Purpose: Many individuals with autism spectrum disorder (ASD) experience challenges with facial emotion recognition (FER), which may exacerbate social difficulties in ASD. Few studies have examined whether FER can be experimentally manipulated and improved for autistic people. This study utilized a randomized controlled trial design to examine acceptability and preliminary clinical impact of a novel mixed reality-based neurofeedback program, FER Assistant, using EEG brain computer interface (BCI)-assisted technology to improve FER for autistic adolescents and adults.
Methods: Twenty-seven autistic male participants (M age: 21.12 years; M IQ: 105.78; 85% white) were randomized to the active condition to receive FER Assistant (n = 17) or waitlist control (n = 10). FER Assistant participants received ten sessions utilizing BCI-assisted neurofeedback training in FER. All participants, regardless of randomization, completed a computerized FER task at baseline and endpoint.
Results: Results partially indicated that FER Assistant was acceptable to participants. Regression analyses demonstrated that participation in FER Assistant led to group differences in FER at endpoint, compared to a waitlist control. However, analyses examining reliable change in FER indicated no reliable improvement or decline for FER Assistant participants, whereas two waitlist participants demonstrated reliable decline.
Conclusion: Given the preliminary nature of this work, results collectively suggest that FER Assistant may be an acceptable intervention. Results also suggest that FER may be a potential mechanism that is amenable to intervention for autistic individuals, although additional trials using larger sample sizes are warranted.
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Sci Rep
January 2025
Key Laboratory of Gas and Fire Control for Mines, Ministry of Education, Xuzhou, 221116, China.
Confined space fires could easily cause serious casualties and property damage, and foam is an effective means of preventing confined space fires. The existing foam generator does not have both momentum and foam expansion rate (FER) and is poorly suited to confined spaces. In order to develop a foam generator suitable for confined space fire protection, an in-depth analysis of the physical foaming characteristics of self-suction foam is required, and the structure of the foam generator is optimized accordingly.
View Article and Find Full Text PDFFront Cell Dev Biol
December 2024
Institute of Precision Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Introduction: Sepsis-induced cardiomyopathy is a common complication of sepsis and is associated with higher mortality. To date, effective diagnostic and management strategies are still lacking. Recent studies suggest that ferroptosis plays a critical role in sepsis-induced cardiomyopathy and ferroptosis inhibitor Ferrostatin-1 (Fer-1) improved cardiac dysfunction and survival in lipopolysaccharide (LPS) induced endotoxemia.
View Article and Find Full Text PDFMicrobiol Res
December 2024
College of Biology, Hunan Province Key Laboratory of Plant Functional Genomics and Developmental Regulation, Hunan University, Changsha 410082, China. Electronic address:
The recruitment of the phosphorus-solubilizing rhizobacteria plays an important role in response to phosphorus deficiency. Through the treatments of Arabidopsis thaliana (Col-0) and the FERONIA (FER) functional deficient mutants (fer-4 and fer-5) with the soil suspension in various phosphorus conditions, we discovered that FER could promote phosphorus-solubilizing rhizobacteria enrichment to rescue the defective plant during phosphorus deficiency. The amplicon sequencing data reflected that the phosphorus-solubilizing rhizobacterial genus Alcaligenes was significantly enriched of Col-0 than fer-4 in low phosphorus conditions.
View Article and Find Full Text PDFSci Rep
November 2024
School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, October Gardens, 6th of October City, 12578, Giza, Egypt.
Facial Emotion Recognition (FER) is a very challenging task due to the varying nature of facial expressions, occlusions, illumination, pose variations, cultural and gender differences, and many other aspects that cause a drastic degradation in quality of facial images. In this paper, an anti-aliased deep convolution network (AA-DCN) model has been developed and proposed to explore how anti-aliasing can increase and improve recognition fidelity of facial emotions. The AA-DCN model detects eight distinct emotions from image data.
View Article and Find Full Text PDFSensors (Basel)
October 2024
School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
In studying the joint object detection and classification problem for facial expression recognition (FER) deploying the YOLOX framework, we introduce a novel feature extractor, called neighborhood coordinate attention Mamba (NCAMamba) to substitute for the original feature extractor in the Feature Pyramid Network (FPN). NCAMamba combines the background information reduction capabilities of Mamba, the local neighborhood relationship understanding of neighborhood attention, and the directional relationship understanding of coordinate attention. The resulting FER-YOLO-NCAMamba model, when applied to two unaligned FER benchmark datasets, RAF-DB and SFEW, obtains significantly improved mean average precision (mAP) scores when compared with those obtained by other state-of-the-art methods.
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