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Kumar, Yogesh
- Glucose Uptake Rate of Microorganisms Living in Hot Springs above 70°c Temperature:A Study of Panamik and Puga Hot Springs in the Ladakh Region, Jammu and Kashmir, India
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Authors
Affiliations
1 Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, IN
1 Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, IN
Source
Current Science, Vol 118, No 4 (2020), Pagination: 644-648Abstract
This study measures in situ microbial glucose uptake rate in two different hot springs in Ladakh, J&K, India with distinct temperatures >74°C and pH > 7.4. For this purpose, the slurry samples from each hot spring were incubated up to 4 h with 13C-labelled glucose in gas-tight incubation bottles at the respective hot-spring sites. The natural δ13C particulate varies from –9.1‰ in Panamik hot spring to –11.7‰ in Puga hot spring. After incubation with 13C-labelled glucose, the δ13C particulate reached a maximum 2472‰ in Panamik and 4365‰ in Puga hot-spring samples. The glucose uptake rate calculated from the final δ13C particulate in the incubation bottles varied from 28 to 147 ng C g–1 h–1 in the Panamik and from 168 to 1196 ng C g–1 h–1 in the Puga samples. This reveals that even at >74°C temperature, thermophiles are capable of running their metabolic machinery, perhaps faster than the heterotrophic microbes/cells under normal temperature condition.Keywords
Exogenous Carbon, Hot Springs, Thermophiles Glucose Uptake.Full Text
References
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PDF Views:87
Authors
Affiliations
1 Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, India; Department of Earth Sciences, Sambalpur University, Jyoti Vihar, Burla, Sambalpur 768 019, IN
2 Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, IN
3 Department of Earth Sciences, Sambalpur University, Jyoti Vihar, Burla, Sambalpur 768 019, India; Present address: Department of Geology, Utkal University, Vani Vihar, Bhubaneswar 751 004, IN
1 Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, India; Department of Earth Sciences, Sambalpur University, Jyoti Vihar, Burla, Sambalpur 768 019, IN
2 Birbal Sahni Institute of Palaeosciences, 53 University Road, Lucknow 226 007, IN
3 Department of Earth Sciences, Sambalpur University, Jyoti Vihar, Burla, Sambalpur 768 019, India; Present address: Department of Geology, Utkal University, Vani Vihar, Bhubaneswar 751 004, IN
Source
Current Science, Vol 122, No 8 (2022), Pagination: 885-887Abstract
No Abstract.Keywords
No keywordsFull Text
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- Convolutional neural network architecture for detection and classification of diseases in fruits
Abstract Views :164 |
PDF Views:94
Authors
Affiliations
1 Amity University, Noida 201 313, IN
1 Amity University, Noida 201 313, IN
Source
Current Science, Vol 122, No 11 (2022), Pagination: 1315-1320Abstract
Artificial intelligence is now becoming a part of people’s everyday lives. It can help farmers detect any disease in the early stage and take pre-emptive actions to save their crops and control disease spread, thus preventing crop wastage as well as increasing their income. The present study uses a combination of 13 convolutional neural network (CNN) models to classify five types of fruits and their leaf images into 41 classes, including diseased and healthy. Results show that the average accuracy of this CNN architecture is above 90% for all 13 individual models. One of the CNN models has been compared with three pre-trained models, i.e. MobileNet, DenseNet121 and InceptionV3 trained using the same dataset. It shows that the CNN architecture used in this study has higher accuracy while also being simple and easy to train.Keywords
Agriculture, Artificial Intelligence, Convolutional Neural Network, Deep Learning, Fruit and Leaf Disease DetectionFull Text
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