Aditya Anil Kadam
@shahucollegelatur.org.in
Assistant Professor
Rajarshi Shahu Mahavidyalaya (Autonomous), Latur
@shahucollegelatur.org.in
Assistant Professor
Rajarshi Shahu Mahavidyalaya (Autonomous), Latur
Food contamination monitoring requires rapid, reliable, and biologically relevant detection strategies. This study proposes a computational framework for the identification and prioritization of biosensor targets associated with food contamination. Candidate targets were collected from biological databases and scientific literature and categorized into enzymatic activities, adaptation metabolites, biofilm markers, and spoilage indicators. A weighted scoring framework based on specificity, detectability, food relevance, signal-generation potential, and literature support was applied to rank candidates. Enzyme-associated targets, particularly α-galactosidase, esterases, and PYRase, demonstrated the highest biosensor potential. The framework provides a systematic foundation for future biosensor development and experimental validation in food safety applications.
Hot Jupiter formation is traditionally explained using host-star properties such as mass and metallicity. This study investigates whether local stellar environments contain additional information associated with Hot Jupiter occurrence. Using Gaia-derived stellar neighborhoods, exoplanet catalogs, and graph-based environmental metrics, we analyzed relationships between planetary systems and their surrounding stellar populations. Multiple statistical, causal, and robustness tests identified significant associations between Hot Jupiter hosts and chemically enriched, dynamically quiet environments characterized by lower mass entropy. Alternative explanations based solely on host-star properties were insufficient to explain the observed patterns. These findings suggest that local stellar environments may influence giant planet formation and migration pathways.
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