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Skilled sensorimotor deficit is an unsolved problem of peripheral nerve injury (PNI) led by limb trauma or malignancies, despite the improvements in surgical techniques of peripheral nerve anastomosis. It is now accepted that successful functional recovery of PNI relies tremendously on the multilevel neural plasticity from the muscle to the brain. However, animal models that recapitulate these processes are still lacking. In this report, we developed a rat model of PNI to longitudinally assess peripheral muscle reinnervation and brain functional reorganization using noninvasive imaging technology. Based on such model, we compared the longitudinal changes of the rat forepaw intrinsic muscle volume and the seed-based functional connectivity of the sensorimotor cortex after nerve repair. We found that the improvement of skilled limb function and the recovery of paw intrinsic muscle following nerve regeneration are incomplete, which correlated with the functional connectivity between the primary motor cortex and dorsal striatum. Our results were highly relevant to the clinical observations and provided a framework for future investigations that aim to study the peripheral central sensorimotor circuitry underlying skilled limb function recovery after PNI.Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.Depression is a common psychological and mental disorder, characterized by low mood, slow thinking and low will, and even suicidal tendencies in severe cases. It imposes a huge mental and economic burden on patients and their families, and its prevention and treatment have become an urgent public health problem. It is worth noting that there is a significant gender difference in the incidence of depression. Studies have shown that females are far more likely to suffer from depression than males, confirming a close relationship between estrogen and the onset of depression. Moreover, recent studies suggest that the brain-derived neurotrophic factor- (BDNF-) mammalian target of rapamycin complex-1 (mTORC1) signaling pathway is a crucial target pathway for improving depression and mediates the rapid antidepressant-like effects of various antidepressants. However, it is not clear whether the BDNF-mTORC1 signaling pathway mediates the regulation of female depression and how to regulate female depression. Hence, we focused on the modulation of estrogen-BDNF-mTORC1 signaling in depression and its possible mechanisms in recent years.Tactile perception, the primary sensing channel of the tactile brain-computer interface (BCI), is a complicated process. Skin friction plays a vital role in tactile perception. This study aimed to examine the effects of skin friction on tactile P300 BCI performance. Two kinds of oddball paradigms were designed, silk-stim paradigm (SSP) and linen-stim paradigm (LSP), in which silk and linen were wrapped on target vibration motors, respectively. In both paradigms, the disturbance vibrators were wrapped in cotton. The experimental results showed that LSP could induce stronger event-related potentials (ERPs) and achieved a higher classification accuracy and information transfer rate (ITR) compared with SSP. The findings indicate that high skin friction can achieve high performance in tactile BCI. This work provides a novel research direction and constitutes a viable basis for the future tactile P300 BCI, which may benefit patients with visual impairments.Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the other algorithms. The experimental results visualize the data using the boxplot for better understanding of attribute distribution. The performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. selleck chemical WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. The comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image.