Neuroscience
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Aggression is a social behavior that is critical for survival and reproduction. In adults, circulating gonadal hormones, such as androgens, act on neural circuits to modulate aggressive interactions, especially in reproductive contexts. In many species, individuals also demonstrate aggression before reaching gonadal maturation. ⋯ Pregnenolone, androgens, and estrogens are generally non-detectable and are not affected by an STI. In peripheral tissues, steroid concentrations are very high in the adrenals. These data suggest that adrenal steroids, such as progesterone and corticosterone, might promote juvenile aggression and that juvenile and adult songbirds might rely on distinct neuroendocrine mechanisms to support similar aggressive behaviors.
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Ischaemic stroke can induce changes in the abundance of gut microbiota constituents, and the outcome of stroke may also be influenced by the gut microbiota. This study aimed to determine whether gut microbiota transplantation could rescue changes in the gut microbiota and reduce ferroptosis after stroke in rats. Male Sprague-Dawley rats (6 weeks of age) were subjected to ischaemic stroke by middle cerebral artery occlusion (MCAO). ⋯ In addition, FMT diminished MDA and iron levels and elevated GSH levels in the ipsilateral brain. Western blot analysis showed that FMT increased GPX4 and SLC7A11 protein expression and decreased TFR2 protein expression in the ipsilateral brain after stroke. FMT can reverse gut microbiota dysbiosis, reduce cerebellar infarct volume, and decrease ferroptosis after stroke.
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Face processing includes two crucial processing levels - face detection and face recognition. However, it remains unclear how human brains organize the two processing levels sequentially. While some studies found that faces are recognized as fast as they are detected, others have reported that faces are detected first, followed by recognition. ⋯ Our findings showed that the networks trained on face recognition also exhibited the "detection-first, recognition-later" pattern. Moreover, this sequential organization mechanism developed gradually during the training of the networks and was observed only for correctly predicted images. These findings collectively support the computational account as to why the brain organizes them in this way.
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Contests may be highly effective in eliciting high levels of effort, but they also carry the risk of inefficient resource allocation due to excessive effort (overbidding), squandering valuable social resources. While a growing body of research has focused on how group identity exacerbates out-group conflict, its influence on in-group conflict remains relatively unexplored. This study endeavors to explore the impact of group identity on conflicts within and between groups in competitive environments, thereby addressing gaps in the current research landscape and dissecting the involved neurobiological mechanisms. ⋯ Subsequently, after the task, additional activation was observed in the right temporal lobe. Results from functional connectivity studies indicated that group identity tasks modify decision-making processes by promoting group norms, empathy, and blurred self-other boundaries for in-group decisions, while out-group decisions after the group identity task see heightened cognitive control, an increased dependence on rational judgment, introspection of self-environment relationships, and a greater focus on anticipating others' behaviors. This study reveals the widespread occurrence of overbidding behavior and demonstrates the role of group identity in mitigating this phenomenon, concurrently providing a comprehensive analysis of the underlying neural mechanisms.
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The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. ⋯ Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.