Automatic classification of temporomandibular joint disorders by magnetic resonance imaging and convolutional neural networks.

Background/purpose: In this study, we utilized magnetic resonance imaging data of the temporomandibular joint, collected from the Division of Oral and Maxillofacial Surgery at Taipei Veterans General Hospital. Our research focuses on the classification and severity analysis of temporomandibular joint disease using convolutional neural networks.

Materials And Methods: In gray-scale image series, the most critical features often lie within the articular disc cartilage, situated at the junction of the temporal bone and the condyles.

Q-score as a reliability measure for protein, nucleic acid, and small molecule atomic coordinate models derived from 3DEM density maps.

Unlabelled: Atomic coordinate models are important in the interpretation of 3D maps produced with cryoEM and sub-tomogram averaging in cryoET, or more generically, 3D electron microscopy (3DEM). In addition to visual inspection of such maps and models, quantitative metrics convey the reliability of the atomic coordinates, in particular how well the model is supported by the experimentally determined 3DEM map. A recently introduced metric, Q-score, was shown to correlate well with the reported resolution of the map for well-fitted models.

Derivation of human toxicokinetic parameters and internal threshold of toxicological concern for tenuazonic acid through a human intervention trial and hierarchical Bayesian population modeling.

Background: Tenuazonic acid (TeA), a mycotoxin produced by Alternaria alternata, contaminates various food commodities and is known to cause acute and chronic health effects. However, the lack of human toxicokinetic (TK) data and the reliance on external exposure estimates have stalled a comprehensive risk assessment for TeA.

Objective: To bridge this gap, a human TK trial and population-based TK (PopTK) modeling were applied to determine human TK parameters of TeA, and the results were applied for risk screening using population biomonitoring data and threshold of toxicological concern (TTC)-based approaches.

MyEcoReporter: a prototype for artificial intelligence-facilitated pollution reporting.

Background: Many chemical releases are first noticed by community members, but reporting these concerns often involves considerable hurdles. Artificial Intelligence (AI)-enabled technologies, especially large language models (LLMs), can potentially reduce these barriers.

Objective: We hypothesized that AI-powered chatbots can facilitate reporting of pollution incidents through text messaging.