AIMC Topic: Organic Chemicals

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Prediction of acute toxicity of organic contaminants to fish: Model development and a novel approach to identify reactive substructures.

Journal of hazardous materials
In this study, count-based Morgan fingerprints (CMF) were employed to represent the fundamental chemical structures of contaminants, and a neural network model (R² = 0.76) was developed to predict acute fish toxicity (AFT) of organic compounds. Model...

Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.

Food research international (Ottawa, Ont.)
Oleogelators are considered food additives that require approval from regulatory authorities. Therefore, classifying these ingredients that may have characteristics (e.g., waxiness), cost and origin (e.g., animal or vegetable) is crucial to ensure co...

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes.

Environmental science. Processes & impacts
The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting t...

CAM/TMA-DPH as a promising alternative to SYTO9/PI for cell viability assessment in bacterial biofilms.

Frontiers in cellular and infection microbiology
INTRODUCTION: Accurately assessing biofilm viability is essential for evaluating both biofilm formation and the efficacy of antibacterial treatments. Traditional SYTO9 and propidium iodide (PI) live/dead staining in biofilm viability assays often ace...

Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals.

Environmental science & technology
Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation. However, understanding the underlying chemical mechanisms still faces challenges due to the...

The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis.

The Science of the total environment
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM)...

Environmental drivers of dissolved organic matter composition across central European aquatic systems: A novel correlation-based machine learning and FT-ICR MS approach.

Water research
Dissolved organic matter (DOM) present in surface aquatic systems is a heterogeneous mixture of organic compounds reflecting its allochthonous and autochthonous organic matter (OM) sources. The composition of DOM is determined by environmental factor...

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone.

Journal of the American Society for Mass Spectrometry
The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mix...

Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

Journal of hazardous materials
Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based o...

Identifying Organic Chemicals with Acetylcholinesterase Inhibition in Nationwide Estuarine Waters by Machine Learning-Assisted Mass Spectrometric Screening.

Environmental science & technology
Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (∼1%) has been explained by known chemicals of concern. Here, we integra...