Showing papers by "Sanjay Ghosh published in 2021"

Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery

Abstract: Crop mapping is a challenging task due to spectral similarity of various crops. This study aims to: (1) identify major crops in Roorkee, India, using Sentinel-2A data. (2) test the efficacy of ense...

Ribosome profiling reveals ribosome stalling on tryptophan codons and ribosome queuing upon oxidative stress in fission yeast.

Abstract: Translational control is essential in response to stress. We investigated the translational programmes launched by the fission yeast Schizosaccharomyces pombe upon five environmental stresses. We also explored the contribution of defence pathways to these programmes: The Integrated Stress Response (ISR), which regulates translation initiation, and the stress-response MAPK pathway. We performed ribosome profiling of cells subjected to each stress, in wild type cells and in cells with the defence pathways inactivated. The transcription factor Fil1, a functional homologue of the yeast Gcn4 and the mammalian Atf4 proteins, was translationally upregulated and required for the response to most stresses. Moreover, many mRNAs encoding proteins required for ribosome biogenesis were translationally downregulated. Thus, several stresses trigger a universal translational response, including reduced ribosome production and a Fil1-mediated transcriptional programme. Surprisingly, ribosomes stalled on tryptophan codons upon oxidative stress, likely due to a decrease in charged tRNA-Tryptophan. Stalling caused ribosome accumulation upstream of tryptophan codons (ribosome queuing/collisions), demonstrating that stalled ribosomes affect translation elongation by other ribosomes. Consistently, tryptophan codon stalling led to reduced translation elongation and contributed to the ISR-mediated inhibition of initiation. We show that different stresses elicit common and specific translational responses, revealing a novel role in Tryptophan-tRNA availability.

Assessment and prediction of urban growth for a mega-city using CA-Markov model

Abstract: Most of World’s mega-cities are facing high population growth. To accommodate the increased population, new built-up areas are emerging at the periphery or fringe area of cities. New urbanisation h...

In-vivo protein nitration and de-nitration facilitate Vibrio cholerae cell survival under anaerobic condition: Consequences of Nitrite induced protein nitration

Abstract: Protein tyrosine nitration (PTN), a highly selective post translational modification, occurs in both prokaryotic and eukaryotic cells under nitrosative stress1. It is reported that the activities of many proteins are altered due to PTN2. PTN is found to be associated with many pathophysiological conditions like neurodegenerative and cardiac diseases etc.3. However, its physiological function is not yet clear. Like all other gut pathogens Vibrio cholerae also faces nitrosative stress in the gut environment which makes its proteome more vulnerable to PTN. Here, we report for the first time in-vivo PTN in V. cholerae. We show that in-vivo protein nitration is nitrite dependent and nitration-denitration phenomenon actually facilitates V. cholerae cell survival in anaerobic or hypoxic condition. In our study, we found that the extent of in-vivo nitration is negatively correlated with the intracellular nitrite content and maximum nitration occurs during log phase of V. cholerae. Most interestingly, a significant denitration was associated with increase in intracellular nitrate content during anaerobic incubation of aerobically grown late log phase cultures. In-vivo nitration could provide an avenue for toxic nitrite storage and nitrosative stress tolerance mechanism in many gut pathogens, whereas denitration could supply nitrate for cell survival in anaerobic nitrate deficient environment.

Estimation of snow depletion curve for gangotri basin using multi-source remote sensing data

Abstract: . Snow Depletion Curve derived from satellite images is a key parameter in Snowmelt Runoff Model. The fixed temporal resolution of a satellite and presence of cloud cover in Himalayas restricts accuracy of generated SDC. This study presents an effective approach of reducing temporal interval between two consecutive dates by integrating normalized Snow Cover Area estimated from multiple sources of satellite data. SCA is extracted by using Normalized Difference Snow Index for six snowmelt seasons from 2013 to 2018 for Gangotri basin situated in Indian Himalayas. This work also explores potential of recently launched Sentinel-3A for estimating SCA. Normalized SCA is utilized to eliminate the effect of difference in spatial resolution of various satellites. The result develops an important linear relation between SDC and time with a decrease in snow cover of 0.005/day that may be further refined by increasing the number of snowmelt seasons. This relationship may help scientific community in understanding hydrological response of glaciers to climate change.

Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging

Abstract: Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and temperature monitoring of climate or CPU and GPU. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the spatio-temporal dynamics of model parameters and noise are modeled to have Kronecker product covariance structure. Inference in our framework is based on majorization-minimization optimization and has guaranteed convergence properties. Our highly efficient algorithms exploit the intrinsic Riemannian geometry of temporal autocovariance matrices. For stationary dynamics described by Toeplitz matrices, the theory of circulant embeddings is employed. We prove convex bounding properties and derive update rules of the resulting algorithms. On both synthetic and real neural data from M/EEG, we demonstrate that our methods lead to improved performance.