AIMC Topic: Escherichia coli

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Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins.

G3 (Bethesda, Md.)
Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknome." This large knowledge shortfall is one of the final frontiers of biology. Machine learning (ML) approaches are enticing...

Fluorescent sensor array for rapid bacterial identification using antimicrobial peptide-functionalized gold nanoclusters and machine learning.

Talanta
Bacterial infectious diseases pose significant challenges to public health, emphasizing the need for rapid and accurate diagnostic tools. Here, we introduced a multichannel fluorescent sensor array based on antimicrobial peptide-functionalized gold n...

Antibacterial, Antifreezing, and Tough Electronic Skin Based on a Tanned Collagen Fiber Network for Underwater Grabber Application.

ACS applied materials & interfaces
The environment of underwater salvage is very special, and many factors such as real-time water conditions, the depth of salvage, and the complexity of underwater conditions could greatly affect the smooth progress of the salvage process. How to accu...

Smartphone-Powered Automated Image Recognition Tool for Multianalyte Rapid Tests: Application to Infectious Diseases.

Analytical chemistry
Point-of-Care Testing (POCT) is rapidly increasing, providing quick, user-friendly, and portable diagnostic tools. Lateral flow assays (LFAs) have been central to POCT, administering fast and cost-effective diagnosis. However, traditional LFAs are li...

Novel robotic tools used for the detection of faecal shedding of Escherichia coli resistant to critically important antimicrobials in healthy dogs.

Veterinary microbiology
Escherichia coli recovered from dogs with clinical conditions such as urinary tract infections are often used to assess populations for resistance to critically important antimicrobials (CIAs). Despite the potential importance of such strains, the nu...

Improving prediction of bacterial sRNA regulatory targets with expression data.

NAR genomics and bioinformatics
Small regulatory RNAs (sRNAs) are widespread in bacteria. However, characterizing the targets of sRNA regulation in a way that scales with the increasing number of identified sRNAs has proven challenging. Computational methods offer one means for eff...

Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli.

New biotechnology
In order to improve predictability of outcome and reduce costly rounds of trial-and-error, machine learning models have been of increasing importance in the field of synthetic biology. Besides applications in predicting genome annotation, process par...

GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions.

BMC bioinformatics
Bacterial small RNAs (sRNAs) are pivotal in post-transcriptional regulation, affecting functions like virulence, metabolism, and gene expression by binding specific mRNA targets. Identifying these targets is crucial to understanding sRNA regulation a...

Decoding the role of the arginine dihydrolase pathway in shaping human gut community assembly and health-relevant metabolites.

Cell systems
The arginine dihydrolase pathway (arc operon) provides a metabolic niche by transforming arginine into metabolic byproducts. We investigate the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and healt...

Prediction of bloodstream infection using machine learning based primarily on biochemical data.

Scientific reports
Early diagnosis of bloodstream infection (BSI) is crucial for informed antibiotic use. This study developed a machine learning approach for early BSI detection using a comprehensive dataset from Rigshospitalet, Denmark (2010-2020). The dataset includ...