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Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinsons disease, Essential Tremor, Dystonia, Essential Tremor, Huntingtons disease, and Tourettes syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

In this study, magnetic resonance imaging (MRI) based on a deep learning algorithm was used to evaluate the clinical effect of the small-incision approach in treating proximal tibial fractures. Super-resolution reconstruction (SRR) algorithm was used to reconstruct MRI images for analysis and comparison. The research objects were 40 patients with proximal tibial fractures. According to the random number method, patients were divided into a small-incision approach group (22 cases) and an ordinary approach group (18 cases). The peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) of the MRI images before and after the reconstruction of the two groups were analyzed. The operative time, intraoperative blood loss, complete weight-bearing time, complete healing time, knee range of motion, and knee function of the two treatments were compared. The results showed that after SRR, the PSNR and SSIM of MRI images were 35.28 and 0.826 dB, respectively, so the MRI image display effect was better. The operation time in the small-incision approach group was 84.93 min, which was significantly shorter than that in the common approach group, and the intraoperative blood loss was 219.95 mL, which was significantly shorter than that in the common approach group (P < 0.05). The complete weight-bearing time and complete healing time in the small-incision approach group were 14.75 and 16.79 weeks, respectively, which were significantly shorter than those in the ordinary approach group (P < 0.05). The half-year knee range of motion and 1-year knee range of motion in the small-incision approach group were 118.27° and 128.72°, respectively, which were significantly higher than those in the conventional approach group (P < 0.05). After 6 months of treatment, the rate of good treatment was 86.36% in the small-incision approach group and 77.78% in the ordinary approach group. After 1 year of treatment, the rate of excellent and good treatment was 90.91% in the small-incision approach group and 83.33% in the ordinary approach group. The rate of good treatment for half a year and 1 year in the small incision group was significantly higher than that in the common approach group (P < 0.05). In conclusion, MRI image based on deep learning algorithm has a high resolution, good display effect, and high application value. The small-incision approach can be applied to the treatment of proximal tibial fractures, which showed good therapeutic effects and a high positive clinical application value.

Electrolysis tanks are used to smelt metals based on electrochemical principles, and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures, thus affecting normal production. Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks, an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5 (YOLOv5) is proposed. Firstly, we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) by changing the U-shaped network (U-Net) to Attention U-Net, to preprocess the images; secondly, we propose a new Focus module that introduces the Marr operator, which can provide more boundary information for the network; again, because Complete Intersection over Union (CIOU) cannot accommodate target borders that are increasing and decreasing, replace CIOU with Extended Intersection over Union (EIOU), while the loss function is changed to Focal and Efficient IOU (Focal-EIOU) due to the different difficulty of sample detection. On the homemade dataset, the precision of our method is 94%, the recall is 70.8%, and the map@.5 is 83.6%, which is an improvement of 1.3% in precision, 9.7% in recall, and 7% in map@.5 over the original network. The algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection, which can lay a technical foundation for improving production efficiency and reducing production waste. [ABSTRACT FROM AUTHOR]

Background: Anxiety and social withdrawal are highly prevalent among patients with Alzheimer's disease (AD). However, the neural circuit mechanisms underlying these symptoms remain elusive, and there is a need for effective prevention strategies. Objective: This study aims to elucidate the neural circuitry mechanisms underlying social anxiety in AD. Methods: We utilized 5xFAD mice and conducted a series of experiments including optogenetic manipulation, Tandem Mass Tag-labeled proteome analysis, behavioral assessments, and immunofluorescence staining. Results: In 5xFAD mice, we observed significant amyloid-β (Aβ) accumulation in the anterior part of basolateral amygdala (aBLA). Behaviorally, 6-month-old 5xFAD mice displayed excessive social avoidance during social interaction. Concurrently, the pathway from aBLA to ventral hippocampal CA1 (vCA1) was significantly activated and exhibited a disorganized firing patterns during social interaction. By optogenetically inhibiting the aBLA-vCA1 pathway, we effectively improved the social ability of 5xFAD mice. In the presence of Aβ accumulation, we identified distinct changes in the protein network within the aBLA. Following one month of administration of Urolithin A (UA), we observed significant restoration of the abnormal protein network within the aBLA. UA treatment also attenuated the disorganized firings of the aBLA-vCA1 pathway, leading to an improvement in social ability. Conclusions: The aBLA-vCA1 circuit is a vulnerable pathway in response to Aβ accumulation during the progression of AD and plays a crucial role in Aβ-induced social anxiety. Targeting the aBLA-vCA1 circuit and UA administration are both effective strategies for improving the Aβ-impaired social ability. [ABSTRACT FROM AUTHOR]

Highlights Natural fiber/resin composites exhibit poor fiber–matrix compatibility and easy combustion. In this study, jute‐fiber‐reinforced epoxy resin was used to prepare composite materials by adding marine shell powder, which is a natural non‐combustible inorganic filler that originates from a variety of sources. The morphology, composition, thermal stability, combustion performance, and mechanical properties of the prepared composites were tested using several characterization methods. The results show that the marine shell powder was evenly distributed in the epoxy resin and did not affect the adhesion between the epoxy resin and the jute fiber. When 20% marine shell powder (SMJF20) was added to the jute‐fiber‐reinforced epoxy resin composite, the pyrolytic residual content was 21.88% (60.2% higher than that without), and the maximum heat release rate was 350.75 kW/m2 (22.8% lower than that without). The total heat release and total smoke release of SMJF20 were also 13.9% and 30% lower than that of MJF, respectively. The addition of marine shell powder did not significantly affect the tensile strength, whereas the tensile modulus of SMJF20 increased by approximately 13.9%, the fracture strain decreased by approximately 13.4%, and the bending strength and modulus decreased by approximately 12.9% and 13.0%, respectively. Marine shell powder is an ideal biomass filler for jute‐fiber‐reinforced epoxy resins. Jute‐fiber‐epoxy composites fortified with non‐combustible marine shell powder. Effective filler distribution without compromising epoxy‐jute fiber adhesion. 20% marine shell powder significantly reduces heat release and smoke release. Tensile strength unaffected; tensile modulus and residual content increased. Marine shell powder proves ideal for enhancing epoxy resin with jute fibers. [ABSTRACT FROM AUTHOR]

Due to the complex structure of high-canopy-density forests, the traditional individual tree segmentation (ITS) algorithms based on ALS point cloud, which set segmentation threshold manually, is difficult to adequately cover a variety of complex situations, so the ITS accuracy is unsatisfactory. In this paper, a top-down segmentation strategy is adopted to propose an adaptive segmentation method based on extreme offset deep learning, and the ITS set aggregation strategy based on gradient change criterion is designed for the over-segmentation generated by random offset, and the precise ITS is realized. Firstly, the segmentation sub-plot is set as 25 m × 25 m, the regional point cloud and its treetop are marked, and the offset network is trained. Secondly, the extreme offset network is designed to carry out spatial transformation of the original point cloud, and each point is offset to the position near the treetop to obtain the offset point cloud with a high density at the treetop, which enhances the discrimination among individual trees. Thirdly, the self-adaptive mean shift algorithm based on average neighboring distance is designed to divide and mark the offset point cloud. Fourthly, the offset point cloud, after clustering, is mapped back to the original space to complete the preliminary segmentation. Finally, according to the gradient change among different canopies, the ITS aggregation method is designed to aggregate adjacent canopies with a gentle gradient change. In order to investigate the universality of the proposed method on different stand structures, two coniferous forest plots (U1, U2) in the Blue Ridge area of Washington, USA, and two mixed forest plots (G1, G2) in Bretten, Germany, are selected in the experiment. The learning rate of the deep network is set as 0.001, the sampled point number of the sub-plot is 900, the transformer dimension is 512 × 512, the neighboring search number of points is 16, and the number of up-sampling blocks is 3. Experimental results show that in mixed forests (G1, G2) with complex structures, the F-score of the proposed method reaches 0.89, which is about 4% and 7% higher than the classical SHDR and improved DK, respectively. In high-canopy-density areas (U2, G2), the F-score of the proposed method reaches 0.89, which is about 3% and 4% higher than the SHDR and improved DK, respectively. The results show that the proposed method has high universality and accuracy, even in a complex stand environment with high canopy density. [ABSTRACT FROM AUTHOR]