Aijrfans vol1 print 1 by iasir journals

ISSN (Print): 2328-3777 ISSN (Online): 2328-3785 ISSN (CD-ROM): 2328-3793

Issue 7, Volume 1 June-August, 2014

American International Journal of Research in Formal, Applied and Natural Sciences

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, aijrfans@gmail.com

PREFACE We are delighted to welcome you to the seventh issue of the American International Journal of Research in Formal, Applied and Natural Sciences (AIJRFANS). In recent years, advances in science, engineering, formal, applied and natural sciences have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. AIJRFANS is publishing high-quality, peer-reviewed papers covering topics such as Biotechnology, Cognitive neurosciences, Physics, Chemistry, Information coding and theory, Biology , Botany & Zoology, Logic & Systems, Earth and environmental sciences, Computer science, Applied and pure Mathematics, Decision Theory & Statistics, Medicine, Algorithms, Anatomy, Biomedical sciences, Biochemistry, Bioinformatics, Ecology & Ethology, Food & Health science, Genetics, Pharmacology, Geology, Astronomy & Geophysics, Oceanography, Space sciences, Criminology, Aerospace, Agricultural, Textile, Industrial, Mechanical, Dental sciences, Pharmaceutical sciences, Computational linguistics, Cybernetics, Forestry, Scientific modeling, Network sciences, Horticulture & Husbandry, Agricultural & Veterinary sciences, Robotics and Automation, Materials sciences and other relevant fields available in the vicinity of formal, applied and natural sciences.

The editorial board of AIJRFANS is composed of members of the Teachers & Researchers community who are actively involved in the systematic investigation into existing or new knowledge to discover new paths for the scientific discovery to provide new logic and design paradigms. Today, modern science respects objective and logical reasoning to determine the underlying natural laws of the universe to explore new scientific methods. These methods are quite useful to develop widespread expansion of highâ&#x20AC;?quality common standards and assessments in the formal, applied and natural sciences. These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest

researchers in the field of formal, applied and natural sciences. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory,

GetCITED,

CRCnetBASE,

Google

DOAJ,

SSRN,

Scholar,

TGDScholar,

Microsoft

Academic

WorldWideScience, Search,

INSPEC,

CiteSeerX, ProQuest,

ArnetMiner, Base, ChemXSeer, citebase, OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley, ResearchGate, cogprints, OCLC, iSEEK, Scribd, LOCKSS, CASSI, E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the AIJRFANS for entrusting us with the important job. We are thankful to the members of the AIJRFANS editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this seventh issue, we received 82 research papers and out of which only 19 research papers are published in one volume as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of formal, applied and natural sciences.

This issue of the AIJRFANS has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in formal, applied and natural sciences and may open new area for research and development. We hope you will enjoy this seventh issue of the American International Journal of Research in Formal, Applied and Natural Sciences and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Formal, Applied and Natural Sciences (AIJRFANS), ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 (June-August, 2014, Issue 7, Volume 1). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Biotechnology  Cognitive neurosciences  Physics  Information coding and theory  Chemistry  Biology , Botany & Zoology  Logic & Systems  Earth science  Computer science  Applied and pure Mathematics  Decision Theory  Statistics  Medicine  Algorithms, and formal semantics  Anatomy  Biomedical sciences  Biochemistry  Bioinformatics  Ecology  Ethology  Food science  Genetics  Health sciences  Pharmacology  Geology  Surface sciences  Astronomy  Geophysics  Oceanography  Space sciences  Criminology  Aerospace  Agricultural  Chemical  Textile  Industrial, Mechanical  Military science  Operations research  Healthcare sciences  Dental sciences  Pharmaceutical sciences  Biostatistics  Computational linguistics  Cybernetics  Forestry  Scientific modeling  Network sciences  Horticulture & Husbandry  Agricultural & Veterinary sciences  Neural and fuzzy systems  Robotics and Automation  Materials sciences

TABLE OF CONTENTS (June-August, 2014, Issue 7, Volume 1) Issue 7, Volume 1 Paper Code

Paper Title

Page No.

AIJRFANS 14-306

FFT based DTMF detection by using Spartan 3E FPGA S Nagakishore Bhavanam , Dr. P. Siddaiah, Dr. P. Ramana Reddy

01-06

AIJRFANS 14-309

Effect of Moringa oleifera leaf powder on sperm count histology of testis and epididymis of hyperglycaemic mice mus musculus Navodita Priyadarshani and M.C.Varma

07-13

AIJRFANS 14-312

Long term addition of organics to sustain the system productivity of Rice (Oryza sativa L.) –Wheat (Triticum aestivum L.) under Indo-Gangetic Plain of India D. K. Singh, P. C Pandey and Shilpi Gupta

14-18

AIJRFANS 14-318

Arsenic Extrusion and Energy Derivation as Survival Mechanism in a Novel Exiguobacterium Isolated from Arsenic-contaminated Groundwater of West Bengal Rajdeep Chowdhury, Prithviraj Karak, Raghunath Chatterjee, and Keya Chaudhuri

19-27

AIJRFANS 14-319

NATURAL OCCURRENCE of ASPERGILLI and PENICILLI and CO-CONTAMINATION of AFLATOXINS and STERIGMATOCYSTIN in SOME MARKET SAMPLES of WALNUT KERNELS from J&K (INDIA) Rohini Sharma and Geeta Sumbali

28-36

AIJRFANS 14-325

Antibacterial Activity of Some Plant Extracts Along With Antioxidant Activity of Potent Ones Upma Srivastava, Swati Ojha, Pooja Singh and N N Tripathi

37-40

AIJRFANS 14-331

The study of Excess Molar volume and deviation in viscosity of binary mixtures of Ethyl Propionate in Pentanol-1 and Hexanol-1 at 308K Ultrasonically R.C.Verma, A.P.Singh and Vinod Kumar Yadav

41-42

AIJRFANS 14-334

A Survey Paper on Various Median Filtering Techniques for Noise Removal from Digital Images Prateek Kumar Garg, Pushpneel Verma Ankur Bhardwaz

43-47

AIJRFANS 14-335

Role of Epidemiological Factors in Accumulation of Oxalates in Forage Crops Pritam Kaur Sidhu, Jaspal Singh Lamba, Ganesh Kumbhar, G.S.Sekhon, Sunil Verma and Mohinder Partap Gupta

48-52

AIJRFANS 14-343

Study of amount of Oxygen (BOD, OD, COD) in water and their effect on fishes Priyanka Sharma, Dr.Sujata Gupta

53-58

AIJRFANS 14-349

Determination of Spatial Crop Coefficient of Chickpea Using Remote Sensing and GIS A.R.Pimpale, P.B. Rajankar, S.B. Wadatkarand I.K. Ramteke

59-64

AIJRFANS 14-352

Karyomorphological studies on the plants of Duchesnea indica (Andr.) Focke B.T Umesh, John E Thoppil

65-68

AIJRFANS 14-355

Interaction effect of sowing dates and different treatments on disease incidence and intensity of Phoma sp.on Safed Musali R.W.Ingle, Saket Shende, V.V.Deshmukh and M.S.Joshi

69-73

AIJRFANS 14-358

SYNTHESIS OF PYRIDO[2,3,4-kl]ACRIDINES UNIT, A BUILDING BLOCK FOR SOME MARINE ALKALOIDS Preeti Zade and M.M.V.Ramana

74-78

AIJRFANS 14-360

Study of Physico-chemical Characterstics in River Ganga at Bithoor Ghat in District Kanpur in Uttar Pradesh R.C.Verma & Archana Bansal

79-80

AIJRFANS 14-362

Lithostratigraphy and evidence of an extensive tectonic of Lower Permian age in the continental deposits of M’tal (Western Rehamna, Morocco) Hafid Saber, Abdelkbir Hminna, Abdellatif Jouhari, Aziz Rmich

81-87

AIJRFANS 14-372

Synthesis and Biological Activities of Some New Amides of Amino Acid Chandra Mohan Saxena, Archna Saxena, Naveen Kumar Shukla

88-90

AIJRFANS 14-376

RAPD Based Genetic Diversity of Freshwater Snail, Pila Gracilis in Bangladesh Shamita Mahzabin, Pinak Guswami, A.K.M.Al-Amin Leamon, Shad Ebna Rahaman, Md. Faruque Miah, M. M. A. Quddus

91-96

AIJRFANS 14-377

Spectral and Anti Bacterial Characterization of the adduct of Adipic acid with [NP(OH)2]3 Atul Gupta & S.P.S. Jadon

97-103

American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 AIJRFANS is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

FFT based DTMF detection by using Spartan 3E FPGA S Nagakishore Bhavanam1 , Dr. P. Siddaiah2, Dr. P. Ramana Reddy3 Department of Electronics & Communication Engineering Research Scholar, University College of Engineering & Technology, JNTU Ananthapuramu, INDIA. 2 Professor & Dean, University College of Engineering & Technology, Acharya Nagarjuna University, Guntur, INDIA. 3 Assoc. Prof., University College of Engineering & Technology, JNTU Ananthapuram, INDIA.

Abstract: DTMF is a method of representing the digits with tones for transmission. Dual Tone Multi Frequency (DTMF) tones are used by all touch tone phones to represent the digits on a touch tone keypad. DTMF technology provides a robust alternative to rotary telephone systems and allows user-input during a phone call. This feature has enabled interactive, automated response systems such as the ones used for routing customer support calls, telephone banking, voicemail, and other similar applications. This paper explains about the FFT based DTMF detection by using Spartan 3e FPGA. FFT based Technique is basically used for detecting the DTMF. Mentor Graphics Modelsim Xilinx Edition (MXE) is used for the Functional Verification and Xilinx ISE is used for Synthesis & simulation respectively. The Xilinx Spartan 3e FPGA Family board is used in this paper. Keywords: FFT; DTMF; Xilinx ISE; Modelsim; Spartan 3E FPGA I. INTRODUCTION Dual Tone Multiple Frequency (DTMF) signaling is used in telephone dialing, interactive banking systems, digital answer machines. DTMF signaling represents each symbol on a telephone touchtone keypad (0 to 9, *, #, A-D) using two sinusoidal tones, as shown in Fig. 1. When a key is pressed, a DTMF signal consisting of a row frequency tone plus a column frequency tone is transmitted. Keys A,B,C,D are not on commercial telephone sets, but are used in military and radio signalling applications. The maximum dialling rate is 10 symbols/s in the Bellcore standard [1], [2] and 12.5 symbols/s in the International Telecommunication Union (ITU) Q.24 standard [3]. ITU specifications require that, the valid DTMF signals have their high and low frequency tones within a tolerance of ±1.5% of an ideal DTMF frequency. If the tolerance of either tone is outside ±3.5%. Then the signal should be rejected as invalid. ITU specifications place requirements on the duration, and pauses between valid DTMF signals. ITU specifications require 100% detection of valid DTMF signals at 15 dB SNR (Signal to Noise Ratio). Bellcore provides test tapes to measure the performance of a DTMF detector against talk-off, which is false detection of speech signals as DTMF signals.

Fig.1 DTMF Scheme for Touch Tone Dialing (Courtesy from IEEE Transactions on Signal Processing)

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II. PROPOSED ARCHITECTURE DESIGN The proposed Architecture can be showed in Fig.2.

Fig.2 DTMF Detection General Module

It consists of the following modules: 1. Hex key pad 2. DTMF signal generator 3. Additive white Gaussian noise 4. Frequency Detection block 5. Magnitude / index estimator 6. Frequency to digit look-up table 1. Hex keypad: The Hex Keypad gives the input to the module. It is an external component. The signal from column is taken as input. Row and display are the output signals. 2. DTMF test signal generator: The DTMF test signal generator block generates that, the carrier frequencies necessary. It consists of two blocks 2.1 Frequency word selector: In this block the carrier waves are generated according to the key pressed ‘o’. For example: If key 9 is being press the frequencies that are generated are 852 Hz (Low frequency group) & 1477 Hz (High frequency group). These frequency waves are generated by a DDS core. 2.2 DDS Core: The LogiCORE™ IP DDS (Direct Digital Synthesizer) Compiler core sources sinusoidal waveforms for use in many applications. A DDS consists of a Phase Generator and a SINE/COS Lookup Table. These parts available individually or combined via this core. Direct digital synthesis (DDS) is a method of producing an analog waveform—usually a sine wave by generating a time-varying signal in digital form and then performing a digital-to-analog conversion. Because operations within a DDS device are primarily digital, it can offer fast switching between output frequencies, fine frequency resolution, and operation over a broad spectrum of frequencies. With advances in design and process technology, today’s DDS devices are very compact and draw little power. 3. Additive white Gaussian Noise: s

The tone out, which is the output from tones generator is mixed with noise in this module. The output is named as noise bits. Wideband Gaussian noise comes from the many natural sources, such as the thermal vibrations of atoms in conductors (referred to as thermal noise or Johnson-Nyquist noise), shot noise, black body radiation from the earth and other warm objects, and from celestial sources such as the Sun. The AWGN channel is a good model for many satellite and deep space communication links. It is not a good model for most terrestrial

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links because of multipath, terrain blocking, interference, etc. However, for terrestrial path modeling, AWGN is commonly used to simulate background noise of the channel under study, in addition to the multipath, terrain blocking, interference, ground clutter and self interference that modern radio systems encounter in terrestrial operation. 4. Tone Generator: The main propose of Tone Generator block is take two cosine waves from DDS cores and add them in order to produce one wave called tone out. 5. Frequency Detector: Input to the Frequency Detector module is noise bits, which is the output from additive white Gaussian noise. 6. The Output of the block is indices and magnitudes. As per the scope of the project, there are three variants of frequency detector block 1. FFT-128 core 2. Goertzel algorithm 3. Resource sharing This paper presents about FFT based DTMF Detection. III. FFT - 128 CORE FFT- 128 core: The below figure represents the FFT core as a Frequency detector module.

Fig.3 Block Diagram with FFT-128 Core as Frequency Detection Module

The Xilinx LogiCORE™ IP Fast Fourier Transform (FFT) implements the Cooley-Tukey FFT algorithm, a computationally efficient method is for calculating the Discrete Fourier Transform (DFT). A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and it’s inverse. There are many distinct FFT algorithms involving a wide range of mathematics, from simple complex-number arithmetic to group theory and number theory. A DFT decomposes a sequence of values into components of different frequencies. This operation is useful in many fields (see discrete Fourier transform for properties and applications of the transform) but computing it is directly from the definition is often too slow to be practical. An FFT is a way to compute the same result more quickly: computing a DFT of N points in the naive way, using the definition, takes O(N2) arithmetical operations, while an FFT can compute the same result in only O(N log N) operations. The difference in speed can be substantial, especially for long data sets where N may be in the thousands or millions in practice, the computation time can be reduced by several orders of magnitude in such cases, and the improvement is roughly proportional to N/log (N). This huge improvement made many DFT-based algorithms practical; FFTs are of great importance to a wide variety of applications, from digital signal processing and solving partial differential equations to algorithms for quick multiplication of large integers.

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The most well known FFT algorithms depend upon the factorization of N, but (contrary to popular misconception) there are FFTs with O (N log N) complexity for all N, even for prime N. Many FFT algorithms only depend on the fact that is an N th primitive root of unity, and thus can be applied to analogous transforms over any finite field, such as number-theoretic transforms. IV. SIMULATION RESULTS The below figures can represents the simulation results of FFT based DTMF Detection with maximized view by using MODELSIM. FFT based DTMF Detection:

Fig. 4 Simulation Results of FFT

Fig. 5 Simulation Results of FFT in Maximized View

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V. SYNTHESIS REPORT Synthesis Report for FFT based DTMF Detection:

Device utilization summary: ----------------------------------Selected Device: 3s500efg320-4 Number of Slices Number of Slice Flip Flops Number of 4 input LUTs Number used as logic Number used as Shift registers Number used as RAMs Number of IOs Number of bonded IOBs Number of GCLKs

: 2646 out of 4656 56% : 3256 out of 9312 34% : 4268 out of 9312 45% : 2589 : 431 : 1248 : 35 : 31 out of 232 13% : 2 out of 24 8%

Timing Summary: ----------------------Speed Grade: -5 Minimum period Minimum input arrival time before clock Maximum output required time after clock Maximum combinational path delay

: 17.669ns (Maximum Frequency: 56.596MHz) : 7.265ns : 18.417ns : No path found VI. CONCLUSION

This paper presents about the FFT based DTMF detection by using Spartan 3e FPGA because of FPGA technology is increasing its applications in communication technologies. The area, timing and power results are analyzed. For this design the Speed Grade Minimum period is 17.669ns (Maximum Frequency: 56.596MHz), Minimum input arrival time before clock is 7.265ns, Maximum output required time after clock is18.417ns. In future this paper will extend to Goertzel Algorithm based DTMF detection by Using Resource Sharing Approach on High Speed FPGSs like Virtex SP6 & Zynq 7000 series FPGA family. VI. [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

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Effect of Moringa oleifera leaf powder on sperm count, histology of testis and epididymis of hyperglycaemic mice Mus musculus Navodita Priyadarshani and M.C.Varma University Department of Zoology, T.M Bhagalpur University, Bhagalpur, 812007, INDIA. Abstract: The aim of the study is to evaluate the effect of leaf powder of Moringa oleifera Lam. on male reproductive system of Swiss albino mice Mus musculus. The sperm count, its mobility and mortality, histology of testis and epididymis of normal and hyperglycaemic male Swiss albino mice have been investigated and attempt has been taken to evaluate the efficacy of Moringa leaf powder in repair mechanism in case of hyperglycaemia. Three sets of animal, i.e. Control (Group I), Hyperglycaemic (Group II) and Hyperglycaemic fed with Moringa powder (Group III) were taken for the experiment. Both normal and hyperglycaemic mice were fed with 200mg/ kg body weight of Moringa leaf powder. The sperm count (million /mm3) recorded decrease in hyperglycaemic mice (Group II) as compared to control (Group I) but improved in treated mice(Group III), the mobility of sperm also decreased in hyperglycaemic mice but mortality increased in hyperglycaemic mice. In treated mice (Group III), the sperm count significantly increased, sperm mobility also increased but sperm mortality decreased significantly. There was a slight decrease in weight of testis (0.478±0.008gm to 0.33±0.006 gram) respectively when compared to control mice. The value improved after supplementation of Moringa oleifera leaf powder (0.33±0.006 gm to 0.415±0.005gm). Similar trend was recorded for Epididymal weight i.e. slight decrease in the weight of epididymis of diabetic mice (from 0.1444±0.003 gm to 0.0521±0.004) and a gradual increase was noticed in the weight from 0.00521±0.004gm to 0.1055±0.001gm) after 21 days treatment. Keywords: Hyperglycaemia, Moringa oleifera leaf powder, sperm count, testis, epididymis, Mus musculus I. Introduction Reproductive disorders in hyperglycaemic males have been studied in the present work. Different experiments have demonstrated several kinds of male reproductive dysfunctions in hyperglycaemia and associated Diabetes Mellitus both in structure and physiology (Handelsman et al., 1985; O’Neill et al., 2009). Hyperglycaemia has been identified has one of the major factors affecting male reproductive functions at manifold levels including its detrimental effects on endocrine control of spermatogenesis and or by impairing erection and ejaculation (Petroianu et al., 2009). Shrilatha and Muralidharan (2007) have reported that the early oxidative stress may cause by the release of free radicals leading to metabolic diseases like hyperglycaemia and Diabetes mellitus. It might cause stress in testis and Epididymal sperm and may lead to the progression of genotoxicity. Ricci et al, (2007) found that insulin-dependent diabetes is accompanied by reduced semen volume and decreased vitality whereas increased mortality of the spermatozoa, but no change in seminal viscosity. The high level of blood sugar may affect sperm quality and therefore decreases male fertility and the potentials. Reports indicate higher rates of infertility and poor reproductive outcomes among hyperglycaemias in comparison to healthy men (Joao et al, 2009). Herbal therapy can alleviate male infertility, irrespective of the etiology of such diseases (Anthony et al., 2006). A large number of plants have been tested for the possible fertility regulatory properties (Bhatia et. al., 2010). Some medicinal plants are extensively used as aphrodisiac to relieve sexual dysfunction or as fertility enhancing agents. They provide a boost of nutritional value thereby improving sexual performance and libido (Yakubu et al, 2007; Sumalatha et al., 2010). Moringa oleifera Lam is a medicinally important plant, belonging to family Moringaceae. The plant is well recognized in India, Pakistan, Bangladesh and Afghanistan as a folkloric medicine (Mughal et al., 1992). It is a small or medium sized tree up to 10 m tall, with thick, soft, corky, deeply fissured bark, growing mainly in semiarid, tropical and subtropical areas. Different parts of the tree have been used in the traditional system of medicine. In India, it is revealed that the M. oleifera leaves is being used traditionally as an aphrodisiac (Lalas and Tsaknis, 2002). The leaves are used for its protective effect by decreasing liver lipid peroxides, as an antimicrobial agent (Faizi et al, 1998). The leaves are also reported as a potent antioxidant activity (Ghasi et al.,

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2000). The leaf juice is believed to control glucose levels and also applied to reduce glandular swelling (Makonnen et al., 1997). The stem bark is used as abortifacint (Nath and Sethi, 1992). II. Materials and methods Experimental animals: Three month old Male Swiss Albino mice (Body weight: 25 ± 5 g) obtained from CDRI Lucknow and were maintained at the Animal House of University Dept. of Zoology, Bhagalpur. They were kept in stainless steel cages in a temperature and humidity controlled condition with 12 h light/dark cycle. Food and water were given to the animals ad libitum. Animals were kept as accepted principles for laboratory animal use and care as per the guidelines of CPCSEA. The mice were of12 weeks of age and acclimatized for one week before the experiment. Leaf powder: Powder product of Moringa oleifera Lam. leaves were obtained from Sanjeevani Herbals, Salem, Tamil Nadu, which is a Government approved supplier of scientific grade articles. The powder is a spray dried product of Moringa leaves, standard in quality. Induction of hyperglycaemia: Experimental animals were kept on fast for 18 h prior to induction of hyperglycaemia. It was induced by intra-peritoneal administration of Alloxan monohydrate (Rodriguez et al., 1999). The total dose of Alloxan-monohydrate (450 mg/kg/bw) was administered in three injections at intervals of 48 h (150 mg/kg/bw each time). Experimental design: Experimental animals were divided in to three groups each having 6 animals. Group-I (Control), Group-II (Diabetic control), Group-III (Diabetic control mice fed with leaf powder of Moringa oleifera Lam.). The total experimental protocol was maintained for 3 weeks i.e.21days after induction of hyperglycaemia (Nambiar and Seshadri, 2001). III. Histological observation After 21 days of experiment, mice were sacrificed and their organs were removed and paraffinised, Haematoxylin - Eosine stained sections of testis and epididymis were observed under light microscope.(Pears,1985) on 10 X and 40 X magnification. Organ Weight Measurement Mice were sacrificed by cervical dislocation at the end of the experiment. The entire male reproductive organs were exposed and both the left and right testis were dissected and weighed together. Sperm Counting and Head Morphology The left and the right cauda epididymis were incised and the sperm were allowed to swim for 15 min. Solution of 1: 10 dilution is made by adding 90 ml of water to 10 ml of sperm suspension. Sperm counts were done by using haemocytometer. For head morphology study, the sperms were collected from epididymis and vas deference. The suspension was smeared dried and fixed with fixative (three volume of absolute methanol and one volume of glacial acetic acid ), stained with haematoxylin for 15 mins and washed then stained with 1 % eosine for 10 mins, washed and left to dry at room temp(Wyrobek, 1979). Calculations Sperm count =dilution x (count in 5 squares) x 0.05x106 Sperm motility = Motile sperm ×100 Motile + non- motile sperms IV. Results and Discussion Hyperglycaemia is associated with reproductive impairments in both males and females. Male reproductive alterations have been widely reported in individuals suffering with diabetes. (Murray et al,.1983; Seethalakshmi et al. 1987, Scarano et al. 2006). In men affected by insulin-dependent diabetes, sperm have severe structural defects (Baccetti et al. 2002), significantly lower motility, and lower ability to penetrate mice eggs (Urner and Sakkas, 1996). The administration of doses of Alloxan Monohydrate to male mice induces a decrease in testicular testosterone production (Arikawe et al. 2006). The present study thus confirms that in hyperglycaemia (Group II), almost all sperm parameters had a statistically significant reduction in comparison with controls (Group II) and also demonstrated that spermatozoa of hyperglycaemic mice recovered significantly in mice of Group III which were under treatment with Moringa. This experimental design thus establishes and confirms the role of Moringa as a potent agent for Hyperglycaemia. The sperm count decreased from 18.36 ±0.044 to8.36 ±0.041(table1) in Group II, whereas after Moringa leaf powder administration for 21 days treatment significantly increased the sperm count i.e. 16.11±0.148(table1) .The same pattern was found in sperm motility, where the sperm motility decreased in diabetic animal (Group II) from 82.2±0.629% to 53.1±0.809%(table1), it significantly increased to 73.6±0.650% among animals of Group III. In the present experiment, hyperglycaemia induced sperm abnormalities in mice have been compared with control mice and subsequent repair mechanism with Moringa application. It has been established that Insulin signalling is important for spermatogenesis, sperm maturation and capacitation, and insulin deficient mice

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showed decreased sperm quality, decreased steroidogenesis and sperm maturation as also reported by Kim and Moley, 2008. Bucholtz et al., 2000 opined that Insulin is known to influence the hypothalamic-pituitary axis . Spaliviero et al., 2004 suggested that low plasma insulin levels can significantly decrease testosterone formation, which is known to limit the process of spermatogenesis. Similar observations have been recorded in the present experiment justifying the above views. The hyperglycaemic mice (Group II) have serious effect on their sperm morphology, sperm count, and testicular weight and Epididymal weight (Table: 1 and 2). Weight of testes largely depends on the mass of the various spermatogenic cells. Hence, the depletion in the spermatogenic elements might be the possible cause of the reduction in the testes weight. This observation finds supports from observations of different workers as Sherins and Hawards, (1978); Takihara et al., (1987); Mathur et al.( 2001, 2003, 2005) and Sharma et al. (2008). Wyrobek et al., (1983) suggested that several kinds of mutation can lead to abnormal sperm morphology. The administration of M. oleifera L. powder in the treated male mice i.e. Group III showed significantly higher testes weight gaining from 0.33±0.006 to 0.415±0.005and epididymis weight recovering from 0.052±0.004 to 0.106±0.001 (Table-2) in comparison to animals of Group II. It may be due to its leaves as they are excellent source of Vitamin B, Calcium, Protein and Potassium. Beta-carotene and other phytochemicals with known powerful antioxidant ability – Kaempferol, Quercetin, Rutin and Caffeoylquinic acids; powerful antioxidant vitamins - C, E, and A and essential micronutrients with antioxidant activity - Selenium and Zinc as explained by Fuglie, (1999); Jaiswal et al.,(2009) and Vongsak et al., (2013). D’cruz and Mathur (2005) proved that the sperm cytoplasm contained very low concentrations of scavenging enzymes therefore an increase in the antioxidant enzyme system levels by Moringa treatment can favour the reproductive process and also enhances spermatogenesis. Sudha et al (2010) also found that methanolic extract of Moringa does not affect sexual behaviour or serum androgen level but enhances seminiferous tubules, testis and Epididymal weight and seminal vesicles in the male rats. Distinct changes have been observed in sperm structure among mice of Group I when compared to animals of Group II (Fig2). Photomicrographs showing headless sperm, round head sperm and coiled tail sperm in abundance as well as banana head sperm and amorphous head sperm, few swollen head sperm confirm the effect of hyperglycaemia among mice inducing such changes. HE stained section of Alloxan monohydrate induced hyperglycaemic mice showed significant alterations in the histological structural patterns in the testis. Abnormalities in testicular tissues are intense intercellular spaces, irregular diameter of seminiferous tubules, and high amount of necrotic cell in the lumen compared with controls (fig 5).In addition they showed that the Epididymal sperm motility is also decreased in diabetic mice (fig5). Similar changes accompanied by the accumulation of immature cells within the tubular lumen were also observed in rats under the influence of Alloxan monohydrate induced diabetic mice (Cameron;1985) The release of immature germ cells within the tubular lumen in Alloxan monohydrate treated animals reported here represents a degenerative process. The restoration of the morphological features of the seminiferous tubules in the Moringa oleifera leaf powder at fixed dose for three weeks in different groups of mice indicated an apparent reversibility. This is noticed by the presence of abundant spermatid in their seminiferous tubules and the thickened Epididymal epithelial lining (Figure -5) compared to the control group. Lumen formation which is also an indication of the degree of spermatogenesis was highly seen in sections in mice treated by Moringa oleifera leaf powder. On the basis of above discussed data and facts it can be concluded that the Moringa oleifera powder significantly reduce the alteration arisen in reproductive ability and associated structures in the Alloxan monohydrate induced male diabetic mice. Table-1: Diabetes Induced Male Reproductive Changes and their treatment with Moringa oleifera leaf powder Sperm Parameter

Group of mice GROUP I

GROUP II

GROUP III

Sperm count (million /mm3)

18.36±0.044

8.36±0.041

16.11±0.148

Sperm motility

82.2±0.629

53.1±0.809

73.6±0.650

17.8±0.629

46.9±0.809

26.4±0.650

(%)

Sperm mortality (%)

N=10 Values are given as mean ±SEM for groups of ten mice. Values are statistically *significant (p<0.05); ** highly significant (p<0.01). Table-2: Diabetes Induced Male Reproductive Changes and their treatment with Moringa oleifera leaf Powder for three weeks on organ weight testis (Gram). Organ weight

Group I

Group II

Group III

Testis weight

0.478±0.008

0.33±0.006

0.415±0.005

Epididymis weight

0.144±0.003

0.052±0.004

0.106±0.001

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N=10 Values are given as mean ÂąSEM for groups of ten mice. Values are statistically *significant (p<0.05); ** highly significant (p<0.01). Fig. 1: Different abnormalites of sperm morphology. A

Fig. 2: Photographs showing structures of Sperms among Hyperglycaemic mice. A-Normal sperm, BHeadless sperm; C- Round head sperm; D-Coiled tail defect, Double tail Sperm, Tail bent defect,(40x) E

Fig. 3: Photographs showing Sperm structure abnormalities induced by hyperglycaemia in Swiss Albino Mice. E-Round headed sperm,(40x)

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T.S. OF EPIDIDYMIS OF CONTROL MICE

T.S. OF EPIDIDYMIS OF DIABETIC MICE

T.S. OF EPIDIDYMIS OF TREATED MICE

Fig. 4: Microphotographs showing histological changes in the tissue architecture. The epithelial lining is degenerating in diabetic group as well as the density of sperm in lumen is lessen. C

T.S OF TESTIS OF CONTROL GROUP(I)

T.S. OF TESTIS OF DIABETIC GROUP (II)

T.S. OF GROUP(III)

TESTIS

TREATED

Fig. 5: Photomicrographs of T.S. of hyperglycaemic Testis A-Control, B- 14 days, References

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Long term addition of organics to sustain the system productivity of Rice (Oryza sativa L.) –Wheat (Triticum aestivum L.) under Indo-Gangetic Plain of India D. K. Singh*1, P. C Pandey2 and Shilpi Gupta3 1&2 Professor and 3SRF 1,2,3 Department of Agronomy, College of Agriculture, G. B. Pant University of Agriculture & Technology, Pantnagar – U S Nagar-263145, Uttarakhand (India) Abstract: The rice-wheat cropping system is India’s most widely adopted system, covering over 10.5 million hectare mostly in the country’s north-west zone. The average productivity of the country of both rice and wheat is low 2,130 and 2,670 kg/ha, respectively. During the last several decades, the rice (Oryza sativa)– wheat (Triticum aestivum) based cropping system in India significantly contributed in enhancing the food grain production & achieving the food self sufficiency & food security. To find out the effect of organic inputs on productivity, soil fertility and sustainability in rice- wheat rotation, a long term field experiments was started from the year 1986 at G.B.P.U.A &T, Pantnagar on silty loam soil and after 10 years of continuous research in the same experiment, the study of another eleven years (1998-2009) on rice–wheat crop cycles were undertaken. Here we reviewed that application of Sesbania green manure along with FYM significantly enhanced the average grain yield of rice (5.96 t/ha), wheat (4.71 t/ha and average total system productivity in terms of rice equivalent (11.68 t/ha). Soil fertility status was also found to significantly enhance over control and recommended dose of fertilizers. The sustainability index for rice – wheat system was highest (0.90) with 100 % use of organic nutrient sources i.e. Sesbania along with FYM treatment on long term basis over control and recommended NPK. Therefore, here we demonstrate that the continuous use of either 100 % of organic nutrient sources i.e. Sesbania green manure + FYM or organics along with recommended doses of NPK sustained the system productivity of rice – wheat and also maintain the soil health through maintaining soil physical and biological properties in Indo – Gangetic plains of India. Keywords: Rice- wheat, green manure, FYM, system productivity, sustainability index, Indo- Gangetic plains I. Introduction The Indo- Gangetic Plains occupies nearly one-sixth of the total geographical area of the sub-continent and is home to nearly 42 per cent of the total population of 1.3 billion of South Asia. The population is increasing at about 2.0 per cent per year, representing 24 million more mouths to be fed annually. Rice-wheat rotations are practiced on nearly 13.5 million ha (Ladha et al, 2000) with another 12 million ha in China. In South Asia that is almost one-sixth of the cultivated area, and rice-wheat cropping produces more than 45 per cent of the region's food. Demand for rice and wheat will grow at 2.5 per cent per year over the next 20 years. At the same time, the per capita rice-wheat growing area has shrunk from 1,200 m2 in 1961 to less than 700 m2 in 2001. Thus, future growth in food production will have to come from yield increases. The Indo-gangetic plains climate is sub-humid with a distinct wet monsoon summer season and a dry, cool winter season. This allows rice and wheat to be grown in a double cropping pattern in one calendar year, rice in the summer and wheat in the winter. Temperature can exceed 45 oC in the summer and frost occurs in some areas in the winter. Soils are mainly alluvial, as a result of depositions of the Indus and Ganges river systems. Many soils are alkaline, although acid soils are also present in the piedmont and some floodplains. Soils range in texture from loamy sands to silty clay loams. The Indo-Gangetic plains are endowed with extensive canal irrigation systems using water storage reservoirs in the Himalayan mid-hills. Canal irrigation is supplemented with tube-well water and most of the rice-wheat areas are irrigated or partially irrigated. The Indo-Gangetic plains is probably one of the most fertile and productive agricultural areas in the world. Rice and wheat are grown annually in sequence on more than 13.5 million hectares in the Indo‐Gangetic Plains of South Asia, where the rice‐wheat rotation is vital for food security and livelihood for millions of rural and urban people. This cropping system so far has maintained the balance between food supply and population growth but recent evidence shows that productivity and sustainability of this system is threatened as yield of both rice and wheat are either stagnant or decreasing and total factor productivity is declining for the following reasons:

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inefficiencies in the current production system, increasing shortage of resources especially water and labor, changing climate and socioeconomic changes such as urbanization, labor migration, preference of nonagricultural work, rapid economic growth led to increased labor requirement in nonagricultural sectors (Ladha et al., 2003). During the last several decades, the rice (Oryza sativa) –wheat (Triticum aestivum) based cropping system in India significantly contributed in enhancing the food grain production & achieving the food self sufficiency & food security. The rice-wheat cropping system is the backbone of India’s food security. The magnitude of the contribution of rice-wheat cropping system to the country’s food security can be gauged from Punjab alone, which has less than 2% of the country’s cultivated land, and provides 60% of the wheat and 40% of the rice to the Public Distribution System and national buffer stocks (Swaminathan, 2007). The rice-wheat cropping system is India’s most widely adopted system, covering over 10.5 million hectare mostly in the country’s north-west zone (Paroda et al., 1994). The average productivity of the country of both rice and wheat is low 2,130 and 2,670 kg/ha, respectively. Currently there is growing concern about the sustainability of RWCS as the growth rate of rice and wheat yield either stagnating or declining (Paroda 1996). The combination of poor soil fertility and inadequate, unbalanced, and inefficient use of fertilizers contributes much to this problem (Yadav et al., 2000; Dwivedi et al., 2001). Continuous rice- wheat cropping without adequate and balanced nutrition has resulted in a widespread problem of multiple nutrient deficiencies (Timsina and Connor, 2001). II. Materials and Methods The long term field experiment was initiated during 1988- 89 in the experimental farm of Norman E. Barlough Crop Research Centre at G. B. Pant University of Agriculture and Technology, Pantnagar, district Udham Singh Nagar, Uttarakhand in the Tarai region of Indo- Gangetic Plains. It lies at 243.8 m above from sea level, about 30 km southward of foothills of Shivalik range of Himalayas at 29 0 N latitude, 790 290 E longitude with sub humid, sub tropical type of climate. The soil of experimental field at the beginning of the experiment (1988-89) was silty loam in texture, rich in organic carbon (1.22 %), medium in available nitrogen (336 kg/ ha), phosphorus (20 kg/ ha) and potassium (216 kg/ ha). The experiment included two crops per year under ricewheat cropping system. The treatments which were arranged in a complete randomized block design with three replications included unfertilized control, application of recommended NPK (N 120P60K40 - T1), application of N180 P60 & K40 (T2), Sesbania + FYM@ 5t/ha for rice (as starter dose to green manure crop) & FYM @ 10t/ha for wheat (T3), N120P60K40 (N through neem coated urea) (T4), N120P60K40 (T1)+ straw mixed@ 4t /ha (T5), Sesbania with P & K (60: 40) for rice & recommended NPK (N120P60K40) for wheat (T6) and N120P60K40 (T1) + straw burnt@ 4t /ha (T7). Wherever the green manuring treatment was there, Sesbania grown in- situ before rice transplanting and incorporated 40- 60 days after sowing. Prilled urea (N@ 120 kg/ha) along with wheat straw @ 4 t/ ha was mixed to soil before rice transplanting. Straw of wheat was burnt and mixed into top 15 cm soil before 20- 25 days of rice transplanting and the plots were irrigated. Similarly, rice straw was used before wheat sowing. FYM @ 5 t/ ha and @ 10 t/ ha for rice and wheat, respectively was applied just 25 days before planting in treatment T3. In treatments T1, T2, T4, T5 and T7, half dose of N and full dose of P & K was applied at the time of field preparation i.e. previous day of planting. Rice variety Pant dhan-4 and wheat variety UP-2338 was used for experimentation. In case of rice nursery sowing and transplanting was done in June and in wheat, the crop was sown in first week of December. Sesbania was incorporated as green manure prior to basmati rice only. All the agronomic practices and plant protection measures were followed as per standard recommendations. Rice crop was harvested on last week of October and wheat crop was harvested on first fortnight of April, every year. (A) Sampling and Analysis of Soil Soil samples were collected from the surface layer (0-15 cm) of all the plots after the completion of crop cycle in April, every year. Soil samples were air dried and ground to pass through a 2 mm sieve. All soil meant for chemical analysis were stored at room temperature until required for analysis. Soil organic carbon was determined by the method of Walkley & Black (1934) as described by Jackson (1967) and expressed in percentage (%). Available nitrogen in soil was determined by alkaline KMnO4 method (Subbiah and Asija, 1956) and expressed in terms of kg ha-1. Available phosphorus in soil was determined by 0.5 M NaHCO 3 (pH 8.5) extraction method (Olsen et al., 1954) and available potassium extracted by neutral normal ammonium acetate method (Pratt, 1965) and concentration in aliquot was determined by flame photometer. All chemical results were given as means of triplicate analysis. (B) Grain Yield Grain yield of rice and wheat was determined from the net plot area by harvesting all the tillers and hills excluding the border line area. The grain were separated from the straw, dried & weighed. (C) System Productivity Data on grain yield was recorded after each season of crop harvest and system productivity in terms of rice grain equivalent yield (RGEY) was evaluated using equation i.e RGEY (q/ha ) = rice yield + {(yield of rabi crop X price of rabi crop)/ price of rice}

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(D) Sustainability Index Sustainability index was calculated to assess soil quality under the influence of different fertilizer management practices and was evaluated using the equation i.e. Sustainability Index = { (Mean yield- standard deviation)/ MaximumYield}. (E) Statistical Analysis All the data using samples from replicate field plots for each treatment was analyzed statistically. Analysis of variance (ANOVA) was done to determine the effect of treatment using Gomez and Gomez (1984). Critical Difference (C.D.) values were calculated using standard errors of mean (S.Em.Âą) at 5 per cent level of significance. III. Result and Discussion (A) Trends in Rice yield Yield of rice during 11 years resulted to a significant effect of different nutrient treatments applied to them (Fig.1). Grain yield for all the treatments were greater than the un- amended control. During 1998- 2002 rice yield was highest with the N180P60K40 treatment which was at par with recommended NPK (T 1), use of Sesbania in combination with FYM treatment (T3), Sesbania with P & K (60: 40) for rice and recommended NPK (N120P60K40) for wheat (T6) and recommended NPK with straw burnt (T 7). Lower grain yield in the plots amended with green manure & FYM may have been associated with the less available nutrients in the initial years of transition as nutrient processes in first year organic systems change from inorganic N fertilization to organic amendments and this effect has been widely studied (Harris et al., 1994) and slower release rates of organic materials (Liebhardt et al., 2000; MacRae et al., 1990). However, after 11 years the maximum mean yield was recorded with the use of Sesbania in combination with FYM treatment (5.96 t/ha) than did any other treatments. This increase in grain yield after continuous use for six years with FYM application has been found to be associated with the better root development and more root length density responsible for increasing the capacity of rice plant to extract nutrient from deeper soil layers. Moreover, this organic manure might have helped in improving nutrient availability from soil for a prolonged period, which ultimately increased the crop yield and this is also studied (Nayak et al., 2007). The increase in grain yield of rice over recommended doses under different treatments ranged from 5 to 25%. (B) Trends in wheat yield Overall yield of wheat was low in almost all fertility treatments which may be attributed to late sowing of wheat (generally during first fortnight of December) which is general phenomenon of rice-wheat cropping system in Indo- Gangetic Plains. Highest grain yield of wheat was recorded in FYM (T 3) for the first two years of experimentation which was at par with the application of recommended NPK (N through NCU). In 2000-01, it shifted towards N180P60K40 (T2) which was statistically at par with T1, T3 & T6. In the year 2002-03 significant higher yield was obtained again with N180P60K40 (T2) which was statistically at par with T1 & T3. During the year 2005-06 to 2008-09 significantly higher yield was recorded with FYM treatment than all other treatments. Long term study showed that there was a regular decrease of wheat yield after 2002-03 till 2008- 09 with recommended dose of NPK and N180P60K40, this may be due to limiting factor of micronutrient and other soil properties. Same trend was also observed by Yang et al., 2011. However, the wheat yield was regularly increasing after 2002-03 till 2008-09 with the application of FYM (T 3). This may be due to the effect of higher levels of organic matter, which improves soil physical & chemical properties and add significant quantities of N, P, K, Ca and Mg (Edmeades, 2003). (Fig. 2) (C) Total System Productivity Total system productivity (average of 11 years) was observed to be higher in all the treatments as compared to the control and recommended NPK dose (Fig. 3). The treatment where Sesbania + FYM to rice and FYM (10 t/ha) to wheat were applied, recorded much higher system productivity (11.68 t/ha) followed by N 180P60K40 (T2) and N120P60K40 + straw burnt @ 4t /ha (T 1). However, lowest system productivity after control (4.00 t/ha) was recorded with recommended NPK (9.11 t/ha), N through NCU (T 4). (D) Sustainability Index Sustainability index was observed to be higher in all the treatments as compared to the control and recommended NPK dose (Fig. 4). Sustainability index was calculated to assess soil quality under the influence of different fertilizer management practices. The long-term application of organic manures in rice-wheat cropping system increased the index value because it increased the nutrient index, microbial index and crop index of soils (Kang et al., 2005). The use of only chemical fertilizers in the rice-wheat cropping system resulted in poor soil microbial index and crop index. In rice- wheat system, additional application of FYM at 10 t ha -1

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before sowing wheat made the system more sustainable than application of N120P60K40 the sustainability index values were 0.90 (the highest for this system) and 0.76, respectively. (E) Soil Properties After 11 years of completion of crop cycle, results (Table 1) revealed that the application of Sesbania green manure maintained the organic carbon (1.23 %) than other treatments which was found to be at par with T5 (recommended NPK with straw mixed), T6 (Sesbania with P & K (60: 40) for rice and recommended NPK (N120P60K40) for wheat) and T7 (recommended NPK with straw burnt). The increase of organic carbon in organic system is very slow (Clark et al., 1999). In all other treatment there was a significant decline in the organic carbon per cent and this might be due to the continuous cropping of rice and wheat. Significantly higher available nitrogen was recorded with Sesbania + FYM treatment followed by T 4 and T5 and lowest was recorded under control. FYM and Sesbania amended plots had higher content of available phosphorus than other treatments; this was due to residual accumulation of nutrient applied through organic sources. Lower availability of plant nutrient in plots applied with organic amendments was expected due to the slower release of organic material, particularly during initial years (Liebhardt et al., 2000, MacRae et al., 1990). However, with the time it showed successive increase. Available potassium was significantly higher when straw was burnt as burning leads to direct addition of potassium (at least 40 kg/ha) followed by T 3, similar results were observed (Clark et al., 1999 and Reganold et al., 2001). The role of green manuring as a source of organic matter and nitrogen and its capacity to mobile soil phosphorus and other nutrients is well recognized. Studies conducted by Gupta (1998) indicated that green manuring increased the available N, P and K content of soil to the extent of 61, 107 and 75 percent, respectively. IV. Conclusion Organic farming opens up the prospects of producing high yield, grain quality and adequate soil fertility by using organic amendments as compared to inorganic/ chemical fertilizers on long term basis. The long term application of organics invariably led to increase in productivity and can boost better energy and environmental balance and makes a substantial contribution to conserving agricultural diversity and therefore, incorporation of green manure and/or farm yard manures were found to be beneficial to build up the soil organic matter and sustain the productivity of the system. Reference [1] [2]

[3] [4] [5] [6]

[7]

[8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

Ladha, J.K., K.S. Fischer., M. Hossain., P.R. Hobbs and B. Hardy. 2000. Improving the productivity of rice-wheat systems of the Indo-Gangetic Plains: A synthesis of NARS-IRRI partnership research. IRRI Discussion Paper No. 40 Ladha, J.K., J.E. Hill., J.D. Duxbury., R.K. Gupta and R.J. Buresh. 2003. Improving the productivity and sustainability of rice‐wheat systems: issues and impact. American Society of Agronomy Special Publication 65. Madison, Wis. (USA): ASA, CSSA, SSSA. 211 p Swaminathan, M.S. 2007. Presidential address, National Academy of Agricultural Sciences, 6th January, 2007, New Delhi Paroda, R.S., T. Woodhead and R.B. Singh. 1994. Sustainability of rice–wheat production systems in Asia’, RAPA Publication 1994/11, FAO Regional Ofﬁce for Asia and the Paciﬁc, Bangkok, Thailand, pp. 209 Paroda, R.S. 1996. Sustaining the Green Revolution: new paradigm. In: B P Pal memorial lecture in 2nd International Crop Science Congress, New Delhi, 22 November Yadav, R.L., B.S. Dwivedi., K. Prasad., O.K. Tomar., N.J. Shrupali and P.S. Pandey. 2000. Yield trends and changes in soil organic C and available NPK in a long term rice-wheat system under integrated use of manures and fertilizers. Field Crops Research., 68: 219-246 Dwivedi, B.S., Shukla, A.K., Singh, V.K., Yadav, R.L. 2000. Results of participatory diagnosis of constraints and opportunities (PDCO) based trials from the state of Uttar Pradesh. In: Subba Rao A and Srivastava S (eds.) Development of Farmers’ Resource-Based Integrated Plant Nutrient Supply Systems: Experience of a FAO–ICAR–IFFCO Collaborative Project and AICRP on Soil Test Crop Response Correlation. IISS, Bhopal, India, pp 50–75 Timsina, J. and D.J. Connor. 2001. The productivity and management of rice-wheat cropping systems: issues and challenges. Field Crops Research., 69: 93-132. Walkley, A and T.A. Black. 1934. An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science. 37:29-38 Jackson, M.L. 1967. Soil Chemical Analysis, Prentice Hall Pvt. Ltd., New Delhi, India. pp.498 Subbiah, B.V. and Asija, G.L. 1956. A rapid procedure for estimation of available nitrogen in rice soils. Current Science., 25:259:260 Olsen, S.R., C.V. Cole., F.S Watanabe and L.A. Dean. 1954. Estimation of available phosphorous in soils by extraction with sodium bicarbonate. U.S., Washington; D.C. Circ. 939:49 Pratt, P.F. 1965. Potassium. In: Methods of Soil Analysis, Vol. II. Chemical and Biological Properties. American Society of Agronomy, Madison, USA. pp. 1023-1030 Gomez, K.A. and A. Gomez. 1984. Statistical Procedures for Agricultural Research (21 E), John Willey and Sons, New York Harris, G.H., Hesterman, O.B and Paul. 1994. Fate of legume and fertilizer nitrogen- 15 in a long term cropping experiment. Agronomy Journal., 86: 910- 915 Liebhardt, W.C., R.W. Andrews., M.N. Culik., R.R. Harwood et al. 2000. Yields and nutrient budgets under compost, raw dairy manure and mineral fertilizer. Compost Sci Util., 8: 207- 216 Mac Rae R.J., S.B. Hill., G.R. Mehuys and J. Henning. 1990. Farm scale agronomic and economic conversion from conventional to sustainable agriculture. Advances in Agronomy., 43: 155-198 Nayak, D.R., Y.J. Babu and T.K. Adhya. 2007. Long-term application of compost influences microbial biomass and enzyme activities in a tropical Aerie Endoaquept planted to rice under flooded condition. Soil Biology and Biochemistry., 39: 1897-1906.

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[19] [20] [21] [22] [23]

Edmeades, D.C. 2003. The long term effects of manures and fertilizers on soil productivity and quality: A Review. Nutrient Cycling in Agroecosystems., 66: 165- 180 Kang, G.S., V. Beri., B.S. Sidhu and O.P. Rupela. 2005. A new index to assess soil quality and sustainability of wheat-based cropping systems. Biology and Fertility of Soils., 41 (6): 389-398. ISSN 0178-2762 Clark, M.S., W. R. Horwath., C. Shennan., K.M. Scow., W.T. Lanini and H. Ferris. 1999. Nitrogen, weeds and water as yield limiting factors in conventional, low input and organic tomato systems. Agriculture Ecosystem & Environment., 73: 257- 270. Reganold, J.P., J.D. Glover, P.K. Andrews and H.R. Hinman. 2000. Sustainabilty of three apple production systems. Nature., 410: 926- 929. Gupta, C. 1998. Integrated use of organic manures with urea fertilizers in lowland rice. PhD Thesis, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttar Pradesh

Fig. 1: Rice grain yield of 11 years as influenced by different fertility treatments.

Fig. 2: Wheat grain yield of 11 years as influenced by different fertility treatments.

Fig. 3: Total system productivity in terms of rice grain equivalent yield of 11 years in continuous rice-wheat crop cycle.

Fig. 4: Sustainability index of different fertility treatments after 11 years of experimentation

Table 1: Organic carbon and available nutrient status of soil as influenced by different fertility treatments after 11 years of rice-wheat crop cycle Treatments Control T1 T2 T3 T4 T5 T6 T7 SEm Âą CD(p=0.05) Initial

Organic carbon (%) 0.84 0.97 0.95 1.23 0.96 1.16 1.21 1.15 0.03 0.078 1.22

Available N (kg/ha) 145.6 351.2 359.5 373.7 367.5 367.1 353.0 357.8 4.54 13.77 336.0

Available P (kg/ha) 12.8 24.0 24.1 30.7 27.2 25.2 25.9 22.7 2.21 6.71 20.0

Available K (kg/ha) 173.5 201.4 197.5 209.8 193.9 206.0 199.0 298.6 0.82 2.74 216.0

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Arsenic Extrusion and Energy Derivation as Survival Mechanism in a Novel Exiguobacterium Isolated from Arsenic Contaminated Groundwater of West Bengal Rajdeep Chowdhury1, Prithviraj Karak2, Ashish K Sen3, Raghunath Chatterjee3, and Keya Chaudhuri3* Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Rajasthan, Bankura Christian College, Bankura, West Bengal Indian Institute of Chemical Biology (IICB), Jadavpur, Kolkata -700032, INDIA. Abstract: The Bengal-Delta plain at Indo-Bangladesh border is severely affected with groundwater arsenic contamination. The microbial diversity of this site is totally uncharacterized. Here we report the existence of a novel Exiguobacterium strain that could thrive under high arsenic concentrations in the arsenicinfested water of the above region. Drinking water was sampled for presence of bacterial isolates. Analysis of the 16SrRNA sequence of the bacteria isolated revealed them to be members representing various genera. Of particular interest was a new species of genera Exiguobacterium (=MTCC 7757 T=JCM 13946T) that thrived in extremely high arsenic concentration, arsenate (30mM) and arsenite (20mM). The growthrate of the bacterium cultured in arsenate-supplemented medium increased significantly; it gained metabolic energy from arsenate-amended aerobic growth conditions. On analysis of the ars-operon, the strain was positive for arsB, but the genetic contribution to arsenate reduction (arsC) was not recognized, though a differential arsenate reductase activity could be observed. An increased expression of arsB, as an associated process to arsenate reduction, confirmed that arsenic extrusion principle worked behind its survival. Identification of such a bacterium could add to the diversity of bacteria specific to that geographical location and also help us in delineating putative novel arsenic resistance mechanisms operative for survival. Keywords: Arsenic-resistance, Bacteria, Exiguobacterium. I. Introduction Increasing human activities have modified the global cycle of arsenic; it is now ranked first in a list of 20 hazardous substances by the Agency for Toxic Substances and Disease Registry and Environmental Protection Agency [1]. The contamination of groundwater by sediment-derived arsenic in the alluvial aquifer of the Bengal Delta Plain threatens millions of its inhabitants. Nine districts (Malda, Murshidabad, Nadia, North-24-Parganas, South-24-Parganas, Bardhaman, Howrah, Hoogly and Kolkata) are the most severely affected ones where > 300 Âľg/L arsenic concentrations are found [2]. Such contamination of ground water in these agricultural sites with arsenic can probably have distinct effects on the groundwater microbial populations. However, little is known of the microorganism diversity at these sites. Arsenic compounds are highly toxic for most microorganisms, yet certain microorganisms have evolved a variety of mechanisms to cope with toxicity of arsenic. Diverse microbial flora that is resistant to arsenic, like Planococcus bengal [3], Deinococcus indicus, Bacillus indicus [4], Pseudomonas fluorescens [5], Bacillus subtilis [6], Thermus aquaticus and Thermus thermophilus [7], Yersinia enterocolitica and Yersinia intermedia [8], Streptomyces noursei [9] and Desulfitobacterium sp. [10] has already been reported from various habitats. Microorganisms inhabiting these arsenic-polluted environments develop metal resistance mechanisms including minimizing the amount of arsenic that enters the cell [11]; transformation of arsenic species through oxidation [12], and reduction [6], achieve anaerobic growth using arsenate as a respiratory electron acceptor for the oxidation of organic substrates and also by conferring arsenic resistance through specific pumps that extrude arsenite (AsIII). The efflux pump generally consists of a membrane anion channel (arsB) that pumps arsenite following conversion of arsenate (AsV) to arsenite by a soluble reductase (arsC) [13]. However, the microbial diversity, biological mechanisms and the genetic contributions underlying the survival of bacteria under extreme arsenic concentrations in the Bengal Delta plain remain largely uncharacterized. Hence, the objective of this study was to enumerate, isolate and identify arsenic-resistant microorganisms from the arsenic-contaminated ground water of West Bengal; investigate their modes of resistance and to assess their tolerance to arsenic. In

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the present study, we report the isolation and characterization of arsenic-resistance of a novel species of Exiguobacterium that was isolated from arsenic-contaminated bore-well water of West Bengal, India. II. Materials and Methods A. Isolation of strain. We collected water samples from bore wells of arsenic-contaminated regions of North 24 Parganas, West Bengal, India and tested for presence of arsenic-resistant bacterium. The arsenic content of the sample was analyzed at School of Environmental Studies, Jadavpur University, Kolkata. Flow injection-hydride generation-atomic absorption spectrometry (FI-HG-AAS) was used for estimation of arsenic [14]. B. Culture conditions and arsenic resistance. Presence of bacteria in water samples were observed by dilution plating technique on Luria-Bertani (LB) agar (1.0% tryptone, 0.5% yeast extract, 0.5% NaCl, pH 7.4, 1.5% Bactoagar, all w/v) (Difco, USA). After incubating the plates at 37 °C for 2 days, different colony morphotypes were visualized. LB agar without arsenic was primarily used for growth, maintenance and biochemical tests. The optimum pH and temperature for growth were 7.4 and 37°C respectively. To follow culture growth, samples grown in LB broth (without agar) and were measured with a Hitachi U-1100 spectrophotometer (Tokyo, Japan). For arsenic sensitivity of the colony, morphotypes cultures were grown in LB broth containing either sodium arsenate (Na2HAsO4) or arsenite (NaAsO2). Strains were grown at 37°C in LB broth containing either 500µM, 1.25mM, 2.5mM, 5mM, 10mM, 20mM, 30mM and 40mM Na2HAsO4 or NaAsO2. C. Morphology, motility and physiological tests. Scanning electron microscopy (SEM) was used to visualize bacteria. The bacteria were suspended in 100μl of 4% glutaraldehyde in 1, 4-piperazine diethanesulfonic acid buffer (0.1M; pH 7.3). The samples were rinsed in sodium cacodylate buffer (0.1M; pH 7.4) and vacuum filtered on 13mm, 0.45μm-pore-size membrane filters (Millipore). The moist samples were then post fixed on the filters with 1% osmium tetroxide in sodium cacodylate buffer. The samples went through serial ethanol dehydration (75 to 95%), followed by immersion in trichorotrifloroethane (Freon 113) and rapid air-drying. The bacteria were mounted on stubs, coated with gold palladium (15nm), and examined in SEM (JEOL, JSM-5200). For the various physiological tests listed in Table 1, the cultures were grown at 37°C in LB broth and tests were performed [15]. Gram reaction was determined using the HiMedia Gram Staining kit. In addition, the ability of the isolated strain to utilize carbon compound as sole carbon source was investigated by supplementing minimal medium [10.5g/l K2HPO4, 4.5g/l KH2PO4, 1g/l (NH4) 2 SO4, 15g/l agar] with 5g/l filter sterilized carbon compound. The sensitivity to various antibiotics was checked in LB. LB broth was also used to check the NaCl tolerance tests with 0-15% of NaCl. Bacterial protein of was measured by Bradford (BioRad, USA). D. DNA extraction and mol % G + C determination. DNA was isolated by the method of Sambrook et al [16]. Briefly, several colonies were picked from the agar plate and the biomass was re-suspended in 0.1mL TE buffer. The cells were pelleted and suspended in TE. TE buffer containing 3% (w/v) sodium dodecyl sulphate was added to the cells and mixed. The cell lysate was extracted with TE buffered phenol and chloroform. The aqueous phase was transferred and two volumes of ice-cold ethanol were added. The DNA was dissolved in sterile TE buffer. DNA preparations contained around 50-200ng DNA/mL. The mol% G+C content of the DNA was determined from melting point (Tm) curves obtained using a Hitachi Spectrophotometer (Hitachi, Japan) [17]. The equation of Schildkraut [18] was used to calculate the G+C content (mol %) of DNA. E. Fatty acid analysis. Cells were grown in LB broth at 37 °C to early stationary phase, harvested by centrifugation (7000g, 10 min) and washed with sterilized 0.1M phosphate buffer saline (pH6.8). Cellular fatty acid methyl esters were obtained using method described by Sato et al [19] and were analyzed as described by Reddy et al [20]. The peaks were identified by GLC-MS (Shimadzu, Japan) using DB-5 column (30mM x 0.25mM x 0.25mM) and a temperature program of 160°C–2min– 3°C/min-220°C-15 min. F. Amplification and sequencing of 16S rRNA genes. The chromosomal DNA of the strain was isolated and DNA concentrations were quantified by UV spectrophotometry at 260nm (Hitachi, Japan). The bacterial 16SrRNA gene was amplified with universal 16S rDNA primers, forward 5’CCGAATTCGTCGACAACAGAGTTTGATCCTGGCTCA-3’, and reverse 5’CCCGGGATCCAAGCTTACGGCTACCTTGTTACGACTT-3’ [21]. PCR conditions were 95°C for 3min, annealing 45°C, 1min and extension 72°C, 2min. The thermal profile for 35 cycles was 94°C-30 sec, 45°C-1min and 72°C-2min, final extension-72°C-5min. The purified PCR product was sequenced using an ABI PRISM model Avant 3700 automatic DNA sequencer and the Big Dye Terminator sequencing kit (Applied Biosystems, USA). 16SrRNA gene sequence accession number: The nucleotide sequence for this study has been deposited in EMBL Database and appears under accession number DQ375558. G. BLAST analysis and molecular phylogenetic tree. Sequence similarity with other type strains of the genus Exiguobacterium was assessed using BlastN with the 16S rRNA sequence of strain as query in the nonredundant database of NCBI. From pairwise alignments, sequence identities were recorded for 16S rRNA sequence of the reported strains. Phylogenetic analysis was performed by MEGA version 3.0 [22]. For the

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construction of phylogenetic tree 16S rRNA gene sequence was aligned by multiple sequence alignment using ClustalW 1.6 algorithm, with type strains of the genera, Exiguobacterium, and closely related Bacillus, Arthrobacter and Sporosarcina retrieved from GenBank. To have sequence similarities into evolutionary distances both Jukes-Cantor model, which is based on simple probabilistic approach, and Kimura 2-parameter model, were used, considering gamma correction with value of 0.25. To determine evolutionary position of the strain, phylogenetic trees were constructed using the Neighbor Joining and UPGMA method with 16S rRNA sequence of the strain along with species of closely related genera. For estimation of sampling error and to evaluate robustness of the trees, bootstrapping was performed with 1000 replicates & 70000 random seeds. To assess stability among clades of phylogenetic tree, the original tree was compared with consensus bootstrap tree. H. Detection of arsB and arsC genes. Primers used for amplification arsB were adopted from previous studies [23]; they are as follows: ArsBF 5’-ATGGCAACCGAAAGGTTTAG-3’ and ArsBR 5’GTTGGCATGTTGTTCATAAT-3’. The arsC, primers are as follows: ArsCF 5’AACAGTTGCCGCAGCATTCT-3’ and ArsCR 5’-ATGCGCTCCAGCTCACGCTT-3’. The PCR products were then sequenced. PCRs amplification consisted of one cycle at 94°C for 2min and 28 cycles at 94°C for 30s, 55°C for 30s, 72°C for 1min, and a final cycle at 94°C for 1min, at 55°C for 1min, and at 72°C for 5min. The amplified sequences were verified by sequencing and blasts using NCBI Blast. I. Arsenate reductase enzyme assay. Bacterial cells were grown to stationary phase in LB supplemented with 20mM of As(V), harvested by centrifugation, washed in reaction buffer (RB, 10mM Tris, with 1mM Na2EDTA & 1mM MgCl2), and finally resuspended in 15mL of RB. Cells were disrupted by sonication, and unbroken cells removed by centrifugation at 8000g. Arsenate reductase activity was measured by NADPH oxidation, which is coupled to reductase activity [23]. NADPH oxidation was initiated at 37°C by mixing 50µL of crude extract in 820µL of RB, 30µL of 10mM DTT, 50µL of 2mM As(V), and 50µL of 3mM NADPH. As(V) concentrations (10, 20 & 30 mM) were assayed. Measurement was recorded at 340nm, where 0.15mM NADPH has absorbance of approx. 1.0. Absorbance decreases as NADPH is oxidized coupled to As(V) reduction. J. Determination of arsenate and arsenite transformation. A flow injection-hydride generation-atomic absorption spectrometry was used for the estimation of arsenic. For evaluation of speciation of arsenic, 5% KI and HCl (35%) was added to diluted samples, incubated at RT for 45min for complete conversion of As(V) to As(III) state, and arsenic was estimated by AAS. An equal amount of unreacted solution was used to have the initial concentration of As(III). Estimate of As(V) present was obtained from the difference in arsenic content between the two samples. The experiment was performed in triplicate to record standard error of the mean. III. Results A. Morphology and physiological characterization. The analyzed strain was Gram-+, motile rod, varying in shape, size of rods being about 1.0-1.5µm in diameter x 0.8-3.0µm in length. Cells as observed under SEM occurred singly, in pairs or in groups (Figure 1a). The strain produced bright orange, convex and shiny colonies. The colonies were 2-3mM in diameter after 2 days at 37°C. Growth occurred at 4 to 41°C, with optimum temperature for growth being 37°C. Optimal pH was 7.4. The organism grows weakly at acidic pH. It grows in the presence of 1–5% (w/v) NaCl. The strain is positive for catalase, indole, oxidase and utilizes glycogen, cellobiose, D-ribose, acetic acid. All strains utilized following substrates: dextrin, d-fructose, α-d-glucose, maltose, maltotriose, d-mannose, sucrose, pyruvic acid, glycerol, adenosine, inosine, uridine. The following substrates were not used: inulin, l-arabinose, d-galacturonic acid, d-melibiose, methyl α-d-galactoside, methyl βd-galactoside, methyl α-d-glucoside, methyl α-d-mannoside, xylitol, α-hydroxy-butyric acid, β-hydroxybutyric acid, α-ketoglutaric acid, d- and l-malic acid, succinic acid, N-acetyl l-glutamic acid, l-asparagine and l-glutamic acid. Other physiological properties are listed in Table 1. The strain is sensitive to kanamycin; the minimum inhibitory concentrations are enclosed in brackets - (50µg/ml) and ampicillin (25µg/ml), gentamicin (30µg/ml), chloramphenicol (30µg/ml), tetracycline (30µg/ml) & streptomycin (25µg/ml). B. Arsenic resistance of the isolated strain. The strain was isolated from water of an arsenic-contaminated bore well in West Bengal, India. When analyzed by AAS the arsenic content of the water was approximately 210µg/lit which is high compared to the World Health Organization (WHO) recommended permissible value of arsenic in drinking water of 10µg/l [24]. To check resistance to arsenic, the bacterium was grown in stable, high arsenic concentrations and also in gradually increasing concentrations of both arsenate and arsenite (Figure 1b & c). There was a marked increase in growth of the bacterium in arsenate-supplemented medium (30mM) (Figure 1b). The bacterium grew in the presence of high concentrations of arsenite (20mM) but no significant growth Table 1 Phenotypic properties of type strains of Exiguobacterium species and the novel isolate. Characteristics

Novel Strain

E. aurantiacum

E. mexicanum

E. aestuari

E. acetylicum

E. undae

E. antarcticum

E. oxidotolerans

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JCM 13946T

DSM 6208Ta

DSM 16483T b

DSM 16306Tc

DSM 20416Ta

DSM 14481Ta

DSM 14480Ta

DSM 17272Td

−

α-Cyclodextrin

−

β-Cyclodextrin

−

Glycogen

−

Cellobiose

−

D-Galactose

−

D-Mannitol

−

D-Raffinose

−

D-Ribose

−

D-Xylose

−

Acetic acid

−

Methylpyruvate

−

Methylsuccinate

−

Propionic acid

−

D-Alanine

−

L-Alanine

−

L-Serine

−

2,3-Butaneidol

−

Thymidine

Adenosine 5′monophosphate

−

Thymidine 5′monophosphate

−

Uridine 5′monophosphate

−

Fructose 6-phosphate

−

Glucose 1-phosphate

−

Glucose 6-phosphate

−

Tween 40

−

Tween 80

−

Oxidase Utilization of:

L-Lactic

acid

−

Characteristics are scored as: −, negative; +, positive; W, weak. [29], [30], [31], [32], [33] a

Table 2 Arsenic resistance as observed in different bacterial strains. Bacterial Strains Resistance to arsenate (mM)

Resistance to arsenite (mM)

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Bacillus arsenicus (JCM 12167T) a

20.0

0.5

Bacillus indicus (DSM 15820 )

20.0

3.0

Bacillus subtilis (NCDO 1769T)c

4.0

0.5

30.0

20.0

T b

Identified Strain a

, [34];b, [4];c, [6];

Figure 1a A scanning electron micrographic picture of the bacterium resembling rod like structure as evident at x 7,500 magnification. A representative figure is presented. Figure 1b Growth of the novel isolate in arsenite (20mM) and arsenate (30mM) medium when compared to untreated control with increasing time in the “X” axis. A spur in growth rate (OD600) at arsenate-supplemented medium is evident from the figure. Data are mean from three independent experiments. Figure 1c Growth of the isolate at increasing arsenite and arsenate concentrations (0-100mM). Cells harvested 24 h after inoculation. Data are mean from three independent experiments. Figure 1d Cellular protein yield for the isolate at arsenite (20mM) and arsenate (30mM); comparison drawn with “no arsenic control”. Cells were harvested and protein content measured, 24 h after inoculation. The symbol * denote statistically significant values compared to “no arsenic control”.

increase was however observed. Table 2 summarizes a comparative analysis of arsenic resistance of the newly identified strain with some other arsenic-resistant bacteria. The biomass of the bacterium growing under increasing arsenate concentrations was also found to be increased as evident from a boost in total protein (Figure 1d). To further demonstrate the isolated strain gained metabolic energy from arsenate, total cellular protein was determined. The total cell yield was approximately twofold higher in the presence of 30mM arsenate when compared to no arsenic control after 24h of incubation. C. Cellular fatty acids. The main cellular fatty acids in the strain were branched. The fatty acid composition indicates that a dominant fatty acid does not occur that would define the genus. Though some major components are determinable, at times they do not even occur in the nearest or all members of the phylogenetic subcluster. Iso-C17:0 is the major fatty acid constituting approximately 24% of the cellular fatty acids. The other major fatty acids (>5%) were iso-C13:0, anteiso-C13:0, iso-C15:0, C16:0, iso-C16:0 and C17:0; additionally minor components are listed in Table 3. Table 3 Fatty acid compositions of the novel isolate and other type strains of the genus Exiguobacterium. Fatty acids

Novel isolate

E. aurantiacum

E. mexicanum

E. aestuari

E. marinum

E. artemiae

E. acetylicum

E. undae

E. antarcticum

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E. oxidotolerans

Chowdhury et al., American International Journal of Research in Formal, Applied & Natural Sciences, 7(1), June-August, 2014, pp. 19-27

JCM 13946T

DSM 6208Ta

DSM 16483Td

iC11:0

1.8

2.0

1.5

iC12:0

3.0

2.1

C12:0

4.3

2.0

8.3

iC13:0

6.1

18.0

11.2

11.5

13.2

5.0

9.0

12.0

8.5

aiC13:0

16.6

12.0

8.9

15.6

18.1

12.0

6.0

9.0

11.0

9.0

1.2

1.0

2.0

1.0

2.7

1.3

13.0

3.0

2.0

iC14:0

DSM 16306Tb

DSM 16484Tb

DSM 16484Td

1.7

2.6

1.6

3.0

6.1

C14:1 ω5c iC15:0

DSM 14480Ta

DSM 17272Tc

2.0

3.0

1.4

1.0

2.0 9.4

4.0

1.7

aiC15:0 C16:1ω11c

3.3

iC16:0

6.8

10.0

13.1

10.4

11.8

8.0

10.0

11.0

20.7

3.2

2.6

2.9

1.0

3.0

2.0

4.2

1.9

26.0

8.0

18.0

10.3 7.1

5.0

1.4

C16:1ω5c

2.0

7.1

2.0

C16:1ω7c

6.5

C16:0

13.4

iC17:0

23.4

aiC17:0

9.3

27.0

32.8

6.0

C18:1ω9c

2.6

13.0

4.3

22.9

10.0

17.0

13.0

2.9

27.2

34.4

12.2

1.0

7.0

5.0

23.3

8.2

7.1

2.1

1.0

2.0

1.1

2.0

3.0

5.0

3.0

6.0

1.0

6.0

5.0

2.0 2.7

5.0

7.0

3.0

5.3

C18:1ω7c

C18:0

DSM 14481Ta

1.0

1.3

C14:0

DSM 20146T

1.7

7.7

6.1

Only values >1% are indicated; values >10% are given in bold.a [29], b[35], c[32], d[30]

D. DNA composition and 16S rRNA analysis. The G + C content of DNA of the isolated strain is 47 mol%. BLAST analysis using 16S rRNA gene sequence (1488bp) indicated that it fell within the radiation of the cluster comprising type species of the genus Exiguobacterium (GenBank DQ375558.1). The strain exhibited a maximum identity of 98% with Exiguobacterium aurantiacum DSM6208T while about 95-97% sequence identity was observed with other Exiguobacterium species. A phylogenetic tree along with the bootstrap values expressed as percentage of 1000 replications, based on Neighbor-Joining method using Kimura 2-parameter model and gamma correction with a gamma value of 0.25 is shown in Figure 3. It is evident that the strain forms a clade with Exiguobacterium aurantiacum DSM6208T (Figure 2) (bootstrap 69%) and the later branch with Exiguobacterium mexicanum DSM 16483T (bootstrap 95%). Phylogenetic trees constructed by UPGMA, using the evolutionary distances computed with Jukes-Cantor and Kimura 2-parameter models, also yielded similar stable groupings. Therefore, it appears that, based on 16S rRNA gene sequence analysis, the isolated strain could be classified as representing a novel species of genus Exiguobacterium (=MTCC 7757T=JCM 13946T). E. Detection of arsB and arsC genes. The bacteria survived under harsh arsenic concentrations and also derived growth advantage from arsenate in the medium; we further verified other putative means employed by the bacterium to sustain in high arsenic concentrations. Arsenate reduction is considered a method of detoxification in many bacteria followed by its extrusion. Arsenate reduction in bacteria is generally catalyzed via the ars operon encoding an arsenate reductase (arsC) and an arsenite efflux pump (arsB) [13]. To find the actual contribution of the operon in our isolate, amplification of the Ars operon genes by PCR was carried out. A positive PCR product for arsB gene was obtained; however we were not able to amplify arsC. The amplified sequences were further verified by sequencing and homology blasts using NCBI Blast. The arsB gene exhibited maximum homology with arsenite/antimonite transporter (arsB) of Shigella sp and Halomonas sp. To analyze

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the activity of the arsenite extrusion pump, RNA from the bacterium was converted to cDNA and was subjected to semi-quantitative RT-PCR using primers for arsB. An increase in activity indicating an active extrusion of arsenic was observed at exponential phase of the bacterium at 20mM arsenate (Figure 3a). Figure 2 Neighbour-joining tree based on 16S rDNA sequences showing position of the novel isolate compared to other Exiguobacterium species, along with few Bacillus, Arthrobacter and Sporosarcina species. Bootstrap values (expressed as percentages of 1000 replications) are shown at branch points.

F. Arsenate reductase assay. Though we didn’t get positive results for amplified arsC gene, yet we went for an enzymatic assay that evaluates cellular arsenate reductase activity. The bacterium was allowed to grow in different concentrations of arsenate and compared to “no arsenic” control. arsC activity was measured based on NADPH oxidation coupled to arsC reductase activity. Absorbance decreases as NADPH is oxidized coupled to arsenate reduction to arsenite. Interestingly a marked reduction in absorbance, signifying high arsenate reductase activity was observed in the identified strain with increasing concentrations of arsenate (Figure 3b). No such activity was observed if the bacterium was grown at increasing concentrations of arsenite (Figure 3c). This does potentially signify the presence and activity of an arsenate reductase in the bacterium. G. Detection of arsenite and arsenate in the growth medium. No significant conversion of arsenite to arsenate was observed during the growth of the bacterium; only trace amounts of arsenate (<1mM) in the medium was obtained when the bacterium was cultured (24h) in arsenite supplemented medium (20mM); however at similar arsenate concentration (20mM), around 5mM arsenite was detected in the growth medium, indicating an active conversion or reduction of arsenate to arsenite, probably by an hitherto unknown arsenate reductase. The arsC enzymatic activity analysis along with active conversion of arsenate thus corroborates with the arsenate reduction theory, which is probably followed by its active extrusion from the bacterium by arsB. Species of the genus Exiguobacterium are known to be alkaliphilic [25]; therefore, arsenate reduction probably acts to buffer medium acidification, favoring the growth of these bacteria. In fact, the bacterial strain exhibited a significant spur or acceleration in growth and hence in final OD600 as the external arsenate (not arsenite) concentration increased in the medium, suggesting that the bacterium enjoyed growth advantage. As mentioned earlier, arsenate at concentrations up to 30mM had a stimulatory effect on the growth rate (Figure 1).

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Figure 3a Semi-quantitative-RT-PCR gel pictograph, demonstrating an increase in ArsB transcriptional activity, on culture of bacterium at different concentrations of arsenate in the medium. The expression of 5S rRNA gene served as house keeping gene. A representative figure from three replicate experiments is presented. Figure 3b, c Bar diagram representing the arsenate reductase enzyme activity at different concentrations of arsenate (b) and arsenite (c). A decrease in absorbance measured at 340nm indicates an arsenate reductase activity. Data are mean from three independent experiments. The symbol * denote statistically significant values compared to â&#x20AC;&#x153;arsenic untreatedâ&#x20AC;?.

IV. Discussion Arsenic is a well-established toxic compound that is found naturally. Arsenic is also released into the environment by human activity through farming, industrial activity and burning of fossil fuels. Presently arsenic poses a global health problem by contamination of drinking water. It has been estimated that more than 70 million people in West Bengal and Bangladesh alone are exposed to arsenic at concentrations exceeding the WHO recommendations [24, 26]. Due to this natural occurrence and toxicity of arsenic, virtually all organisms have evolved resistance systems to different concentrations of arsenic, bacteria are no exceptions. Our research in this study is focused on identification of arsenic resistant bacterium from one of the most highly arsenicinfested area, and understanding the molecular significance behind such resistance. Very few studies have been conducted till date that identify and explain the genetics involved in arsenic resistance of bacteria thriving under such high arsenic environment. From the strains tested, an isolate belonging to the genus Exiguobacterium (9597% sequence identity) was identified following 16SrRNA analysis (=MTCC 7757T=JCM 13946T). Further phylogenetic analysis revealed that the isolated strain exhibited quantitative differences in fatty acid composition and phenotypic differences with other strains of the genus Exiguobacterium; the closest evolutionary affiliation was observed with E. aurantiacum. On the basis of phenotypic and genotypic data, it was proposed that the strain belonged to a new species of the genus Exiguobacterium. However, what was more fascinating was that it exhibited high arsenic resistance, as in arsenate (30mM) and arsenite (20mM). No other bacteria have previously been reported to survive under such high arsenic concentration in India. While analyzing the mechanism involved, we observed that the bacteria were able to convert arsenate in the media to arsenite followed by its extrusion as its survival strategy. Not only that the bacteria were able to gain growth advantage from the media supplemented with arsenate and not arsenite, as evident from a spur in growth rate. Microorganisms have evolved resistance strategies in order to counter the deleterious effects of arsenic; bacterial arsenic resistance systems are predominantly of the extrusion category, i.e. the metal ions are actively pumped out of cell. The genes encoding the resistance machineries are generally arranged in operons. The minimum set of genes required for arsenic resistance is arsB and arsC [13]. The presence of these two genes was verified in the Exiguobacterium strain identified by PCR followed by sequencing. The isolate was positive for arsB but negative for arsC, and therefore an arsC-independent novel gene may exist for the reduction of arsenate or the arsC in this bacterium has low similarity with previously published ones, and therefore the presence could not be detected. In this respect, it has been previously reported that the arrangement and sequences of the ars operon genes in different bacterial genera can vary considerably [27]. Given the fact that a positive response to arsenate reductase enzymatic activity for the bacterial strain in arsenate media was observed, such a conclusion is not at all redundant. We further analyzed the expression of arsB at the RNA level in arsenate-amended

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medium; which was found to be significantly elevated. Therefore it seems that the new bacterial strain identified, thrives in high arsenic concentrations by preferentially converting arsenate to arsenite by a hitherto unknown reductase and then extruding it out of the cell by arsB. Though a plasmid mediated resistance has been observed in some bacteria exhibiting resistance to arsenic, antimonite, these bacteria we identified were found not to harbor any plasmids; negating such an epigenetic mode of resistance [28]. Since the isolate was resistant to both As(III) and As(V) they could represent good candidates for bioremediation processes of native polluted sediments. This study further provides original results on levels of bacterial resistance to arsenic present in the aquifers Bengal delta plain and to assigning genera of bacterial strains isolated from arsenic-polluted sediments. V. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]

[28] [29] [30] [31] [32] [33] [34] [35]

ATSDR-Arsenic, Toxicological Profile for Arsenic (Update). U.S. Department of Public Health and Human Services, Public Health Service, Atlanta, GA., 2007. Chakraborti, D., et al., Groundwater arsenic contamination and its health effects in the Ganga-Meghna-Brahmaputra plain. J Environ Monit, 2004. 6(6): p. 74N-83N. Chowdhury R, S.A., Karak P, Chatterjee R, Giri AK, Chaudhuri K., Isolation and characterization of an arsenic-resistant bacterium from a bore-well in West Bengal, India. Annals of Microbiology, 2009. 59 (2) p. 1-6. Suresh, K., et al., Deinococcus indicus sp. nov., an arsenic-resistant bacterium from an aquifer in West Bengal, India. Int J Syst Evol Microbiol, 2004. 54(Pt 2): p. 457-61. Prithivirajsingh, S., S.K. Mishra, and A. Mahadevan, Detection and analysis of chromosomal arsenic resistance in Pseudomonas fluorescens strain MSP3. Biochem Biophys Res Commun, 2001. 280(5): p. 1393-401. Sato, T. and Y. Kobayashi, The ars operon in the skin element of Bacillus subtilis confers resistance to arsenate and arsenite. J Bacteriol, 1998. 180(7): p. 1655-61. Gihring, T.M., et al., Rapid arsenite oxidation by Thermus aquaticus and Thermus thermophilus: field and laboratory investigations. Environ Sci Technol, 2001. 35(19): p. 3857-62. Bansal, N., I. Sinha, and J.S. Virdi, Arsenic and cadmium resistance in environmental isolates of Yersinia enterocolitica and Yersinia intermedia. Can J Microbiol, 2000. 46(5): p. 481-4. Friedrich, W., E.J. Bormann, and U. Grafe, Isolation and biological properties of arsenate-resistant strains of Streptomyces noursei. Z Allg Mikrobiol, 1984. 24(1): p. 13-9. Niggemyer, A., et al., Isolation and characterization of a novel As(V)-reducing bacterium: implications for arsenic mobilization and the genus Desulfitobacterium. Appl Environ Microbiol, 2001. 67(12): p. 5568-80. Dopson, M., et al., Growth in sulfidic mineral environments: metal resistance mechanisms in acidophilic micro-organisms. Microbiology, 2003. 149(Pt 8): p. 1959-70. Osborne, F.H. and H.L. Enrlich, Oxidation of arsenite by a soil isolate of Alcaligenes. J Appl Bacteriol, 1976. 41(2): p. 295-305. Rosen, B.P., Resistance mechanisms to arsenicals and antimonials. J Basic Clin Physiol Pharmacol, 1995. 6(3-4): p. 251-63. Chowdhury, R., et al., In vitro and in vivo reduction of sodium arsenite induced toxicity by aqueous garlic extract. Food Chem Toxicol, 2008. 46(2): p. 740-751. Smibert, R.M.K., N. R Phenotypic characterization. In Methods for General and Molecular Bacteriology. American Society for Microbiology., 1994: p. 607-655. Sambrook J, F.E.F.M., T. , Mol. Cloning: A Laboratory Manual ed. Nolan C: New York: Cold Spr Harr LabPress., 1989. 1. Shivaji, S., et al., Isolation and identification of Pseudomonas spp. from Schirmacher Oasis, Antarctica. Appl Environ Microbiol, 1989. 55(3): p. 767-70. Schildkraut, C., Dependence of the melting temperature of DNA on salt concentration. Biopolymers, 1965. 3(2): p. 195-208. Sato, N.S.M., N., Membrane lipids. Methods Enzymol., 1988. 167: p. 251-259. Reddy, G.S., et al., Arthrobacter roseus sp. nov., a psychrophilic bacterium isolated from an antarctic cyanobacterial mat sample. Int J Syst Evol Microbiol, 2002. 52(Pt 3): p. 1017-21. Weisburg, W.G., et al., 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol, 1991. 173(2): p. 697-703. Kumar, S., K. Tamura, and M. Nei, MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment. Brief Bioinform, 2004. 5(2): p. 150-63. Anderson, C.R. and G.M. Cook, Isolation and characterization of arsenate-reducing bacteria from arsenic-contaminated sites in New Zealand. Curr Microbiol, 2004. 48(5): p. 341-7. WHO, Guidelines for drinking water quality. Health criteria & other supporting info. Geneva: WHO, 1996. 2nd ed,2: p. 940-94. Collins MD, L.B., Farrow JAE, Schleifer KH, Chemotaxonomic study of an alkalophilic bacterium, Exiguobacterium aurantiacum gen. nov. sp. nov. J Gen Microbiol, 1983. 129: p. 129:2037â&#x20AC;&#x201C;2042. Anawar, H.M., et al., Arsenic poisoning in groundwater: health risk and geochemical sources in Bangladesh. Environ Int, 2002. 27(7): p. 597-604. Butcher, B.G., S.M. Deane, and D.E. Rawlings, The chromosomal arsenic resistance genes of Thiobacillus ferrooxidans have an unusual arrangement and confer increased arsenic and antimony resistance to Escherichia coli. Appl Environ Microbiol, 2000. 66(5): p. 1826-33. Cervantes, C., et al., Resistance to arsenic compounds in microorganisms. FEMS Microbiol Rev, 1994. 15(4): p. 355-67. Fruhling, A., et al., Exiguobacterium undae sp. nov. & Exiguobacterium antarcticum sp. nov. Int J Syst Evol Microbiol, 2002. 52(Pt 4): p. 1171-6. Lopez-Cortes, A., et al., Exiguobacterium mexicanum sp. nov. and Exiguobacterium artemiae sp. nov., isolated from the brine shrimp Artemia franciscana. Syst Appl Microbiol, 2006. 29(3): p. 183-90. Crapart, S., et al., Exiguobacterium profundum sp. nov., a moderately thermophilic, lactic acid-producing bacterium isolated from a deep-sea hydrothermal vent. Int J Syst Evol Microbiol, 2007. 57(Pt 2): p. 287-92. Yumoto, I., et al., Exiguobacterium oxidotolerans sp. nov., a novel alkaliphile exhibiting high catalase activity. Int J Syst Evol Microbiol, 2004. 54(Pt 6): p. 2013-7. Chaturvedi, P., et al., Exiguobacterium soli sp. nov., a psychrophilic bacterium from the McMurdo Dry Valleys, Antarctica. Int J Syst Evol Microbiol, 2008. 58(Pt 10): p. 2447-53. Shivaji, S., et al., Bacillus arsenicus sp. nov., an arsenic-resistant bacterium isolated from a siderite concretion in West Bengal, India. Int J Syst Evol Microbiol, 2005. 55(Pt 3): p. 1123-7. Kim, I.G., et al., Exiguobacterium aestuarii sp. nov. and Exiguobacterium marinum sp. nov., isolated from a tidal flat of the Yellow Sea in Korea. Int J Syst Evol Microbiol, 2005. 55(Pt 2): p. 885-9.

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Available online at http://www.iasir.net

NATURAL OCCURRENCE of ASPERGILLI and PENICILLI and COCONTAMINATION of AFLATOXINS and STERIGMATOCYSTIN in SOME MARKET SAMPLES of WALNUT KERNELS from J&K (INDIA) Rohini Sharma 1 and Geeta Sumbali 2 Department of Botany, University of Jammu, Jammu-180006, India. Abstract: Walnut is a major nut of J&K State, which is both exported as well as largely consumed domestically. Mycoflora assessment of in-shell, shelled half and shelled broken kernels indicate that all the three grades are capable of harbouring toxigenic fungi and therefore may have mycotoxin contamination. Among the recovered mycoflora, Aspergillus and Penicillium species were the most dominant. Therefore, investigations was carried to determine contamination of aflatoxins B 1, B2 and sterigmatocystin in dried walnut kernels. Using TLC and HPLC methods, a total of 90 market samples were analysed for these mycotoxins. Maximum number of samples (23.3percent) were detected to be positive for aflatoxin B 2 contamination, followed in decreasing order by aflatoxin B1 (11.1percent) and sterigmatocystin (3.3percent). Co-contamination of AF’s and STC was found in 2.22% of the investigated samples. Although a large proportion of samples had fairly low levels of individual mycotoxins, yet it is of concern as the co-occurrence of mycotoxins may generate additive or synergistic effect in humans, especially if the respective grades are consumed on a daily basis. Key words: Walnut, aflatoxins B1, B2, sterigmatocystin, TLC, HPLC

I. INTRODUCTION Walnut (Juglans regia L.) is one of the finest nuts from temperate regions. It has high nutritional value, very positive effects on human health [1] , [2] and appreciated medicinal importance [3], [4]. Walnut is a significant component in a variety of traditional dishes that are eaten raw or roasted in some countries [5]. Additionally, it is of great economic value for the various food industries [6]. Unfortunately, walnut is sensitive to pre and postharvest fungal invasion and as a result of inappropriate storage conditions, it may be contaminated by toxigenic fungal species [7]. Infection can occur during growth, harvesting, transportation or storage of walnuts [8]. Numerous fungi have the ability to attack walnut during storage under improper conditions [9]. Among these, Aspergillus and Penicillium are the most dominant fungi that invade commoditized walnut [10]. These two common groups of storage fungi are involved in the production of mycotoxins [11] and simultaneously deteriorate dried commodities as they are able to grow at water activity (a w) as low as 0.64 [12]. Unfortunately, accumulation of such mycotoxic compounds could affect the nut quality [13], [14] and harm the consumer [15], [16]. The present study is aimed to investigate the natural occurrence of Aspergillus and Penicillium species associated with dried walnut kernels and the first report of co-occurrence of aflatoxins and sterigmatocystin detected from market samples of J&K state. II. MATERIALS AND METHODS A. Isolation and identification of aspergilli and penicilli associated with walnut kernels Market samples of walnuts kernels (in-shell, shelled half and shelled broken) were collected in pre-sterilized polythene bags from local markets of J&K. These sample bags were sealed over flame to avoid external contamination and brought to the laboratory for further studies. Aspergilli and penicilli associated with the walnut kernels were determined by following the method of [17]. In this method, 5g sample was taken in an Erlenmeyer flask (250 ml capacity), containing 45ml sterilized distilled water and shaken vigorously on a rotary shaker for 30 minutes to obtain a homogenous suspension. Ten fold serial dilutions were prepared and 1ml portion of suitable dilution was poured in petriplates by using a sterilized pipette. For recovery of maximum number of Aspergillus and Penicillium species from each sample, three different media- modified Czapek Dox agar (CDA), dichloran 18% glycerol agar (DG-18) and malt salt agar (MSA) were used. For each of these three media, 5 replicates were maintained. The medium was poured by making a gentle rotational movement of the petriplates to ensure uniform spreading of the sample. These petriplates were incubated for 7 days at 28±2 oC.

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After incubation, observations for fungal colonies were made. Identification of the isolated aspergilli and penicillia was done by using relevant literature [18], [19]. Percentage frequency of occurrence of each species was calculated as follows: Frequency (%) = Number of samples from which an organism was isolated X 100 Total number of samples tested B. Extraction of Aflatoxins and Sterigmatocystin From Walnut Kernels A total of 90 samples of various grades of walnut kernels were analysed for a flatoxin and sterigmatocystin contamination by using modified multimycotoxin method developed by Roberts and Patterson [20]. In this method, 25g of finely ground kernels were taken in an Erelenmeyer flask (250 ml capacity) and 100 ml mixture of acetonitrile and 4% potassium chloride (90:10 v/v) was added to it. The flask containing mixture was kept for horizontal shaking on a rotary shaker for 30 minutes. Thereafter, extract was filtered through Whatman no. 41 filter paper and the filtrate was defatted twice with 50 ml iso-octane in a separating funnel (250ml capacity). When the layers separated clearly, upper iso-octane layer was discarded and the lower acetonitrile layer was reextracted with 50ml iso-octane. Discarded the upper lipid containing layer and added 12.5ml distilled water to the lower acetonitrile layer. This layer was extracted thrice by using 20ml chloroform each time. Lower chloroform – acetonitrile layer was collected in a conical flask and drained through Whatman no. 41 filter paper having a bed of anhydrous sodium sulphate. The extract was collected in a beaker and marked as extract I. The aqueous layer left in the separating funnel was acidified with 1ml of 1.0N HCL and the acidic mycotoxins were extracted from it thrice by using 10 ml chloroform each time. Lower chloroform layers were combined, passed through anhydrous sodium sulphate bed, collected in a beaker and marked as extract II. Extracts I and II were combined and then evaporated to dryness on a water bath. After evaporation, the residue was dissolved in 1ml of chloroform and stored in small screw cap vials for qualitative and quantitative estimation of aflatoxins and sterigmatocystin. C. Estimation of aflatoxins (AF’s) Qualitative estimation of aflatoxins was done by spotting known amount of sample extracts (100µl) with the help of micropipette on the activated TLC plates. The aflatoxin standards (B 1 , B2 , G1 and G 2) were also spotted on the TLC plates as reference spots. The TLC plates were then developed with a solvent system consisting of toluene: isoamyl alcohol: methanol (90: 32 : 2 v/v). Developed plates were examined under long UV light (365 nm) and the various spots of aflatoxins were located and marked with a sharp needle after comparing their fluorescence colour and Rf value with the standard spots. Chemical confirmation of aflatoxins was done by spraying 0.25 % H2SO4 , which changed the blue fluorescent spots to yellow [21]. High Performance Liquid Chromatography (HPLC) was used for quantitative estimation of aflatoxins. This was done by standardizing the method described by Sigma -Aldrich [22]. The analytical equipment for HPLC consisted of a liquid chromatography pump (Class – LC -10 Schimadzu) , an autoinjection system SIL-10 A with a 50µl sample loop , a variable wavelength absorbance UV/VIS detector SPD-10 A set at 365 nm. The analytical column was C-18 (250 x 4.6 mm), filled with ODS (M) , RP-18 material, 5 µm particle size (Merck). The mobile phase consisted of acetonitrile : methanol : water (30:10:60), at a flow rate of 1.5 ml/min. Analysis was performed at room temperature (25-30o C) and data was recorded in HP Desk Jet 670. Injection volume for extract solution varied between 10-40 µl. Estimation was done by comparison of retention time (afla B 1-6.2 min and B2-5.2 min) and peak areas observed in the aflatoxin standards with those observed for samples. D. Estimation of sterigmatocystin (STC) Detection of sterigmatocystin was done by following the method of Athnasios and Kuhn [23]. In this method, 100µl of sample extracts were spotted on TLC plates along with the standard. The plates were developed in a solvent system consisting of benzene : acetic acid (9:1 v/v), air dried and sprayed with 20% AlCl 3 solution and heated for 10 minutes at 80ºC. Sterigmatocystin spots were located under short wave UV light as yellow fluorescent spots. Quantitative estimation of sterigmatocystin was done by modifying the HPLC method given by Engelhart et al [24]. The mobile phase consisted of methanol: water (80:20v/v) at a flow rate of 1.5ml/min. A variable wavelength UV/VIS detector set at 365 nm was used. Injection volume for extract solution was 10µl. Quantification was done by comparing retention time 3.9 min and peak area observed in the standard with those observed for samples. III. RESULTS AND DISCUSSION A. Aspergilli and Penicilli associated with Walnut kernels During the present investigation, Aspergillus and Penicillium species were the most dominant represented by 25 and 34 species respectively (Table 1). Some of the common xerophilic aspergilli and penicilli recovered from the walnut samples included Aspergillus flavus, A. niger, A. japonicus, A. fumigatus, A. ochraceus, A. versicolor, A. tamarii, A. sydowii, Penicillium brevicompactum, P. chrysogenum, P. citrinum, P. fellutanum,

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P.griseofulvum and P.waksmanii. Dominance of Aspergillus and Penicillium species from dried fruits and nuts has also been reported earlier by Sekar et al. [25] and Kumar et al. [26]. Species of Aspergillus and Penicillium are considered versatile in their water activity (aw) requirements and these are most numerous in low aw habitats [27]. Aspergilli and penicilli grow on a large number of substrates and their ability to thrive at high temperatures (30-40oC) and relatively low available water (xerophilic nature) makes them well suited to colonize a number of nut crops. The maximum number of species of penicilli and aspergilli were isolated from in-shell samples (36) followed in decreasing order from shelled broken (33) and shelled half kernels (25). These results show that post-harvest operations have a major influence on the walnut mycoflora as they were found to be colonized by several species of Aspergillus and Penicillium. As walnuts contain substantial amount of fatty acids and other growth promoting elements, the invading mycoflora deteriorate the quality of walnuts during prolonged storage. Further, differences in the number and type of aspergilli and penicilli associated with the three grades of walnut kernels may be due to the fact that the shelled half kernels and shelled broken kernels undergo processes like sun-drying and sorting before marketing, which leaves behind lesser number of contaminated kernels. However, differences detected in the mycoflora of shelled half kernels and shelled broken kernels might be due to the fact that the broken grade of walnut kernel provides larger surface area for the colonization of fungal species than that of the shelled half kernels. Perusal of results (Figure 1) also show that the maximum number of fungal species (36) were associated with the in-shell grade of walnuts as it provides natural congenial atmosphere for the growth of fungal species on the kernels within the shell. In addition, temperature and humidity are also maintained within the shell, which favour the growth and multiplication of a large number of fungal species. B. Mycotoxins associated with walnut kernels (a) Detection of Aflatoxins During the present investigation, only two members of the aflatoxin group, that is, AFB 1 and AFB2 were detected as contaminants of some of the samples of in-shell, shelled half and shelled broken kernels of walnut. This is probably due to the presence of A. flavus as a dominant contaminant in all the screened samples (Table 1). However, inspite of the dominance of A. flavus in the investigated grades of walnut kernels, the magnitude of AFB1 and AFB2 contamination varied with the type of kernel grade, storage conditions and aflatoxigenic potential of the A. flavus strains. Earlier, toxigenic strains of A. flavus have been reported to produce aflatoxins in nuts and other dried fruits when the environmental conditions are favourable for their growth [28]. During the present investigation, 23.33% of the samples were detected to be positive for AFB 2 and only 11.11% samples were positive for AFB1 contamination (Figure 2). Inspite of the high frequency percent of A. flavus in all the three grades of walnuts (Tables 1), production of aflatoxins in these grades was comparatively low (Table 2). Earlier, Abbas et al. [29] also recorded great variation in aflatoxin production by A. flavus. Other researchers who have reported aflatoxin contamination from walnuts and other nuts include Andrade [30], Khodavaisy et al. [31] and Abdulla [32]. While investigating the walnut kernels of J&K state mycoflora, A. parasiticus, which is reported to produce AFG1 and AFG2 [33] was detected in only 17 percent of the shelled half samples (Table 1). However, all the A. parasiticus positive samples were found to be negative for AFG 1 and AFG2 contamination, which may probably be due to the association of atoxigenic strains of A. parasiticus or due to the unfavourable storage conditions. Earlier, Steiner et al. [34] and Juan et al. [35] detected AFG1 contamination from dried figs. As depicted in figure 3, among the in-shell kernels of walnuts, incidence of AFB2 contamination was more than that of AFB1 contamination. However, concentration of AFB 1 was detected to be higher in the in-shell kernels and ranged between 0.123-0.246 µg/g, whereas the concentration of AFB2 ranged between 0.064-0.233 µg/g (Table 2). Only two aflatoxin positive samples were detected to have both AFB1 and AFB2 contamination. In both these samples, concentration of AFB1 was detected to be higher than that of AFB 2. Earlier, Fuller et al [36] and Luttfullah and Hussain [37] detected more than 1 µg/kg of AF’s in the in-shell walnut samples procured from California and Pakistan. Perusal of data presented in table 2 shows that the shelled half kernels were even more contaminated with aflatoxins than the in-shell walnut kernels. Among the investigated samples of shelled half kernels, only 10.0% samples were positive for AFB 1 contamination, whereas AFB2 was detected from 30.0% samples. The contamination range of AFB 1 was higher (0.391-0.983 µg/g) than that of AFB2 (0.146-0.297 µg/g), which was however detected in more number of investigated samples (Table 1). Only three samples were detected to have both AFB1 and AFB2 contamination (Table 1). In case of shelled broken kernels, it was found that A. flavus possessed lower frequency percentage (33 percent) in comparison to other two grades of walnut kernels (Table 1). Corroborating with these results, it was detected through HPLC analysis that 16.66 percent samples of the broken kernels were contaminated with AFB 1 (0.45-4.55µg/g), whereas AFB2 was found to be present in 20.0 percent of the investigated samples and the contamination range varied from 0.19- 0.51µg/g. Low spore count of A. flavus and aflatoxin contamination has also been detected from pistachio nuts and almonds [38]. Detection of aflatoxins in shelled broken kernels is of great concern as this particular grade of walnut is commonly used in bakery and confectionary and may affect the health of consumers.

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Levels of AFB1 and AFB2 detected in aflatoxin positive samples of in-shell and shelled half kernels were below the permissible limits, which may be due to the presence of antioxidants within the walnut kernels. According to Molyneux et al. [39], presence of specific antioxidant hydrolysable tannin in walnuts can suppress AF’s formation. The antioxidants present in the nuts also play an important role against microbial growth and proliferation [40], [41]. Earlier, Patterson et al. [42] reported that many fungi that produce mycotoxins can also degrade them under specific conditions. However, the maximum level for AFB1 in dried fruit for human consumption is 2 µg/kg, for total aflatoxins it is 4 µg/kg and for dried fruit to be subjected to sorting or other physical treatment, the maximum levels are 5 µg/kg for AFB 1 and 10 µg/kg for total aflatoxins [43]. (b) Detection of Sterigmatocystin Sterigmatocystin (STC) is a mycotoxin produced by many Aspergillus species viz., A. nidulans, A. sydowii and A. versicolor . Only 6.66% samples of in-shell kernels were detected with contamination of sterigmatocystin, which varied from 1.133-2.237 µg/g (Table 1). However, only one sample (3.33%) of shelled half kernels was found to be positive for sterigmatocystin with concentration of 1.709 µg/g and no sample of shelled broken kernel was contaminated with this mycotoxin (Figure 3). Absence of STC in the shelled broken kernels was quite surprising as these samples harboured Aspergillus versicolor, which is known producer of STC. Sterigmatocystin is a carcinogenic compound and classified as 2B carcinogen by the International Agency for Research on Cancer [44]. There are many reports about toxicity and mutagenicity of STC [45], [46]. STC is a precursor of AFB1 during biological transformation [47]. As shown in figure 2, very few samples of walnut kernels were found to be contaminated with STC and therefore there is no significant risk to the consumers. During the present investigation, co-occurrence of aflatoxins and sterigmatocystin was also observed in 2.22% of the investigated samples. Further, it was also found that in such samples, amount of sterigmatocystin was much more than that of aflatoxins (Table 1). IV. CONCLUSION All these facts allow a conclusion that different grades of walnuts that are sold in J&K markets are susceptible to fungal deterioration and mycotoxin contamination, which could be due to the favourable climatic conditions especially humidity and temperature. Although a large proportion of samples had fairly low levels of individual mycotoxins, this should be of concern as the co-occurrence of these mycotoxins may generate additive or synergistic effect in humans, especially if the respective grades are consumed on a daily basis. ACKNOWLEDGEMENT The authors are thankful to Head, Department of Botany, University of Jammu for providing laboratory facilities. REFERENCES [1] [2] [3]

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Kuhn, “Improved thin layer chromatographic for the isolation and estimation of sterigmatocystin in grains”, J. Assoc. Off. Anal. Chem, 60, 1977, 104-106. S. Engelhart , A. Loock , D. Skutlarek, H. Saqunski, A. Lommel, H. Farber and M. Exner, “Occurrence of toxigenic Aspergillus versicolor isolates and sterigmatocystin in carpet dust from damp indoor environments”, Appl. Environ. Microbiol, 68, 2002, 3886-90. P. Sekar, N. Yumnam and K. Ponmurugan, “Screening and characterization of mycotoxin producing fungi from dried fruits and grains”, Adv. Biotech, 2008, 12-15. A. Kumar, R. Shukla, P. Singh, B. Prakash and N.K. Dubey, “Chemical composition of Ocimum basilicum L. essential oil and its efficacy as a preservative against fungal and aflatoxin contamination of dry fruits”, Int. J. Food Sci. and Tech, 46,2011, 18401846. A.D.Hocking , “Xerophilic fungi in intermediate and low moisture foods”, In: Handbook of applied mycology. Vol. 3. Foods and feeds. (eds. Arora D.K., Mukerji, K.G. and Marth, E.H.), Marcel Dekker Inc, 1991. K.R.N. Reddy, H.K. Abbas, C.A. Abel, W.T. Shier, C.A.F. Oliveria and C.R. Raghavender, (2009). “Mycotoxin contamination of commercially important agricultural commodities”, Toxin Rev, 28, 2009, 154-168. H.K. Abbas , M.A. Weaver, R.M. Zablotowicz , B.W. Horn and W.T.Shier, “Relationships between aflatoxin production and sclerotia formation among isolates of Aspergillus section Flavi from Mississippi Delta”, Europ. J. Pl. Pathol, 112, 2005, 283-287. P.D. Andrade , M. Homem de Mello, J.A.Franca and E.D. Caldas, “Aflatoxins in food products consumed in Brazil: a preliminary dietary risk assessment”, Food Addit. Contam, 2012, 1-10. S. Khodavaisy, A. Maleki, B. Hossainzade, S. Rezai, F. Ahmadi, A. Validi, A. Rashidi and E. Ghahramani, “Occurrence of fungal contamination in pistachio and peanut samples from retail shops in Sanandaj province, Iran”, Afr. J. Microbiol. Res, 6, 2012, 6781-6784. N.Q.F. Abdulla, “Evaluation of fungal flora and mycotoxin in some important nut products in Erbil local markets”, Res. J. Environ. Earth Sci, 5, 2013, 330-336. J.C. Frisvad, U. Thrane and R.A. Samson, (2007). “Mycotoxins producers. In: Food mycology”, A multifaceted approach to fungi and food. (eds. Dijksterhuis, J. and Samson, R.A.), CRC Press, Taylor and Francis group: Boca Raton, FL, USA, pp. 135159,2007. W.E.Steiner, R.H. Rieker and R. Battaglia, (1988). “Aflatoxin contamination in dried figs: distribution and association with fluorescence”, J. Agric. Food Chem, 36,1988, 88-91. C. Juan, A. Zinedine, J.C. Molto, l. Idrissi L and Manes J (2008). Aflatoxins levels in dried fruits and nuts from Rabat-Sale area, Morocco. Food Contr. 19: 849-853. G. Fuller, W.W. Spooncer , A.D. King, J. Schade and B. Mackey, “Survey of aflatoxins in California tree nuts”, Journ. American Oil Chem. Soc, 54, 1977, 231-234. G. Luttfullah and A. Hussain, “Studies on contamination level of aflatoxins in some dried fruits and nuts of Pakistan”, Food Contr, 22,2011, 426-429. K.R.N. Reddy, N.I. Farhana and B. Salleh , “Occurrence of Aspergillus spp. and Aflatoxin B1 in Malaysian foods used for human consumption”, J. Food, Sci, 76,2011, 99-104. R.J. Molyneux, N. Mahoney, J.H.Kim and B.C.Campbell, “Mycotoxins in edible tree nuts”, Int. Food Microbiol, 119, 2007, 72-78. R. Puupponen-Pimia, L. Nohynek , C. Meier, M. Kahkonen, M. Heinonen, A. Hopia and K.M. Oksman-Caldentey, “Antimicrobial properties of phenolic compounds from berries”, J. Appl. Microbiol, 90,2001, 494-507. B. Prakash , R. Shukla, P. Singh, A. Kumar, P.K. Mishra and N.K.Dubey, “Efficacy of chemically characterized Piper betle L. essential oil against fungal and aflatoxin contamination of some edible commodities and its antioxidant activity”, Int. J. Food Microbiol, 142,2010,114-119. R.R.M. Paterson, A. Venancio and N. Lima N, “Mycotoxins- the experience and expertise of Micoteca da Universidade do Minho. In Biological resource centres and the use of microbes, European Culture Colections Organisations XXII. pp. 217-233, 2003. [EC] European Commission, “EC401/2006, laying down the methods of sampling and analysis for the official control of the levels of mycotoxins in foodstuffs”, Off. J. Europ. Union, 70, 2006, 12-34. [IARC] International Agency for Research on Cancer, “IARC Monographs on the evaluation of carcinogenic risks to humans. Summaries and evaluations, sterigmatocystin. Suppl. 7, 7 1987. M. Sekijima, D. Whong, H. Watabe, K. Sugai, S. Sekita and Y. Ueno, “Mutagenicity of sterigmatocystin and related compounds”, Mut. Res. Environ. Mutagen. and Related Subjects, 272, 1992, 281-282. V. Sivakumar, J. Thanislass , S. Niranjali and H. Devaraj, “Lipid peroxidation as a possible secondary mechanism of sterigmatocystin toxicity”, Human and Experi. Toxicology, 20, 2001, 398-403. H.H Wilkinson, A. Ramaswamy, S,C. Sim and N.P.Keller, “Increased conidiation associated with progression along the sterigmatocystin biosynthetic pathway”, Mycolog, 96,2004, 1190-1198.

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Table 1. Percentage frequency of Aspergillus and Penicillium species recovered from dried walnut kernels. Fungal Species Aspergillus candidus A. carneus A.chevalieri A. clavatus A.ficuum A.flavipes A. flavus A. flavus var. columnaris A. fumigatus A. glaucus A. japonicus A. niger A. ochraceus A. oryzae A.parasiticus A. penicilloides A.subolivaceus A. sulphureus A. sydowii A.tamari A. terreus A.terricola var. americana A.terricola var.indicus A.tubingensis A. ustus A. versicolor A.wentii Penicillium arenicola P. aurantiogriseum P. brevicompactum P. canescens P. chrysogenum P. citrinum P.corylophilum P. expansum P. fellutanum P.fennelliae P. griseofulvum P.griseoroseum P. granulatum P.hirsutum P.islandicum P. italicum P.janczewskii P. dalae P. melinii P.miczynskii P. olivicolor P. olsoni P. oxalicum P.paxilli P.piceum P.pinophilum P. puberulum P. purpurogenum P.restrictum P. variabile

In-shell kernels

Shelled half kernels

Shelled broken kernels

17.0 14.0 14.0 53.0 23.0 17.0 13.0 33.0 63.0 20.0 17.0 7.0 7.0 17.0 17.0 13.0 17.0 10.0 7.0 37.0 3.0 33.0 27.0 3.0 23.0 20.0 7.0 7.0 7.0 3.0 7.0 17.0 10.0 7.0 10.0 -

3.0 10.0 13.0 7.0 40.0 17.0 50.0 17.0 7.0 10.0 10.0 17.0 23.0 23.0 17.0 13.0 7.0 13.0 3.0 7.0 3.0 13.0

17.0 10.0 33.0 33.0 17.0 43.0 17.0 17.0 17.0 7.0 10.0 10.0 13.0 7.0 27.0 20.0 20.0 10.0 7.0 7.0 7.0 10.0 7.0

13.0 7.0 7.0 3.0 20.0 13.0 10.0 -

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Rohini Sharma et al., American International Journal of Research in Formal, Applied & Natural Sciences, 7(1), June-August, 2014, pp. 2836 P.velutinum P.verrucosum P. viridicatum P. waksmanii

33.0

10.0 10.0 23.0

3.0 7.0 7.0 -

Table 2. HPLC analysis of walnut kernels for aflatoxin (AFB 1 and AFB2) and sterigmatocystin (STC) contamination (µg/g). IN-SHELL KERNELS

SAMPLES ANALYSED

SHELLED HALF KERNELS

SHELLED BROKEN KERNELS

AFB1

AFB2

STC

AFB1

AFB2

STC

AFB1

AFB2

STC

4.556

0.233

2.237

0.195

0.275

0.391

0.288

0.261

0.297

0.070

0.146

0.256

0.480

0.512

0.246

0.134

0.129

0.901

0.282

0.064

0.269

0.123

0.067

0.983

0.257

1.709

0.486

0.263

0.455

1.133

0.206

0.494

0.258

0.269

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No. of positive samples %age of positive samples

2 6.66

6 20.0

2 6.66

3 10.0

9 30.0

1 3.33

5 16.66

6 20.0

25 20 15 Aspergillus 10

Penicillium

0 In-shell

Shelled half

Shelled broken

Figure 1: Total number of Aspergillus and Penicillium recovered from market samples of dried walnuts kernels.

3.30% 11.11%

AFB1 AFB2 STC

23.33%

Figure 2: Percentage of walnut kernel samples contaminated with aflatoxins (AFB 1 and AFB2) and sterigmatocystintoxins.

9 8 7 6 5 4 3 2 1 0

IN-SHELL

SHELLED HALF SHELLED BROKEN

AFB1

AFB2

STC

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Figure 3: Mycotoxin contamination (Âľg/g) detected from market samples of in-shell, shelled half and shelled broken walnut kernels.

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

ANTIBACTERIAL ACTIVITY OF SOME PLANT EXTRACTS ALONG WITH ANTIOXIDANT ACTIVITY OF POTENT ONES Upma Srivastava, Swati Ojha, Pooja Singh* and N N Tripathi Department of Botany, DDU Gorakhpur University, Gorakhpur-273009, India Abstract: The in vitro antibacterial activity of 14 methanolic extracts was investigated by disc diffusion method against a gram negative bacteria Staphylococcus aureus. Amongst the extracts tested, the extracts of Datura stromonium, Ocimum basilicum, Cymbopogon citratus and Eucalyptus sideroxylon showed significant antibacterial activity against the bacterial pathogen. D. stromonium showed highest antibacterial activity followed by O. basilicum extract. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay was used to determine the antioxidant activity of potent extract. D. stromonium showed the appreciable antioxidant activity. The highest antioxidant activity was observed at 60 mg/ml concentration with a percent inhibition of 71.66 and IC50 value 9.71 mg/ml. Moreover, the radical scavenging activity of extract was lower than that observed for the synthetic antioxidant BHA and BHT. The results provide evidence that the extract of D. stromonium and O. basilicum can be further recommended in the treatment of the infections caused by the bacterial pathogen and D. stromonium is a potential source of natural antioxidants. I. INTRODUCTION From antiquity, nature has been a rich store of remedies for relief from various ailments affecting mankind. Plants have been used for thousands of years in traditional medicine. The use of plants for treating diseases is as old as the human species. Plants produce a wide variety of secondary metabolites such as vitamins, terpenoids, tannins, flavonoids, alkaloids and other metabolites, which are rich in antimicrobial and antioxidant activities [1] [2]. Popular observations on the use and efficacy of medicinal plants significantly contribute to the disclosure of their therapeutic properties, so that they are frequently prescribed, even if their chemical constituents are not always completely known. A number of plants have been documented for their biological [3] [4] and antimicrobial properties [5] [6]. It can be assumed, that although the bulk of traditional antibiotics can still manage drug-resistant bacteria, many commonly used antibiotics are no longer effective [7] [8]. Bacteria have the genetic ability to transmit and acquire resistance to drugs, which are utilized as therapeutic agents. Drug resistance can be described as a state of decreased sensitivity to drugs that ordinarily cause growth inhibition or cell death. More strains of pathogens have become antibiotic resistant, and some have become resistant to several antibiotics and chemotherapeutic agents, the phenomenon of multidrug resistance. Limited treatment options for infections caused by such multiresistant microorganisms prompted the search for novel compounds with a broad spectrum of activity and new therapeutic strategies. In an effort to expand the spectrum of antimicrobial agents from natural resources, ten medicinal plants belonging to seven families, have been selected to assess their antibacterial potential. II. MATERIALS AND METHODS Plant materials: The leaves 15 plants were collected from different regions of Gorakhpur district. The leaves were plucked and packed in polythene bags. Plants were initially identified by morphological features and then confirmed from the herbarium database present in the herbarium of DDU Gorakhpur University Gorakhpur. The scientific names and family of the 16 plant materials are detailed in Table 1. Preparation of Plant material: The fresh leaves were washed with tap water and then with 90 per cent alcohol, chopped into smaller pieces with a knife and then kept in the shade for 14 days to dry and then crushed using pestle and mortar and further reduced to powder using electric blender and then stored in airtight closed bottles until tested and analyzed. Extraction procedure: 10 g of the powdered sample of the plant was soaked in 100 ml of methanol in a 250 ml conical flask at room temperature with shaking after every 4 for 24 h. The extract was filtered using muslin cloth and then Whatman no.1 filter paper. The filtrates were then evaporated to dryness in a rotary evaporator maintained to remove residual solvents and then stored in screw capped bottles for further use. The extracted powder was resuspended in the methanol at desired concentrations before it was tested for the antibacterial activity.

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Microbial strains and Preparation of inoculums: Gram positive bacteria Staphylococcus aureus (MTCC No. 9542) was used for evaluation of antibacterial assay. The stock culture was maintained in nutrient agar (NA) slant at 4°C and sub-cultured monthly. Working cultures were prepared by inoculating a loopful of each test microorganism in 10 ml of nutrient broth (NB) from NA slants. Broths were incubated at 37°C for 18-20 hours. The suspension was diluted with sterile distilled water to obtain approximately 10 6 CFU/ml. Determination of antibacterial activity: The antibacterial activity of plant extracts was evaluated using disc diffusion method [9]. 10ml of sterilized nutrient agar medium was poured in 80mm Petridishes and was allowed to solidify. The plates were seeded by spreading 0.1 ml of overnight inoculum and allowed to set for 20-25mins. For, screening, sterile, 6mm diameter filter paper discs were soaked in plant extracts at 500 µg/ml concentration and placed on the surface of inoculated media agar plates using sterile forceps and then gently pressed down onto the agar surface. Disk soaked with the solvent was used as control. The positive control plates were inoculated with test organism. All the plates were incubated at 35-37ºC for 24h. Clear inhibition zones around the discs indicated the presence of antibacterial activity. Diameter of inhibition zones were measured in millimeters. An inhibition zone of 10mm or more was considered as high antibacterial activity. Determination of MIC (Minimum inhibitory concentration) values: The minimum inhibitory concentration value for bacterial pathogen was determined by agar dilution technique of CLSI with slight modifications [10]. A series of twofolds dilution of extract concentrations (25 µg/ml- 3200 µg /ml) was prepared in Petridishes. 10ml of sterilized and molten nutrient agar medium was poured in each dish already containing 100µl amount of extracts. Plates were dried at 35ºC for 30minutes prior to spot inoculation with 5µl of overnight bacterial culture (adjusted to 0.5 MacFarland standard) containing approximately 10 6 CFU/spot using an sterilized inoculating loop. Nutrient agar with solvent was used as positive control. The inoculum spots were allowed to dry at room temperature and plates were incubated at 35-37ºC for 24h. MICs were determined as the lowest concentration of oil inhibiting the visible growth of microorganisms on agar plate disregarding the presence of 1 or 2 colonies. Determination of MBC (Minimum bactericidal concentration) values: The MBC of the extracts was determined as described by Mishra et al.,[11]. Fresh nutrient agar medium was poured into Petriplates and allowed to solidify. Inoculum from various poisoned plates of MIC experiment showing no growth was submitted to subculture on freshly prepared plates. The lowest concentration of antimicrobial agent from which bacteria do not recover on fresh medium was treated as MBC. DPPH free radical scavenging activity: Effect of extracts on DPPH radical was estimated using method of Güllüce et al. [12] with slight modifications. 0.004% of DPPH (Hi Media) was prepared in methanol and 2ml of this solution was mixed with different concentrations of extracts (10, 20, 30, 40, 50 and 60 mg/ml) dissolved in methanol. Reaction mixture was vortexed thoroughly and left for 30mins. After 30mins absorbance of the mixture was measured at 517nm in an UV spectrophotometer (Hitachi) against a blank (pure methanol). Control sample was also prepared as above without any oil. Ascorbic acid, BHT (Butylated hydroxytoluene) and BHA (Butylated hydroxyanisole) was taken as reference standards. Experiments were performed in triplicate and averaged. IC 50 value was determined from percent inhibition versus concentration graph. Percent inhibition was calculated from control using following equation: [Abscontrol- Abssample] Radical scavenging activity (%) = × 100 Abscontrol Where, Abscontrol= Absorbance of DPPH radical + methanol Abssample = Absorbance of DPPH radical + sample oil/standard III. RESULTS Antibacterial activity: Results from antibacterial disc diffusion assay are summarized in Table 1. Some of the extracts showed moderate to high inhibiting activity while most of the extracts did not found effective against tested the tested bacterial pathogen. The zones of inhibition ranged from 10-30mm. Results showed that Datura stramonium and Ocimum basilicum extracts showed significant antibacterial activity against the bacteria tested. Additionally, the extracts of Citrus aurantifolia, Cymbopogon citratus and Eucalyptus sideroxylon also showed moderated inhibitory activity. D. stramonium showed highest activity forming 30mm zone of inhibition against Staphylococcus aureus followed by O. basilicum which formed 16.67mm inhibition zone. The zone of inhibition formed by other extracts was negligible. Furthermore, the antibacterial activity of most effective extract against Staph. aureus quantitatively was assayed by determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC).

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Table 1: Antibacterial activity of different plant extracts against Staph. aureus based on Disc Diffusion Method Plant extracts Cymbopogon citratus (DC.) Stapf Datura stramonium (LINN.) Eucalyptus sideroxylon (Cunn.) Euphorbia hirta Linn. Haemelia patens (Jacq.) Hyptis suaveolens (Linn.) Poit. Ocimum basilicum Linn. Ocimum canum Linn. Ocimum gratissimum Linn. Ocimum sanctum Linn. Piper longum Linn. Piper methysticum G.Forst.

Family Poaceae Solanaceae Myrtaceae Euphorbiaceae Rubiaceae Lamiaceae Lamiaceae Lamiaceae Lamiaceae Lamiaceae Piperaceae Piperaceae

Zone of Inhibition (mm) 11.33±0.46 30.66±0.45 14±0.81 16.67±0.94 -

- no visible zone of inhibition Minimum inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC): D. stramonium exhibited strong action against Staph. aureus with MIC value of 800 µg/ml followed by the extract O. basilicum with 1600 µg/ml MIC value (Table 2). MBC values were found to be 1600 µg/ml and 3200 µg/ml for D. stramonium and Ocimum basilicum respectively (Table 2). Table 2. MIC and MBC data of D. stramonium and O.basilicum extracts against Staph. aureus µg/ml. Bacterial strain Staph. aureus

D. stramonium

O. basilicum

MIC

MBC

MIC

MBC

800

≤1600

1600

≤3200

DPPH radical scavenging assay: The DPPH radical scavenging activity of most potent extract, D. stramonium and references are shown in Figure1. D. stramonium methanolic extarct notably reduced the concentration of DPPH free radical, with an efficacy lower than that of reference BHA (Butylated hydroxyl anisole) and BHT (Butylated hydroxytoluene). The results showed significant decrease in the concentration of DPPH free radical due to the scavenging ability of extract and reference. Decrease in concentration of DPPH was observed with the increase in concentration of extract. The highest antioxidant activity was observed at 60µg/ml concentration (75.66%). IC 50 value of extract was found to be 9.17 mg/ml. IV. DISCUSSION The present study was designed to obtain preliminary information on the antibacterial l activity of some methanolic plant extracts. Disc diffusion method was used in this study. Out of 12 extracts tested, only methanolic extracts of D. stramonium and O. basilicum exhibited good antibacterial activity and gave zone of inhibition followed by the methanolic extracts

Fig1: Free radical scavenging activity of extracts at different concentrations and reference antioxidants showing highest percent inhibition of DPPH radical.

of C. aurantifolia, C. citratus and E. sideroxylon against Staph. aureus Reference 13 reported that methanol was the most effective solvent for plant extraction than any other solvents. Reference 14 also found methanol as the most effective solvent. D. stramonium showed highest inhibitory activity against bacterial pathogen. The present study is comparable with the reports of Sharma and Sharma [15] and Johnson et al., [16] and Sreenivasa et al., [17]. O. basilicum extarct was proved good in inhibiting E. coli after D. stramonium as reported by Hossain et al., [18]. It has been hypothesized that the inhibition involves phenolic compounds, because these compounds sensitize the phospholipid bilayer of the microbial cytoplasmic membrane causing increased permeability, unavailability of vital

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intracellular constituents and impairment of bacterial enzymes. Though the minimum inhibitory concentration is high, nevertheless it showed that plant extract under invitro study has broad antibacterial activity. Generally, it is well known that Gram negative bacteria are more resistant than Gram positive bacteria. Many studies demonstrated correlation between phenolis content and antioxidant activity [19]. On the other hand Bajpai et al., [20] reported no correlation between total phenolic content and antioxidant capacities of a number of medicinal plant extracts. The phenolic compounds may contribute directly to the antioxidative action. DPPH is a stable free radical which accepts an electron or hydrogen radical to become a stable diamagnetic molecule, which is widely used to investigate radical-scavenging activity. In DPPH radicalscavenging assay, antioxidants react with DPPH, and convert it to yellow coloured α,α-diphenyl-β-picryl hydrazine. The degree of discolouration indicates the radical-scavenging potential of the antioxidant activities [21]. Figure1. Shows DPPH scavenging activity of D. stramonium extracts at different concentrations in comparison with well known synthetic antioxidants. The antioxidant activity reflected by the DPPH radical scavenging assay was clearly observed in the methanolic leaf extract of D. stramonium. D. stramonium extract was found to have good antioxidant activity as well promising antibacterial activity. The DPPH assay proved that the antioxidant activity of the extract was appreciable. It can be used as potential source of natural antioxidants. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

[13] [14] [15] [16]

[17] [18]

[19] [20] [21]

CC Wong, HB Li, KW Cheng, and F Chen (2006). “A systematic survey of antioxidant acitivity of 30 chinese medicinal plants using the ferric reducing antioxidant power assay”. Food Chem., 97, 705-711. JC Baker, RA Owens, BD Whitaker, NM Mock, DP Roberts, KL Deahl and AA Aver’yanov (2010).“Effect of viroid infection on the dynamics of phenolic metabolites in the apoplast of tomato leaves”. Physiol. Mol. Plant Pat., 74, 214-220. JK Grover, S Yadav and V Vats (2002).“Medicinal plants of India with anti-diabetic potential”. J. Ethnopharmacol., 81, 81–100. HP Gajera, SV Patel and BA Golakiya (2005). “Antioxidant properties of some therapeutically active medicinal plants— an overview”. JMAPS., 27, 91–100. DS Arora DS (1998). “Antimicrobial activity of tea (Camellia sinensis)”. Antibiot. Chemother., 2, 4–5. EA Polambo and SJ Semple (2001). “Antibacterial activity of traditional Australian medicinal plants”. J. Ethnopharmacol., 77, 151– 157. SB Levy (1998), “The challenge of antibiotics resistance”. Sci. Am., 278, 46-53. GD Wright (2010), “Antibiotic resistance in the environment: a link to the clinic?”. Curr. Opin. Microbiol., 13, 589-594. NCCLS (National Committee for Clinical Laboratory Standards) (1997), “Performance Standards for Antimicrobial Disk Susceptibility Test”. Approved Standard M2-A6: Wayne PA. National Committee for Clinical Laboratory Standards (2002). “Performance standards for antimicrobial disk and dilution susceptibility tests for bacteria isolated from animals”. AK Mishra, A Mishra, S Tripathi and NN Tripathi (2008). “Susceptibility of Enterococcus faecalis to plant volatiles oils”. J. Microb. World, 10,108-112. Güllüce M., M. Sökmen, D. Daferera, G. Ağar, H. Ozkan, N. Kartal, M. Polissiou, A Sökmen and F. Sahin (2003), “In vitro antibacterial, antifungal, and antioxidant activities of the essential oil and methanol extracts of herbal parts and callus cultures of Satureja hortensis L”. J. Agric. Food Chem., 51, 3958-65. JN Eloff (1998). “Which extractant should be used for the screening and isolation of antimicrobial components from plants?” J. Ethnopharmacol., 60, 1-8. M Soniya, T Kuberan, S Anitha and P Sankareswari (2013). “In vitro antibacterial activity of plant extracts against Gram positive and Gram negative pathogenic bacteria”. International Journal of Microbiology and Immunology Research. 2, 001-005. Pallavi Sharma and Ram Avatar Sharma (2013). “Comparative Antimicrobial Activity and Phytochemical Analysis of Datura stramonium L. Plant Extracts and Callus In vitro”. European Journal of Medicinal Plants. 3, 281-287. D Benito Johnson, B N Shringib, Dinesh Kumar Patidar, Nehru Sai Suresh Chalichem, Ashok Kumar Javvadi (2011). “Screening of Antimicrobial Activity of Alcoholic & Aqueous Extract of Some Indigenous Plants”. Indo-Global Journal of Pharmaceutical Sciences, 1, 186-193. S Sreenivasa, K Vinay, NR Mohan (2012). “Phytochemical Analysis, Antibacterial and Antioxidant Activity of Leaf Extract of Datura Stramonium”. International Journal of Science Research, 01, 83-86. MA Hossain, MJ Kabir, SM Salehuddin, SM Rahman, AK Das, SK Singha, MK Alam and A Rahman (2010). “Antibacterial properties of essential oils and methanol extracts of sweet basil Ocimum basilicum occurring in Bangladesh”. Pharm Biol., 48, 504-11. doi: 10.3109/13880200903190977 JH Yang, H.C. Lin and J.L. Mau (2002). “Antioxidant properties of several commercial mushrooms”. Food Chem., 77, 229-235 (2002). M Bajpai , A. Pande , S.K. Tewari and D. Prakash (2005). “Phenolic contents and antioxidant activity of some food and medicinal plants”. Int. J. Food Sci. Nutr., 56, 287-291. MS Blois (1958) “Antioxidant determinations by the use of a stable free radical,” Nature, 181, 1199–1200.

ACKNOWLEDGEMENTS Authors are thankful to the Head, Department of Botany, DDU Gorakhpur University, Gorakhpur for providing laboratory facilities.

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The study of Excess Molar volume and deviation in viscosity of binary mixtures of Ethyl Propionate in Pentanol-1 and Hexanol-1 at 308K Ultrasonically R.C.Verma1, A.P.Singh2 and Vinod Kumar Yadav3 Deptt.of Chemistry, Janta P.G.College, Bakewar (Etawah), Uttar Pradesh, India 3 Deptt. Of Chemistry, P.D.Mahila P.G.College, Fatehgarh, (Farukhabad), Uttar Pradesh, India 1,2

Abstract: Density, Ultrasound velocity and viscosities of Ethyl propionate with pentanol-1 and hexanol-1 have been measured over entire range of composition at 308 K and atmospheric pressure. The computed acoustic and thermodynamic properties of Ethyl propionate in higher alcohols will give the excess values of essentropic compressibility, molar volume and viscosity. The excess value will decide the nature and extent of molecular interaction of Ethyl propionate in pentanol-1 and hexanol-1 Keywords: Molar volume,Viscosity, Ethyl propionate, Pentanol-1,hexanol I. INTRODUCTION Ultrasound velocity, density and viscosity related parameters such as isentropic compressibility, intermolecular free length, molar and available volume, yield valuable information about intermolecular interaction between the non-polar and polar molecules. The interaction behaviour is due to deviation from ideality cause the solvent interaction,1-3 Subbarngaiah et al,4 and Erying and Hirschfelder et al 5 investigated ultrasonic behaviour of aqueous solution and discuss the results by hydrogen bonded complex formation. Verma et al 6-8 reported various thermodynamic parameters in binary mixtures of higher alcohols with benzene, toluene carbon tetrachloride and thiophene. The present investigation deals with the study of excess isentropic compressibility, molar volume, available volume and viscosity for binary mixtures of Ethyl propionate in alcohols. II. EXPERIMENTAL Ethyl propionate, pentanol-1 and hexanol-1 were used after single distillation. Binary mixtures were prepared by mixing known volume of each liquid in air tight Stoppard glass bottles. Care was taken to avoid contamination during mixing. Ultrasonic velocity was measured by Ultrasonic Interferometer M-80 manufactured by M/S Mittal Enterprises, New Delhi having accuracy of about 0.057. Density of pure liquid and binary mixtures were measured by using double walled Picknometer. The Picknometer was calibrated with distilled water. The value obtained were tally with the literature values. The viscosities have been determined by using Ostwald viscometer. The accuracy in density measurements was 0.0002. Mplar volume ( Vm) were calculated using following relation Vm= M/ Where M is effective molecular weight and  is the density. Excess value of molar volume (VmE) have been calculatrd by following formula VmE=Vmexp-(X1Vm1+X2Vm2) Where Vmexp, Vmexp and Vm2 are molar volumes of mixture and pure component 1 and 2 respectively and X1 and X2 are molar fraction of component 1 and 2. Excess viscosity has been calculated by using the relation. E=exp-(X11+X22) III. RESULT AND DISCUSSION The values of ultrasonic velocity, density, viscosity isentropic compressibility and their excess parameters are represented in Table 1 and 2. As it can be seen from Table 1 and 2 that the ultrasonic velocity decreases with increasing mole fraction of ethyl propionate. It is obvious that the moles of ethyl propionate are so lighter in density in comparable to pentanol-1 and hexanol-1. The VmE and sE values are negative for ethyl propionate with pentanol-1 as well as negative for ethyl propionate with hexanol-1. The E values are positive for ethyl propionate with pentanol-1 as well as positive

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for ethyl propionate with hexanol-1. Treszezanowics and Benson9 suggested that VmE is the resultant contribution from several opposing effects.These may be divided arbitrary in to the three types, namely chemical, physical and structural. Physical contribution that is non-specific interaction between the real species present in the mixture, contributes positive term to VmE. The chemical or specific interaction result in a volume decreases and these include change transfer type forces and other complex forming interactions. This effect contributes negative value of VmE. The structural contribution arising from geometrical fitting (interstitial accommodation) of one component into another due to the differences in the free volume and molar volume between components lead to a negative contribution to VmE. The negative deviations in viscosity –ve nE. It expect non-specific molecular interactions between the unlike molecules. The tabulate experimental and computed data throw light on molecular interaction. The nature and extent of interaction define molecular interaction between the binary mixtures. The haxonal-1 having more carbon atom in alkyl group has least repelling power to other molecules and toluene. REFERENCES [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8].

[9].

V. Rajendran, Ind. J. Pune and Appl. Phys., 34, 5256(1996). S. N. Jajoo, C.S. Adgainkar and V.S. Deogaonkar, Acoustica, 46,111(1980). B. Jaconson, Acta. Chem. Scand.,6, 1485(1952). K. Subbarangaiah, N.M. Murthy and G. Subdrahmanyan, Bull. Chem.Soc. Jpn., 54, 2200(1981). H. Erying and J. O. Hirschfelder, J. Phys. Chem., 41, 249(1937). R.C. Verma and S. Singh, Orient. J. Chem., 22(3), 671-673(2006). Verma RC., Kumar A, Raghav S and Singh A.P. 2nd. J.Chem. Sci. 10(3) 2012, 1664-1668 Verma RC, Raghav S, Chauhan,N., Rauki and Singh AP Res. J. of ree. Sci.,2(ISC-2012), 2013,1-6 A.J. Treszczanowicz and G.C. Benson, J. Chem. Thermodyn., 10, 961(1976).

Mole Fraction (X1)

Ultrasonic Velosity V(m/s)

Density (ρ) (gm/ml)

Excess isentropic compressibility (βsE) cm2/dyne.1012

0.0000 0.0794 0.1826 0.2497 0.3412 0.4372 0.5381 0.6444 0.7565 0.8749 1.0000

1218 1210 1201 1192 1183 1173 1164 1154 1144 1133 1123

0.8025 0.7993 0.7948 0.7899 0.7846 0.7804 0.7744 0.7694 0.7638 0.7591 0.7526

0.00 -0.24 -0.24 -0.23 -0.21 -0.21 -0.18 -0.17 -0.12 -0.06 0.00

Excess molar volume (VmE) ml/mole

Excess viscosity (ηE) c.p

0.00 -0.24 -0.32 -0.35 -0.31 -0.44 -0.30 -0.31 -0.21 -0.27 0.00

0.0000 0.0553 0.0594 0.0576 0.0524 0.0457 0.0371 0.0280 0.0180 0.0090 0.0000

Table 1: Mole fraction (X1) of Ethyl propionate, untrasonic velosity, density, excess isentropic compressibility, excess molar volume and excess viscosity for Ethyl propionate with Pentanol-1 at 308K. Mole Fraction (X1)

Ultrasonic Velosity V(m/s)

Density (ρ) (gm/ml)

Excess isentropic compressibility (βsE) cm2/dyne.1012

Excess molar volume (VmE) ml/mole

Excess viscosity (ηE) c.p

0.0000 0.0944 0.1900 0.2867 0.3847 0.4840 0.5846 0.6864 0.7896 0.8941 1.0000

1280 1266 1247 1229 1213 1196 1180 1166 1150 1137 1123

0.8082 0.8017 0.7971 0.7920 0.7852 0.7808 0.7759 0.7692 0.7649 0.7575 0.7526

0.00 -0.51 -0.51 -0.48 -0.44 -0.43 -0.40 -0.38 -0.23 -0.08 0.00

0.00 0.14 -0.01 -0.09 -0.11 -0.08 -0.20 0.00 -0.22 0.11 0.00

0.0000 0.1480 0.1520 0.1410 0.1264 0.1150 0.1010 0.0812 0.0560 0.0301 0.0000

Table 2 : Mole fraction (X1) of Ethyl propionate, untrasonic velosity, density, excess isentropic compressibility, excess molar volume and excess viscosity for Ethyl propionate with Hexanol-1 at 308K.

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A Survey Paper on Various Median Filtering Techniques for Noise Removal from Digital Images Prateek Kumar Garg 1, Pushpneel Verma 2, Ankur Bhardwaz3 Department of Computer Science and Engineering Bhagwant Institute of Technology, Muzaffarnagar, Uttar Pradesh, INDIA Abstract: One of the noise types that is normally degrades digital images, including grayscale digital images, is impulse noise. Therefore, researches regarding to impulse noise removal have become one of the active researches in the field of image restoration. The existence of impulse noise is one of the most frequent problems in many digital image processing applications. Median based filter is normally becoming the choice to deal with this type of noise. However; there are many variations of median filter in literature. In addition to standard median filter there are weighted median filter, iterative median filter, recursive median filter, directional median filter. Switching median filter, and adaptive median filter. Therefore, this paper will survey these median filtering frameworks. Index Terms: Impulse noise, median filter, standard median filter, weighted median filter, iterative median filter, recursive median filter, directional median filter, switching median filter, adaptive median filter.

I. INTRODUCTION Similar to other digital signal, digital images are sometimes could be corrupted by noise. One of the noise types normally related to digital image is impulse noise [1].Impulse noise is a set of random pixels which has a very high contrast compared to surroundings. In general impulse noise appears as a sprinkle of bright or dark spots on the image, and the normally these spots have relatively high contrast towards their surroundings areas.Therefore, even at low corruption level, impulse noise can significantly degrade the appearance and quality of the image [2],[3].Malfunctioning pixels in camera sensors, faulty memory locations in hardware or transmission of the image in a noisy channel, are some of the common causes for impulse noise[4]. A popular solution to deal with impulse noise is by using rank –order filters. This type of filters is order-statisticfilters. This type of filters is non linear and works in spatial domain. It uses sliding window approach, where on each sliding –iteration, only the value of the pixel corresponds to the center of the window is changed. This value is obtained based on the ordered intensity values of the pixels contained in the area defined by the filtering window [4], [5]. Among these rank –order filters, median based filters are the most popular technique to reduce both bipolar and unipolar impulse noise [4], [5].Generally median filters uses median value in its filtering process. A median filter works in a window of size WM × WN, where WM and WN are both odd. It replaces the center pixel with a value equal to the median of all the pixels in window. So using a median filter will help reduce least and highest intensity value pixels, generally represented by the impulse noise and so the picture clarity improves. [5]. II. VARIOUS MEDIAN FILTERING TECHNIQUES Much wider range of algorithms is provided to filter the digital images from the impulse noise. Here in this Survey paper we study various median filtering techniques to remove impulse noise. A. Standard median filter (SMF) The standard median filter [6] is a simple rank selection filter also called as median smoother, introduced by tukey in 1971 that attempts to remove impulse noise by changing the luminance value of the center pixel of the filtering window with the median of the luminance values of the pixels contained within the window. Although the median filter is simple and provides a reasonable noise removal performance, it removes thin lines and blurs image details even at low noise densities. The filtered image S = {S(i,j)} from SMF can be defined by the following equation[1]: S(i,j) = Median(k,l)ϵ Wm,n{D(i+k, j+l)} (1) WhereWm,nis a sliding window of sizemn pixels centered at coordinates (i, j). The median value is calculated by using equation (1) with ns= mn

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Although S M F can significantly reduce the level of corruption by impulse noise, uncorrupted pixel intensity values are also altered by SMF. This undesired situation happens because SMF does not differentiate between uncorrupted from corrupted pixels. Furthermore, SMF requires a large filter size if the corruption level is high. Yet, large filter of SMF will introduce a significant distortion into the image [7]. It is worth noting that equation (1) is normally using sorting algorithm such as quick-sort or bubble-sort to arrange the samples in increasing or decreasing order. Even though sorting algorithm can be easily implemented, sorting procedure requires long computational time when Wm,nis alarge filter because the number of samples (i.e. ns= mn) is big. Thus, in order to avoid from using any direct sorting algorithm, the use of local histograms has been proposed for median value calculation. The time required to form local histogram can be reduced by using a method proposed by Huang et al. [8], where instead of updating mnsamples, only 2msamples need to be updated in each sliding-iteration. B. Weighted Median Filter (WMF) Weighted median filter is one of the branch of median filter (WMF). It was first introduced by Justusson in 1981, and further elaborated by Brownrigg. The operations involved in WMF are similar to SMF, except that WMF has weight associated with each of its filter element. These weights correspond to the number of sample duplications for the calculation of median value. The filtered image S= {S(i, j)} from WMF can be defined by the following equation [7]: S(i,j) = Median(k,l)ϵ Wm,n {Wm,n(k,lD(i+k, j+l)} (2) Where operator indicates repetition operation. The median value is calculated using equation (1) with ns is equal to the total of Wm,n(k,l). Normally, the filter weight Wm,n is set such that it will decrease when it is located away from the center of the filtering window. By doing so, it is expected that the filter will give more emphasis to the central pixel, and thus improve the noise suppression ability while maintaining image details [9-12]. However, the successfulness of weighted median filter in preserving image details is highly dependent on the weighting coefficients, and the nature of the input image itself. Unfortunately, in practical situations, it is difficult to find the suitable weighting coefficients for this filter, and this filter requires high computational time when the weights are large [13–15]. 1. Central Weighted Median Filter (CWMF) It is special type of median filter. CWM is a filtering technique in which filter gives moreweight only to the central value of a window, and thus it is easier to design and implement than general WM filters [16]. 2. Adaptive Weighted Median Filter (AWMF) Adaptive weighted median filters (AWMF), which is an extension to WMF. By using a fixed filter size Wm,n,, the weights of the filter will be adapted accordingly base on the local noise content. This adaptation can be done in many ways, mostly based on the local statistics of the damaged image[17]. C. Recursive Median Filter Several researches in median filtering, such as [16], use recursive approach in their methodology. Theoretically, recursive median filters can be considered analogous to infinite impulse response (IIR) filter because their outputs at curtain position are determined not only from the input intensities, but also from the calculated outputs at previous locations. In implementation of recursive median filter, normally the degraded image and the filtered image share the same data array. In this method, the already processed pixels are now considered as noise free input pixels. Thus, by replacing the input pixels with these values, it assumes that the median value calculation will be more accurate. However, if the filter fails to remove the noise at previous locations, the error might be propagated to other area of the image. Furthermore, it is worth noting that the result from recursive median filter is dependent to the direction of filtering. D. Iterative Median Filter Iterative method requires the same procedure to be repeated several times. In general, iterative median filter with ni iterations, requires ni -1 temporary images. Iteration procedure enables median filtering process to use smaller filter size and reduce the computational time, while maintaining local features or edges of the image. The number of iterations nican be set by the user, or the iteration process stops when the output image converged (i.e. the current output image is equal to the previous output image). In practical, the number of iterations needed is dependent to the level of corruption and also the nature of the input image itself [2124].

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E. Directional Median Filter Directional median filter, or also known as stick median filter, works by separating its 2-D filter into several 1-D filter components [18-20]. Each filter component or stick, presented as a straight line, corresponds to a certain direction or angle. For a window of size mn pixels, there are m+n-2 sticks that will be used. The computed median values from these 1-D filters are then combined to obtain the final result. In [20], the output intensity is defined as: S(i,j) =max{Median(k,l)ϵ Wϴ {D(i+k, j+l)}}

(3)

WhereWis is the stick. Here, the output intensity is defined as the largest median value determined at each location. F. Switching Median Filter Nowadays, one of the popular median filtering approaches is switching median filter, or also known as decision based median filter. This approach has been used in recent works, such as [26-29]. Switching median filter tries to minimize the undesired alteration of uncorrupted pixels by the filter. Therefore, in order to overcome this problem, switching median filter checks each input pixel whether it has been corrupted by impulse noise or not. Then it changes only the intensity of noisy pixel candidates, while left the other pixels unchanged. Normally, switching median filter is built from two stages. The first stage is for noise detection, while the second stage is for noise cancellation. The output from the noise detection stage is a noise mask M. This mask is a binary mask. Noise detection procedure used by researchers are normally depending on the noise model been used. For fixed-valued impulse noise (i.e. salt-and-pepper noise), mostly the noise detection is done by thresholding the intensity values of the damaged image. Other popular noise detection methods include by checking the difference between intensity of the current pixel with its surrounding, inspecting the difference of the damaged image with its median filtered versions, or by applying some special filters. Next, mask M will be used in the noise cancellation stage, where only pixels with M = 1 are processed by the median filter. For the calculation of median, only "noise-free" pixels (i.e. pixels with M = 0) are taken as the sample. G. Adaptive Median Filter The Adaptive Median Filter is designed to eliminate the problems faced with the standard median filter. The basic difference between the two filters is that, in the Adaptive Median Filter, the size of the window surrounding each pixel is variable. This variation depends on the median of the pixels in the present window. If the median value is an impulse, then the size of the window is expanded. Otherwise, further processing is done on the part of the image within the current window specifications. „Processing‟ the image basically entails the following: The center pixel of the window is evaluated to verify whether it is an impulse or not. If it is an impulse, then the new value of that pixel in the filtered image will be the median value of the pixels in that window. If, however, the center pixel is not an impulse, then the value of the center pixel is retained in the filtered image. Thus, unless the pixel being considered is an impulse, the gray-scale value of the pixel in the filtered image is the same as that of the input image. Thus, the Adaptive Median Filter solves the dual purpose of removing the pulse noise from the image and reducing distortion in the image. Adaptive Median Filtering can handle the filtering operation of an image corrupted with impulse noise of probability greater than 0.2. This filter also smoothens out other types of noise, thus, giving a much better output image than the standard median filter.[1] H. Median Filter Incorporating Fuzzy Logic In order to preserve the local details of the image, median filter should only change the intensity of corrupted pixels on the damaged image. However, it is very difficult to detect the corrupted pixels from this image correctly. Even for fixed-valued impulse noise (i.e. salt-and-pepper noise), where the noise only takes values 0 and L-1, simple thresholding method still cannot classify the pixels effectively. This is because some of the uncorrupted pixels are also been presented by these two values. Thus, researches such as [14], [23], and [30-33], incorporate fuzzy logic approach into median filtering process. There are several ways on how fuzzy logic been used in median filtering process. Fuzzy logic can be used to grade how high a pixel has been corrupted by impulse noise. Normally, based on this fuzzy degradation measure, a proper correction will be applied. On the other hand, some of the methods use fuzzy logic as a decision maker that selects a proper filter, from a filter bank, for a given input image. In order to use fuzzy logic, the damaged image must first undergo a fuzzification process. Normally, the input for the fuzzification process is the intensity of the pixels, or the intensity differences between the current pixels with its surrounding. The system then executes the noise filtering process based on the fuzziness values obtained.

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The results are then found through a defuzzification process. Although fuzzy logic can improve impulse noise suppression, methods such as [32-33] use too many fuzzy rules to obtain an acceptable result. As a consequence, this condition makes their filtering methods becoming computational expensive. Furthermore, their restoration results are also too dependent to the number of membership functions, and also to the parameters that control the shape of the membership functions. Therefore, such methods are difficult to be implemented as an automatic impulse noise reduction filter, and also cannot be used for real-time processing. III. Summary This paper surveys eight common median filtering techniques. Each technique has its own advantages, and disadvantages. From literature, we found that most of the recent median filtering based methods employ two or more than two of these frameworks in order to obtain an improved impulse noise cancellation. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

[11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]

Rafael C. Gonzalez, Richard E. Woods, “Digital ImageProcessing”, 2nd edition, Prentice Hall, 2002. Maria Petrou, PanagiotaBosdogianni, “Image Processing:The Fundamental”, John Wiley & Sons Ltd, 2000. Jung-Hua Wang, Lian-Da Lin, “Improved median filter usingmin-maxalgorithm for image processing”, ElectronicsLetters, vol. 33, no. 16, pp.1362-1363, July 1997. Raymond H. Chan, Chung-Wa Ho, Mila Nikolova, “Saltandpepper noise removal by median-type noise detectorsand detail preserving regularization”, IEEE Trans. ImageProcessing, vol. 14, no. 10, pp. 1479-1485, October2005 ThotaSusmitha., GaneswaraRao M.V, Kumar Dr.P.Rajesh, “FPGA Implementation of Adaptive Median Filter for the Removal of Impulse Noise”,International Journal of Electronics & Communication Technology,Vol. 2, SP-1, Dec . 2011. S. E. Umbaugh, Computer Vision and Image Processing, Prentice-Hall, Englewood Cliffs, NJ,USA, 1998. R. K. Yang, L. Yin, M. Gabbouj, J. Astola, and Y. Neuvo, “Optimal weighted median filtering under structural constraints,” IEEETransactions on Signal Processing, 1995, vol. 43, no. 3, pp. 591-604. T. S. Huang, G. J. Yang, and G. Y. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Transactions on Acoustics, Speechand Signal Processing, 1979, vol. 27, no. 1, pp. 13-18. T.-C. Lin, “A new adaptive center weighted median filter for suppression impulsive noise in images,” Information Sciences, 2007, vol. 177, no. 4, pp.1073-1087. V. R. Vijay Kumar, S. Manikandan, P. T. Vanathi, P. Kanagasabapathy, and D. Ebenezer, “Adaptive window length recursive weighted median filter for removing impulse noise in images with details preservation,” ECTI Transactions on Electrical Eng., Electronics, andCommunications, 2008, vol.6, no.1, pp. 73-80. S.-J. Ko and Y. H. Lee, “Center weighted median filters and their applications to image enhancement,” IEEE Transactions on Circuitsand Systems, 1991, vol. 38, no. 9, pp. 984-993. T. Sun, “Center weighted median filters: Some properties and their applications in image processing,” Signal Processing, 1994, vol. 35, no. 3, pp. 213-229. K. Arakawa, “Median filter based on fuzzy rules and its application to image restoration,” Fuzzy Sets and Systems, 1996, vol. 77, no. 1, pp. 3–13. A. Asano, K. Itoh, and Y. Ichioka, “Optimization of the weighted median filter by learning,” Optics Express, 1991, vol. 16, no. 3, pp. 168–170. G. R. Arce and J. L. Paredes, “Recursive weighted median filters admitting negative weights and their optimization,” IEEE Transactionson Signal Processing, 2000, vol. 48, no. 3, pp. 768–779. S. J. Ko, and Y. H. Lee, 1991. Center weightedmedian filters and their applications toimage enhancement, IEEE Transactions, pp984-993 C. S. Panda, S. Patnaik, Filtering Corrupted Image and Edge Detection inRestored Grayscale Image UsingDerivative Filters, International Journal ofImage Processing, (IJIP) Volume (3): Issue(3), pp 105-119. Y. Q. Dong and S. F. Xu, “A new directional weighted median filter forremoval of random-valued impulse noise,” IEEE Signal ProcessingLetters,2007, vol. 14, no. 3, pp. 193–196. A. Hussain, M. A. Jaffar, and A. M. Mirza, “A hybrid image restoration approach: Fuzzy logic and directional weighted median based uniform impulse noise removal,” Knowledge and Information Systems, 2010,vol. 24, no. 1, pp. 77–90. R. N. Czerwinski, D. L. Jones, and W. D. O‟Brien Jr, “Ultrasound speckle reduction by directional median filtering,” In Proceedings ofInternational Conference on Image Processing 1995, 1995, pp. 358–361. Z. Wang and D. Zhang, “Progressive switching median filter for theremoval of impulse noise from highly corrupted images,” IEEETransactions on Circuits and Systems II: Analog and Digital SignalProcessing, 1999, vol. 46, no. 1, pp. 78–80. R. H. Chan, C. Hu, and M. Nikolova, “An iterative procedure for removing random-valued impulse noise,” IEEE Signal ProcessingLetters,2004, vol. 11, no. 12, pp. 921–924. C. Spence and C. Fancourt, “An iterative method for vector medianfiltering,” In IEEE International Conference on Image Processing,2007 (ICIP 2007), 2007, vol. V, pp. 265–268. A. R. Forouzan and B. N. Araabi, “Iterative median filtering for restoration of images with impulse noise,” In Proceedings of the 200310th IEEE International Conference on Electronics, Circuits andSystems, 2003, vol. 1, pp. 232–235. G. R. Arce and J. L. Paredes, “Recursive weighted median filters admitting negative weights and their optimization,” IEEE Transactionson Signal Processing, 2000, vol. 48, no. 3, pp. 768–779. P.-E. Ng and K.-K. Ma, “A switching median filter withboundarydiscriminative noise detection for extremely corrupted images,” IEEETransactions on Image Processing, 2006, vol. 15, no. 6, pp. 1506–1516. C.-H. Hsieh, P.-C.Huang, and S.-Y. Hung, “Noisy image restoration based on boundary resetting BDND and median filtering with smallest window,” WSEAS Transactions on Signal Processing, 2009, vol. 5, no. 5, pp. 178–187. V. Jayaraj, D. Ebenezer, and V. R. Vijayakumar, “A noise free estimation switching median filter for detection and removal of impulse noise in images,” European Journal of Scientific Research, 2011, vol. 51, no.4, pp. 563–581. H. Ibrahim, “Adaptive switching median filter utilizing quantized window size to remove impulse noise from digital images,” AsianTransactions on Fundamentals of Electronics, Communication andMultimedia, 2012, vol. 2, no. 1, pp. 1-6. A. Toprak and I. Guler, “Suppression of impulse noise in medical images with the use of fuzzy adaptive median filter,” Journal of

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Medical Systems, 2006, vol. 30, no. 6, pp. 465–471. A. Toprak and I. Guler, “Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter,” Digital Signal Processing, 2007, vol. 17, no. 4, pp. 711–723. A. Toprak, M. S. Ozerdem, and I. Guler, “Suppression of impulse noise in MR images using artificial intelligent based neurofuzzy adaptive median filter,” Digital Signal Processing, 2008, vol.18, no. 3, pp. 391–405. W. Luo, “Efficient removal of impulse noise from digital images,” IEEE Transactions on Consumer Electronics, 2006, vol. 52, no. 2, pp .523- 527.

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

ROLE OF EPIDEMIOLOGICAL FACTORS IN ACCUMULATION OF OXALATES IN FORAGE CROPS Pritam Kaur Sidhu1*, Jaspal Singh Lamba2, Ganesh Kumbhar1, Gurnam Singh Sekhon3, Sunil Verma1 and Mohinder Partap Gupta1 1 Animal Disease Research Centre, GADVASU, Ludhiana, INDIA. 2 Department of Animal Nutrition, GADVASU, Ludhiana. INDIA. 3 Agriculture Development Officer, Sudhar, Ludhiana, INDIA. Abstract: Napier Bajra Hybrid (NBH), an interspecies cross of bajra (Pennisetum glaucum) and napier grass (Pennisetum purpureum) is a multicut fodder and its use is very common due to its high yield, nutritive value, digestibility, palatability and survivability for longer periods. An occurrence of five outbreaks associated with NBH poisoning in farm animals in Punjab prompted us to investigate the epidemiological factors contributing towards accumulation of oxalate in NBH and other fodder crops. Samples of NBH (n=390), bajra (n=42), Megathyrsus maximus (guinae grass, n=30), Chenopodium album (bathu, n=21) and Sorghum bicolor (chari, n=50) were collected from villages of Punjab and analyzed for oxalate and nitrate concentration. Highest concentration (3.48%-5.98%) of oxalate was found in bathu followed by NBH (2.58%-5.62%). An oxalate concentration was ≤ 2% in bajra, chari and guinea grass. The effect of agronomic and climatic conditions on the accumulation of oxalates in NBH was studied. The oxalate levels (1.65%-2.20%) in first and second cutting of the crop were approximately same and third cutting showed maximum oxalate concentration (2.00%-2.85%). The data suggested that oxalate accumulation may occur in NBH; however, chari and guinea grass can accumulate higher nitrate. Application of nitrogen fertilizer on NBH didn’t influence oxalate concentration directly. Keywords: Farm animals, Forages, Napier bajra hybrid, Oxalate, Nitrate.

I. Introduction A wide range of plants including food stuff, Chenopodium album (bathu), Spinacea oleracea (spinach) and forages, Cenchrus ciliaris (buffel grass), Pennisetum clandestinum (kikuyu grass), Pennisetum purpureum (napier grass), Digitaria decumbens (pangola grass), Amaranthus spp. (pigweed), Rheum rhaponticum (rhubarb), Salsola kali (Russian thistle), Setaria sphacelata (setaria) and Beta vulgaris (sugar beets) can accumulate large amounts of oxalate in some circumstances leading to poisoning in farm animals ([12], [20], [2], [22], [13], [19]). Ingested oxalate complexes with dietary calcium (Ca) and forms insoluble Ca oxalate leading to hypocalcaemia and nephrotoxicity in ruminants. In India, Pennisetum glaucum (bajra), Trifolium alexandrinum (barseem), Sorghum bicolor (chari), Megathyrsus maximus (guinae grass), Zea mays (maize), Brassica campestris (mustard), napier bajra hybrid (NBH) and Avena sativa (oats) are the commonly used forages for dairy animals. Napier bajra hybrid, an interspecies cross of bajra and napier grass is preferred over other forages due to its advantages like survivability for longer periods with casual management, high yield, nutritive value, digestibility and palatability [11]. Five outbreaks of NBH poisoning in farm animals associated with excessive accumulation of oxalate occurred in Punjab during the year 2009-2010. The investigation of these outbreaks confirmed that NBH may accumulate oxalate in amounts that can be fatal to livestock. An oxalate concentration in the NBH of affected farms was 4.0%-10.5% [21]. These incidents prompted us to investigate the epidemiologic evidences that play role in accumulation of oxalates in NBH. Sudden deaths of cattle, sheep and horses due to grazing of buffel grass, Panicum maximum, kikuyu grass, setaria, rumex and oxalis spp. had been documented due to accumulation of higher oxalates in these plants ([20], [5], [8], [10], [19]). The NBH contains oxalate ≤ 2.42% when it is cut at the recommended medium height of 100-135cm [4]. It is known that the oxalate concentration in the napier grass may vary with the season, variety, stage of growth, part of the plant and application of nitrogen fertilizer in the crop ([13], [17]). Hence, the study was planned to generate epidemiological data on the oxalate concentration in the commonly used forage crops and agronomic and climatic factors responsible for altering the levels of oxalate in the NBH. Nitrate concentrations were also determined in fodder samples to establish relationship between accumulation of oxalates and nitrates in these forages.

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II. Materials and Methods The study was divided into two parts. In the first part of the study, the samples of NBH were either brought by the farmers or obtained from the farms of Punjab where farmers complained that animals were off-feed and constipated. In addition, bajra, bathu, chari and guinea grass were collected from the villages of Punjab and analyzed for oxalate and nitrate concentrations. In the second part of the study, NBH samples were collected from the farms where the fodder was being used for livestock without any complaint. Data pertaining to epidemiology and history of application of fertilizers for NBH were recorded. Oxalate levels in forages were determined by the titrimetric method as described by Moir in [9] and nitrate concentrations in the fodder were determined by the colorimetric method using Spectrophotometer [1]. A co-relation between nitrate and oxalate in fodder was established. Oxalates were determined in the whole plant; and in the stem and leaves of the same plant separately. III. Results Part-1 The results of tests done for oxalate and nitrate of five forages studied in the first part of the study have been presented in the Table 1. The collected fodder samples (n=288) were suspected to have adverse effects on the health of cattle and buffaloes that were consuming the fodder. According to farmers, the animals were becoming partially/completely off-feed and/or constipated after a one-two week consumption of fodder. At four farms, the farmers were worried due to decrease in milk production of milking cattle after the change in fodder. The concerned owners were advised to stop feeding the fodder immediately and fodder samples were analyzed for the role of oxalate and nitrate in affecting the health of animals. Highest concentration of oxalate (3.48%5.98%) was determined in bathu followed by NBH (2.58%-5.62%). Guinea grass, bajra, and chari showed oxalate concentration ≤ 2%. In NBH, oxalate concentration was ≤3% in 93% samples and 7% samples contained oxalate >3%. Bathu was found to contain >3% oxalate in all the samples. A low oxalate level (<1%) was detected in 75% samples of bajra and Chari; and 25% samples had slightly higher oxalate (1%-2%). However, guinea grass exhibited > 1% oxalate in 60% of samples. Part-II In the second part of the study, 245 NBH samples which were being fed to cattle and buffaloes were collected from the villages of Punjab. Animals were doing well with this green fodder. There was no apparent illness in animals and milk yield was also quite good. Two varieties of NBH (PBN-83, n=40 and PBN-233; n=105), commonly used in Punjab were investigated for levels of oxalate under different climatic and agronomical conditions. The height of plants ranged from 100cm to 150cm at the time of collection of samples. The PBN-83 variety accumulate more oxalate than PBN-233, because 90% samples of PBN-233 had oxalate level <2% and ~ 50% samples contained > 2% oxalate in PBN-83. Maximum level of oxalate (2.62%) was also higher in PBN-83 than PBN-233 (2.25%). Leaves of the plant had greater concentration of oxalate than stem part. The plants grown in the fields fertilized with heavy amount (> 50kg/acre) of nitrogenous fertilizer accumulate oxalate in the range of 0.80%-2.22 % and the range was 0.95%-1.95% when fertilizer used was < 50kg/acre. Under normal weather conditions, oxalate levels were up to 1.5%, whereas the levels rose to 2.62% during stressful conditions of hot and humid weather of summer. A hundred samples of PBN-233 variety (25 samples for each cutting) were studied to determine the influence of cutting on accumulation of oxalate in the plant. The amount of oxalate (1.75%-2.05%) was same in the first and second cutting (1.65%- 2.20%) of the crop. The third cutting of the crop accumulated maximum amount of oxalate (2.00%- 2.85%) followed by fourth cutting. IV. Discussion Napier bajra hybrid is cultivated as forage crop in sub-tropical regions of Asia, Africa, Southern Europe and America. The grass is gaining popularity because once planted it supplies fodder continuously for a period of three years and it combines high quality and faster growth of bajra with deep root system of napier grass. It tolerates soil pH ranging from 5 to 8. The NBH contains about10.2% crude protein and 30.5% crude fibre, 10.9 % ash with Ca and phosphorus and is superior in nutritive value and palatability to napier grass when cut at the right stage [11]. The forage is less fibrous and more acceptable to ruminants due to its juicy and succulent characteristics at early stages of growth. The oxalate content of some of the varieties may be high. Like other tropical grasses some of the varieties of NBH can accumulate oxalate to a level which is toxic to animals under certain circumstances and have been associated with sudden deaths of cattle, buffaloes and horses due to high oxalate content ([3], [21]). Oxalates binds with Ca to produce severe effects such as hypocalcaemia and oxalate nephrosis can occur due to deposition of Ca oxalate crystals in the kidneys. The oxalates are absorbed from the gastrointestinal tract and combine with serum Ca and magnesium. The acute hypocalcaemia impairs normal cell membrane function and may lead to muscle tremors, weakness, collapse and death. Having in mind, above said advantages and risks of using NBH as fodder for livestock, the epidemiological data associated with it were generated and compared with other commonly using forages (bajra, chari, guinea grass) and a foodstuff (bathu) that is fed to animals during summer season when there is scarcity of fodder. In the part I of the study, oxalate

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levels in NBH were <2% under normal management and climatic conditions, but it rose up to >2.65% under adverse circumstances viz., during use of excessive fertilizer, stressful climatic conditions and in later cuttings (3rd and 4th cuttings) of the crop. It supports the literature claim that the oxalate level of NBH is <2.42% when it is cut at the recommended medium height of 100cm-135cm under normal conditions [4]. In ruminants, the suggested safe limit of oxalates in forages is <2% to avoid adverse effects of oxalate on the health of livestock [19]). It suggested that under normal agro-climatic conditions, NBH can be used as a green fodder for ruminants without any problem when cut at the recommended age and height. There was difference in oxalate levels between two varieties of NBH that indicated that variety of plant may influence the accumulation of oxalate content due to their inherent ability to accumulate oxalates. Previous reports indicated that oxalate content in grasses differ among varieties within the same species ([13]), [18]). According to Libert as mentioned in [6] genotype of plant may account for 72% of the variability in oxalate content. This might be due to the difference in proportion of leaves in different varieties of plants because leaves contain higher amount of oxalate that stem part. Lower oxalate levels in variety PBN-233 than PBN-83 indicated that PBN-233 is safer for use as fodder in livestock. In the present study, higher amount of oxalate in leaves of NBH than stem part supports this hypothesis. Rahman and co-workers [13] also found higher oxalate levels in leaves than stem of napier grass. The present study indicated that oxalate levels in NBH may increase with the number of cutting as the higher amount was determined in the samples of third and fourth cutting. The results supported our speculation in previous outbreak investigations where high amount of oxalate (4%-10%) level in NBH was partly associated with the third cutting of the fodder [21]). As indicated by the history given by the farmers, the reason for the increase in oxalate levels with the number of cuttings in NBH may be due to application of nitrogenous fertilizer in the fields after every cutting. However, there was no direct co-relation between oxalate concentration and nitrogen fertilizer used for the crop in field. These results were in line with the observations made by the previous workers ([23], [15], [16]). In the Part I of the study, the oxalate levels (2.6%-5.6%) in NBH samples were higher than the NBH samples of the part II study. Considering the results and symptoms shown by the animals high oxalate levels can be associated with health problems of cattle and buffaloes. The majority of NBH samples were aged and overripe with very thick and hard stem and samples were collected in stressful weather of summer (very hot and humid). The day temperature varied between 39○C -44○C with 90%-96% humidity. These might be the reasons for the excessive oxalate accumulation in the NBH. It has been reported that oxalate concentration of NBH is directly proportional to the thickness of stem; thicker the stem higher the oxalate concentration [3]. Other commonly used forages (guinea grass, bajra, and chari) appeared to be safe as for as oxalate concentration is concerned. An oxalate level of <1% was found in 90% of samples. Bathu may pose problems to livestock if eaten as single fodder as the oxalate content was 2-3 folds higher than recommended safe limit of oxalates (<2%) for ruminants. The findings were in accordance with Thakur and colleagues [22] that determined high concentration (2.26% soluble and 5.65% of insoluble) oxalate in bathu. In part I of the study, the fodder samples were also analyzed for nitrate concentration to establish the relationship between oxalate and nitrate accumulation and cause of illness in animals. Nitrate levels were highest in chari followed by bathu and NBH. There was direct correlation between nitrate levels of plants and use of fertilizer (urea) in these fields. The nitrate level >1000 ppm was detected in the samples that were fertilized with >50kg/acre urea. Some fields were added with cow and buffalo dung along with urea. This can be one of the reasons for the excessive accumulation of nitrates in fodder samples in addition to climatic factors. The results indicated that there is no correlation between use of nitrogenous fertilizer and oxalate levels of the plants. The oxalate levels in chari was <1% in 75% of samples despite having nitrate levels >2000ppm in 50% of samples. Similar findings were obtained for bajra, bathu, guinea grass and NBH. However, samples of NBH having oxalate concentration >3% showed nitrate concentration ≥2000 ppm. It is assumed that nitrate accumulation in NBH might be influencing oxalate levels of plant but not directly due to the use of nitrogenous fertilizer. Earlier reports also documented that oxalate accumulation in forage was not linked to the application of urea ([23], [14], [15]), [17]). It is concluded that oxalate content of NBH is within the safe limit (≤2%) for ruminants under normal management and climatic conditions. If plant is not cut at the recommended age and height the forage can accumulate excessive amounts (>5%) of oxalate that can be toxic to livestock. The climatic conditions also contribute in enhancing the accumulation of oxalate in NBH especially extremely stressful weather. Feeding of bathu may pose health risk to ruminants due to high oxalate content (3.48% -5.98%). The data suggested that oxalate accumulation is not a problem with chari, bajra and guinea grass. These forages can accumulate higher nitrate when excessive nitrogen containing fertilizer is applied and climatic conditions for the growth of the plant are not favourable. There was no direct co-relation between oxalate concentration of the NBH and nitrogen fertilizer used for the crop. However, there was a trend of higher nitrate levels in NBH containing oxalates more than 3%. Experimental studies with greater number of samples and controlled agronomical conditions are

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required to confirm these findings and for exploring other factors contributing to accumulation of oxalate in NBH. References [1] Catalado D A, Haroon M, Schrader L E and Youngs V C. 1975. Rapid colorimetric determination of nitrate in plant, tissues by titration of salicylic acid. Communication Soil Sci Plant Analysis, 6: 71-80. [2] Cheeke P R. 1995. Endogenous toxins and mycotoxins in forage grasses and their effects on livestock. J. Anim. Sci, 73: 909-918 [3] Dhillon K S., Paul B S., Bajwa R S and Singh J. 1971. A preliminary report on a peculiar type of napier grass (Pennisetum purpureum, ‘Pusa giant’) poisoning in buffalo calves. Indian J. Anim. Sci., 41:1034-1036. [4] FAO, Factsheet, Pennisetum Purpureum . In: http://www.fao.org/aga/agap/frg/afris/Data/htm.2007 [5] Groenendyk S and Seawright A A. 1974. Osteodystrophia fibrosa in horses grazing Setaria sphacelata. Aust Vet J ; 50:131-132. [6] Libert B. 1987. Genotypic and non-genetic variation of oxalate and malate content in rhubarb (Rheum spp. L.). J. Hort. Sci., 62:513-521. [7] Marais J P. 1990. Effect of nitrogen on the oxalate calcium content of kikuyu grass (Pennisetum clandestimun Hoscht). J. Grassland Soci. South Africa; 7:106. [8] McKenzie R A, Bell A M, Storie G J, et al. 1988. Acute oxalate poisoning of sheep by buffel grass (Cenchrus ciliaris). Aust Vet J; 65:26. [9] Moir K W. 1953. The determination of oxalic acid in plants. Queensland J. Agric. Sci; 10:1. [10] Panciera R J, T Martin, G E Burrows, D S Taylor and L E Rice. 1990. Acute oxalate poisoning attributable to ingestion of curly dock (Rumex crispus) in sheep. J. Am. Vet. Med. Assoc; 196:1981-1984. [11] Pandey K C and Roy A K. 2011. Forage Crops Varieties. IGFRI, Jhansi (India) [12] Peet R L, J Dickson and M Hare. 1990. Kikuyu poisoning in goats and sheep. Aust. Vet. J., 67:229-230. [13] Rahman M M, Nimi M, Ishi Y, and Kavamura O. 2006. Effect of season, variety and botanical fractions on oxalate content of napier grass (Pennisteum purpureum Schumach). Grassland Sci, 52:161-166. [14] Rahman M M, M Yamamoto, M Niimi and Kawamura O. 2008a. Effect of nitrogen fertilization on oxalate content in Rhodes grass, Guinea grass and Sudan grass. Asian-Aust. J. Anim. Sci, 21:214-219. [15] Rahman M M, Y Ishii, M Niimi and Kawamura O. 2008b. Effects of levels of nitrogen fertilizer on oxalate and some mineral contents in napier grass (Pennisetum purpureum Schumach). Grassl. Sci; 54:146-150. [16] Rahman M M, Y Ishii, M Niimi and Kawamura O. 2009. Effect of clipping interval and nitrogen fertilization on oxalate content in pot-grown napier grass (Pennisetum purpureum). Trop. Grassl; 43:73-78. [17] Rahman M M, Y Ishii, M Niimi and Kawamura O. 2010. Effect of application form of nitrogen on oxalate accumulation and mineral uptake by napier grass (Pennisetum purpureum). Grassl. Sci; 56:141-144. [18] Rahman M M, and Kawamura O. 2011. Oxalate accumulation in forage plants: some agronomic, climatic and genetic aspects. Asian-Aust. J. Anim. Sci; 24:439-448. [19] Rahman M M, Abdullah R B, Wan Khadijah W E. 2013. A review of oxalate poisoning in domestic animals: tolerance and performance aspects. J Anim Physiol Anim Nutr (Berl). 97(4): 605-14. doi: 10.1111/j.1439-0396.2012.01309.x. [20] Seawright A A, Groenendyk S and Silva K I. 1970. An outbreak of oxalate poisoning in cattle grazing Setaria sphacelata. Aust. Vet. J.; 46:293-296. [21] Sidhu P K, Kumbhar G, Verma S K, Gupta M P. 2013. Poisoning in bovines and equines due to accumulation of oxalate in perennial fodder (Pennisetum glaucum×Pennisetum purpureum) in Punjab during 2009-2010. The Journal of Veterinary Science. Photon; 114: 153-160. [22] Thakur M, Kumari M and Pundir C S. 2001. Determination of oxalate in foodstuffs with arylamine glass-bound oxalate oxidase and peroxidise. Current Sci.; 81:248-251. [23] Williams M C, Smith B J and Lopez R. 1991. Effect of nitrogen, sodium and potassium on nitrate and oxalate concentration in kikuyu grass. Weed Technol.; 5: 553-556.

Acknowledgments This work was supported by RKVY grant awarded by Guru Angad Dev Veterinary and Animal Science University, Ludhiana, India.

Table 1: Oxalate and nitrate concentration in forage crops suspected for ill-health of dairy animals. Plant Species

Nitrate (No3-N) ppm

Oxalate (%)

Napier bajra hybrid (n = 145)

790 – 2100

2.58 - 5.62 (< 3% = 135; > 3% = 10)

Guinae grass (n = 30)

624-1285

1.05 - 2.40 (≤ 1% = 12; > 1% = 18)

Bajra (n = 42)

850- 2620

0.63 - 1.98 (≤ 1% = 32; > 1% = 10)

Bathu (n = 21)

1000 - 2210

3.48- 5.98 (> 3% = 21)

Chari (n = 50)

1130 – 2800

0.92- 1.70 (≤ 1% = 38; > 1% = 12)

n= number of samples Table 2: NBH oxalate concentration in different agronomical and climatic conditions. Conditions influencing oxalate levels Variety

NBH-83

Amount of Oxalate (%) 1.18 - 2.62 (n = 40)

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Pritam Kaur Sidhu et al., American International Journal of Research in Formal, Applied & Natural Sciences, 7(1), June-August, 2014, pp. 48-52 (1.18- 2.00, n=22; 2.00-2.62, n=18)

Stem

0.68 – 2.25 (n = 105) (0.68- 2.00, n = 90; 2.00-2.25, n=15) 0.46 – 1.52 (n=40)

Leaves

0.70 - 2.35 (n = 40)

< 50 Kg per acre

0.95- 1.95 (n = 80)

> 50 Kg per acre

0.80 – 2.22 (n = 45)

Extreme winter & summer

1.44- 2.62 (n=25)

Normal winter and summer

0.65- 1.48 (n = 25)

NBH-233 Plant parts

Nitrogen Fertilizer application Season

Cutting

1 cutting

1.75-2.05 (n = 25)

2nd cutting

1.65- 2.20 (n =25)

2.00- 2.85 (n= 25)

2.25- 2.65 (n= 25)

3 cutting 4 cutting

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Study of amount of Oxygen (BOD, OD, COD) in water and their effect on fishes *Priyanka Sharma and **Dr. Sujata Gupta *Research Scholar, Mewar University, Chittorgarh, Rajasthan, India. **A.P., D.A.V College, Dehradun,Uttarakhand, India. Abstract: Present investigation is carried out during December 2012 to April 2013, for which three rivers were chosen i.e. Alaknanda, Bhagirathi, and Ganga to assess the effect of pollution on water and fish diversity of river Ganga-Uttarakhand (DevPrayag to Hardiwar). Water and fish samples were collected from all 4 sites .The samples of water were analyzed for amount of Oxygen(BOD,OD,COD) and fish samples were analyzed how a reduction in dissolved oxygen concentration is one of the most important factor and direct effects of fish life cycle because less DO in water can cause mortality, reduced growth rates, and altered distributions and behaviors of fishes as well as less DO can lead to large reductions in the abundance, diversity, and harvest of fishes within affected waters . During the course of study a total of 35 samples of mainly 5 species Catla Catla, Labeo Rohita, Cirhinus mrigala, Hypphthal michthys molitrix, , Cyprinus carpio were collected from all 4 sites and all these specimen were caught with the help of cast net. Keywords: Water samples, COD, mortality, distribution, behaviors. I. Introduction Uttrakhand is surrounded by great Himalayas in the North, Shivalik hills in the South, Ganga in the East and Yamuna in the West. Region of Uttrakhand enjoy moderate climate with maximum temperature of summers (April to July) is around 36 Degree Celsius while the minimum temperature of winters (November- February) is around 5 Degree Celsius. But due to rapid increase in pollution, rising standard of living and exponential growth of industrialization and urbanization have polluted the water resources of Uttrakhand. In addition to that, dumping of city garbage, human and animal excreta, agricultural wastes, pesticides, burning of human bodies, community bathing and faulty social and religious practices. According to an estimate about 1965 9 tons of polluted matter enter the river enter the river every year of which 55.4% is contributed by Uttrakhand and Uttar Pradesh while 18.8% by West Bengal. At Haridwar (Uttrakhand), Ganga water is not free from pollution, which starts from Rishikesh itself where industrial wastes from Bharat Heavy Electrical s Limited (BHEL) have polluted the water. The waste from Indian Drug Production Limited (IDPL) adds to the problem. About 15 large and small sewage drain discharge and about 42 mid municipal sewage into river, community bathing, discharge milk pots, and bunches of flowers and leaves etc. into the river. Pollution get accentuated sharply during Kumbh (every 12 years) then up to 5 million devotes descend in small town to bathe in the holy river. Most of the river water is drained out of irrigation canal at Haridwar which also decrease the pollution absorbing capacity of the river. Consequently, the problem was taken up when effluents of these industries go into the water system and change the physiochemical quality of the water and make it unfit for drinking and creates difficulty for survival or aquatic life. Since all natural water waste contain bacteria and nutrients, almost any waste compound introduced into such water waste contain bacteria and nutrients will initiate biochemical reactions. These biochemical reactions are measured as BOD and COD in laboratory (Tehovanoglous et. al 2003). Both the BOD and COD tests are measure of relative oxygen-depletion effect of waste contaminant. Both have been widely adopted as a measure of pollution effect. The BOD test measures the oxygen demand biodegradable pollutants whereas the COD tests measure the oxygen demand of oxidizable pollutants. Chemically, waste water is composed of organic and inorganic components as well as various gases. Organic components may consist of carbohydrates, proteins, fats and greases, surfactants, oils, pesticides, phenols etc (Tehovanoglous et. al 2003, Maiti 2004).Further in this paper we explained how variation in oxygen quantity (BOD, COD, DO) affect the life of fishes of Uttarakhand (Ganga).

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II. Study Area Devprayag is located in 30.146315 N 78.598251 E, in Tehri Gharwal District in the state of Uttrakhand, India and is one of the Panch Prayag of Alakhnanda river where Alakhnanda and Bhagirithi rivers meet and take the name Ganga. The original path of Ganga River is on South west direction, then it moves through Easterly direction and final in last lap, it flows again southwards and merges into the sea. During its middle course on easterly direction, a number of big and small tributaries have joined on the northern side from the Himalayan sub-basin, namely, Ramaganga, Gomati, Ghagra, Gandhak and Kosi, all of which have their origins within the mountain range of the Himalyas in Nepal. Therefore, the contribution of flow of these tributaries is from Nepal within the Himalyan range and also from the Indian soil on the Southern side of the Himalyan foothills. There is another tributary, Mahayana which joins the river in Bangladesh. III. Method Fish and water samples were collected from 3 rivers of Uttrakhand. The areas from which samples were collected include Devprayag and Haridwar. The river for this study was Alakhnanda, Bhagirathi and Ganga. Water samples were collected once every month during February to April 2014. Site of collection are : 1. Haridwar : Har ki Paudi and Brahmakunda 2. Devprayag: Alakhnanda and Bhagirathi For water, samples were collected in clean 20 polythene bottle from all 4 river sites. Their samples were used in titration method to measure their BOD, DO and COD. For fish, total 35 samples of mainly 5 species Catla Catla, Labeo Rohita, Cirhinus mrigala, Hypphthal michthys molitrix, carpio were collected from all 4 sites and all these specimen were caught with the help of cast net. These fishes were transported to plastic container to lab. Then these fishes were analyzed to check the effect of BOD, COD, OD made in their structure in addition to this some observation were made on the research sites. Table1-Oxygen analysis of river water of Uttarakhand (mg/l) Samples

I-H-H

II-H-B

III-D-A

IV-D-B

Name of water body

Ganga

Alakhnanda

Bhagirathi

Location

Har ki Paudi

Brahmakund

Daveprayag

City

Haridwar

Devprayag

Colour Odour

Clear Odourless

DO (mg\l)

9.5

8.2

10.1

9.9

BOD (mg\l)

2.8

5.1

2.1

3.2

COD (mg\l)

12.2

34.2

6.1

12.9

I-H-H (Haridwar-Har ki B(Devprayag-Bagirathi)

pauri),

II-H-B(Haridwar-Brahmakund),

III-D-A(Daveprayag-Alakananda),IV-D-

IV. Observation and Results A. Effect on water COD (Chemical Oxygen Demand) range from 6.1 to 34.2 mg per litre. The highest value 34.2 mg per liter is observed at Haridwar (Brahmakund). While the lowest value of 6.1 mg /l is observed at Devprayag (Alakhnanda).DO varies from 8.2 to10.1 mg/l. The average value of DO is meeting the criteria at all monitoring locations. Except some period of year, the DO is not meeting the criteria in river Ganga at 2 sites i.e. Har ki paudi and Brahmakund in Haridwar. BOD ranged from 5.1 to 2.1 mg/l .The highest value 5.1mg per liter is observed at Haridwar (Brahmakund). While the lowest value of 2.1mg /l is observed at Devprayag (Alakhnanda).DO varies from 8.2 to10.1 mg/l. B. Effect of Oxygen depletion on Fishes

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With the help of Table 1, it is clear that effect of pollution is more on site 1 &2 as compared to 3 &4. First observation made was that fishes from site of low DO lacking swim bladder which led to increase in mortality on these sites. Mortality of adults was high as compared to young fishes within a water body. Second observation made during this study was that fishes in area where DO was less become lethargic and stop feeding which explained that dissolved oxygen is not only related with breathing but also with feeding of fishes. As oxygen level decreased, the fishes do not have enough energy to swim, feeding and utilizing yet more oxygen. Often it was recognized that due to less DO in water, some of the fishes get prone to some deadly diseases. Thirdly, it was observed that the ventilation rate was increased to bring more water in contact with the gill within a unit of time. There are, however, limits to increase flow attainable, the space between secondary lamellae is narrow and water will tend to be forced fast the tip of primary lamella when the respiratory water flow was high, thus by passing the respiratory surface. Fourth observation was that most of the fishes of sites 1&2 were suffering from oxygen deficiency disease called Asphyxiation (disease more common in cyprinids). Symptom of these diseases are fish do not take food, skin became pale in color, congestion of cyanotic blood in the gill, adherence of gill lamellae, small hemorrhages in the front of the ocular cavity and in the skin of gill covers. In the majority of predatory fishes the mouth gaps spasmodically and the operculum over the gills remains loosely open. Not only this, fish reduced food intake, leading to reduction in growth. Fifth observation was that Low DO concentrations can contribute to poor spawning success by troublemaking spawning activities and limiting the amount of energy available for the production of viable eggs and larvae. The physiological pressure and energy demands resulting from exposure of adults to high water temperatures and low DO concentrations can reach levels that affect the amount of energy obtainable for the production of viable eggs and larvae. Sixth change was that all fishes have an initial limiting threshold for DO below which they experience a turn down in the ability to perform certain activities and functions. Exposure to low DO concentrations can affect the behaviour of fish, resulting in changes in distribution, habitat use, activity, and respiration mode. Fish can stay away from mortality and other adverse effects of low DO concentrations through a number of behavioural responses that reduce either their exposure to low DO concentrations or their need for oxygen. Potential behavioural responses to low DO concentrations include avoidance, changes in activity, increased use of air breathing, increased use of aquatic surface respiration, and habitat shifts. The concentration of DO that will activate avoidance behaviours varies among species and life stages, depending on their tolerances of low DO concentrations. Field and laboratory studies indicate that fish tend to avoid oxygen concentrations that are two to three times higher than those that cause 50% mortality in 24-hour and 96-hour exposures, roughly equal to the concentrations associated with reduced growth in laboratory experiments (Breitburg 2000). Such behaviour indicates that fish can avoid hypoxic waters and select more highly oxygenated waters if available .Some species may also use air breathing or aquatic surface respiration to increase oxygen uptake under such conditions (Weber and Kramer 1983). Where alternative habitats are limited or not accessible, low DO concentrations coupled with high water temperatures can block or delay migration or restrict fish to small refuges where they may experience increased susceptibility to predators, disease, and food limitation .Changes in action in response to low DO concentrations include increased gill ventilation and swimming activity (associated with avoidance behaviour) followed by decreases in activity, depending on the period of exposure. Reduced motion levels can help reduce oxygen requirements and allow fish to stay alive exposure to low DO concentrations when escaping is ineffective. However, such a response can reduce feeding and spawning opportunities, potentially foremost to reduced growth or reduced reproductive success, depending on the duration of exposure. These responses can be important strategies for reducing or avoiding unfavourable effects caused by straight exposure to low DO concentrations but also can increase the possible for adverse effects from other factors (e.g., increased predation risk).Therefore, the degree to which fish exhibit these responses in nature likely will be influenced by the differences in energy costs and mortality risks associated with alternative responses. In seventh observation we took some general effects like (a) Susceptibility to Predatino all fishes have an incipient limiting threshold for DO below which they experience a turn down in the ability to perform certain activities and functions. Experience to low DO concentrations can raise the susceptibility of fish to predation by altering normal behaviour, reducing activity levels, and reducing swimming performance. Prolonged or frequent exposure to hypoxia may reduce growth rates enough to reduce the size of fish and thereby increase the period of time that fish are weak to predators.(b)Susceptibility to Parasites/Pathogens Environmental conditions, including natural and anthropogenic stressors, can heavily influence the parasite-host interaction because they regulate the physiological condition of both the host and the parasite. Traumatic environmental conditions, such as low DO concentrations, can increase the vulnerability of fish to infectious diseases and parasites. The compounding effects of numerous stressors

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elicit significant physiological and behavioural responses that may result in increased rates of mortality. Parasites and pathogens alone are known to cause significant changes in reproduction, endurance, and growth of individual fish. Affected fish often become incapacitated, reproduce less, and become more susceptible to predation and less able to tolerate environmental extremes. Though it may be difficult to divide the combined effects of numerous stressors acting at the same time, combining low DO concentrations with parasites and pathogens likely amplifies negative effects.(c)Susceptibility to Contaminants Fish species may be negatively affected by chemical pollution in urban or agricultural runoff . The toxicity of particular chemicals to fish often changes depending on water quality parameters, including DO concentrations, pH, salinity, and hardness. Poisonous substances and low DO concentrations change the physiology of fish and can affect the function and behaviour of fish in the field. In broadspectrum, organisms living near their environmental tolerance limits (such as low DO concentrations) are more vulnerable to additional chemical stress, especially when exacerbated by enlarged temperatures or low food supplies. An boost in susceptibility to toxic substances may be caused by an increase in respiration attributable to low DO concentrations. Fish respiring more bring more water, and therefore more toxic substances, across the gills and into their systems. Decreased DO concentration causes harmful effects on fishes can be explained by the bio energetic principle proposed by Fry (1971), according to that, the DO concentration that can be explained as upper threshold below which oxygen causes direct mortality as shown in graph within the range, the potential magnitude of adverse effects increases with decreasing oxygen concentration and increasing duration and frequency of exposure. The initial limiting level is important threshold below which the lack of available oxygen resists the ability of fish to perform at maximum levels and increases physiological stress and expenditure of energy to meet oxygen demand.

Rombough 1988, Cech et al 1990 explained different species of fishes have different ability to tolerate low oxygen concentrations, depending on the natural and range of DO concentration that fishes encounter in their preferred habitat. In fish metabolic rate, respiration and feeding activity, growth is highly affected by the concentration decrease, as a results of all this disease attack fishes which lead to mortality of fishes (Tom 1998).Wederm eye 1996 studied that not only physiological or metabolic activities but production of fishes also get affected by decrease oxygen concentration .He also added that, DO requirement varies with species, body size and activities of fishes. Tom (1998) said that oxygen requirements per unit weight of fish significantly decline with increasing individual weight. Randolph and Clemenens (1976) found that feeding patterns of catfish varied with temperature and oxygen availability. When the oxygen content drops below 59% fish starts to lose its appetite. V. Conclusion India is gifted with rich water resources nearly 45000 km long riverine system crisscrosses the length and breadth of the country. Out of this Ganga basin is extraordinarily varied in altitude, climate, land use and cropping patterns. Ganga has been a cradle of human civilization since time immemorial. It is one of the most sacred rivers in the world and is deeply regarded by the people of the country. India has 12 basins, 14 minor and dessert river basin. Out of this Ganga is the largest river basin which flows through the state of Uttarakhand, Uttar Pradesh, Haryana, Himachal Pradesh, Bihar, Jharkhand and West Bengal. But these water resources are losing their beauty and life every day. The reason behind these losses are swiftly increasing pollution, growing standards of living and exponential growth of industrialization and urbanization have exposed the water resources, in general and reverse in particular to various forms of degeneration. Several Indian rivers, including the Ganga in several stretches particularly during lean flows, have become unfit even for bathing. The Ganga revered for its purity subsistence every day. The river in which millions wash off their sins is now left

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with 15% of its original water, while the remaining 85% comprises sewage, slu dge, several conservation efforts backed by crores of rupees have failed to restore the sensitivity of the national river. The river Ganga water quality evaluate on the basis of pollution indicate us (DO, BOD & COD) indicate the dissolved oxygen level of river Ganga increasing very rapidly so the need is, in fact, made all the more urgent by the recent spurt of human activities in this region in exploiting its water resources for hydroelectric purposes. Not only are the rivers directly affected by the developmental activities, but they are also affected by other threats like introduction of exotic species, over fishing and the disposal of industrial and domestic wastes from new industries and settlements. Before the rich species diversity of this region of the subcontinent is lost forever, some immediate action and some more research work will be done to save Ganga. References 1. 2. 3. 4. 5. 6. 7. 8.

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Kumar Ashok , Bisht B.S., Joshi V.D. , Singh A.K. and Talwar Amitabh. Physical, Chemical and Bacteriological Study of Water from Rivers of Uttarakhand J Hum Ecol, 32(3): (2010) ,pp.169-173 Maiti SK 2004. Handbook of Methods in Environmental Studies, Water and Waste Water Analysis, Vol. 1, Jaipur: ABD Publishers. Mane VR, Chandorkar AA, Kumar R 2005. Prevalence of pollution in surface and ground water sources in the rural areas of Satara Region, Asian Journal of Water,Environment and Pollution. 2: 81-87. Massoud Tajrishy, Ahmad Abrishamchi 2005. Integrated Approach to Water and Wastewater anagement forTehran, Iran, Water Conservation, Reuse and Recycling. Proceedings of the Iranian-American Workshop. Washington, D.C.: National Academies Press. Merritts D, DeWet A, Menking K 1998. Environmental Geology: An Earth System Science Approach. New York: W.H. Freeman and Company Randolph, K.N., and H.P. Clemens. 1976. Some factors influencing the feeding behaviour of channel catfish in culture ponds. Transactions of American Fisheries Society 105: 718724. Rombough PJ. Growth, aerobic metabolism, and dissolved oxygen requirements of embryos and alevins of steelhead, Salmo gairdneri . Can J Zool. 1988;66(3):651–660. doi: 10.1139/z88-097. Rui-feng Liang, Bo Li, Ke-feng Li, and You-cai Tuo Effect of total dissolved gas supersaturated water on early life of David’s schizothoracin (Schizothorax davidi) J Zhejiang Univ Sci B. Jul 2013; 14(7): 632–639. Schlesinger WH 1991. Biogeochemistry: An Analysis of Global Change. New York: Academic Press Inc. Tchobanoglous G, Burton FL, Stensel HD 2003. Wastewater Engineering (Treatment Disposal Reuse). 4th Edition.New York: Metcalf and Eddy Inc. Thetmeyer, H., Waller, U.,Black, K.D., Inselmann, S., Rosenthal, H., 1999. Growth of European sea bass (Dicentrarchus labrax L.) under hypoxic and oscillating oxygen conditions. Aquaculture, 174, 355-367. Tom L.1998, Nutritional and feeding of fish. Kluwer Academic Publishers. Second edition. Wiklund T.and Dalsgaar I., Occurrence and significance of atypical Aeromonas salmonicida in non-salmonid and salmonid fish species: A review. Dis Aquat Org, 32: (1998), pp.49-69. Wiklund T. and Bylund G., Fin abnormalities of pikeperch in coastal areas off the Finnish south coast. J. Fish Biol. 48: 1996, pp.652657. Wiklund, T., Lounasheimo, L., Lom, J. and Bylund, G ., Gonadal impairment in roach Rutilus rutilus from Finnish coastal areas of the northern Baltic Sea. Dis. aquat. Org. 26: (1996),pp.163-171. WHO 1999. Guidelines for Drinking Water Quality. 2nd Edition. Geneva: WHO

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DETERMINATION OF SPATIAL CROP COEFFICIENT OF CHICKPEA USING REMOTE SENSING AND GIS A.R.Pimpale1, P.B. Rajankar2, S.B. Wadatkar 3, I.K. Ramteke4 Ph.D. Scholar, Department of Irrigation and Drainage Engineering, Dr. PDKV, Akola, India 2 Associate Scientist, Maharashtra Remote Sensing Applications Centre, Nagpur, India 3 Head, Department of Irrigation and Drainage Engineering, Dr. PDKV, Akola, India 4 Scientific Associate, Maharashtra Remote Sensing Applications Centre, Nagpur, India

Abstract: Spectral vegetation indices have been often used for quantitative monitoring of biometric parameters of vegetation. Remotely sensed data vegetation indices data can be used to obtain rapid, accurate estimates of viable canopy attributes and related parameters. Crop coefficient is parameter of special interest for water management applications. Crop coefficient (Kc) based estimation of crop evapotranspiration(Etc) is one of the most commonly used methods for irrigation water management. The standardized FAO56 Penman-Monteith approach for estimating ETc from reference evapotranspiration and tabulated generalized Kc values has been widely adopted worldwide to estimate ET c. It is very complicated to calculate site specific or spatial evapotranspiration which can lead to inaccurate determination of water requirement by using the above said method. In this distributed study, a modified approach for estimating spatial Kc values from remotely sensed data is presented. A study was conducted in five centrally located districts of Maharashtra viz. Solapur, Beed, Osmanabad, Pune and Ahmednagar. The ground truth work was carried out in the month of Dec 2011 and Dec 2012 for collecting the information on sowing date, crop stage and other site specific parameters pertaining to the rabi dominant crops like rabi sorghum, wheat and chickpea. This paper elaborate about study carried out pertaining to chickpea only. Multi temporal satellite images of IRS-P6 AWiFS sensor corresponding with chickpea growing period (2011 and 2012 season) were used and Normalized Difference Vegetation Index (NDVI) was generated for the corresponding dates. Ground truth information collected with the aid of GPS device and geotagged camera was precisely transformed on the multi temporal stack of satellite images. Vector corresponding to the field sizes were digitized and beneath pixels were used for estimation of spectral indices. The multidate NDVI values of the identified locations of chickpea were correlated with the weekly crop coefficient values recommended by Mahatma Phule Krishi Vidyapeeth, Rahuri M.S.. It was observed that the NDVI pattern during the growing period is similar to the corresponding crop coefficient pattern. A regression model was developed to establish the relationship between NDVI and the crop coefficients (Kc) for chickpea crop, It was found that there exists a good linear relationship between Kc and NDVI with a R 2 value of 0.874 and a low root mean square difference. The results indicate that this approach can be a very useful tool for a large scale estimation of spatial evapotranspiration using the estimated crop coefficient. Keywords: crop coefficient, vegetation indices, NDVI, chickpea,, AWiFS, evapotranspiration

I. INTRODUCTION India is the world leader in chickpea (Cicer arietinum) production followed by Australia and Pakistan. Chickpeas are an excellent source of the essential nutrients, iron, folate, phosphorus, protein and dietary fiber. Maharashtra have 11% share in production of chickpea in the country. Chickpea is normally grown on residual moisture developed in the soil due to rainfall of the kharif season. Moisture supply during the growing period have a strong influence on chickpea plant phenology. Flowering and pod setting stages are the most sensitive stages to water stress. There is drastic reduction in yield because of short of sufficient moisture during sensitive stages. However its production can be increased to a great extent by 2-3 life saving irrigations. This calls for accurate water requirement assessment at appropriate time. Excess of water may lead to root borne diseases. On the other hand water stress may decline production. So, the knowledge of optimal water requirement is the key concern. Crop water requirement is the amount of water required to compensate the evapotranspiration (ETc) loss from the cropped field. A crop coefficient(Kc) relates the actual ETc at a given stage of crop development to reference ET (i.e. ETo) calculated from meteorological data by different equations depending upon data availability. If sufficient weather data are available, the standard FAO Penmanâ&#x20AC;&#x201C;Monteith formula can be used for calculations of daily ETo (Allen et al., 1998). Actual ET i.e. ETc is then calculated using the estimated ETo. ETc = Kc x ETo (1)

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Crop coefficients are generally derived through field experiments using lysimeters planted with the crop being studied and are typically at monthly intervals corresponding to the main growth stages of the crop. These crop coefficients are usually developed for crops grown under optimum agronomic conditions. Thus the calculated values are only useful approximations of the actual ET and water requirements for a given crop. Doorenbos and Pruitt (1977) in FAO-24 publication have given tabulated values of Kc for different crops using lysimeters for the major stages of crop development. However they have suggested to use modified/locally developed Kc values. Lysimeter studies at Mahatma Phule Krishi Vidyapeeth Rahuri (2012) have recommended such weekwise Kc values for chickpea crop Precision irrigation management requires information for determining ETc under variable climatic and field conditions. Hence, accounting for spatial and temporal variations in water use with present crop coefficient procedures is extremely difficult. Use of such crop coefficients for irrigation scheduling can lead to overirrigation of crops, which can be a serious concern especially in water-short arid and semi-arid areas of the world (Santos et al., 2007). Satellite remote sensing offers a means to overcome some of the shortcomings of time-based Kc curves by providing real-time and/or near real-time spatial information on Kc and ETc use as per the actual cropping patterns. The potential for using multispectral vegetation indices(VIs) as near real-time surrogates for crop coefficients was proposed by Jackson et al. (1980) who pointed out the similarity between the seasonal pattern of a VIs for crops and that of the crop coefficient. The concept was eventually established by Bausch and Neale (1989) who derived Kc for corn in Colorado based on several VIs. Limited research on this aspect has been done so far to expand the development of VI-based crop coefficients for field crops. Considering this a study was undertaken for rabi crops sorghum, wheat and chickpea in part of Maharashtra where these crops are dominant. Very limited work pertaining to this aspect has been attempted in Indian scenario, hence paper assumes greater importance specially in case of chickpea II. MATERIALS AND METHODS The Study area The study area comprises of dominant rabi crops (sorghum, wheat and chickpea) growing centrally located five districts of Maharashtra i.e. Pune, Solapur, Ahmednagar, Beed and Osmanabad.(Fig 1). The study area spans between 73° 15' 57" to 76° 47' 36" E longitude and 19° 59'40" to 17° 04'50" N latitude covering an area of 65,716 Km2 ( Fig.1). A raster file containing above Area of Interest (AOI) was selected attributing to following geographical co-ordinates. Top left : Latitude 20°10' 45.801" N Longitude 73° 12' 29.954"E Top Right : Latitude 20°10' 45.801" N Longitude 76 °59' 26.631"E Bottom left : Latitude 17° 03' 56.586"N Longitude 73° 12' 29.954"E Bottom Right : Latitude 17° 03' 56.586"N Longitude 76 °59 '10.791"E Remote Sensing Data and Software Used Multispectral images of IRS- P6, AWiFS sensor of five consecutive months of rabi season (Oct-Feb) for the year 2011-12 and 2012-13 were obtained from NRSC, ISRO, Hyderabad (Table 1 and Table 2). AWiFS (Advanced Wide Field Sensor) have four bands, band- 1 is green (0.5-.59 μm), band-2 is red(.62-.68 μm) , band3 is Near Infra Red (NIR)(.77-.86 μm) and band-4 is Middle infra red(MIR)(1.55-1.70 μm) with 56m resolution near Nadir and 70m near edge. It covers swath of 740 km with radiometric resolution 10 bit . The projection and datum of the data products are Lambert Conformal Conic and WGS 84 respectively. The ERDAS IMAGINE Software v. 9.1 and ArcGIS v. 10.1 software were used for all analysis related to remote sensing and geogarphical information system (GIS) applications. Ground Truth Data Ground truth information corresponding to the time of satellite data acquisition was collected to validate the image interpretations. 16 fields in each year were selected with relatively good spatial resolution. Detailed information like location coordinates of training sites by GPS and geotagged camera, sowing time, growth stage of crop, ground cover percentage, competing crops, crop infestation, soil type, moisture condition and crop calendar etc., were collected during field visit in December 2011 and December 2012. Normalized Difference Vegetation Index (NDVI) The most widely used and accepted vegetation index i.e. Normalized Difference Vegetation Index (NDVI) was selected for the study . The NDVI was calculated using spectral reflectance from visible and near infrared bands using following relationship (Rouse et al. 1973). Where, NIR and R are reflectance in near infrared and red wavelength bands respectively. The digital Analysis

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Digital analysis was carried out using Remote Sensing, GIS and Image processing software ArcGIS and ERDAS Imagine. It consisted of importing bandwise and layer staking the satellite data, digitization of study area, georeferencing the AWiFS images, clipping/subseting the area of interest from larger image and radiometric normalization. Ground truth sites were marked and a point and polygon vector layers were digitized ArcGIS. Non crop mask, toposheet layer and road network layer were also utilized for precise interpretation. The NDVI of each pure chickpea crop polygon was extracted by using signature editor and generating statistics. The mean multi temporal values of chickpea polygon NDVI were considered for further analysis. Crop Coefficients of Chickpea Crop (Kc) The weekwise crop coefficients of the chickpea crop obtained from lysimeter studies recommended by Mahatma Phule Krishi Vidyapeeth Rahuri, Maharashtra were used for the study.(Table 3) III. RESULTS AND DISCUSSION Generation of NDVI profile The Model Buider fuction in ERDAS Imagine was applied to get Normalized Difference Vegetation Index (NDVI) of each pixel of the subset image and NDVI image of the subset (AOI) was obtained for each date of pass. Thus 8 NDVI images each for the year 2011-12 and 2012-13 were obtained. A layer stack of the NDVI images was prepared for each year. Vector layers (point and polygon) of crops and non crop mask were added. Pure crop pixels of chickpea already marked by polygons were selected as AOI and added in signature editor in ERDAS Imagine to get statistics of NDVI of the crop polygons for each date. The statistics consisted of maximum value, minimum value, mean and standard deviation. This statistics corresponding to NDVI of each chickpea polygon was obtained The mean values of the chickpea crop polygon NDVI were used for further study. The NDVI values were distributed for different weeks after sowing of the crop with consideration of ground truth date and ground truth information. Table 4 shows the average weekwise values of NDVI for pure chickpea crop pixels. NDVI–KcPattern: MPKV Rahuri has recommended chickpea crop coefficients based on method of calculating reference evapotranspiration (ET o) i.e. Penman-Monteith Method, Pan evaporation method and Hargreaves– Samani method. These are denoted as KcPM, KcPE and KcHS respectively. The NDVI and Kc values were plotted against the number of weeks after sowing (Fig 2). It is clear from the figure that the NDVI and Kc curves have similar pattern with slight difference at the end. It is observed that the chickpea crop NDVI increases with the growth of the crop during initial, crop development and mid season stage and then decreases slowly during late season stage, whereas the Kc values also increase up to crop development stage but decrease fast during late season stage as compared to NDVI. Since watering is mostly required up to crop development stage the relation of NDVI and Kc can be utilized for irrigation scheduling. Relation between NDVI and Kc The averaged weekly NDVI values of the selected sites were determined through the satellite images and correlated with the weekly crop coefficients recommended by MPKV Rahuri to develop regression equations. It was found that the chickpea crop NDVI have similar trend as that of chickpea crop coefficients and can be correlated linearly. Figs. 3, 4 and 5 show correlation of NDVI with crop coefficients of KcPM, KcPE, KcHS with r2 values of 0.874, 0.837 and 0.775 respectively. The regression (correlation) equations so developed are: KcPM = 3.094 NDVI - 0.354 (2) KcPE = 2.942 NDVI - 0.344 (3) KcHS = 2.535 NDVI - 0.220 (4) All the three equations show good linear relation indicating the correlation of chickpea crop Kc with NDVI. It is clear that recommended Kc obtained with lysimeter studies using Penman–Monteith method for ETo have shown highest correlation with NDVI followed by Pan Evaporation method and Hargeaves-Samani method. This shows that the NDVI is highly correlated with chickpea crop Kc in linear relation. IV. CONCLUSIONS Satellite remote sensing data and GIS techniques were applied to estimate chickpea crop coefficients. The regression equation Kc = 3.094 NDVI - 0.354 may be used for determination of Kc. This can help to calculate accurate and spatial water demand of chikpea. The results obtained are supporting the similar study conducted by Misra et al. (2005) for paddy crop (West Bengal), Ray and Dadhawal(2000) in Mahi river irrigation (2006) and Gontia and Tiwari(2010) for wheat crop in Tarafeni South Main canal irrigation command. Therefore the information generated can be used to schedule amount of water for higher production of chickpea. The present investigation can pave an innovative approach for optimizing irrigation water scheduling. This can also be extrapolated in the areas where similar kind of agroclimatic conditions exists. ACKNOWLEDGEMENT Authors are thankful to Dr S.D.Gorantiwar, Head Irrigation and Drainage Engineering, College of Agriculture Engineering, Mahatma Phule Krishi Vidyapeeth Rahuri for his valuable suggestions time to time.

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REFERENCES Allen, R. G., L.S.Pereira, D.Raes and M. Smith,1998. Crop Evapotranspiration–Guidelines for computing crop water requirements. Irrigation and Drainage Paper 56. FAO, Rome, Italy. Anonymous, 2012. Krishidarshani MPKV Rahuri, 9-10 Bausch W. C. and C.M.U. Neale. 1989. Spectral inputs improve corn crop coefficients and irrigation scheduling. TRANS of ASAE 32(6), 1901-1908 Doorenbos J and W.O. Pruitt 1977. Crop water requirement. Irrigation and Drainage Paper No.24.(revised), FAO, Rome, Italy. Gontia, N K and K.N. Tiwari. 2010. Estimation of crop coefficient and evapotranspiration of wheat (Triticuum aerstivum) in a irrigation command using remote sensing and GIS. Water Resources Management 24, 1399–1414. Jackson R.D, S.B. Idso, R.J. Reginato and P.J. Pinter, 1980. Remotely sensed crop temperatures and reflectances as inputs to irrigation scheduling. In: Irrigation and drainage special conference proceedings, 23–25 July, Boise, Idaho. ASCE, New York, 390–397. Jensen, M.E., R.D. Burman and R.G. Allen. 1990. Evapotranspiration and Irrigation Water Requirements. ASCE Manual No. 70. New York, N.Y. Misra P; Tiwari K N, V.M. Chowdhary and N.K. Gontia 2005. Irrigation water demand andsupply analysis in command area using Remote Sensing and GIS. Hydrol J IAH28(1-2),59-69 Ray S S and V.K. Dadhwal 2000. Estimation of evapotranspiration of irrigation command using remote sensing and GIS. Agric Water Manag. 49,239-249. Rouse J W, R.H. Hass, J. A . Schell and D.W. Deering 1973. Monitoring vegetation system in great plains with ERTS. Proc. 3rd ERTS-1 symp,GSFC,NASA,SP-351,48-62. Santos C., I.J. Lorite , M. Tasumi, R.G. Allen, and E.Fereres 2007. Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level. J. Irri. Sci. 3, 277–288.

India

Maharashtra

Fig. 1: The study area 1.4 NDVI KcPM KcPE KcHS

Kc, NDVI

1.2 1 0.8 0.6 0.4 0.2 0 0

10 Weeks

Fig 2 NDVI and Kc pattern for chickpea

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1.4 y = 3.0948x - 0.3549 R² = 0.8749

1.2

Kc PM

1 0.8

0.6 0.4 0.2 0 0.18

0.28

0.38 NDVI

0.48

0.58

Fig. 3: Relationship of crop coefficient KcPM with NDVI 1.40 y = 2.9427x - 0.3447 R² = 0.8375

1.20

Kc PE

1.00 0.80 0.60 0.40 0.20 0.00 0.18

0.28

0.38 NDVI

0.48

0.58

Fig. 4 Relationship of KcPE with NDVI values for Chickpea

1.2 1

y = 2.5356x - 0.2208 R² = 0.7753

0.8 0.6 0.4 0.2 0 0.18

0.23

0.28

0.33 0.38 NDVI

0.43

0.48

0.53

Fig. 5 Relationship of KcHS with NDVI values for Chickpea

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Table 1: Multi-date IRS-P6 AWiFS 2011-12 data used for the study Sr No

Satellite

Sensor

Path

Row

Date of Pass

IRS-P6

Awifs

097

059

25-10-2011

IRS-P6

Awifs

100

059

09-11-2011

IRS-P6

Awifs

097

059

18-11-2011

IRS-P6

Awifs

096

059

17-12-2011

IRS-P6

Awifs

098

059

27-12-2011

IRS-P6

Awifs

095

059

19-01-2012

IRS-P6

Awifs

096

059

24-01-2012

IRS-P6

Awifs

098

061

03-02-2012

Table 2: Multi-date IRS-P6 AWiFS 2012-13 data used for the study Sr No

Satellite

Sensor

Path

Row

Date of Pass

IRS-P6

Awifs

097

058

19-10-2012

IRS-P6

Awifs

098

059

11-11-2012

IRS-P6

Awifs

098

059

29-11-2012

IRS-P6

Awifs

098

059

11-12-2012

IRS-P6

Awifs

098

062

27-12-2012

IRS-P6

Awifs

097

059

11-01-2013

IRS-P6

Awifs

097

23-01-2013

IRS-P6

Awifs

097

059

04-02-2013

Table 3: The crop coefficients for chickpea Week after sowing 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Method of calculating reference ET Penman Monteith Pan Evaporation Hargeaves-Samani 0.85 0.77 0.83 0.84 0.75 0.79 0.88 0.79 0.80 0.95 0.86 0.83 1.04 0.95 0.89 1.12 1.04 0.95 1.18 1.11 1.01 1.21 1.15 1.05 1.20 1.15 1.06 1.15 1.10 1.04 1.05 1.01 0.97 0.91 0.88 0.86 0.75 0.72 0.72 0.57 0.53 0.55 0.38 0.35 0.37 0.23 0.19 0.21 0.12 0.09 0.11

(Ref. Krishidarshani 2012 MPKV Rahuri pp. 9-10) Table 4: NDVI of pure chickpea crop pixels Week No

NDVI values 2011-12 2012-13

Average NDVI

0.235204

0.282037

0.258621

0.493533

0.486944

0.490238

0.495693

0.494649

0.495171

0.500975

0.504399

0.502687

0.425996

0.416228

0.406461

0.298672

0.330111

0.314391

0.206559

0.232471

0.219515

0.192761

0.216209

0.204484

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Karyomorphological studies on the plants of Duchesnea indica (Andr.) Focke B.T Umesh1, John E Thoppil2 Assistant Professor, Department of Biotechnology, MES College, Marampally, Aluva, Ernakulam, Kerala, INDIA 2 Associate Professor, Department of Botany, University of Calicut, Kerala, INDIA 1

Abstract: The present research was conducted for karyomorphological investigation on a lesser known but ethanomedicinally and ethanopharmacologically important species of genus Duchesnea. For this study, 1 normal HCl, Para dichloro benzene, aessculin and aceto orcein were used as pretreatment solution and for staining. Chromosome characteristics including long arm, short arm, chromosome lengths, total length of chromosome set, arm ratio index and centromeric index were measured. The studies showed that the somatic chromosome number in the genus Duchesnea indica was 2n = 84 (dodecaploid, n = 7). The karyotype formula was found to be K (2n) = 84= A12 B60 C12.. The total chromosome length of the cells of the parent plant was noticed as 34.566 μm and that of the variant was 35.7675 μm. The disparity indices observed in the chromosome complement of parent was 61.6755. The total forma percentage was estimated as 40.6758. The average chromosome length of the parent plant was found as 0.6176 μm. Keywords: Duchesnea indica, Indian symmetry.Chromosome morphology.

Strawberry,

Chromosome,

Karyotype,

Chromosome

I. Introduction Duchesnea indica, commonly called as mock strawberry (Indian Strawberry) is a trailing herb of Rosaceae [4]. It is a common herb widely distributed in south East Asia[1] The whole plant is used as an anti cancer herb in Chinese medicine[2,3] The plant is anticoagulant, antiseptic, depurative and febrifuge. It can be used in decoction or the fresh leaves can be crushed and applied externally as a poultice. It is used in the treatment of boils and abscesses, eczema, ringworm, stomatitis, laryngitis, acute tonsillitis, snake and insect bites and traumatic injuries. A decoction of the leaves is used in the treatment of swellings. An infusion of the flowers is used to activate the blood circulation. The fruit is used to cure skin diseases. A decoction of the plant is used as a poultice for abscesses, boils, burns etc [4,5]. Cytological techniques are very useful in determining the chromosome constitution of an organism and it facilitates recognition of the individual chromosomes. In plant taxonomy, breeding, and genetic studies, information about chromosome karyotype can be useful in species identification and analysis of hybrid populations [11, 12]. In case of this Duchesnea species chromosome counts have been reported from time to time by various cytologists but detailed description and visual presentation of karyotype of these species have not been published. The data on chromosome number and karyotypic analysis are prerequisites to the overall understanding of the genome. Therefore, an attempt has been made to analyze the detailed karyotype of D.indica. II. Materials and methods The experiment was conducted in the Cell Biology and Genetics laboratory of the Department of Botany, University of Calicut, Malappuram, Kerala, India. The species Duchesnea indica (Andr.) Focke was collected from Ooty, Tamilnadu, South India and authenticated at Calicut University Herbarium [Umesh B. T., 86006 (CALI)] where voucher specimens are deposited. The plants were grown at the experimental garden attached to the Department of Botany, Calicut University. The root tips of Duchesnea indica (Andr.) Focke was collected from the experimental plot of Calicut University Botanical garden. Somatic chromosome spreads were prepared with the help of improved techniques (Sharma and Sharma, 1990). Young root tips were collected from the plant at the period of peak mitotic activity (9 am - 10 am). Root tips were thoroughly washed in distilled water and treated in pre-treatment chemicals. The root tips that were not required immediately for slide preparation were stored in 70% ethanol in a refrigerator. Saturated solution of para dichloro benzene with traces of aesculin was used for pre treatment. Small quantity of saponin was also added to remove the oil particles from the cells. The pre treatment solution is initially chilled at 0 to 5 0C for 4 to 5 minutes and the root tips were treated at 12 to 15 0 C for 2 to 3 hours. The treated root tips were then washed with distilled water and fixed in 1 : 3 acetic acid -

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ethanol mixture (modified Carnoy’s Fluid) overnight. The root tips were washed in distilled water followed by treatment in 1N HCl for 5 minutes at room temperature. After washing the root tips thoroughly to remove the traces of acid, they were stained with 2% aceto orcein for 3 - 4 hours. Stained root tips were washed in 45% acetic acid to remove the excess stain and squashed. The slides were scanned under Olympus microscope CX 21 and the photographs of well spread mitotic plates were taken using Olympus Camedia C - 4000 Zoom digital compact camera attached to the microscope. III. Karyomorphological Analysis:Karyograms were generated with the aid of computer soft wares such as Adobe Photoshop, CHIAS and a data based analyzing system (Microsoft EXCEL). Photographs were scanned and stored as digital images. These digital images were converted into grey scale images using Adobe Photoshop programme. Identification numbers were allotted to each chromosome and then loaded to CHIAS for karyomorphometric analysis. The arm length of each chromosome was measured after determining the centromeric position. Then the centromeric indices were calculated. Homologous chromosomes were identified and classified on the basis of arm ratio and centromeric indices, according to Abraham and Prasad [19]. The images were measured for long arm, short arm, and total length of chromosome set, arm ratio and centromeric index. The symmetry of karyotype was classified according to Stebbins [5]. Along with this Total Forma percentage (TF %) [18] was also calculated. Disparity index (DI) of the chromosomes were calculated as per Mohanty et al. by using the formula, Longest chromosome - shortest chromosome DI = ---------------------------------------------------------- X 100 Longest chromosome + shortest chromosome The total forma percentage was calculated using the formula; Total sum of length of short arm of chromosomes TF % = ---------------------------------------------------------------× 100 Total sum of total length of all the chromosomes Details of chromosome nomenclature in relation to centromeric location based on arm ratios and centromeric indices (Abraham and Prasad, 1983). Nomenclature Median Nearly median Nearly submedian Submedian Nearly submedian Nearly subterminal Sub terminal Nearly subterminal Nearly terminal Terminal

Notation M nm nsm(-) SM nsm (+) nst(-) ST nst(+) nt T

R1 s/l 1.000 0.99 to 0.61 0.60 to 0.34 0.33 0.3 to 0.23 0.22 to 0.15 0.14 0.13 to 0.07 0.06 to 0.01 0.00

R2 l/s 1.00 1.01 to 1.63 1.64 to 2.99 3.00 3.01 to 4.26 4.27 to 6.99 7.00 7. 01 to 14 . 38 14. 39 to 19. 99 

l1 100s/c 50.00 49.99 to 38.01 38.00 to 25.01 25.00 24. 99 to 18. 20 18.19 to 12. 51 12.50 12. 49 to 5.01 5.00 to 0.01 0.00

I2 100 l/c 50.00 50. 01 to 61.99 62. 00 to 74. 99 75.00` 75. 01 to 81. 80 81. 81 to 87. 49 87.50 87 51 to 94.99 95.00 to 99.99 100. 00

R1 and R2 = Arm ratios I 1 and I 2 = Centromeric indices s - short arm length l - long arm length C-total chromosome length III. Results The chromosome number of this species is 2n=84 (figure 1(a)). Karyomorphometric characters of the mitotic chromosomes are shown in table 1. Chromosome complement analysed in detail (Plates 5 – 7; Tables 4 – 6). The number of chromosomes with secondary constrictions was found to be 12. Changes in chromosome length, disparity index and total forma percentage were noticed. The chromosomes ranged in size from 1.5323 µm to 0.3838 µm. The total chromosome length of the cells of the plant was noticed as 34.566 µm. The disparity indices observed in the chromosome complement was 61.6755. The total forma percentages was 40.6758. The general description of common types of chromosomes :Type A: - Chromosome with secondary constriction ranging from 1.6191 µm to 0.8027 µm with nearly median/ nearly submedian primary constriction. Type B: - Chromosome with length of 1.2879 µm to 0.3838 µm with nearly median primary constriction. Type C: - Chromosome with length ranging from 1.3603 µm to 0.4402 µm with nearly sub median primary constriction.

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The nomenclature of chromosome was depicted according to the system followed by Abraham and Prasad (Table 1). The karyotype formula of the plant was A12 B60 C12. The result of the experiment is reported as under: Duchesnea indica (Andrew) Focke. (2n = 12x = 84 = A12 B6C60 D6) Normal somatic chromosome number Chromosomes with secondary constriction Total chromosome length Range of chromosome length Average chromosome length Disparity index TF value

: 84 : 12 : 34.5660 μm : 1.6191μm - 0.3838μm : 0.6176 μm : 61.675 : 40.6755

Table 1. Karyomorphometrical details of Duchesnea indica (Andr.) Focke Chr. Type

No.of Pairs

Total Length (μ m)

s (μ m)

l (μ m)

R1 (s/l)

R2 (l/s)

l1 (s/c%)

l2 (l/c%)

Nature of Primary Constriction.

1.5323

0.5809

0.9514

0.6106

1.6378

37.9103

62.0897

nsm(-)

1.2879

0.5407

0.7472

0.7236

1.3819

41.9831

58.0169

1.2663

0.4777

0.7886

0.6058

1.6508

37.7241

62.2759

nsm(-)

1.2207

0.5088

0.7119

0.7147

1.3992

41.681

58.319

1.1625

0.4128

0.7497

0.5506

1.8161

35.5097

64.4903

nsm(-)

1.1291

0.3994

0.7297

0.5473

1.827

35.3733

64.6267

nsm(-)

1.1103

0.4544

0.6559

0.6928

1.4434

40.9259

59.0741

1.0994

0.5296

0.5698

0.9294

1.0759

48.1717

51.8283

1.0974

0.4698

0.6276

0.7486

1.3359

42.8103

57.1897

1.0942

0.3469

0.7473

0.4642

2.1542

31.7035

68.2965

nsm(-)

1.0833

0.3942

0.6891

0.5721

1.7481

36.3888

63.6112

nsm(-)

1.0705

0.4685

0.602

0.7782

1.285

43.7646

56.2354

1.0111

0.4567

0.5544

0.8238

1.2139

45.1686

54.8314

1.0020

0.4634

0.5386

0.8604

1.1623

46.2475

53.7525

0.9975

0.1947

0.8028

0.2425

4.1233

19.5188

80.4812

nsm(+)

0.9417

0.4225

0.5192

0.8138

1.2289

44.8657

55.1343

0.9333

0.461

0.4723

0.9761

1.0245

49.3946

50.6054

0.9291

0.4572

0.4719

0.9688

1.0322

49.2089

50.7911

0.9198

0.4133

0.5065

0.816

1.2255

44.9337

55.0663

0.8947

0.3842

0.5105

0.7526

1.3287

42.9418

57.0582

0.8715

0.3381

0.5334

0.6339

1.5776

38.7952

61.2048

0.8390

0.3133

0.5257

0.596

1.6779

37.3421

62.6579

nsm(-)

0.8027

0.3207

0.482

0.6654

1.503

39.9527

60.0473

0.7207

0.3078

0.4129

0.7455

1.3415

42.7085

57.2915

0.6864

0.2928

0.3936

0.7439

1.3443

42.6573

57.3427

0.5010

0.1981

0.3013

0.6575

1.5209

39.5409

60.1397

0.4899

0.2442

0.2457

0.9939

1.0061

49.8469

50.1531

0.4839

0.2134

0.2705

0.7889

1.2676

44.1

55.9

0.4830

0.2371

0.2459

0.9642

1.0371

49.089

50.911

0.4774

0.2373

0.2401

0.9883

1.0118

49.7067

50.2933

0.4640

0.2268

0.2372

0.9562

1.0459

48.8793

51.1207

0.4586

0.1627

0.2959

0.5498

1.8187

35.4775

64.5225

nsm(-)

0.4477

0.1893

0.2584

0.7326

1.365

42.2828

57.7172

0.4452

0.1981

0.2471

0.8017

1.2473

44.4969

55.5031

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Chr. Type

No.of Pairs

Total Length (μ m)

s (μ m)

l (μ m)

R1 (s/l)

R2 (l/s)

l1 (s/c%)

l2 (l/c%)

Nature of Primary Constriction.

0.4394

0.213

0.2264

0.9408

1.0629

48.4752

51.5248

0.4371

0.2024

0.2347

0.8624

1.1596

46.3052

53.6948

0.4269

0.1982

0.2287

0.8666

1.1539

46.4277

53.5723

0.4260

0.1622

0.2638

0.6149

1.6264

38.0751

61.9249

0.4231

0.2214

0.2217

0.9986

1.0014

52.3281

52.399

0.4181

0.1883

0.2298

0.8194

1.2204

45.0371

54.9629

0.4066

0.1849

0.2217

0.834

1.199

45.4747

54.5253

0.3838

0.1509

0.2329

0.6479

1.5434

39.3174

60.6826

s = Short arm length, l = Long arm length, c = Total chromosome length, R 1 = arm ratio 1 R2 = arm ratio 2, I1 = Centromeric index 1, I2 = Centromeric index 2 IV. Discussion To examine the karyomorphological changes in the chromosome complement of the tissue culture derived plants, mitotic studies were carried out on actively growing root tip meristem of in vitro plants and also on callus cells. Then it was compared with the in vivo plant. The ploidy level of all the cells studied, i.e., in vivo, in vitro and callus were found to be invariably dodecaploid (2n = 12x = 84). Chromosome morphology of the regenerated plants showed slight variation. Some of the chromosomes are not the exact replica of the parent plant and exhibited structural changes, like variation in total chromosome length, average chromosome length, centromeric positions, disparity index and total forma percentage. The chromosomes ranged in size from 1.5323 μm to 0.3838 μm in the parent plant, from 1.5323 μm to 0. 4402 μm in the variant and from 1.6191 μm to 0.3838 μm in the callus. The total chromosome length of the cells of the parent plant was noticed as 34.566 µm and that of the variant was 35.7675 µm. The total chromosome length of the callus was 33.3151 µm. The disparity indices observed in the chromosome complement of parent, variant and callus were 61.6755, 55.3662 and 59.9394 respectively. The total forma percentages of the parent, variant and callus were estimated as 40.6758, 42.0408 and 41.5358. The karyotype formula of the parent and the in vitro plant was A12 B60 C12. V. References [1] [2] [3] [4] [5] [6] [7] [6] [7] [8] [9] [10] [11] [12]

SK Jain., Medicinal plants. National Book Trust, India. 3, 1979 SP Ambasta. The useful Plants. Publication and information Directorate, New Delhi, ; 186, 1994 JA Duke. and ES Ayensu. Medicinal Plants of China. Reference Publications Inc., China. 1985. CSIR.. The Useful Plants of India, Publication and Information Directorate, CSIR, New Delhi, 186, 1994 G Baquar, SR Hussain,” Chromosome studies in some flowering plants of West Pakistan 1”, Phyton, 24: 49-55, 1967 GL Stebbins,” Chromosomal evolution in higher plants”, Edward Arnold Ltd, London, 1971. MT Monforte, N Miceli, MR Mondello, R Sanogo, A Rossitto, EM Galati,” Antiulcer activity of Salvadora persica on experimental ASA- induced ulcer in rats: ultrastructural modifications”, Pharmaceutical Biology, 39: 289-292, 2001 S Bala, RC Gupta, “Chromosomal diversity in some species of Plantago (Plantaginaceae) from north India”, International Journal of Botany, 7:82-89, 2011 SS Sindhu,” Chromosome studies of some mangroves”, Proceeding of Indian Science Congress, 48 (3): 302-303, 1961 Y Huziwara,” Karyotype analysis in some genera of compositae VIII. Further studies on the chromosomes of aster”, American Journal of Botany, 49: 116-119, 1962 Z Abraham, NP Prasad, “A system of chromosome classification and nomenclature”, Cytologia, 48: 95-101, 1983 Z Nazari, NH Mirzaie, KGR Bakhshi, F Asadicorom, “Karyotypic characteristics of Moringa peregrina (forssk.) Fiori in Iran”, Iranian Journal of Medicinal and Aromatic Plants, 27, 635-646, 2012 D’ Amato, F. 1952. Polyploidy in the differentiation and function of tissues and cells in plants: A critical examination of literature. Caryologia 4: 311 - 357. Das Gupta, A. and Datta, P. C. 1976. Cytotaxonomy of Piperaceae. Cytologia 41: 697 - 706.

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American International Journal of Research in Formal, Applied & Natural Sciences

Available online at http://www.iasir.net

Interaction effect of sowing dates and different treatments on disease incidence and intensity of Phoma sp.on Safed Musali R.W.Ingle, Saket Shende, V.V.Deshmukh and M.S.Joshi Department of Plant Pathology, Dr.PDKV, Akola, Maharashtra 444104, India Abstract: The Safed musli (Chlorophytum borivilianum) is important medicinal herbaceous plant belongs to the family Liliaceae. It is distributed mainly in the Southern Rajasthan, North Gujarat and Western Madhya Pradesh and some part of Vidrabha region. The field experiment was carried out during 2011-12 at Nagarjuna Medicinal and Aromatic Plant Garden, Dr. P.D.K.V., Akola to see the interaction effect of sowing dates and different treatments on incidence and intensity of foliar diseases Phoma on Safed Musli. From the resealts it is revealed that the lowest incidence and intensity of foliar disease caused by Phoma sp. was observed, in treatment D3 i.e. (30th June) and T3 (T. viride + P. fluorescens + Carbendazim + Mancozeb).

I. INTRODUCTION India is one of the twelve mega biodiversity centers in the world with a wealth of 8000 species of medicinal plants. The world of naturals is storming the globe with scientific rationale and trends that are fast emerging to support better health and life through plant and plant products.(Raghavendra et al., 2005) The demand for the products obtained from these plants such as phytochemical, steroidal, biologically active compounds, alkaloids, etc. is increasing in the national and international market. Safed Musli is originally grown in thick forest in natural form and is a traditional medicinal plant. Mainly its tuberous roots are used in ayurvedic medicines. (Gutierrez and Cundom, 2006). Safed musali (Chlorophytum borivilianum) is herbaceous belongs to the family Liliaceae. It is distributed mainly in the Southern Rajasthan, North Gujarat and Western Madhya Pradesh and some part of Vidrabha region. Now this crop has been brought under commercial cultivation in Gujarat, Rajasthan, Maharashtra, Karnataka, Madhya Pradesh, and Tamilnadu etc.(Bordia et al.1990) Because of monocroping the disease intensity and incidence may increase in recent future. The Safed Musli crop which is affected by the various diseases cause huge damage to crop as root , collar , tuber root, leaf and sheath blight, anthracnose , leaf spot and rust The root rot fungus viz., Fusarium solani, Rhizoctonia bataticola (Taub.) Butler, Rhizoctonia solani, Sclerotium rolfsii Sacc. and Phythium spp.The attack of Macrophomina phaseolina causing leaf spot in Madhya Pradesh region. The damage recorded was about 52% (Mandal et al. 2004.). The root rot (Sclerotium rolfsii Sacc.) caused losses when it serves about 10-15% in the field (Singh et al. 2007). The blight disease (Colletotrichum dematium (Pers.ex Fr.)) caused losses up to 30% in severely affected fields recorded from Vidarbha region (Tekade 2008). II. MATERIAL AND METHODS A. Collection of disease samples The diseased samples of Safed Musli were collected form Nagarjun Research Station, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola. The samples were examined for the presence of pathogen under research microscope and preserved for further investigation. B. Record of medicinal plant diseases under natural field condition The initiation and development of diseases on Safed Musli were recorded and weekly observations were taken and the incidence / occurrence of diseases were on per cent plant incidence. The disease occurrence was recorded by selecting the five plants for foliar diseases. C. Observations Observations on per cent disease incidence and intensity were recorded periodically. Incidence was calculated from number of infected and healthy plants. The per cent disease incidence of Safed Musli was calculated by following formula. Number of infected plants Per cent Disease Incidence = ------------------------------------ x 100 Total number of plants

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The average intensity was worked out by using the following formula. Summation of all numerical ratings Per cent Disease Intensity = ----------------------------------------------- x 100 Total numbers of leaves examined X maximum ratings D. Experimental layout The field experiment was conducted at Nagarjun Medicinal and Aromatic plant Garden, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola in Split Plot Design with three replications with 2 factors main factor sowing date and sub factor as rhizome treatment. Crop : Safed Musli Season : Kharif Pathogen : Phoma sp., Plant to Plant distance : 10 cm Row to Row distance : 30 cm Plot size : 2.4 x 2.1 m2 Replication : Three Design : Split Plot Design E. Treatment details Main factor Sr. no.

Date of sowing

10th June 2011

20th June2011

30th June 2011

Sub factor Sr. No.

Treatment No.

1 2 3 4

T1 T2 T3 T4

Treatment Details Rhizome treatment with Trichoderma viride + Pseudomonas fluorescens Rhizome treatment with Carbendazim + Mancozeb Rhizome treatment with T1 + T2 Control

III. RESULTS AND DISCUSSION Infected samples of Safed Musli were collected from the field of Nagarjun Medicinal and Aromatic Plant Garden, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola and constantly observed for disease occurrence and their management during 2011-2012. Isolation of fungal pathogen from diseased samples was made on potato dextrose agar by tissue isolation technique. Phoma sp. The mycelium is scanty, light brown in colour. The pycnidium is mostly thin walled and brown in colour. Sometimes the pycnidia may be superficial. The whitish, pinkish or pale coloured ooze comes from the ostiole. Pycnidiospores are single celled, hyaline and oval. Similar morphological charactors were found to Boerema et al. (2004) Effect of sowing date on incidence and intensity of foliar disease caused by Phoma sp. From the results it was revealed that at 46 DAS the lowest disease incidence 2.98% was observed in D1 with disease intensity 1.11% followed by D3, 2.39% (3.75%) and D2 3.70% (5.73%). Similarly, the lowest disease incidence and intensity was recorded in D1 6.08% (4.18%) followed by D3, 8.24% (5.23% ) and D2 14.98% (8.21 %) at 60 DAS. At 74 DAS the lowest disease incidence and intensity was recorded in D3 13.02% (8.05%) followed by D1 15.90% (10.11%) and D2 18.50% (10.16%). The lowest disease incidence and intensity at 88 DAS was observed in D3 i.e. 20.26% followed by D1(20.85%) and D2 (25.75%). While lowest intensity was observed in D 1, 11.55% followed by, D3, 13.12 and D2 17.63%. At 102 DAS lowest disease incidence was observed in D3, 24.24% followed by D1, 27.38% and D2 33.29%. Where as lowest disease intensity was observed in D3, 17.63% followed by D1, 19.61% and D2, 25.86%. The results in present investigation indicated that gradual increase in disease incidence and intensity with increase in days after sowing. The 2nd date sowing showed highest per cent of disease incidence and intensity followed by D1 and D3 i.e. the least one. The present findings explain that D 3 is most appropriate sowing date

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for Safed Musli regarding the incidence of Phoma sp. This may be because of the climatic conditions present during growth periods of different sowing dates. Similar results were recorded by Breteg et al. (2000) and Patil et al. (2010) that late sowing helps to reduce disease incidence of Phoma sp. It may be due to fewer amounts of primary inoculation and climatic condition. Table 2. Effect of sowing dates on incidence and intensity of foliar disease caused by Phoma sp. Treatmen t detiails DATE 1

46 DAS Incidenc Intensit e y

60 DAS Incidenc Intensit e y

74 DAS Incidenc Intensit e y

88 DAS Incidenc Intensity e 11.55 20.85 (19.75)* (27.10)** *

102 DAS Incidenc Intensity e 19.61 27.38 (26.17)* (31.46)** *

2.98 (1.86)*

1.11 (1.26)*

6.08 (2.55)*

4.18 (2.15)*

15.90 (3.95)*

10.11 (3.20)*

DATE 2

5.73 (2.47)

3.70 (2.02)

14.98 (3.88)

8.21 (2.91)

18.50 (4.27)

10.16 (3.23)

25.75 (30.41)

17.63 (24.76)

33.29 (35.21)

25.86 (30.54)

DATE 3

3.75 (2.03)

2.39 (1.67)

8.24 (2.93)

5.23 (2.36)

13.02 (3.66)

8.65 (3.02)

20.26 (26.60)

13.12 (21.17)

24.24 (29.40)

17.63 (24.78)

`F’ Test SE(m)+ CD at 5%

Sig. 0.10 0.39

Sig. 0.01 0.05

Sig. 0.13 0.49

Sig. 0.05 0.19

Sig. 0.10 0.41

Sig. 0.07 0.26

Sig. 0.32 1.24

Sig. 0.52 2.03

Sig. 0.24 0.95

Sig. 0.24 0.94

Figure in parenthesis * indicate Square root values. Figure in parenthesis ** indicate arc sin values. Effect of rhizome treatment on incidence and intensity of foliar disease caused by Phoma sp. The data dipected (Table 2) indicated that the lowest disease incidence was observed in treatment T 3 (Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb) 3.35% Followed by T 1 (Trichoderma viride + Pseudomonas fluorescens), 3.76% and T2 (Carbendazim + Mancozeb), 4.02% as compared to T4 control 5.49%. where as lowest intensity was recorded in treatment T 2 (Carbendazim + Mancozeb), 1.77% followed by T3 (Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 2.08% and T1 (Trichoderma viride + Pseudomonas fluorescens), 2.11% as compared to T 4 control 3.63% at 46 DAS. At 60 DAS the lowest disease incidence at 60 DAS observed in treatment T3 (Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 7.97% followed by T 1 (Trichoderma viride + Pseudomonas fluorescens), 8.41% and T2 (Carbendazim + Mancozeb), 8.87% as compared to T 4 control 13.82%. while lowest disease intensity was observed in treatment T 1 (Trichoderma viride + Pseudomonas fluorescens), 4.47% followed by T 3 (4.85%) and T2 (5.11%) as compared to control 9.07%. Table 3. Effect of seed treatments on incidence and intensity of foliar disease caused by Phoma sp. Treat details

46 DAS Incidenc Intensit e y

60 DAS Incidenc Intensit e y

74 DAS Incidenc Intensit e y

88 DAS Incidenc Intensity e 13.75 21.00 (21.67)* (27.24)** * 19.57 12.91 (26.21) (20.93) 19.06 12.32 (25.82) (20.40)

102 DAS Incidenc Intensity e 21.00 27.15 (27.20)* (31.34)** * 25.27 19.41 (30.11) (26.06) 24.85 18.18 (29.83) (25.11)

3.76 (2.05)*

2.11 (1.59)*

8.41 (2.96)*

4.47 (2.22)*

13.64 (3.71)*

8.68 (3.03)*

4.02 (2.07) 3.35 (1.95)

1.77 (1.47) 2.08 (1.58)

8.87 (2.99) 7.97 (2.85)

5.11 (2.34) 4.85 (2.29)

13.71 (3.70) 13.39 (3.67)

8.14 (2.93) 9.07 (3.07)

TREAT 4

5.49 (2.42)

3.63 (1.96)

13.82 (3.69)

9.07 (3.05)

22.49 (4.74)

12.66 (3.57)

29.52 (32.88)

17.40 (24.58)

35.93 (36.81)

25.54 (30.29)

`F’ Test

Sig.

SE(m)+

0.08

0.05

0.08

0.06

0.07

0.06

0.30

0.40

0.32

0.38

CD at 5%trreyu y

0.23

0.15

0.25

0.17

0.21

0.19

0.88

1.19

0.95

1.14

TREAT 1 TREAT 2 TREAT 3

Figure in parenthesis * indicate Square root values. Figure in parenthesis ** indicate arc sin values. At 74 DAS, the lowest disease incidence was observed in treatment T 3 (Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 13.39% with lowest disease intensity in treatment T2 (8.14%), as compared to control 12.66%. At 88 DAS the lowest disease incidence and intensity was recorded in treatment T3 (Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 19.06% (12.32%). Similarly, the lowest disease incidence and intensity was observed in T 3 (Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 24.85% (18.18%)

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In present investigation the combination of biocontrol agent and chemical fungicide was found superior. As the per cent disease incidence and intensity were lowest and highest per cent incidence found in T 4 i.e. control. The disease incidence was increasing gradually in all four treatments. The lowest disease incidence found in T 3 followed by T2, T1 and in T4 the highest disease incidence was found caused by Phoma sp.The present investigations revealed that the combination of biocontrol and chemical fungicide shows effective as rhizome treatment. It may be because broad spectrum antibiotic produced by Pseudomonas fluorescens such as 2-4diacetylphloroglucinol and antibiosis prove major mechanism involved in their biocontrol activity. Synergism between different biocontrol agent and chemical fungicide was often observed as an effective mean for their integration with existing disease management practices. Breteg et al. (2000) found similar results. Interaction of sowing dates and rhizome treatment over incidence and intensity of disease caused by Phoma sp. Table. Effect of interaction of sowing dates and rhizome treatments on foliar disease caused by Phoma sp. Phoma sp. 46 DAS T1 Intensity D1 D2 D3 F test S E (M)+

1.31(1.34)* 3.30 (1.94) 1.73 (1.49)

T2 Intensity 1.00 (1.22)* 3.05 (1.88) 1.25 (1.31)

CD @ 5 %

D1 D2 D3 F test S E (M)+

T3 Intensity 0.99 (1.22)* 2.56 (1.75) 2.70 (1.77) Sig. 0.09

T4 Intensity 1.14 (1.28)* 5.88 (2.53) 3.86(2.09)

0.26

T1 Incidence 6.43 (2.63)* 11.27 (3.42) 7.52 (2.83)

Phoma sp. 60 DAS T2 T3 Incidence Incidence 5.47 (2.42)* 4.97 (2.33)* 13.60 (3.71) 12.63 (3.62) 7.53 (2.83) 6.30 (2.60) Sig. 0.98

CD @ 5 %

T4 Incidence 7.47 (2.82)* 22.40 (4.78) 11.60 (3.48)

2.90 Phoma sp. 60 DAS T1 Intensity

T2 Intensity

T3 Intensity

T4 Intensity

3.91 (2.10)*

3.60 (2.02)*

3.39 (1.97)*

5.84 (2.52)*

5.92 (2.53)

6.83 (2.69)

7.18 (2.77)

12.90 (3.66)

D3 F test

3.57 (2.02)

4.89 (2.32)

3.99 (2.12) Sig.

S E (M)+

0.56

CD @ 5 %

1.67

8.46 (2.99)

Figure in parenthesis * indicate Square root values. Figure in parenthesis ** indicate arc sin values. The results on interaction effect revealed that at 46 DAS the results were statistically non significant over the incidence of disease. Regarding intensity at 46 DAS, the lowest disease intensity was observed in D 1T3 (10th June 2011 + Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 0.99% followed by D 1T2 (10th June 2011 + Carbendazim + Mancozeb), 1.00%; D 3T2 (30th June 2011 + Carbendazim + Mancozeb), 1.25%; D1T1 (10th June 2011 + Trichoderma viride + Pseudomonas fluorescens), 1.31%; D3T1 (30th June 2011 + Trichoderma viride + Pseudomonas fluorescens), 1.73%; D2T3 (20th June 2011 + Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 2.56%; D3T3 (30th June 2011+ Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 2.70%; D2T2 (20th June 2011+ Carbendazim + Mancozeb), 3.05%; D2T1 (20th June 2011 + Trichoderma viride + Pseudomonas fluorescens), 3.30%; over control D1T4 (10th June 2011+ control),1.14%; D3T4 (30th June 2011+ control), 3.86%; and D2T4 (20th June 2011 + control), 5.88%. The lowest disease incidence at 60 DAS observed in D1T3 (10th June 2011 +Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 4.97% (3.39%) followed by D 1T2 (10th June 2011 + Carbendazim + Mancozeb), 5.47% (3.60%); D3T3 (30th June 2011 + Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 6.30% (3.99%); D1T1 (10th June 2011 + Trichoderma viride + Pseudomonas

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fluorescens) 6.43% (3.91%); D1T4 (10th June 2011+ control), 7.47% (5.84%); D3T1 (30th June 2011+ Trichoderma viride + Pseudomonas fluorescens), 7.52% (3.57%); D3T2 (30th June 2011 + Carbendazim + Mancozeb), 7.53% (4.89%); D2T1 (20th June 2011+ Trichoderma viride + Pseudomonas fluorescens), 11.27% (5.92%); D2T3 (20th June 2011 + Trichoderma viride + Pseudomonas fluorescens + Carbendazim + Mancozeb), 12.63% (7.18%); D2T2 (20th June 2011 + Carbendazim + Mancozeb) 13.60% (6.83%) over control D 3T4 (30th June 2011+ control), 11.60% (8.46%); D2T4 (20th June 2011 + control), 22.40% (12.90%). The highest incidence and intensity were found at 60 DAS in D 2T4 combination and lowest in D3T3 combination. Among combination of sowing date, bioagent and chemical fungicide D 3T3 found superior. Late sowing with treatment of bioagent and chemical fungicide gave best control of disease incidence and intensity of Colletotrichum dematium (Pers.ex Fr.) for Safed Musli. Similar results were reported by Breteg et al. (2000) and Patil et al. (2010) Literature Cited Boerema ,G.H. , J. de Gruyter , M .E. Noordeloos and M.E.C. Hamers .2004. Phoma sp. Identification manual. Bordia P. C., P. Seth and M. M. Simlot. 1990. Safed Musli (Chlorophytum borivillianum) in the Arawali region and preliminary observations. Paper presented in the National Symposium on Conservation and Management of living Resources. University of Agricultural Sciences, G.K.V.K., Banglore. 10-12 January. Bretag, T.W., P.J.Keane and T.V.Price. 2000. Effect of sowing date on the severity of ascochyta blight in field peas (Pisum sativum L.) grown in the Wimmera region of Victoria. Australian Journal of Experimental Agriculture. 40 (8): 1113-1119. Gutierrez, S. A. and M. A. Cundom .2006. Frist record of Sclerotium rolfsii Sacc. on Chlorophytum comosum in Argentina . Australian plant Disease notes ,1:11-12. Mandal, kunal., S .Maiti, D. R. Saxena and M. Saxena. 2004. A new leaf spot of disease of Safed Musli. J. Mycol. Pl. Pathol. 34 (1) : 163. Patil, V. A., B. P. Mehata and A. J. Deshmukh. 2010. Field evaluation of different fungicides against Phoma leaf spot disease of Indian bean. International Journal of Pharma and Bio Science. 1(2) : 1-5. Raghavendra, B. V, S. Lokesh and T. Vasath kumar. 2005. First report of tuber rot of safed musli (Chlorophytum borivilianum) caused by Fusarium solani in India. Journal of Australian Pl. Path. 34 (2) : 275-276. Singh, H. B., A. Singh, A. Tripati, S. K. Tiwari and J. K. Johri. 2007. Collar rot of Chlorophytum borivilianum caused by Corticum rolfsii. National Botanical Research, Lucknow. Bulletin OEEP/EPPO Bulletin. 31, 111-117. Tekade, Aparna, Mina koche and B. T. Raut. 2008. Influence of weather factors on fungal disease of musli. J. Pl. Dis. Sci. 4 (2) : 173-175.

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SYNTHESIS OF PYRIDO[2,3,4-kl]ACRIDINES UNIT ,A BUILDING BLOCK FOR SOME MARINE ALKALOIDS Preeti Zade*and M.M.V.Ramana# *Department of Applied Sciences, Bharati Vidyapeeth College of Engineering, Navi Mumbai, INDIA #Department of Chemistry, Universityof Mumbai, Vidyanagari, INDIA Abstract: Many polycyclic fused-ring alkaloids containing pyrido(2,3,4-kl)acridine (1) skeleton1 have been isolated from a variety of marine sources such as sponges, molluscs, andtunicates, most of which have been reported to have cytotoxic ,antitumor, and antiviral activities 2 This is due to their interesting biological activities and challenging structures. It seems that any contribution towards the synthesis of pyridoacridine units is worthwhile since most of the strategies involve the synthesis of such units. The route we present here involves two important key steps, cyclization to form the quinolinone moiety and intramolecular nitrene insertion to build a tetracyclic system. Keywords: marine alkaloids, quinolinone, nitrene insertion, pyrido(2,3,4-kl)acridine. I. INTRODUCTION Marine natural products have attracted the attention of biologists and chemists the world over for the past five decades. As a result of the potential for new drug discovery, marine natural products have attracted scientists from different disciplines, such as organic chemistry, bioorganic chemistry, pharmacology, biology and ecology. This interest has led to the discovery of thousands of marine natural products to date and many of the compounds have shown very promising biological activity. The ocean is now considered to be a great source of potential drugs. Marine natural products are small- to medium- molecular weight compounds produced by marine plants, invertebrates and microbes that have stimulated interdisciplinary studies by chemists and biologists. From marine toxins that impact public health concerns to the search for new drugs from the sea, the study of biologically active marine natural products. Although elegant synthetic methodologies for most of these alkaloids have been developed and total synthesis of many of these natural products have been achieved, such alkaloids are still continuing to be the focus of many synthetic groups 3, 4. One of the most interesting groups of compounds of marine origin is the family of polycyclic aromatic alkaloids derived from the pyrido[kl]acridine skeleton. They have been normally isolated from sponges or tunicates, although they have been assumed to derive from associated microorganisms because of the wide diversity of their natural sources. These compounds exhibit very interesting biological properties, including excellent antitumor activities but they are normally isolated in minute amounts and their natural sources are not readily available. Since these factors have precluded their systematic study, there is a clear need for synthetic routes to the natural products themselves and to their analogues in order to define the structural requirements for their biological properties. II. RESULTS AND DISCUSSION The synthesis of this important pyridoacridines intermediate (LX)(Figure-1) has been achieved in 7 steps with overall 76% yield. Our synthesis stared with the condensation of commercially available benzoylacetic ethyl ester (XLV) and 2- methoxy -5-nitrobenzenamine (XLIV) which led to the formation of the amide (XLVI) in 81% yield. Such a condensation reaction is carried out by heating for longer time in a Dean stark apparatus. Hence, we have to carry out in a microwave oven to decrease the time required for condensation.The cyclization of the corresponding product (XLVI) with 80% H2SO4,which smoothly yielded the quinolinone (XLVIII) in 49% yield. After constructing the quinolinone ring successfully, we concentrated on building the last ring, utilizing the intramolecular nitrene insertion methodology. Conversion of (LI)to the corresponding azide (LV) was then carried out by the classical nitrous acid followed by sodium azide combination of reactions. The next step in the synthesis was the key intramolecular nitrene insertion reaction. The synthesis of the tetracycliccompound (LXII) constitutes achievement of one of the major objectives, namely the synthesis of an advanced tetracyclic intermediate in which the B ring is functionalized. Analogues of natural products containing pyridoacridine units could be available from it by manipulation of the quinolinone ring. The key compound (LX) was then easily prepared from (LIX) by conversion to the chloroquinoline in 83% yield

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MATERIALS AND METHODS The melting points were determined using capillary tube and are uncorrected. The FTIR spectra were recorded on Spectrum One Perkin Elmer (US). The 1H NMR spectra were recorded on a Bruker AVANCE (300 MHz) spectrometer (with TMS as internal reference). 13C NMR spectra were recorded on Bruker AVANCE (75MHz) spectrometer. Mass spectra were recorded on API-3000 MD-series (US). Elemental analyses were carried out in EA 3000, Euro Vector, Italy. The purity of the compounds was checked by TLC on pre-coated SiO2 gel (200mesh). EXPERIMENTAL SECTION 2- Methoxy -5-nitrobenzenamine (XLIV) was prepared as per procedure reported in literature 5. Preparation of 2- methoxy -5-nitrobenzenamine(XLIV): 2- Methoxy -5-nitrobenzenamine was prepared from 2-methoxy aniline (o- anisidine) (0.01 mol) (XLI), sulfuric acid (85%, 15ml)(XLII) and guanidinium nitrate(0.01 mol) (XLIII) as described in experimental section in 70% yield as an orange coloured solid of 2-methoxy-5-nitrobenzenamine (XLIV) m.p.1180C, (lit.5). The IR, 1 H-NMR spectral data of this compound was also in agreement with the reported data 5. Now having 2- methoxy -5-nitrobenzenamine (XLIV) and commercially available benzoylacetic ethyl ester (XLV) in hand we carried out condensation reaction in microwave. Solvent free microwave enhanced condensation of 2- methoxy -5-nitrobenzenamine (XLIV) and benzoylacetic ethyl ester (XLV) : Formation of N-(2-methoxy-5-nitrophenyl)-3-oxo-3phenylpropanamide (XLVI): A mixture of 2-methoxy-5-nitroaniline (XLIV) (0.009 mol) and benzoylacetic ethyl ester (XLV) (0.009 mol) was irradiated in a microwave oven. The reaction on work up as described in experimental section gave light yellow crystals of N-(2-methoxy-5-nitrophenyl)-3-oxo-3-phenylpropanamide (XLVI) in 81% yield, m. p. 183-185°C (lit.6). 8-methoxy-5-nitro-4-phenylquinolin-2(1H)-one (XLVIII) was prepared as per procedure reported in literature7. Cyclization of N-(2-methoxy-5-nitrophenyl)-3-oxo-3-phenylpropanamide (XLVI) in 80% H2SO4: Formation of 8-methoxy-5-nitro-4-phenylquinolin-2(1H)-one(XLVIII): N-(2-Methoxy-5-nitrophenyl)-3-oxo-3-phenylpropanamide (XLVI) (0.014mol) was stirred in H2SO4 (80%, 100 ml) (XLVII) as described in experimental section to give an orange coloured crystalline solid of 8-methoxy-5nitro-4-phenylquinolin-2(1H)-one (XLVIII), (yield: 49%), m.p. 155-1580C. Elemental analysis of this compound agreed with the molecular formula C 16H12N2O4 required for 8-methoxy-5nitro-4-phenylquinolin-2(1H)-one (XLVIII). Mass spectrum showed M+ at m/z 296, thus confirming the formation of cyclization product (Scheme-10). The spectral data for the above compound UV Spectrum: 236.0 (3.76),275.5(3.62) IR Spectrum : 3370(ν amide N-H),1624.93 (ν amide C=O),1605.65 (ν Ar C=C),1326.99 and 1514.48 (ν – NO2),1017.07 (ν C-O). 1 H-NMR Spectrum :9.853(s, 1H; H-1) ,7.643-7.692(m, 5H; Ar H- 2',3',4',5',6'),7.518-7.548(d,1H ; H-6), (J = 9 Hz),6.982 -6.952(d, 1H; H-7), (J = 9 Hz), 5.728 (s, 1H; H-3), 3.982 (s, 3H; -OCH3). 13 C-NMR Spectrum : 159.9(C-2, C=O), 158.8 (C-8), 139.7 (C-5), 139.0 (C-7), 137.1 (C-6),129.5 (C-4), 128.4(C-8a),128.2 (C-4a), 127.5 (C-3),127.3 (C-1'), 125.2(C-2'),123.4 (C-3'), 120.7 (C-5'),115.6 (C-6'),111.4 (C-4'),56.2(-OCH3). Thus the above pmr data suggests the formation of the product having the structure as (XLVIII) Mass Spectrum: m/z 296 (M+), 281(91), 267(48),250(35), 225(48), 207(100),191(25) 8-methoxy-5-nitro-4-phenylquinolin-2(1H)-one (XLVIII) was then subjected to reduction by using Zn dust and formic acid as reducing agent8. Reduction of 8-methoxy-5-nitro-4-phenylquinolin-2(1H)-one (XLVIII) with Zn dust (XLIX) /HCOOH) (L): Formation of 5- amino-8-methoxy-4-phenylquinolin-2(1H)-one (LI): 5- Amino-8-methoxy-4-phenylquinolin-2(1H)-one (LI) was prepared from 8-methoxy-5-nitro-4phenylquinolin-2(1H)-one (XLVIII) (0.005mol) , Zn dust (0.006 mol) (XLIX) in methanol (5ml) and 90% HCOOH (2.5ml)(L) as described in experimental section as the yellow crystalline solid, (yield: 78%) m.p. 216-218°C (lit.6).

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Elemental analysis of this compound also agreed with the molecular formula C 16H14N2O2 calculated for 5amino-8-methoxy-4-phenylquinolin-2(1H)-one (LI). The IR, 1 H-NMR and mass spectral data of this compound was also in agreement with the reported data 6.

NO2

NO2 OC2H5

85%H2SO4

NH.HNO3

(XLV) NH2

H2N

OMe

(XLII)

NH2

N H OMe

OMe

(XLVI)

(XLIII) (XLIV) Aq.H2SO4(XLII)

NH2

NO2

NaNO2,NaN3 (LIII)/(LIV)

N H OMe

Zn/HCOOH (XLIX)/(L)

H2SO4 (LII)

N H

OMe

(LV)

OMe

(XLVIII)

(LI) Nitrene insertion

Xylene (LVI) reflux

HN HN

POCl3 (LVII),Reflux

N N H

O OMe

(LIX)

OMe

(LVII)

Zn/HCOOH (XLIX)/(L)

N OMe

(LX)

Figure 1

Diazotization of 5- amino-8-methoxy-4-phenylquinolin-2(1H)-one (LI) with sodium nitrite – Conc.H 2 SO4 followed by reaction with sodium azide: Formation of 5-azido-8-methoxy-4phenylquinolin-2(1H)-one (LV): 5-azido-8-methoxy-4-phenylquinolin-2(1H)-one (LV) was prepared from 5-amino-8-methoxy-4phenylquinolin-2(1H)-one (LI) (0.007 mol), concentrated H 2 SO 4 (1.72 ml) (LI), sodium nitrite

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(0.001mol) (LIII) and sodium azide (0.001mol)(LIV) as described in experimental section as yellow crystalline solid 11 ,(yield: 90%),m.p.2610C. Elemental analysis of this compound also agreed with the molecular formula C 16H12N4O2 calculated for 5-azido8-methoxy-4-phenylquinolin-2(1H)-one (LV). The IR, 1 H-NMR and mass spectral data of this compound was also in agreement with the reported data 6. Conversion of 5-azido-8-methoxy-4-phenylquinolin-2(1H)-one (LV) into 4-methoxy -3H-pyrido [2,3,4-kl]acridin-2(7H)-one(LVII)by refluxing in xylene 5-Azido-8-methoxy-4-phenylquinolin-2(1H)-one (LV) (2 mmol) was refluxed in xylene (LVI) for 1.5 hours. The reaction on work up as described in experimental section gave a brown crystalline solid of 4methoxy -3H-pyrido [2,3,4-kl]acridin-2(7H)-one (LVII),(yield: 75%), m. p. 270 °C (lit6. m.p.270°C). The IR, 1 H-NMR and mass spectral data of this compound was also in agreement with the reported data 6. Reaction of 4-methoxy -3H-pyrido[2,3,4-kl]acridin-2(7H)-one(LVII ) with POCl 3: Formation of 2chloro-4-methoxy -7H-pyrido[2,3,4-kl]acridine (LIX) 4-Methoxy -3-H-pyrido [2,3,4-kl]acridin-2(7H)-one (LVII) ( 0.75mmol) was refluxed in POCl3 (30 ml)(LVIII) for 1 hour. The reaction on work up as described in experimental section to give a crystalline solid of 2-chloro-4-methoxy -7H-pyrido [2,3,4-kl]acridine (LIX),(yield: 83%),m.p.202°C (lit6. m.p.202°C). The IR, 1 H-NMR and mass spectral data of this compound was also in agreement with the reported data 6. Reductive dechlorination is usually carried out by Pd -C/H 2 in presence of triethylamine. We thought of carrying out this reaction by Zn dust and formic acid. Reaction of 2-chloro-4-methoxy -7H-pyrido [2, 3,4-kl]acridine (LIX) with Zn dust (XLIX) and formic acid(L) : Formation of 4-methoxy -7H-pyrido [2,3,4-kl] acridine (LX) A suspension of 2-chloro-4-methoxy -7H-pyrido [2,3,4-kl]acridine (LIX) (0.002mol) , Zn dust (0.006 mol)(XLIX) in methanol (5ml) was stirred with 90% HCOOH (2.5ml)(L) at room temperature. The mixture was work up as described in experimental section to give a red crystalline product of 4-methoxy -7H-pyrido [2,3,4-kl] acridine (LX),( yield: 76%), m.p. 224°C (lit. 6). Elemental analysis of this compound also agreed with the molecular formula C16H12N2O calculated for 4methoxy -7H-pyrido [2,3,4-kl] acridine (LX). The IR, 1 H-NMR and mass spectral data of this compound was also in agreement with the reported data 6. Synthesis of 4-methoxy -7H-pyrido [2,3,4-kl] acridine (LX) has been carried out in 7 steps for first time. Both these method involves nitrene insertion as the key steps for generating pyridoacridine framework. References [1]. [2].

[3].

[4]. [5]. [6]. [7]. [8]. [9].

Ma del Mar Blanco, Carmen, A., J. Carlos Menendez, Tetrahedron, 55,1999, 12637-12646. (a) M.H.G. Munro, R.T. Luibrand, and J.W. Blunt. In Bioorganic marine chemistry. Vol. 1. Edited by P.J. Scheuer. Springer, New York. 1987; (b) T. Ozturk. In The alkaloids.Vol. 49. Edited by G.A. Cordell. Academic Press, New York.1997; (c) C.J. Moody and R. Thomas. Adv. Heterocycl. Nat.Prod. Synth. 2, 377 (1992); (d) T.F. Molinski. Chem. Rev. 93,1825 (1993). (a) Molinski, T. F.; Chem. Rev. 1993, 93, 1825–1838. (b) Salomon,C. E.; Faulkner, D. J.; Tetrahedron Lett. 1996, 37, 9147– 9148. (c) Plubrukarn, A.; Davidson, B. S.; J. Org. Chem. 1998, 63, 1657– 1659. (d) Copp, B. R.; Jompa, J., Tahir, A., Ireland, C. M.; J. Org.Chem. 1998, 63, 8024–8026. (e) Eder, C., Schupp, P., Proksch, P.,Wray, V., Steube, K.; Muller, C. E.; Frobenius, W., Herderich, M.,Van Soest, R. W. M. ;J. Nat. Prod. 1998, 61, 301–305. (f) de Guzman, F. S., Carte, B., Troupe, N., Faulkner, D. J., Harper,M. K., Concepcion, G. P., Mangalindan, G. C., Matsumoto, S. S.;Barrows, L. R.; Ireland, C. M.; J. Org. Chem. 1999, 64, 1400–14021.(g)Schmitz, F. J., Agarwal, S. K., Gunasekera, S. P., Schmidt, P. G., Shoolery, J. N.; J. Am. Chem. Soc. 1983, 105, 4835. Delfourne, E., Bastide, J.; Med. Res. Rev. 2003, 23, 234. Ramana ,M. M. V., Malik ,S. S. , Parihar, J. A.; Tetrahedron Letters, 2004, 45, 8681–8683. Turan, O., Alexander, Mc.; Canadian Journal of chemistry , 2000, 78(9), 1158-1164. Kobayashi, J., Cheng, J., Walchi, M. R., Nakamura, H., Hirata, Y., Sasaki, T., Ohizumi, Y.; J. Org. Chem. 1988, 53, 1800. Takemoto, T., Daigo, K., Arch. Pharm. ( Weinheim, Ger. ), 293, 1960, 627-633. Takemoto, T., Daigo, K., Sai, T. (1965a) Yakugaku Zasshi, 85, 33-37; Chem. Abstr., 62, . 1965,12049.

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[10]. [11].

Impellizzeri, G., Mangiafico, S., Oriente, G., Piattelli, M., Sciuto, S., Fattorusso, E., Magno, S., Santacroce, C., Sica, D., Phytochemistry, 14, 1975, 1549-1557. Daigo, K., Yakugaku Zasshi, , 79, 1959,350-353; Chem. Abstr., 53, 1959, 14218.

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Study of Physico-chemical Characterstics in River Ganga at Bithoor Ghat in District Kanpur in Uttar Pradesh R.C.Verma1 & Archana Bansal2 Deptt. of Chemistry, Janta P.G. College, Bakewar (Etawah) Deptt. of Chemistry, Sai Nath University, Ranchi Abstract: Water samples from river Ganga at Bithoor Ghat in Kanpur in Uttar Pradesh were collected and physico-chemical parameters were determined using standard analytical procedure in Jan. to March 2013. pH (7.3-7.7),Chloride and phosphate contents of water samples were determined 13-15 and 0.06-0.09 mg/l respectively, Total hardness 104.2-130.2 mg/l, fluoride level were also 6.0-6.2 mg/l,DO of samples were 7.68.1 mg/l, BOD were 2.5-3.5mg/l and COD were 24-29 mg/l. These results were said to their agreed with the limits set by World Health Organization ( WHO ) for drinking water. Key words: Physico-chemical, WHO, drinking water. I. INTRODUCTION Water the most essential requisites that nature has provided to sustain life on earth.About80% earth surface is covered by water. The deteriorate quality of water create various problems for mainkind. The growth in population, about 90% of which occur in urban areas, increases the demand for water for domestic and industrial uses. Water pollution from domestic and human waste is the main cause for human being water born disease. The industrial water pollution is due to inadequate measure adopted in the industry for the abatement of pollution. It is need of time to protect environment for present and future generations. The purpose of study in to prepare qualitative assessment of abiotic and biotic conditions prevailing in river Ganga. II. MATERIAL AND METHOD The Kanpur on National Highway no.1 and 2 and falls on the Broad Guage NR Railway line between Delhi and Kolkata. Water samples were collected in clean polythene bags and subjected to chemical analysis for measurement of different parameters such as temperature, pH, DO, BOD, COD, fluoride, chloride, phosphate, hardness and total dissolved by standard analytical method in Jan.2013. III. RESULT AND DISCUSSION The values of different parameter with respect to sampling station are given in Table-1. The temperature of water were 16.1-22.90C.Maximum value is 22.9 in March and minimum value is 16.1 C in Jan.2013 The WHO (1992 ) did not recommend any definite temperature for drinking water. The pH value were 7.3-7.7 .Maximum value of pH is 7.7 while minimum is 7.3 in March. Total dissolved were 140.0-144.0 mg/l which are under limits. The total hardness of water were 104.2-130.2 mg/l. The maximum value is 130.2 in March while minimum value is 104.2 mg/l in Feb.2013.The level of hardness are below the levels (300 mg/l ) as laid down by Indian standard and thus water is soft. Fluoride level were 6.0-6.2 mg/l .Maximum value is 6.2 in March while minimum is 6.0 mg/l which are low. The chloride contents of water were 11- 15 mg/l which is below the prescribed limit ( 250mg/l).The COD value of water were 24- 29 mg/l. Maximum value of COD is 29 in Feb. while minimum 24 mg/l in Jan.2013. Table-1.Physico-chemical characteristics in river Ganga at Bithoor Ghat, Kanpur Characteristics Jan 2013 Water Temperature (0C) pH TDS (mg/l) Total hardness (mg/l) Chloride (mg/l) Phosphate (mg/l) Fluoride (mg/l) DO (mg/l) BOD (mg/l) COD (mg/l)

Feb 2013 16.1 18.9 7.5 7.7 140 142 112.6 104.2 13 11 0.07 0.08 6.0 6.1 8.1 7.8 2.5 3.5 24 29

Value March 2013 22.9 7.3 144 130.2 15 0.09 6.2 7.6 3.5 28

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The DO value of water were 7.6-8.1 mg/l. Maximum value of DO is 8.1 in Jan. while minimum is 7.6 in March 2013 which are permissible limit. The BOD value of water were 2.5 -3.5mg/l.Maximum value of BOD is 3.5 in March while minimum value is 2.5 mg/l in Jan.2013. IV. CONCLUSION It is need of time to protect environment for present and future generations. The purpose of study in to prepare qualitative assessment of abiotic and biotic conditions prevailing in river Ganga. REFERENCES [1].

[2]. [3]. [4]. [5]. [6]. [7]. [8]. [9].

American Public Health Association, American Water Works Association, and Water Pollution Control federation, Standard methods for the examination of water and wastewater,18th Ed. Washington, D.C. USA, American Public Health Association variously paginated.1992. ICMR Manual of standard of quality for drinking water supplies .ICMR, New Delhi 1975 WHO (World Health Organization).Environmental Health Criteria,vol.134-Cadmium international Programme on Chemical Safety (IPCS ) Monograph. Geneva, Switzerland.1992 SI, Indian Standard Specification for drinking water:ISI,1983,10500 Indian Standard methods of sampling and test (Physical &Chemical) for water used in industry, Indian Standard Institution, New Delhi IS,1964;3025 Rai, M. and Srivastav, R.M; Metallic status in and around Chopan River Raghogarh, Cur. W. Envir. 2006,1 (1 ):91-93. Chouhan, RPS, Singh M.P, Suraiya A, Singh S: Study of physico-chemical characteristics of Municipal drinking water supply of Sidhi District:Cur.W.Envir,2006;1 (1 )73-75 Rajesh CV, Jitendra G, Reena G and Raghav S: Study of Physico-chemical Characteristics and heavy metals in river Sengar at Jaswant Nagar District Etawah in Uttar Pradesh: Int. J. of pharm. res. and bio-sc:2014,3 (3 )108-111. Vishwakant, Verma, R.C. and Saxena, R.S. Study of some limnological properties of Harchandpur pond, District Etah (U.P.) India. Cur. W. Envir. 2006, 2 (1 ): 35-38 (2007).

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Lithostratigraphy and evidence of an extensive tectonic of Lower Permian age in the continental deposits of M’tal (Western Rehamna, Morocco) Hafid Saber1, Abdelkbir Hminna1, Abdellatif Jouhari1, Aziz Rmich1 1 Chouaïb Doukkali University, Faculty of Sciences, Geology department, P.C. 20, 24000, El Jadida, Morocco. Abstract: In this study, a synthetic lithostratigraphic profile was established in the Permian basin of M’tal. It is made of a detrital series of approximately 450 m. Two formations could be identified: A basal conglomeratic with abundant matrix (Ouled Mira formation) overlayed by a sandstone-silts formation (Bir Enhass formation). A systematic analysis of the synsedimentary fracturing reveals that this fault network and the opening of the M’tal basin was activated during East-Western extension. Keywords: Lower Permian, Late-Hercynian, Tectonic, Geodynamic, Western Rehamna, Morocco. I. Introduction The Permian deposits are known in Morrocan Meseta and High Atlas. Those basins was studied in detail in the central Morocco ([1], [2], [3], [4], [5]), in the eastern and southern Rehamna ([1], [6], [7]) and in the High Atlas ([8], [9], [10]). The present study shows for the first time, the evidence of an extensive tectonic of probably lower Permian age in the basin of M’tal (Western Rehamna) (Fig. 1). Indeed, through the existing work, the late-hercynian geological history is slightly known between the westphalian and the deposit of the first terms of Permo-triassic in this region. During this period, multiple sedimentary and tectonic events have occurred. We will study particularly those events. This analysis is followed by a comparison with the similar events described in the other late Palaeozoic basins of Morocco.

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Fig. 1 : Location of the M’tal basin in Moroccan Meseta. II. Location and tectonic features The sedimentary basin of M’tal outcrops within a submeridian gutter of approximately 8 km in length and 3 km in width. It is located on the western extremity of the Palaeozoic Hercynian massif of Rehamna, approximately 100 km south from of El Jadida, on the road to Marrakech, near Jemaâ M’tal locality (Fig. 1). This basin is

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situated parallelly to the submeridian (N176-170) M’tal Fault which marked the western margin of the basin. The fault, approximately 20 km in length, is located in the south of Jemâa M’tal locality at 500 m and skirts the road of Marrakech. The equivalent of these formations, in the basin of Mechraa Ben Abbou (southern Rehamna), is lower Permian [11]. Based on the analogy of the facies, the geological history and the geographical proximity, the deposits of the basin of M’tal can be assigned to the same age. III. Lithostratigraphic study With an aim of specifying the detailed lithology of the various formations of the Permian basin of M’tal and of evaluating its thickness, as well as the conditions of deposits, four sections (A, B, C, D) was realised through the basin. Owing to these cross sections, a synthetic lithostratigraphic profile was established. It is made of a detrital series of approximately 450 m (Fig. 2). Two formations could be identified: • A basal conglomerate with abundant matrix training whose pebbles are sometimes imbricated (Ouled Mira formation), • A sandstone-silts formation (Bir Enhass formation). log

Formation

Facies

sedimentary structures

Sequences

Bir Enhass formation

Siltstone and fine-grained sandstones

fine-grained sandstones with interbedded coarse-grained sandstones and siltstone levels

Ouled Mira formation

Interbeded conglomeratic coarse-grained sandstones

basal conglomerate

20 m

0 Paleozoïc

Fig. 2 : Synthetic lithostratigraphic profil of the Permian basin of M’tal. A. The conglomeratic formation of Ouled Mira (400 m thickness) It is observed in the North of the basin, organized in monoclinal strata of variable thickness which rest in angular unconformity on the folded and deformed Palaeozoic base (locations of the cross sections: see the map in Fig. 3), with intercalations of sandstone-silts levels towards the top. The dip of the layers of this formation is of approximately 30° towards NW. A detailed lithostratigraphic profile was established (Fig. 4).

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Detailed section of Ouled Mira formation and Description of the facies: Conglomerates: Of variable thickness (2 to 8 m) (Fig. 4), they are massive with joined to sub-joined pebbles, with imbricated pebbles who inform us on the direction of the currents which supply the detrital matter of the basin. The components are represented primarily by pebbles of quartzite, sandstone and some fragments of schist, angular to sub-angular resulting from the deposit of Palaeozoic substratum elements; the argillaceoussandy matrix is less abundant. The benches of finer conglomerates are in the middle of the formation with variable thickness (10 to 30 cm). 220

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Fig. 3 : Geologic map of M’tal basin. Sandstones: They are presented in fine sandstone levels, seldom coarse to slightly microconglomeratic, of centimetric to decimetric thickness; their colors are red to brown. The texture is detrital, with sub-angular grains dispersed in calcitic or argillaceous matrix. The microscopic study shows that it is formed by : - Angular to sub-angular Quartz. They occupy 50% of the rock. - Feldspars represented primarily by plagioclases. - Matrix, less abundant, is formed by a carbonated mudstone.

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Silts: They are not very frequent and are presented in thin and discontinuous levels, resting on the sandstones facies or directly on the conglomerates. log

sedimentary structures

Facies

Sequences

Siltstone and fin e-gr ain ed sandstones

fine -gra in ed sandsto nes with interb edd ed c oa rse-gra in ed san dston es

I nterbeded con glomeratic fine -grain ed san dston es

10 m 0

Paleo zoĂŻc

Fig. 4 : lithostratigraphic profil of the Ouled Mira formation. B. Sandstone-silts of Bir Enhass formation Of 60 meters thickness and red color, a detailed profile, of NW-SE direction, was realised (Fig. 5) along the Western outcrops of the basin. It corresponds to the top of the Permian of M'tal. It consists of terrigenous materials organized in centimetric to metric levels of sandstone and silts. log

Facies

sedimentary structures

Sequence

Siltstone and fin e-gr ain ed sandstones

fine-grain ed sandsto nes with interb edd ed coarse-grain ed san dston es

1m 0

Fig. 5 : lithostratigraphic profil of the Bir Enhass formation.

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Description of the facies Sandstones: We find two types: coarse sandstones and fine sandstones. Coarse Sandstones: They are presented in levels from 1 to 4 m thickness, at a slightly erosive base. The granulometric sorting is good and one finds there only some dispersed gravels of quartz, quarzites or sandstones. Fine Sandstones: They arrive directly on the precedents or interstratified in the silts. They are not very hardened, friable, with a little or not erosive base. The thickness varies from 20 cm to 80 cm, grano-classified, they end in not very hardened silts. Silts: They are most important in volume compared to the other facies, we frequently found them in the top of the fine sandstones, with levels from 2 to 10 m thickness. They have a variable colors red-brown, red-brick. The colouring of these silts depend on the presence of iron oxydes. IV. Environments of deposition The massive conglomerates, with less abundant matrix, indicate a slight displacement by mass transportation (debris-flows) and correspond to gravity sliding. This type of sedimentation is often related to tectonic activity and/or to high reliefs. These facies can be observed preferentially near the eastern borders of the basin. The large size reached by some blocks suggests a close origin. On the other hand, channels with surrounding pebbles indicates a long transportation. The conglomerates of stratified contingnous coarse pebbles attest to an aqueous environment with channels. The abundance of coarse deposits indicates higher reliefs than during the lower Permian. The upper siltstones sandstone formation corresponds to channel deposits in a fluvial type environment. The red siltstones suggest deposition in a flooding plain. V. The filling up of the basin The occidental border of Permian basin of M’tal is a tectonic limit inherited from the pre-Permian substratum: the border fault of synchronal activity to the filling up of the basin: the basin side is formed of detritic sediment and flysch of the paleozoic. The direction of the paleocurrent which was the origin of the transport and deposit of red continental molasses of M’tal was studied in the level of the outcrop favorable to such study. The pebble imbrications observed in the conglomerates shows the dominance of N80° to N110° direction with the filling up direction to the east towards the west. The lenses observed in the land have the same result with the precedent. The first levels of the filling up, wish are proximate, locate in eastern part of the basin. Towards the west and precisely towards the distal zones, the sedimentation relatively fines. Also, there are lateral torrential deposits limited to the northern and the southern border zones basin, and are associated to fluviatil deposits coming from the east. VI. Tectonic The M’tal basin was affected by brittle late-Hercynian tectonic during which an important network of normal faults developed; the majority of them are synsedimentary faults (Fig. 6). This network corresponds probably to a late-Hercynian direction since it has an orientation close to that of the conglomeratic furrow of a lower Permian age. This raises the question of the tectonic control of late-Hercynian faults during the formation of this collapse basin.

Fig. 6 : Cross section showing the emplacement of normal faults. A systematic analysis of synsedimentary fractures reveals that this fault network was active during East-Western

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extension. The reactivation of the Hercynian faults, whose direction was compatible with the extensional stress field, could have played a determining role in the orientation and the localisation of conglomeratic furrow. Thus, these faults can be interpreted as: old brittle faults inherited from the Hercynian time, or faults formed during the development of the semi-graben during the lower Permian. Therefore, the dip of these faults is compatible with an extensive strain as hemi-graben, with a maximum stress 1 vertical (the axis number 3, Fig. 7), and a minimal stress 3 (the axis number 1) horizontal close to the direction of the East-West extension that could have dominated during the phase of sedimentation. N

Fig. 7: Kinematic axes deduced from normal synsedimentary faults (Schmidt projection, lower hemisphere). VII. Conclusion The sediments of M’tal basin are detritic sediments (conglomerate, sandstone and silts) fluviatile generally red of intermediate zone at the base (with coarse element) and also of zone at the summit characterized by fine elements (down part of drainage pattern). They are organized into banks inclined towards the west of the basin, grano-decreasing on a thickness of almost – 500m. The enrichment of the basin in terrigenous element is attributed to the erosion of Palaeozoic basement. They are covered by Jurassic or recent plio-quaternary formations. The Permian series of M’tal is subdivided on two formations: - Conglomeratic formation of gritty intercalation. They are badly classified, its elements has variable size in less abundant matrix. - A sandstone-silts formation (Bir Enhass formation). Analysis of the tectono-sedimentary structures confirms that the sedimentation and the opening of the M’tal basin was controlled by accidents affecting the basement under an extensive regime of East-West orientation. A similar extension had been observed in the western High Atlas in the basins of the Ida Ou Zal and Ida Ou Ziki ([7], [12], [13]) during the same epoch. This local extension can be also generated under a transtensif mode as the Permian basins of Khenifra [1], of Mechraa Ben Abbou ([1], [6]) and of Souss ([7], [8], [12], [13], [14]). References [1] [2]

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[4] [5] [6] [7] [8] [9]

El Wartiti, M. (1990). Le Permien du Maroc mesetien : étude géologique et implications paléogéographiques. Thèse d'Etat, Université Mohamed V, Rabat, 458 p. Youbi, N., (1998). Le volcanisme "post-collisionnel" : un magmatisme intraplaque relié à des panaches mantelliques. Etude volcanologique et géochimique. Exemples d’application dans le Néoprotérozoïque terminal (PIII) de l’Anti-Atlas et le Permien du Maroc. Thèse d’Etat, Univ. Cadi Ayyad, Marrakech, Maroc, 519 p. Hmich D., Schneider, J.W., Saber H., Voigt S., El Wartiti, M. (2006). New continental Carboniferous and Permian faunas of Morocco – implications for biostratigraphy, palaeobiogeography and palaeoclimate. In: Lucas S.G., Cassinis G. & Schneider J.W. (eds.), Non-marine Permian biostratigraphy and biochronology. Geological Society of London Special Publications 265: 297-324. Voigt, S., Lagnaoui, A., Hminna, A., Saber, H., Schneider, J.W., (2011a). Revisional notes on the Permian tetrapod ichnofauna from the Tiddas Basin, central Morocco. Palaeogeography, Palaeoclimatology, Palaeoecology 302, pp. 474–483. Voigt, S., Saber, H., Schneider, J.W., Hmich, D., Hminna, A., (2011b) - Late Carboniferous-Early Permian tetrapod ichnofauna from the Khenifra Basin, central Morocco. Geobios, 44, pp. 399–407. Khounch, H. (1988). Le bassin permien de Mechraâ Ben Abbou (Rehamna) sédimentologie, dynamique d'ouverture et de comblement. Thèse de 3° Cycle, Univ. Cadi Ayyad Marrakech, 184 p. Saber, H., El Wartiti, M., Broutin, J., Toutin Morin, N. (1995). L’intervalle stéphano-permien (fin du cycle varisque au Maroc). Gaia, Lisbonne 11, 57–71. Saber, H., El Wartiti, M., Hmich, D., Schneider, J.W., (2007). Tectonic evolution from the Hercynian shortening to the Triassic extension in the Paleozoic Western High Atlas (Morocco). Journal of Iberian Geology 33 (1), pp. 31-40. Voigt, S., Hminna, A., Saber, H., Schneider, J.W., klein, H., (2010). Tetrapod footprints from the uppermost level of the Permian

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[10] [11]

[12] [13] [14]

Ikakern Formation (Argana Basin, Western High Atlas, Morocco). Journal of African Earth Sciences, 57, pp. 470–478. Hminna, A., Voigt, S., Saber, H., Schneider, J.W., Hmich, D., (2012). On a moderately diverse continental ichnofauna from the Permian Ikakern Formation (Argana Basin, Western High Atlas, Morocco). Journal of African Earth Sciences, 68, pp. 15-23. Damotte, R., El Wartiti, M., Freytet, P., Khounch, H., Lethiers, F., Totin-Morin, N. (1993). Le bassin de Mechraa Ben Abbou (Rehamna, maroc): son insertion dans le contexte permien du Maroc central et mésétien, in 118ème Congr. nat. Soc. hist. scient., 4ème Colloque Géologie africaine, Bassins sédimentaires africains, Paris (ed. Comité Trav. Hist. Scientif.), 53-72. Saber, H., El Wartiti, M. (1996). Histoire sédimentaire et tectonique tardi-hercynienne des bassins de l’Oued Zat et Ida Ou Zal (Haut-Atlas occidental, Maroc) : bassins en transtension sur décrochements. Journal of African Earth Sciences 22, 301–309. Saber, H., El Wartiti, M., Broutin, J. (2001). Dynamique sédimentaire comparative dans les bassins stéphano-permiens des Ida Ou Zal et Ida Ou Ziki (Haut Atlas Occidental, Maroc). Journal of African Earth Sciences, Vol. 32, 4, 573–594. Saber, H. (1998). Le Stéphano-Permien du Haut Atlas occidental : étude géologique et évolution géodynamique (Maroc). Thèse d’Etat ès-Sciences, Université Chouaïb Doukkali, El-Jadida, Maroc, 212 p.

List of figures Figure 1 : Location of the M’tal basin in Moroccan Meseta. Figure 2 : Synthetic lithostratigraphic profil of the Permian basin of M’tal. Figure 3 : Geologic map of M’tal basin. Figure 4 : lithostratigraphic profil of the Ouled Mira formation. Figure 5 : lithostratigraphic profil of the Bir Enhass formation. Figure 6 : Cross section showing the emplacement of normal faults. Figure 7 : Kinematic axes deduced from normal synsedimentary faults (Schmidt projection, lower hemisphere).

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SYNTHESIS AND BIOLOGICAL ACTIVITIES OF SOME NEW AMIDES OF AMINO ACID Chandra Mohan Saxena, Archna Saxena, Naveen Kumar Shukla Department of Chemistry, D.B.S. (P.G.) College, Govind Nagar, Kanpur-208006 (U.P.), India Abstract: The biological activity of some new amides of amino acid were synthesized by the reaction of Glycine and ethyl di amine with some organic acid like maleic acid and malic acid. The compounds were characterized by their elemental analysis UV, IR, NMR and Mass spectral analysis along with their biological activities against different pathogenic microbial strains. Compounds show higher to moderate biological activities against the micro organisms. Key words: Glycine, Ethyl di amine, Maleic acid, Malic acid and Biological activity. I. INTRODUCTION During the past two decades , the incidence of infections caused by Opportunistic fungal pathogens has increased substantially in immune compromised patients. Due to the emergence of mono and multi drugs resistant strains of mycobacterium tuberculosis and AIDS epidemic etc, there is a search for new drugs leading to new structure classes along with novel mechanism of action become the need of the present time. The literature survey revealed that nitrogen and sulphur containing compounds [1-3] are potentially active against cancer, viral and fungal disease[4-5] Amines in general have been known to be biologically active[6] and the effect of presence of various constituents in the amines increases their antimicrobial and antifungal activity which has been investigated [7-8] The compounds having amino acid have proven to be potentially active against various fungal strains and many of them got wide acceptance clinical trials [12-13]. The differential inhibition of cytochrome p-450 between pathogenic bacteria and fungal strains and human being is the basis for the clinically important amino acid as antimicrobial agents. It may be found that the inhibition can be determined by the differential complementarities between the structure of antimicrobial agent and the active sites of enzymes responsible for microbial activities. Further the compounds containing amide moiety has also attracted attention due to their important role in various industrial and biological processes [14]. Moreover the amide moieties having fundamental interest in order to understand the role of metallo proteins in the control of cell metabolism. In view of the important behaviour of amine and amino acid, the present communication deals with the synthesis , characterization and biological activities of some new amides of amino acids. II. METHODOLOGY The synthesis of amides of amino acid was carried out by the simple reaction of glycine, ethyl di amine with respective carboxylic acid in water followed by refluxing the reaction mixture for about 24 hours. The vacuume distillation of reaction mixture afforded an off white colour crystalline solid. The newly synthesized compounds have sharp melting point. The general method of preparation of amides of amino acid as follows. A. REACTION OF GLYCINE AND ETHYLENE DI AMINE WITH MALEIC ACID (SYNTHESIS OF DECA CYCLO , 4- ENE,1,3,6,8 TETRA CARBOXY TETRA AMIDE) Glycine (2 mole 0.2 M ) and ethyl di amine ( 1 mole 0.1M) were taken in a round bottom flask fitted with an air condenser . Since the reaction is exothermic therefore the flask was kept in a ice bath for half an hour before reflux ion. Now the reaction mixture was refluxed for 3-4 hour on a water bath followed by slow addition of an aqueous solution of maleic acid (1 mole 0.02 M). The reaction content was further refluxed again 8-10 hours. The resulting mixture was reduced to half of its volume and kept overnight, white shining crystals are obtained which was further recrystallized in ethanol. B. REACTION OF GLYCINE AND ETHYL DI AMINE WITH MALIC ACID (SYNTHESIS OF DECA CYCLO, 4- HYDROXY 1,3,6,8 TETRA CARBOXY TETRA AMIDE) Glycine (2 mole 0.2 M ) and ethyl di amine ( 1 mole 0.1M) were taken in a round bottom flask fitted with an air condenser . Since the reaction is exothermic therefore the flask was kept in a ice bath for half an hour before reflux ion. Now the reaction mixture was refluxed for 3-4 hour on a water bath followed by slow

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addition of an aqueous solution of malic acid (1 mole 0.02 M). The reaction content was further refluxed again 8-10 hours. The resulting mixture was reduced to half of its volume and kept overnight, light brown shining crystals are obtained which was further recrystallized in ethanol. III. BIOLOGICAL ACTIVITY The biological activity of the amides of amino acid was carried out by disk diffusion method using gentamycin as standard. In this technique filter paper (Whatt Mann No -1) sterile disk of about 5 mm diameter, impregnated with the test compounds (10 mg/ml of ethanol) along with standard were placed on nutrient agar plate at 370C for 24 hours in BOD incubator. The inhibition zone around the dried impregnated disc was measured after 18 hours gives bactericidal activity and after 24 hours gives fungal test organisms. A suitable artificial culture media is prepared to allow the growth of micro organism having necessary nutrients, growth promoting factors and free from contamination. Following culture media have been used for growing bacteria and fungi. IV. RESULTS AND DISCUSSIONS The amides of amino acids are given below: Glycine + Ethyl di amine + Organic acid -----→ Amides of amino acid The amides of amino acids were crystallized after vaccum distillation. The compounds have sharp melting point. The further characterizations of these entire compounds were done by elemental analysis UV, IR, NMR and Mass spectroscopy followed by Biological activities. A. IR SPECTRA The infrared spectra of nearly synthesized amides of amino acids were recorded in KBr / CsI pellets in the range of 4000 – 200 cm-1. The IR spectra of the entire compound clearly exhibit absorption bands due to amide and methylene groups. The absorption frequencies due to carbonyl groups in amide have been fully assigned. 1 B. H-NMR SPECTRA The 1H NMR spectra of the amide of amino acids were recorded in CDCl3 at room temperature using TMS as standard. The peaks values suggested the presence of secondary amide group along with methylene groups in the compound. The peaks located at δ 8.0 ppm ( for secondary amides ) while the peaks values appears as δ 2.46, δ 3.46 and δ 4.09 in case of first compound suggested the presence of six methylene group in the compound. The peaks value for other compound is very close to the first compound indicating the presence of secondary amide s and methylene proton. V. BIOLOGICAL ACTIVITY Biological activity of these amide of amino acids was tested against different microbial strains using 10 µg /ml concentration of the compound , All these compounds shows higher to moderate activity against pathogenic microbial strains. The presence of polar groups in the molecule increases the water and lipid solubility, which is necessary for biological efficacy. These nitrogen compounds generally forms complexes with metallo enzymes, particularly those which are responsible in basic physiology such as cytochrome oxidase. It may found that the compounds having nitrogen content generally reacted with peptidoglycan layer of bacterial cell wall and damage it by penetrating it followed by death of bacterial cell. Sometimes the nitrogen containing compounds at lower concentration may cause the bacteriostatic condition by slow down the growth of bacterial cell . VI. CONCLUSION The amides of amino acid presented in the manuscript have great potential as biological activity. These compounds may be further exploited for the development of new drugs for the treatment of microbial infections along with their further aspects as gastro protective agents. ACKNOWLEDGEMENTS The authors are highly thankful to Dr. Nagendra Swarup, Secretary Board of Management Dyanand Shiksha Sansthan. Dr. Ashok Kumar Srivastava, Principal D.B.S. College, Kanpur. Dr. Sunil Kumar Srivastava, Bursar D.B.S.College, Kanpur and Dr. S. C. Dixit , Head of the Chemistry Department, D.B.S.College Kanpur for providing necessary laboratory facility. I am also thankful to UGC New Delhi for providing funding facility and CDRI Lucknow for elemental analysis, UV, IR, NMR and Mass spectral analysis and Director NBRI Lucknow for biological activity.

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Gaspar A., Garrido E.M., Esteves M., Quezada E.,Milhazes N., Garrido J., Borges F., New insights into the antioxidant activity of hydroxycinnamic acids: Synthesisand physicochemical characterization of novel halogenatedderivatives, Eur. J. Med. Chem., 44, 2092 â&#x20AC;&#x201C; 2099 (2009)

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RAPD Based Genetic Diversity of Freshwater Snail, Pila gracilis in Bangladesh 1

Shamita Mahzabin, 1Pinak Guswami, 1A.K.M.Al-Amin Leamon, 1Shad Ebna Rahaman, 1Md. Faruque Miah 1 Department of Genetic Engineering and Biotechnology, Shahjalal University of Science and Technology, Sylhet, Bangladesh

Abstract: Genetic diversity of the freshwater snail, Pila gracilis from a natural population of Bangladesh was evaluated by applying Random Amplified Polymorphic DNA (RAPD) assay with three RAPD primers. A total of 54 DNA bands were revealed with the size of the bands ranged from 85- 1670 bp whereas 11 polymorphic loci were observed. Highest and lowest polymorphism was showed by the primer B03 (22.22%) and the primer C04 (19.04%). Considering different parameters, relatively higher genetic diversity was found among the collected individuals. Lowest and highest values of inter-individual pair wise similarity was recorded 0 and 6 whereas the genetic distance was observed with values highest 1.0 and lowest 0.538 respectively. Conversely, the highest and lowest linkage distance among the individuals was observed 17.0 and 8.0 respectively. The highest and lowest Neiâ&#x20AC;&#x2122;s genetic similarity values were recorded 0.352 and 0.00 respectively. Finally, from the cluster analysis all the experimental individuals were found to have distant relationship. The study indicates that higher genetic diversity was found among the individuals of this Pila gracilis, however, it is not sure that the population of Pila gracilis in the nature of Bangladesh is good genetic status where only few individuals were investigated. It is, therefore, necessary to conduct the experiment with large number of species and more RAPD primers. Keywords: Pila gracilis, genetic diversity, PCR- RAPD, Bangladesh I. Introduction About 450 snail species are available in Bangladesh and they are the most abundant and commercially valued mollusks which are belong to the family of Ampullariidae [1], [2]. This freshwater apple snail is of greatest demand all around the world due to their high protein, fat, vitamin and mineral content as well as their highly delicious meat quality [3]. This eco-friendly snails are also known as excellent source of some required trace and minor elements which is needed for the proper growth and development of any organism and can also be used as high-nutrient supplementary feed for domestic animals, birds and even for shrimp and fish culture [4]-[7]. In addition, fertilizer, lime, etc. are also produced from snail shell which is regularly used in agriculture and aquaculture [7]. It has become increasingly obvious that people whose capital input is relatively lower should think about alternative sources of income, ideas and business, which are at least affordable by themselves. The members of the genus Pila are a major group of apple snails vastly comprises P. ampullacea, P. angelica, P. gracilis, P. pesmei and P. polita, among which Pila gracilis was chosen for this experiment which is found in a wide range of close and running freshwater habitats including in paddy fields, irrigation canals and similar habitats in freshwater and it can aestivate through the dry season. However, the number of fresh water snails has started to decline due to various reasons like siltation, dam construction and other channel modifications, industrial and agricultural pollution, etc. All of which have concurrently degraded the habitats on which most of these species are dependent [8]. Not only this, over exploitation of snail fauna for intensive prawn farming as well as for the paddy cultures, the natural habitat of the apple snails are also rapidly being destroyed resulting in a major threat to the existence of apple snails which badly requires scientific management [9],[10]. Therefore, particular attention through suitable management of habitat is necessary for the survival of these species with small distributional ranges [11]. It has been acknowledged that several works were done on their abundance and nutritional perspective but their genetic diversity is still not well studied although both of the genetic and ecological environments play a vital role in controlling the genetic structure of a specific population [12]. Very limited research has been undertaken on this species even most noticeably no genetic research has been conducted till today [13], [14]. Due to high socioeconomic and ecological importance of the species Pila gracilis, number of this species are reducing day by day from the nature of Bangladesh as a consequence of over exploitation. The objective of this study was to assess the genetic diversity of the freshwater snail, Pila gracilis and to examine the genetic relationships among individuals of this species for knowing the genetic structure and developing conservation strategies and management program

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of this snail in the nature of Bangladesh. This is the first time genetic based study in Bangladesh that has been conducted on this particular species. II. Materials and Methods Sample Collection and Species Identification The apple snail Pila gracilis were collected by passing a dip net through the upper surface of sediment, water and vegetations from the experimental fish pond which is under the Department of Genetic Engineering and Biotechnology (GEB) at Shahjalal University of Science and Technology (SUST), Sylhet, Bangladesh. Collected snails were transferred to the general laboratory of the Department of GEB, SUST and kept in prelabeled plastic containers as well as the species of Pila gracilis morphologically identified according to the [9],[15]. Tissue Isolation and Preservation A hammer was used to gently crack the shell of the snails. A sterilized forceps was used to pry the shell from the soft tissue of the snail. 7 individuals of Pila gracilis were dissected one after another using scissors, needles etc. to find out the adductor muscle. Collected adductor muscles were kept in seven different eppendorf tubes containing 100% ethanol and preserved them at – 20°C until DNA extraction. Tissues were collected in the general laboratory but DNA extraction, PCR amplification and Gel electrophoresis were performed in the Animal Biotechnology Laboratory (ABL) and USDA Laboratory of this Department. DNA Extraction A long protocol of DNA extraction from Molluscs and according to this protocol, DNA was extracted from this experimental Pila species where visceral tissues were used [16], however, in this experiment, adductor muscles were used. Checking Quality of Extracted DNA DNA quality was checked by gel electrophoresis on 1% agarose gel with 3µl DNA where 1kb plus ladder was used to compare migration of DNA and the gel was run at 70 volt for 40 minutes. This gel was then placed in gel documentation system and photograph was taken by digital camera (Panasonic”DMC-FS20) while clear bands with good concentration of DNA were found from each of the individuals. PCR Amplification In this experiment, three decamer RAPD primers such as B 03 (5'- CAT CCC CCT G-3'), C 04 (5'- CCG CAT CTA C-3') [17] and OPB 12 (5’-CCT TGA CGC A-3’) [18] were adopted for studying genetic diversity. PCR reactions were performed each sample in a 15µl reaction mixture for each sample with 8µl of master mix (Promega Hot Start), 1µl of primer, 2µl of template DNA and 4µl deionized distilled water for RAPD primers were used. PCR reaction was conducted for pre heating 94˚c 3 minutes, denaturation at 94ºc for 1 minutes; annealing temperature for this PCR was 34˚c (for B 03 and OPB 12) and 35ºc (for C 04) in 1 minute and 2 minutes for elongation or extension at 72˚c. A final step of 7 min for 72ºc was added to allow complete extension of the amplified fragments. The PCR was run for 35 cycles. Checking PCR-RAPD Products PCR products were checked by electrophoresis on 2% agarose gel with 3µl DNA where 1kb plus ladder was used to compare migration of DNA. The gel was run at 70 volt for 40 minutes. This gel was then placed in gel documentation system and photograph was taken by digital a camera (Nikon Coolpix P100 26X Zoom 10.3 Megapixel camera). Data Analysis Using different software and equations, RAPD data of this experiment was interpreted. The software AlphaEaseFC 4.0 was used for measuring molecular weight of bands. Pair wise similarity was calculated by D = 1- Nxy / Nx+Ny- Nxy, where, D = the genetic distance between sample x and y, Nxy = number of band shared by sample x and y, Nx =the number of bends in sample x, Ny =the number of bends in sample y. Nei’s genetic similarity among individuals were measured by F= 2Nxy/ Nx+Ny, where, F= Nei‟s genetic similarity, Nxy= Number of shared Band between X and Y, Nx= Number of bend in X, Ny = Number of band in Y. Polymorphism information content (PIC) was measured as n PIC = 1- ∑ Pij2 j=1 Where, Pij is the frequency of the jth allele for the ith marker Summed over ‘n’ alleles. Linkgae distance based on new.sta and Intra-individual relationship through dandogram was analyzed by using a softwere “Statistica”. III. Results DNA Profile and Data Scoring In this experiment, three arbitrary primers were used to study genetic diversity among 7 individuals of Pila gracilis. DNA profiling was compared with 1kb plus ladder was used and bands were found between 75bp to 20000 bp length (Gene ruler TM). Each amplified band profile was defined by either the presence of bands (1)

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or absence of bands (0) at particular positions on the gel. This was done separately for each individual and each primer. Only few bands were observed in RAPD gel of primer OPB 12 where one to four bands were seen in different individual and the bands size was ranged from 126-1670 bp length. The primer B 03 and C 04 were produced only some bands and maximum 4 bands were seen in each individual while the RAPD primer B 03 ranged from 143bp to 1105bp length and the size of amplified products of the primer C 04 was ranged from 85485 bp length. Bands Summary A total of 54 bands with 11 polymorphic bands were detected among the individuals of Pila gracilis (Table 1). The highest number of bands (21) was amplified by the primer C04 and the lowest number of bands (15) was amplified by the primer OPB12. Highest polymorphism (22.22%) was showed by the primer B03 and lowest polymorphism (19.04%) was showed by the primer C04 among the tested individuals. The highest to lowest numbers of bands with 3, 2.57 and 2.14 were amplified per individual by C04, B03 and OPB 12 respectively. Primer B03 showed highest polymorphism information content (PIC) (0.8765) while an average PIC was 0.865. Table 1: Summary of the bands revealed from three primers based on RAPD band analysis. Primers

B03 C04 OPB12 Total Average

Size of DNA bands (bp) 143-1105 85-485 126-1670 -

Total number of DNA bands

Number of polymorphic loci

18 21 15 54 18

4 4 3 11 3.66

P. gracilis Percentage of polymorphic loci (%) 22.22 19.04 20 20.42

Number of bands per individual 2.57 3 2.14 -

Polymorphism information content (PIC) 0.8765 0.8117 0.907 0.865

Inter-individual Pair Wise Similarity Indices The inter-individual pair wise similarity indices was recorded 6, 4, 3, 2 and 1 respectively but in two cases no inter individual pair wise similarity was seen, though highest similarity was seen only the individual pair 6 and 7 (Table 2). Table 2: Inter-individual pair wise similarity Individuals Individual 1 Individual 2 Individual 3 Individual 4 Individual 5 Individual 6 Individual 7

Individual 1 ---

Individual 2 2 ---

Individual 3 3 2 ---

Individual 4 0 1 2 --

Individual 5 2 2 3 1 ---

Individual 6 2 3 4 2 2 ---

Individual 7 0 2 3 2 1 6 ----

Genetic Distance The genetic distance among individuals of Pila gracilis was found highest 1.000 and the lowest genetic distance (0.750) was recorded. Relatively higher distance was recorded in this experiment (Table 3). Table 3: Genetic distance among individuals of Pila grecilis Individuals Individual 1 Individual 2 Individual 3 Individual 4 Individual 5 Individual 6 Individual 7

Individual 1 --

Individual 2 0.800 --

Individual 3 0.786 0.867 --

Individual 4 1.000 0.889 0.846 --

Individual 5 0.818 0.818 0.800 0.900 --

Individual 6 0.846 0.750 0.750 0.818 0.857 --

Individual 7 1.000 0.857 0.833 0.833 0.938 0.538 --

Nei’s Genetic Similarity Considering the Nei’s genetic similarity analysis it was found highest and lowest value 0.631 and (0.00) respectively. Highest value was found only in one pair of individual while two pairs of individuals showed lowest value of this Nei’s genetic similarity. However, in general lower Nei genetic similarity of these individuals was observed (Table 4). Table 4: Nei’s genetic similarity among individuals of Pila gracilis Individuals Individual 1 Individual 2 Individual 3 Individual 4 Individual 5 Individual 6 Individual 7

Individual 1 --

Individual 2 0.333 --

Individual 3 0.352 0.235 --

Individual 4 0.00 0.200 0.267 --

Individual 5 0.308 0.308 0.333 0.182 --

Individual 6 0.267 0.400 0.400 0.308 0.250 --

Individual 7 0.00 0.250 0.286 0.286 0.118 0.631 --

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Linkage Distance The values of pair-wise comparisons of linkage distance were computed from combined data for these three primers ranged from 8.0 to 17.0 (Table 5). Comparatively higher linkage distance was observed between all other individualâ&#x20AC;&#x2122;s pair. Table 5: Squared Euclidean distances (new.sta) Individuals Individual 1 Individual 2 Individual 3 Individual 4 Individual 5 Individual 6 Individual 7

Individual 1 0 8 9 10 9 11 17

Individual 2

Individual 3

Individual 4

Individual 5

Individual 6

Individual 7

0 11 8 9 9 13

0 9 10 10 14

0 9 9 11

0 12 16

0 8

Genetic Relationships among Individuals A cluster analysis using UPGMA based on linkage distance was done to resolve the phylogenetic relationships among experimental individuals of Pila gracilis. The UPGMA clustering system generated six clusters (Figure 1) in total in which two genetic clusters (Cluster 1 & 5) formed at linkage distance 8 between individual 1 & 2 and individual 6 & 7. Individuals from each of these two clusters were observed very much related to each other. At linkage distance 9, two clusters were formed where cluster 1 forms second cluster with individual 4 and individual 4 & 5 forms cluster 3. The fourth cluster was seen near about linkage distance 10 and linkage distance was observed more than 12 in cluster number 6 which was indicated that individual 6 & 7 were distantly related with the rest of the individuals.

Figure 1. Genetic relationships of experimental individuals of Pila gracilis IV. Discussion The effectiveness of RAPD in detecting polymorphism among individuals of Pila gracilis, their applicability in population studies, and the establishment of genetic relationships has been demonstrated with this study. The final target of this study was to investigate intra-species genetic variation in the population of the species Pila gracilis from the experimental samples. Considering different parameters, moderately higher degree of genetic diversity was found among individuals of the collected experimental samples using RAPD analysis. This study reflects the fact that, this species has experienced moderate genetic deterioration. However, the vulnerable state of this species may cause much genetic deterioration in the coming future, if proper steps for conservation and stock enhancement are not taken. A study was conducted in Thailand on analysis of the genetic diversity of introduced Pomacea canaliculata and native apple snails (Pila) by RAPD technique which is quite similar to this study (Thaewnon-ngiw et. al. 2003). Three primers OPA07, OPB10 and UBC122 exhibiting reproducible and easy scoring results were selected for analysis of genetic diversity and identification of molecular markers of apple snails in Thailand whereas in current research three primers such as B03, C04 and OPB12 were used for the genetic diversity study of Pila gracilis. According to research in Thailand, their overall species for instance Pomacea canaliculata, and four native apple snails; Pila ampullaceal, P. angelica, P. pesmei and P. polita were found polymorphic and the average number of polymorphic bands of each species was nearly identical although a lower level of

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polymorphism was observed in P. angelica. Those result suggested that the potential of RAPD analysis for determination of inter and intra-specific genetic differences of apple snails in Thailand. Another important notification is that, genetic diversity and molecular diagnostic markers of exactly these species in Thailand were also studied by PCR-RFLP of cytochrome oxidase subunit I and COI surprisingly the result was contradictory to that from RAPD analysis. This should have resulted from limited sample sizes of native apple snails in that study. In the present study, three random primers were amplified 54 DNA bands in total with an average of 18 bands where the size of the band ranges from 85- 1670 bp with a very high specificity. All the primers revealed 11 polymorphic loci and the intra-specific polymorphism was 22.22 %, CO-04 was recorded in an average of 20.42 %. The same primers were used on two different apple snails Pila polita (Leamon, 2014) and Pila globosa (Rahaman, 2014) respectively while total 55 DNA bands in Pila polita and 56 DNA bands in Pila globosa were found within the same size. Also less inter-individual pair wise similarity indices was found among the individuals of Pila gracilis which was mostly similar of the findings of Pila polita (Leamon, 2014) and Pila globosa (Rahaman, 2014). Contrary, relatively higher genetic distance was observed in Pila gracilis but much higher distance was recorded in Pila polita (Leamon, 2014) and Pila globosa (Rahaman, 2014) by the same primers. Lower Nei’s genetic similarity was recorded in Pila globosa (Rahaman, 2014) however moderate similarity was found in Pila polita (Leamon, 2014) and the result of the present study agrees on that result. Other result of this experiment, comparatively higher linkage distance was observed between the individual pairs which was similar to the studies on Pila polita (Leamon, 2014) and Pila globosa (Rahaman, 2014). V. Conclusion In this study, genetic diversity of the freshwater snail Pila gracilis was observed. Overall genetic diversity of this species was found satisfactory level in the experimental ecosystem. Though, the genetic distance value showed higher genetic diversity but as only seven individuals of this species were studied with very few banding patterns. To find out a very good genetic status, large number of individuals should be analyzed with more RAPD primers. However, this was the first genetic diversity based research of this species which could be proved as a base line study for future investigation on this species. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[14]

[15]

[16] [17] [18]

P. Gain, (1998). Chanda beel: shrimps attacks snails and environment. pp. 17-20. In: P. Gain (ed.), Earth Touch. The Society for Environment and Human Development (SEHD), Dhaka, Bangladesh. S.A.A Nahid., Henriksson P.J.G. and Wahab M.A. (2013). Value-Chain analysis of freshwater apple snail (Pila globosa) used for on-farm feeds in the freshwater prawn farming sector in Bangladesh. 3 (2): 22-30. D. Nargis, Talukder, S. H. A, Pramanik and M. R Hasan (2011). Nutritional Value and Physico-Chemical Characteristics of Apple Snail Pila globosa (Swainson) and Lymnaea luteola Lamark, J. Sci. Ind. Res. 46(4): 539-542. J.Abedin, and Kabir, K. (1999). Cost benefit analysis of gher system under Khulna areas before project intervention. A survey report prepared by Greater Options for Local Development through Aquaculture Project of CARE-Bangladesh. M.S Jahan and M.R Rahman. (2000). Prospects of snail culture in Bangladesh. Environment & Agriculture: At the cross road of the New Millennium, pp: 522-526. R.D Nath, M.L. Rahi, G.S. Hossain, and K.A. Huq, (2008). Bangladesh status of fresh water snail in Khulna district. Bangladesh Res.Pub. J. 1 (4): 337-347. Baby R. L, Hasan.I, Kabir K. A. and Naser. M.N (2010). Nutrient Analysis of Some Commercially Important Molluscs of Bangladesh. 2 (2):390-396. P.D Johnson., Research scientist, Tennessee Aquarium Research Institute, Cohutta, (2009). Freshwater Snail Biodiversity and Conservation. Publication 420-530. M.S Jahan., S.M Akter., M.M Sarker, M.R Rahman and M.N Pramanik. (2001). Food Presence & Breeding Ecology of the Apple Snail Pila Globosa (Swainson) in simulated habitat. J. Asiat. Soc. Bangladesh, Sci, 27(1): 117-124, June 2001 S.A.A Nahid., P.J.G. Henriksson and M.A. Wahab (2013). Value-Chain analysis of freshwater apple snail (Pila globosa) used for on-farm feeds in the freshwater prawn farming sector in Bangladesh. 3 (2): 22-30. F.Kohler, M .Seddon, Arthur E. Bogan, D.V Tu, P.S Aroon., and D. Allen (2010). The status and distribution of freshwater molluscs of the Indo-Burma region. J. S. Jones (1973). Climatic selection has an important effect on some patterns of gene distribution in snail populations. Science 9 November 1973: 182:4112 pp. 546-552. B .Thaewnon-ngiw, S.Klinbunga, K.Phanwichien, N.Sangduen, N. Lauhachinda, and P.Menasveta, (2003). Genetic diversity of introduced Pomacea canaliculata and native (pila) apple snails in Thailand revealed by Randomly Amplified polymorphic DNA (RAPD) analysis. AJSTD, 20 (3, 4): 289-306. B .Thaewnon-ngiw, S.Klinbunga, K.Phanwichien, N.Sangduen, N. Lauhachinda, and P.Menasveta, (2004). Genetic Diversity and Molecular Markers in Introduced and Thai Native Apple Snails (Pomacea and Pila). Journal of Biochemistry and Molecular Biology, 37 (4): 493-502. S. Klinbunga., B. Thaewnon-ngiw , K. Phanwichien., N. Sangduen, N. Lauhachinda, P. Menasveta (2003). Genetic diversity of introduced (Pomacea canaliculata) and native (Pila) apple snails in Thailand revealed by Randomly Amplified Polynorphic DNA (RAPD) Analysis, 20 (3, 4): 289-306. M. K. Krause, (1997). Molecular Approaches to Zoology-Molecular Zoology: Advances, Strategies, and Protocols Joan D. Ferraris Stephen R. Palumbi. Bioscience 47 (3):194-196 K. Schwenk, A. Sand, M. Boersma, M. Brehm, E. Mader, D. Offerhaus and P. Spaak (1998). Genetic markers, genealogies and biogeographic patterns in the cladocera. Aquatic Ecology 32: 37–51. M. S. Alam, M. S. Islam and M. S. Alam, (2010). DNA Fingerprinting of the Freshwater Mud Eel, Monopterus cuchia (Hamilton) by Randomly Amplified Polymorphic DNA (RAPD) Marker. International Journal of Biotechnology and Biochemistry, 6(2): 271-278.

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Acknowledgements Authors are very much indebted to the Biotechnology Research Centre, University of Dhaka, Bangladesh for providing financial supports through the research project entitled “Apple snail (Pila spp): Molecular identification and population genetics for conservation and management in Bangladesh”.

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Available online at http://www.iasir.net

Spectral and Anti Bacterial Characterization of the adduct of Adipic acid with [NP(OH)2]3 Atul Gupta & S.P.S. Jadon Department of Chemistry, S.V. College, Aligarh (U.P.) 202001, India Abstract: The adduct of [NP(OH)2] 3 with adipic acid synthesized was analyzed qualitatively, quantitatively, mass, I.R., U.V. and 1HNMR spectrometrically. On the basis of analytical data and mol. wt., its molecular formula is assigned as (COO)2 [P3N3(OH)4] [CH2]8 (COOH)2

which is soluble in water and other non aqueous solvents. The adduct is found 18mm active against proteus (gram – ve). Keyword: Spectrometrically, conductor, transition, frequencies.

I. Introduction (NPCl2)3 and (NPH2)3 trimers and their complexes with metals have been reported1-9. The reaction products of [NP(OH)2]3 with acrylic acid, cinnamic acid and olic acid have also been synthesized and investigated 10. The reaction product of [NP(OH)2]3 with salicylic acid, Hippuric acid and Nicotinic acid have also been investigated11-12. Therefore the compound of [NP(OH) 2]3 with adipic acid was prepared and its studies are being presented herewith. II. Experimental [NP(OH)2]3 was synthesized by the reaction of NaOH on [NP(Cl)2]3 by using Anala R grade chemical. The product [NP(OH)2]3 was mixed with Adipic acid (1:1 ratio) in alcohol following by the addition of 1 ml conc. H2SO4 and refluxed for 6h until the completion of reaction. The mass, formed was filtered, washed with alcohol and ether successively, dried and stored in a vacuum desiccators over fused CaCl 2. The quantitative estimations for the C, H, N, get done from the CDRI Luknow and SAIF Chandigarh. The molecular weight was determined K  1000  w1 by using Rost’s process using the equation, (M) = f and camphor as solvent. T  w2 Mass, I.R., U.V. and 1HNMR spectra were carried out subsequently on Jeal SX – 102 (FAB), Shimadzu 8201 PC (4000 – 400 cm-1), Perkin Elmer-15 PC, ( 200 nm – 800 nm) and Bruker DRX – 300, spectrometers at room temperatures. The adduct was tested against E.coli and proteus (gram-ve) bacteria, klebsilla (gram+ve) and candida albican fungi using invitro technique. III. Results and Discussion The palm coloured adduct of [NP(OH)2]3 with Adipic acid is soluble in water, the presence of N & P was confirmed by testing for NH4+ and PO43 ions13. (COO)2

[P3N3(OH)4] [CH2]8 (COOH)2 Molecular formula for adduct was established on the basis of analytical data % found (cal.) P 18.87 (18.86), N 8.52 (8.51) O 38.96 (38.94), C 29.22 (29.21) H 4.46 (4.46) and molecular weight 492.75 (493.0) gm mol. The molecular formula is supported by the mass line m/z 492 (m1) which is observed in its mass spectrum, (Fig. 1) indicating that two molecule of Adipic acid have reacted with one molecule of [NP(OH)2]3 with elimination of two water molecule in the presence of conc. H 2SO4.

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O [NP(OH)2]3 + 2[HOOC

(CH2)4

conc. H2SO4

OH]2

(COO)2 [P3N3(OH)4] [CH2]8

+ 2H2O (COOH)2

……(1)

The other mass lines in its mass pattern fig. (1) may be explained by the FAB, fragmentation process. O O HOOC

(CH2)4

C O N

O C P

COOH

(CH2)4

m/z 492 (M-1) -CO2

-OH O HOOC

(CH2)4

C O HO HO

N P

O C P N

(CH2)4

HOOC

COOH

(CH2)4

C O HO

P OH

N P

O C P N

N P

(CH2)4

OH OH

m/z 449

m/z 475 (M+1)

O COOH

(CH2)4

C O

O C P

N N HO OH HO P N P OH m/z 391 (M-1)

O C

O (CH2)4

C O

N O

O C P N

N P O

m/z 355 (M-1)

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O C

O (CH2)4

O P

N P

m/z 325 (M+3)

O H

(CH2)4

O N P

O P N

N P

m/z 293 (M-1)

O H

(CH2)4

O N P

O P N

N P

m/z 277 (M-2)

O C

O O N

O P N

N P

m/z 257 (M+2)

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O N P

O P N

N P

m/z 221 (M-2)

O C

O N P

P N

N P

m/z 179

O O N

P m/z 136/137 (M+2)

Fig. 1: Mass Spectrum of Compound The vibrations observed in the I.R. spectrum (fig. 2) of adduct are compare to that of ligand and it is found that the bands at lower region at 518.0, 678.6, 735.8, 853.1, 886.1 cm-1 are respectively for PN and POH groups. The other assignment at 1009.5, 1068.6, 1174.0 cm-1 for –P–O–C–, 1291.2 cm-1 for C=OP14 1689.8 cm-1 for

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C=OO, 2131.5, 2329.4 cm-1 for P=N, indicating that two POH groups of the hydroxyl phosphazene have reacted with two carboxylic groups of Adipic acid forming POC linkage with the elimination of two water molecules as shown by the reaction-1. The vibrations 2609.6, 3074.5 cm-1 for two CH2 COOH groups alongwith the frequency at 3406.23568.4 cm-1 shows the presence of 4 free N-POH groups in the compound. From the I.R. spectral datas the structure (fig- 5) of the adduct is confirmed.

(a)

(b)

Absorbance

Fig. 2: I.R. Spectrum (a) Ligand (b) Compound Further to know the nature of the compound its electronic spectrum (fig- 3) recorded is interpritated on the basis of available literature15, out of the two bands observed at 200nm and 280nm, the former band is for the charge transfer transition because its energy is 6.2ev more than 4.5ev for a ionic environmental while later transition is for p –p bonding having 4.43ev energy for a covalent bond formed in PN ring. The values of oscillator strength (f) of the order of 10-5 is also for spin orbital coupling i.e., p –p bonding. The low values of band gap energy and high value of number conducting electron infers that the adduct is a good conductor of heat and electricity.

Wave length  (nm) Fig. 3: U.V. Spectrum of Compound

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In proton NMR spectrum of the adduct, the two signals at the chemical shift ()1.229 and 1.488ppm are due to two OH of two carboxylic groups, while the two of the doublet signals (fig-4)at  2.199,2.414,2.500,2.725, ppm which are due to four OH groups attached to two p atoms opposite to each other. A doublet signal at the chemical shift of  3.160-3.393ppm is on account of symmetric P=N bands, present in the adduct, A set of the triplet signal in the range of the chemical shift () 4.211, 5.033, 6.170, ppm show the presence of symmetric CH2COO groups, the remaining a triplet signal at the chemical shift,  8.085, 8.925, 8.944ppm is due to P-N bands in the phosphazide ring which Is clearly observed in its high resolved spectrum (fig. 4).

Fig. 4: 1HNMR Spectrum of Compound Thus the HNMR spectrum of the adduct clearly indicate that the two OH groups of the [NP(OH)2]3has reacted with two carboxylic groups with the elimination of two water molecule as shown by reaction-(1) with the formation of adduct tetrahydroxy phosphazide di adipicate inferring the structure (fig. 5). The adduct is found 18mm active against proteus (gram-ve) bacteria inferring that it may be used as medicine for urinary tract infection while it is ineffective against other bacteria and fungi. (COO)2 IV. Conclusion [P3N3(OH)4](CH2)8 The adduct of the Adipic acid with [NP(OH)2]3 synthesized is assigned as (COOH)2 which is supported by its mass pattern and I.R. vibrations. On the basis of its UV and 1HNMR spectra it is conferred that the adduct has  bonding due to PN ring along with ionic environment showing the good conductor nature and structure as fig. 5. Acknowledgements Authors wish to thank to Directors, C.D.R.I. Lucknow & S.A.I.F. Chandigarh to provide instrumental facilities. We are also thankful to Dr. Ajay, Jai Hospital, Agra for bacteriology Analysis. 1

O HOOC

(CH2)4

C O HO HO

N P

O C P N

N P

(CH2)4

COOH

OH OH

m/z 492 (M-1) Fig. 5: Structure of Adduct, Tetrahydroxy phosphazide di-adipicate References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9].

H. Binder, Z. Inorg. Alleg. Chem.(Gen.), 130, 383 (1971). Y. Busleav, B.V. Levin, M.Z.G. R.y. S.P. Petrosynnts and B.V. Micronova, Zh. Neorg. Khim., 14, 3245 (1969). H.W. Raesky and H. Weizer, Ber, 106, 280 (1973). H.R. Sllock, Inorg. Chem., 38, 280 (1999). O.S Jung. Inorg. Chem., 38, 5447 (1999). S.P.S Jadon, Asian J. Chem., 15, 151; 2003; 17, 1312 (2005). A. Sundermannand and W.W. Scholler, Inorg. Chem., 38, 6261 (1999). N. Jain and S.P.S. Jadon, Asian J. Chem., 18, 730 (2006). N. Jain and S.P.S. Jadon Int. J. Chem. Sci. 4, 285 (2006).

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Illa Rani and S.P.S. Jadon, Asian J. Chem., 20(7), 5711-5716 (2008), Int. J. Chem. Sci. 6(2), 519-525 (2008). Atul Gupta and S.P.S. Jadon, Int. J. Chem. Sci. 7(4), 2867-2871 (2009). Atul Gupta and S.P.S. Jadon, RJPBCS, 3(4), 11-19 (2012). A.I. Vogel, “A Text Book of Quantitative Inorganic”, Longman, London (1961). K. Nakamoto, “Infrared and Raman Spectra of Inorganic and Coordination Compounds”, John Willey & Sons. NY (1978). B.N. Figgis, “Introduction to Ligand Fields”, Wiley Eastern Limited, New Delhi (1976).

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