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RISS 인기검색어
- 검색키워드
- 저자 : Santanu Nath (검색결과 2 건)
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Laishram Menalsh,Mandal Satyendra Nath,Haldar Avijit,Das Shubhajyoti,Bera Santanu,Samanta Rajarshi 아세아·태평양축산학회 2023 Animal Bioscience Vol.36 No.6
Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer’s field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.
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Rituparna Maiti,Biswa Ranjan Mishra,Monalisa Jena,Archana Mishra,Santanu Nath 대한정신약물학회 2021 CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE Vol.19 No.1
Objective: Serum melatonin, a biomarker of circadian rhythm, can upregulate Growth-associated protein 43 (GAP-43) which is involved in neural regeneration and plasticity. The present study was conducted to investigate the adequacy of the first-line antipsychotic drugs to improve sleep and circadian rhythm disruptions by assessing the effect of haloperidol and risperidone on serum melatonin and GAP-43 in schizophrenia. Methods: In this cohort study, 100 schizophrenic patients were recruited, and clinical evaluations were done using the Positive and Negative Syndrome Scale (PANSS) and the Pittsburgh sleep quality index (PSQI). The patients with predominantly positive symptoms taking haloperidol (Group I) and patients with predominantly negative symptoms taking risperidone (Group II) were admitted and serum melatonin, arylalkylamine N-acetyltransferase, GAP-43 and urinary melatonin were estimated. After 8 weeks, all clinical and biochemical parameters were repeated. Results: Serum melatonin (2:00 hours) was significantly decreased in both haloperidol (2.42; 95% confidence interval [95% CI]: 0.67−4.17; p = 0.008) and risperidone group (3.40; 95% CI: 0.54−6.25; p = 0.021). Urinary melatonin was significantly decreased in both haloperidol (p = 0.005) and risperidone group (p = 0.014). PSQI score was significantly increased in both haloperidol (p = 0.001) and risperidone group (p = 0.003). Serum GAP-43 was significantly decreased in both haloperidol and risperidone group (p < 0.001). PANSS decreased significantly in both the groups and there was a significant negative correlation between serum melatonin at 2:00 hours and PANSS (r = −0.5) at baseline. Conclusion: Monotherapy with haloperidol and risperidone can achieve symptomatic improvement but cannot improve sleep and circadian rhythm disturbances in schizophrenia.
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