Exploring Pathogen Presence Prediction in Pastured Poultry Farms through Transformer-Based Models and Attention Mechanism Explainability.

In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score-an evaluation metric for model performance-thus fulfilling an essential need in predictive microbiology. Additionally, the emphasis is on making our model's predictions explainable.

Predicting MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility.

spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR.

Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach.

Background: Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa.

An ensemble learning approach to identify pastured poultry farm practice variables and soil constituents that promote prevalence.

Animal sourced foods including contaminated poultry meat and eggs contribute to human non-typhoidal salmonellosis, a foodborne zoonosis. Prevalence of in pastured poultry production systems can lead to contamination of the final product. Identification of farm practices that affect prevalence is critical for implementing control measures to ensure the safety of these products.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey.

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread.

Preharvest Environmental and Management Drivers of Multidrug Resistance in Major Bacterial Zoonotic Pathogens in Pastured Poultry Flocks.

Due to nutritional benefits and perceived humane ways of treating the animals, the demand for antibiotic-free pastured poultry chicken has continued to be steadily rise. Despite the non-usage of antibiotics in pastured poultry broiler production, antibiotic resistance (AR) is reported in zoonotic poultry pathogens. However, factors that drive multidrug resistance (MDR) in pastured poultry are not well understood.

Quantitative analysis of Streptococcus pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC ESI MS/MS.

Understanding the growth of bacterial pathogens in a micronutrient restricted host environment can identify potential virulence proteins that help overcome this nutritional barrier to productive infection. In this study, we investigated the pneumococcal protein expression response to iron limitation using an in vitro model. We identified S.

Effects of subminimum inhibitory concentrations of antibiotics on the Pasteurella multocida proteome.

Subminimum inhibitory concentrations (sub-MICs) of antibiotics can be therapeutically effective, but the underlying molecular mechanisms are not well-characterized. We analyzed the Pasteurella multocida proteome response to sub-MICs of amoxicillin, chlortetracycline, and enrofloxacin using isotope-coded affinity tags (ICAT). There were parallel effects on inhibition of growth kinetics and suppression of protein expression by clusters of orthologous groups (COG) categories.