecosystems (Ed.by D. Dyjykova and J. Kvet) pp.278-84.

Sahai R. and Sinha A.B. 1970. Contribution to the ecology of Indian aquatics. I. Seasonal changes inbiomass of water hyacinth (Eichhornia crassipes Mart. Solms.). Hydrobiologia. 35: 376-82.

Sayantan D. and Shardendu. 2013. Amendment in phosphorus levels moderate the chromium toxicityin Raphanus sativus L. as assayed by antioxidant enzymes activities. Ecotoxicol. Environ. Saf.95: 161-70.

Shardendu and Ambasht R.S. 1991. Relationship of nutrients in water with biomass and nutrientaccumulation of submerged macrophytes of a tropical wetland. New Phytol. 117: 493-500.

Srivastava V.C. 1973. The limnology, primary production and energetic of Chilw, Gorakhpur. Ph.D.Thesis, Gorakhpur University.

Verma K.R. 1979. Phytosociology, productivity and energetic of macropbhytes of Gujar lake(Khetasarai) Jaunpur. Ph.D. thesis, Banaras Hindu University.

Westlake D.F. 1963. Comparison of plant productivity. Biol. Rev. 38: 385-425.

Westlake D.F. 1965. Some basic data for investigations of the productivity of aquatic macrophytes.Primary production in aquatic environment. (Ed. By C. R. Goldman). Mem. Ist Ital. Idrobiol.18 (suppl.), pp. 229-48.

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PHYLOGEN ETIC STUDY OF C ELLULASE PRODUC INGSTREPTOMYCES SP. MTCC 7779 AND EFFECT OF NUTRITIONALFACTORS ON THE ENZYME ACTIVITY

Nirupa Kumari, Supriya Sharma and Birendra Prasad*

Microbial & Molecular Genetics Lab.,Department of Botany, Patna University, Patna-800 005, India

*Corresponding author ([email protected])

Abstract : Effect of carbon, nitrogen and metal ions on cellulase enzyme activity was investigated inshake culture condition. Strept omyces sp. MTCC 7779 was screened out from 105 strains ofactinomycetes isolated from soil of Patna, India, due to its ability to produce substantial amount ofcellulase enzyme in plate containing Carboxy Methyl Cellulose as sole carbon source. Maximumendoglucanase and -glucosidase activity was found in 72 hours old culture filtrate at pH 6.8 andtemperature 35±2ºC.The organism also hydrolysed different agro-wastes materials such as bagasseand corncob, along with CMC and filter paper. To standardize the additives in the media, NaNO3 wasfound to be the best nitrogen source for the production of cellulase enzyme. Endoglucanase and

-glucosidase activities enhanced almost double when medium was supplemented with five times moresodium nitrate. Simultaneously, amount of reducing sugar in broth was also enhanced in same ratio.

Among the eleven different types of metal ions, Fe+2 and Mn+2 were found to be significantstimulator for both cellulase activity as well as yield of reducing sugar. Ions like Zn+2 and K+1 were leaststimulator for enzyme production and activity. In synergistic effect, Fe+2 and Mn+2 greatly enhanced thesubstrate enzyme affinity and showed almost triple value of Vmax. While the value of Km greatly enhancedin the presence of Zn+2 and K+1.

The 16S rRNA region of this strain was amplified and sequenced. The Neighbor joining andMaximum Parsimony algorithm with topology tree of 16S rRNA was constructed. Based on observationand phylogenetic analysis, the strain showed 98.79 % similarity with Streptomyces carpaticus, 97.95% with S. cheonansis and 96.24% with S. xiamenensis. Sequence data has been deposited at NCBI,Bethesda, USA having Gene Bank Accession No. GU562884.

Keywords: Streptomyces, Cellulase, Nutritional factors, Metal ions-stimulator, phylogenetic analysis.

Introduction : Microbial degradation of organic wastes, especially cellulosic agro-wastes have beenin practice for obtaining commercially useful compounds such as ethanol, glucose and single cellprotein (Solomon et al., 1999). Cellulase and hemicellulase have been evaluated for their ability tobeneficially modify pulp and paper characteristics (Kibblewhite 1996; Suurnnakki et al., 2000;Torres et al., 2000; Roncero et al., 2000) as well as in separation of gluten from wheat flour(Cavaco-Paulo 1998; Bhat 2000). Economical production of cellulases has generally been considered

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to be the main aspect for feasible production of bioethanol from lignocellulosic biomass using cellulase-based processes (Gadgil et al., 1995; Lynd et al., 2002). Most of the cellulases for such commercialuses have been obtained from different microorganisms, mainly thermophillic fungi (Maheshwari etal., 2000; Jatinder et al., 2006).

Actinomycetes, having fungus like morphological and nutritional characteristics, normally inhabitthe soil where they develop mycelial form. Ability of the member of this class to thrive in diverse habitatis based on their nutritional capabilities conferred by a vast array of hydrolytic enzymes that allow themto recycle polysaccharides, proteins and fats that form essential residues of plants and animals (Sampathand Chandrakasan, 1988; Hodgson, 2000). Various species belonging to the genus Streptomyces arethe main representative of this group of microorganisms, all of which have a complex life cycle thatresponds to different signals, among which the nutrient limitation plays a key role. Streptomyces strainshave, therefore, been commercially useful in production of a number of bioactive compounds such asantibiotics (Champness, 1988) and enzymes degrading complex polysaccharides (Ellaiah and Srinivasulu,1996; Jang and Chang, 2005).

As an approach to produce a more affordable enzyme, most of the researches have beencarried out using cheaper lignocellulosic biomass such as bagasse, sawdust and corncob (Ojumu et al.,2003). Although the enzymatic degradation of cellulosic material is relatively a slow process, search fornovel microbial strains capable of producing enhanced levels of thermostable cellulase is still continuing.

This report describes the isolation of a mesophilic strain of actinomycetes, Streptomyces sp.MTCC 7779 from decomposing agro-was tes. This strain bears some novelty as it produces athermostable endo-1, 4- -D-glucanase in solid-state fermentation showing a higher yield of reducingsugar (RS) from bagasse (2450 mg/l) and corncob (2250 mg/l) as the carbon source. Successfulattempts have also been made to optimize the nutritional requirements for maximum production ofenzyme. The effect of nitrogen sources and different metal ions has also been assessed on the cellulaseactivity and yield of RS for further commercial application of this strain. Impact of these metal ions onthe value of Km and Vmax has also been investigated.

Material and Methods :

Isolation of cellulase producing strains of actinomycetes

Strains of actinomycetes were isolated from decomposing agro-wastes using starch caseinagar (SCA) media containing per litre, 10g soluble starch, 0.3 g hydrolyzed casein, 2g KNO3, 2gNaCl, 2g K2HPO4, traces of MgSO4, 7H2O; CaCO3, FeSO4, 7H2O and 1.5 % (w/v) agar. Preliminaryscreening for hyper-cellulase producing strains were carried out by examining the “halos” formed onsolid agar plates containing carboxy methyl cellulose (CMC) as the substrate followed by congo redstaining and washing (Teather and Wood, 1982).

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Preliminary characterization and identification

The strain was characterized for taxonomic identification based on the parameters described inBergey’s manual of determinative bacteriology (Williams et al., 1982). Clonal culture of the strain wasdeposited in MTCC & Gene Bank, IMTECH, Chandigarh (India) with Accession NumberMTCC7779. 16S rRNA gene sequencing of strain MTCC 7779 was also carried out to ascertain thespecies.

Phylogeny analysis

The 16S rDNA sequences of closely related validly published taxa were retrieved from theGenbank data base using BLASTN (Altschul et al., 1997).

Growth conditions and production of Reducing Sugar (RS)

Broth cultures were raised in 50ml media in 250ml Erlenmeyer flasks in a shaking incubator(JeioTech, Korea) at 150rpm and 35+10C. CMC of high viscosity and highest purity grade (HiMedia)was used as the carbon source for assessment of the yield of reducing sugar (RS) as well as endo-1, 4-

-D-glucanase activity.

Test of carbon sources

A total of four carbon sources (CMC, Whatman No. 1 filter paper, bagasse and corncob)were selected as the test substrates. The strain was grown separately in a modified basal synthetic saltmedium (BSM), pH 6.5 (containing per litre 3g NaNO3; 0.5g MgSO4, 7H2O; 0.5g K2HPO4, 1g KCland traces of ZnSO4; MnSO4; FeSO4, 7H2O; CaCl2,) supplemented with 0.5% (w/v) of the respectivecarbon source.

Quantitative estimation of cellulase

The enzyme sample was prepared by passing the culture filtrates through Whatman No.1 filterpaper with the help of suction pump. The filtrate was centrifuged at 1500xg for 30 minutes at 40C (BeckmanJ2-21M/E). The supernatant was used as the crude enzyme in catalytic reaction. The reaction mixturecontained in a total volume of 2ml, 1.4ml sodium citrate buffer (50mM) pH 6.5, 0.5ml of CMC (1% w/v)and 0.1ml of appropriately diluted enzyme. The mixture was incubated at 500C for 20 minutes. Thereaction was terminated by adding 3ml of 3,5-dinitrosalicylic acid (DNSA) followed by heating in a waterbath at 1000C. The colour thus developed was read at 540nm spectrophotometrically (Hitachi U-3210)using glucose as the standard (Miller, 1959). The same process was used for FP cellulase activity exceptthat the substrate was replaced by filter paper in a 50mM citrate buffer, pH 5.6.

For -glucosidase activity, p-nitrophenyl -D-galactopyranoside was used as the substrate in50mM sodium phosphate buffer, pH 6.5 and the reaction was stopped by adding 3ml of 1M sodiumcarbonate. The amount of p-nitrophenol produced was determined at 400nm using p-nitrophenol asthe standard (Berghem and Pettersson, 1974).

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Estimation of reducing sugar(RS)

RS was estimated by the method described by Miller (1959). The reaction mixture contained1.5ml of crude enzyme and 3ml of DNSA. The enzyme activity was expressed in terms of amount ofRS in mmole released in ml-1 min-1 using glucose as the standard.

Effect of nitrogen sources on the yield of RSG42

Four different nitrogen sources (urea, peptone, NH4NO3 and NaNO3) were tested to selectthe best nitrogen source keeping the nitrogen concentration at 3g/l. The cultures were raised in a timecourse study and filtrates were processed for enzyme assays.

Effect of cations on the yield of reducing sugar and endoglucanase activity

Eleven different compounds (CoCl2.6H2O, CuSO4.2H2O, NaCl, MgSO 4.7H2 O, KCl,MnSO4.H2O, ZnSO4.7H2O, FeSO4.7H2O, CaCl2, HgCl2and Na2MoO4.2H2O) were used for testingthe effects of cations on the production of RS and enzyme activity. The yield of RS was studied byinoculating 108 spores of Streptomyces sp. in 50ml of enzyme production medium supplemented with5mM of respective cation. The effect of cations on enzyme activity was studied by adding them in thereaction mixture at a concentration of 1mM.

Results : A total of 22 actinomycetes strains isolated from decomposing agro-wastes were screenedfor cellulase production. One isolate showed significantly high level of endoglucanase activity on CMCplates as it produced a “halos” of 30mm diameter after staining with congo red. Significantly, the strainproduced a unique type of grayish black spores in chains, and reticuliniaperti arrangement of the sporeson mycelia (Fig. 1).

The endoglucanase, -glucosidase and FPase activities were studied in culture filtrates obtainedin the presence of four different carbon sources (CMC, FP, bagasse and corncob) at an interval of 24hours upto 120 hours of incubation. Maximum endoglucanase activity was observed with corncob asthe carbon source whereas maximum -glucosidase activity in the presence of CMC. A nearly similarlevel of -glucosidase activity was observed with corncob and bagasse in 72 hours old culture andbeyond which the enzyme activities decreased rapidly. FPase activity was maximal in culture grown onfilter paper (Table-1).

Carbon sources significantly influenced the production of reducing sugar and enzymatic activities.Table-1 shows the endoglucanase, -glucosidase and FPase activity of the partially purified enzymeobtained after 72 hours. The maximum endoglucanase activity was found in corncob (2.50±0.091U/ml). Maximum -glucosidase activity was observed in carboxy methyl cellulose (0.69±0.021U/ml)and maximum FPase activity (0.150±0.0004 U/ml) appeared in the culture filtrate containing filterpaper as a sole carbon source.

The nitrogen source and substrates that regulate cellulase production were also evaluated inpresence of a fixed concentration (3 gL-1) of urea, peptone, NH4NO3 and NaNO3. The inorganic

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nitrogen source was found to be the most suitable in increasing the cell mass and yield of RS (data notshown). In order to optimize the media composition for enhanced enzyme activities and yield of reducingsugar, cultures were raised in presence of 5X-elevated level of NaNO3 (15 gL-1) in the basal mediumcontaining different carbon sources under test. The results thus obtained revealed that endoglucanaseand FPase were secreted at 4.08 and 2.61 folds higher levels as compared to the control (Table-2).The corresponding enzyme activities were also increased considerably in the culture broth supplementedwith extra NaNO3. Maximum reducing sugar (11475±52.13 g/L) and their correspondingendoglucanase (3.8±0.05 U/ml) activity occurred in broth containing corncob as a sole carbon source.A slightly lower value of reducing sugar (11180±65.5 g/L) and endoglucanase (3.75±0.024 U/ml)were observed in case of bagasse.

The effect of addition of cations on the enzyme activity and yield of RS was tested in presenceof CMC in basal medium. A significant enhancement in the enzyme activity and corresponding yield ofreducing sugar were observed only in the presence of Fe+2 and Mn+2 (Fig. 2). No significant change inthe enzyme activity as compared to the control could be detected in presence of Co+2, Cu+2, Mo+2,Zn+2 and Ca+2 whereas Na+, Mg+2 and K + moderately stimulated the enzyme activity.

The combination of two most stimulator metal ions (Fe+2 and Mn+2) and two moderate stimulatormetal ions (Zn+2 and K+1) have also been tested to evaluate the values of Km and Vmax in the presence ofabove mentioned combination of metal ions. The Vmax value for endoglucanase enzyme almost triple(7.692 U/ml) in presence of Fe+2 and Mn+2 (Table-3). In contrast, the value of Vmax greatly reduced inpresence of Zn+2 and K+1 for endoglucanase enzyme. Similar result was observed for -glucosidaseenzyme. The Vmax value was 1.428 mole/ml/min, it was almost double in comparison to control (0.714

mole/ml/min). The Vmax value of -glucosidase enzyme was reduced to 0.500 mole/ml/min in presenceof Zn+2 and K+1. Km value also fluctuated in combination of these metal ions. The enzyme produced byStreptomyces sp. having good affinity with their substrate. The affinity increase significantly in presenceFe+2 and Mn+2 and greatly reduced in presence of Zn+2 and K+1 ions for both enzymes (endoglucanaseand -glucosidase) (Fig.3).

For phylogentic analysis of this strain, a 1426 bp sequence of 16S rRNA gene wasamplified from the genomic DNA with the use of universal primer (Forward Primer 5’-AGAGTTTGATCCTGGCTCAG-3’ and reverse 5’ TACGGCTACCTTGTTACGACTT-3’)and its sequence was submitted to GenBank,NCBI, Bethesda, USA with accession no.GU563884.1). The 16S rRNA gene of different Strept omyces species was obtained byBLASTN search, however 21 strains of Strept omyces species were selected on the basis ofhigh identity (%) with good E value for phylogenetic analysis (F ig. 4). It showed about 98.79%similarities with Strept omyces carpaticus , 97.95 % with S. cheonansis and 96.24% with S.menens is . No 100% identity was observed with preexisting Streptomyces sequencesdeposited in NCBI.

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Enzyme Values Control Fe+2 & Mn+2 Zn+2 & K+1 Km(mg/ml) 0.606 0.4651 1.33Endoglucanase Vmax (IU/ml) 2.22 7.692 2.127

Km (mM) 0.25 0.208 1.430 -glucosidaseVmax (IU/ml) 0.714 1.428 0.500

Table-3. Values of Km and Vmax in different conditions

Table-1. Effect of different Carbon sources on the production of EG (Endoglucanase), -glucosidaseand FPase by Streptomyces. sp. MTCC 7779 under shaking condition at 350+10C.

*Yield of reducing sugar multiply in fold shown in bracket.

Table-2. Total yield of reducing sugar and its corresponding endoglucanase activity in control conditionas well supplemented with 5x Nitrogen sources.

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Fig.2.Cellulase production and corresponding activity in the presence of different metal ions

Fig.1. Microphotograph of Streptomyces sp. MTCC 7779 (Retinoculoperti arrangement ofspores on mycelia)

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(a) Endoglucanase activity inpresence of combination of metal ions (Fe+2 + Mn+2 and Zn+2 + K+1) along with control

(b) -gluosidase activity in presence of combination of metal ions (Fe+2+Mn+2 and Zn+2+ K+1)alongwith control

Fig.3. Linewever-Burk plot of Cellulase produced by Streptomyces sp. (Effect of concentration ofsubstrate shown in inset

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Fig. 4. Phylogeny trees are based on the nucleotide sequence of 16S rRNA genes. The trees wereconstructed by using MEGA 4.1 software. Neighbor-Joining algorithm with topology was usedfor tree construction.

Discuss ion : Cellulose accumulates in terrestrial environments , where a variety of cellulolyticmicroorganism, existing in virtually every niche and clime, dispose it (Leschine, 1995). Maximumproduction of reducing sugar and -glucosidase were obtained in 72 hours old culture filtrate byStreptomyces sp. Jang and Chen (2004) also reported that the production of endoglucanase reachedthe maximum between 3rd to 5 th days whereas -glucosidase production occurred on the 9th day fromStreptomyces sp. Our strain Streptomyces sp. Has more enzymatic activities than Micromonosporachalcae (Gallaghher et al., 2004) in which maximum endoglucanase and -glucosidase activities wereobserved after 8 days and 16-18 days, respectively. After 5th days, a sharp decline in enzyme activitieswas observed. Ojumu et al. (2004) also suggested that the depression of cellulase activity between 4-5 days might be due to cumulative effect of cellobiose, a dimmer of glucose. According to Spiridonov& Wilson (1998) and Gutierrez-Nova et al., (2003) the catabolic repression plays an important role inthe regulation and secretion of inducible enzymes. Such repression effect has also been observed inother organisms.

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Maximum yield of reducing sugar was occurred in cultural filtrate containing bagasse as a solecarbon source. Chowdhury et al. (1991) also reported that the bagasse was more easily saccharifiedcarbon source than any other agricultural wastes. Filter paper considered as the most resistant tobiodegradation. The highest cellulase productivity with corncob and bagasse may be due to its veryhigh percentage of cellulose that is the major component of the cell wall of agro-wastes (Ojumu et al.,2003). Levels of various enzyme activities differed with different carbon sources and maximumendoglucanase, FPase and -glucosidase activities were observed in case of corncob, filter paper andCMC, respectively. Different levels of cellulolytic enzyme produced on these carbon source indicatedthat the heterogeneous nature of the carbon source play an important role in the induction of theseenzymes (Jatinder et al., 2006). A significant enhancement in the total yield of RS in presence ofbagasse (4.5-folds), corncob (5.1-folds) and CMC (4-folds) was observed in media supplementedwith elevated level of NaNO3 whereas in presence of FP, the yield was only 2.6-folds higher thancontrol sample. Similar result has also been reported by Rajoka (2004), and it has been proven thatNaNO3 was best source of nitrogen for production of cellulase enzyme.

According to Tejirian and Xu (2010), only a few metal ions act as inhibitors of cellulases, Fe+2

and Fe+3 act as inhibitors of cellulase enzyme. Result of the present work was not in support of previousresult. During present investigation, maximum production of endoglucanase and -glucosidase activitieshas been observed in the presence of Fe+2 and Mn+2 ions. Manoliu et al., (2005) showed that theendoglucanase and -glucosidase activity tremendously increased in the presence of 80ìml/L of 45%petroleum ferrofluid in broth culture on 11th day in stationary phase containing fungal strain Chaetomiumglobosum. According to Siddiqui et al., (1997) the low concentration of Mn+2 activate the enzymeswith apparent activation constant. The ions like Cu+2 and Co+2 were slightly activating the enzymeactivity under assay conditions, while Hg+2 inhibited the activity (Ferchak and Pye, 1983). It has beensuggested that the metal inactivation of the cellulase proceeds by chelation involving carboxylates at theactive center, thereby perturbing the tryptophan residue (s) in the binding site of the enzyme (Clarkeand Admas, 1987). Acoording to Licus et al. (2001) and Yin et al. (2010) metal could interact withthe hydrophobic group of amino acids, resulting in the decreased enzyme activity.

The combination of Zn+2 and K+1 act as uncompetitive inhibitors [Fig-3(a) & (b)] for enzymeendoglucanase and -glucosidase. Zn+2 and K+1 combine with enzyme substrate complex, and forminhibitor complex. These complexes reduce the catalytic efficiency of the enzyme, so that the velocity ofthe reaction decreased gradual (Table-3). Such inhibition does not overcome by high concentration ofsubstrate. In the presence of Fe+2 and Mn+2 ions the Km value reduces, therefore it is suggested that theaffinity of enzyme and substrate become high in the presence of Fe+2 and Mn+2. So these two metal ionsacts as activator and greatly increases the rate of reaction.

Pernodet et al., (1989) reported that the 16S rRNA and 23S rRNA genes of variousStreptomyces species were partially sequenced and used for defining all member of the genus, groups

47


of species or individual species. As shown in Fig. 3 (NJ Algorithm with topology) two strains belongingto Streptomycetaceae have been relatively closely related to Streptomyces sp. MTCC 7779 has theown branch with Streptomyces carpaticus (DQ442494.1).

Conclusion : The present study led us to conclude that the carbon and nitrogen sources play a vitalrole in production of hydrolyzing enzymes. Streptomyces sp. MTCC 7779 is capable of producingcellulase from bagasse and corncob in huge amount. Cellulase enzyme production from these carbonsources could be harvested at 72 hours in shake culture, the time at which the activity is highest. Thisfeature may be advantageous in commercial application of the enzyme of mesophilic actinomycetes forthe saccharification of natural cellulosic substrates. On the basis of phylogenetic study of this strain no100% identity has been observed with preexisting Streptomyces sequences deposited in NCBI.Therefore, it may be a novel species of Streptomyces.

References:

Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W and Lipman DJ (1997) GappedBLAST and PSI-BLAST: a new generation of protein database search programs. NucleicAcids Research, 25:3389-3402.

Berghem B and Pattersson G (1977) Location and formation of cellulase in Trichoderma

viride. Journal of Applied Bacteriology, 42:65-75.

Bhat MK (2000) Cellulase and related enzymes in biotechnology. Biotecnology Advances, 18:355-383.

Cavaco- Paulo A (1998) Mechanism of cellulase action in textile processes. CarbohydratePolymer, 37:273-277.

Clarke AJ and Adams SL (1987) Irreversible inhibition of Schizophyllum commune cellulase by divalenttransition metal ions. Biochimica et Biophysica Acta (BBA)-Protein Structure and MolecularEnzymology, 916:213-219.

Champness WC (1988) New loci required for Streptomycces coelicolor morphologicaldifferentiation. Journal of Bacteriology, 170:1168-1174.

Chowdhury NA, Oniruzzaman M, Nahar N and Choudhury N (1991) Production of Cellulases andsaccharification of lignocellulosic by Micromonospora sp. World Journal of Microbiology andBiotechnology, 7:603-606.

Ellaiah P and Srinivasulu B (1996) Production of extracellular protease by Streptomyces fradie.Hindustan Antibiotic Bulletin, 38:41-47.

Ferchak JD and Pye EK (1983) Effect of cellobiose, glucose, ethanol, and metal ions on the Cellulaseenzyme complex of Thermomonospora fusca . Biotechnology Bioengineering, 25:2865-2872.

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Gadgil NJ, Daginawala HF, Chakrabarti T and Khanna P (1995) Enhance cellulose production by amutant of Trichoderma ressei. Enzyme and Microbial Technology, 17:942-946.

Gallagher J Winters A, Barron N, Mchale L, McHale AP (1996) Production of cellulase and-glucosidase activity during growth of the actinomycetes Micromonospora chalcae on cellulose-

containing media. Biotechnology Letters, 18(5):537-540.

Gutierrez-Nova N, Herrera-Herrera A, Mayorga-Yeyes L, Salgado LM, and Ponce-Noyola T (2003)Expression and Characterization of the celcflB gene from Cellulomonas flavigena encoding aendo-beta-1,4- glucanase. Current Microbiology, 47:359-363.

Hodgson DA (2000) Primary metabolism and its control in streptomycetes: a most unusual ofAdvances in Microbial Physiology, 42:47–238.

Jatinder K, Chadha BS and Saini HS (2006) Optimization of medium components for of cellulases byMelanocarpus sp. MTCC 3922 under solid-state fermentation. World Journal of Microbiologyand Biotechnology, 22:15-22.

Jang HD and Chang KS (2005) Thermostable cellulases from Streptomyces sp: scale-up productionin a 50-l fermenter. Biotechnology Letter, 27(4):239-42.

Kibblewhite PR and Clark TA (1996) Enzymatic modification of radiata pine kraft fibre and hand sheetproperties. Appita Journal, 49:390-396.

Leschine SB (1995) Cellulose degradation in anaerobic environments. Annual Review of Microbiology,49:399-426.

Licus R, Robles A, Garcia MT, DE Cienfuegos GA and Galvez A (2001) Production, purification andproperties of an endoglucanase produced by the hyphomycete Chalara (syn.Thielaviopsis)paradoxa CH32. Journal of Agricultural and Food Chemistry, 49:79-85.

Lynd LR, Weimer PJ, VanZyl, WH and PetoriusIS (2002) Microbial cellulose utilization: fundamentalsand biotechnology. Microbiology and Molecular Biology Reviews, 66:506-577.

Manoliu Al, Oprica L, Creanga, DE (2005) Ferrofluid and cellulolytic fungi. Journal of Magnetism andMagnetic Materials, 289:473-475.

Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugars. AnalyticalChemistry, 31:426-428.

Ojumu TS, Soloman BO, Betiku L, Stephen K and Amigun B (2003) Cellulase production byAspergillus flavus Linn. isolated NSPR 101 grown on baggase and corncob. African Journal ofBiotechnology, 2(6):150-152.

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Pernodet JL, Alegre MT, Boccard F, Guerineau M (1989) Organization and nucleotide sequence of aribosomal RNA gene cluster from Streptomyces ambofaciens. Gene, 79:33–46.

Roncero Ma B, Torres AL, Colom JF and Vidal T (2000) Effect on xylanase treatment on fiber morphologyin totally chlorine free bleaching (TCF) of Eucalyptus pulp. Process Biochemistry, 36:45-50.

Rajoka MI (2004) Influence of various fermentation variables on exo – glucanase production inCellulomonas flavigena. Electronic Journal of Biotechnology, 7:256-263.

Sampath P and Chandrakasan G (1998) Physiological and nutritional factors affecting Biosynthesis ofextracellular protease by Streptomyces . New Microbiology, 21:55-63.

Siddiqui KS, Rashid MH and Rajoka MI (1997) Kinetic analysis of the active site of an intracellular-glucosidase from Cellulomonas biazotea. Folia Microbiologica, 42:53-58.

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Solomon BO, Amigum B, Betiku E, Ojumu TV and Lyokun S (1999) Optomization of Cellulase productionby Aspergillus flavus Linn. isolate NSPR 101 grown on Baggase. JNSChE, 6:61-68.

Suuranakki A, Tenkanen M, Siika-aho M, Nikupaavola ML, Viikari L and Buchert J (2000)Trichoderma ressei cellulases and their core domains in the hydrolysis and modification of chemicalpulp. Cellulose, 7:189-209.

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Torres AL, Rocerno, Ma B, Colom JF, Pastor, F.I.J., Blanco A and Vidal T (2000) Effect of a novalenzyme on fibre morphology during ECF bleaching of oxygen delignified eucalyptus kraft pulps.Bioresources Technology, 74:35-140.

Tejirian A and Xu F (2010) Inhibition of cellulase-catalyzed lignocellulosic hydrolysis by iron and oxidativemetal ions and complexes. Applied Journal of Environmental Microbiology. 76:7673-7682.

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51


ENHANCED CLONAL PROPAGATION IN RAUVOLFIA TETRAPHYLLAL. USING ADENINE SULPHATE

Rachna Kumari, M.P. Trivedi# and Birendra Prasad*

Microbial & Molecular Genetics Lab.,Department of Botany, Patna University, Patna-800 005, India

#Department of Botany, Patna Science College, Patna University, Patna-800 005, India*Corresponding author ([email protected])

Abstract : Nodal explant with axillary buds of Rauvolfia tetraphylla L. were inoculated on MSmedium supplemented with BAP (0.5-3.0 mg/l) + NAA (0.5mg/l), Kinetin (3-10mg/l) + 2,4-D(0.5-1.0mg/l), BAP (0.5-3mg/l) + 2,4-D (0.1-1mg/l) and BAP (0.5mg/l) + Adenine sulphate (AS)(3-20mg/l) + IBA (0.2mg/l). An optimal micropropagation result was achieved using BAP + AS(3mg/l) + IBA. Maximum shoot length appeared in BAP + AS (14mg/l) + IBA and number of shootswere also enhanced (more than 2 at all concentration after AS treatment). The highest shootregeneration frequency (90%) was achieved on MS medium supplemented with 3 mg/l AS aftereight weeks prior to transfer in rooting media. The regenerated shoots showed best rooting on MSmedium containing 2mg/l IBA. Micropropagated plantlets were hardened in mixture of soil :vermicompost : sand in 2:1:1 proportion, aseptically. After 3 months of its survival, they were transferredto greenhouse.

Key words: Rauvolfia tetraphylla, Benzyl amino purine (BAP), Naphthalene acetic acid (NAA),Kinetin, 2,4-Dichlorophenoxy acetic acid (2,4-D), Adenine sulphate (AS), Indole-3-butyric acid (IBA)

Introduction: Rauvolfia tetraphylla L. is an endangered woody shrub of family Apocynaceae. Theroots often used as a substitute of R. serpentina because of the presence of alkaloid which is localizedin the roots (Patil and Jeyanthi, 1997). The plant is medicinally important in the treatment of hypertensionand used as a sedative or tranquilizing agent. Rauvolfia species is threatened in India due to itsindiscriminate collection and over exploitation of natural resources for commercial purposes to meetthe requirements of pharmaceutical industry. Hence, the conservation by in vitro propagation of thesevaluable genotypes is better option (Faisal and Anis, 2002), to satisfy the growing commercial demandof the plant.

Adenine sulphate (AS) is a potent growth regulant for in vitro propagation. Adenine sulphateinduces higher rates of adventitious shoot formation in Rauvolfia serpentina (Ilahi et al., 2007). Thereare various reports on in vitro propagation of R. tetraphylla through bud, shoot and nodal cuttings asexplant using growth regulators such as IAA, IBA, NAA, BAP and kinetin by several workers (Sharma

52


et al., 1999; Ghosh and Banergee, 2003; Harisaranraj, 2009). The existing protocol gave poor plantregeneration. The present work describes an in vitro shoot multiplication from nodal explant of R.tetraphylla using AS growth regulator.

Materials and Methods : Young shoots of R. teraphylla were collected from one year old plantsgrowing in the green house of botanical garden of Patna Science College, Patna. The shoots were firstwashed with running tap water for half an hour and then treated with detergent (Labolene 5 %, v/v for5 minutes) followed by 0.5% savlon for 5 minutes and finally washed with sterilized tap water. The plantmaterial then surface sterilized with 0.1% (w/v) HgCl2 for 3 minutes before rinsing four times withsterilized double distilled water.

The basal medium was MS salt and vitamins (Murashige and Skoog, 1962) supplementedwith 3% (w/v) sucrose and gelled with 0.8% (w/v) agar. The pH of the medium was adjusted to 5.8with 1N HCl or NaOH before autoclaving at 121oC for 20 min. After sterilization, the explants wereinoculated on MS Medium and were maintained in culture room at 25+2oC under photoperiod of 16

-2s-1 provided by white fluorescent light at 50 -60% relativehumidity. For each treatment, 30 replicates were used.

For hardening, microcuttings were transferred to plastic tray in a sterile mixture of soil :vermicompost : sand (2:1:1), covered with transparent polythene bags, irrigated with sterilized waterand maintained aseptically at temperature 25 + 2oC and 90-100% humidity.

Result and discussion : A variety of hormonal combinations were tried to induce multiple shootproduction from nodal explant. The number of shoots per explant, their length as well as growth frequencywere low in MS basal media supplemented with growth regulators BAP (0.5-3.0mg/l) + 2,4-D (0.5-1.0 mg/l) and BAP (0.5-3.0mg.l) + NAA (0.5mg/l). At higher concentration i.e. 2.0 mg/l BAP andlower concentration of NAA (0.5mg/l), shoot proliferation was comparatively better but it did notshow more than 2 shoots/explant and also a shoot length not greater than 3.0cm (Fig.1). Harisaranrajet al., (2009) induced multiple shoots from nodal cutting of R. tetraphylla cultured on MS mediumcontaining 2.0 mg/l BAP and 0.5 mg/l NAA and got average result. Salma et al., (2008) also used thiscombination on mass propagation of R. serpentina and got better result. At lower concentration ofBAP (0.5mg/l) and higher concentration of 2,4-D and NAA (1.0 and 0.5, respectively) the budmultiplication frequency was reduced. Some of the buds appeared to be healthy and some showedreddish brown colouration at their base.

Effect of kinetin at concentration between 3.0 to 10.0 mg/l with 2,4-D (0.5 to 1.0 mg/l)showed poor percentage of shoot growth and decreased shoot quality because most of the shootswere reddish brown at base and necrotic. It means lower concentration of kinetin was optimum for

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micropropagation of R. tetraphylla. Similar result was observed by Harisranraj et al. (2009), in cultureof R. tetraphylla on liquid media.

The proliferation of shoots in AS using concentration 3.0, 5.0, 8.0, 11.0, 14.0and 20.0 mg/l was maximal. The shoot regeneration frequency was highest (90%) in MSmedium supplemented with BAP (0.5 mg/l) + AS (3.0 mg/l) + IBA (0.2 mg/l). After eightweeks of culture, 3-5 multiple shoots were obtained from each explant. A tremendousincrease in height of the plant was noticed (3.8 to 6.3 cm per explant) (Table- 1; F ig. 2).Maximum shoot length appeared in BAP + AS (14m/l) + IBA (0.2mg/l). A frequent increasein adventitious shoot in R. serpentina was also noticed by Ilahi et al. , (2007), whenincorporated AS in basal MS media with BAP. The combination of BAP with adenine sulphatehas s timulatory effect on overall growth and number of shoots production. Gabriela (2011)has also observed the formation of callus at the base of the explant in Trifolium repens L.in the combination of BAP and adenine sulphate. To promote maximum shoot multiplication,higher concentration of AS i.e. 20 mg/l was incorporated with basal MS media, while theconcentration of BAP and IBA was maintained at the same level i.e. 0.5 mg/l and 0.2 mg/l,respectively. This combination did not show much promotive response for enhancement ofhigher percentage of shootlet /explant (nodal cuttings) but it was found to be promotive incase of adventitious shoot formation from callus in many plant species (Zibbu et al. , 2010;Gabriela, 2011).

In v itro rooting of microshoots excised from proliferating cultures were carriedout in MS full strength medium supplemented with 0.5, 1.0, 2.0, 3.0 and 5.0 mg/l Indole-3 - butyric acid. After one week shoots were subcultured on to plain MS medium.Roots got initiated from 0.5 to 3.0 mg/l concentration of IBA after twenty five days ofculture (Table-2). Rooting frequency was highest at 2.0 mg/l concentration of IBA. Atconcentration of 3.0 mg/l rooting was also satisfactory but IBA at higher concentration(5.0 mg/l) inhibited root ing. The suita bility of IBA at concentrat ion of 2.0mg/l andcombination of IBA and IAA (1.0 + 1.0mg/l) for rooting of microshoots of Rauv olf iaplants has also been reported by many workers (Faisal et al. , 2005; Ihsan Ilahi, 2007;Salma et al. , 2008). Further three months acclimatization of micropropagated plants in amixture of soil: vermicompost: sand in 2:1:1 proportion gave eighty percent survivabilityin growth chamber. F inally the pla nts were tra nsferred to gre enhouse where t hesurvivability was observed to be about 40%.

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Fig.1 . S tunte d grow th of a t BA P (2 .0 mg/l) &

N AA (0.5 mg /l)R . te trap hy lla

F ig. 2 . regeneration of a fter A S treatm ent

- Induction of shoots on

BAP+AS (3.0 m g/l ) + IB A

- M ultip lication of shoots on

BAP+ AS (14.0 mg/l) + IBA

- Induction of roots in M ic roshoot

- Acc lim atiz ed p lants

In vi troR . te traphylla

A

B

C

D

Table-1: Effect of Phytohormone on nodal cutting of R. tetraphylla for micropropagationG rowth regulant

Regenerationfrequency

(%)

Average No. of shoot /

explant

Average length of

shoot/explant (cm)

Callus induction at

base Shoot quality

BAP+AS+IBA

0.5+3.0+0.2 90.0 5.0+0.58 4.0+1.0 ++ Healthy

0.5.+5.0+0 .2 33.0 3.2+ 0.58 3.8 +0.53 + Healthy

0.5+8.0 + 0.2 60.0 4.0+0.58 4.0+ 0.58 + Healthy

0.5+11.0 +0.2 62.5 3.0+0.00 5.8 +0.53 + Healthy

0.5+14.0+0.2 67.0 3.0+0.58 6.3+0.58 + Healthy

0.5+20.0+0.2 80.0 2.6+0.58 5.0 +1.0 + Healthy

Values represent mean + SE of 30 replicates per treatment, recorded after two months, (-) no response,(+) slight callusing, (++) moderate callusing

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Table -2 Effect of IBA on root induction from in vitro raised microshoot of R. tetraphyllaafter two month of culture

Values represent mean + SE.References :

Faisal, M and M Anis (2002) Rapid invitro propagation of Rauv olfia tetraphylla L. Anendangered medicinal plant. Journal of Physiology and Molecular Biology of P lants, 8 (2):295-299.

Faisal, M, N Ahmad and M Anis (2005) Shoot multiplication in Rauvolfia tetraphylla L. usingthidiazuron. Plant cell, Tissue and organ culture, 80: 187-190.

Gabriela Vicas (2011) Effect of adenine sulphate (ADSO4) on the invitro evolution of white clovervariety (Trifolium repens L.). Analele Universitatii din Oradea Fascicula Protectia Mediului, XVII:203-210.

Ghosh, K C and N Banerjee (2003) Influence of plant growth regulators on invitro micropropagationof Rauvolfia tetraphylla L. Phytomorphology , 53:11-19.

Harisaranraj, R, K Suresh and S Saravanababu (2009) Rapid clonal propagation Rauvolfia tetraphyllaL. Academic Journal of Plant Sciences, 2 (3): 195-198.

Ihsan Ilahi, Fazal Rahim and Mussarat Jabeen (2007) Ehhanced clonal propagation and alkaloidbiosynthesis in cultures of Rauvolfia. Pakistan Journal of Plant Sciences, 13 (1): 45-56.

Murashige, T and F Skoog (1962) A revised medium for rapid growth and bioassays with tobaccotissue cultures. Physiologia Plantarum, 15; 473-497.

Patil, V M and Jeyanthi (1997) Micropropagation of two species of Rauvolfia (Apocynaceae). CurrentSciences, 72 (12): 961-965.

Salma, U, M S M Rahman, S Islam, N Haque, M Khatun, T A Jubair and B C Paul, (2008) Masspropagation of Rauvolfia serpentina L. Benth. Pakistan Journal of Biological Sciences, 11 (9):1273-1277.

t

4.0 + 1.0

4.0+ 1.0

4.3 + 0.58 2.3 + 0.58

-

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Sharma D, S Sharma and A Baruash (1999) Micropropagation and invitro flowering of Rauvolfiatetraphylla: a potent source of antihypertensive drug. Planta Medica, 65: 277-278.

Zibbu Garima and Amla Batra (2010) Effect of adenine sulphate on organogenesis via leaf culture in anornamental plant : Thevetia peruviana (Pers.) SCHUM. International Journal of Pharma and BioSciences, V1 (2): 1-9.

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VEGETATION OF PATNA DISTRICT OF BIHARNupur, Maheshwar Prasad Trivedi and Indrani Trivedi

Department of Botany, Patna Science College, Patna - 800005

Key words : Vegetation, Patna, Bihar.

The unique location and varied climatic conditions of Patna support luxuriant vegetationcover with huge species diversity. The vegetation aspects of the region are being presented.

Introduction : Vegetation is actually the totality of plant growth including large or small population ofeach species intermixed in a region. It is continuous though it differs from place to place according toenvironment gradient (Whittaker, 1967; Grime, 1987). It is the mere grouping of individual plants.Vegetations are brought about by plants modifying the habit of place in which they grow.

The present vegetation has suffered indeed from plants, animals, soil, climate and man.Thus, the vegetation of the region that we see around us is much interfered.

Materials and methods : The materials for the investigation are vegetation of Patna District inBihar. The area is predominantly urban. It is situated between 24°97' – 25°57' N latitude and 84°44'– 86°4' E longitude at an elevation of about 60 m above sea-level. It covers an area of 3202 Km2.The town is 20 kilometer long from east to wes t and 5 kilometer broad from north to south. The cityis situated on the land between the rivers Ganga on the north and the Punpun on the south. Adjoiningareas of Patna are Danapur, Maner, Khagaul, Bihta, Naubatpur, Masaurhi, Punpun, Fatuha, Barhand Mokama.

Extensive field studies have been done for which several rounds of trips were arranged.During each trip plants were collected, pressed, preserved and photographed. Listing of all thespecies has been done separately. Phytosociological studies of weeds and ruderals have also beendone.

Results and Discussion : The flora of Patna has relatively different composition and characteristicson account of variable rainfall, temperature, geology, topography and substratum of the locality whichinfluence the floristic and vegetation differently in various phytogeographic regions.

The only natural habitats available are a few marshy places and grass lands. The roadstraversing through the residential colonies have the trees planted on their sides for the purpose of shadeand beautification. Largest collection of species is in the Sanjay Gandhi Biological Park and KumhrarPark which are places of tourist interest.

With the onset of monsoon in different adjoining areas of Patna like Maner, Danapur, Barh,Mokama, etc., green herbs make their appearance in every nook and corner. Among them Cleome

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viscosa Linn., Cleome gynandra Linn., Desmodium gangeticum D.C., Indigofera hirsuta Linn.,Cassia occidentalis Linn., Cassia tora Linn., Dentella repens Forst., Tridax procumbens Linn.,Xanthium strumarium Linn., Heliotropium indicum Linn., Evolvulus alsinoides Linn., Phyllanthusfraternus, Cyperus distans Linn. f., Brachiaria ramosa Stapf. are more common.

The hedges of gardens and parks are formed by Murraya paniculata (Linn.) Jacq.,Lawsonia inermis Linn., Tabernaemontana divaricata (Linn.) R. Br., Duranta repens Linn. andothers. The comparatively moist waste places of different area of Patna are colonized by shrubs likeLawsonia inermis Linn., Ipomoea fistulosa Mart. ex. Choisy, Adhatoda zeylanica Medic., Lantanacamara Linn., Vitex negundo Linn. and others.

In the rainy season vegetation acquires remarkable luxuriance. The most conspicuousclimbers in the rainy season are Tinospora cordifolia (Willd.) Miers, Cayratia trifolia (Linn.) Domin.,Clitoria ternatea Linn., Coccinia grandis (Linn.) Voigt., Zizyphus oenoplia Mill., Basella alba Linn.and others. With the rainy season coming to an end, climbers like Cocculus hirsutus (Linn.) Deils,Cuscuta reflexa Roxb. Antigonon leptopus Hook and Arn. become prominent. During summer,climbers like Capparis zeylanica Linn., Zizyphys oenoplia Mill. and others are spread over bushes inmost of the areas.

The southern bank of river Ganga is lined with stone boulders with many pucca ghats; in thecrevices of rocks many amphibious and wet meadow species are seen growing, viz., Ranunculussceleratus Linn., Salvia plebeia R.Br., Alternanthera polygonoides (Linn.) R.Br., Polygonumbarbatum Linn., P. glabrum Willd., P. hydropiper Linn., Rumex dentatus Linn. and others. By earlysummer these plants are replaced by dry meadow species such as Argemone mexicana Linn., Scopariadulcis Linn., etc.

The lands along roads and railway tracks are inhabited by dry-meadows comprising largenumber of herbs and undershrubs like Argemone mexicana Linn., Cleome gynandra Linn., C. vicosaLinn., Tephrosia purpurea (Linn.) Pers., Cassia occidentalis Linn., C. tora Linn., Ageratumconyzoides Linn., Parthenium hysterophorus Linn., Lippia javanica (Burm. f.) Spreng, Chrozopherarottleri (Geiss.) Juss. ex spreng, Croton bonplandianum Baill., Amaranthus spinosus Linn., A. viridisLinn.,etc..

In the different areas of Patna, Mango and Guava orchards are common. The ecologicalcondition of these orchards favour the growth of several shade loving species, such as: Malvastrumcoromandelianum (Linn.) Gracke, Urena lobata Linn., Oxalis corniculata Linn., Desmodiumganget icum (Linn.) DC., Ageratum conyzoides Linn., Vernonia cinerea Linn., Cynoglossumlanceolatum Forsk and Achyranthes aspera Linn.

The lawns and parks are gradually scrapped off succulent grasses and colonized by coarserones. Mixed with grasses grow a number of herbaceous colonizers, e.g. Alysicarpus monilifer (Linn.)

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DC., Desmodium triflorum (Linn.) DC, Indigofera linifolia (Linn. f.) Retz., Boerhaavia diffusaLinn., Polygonum plebeium R.Br. In sheltered spots, as under the benches and near the railings ofparks, grow Vernonia cinerea (Linn.) Less, Achyranthes apsera Linn., Amaranthus spinosus Linn.,A. viridis Linn. and other erect forms . In many lawns and parks, the more xerophytic grasses likeImperata cylindrica (Linn.) Beauv., Dichanthium annulatum (Forst.) Stapf., etc. are replacing thecommon doob grass. The parks which have restricted entries and protected against grazing arecomparatively damper. They show a luxuriant growth of moisture loving grasses.

Due to rapid inflow of population many cultivated lands are being converted into dwellingsites. Despite this habitational pressure, gradually the outskirts, particularly the eastern and southernsides are still under cultivation. In the rainy season, paddy is grown on low lands and maize, millets andpigeon peas on higher lands. In winter wheat, barley, gram, pea and oil seeds are grown. The commonvegetable crops sown in the areas are Brasscia oleraceae Linn. var. capitata Linn. (cabbage), B.oleraceae Linn. var. botrytis Linn. (cauliflower), Raphanus sativus Linn. (radish), Abelmoschusesculentus (Linn.) Moench., Cucumis sativus Linn., Cucurbita maxima Duch., Luffa cylindricaRoem., Luffa cylindrica Roem., Momordica charantia Linn., Daucus carota Linn., Lycopersiconeculentum Mill., Solanum melanogena Linn., Allium cepa Linn., Allium sativum, Amorphophalluscampanulatus Bl. and others.

Weeds growing in the crop fields compete with crop plants for various growth requirementsand deplete the soil of nutrients. They spread diseases of crop plants either as their primary carrier or assecondary host. The weeds found with rainy season (Kharif) crops are Caesulia axillaris Roxb.,Eclipta prostrata Linn., Bacopa monneiri (Linn.) Pennell, Scoparia dulcis Linn., Alternantherapolygonoids (Linn.) R.Br., A. sessilis Linn., Polygonum glabrum Willd., Cyperus rotundus Linn.,Cynodon dactylon (Linn.) Pers. etc. and the weeds of winter season (Rabi) crops are Argemonemexicana Linn., Fumaria parviflora Lamk., Medicago lupulina Linn., Melilotus alba Desv., Caesuliaaxillaris Roxb. Eclipta prostrata Linn., Anagallis arvensis Linn., Ipomoea aquatica Forsk., Solanumnigrum Linn., S. surattense Burm f., Chenopodium album Linn., Croton bonplandianum Baill.,Cynodon dactylon (Linn.) pers. and several others.

The walls of delapdated houses show a luxuriant growth of weeds e.g. Blumea mollis(Don.) Merrill, Tridax procumbens Linn., Vernonia cinerea (Linn.) Less., Lindenbergia indica (Linn.)O. kuntze, Boerhaavia diffusa Linn., Commelina benghalensis Linn., Brachiaria reptans (Linn.)Gard et Hubb. and others. On older ruins, there are seen Ficus bengalensis Linn., F. racemosa Linn.,F. religiosa Linn. and others. The dust heaps and garbage dumps are harboured by the commonweeds , viz. Argemone mexicana Linn., Cleome gynandra Linn., C. v iscosa Linn., Crotonbonplandianum Baill. and others. In different areas of Patna, new building construction sites are generallylow- lands having dry-meadow species like Cassia occidentalis Linn., C. tora Linn., Solanumsurattense Burm. f. and Croton bonplandianum Baill., etc.

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The aquatic floristic components of the area include species like Nymphaea nauchaliBurm. F., Trapa bispinosa Roxb., Utricularia aurea Lour., Hygrophila auriculata Heine, Eichhorniacrassipes Solms., Pistia stratioides Linn., Wolffia arrhiza and others. At certain places with excessiveorganic matters, Eichhornia crassipes grows too densely to choke out all other plants in its vicinity.Azolla pinnata R. Br. is often seen forming red carpets in some local ponds.

Newly introduced species or invasive species which are now growing successfully in thearea but were not recorded by Haines (1921-25) and Srivastava (1956) are Parthenium hyterophorusLinn., Mecardonia procumbens (Miller) Small., Alternanthera polygonoides (Linn.) R. Br., Ageratumhoustonianum Mill. Gard. and others.

The phytosociological studies of weeds and ruderals were made at four sampling sites ofPatna-Masaurhi, Punpun, Danapur and Mokama. In Masaurhi 26 species, in Punpun 25 species, inDanapur 24 species and in Mokama 21 species were sampled for their percentage frequency, frequencyclass, density and abundance.

The most frequent species are Eclipta alba, Commelina benghalensis and Millingtoniahortensis in Masaurhi, Cynodon dactylon and Commelina benghalensis in Punpun, Cynodon dactylonand Vandellia crustacea in Danapur, and Vicia hirsuta and Grangea maderaspatana in Mokama. Theminimum frequency was shown by Atylosia scarabaeoides, Cayrotia trifolia and Launaea asplenifoliain Masaurhi, Oxalis corniculata, Blumea lacera , Desmodium gangeticum and Celosia argentea inPunpun, Phalaris minor and Portulaca in Danapur, and Gnaphalium indicum , Chenopodium album,Vernonia cinerea, Celosia argentea, Lathyrus aphaca and Xanthium strumarium in Mokama.

Surprisingly the histograms of almost all sampling sites are in accordance with Raunkiaer’slaws of frequency (1-4). The frequency class A is approximately 40% in Masaurhi, Punpun and Danapurwhile in Mokama it is 45%. The frequency class B was highest in Masaurhi but in Danapur the frequencyclass C is greater than B. It is the only place where Raunkiaer’s law was not followed (Nupur, 2009).No doubt, the area is wide and continuous study is needed for a wider conclusion and acceptability.

Acknowledgement : The authors are thankful to the Head, Department of Botany, Patna Universityfor facilities and valuable suggestions.

References :Grime, J.P. (1987). Plant strategies and vegetation processes. Wiley Interscience, New York NY.

Haines, H.H. (1921-1925). “The Botany of Bihar and Orissa” 6 parts, London.Nupur (2009). Problem and Conservational approach of angiospermic biodiversity of Patna and Vaishali

of Bihar, Ph.D. Thesis, Patna University, Bihar (Published).Srivastava, J.G. (1956). The vegetation of Patna District (Bihar). J. Ind. Bot. Soc. 38 : 186-194.Whittaker, R.H. (1967). Gradient analysis of vegetation. Biological Review, 42 : 207-264.

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SOIL MOISTUR E AND CON SERVATION POTEN TIAL OF SOM EGRASSES AND WEEDS IN URBAN AREAS OF PATNA DISTRICT

Indrani Trivedi and U.K.Sinha

Department of Botany,Patna University,Patna-800005.

Abstract: The soil binding capacity and moisture conservation potential of some grasses and weeds inurban areas of Patna district has been worked out. An efficient grass and weedy species is one whichdeclines the run-off velocity, able to dissipate the rain drop energy, retains the soil particles and improvesthe infiltration rate. The average root diameter was highest in Amaranthus virdis and lowest in Evolvulusalsinoides. The Dichanthium caricosum and Trianthema monogyna were adjudged the best soilbinders.Soil moisture conservation potential is highest in Dichanthium caricosum, Sagitaria sagitifoliaand lowest in Oxalis corniculata.

Key Words : Soil binding capacity,Soil moisture, grasses,weeds, urban area, Patna district

Introduction: The explosion of population in India has exerted tremendous pressure on the landfor production of food ,fuel and fodder. On the other hand problem of soil degradation is at alarmingrate.Physical degradation is a major limitation resulting in soil erosion. In India about 5334 milliontonnes of soil is eroded every year and about 29% of it is lost permanently to seas. Also as perestimate, about 16.35 tonnes per hectare per year soil is being eroded which is more than thepermissible limit of about 4.5. Trees are helpful in reducing the rain drop but grasses and weeds alsoprovide a cover on earth surface to intercept the rain water. Various weeds and grasses have theirown ability to check the erosion, improve infiltration or form a sod over ground. An efficient grassand weedy species is one which reduces the velocity of run off, able to dissipate the rain dropenergy, retains the soil particles and improves the infiltration rate (S ingh and Ratan, 2008). In thepresent study an inves tigation has been made to work out soil moisture and conservation potential ofsome grasses and weeds.

Materials and methods: The materials for the present investigation are grasses and weeds growingnaturally in diverse niches. Roots growth, their diameter and conservation efficiency were worked out.

Root growth-Root excavations of selected species were done by carefully working side ways anddown wards till the root tips were exposed (Bohm, 1979). The roots thus excavated were cleaned inwater and separated for data recording. The roots of selected species were sampled from the quadratswhich were clipped for estimation of above ground biomass. The soil monolith of 25x25x25cm wasremoved. The dug out monolith was carried to laboratory and flooded with water. The roots wereseparated by hand.

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Root diameter-After excavations and washing, the roots of each plant were separated and dividedinto four categories viz. main, primary, secondary and tertiary roots. The diameter of all the four categorieswas taken at 3 points along the length with the help of a vernier caliper and average diameter of eachcategory of roots was worked out.

Root diameter was measured with the help of vernier caliper and root volume was measuredby placing the roots in a measuring cylinder containing known volume of water. The increase in volumeindicated the root volume. The binding capacity of the root was calculated by the formula F=V\R²where F is the binding factor, V is the volume in ml and R is the average radius in mm of the roots. Theevaluation was made in natural condition (tables 1and 2).

Moisture conservation-

Water (%) by mass:

Wet mass of soil = (wet mass of soil + box)-(mass of box)

Dry mass of soil = (dry mass of soil + box)-(mass of box)

Water (%) by mass = (wet mass - dry mass / dry mass) x 100

Results and discussion: The average root diameter was highest in Cassia tora (6.55mm) whilelowest in Evolvulus alsinoides (1.275mm).The grasses like Cynodon dactylon, Cyperus rotundusand Dichanthium caricosum have more or less similar diameters( Table 1).

The soil binding capacity of Dichanthium caricosum was the highest (90.32) followed byCyperus rotundus (63.775).Cynodon dactylon showed 52.426 soil binding factor. Among the weedsTrianthema monogyna (43.103) and then Amaranthus viridis (39.836) showed the highest value andleast (7.665) by Portulaca quadrifida( Table 2).

Soil moisture conservation potential is highest in Dichanthium caricosum (17%) Sagitariasagitifolia (17%) and lowest in Oxalis corniculata(1%).

Singh and Ratan (2008) have worked out the various parameters for grass ability to reduceerosion and enhance infiltration. They have concluded that Heteropogon contortus (lumpa grass) isbest suited grass in Bundelkhand region including Jalaun district (Tyagi,1997). Similarly Muthana (1981)also recommended H. contortus,Dichanthium annulatum and Pennisetum purpureum most suitable forthe above mentioned region.

Singh and Soni (2009) have worked out the soil conservation value and on various parametersfor revegetation and consolidation of uranium tailings at Jaduguda in Jharkhand.

Munshower,1993 emphasized that native species were less competitive and can be usedin rehabilitation and the disturbances permit the germination and development of non-seededspecies.

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Dadhwal and S ingh (1993) s tudied the rooting behavior of five trees, two shrubs andsix grass species. The best root growth, soil binding capacity and nodulation were found inLeucaena leucocephala, Pennisetum purpureum, Eulatiopsis binata and Cymbopogonfulv us.

The higher values of binding capacity of species may be due to their well developed lateralroots and higher root volume(Trivedi and S inha,2012). Biomass is increased due to the productionof greater number of lateral roots .The absorption of water is also enhanced under moisture stressconditions (Vasistha, 1992)Suitability of grasses, weeds and others is being judged for reclamationof varying lands.

Aknowledgements : We are thankful to Prof. S.N. Sharma, HOD, Botany, Patna University forproviding laboratory facilities.

References:

Bohm, W.1979. Methods of studying root system: Ecological studies. Spring-Verlag, Berlin, NewYork. 33

Dadhwal, K. S. and Singh, B. 1993. Rooting behavior of different plant spcies in lime stone minedarea. Indian Forester. 119(2):71-74.

Muthana, K. D.1981. Forage forest particles envisaged for the development of Bundelkhand region(U.P.). Indian J.Range Mgmt. 2(1 and 2):73-79.

Munshower, F.F.1993. In: Practical handbook of disturbed land revegetation. Lewis publishers,London, Tokyo.

Singh Lal and Soni Prafulla. 2009. Species selection for revegetation and consolidation of Uraniumtailings at Jaduguda in Jharkhand, India , The Ecoscan 3(1&2) :19-25.

Singh U.N and Ratan Neel. 2008. Assessment of soil and water conservation of some grassspecies in light Olive-Brown soils of Jalaun Based on overall performance index, TheEcoscan 2(2) :219-222.

Tyagi , R. K.1997. Grassland and fodder atlas of Bundelkhand. Indian grassland and fodder researchinstitute, Jhansi (India), pp. 39-40.

Vasis tha ,H. B. 1992. Growth behaviour of some colonizing plant species of rock phosphate minespoils areas of Doon Valley. Ph.D. Thesis submitted to H. N. B. Garhwal university Springer,(Garhwal).

Trivedi Indrani and Sinha U.K. 2012 Soil binding capacity of some grasses and weeds in urban arasof Patna district.Int. J. Mendel,29 (1-4),23-24.

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Species

Main roots

Primary roots

Secondary roots

Tertiary roots

Average root diameter

Dichanthium caricosum 5.2 2.7 1.3 0.1 2.32 Cyperus rotundus 4.5 3.7 1.8 1.2 2.80 Cynodon dactylon 3.7 2.3 1.6 1.1 2.17 Trianthema monogyna 4.4 3.5 1.8 1.2 2.72 Amaranthus viridis 10.8 7.7 4.7 1.3 6.12 Evolvulus alsinoides 2.5 1.8 o.6 0.2 1.27 Oxalis corniculata 3.8 2.6 1.3 0.2 1.97 Phyllanthus fraternus 3.6 2.4 1.7 0.9 2.15 Brachiaria ramosa 3.8 2.6 1.3 0.9 2.15 Molunga pentaphylla 5.4 4.7 2.3 0.9 3.32 Launea pinnatifida 5.4 3.6 2.3 1.4 3.17 Acalypha indica 5.6 3.7 2.5 1.8 3.40 Sagittaria sagittifolia 10.4 7.3 3.6 1.8 5.77 Ruellia tuberosa 10.9 6.4 3.9 1.8 5.75Parthenium hysterophorus 8.2 4.5 3.7 1.4 4.45Asplenium indicum 3.8 2.3 1.2 0.9 2.05Lindernia spp. 6.2 4.3 2.3 1.4 3.55Euphorbia hirta 5.6 4.5 2.7 0.9 3.42Anisomeleus indica 6.8 5.7 4.3 1.6 4.60 Solanum nigrum 5.8 3.3 1.8 0.9 2.95 Vernonia cineraria 10.4 6.4 3.7 1.3 5.45 Poulszia indica 12.4 8.9 3.6 1.6 6.62 Brachiaria reptans 7.9 6.7 5.2 4.6 6.10 Nicotiana plumbaginifolia 9.5 4.9 1.7 0.2 4.07 Portulaca quadrifida 7.3 5.4 3.2 1.2 4.27

Table 1: Root diameter (mm) of selected plant species

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Table 2: Binding capacity (Root Conservation Value)

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Table 3 : Mois ture conservation value

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PHYTOTHERAPEUTIC ROLE OF CENTELLA ASIATICA L.Bimal K. Mehta

Guest faculty (Biotechnology and Env. Sc.)Dept. of Botany,

Patna Science College, P.U. Patna

Abstract : Centella asiatica L. has been used for centuries (about 3,000 years) as a medicine in theAyurvedic tradition of India. The plant extracts were incorporated into the Indian Pharmacopeia,wherein addition to being recommended for wound healing, treatment of skin conditions such as leprosy,lupus, varicose ulcers, exzema and psoriasis and in brain stimulation, treatment of venous hypertension,microangiopathy and in gastric ulcers. The centella asiatica L. is a rejuvenative nervine recommendedfor nervous disorder, epilepsy, senility and premature aging and also as a brain tonic. The samples of theIndian plants collected from different places showed the presence of glycosides : indocentelloside,brahmoside, brahminoside, asiaticoside, thankuniside and isothankuniside etc. A new pligosaccharidecentellose,” Kaempferol, quercetin and stigmasterol have also been reported.

With the development of science many new drugs of synthetic origin have come into existencebut times have changed and we are back to the herbs and herbal products that our ancestors used.

Key words : Phytotherapeutic role, Centella asiatica

Introduction : Human beings have to depend on Nature for sustenance and survival. The traditionalsystem of medicine in india dates back to the age of the Rigveda (2500 to 1600 B.C.). With the developmentof science, many new drugs of synthetic origin have come into existence and with the rapid growth of thepharmaceutical industry the value and use of the herbal medicines has come down in the recent past. Timeshave changed and we are back to the herbs and herbal products that our ancestors used.

Centella asiatica is a perennial plant native to India, China and Indonesia,. The plant commonlyknows as “Brahmi” belongs to the family Apiaceae (=umbelliferae.) It is found throughout our country,more in the tarai regions of the himalayas and Bihar near marshy place or river banks. Plant is a trailingherb, branched with soft node and internode, stem rooting at nodes.

Leaves are orbicular, reniform 1.25cm to 6.25cm in diameter, glabrous with crenate margin.Flowers are sessile, cluster of 3 to 6, medium sized, multicoloured. Corolla with two rows of petals andwith white petaloids intermingled with stamens in its centre. White petals with variegations and sometimespink streaks . Flowering takes place during march-April. Fruits are globular nearly 8mm in diameterwith 7-9 raised ribs over which the seeds appear. The plant can be harvested at any times of the yearand is used fresh or dried. In common with most traditional phyto therapeutic agents, C. asiatica isclaimed to possess a wide range of pharmacological effects being used for wound healing capacity(Suguna et al. 1669), Mental disorder (Apparao et al. 1973), fungicidal, antibacterial (Oyedeji et al.2005), antioxidant and anticancer properties (Jayashree et al. 2003) Centella asiatica has also been

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reported to be useful in the treatment of inflammations, diarrhoea, asthma, tuberculosis and various skinlesions and ailments like leprosy and psoriasis. In addition, numerous clinical reports verify the Ulcerpreventive and anti depressive, sedative effects of C. asiatica preparations, as well as their ability toimprove venous insufficiency and microangiopathy.

Popularity of C. asiatica is mostly due to its efficacy and versatility, especially referring to itsreputation as a wound healing agent and brain stimulant (promoting brain growth and improving learningand memory.) Many scientists in the world have been conducting quite extensive experimental andclinical investigations and focusing their interests on searching for some promising compounds with higheffectiveness and low toxicity for the benefit of human health.

The present paper deals about the recent advances in the phytochemistry and bioactivities ofC. asiatica, particularly mentioning its principally active mass-triterpenoids.

Materials and Methods : The present study is based on extensive laboratory studies on Centellaasiatica (L). The study was limited to the Urban and rural localities of Patna, (Bihar). The traditionalhome remedies are still alive here. Interviews were conducted involving some patients, Ayurvedic doctorsand Vaidya. The diagnosis were based on clinical features. The dried powdered plant material (Leaves,roots, aerialparts, stem, seeds) was extracted with chloroform in a Soxhlet extraction apparatus. Thesolvent was removed under reduced pressure and semi solid mass was obtained (Yield 16.7%). Theextract showed positive test for alkaloids, volatile oils and saponins. The alkaloid were identified bychromatographic comparison with reference compounds and it was further confirmed. The extract atthe different doses of 50, 100 and 200 mg/kg was suspended in aqueous between 80 solution (2%).The dose range is usually 60-120 mg/day, although higher doses may be provided in some situations.(Karting, T. 1986). Nausea has been reported in high level of intake. It should not be taken internally asa supplement by children under the age 4 or breast feeding/ Pregnant mothers. People taking sedativesshould not use Centella asiatica L. as a supplement.

Results and Discussion :

Phytochemistry

Triterpenoids : Triterpene is a major and the most important component of C. asiatica, regarded asa marker constituent in terms of quality control. The triterpenes obtained from C. asiatica are mainlypentacyclic triterpenic acids and their respective glycosides, including asiatic acid, asiaticoside, madecassicacid, madecassoside, brahmoside, brahmic acid, brahminoside, thankuniside, isothankuniside,centelloside, madasiatic acid, centic acid, cenellic acid, betulinic acid, indocentic acid, etc. Earlier workon this plant has led to the isolation of many triterpenoid constituents. Brinkhaus et al., (2000) hasalready reviewed the chemical profile of C. asiatica before 2000, thus we predominantly collectphytochemistry information on novel compounds isolated from C. asiatica in recent years. Shukla etal., (2000) separated a new ursane triterpenoid from C. asiatica and exhibited its dose-dependentgrowth inhibitory activity against larvae of Spilarctia oblique. Later on, Matsuda et al.,(2001) isolateda new olean-13-ene triterpene, Centellasapogenol A, and its Oligoglycoside from C. asiatica.

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Continuously, in their comparative study on the genetype cultivated in Sri Lanka, however, they obtainedtwo new ursane-type triterpeneoligoglycosides, Centellasaponins B and C, and an Oleanane-typetriterpene Oligoglycoside, Centellasaponin D. Kuroda et al, 2001 also seperated 5 new triterpeneglycosides from the aerial parts of C. asiatica and none of these saponins revealed significant cytotoxicity.Moreover, Jiang et al., (2005) identified four new triterpenoid glycosides named Asiaticoside C, D, E,and F from the BuOH fraction of C. asiatica. In addition, drawing assistance from the technology ofbiotransformation, Monti et al., (2005) prepared an array of novel derivatives of asiaticoside withmolecular diversity and functional variety.

Being the chief bioactive substances in C. asiatica, triterpenoid derivatives play an importantrole in the aspect of medicinal application. Several of their traditional uses have been scientificallyvalidated and some of the active principles have also been reported.

Flavonoids : C. asiatica has also been reported to containnumerous flavonoids, including quercetin and kaempferol, catechin, rutin and naringin, as a major part ofthe total phenolic contents, some of which are major contributors in particular to the antioxidative activityof C. asiatica (Zainol et al, 2003). Based on the hypothesis of free radical mediated toxicity in oxidativestress process and depending on its antioxidant properties, C. asiatica has been recently indicated toshow anti-lipid peroxidative and free radical scavenging activities (Hussin et al. 2007 ; Wong et al. 2006).In addition, (Matsuda et al. 2001) isolated a flavonol, petuletin, and kaempferol, 3-O- -D-glucuronidefrom the aerial parts of C. asiatica cultivated in Vietnam, both of which exhibited potent inhibitory activityon aldose reductase in rats. And bioflavonoids of C. asiatica have ever been exhibited to be efficacious invenous insufficiency, probably due to their actions on mucopolysaccharide metabolism.

Other components : Coherent researches on C. asiatica also revealed the presence of Polysaccharides,Polyyne-alkene, amino acids, fatty acids, sesquiterpenes, alkaloids, sterols, carotenoids, tannin,chlorophyll, pectin, inorganic salts, etc.

Phytotherapeutic Action

Wound healing properties : C. asiatica have been shown to produce different actionson the various phases of wound repair (Suguna et al. 1996). Scientific studies proved that triterpenesfrom C. asiatica stimulated extracellular matrix accumulation in rat experimental wounds, as furtherevidenced in vitro by gene-expression alternation in a human dermal fibroblast (Coldren et al. 2006).Asiatic acid was the only component responsible for the collagen synthesis stimulation, whilemadecassoside was able to increase significantly collagen secretion. Advanced studies indicated thatasiaticoside induced type I collagen synthesis via the activation of the TGF- receptor I kinase-independent Smad pathway, which forged a basis for molecular understanding of Centella’s bioactivityon wound healing (Lee at al. 2006).

Brain stimulating effects : C. asiatica possesses various CNS effects such as stimulatory-nervinetonic, rejuvenant, sedative, tranquilizer, especially memory improvement and intelligence promotingproperty. Some of these bioactivities have been demonstrated experimentally. Scientific findings exhibited

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that the aqueous extract of C. asiatica has cognitive enhancing effect and an antioxidant mechanism isinvolved (Rao et al. 2005 ; Veerendra et al. 2002). Additionally, C. asiatica leaf extract was not onlyshowed to improve spatial learning performance and enhance memory retention in neonatal rats duringgrowth spurt period, but also found efficient in enhancing hippocampal CA3 neuronaldendritic arborizationin rats, thus providing evidence to show the effect of this plant extract on the brain regions involved inlearning and memory (Mohan Das et al. 2005, 2006).

Treatment of venous hypertension and microangiopathy

Studies done in accordance with standardized scientific criteria have shown that triterpeniccomponents in C. asiatica exhibit a positive effect in the treatment of venous insufficiency andmicroangiopathy and several prospective, placebo-controlled, randomized trials convinced theeffectiveness of the total triterpenic fraction of C. asiatica by improving microcirculation, edema anddecreasing capillary permeability (De Sanctis et al. 2001).

Actions on gastric ulcer : Many scientific findings suggest the potential use of C. asiatica and itsactive ingredient as anti-gastric ulcers drugs. (Cheng et al. 2004) displayed the healing effects of C.asiatica water extract and asiaticoside on acetic acid induced gastric ulcers in rats, by significantlyattenuating the myeloperoxidase activity, promoting epithelial cell proliferation and angiogenesis, andupregulating expression of basic fibroblast growth factor in the ulcer tissues, therefore strengthening themucosal defensive factors. Centella extract was also reported to show anti-ulcerogenic activity againstvarious physical and chemical factors, such as ethanol-, aspirin-, cold-restraint stress- and pyloricligation induced gastric ulcers in rats (Sairam et al. 2001). In addition, (Guo et al. 2004) showed thatC. asiatica water extract and asiaticoside have an anti-inflammatory property that is brought about byinhibition of NO synthesis and thus facilitates ulcer healing.

Anticancer activity :light on C. asiatica, in search of potential bioactive molecules against tumor. (Babu et al. 1995) foundcrude extract of C. asiatica as well as its partially purified fractions exhibited selective cytotoxicity invitro and anti-tumour properties in vivo.

Other effects : In addition to above-mentioned activities, triterpenoids in C. asiatica were also claimedto be effectively applied for anti-bilharzial, antifertility, anti-herpes simplex virus, radioprotection, cosmetics,immunomodulatory and antagonizing liver fibrosisAdmitting of no exception, C. asiatica, despite of itsmultifarious favorable uses, has been inevitable to show several adverse effects, including mutagenicity,allergic contact dermatitis, and hepatotoxicity, perhaps mainly evoked by its triterpenoids components.

The requirement of C. asiatica in pharmaceutical industries has been sharply increasing, thusleading to the over exploitation of this herb. It has already been listed as threatened species by theInternational Union for Conservation of Nature and National Resources (IUCN) and an endangeredspecies. Therefore application of tissue culture approaches for rapid multiplication of elite clones andgermplasm conservation is of vital importance. However, further studies are still needed to be done forthe evaluation of the genetic resources of the plant for variation in morphological, growth, and herb and

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yield related characters to identify high herb and madecassol yielding populations suitable for use inagronomical and plant breeding programs.

A great progress has been made over the past decades in study of biologically active componentsand bioactivities of C. asiatica, but the results are still unsatisfactory. More scientific data are requiredbefore recommendation for increase in its utilization can be given with confidence.

References :

Apparao MVR, Srinivasan K and Rao K (1973) The effect of mandookparni (Centella asiatica) on thegeneral mental ability (Medhya) of mentally retarded children[J]. J Res Indian Med, 8: 9-16.

Babu TD, Kuttan G and Padikkala (1995) J. Cytotoxic and anti-tumor properties of certain texa of umbelliferaewith specific reference to Centella asiatica (L.) urban[J]. J Ethnopharmacol, 48(1) : 53-57.

Brinkhaus B, Lindner M and Schuppan D. (2000) Chemical, pharmacological and clinical profile ofthe East Asian medical plant Centella asiatica[J]. Phytomedicine, 7(5) : 427-448.

Cheng CL, Guo JS and Luk J (2004) The healing effects of Centella extract and asiaticoside on aceticacid induced gastric ulcers in rats[J]. Life Sci, 74(18) : 2237-2249.

Coldren CD, Hashim P and Ali JM (2003) Gene expression changes in the human fibroblast inducedby Centella asiatica triterpenoids[J]. Planta Med, 69(8) : 725-732.

De Sanctis MT, Belcaro G and Incandela L (2001) Treatment of edema and increased capillary filtration invenous hypertension with total triterpenic fraction of Centella asiatica: a clinical, prospective, placebo-controlled, randomized, dose-ranging trial[J]. Angiology, 52(Suppl 2) : S55-59.

Guo JS, Cheng CL and Koo MW. (2004) Inhibitory effects of Centella asiatica water extract and asiaticosideon inducible nitric oxide synthase during gastric ulcer healing in rats[J]. Planta Med, 70(12) : 1150-1154.

Hussin M, Abdul-Hamid A and Mohamad S (2007) Protective effect of Centella asiatica extract andpowder on oxidative stress in rats[J]. Food Chem, 100(2) : 535-541.

Jayashree G, Kurup MG and Sudarslal VS (2003) Anti-oxidant activity of Centella asiatica onlymphoma-bearing mice[J]. Fitoterapia, 74(5) : 431-434.

Jiang ZY, Zhang XM and Zhou (2005) J. New triterpenoid glycosides from Centella asiatica[J].Helv Chim Acta, 88(2) : 297-303.

Karting, T. (1986) Clinical application of Centella asiatica (L) urb. in herbs spices and medicinalplants : Recent Advances in Botany, Horticulture, and Pharmacology; Vol. 3, Craker LE, SimonJE (eds) Phoenix, AZ : Oryx Press, 145-173.

Kuroda M, Mimaki Y and Harada H (2001) Five new triterpene glycosides from Centella asiatica[J].Nat Med, 55(3) : 134-138.

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Lee J, Jung E andKim Y (2006) Asiaticoside induces human collagen I synthesis through TGFbeta receptorI kinase (TbetaRI Kinase)-independent smad signaling[J]. Planta Med, 72(4) : 324-328.

Matsuda H, Morikawa T and Ueda H (2001) Medicinal foodstuffs. XXVI. Inhibitors of aldosereductase and new triterpene and its oligoglycoside, centellasapogenol A and centellasaponinA, from Centella asiatica (Gotu Kola)[J]. Heterocycles, 55(8) : 1499-1504.

Matsuda H, Morikawa T and Ueda H (2001) Medicinal foodstuffs. XXVII. Saponin constituents ofgotu kola (2): structures of new ursane- and oleanane-type triterpene oligoglycosides,centellasaponins B, C, and D, from Centella asiatica cultivated in Sri Lanka[J]. Chem PharmBull (Tokyo), 49(10) : 1368-1371.

Mohandas Rao KG, Muddanna Rao S and Gurumadhva Rao S. (2005) Centella asiatica (linn) inducedbehavioural changes during growth spurt period in neonatal rats[J]. Neuroanatomy, 4(1) : 18-23.

Mohandas Rao KG, Muddanna Rao S and Gurumadhva Rao S. (2006) Centella asiatica (L.) leafextract treatment during the growth spurt period enhances hippocampal CA3 neuronal dendriticarborization in rats[J]. Evid Based Complement Alternat Med, 3(3) : 349-357.

Monti D, Candido A, Silva MMC. et al. (2005), Biocatalyzed generation of molecular diversity:selective modification of the saponin asiaticoside[J]. Adv Synth Catal, 347(7-8) : 1168-1174.

Oyedeji OA and Afolayan AJ. (2005) Chemical composition and antibacterial activity of the essentialoil of Centella asiatica growing in South Africa[J]. Pharm Biol, 43(3) : 249-252.

Rao SB, Chetana M and Uma Devi P. (2005) Centella asiatica treatment during postnatal periodenhances learning and memory in mice[J]. Physiol Behav, 86(4) : 449-457.

Sairam K, Rao CV and Goel RK. (2001) Effect of Centella asiatica Linn on physical and chemicalfactors induced gastric ulceration and secretion in rats[J]. Indian J Exp Biol, 39(2) : 137-142.

Shukla YN, Srivastava R,Tripathi AK, et al. (2000) Characterization of an ursane triterpenoid from Centellaasiatica with growth inhibitory activity against Spilarctia bliqua[J]. Pharm Biol, 38(4) : 262-267.

Suguna L, Sivakumar P and Chandrakasan G. (1996) Effects of Centella asiatica extract on dermalwound healing in rats[J]. Indian J Exp Biol, 34(12) : 1208-1211.

Veerendra Kumar MH and Gupta YK. (2002) Effect of different extracts of Centella asiatica oncognition and markers of oxidative stress in rats[J]. J Ethnopharmacol, 79(2) : 253-260.

Wong SP, Leong LP and Koh JHW. (2006) Antioxidant activities of aqueous extracts of selectedplants[J]. Food Chem, 99(4) : 775-783.

Zainol MK, Abd-Hamid A, Yusof S, et al. (2003) Antioxidative activity and total phenolic compounds of leaf,root and petiole of four accessions of Centella asiatica (L.) Urban[J]. Food Chem, 81(4) : 575-581.

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ETHNO – BOTANICAL STUDIES ON MACROPHYTES OF BHOJPUR,(BIHAR)

Babita Singh

Department of Botany,Patna Science College, Patna – 800005.

Abstract : Bhojpur forms the district of Bihar Aquatic angiosperms of this area have been almostignored. This group of plants has got the potential for exploitation as animal feed, human food, medicines,soil additives and fuel production. Present paper deals with the taxo-ethno-botanical information ofsome of important macropyhtes of Bhojpur. About 10 plants have been described. The information hasbeen collected from the reliable and authentic sources by paying the periodical visit to collect aquaticweeds for voucher specimens as well as to get the ethno-botanical informations from the old personsand practioners of ayurvedic medicines.

Key words : Ethno-Botanical studies, Macrophytes, Bhojpur

Introduction : Ethno-botany is a branch of botany dealing with the utilization of plants and their partsby the tribals or rural people from the times immemorial. It is a science in which the relationship betweenthe tribals people and the plants studied and deals with the fact that plants have close relationship withman directly or indirectly almost in every field. Not much work has been done except S.K Jain (1963),Bhargava (1981), Mahesh wari and Singh (1984).The plants growing around have greatly influencedthe natives from time to time directly or indirtectly Hindus use Ocimum sanctum(LAMIACEAE) tobathe their idols. Flowers of Nelumbo nucifera , Nymphae stellata and Nymphea nymphoides areoffered to lord Shiva. People use aquatic weeds as fodder and also as vegetable after cooking, Thetribals use some of the aquatic weeds for burns cut etc for themselves and for their burns or cut.

Materials and Methods : Periodical visits were made to visit all places of Bhojpur for the collectionof aquatic and wetland angiosperms. Information regarding the ethno-botanical and ethno-medicinalplants were collected from men and women of all the spheres of life. Elderly persons in the remoteareas treating tribal people by the local vaidya were also consulted. Repeated quarries were made toget data verified and confirmed.

Observation and Result : Observations made during the course or this study are enumerated as such :-

1. Trapa natans Linn.

Family : TRAPACEAECommon Name : Singhara

Taxonomic Notes : A much branched, annual, aquatic rooted herb, with asssimilatry root stock,Leaves :rhomboid, with swollen petiole and purple-tinged beneath. Flower : white, solitary axillary.Sepals : Persistent, Spinous. Nut : angled.

Commonly grown for fruits but occasionally found growing in pond as escape.

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Medicianl use : The flour of dried nuts are laxative. The unripe nut are used in the treatment ofabdominal disorders.

2. Utricularia inflex (Linn.) Cl.

Family : LENTIBULARIACEAE

Common name : Bladder wort

Taxonomic Notes : A wild annual, rooted, aquatic herb, with leaves pinnately divided into capillarysegments each with small bladder at their base. Flower : 3mm across, yellow in aerial raceme. Calyx :acrescent. Capsule : globose, with minute seeds

Commonly found growing in shallow water of ponds and paddy crop feild.

Medicinal use : The paste of flowers are applied externally in headache.

3. Typha ungustata Bony & chaub

Family : TYPHACEAE

Common name : Patera

Taxonomic Notes : A wild, annual, unbranched grass, with leaves exceeding the flower which arearranged in cylindric spikes. The male and female flowers are seprated by a long interval. Female :pale-brown Male flower :mixed with clavate toipped pistillodes.

Commonly found growing on the edges of ponds and ditches.

Medicinal uses : The infusion of floral spikes are used in the treatment of sterility among women.

4. Alternanthera philoxeroides (mart.) Griseb

Family : AMARANTHACEAE

Common name : jangli kulphi

Taxonomic Notes : A wild annual, aquatic herb with rooting at nodes. Stem : fistular, glabrous. Leaves: oblong lanceolate, opposite decussate. Flowers :6mm long, white, in axillary, globes head. Bracts andbracteoles : membranous, subequal: persistent.

Commonly found growing on the slopes of pond and abundant in ditches.

Medicinal use : The paste of entire plants are used in diarrhoea of cattles.

5. Ranunculus sceleratus Tulin

Family : RANUNCULACEAE

Common Name : jaldhania

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Taxonomic Notes : A wild annual subaquatic herb, 40-50 cm tall with, fistula internodes, Leavesvariable, lower long petioled while uppermost sessile. Flowers : 6-9 mm across, yellow. Achenes :obliquely abovate on an ablong receptacles called torus.

Commonly found growing in most places on the bank of rivers pond and ditches

Medicinal use : Extract of Leaves are used in intermittent fevers

6. Nymphaea stellata Willd.

Family : NYMPHAEACEAE

Common name : Bhent

Taxonomic Notes : A wild annual, rooted aquatic herb, with rotund leaves which gets streaked withpurple beneath, Flowers : white conspicuous in solitary axillary on highly elongated peduncle.

Commonly found growing in shallow stagnant water of ponds and ditches.

Medicinal use : The decoction of roots are applied externally in sores as antiseptic

7. Potamogeton indicus Roxb.

Family : POTAMOGETONACEAE

Common Name : Wild Kumbhi

Taxonomic Notes : A wild annual, floating herb, with purple streaked internodes. Submerged Leaveslanceolate, thin while Floating ones elliptic, thicker. Stipules : scarious. Spikes above the surface water.

Commonly found growing in shallow water of ponds .

Medicinal use : The Leaves are diuretic and used in kidney problems.

8. Ceratophyllum demersum Linn

Family : CERATOPHYLLACEAE

Common Name : Kajri

Taxonomic Notes : A wild annual rootless, submerged, aquatic herbs, with verticillate leaves. Flowers: unisexual monoecious. Male & Female flowers : solitary. Nutlets : avoid, coriaceous.

Commonly found growing in shallow water of ponds and ditches

Medicinal use : The Paste of entire plants are used externally in cutaneous affections .

9. Nymphaea nymphoides Rox

Family : MENYANTHACEAECommon Name : Kumudini

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Taxonomic Notes : A wild annual rooted aquatic herb, with rooting at nodes . Leaves :suborbicular,with purple-tinged beneath. Flower : white, in clusters at the base of petiole.

Commonly found growing on the edges of ponds and canals

Medicinal use : The paste of leaves are used in jaundice.

10. Ammania baccifera Linn.

Family : LYTHRACEAE

Taxonomic Notes : A wild, annual, erect, glabrous herb, with tap-root stock. Stems : angular, withvariable length of internodes, Leaves : Opposite, lanceoiate, with cuneate and 1- nerved at base.

Commonly found growing in moist paddy field after harvesting.

Medicinal use : The paste of root are diuretic.

References :

Bhargava, N. 1981 Plants in folk life and folk love in Andaman and Nicobar Island.

Jain, S.K., 1983. Studies in Indian Ethnobotany, IRL. Bull, 1(2): 126-128.

Jain, S.K., 1980. Glimpses of Indian Ethnobotany, Oxford and IBH Publishing company, New Delhi.

Maheshwari, J.K. and J.P. Singh, 1984 Contribution of the ethno-botany of Bhora Tribe of Bijnor andPauri Garhwal district, U.P.J. Econ, Taxon. Bot 5:251-259.41

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FLAVONOID DIVERSITY AND SYSTEMATIC EVALUATION OFCROTOLARIA SPECIES

Fahmida Rahman*, Naheed Ahmad**, Md. Khursheed Alam***

*Research Scholar, Department of Botany, Patna University, Patna - 5**Associate Professor, Department of Botany, Patna University, Patna - 5***Guest Faculty, B.Sc. Biotechnology, Patna Science College, Patna - 5

Abstract : Crotolar ia belongs to family Fabaceae, comprising of both wild and cultivated species.The genus having tremendous economic and pharmaceutical importance. It has eluded classificationtill today due to presence of homoplastic characters. Various species of Crotolar ia are rich insecondary metabolites and other phytochemicals like phenols, flavonoids, anthocyanins, alkaloidsetc. They are best chemosystematic markers and are very much helpful in taxonomic characterizationof the genus Crotolar ia. In the present study, distribution of flavonoids in two species of Crotolariaviz. Crotolar ia juncea L. and Crotolaria striata DC has been compared and their chemosystematicevaluation carried out.

Key words : Flavonoid, BAW, Spectrophotometer

Introduction : The genus Crotolaria of the family Fabaceae has been withstanding adequate generalclassification for more than 100 years. It can serve as a group where the distribution of some keycharacters are contradicted by the presence of others.

The large amount of the homoplastic characters has often lead to extreme lumping of generain order to minimize contradiction in character distribution. Attempts have been made to get a homogenericdelimitation (Van Wyk and Schutte 1995). However till today genus Crotolar ia has ambiguousclassification.

The present study is an attempt to make insight into the flavonoid patterns of two species ofCrotolaria named C. juncea L. and C. striata DC.

The flavonoids are a group of polyphenolic compounds of C3– C6– C3 basic skeleton,which are widely distributed throughout the plant kingdom and consis ts of about 300 varieties.Some of the important categories are Flavones, F lavonones, Flavonols, anthocyanin etc. Theyare secondary metabolites and their production are genetically and physiologically controlled,hence they are stable and reliable characters for taxonomic analys is of any taxa (Heywood V.H.1973). These are also used as active principle against various diseases (Crawford D.J . 1978).

Multiple mechanism have been proposed to explain the diversity of phytochemicalsbetween different plants (Harborne J.B. 1977). The structural variation of flavonoids in each plantgroup is due to change in number and position of hydroxyl constituents of methyl and occasionallyisoprenoid group.

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Materials and Methods :Leaves of 2 species of Crotolaria namely C. juncea and C. striata wereanalysed for their flavonoid compounds.

The species were collected through extensive survey of Patna and its locality. These werestudied morphologically and grown in the Botanical Garden of Deptt. of Botany Patna University. C.juncea L. was shrub with linear oblong leaves and yellow flowers, whereas C. striata DC. wasundershrub having long petioled trifoliate leaves and yellow flowers.

For extraction of Flavonoid 1 gm. of dried leaves were extracted with methanol anddistilled water in 1 : 1 ratio, at room temperature. Extracts were Centrifuged at 8000 rpm for 5minutes, pellets discarded and supernatant collected as Extraction sample, concentrated onwater bath for 10 minutes and chromatographed two dimensionally on whatman No. 1 paperus ing 2 solvent combinations, i.e. BAW (n-Butanol; Acetic acid glacial : Water, 4:1:5) versus15% Acetic acid and BAW versus distilled water, following standard procedure of HarborneJ.B. (1970).

Flavonoids were identified by comparing with authentic makers along with Rf values andcolour in UV light before and after fuming with Ammonia vapours.

Compounds were repeatedly purified by paper chromatography, till the absorption propertiesbecame constant. Hence and elute of a paper blank in 95% ethanol (usually about 150 cm2) was takenand applied (spotted) to the paper, and run in BAW and 15% HOAc, separately. After the purificationof compounds, the spots of chromatogram were cut and shaken in 95% ethanol for 30 minutes. Thesolution was filtered and allowed to concentrate, and directly used for spectral analyses on UV-240spectrophotometer.

Results and Discussion : A total of 21 spots were observed in the chromatographic profiles of thetwo species of Crotolaria. The total numbers of spots in C. juncea were 15 and in that of C. striatawere 12. The reported Rf and spectral values helped in identification of flavonoid compounds. Out of21 spots, only 10 could be identified. (Table-1)

Characterisation of the compounds revealed that the occurrence of Quercitin, Fricin,Apigenin, Genticin and Luteolin-6 glucosides were common to both the species of crotolaria. C.juncea was distinct in its profile due to the presence of Butien, Naringin, Vixetin and Kaempferol.Whereas, C. striata was charactersied by Isovitexin and Diadzein. Luteolin was also detected inthese two species.

The paired affinity values between species considered was 40. The group affinity was 140and isolation values were 40% and 45% in C. juncea and C. striata respectively.

Work on flavonoid of Crotolaria has been carried out by various workers. LikeSubramanian SS and Nagarajan S. 1969, reported Luteolin-4-glucoside in C. retusa.Dampsey, J.M. 1975, reported presence of 3,5,7- trihydroxy, flavone in aerial parts of C.alata .

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Morris and Kays 2005, reported that most of the crotolaria species contain flavones likeApigienin & Luteolin. The present investigation is substantiating their views.

The two species C. juncea and S. striata , although possess morphological plasticity. Theyare completely distinct in their flavonoid pattern and their separate identity within the genus CrotolariaL. is justified.

References :

Crawford, D.J. (1987) The Bot. Rev., 4431 .

Dampsey, J.M.. (1975) Fiber Crop. The University Press of Florida, Gainesvilla, Fla .

Harborne, J.B. (1977) Biochem. Syst. Evol., 5,7 .

Heywood, V.H., (1973) Pure and Appl. Chem., 34, 355.

Morris, J.B. and Kays, S.E. (2005) Total dietary fiber variability in a Cross Section of C. junceagenetic resources Crop Sci. 45 : 1826-1879 .

Subramanian, S.S. and Nagarajan, S. (1969) Flavonoids of the Seeds of Crotalaria retusa and C.striata. Curr. Sc. (India) 3865.

Van Wyk, B.E. and Schutte, A.L.. (1995) Phylogenetic relationship in the tribes podalyrieae, Liparieaeand Crotalarieae. In Advances in legume systematics Edited by M. Crisp and J.J. Doyle,Royal Botanic Garden, Kew, U., pp. 283-308 .

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TABLE-1 :

Flavonoid profile and Chromatographic analysis of C. juncea L. and C. striata DC.

Fluorescence Rf × 100 Spectral values

Spots

No.

Visible light

UV UV +NH

3

BAW

15% AcOH

39%AcO

H

Water

Phenol

Forestal

EtOH EtOH +NaOAc

EtOH+H3PO

4

Chemical indentity

1. Ns Br.gr

Br.gr.

13.1 86.5 — — — — + — — —

2. Br Br Br 15.4 80.2 — — — — + — — —

3. D.Br — — 20.2 — 0.5 63.1 — — 250;302,330,405

301,320380,420,510

200,301

?

4. Br.ochre

— — 69.8 — 86.0 51 — — + — —

5. YL — — 70.8 — — — — — 224,285,332

— — Hesperidin

6. Br.ochre

— — 47.2 60 63.1 60.1 — — 261 270,368 270,344

Luteolin 7-gl ucoside

7. YL — — 56.0 — — — — — 227, 280, 335

— — Naringin

8. L. YL — — 58.6 — — — — — 253.370

261,265 380 Quercetin

9. YL. Gr. — — 65.0 72.1 — 8 85.1 70.8 245,265,

— — Tricin

10. F. YL. — — 7.5 — — — 30 71.3 220,280, 345

— — Phlorodizin

11. YL. — — 75.4 — — — 64.2 — + — — ?

12. Br. — — 76.6 — — 0 65.0 — 260,380

— — Butein

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Fluorescence Rf × 100 Spectral values

Spots No.

Visible light

UV

UV +NH

3

BAW

15% AcO

H

39% AcOH

Water

Phenol

Forestal

EtOH EtOH +NaOA

c

EtOH +H3PO

3

Chemical indentity

13. Br — — 77.8 56.1 — 0 57.0 54 265,366

— — Kaempferol

14. Br.YL — — 83.5 — — — — — + — — —

15. F.YL — — 85.6 65.9 — — 87 82 265331 — — Apigenin

16. D.Orche

— — 92 10.2 — — — — + — — ?

17. F.Gr — — 93 23.6 — — — — + — — ?

18. Grey — — 95.9 — — — — — + — — ?

19. Grey — — 90.1 — — — — — + — — ?

20. D.Br. — — 27.4 65.2 — — — — + — — ?

21. Ns. — — — — — — — — + — — —

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PHYS IOLOGICAL EFFE CTS OF GIBBERELLIC AC ID ONCYTOPLASMIC-GENETIC MALE STERILE ( CMS ) LINE OF RICE.

R.K. Mandal* and Shyam Deo Mehta**

*Associate Professor in Botany, Patna Science College, Patna

**Principal, Gautam Budh Evening Collage, Kumhrar, Patna.

Abstract : Gibberellic acid (GA20) on various concentrations viz-10 PPM, 20 PPM and 30 PPMwas sprayed on cytoplasmic. genetic male sterile (CMS) line of rice (V20A) to obserce its effect onvarious agronomic characters viz-plant height, panicle exsertion and seed setting percentage. Withincrease in GA concentrations there were increased in all asronomic characters included in the experimentBut by use of 30 PPM of GA on CMS line V20A panicle exsertion and seed setting percentageincrease were maximum. Hence 30 PPM concentration of GA should be recommended for seedproduction.

Ke y words : Gibberellic acid, V20A, panicle exsertion, seed setting percentage, 30 PPMconcentration

Introduction : With the spectacular success in hybrid rice breeding in China, a newvista has emergedin rice production. The hybrid rice varieties developed in China not only recorded an increase of 20-30% in yield in comparison to the best commercial varieties, but were also claimed to be resistant to thedisease and pests. The tolerance of environmental stress and fertilizer response of the hybrid rice werereported to the superion Jones (1926) This success attracted the attention of rice breeder all over theworld on account of yield advantage and superior physiological efficiency of the hybrid rice. The desirablecharacters observed in the CMS lines were shorter plant height, better tillering ability, longer paniclelength, longer duration of flowering and flower opening all favouring a higher seed set. Among thedrawbacks evident in the (CMS) liner were poor panicle exsertion and lower stigma exsertion, Thiscauses decrease in yield.

Materials and Method : The cytoplasmic genetic male sterile line viz-V20A obtained fromInternational rice research Institute (IRRI) Manila, Philippines was grown in the field during kharif tosee the effect of different doses of gibberellic acid on various agronomical characters viz-Plant height,panicle exesrtion and seed setting percentage. The experiment was conducted in randomized blockdesign with four replications and three concentration of gibberellic acid i.e 10 PPM,20 PPM and 30PPM were used. these three concentration of GA were applied once on CMS line V20A at the time ofinitial heading and observation was made after 20 days of GA application and seed setting percentagewas recorded after 25 days on 10 plants.

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Results and Discussion : Observation on Plant height, panicle exsertion and seed set as influencedby the spray of gibberellic acid are given it Table 1.

It was observed that plant height in CMS line was increased in all doses of GA treatment. Thefinal plant height was maximum at 30 PPM concentration of GA. The increase in plant height in CMSline was. not desirable character as it decreases seed set percentage Rutger and carnahan (1981),Chaudhary et.al (1982), Jones Duan et.al (1997) and Ahmed et.al (1988)

In the present investigation it was recorded that the spraying of 20 PPM and 30 PPMGA on CMS line improved the panicle exsertion. In control it was observed that 1/3 part ofpanicle remained inside the sheath which results in poor seed set on CMS lines Line and Yuan,(1980) also recommended spraying of 20 PPM GA at heading on CMS lines for improvingpanicle exsertion and seed set.

Due to poor panicle exsertion seed set on CMS lines were poor. It fact one third to halfpanicle remain enclosed in the leaf sheath and can not receive any pollen and hence do not getseed. Poor panicle exsertion in a CMS line was considered to be due the presence of sterilecytoplasm. The sterile cytolplasm inhibited panicle exsertion (Virmani and Athwal, (1973), Linand Yuan, (1980), Virmani et.al (1985) and chenXionghu of al (1996).; In the present experimentit was found that spraying of 10 PPM, 20 PPM and 30 PPM concentration of GA increased theseed set percent 32.1, 49.5 and 57.9 respectively. This is in agreement with the result of Linand Yuan (1980) and Honda et.al (1996). But in present investigation it was found that 30PPM, GA should be recommended ins tead of 20 PPM for general spraying on CMS lines inseed production plots.

Table 1.

Effect of Gibberellic Acid on cytoplasmic genetic male sterile line V20A

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References :

Ahmed, M.I, Singh, S.Viraktamath. B.C., Ramesha M.S. and Vigakumar, S.S. and Khush, G.S. (1982);Hybrid and composite breeding approach for rainfed lowland rice. IRRI Saturday seminar on9th January (1982). Los Banos, Philippines.

Chen Xionghce, wan Banghce, Wu changwel and Liang kegin. (1996). Study on the flowering habit ofphoto-thermo sensitive genic male sterile rice. Journal of south China Agri. Univ. 2: 1-6.

Honda, I., sado, I; Yanagiswa, T.; kato, H., ikeda, R.; Hira sawa, it and takashashi, N. (1996)characterization of endogenous gibberelli As in dwarf rice mutants. Bioscience, Biotechnologyand Biochemistry. 6 (12) : 2073-2075.

Jones, Duan; Liang cheng ye; Huang Yuwen; Lie Hong Xian. (1997). Studies on seed setting percentageof hybrid rice. Journal of Tropical and subtropical Botany. 5 (1) : 71-77. Jun J.W. (1928)Inheritance of earliness and other agronomic characters in rice. Jour. Agric Res 36 : 581-601.

Lin, S.C. and Yuan, L.P. (1980). Hybrid rice breeding in china. In Innovative approaches to ricebreeding. PP. 36-51 Int. Rice Res. Inst-, Los benos, Philippines.

Rutger, J.N. and Carnahan, H.L. (1981). Crop Sci. 21: 373-376

Virmani, S.S. and Athwal, D.S. (1973). Genetic Variability for floral characters influence of out crossiof in oryza sativa L. crop. Sci. 13 : 66-67.

Virmani, S.S.; Rajik Govinda, Dalmacio, R.D. and Aurin, P.A. (1985). Current Knowledge of an outlook on cytoplasmic-genetic male sterility and fertility Restoration in Rice. In Rice Genetics PP.633-647. Proc. Int. Rice Genet. Symp., 27-31 May (1985). Int. Rice Res. Inst. P.O. Box 933.Manila, Philippines.

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89


EFF ECT OF MIX ED SO LVENT S ON THER MOD YNA MICPROPERTIES OF 4-NITROPTHALIC ACID

Dilip Kumar Verma*, Rajnish Kumar Singh and Prahlad Kumar

Department of Chemistry, Patna University, Patna-800005Email : [email protected].

Abstract : The study of solvent effect (water-dioxan mixture) on thermodynamic properties of 4-nitropthalic acid was done at 250C by fixing ionic strength. The PKa value increases with increasein mole percent of dioxane which is explained with dielectric constant of medium. The free energychange is also calculated to illustrate the effect of mixed solvent on thermodynamic properties oforganic acid.

Keywords : Nitropthalic acid, water-dioxan mixture, free energy change, ionic strength, dielectricconstant.

Introduction : The thermodynamic properties depend on many factors such as charges and size ofthe constituent ions, environment of the medium and temperature of the system. The study onsolvent on dissociation equilibria is much revealing about the structure of ions which is essential forsatisfactory understanding of the reaction. This is essential not only to confirm . The nature of ion-solvent interaction and support the model suggested in aqueous medium but also to investigate, therole of solvent in changing the model. A lot of works1-5 have been done regarding the study of theeffect of mixed solvents. Our main purpose is to observe the effect of mixed solvents on thermodynamicproperties of organic acid.

Expe rimental Method : Potentiometric method which has been proved to be very accuratemethod for determining the dissociation constant of acid in aqueous medium can be employedsatisfactorily for other mediums also. The dissociation constant of 4-nitropthalic acid in waterdioxan mixture composition varying (from 5% v/v to 25% v/v dioxan) at 250C and at constant ionicstrength was determined. The ionic strength was made constant using sodium chloride. The valuesof dissociation constant PK1, “G0, dielectric constant of o-nitropthalic acid at 250C in variouscompositions of dioxan- water mixture are given in table I.

Results and Discussion : When data in Table (1) was analysed, it is seen that PK increase withincrease in mole percent of dioxan. The variation of PK values with change in solvent compositioncan be analysed in terms of variation of dielectric constant of medium. The dielectric constant of themedium will be affected by the presence of ions. The increasing dioxan percentage in solventmixture is either to decrease bulk dielectric which disfavours dissolution or to increase the basicityof the medium which favours dissociation. A number of workers6-8 have justified the former case.This is also seen in variation of PK against 100/D in table I.

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It is seen that “G0 value is positive. This shows that unionized from will be more abundantlypopulated than the ionised from.9 The addition of dioxan increases “G0 values. This means thationization becomes more and more difficult with increase in the percentage of dioxan. This indicatesthat proton exchange between dioxan molecule and acid molecule is less prominent than the effect ofdielectric variation. Otherwise, dioxan being more basic than water would have facilitated ionisationresulting in the decrease of “G0 values.

References:

Rosotti F.J.C. and Rosotti H.S. (1961) : “The determination of stability constant” McGraw Hill, NewYork, P.27.

Bag S.P. and Lahiri, S. (1975) J. Indian Chem. Soc., 52, 30.

Denison J.T. and Ramsay, J.B. (1955) J. Am. chem. Soc., 22, 2615.

Dunsmore H.S. and Speakman J.C. : (1954) Trans Farad. Soc.,20, 236.

Gilkerson, W.R. (1956) J. Chem. Phys., 25, 1199.

Kesherwani, A.K. & Khan, F. (2002) Bull. Electrochem.,18, 413.

Shabana Begum S, Siva Kumar , C.L. Mayanna S.M. and Murlidharan V.S. (2000) Electrochem.Acta, 18, 89.

Singh, Ratan, Verma P.S. & Jain D.S. (1991) Electrochem. Soc. India, 7:, 40, 47.

Srivastava, S.B. Prakash Om and Prakash Sheo (1975) J. Chem. Thermodynamics, 7, 997.

TABLE – IValues of 100/D, Dissociation Constant, PK, “G0 at 250C

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ULTR AVIOLET-VISIBLE & INFRARED SPECTRAL STUDY OFCOMPLEXES OF CO (III) WITH MIXED LIGANDS BIGUANIDE C2H7N5AND PYRIDINE C5H5N

Bina Rani*, Madhu Kumari Gupta** and Radhakant Prasad***

*Reader in Chemistry, Magadh Mahila College, Patna University, Patna-1

**PGT (Chemistry), Kendriya Vidyalaya, Bengdubi, Darjeeling, West Bengal.

***Professor, Dept. of Chemistry, Patna Science College, Patna University, Patna-1

Abstract : Complexes of transition metal cobalt in oxidation state III with mixed ligands biguanide(C2H7N5) and pyridine (C 5H5N) have been prepared and their elemental studies have been studied. Inthis research paper, an effort has been made to characterize their spectroscopic characters. The blueprints of the structure of the coordination complexes which have been prepared are illustrated with thehelp of UV & IR spectroscopic data.

Key words:- UV (Ultraviolet & visible), IR (Infra-red), biguanide, pyridine, transition metal.

Introduction : Biguanide sulphate C 2H7N5•H2SO4 •H2O is a bidentate chelating molecule and itscomplexes with bivalent and trivalent ions are known1-4.

H N – C – NH – C – NH(1) || (3) || (5) NH NH (2) (4)

3 3+ +

Biguanide : The ligand biguanide is found to coordinate with N(2) and N(4) as shown in figure givenabove forming usual six membered chelate rings with metal atoms from each biguanide unit. Thecoordination complexes of a number of biguanides with various metal ions have been investigatedextensively5,6.

Biguanides may be prepared by the condensation of two molecules of guanidine.

-NH3

H2N – C – NH H + H2N – C – NH2

|| || Fusion

NH NH

Guanidine guanidine

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H2N – C – NH – C – NH2

|| ||

NH NH

Biguanide or diguanide

Actually, guanidine hydrochloride is used and fused at 180-1850C. Biguanide may also beregarded as guanylguanidine. The name “diguanide” is not as prevalent as one finds difficulty whilenaming dibiguanides for in this case one has to call di-diguanide.

The pyridine is an organic molecule which acts as a ligand. Many coordination compoundshave been prepared till now containing pyridine as a ligand. It coordinates with its nitrogen atom presentin the ring.

pyridine

The detailed studies on structural and biochemical aspects of coordination complexes of mixedligands (bidentate biguanide and pyridine) with cobalt metal are lacking. To investigate the coordinatingbehaviour of bidentate biguanide C2H7N5 and pyridine C5H5N we have prepared and characterized thecomplexes of Co (III) with mixed ligands.

Results and discussion : The acidic solution of CoSO4, cobalt sulphate reacts with basic solution ofbiguanide [BigH2

+] (OH-) 2 forming yellow silky precipitate of [Co (BigH+2)3] (OH) 2 was obtained

which was filtered quickly to avoid oxidation on the buchner funnel and was washed with ice coldwater. The yellow coloured complex so obtained was then mixed with a little amount of distilled waterto prepare the suspension of the complex which was then transferred to an aeration flask. To thissuspension a small amount of pyridine, C5H5N (A.R.) was added and then a brisk current of air waspassed through it to oxidize Co (II) complex to Co (III) complex [Co (BigH)

2 (Py) 2 ](OH)3

(Dipyridinebisbiguanidium-cobalt(III)hydroxide). Due to aeration, the silky yellow bisbiguanidium cobalt[II] hydroxide [Co (BigH+

2)3] (OH) 2 gradually dissolved to a dark red solution with the separation ofa slight black oxide of cobalt due to decomposition of the complex. The mixture was then filteredthrough a quantitative filter paper and the filtrate was left in cold for crystallization. The complexsulphate[Co(BigH) 2 (py)2]2(SO4)3 .12H2O was obtained when red solution of the complex base[Co(BigH+)2py2](OH)3 was neutralization with dilute sulphuric acid in cold as red crystals associatedwith a small amount of the trisbiguanidium cobalt (III) sulphate. The complex nitrate, [Co(BigH)2(py)2](NO3)3, was obtained by the neutralization of the solution of the complex base

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[Co(BigH)2py2](OH)3 with dilute HNO3 (nitric acid) in cold. The complex carbonate [Co(BigH)2(py)2]2(CO3)3 was prepared by treating the complex base [Co(BigH)2py2](OH)3 with either ammoniumbicarbonate NH4(HCO3) or sodium bicarbonate NaHCO3. The complex chloride [Co (BigH) 2(Py) 2]Cl3.6H 2O was prepared by dissolving the ca rbonate in dil. Hydrochloric acid.Dipyridinebisbiguanidiumcobalt (III) thiosulphate[Co(BigH)2](S2O3)3•6H2O was prepared by neutralizingthe complex chloride with solution of sodium thiosulphate dropwise.The complex oxalate [Co (BigH)2

(Py)2]2 (C2O4)3.8H20 was prepared either by neutralizing the complex base with oxalic acid or byadding sodium oxalate to the complex chloride solution.

The trihydroxide was soluble in water and was alkaline to litmus. The sulphate was soluble inhot water and nitrate was in alcohol. Besides this all complexes were found to be soluble in DMSO.

In case of Dipyridinebisbiguanidecobalt (III) complexes, cobalt has +3 oxidation states withlow spin type. The electronic absorption spectra of the complexes display band near 500nm (20,000cm-

1) and 357nm (28,000cm-1). This is attributed to transition of d-electrons from 1A1g (ground state) tonext upper higher level 1T1g and 1T2g states respectively. In Co (III) complexes the spin forbiddentransition 1A1g

5Eg or1A1g 5T2g are seldom observed.

Here,

uv 2 = Dipyridinebisbiguanidiumcobalt (III) sulphate

uv 3 = Dipyridinebisbiguanidiumcobalt (III) thiosulphate

uv 4 = Dipyridinebisbiguanidium cobalt(III)oxalate

The appearance of strong absorption bands in the region of 4000cm-1 to 2500cm-1 usuallycomes from stretching vibrations between hydrogen and some other atoms with mass 19 or less. TheO—H and N—H stretching frequencies fall in the 3700 to 2500 cm-1 region with various intensities.The ligand biguanide sulphate (BigH+HSO4

-).H2O contains =NH, —NH, – NH3+ and SO4

2- groups.The various modes of IR vibrations of =NH, N—H, —N+H3 and SO4

2- groups display IR bands in3350 to 625 cm-1 region. The N—H stretching in ammonia and alkyl derivatives of ammonia is observedin the region 3500-3300cm-1. The position of absorption depends on the degree of H- bonding. TheN—H and NH2 stretches are observed between 3479.4 to 3239.7 cm-1.

The N—H stretching in – N+H3 group is obtained at 3156 and 3022.9 cm-1. Further the peaks2581.6 cm-1, 2401.6cm-1 and 2265.3 cm-1 are due to N – H stretching in >N+H group. The N—H andNH2 deformation vibration observed at 1522.3 cm-1 and ä-NH2 vibration at 1427.5 cm-1. N—Hwagging observed at 762cm-1. The C—N (unconjugated) stretching observed at 1025.9 cm-1 and928.3 cm-1. The C=N stretching observed at 1658.2cm-1.

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The ionic sulphate group displays as broad and very strong band at 1216 cm-1 due to õ3

vibration of SO42- ion. The strong and sharp band at 528cm-1 is attributed to õ4 vibration of SO4

2-

group. The free sulphate ion has regular tetrahedral symmetry (Td). On formation of bond with metal orhydrogen atom with oxygen or atoms of sulphate group[10.]. The sulphate ion has four different modes ofvibrations õ1, õ2, õ3 and õ4. The õ1and õ2 vibrations of sulphate are not IR active. The õ3 and õ 4

vibrations split into two bands while the õ3 and õ4 split into three bands on bidentate bonding of sulphategroup. The bidentate bonding of sulphate acts as chelating or bridging molecule [11]. The IR spectra ofchelating or bridging sulphate groups are differentiated clearly by position of splitting of õ3 (SO4

2-)stretching vibrations [10, 12].

As it is well known the N – H stretching decreases on complex formation with metals which isshown in the complexes M1, M2, M3, M4, M5, M6 & M7. All these complexes have shown the presenceof Co – N & N – Co – N vibration near 500nm.

Here,

M1 =Dipyridinebisbiguanidium cobalt (III) hydroxide

M2 = Dipyridinebisbiguanidium cobalt (III) sulphate

M3 = Dipyridinebisbiguanidium cobalt (III) nitrate

M4 = Dipyridinebisbiguanidium cobalt (III) chloride

M5 = Dipyridinebisbiguanidium cobalt (III) carbonate

M6 = Dipyridinebisbiguanidium cobalt (III) oxalate

M7 = Dipyridinebisbiguanidium cobalt (III) Thiosulphate

M10= Biguanide sulphate monohydrate

All these complexes have shown the presence of Co – N & N – Co – N vibration near 500nm.

In the high frequency region (about 650cm-1), the pyridine (py) vibrations show very little shiftupon complex formation [11]. However, those at 604(in plane ring deformation are shifted to higherfrequencies upon co-ordination to a metal.

Clark and William [10] have carried out an extensive far- infrared study on metal pyridinecomplexes. The C – H [11] out of plane bendings (1000-700cm-1) of pyridine ring were assigned fromthe analysis of combination bands at 2000-1600cm-1. This shows combination of pyridine with thecobalt metal to form complexes. The pyridine combines with Co metal by its N atom.

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Experimental : The ligand biguanide sulphate was prepared by the reported method:

Biguanide sulphate C2H7N5•H2SO4•H2O was prepared according to the method describedby Smolka1 and Friedrich with slight modification which resulted in better yield. In this method, amixture of ammonium iodide (NH4I) and dicyandiamide, C2H4N4 (mol.wt.-84.08) [dried at 100 0C] in2:1 proportion was intimately mixed in mortar and pestle and the mixture so obtained was then transferredto a dry pyrex beaker and heated over asbestos board by means of a Bunsen burner. During thisprocess, the mixture was constantly stirred with the help of a thermometer and temperature was raisedgradually to 1550 C. At this temperature, the mixture was changed to a thick liquid and was maintainedat this temperature [155+2]0C for ten minutes. The molten mass was then poured into a large volume ofwater and then filtered from any solid residue. The filtrate so obtained was then treated with a solutionof cuprammonium sulphate, [Cu (NH3)4] SO4. Cuprammonium sulphate was prepared by adding liquorammonia (NH3) to copper (II) sulphate solution. As a result, rose coloured precipitate of copperbiguanide sulphate Cu(C2H6N5)2•H2SO4 was obtained at once. This was filtered in buchner funnel andwas washed thoroughly with cold water. The precipitate should be kept over the buchner funnel tillwater get drained. The moist copper biguanide sulphate Cu(C2H6N5)2 •H2SO4 was then decomposedwith cold solutions of about 10% sulphuric acid H2SO4. A blue solution was obtained which on keepingin the cold [120C] deposited large crystals of biguanide sulphate. The yield was found to be best whena mixture of 8gms of dicyandiamide with 16gms of ammonium iodide was fused. Bes ide this thetemperature should also be maintained properly for good yield.

Preparation of the metal complexes :

Dipyridinebisbiguanidiumcobalt(III) hydroxide

[Co(BigH)2py2](OH)3

It was prepared by adding calculated amount of biguanide sulphate dissolved in slight excess ofsodium hydroxide to a solution of cobalt (II) sulphate with continuous stirring. As a result, the yellowsilky ppt. was obtained which was filtered quickly to avoid oxidation on the buchner funnel and waswashed with ice cold water. The yellow coloured complex so obtained was then mixed with a littlewater to make suspension of the complex which was then transferred to an aeration flask. To thissuspension a small amount of pyridine, C5H5N (A.R.) was added and then a brisk current of air waspassed through it to oxidize Co (II) complex to Co (III) complex. Due to aeration, the silky yellowbisbiguanidium cobalt [II] hydroxide gradually dissolved to a dark red solution with the separation of aslight black oxide of cobalt due to decomposition of the complex. The mixture was then filtered througha quantitative filter paper and the filtrate was left in cold for crystallization. Dark violet permanganatelike crystals gets deposited slowly in course of a day or two. These were filtered, washed with ice cold

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water and finally with absolute alcohol .The product was then dried in a CO2 free atmosphere. Thesubstance was soluble in water and was alkaline to litmus. The dried product on analysis was found tocontain Co = 12.39%

N = 35.60%

Required for [Co (BigH) 2 (Py) 2 ](OH)3

Co = 12.54%

N = 35.75%

Where “BigH” stands for one molecule of biguanide and “Py” for one molecule of pyridine.

The complex on heating evolved pyridine at about 950 C.

Dipyridinebisbiguanidiumcobalt (III) sulphate

[Co(BigH) 2 (py)2]2(SO4)3.12H2O

The red solution of the complex base [Co (BigH+) 2(py)2](OH)3 obtained as described aboveon neutralization with dilute sulphuric acid in cold deposited red crystals associated with a small amountof the trisbiguanidium cobalt (III) sulphate .This was removed by fractional crystallization from water.Trisbiguanidium cobalt (III) sulphate was found to be more soluble than the dipyridinebisbiguanidiumcobalt (III) sulphate. The pure crystals were filtered & washed with cold water and alcohol and driedin air The air dried sample on analysis was found to contain

Co = 9.00%

N = 24.85%

SO4-2 = 21.59%

Required for [Co (BigH) 2 (py) 2] 2(SO4)3.12H2O

Co = 8.78%

N = 25.04%

SO4-2 = 21.46%

Water could not be determined by heating as the complex evolved pyridine also when heated at 950C.

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Dipyridinebisbiguanidiumcobalt(III)nitrate

[Co(BigH) 2(py)2](NO3)3

For the preparation of dipyridinebisbiguanidiumcobalt(III)nitrate [Co(BigH) 2(py)2](NO3)3, thesolution of the complex base [Co(BigH)2py2](OH)3 was neutralized with dilute HNO3 (nitric acid) incold. As a result deep violet crystals were separated, filtered and washed with ice cold water. Furtherit was washed with alcohol & dried in air .The complex was found to be slightly soluble in alcohol.

Found Co = 9.54%

NO3- = 31.52%

[Co (BigH) 2(py)2](NO3)3 requires

Co = 9.75%

NO3- =30.74%

Dipyridinebisbiguanidiumcobalt(III)carbonate

[Co (BigH) 2(py)2]2(CO3)3

This was prepared by treating the complex base [Co(BigH)2py2](OH)3 with either ammoniumbicarbonate NH4(HCO3) or sodium bicarbonate NaHCO3. As a result, red precipitate of complexcarbonate was obtained which was filtered and washed.

Found Co = 12.05%

[Co (BigH) 2(py)2]2(CO3)3 requires

Co =11.59%

Dipyridinebisbiguanidiumcobalt(III)chloride

[Co (BigH) 2(Py) 2] Cl3.6H2O

The method of preparation as applied in case of the complex sulphate and nitrate could not beused for the preparation of complex chloride. When the complex was neutralized with dilute hydrochloricacid in cold, the complex chloride could not be crystallized out, but a gummy mass was obtained.

However, when a solution of complex base [Co(BigH) 2(py)2]2(CO3)3 was treated withammonium carbonate, (NH4)2CO3 or sodium bicarbonate, NaHCO 3 red precipitate of the complex

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carbonate was obtained. The precipitate was filtered and washed. When the precipitate was just dissolvein cold and dil HCl and cooled deposits dark red crystals. The crystals were filtered. Wash with icecold water and alcohol & dried in air.

Required [Co (BigH) 2(Py) 2] Cl3.6H2O

C = 9.35%

N = 26.53%

Cl = 16.79%

The compound disengages pyridine when heated at 950C.

Dipyridinebisbiguanidiumcobalt(III)thiosulphate

[Co(BigH)2(py)2]2(S2O3)3•6H2O

It was prepared by neutralizing the complex chloride with solution of sodium thiosulphate dropwise. As a result, the complex thiosulphate immediately precipitated as red crystals. The substance wasfiltered, washed with cold water and alcohol. The complex was then dried in air.

Found Co = 9.25%

S2O3 = 27%

Required for [Co (BigH)2](S2O3)3•6H2O

Co = 9.20%

S2O3 = 26.23%

Dipyridinebisbiguanidiumcobalt(III)oxalate

[Co (BigH)2 (Py)2]2 (C2O4)3.8H20

It was prepared either by neutralizing the complex base with oxalic acid or by adding sodiumoxalate to the complex chloride solution. The red crystalline precipitate Dipyridinebisbiguanidium cobalt(III)oxalate,

[Co (BigH)(Py)2 ]2 (C2O4)3.8H20 so obtained was filtered washed with cold water and alcoholand dried in air.

Found

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Co = 5. 67%

[Co (BigH) 2(Py)2] (C2O4)3.8H20 requires

Co = 5.51%

On heating at 95 0C decomposed with the loss of water and pyridine. The aqueous solution, on heatingslowly evolved pyridine and the colour changed to violet. Even in cold it disengaged pyridine, but slowly.

Conclusion : From the stoichiometry and the physico-chemical properties studied about the cobaltcomplex with the ligand biguanide and pyridine, the probable structures of complexes are shown below:

NH || C – H2N N = C – N +H3

HN Co NH (O H)3 H3N+— C = N NH2 – C = NH py py

Dipyridinebisbiguanidiumcobalt(III)hydroxide

NH || C – H2N N = C – N+H3 HN Co NH (SO4 )3 • 12H2O H3N

+— C = N NH2 – C = NH py py 2

Dipyridinebisbiguanidiumcobalt(III)sulphate

NH | |

C – H2N N = C – N+H3

HN Co NH (NO3)3

H3 N+— C = N NH2 – C = NH py py

Dipyridinebisbiguanidiumcobalt(III)nitrate

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NH ||

C – H2N N = C – N+H3

HN Co NH (CO3)3

H3N+— C = N NH2 – C = NH

py py 2

Dipyridinebisbiguanidiumcobalt(III)carbonate

NH ||

C – H2N N = C – N+H3

HN Co NH (C2O4)3

H3N+— C = N NH2 – C = NH py py 2

Dipyridinebisbiguanidium cobalt(III)oxalate

NH | |

C – H2N N = C – N+H 3 HN Co NH Cl3• 6H2O

H 3N+— C = N NH2 – C = NH py py

Dipyridinebisbiguanidiumcobalt(III)chloride

Acknowledgement : I am particularly grateful to the sophisticated analytical instrument faculty,Central Drug Research Institute, Lucknow, for recording UV spectra, IR spectra and elemental analysisof my newly prepared compounds.

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I could not forget the help and the guidance by Dr. Dhananjai Singh who has helped me tointerpret the UV and IR data of the research work & tackle the obstacles faced by me. So, I am verygrateful to him and have no words to express my gratitude.

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75,219.Prasad R.K., Bina Rani and Singh Dhanajai (2006) : J. Indian Chem, 83,718Prasad R.K. , Bina Rani and Dhanajai Singh and Kumar Prahlad (2010) : J. Indian Chem.

Soc., 87, 1313.Nakamoto K. IR& Raman Spectra of Inorganic and Co-ordination compounds, Johnwiley,

New York.Barrailough C. & Tobe M.L. (1961) : J. Chem. Soc. 1993Earnshow A., Larkworthy L.K.& Patel K. C. (1969) : J.Chem. Soc. A, 1339.Clark R.J.H. and William C. S. (1965) : Inorg. Chem. ,4, 350Kakiuchi Y., Kida S. & Quagliano J. V. (1963) : Spectrochem. Acta, 19, 201.Sugden. J. Chem. Soc. 1932, 246

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AR SENIC C ONTAM INATION IN GR OUND WATER OF BIHAR:CAUSES, ISSUES AND CHALLENGES

Madhurendra Nath Sinha 1, Ranvir Nandan2, Rabindra Kumar3

1. Professor of Geology and Head of Department, Department of Geology,Patna Science College, Patna University, Patna 800005.

email : [email protected]. Associate Professor, Department of Geology, B N College,

Patna University, Patna 8000053. Associate Professor, Department of Geology,

Patna Science College, Patna University, Patna 800005email : [email protected]

Abstract: Many district of Bihar are having arsenic in its groundwater. The various causes of arseniccontamination are mostly through geogenic channel. Human interference is not much responsible forthe problem. This paper tries to make an attempt to understand the groundwater arsenic contaminationscenario, causes, issues and challenges in Bihar. The problems on social, economic and environmentalissues are discussed in details. Almost 75-80 per cent of the rural populations rely on their drinkingwater from the groundwater sources. Excess use of arsenic in drinking water leads to several diseaseincludes primary (black spots in the body, Keratosis) and secondary (white black spots in the body,Hyper-Keratosis, Non-pitting edema and liver and kidney disorders) health impacts in the long run.It has also tertiary health impacts causes gangrene of the distal organs, cancer of the skin, lungs andurinary bladder and kidney and liver failure. It has impacts on human health, food chain nuisance andsocio-economic conditions hampers among the affected stakeholders.Key words : Arsenic contamination, Groundwater, Bihar

The presence of Arsenic is hampering agricultural activity due to decline in soil fertility andproductivity. Social problems like depression, suicidal tendency and social ignorance are common.Young men and women with arsenicosis problems are not getting married. Contamination in drinkingwater hinders the social and economic activity to the effected person. The challenges are on themitigation (at macro) and adaptation (micro as well as macro) activity.

Majority of the population residing in the arsenic prone belt are from low income and are notaware about the problems of the arsenic menace. Therefore both short term mitigation (hand pump treatmentplan or sanitary dug well) and long term mitigation (alternative source of surface water) strategy is needed.

Introduction : Bihar along with few other states of India is facing an acute problem due to presence ofarsenic menace in its groundwater. Groundwater is the main source of drinking water and it constitutesmore than 80 per cent of drinking water source in rural Bihar. The other sources of drinking water arefrom surface water, dug well, pond and from natural sources (lakes, rivers etc.). Few percentages ofrural households are using drinking water from protected dug wells. The groundwater sources wereconsidered safe for drinking water but over the past few years, they have reported contamination and

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pollution problems in its root due to rapid urbanisation, industrialisation and excess extraction ofgroundwater for irrigation purpose. Around forty percent districts of Bihar has reported arseniccontamination problem in its groundwater. This comprises of more than 67 blocks from 15 districtsand covering more than 1500 habitations across the state where arsenic contamination in groundwaterexceeds the BIS limits for safe drinking water of 50 particle per billion (ppb) and more. If weconsider the WHO limits of 10 ppb, the coverage area will be much more and the population whichis facing the danger of arsenic hazard will be thrice of the Bureau of Indian Standard (BIS) limit.

It has been estimated that more than 13.85 million people could be under the threat ofcontamination level of above 10 ppb/l, out of which more than 6.96 million people could be above50 ppb/l, against the total population of these area is around 50 million (MoWR, 2010). The actualproblem of arsenic menace among the population will be increasing at an alarming rate by every newsurvey done by Central Ground Water Board (CGWB) and Public Health Engineering Department(PHED), Govt. of Bihar.

Arsenic is a shiny metalloid that dissolves in water. It is a natural mineral, present in the soiland aquifers, and the concentrations above the safe level in drinking water may cause significanthealth risks. Most arsenic enters water supplies either from natural deposits in the earth or fromindustrial and agricultural pollution. Arsenic is a natural element of the earth’s crust. Although surfacewater are mostly considered safe for drinking water but groundwater sources are arsenic contaminatedin the range of 40 – 140 metre. It is used in industry and agriculture, and for other purposes. It is alsoa by-product of copper smelting, mining and coal burning.

Access to safe water supply is one of the most important factors of health and socio–economicdevelopment (Cvjetanovic, 1986). More than 150 million people are affected worldwide by arseniccontamination in 70 countries, out of which 50 million people in Bangladesh and 30 million people inIndia are at risk (Ravenscroft et al., 2005). Arsenic is toxic in nature and the excess quantity of itsuse in drinking water leads to several health hazards. Drinking arsenic contaminated water over along period results in various health effects including skin problems such as colour changes on theskin, and hard patches on the palms and soles of the feet (WHO, 2010). It also leads to skin cancer,cancer of the bladders , kidney and lung, and diseases of the blood vessels of the legs and feet, andalso possibly diabetes, high blood pressure and reproductive disorders (ibid). Given the background,this paper has attempted to understand the issues and challenges posed by arsenic groundwatercontamination problems and its menace of the affected population in Bihar. The paper is divided intofour sections. Followed by brief introduction and background problem, the second section is onwater pollution and arsenic scenario. In this section, water pollution and arsenic scenario have beendiscussed in details. Third section deals with is sues and challenges faced by arsenic in drinking water.This section also explains about the possible solutions for the emerging challenges. The fourth sectioncomprises of concluding remarks.

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Figure 1. Map of Bihar showing the major rivers and physiography.

Water Pollution and Arsenic Scenario: Air and water pollution are major environmental problemscurrently exist in India along with depletion of non-renewable resources and degradation ofrenewable resources (Sankar, 2000). Resources are economic goods while pollutants, thedegrader of resources are economic bads. Pollutants are the reverse of the resources (Dasgupta,2010) and pollution is thus the reverse of conservation (Dasgupta, 1993 and 2007). Pollutioncan be thought of as a pure public bad and hence pollution reduction as a public good (Baligaand Maskin, 2005). Pollution is treated as negative externalities in economics literature (Pigou,1920, Sankar, 2005). When certain actions of producers or consumers have unintended effectson other producers or consumers externality arises. Externality is of two kinds positive andnegative. Externalities may be global public bads (emissions of greenhouse gases, climate change,depletion of ozone layer, loss of bio-diversity and extinction of endangered species and otherare some of the examples of global public bads) which have global effect and local public bads(problem of groundwater or surface water in a region, land degradation, air and vehicular pollutionsand others are some of the examples of the local public bads) which have local or regionaleffect. Climate change problem aggravate the availability of water in the country as it threatensthe water cycle. As the population increases the demand for agriculture also grows and thedemand of water thus increase. Table 1 provides information on projected water demand inIndia by different uses.

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Table 1. Projected Water Demand in India (by different uses)

Water Pollution : Water pollution is major concern in India and particular in Bihar. With around 17percent of the world’s population but only 4 percent of its usable freshwater, India has a scarcity ofwater. World oceans cover about 3/4th of earth’s surface. But fresh water constitutes small proportions.About 2.7 percent of the total water available on the earth is fresh water, of which about 75.2 percentlies frozen in Polar Regions and another 22.6 percent is present as groundwater (Ministry of WaterResources India (MoWR), 2007; and United Nations Report (UN), 2005). A small proportion (about2 percent) of total fresh water is available in lakes, rivers, atmosphere, moisture, soil and vegetation.

Water resources of a country constitute one of its most significant economic assets and thedifferent forms of water resource development differ for various uses, fluctuate from country to countrydepending on its climatic, physiographic, and socio-economic conditions and development (Jain, 1977).India is rich in both surface water and groundwater resources. India has total annual replenishablegroundwater resources of 433 billion cubic meters (BCM), net annual groundwater availability of 399BCM, annual ground water draft for irrigation, domestic and industry is around 233 BCM, and stage ofgroundwater development is around 58 percent. Annual precipitation (includes snowfall) in India is4000 cubic kilometers while average annual availability of water resources is around 1869 cubickilometers. Per capita water availability is 1820 cubic meters according to 2001 ministry of waterresources sources. Estimated utilized water resources is 1122 cubic kilometers in which surface waterresources share is 690 cubic kilometers and groundwater resource share is 431 cubic kilometers. Biharis rich in groundwater resources. In Bihar, annual replenishable groundwater resources, net annualgroundwater availability and annual groundwater draft are 29, 27.42, and 10.77 BCM. The stage ofgroundwater development in Bihar is 39 percent and the annual rainfalls (in mm) are 1232. The percapita water availability is decreasing in both Bihar and India. In 2001, per capita availability of water(in cu. m) was 1950 and 1816 for Bihar and India. It has further decline to 1545 and 1200 (in cu. M)in 2011. The decline in availability of groundwater in Bihar is due to the uncontrolled population growth,excess dependence on groundwater (85 percent), over extraction of groundwater for irrigation,uncontrolled deforestation. This leads to overall water quality problems. But water is becoming

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increasingly scarce over the years. Uncontrolled growth of population, expansion of irrigation channelsand developmental activity are responsible for the decline in water availability problems. It also leads toproblems in water quality which affects the health and other problems. Different groundwatercontamination problems in Bihar are given in table 2.

Table2. Different Groundwater Contamination in Bihar

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. Source: Author’s own compilation from various sources.

Arsenic Contamination – Causes and Sources : Arsenic is found in the natural environment inplenty in the earth’s crust and in small magnitudes in rock, soil, water and air and is always present ascompounds with oxygen, chlorine, sulphur, carbon and hydrogen on one hand, and with lead, gold andiron on the other (MoWR, 2010b). It can exist in both organic and inorganic form but inorganic arsenicis more toxic than organic arsenic. Inorganic arsenic compounds are known to be more humancarcinogens. Arsenic in element form is insoluble in water and soluble in oxidized form. Countriesincluding Argentina, Bangladesh, Chile, Ghana, Mexico, Mongolia, India, Taiwan, Vietnam, and UnitedStates are exposed to arsenic problems because the sources of arsenic are primarily natural rather thananthropogenic or geothermal. Inorganic arsenic of geological origin has been recognised as the mainform of arsenic in groundwater. Sparks (2005) suggested three source of arsenic in soil and aquaticecosystem. It consists of iogenic, Geogenic and anthropogenic sources of arsenic. By and large geogenicsources are responsible for arsenic contamination but anthropogenic activities also cause contamination.The anthropogenic sources of arsenic occur due to human activities. The main source of anthropogeniccan be further classified in three categories viz. agricultural, industrial and others. Agricultural sources ofarsenic can be from pesticides, herbicides, seed treatment, cattle deep and fertilizer mainly, while industrialsources are from timber treatment, tannery, electro plastic, and paints and chemicals. Other anthropogenicsources are from sewage and smelting.

Arsenic Scenario : Arsenic is a heavy metal and regarded as a toxic element. Excess of arsenic indrinking water over long run is considered as a human health hazard and leads to different diseases. Inextreme cases it leads to an end of human life. Seven states of India have reported arsenic contaminationin groundwater and it is increasing at increasing rate (MoEF, 2009). Out of reported seven states, Biharand West Bengal have severe impact of the livelihoods of the stakeholders due to arsenic menace.

More than 70 countries are globally affected directly or indirectly with arsenic contamination indrinking water which affects more than 150 million people across the globe. Around middle of the 20thcentury, arsenic poisoning surfaced in those areas where people ingested arsenic contaminated water.The major affected countries from arsenic poisoning were Argentina, Chile, Mexico, Taiwan, and some

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part of the United States. In global arsenic contamination scenario, 38 countries are affected moreseverely at present. At the last quarter of 20th century three Asian countries (Bangladesh, China, andIndia) came to lime light due to their suffering from groundwater arsenic contamination. The majorsource of arsenic contamination was contaminated hand tube-wells. As of 2010 September, 13 Asiancountries are arsenic affected and the level of arsenic contamination in Asian countries is more severethan the rest of the world. Bangladesh is the worst affected country, as 60 of its total 64 districts havearsenic groundwater contamination above World Health Organization (WHO), 2001 guidelines of10mg/l for safe drinking in water. In India, flood plains of all the states in Ganga and Brahmaputra riversare arsenic affected.

The first case of arsenic in India was reported in 1976 from Chandigarh, where some patientswere suffering from noncirrhotic portal hypertension (NCPH) and later it was found that the water usedby patients who suffered from NCPH came from arsenic contaminated tube wells (MoWR, 2010b). In1982 a patient from North - 24 Parganas district of West Bengal, whose skin lesions were not like theusual skin diseases and later similar problem finds to many patient from the same village suffered fromsuch problems in soles of their feet, palms of their hands, ulcer in hands and bodies and found that dueto the excess availability of arsenic in tube wells in drinking water (MoWR, 2010a). Soon after theincident four districts of West Bengal (North 24 Parganas, South 24 Parganas, Nadia, and Murshidabad)were found on arsenic menace in ground water. In 1983, 33 villages of 4 districts were identified,having arsenic contamination. By the end of 2004, 3200 villages of 85 blocks from 9 districts wereidentifiedhaving arsenic contaminated water and by the end of 2008, more than 3417 villages of 111 blocks from9 districts have reported arsenic contaminated groundwater (MoWR, 2010b).

In 2002, two villages (Barisban and Semaria Ojhapatti) from the Bhojpur district of Bihar inthe middle Ganga plain reported excess of arsenic contamination exceeding 50 mg/l (Chakraborty etal., 2003, Sinha et al. , 2010) . As of 2009, out of 38 districts of Bihar, 57 blocks from 15 districtshaving total population more than 10 million have been reported to have arsenic groundwatercontamination above 50 mg/l (MoWR, 2010a and 2010b, MoEF, 2009). Due to the excess arseniccontaminated drinking water, 18 babies were born blind in the Bhojpur district. The demographicsurvey done by many organizations mainly in Bihar and West Bengal estimated that more than 13.85million people could be under the threat of contamination level above 10 mg/l, in which more than6.96 million people could be above 50 mg/l, against the total population of those areas of the orderof 50 million (MoWR, 2010b).

Live-stock in large number has also been exposed to arsenic contaminated groundwater. In thearsenic affected areas, arsenic contaminated groundwater is also used for agricultural irrigation. Thisleads to the possibility of arsenic exposure through food chain not only in contaminated areas but alsoin areas with no contamination due to open market sale of food products.

Out of seven states, two states of India namely Bihar and West Bengal are worst affected byarsenic contamination in their groundwater. Altogether more than 40 percent of the people from Bihar

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and West Bengal are affected by arsenic contamination in groundwater which causes serious threats tothe people of the state in health and other hazards which threats to the socio - economic status of theaffected people. Table 3 presents the arsenic contamination problem in Bihar.

Table 3. Occurrence of high Arsenic in Groundwater in Bihar (> 50 ppb/l)

Source: PHED, Govt. of Bihar (2012)

Issues and Challenges : Scarcity of safe drinking water in the rural areas of Bihar acquainted withsocial and economic issues. It also threats the environment as well as major health problems.Contamination in drinking water hinders the social and economic activity to the affected person. Theevidence on the adverse impacts of water pollution in general and on human health in particular is wellknown. High concentration of contamination in drinking water – arsenic, fluoride, iron, nitrate and lead-contribute to both human mortality and morbidity. Prolonged exposure to water contamination couldlead to different disease. Epidemiological studies show that arsenic in drinking water cause cancer(Canter 1997, Chakraborty and Saha, 1987). Arsenic contamination (Sinha 2010) in drinking waterover long run can cause the problems in the reproductive system, birth defects and harm the central andperipheral nervous system (Canter, 1997) and excess acquaintance of arsenic during pregnancy canadversely affected reproductive endpoints (Mukherjee, 2006). The dose-response relation betweenlow arsenic concentrations in drinking water and arsenic-induced skin Keratosis and hyper pigmentationis well characterized (Haque et al., 2003). The arsenic related skin disease may be associated withincreased risks of skin, bladder and lung cancer (NRC, 1999). Without skin lesion also cancer riskscan prevail (ibid). Such health problem has involved economic, social and environmental costs to theaffected stakeholders. Arsenic in drinking water hinders the social as well as economic costs to thesociety in general and affected households in particular. Health problems caused by pollution have

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economic costs arising from the expenses incurred in treating the disease and loss of productivity(Bates, 1990, Ostro, 1994, Banerjee, 2001, Adhikari, 2012). Skin lesions poses an important publichealth concern in Bangladesh and West Bengal, India as advanced forms of Keratosis are painful andif untreated can lead to social isolation among the affected villages (Haque et al., 2003, Haque andKhan, 2011).

While most of the arsenic studies are concentrated on arsenic are epidemiological studies. Inepidemiological studies it has been tried to link exposure of arsenic in drinking water over period oftime causes acute illness. The studies are cross-section in nature and focused more on current exposureto the illness. The long term (5 to 10 years) exposure of arsenic in drinking water forms the basis of skinlesions (Keratosis, Melanosis), hyper pigmentation, and increased risks of lung, bladder and skin cancers,birth defects and peripheral nervous system. Arsenic related exposure hinders the social and economiccost of the affected person. There are very few studies available which has focused on both social andeconomic factors and tried to estimate the socio-economic cost involved to the household due toexposure of arsenic in drinking water. Arsenic in drinking water causes different types of cancer. NationalAcademy of Sciences (NAS) 1999 suggested that arsenic level in tap water and its total cancer risk.Table 4 presents Arsenic in Drinking Water and Cancer.

Table 4. Arsenic in Drinking Water and Cancer

Source: National Academy of Sciences (1999)

The arsenic problem has a major effect on the socio - economic structure. The socio – economicproblems can be mainly categorised into three classes as agricultural problem, health problem andother problems. Excess presence of contaminated water leads to decrease in agricultural productivity,soil fertility, and also enters into the food chain which creates health problems. Brammer (2008) suggestedthat in India, Nepal and Bangladesh arsenic contaminated water used for irrigation enter into the foodchain. All these three problems lead to both social and economic problems. Skin lesions, bladder andcancer, and mortality are few of the health problems. Social ignorance, depression and suicidal tendencyare among few social problems. Arsenic contamination has widespread social problems among the

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affected households. Social problems are linked with the health and the economic problems. Arsenicrelated diseases are not spreadable disease. It is common myth among the households in rural areasthat it is a spreadable disease. The possible solution is to initiate awareness programme by the governmentat the community or grassroots level. Arsenic groundwater contamination has severe economic effecton the people residing in the areas where the menace is found. Study found that poor people are moreprone to suffer from such problem. There is dearth of studies on economic aspects of arsenic problems.The chronic effects of inorganic arsenic exposure via drinking water include skin lesions, such as hyperpigmentation and black foot disease, and respiratory symptoms, such as cough and bronchitis. Besides,there is sufficient evidence to link bladder and lung cancers with ingestion of inorganic arsenic (NRCReport, 2006).

Arsenic contaminated groundwater is used for agricultural irrigation resulting in excessive amountof available arsenic in the crops in that area. It has been reported that second to the ingestion of arsenic,after the direct consumption as drinking arsenic contaminated water, is through food chain, particularlyuse of contaminated rice followed by vegetables. This eventually indicates that the effects of this occurrenceare far-reaching; sooner we search sustainable solutions to resolve the problems, lesser be its futureenvironmental, health, socio-economic and socio-cultural hazards (MoWR, 2010b). The fertilizers andpesticides used for agricultural purpose also cause arsenic contamination. Rice and vegetables havemore effects on arsenic contaminated water. Brammer (2008) in his study suggested that arsenic-polluted water used for agriculture irrigation is a health hazard for the people eating food from the cropsirrigated in the areas of India, Bangladesh and Nepal in recent times. Arsenic contaminated water usedfor irrigation can adversely affect the soil quality and hence reduce food production.

Arsenic contaminated groundwater used for irrigation in the countries of south and south-eastAsia is adding arsenic to soils and rice. This poses a serious risk to sustainable agricultural productionand also the livelihoods and health of the affected population of those countries (Brammer, 2009). Thepossible mitigation strategy or measures should be needed. Two possible options can be possible. Thefirst is to provide the alternative irrigation sources and the second will be removing the contaminatedsoil by using the appropriate technology.

Concluding Remarks : Bihar was one of the least developed states of India both in terms of percapita income and human development index. However recent developments have made Bihar abetter place for habitation. In the last few decades pollution of water level has increased due to excessexploitation of groundwater resources for irrigation and drinking purposes, rapid increase inindustrialisation and urbanisation. Groundwater level is increasingly falling in many parts due to excessdrawls and recurrent draught like conditions leading to contamination problems with nitrate, fluoride,arsenic and other chemicals and also contributes to contaminating potable water sources.

Accesses to safe and clean drinking water along with sanitation are basic human needs. Theyare fundamentally linked to the health and wellbeing of the people. The majority of the people are facingarsenic in their drinking water is from poor socio-economic background. They are either not aware or

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if aware are forced to take drinking from same source due to lack of alternative sources of water. AsPrime minister of India Dr. Manmohan Singh rightly said in his 2012 IWW speech that “With around17% of the world’s population but only 4% of its usable freshwater, India has a scarcity of water. Rapideconomic growth and urbanisation are widening the demand supply gap. Climate change could furtheraggravate the availability of water in the country as it threatens the water cycle. Our water bodies aregetting increasingly polluted by untreated industrial effluents and sewage.

Groundwater levels are falling in many parts due to excess drawls leading to contamination withfluoride, arsenic and other chemicals. The practice of open defecation, which regrettably is all toowidespread, contributes to contaminating potable water sources”. If we cannot be aware and takeaction then the condition of contamination will be worse than Bangladesh which will certainly affectsustainable health of the stakeholders in all aspects of life.

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A PLANE SYMMETRIC UNIVERSE FILLED WITH VISCOUS FLUIDIN A MODIFIED BRANS-DICKE COSMOLOGY

L. N. Rai, S. Alam and Priyanka Rai

Department of Mathematics, Patna Science College,Patna University-800 005, Bihar, India

Abstract : A plane symmetric cosmological model filled with viscous fluid has been derived in theBrans-Dicke theory. Some physical and geometrical properties of this model have been discussed.Finally, this model has been transformed to the original form (1961) of Brans–Dicke theory.

Key Words : Plane symmetric universe, Viscous fluid, Cosmological model, Brans-Dicke theory.

Introduction : Endo and Fukui [1] have studied the variable cosmological term from the point of viewof cosmology in Brans-Dicke theory [2] and elementary particle physics.

In this paper, we have considered the modified Brans-Dicke theory with the variablecosmological term as an explicit function of a scalar field f as proposed by Bergmann [3] and Wagoner[4] and discussed in detail by Endo and Fukui [1].

The Brans-Dicke field equations with cosmological term Q are [1] :

,ki j ij ij , i , j ij ,k i; j ij2

8 1 1G g Q T – g ( – g )2 (1.1)

8(2 3 ) (1.2)

(2 3 ) (1 – ) 8 (1 – )Q . T ,4 4 (1.3)

) deviates from that of Bransand Dicke, w is coupling constant and ijT is energy-momentum tensor. Semicolons denote covariantderivative with respect to the metric ijg and commas mean partial derivatives with respect to thecoordinate ix . The theory can also be represented in a different formunder a unit transformation (UT) [5] in which length, time and reciprocal mass are scaled by the function

12 (x). Then under the conformal transformation

ij ij ijg g g (1.4)

equations (1.1) — (1.3) have the form

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,kijij ,i , j ,kij ij

1 1G g Q (8 )T (2 3)( – g )2 2

(1.5)

8(2 3)

(1.6)

Q . T,(1.7)

where the barred quantities are defined in terms of ijg as their unbarred counterparts aredefined in terms of the unbarred metric ijg and all barred operations are performed with respect to thebarred metric and barred Christoffel symbols.

In section – 2, we have studied a plane symmetric cosmological model filled with viscous fluidin Brans-Dicke theory of gravitation. In section-3, we have discussed about some physical andgeometrical properties of this model. Lastly in section-4, we have transformed this model to the 1961form of Brans-Dicke theory.

The Field Equations: The geometry of the universe is described by the line element.1 a 1–a

2 2 2 2 22 2ds –dT Tdx T dy T dz (2.1)

where a being a constant.

The energy–momentum tensor for the viscous fluid distribution is given by [6]:

j j j j j l l jji ;i i; i;l i ;li iT p v v pg – v v v v v v v v

l j j;l ii

2– – v g v v ,3

(2.2)

together with

iiv v –1 (2.3)

where p and are the pressure and density respectively, , and are the two coefficients ofviscosity, iv is the flow vector satisfying equations (2.3) and semicolons signifies covariant differentiation.

The coordinates are assumed to be comoving so that

1 2 3 4v v v 0 and v 1.

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The pressure and density in the model (2.1) are given by1

2 2 222

( a – 5 ) 1 ( 5 – a ) 88 p 1 ta n lo g (K T ) Q2 8 ( 2 3 ) T16T (2.4)

12 2 22

2( a – 5 ) 1 (5 – a )8 1 – ta n lo g ( KT ) – Q .

2 8 ( 2 3 )16T (2.5)

Also scalar field is given by

12 2(5 – a )log se c lo g( K T )8( 2 3 ) (2.6)

and1

2 2 22

2(1 – ) (a – 5) (5 – a )Q se c lo g(K T )

4 8(2 3 )8T (2.7)

where K is a constant.

Physical & Geometrical Properties

The model has to satisfy the reality conditions [7] :

(i) p 0 a n d (ii) 3p 0

which requires that2 3

a 5 , – , Q 0 (i.e . 1) .2

(3.1)and

12 2 2

22

(5 – a ) (5 – a )Q se c l og (K T ) .8(2 38T (3.2)

The flow vector of the distribution for the model (2.1) is given by 1 2 3 4v v v 0 an d v 1 .

Obviously i j;jv v 0.

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Therefore the flow is geodesic.

The rotation tensor

i; j j; iij v – v 0.Thus, the fluid filling the universe is non-rotational.

The expansion scalar i;i

1 v3 is given by

1 .3T (3.3)

Shear tensor i ; j j; i i jij ij1 (v v ) – (g – v v )2 is given by

111 ,6

a–12

22(3a–1) T ,

12(3.4)

–a–12

33– (3a 1) T ,

12 44 0.

Also the shear s is2 ij 2

ij 21 1 [1 3a ].2 48T (3.5)

The volume element of the model is given by

1 122 2V (– g ) (– T ) T. (3.6)

Here volume is directly proportional to time. If the time increases then volume increases (i.e.the models are expanding with time) and if the time decreases then volume decreases (i.e. the modelsare contracting).

Deceleration parameter

2 2d 1q – 3d T 3 is given by

2

4(9 T 1)q –

81T (3.7)

The deceleration parameter acts as an indicator of the existence of inflation. If q > 0, the modeldecelerates in its standard way while q < 0, the model inflates. The present model represents a deceleratemodel if and inflationary model if .

2 1T – 9 and inflationary model if 2 1T – 9 .

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The surviving components of the conformal curvature jkhiC tensor for the line-element (2.1) are

1 4 2 3 21 4 2 3 2

1C C [1 a ] ,

24T

1 2 3 4 21 2 3 4 2

1C C – [1 6 a a ],48T (3.8)

1 3 2 4 21 3 2 4 2C C – [1 – 6 a a ].

48TThe pressure, density, scalar field and cosmological constant are singular at

12

21 2 (2 3 )T e xp .K (5 – a ) (3.9)

The model exists for a finite time12

21 1 2(2 3 )T e xp .K K (5 – a )

viscous fluid distribution in general relativity [8].

Transformations of the Solutions and Discussion : Under the transformations

iji j ij1g g g ,

ij ijijT T T ,

2T T T ,

2p p p, (4.1)

2 ,

e ,

Q Q Q,

1i ii 2v v v ,

the solutions of the field equations (1.5)-(1.7) are changed into 1961 form of Brans-Dicketheory [2]. We now apply these transformations to the solutions obtained from field equations (1.5)-(1.7). Thus

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12 2(5 – a )se c lo g(K T )

8( 2 3 ) (4.2)

12 2

ij ij(5 – a )g co s lo g (K T) g8(2 3 ) (4.3)

i.e.

12 2

11(5 – a )g cos lo g (K T ) T8(2 3 )

11 a2 2

222

(5 – a )g c os lo g( K T ) T8( 2 3 )

11–a2 2

233

(5 – a )g co s lo g(K T) T8( 2 3)

12 2

44(5 – a )g – co s lo g (K T ) .

8 (2 3 )

12 2 2

42

(a – 5) (5 – a ) 1 –8 p se c lo g (K T ) 18 (2 3 )32T

12 2 2

22

( a – 5) (5 – a )s ec log (K T )8 (2 3 )32T

12 2

28 (5 – a )se c lo g (K T )T 8(2 3 ) (4.4)

12 2 2

42

(5 – a ) (5 – a (1 – )8 se c lo g (K T) 18 (2 3 )32T

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12 2 2

22

3 (a – 5 ) (5 – a )se c lo g(K T )8(2 3 )32T (4.5)

11 2 2

4 2 (5 – a )v se c log (K T )8 (2 3 ) (4.6)

12 2 2

32

(1 – ) (a – 5) (5 – a )Q se c lo g(K T )4 8(2 3 )8T (4.7)

symmetric universe filled with viscous fluid in the Brans-Dicke theory [2]. The model obtained in thispaper is new and like other models filled with viscous fluid, they may be used in the relativistic cosmologyfor the description of very early stages of the universe expansion.

References:

Endo, M., Fukui, T. (1977) : Gen. Relativ. Gravitation, 8, 833,

Brans, C.H., Dicke, R.H. (1961) : Phys. Rev., 124, 925,

Bergmann, P.G. (1968) : Int. J. Theor. Phys., 1, 25,

Wagoner, R.V. (1970) : Phys. Rev., D1, 3209,

Dicke, R.H. (1962) : Phys. Rev., 125, 2163,

Landau, L.D., Lifschitz, E.M. (1963) : Fluid Mechanics, Vol. 6, 505,

Ellis, G.F.R. (1971) : General relativity and cosmology, (ed.) R.K. Sachs, Academic Press,New York and London, 117,

Prakash, S. (1981) : Curr. Sci., 50, 78,

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125


MULTICOLLINEARITY, ITS EFFECTS AND CONSEQUENCESL.N. Rai and Alakh Niranjan

Department of Mathematics,

Patna Science College, Patan-5, Bihar, India

Abstract :In this paper we have extensively discussed about multicollinearity its effects and consequences.Furthermore, exact multicollinearity versus orthogonality has been extensively studied in recent literaturewhere multicollinearity ( p 0 ) does imply non orthogonality ( p 1). In presence of multicollinerityto a singular matrix p 0 , there is exact multicollinearity following the condition that defined quantityK* as infinity.

In this context estimate of both efficient vector and dispersion matrix have been evaluated byFarrar and Glauber (1967) related with the study of multicollinearity specifying the character of matrixnotations on assumption of parent orthogonality to the determinant |R| or conveneient transformation of|R| for which test of significance of rejection of hypothesis 0H : |R| = 1 at specified level of significance.In addition a light has been given on Haitovasky’s Chi square, while the extent of multicollinearity couldbe assertained for measuring the departure of R-matrix from singularity. Also, lower bounds for 2E( )and 2V( ) have been obtained in this regard and indicating the distance between b and b when eigenvalues are considered in this study. We have also stressed on application of measures based on multiplecorrelation implicating a test known as variance ratio test contributing matrix inversion explained withsum of squares due to error, total and regression expressed for sampling variance and square of regressioncoefficient consisted with the study of multicollinerity. Lastely, solution existence and consequences ofmulticollinearity have been made on studying different approaches in the context of multicollinearity tomore extended forms.

Key words : Multicollinearity, Regression Analysis, Orthogonality, Singular Matrix, Eigen values,Regression Coefficient.

Introduction : The present study would make an attempt to concentrate on the concept ofmulticollinearity and also to visualize its effect and consequences with greater extent by means ofregression analysis study.

In this connection, the first aspect is to study relating the term ‘multicollinearity’, which hasbeen brought in the case of regression analysis due to Frissh (1934) that exists for taking thedecis ion of exact relation. That means multicollinearity was often referred for the existence ofmore than one exact linearship while the term collinearity means the existence of single linearrelationship. Though linear regress ion model is also reformed for existence of all explanatoryvariables.

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However, in consideration of these two studies there maintains some distinction between thesetwo used terms. In recent literature the term multicollinearity is used to denote the presence of linearship(or near - linear relationships) among the explanatory variable.

In the case of regression model

Y = X +

as we know there is one of the basic implicit assumptions of the classical linear regression thatthere does not exist exact linear relationship among the observed values of the explanatory variables. Inother words, it can be said that the data matrix X which is of order (n x p) following with rank. Thereason for the assumption is that the least square estimation b = / –1(X X) /X Y requires the inversionof /X X which is impossible of the rank of b, and hence the rank of /X X is less than p that showslinear dependence between the explanatory variables.

This indicates the case of extreme multicollinearity which exists when some or linear all of theexplanatory variables in a relation are perfectly linearly correlated. In this situation the parameter vectoris not estimable.

Thus, the least square estimation procedure breaks down there.

In fact, an exact linear relationship is highly improbable in the case of practical work but thegeneral interdependence of economic phenomenon may easily result in the appearance of approximatelinear relationships for the study of regressors.

Johnson (1963) has resorted to a asymptotic definition. Multicollinearity is the name given tothe general problem which arise when some or all of explanatory variables in a relation are as highlycorrelated one with another that it becomes very difficult, if not impossible, to disentangle their separateinfluence and obtain a reasonably precise estimate of their relative effects.

Concept Relating To Exact Multicollinearity Versus Orthogonality : The present section wouldneutral to examine the exactness of multicollinearity with the concept of orthogonality. It is true thatexact multicollinearity exists when the rank of X is less than p i.e. if the columns of X are denoted by

1X , 2X ......... pX there exists non zero constants ia (i = 1,2, .......... p) such that

i ia X 0 (2.1)

By examining such existed relation, an assumption can be made that multicollinearity existswhen there is relationships among explanatory variables and they are perfectly linearly correlated.

The matrix is said to have orthogonal regressors when it is such that /X X = 1 i.e all eigenvaluesof /X X are equal to unity and X consists of orthogonal regressions. Hence orthogonality of regressors(or explanatory variables) implies that /X X 1.

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In order to concentrate on near ‘multicollinearity’ that exists when there are non-zero constantssuch that

i ia X 0.When the matrix /X X is singular, its inverse does not exist in the usual sense. Exact-

multicollinearity implies that /X X is singular i.e. the smallest eigen values p near multicollinearity,,means that /X X is singular and the smallest eigen value is close to zero. In case of near orthogonality,,eigenvalues are different from unity. In particular, the smallest eigen values p 1 that does not imply

p 0 .

Thus non-orthogonality does not necessarily imply singularity or muiticollinearity. However,muiticollinearity p( 0) does imply non-orthogonality ( p 1).

Conditioned Matrix :

Numerical analysts have mainly uncared with near singularity of matrix and hence derived theso called “conditioned number” K* to index the extent of all conditioning of a matrix. It is usuallydefined as

1 –1* 2 21 1K

where 1 2 p........................ .

This is a ratio of what is called the singular value of X [i.e. square - test of the eigen values of/X X] . The meaning of term “Singular” here is not be confused with the singularity of the matrix itself.

The presence of muiticollinearity can be checked by merely looking at p without carrying about 1. For a singular matrix /

pX X, 0 there is exact-multicollinearity, and K* is infinite.

For orthogonal data, we have /X X 1 we have K* is equal to unity, thus the conditionnumber lies in the half upon interval (1,0). For any two matrices, the larger is the value of K* theworse conditioning. All ill-conditioned matrix of available data is defined by a large conditionnumber. Of course, there is some ambiguity large similar to the s ingularity in defining elements tozero.

Chi-Saquare (X2) As Measure of Multicollinearity : This study is followed by Bartlett’s (1950). If

the regressor variables are standardized, then /X X contains elements that are the simple correlation

coefficient among the regressors. In that case /X X falls in the interval (0,1).

If /X X = 0, one or more exact-linear dependencies exists among the columns of X. Similarly

if /X X =1 then column of X are orthogonal.

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In fact, most standard multiple regression computer programme have built-in checks for non-singularity of the /X X matrix. These lists commonly use that determinant of a singular matrix is zero.

Farrar and Glauber (1967) attempted to define a standard comparison for /X X by defining

multicollinearity as a departure of the matrix X from orthogonality. Estimate of both efficient vector andits dispersion matrix viz.

/ –1 /b (X X) X Yand V(b) = 2 / –1(X X)require the operation.

The test must commonly used and rely on the property that determinant of a singular matrix iszero. Defining a small positive test value, u > 0 a solution is attempted if

/X X u.

Computations hailed otherwise if /X X is based on a normalised correlation matrix (unless

stated otherwise) then 0 < /X X = |R| < 1.

As X approaches singularity (perfect multicollinearity) then |R| approaches one.Unfortunately the gradient between these limits is not well defined. If under in assumption of

parent orthogonality to the determinant |R| or convenient transformation of |R| could be found, theresulting statistic could provide a useful measure of the presence and severity of multicollinearity withina set of predictor variables.

However, Bartlett(1950) contributed2 2 p(p – 1)X –[n – 1– (2p 5) / 6] log| R |~ X [ ]

2n = no. of sample, and

p = no. of variables.

this contribution due to Bartlett on the basis of comprising the lower moments of Wilksdistribution with these 2X distribution.

A high value of Chi square indicates the existence of multicollinearity. Its severity can be measuredby the level of significance at which Ho: |R| = 1 is rejected.

In this connection, Monte Carle pointed out that for n = 20, p = 10 and a = 0.05, then the nullhypothesis is rejected following with the concept that when the elements of R are larger (in absolute,value) than 0.36.

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In the same way, when n = 200, p = 10, a = 0.05.

One is sure in virtual sense that the null hypothesis Ho is rejected when the elements of R arelarger (in absolute value) than 0.09.

Furthermore, William and Watte(1978) have presented a geometric interpretation of 1

/ 2XX

when X is in standardised form. 1

/ 2X X represents a ratio of the volumes of the joint confidenceregion based on available design relative to that of an orthogonal reference design in which the regressorshave the same variability as in the original design.

Although the Bartlett test defines multicollinearity as a departure from orthogonality, that doesnot need any solution of the least square. On the contrary, the least squares solution for multiple regressionpre supposes (in non-trivial cases) intercorrelated predictor variables, otherwise the sample correlationcoefficients may be computed for each predictor variable separately.

The only relevant requirement in the context of multicollinearity is the full rank requirement.According to Haitovasky’s Chi-square, the extent of multicollinearity can be measured as the departureof R-matrix from singularity.

A Heuristic statistic which is consistent.

Which is consistent with the concept due to Haitovasky’s(1969) given by 2X = [n–1–(2p +5)/6] log (1–|R|).

A small value of X2 indicates the existence of multicollinearity, its severity can be measured bythe level of significance at which the null hypothesis oH ;| R | o is accepted.

Explanation For Eigen Values Mathematically As Measure Of Multicollinearity

Writing the equation as discussed in proceeding study such as

/ –1 /b – (X X) X U (5.1)

Let D define the distance between b and b such that;

= (b – ) = / –1 /(X X) X UThe squared distance between b and b is obtained as;

2 = / –1 / / –1 /(X X) X U[(X X) X U]

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E( ) = / –1 / / –1 /E[(X X) X U] [(X X) X U]

= / –1 / / –1 /E tr [(X X) X U] [(X X) X U]

= / –1 / / –1 / /E tr [(X X) X U] [(X X) X U]

= / –1 / / / –1tr E[(X X) X U U X(X X) ]

= 2 / –1tr[(X X) ]

Now /X X is real symmetric matrix, as its inverse. Therefore,p

2 2

i 1 i

1( ) ( ) (5.2)

where i is the i th eigen value of /X X . Considering the variance of 2 we have

V 2( ) = 2 2 2E[ – E( )]

= 4 4 / 2E[ – tr (X X) ]

But, 4 / –1 / / –1 / / –1 / / –1 /( ) [(X X) X U]' [(X X) X U] [(X X) X U] [(X X) X U]

Thus, 4 / –1 / / –1 / / –1 / / –1 /E( ) tr E[(X X) X U]' [(X X) X U] [(X X) X U] [(X X) X U]

= / / / –2tr E(UU UU ) (X X)

Since U is assumed normally distributed, we can write

/ / 4E(UU UU ) 2

4 4 / –2V( ) 2 tr (X X)

4 / –22 tr (X X)

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p4 –2

ii 1

2 (5.3)

Considering the equations (5.2) and (5.3), if min is the smallest value of /(X X) then,

(a) A lower bound for 2

2

minE( ) is

(b) A lower bound for 4

2

minV( ) is

Thus, if the particular variables are related in such a manner as to result in /X X matrix with oneor more small eigenvalues, the distance from b and will tend to be greater since the eigenvalues of amatrix are just the zeros of the characteristic polynomial, the fact that the eigen values depend continuallyon the element of the matrix follows immediately if it is known that zeros of a polynomial dependcontinually on its coefficients.

Then, a rule based on eigen values of the R matrix could logically suppliant the correlationcoefficient rule except that some information is lost when we deal with the characteristic polynomial,since there are many distant matrices with a given characteristic polynomial.

But multicollinearity is essentially a problem of small |R|, and it is irrelevant what the specificelements of R are that produces |R|.

Pointing, if 1 2 p, ,.................................... are the eigenvalues of R (not necessarilydistinct) then

|R| = p

ii 1

The small eigenvalues, therefore, result in small R. In fact, a singular matrix implies the existenceof one or more zero eigenvalues. A rule can be established to constraint the smallest eigenvalue to begreater than a specified value. Usually, 0.3 is the specified value as suggested by Daling andTamura(1970).

Measures Based On Multiple Correlation : Klein(1960) suggested that the multicollinearity is saidto be harmful if | ijr | > yR

for all i = j, where ijr is the zero order correlation between the predictorvariables and Ry is the multiple correlation between responses and the predictor variables. In thisconnection, Farrar and Glauber(1967) found same drawbacks in Klein’s rules and they have developeda set of three tests for multicollinearity, viz.

(i) test based on Chi-Square.

(ii) F-test locating which variables are multicollinear.

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(iii) t-test for finding out the pattern of multicollinearity, i.e. for determining which variablesare responsible for appearance of multicollinearity.

A particular variable iX would be said to be harmfully multicollinear of i y| R | | R | .

Where iR is the multiple correlation of iX with other predictor variables and Ry is the multiplecorrelation of dependent variable with the entire set of predictor variables. The notion of the rule can bedeveloped as follows.

We can define the variance of the OLS estimator as follows :

2 / –1V(b) (X X)/ /1 1 12/ /

1

X X X Z

Z X Z Z (6.1)

where without loss of generality, 1X is a vector of observations on the first predictor, and Z is a matrixof observations of the remainder of the predictor. Applying the partitioned matrix inversion rule in

equation (6.1), we have

/ / –1 /2 1 1 1 1X X X Z(Z Z) Z X AV(b)

B B (6.2)

where A and B are vectors and C is a matrix. However, / / / –1 /1 1 1 1[X X – X (Z Z) Z X ] is the

error sum of squares of the regression of the first predictor variable on the remaining particular variable.

Now from the equation (6.2), the variance of the first coefficient estimate is

2 / / / –1 / –11 1 1 1 1V(b ) [X X – X (Z Z) Z X ]

2 –11(SSE )

2 –1 –11 1(SST – SSR ) ) (6.3)

where SSE1, SST1. and SSR1 are the errors, total and regression, sum of squares respectively.Again defining SSE, SST and SSR to be the analogous sums of squares for the regression of

the criterion variable on all of the particular variables, including the first particular variable. An estimateof s2 is

S2 = (SST – SSR) / m-p-1) (6.4)

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Also, R12 = SSR1 / SST1 (6.5)

Therefore, SST1 – SSR1 = SST1 (1 – R12) (6.6)

Analogously, SST – SSR = SST (1 – Ry2) (6.7)

Substituting equations (6.4), (6.6) and (6.7) in (6.3) we have an estimate of V(b1) as

2 21 y 1 1

1V(b ) [ ] [SST(1– R ) / SST (1– R )(m p 1)

2 21 y 1

1[ ] [SST / SST ] [(1– R ) /(1– R )(m p 1)

2 2 2 2y 1 y 1

1[ ] [S / S ] [(1– R )/ (1– R )](m p 1) (6.8)

Since the choice of the first predictor variable arbitrary equation (6.8) can be generalized as

2 2 2 21 y i y 1

1V(b ) [ ] [S / S ] (1– R ) / (1– R )(m p 1) .

Thus, the magnitude of the estimated variance of such estimated coefficient, given the ratio of2yS and 2

iS , depend not only on the intercorrelation between the (ith) predictor variable and the rest,but also on the relationship between the criterion variable and the predictor variables.

Concentration On Possible Solution Of Multicollinearity Problem : In the present section ourattempt is to focus on possible solution of multicollinearity which seems a vital problem relating topresent study of research work. When multicollinearity is present in a set of multicollinearity variables,the OLS estimates of the individual regression coefficients tend to be unstable and can made to enumerousinferences. After deleting its presence, some alternative methods that prove a more informative analysisof the data than the OLS method. The possible solutions for multicollinearity are

(a) Dropping Variable(s) : It is assertained that multicollinearity arises due to lack of sufficientinformation in the sample to permit reliable estimation of the individual parameters. In some situation itmay be the case that one is not interested in all the parameters. In such cases we can get estimators forthe parameters and one is interested in that have smaller mean square errors than the OLS estimators,

Considering the case

Yi = 1 X1i + 2 X2i + Ui (7.1)

and the problem is that 1X and X2 are very highly correlated. In this situation one of the highly correlatedvariables may be dropped. Therefore dropping X2 the existing model becomes asYi = 1 X1i + Vi————— (7.2)

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where Vi is said as new disturbance term.

The estimator 1b of b1 obtained from (7.1) is the OLS estimator, b*1 of the same b1 obtainedfrom (7.2) is called the omitted variable (OL) estimator. The estimator *

1b is biased estimator becauseof the omitted variable X2, while b1 is an unbiased estimator.

Further, it can be shown that b*1 has a smaller variance. Since the estimate of b1 from (7.2) isgiven by

b*1 = 2i 1i 1iY X x

It can further be shown that*1 1 21E(b ) b (7.3)

where b21 is the slope coefficient in the regression of X2 on X1.

This shows that b*1, the estimated variable estimator is a biased estimator. If both b21 and 1in (7.3) are positive, then *

1E(b ) will be greater than 1 leading to a positive bias. Similarly, if theproduct b21 is negative, on an average *

1b will under estimate 1, leading to a negative bias.

Thus, dropping a variable from the model to alleviate the problem of multicollinearity may leadto the specification bias. Hence the solution may be worse than biases in certain situations.

Existence Of First Differences In Case Of Multicollinearity : The existence of first differences isexisting in a common trend where source of multicollinearity is possible, then in such situation, the ratioor first differences technique is often used in time series analysis. However, the transformations usedunder the technique have adverse effect on the properties of the resulting residuals.

In using ratio introduction of heterosedasticity, auto correlation may be introduced with the helpof using first differences by consideration of the following model, where t is used for time ot . Thus wehave,

t 1 1t 2 2t tY X X U ......... (given for t time)

Also, t 1 1(t –1) 2 2(t–1) t–1Y X X U .. (given for time t-1)

Using first differences, the following result exist in the following manner given as,

t t t–1Y Y – Y

1t 1t 1,t–1X X – X

2t 2t 2 t–1X X – X ,

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Thus, by using first differences, there is found that for the case of regression model, therereduces the severity of multicollinearity because 1tX and 2tX are existing for highly correlatedvariables as there exists for 1tX and 2tX .

In order to concentrate on ratios, the given regression model may exist as

t 1 1t 1 2 2t 1t t 1tY X t X X X U X (on division of 1tX )

1 2 2t 1t 1t t 1tX X X U xObvious, resulting residuals will be heterosedastic indicating (1/ 1tX ) is used as an explanatory

variable.

Hence, a confusion may be drawn that the ratios or first differences provides for existence ofmulticollinearity, unless an assumption is being made for disturbance Ut in the original model of regressionbecause the transformations as made above provide independent and homosedastic residuals in thetransformed equations.

There are some special methods existing in case of multicollinearity, where traditional residualmeasure, are being applied as suggested by Ranger Erisch under the conditions of more data. But thedifficulty arises for expansion or impractical due to cancellation of data that is not possible.

Concentrating on the study of multicollinearity, where some methods relating to given problemssuch as (a) Chi-Square as a measure of collinearity (b) eigen values as a measure of collinearity (c)measures based on multicorelation as they have been discussed in earlier sections of this paper.

However, possible solutions for multicollinearity, the following measures (as discussed in thepresent section) have been under taken into study such as:

(i) dropping variable,(ii) using extraneous estimates(iii) using ratio or first differences,(iv) using some special method.

Though, these solutions have been discussed elaborately but in conclusion a justification mightbe laid there that multicollinearity as defined earlier is a statistical, rather than a mathematical condition.As such, one thinks, one speaks, in terms of the problem a severity rather than of its existence or non-existence. It is true that the effect on estimation and specification of interdependence in X-reflected byvariances of estimated regression coefficient and a tendency towards misspecification also dependspartly on the strength of dependence between Y and X.

Consequences Of Multicollinearity : In order to concentrate on consequences of multicollinearity,a brief concept necessiates there. The presence of multicollinearity has a number of potentially seriouseffects on the least square estimates of the regression coefficients. Some of these effects may be easily

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demonstrated. It is true that collinearity does not destroy the property of minus variance. But this doesnot mean, that variance of as OLS estimator will necessarily be small (in relation to the value of theestimator) in any given sample.

Further, in consideration of high variance estimates of regresion coefficient the following conditionsmay be taken into account as;

If the intercorrelation between the explanatory is perfect then the two conditions hold there.

(i) the estimates of coefficients are indeterminate i.e. the value of b explode, and

(ii) the standard errors of these estimates become infinitely large. For near multicollinearityXp > 0 and also Mean Square Error (MSE) tends infinitely i.e. b is subject to very largevariance (Since MSE(b) 2 –1

p ).

In this connection, Marquardt(1970) remarked by consideration of correlation matrix /X Xindicating rij for (i,j) the element of the inverse matrix / –1(X X) asserted on variance inflation factor(VIF)and the author contributed

ijVIF(i) r 5that indicates a harmful multicollinearity.

In this connection, Theil(1971) showed the following resultij 2

i2i

1r [ X ](1– R )

where 2 /i i i[| X | X X ] and 2

iR represents the squared multiple correlation coefficient when

Xi(ith col of X) is regressed on remaining (p-1) regressors.

Again, stressing on multicollinearity when it is present, there is a linear relation among regressorsas discussed in present study and 2

iR will be large (close to 1).

Thus, the denominator of iir will close to zero. Hence iir will be large. In otherwords,multicollinearity leads to high variance of regression coefficients.

In addition to, giving an explanation of the difficulty with the estimated student’s value based onmulticollinear data is that these values are highly unstable and often change their sign and relative magnitudewith minor perturbation in data.

Thus, the exact causes of wrong signs may be many, and what appears to be a wrong signs maynot even be wrong. Most regression practitioners knew about this problem, even though it is not a well-defined problem, in a puristic sense.

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When the estimate regression coefficient bi are interpreted as practical derivatives y iX thewrong sign problem is particularly serious.

Hence, it is therefore, other can be assertained that the concept of stability of bi values can berigorously defined by using some classical concepts in perturbation theory developed by VonNeumann(1941), Wilkinsen(1965) and others.

References :

Frissh, R (1937) : Statistical Confluence Analysis by Means of Complete Regression Systems, Instituteof Economics, Oslo University, Publ. No. 5. cited in 1977 Maddala, G.S.

Farrar, D.E. and Glauber, R.R. (1967) : Multicollinearity In Regression Analysis : The Problem Revisited,Review of Economics and Statistics, 49, 92–107

Feldstein, M.S. (1973) : Multicollinearity and the Mean Squared Error of Alternative Estimators,Econometrics, 41, 337 – 346

Fomby, T.B. and Hill, R.C. (1978) : Multicollinearity and Minimax Conditions for the Bock Stein –Like Estimator, Econometrics, 47, 211 – 212

Haitovasky, Y. (1969): Multicollinearity In Regression Analysis; Comment, Review of Economics andStatistics, 51, 486-489

Kumar, T.K. (1975) : Multicollinearity In Regression Analysis, Review of Economics and Statistics,57, 365-366

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139


SYNCHROTRON SPECTRA OF ASTROPHYSICAL JET M87 AND THEPROCESS OF DIFFUSIVE SHOCK ACCELERATION

Sumita Singh

Postgraduate Department of Physics, Patna University

Email :[email protected]

Abstract : The theory of acceleration of the synchrotron emitting electrons or positrons in the extragalacticjets has to incorporate the fact that the spectrum is continuous from radio to X-rays.With reference tothe M87 jet it has been observed that the spectral shape is uniform along the jet. And there is a constantupper spectral cutoff of the synchrotron electrons at

610*9.0c

Structure of M87 : The structure of M87 comprises of various knots which arise due to shocks in thejet flow. The two characteristics as described above are applicable to the smallest scales of about10pc. The synchrotron half-life at the upper cutoff is

yrTB 216ccsyn )1/()10/(185)(

(Meisenheimer et al 1996). This implies that the energy losses should affect the spectrum at adistance from the acceleration site ranging from

syn

syn

42

10

pc to

pc

in the inner jet (knot F) to 2acc B at knot A.

This can be explained using the model of shock acceleration.

Numerical Analysis & Results : The sequential procedure for treating synchrotron losses involvesneglecting the synchrotron losses o that DSA forms a distribution that extends well beyond the synchrotroncutoff, and then allowing synchrotron losses to modify this distribution. In order to check the validity ofthis sequential procedure we treat DSA in another manner that allows one to include the accelerationand the synchrotron losses at the same time, rather than sequentially. The numerical results show thatthe two procedures produce indistinguishable results which are illustrated in the figures. In these figuresthe logarithm of the distribution is plotted as a function of log N(X), so that a power law distributioncorresponds to straight line. The absolute values of f(p) and of p are unimportant. The synchrotroncutoff momentum pc is a free parameter and is chosen to be either three (pc/po = 103) or six (pc/po =106) orders of magnitude above the infection momentum. All the shocks have the same strength, specifiedby the value of r, and the calculations are performed both for strong shocks with r = 3.8 and for shocks

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with r = 2.0. The adiabatic decompression after each shock moves the curve to the left [by –(logr)/3],without changing its shape, so that after N shocks the lowest energy particles in the distribution haslogp = -N(logr)/3.

Here å is the compression ratio, is the power law index and a is related to the power lawindex linearly and Y and X both are the ratios of p &po. Log N(X) stand for log f(p).

In the theory of DSA it is usually assumed that there is injection at every shock. Hence thecases are shown in the figures, where there is only a single initial injection with this distribution subjectedto many shocks in not realistic in practice. One expects the distribution after N shocks to consist of thesum over the distribution injected at the first shock subjected to N-1 shocks ,and so on to the distributioninjected at the Nth shock subjected to only one shock. This sum is performed in evaluating the distributionshown. As N is increased the slope of distribution decreases monotonically and approaches b = 3 atlow p>po, in accord with the theoretical predictions (White 1985; Achterberg 1990; Schneider 1993).Nearer the synchrotron cutoff, after about 10 shocks, a peak in the slope starts to develop and becomesincreasingly prominent with increasing N. This pick may be attributed to the contribution from theplateau-like portions of the distribution resulting from injection at the earliest shocks.

The forgoing results show four notable effects of synchrotron losses on multiple DSA: (a) itprovides a high-p synchrotron cutoff (denoted pc) beyond which no particle can be accelerated byDSA; (b) for a single initial injection, a plateau distribution, f(p) = const., develops at p<0.1pc; (c) thecumulative effect of injection at every shock leads to distribution f(p) á p-3 for p <<pc; and (d) thedistribution in (c) has a slope that rises gradually to a peak (with bmin ~ 2 at p ~ 0.1pc).

Results and discussions : We present the results of numerical calculations that show the effect ofsynchrotron losses on diffusive shock acceleration (followed by adiabatic decompression) at multipleshocks. Our main results can be summarized as follows:

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(1) Synchrotron losses are most important during the acceleration process, when theelectrons are in the compressed – B region just downstream from the shock. Synchrotronlosses imply a synchrotron cutoff, p = pc, to the distribution of accelerated particles;DSA cannot cause any particle to be accelerated to p > pc.

(2) It is shown analytically that two procedures for treating the combination of synchrotronlosses and DSA are equivalent. In one treatment, the effects of synchrotron losses areincluded in the momentum change in each cycle of particle crossing the shock fromupstream to downstream and back. In the other procedure, used in our numericalcalculations, synchrotron losses are first neglected to find the distribution of electronsresulting from DSA alone, and then the synchrotron losses are allowed to modify thisdistribution. The two procedures are equivalent provided that the time for which thesynchrotron losses are allowed to modify this distribution.

(3) Just below the synchrotron cutoff , the distribution of particles injected at an initialshock and subjected to DSA at many shocks without further injection tends to form aplateau distribution [f(p) independent of p] which corresponds to an energy spectrumN(e) á å2.

(4) The distribution below the synchrotron cutoff due to the cumulative effect of injection atevery shock tends to distribution f(p) á p-b with 3b at p<< pc, with the distributionbecoming somewhat flatter such that the slope has peak (with 2b ) just below pc (at

0.1 cp for strong shocks). Such a distribution, if the source were homogeneous (whichit is not due to the shocks), would corresponds to a flat synchrotron spectrum

[ ( 3) / 2 0b ] becoming a weakly inverted spectrum ( 0.5) with a peak just belowa sharp cutoff due to synchrotron losses.

It can be concluded that it is possible for multiple DSA coupled with synchrotron losses toaccount for a flat synchrotron spectrum. This may be a viable explanation for the flat synchrotronspectra observed in some Galactic Centre sources.

The forgoing results apply to DSA at a single shock, and it is of interest to consider DSA at asequence of shocks. It is assumed that a new distribution of particles is injected at each shock and theboth these injected particles and the particles injected at earlier shocks are subjected to DSA. Inbetween shocks the magnetic field is decompressed to its initial value, which leads to adiabatic energyloss by all particles between the shocks.

One other notable feature of the simple theory is the treatment in terms of test particles. In factthe accelerated particles must contribute to the stresses; by continually reflecting off the scatteringcenters embedded in the upstream and downstream flows, the accelerated particles transfer momentumacross the shock, tending to slow down the relative flow. DSA can be very efficient under relativelymild conditions the accelerated particles can provide the most important dissipation mechanism for

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shocks, in the sense that a large fraction of the shock energy is ultimately transferred to acceleratedparticle. In addition, if the number density of injected particles is high enough, acceleration of theseparticles can lead to them providing the dominant stress in the shock, resulting a shock structure that isquite different from that in the absence of the accelerated particles. In adopting a test particle approachit is assumed that such dynamical effects of the accelerated particles are not important.

References :

Biretta, J. A., Owen, F. N. , (1990) in Parsec – scale Jets, eds. J. A. Zensus and T. J. Pearson,(Cambridge: Cambridge Univ. Press), 125.

Melrose, Don , Crouch, Ashley, (1997) Effect of Synchrotron Losses on Multiple DiffusiveShock Acceleration

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143


ON THE DETERMINATION OF REORDER LEVEL BASED ONNEGATIVE BINOMIAL DISTRIBUTION

Arun Kumar Sinha

Department of Statistics and Patna Science College,Patna University, Patna 800005 (Bihar) India

([email protected])

Abstract : An attempt has been made in this paper to propose a method for the determination of the“reorder level” or “float size” of a stock by extending the work done earlier in this direction. Also, atable has been prepared for this purpose, which appears to be more useful, convenient and extensivecompared to the available tables in many respects.

Keywords: Float size, risk level, normal approximation, cumulative probabilities,

Poisson distribution, gamma distribution

Introduction : The main aim of an inventory management is to maintain an optimum level of stocks tomeet future demands. Taylor (1961) while discussing the inventory management for aircraft maintenancepointed out two types of problems that are usually encountered. The first one focuses at the determinationof the “reorder level”, which is the level to which a stock is allowed to fall before an order for new itemsis placed. The second problem arises in the replacement of defective parts of the aircrafts. It requiresthat a store must have some extra components of each type needed. The surplus is called the “floatsize” of each type of the component required. It means that the solution to the second problem is tohave an estimate of the float size for each component. However, if we consider the placing of a defectivecomponent for repair as a “demand” and the interval between its removal from an aircraft and its entryinto the store as the lead time, the second problem reduces to the same as the first one. It is, therefore,evident that “float size” is the same as “reorder level”. The author has used the negative binomialdistribution (NBD) for the purpose. Also, Brown (1965) has described the probability distribution forthe study of demand of replacement parts in the air force supply system. The author has prepared atable that is divided into two parts. The first part gives the probability of ‘n’ demands. The cumulativeprobabilities of ‘n’ or fewer demands are mentioned in the second part of the table. An attempt hasbeen made, in this paper, to extend the work of the earlier authors by employing the approximationssuggested by Bartok (1966) and also to prepare a table that is more extensive and convenient in manyrespects. One could obtain the value of the float size or reorder level from the table corresponding tothe estimates of the parameters of the NBD and the desired risk level. The estimate of the parametersis calculated on the basis of the observed demand of the component. The technique proposed in thepaper may be used in areas other than the maintenance of aircrafts. The application of the technique hasbeen illustrated in the maintenance of the inventory of the LPG (Liquefied Petroleum Gas) cylinders forcooking.

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Model : If ‘a’ is the average number of demand per unit of time then the number of demand ‘x’ duringa fixed interval of time ‘t’ follows the Poisson distribution as given below:

P(X = x) = e-at(at) x/x! ; x = 0, 1, 2, …

This assumption holds even if the demand occurs during the scheduled checks. Further, thelead time follows the gamma distribution as mentioned below:

f(t) = (e-bt bk tk-1) / (k – 1)! ; k> 0

This is a general assumption because it includes a wide range of lead time distribution. Thesetwo assumptions lead to the probability P(X = x) of exactly ‘x’ demands during the lead time which ismentioned below:

where q = a / (a+b) and p = b / (a+b) ; x = 0, 1, 2, …

This shows that ‘X’ follows the NBD with parameters k and p. The probability of more than ‘n’demands at the end of a lead time is given by:

Our problem is to find out ‘n’ for a given risk level (Pn), p and k. On the basis of the mean andthe variance of the observed demand, we compute the estimates of p and k as follows:

As it is difficult to obtain the value of ‘n’ directly from the NBD, the normal approximation tothe distribution is used.

Two normal approximations out of a number of approximations proposed by Bartko (1966)have been selected. The selection has been made after taking into consideration the maximum error Ei

(n, k, p) defined by:

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145


where NB i (n,k,p)denotes the ith approximation to the cumulative negative binomial probability.The first approximation is given by:

We have, thus, obtained the following quadratic equation:

n2 + (1-kq/p)n + (k 2q2/p2 – kq/p + 1/4 – z2kq/p2) = 0 (1)

Bartko (1996) has calculated the maximum errors due to this approximation which is reproducedbelow :

The second approximation that we have chosen has been referred to as the Camp-Paulsonapproximation by Bartko (1966). According to this:

We have derived the following equation of the sixth degree in ‘n’ on the basis of the aforesaidapproximation:

531441 n6 + (4374 ABC – 5832 AB3 + 1968 E) n5 + (A2C3 + 20412 ABC – 27216 AB3 –54 ABCD + 243 E2 + 19683 F) n4 + (4 A2C3 + 38070 ABC – 50760 AB3 – 210 ABCD +486 EF + E3) n3 + (6 A2C3 + 35472 ABC – 47296 AB3 – 306 ABCD + 243 F2 + 3 E2F) n2

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146


+ (4 A2C3 + 16512 ABC – 22016 AB3 -198 ABCD + 3 EF2) n + (A2C3+ 3072 ABC – 4096AB3 – 48 ABCD + F3) = 0 (2)

where A = kq/p, B = (9k – 1)/k, C = B2 – 9z2/k2, D = 9z2, E = 144 – D and F = 64 – D

The highest real roots of both the equations developed for different values of the parameters ofthe NBD at the risk levels 0.005, 0.010, 0.025, 0.050 are shown in Table 1. The second equationprovides a better solution because of small maximum errors. The errors due to the Camp-Paulsonapproximation have been computed by Bartko (1966). These are reproduced below:

In order to determine the reorder level or float size ‘n’ of an item or defective component of anaircraft, we first of all estimate the parameters of the NBD on the basis of the observed demands.Corresponding to these estimates and the desired risk level we obtain the value of the float size orreorder level from the table that we have prepared. In case the value of ‘n’ is not given for a particularset of the estimates and the desired risk level we could easily compute its value from the equation (2) onthe basis of the higher root obtained from the equation (1. Also, the value could be calculated directlyfrom the equation (2). If Table 1 does not serve the purpose of an organization then it could be modifiedby taking into account the variations in the estimates of the parameters and the desired risk levels.

Describing the NBD as a tool in the study of demands for replacement parts Brown (1965) hasprepared a table of the distribution that is divided into two parts. Part 1 gives the individual probabilityand Part 2 provides the cumulative probability of ‘x’ demands or less for 13 different values of meanand 10 different ratios of variance – to – mean. It is obvious that the table we have prepared is muchmore convenient because it readily provides the values of float size or reorder level for the given valuesof mean and variance of the observed demands at the desired risk level. This is also a fact that the valueof ‘n’ is required for the maintenance of the optimum level of a stock, not the exact or the cumulativeprobability of demands. The tables of Williamson and Bretherton (1963) are also not helpful for thisreason. This fact establishes the superiority of our table over those of Williamson and Bretherton (1963)and Brown (1965).

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Numerical Example : In order to illustrate the technique let us consider the observed distribution ofthe daily demands of the LPG cylinders for cooking placed at a cooking gas agency. The data are givenbelow:

We have computed the average demands ( ) = 166.16667 and s2 = 732.966618 on the basisof the demands placed between Sept 1 and Sept 6, 1986 and subsequently, the estimates of p and khave been obtained as follows:

On the basis of equations (1) and (2) we have calculated the values of the reorder level or floatsize (n) as 210.207 and 212.313 and 218.736 and 222.337 at the risk levels 0.05 and 0.025 respectively.This suggests that the agency needs to maintain an inventory of only 210 or 212 cylinders at 5% risklevel.

References :

Bartko, J.J. (1966). Approximating the negative binomial. Technometrics, 8, 345-350.

Brown, B. (1965). Some tables of the negative binomial distribution and their use. Memorandum RM4577 PR, The Rand Corporation, California, USA.

Taylor, C. J. (1961). The application of the negative binomial distribution to stock control problems.Operations Research Quarterly, 12, 81-88.

Williamson, E. and Bretherton, M. H. (1963). Tables of the negative binomial probability distribution.Wiley, New York.

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Table 1. The reorder level or float size ‘n’ for the values of k (1 (1) 5 (5) 50 (25) 100 (50) 200) andp (0.05 (0.1) 0.95) at the risk levels 0.005, 0.010, 0.025 and 0.050 corresponds to the standardnormal variates 2.576, 2.326, 1.960 and 1.645 respectively. For each p, the first and the second rowdenote the values based on equation (1) and (2), respectively.

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149


k = 5 k = 10 0.05 206.785

243.127195.888223.490

179.934196.663

166.204175.339

348.295384.867

332.884360.910

310.322327.630

290.905300.650

0.15 63.23774.685

59.80168.503

54.771 60.054

50.442 53.352

106.235 117.766

101.376110.218

94.262 99.728

88.140 91.222

0.25 34.45440.918

32.51737.436

29.683 32.678

27.242 28.881

57.719 64.237

54.98059.982

50.971 54.066

47.520 49.267

0.35 22.05426.380

20.76624.060

18.88120.886

17.25918.359

36.83641.204

35.01538.368

32.34934.426

30.05431.227

0.45 15.10418.238

14.18316.571

12.834 14.289

11.673 12.471

25.147 28.318

23.84426.280

21.937 23.446

20.295 21.148

0.55 10.61612.989

9.93411.743

8.936 10.038

8.077 8.681

17.617 20.023

16.65318.501

15.241 16.387

14.026 14.673

0.65 7.4359.275

6.9268.330

6.181 7.036

5.540 6.007

12.299 14.170

11.57913.018

10.526 11.417

9.619 10.121

0.75 5.0076.451

4.6345.735

4.088 4.758

3.619 3.981

8.264 9.739

7.7378.870

6.965 7.666

6.301 6.693

0.85 3.0074.137

2.7523.613

2.3792.920

2.0582.335

4.9766.138

4.6165.508

4.0894.638

3.6353.938

0.95 1.1191.971

0.9871.751

0.795 1.178

0.629 0.822

1.944 2.829

1.7582.434

1.485 1.895

1.251 1.467

k = 15 k = 20 0.05 478.984

515.621460.109488.294

432.477 450.024

408.695 418.701

604.070 640.736

582.276610.548

550.369 568.053

522.908 533.067

0.15 145.821157.377

139.870148.765

131.157 136.700

123.659 126.823

183.641 195.209

176.769185.693

166.708 172.295

158.050 161.263

0.25 79.06185.598

75.707803.741

70.79673.936

66.57068.364

99.407105.954

95.534100.587

89.86493.029

84.98486.806

0.35 50.33954.723

48.10851.486

44.843 46.951

42.033 43.238

63.180 67.572

60.60463.995

56.834 58.959

53.589 54.813

0.45 34.27637.460

32.68035.135

30.344 31.876

28.333 29.209

42.930 46.123

41.08843.554

38.390 39.936

36.069 36.959

0.55 23.94126.360

22.76024.625

21.03121.196

19.54320.209

29.91432.341

28.55130.426

26.55427.730

24.83625.513

0.65 16.65718.543

15.77617.230

14.486 15.393

13.376 13.893

20.754 22.648

19.73721.200

18.247 19.164

16.965 17.492

0.75 11.15112.640

10.50611.654

9.561 10.276

8.743 9.153

13.847 15.344

13.10114.258

12.010 12.734

11.071 11.485

0.85 6.6957.870

6.2527.158

5.6066.168

5.0505.366

8.2789.465

7.7698.685

7.0237.594

6.3816.705

0.95 2.6383.541

2.4103.103

2.076 2.500

1.789 2.018

3.264 4.179

3.0013.704

2.616 3.048

2.284 2.521

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150


k = 25 k = 30 0.05 725.577

762.257701.210729.537

665.537 683.313

634.835 645.097

844.541 881.229

817.849846.215

778.771 796.613

745.138 755.476

0.15 220.332231.907

212.649221.592

201.401207.017

191.720194.966

256.221267.801

247.805256.761

235.483241.121

224.879228.148

0.25 119.118125.670

114.787119.852

108.448 111.630

102.992 104.832

138.376 144.932

133.633138.705

126.688 129.883

120.712 122.565

0.35 75.59879.995

72.71876.118

68.503 70.640

64.875 66.111

87.715 92.116

84.56187.967

79.943 82.089

75.969 77.215

0.45 51.28254.480

49.22251.696

49.206 47.762

43.611 44.510

59.419 62.621

57.16359.642

53.859 55.421

51.016 51.922

0.55 35.66438.096

34.13936.021

31.907 33.091

29.986 30.671

41.254 43.690

39.58441.471

37.139 38.328

35.035 35.725

0.65 24.68426.584

23.54725.016

21.881 22.805

20.448 20.981

28.496 30.399

27.24928.723

25.425 26.354

23.854 24.392

0.75 16.42017.924

15.58716.750

14.36715.097

13.31713.737

18.90620.415

17.99319.161

16.65717.392

15.50715.831

0.85 9.78010.974

9.21110.133

8.3778.954

7.6597.988

11.22312.422

10.59911.527

9.68610.267

8.8999.232

0.95 3.8474.770

3.5534.264

3.122 3.561

2.752 2.994

4.400 5.330

4.0784.794

3.606 4.050

3.200 3.447

k = 35 k = 40

0.05 961.579998.271

932.747961.142

890.538 908.431

854.211 864.607

1077.091 1113.785

1046.2681074.686

1001.145 1019.079

962.309 972.752

0.15 291.503303.085

282.412291.379

269.104 274.757

257.649 260.937

326.304 337.888

316.585325.560

302.358 308.024

290.113 293.415

0.25 157.292163.851

152.169157.248

144.668 147.872

138.212 140.077

175.937 182.498

170.460175.544

162.441 165.653

155.540 157.413

0.35 99.605104.008

96.19899.609

91.210 93.363

86.918 88.171

111.314 115.720

107.672111.087

102.340 104.499

97.751 99.010

0.45 67.39470.588

64.95667.439

61.38862.955

58.31659.229

75.23978.446

72.63375.119

68.81870.390

65.53566.452

0.55 46.72449.163

44.92046.810

42.279 43.473

40.006 40.700

52.098 54.540

50.17052.063

47.346 48.544

44.917 45.614

0.65 32.21734.124

30.87132.349

28.900 29.833

27.204 27.746

35.867 37.776

34.42835.909

32.321 33.257

30.508 31.053

0.75 21.32722.839

20.34021.512

18.89719.636

17.65518.082

23.69525.210

22.64123.816

21.09721.839

19.76820.200

0.85 12.62013.823

11.94612.878

10.960 11.545

10.111 10.447

13.982 15.189

13.26214.197

12.207 12.796

11.299 11.638

0.95 4.9295.864

4.5815.303

4.071 4.520

3.633 3.884

5.440 6.380

5.0685.794

4.523 4.975

4.054 4.308

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k = 45 k = 50 0.05 1191.356

1228.052 1158.664 1187.100

1110.803 1128.770

1069.612 1080.093

1304.577 1341.274

1270.117 1298.568

1219.6671237.662

1176.248 1186.761

0.15 360.711 372.297

350.403 359.384

335.313 340.990

322.325 325.639

394.790 406.377

383.924 392.910

368.018373.704

354.327 357.652

0.25 194.361200.923

188.551193.639

180.046183.264

172.726174.606

212.599219.162

206.475211.567

197.510200.733

189.794191.679

0.35 122.877127.284

119.014122.432

113.358115.521

108.491109.754

134.316138.724

130.243133.664

124.282126.449

119.151120.419

0.45 82.97986.187

80.21582.704

76.16977.744

72.68673.606

90.63093.840

87.71790.208

83.45285.031

79.78180.704

0.55 57.394 59.838

55.349 57.245

52.354 53.554

49.777 50.478

62.626 65.071

60.469 62.368

57.31358.515

54.596 55.299

0.65 39.459 41.370

37.932 39.416

35.698 36.636

33.774 34.337

43.002 44.802

41.393 42.878

39.03739.978

37.010 37.560

0.75 26.020 27.538

24.902 26.080

23.265 24.010

21.857 22.289

28.310 29.830

27.131 28.311

25.40626.153

23.921 26.375

0.85 15.315 16.524

14.551 15.488

13.432 14.023

12.469 12.811

16.623 17.835

15.818 16.758

14.63815.232

13.624 13.967

0.95 5.936 6.879

5.541 6.270

4.963 5.418

4.466 4.722

6.419 7.363

6.003 6.735

5.3945.851

4.869 5.128

k = 75 k = 100

0.05 1859.379 1896.075

1817.174 1845.671

1755.386 1773.473

1702.208 1712.830

2401.655 2438.349

2352.921 2381.447

2281.5742299.716

2220.169 2230.856

0.15 561.618 573.208

548.310 557.314

528.829 534.545

512.062 515.421

724.500 736.088

709.131 718.144

686.635692.369

667.274 670.653

0.25 301.780 308.347

294.280 299.383

283.300 286.541

273.850 275.755

388.735 395.304

380.075 385.184

367.396370.648

356.484 358.401

0.35 190.174194.587

185.187188.617

177.886180.065

171.602172.883

244.552248.968

238.794242.229

230.363232.549

223.107224.396

0.45 127.933131.148

124.364126.863

119.141120.729

114.645115.579

164.176167.394

160.056162.559

154.024155.618

148.833149.772

0.55 88.07390.524

85.43287.338

81.56682.777

78.23978.951

112.737115.191

109.688111.597

105.224106.440

101.382102.099

0.65 60.18962.109

58.21959.711

55.33456.282

52.85153.408

76.79278.716

74.51776.014

71.18572.138

68.31868.880

0.75 39.372 40.900

37.929 39.116

35.816 36.570

33.997 34.439

50.007 51.539

48.340 49.532

45.90046.658

43.800 44.246

0.85 22.900 24.121

21.914 22.862

20.469 21.070

19.226 19.577

28.884 30.111

27.745 28.699

26.07826.683

24.642 24.997

0.95 8.698 9.658

8.189 8.932

7.443 7.909

6.801 7.067

10.826 11.794

10.238 10.288

9.3769.846

8.635 8.907

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k = 150 k = 200 0.05 3464.512

3501.2003404.8253433.381

3317.444 3335.648

3242.238 3253.004

4509.654 4546.338

4440.7334469.308

4339.835 4358.077

4252.995 4263.802

0.15 1043.4141055.006

1024.5951033.619

997.043 1002.798

973.331 976.734

1356.746 1368.338

1335.0201344.046

1303.202 1308.968

1275.821 1279.238

0.25 558.790565.361

548.184553.300

532.656 535.919

519.291 521.222

725.698 732.270

713.450718.571

695.520 698.791

680.088 682.027

0.35 350.746355.164

343.693347.133

333.367335.562

324.480325.779

454.845459.265

446.701450.145

434.778436.979

424.517425.821

0.45 234.828238.049

229.782232.291

222.395 223.996

216.037 216.984

303.983 307.206

298.156300.668

289.626 291.231

282.284 283.236

0.55 160.707163.166

156.973158.888

151.506 152.727

146.800 147.523

207.569 210.029

203.257205.175

196.944 198.169

191.250 192.237

0.65 108.884110.913

106.198107.700

102.118 103.076

98.606 99.173

140.350 142.281

137.132138.637

132.421 133.382

128.366 128.936

0.75 70.53372.070

68.49269.689

65.503 66.267

62.931 63.382

90.453 91.994

88.09689.297

84.646 85.413

81.676 82.129

0.85 40.34641.580

38.95139.911

36.90837.520

35.15035.510

51.39352.631

49.78250.747

47.42448.039

45.39445.757

0.95 14.82115.798

14.10014.859

13.045 13.525

12.137 14.415

18.601 19.585

17.76918.534

16.551 17.036

15.502 15.785

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SIM ULATIN G AVERAGE TIME C OMPLEX ITY O F SR SWOR :A STATISTICAL APPROACH

Anchala Kumari1 and Soubhik Chakraborty 2

1Department of Statistics, Patna University, Patna-800005, India2Department of Applied Mathematics, BIT Mesra, Ranchi-835215, India

Abstract : Algorithm is a fundamental concept in computer science. Developing an optimal algorithmfor solving a problem depends on its complexity (which can be computational, time, space, someweighted combination of time and space or even monetary cost) which provides a quantitative judgmentto select the best algorithm amongst the several ones.

Over the decades a lot of work has been done to measure the computational complexity butnone of the measures is realistic in nature as it merely expresses the order of complexity by computingthe minimum number of operations required which in turn is expressed as the function of input parameters.In this paper attempt has been made to focus on the statistical approach to simulate the average timecomplexity of an algorithm T for drawing a random sample of size (n) from a population of size(N),sampling been done without replacement.

It has been investigated that for (i)n=”N the average time complexity is of O(Nlog2N) and (ii)for n arbitrarily chosen ,complexity is O(n) .While estimating the parameters of the model , emphasishas been made on pattern recognition than on estimation since the estimates are system dependent.

Keywords: Simple Random Sampling Without Replacement; Algorithm Complexity, ComputerExperiment.

Introduction : Simple random sampling without replacement (srswor) is a procedure for selecting asample of size n from a population of size N such that the unit of the population selected at a particulardraw is not returned before the next draw and an equal probability of selection (equal to reciprocal ofnumber of units in the population ) is ensured to each unit of the population at the first and eachsubsequent draw. In his book [5], Prof. Donald Knuth (Stanford University) has discussed about analgorithm for srswor .Some more algorithms related to srswor are available in W. Kennedy and J.Gentle’s book [4]. These works suffer from the drawback that the authors have suggested the selectionprocedure only, but have not focused on the order in which the observations come into the sample. Inthis paper the algorithm described focuses on both the aspects: the selection procedure as well as theorder in which the observations come into the sample.

The algorithm for drawing a sample of size n from a population of size N is based on theconcept that the sample drawn is a random permutation of the digits 1, 2,…….N taking n at a time. Asthe population can be labeled 1, 2,…..N ,the sample consists of those units with the same labels asthose appearing in the random permutation. The n numbers so selected not only ensure the randomselection of the units but also specifies the order in which the units come into the sample. The computer

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program for finding the execution time of the algorithm T written in Visual C++ follows in the nextsection.

Visual C++ code

#include<iostream.h> #include<sys/timeb.h> #include<time.h> #include<math.h> #include<stdlib.h> void main() {

int npop,ns,k;int *a,*s;clock_t start,end;cin>>npop;a=new int[npop];

for(int i=0;i<npop;i++)*(a+i)=i+1;

ns= pow(npop,.5);s=new int[ns];start=clock();

for(int j=0;j<ns;j++){ k=1+rand()%npop; *(s+j)=*(a+k); if(k!=npop)

{for(i=k;i<npop;i++)a[i]=a[i+1];

}npop=npop-1;

}end=clock();

double elapsed=double(end-start)/CLOCKS_PER_SEC;

cout<<“elapsed time=”<<elapsed<<endl;

}

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For n arbitrarily chosen, the statementns= pow (npop,.5) is replaced by the statementcout<<ns<<endl;The logic here is that first an array a[] of size N with units 1,2,3,…N is created such that a[i]= i,i=1,2,3…N. At first draw , a random number k is selected from 1 to N. Then a[k] becomes the firstelement of the sample. The elements from a[k+1] to a[N] are sifted to one place on the left to fill the gapcreated by removal of the element a[k] in the array(a[]). In case the random number k is equal to N ,sifting of the elements to the left is not done. In either case, N is set equal to N-1 before drawinganother random number k. The process continues till all the n units come into the sample. This meansthe process has to be repeated n times.

Analysis and Results :

Case 1 n=”N

The average run time is obtained for different values of population size (input parameter) orequivalently for different values of sample size (n=”N), average being taken over 100 trials for eachvalue of population size and is given in the table below.

Table 1 average execution time n= “N

N : 10000 40000 90000 160000 250000

Avg time (sec): 0.0154 0.0532 0.206 0.4156 0.7656

N 360000 490000 640000 810000 1000000

Avg time (sec): 1.297 2.0344 3.0902 4.3084 6.006

The average time when plotted against different values of N using MINITAB statistical packageagrees to the average complexity lying between O(N) and O(N2). Fig1 and fig 2 shows time as linear andquadratic functions in N(population size) with 100R2 values (coefficient of determination) equal to 97.4and 100 respectively. For a good literature on applied regression analysis, the reader is referred to [3].

Fig.1 N-time plot time->O(N)

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Fig.2 N-time plot time->O(N 2)

In fig.1 we see that the some of the points lie even outside the 95%confidence bound where asin fig. 2 confidence bounds and regression curve coincide together. The polynomial curve gives agood fit with R2 = 100% .It may be argued that the contribution due to N2 term is almost negligible inthe equation time=-.554417+.0000026N+.0000000N2 .The N2 term contributes in reducing thedistance between the confidence bounds.Further we have O(N)<O(Nlog2N)<O(N 2)And O(Nlog2N) +O(N) = O(Nlog2 N)Thus we may experiment with the modelY=a + bNlog2NThe estimated values of a,b are ^ ^ a = -.3639 b =.0000003line of regression is time= - .3639 + .0000003 Nlog2NR2 = 98 .2%Normal probability plot of the residuals approximates to linear function as shown below.

Fig .3. Normal probability plot of residuals

0 500000 1000000

0

1

2

3

4

5

6

N

t ime = - 0.0 5544 17 + 0. 0 00 00 26 N + 0.000 000 0 N**2

S = 0 . 041 39 92 R- Sq = 100 . 0 % R- Sq(a dj) = 100 . 0 %

Regre ssion

95% CI

Regression Plot

-1 0 1 2

-1

0

1

Standardized Residual

No r m a l P ro b a b i l i ty P lo t o f t he R e s id u a l s(resp ons e is time)

Analysis of variance table is as given below

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Table 2 : ANOVA for regression analysis

Source DF SS MS F P

Regression 1 37.396 37.396 427.42 0.000

Residual Error 8 0.700 0.087

Total 9 38.096

From the table it is clear that F is highly significant and regression sum of squares contributesmarkedly to total sum of squares, implying thereby a good fit .

Case 2 n is any arbitrary value

Here no restriction is imposed on the sample size .Keeping population size fixed at 20000samples size n is arbitrarily chosen. We get the following table of average execution time over 100trials.

Table 3 Average execution time

Sample size n : 4000 4500 5000 5500 6000Avg run time y (sec): 0.3126 0.3282 0.3590 0.3810 0.4064Sample size n : 6500 7000 7500 8000Execution time y: 0.4284 0.4560 0.4750 0.4910

Execution time when plotted against the values of n agrees to a linear pattern confirmingthereby to the fact that complexity of the algorithm is of order O(n).

The model equation is time= .125658 + .0000464 n

R2 = 99.7%

Fig.4 n-time plot

4000 5000 6000 7000 8000

0.3

0.4

0.5

n1

t im e1 = 0. 1 2 56 58 + 0.0 0 00 464 n1

S = 0. 00 3 86 24 R- Sq = 99 . 7 % R -S q( a d j) = 99 . 6 %

Regres sion

95% CI

Regression Plot

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All the points lie within 95% confidence bounds implying that the complexity can be well explained bya linear function in n , the sample size. The very high value of F in the ANOVA table below alsosupports to this fact.

Table 4 ANOVA applied to regression

Analysis of Variance

Source DF SS MS F PRegression 1 0.0323222 0.0323222 2166.59 0.000Error 7 0.0001044 0.0000149Total 8 0.0324267

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The middle term follows from the fact that mean of 1,2,3-----m is (m+1)/2 where m equals N, N-1,……..N-n+1 at j=1,2,3,……n respectively. or, E(T(n))= nN/2 - n2/4 +3n/4 which leads to O(nN) and for n= O(N3/2)For large N, we have N< 2 or log2N< 2N <N3/2 Thus the experiment with O(N log2N) complexity provides an empirical bound-estimate lower than theoretical least upper bound which is quite sensible and realistic as well. Also for n an arbitrary value O(n)<O(nN).Conclusion : In both the situations n = N or n is arbitrarily chosen, empirical bound estimates for thecomplexity of srswor is less than the theoretical lower upper bound. As a final comment, we emphasizethat empirical O is actually a bound-estimate and itself not a bound, also when it is obtained by workingdirectly on time as in this case, it is estimating a weight-based statistical bound which weighs eachoperation every time it comes against the corresponding time it consumes. Unlike the mathematicalbounds which are count based and operation specific, it has the provision of mixing operations ofdifferent types conceptually which is permitted due to weighing and which is very realistic since in allreal time applications, operations perform collectively. Empirical O is written as O with a subscriptemp. So we can write for the first study Tavg(n) =O(N log2N) where N=n2

And for the second study Tavg(n) = O(n). Empirical O is obtained by supplying numerical valuesto the weights obtained by running computer experiments. A computer experiment is a series of runs ofa code for various inputs and whether the response variable will be the output or a complexity dependson the investigator’s interest (e.g. we can run a sorting algorithm to get the sorted array (output) just aswe can do it to measure sorting time or measure the number of comparisons or the number of interchanges(time/computational complexity respectively) depending on the investigator’s interest). For more onempirical O, statistical bounds and the link between algorithmic complexity and computer experiments,see [2]. If the computer experiment is designed and analyzed properly, it increases the credibility of thebound estimate, the so called empirical O.

References :

Aho .V, Hopcroft J, Ullman J.(2000). Data Structure and Algorithms, Pearson Education Reprint.

Chakraborty, S and Sourabh, S. K.(2010). A Computer Experiment Oriented Approach to AlgorithmicComplexity, Lambert Academic Publishing, Germany.

Draper N , Smith H.(1998). Applied regression analysis, Wiley-interscience, 3rd ed. 8

Kennedy J , Gentle James E.(1980). Statistical Computing, Marcel Dekker,

Knuth .D.E.(1998).The Art of Computer Programming,vol.2: 3rd ed., Addison Wesley LongmanPublishing Co,Inc.Boston, MA,USA ISBN; O-201-89684-2

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161


PES TIC ID E AC C UMULATION , ALTERATION O F SER UMVITELLOGENIN AND FSH LEVEL AND OCCURRENCE OF HEPATICNEOPLASM IN AIR BREATHING FISH FROM WETLANDS OF NORTHBIHAR, INDIA

Prakriti Verma and Prabha Rani.

Department of Zoology, Patna UniversityPatna- 800 005, Bihar, India.

Corres.Author- [email protected]

Abstract : In the present investigation a number of selected wetlands from the north Bihar particularlySupaul and Saharsa district were surveyed. Fish soil and water sample were collected from the varioustest zones for the assessment of organocholrine pesticide accumulation. A comparative analysis of thetoxic status of fish from these wetlands based on pesticide accumulation and histopathology of livercells were done. Various organochlorine group of pesticide incurred were HCHs, Endosulfan DDT,DDE etc. Comparative survey of test zones showed the variation in accumulation of HCHs and AldrinDDTs. Fishes and water sample from the reference site showed almost negligible percentage of thesepesticides. Fishes collected from different test zones were screened for the presence of neoplastic andpreneoplastic liver lesion based upon histopathological and ultrastructural findings. The histoarchitectureof liver of C. batrachus from the test reveals changes in the mitochondrial organization, reduced ERcristae, scanty hepatoplasm and tendency of SER to transfer into glycogenated bodies, apoptic bodies,proliferation of RER and cellular hyper activity. Blood of test fish also revealed decreased serum FSHlevel & less concentration of vitellogenin.

Key words : Pesticide accumulation, Serum Vitellogenin FSH level, hepatic neoplasm, air breathingfish, wetlands, North Bihar

Introduction : Northern Bihar is lamented with a vast source of naturally occurring low lying areas,flood plains, wet land and paddy fields inhabiting a good population of air breathing cat fishes likeClairas batrachus (Linn) and Heteropneustes fossilis (Bloch).. The sudden death of fish indicatesheavy pollution which can be measured in terms of biochemical, physiological or histological response(Sounders 1969, Nath Dutta, et al 2003). In aquatic environment pesticide undergo a biotic degradationby hydrolysis, and enter in aquatic organism directly through gill or epithelial tissues. The harmful chemicalsaccumulate in specific organs and then get biomagnified. Fishes take up most of the xenobiotics fromthe surrounding water by passive diffusion through gills or gastro-intestinal tract. After uptake the chemicalare transmitted and deposited in the fatty portion of the tissues (Kumari et al 2001). Liver is the targetorgan, which not only resists the deleterious effects of pesticides but also detoxifies it. Entering to anorganism xenobiotic bind to specific cellular structure called receptor that is localized on the cell surfaceor inside the cell either in its cytoplasm or on cell organelles (Yamaguchi 2003). Several reports areavailable for the study of various pesticide residues in fish and its impact on various organ have been

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well established (Muir 2003, Carla 2004 & Couch 1993, Baile 1991) but a systematic approachregarding prevalence of a various organochlorine pesticides in soil & fish liver sample from selectedwetland test zones of North Bihar are lacking. In the present investigation a few wetland test zones ofnorth Bihar have been selected to see correlation between organochlorine pesticides residues in soiland liver of fish and occurrence of neoplasm.

Materials and Methods : Clarias batrachus were captured by local fisherman from the three testzone wetlands in around Saharsa and Supaul District of Bihar viz – Chitragupta Mandir Chaur – (A),Gramharnia Chaur (B) and Hardi chaur (C) and screened to find out the neoplastic and pre-neoplasticlesions. Approximately 200 fish adult/ female were screened (Table – I) and collected from thesewetlands. Unhealthy fishes were dissected on spot and liver tissues were sampled for the estimation ofpesticide residue (Table - III) and histopathological analysis. Among the fish haul, healthy Clariasbatrachus of 14-27" length and 50-110 gm±10 gm wt were brought to the laboratory and afterdisinfections with 0.1% KMNO4 solution they were kept at room temperature in large plastic pool foracclimatization. They were fed with pelleted food made up of wheat flour and egg with a pinch of starchas binder @ 4-5% of their body weight. They were also fed with chopped goat liver on every 3rd dayto fulfill their dietary requirement.

Quantification of pesticide residues in soil : Collection of soil sample – Soil sediments were collectedat the four edges of each wetlands/Chaur using a spade. The entire samples were sealed in polythenebags, storing at 0oC and transported to the laboratory within two days. Freeze dried sample were thenpassed through 1.0 nm sieve to separate sample and other debris. After crushing with anhydrous sodiumsulphate powdered soil sample were further processed in n-Hexane and send to ITRC (Industrial ToxicologyResearch Institute), Lucknow for estimation of organochlorine pesticide residues (Table– II).

Collection of Blood Sample and serum analysis FSH & and Vitellogenin – At autopsy, the fisheswere anesthetized with 0.1g/L of MS22, Blood sample were collected in a citrated hypodermic syringeby resorting to cardiac puncture. The blood was centrifuged @ 15000 rpm for 15 minute and clearsupernatant fluid was stored as blood serum in appendorf at 4oC in deep freeze for further analysis.Serum FSH was done on Merk “minimios” ELISA reader by Herichson Method. The serum FSH andvitellogenin of both the group of fishes have been depicted in Table– III & IV.

Detection of the egg yolk precursor vitellogenin (vtg) in blood, tissue sample of female juvenile, malefish in a sample and sensitive biomarker for endocrine disrupt the chemicals (EDCs) with estrogenic effect.Measurement of vtg has become an accepted routine screening test for estrogenic and anti-androgenic effectof EDCs in fish. The lyophilized coup vtg std. was calibrated against purified cat fish vtg. Using the followingformulas (Norberg and Haux 1988) vtg concentration mg/mg = absorbance at 286 nm/0.66, range = 0.24ng/ml. The comp vtg ELISA kit were purchased from Bioscience Laboratory, Norway.

Histopathological Analys is : The liver tissue of reference site and those of various test zones werefixed for light and electron microscopy. For light microscopy, tissues were fixed in neutral formalin.After dehydration through graded series of alcohol, clearing, embedding, microtomy 5m section were

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stained by Delafield’s haemotoxylin and eosine & mount in DPX and photographs were taken ontrinocular microscope (Labomed CXRIII) fitted with Olympus digital 14 megapixel camera. For electronmicroscopic studies tissues were fixed in 2.5% gluteraldehyde in 0.1M phosohate Buffer (pH=7.4) at4oC. After one hour the tissues were placed in 1% OSO 4 in 0.2M Phosphate Buffer solution (pH 7.4)at 4oC for 2 hours, followed by its dehydration in graded series of alcohol and amyl acetate, cleaned intoluene and embedded in araldite mixture. Ultrathin sections were obtained through Reichart JungSupersora ultra microtome stained in Uranyl acetate and lead citrate and transformed to former coatedcopper grid and viewed under Philip’s EM-10 transmission Electron Microscopy at SIF-EM FacilityUnit, Dept. of anatomy, AIIMS, New Delhi. The Micrographs have been shown in Text graphs. ElectronMicroscopy of liver cell.

ObservationsTABLE – I

Resume of the tes t fish Clarias batrachus surveyed from different tes t zones after externaland internal screening during 2010-2011

(-) represent absence of les ion for the approx. 200 individual studied.

TABLE – IIConcentration of organochlorine pesticide residue in soil of different test zone of North

Bihar during 2010-11

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TABLE-IIIFluctuation of Serum vitellogenin level in test fish collected from different test zones

TABLE-IVFluctuation of Serum Follicle Stimulating Hormone level in test fishes collected from

different test zones during the post, Pre, spawning and resting period

Values are expressed in Mean±SE for No. of observations 10 in each case.

The liver of normal fish has continuous mass of hepatic cells with cord like formation. Thecells were large with more or less centrally placed nucleus and homogenous cytoplasm. Cleardivision of hepatic cells into lobules has not been observed in most of the hepatic cells. Hepatocyteswere intact with dense cytoplasm. Architecture of hepatic artery was very distinct. Sinusoidalspaces were well organized and opened into central vein. The histoarchitecture of liver of C.batrachus from the test zone (B) wetlands had a number of necrotic changes and enlargedperisinusoidal areas, increased eosinophilic inclusion, pyknotic and heterochromatized nuclei.Vacuolation refers to the Initiation of pre-neoplastic changes occurring in liver. Most pronouncedabnormalities are vacuolar degeneration, Karyomegely, fibrosis of central vein, focal vacuolationand multi-focal hemosidorosis and occurrence of appotic bodies. Few or no lesion was observedin the tissue of fish taken from reference site and site A. Few histological examination of liver tissuefrom test zone C also revealed re-organization of liver tissue, characteristic of micro and macro-nodular cirrhos is. Pronounced feature of ductular metaplasia of hepatocyte leading toneocholangiolar structure. Hepatocyte tending to form rosette with the bile canaliculi located inthe center. EM Studies also reveals changes in the mitochondrial organization, reduced ER cristae,scanty hepatoplasm and tendency of SER to transferred into glycogenated bodies, apoptic bodies,proliferation of RER and cellular hyper activity. Fish liver were also analysed for accumulation oforganochlorine pesticides such as HCH, Aldrine, Dieldrin, Endosulphan and derivatives of DDTs.Liver lesion and other abnormalities detected were statistically associated with sedimentcontamination and water concentration of DDT & its metabolities. (Table – II) Serum level ofFSH and vitellogenin level of test fish from these test zones also reveals decreasing trend ascompared to the reference site (Table – III & VI).

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Discussion : A comparative analysis of pesticides in soil and fish liver shows accumulation of variousorganochlorine pesticides viz. áHCH âHCH ãHCH , aldrin, endosulphan, DDE, DDT etc. the presence

from different test zone showed the variation in the accumulation of áHCH âHCH ãHCH , aldrin andDDT whereas fishes collected from reference site showed almost negligible percentage of organochlorinepesecticides. Presence of pesticides in fish liver reveals the persistent use of pesticides which has alsobeen reported by Halden (1965). Mass mortality and behavior of Atlantic salmon on stream is pollutedby agricultural pests (Saunders, 1969). Pesticidal poisoning in fish is considered to be very serious, asfish forms a major food resource for mankind affecting the consumer’s health (Dubois, 1971) and mayalso adversely affect the yield of yeast. Presence of pesticide residues in liver tissues in the presentinvestigation can be correlated with the reports of (Yamagudi, 2003), in the muscles of fish from UpperThames river. Muir et al (2003) have also observed DDT, HCH & PCBs in fishes from Barents SeaCanadian Arctic. Occurrence of neoplasm and other degenerative changes in the present investigation,have also well documented about the pathogenesis of liver lesion with anthropogenically introducedcontaminants Robert et al (1991). Mayers et al (1992) have shown that hepatic neoplasm as biomarkersof contaminant exposure in fish. They have also measured fluorescent aromatic compounds in Bile andPolychlorinated biphenyl PCBs. Carla M. et al (2004) have examined fish liver for toxicopathic lesionand analyzed for selected chlorinated hydrocarbon such as PCBs, DDTs,and di-aldrin.George et al(1996) have shown Rainbow trout liver as an alternative model for environment carcinogenesis research.Parallel diagnosis of cell and tissue pathologies in c. batrachus liver showed that lysosomal per turbationsensitivity reflected onset of progression of liver injury comprising focal to extensive necrosis and fibrosis,as indicated by highly significant correlation between the breakdown of lysosomal stability and degreeof liver lesion. Injury of lysosomal membrane by lypopholic toxic compound may lead to leakage of thehydrolytic lysosomal enzyme causing disturbance of cell function, resulting in degeneration and possiblyneoplasm (Moore 1985). Further (Moore et al 2007) very well illustrated about the hepatocellularneoplasm in adult winter flounder from Boston Harbour, as in the present investigation Mark et al(2007) have studied the progression of hepatic neoplasia in medaka exposed to diethylnitrosamine.Couch (1993) have very well compared about neoplastic hepatocyte with normal one under Light andElectron microscopy. He has also well documented the hepatocellular carcinomas in teleost fish. Mooreet al (1991) in their studies have used the cellular marker of pollutant exposure and liver damage infish. Donald et al (1984) have shown the effect of chemical pollutants poses stress in bottom dwellingfish and these are more prone to liver neoplasm and other diseases. Angela et al (1992) have shownthe histochemical and cytochemical indices of toxic injury in the liver of dab Limanda limanda. Thecellular death associated with this type of necrosis not only induces an inflammatory response, but alsodecreases the functional no. of cells in the tissue with deleterious consequences for the organ function.Increase in bile duct and disappearance of cellular limit suggest drastic alteration in the distribution oforganelle. Electron microscopy reveals proliferation of ER, loss of cristae of mitochondria and cellularhypertrophy. The study confirmed the lesion described above revealed the incidence of cell death in

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individual of all tested group and reminiscent of programmed cell death. With nuclear shrinkage, irregularshape and heterochromatization, which ultimately affect the metabolism of hepatocyte involved in importantbiochemical pathway as confirmed by decrease in FSH, and Vitellogenin level. In all the wetland testzones, test zone B seems to be the most toxic followed by test zone C and test zone A (Reference site)as shown in the text graph (II). Organochlorine pesticide accumulation in soil and occurrence of liveranomalies itself confirm it.It may be concluded that the bottom dwelling air breathing Clarias batrachusfrom these wetlands is being worst sufferer by the accumulation of organochlorine pesticides and otherpollutants.

Acknowledgements : Authors are thankful to Women Scientist Scheme, SERC Division, Departmentof Science & Technology New Delhi for providing fund (Project No. DSTNo:SR/WOS-A/LS-17/2008), Vice-Chancellor, Patna University and Department of Zoology, Patna University for providingresearch facility, members of EM Facility Unit, Department of Anatomy AIIMS, New Delhi for kindcooperation for TEM.

Reference :

Baile and Oberai (1991), Rawat et al (2002) reported the histopathological changes in the fish Clariasbatrachus when subjected to endosulfan.

Donald, C., Mallins, Bruce B., mccain, Donald, W., Brown, Sin-Lam Chan, Mark S. Myers,John T. Landahl, Patty G. Prahaska, Andrew J. Friedman, Linda D. Rhodes, Douglas G.Burrows, William D. Gronlund and Harold O. Hodgins. (1984): Chemical Pollutants inSediments and Diseases of Bottom- Dwelling fish in Puget Sound, Washington, Environ. Sci.Technol., 18, No.9, p. 705.

Dubois, K.P. (1971) : Acute toxicity of organophosphorus compounds to mammals. Bull. Wld. Hlth.Org. 44: 233-240.

Dutta, et al (2003) has reported sublethal malathion induced changes in the ovary of an air breathingfish H.fossilis.

Halden, A.V. (1965): Contamination of fresh water by persistent insecticide and their effect on fish.Ann. Appl. Biol. 55: 332-335.

John A. Couch (1993): Light and electron microscopic comparisons of normal Hepatocytes andneoplastic Hepatocytes of well-differentiatedhepatocrellular carcinomas in a teleost fish, Dis. Aquat.Org. 16: 1-14.

Mark, S. Ohikhiro and David E. Hinton (2007): Progression of hepatic neoplasia in Medaka (Oryizyslatipes) exposed to diethyl nitrosamine. Springer link Journal articles pp.1-2.

Moore, M.J., Smolowitz,R. Stegeman, J.J. (1994/2007): Cellular alteration preceding neoplasia inPseudopleuronectes americaus from Boston Harpour. Marine Environ. Research. MERSDW28, No.1/4 pp.425-429.

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Moor, M.N., Lowe. D.M., Buke. D., Dixon P. (1991): Molecular and cellular markers of pollutantexposure and liver damage in fish. ICES. CM 1991/E: 23.

Muir, D. Savinova, T., Saninov, V., Alexeeva, L., Potelov , V., Svetochev, V. (2003): Bioaccumulationof pcbs and chlorinated pesticides in seals, fishes and invertebrates from the white sea, Russia.Sci Total Environ. 306(1-3): 111-31.

Myers, M.S., Olson, O.P., Johson, L.l., Stehr, C.S., Hom, T., (1992) : Hepatic lesions other thanneoplasms in subadult flatfish from puget sound, Washington: Relationship with contaminantexposure. Response of Marine Organism to Pollutant Part I, P.45-51

Robert A., Murchelano and Richard E. Wolke (1991) Neoplasm and Nonneoplastic liver lesion inwinter flounder, Pseudopleurotectes americanus from Bosten Harbour, Massachusettes. Environof Health Perspective 90, pp.17-26.

Rauhani-Rankouhi, T., Van Holsteign, I., Letcher, R.J., Giery, J.P., Van den Berg, M., (2002): Theeffects of pre-exposure with environmental and mnatural estrogen on vitellogenic production incarp. (Cyprinus carpio) Hepatocytes. Toxicol. Sci. 67, 75-80.

Saunders, J.W. (1969): Mass mortalities and behavior of brook trout and atlantic salmon onstreampolluted by agriculture particles J. Fish Res. Bul. Con. 26: 695-699.

Yamaguchi N. Gazzard. D. Scholey, G. Macdonald, D.W. (2003): Concentration and hazard assessmentof pcbs, organochlorine pesticides and mercury in fish species from the Upper Thames: riverpollution and its potential effects on top predators. Chemosphere. 50(3): 275-73.

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A SERO-GENETIC STUDY ON THE DISTRIBUTION OF RH-ANTIGENAMONG MAJOR SCHEDULED CASTE POPULATIONS

Parimal Kumar Khan and Manoj Vibhakar

Department of Zoology, Patna University, Patna

Abstract : An immune- haematological test was carried out to study the distribution of Rh- antigenamong the four endogamous scheduled caste populations of the district of Patna (Bihar). The populationssurveyed were the Dusadhs ,the Chamars,the Pasis and the Musahars.

All the four populations exhibited a relatively high incidence of Rh-ve blood group with almostno statistically significant difference among them. It was comparatively higher among the Dusadhs(5.62%)and the Chamars (5.29%) followed by a slightly lower frequency among the Musahars(4.41%) andthe Pasis(3.71%). The frequency dominant (D) and recessive (d) alleles showed a similar pattern oftheir incidence in all the four populations.

Introduction : India, with its 1.2 billion people [1], has the second largest population in the world,being represented by over 4000 endogamous group, many structured in the Hindu caste system as‘Jatis’ [2]. Each endogamous group, reproductively isolated from each other, represents a Mendelianpopulation [3]. The practice of endogamy , being performed for centuries, has led to the evolution ofdifferent gene pools, one for each caste. Population geneticists use to analyze the frequencies of variousgenetic markers to study the quantitative variation in differences human populations (gene pools). Asmany facets of the several population groups of India are still relatively unexplored, the present study ofsample of scheduled castes (with respect to the distribution of Rh-blood group) aims to examine thegenetic polymorphism among them.

The Rh-factor was discovered by Karl Landsteiner and A.S. Wiener [4] from rabbits immunizedwith the blood of a monkey, Rhesus macaque; the symbol ‘Rh’ came from the first name of thespecies of monkey. The resulting antibodies agglutinated the red corpuscles of the monkey. On thebasis of the presence or absence of Rh antigen, the whole human population has been divided into Rh+ve

and Rh-ve groups. The first human blood found to lack all known antigens, Rh-null, was reported by Voset al.[5]. Initially the genetic mechanism of the Rh-system seemed to be governed by a single pair ofalleles, R and r, as postulated to account for the difference between Rh+ve and Rh-ve individuals. Wiener[6]developed a hypothesis based upon a series of multiple alleles. According to an earlier hypothesis[7],three pairs of gene are involved in the production of Rh antigen that are not alleles but are located neareach other on the same chromosome. The dominant forms of these genes are represented by C, D andE, and its recessive form by c, d and e. A person is classified Rh+ve on the basis of the presence of atleast one dominant (D) allele, whereas the dd genotype ensures the Rh-ve blood group. While the C,

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c, E and e alleles specify related antigens, they are less immunologically insignificant. Later, Rosenfieldet al.[8] developed a new Rh notation system to be represented by only two alleles, D (dominant) andd(recessive).

Methodology : A test for Rh (D) incompatibility was performed by the process of slide agglutinationmethod[9] with the help of SpanClone anti-D (Rho) monoclonal IgM antisera (Span Diagnostics Ltd.,Surat, India). One drop of anti-D was dispensed on a clean dry slide, and a drop of blood was thenadded to it, mixed well with applicator stick and the slide was tilted back and forth for 2 minutes.Presence of D antigen resulted in agglutination of the test RBCs and the blood was assigned Rh(+ve).No agglutination with anti-D antiserum indicated the absence of D-antigen, the blood group being Rh(-

ve). Agglutination of erythrocytes therefore indicated incompatibility, whereas even distribution oferythrocytes indicated no reaction. Frequencies of dominant (D) and recessive (d) alleles were calculatedfrom the number of phenotypes scored.

Re sults and Discussion : The incidence of Rh-v e subjects in all the four populations was slightlyhigher than the range known for Indian populations ( Fig. 1,Table 1). It was comparatively higheramong the Dusadhs (5.62%) and the Chamars(5.29 %) with decreasing magnitude among theMusahars (4.41%) and the Pasis (3.71%). Statistically insignificant inter-sex variations in thefrequency of Rh-v e persons were observed within each population (Table 1) . All the fourpopulations, however, did not differ in the incidence of this trait among themselves ( exceptbetween the Pasis and the Dusadhs) (Table 3). Such a high incidence of Rh-ve blood group is notunique for these populations alone, because Pingle et al. [10] have found that in Rajgonds, a tribalgroup of Andhra Pradesh, it is 6.18%, and as high as 10 to 17%among the Saraswat Brahmins ofWestern India [11].

The frequency of dominant (D) and recessive (d) alleles showed a similar pattern of theirincidence in all the four populations (Table 2). Consequently, the expected frequency of individualshomozygous (DD) for the dominant allele was very high to be followed by heterozygous (Dd) andrecessive (dd) ones.

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Table1Frequency dis tribution of subjects of Rh-blood group system in the different populationsz

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References :Census of India (2011) Ministry of Information and Broadcasting, Government of India, New Delhi.Singh K S(1998) India’s communities. People of India.National Series, vol-IV, Oxford University

press, India.Wright S ( 1951) The genetical structure of populations. Ann Eugen,15, 325-354.Landsteiner K and Weiner A S (1940) An agglutinable factor in human blood recognized by immune

sera for rhesus blood .Proc Soc Exper Biol,43,223.Vos U, Gutler L and Cleve H(1961) A sample with no detectable Rh antigen.Lancet, 1,14.Weiner A S (1970) Blood groups and disease. Am J Hum Genet, 22,476-483.Fisher R A and Race R R(1946) Rh gene frequencies in Britain. Nature,157,48.Rosenfield R E , Allen F H and Rubinstein P(1973) Genetic model of the Rh blood group system.Proc

Natt Acad Sci USA, 70,1303.Bhasin M K and Chahal SMS(1996) A Laboratory Manual for Human Blood Group Analysis. Kamla

Raj Enterprises, Delhi.Pingle U, Mukherjee B N and Das S K(1981) Blood samples belonging to five tribal groups of Andhra

Predesh . J Morphol Anthropol ,73,339-348.Bhatia HM , Shanbhag H , Bharucha Z S , Bapat J, Sathe MS, Sharma RS , Kabeer H, Ucha ZS and

Surlacar L (1976) Genetic studies among endogamous groups of Saraswat Brahmins in WesternIndia.Hum Hered, 26,458-467.

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