Environmental Health

Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches.

Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals on electrocardiograms (ECGs). LQTS can lead to severe clinical manifestations, including syncope, ventricular arrhythmias, and sudden cardiac death. Effective genotype-specific management strategies are essential to mitigate the risk of life-threateni...

A domain adversarial network for small-sample obstructive sleep apnea detection using a single-lead piezoelectric sensor.

OBJECTIVE: In recent years, increasing attention to sleep health has accelerated development of wearable devices for home monitoring. Two critical challenges remain: providing an unobtrusive sensing solution suitable for long-term deployment, and designing algorithms that generalize across diverse users and acoustic environments when annotated data are limited. APPROACH: We develop a single-lead b...

An integrated SWAT-XGBoost-SHAP framework identifies key drivers of critical source areas during critical periods in a small watershed.

Non-point source (NPS) pollution has emerged as a critical environmental issue, significantly impacting water quality and ecosystem health at the wate...

Pollutant-Specific Pathways Linking Gaseous Air Pollution to Adolescent Depressive Symptoms: Direct and Sleep-Mediated Effects in a Population-Based Retrospective Cohort Study.

OBJECTIVE: This study aimed to examine associations between multiple gaseous air pollutants across different developmental periods and depressive in l...

Characterizing Middle-aged and Older Adults' Perceptions of the Cultural Sensitivity and Quality of Generative Artificial Intelligence-authored Text Messages to Promote Physical Activity.

OBJECTIVE: Generative artificial intelligence (GenAI) could be used to write text message content in physical activity behavior change interventions f...

Raman analysis of black carbon using artificial neural networks for emission source classification.

Black Carbon (BC), a major component of Particulate Matter (PM), plays a crucial role in air pollution, climate change, and public health impacts, due...

Selective reuse of prior ensemble data improves the latest air temperature forecast over North America.

Accurate subseasonal to seasonal (S2S) weather forecasts are critical for managing risks to society, yet improving forecast skill remains challenging....

Machine-Learning Source Apportionment of Particulate Pollution Aids Urban Emission Regulations.

Reducing urban particulate pollution is essential for improving air quality and reducing health risks. Achieving this objective requires accurate iden...

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 a...

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), wher...

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 ...

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...