Environmental Health

Development of fuzzy logic algorithm for predicting heavy metal content in poultry product.

Poultry products are important global protein sources but are vulnerable to contamination by toxic metals such as copper, cadmium, lead, and arsenic. Excessive intake of these metals poses health risks, necessitating reliable yet accessible detection methods. This study developed a fuzzy logic framework using the Mamdani inference system and triangular membership functions in MATLAB R2021b to esti...

Beyond Residential Ambient Concentrations: Quantifying Exposure Error and Advancing Personal PM2.5 Prediction with a Scalable Modeling Framework.

Accurate assessment of personal PM2.5 exposure is essential but challenging in large-scale epidemiology, as conventional residential ambient data often lead to exposure misclassification. This study aimed to quantify errors in ambient data proxies and develop a scalable modeling framework for personal exposure prediction using readily available data. We conducted a panel study with 12 adults from ...

Advancing Screening of Aquatic Persistent, Mobile, and Toxic Substances via Integration of Graph Convolutional Networks and Multitask Learning.

The accurate identification of persistent, mobile, and toxic (PMT) substances, which are typical emerging contaminants, is critical for assessing risk...

HMRPred: A Machine Learning-Based Web Resource for Identification of Heavy Metal Resistance Proteins.

Heavy metal contamination poses a significant threat to environmental health, agriculture, and microbial ecosystems, necessitating the identification ...

Hierarchical Cross-scale Machine Learning for Enhanced Interpretation and Prediction of Phosphorus Removal by Metal Oxides Materials.

Phosphorus pollution necessitates advanced water remediation technologies. Metal-oxide materials show significant promise but face complexity arising ...

The association of environmental exposure with multiple sclerosis severity score: A study based on sequential data modeling.

Introduction Multiple Sclerosis (MS) is a chronic neuroinflammatory disease influenced by clinical, demographic, and environmental factors. Predicting...

AI-driven optimization of sustainable solvent-based extraction: A deep learning approach for green sample preparation.

BACKGROUND: This study introduces a data-driven deep learning framework for optimizing the extraction of plant biomolecules, aiming to improve both ef...

Evaluating deep learning time series models for PM2.5 forecasting across diverse horizons.

Air pollution, particularly PM2.5, poses a major health challenge in urban areas such as Chengdu, China, where basin topography and intense emission s...

Machine learning reveals ecological thresholds and predicts future bacterial community shifts in a chronically eutrophic estuary.

Effectively managing eutrophic estuaries under climate change requires a quantitative understanding of how microbial communities respond to multiple e...

A Smartphone-Read, Machine Learning-Enhanced Lanthanide Hydrogel Sensor for Multimodal and On-Site Detection of Kanamycin in Food Safety.

Residual kanamycin from veterinary use poses a persistent threat to environmental safety and public health, as its accumulation in water systems and a...

A novel graph convolutional neural network on K neighbors model for fine-grained air pollution distribution mapping based on sparse monitoring.

This article aims to predict the concentration of air pollutants at any unmonitored location based on sparse monitoring points in the monitoring area,...

Associations between prenatal exposure to indoor air pollution and ambient particulate matter with infant growth.

Despite growing concern, the developmental effects of prenatal exposure to indoor and ambient particulate pollution remain poorly characterized. This ...

Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study.

BACKGROUND: Living near greenspace is associated with decreased cardiovascular disease (CVD). Greenspace estimates, however, typically represent all t...

Combined effects of persistent organic pollutants and endocrine-disrupting chemicals on thyroid disease risk: A WQS-XGBoost analysis of US population data.

To assess the combined effects of mixed persistent organic pollutants and endocrine-disrupting chemicals on thyroid disease risk, we analyzed data fro...

[Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].

OBJECTIVES: To enhance the accuracy and reliability of 12-lead electrocardiogram (ECG) automatic diagnosis. METHODS: Herein we propose a 12-lead ECG a...

uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm.

INTRODUCTION: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains cha...

Ambient Air Pollution, Greenness and Frailty in an Elder Asian Population: A Multi-Center Study with Long-Term Exposure.

BACKGROUND: Long-term exposure to air pollution has been linked to adverse health outcomes in older adults; however, its association with frailty, par...

A Framework Integrating Single-Cell Metallomic Data in Health Effect Analysis via Quantile Features and Machine Learning.

Heavy and essential metals coexist in a cell, collectively participate in cellular biological processes, and lead to overall health consequences. Howe...

Surface water quality prediction via an MLA-Mamba hybrid neural network with GRPO optimization.

Surface water quality forecasting is crucial for pollution early warning and sustainable water resource management. However, accurate prediction of ke...

Indoor air quality in primary schools: real-time monitoring and predictive modeling of PM10 in Kenitra, Morocco.

The health of children may be adversely influenced by the air quality in schools because they are more sensitive to indoor air pollutants. PM10, which...