A previous design of a biofeedback system for Neurorehabilitation in an interactive multimodal environment has demonstrated the potential of engaging stroke patients in task-oriented neuromotor rehabilitation. This report explores the new concept and alternative designs of multimedia based biofeedback systems. In this system, the new interactive multimodal environment was constructed with abstract presentation of movement parameters. Scenery images or pictures and their clarity and orientation are used to reflect the arm movement and relative position to the target instead of the animated arm. The multiple biofeedback parameters were classified into different hierarchical levels w.r.t. importance of each movement parameter to performance. A new quantified measurement for these parameters were developed to assess the patient's performance both real-time and offline. These parameters were represented by combined visual and auditory presentations with various distinct music instruments. Overall, the objective of newly designed system is to explore what information and how to feedback information in interactive virtual environment could enhance the sensorimotor integration that may facilitate the efficient design and application of virtual environment based therapeutic intervention.
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http://dx.doi.org/10.1109/IEMBS.2006.260409 | DOI Listing |
Paxillin (PXN) and focal adhesion kinase (FAK) are two major components of the focal adhesion complex, a multiprotein structure linking the intracellular cytoskeleton to the cell exterior. PXN interacts directly with the C-terminal targeting domain of FAK (FAT) via its intrinsically disordered N-terminal domain. This interaction is necessary and sufficient for localizing FAK to focal adhesions.
View Article and Find Full Text PDFGene expression is coordinated by a multitude of transcription factors (TFs), whose binding to the genome is directed through multiple interconnected epigenetic signals, including chromatin accessibility and histone modifications. These complex networks have been shown to be disrupted during aging, disease, and cancer. However, profiling these networks across diverse cell types and states has been limited due to the technical constraints of existing methods for mapping DNA:Protein interactions in single cells.
View Article and Find Full Text PDFFront Psychol
December 2024
Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands.
The key function of storytelling is a meeting of hearts: a resonance in the recipient(s) of the story narrator's emotion toward the story events. This paper focuses on the role of gestures in engendering emotional resonance in conversational storytelling. The paper asks three questions: Does story narrators' gesture expressivity increase from story onset to climax offset (RQ #1)? Does gesture expressivity predict specific EDA responses in story participants (RQ #2)? How important is the contribution of gesture expressivity to emotional resonance compared to the contribution of other predictors of resonance (RQ #3)? 53 conversational stories were annotated for a large number of variables including Protagonist, Recency, Group composition, Group size, Sentiment, and co-occurrence with quotation.
View Article and Find Full Text PDFMayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFNat Sci Sleep
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China.
Purpose: To develop a deep learning (DL) model for obstructive sleep apnea (OSA) detection and severity assessment and provide a new approach for convenient, economical, and accurate disease detection.
Methods: Considering medical reliability and acquisition simplicity, we used electrocardiogram (ECG) and oxygen saturation (SpO) signals to develop a multimodal signal fusion multiscale Transformer model for OSA detection and severity assessment. The proposed model comprises signal preprocessing, feature extraction, cross-modal interaction, and classification modules.
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