AIMC Topic: Fungi
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Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases.
Genome research
Feb 14, 2025
Metagenomics, particularly genome-resolved metagenomics, have significantly deepened our understanding of microbes, illuminating their taxonomic and functional diversity and roles in ecology, physiology, and evolution. However, eukaryotic populations...
Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.
Environment international
Feb 4, 2025
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...
Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PloS one
Jan 24, 2025
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art painting...
Machine learning models for predicting indoor airborne fungal concentrations in public facilities utilizing environmental variables.
Environmental pollution (Barking, Essex : 1987)
Jan 16, 2025
Airborne fungi are major contributors to substandard indoor air quality, with potential implications for public health, especially in public facilities. The risk of chronic exposure can be significantly reduced by accurately predicting airborne funga...
Targeted isolation and AI-based analysis of edible fungal polysaccharides: Emphasizing tumor immunological mechanisms and future prospects as mycomedicines.
International journal of biological macromolecules
Nov 26, 2024
Edible fungal polysaccharides have emerged as significant bioactive compounds with diverse therapeutic potentials, including notable anti-tumor effects. Derived from various fungal sources, these polysaccharides exhibit complex biological activities ...
Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.
Sensors (Basel, Switzerland)
Nov 9, 2024
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as , , , , and were chosen based on their ecological importance and distinct morphological characteristics. Th...
Effective detection of indoor fungal contamination through the identification of volatile organic compounds using mass spectrometry and machine learning.
Environmental pollution (Barking, Essex : 1987)
Oct 28, 2024
Indoor fungal contamination poses significant challenges to human health and indoor air quality. This study addresses an effective approach using mass spectrometry and machine learning to identify microbial volatile organic compounds (MVOCs) originat...
Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.
Biosensors & bioelectronics
Oct 21, 2024
The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition parti...
Optimization of fungal secondary metabolites production via response surface methodology coupled with multi-parameter optimized artificial neural network model.
Bioresource technology
Sep 20, 2024
Filamentous fungi's secondary metabolites (SMs) possess significant application owing to their distinct structure and diverse bioactivities, yet their restricted yield levels often hinder further research and application. The study developed a respon...
Investigating PCB degradation by indigenous fungal strains isolated from the transformer oil-contaminated site: degradation kinetics, Bayesian network, artificial neural networks, QSAR with DFT, molecular docking, and molecular dynamics simulation.
Environmental science and pollution research international
Sep 6, 2024
The widespread prevalence of polychlorinated biphenyls (PCBs) in the environment has raised major concerns due to the associated risks to human health, wildlife, and ecological systems. Here, we investigated the degradation kinetics, Bayesian network...
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