A Closed-Loop Brain Stimulation Control System Design Based on Brain-Machine Interface for Epilepsy

In this study, a closed-loop brain stimulation control system scheme for epilepsy seizure abatement is designed by brain-machine interface (BMI) technique. In the controller design process, the practical parametric uncertainties involving cerebral blood flow, glucose metabolism, blood oxygen level dependence, and electromagnetic disturbances in signal control are considered. An appropriate transformation is introduced to express the system in regular form for design and analysis. Then, sufficient conditions are developed such that the sliding motion is asymptotically stable. Combining Caputo fractional order definition and neural network (NN), a finite time fractional order sliding mode (FFOSM) controller is designed to guarantee reachability of the sliding mode. The stability and reachability analysis of the closed-loop tracking control system gives the guideline of parameter selection, and simulation results based on comprehensive comparisons are carried out to demonstrate the effectiveness of proposed approach

Spatial Autoregressive Coding for Graph Neural Recommendation

Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow models especially random-walk-based algorithms fail to adequately exploit neighbor proximity in sampled subgraphs or sequences due to their optimization paradigm. GNN-based algorithms suffer from the insufficient utilization of high-order information and easily cause over-smoothing problems when stacking too much layers, which may deteriorate the recommendations of low-degree (long-tail) items, limiting the expressiveness and scalability. In this paper, we propose a novel framework SAC, namely Spatial Autoregressive Coding, to solve the above problems in a unified way. To adequately leverage neighbor proximity and high-order information, we design a novel spatial autoregressive paradigm. Specifically, we first randomly mask multi-hop neighbors and embed the target node by integrating all other surrounding neighbors with an explicit multi-hop attention. Then we reinforce the model to learn a neighbor-predictive coding for the target node by contrasting the coding and the masked neighbors' embedding, equipped with a new hard negative sampling strategy. To learn the minimal sufficient representation for the target-to-neighbor prediction task and remove the redundancy of neighbors, we devise Neighbor Information Bottleneck by maximizing the mutual information between target predictive coding and the masked neighbors' embedding, and simultaneously constraining those between the coding and surrounding neighbors' embedding. Experimental results on both public recommendation datasets and a real scenario web-scale dataset Douyin-Friend-Recommendation demonstrate the superiority of SAC compared with state-of-the-art methods.Comment: preprin

An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator

With the wide application of intelligent manufacturing and the development of diversified functions of industrial manipulator, the requirements for the control accuracy and stability of the manipulator servo system are also increasing. The control of industrial manipulator is a time-varying system with nonlinear and strong coupling, which is often affected by uncertain factors, including parameter changing, environmental interference, joint friction and so on. Aiming at the problem of the poor control accuracy of the manipulator. Under the complex disturbance environment, control accuracy of the manipulator will be greatly affected, so this paper proposes an adaptive type-2 fuzzy sliding mode control (AT2FSMC) method applied to the servo control of the industrial manipulator, which realizes the adaptive adjustment of the boundary layer thickness to suppress the trajectory error caused by the external disturbance and weakens the chattering problem of the sliding mode control. The simulation results on a two-axis manipulator indicate that, with the presence of external disturbances, the proposed control method can help the manipulator maintain control signal stability and improve tracking accuracy. It also suppressed chattering produced by sliding mode control (SMC) and strengthening the robustness of the system. Compared with other conventional trajectory tracking control methods, the effectiveness of the proposed method can be reflected. Finally, the proposed method is tested in an actual manipulator to complete a practical trajectory to prove its feasibility

Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed Individual and Structural Graph Information Bottlenecks (IS-GIB). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings. To leverage the structural intra- and inter-domain correlations, we propose Structural Graph Information Bottleneck (S-GIB). Specifically for a batch of graphs with multiple domains, S-GIB first computes the pair-wise input-input, embedding-embedding, and label-label correlations. Then it minimizes the mutual information between input graph and embedding pairs while maximizing the mutual information between embedding and label pairs. The critical insight of S-GIB is to simultaneously discard spurious features and learn invariant features from a high-order perspective by maintaining class relationships under multiple distributional shifts. Notably, we unify the proposed I-GIB and S-GIB to form our complementary framework IS-GIB. Extensive experiments conducted on both node- and graph-level tasks consistently demonstrate the superior generalization ability of IS-GIB. The code is available at https://github.com/YangLing0818/GraphOOD.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE

Osteoma in the upper cervical spine with spinal cord compression

Osteoma is a common benign tumor. It occurs dominantly at the skull bone. Outside skull osteoma is rare, and primary intra-canal osteoma is extremely rare. To the author’s knowledge, only 14 cases of osteomas of the spine had been reported, in which only seven cases were in English literature. The authors reported two rare cases of intra-canal osteoma of the upper cervical spine with cord compression. Included are pertinent history, physical examination, rontgenographic evaluation before and after operation, surgical interventions, pathological study, and outcome. The available literature is also reviewed. On systemic examination and rontgenographic study, these two cases were found to have bone tumor in the upper cervical canal. Surgical interventions were performed, one with an en bloc excision, the other with a subtotal excision. The pathological study demonstrated a diagnosis of osteoma. After a follow-up with 20 and 15 months, the clinical symptoms of both cases significantly improved

Lnc-SNHG1 Activates the TGFBR2/SMAD3 and RAB11A/Wnt/β-Catenin Pathway by Sponging MiR-302/372/373/520 in Invasive Pituitary Tumors

Background/Aims: Long noncoding RNAs (lncRNAs) are critical regulators in various diseases including human cancer and could function as competing endogenous RNAs (ceRNAs) to regulate microRNAs (miRNAs). Methods: Quantitative real-time PCR (qRT-PCR) was used to analyze the expression of lnc-SNHG1 and miR-302/372/373/520 in pituitary tumor tissues and cell lines. Cell proliferation was investigated using MTT and cell count assays. The mechanisms by which lnc-SNHG1 affects pituitary tumor progression were investigated using Western blot assays, transwell migration assays, immunohistochemistry, immunofluorescence, luciferase reporter assays, tumor xenografts, and flow cytometry Results: We found that lnc-SNHG1 was overexpressed in invasive pituitary tumor tissues and cell lines. Ectopic expression of lnc-SNHG1 promoted cell proliferation, migration, and invasion, as well as the epithelial-mesenchymal transition (EMT), by affecting the cell cycle and cell apoptosis in vitro and tumor growth in vivo. Further study indicated that overexpression of lnc-SNHG1 markedly inhibited the expression of miR-302/372/373/520 (miRNA-pool) which is down-regulated in invasive pituitary tumor cells. Moreover, overexpression of lnc-SNHG1 significantly promoted the expression of TGFBR2 and RAB11A, the direct targets of miR-302/372/373/520. Finally, lnc-SNHG1 activates the TGFBR2/SMAD3 and RAB11A/Wnt/β-catenin pathways in pituitary tumor cells via sponging miR-302/372/373/520. Conclusions: Our data suggest that lnc-SNHG1 promotes the progression of pituitary tumors and is a potential therapeutic target for invasive pituitary tumor

Thoracic myelopathy caused by ossification of ligamentum flavum of which fluorosis as an etiology factor

PURPOSE: To evaluate the clinical feature, operative method and prognosis of thoracic ossification of ligamentum flavum caused by skeletal fluorosis. METHODS: All the patients with thoracic OLF, who underwent surgical management in the authors' hospital from 1993–2003, were retrospectively studied. The diagnosis of skeletal fluorosis was made by the epidemic history, clinical symptoms, radiographic findings, and urinalysis. En bloc laminectomy decompression of the involved thoracic levels was performed in all cases. Cervical open door decompression or lumbar laminectomy decompression was performed if relevant stenosis existed. The neurological statuses were evaluated with the Japanese Orthopaedic Association (JOA) scoring system preoperatively and at the end point of follow up. Also, the recovery rate was calculated. RESULTS: 23 cases have been enrolled in this study. Imaging study findings showed all the cases have ossification of ligamentum flavum together with ossification of many other ligaments and interosseous membranes, i.e. interosseous membranes of the forearm in 18 of 23 (78.3%), of the leg in 14 of 23 (60.1%) and of the ribs in 11 of 23 (47.8%). Urinalysis showed markedly increased urinary fluoride in 14 of 23 patients (60.9%). All the patients were followed up from 12 months to 9 years and 3 months, with an average of 4 years and 5 months. The JOA score increased significantly at the end of follow up (P = 0.0001). The recovery rate was 51.83 ± 32.36%. Multiple regression analysis revealed that the preoperative JOA score was an important predictor of surgical outcome (p = 0.0022, r = 0.60628). ANOVA analysis showed that patients with acute onset or too long duration had worse surgical result (P = 0.0003). CONCLUSION: Fluorosis can cause ossification of thoracic ligamentum flavum, as well as other ligaments. En bloc laminectomy decompression was an effective method. Preoperative JOA score was the most important predictor of surgical outcome. Patients with acute onset or too long duration had worse surgical outcome