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Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer’s disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient’s Mini-Mental State Examination scores. Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

Partial multi-label learning (PML) deals with the problem where each training example is associated with an overcomplete set of candidate labels, among which only some candidate labels are valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. The PML training procedure is prone to be misled by false positive labels concealed in the candidate label set, which serves as the major modeling difficulty for partial multi-label learning. In this paper, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking coupled with virtual label splitting or maximum a posteriori (MAP) reasoning. Experimental studies show that the proposed approach can achieve highly competitive generalization performance by excluding most false positive labels from the training procedure via credible label elicitation.

In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.

Dentate nuclei (DN) are vital structures in the anatomical circuits that link the cerebellum to the cerebrum. However, the characteristics of DN functional connectivity (FC) in schizophrenia remain largely unknown. In this study, we investigated the FC of the DN in patients with schizophrenia and examined their possible clinical correlates using resting-state functional magnetic imaging data. We found that the patient group had greater DN FC with the parietal lobe (e.g., postcentral gyrus and superior parietal lobule) and less DN FC with the prefrontal cortex (e.g., superior frontal gyrus), posterior cingulate cortex, and regional cerebellum (e.g., vermis 4-5 and crus I) than did the control group. Furthermore, some abnormal connectivities of the DN with these regions significantly correlated with psychiatric symptoms. These results suggest that the DN circuits are disturbed and may participate in the pathophysiology of schizophrenia.

Background Dengue virus (DENV) infection is increasingly common in southern China and can be transmitted through blood transfusion but is not currently part of donor screening throughout the region. We assessed DENV prevalence among donors at the Xishuangbanna Blood Center, Yunnan, to support development of DENV screening strategies. Methods Blood samples were collected randomly between June 2019 and August 2019. These were screened for anti-DENV IgG and IgM using enzyme-linked immunosorbent assay (ELISA). Then, all reactive samples and some randomly-chosen non-reactive samples were used to detect DENV RNAs using real-time polymerase-chain-reaction (RT-PCR) assays. After RT-PCR, samples were further tested for soluble nonstructural protein 1 (NS1) using the colloidal gold method. Donors demographics were also collected and assessed. Results Over the study period, 2254 donor samples were collected and tested for anti-DENV IgG and IgM by ELISA. This revealed 598 anti-DENV IgG and/or IgM reactive samples, a serological prevalence of 26.53%. Of these, 26 were RT-PCR positive and/or NS1 positive. Significant differences in DENV prevalence were noted by occupation (P = 0.001), education (P ), and ethnicity (P = 0.026). Conclusion The prevalence of DENV in Xishuangbanna Blood Center was higher than most other blood centers that have implemented DENV donor screening. Our study provides first-hand data about the prevalence of DENV and allows the development of a screening strategy for clinical use.

Background. Acute-on-chronic liver failure (ACLF) is a clinical syndrome with acute jaundice and coagulation dysfunction caused by various inducements on the basis of chronic liver disease. Western medical treatment is limited. Previous studies have confirmed that Jieduan-Niwan Formula (JDNW Formula), an empirical prescription for the treatment of ACLF, can inhibit inflammation and resist hepatocyte apoptosis. However, potential targets and mechanisms still need to be explored. Methods. In this study, network pharmacological analysis was performed to investigate the key components and potential mechanisms of JDNW Formula treating ACLF. Firstly, we predicted the potential active ingredients of JDNW Formula and the corresponding potential targets through TCMSP, BATMAN-TCM platform, and literature supplement. Then, the ACLF targets database was built using OMIM, DisGeNET, and GeneCard database. Based on the matching targets between JDNW Formula and ACLF, the PPI network was constructed for MCODE analysis and common targets were enriched by Metascape. Furthermore, the ACLF rat model was used to verify the potential mechanism of JDNW Formula in treating ACLF. Results. 132 potential bioactive components of JDNW Formula and 168 common targets were obtained in this study. The enrichment analysis shows that the AMPK signaling pathway was associated with the treating effects of JDNW Formula. Quercetin was hypothesized to be the key bioactive ingredient in JDNW Formula and has a good binding affinity to AMPK based on molecular docking verification. JDNW Formula and quercetin were verified to treat ACLF by regulating the AMPK/PGC-1α signaling pathway as a prediction. Conclusion. The study predicted potential mechanisms of JDNW Formula in the treatment of ACLF, involving downregulation of inflammatory factor expression, antioxidant stress, and inhibition of hepatocyte apoptosis. JDNW Formula may improve mitochondrial quality in ACLF via the AMPK signaling pathway, which serves as a guide for further study.

To investigate the imaging features and clinic value of magnetic resonance imaging (MRI) and computed tomography (CT) in the diagnosis of clear cell renal cell carcinoma (ccRCC). 139 RCC patients underwent MRI and CT examination before surgery, and the sensitivity, specificity, accuracy, positive and negative predictive values of MRI and CT in diagnosing ccRCC were analyzed and compared. There were 93 true-positive, 11 false-positive, 11 false-negative and 24 true-negative in MRI screening with sensitivity, specificity, accuracy, positive predictive value and negative predictive value of 89.42%, 68.57%, 84.17%, 89.42% and 68.57% respectively. Whereas there were 72 true-positive, 4 false-positive, 32 false-negative and 31 true-negative in CT screening with the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of 69.23%, 88.57%, 74.10%, 94.74% and 49.21% respectively. The sensitivity and negative predictive value of MRI were better than those of CT, while the specificity of CT was better than that of MRI. MRI and CT have respective advantages in the diagnosis of ccRCC. The sensitivity and negative predictive value of MRI are superior to those of CT, while the specificity of CT is superior to that of MRI. Clinically, different examinations are selected according to different situations.

The recently evolving remote healthcare technology could potentially aid the realization of cost-effective and lasting solutions to life-threatening diseases such as heart failure. Such a remote healthcare system should integrate an effectual heart failure risk monitoring and prediction platform. However, developing a heart failure risk (HFR) prediction method that objectively incorporate individual contributive characteristics of HFR risk factors, that are required for adequate prediction remains a challenge. Towards addressing this research gap, a new approach driven by hierarchical neighborhood component-based-learning (HNCL) and adaptive multi-layer networks (AMLN) is proposed. In the proposed method, the HNCL module firstly learns the interrelations among the HFR attributes/ risk factors to construct a set of informative features, regarded as the global weight vector that reflects individual contribution of each risk factor. Subsequently, the constructed global weight vector is applied in building an AMLN model for the prediction of HFR. Moreover, the proposed method’s performances were extensively validated with a benchmark clinical database of potential heart failure patients and compared with previous studies using prediction accuracies, performance plots, receiving operating characteristic analysis, error-histogram analysis, specificity, and sensitivity metrics. From the experimental results, we found that the proposed method (AMLN–HNCL)​ achieved significantly higher and stable predictions with an improvement of approximately 11.10% over the commonly applied method. Additionally, the proposed method recorded 9.09% and 12.48% improvements for specificity and sensitivity, respectively compared to the commonly applied method. The superiority in performances achieved by the proposed method should be because the interrelations amongst the risk factors were adequately learnt and their individual contribution was objectively accounted for in the prediction task. Thus, we believe that the proposed method could potentially facilitate the practical implementation of accurately robust HFR prediction module in the context of the currently emerging remote healthcare system, especially in Internet of Medical Things (IoMT) systems. Also, the method may be applied in wearable mobile health-care gadgets capable of monitoring the heart failure status in individuals.

Background A novel coronavirus has caused an international outbreak. Currently, there are no specific therapeutic agents for coronavirus infections. Convalescent plasma (CP) therapy is a potentially effective treatment option. Methods Patients who had recovered from COVID‐19 and had been discharged from the hospital for more than two weeks were recruited. COVID‐19 convalescent plasma (CCP)‐specific donor screening and selection were performed based the following criteria: 1) aged 18‐55 years; 2) eligible for blood donation; 3) diagnosed with COVID‐19; 4) had two consecutive negative COVID‐19 nasopharyngeal swab tests based on PCR (at least 24 h apart) prior to hospital discharge; 5) had been discharged from the hospital for more than 2 weeks; and 6) had no COVID‐19 symptoms prior to convalescent plasma donation. In addition, preference was given to CCP donors who had a fever lasting more than 3 days or a body temperature exceeding 38.5 Celsius, and 4 weeks after the onset of symptoms. CCP collection was performed using routine plasma collection procedures via plasmapheresis. In addition to routine donor testing, the CCP donors’ plasma was also tested for SARS‐CoV‐2 nucleic acid and S‐RBD‐specific IgG antibody. Results Of the 81 potential CCP donors, 64 (79%) plasma products were collected. There were 18 female donors and 46 male donors. There were 34 first‐time blood donors and 30 repeat donors. The average time between CCP collection and initial symptom onset was 49.1 days, and the average time between CCP collection and hospital discharge was 38.7 days. The average volume of CCP collected was 327.7 ml. All Alanine transaminase ( ALT ) testing results met blood donation requirements. HIV Ag/Ab, anti‐HCV, anti‐syphilis and HBsAg were all negative; NAT for HIV, HBV, and HCV were also negative. In addition, all of the CCP donors’ plasma units were negative for SARS‐CoV‐2 RNA. Of the total 64 CCP donors tested, only one had an S‐RBD‐specific IgG titer of 1:160, all others had a titer of ≥1:320. Conclusion Based on a feasibility study of a pilot CCP program in Wuhan China, we demonstrated the success and feasibility of CCP collection. In addition, all of the CCP units collected had a titer of ≥ 1:160 for S‐RBD‐specific IgG antibody, which met the CCP quality control requirements based on the Chinese national guidelines for CCP. This article is protected by copyright. All rights reserved.

Background Transfusion-related acute lung injury (TRALI) is an important cause of death associated with transfusion, and no specific clinical treatments are available. Endothelial cells are believed to play an important role in the development of TRALI. This study investigated whether IL-35, an endothelial stabilizing cytokine could regulate the severity of antibody-mediated TRALI in vivo. Study design and methods Human microvascular endothelial cells (HMVECs) were cultured in vitro, rIL-35(2 μg/mL) was added before HMVECs activation, and HMVECs were fully activated by LPS (0.5 μg/mL). Then cells were collected for flow cytometry analysis. We used a previously established "two-event" mouse model of TRALI with naive and lipopolysaccharide (LPS)-injected mice as controls. rIL-35(100 μg/kg) was injected into the tail vein for 3 consecutive days before the induction of the TRALI model. Samples were collected 2 hours after TRALI induction and tested for lung tissue myeloperoxidase activity, total protein levels, lung tissue histology, endothelial cell activation assay, and cytokine assay. Results In vitro culture of HMVECs with rIL-35 verified that rIL-35 inhibited endothelial cells. In a mouse model, prophylactic administration of rIL-35 prevented pulmonary edema, increased lung protein levels, and reduced polymorphonuclear neutrophil accumulation in the lung. Conclusions This work suggests that antibody-mediated murine TRALI can be prevented by rIL-35, and that rIL-35 appears to work by inhibiting the activation of lung endothelial cells.

Acute-on-chronic liver failure (ACLF) is a serious and complicated disease that threatens human health because its pathogenesis is unclear, and the outcome of the current therapies has been less than satisfactory. A national famous doctor of traditional Chinese medicine, Qian Ying, created the Jieduan-Niwan Formula (JDNW), based on his long-term clinical experience. However, despite the good clinical outcome, the biological mechanism by which it works is unknown. In the current study, we established an ACLF rat model by administering human serum albumin (HSA) combined with D-galactosamine (D-GalN) and lipopolysaccharide (LPS) to explore the potential mechanism of JDNW in treating ACLF. The rats were treated with JDNW by administration of the model substances and sacrificed after 4, 8, and 12 h. Then we divided the rats into normal group, model at 4 h, model at 8 h, model at 12 h, JDNW at 4 h, JDNW at 8 h, and JDNW at 12 h. Biochemical and histopathological examinations were performed to compare the rats in different groups. Compared with the ACLF model group, expression levels of alanine transaminase, aspartate aminotransferase, total bilirubin, and TNF-α and IL-6 proteins were reduced in the JDNW group at the corresponding time points, the survival rates of rats were increased, and the pathological condition of the liver was improved. In addition, JDNW treatment improved the ultrastructure of hepatocytes and mitochondria and decreased the hepatocyte apoptosis index. E2F1, P53, P73, Apaf-1, p14ARF, caspase-3, caspase-6, and caspase-7 levels in the JDNW group were distinctly lower than those in the untreated rats. Moreover, Bcl-2 and Mcl-1 levels increased. Thus, JDNW decreases ACLF-induced mortality in rats by modulating the E2F1-mediated intrinsic apoptotic pathway.

Although a single-stage off-line power light-emitting diode (LED) driver can achieve low cost and high efficiency, the notorious double-line-frequency flicker issue with a single-stage LED driver limits its usage in high-quality lighting applications. To solve lighting flicker, as well as maintain a low cost and high efficiency, a multiplexing ripple cancellation (MRC) LED driver is proposed in this paper. One switching cycle is divided into two intervals. During the first interval, the proposed LED driver operates as a conventional LED driver that transfers energy from the ac input to LED output, performs power factor correction, and generates the main output voltage. The main output voltage has a double-line-frequency ripple like in a conventional design. During the second interval, the proposed LED driver transfers energy from the ac input again to generate an opposite ripple voltage to cancel the ripple voltage from the main output. In this way, the voltage across the LED load is a dc to achieve flicker-free LED driving performance. More than 99% of the output power goes through one-time power conversion, while less than 1% goes through two-time power conversion. A 7.5-W experimental prototype is built and tested to verify the design concept.

Background. Acute on chronic liver failure (ACLF) is a syndrome of acute liver failure that occurs on the basis of chronic liver disease, which is characterized by a rapid deterioration in a short period and high mortality. High mobility group box 1 (HMGB1) may be involved in the pathological process of ACLF; its specific role remains to be further elucidated. Our previous studies have shown that quercetin (Que) exerts anti-oxidant and anti-apoptotic effects by inhibiting HMGB1 in vitro. The present study aimed to investigate the effect of Que on liver injury in ACLF rats. Methods. The contents of ALT, AST, TBiL, and PT time of rats in each group were observed. HE staining was used to detect liver pathology. The levels of oxidative stress indicators such as MDA, GSH, and 4-HNE in the rat liver were detected. TUNEL assay was used to detect apoptosis in rat hepatocytes. Immunofluorescence and western blot analysis were performed to explore the protective effect of Que on ACLF rats and the underlying mechanism. Results. The results showed that Que could reduce the increase of serum biochemical indices, improve liver pathology, and reduce liver damage in ACLF rats. Further results confirmed that Que reduced the occurrence of oxidative stress and apoptosis of hepatocytes, and these reactions may aggravate the progress of ACLF. Meanwhile, the results of immunofluorescence and western blotting also confirmed that the expression of HMGB1 and extranuclear translocation in ACLF rat hepatocytes were significantly increased, which was alleviated by the treatment of Que. In addition, when cotreated with glycyrrhizin (Gly), an inhibitor of HMGB1, the inhibition of Que on HMGB1 and its translocation, apoptosis and oxidative stress, and the related proteins of HMGB1-mediated cellular pathway have been significantly enhanced. Conclusion. Thus, Que alleviates liver injury in ACLF rats, and its mechanism may be related to oxidative stress and apoptosis caused by HMGB1 and its translocation.

BackgroundLow-frequency transcranial magnetic stimulation (rTMS) over the left temporoparietal cortex reduces the auditory verbal hallucination (AVH) in schizophrenia. However, the underlying neural basis of the rTMS treatment effect for schizophrenia remains not well understood. This study investigates the rTMS induced brain functional and structural alternations and their associations with clinical as well as neurocognitive profiles in schizophrenia patients with AVH.MethodsThirty schizophrenia patients with AVH and thirty-three matched healthy controls were enrolled. The patients were administered by 15 days of 1 Hz rTMS delivering to the left temporoparietal junction (TPJ) area. Clinical symptoms and neurocognitive measurements were assessed at pre- and post-rTMS treatment. The functional (amplitude of low-frequency fluctuation, ALFF) and structural (gray matter volume, GMV) alternations were compared, and they were then used to related to the clinical and neurocognitive measurements after rTMS treatment.ResultsThe results showed that the positive symptoms, including AVH, were relieved, and certain neurocognitive measurements, including visual learning (VisLearn) and verbal learning (VerbLearn), were improved after the rTMS treatment in the patient group. Furthermore, the rTMS treatment induced brain functional and structural alternations in patients, such as enhanced ALFF in the left superior frontal gyrus and larger GMV in the right inferior temporal cortex. The baseline ALFF and GMV values in certain brain areas (e.g., the inferior parietal lobule and superior temporal gyrus) could be associated with the clinical symptoms (e.g., positive symptoms) and neurocognitive performances (e.g., VerbLearn and VisLearn) after rTMS treatment in patients.ConclusionThe low-frequency rTMS over the left TPJ area is an efficacious treatment for schizophrenia patients with AVH and could selectively modulate the neural basis underlying psychiatric symptoms and neurocognitive domains in schizophrenia.

Non-alcoholic fatty liver disease (NAFLD) has become the most prevalent liver disease in China. Sinisan (SNS) is a traditional Chinese medicine formula that has been widely used in treating chronic liver diseases, including NAFLD. However, its underlying biological mechanisms are still unclear. In this study, we employed a network pharmacology approach consisting of overlapped terms- (genes or pathway terms-) based analysis, protein-protein interaction (PPI) network-based analysis, and PPI clusters identification. Unlike the previous network pharmacology study, we used the shortest path length-based network proximity algorithm to evaluate the efficacy of SNS against NAFLD. And we also used random walk with restart (RWR) algorithm and Community Cluster (Glay) algorithm to identify important targets and clusters. The screening results showed that the mean shortest path length between genes of SNS and NAFLD was significantly smaller than degree-matched random ones. Six PPI clusters were identified and ten hub targets were obtained, including STAT3, CTNNB1, MAPK1, MAPK3, AGT, NQO1, TOP2A, FDFT1, ALDH4A1, and KCNH2. The experimental study indicated that SNS reduced hyperlipidemia, liver steatosis, and inflammation. Most importantly, JAK2/STAT3 signal was inhibited by SNS treatment and was recognized as the most important signal considering the network pharmacology part. This study provides a systems perspective to study the relationship between Chinese medicines and diseases and helps to discover potential mechanisms by which SNS ameliorates NAFLD.

The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.

P2X7 receptor is a crucial receptor related to neuronal activation, neurosensitization, and pain transmission, and increasing evidences indicated that glial cells is thought to be a major contributor to the chronic neuropathic pain after nerve injury. In present study, we designed to investigate whether the P2X7R in glial plays a role in chronic neuropathic pain. We divided adult male Sprague Dawley rats were respectively into four groups:(1) vehicle group (Veh), (2) CCI (C group), (3) P2X7 inhibitor group (P group), (4) CCI+ P2X7R inhibition (CP group). Behavior test, real-time polymerase chain reaction, western blot, immunofluorescence staining, and transmission electron microscope were used to analyze the scientific hypothesis. The results of the experiment is that(i) P2X7R of microglia was downregulated by A-70003 after CCI. (ii) Downregulation of P2X7R on microglia is coincident with remission of NP after CCI. (iii) P2X7R of microglia participates in NP via regulating autophagy and apoptosis. In summary, our results support P2X7R inhibition can counteract the CCI-induced NP due to microglia activation via a modulation of autophagy and apoptosis in mPFC and spinal cord. This may provide an importantly neuroprotective mechanism for the improved NP and also help devising new therapeutic to improve chronic pain in patients.

Big data applications increasingly rely on the analysis of large graphs. In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing out-of-core graph processing systems usually incur redundant data accesses and storage and severe competition of I/O bandwidth when handling the CGP jobs, thus leading to very long waiting time experienced by users for the computing results. In this paper, we propose an I/O-efficient out-of-core graph processing system, GraphCP, to support the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model that shares the I/O access and processing of graph data among the CGP jobs and adaptively schedules the loading of graph data, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 10.3x and 4.6x faster than two state-of-the-art out-of-core graph processing systems GridGraph and GraphZ respectively, and 2.1x faster than a CGP-oriented graph processing system Seraph.

Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

Gesture, an important mode of human natural behavior, has the advantages of rich semantics, natural flexibility, simplicity and convenience, and has become a research focus of new human-machine interaction technique. This paper, take the multi-UAV formation human-machine coordinated operation and control as the background, based on the gesture recognition method of surface electromyography(sEMG), and researches the gesture interaction technique for multi-UAV formations, which makes the operators use gestures to complete the flight operation and control of UAV formations without space constraints.

Huijuan Zhang,1,2 Zhe Guo,1,3 Yan Chai,1 Yi-Peng Fang,1 Xiangdong Mu,1,2 Nan Xiao,4 Jun Guo,1,2 Zhong Wang1,5 1School of Clinical Medicine, Tsinghua University, Beijing, 100084, People’s Republic of China; 2Department of Pulmonary and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, People’s Republic of China; 3Department of Liver Intensive Care Unit, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, People’s Republic of China; 4Department of Laboratory Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, People’s Republic of China; 5Department of General Practice Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, People’s Republic of ChinaCorrespondence: Jun GuoDepartment of Pulmonary and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Changping District, Beijing, 102218, People’s Republic of ChinaTel/Fax +86-10-56119539Email junguo_med@mail.tsinghua.edu.cnZhong WangDepartment of General Practice Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, No. 168 Litang Road, Changping District, Beijing, 102218, People’s Republic of ChinaTel/Fax +86-10-56119084Email wz523@mail.tsinghua.edu.cnPurpose: Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections have been increasingly reported worldwide. We aimed to identify the risk factors for nosocomial CRKP infections and assess the clinical outcomes.Patients and Methods: We conducted a case-control study with data collected from January 2016 to December 2018 in China. Controls were selected at a ratio of 1:1 from patients with nosocomial carbapenem-susceptible Klebsiella pneumonia (CSKP) infections. Risk factors for nosocomial CRKP infections and clinical outcomes were assessed with univariate and multivariate analyses.Results: A total of one hundred forty-two patients with CRKP infections and one hundred forty-two patients with CSKP infections were enrolled in this study. Multivariate analysis showed that exposure to antibiotics within 3 months prior to admission (odds ratio OR, 2.585; 95% confidence interval [CI], 1.425– 4.691; P=0.002), exposure to carbapenems (OR, 2.532; 95% CI, 1.376– 4.660; P=0.003), exposure to fluoroquinolones (OR, 3.309; 95% CI, 1.326– 8.257; P=0.010), and the presence of a nasogastric tube (OR, 2.796; 95% CI, 1.369– 5.712; P=0.005) were independent risk factors for CRKP infections. The 30-day mortality rate in the CRKP group was 19.7%, while the in-hospital mortality rate was 28.9%. In the CRKP group, a higher creatinine level (OR, 1.009; 95% CI, 1.002– 1.016; P = 0.013), being in shock at the time of a positive culture (OR, 4.454; 95% CI, 1.374– 14.443; P = 0.013), and co-infection with other resistant bacteria (OR, 4.799; 95% CI, 1.229– 18.740; P = 0.024) were independent predictors of in-hospital mortality in patients with CRKP infections. Kaplan–Meier curves showed that the CRKP group had a shorter survival time than the CSKP group.Conclusion: Nosocomial CRKP infection was associated with exposure to carbapenems and fluoroquinolones within 3 months prior to hospitalization and the presence of a nasogastric tube. Patients infected with CRKP had higher 30-day and in-hospital mortality rates. A higher creatinine level, shock and co-infection with other resistant bacteria were independent predictors of in-hospital mortality in patients with CRKP infections.Keywords: carbapenem-resistant Klebsiella pneumoniae, risk factors, clinical outcomes, nosocomial infection

AC-connected light emitting diode (LED) drivers experience imbalanced energy between input and output in a half-line cycle. To achieve the flicker-free operation, the imbalanced energy needs to be buffered, often by energy-dense electrolytic capacitors. However, electrolytic capacitors are also well-known for their short lifespan. These capacitors are the limiting factor of an LED drivers’ lifespan. High voltage film capacitors and a buck converter have been used in the proposed LED driver to buffer the imbalanced energy. When P in > P LED, the extra energy is transferred from the ac input directly to the high voltage film capacitors. When P in P LED, the energy is transferred from the high voltage film capacitors to the output by the buck converter. The imbalanced energy goes through two power conversion steps in the proposed LED driver, which is one time less than other comparable electrolytic capacitor-less designs, enabling a higher efficiency to be achieved. A 28 W flyback topology based experimental prototype had been built and tested to verify the proposed design.

An important aspect of a Speech tutoring aimed talking-head system (STTS) is the accuracy of produced articulatory movements. Little work has been done for the Articulatory movements' accuracy (AMA) evaluation in STTSs. Although subjective evaluation is reliable, it is time consuming and inconvenient. The traditional objective evaluation is comparing the motion of several points on the surface of the synthetic articulator to the Electromagnetic articulography (EMA) data which describes the motion of corresponding points on the articulatory surface of a speaker. The EMA information is too limited to describe the whole shape changing of deformable articulators for a speech process. To solve this problem, we propose a substantially different objective evaluation method based on a separately recorded medical video. The synthetic articulatory shapes in a speech process are compared to the corresponding shapes tracked from the medical video. This method is translation, rotation, and scaling invariant which allows the comparison of the shapes from the synthetic tongue and the medical images. The time difference problem of synthesis results and medical video is solved by introducing Dynamic time warping (DTW) to the proposed method. Experimental results demonstrate that our method has the ability to evaluate the deformation shape accuracy from an entire articulation process. The comparison results suggest that our method is more accurate than the traditional method especially for deformable articulators.

Background The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole brain WM network in different periods of the nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT), 2) identify the most discriminating WM regions and WM connections as biomarkers of radiation brain injury (RBI), and 3) supplement the understanding of the pathogenesis of RBI, which is useful for early diagnosis in the clinic. Methods A DTI scan was performed in 77 patients and 67 normal controls. A fractional anisotropy map was generated by DTIFit. TBSS was used to find the region where the FA differed between the case and control groups. Each resulting FA value image is registered with each other to create an average FA value skeleton. Each resultant FA skeleton image was connected to feature vectors, and features with significant differences were extracted and classified using a support vector machine (SVM). Next, brain segmentation was performed on each subject’s DTI image using automated anatomical labeling (AAL), and deterministic white matter fiber bundle tracking was performed to generate symmetrical brain matrix, select the upper triangular component as a classification feature. Two-sample t-test was used to extract the features with significant differences, then classified by SVM. Finally, we adopted a permutation test and ROC curves to evaluate the reliability of the classifier. Results For FA, the accuracy of classification between the 0–6, 6–12 and > 12 months post-RT groups and the control group was 84.5, 83.9 and 74.5%, respectively. In the case groups, the FA with discriminative ability was reduced, mainly in the bilateral cerebellum and bilateral temporal lobe, with prolonged time, the damage was aggravated. For WM connections, the SVM classifier classification recognition rates of the 0–6, 6–12 and > 12 months post-RT groups reached 82.5, 78.4 and 76.3%, respectively. The WM connections with discriminative ability were reduced. Conclusions RBI is a disease involving whole brain WM network anomalies. These brain discriminating WM regions and WM connection modes can supplement the understanding of RBI and be used as biomarkers for the early clinical diagnosis of RBI.

Background Transfusion-related acute lung injury (TRALI) is one of the most serious adverse events following transfusion, and there is no specific treatment in clinical practice. However, regulatory T cells (Tregs) have been suggested to play a potential role in the treatment of TRALI. This study investigated whether interleukin (IL)-2 or IL-2/anti-IL-2 complexes (IL-2c), which are mediators of Treg expansion, can modulate the severity of antibody-mediated TRALI in vivo. Study design and methods This study utilized a mouse model of the "two-hit" mechanism: BALB/c mice were primed with lipopolysaccharide (LPS) as the first hit, and then TRALI was induced by injecting major histocompatibility complex Class I antibodies. Mice injected with LPS only or LPS combined with isotype control antibodies served as controls. For the Treg-depleted groups, mice were infused with anti-mouse IL-2Rα first and then subjected to the same treatments as the TRALI group. Regarding IL-2- and IL-2c-treated mice, recombinant murine IL-2 or IL-2c was intraperitoneally administered to mice for 5 consecutive days before induction of the TRALI model. Samples were collected 2 hours after TRALI induction. Results Prophylactic administration of IL-2 or IL-2c to mice prevented the onset of edema, pulmonary protein levels, and proinflammatory factors that inhibited polymorphonuclear neutrophil aggregation in the lungs. Furthermore, the percentage of CD4+ CD25+ FoxP3+ Tregs was expanded in vivo using IL-2 and IL-2c compared to TRALI mice, as was confirmed through analysis of the spleen, blood, and lung. Conclusion This study validates that the protective mechanisms against TRALI involve CD4+ CD25+ FoxP3+ Tregs, which can be expanded in vivo by IL-2 and IL-2c. This results in increased IL-10 levels and decreased IL-17A, thereby prophylactically preventing antibody-mediated murine TRALI.

BACKGROUND: Hepatocellular carcinoma (HCC) commonly occurs in patients with splenomegaly. This study aimed to investigate the impact of splenomegaly with or without splenectomy on long-term survival of HCC patients with portal vein tumor thrombus (PVTT) treated with liver resection (LR). METHODS: HCC patients with PVTT who underwent LR from 2005 to 2012 from 6 hospitals were retrospectively studied. The long-term overall survival (OS) and recurrence-free survival (RFS) were compared between patients with or without splenomegaly, and between patients who did or did not undergo splenectomy for splenomegaly. Propensity score matching (PSM) analysis was performed to match patients in a 1:1 ratio. RESULTS: Of 716 HCC patients with PVTT who underwent LR, 140 patients had splenomegaly (SM group) and 576 patients had no splenomegaly (non-SM group). The SM group was further subdivided into 49 patients who underwent splenectomy (SPT group), and 91 patients who did not received splenectomy (non-SPT group). PSM matched 140 patients in the SM group, and 49 patients in the SPT group. Splenomegaly was an independent risk factor of poor RFS and OS. The OS and RFS rates were significantly better for patients in the non-SM group than the SM group (OS: P

Background Since early 2020, convalescent plasma has been widely used for treating coronavirus disease 2019 (COVID‐19). There is limited information regarding donor tolerability of convalescent plasma donation. In this study, we evaluated the short‐term donor tolerability of convalescent plasma donation. Methods A prospective study of 309 convalescent plasma donation related adverse events were conducted at Wuhan Blood Center of China, from February 12 to April 1, 2020. Additionally, up to 28‐day post‐donation follow‐ups were performed on the donors. Results Sixteen (5.2%) adverse events were reported in 309 donations. All of these were mild vasovagal without loss of consciousness. The frequency of adverse reactions was higher in donors with a per donation volume of >8 mL/kg body weight or ≥ 600 mL

This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1–1 and sample 1–2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%.

Cognitive impairments are the hallmark of schizophrenia and prominent in the early episode stage. However, the underlying pathological mechanisms of cognitive impairments are not fully understood. This study aimed to investigate the abnormal functional connectivity (FC) of the cerebellar dentate nucleus (DN) and its correlation with cognitive impairments in patients with drug-naive and first-episode schizophrenia. Resting-state functional magnetic resonance imaging data were acquired in 47 patients and 43 healthy controls. Cognitive functions were assessed by number sequence span, verbal category fluency, digit-symbol coding tests. The results showed that the patients had deficits in all three cognitive tests compared to the controls. Furthermore, the increased FC of DN with the bilateral postcentral gyrus and decreased FC of DN with the right inferior temporal gyrus and regional cerebellum (e.g., Vermis 4-5 and Crus I) were observed in the patient group compared to the control group. Importantly, these abnormal DN FC significantly correlated with cognitive tests (e.g., number sequence span and digit-symbol coding) and clinical symptoms (e.g., negative symptom) in the patient group. The results suggested that abnormal FC of DN with cortical and subcortical regions was associated with cognitive impairments and symptom severity and might be an underlying neural mechanism in schizophrenia.

Purpose. The purpose of the current study is to detect changes of topological organization of whole-brain functional networks and their relationship with the clinical treatment effects of antiepileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using resting-state functional MRI (RS-fMRI). Patients and Methods. RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 age- and gender-matched healthy controls (HCs). The patients were treated with first-line AEDs for 46.03 months before undergoing a second RS-fMRI scan. Results. CAE children at baseline showed a reduced clustering coefficient (Cp) and local efficiency (El) than the HC group, implying the reduction of functional segregation. CAE children at baseline also showed smaller characteristic path length (Lp) and higher global efficiency (Eg) compared with the HC group, implying the impairment of functional segregation. However, those metrics showed no significant differences between CAE children at follow-up and the HC group which indicated a clear renormalization of topological organization after AED treatments. CAE at follow-up also showed significantly decreased connectivity between several network regions, with which the thalamus is mainly involved. Furthermore, the reduced connectivity change between the left superior parietal gyrus and the left thalamus is positively correlated with the symptom improvements after AED treatment. Conclusion. We highlighted the convergence and divergence of brain functional network dysfunctions in CAE patients and provided crucial insights into pathophysiological mechanisms and the AED effects.

Noise has been proven to have a beneficial role in non-linear systems, including the human brain, based on the stochastic resonance (SR) theory. Several studies have been implemented on single-modal SR. Cross-modal SR phenomenon has been confirmed in different human sensory systems. In our study, a cross-modal SR enhanced brain–computer interface (BCI) was proposed by applying auditory noise to visual stimuli. Fast Fourier transform and canonical correlation analysis methods were used to evaluate the influence of noise, results of which indicated that a moderate amount of auditory noise could enhance periodic components in visual responses. Directed transfer function was applied to investigate the functional connectivity patterns, and the flow gain value was used to measure the degree of activation of specific brain regions in the information transmission process. The results of flow gain maps showed that moderate intensity of auditory noise activated the brain area to a greater extent. Further analysis by weighted phase-lag index (wPLI) revealed that the phase synchronization between visual and auditory regions under auditory noise was significantly enhanced. Our study confirms the existence of cross-modal SR between visual and auditory regions and achieves a higher accuracy for recognition, along with shorter time window length. Such findings can be used to improve the performance of visual BCIs to a certain extent.

Accurate and reliable short-term wind power forecasting is very important for coping with wind power fluctuation and optimizing wind farm operation. Due to the complexity and variability of wind farm surroundings, traditional forecasting methods are difficult to meet the requirements of refined and dynamic modeling, and there are problems such as easy to fall into local optimum, convergence speed and forecast accuracy to be improved. In response to the above requirements, this paper established an intelligent wind power prediction method based on the IPSO-GSA-BP model Firstly, in view of the defects of the universal gravitation algorithm (GSA) such as the lack of jumping out of the local optimal mechanism and the group memory function, the Inproved Particle Swarm optimization algorithm (IPSO) is used to improve the universal gravitation search algorithm from the aspects of particle inertial mass and global search to improve its optimal precision. Then, the BP neural network is optimized by the optimal parameters obtained by the IPSO-GSA. Finally, a simulation experiment was carried out using the operating data of a wind farm in southern China, and compared with other models. The results showed that the method proposed in this paper has higher accuracy and higher reliability in short-term wind power forecasting, and can realize refined and dynamic modeling, so as to provide a new technological path for optimal scheduling management in wind farm.

The liquid hydrogen Storage System (LHSS) combined superconducting magnetic energy storage (SMES) have both features of power-type and energy-type energy storage, and is capable of providing power grid auxiliary services. This paper analyzes basic structure of LHSS combined SMES. Requirements for participating in grid auxiliary services and the VSG control strategy are also analyzed in this paper. A novel two layer control strategy which focus on upper layer energy control and lower layer power control is proposed in this paper. A study case is given to verify the effectiveness of the proposed control strategy.

Background and objective Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions. Methods The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition. Results The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space. Conclusion This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.

Prostate cancer (PCa) is one of the most frequent cancers in men, and its biomolecular targets have been extensively studied. This study aimed to analyze the expression of toll-like receptor 9 (TLR9) and vascular endothelial growth factor C (VEGF-C) and the clinical value of the coexpression of TLR9 and VEGF-C in PCa. We retrospectively evaluated 55 patients with clinically localized, intermediate-risk, or high-risk PCa who underwent laparoscopic radical prostatectomy (LRP) and extended pelvic lymph node dissection (ePLND) without neoadjuvant hormonal therapy at a single institution from June 2013 to December 2016. In all 55 patients, the median number of lymph nodes (LNs) resected was 23 (range: 18-31), and a total of 1269 LNs were removed, of which 78 LNs were positive. Seventeen patients had positive LNs, with a positive rate of 30.9%. In addition, the immunohistochemical results in the above patients revealed that high TLR9 expression was correlated with higher Gleason score (GS) (P = 0.049), increased LN metastasis (P = 0.004), and more perineural invasion (PNI) (P = 0.033). Moreover, VEGF-C expression was associated with GS (P = 0.040), pathological stage (pT stage) (P = 0.022), LN metastasis (P = 0.003), and PNI (P = 0.001). Furthermore, a significant positive correlation between TLR9 and VEGF-C was found (P < 0.001), and the TLR9/VEGF-C phenotype was associated with LN metastasis (P = 0.047). Collectively, we propose that TLR9 stimulation may promote LN metastasis in PCa cells through the upregulation of VEGF-C expression, thereby affecting the prognosis of PCa patients. Therefore, these markers may serve as valuable targets for the treatment of PCa.

With the rapid development of Chinnes highway transportation industry, the problem of road traffic safety has become increasingly prominent. Highway passenger transport accidents are generally fatal and fatal accidents. Traffic accidents not only cause enormous economic losses to transport enterprises, but also have a very bad social impact on local highway transport management departments, which has even become a new social instability factor. This is mainly because there are many problems in autopilot technology, such as low recognition accuracy, poor real-time performance, weak anti-interference ability and so on. However, embedded technology and machine learning can solve these problems well, so autopilot technology will become the mainstream in the future. Firstly, this paper analyses the importance of autopilot technology. Then this paper analyses the machine learning target recognition, vehicle automatic driving system model and vehicle automatic driving system flow. Finally, this paper designs the function of autopilot system.

Machine learning algorithms are crucial for crop identification and mapping. However, many works only focus on the identification results of these algorithms, but pay less attention to their classification performance and mechanism. In this paper, based on Google Earth Engine (GEE), Sentinel-2 10 m resolution images during a specific phenological period of winter wheat were obtained. Then, support vector machine (SVM), random forest (RF), and classification and regression tree (CART) machine learning algorithms were employed to identify and map winter wheat in a large-scale area. The hyperparameters of the three machine learning algorithms were tuned by grid search and the 5-fold cross-validation method. The classification performance of the three machine learning algorithms were compared, the results of which demonstrate that SVM achieves best performance in identifying winter wheat, and its overall accuracy (OA), user&rsquo, s accuracy (UA), producer&rsquo, s accuracy (PA), and kappa coefficient (Kappa) are 0.94, 0.95, 0.95, and 0.92, respectively. Moreover, 50 various combinations of training and validation sets were used to analyze the generalization ability of the algorithms, and the results show that the average OA of SVM, RF, and CART are 0.93, 0.92, and 0.88, respectively, thus indicating that SVM and RF are more robust than CART. To further explore the sensitivity of SVM, RF, and CART to variations of the algorithm parameters&mdash, namely, (C and gamma), (tree and split), and (maxD and minSP)&mdash, we employed the grid search method to iterate these parameters, respectively, and to analyze the effect of these parameters on the accuracy scores and classification residuals. It was found that with the change of (C and gamma) in (0.01~1000), SVM&rsquo, s maximum variation of accuracy score is up to 0.63, and the maximum variation of residuals is 76,215 km2. We concluded that SVM is sensitive to the parameters (C and gamma) and presents a positive correlation. When the parameters (tree and split) change between (100~600) and (1~6), respectively, the RF&rsquo, s maximum variation of accuracy score is 0.08, and the maximum variation of residuals is 1157 km2, indicating that RF is low in sensitivity toward the parameters (tree and split). When the parameters (maxD and minSP) are between (10~60), the maximum accuracy change value is 0.06, and the maximum variation of residuals is 6943 km2. Therefore, compared to RF, CART is sensitive to the parameters (maxD and minSP) and has poor robustness. In general, under the conditions of the hyperparameters, SVM and RF exhibit optimal classification performance, while CART has relatively inferior performance. Meanwhile, SVM, RF, and CART have different sensitivities toward the algorithm parameters, that is, SVM and CART are more sensitive to the algorithm parameters, while RF has low sensitivity toward changes in the algorithm parameters. The different parameters cause great changes in the accuracy scores and residuals, so it is necessary to determine the algorithm hyperparameters. Generally, default parameters can be used to achieve crop classification, but we recommend the enumeration method, similar to grid search, as a practical way to improve the classification performance of the algorithm if the best classification effect is expected.

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.

Due to the advantages of prominent brain responses, less training duration, and robustness to artifacts, steady-state visual evoked potential (SSVEP) based braincomputer interface (BCI) has been recently applied to intelligent wheelchair navigation. Recent researches mainly focused on the implementation of a specific recognition method to analyze the wheelchair navigation performance under fixed stimulation target number. The navigation performance evaluation under different recognition methods and different BCI target numbers is rarely compared. In this paper, the navigation performance comparison between different numbers of stimulation targets and different recognition methods was carried out. The multichannel integrated GT2 circ and canonical correlation analysis (CCA) recognition methods were used to compare the wheelchair navigation performance under different time-window (TW) lengths. And the 3-target and 4-target arrangement layouts were adopted to compare the navigation performance between the “No feedback” and the “Feedback” conditions. Experimental results showed that the GT2 circ recognition method with TW length of 2 s would promote the navigation efficiency and different stimulation target number could be tailored for different wheelchair application purposes, which would bring tutorial instructions to the applicable navigation of the BCI-actuated wheelchair.

Aiming at the influence of user equipment (UE) mobility on computational results feedback of mobile edge computing (MEC) server, a resource allocation strategy based on virtual machine migration is proposed to maximize the system energy efficiency (EE). UEs can harvest radio frequency energy from multiple frequency bands as they move. When the UE moves over a long distance, virtual machine migration is adopted to transfer the computational tasks offloaded by the UE from the initial MEC server to the current MEC server. After the current MEC server completes the computational task, it can directly feed the computational results back to the UE, which can significantly reduce the delay. After the current MEC server completes the computational task, it will feed the computational results back to the UE. By jointly considering offloading computational task by the UE and computational results feedback of MEC server, the problem of power and subcarrier allocation is modeled as a mixed integer nonlinear programming problem. The goal is to maximize the system EE under the constraints of energy consumption, subcarrier allocation, and transmitting power. The suboptimal solution is obtained by introducing the genetic algorithm. Simulation results show that the proposed method has higher EE than partial power or subcarrier allocation methods based on the genetic algorithm.

Introduction: The miniaturization of blood pumps has become a trend due to the advantage of easier transplantation, especially for pediatric patients. In small-scale pumps, it is much easier and more cost-efficient to manufacture the impeller with straight blades compared to spiral-profile blades. Methods: Straight-blade impeller designs with different blade angles, blade numbers, and impeller flow passage positions are evaluated using the computational fluid dynamics method. Blade angles (θ = 0°, 20°, 30°, and 40°), blade numbers ( N = 5, 6, 7, and 8), and three positions of impeller flow passage (referred to as top, middle, and bottom) are selected as the studied parametric values. Results: The numerical results reveal that with increasing blade angle, the pressure head and the hydraulic efficiency increase, and the average scalar shear stress and the normalized index of hemolysis decrease. The minimum radial force and axial thrust are obtained when θ equals 20°. In addition, the minimum average scalar shear stress and normalized index of hemolysis values are obtained when N = 6, and the maximum values are obtained when N = 5. Regarding the impeller flow passage position, the axial thrust and the stagnation area forming in the impeller eye are reduced as the flow passage height declines. Conclusion: The consideration of a blade angle can greatly improve the performance of blood pumps, although the influence of the blade number is not very easily determined. The bottom position of the impeller flow passage is the best design.

This study aimed to identify the neuroanatomical substrates and white matter connectivity in children with autism spectrum disorder (ASD) and the association between gray matter and structural connectivity. A total of 36 children including patients with ASD and healthy controls between 6 and 15 years of age were enrolled in this study. High-resolution structural MRI and functional MRI were performed and analyzed using voxel-based morphometry and tract-based spatial statistics. The relationship between gray matter volume and structural connectivity was generated using Pearson correlation analysis. Voxel-based morphometry analysis showed significantly reduced areas of gray matter in the left cerebellum. Tract-based spatial statistics analysis showed white matter abnormalities in several distinct clusters within the right inferior frontal gyrus (opercular part), the left inferior parietal lobule, and the right mentary motor area. Neither ASD nor healthy controls showed a significant correlation between gray matter volume and white matter integrity. Our study confirmed the presence of several structural and regional abnormalities in ASD children. These findings suggest that there are significant differences in some brain regions in children with autism relative to healthy children, but no association between them.

Current prostheses are limited in their ability to provide direct sensory feedback to users with missing limb. Several efforts have been made to restore tactile sensation to amputees but the somatotopic tactile feedback often results in unnatural sensations, and it is yet unclear how and what information the somatosensory system receives during voluntary movement. The present study proposes an efficient model of stacked sparse autoencoder and back propagation neural network for detecting sensory events from a highly flexible electrocorticography (ECoG) electrode. During the mechanical stimulation with Von Frey (VF) filament on the plantar surface of rats’ foot, simultaneous recordings of tactile afferent signals were obtained from primary somatosensory cortex (S1) in the brain. In order to achieve a model with optimal performance, Particle Swarm Optimization and Adaptive Moment Estimation (Adam) were adopted to select the appropriate number of neurons, hidden layers and learning rate of each sparse auto-encoder. We evaluated the stimulus-evoked sensation by using an automated up-down (UD) method otherwise called UDReader. The assessment of tactile thresholds with VF shows that the right side of the hind-paw was significantly more sensitive at the tibia-(p = 6.50 × 10(−4)), followed by the saphenous-(p = 7.84 × 10(−4)), and sural-(p = 8.24 × 10(−4)). We then validated our proposed model by comparing with the state-of-the-art methods, and recorded accuracy of 98.8%, sensitivity of 96.8%, and specificity of 99.1%. Hence, we demonstrated the effectiveness of our algorithms in detecting sensory events through flexible ECoG recordings which could be a viable option in restoring somatosensory feedback.

Adding noise to a weak input signal can enhance the response of a non-linear system, a phenomenon known as stochastic resonance (SR). SR has been demonstrated in a variety of diverse sensory systems including the visual system, where visual noise enhances human motion perception and detection performance. The SR effect has not been extensively studied in brain-computer interface (BCI) applications. This study compares the performance of BCIs based on SR-influenced steady-state motion visual evoked potentials. Stimulation paradigms were used between a periodically monochromatic motion-reversing simple ring and complex alternating checkerboard stimuli. To induce the SR effect, dynamic visual noise was masked on both the periodic simple and complex stimuli. Offline results showed that the recognition accuracy of different stimulation targets followed an inverted U-shaped function of noise level, which is a hallmark of SR. With the optimal visual noise level, the proposed visual noise masked checkerboard BCI paradigm achieved faster and more stable detection performance due to the noise-enhanced brain responses. This work demonstrates that the SR effect can be employed in BCI applications and can achieve considerable performance improvements.

Exposure to sevoflurane and other inhalational anesthetics can induce cognitive impairment in elderly patients. Studies have indicated that methylene blue (MB) has beneficial effects on multiple neurodegenerative diseases and the mechanism involves mitochondrial function preservation. However, whether MB can attenuate the cognitive decline induced by sevoflurane in aged mice requires further investigation. Forty-five 18-month-old C57/BL mice were used to establish a model of sevoflurane-induced cognitive impairment in which the mice were exposed to 3% sevoflurane for 2 h. Mice in the MB group were intraperitoneally injected with MB at a dose of 5 mg/kg before sevoflurane inhalation. The Morris water maze test was used to evaluate the learning and memory performances. We also examined changes in mitochondrial morphology as well as the expression and interaction of related proteins in the aged hippocampus. Parkin, BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3), mitochondrial dynamin-related protein 1 (Drp1), small ubiquitin-like modifier (SUMO2/3), SUMO-specific proteases 3 (SENP3), and ubiquitin‐like conjugating enzyme 9 expression in the mouse hippocampus was detected by western blotting, and SUMO2/3-Drp1 was examined by coimmunoprecipitation. Exposure to sevoflurane increased SENP3 expression and Drp1 deSUMOylation in the aged hippocampus and resulted in cognitive deficiency. MB attenuated sevoflurane-induced memory loss and mitochondrial fragmentation and decreased Drp1 deSUMOylation in the aged hippocampus. This neuroprotective effect provides a mechanistic explanation for how the SUMOylation status of Drp1 acts as a key switch in the cognitive dysfunction induced by sevoflurane.

This paper introduces the anode-via vertical field plate Schottky barrier diode (AVFP-SBD) structure as an enhanced breakdown voltage AlGaN/GaN SBD, using the 2-D ATLAS Silvaco software. The breakdown voltage enhancement is achieved by integration of the lateral field plate (LFP) and vertical field plate (VFP) into the AlGaN/GaN SBD. The simulation results indicate that the VFP not only provide a uniform electric field along the channel, but also distributes the internal electric field on the side of the anode evenly. We show that the proposed structure significantly enhances the breakdown voltage of the conventional AlGaN/GaN diodes, i.e., from −246 to −354 V. Moreover, optimal breakdown voltage of −2301 V is achieved using the SBD with anode-via both LFP and VFP, through an integration of the LFP into the AVFP-SBD. Such breakdown voltage enhancement is achieved without any deterioration in the forward characteristics of the diode.