Results 81 to 90 of about 21,812 (251)

Self‐Regulated Learning Meets AI: Reinterpreting Self‐Regulation, Co‐Regulation, and Socially Shared Regulation in Human–AI Interaction

New Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability.
Moon‐Heum Cho, Jerusalem Merkebu
wiley   +1 more source

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio, Desiree Latella, Lilla Bonanno, Paulo Cezar Rocha dos Santos, Marcos Maldonado‐Díaz, Meir Plotnik, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò   +8 more
wiley   +1 more source

Influenza Vaccination Responses in Disabled Stroke Patients: A Single‐Center Prospective Observational Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective This study aimed to investigate the immunological response to influenza vaccination, the incidence and severity of influenza infection, and the side effects of the vaccination in patients with ischemic stroke. Methods This prospective observational study was conducted between 2023 and 2024 at Ramathibodi Hospital.
Achiraya Pakngao, Sureerat Suwatcharangoon, Pintip Ngamjanyapron, Kobporn Boonnak, Thanitta Suangtamai, Porpon Rotjanapan   +5 more
wiley   +1 more source

An epilepsy prediction and management system based on federated learning combined with hybrid harmony search and mutual information (HSA-MI)-based feature selection approach

Scientific Reports
Epilepsy represents a widespread neurological disorder that causes unexpected seizure occurrences which produce significant obstacles for daily life activities in people worldwide. Real-time seizure detection accuracy stands vital for safeguarding patients while ensuring timely interventions and bettering their quality of life.
Khan, Mohd Abdul Rahim, Heba, Khaled Mahmoud, Abbass, Abbass Hassan, Shaikh, Mujeeb Ahmed, Tejani, Ghanshyam G., Sharma, Sunil Kumar, Ma’aitah, Mohammad Khaleel Sallam   +6 more
openaire   +3 more sources

Uncovering G Protein‐Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Low‐grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein‐coupled receptors (GPCR) contribute to glioma malignant progression, but their prognostic value remains unclear. This work attempts to formulate a GPCR‐based outcome‐predicting model for LGG. Methods Based on TCGA LGG data, the enrichment scores
Jun Yang, Dongxu Zhang, Dongyuan Liu, Da Lin, Hao Wang   +4 more
wiley   +1 more source

Electroencephalographic Normalization as a Biomarker of Clinical Recovery in Down Syndrome Regression Disorder

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Down syndrome regression disorder is a syndrome characterized by subacute loss of cognitive, behavioral, and functional abilities in individuals with Down syndrome. Electroencephalography abnormalities are frequently observed during evaluation, but it remains unclear whether these findings represent a dynamic marker of disease ...
Jonathan D. Santoro, Mackenzie Silverman, Maeve C. Lucas, Mariam M. Yousuf, Samuel T. Otey, Stella V. Gray, Brittany Jordan, Madeline D. Kahan, Latanya D. Agurs, Michelle Van Hirtum Das, Deborah Holder, Devin King, Eileen A. Quinn, Ryan Kammeyer, Michael S. Rafii   +14 more
wiley   +1 more source

The Multiple Sclerosis Severity Allele rs10191329A and Cognitive Function: A UK Biobank Study

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT The genome‐wide association study of Multiple Sclerosis severity linked the genetic variant rs10191329A to long‐term disability and implicated brain resilience as a determinant of outcome. We hypothesised that rs10191329A might influence cognition in other neurological diseases and healthy controls.
Ioanna Zimianiti, Sheena Waters, Adil Harroud, Pernilla Stridh, Ruth Dobson, Benjamin M. Jacobs   +5 more
wiley   +1 more source

A Mutual Information-Based Framework for Enhancing Graph Neural Networks on Heterophily

IEEE Access
Graph Neural Networks (GNNs) have demonstrated strong capabilities in analyzing structured data, particularly under the assumption of homophily, where neighboring nodes tend to share similar attributes.
Gahee Kim, Seongjin Choi, Se-Young Yun
doaj   +1 more source

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif, Diane Demailly, Gabriella A. Horvàth, Juan Dario Ortigoza Escobar, Nathalie Dorison, Mayté Castro Jiménez, Cécile A. Hubsch, Thomas Wirth, Gun‐Marie Hariz, Sophie Huby, Morgan Dornadic, Zohra Souei, Muhammad Mushhood Ur Rehman, Simone Hemm, Mehdi Boulaymen, Eduardo M. Moraud, Jocelyne Bloch, Xavier Vasques   +17 more
wiley   +1 more source

MI-Dropout: A New Generalized Dropout Strategy Guided by Mutual Information

Dropout is a commonly used regularization method in deep neural networks, which can effectively alleviate overfitting. However, traditional Dropout usually uses a fixed probability to discard neurons, which fails to fully utilize the importance of the current input features.
openaire   +2 more sources