The latest COVID-19 pandemic reveals that unexpected changes elevate depression bringing people apart, but also calling for social sharing. Yet the impact of depression on social cognition and functioning is not well understood. Assessment of social cognition is crucial not only for a better understanding of major depressive disorder (MDD), but also for screening, intervention, and remediation. Here by applying a novel experimental tool, a Face-n-Food task comprising a set of images bordering on the Giuseppe Arcimboldo style, we assessed the face tuning in patients with MDD and person-by-person matched controls. The key benefit of these images is that single components do not trigger face processing. Contrary to common beliefs, the outcome indicates that individuals with depression express intact face responsiveness. Yet, while in depression face sensitivity is tied with perceptual organization, in typical development, it is knotted with social cognition capabilities. Face tuning in depression, therefore, may rely upon altered behavioral strategies and underwriting brain mechanisms. To exclude a possible camouflaging effect of female social skills, we examined gender impact. Neither in depression nor in typical individuals had females excelled in face tuning. The outcome sheds light on the origins of the face sensitivity and alterations in social functioning in depression and mental well-being at large. Aberrant social functioning in depression is likely to be the result of deeply-rooted maladaptive strategies rather than of poor sensitivity to social signals. This has implications for mental well-being under the current pandemic conditions.
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http://dx.doi.org/10.1093/cercor/bhaa375 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States.
Rechargeable Li-CO batteries face challenges of sluggish reaction kinetics and poor rechargeability. Highly efficient electrocatalysts are urgently needed to decompose the discharge product, LiCO. Mn-based transition metal oxides are regarded as promising candidates for improving the cycle performance and reaction kinetics of Li-CO batteries.
View Article and Find Full Text PDFComput Biol Med
December 2024
Electrical and Computer Engineering Department, UC San Diego, La Jolla, CA, USA.
Automated segmentation and detection of tumors in CT scans of the liver and kidney have a significant potential in assisting clinicians with cancer diagnosis and treatment planning. However, current approaches, including state-of-the-art deep learning ones, still face many challenges. Many tumors are not detected by these approaches when tested on public datasets for tumor detection and segmentation such as the Kidney Tumor Segmentation Challenge (KiTS) and the Liver tumor segmentation challenge (LiTS).
View Article and Find Full Text PDFJpn J Radiol
December 2024
MRI Unit, Radiology Department, HT Medica. Carmelo Torres nº2, 23007, Jaén, Spain.
Background And Objective: Structured reports in radiology have demonstrated substantial advantages over unstructured ones. However, the transition from unstructured to structured reporting can face challenges, as experienced radiologists worry about the potential loss of valuable information. In this study, we fine-tuned the Llama 2 model capable of generating structured pituitary MRI reports from unstructured reports.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok, 25913, Republic of Korea.
Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging.
View Article and Find Full Text PDFSci Rep
December 2024
College of Sciences, National University of Defense Technology, 410073, Changsha, China.
Deep Convolutional Neural Networks (DCNNs), due to their high computational and memory requirements, face significant challenges in deployment on resource-constrained devices. Network Pruning, an essential model compression technique, contributes to enabling the efficient deployment of DCNNs on such devices. Compared to traditional rule-based pruning methods, Reinforcement Learning(RL)-based automatic pruning often yields more effective pruning strategies through its ability to learn and adapt.
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