Occurrence, Multidrug Resistance, SCCmec Typing of Methicillin-Resistant Staphylococcus aureus from Farmed Eggs and Environment.

A comparative cross-sectional study was conducted on farmed eggs (n: 480) and environmental samples (n: 72; feed, water, and poultry droppings) from twenty-four deep litter and caged poultry layer farms across Punjab, India. The study noted a significantly higher occurrence of Staphylococcus aureus (31.67%; 95% CI 26.

TPTC: topic-wise problems' trend clusters for smart agricultural insights extraction and forecasting of farmer's information demand.

To meet the challenges of increasing food production demand globally, extracting insights regarding the persistent agriculture-related problems on a nationwide scale is the need of the hour. Policymakers now have limited possibilities for acquiring a comprehensive knowledge of the difficulties that farmers face on a national level. In this direction, the presented work proposes a new artificial intelligence-based pipeline to gain insights at country level regarding the farmers' demand for assistance in India.

Occurrence, Multidrug Resistance, and Multilocus Sequence Typing of Extended-Spectrum Beta-Lactamase/AmpC-Producing from Farmed Eggs.

The present study evaluated the occurrence, antibiogram profile, and sequence types (STs) of multidrug resistant (MDR) from freshly laid eggs ( = 480), feed ( = 24), water ( = 24), poultry droppings ( = 24), and hand swab samples ( = 10) collected from 24 deep litter (DL) and caged poultry layer farms (12 per category) across Punjab, India. The overall contamination rate in DL and cage farms was 32% (95% confidence intervals [CI], 26.6-37.

Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach.

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models' learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed.

Surveillance and response strategies for zoonotic diseases: a comprehensive review.

Out of all emerging infectious diseases, approximately 75% are of zoonotic origin, with their source often traced back to animals. The emergence of zoonoses is driven by a complex interplay between anthropogenic, genetic, ecological, socioeconomic, and climatic factors. This intricate web of influences poses significant challenges for the prediction and prevention of zoonotic outbreaks.