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Experience-driven suppression of irrelevant distractor locations is context dependent.
Atten Percept Psychophys
January 2025
Department of Psychology, The Ohio State University, 225 Psychology Building, 1835 Neil Ave, Columbus, OH, 43210, USA.
Humans can learn to attentionally suppress salient, irrelevant information when it consistently appears at a predictable location. While this ability confers behavioral benefits by reducing distraction, the full scope of its utility is unknown. As people locomote and/or shift between task contexts, known-to-be-irrelevant locations may change from moment to moment.
View Article and Find Full Text PDFIdentification of ubiquitination-related key biomarkers and immune infiltration in Crohn's disease by bioinformatics analysis and machine learning.
Sci Rep
January 2025
General Surgery Department, Jiangsu University Affiliated People's Hospital, Zhenjiang, 212000, China.
Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD.
View Article and Find Full Text PDFMachine Learning Model for Risk Stratification of Papillary Thyroid Carcinoma Based on Radiopathomics.
Acad Radiol
January 2025
Department of Ultrasound, Chengdu Second People's Hospital, Chengdu 610000, China (X.L., X.Q.). Electronic address:
Rationale And Objectives: This study aims to develop a radiopathomics model based on preoperative ultrasound and fine-needle aspiration cytology (FNAC) images to enable accurate, non-invasive preoperative risk stratification for patients with papillary thyroid carcinoma (PTC). The model seeks to enhance clinical decision-making by optimizing preoperative treatment strategies.
Methods: A retrospective analysis was conducted on data from PTC patients who underwent thyroidectomy between October 2022 and May 2024 across six centers.
Automated Diagnosis and Classification of Temporomandibular Joint Degenerative Joint Disease via Artificial Intelligence Using CBCT Imaging.
J Dent
January 2025
Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China. Electronic address:
Objectives: In this study, artificial intelligence techniques were used to achieve automated diagnosis and classification of temporomandibular joint (TMJ) degenerative joint disease (DJD) on cone beam computed tomography (CBCT) images.
Methods: An AI model utilizing the YOLOv10 algorithm was trained, validated and tested on 7357 annotated and corrected oblique sagittal TMJ images (3010 images of normal condyles and 4347 images of condyles with DJD) from 1018 patients who visited Peking University School and Hospital of Stomatology for temporomandibular disorders and underwent TMJ CBCT examinations. This model could identify DJD as well as the radiographic signs of DJD, namely, erosion, osteophytes, sclerosis and subchondral cysts.
InterDIA: Interpretable Prediction of Drug-induced Autoimmunity through Ensemble Machine Learning Approaches.
Toxicology
January 2025
Deparment of clinical pharmacy, Jieyang People's Hospital, 522000, China. Electronic address:
Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these challenges, we developed InterDIA, an interpretable machine learning framework for predicting DIA toxicity based on molecular physicochemical properties. Multi-strategy feature selection and advanced ensemble resampling approaches were integrated to enhance prediction accuracy and overcome data imbalance.
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