AIMC Topic: Biodegradation, Environmental
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Unlocking the potential of Eudrilus eugeniae in mitigating the pollution risk of pesticides and heavy metals: Fostering machine learning tactics to optimize environmental health.
The Science of the total environment
Mar 8, 2025
Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative ...
Polymer Biodegradation in Aquatic Environments: A Machine Learning Model Informed by Meta-Analysis of Structure-Biodegradation Relationships.
Environmental science & technology
Jan 7, 2025
Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural environments. Biodegradable polymers, while promising for reducing environmental impact, account for less...
Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches.
Biodegradation
Dec 28, 2024
The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Eleme...
Interpretation of machine learning-based prediction models and functional metagenomic approach to identify critical genes in HBCD degradation.
Journal of hazardous materials
Dec 25, 2024
Hexabromocyclododecane (HBCD) poses significant environmental risks, and identifying HBCD-degrading microbes and their enzymatic mechanisms is challenging due to the complexity of microbial interactions and metabolic pathways. This study aimed to ide...
Harnessing artificial neural networks to model caffeine degradation by High-Yield biodiesel algae Desmodesmus pannonicus.
Bioresource technology
Dec 10, 2024
In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, phot...
Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.
Journal of chemical information and modeling
Nov 27, 2024
Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and researc...
Machine learning models reveal how polycyclic aromatic hydrocarbons influence environmental bacterial communities.
The Science of the total environment
Oct 22, 2024
Polycyclic aromatic hydrocarbons (PAHs) are harmful and widespread pollutants in the environment, posing an ecological threat. However, exploring the influence of PAHs on environmental bacterial communities in different habitats (soil, water, and sed...
Enhanced phytoremediation of vanadium using coffee grounds and fast-growing plants: Integrating machine learning for predictive modeling.
Journal of environmental management
Oct 9, 2024
Vanadium (V) contamination posed a significant environmental challenge, while phytoremediation offered a sustainable solution. Phytoremediation performance was often limited by the slow growth cycles of traditional plants. A novel approach to enhanci...
Machine learning phenotyping and GWAS reveal genetic basis of Cd tolerance and absorption in jute.
Environmental pollution (Barking, Essex : 1987)
Sep 10, 2024
Cadmium (Cd) is a dangerous environmental contaminant. Jute (Corchorus sp.) is an important natural fiber crop with strong absorption and excellent adaptability to metal-stressed environments, used in the phytoextraction of heavy metals. Understandin...
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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