AI Medical Compendium Journal:
Environmental science & technology

Showing 71 to 80 of 131 articles

Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters.

Environmental science & technology
Machine learning models show promise in identifying geogenic contaminated groundwaters. Modeling in regions with no or limited samples is challenging due to the need for large training sets. One potential solution is transferring existing models to s...

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection.

Environmental science & technology
For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that au...

Deep Learning Bridged Bioactivity, Structure, and GC-HRMS-Readable Evidence to Decipher Nontarget Toxicants in Sediments.

Environmental science & technology
Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typ...

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Environmental science & technology
Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of toxicity data. We the...

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

Environmental science & technology
The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies u...

Machine-Learning-Assisted Descriptors Identification for Indoor Formaldehyde Oxidation Catalysts.

Environmental science & technology
The development of highly efficient catalysts for formaldehyde (HCHO) oxidation is of significant interest for the improvement of indoor air quality. Up to 400 works relating to the catalytic oxidation of HCHO have been published to date; however, th...

Using Machine Learning to Construct the Blood-Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data.

Environmental science & technology
Permeabilities of various trace elements (TEs) through the blood-follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to c...

Stacking Machine Learning Models Empowered High Time-Height-Resolved Ozone Profiling from the Ground to the Stratopause Based on MAX-DOAS Observation.

Environmental science & technology
Ozone (O) profiles are crucial for comprehending the intricate interplay among O sources, sinks, and transport. However, conventional O monitoring approaches often suffer from limitations such as low spatiotemporal resolution, high cost, and cumberso...

Immune Regulation Patterns in Response to Environmental Pollutant Chromate Exposure-Related Genetic Damage: A Cross-Sectional Study Applying Machine Learning Methods.

Environmental science & technology
Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevating cancer risk, yet research rarely focuses on related immunological mechanisms, which play an important role in the occurrence and development of canc...