AIMC Topic: Bacteria

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AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.

Accounts of chemical research
ConspectusThe escalating threat of antimicrobial resistance (AMR) poses a significant global health crisis, potentially surpassing cancer as a leading cause of death by 2050. Traditional antibiotic discovery methods have not kept pace with the rapidl...

Impact of microbial profile integration on machine learning predictions of methane production: synergies and trade-offs with physicochemical parameters.

Bioresource technology
Microbial sequencing data were rarely integrated into the prediction of methane production using machine learning (ML) models because of high dimensionality and the lack of a systematic way to evaluate the change of insight gained from modelling with...

Single-tooth resolved, whole-mouth prediction of early childhood caries via spatiotemporal variations of plaque microbiota.

Cell host & microbe
Early childhood caries (ECC) exhibits tooth specificity, highlighting the need for single-tooth-level prevention. We profiled 2,504 dental plaque microbiota samples from 89 preschoolers across two cohorts, tracking compositional changes with imputed ...

Optimizing phage therapy with artificial intelligence: a perspective.

Frontiers in cellular and infection microbiology
Phage therapy is emerging as a promising strategy against the growing threat of antimicrobial resistance, yet phage and bacteria are incredibly diverse and idiosyncratic in their interactions with one another. Clinical applications of phage therapy o...

Risk-reward trade-off during carbon starvation generates dichotomy in motility endurance among marine bacteria.

Nature microbiology
Copiotrophic marine bacteria contribute to the control of carbon storage in the ocean by remineralizing organic matter. Motility presents copiotrophs with a risk-reward trade-off: it is highly beneficial in seeking out sparse nutrient hotspots, but e...

TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.

BMC bioinformatics
BACKGROUND: 16S rRNA amplicon sequencing is a widely used method for microbiome composition analysis due to its cost-effectiveness and lower data requirements compared to metagenomic whole-genome sequencing (WGS). However, inherent limitations in 16S...

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity.

mBio
Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited....

Distinguishing critical microbial community shifts from normal temporal variability in human and environmental ecosystems.

Scientific reports
Differentiating significant microbial community changes from normal fluctuations is vital for understanding microbial dynamics in human and environmental ecosystems. This knowledge could enable early warning systems to monitor critical changes affect...

Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.

Mikrochimica acta
Most microbiota determination (skin, gut, soil, etc.) are currently conducted in a laboratory using expensive equipment and lengthy procedures, including culture-dependent methods, nucleic acid amplifications (including quantitative PCR), DNA microar...

A novel method for achieving ecological indicator based on vertical soil bacterial communities coupled with machine learning: A case study of a typical tropical site in China.

Journal of hazardous materials
Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM...