Comprehensive validation of bactericidal efficacy of umbelliferone against multidrug-resistant pathogens through multifaceted antimicrobial assays - Neetesh Mandal; Gulshan Sen; Abhishek Pathak; Kuldeep Gauliya; Manish Kumar Manjhi; Devanshi Chandel Upadhyay; Mohammed Latif Khan; Chandrama Prakash Upadhyay (Archives of Microbiology, 2025)

Fostering Nutritional Equity: Biofortification Strategies, Socioeconomic Implications, and Regulatory Policies for Developing Biofortified Staple Crop - Kuldeep Gauliya; Abhishek Pathak; Neetesh Mandal; Manish Kumar Manjhi; Devanshi Chandel Upadhyaya; Aman Raj; Chandrama Prakash Upadhyaya (Journal of Soil Science and Plant Nutrition, 2025)

Integrating sensor data and machine learning to advance the science and management of river carbon emissions - Lee E. Brown; Taylor Maavara; Jiangwei Zhang; Xiaohui Chen; Megan Klaar; Felicia Orah Moshe; Elad Ben-Zur; Shaked Stein; Richard Grayson; Laura Carter; Elad Levintal; Gideon Gal; et al. (Critical Reviews in Environmental Science and Technology, 2025)

Impact of Metal Oxide Nanoparticles on Potato (Solanum tuberosum L.) Tuber Yield in Hydroponics - Neha Joshi; Abhishek Pathak; Devanshi Chandel Upadhyaya; Chandrama Prakash Upadhyaya (American Journal of Potato Research, 2024)

Light and temperature controls of aquatic plant photosynthesis downstream of a hydropower plant and the effect of plant removal - Benoît O.L. Demars; Susanne C. Schneider; Kirstine Thiemer; Peter Dörsch; Ulrich Pulg; Sebastian Stranzl; Gaute Velle; Devanshi Pathak (Science of The Total Environment, 2024)

High‐resolution water‐quality and ecosystem‐metabolism modeling in lowland rivers - Devanshi Pathak; Michael Hutchins; Lee E. Brown; Matthew Loewenthal; Peter Scarlett; Linda Armstrong; David Nicholls; Mike Bowes; François Edwards; Gareth Old (Limnology and Oceanography, 2022)
High‐resolution monitoring of water quality and ecosystem functioning over large spatial scales in expansive lowland river catchments is challenging. Therefore, we need modeling tools to predict these processes at locations where observations are absent. Here, we present a new approach to estimate ecosystem metabolism underpinned by a high‐resolution, process‐based model of in‐stream flows and water quality. The model overcomes the current challenges in metabolism modeling by accounting for oxygen transport under varying flows and oxygen transformations due to biogeochemical processes. We implement the model in a 62‐km‐long stretch of the River Thames, England, using observations spanning 2 yr. Model outputs suggest that the river is primarily autotrophic from mid‐spring to mid‐summer due to high biomass during low‐flow periods, and is heterotrophic during the rest of the year. Ecosystem respiration in upstream reaches is driven mainly by biochemical oxygen demand, autotrophic respiration, and nitrification processes, whereas downstream sites also show a control of benthic oxygen demand in addition to the aforementioned processes. Using empirical modeling, we analyze the sensitivity of our estimated metabolism rates to multiple environmental stressors. Results demonstrate that empirical models could be useful for rapid river health assessments, but need improvements to reproduce peak metabolism rates. The process‐based model, although more complex than existing in situ approaches to metabolism quantification, allows inference when gaps in continuous observations are present. The model offers additional benefits for predicting metabolism rates under future scenarios of environmental change incorporating multiple stressor effects.