Environmental Health

Implementing empirical likelihood within the causal inference framework to study causal effects of air pollution on reproductive development.

To study whether air pollution is detrimental to reproductive development is imperative. In the absence of randomized trials to study the effects of air pollution on human health, data from observational studies have been utilized in which the researchers attempted to capture the causal associations between air pollution and the health outcomes. Many of these studies rely on parametric assumptions...

An explainable machine learning model for accurate estimation of residual phthalate esters in Chinese agricultural soils.

Accurate estimation of diffuse chemical pollution in agricultural soils remains a persistent challenge, particularly for phthalate esters (PAEs), where traditional linear mechanistic models often show biases of 1-2 orders of magnitude. An explainable machine learning framework based on XGBoost was developed to predict residual dibutyl phthalate (DBP) and di-(2-ethylhexyl) phthalate (DEHP) concentr...

Integrated approach for arsenic prediction and health risk evaluation in community tube wells installed by public health department: comparative study of random forest, extreme gradient boosting, and deep neural networks.

Arsenic contamination in groundwater poses a significant public health risk, especially in developing countries with inadequate water quality monitori...

High-resolution LA-ICP-MS imaging combined with attention-enhanced residual network reveal cadmium-induced metal distribution heterogeneity and toxicity in Mytilus galloprovincialis.

Marine bivalves often face heavy metal stress, yet we still lack of high-resolution way to observe the metal pollution status in soft tissue. This stu...

From pollutant profiling to source attribution: An interpretable staged machine learning framework for sewer surveillance.

Illicit industrial discharges into urban sewer networks pose a severe threat to water security, yet source tracking is hindered by data scarcity withi...

MZSGO: multimodal zero-shot protein function annotation via evolutionary signals and textual semantics.

MOTIVATION: Although deep learning has significantly advanced the field of protein function prediction, current approaches are limited by their relian...

LTF-MSPCNet: A synergistic approach combining attention mechanisms and local texture features for oil spill segmentation in SAR images.

Marine oil spills represent a significant environmental pollution incident that severely disrupts the balance and stability of marine ecosystems. SAR ...

Deciphering stone mining-induced hazardous heavy metal contamination in agricultural soils using source attribution, health-dietary risk analysis, and machine learning-driven insights.

Mining activities significantly contribute to the release of hazardous heavy metals (HHMs) into agricultural soils, especially where stone extraction ...

Laccase- and peroxidase-like synergy-driven Cu-MOF nanozyme sensor array for high-throughput screening of phenolic pollutants.

Phenols are widespread high-risk environmental contaminants that exhibit structural diversity and vary in toxicity and environmental risk with substit...

Integrative Dermatology for Longevity: The Synergy of Topical and Internal Approaches.

Skin aging reflects both intrinsic biological decline and extrinsic influences collectively known as the skin exposome, including ultraviolet (UV) rad...

A hybrid bio-inspired model for predicting urban air pollution using deep learning.

Improving transthyretin cardiac amyloidosis detection from electrocardiograms through the Willem artificial intelligence platform.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently underdiagnosed disease in which delay in diagnosis limits the efficacy of t...

Traffic noise, air pollution, and greenness in relation to age-related macular degeneration: Evidence from a cross-national cohort study.

Age-related macular degeneration (AMD) is influenced not only by biological factors but also by the broader urban environment, yet evidence integratin...

Derivation of safety threshold for soil organic chemicals based on source-transport-fate dynamics in the soil-groundwater system: A case study of soil 1,2,3-trichloropropane in the Yangtze River Delta region, China.

1,2,3-Trichloropropane (TCP) is a persistent and highly toxic contaminant that poses substantial risks to groundwater systems. However, deriving groun...

Machine Learning-Assisted Concentration-Independent Recognition of Neonicotinoids Based on Multienzyme-like Activities of FeCu Dual-Atom Nanozyme.

Residues of neonicotinoid insecticides (NEOs) pose serious threats to ecological systems and human health. Conventional nanozyme sensors often suffer ...

Vietnamese Odonata: bridging global biodiversity, ecological, and conservation gaps in a changing world.

Vietnam is a global hotspot, hosting 493 Odonata species, with ~95% evaluated by the IUCN. Odonata species play vital ecological roles in aquatic and ...

Source apportionment of pollution in the Tianjin Haihe river sluice based on the TCN-APCS-MLR model.

River pollution identification in complex watersheds like the Haihe River Sluice faces challenges due to temporal dynamics and nonlinear driving mecha...

Spatiotemporal characteristics and driving factors of coal-burning hotspots in Shandong Province revealed by multi-source data and machine learning.

Coal combustion emissions significantly contribute to air pollution in China, especially in the residential sector, where they are widely dispersed an...

Coupling bayesian optimization and multi-paradigm ensemble learning to predict lead remediation in microbially induced carbonate precipitation: From model performance to crystalline control windows.

Microbially Induced Carbonate Precipitation (MICP), recognized as a highly promising green remediation technology, exhibits lead (Pb) immobilization e...

Machine learning-driven QSAR models for the prediction of metabolic mechanisms and thyroid hormone-disrupting effects of emerging pollutants in the human body: a case study of bisphenol analogues.

Research on theoretical prediction methods for elucidating the reaction mechanisms and thyroid hormone-disrupting effects of emerging pollutants in th...