A cross-sectional study was conducted with 605 practitioners of Brazilian Jiu Jitsu (BJJ) to test the hypothesis that high arousal rituals promote social cohesion, primarily through identity fusion. BJJ promotion rituals are rare, highly emotional ritual events that often feature gruelling belt-whipping gauntlets. We used the variation in such experiences to examine whether more gruelling rituals were associated with identity fusion and pro-group behaviour. We found no differences between those who had undergone belt-whipping and those who had not and no evidence of a correlation between pain and social cohesion. However, across the full sample we found that positive, but not negative, affective experiences of promotional rituals were associated with identity fusion and that this mediated pro-group action. These findings provide new evidence concerning the social functions of collective rituals and highlight the importance of addressing the potentially diverging subjective experiences of painful rituals.
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http://dx.doi.org/10.1002/ejsp.2514 | DOI Listing |
Network
December 2024
Department of Electronics and Communication Engineering, Dronacharya Group of Institutions, Greater Noida, UP, India.
Speaker verification in text-dependent scenarios is critical for high-security applications but faces challenges such as voice quality variations, linguistic diversity, and gender-related pitch differences, which affect authentication accuracy. This paper introduces a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) architecture to address these challenges. The Gender-Aware Network utilizes Convolutional 2D layers with ReLU activation for initial feature extraction, followed by multi-fusion dense skip connections and batch normalization to integrate features across different depths, enhancing discrimination between male and female speakers.
View Article and Find Full Text PDFmBio
December 2024
Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.
Unlabelled: A novel Hendra virus (HeV) genotype (HeV genotype 2 [HeV-g2]) was recently isolated from a deceased horse, revealing high-sequence conservation and antigenic similarities with the prototypic strain, HeV-g1. As the receptor-binding (G) and fusion (F) glycoproteins of HeV are essential for mediating viral entry, functional characterization of emerging HeV genotypic variants is key to understanding viral entry mechanisms and broader virus-host co-evolution. We first confirmed that HeV-g2 and HeV-g1 glycoproteins share a close phylogenetic relationship, underscoring HeV-g2's relevance to global health.
View Article and Find Full Text PDFACS Chem Biol
December 2024
Department of Chemistry, Scripps Research, 10550 N Torrey Pines Rd, La Jolla, California 92037, United States.
Fibroblast growth factor 2 (FGF2) is a multipotent growth factor and signaling protein that exhibits broad functions across multiple cell types. These functions are often initiated by binding to growth factor receptors and fine-tuned by glycosaminoglycan (GAG)-modified proteins called proteoglycans. The various outputs of FGF2 signaling and functions arise from a dynamic and cell type-specific set of binding partners.
View Article and Find Full Text PDFBMC Genomics
December 2024
College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China.
Background: Achieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Mechatronics Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11535, Egypt.
Accurate multi-object tracking (MOT) is essential for autonomous vehicles, enabling them to perceive and interact with dynamic environments effectively. Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in unreliable tracking. Recent multi-modal approaches have improved performance but rely heavily on complex, deep-learning-based fusion techniques.
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