3 resultados para Male urogenital system

em Cochin University of Science


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School of Industrial Fisheries,Cochin University of Science and Technology

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This thesis entitled “Histological ,Histochemical and biochemical characterisation of male morphotypes of macrobrachium rosenbergii(De Man). The giant fresh water prawn Macrobrachium rosenbergii (de Man) is emerging as a prime candidate species in fresh water aquaculture as a global basis and therefore, receiving much attention in recent years.the present work was aimed at to study the histological variations, if any, in the reproductive system viz. testes, vas deferens including androgenic gland, hepatopancreas and the neurosecretory system viz. eye stalk, brain and thoracic ganglion among the male morphotypes and their transitional stages of M.rosenbergii from growouts. This study was also aimed at to bring out the histochemical variations, if any, in the reproductive system comprising of testes, vas deferens including androgenic gland and the hepatopancreas among the male morphotypes and their transitional stages collected from growouts. Biochemical characterisation of various male morphotyes and their transitional stages have also been attempted in order to find out biochemical evidence, if any, in the morphotypic transformation.Histological study of the testes of three male morphotypes viz., SM, SOC and SBC and their 'transitional stages viz., WOC, tSOC, WBC and OBC have been carried out with a view to unravel the structural and functional differences of the testes, if any, of these morphotypes. Studies on the lipid components viz., cholesterol, phospholipid and triglyceride In the muscle tissue and hepatopancreas have been carried out.

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Malayalam is one of the 22 scheduled languages in India with more than 130 million speakers. This paper presents a report on the development of a speaker independent, continuous transcription system for Malayalam. The system employs Hidden Markov Model (HMM) for acoustic modeling and Mel Frequency Cepstral Coefficient (MFCC) for feature extraction. It is trained with 21 male and female speakers in the age group ranging from 20 to 40 years. The system obtained a word recognition accuracy of 87.4% and a sentence recognition accuracy of 84%, when tested with a set of continuous speech data.