AIMC Topic: Bacteria

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Machine Learning Accelerated Discovery of Antimicrobial Inorganic Nanomaterials.

The journal of physical chemistry letters
The growing prevalence of infectious diseases and the increasing threat of bacterial resistance have drawn widespread attention to antimicrobial inorganic nanomaterials. However, the diversity, abundance, and complex mechanisms of these materials pre...

Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance.

mBio
Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of anti...

Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.

Communications biology
Predicting prokaryotic phenotypes-observable traits that govern functionality, adaptability, and interactions-holds significant potential for fields such as biotechnology, environmental sciences, and evolutionary biology. In this study, we leverage m...

The Biogeography of Soil Bacteria in Australia Exhibits Greater Resistance to Climate Change Than Fungi.

Global change biology
Soil microorganisms are crucial to ecosystem health, and their composition and distribution are shaped by a range of environmental factors. However, the effects of accelerating climate change on soil microbiomes remain under-explored. This study exam...

Microbial degradation potential of microplastics in urban river sediments: Assessing and predicting the enrichment of PE/PP-degrading bacteria using SourceTracker and machine learning.

Journal of environmental management
Microplastic mitigation strategies that adapt to various actual aquatic environments require the ability to predict their microbial degradation potential. However, the sources and enrichment characteristics of the degrading bacteria in the plastisphe...

Intelligent FA/FNA alternating strategy for nitrite-oxidizing bacteria inhibition: Data-driven prediction and process control.

Journal of environmental management
Alternating treatment with free ammonia (FA) and free nitrous acid (FNA) is an effective strategy to inhibit nitrite-oxidizing bacteria (NOB) in partial nitrification (PN) process. However, the current alternating treatment relies on manual assessmen...

Recent advances in microbial synthesis of polyphenols.

Current opinion in biotechnology
Polyphenols are plant-derived secondary metabolites known for their antioxidants, anti-inflammatory, and antimicrobial properties, with flavonoids being the most structurally diverse and medically relevant subclass. Traditional plant extraction is li...

XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.

NAR genomics and bioinformatics
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...

Enhancing robustness and generalization in microbiological few-shot detection through synthetic data generation and contrastive learning.

Computers in biology and medicine
In many medical and pharmaceutical processes, continuous hygiene monitoring is crucial, often involving the manual detection of microorganisms in agar dishes by qualified personnel. Although deep learning methods hold promise for automating this task...

Paving the way for bacteria-based drug delivery: biohybrid microrobots emerging from microrobotics and synthetic biology.

Advanced drug delivery reviews
Advances in microrobotics and synthetic biology are paving the way for innovative solutions to long-standing challenges in drug delivery. Both fields have independently worked on engineering bacteria as a therapeutic system, focusing on enhancing pro...