AIMC Topic: Bacteria

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Rapid and accurate identification of foodborne bacteria: a combined approach using confocal Raman micro-spectroscopy and explainable machine learning.

Analytical and bioanalytical chemistry Mar 29, 2025
This study proposes a rapid identification method for foodborne pathogens by combining Raman spectroscopy with explainable machine learning. Spectral data of nine common foodborne pathogens are collected using a laser confocal Raman spectrometer, and...
Food Microbiology Foodborne Diseases Machine Learning Algorithms Spectrum Analysis, Raman Support Vector Machine Bacteria
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Modeling microbiome-trait associations with taxonomy-adaptive neural networks.

Microbiome Mar 29, 2025
The human microbiome, a complex ecosystem of microorganisms inhabiting the body, plays a critical role in human health. Investigating its association with host traits is essential for understanding its impact on various diseases. Although shotgun met...
Humans Bacteria Microbiota Metagenomics Neural Networks, Computer Computer Simulation
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Colonial bacterial memetic algorithm and its application on a darts playing robot.

Scientific reports Mar 28, 2025
In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends the Bacterial Memetic Algorithm by integrating Cultural Algorithms and co-evolutionary dy...
Biological Evolution Algorithms Bacteria Robotics
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Machine learning assisted paper-based fluorescent sensor array with metal-doped multicolor carbon quantum dots for identification and inactivation of bacteria.

Talanta Mar 27, 2025
Bacterial infection is a thorny threat in a variety of fields, including medicine, environment, food, and agriculture. A multifunctional platform that meets the demands of both bacterial identification and real-time inactivation is urgently needed. T...
Copper Silver Fluorescent Dyes Paper Bacteria Machine Learning Carbon Anti-Bacterial Agents Quantum Dots Zinc Fluorescence Biosensing Techniques
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aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.

Genome biology Mar 23, 2025
A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to in...
Adaptation, Physiological Genome-Wide Association Study Bacteria Machine Learning Ecosystem Phylogeny Software
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Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections.

Microbial pathogenesis Mar 19, 2025
A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune system's phagocytosis process and attacking with anti-microbial chemicals such as reactive oxygen and nit...
Humans Artificial Intelligence Biofilms Bacteria Nanoparticles Anti-Bacterial Agents Anti-Infective Agents Early Diagnosis
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Revealing gut microbiota biomarkers associated with melanoma immunotherapy response and key bacteria-fungi interaction relationships: evidence from metagenomics, machine learning, and SHAP methodology.

Frontiers in immunology Mar 18, 2025
INTRODUCTION: The gut microbiota is associated with the response to immunotherapy in cutaneous melanoma (CM). However, gut fungal biomarkers and bacterial-fungal interactions have yet to be determined.
Immunotherapy Treatment Outcome Biomarkers Melanoma Gastrointestinal Microbiome Humans Skin Neoplasms Female Middle Aged Machine Learning Bacteria Adult Aged Male Fungi Metagenomics
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Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Computers in biology and medicine Mar 17, 2025
Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digital health applications, and biological assets, including microorganisms. However, the hypothesis posits that implementing micro- and nanoscale DTs, e...
Convolutional Neural Networks Bacteria Federated Learning Internet of Things Neural Networks, Computer Algorithms Deep Learning Humans
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SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 13, 2025
Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when photons interact with substances, providing detailed molecular structure information. It exhibits various advantages including high sensitivity, specifici...
Drug Resistance, Bacterial Bacteria Spectrum Analysis, Raman Humans Anti-Bacterial Agents
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deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.

mSystems Mar 10, 2025
Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec) in the active sites, which is encoded by an in-frame UGA codon. Compared to eukaryotes, identificat...
Bacterial Proteins Genome, Bacterial Selenoproteins Deep Learning Bacteria Algorithms
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