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.

Characterization of an Arginine Decarboxylase from by Ultrahigh-Performance Liquid Chromatography-Tandem Mass Spectrometry.

Polyamines are polycations derived from amino acids that play an important role in proliferation and growth in almost all living cells. In (the pneumococcus), modulation of polyamine metabolism not only plays an important regulatory role in central metabolism, but also impacts virulence factors such as the capsule and stress responses that affect survival in the host. However, functional annotation of enzymes from the polyamine biosynthesis pathways in the pneumococcus is based predominantly on computational prediction.

Impact of Difluoromethylornithine and AMXT 1501 on Gene Expression and Capsule Regulation in .

(Spn), a Gram-positive bacterium, poses a significant threat to human health, causing mild respiratory infections to severe invasive conditions. Despite the availability of vaccines, challenges persist due to serotype replacement and antibiotic resistance, emphasizing the need for alternative therapeutic strategies. This study explores the intriguing role of polyamines, ubiquitous, small organic cations, in modulating virulence factors, especially the capsule, a crucial determinant of Spn's pathogenicity.

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.