Biology subjects

C, V.

Publications and source records attributed to C, V..

4 recordsLinked to original sources

bioRxiv2026

Heterotrimeric Gβ subunit orchestrates flavonoid metabolism governing drought-tolerance in groundnut (Arachis hypogaea L.)

Groundnut (Arachis hypogaea) is an important oilseed crop, and its production is severely affected by drought worldwide. Improving the drought-tolerance of groundnut without compromising its agronomic traits is a major challenge for researchers. Here, a meta-transcriptome analysis identified tandemly duplicated G{beta} subunits (AhG{beta}1 and AhG{beta}3) as early signaling genes under water deficit conditions. Heterotrimeric G-protein {beta}-subunit is a major signaling element that can regulate diverse responses in plants. Heterologous complementation studies in Arabidopsis G{beta} mutant (agb1-2) demonstrated evolutionary conservation of AhG{beta} function, while the transgenic overexpression and RNAi knockdown in groundnut established AhG{beta}1 as a positive regulator of plant growth, yield, and drought tolerance. Integrated transcriptomic, transcription factor regulatory network, and metabolomic analyses demonstrated that AhG{beta}1 coordinates drought-responsive transcriptional and metabolic reprogramming characterized by suppression of cell wall-associated processes and activation of phenylpropanoid, flavonoid, and isoflavonoid biosynthesis. This reprogramming resulted in enhanced accumulation of antioxidant flavonoids, including quercetin and rutin, reduced ROS accumulation, and improved drought tolerance. Furthermore, exogenous application of these flavonoids enhanced drought recovery, highlighting their role as protective downstream metabolites. Collectively, our findings establish AhG{beta}1 as a conserved regulator linking early G-protein signaling with transcriptional and metabolic reprogramming to maintain redox homeostasis during drought stress and identify promising molecular and metabolic targets for improving drought resilience in groundnut.

plant biology↗

bioRxiv2023

A Basal Ganglia Model for understanding Working Memory Functions in Healthy and Parkinson's Conditions

Working memory is considered as the scratchpad to write, read, and process information to perform cognitive tasks. Basal Ganglia (BG) and Prefrontal Cortex are two important parts of the brain that are involved in working memory functions and both the structures receive projections from dopaminergic nuclei. In this modelling study, we specifically focus on modelling the working memory functions of the BG, the working memory deficits in Parkinsons disease conditions, and the impact of dopamine deficiency on different kinds of working memory functions. Though there are many experimental and modelling studies of working memory properties, there is a paucity of models of the BG that provide insights into the contributions of the BG in working memory functions. The proposed model of the BG is a unified model that can explain the working memory functions of the BG over a wide variety of tasks in normal and Parkinsons disease conditions.

neuroscience↗

bioRxiv2022

A Generalized Reinforcement Learning-Based Deep Neural Network (GRL-DNN) Agent Model for Diverse Cognitive Constructs

Human cognition is characterized by a wide range of capabilities including goal-oriented selective attention, distractor suppression, decision making, response inhibition, and working memory. Much research has focused on studying these individual components of cognition in isolation, whereas in several translational applications for cognitive impairment, multiple cognitive functions are altered in a given individual. Hence it is important to study multiple cognitive abilities in the same subject or, in computational terms, model them using a single model. To this end, we propose a unified, reinforcement learning-based agent model comprising of systems for representation, memory, value computation and exploration. We successfully modelled the aforementioned cognitive tasks and show how individual performance can be mapped to model meta-parameters. This model has the potential to serve as a proxy for cognitively impaired conditions, and can be used as a clinical testbench on which therapeutic interventions can be simulated first before delivering to human subjects.

neuroscience↗

bioRxiv2021

The Flip-flop neuron - A memory efficient alternative for solving challenging sequence processing and decision making problems

Sequential decision making tasks that require information integration over extended durations of time are challenging for several reasons including the problem of vanishing gradients, long training times and significant memory requirements. To this end we propose a neuron model fashioned after the JK flip-flops in digital systems. A flip-flop is a sequential device that can store state information of the previous history. We incorporate the JK flip-flop neuron into several deep network architectures and apply the networks to difficult sequence processing problems. The proposed architectures include flip-flop neural networks (FFNNs), bidirectional flip-flop neural networks (BiFFNNs), convolutional flip-flop neural networks (ConvFFNNs), and bidirectional convolutional flip-flop neural networks (BiConvFFNNs). Learning rules of proposed architectures have also been derived. We have considered the most popular benchmark sequential tasks like signal generation, sentiment analysis, handwriting generation, text generation, video frame prediction, lung volume prediction, and action recognition to evaluate the proposed networks. Finally, we compare the results of our networks with the results from analogous networks with Long Short-Term Memory (LSTM) neurons on the same sequential tasks. Our results show that the JK flip-flop networks outperform the LSTM networks significantly or marginally on all the tasks, with only half of the trainable parameters.

neuroscience↗