AIMC Topic: Pesticides

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AI-Empowered Molecular Editing Opens a New Horizon in Pesticide Discovery.

Journal of agricultural and food chemistry
Rapid evolution of digital technologies has enabled vital tools in pesticide discovery, which are crucial for agricultural productivity and food security. Therein, molecular editors have emerged as basic and critical tools in this field. However, exi...

Use of Artificial Neural Networks (ANNs) to assess xenobiotics in a river catchment using macroinvertebrates as bioindicators.

Aquatic toxicology (Amsterdam, Netherlands)
The Danube flows through various European regions, exposing its aquatic ecosystem to multiple stressors, including dams, canalization, and agricultural activities. Fertilizers, manures, pesticides, animal husbandry activities, irrigation practices, d...

Machine learning-powered fluorescent sensor arrays for rapid detection of heavy metals and pesticides in complex environments.

Biosensors & bioelectronics
The co-contamination of multiple pollutants in complex environmental matrices poses a significant threat to ecosystems and public health, necessitating advanced detection methods. In this study, we developed a machine learning-powered chemical sensor...

Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees.

PloS one
Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision bee...

Research progress on molecularly imprinted polymers (MIPs)-based sensors for the detection of organophosphorus pesticides.

Food chemistry
Organophosphorus pesticides (OPs), widely used in agriculture, have become major focuses of research in food safety and environmental pollution control due to their neurotoxicity and environmental persistence. In recent years, molecularly imprinted p...

A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health.

Scientific reports
Pesticides and other synthetic agrochemicals play a critical role in emerging agricultural practices by enhancing crop productivity and protecting against pests and diseases. However, their widespread application has raised significant concerns about...

Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007-2008.

Frontiers in public health
Age-related macular degeneration (AMD) is the most common cause of irreversible deterioration of vision in older adults. Previous studies have found that exposure to pesticides can lead to a worsening of AMD. In this paper, information on pesticide e...

Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction.

Scientific reports
Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid dist...

Evaluating degradation efficiency of pesticides by persulfate, Fenton, and ozonation oxidation processes with machine learning.

Environmental research
Quantifying organic properties is pivotal for enhancing the precision and interpretability of degradation predictive machine learning (ML) models. This study used Binary Morgan Fingerprints (B-MF) and Count-Based Morgan Fingerprints (C-MF) to quantif...

Unraveling the complexity of organophosphorus pesticides: Ecological risks, biochemical pathways and the promise of machine learning.

The Science of the total environment
Organophosphorus pesticides (OPPs) are widely used in agriculture but pose significant ecological and human health risks due to their persistence and toxicity in the environment. While microbial degradation offers a promising solution, gaps remain in...