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Alzheimer’s disease (AD) is the most common cause of age-related dementia and is currently incurable. The failures of current clinical trials and the establishment of modifiable risk factors have shifted the AD intervention from treatment to prevention in the at-risk population. Previous studies suggest that there is a geographic overlap between AD incidence and spicy food consumption. We previously reported that capsaicin-rich diet consumption was associated with better cognition and lower serum Amyloid-beta (Aβ) levels in people aged 40 years and over. In the present study, we found that intake of capsaicin, the pungent ingredient in chili peppers, reduced brain Aβ burden and rescued cognitive decline in APP/PS1 mice. Our in vivo and in vitro studies revealed that capsaicin shifted Amyloid precursor protein (APP) processing towards α-cleavage and precluded Aβ generation by promoting the maturation of a disintegrin and metalloproteinase 10 (ADAM10). We also found that capsaicin alleviated other AD-type pathologies, such as tau hyperphosphorylation, neuroinflammation and neurodegeneration. The present study suggests that capsaicin is a potential therapeutic candidate for AD and warrants clinical trials on chili peppers or capsaicin as dietary supplementation for the prevention and treatment of AD.

The neurotrophin receptor p75 (p75NTR) plays important roles in regulating amyloid-beta (Aβ) metabolism in the brain. The expression of p75NTR is altered in the brain of patients with Alzheimer's disease (AD). In this study, we aimed to evaluate whether p75NTR mRNA level in the peripheral blood cells is changed among AD patients and its potential to be a biomarker for AD. The study subjects included 26 patients with AD (PiB-PET positive) and 28 cognitively normal controls (PiB-PET negative). RNA was extracted from peripheral blood cells of fast blood. p75NTR mRNA was measured using quantitative real-time PCR assay. p75NTR mRNA levels in blood cells were comparable between AD patients and controls. p75NTR mRNA levels in blood cells were not correlated with MMSE scores, ApoE genotypes, gender, and age. p75NTR mRNA expression in blood cells is not changed in AD patients and is unlikely to be a biomarker for AD Refereed/Peer-reviewed

Welding defect constitute a great danger to the safe usage of petroleum pipelines. In addition, manual welding defect detection has numerous deficiencies, such as subjectivity and inaccurate estimation of geometric parameters. Thus, we proposed an automatic welding defect detection system for X-ray images. In the system, five typical defects (cracks, lack of penetration, lack of fusion, round defects, and stripy defects) and non-defects were chosen for recognition. There are three stages in the system: defect extraction, defect detection, and defect recognition. In the first stage, background subtraction with an adaptive thresholding method was adopted to identify the potential defects. In the second stage, to extract real defects from the massive number of potential defects, the adaptive cascade boosting (AdaBoost) algorithm was employed for binary classification. Grayscale features and geometric properties of the defects were extracted for the classification. In the third stage, the AdaBoost algorithm was extended for multi-classification. In the process of distinguishing defects from non-defects, a high detection rate is necessary. To ensure the high true positive rate (TPR) and the low false positive rate (FPR), we proposed the cascade AdaBoost algorithm with penalty term. The accuracy of the defect detection was 85.5%, and the TPR was 91.66%. Moreover, three comparison tests of support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were employed to validate the superiority of AdaBoost. The experimental results indicate that the proposed detection system can be effective for defect detection.

The COVID-19 pandemic is exacting an increasing toll worldwide, with new SARS-CoV-2 variants emerging that exhibit higher infectivity rates and that may partially evade vaccine and antibody immunity1. Rapid deployment of non-invasive therapeutic avenues capable of preventing infection by all SARS-CoV-2 variants could complement current vaccination efforts and help turn the tide on the COVID-19 pandemic2. Here, we describe a novel therapeutic strategy targeting the SARS-CoV-2 RNA using locked nucleic acid antisense oligonucleotides (LNA ASOs). We identified an LNA ASO binding to the 5’ leader sequence of SARS-CoV-2 ORF1a/b that disrupts a highly conserved stem-loop structure with nanomolar efficacy in preventing viral replication in human cells. Daily intranasal administration of this LNA ASO in the K18-hACE2 humanized COVID-19 mouse model potently (98-99%) suppressed viral replication in the lungs of infected mice, revealing strong prophylactic and treatment effects. We found that the LNA ASO also represses viral infection in golden Syrian hamsters, and is highly efficacious in countering all SARS-CoV-2 “variants of concern” tested in vitro and in vivo, including B.1.427, B.1.1.7, and B.1.351 variants3. Hence, inhaled LNA ASOs targeting SARS-CoV-2 represents a promising therapeutic approach to reduce transmission of variants partially resistant to vaccines and monoclonal antibodies, and could be deployed intranasally for prophylaxis or via lung delivery by nebulizer to decrease severity of COVID-19 in infected individuals. LNA ASOs are chemically stable and can be flexibly modified to target different viral RNA sequences4, and they may have particular impact in areas where vaccine distribution is a challenge, and could be stockpiled for future coronavirus pandemics.

Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals.

With the arrival of big data era, hierarchical storage systems are commonly used to optimize the I/O performance. While data placement strategies have significant influence on the I/O performance, the traditional one-layout-fits-all strategy fails to make optimal decisions and could not utilize hierarchical storage architecture efficiently. Some strategies based on user hints cannot meet the demand of data explosion. Although recent efforts propose several adaptive data placement techniques, fitting diverse scientific workflows and complex system characteristics are still challenging. This paper presents WorkflowRL, an intelligent data placement engine that utilizes reinforcement learning to manage data across multi-layer hierarchical storage system. WorkflowRL extracts factors affecting the I/O performance including the workflow characteristics and system information for feature learning. Then, it interacts with the heterogeneous storage system environment to make optimal data placement decisions. Extensive experiments have been conducted and the results have demonstrated that our data placement strategy can significantly improve the I/O performance with diverse scientific workflows.

Brain&ndash, Machine Interfaces (BMIs) have attracted much attention in recent decades, mainly for their applications involving severely disabled people. Recently, research has been directed at enhancing the ability of healthy people by connecting their brains to external devices. However, there are currently no successful research reports focused on robotic power augmentation using electroencephalography (EEG) signals for the shoulder joint. In this study, a method is proposed to estimate the shoulder&rsquo, s electromyography (EMG) signals from EEG signals based on the concept of a virtual flexor&ndash, extensor muscle. In addition, the EMG signal of the deltoid muscle is used as the virtual EMG signal to establish the EMG estimation model and evaluate the experimental results. Thus, the shoulder&rsquo, s power can be augmented by estimated virtual EMG signals for the people wearing an EMG-based power augmentation exoskeleton robot. The estimated EMG signal is expressed via a linear combination of the features of EEG signals extracted by Independent Component Analysis, Short-time Fourier Transform, and Principal Component Analysis. The proposed method was verified experimentally, and the average of the estimation correlation coefficient across different subjects was 0.78 (&plusmn, 0.037). These results demonstrate the feasibility and potential of using EEG signals to provide power augmentation through BMI technology.

Although current prosthetic hands can provide some hand functions for the amputees, they are impossible to be used in the water. During the amputees’ daily lives, it is inevitable for them to use hands in the water environment, such as washing, swimming. In order to improve the quality of amputees’ lives, it is very necessary to develop a water proof prosthetic hand. We develope conductive silicon surface electromyography (sEMG) sensors to acquire sEMG signals in the water environment. ForsEMG signals collected by flexible water proof electrodes in the water, we develope sEMG signals recognition method. The experimental results show that we can control the prosthetic hand to complete four gestures based on the sEMG signals, and the accuracy is 93.89%. Therefore, it can be concluded that the sEMG signals recognition method and the prosthetic hand control system are feasible.

Neurilemmoma, also known as schwannoma or neurinoma, is a tumor that originates from neural sheath Schwann cells. Giant neurilemmomas derived from the retroperitoneum have rarely been reported. We herein describe a woman with a giant retroperitoneal neurilemmoma that was initially incorrectly diagnosed as an inflammatory abdominal mass. The tumor extended from the patient’s hypogastrium to her pelvic cavity and measured 20 × 15 × 10 cm. The tumor was excised via laparotomy and diagnosed as a retroperitoneal neurilemmoma through histological and immunohistochemical examination. Although rare, particularly in the giant form, neurilemmoma should be considered as an important differential diagnosis in patients with a retroperitoneal tumor or inflammatory abdominal mass. Complete excision should be considered for the potential cure of giant retroperitoneal neurilemmomas.

MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.

Amyloid-beta (Aβ) plays a pivotal role in the pathogenesis of Alzheimer’s disease (AD) and has been regarded as the main therapeutic target for AD. However, most of the Aβ-targeted clinical trials have not succeeded. Therefore, the Aβ-targeted therapeutic strategy on treating this complex disease needs to be re-evaluated. In this review, we analyzed the challenges and critical points of the current anti-Aβ therapeutic strategies. In addition to Aβ, multiple pathological events such as tau hyperphosphorylation, oxidative stress, and neuroinflammation, which are involved in AD pathogenesis and synergistically drive disease progression, could be important targets for AD treatment. Tertiary prevention strategies are needed for the successful management of AD due to its complex and dynamic pathogenesis. Systemic perspective addressing the disease pathogenesis within and outside the brain, as well as the multidomain intervention targeting risk factors and comorbidities, are important approaches for the therapeutic solutions of AD.

A 48-year-old man presented with 3 days of mild horizontal diplopia in the left direction, followed by the onset of headache 17 days later. A physical examination revealed isolated left abducens nerve palsy. Head computed tomography (CT) and magnetic resonance imaging (MRI) scans revealed soft-tissue density neoplasms that occupied the sphenoidal sinus and further invaded to destroy the clivus. Immunohistochemical staining of neoplasms was performed from biopsies samples. The pathological diagnosis was extranodal natural killer (NK)/T-cell lymphoma (ENKL), nasal type, associated with Epstein-Barr virus (EBV). The patient subsequently exhibited secondary symptoms (fever, night sweats), enlarged lymph nodes, renal metastases, and hemophagocytic syndrome, with clinical diagnosis stage IV of ENKL. The patient has a poor prognosis. This report is unique in two aspects: the unilateral abducens nerve palsy as the initial and isolated symptom of ENKL, and the primary sphenoidal sinus ENKL.

Previous studies have suggested that cardiovascular functions might play a critical role in Alzheimer's disease (AD) pathogenesis. However, the relationship among heart function, blood flow of cerebral vessels, and AD remains unclear. In the present study, AD patients (n = 34) and age- and gender-matched cognitively normal controls (n = 34) were recruited. Demographic and comorbidity information was collected. The ejection fraction was measured using echocardiography, and the mean velocity, pulsatility index (PI), and resistance index (RI) of the basilar artery (BA), left terminal internal carotid artery (LTICA), and right terminal internal carotid artery (RTICA) were measured using transcranial Doppler. The data of lacunae, white matter changes, and plaques in the aortic arch and carotid arteries were collected from brain magnetic resonance imaging and computed tomography angiography images. Compared with normal controls, AD patients had lower ejection fractions and cerebral blood flow velocities and higher RI and PI in the BA, LTICA, and RTICA, as well as more plaques in the aortic and carotid arteries. In the multivariate logistic regression analysis, the ejection fraction and the mean velocity of the BA and LTICA were independently associated with AD after adjusting for age, gender, education, vascular risk factors, arterial plaques, and brain ischemic lesions detected in the brain images. These findings suggest that heart function and vascular condition may play important roles in AD pathogenesis. Improving cardiovascular functions could be a promising approach for the prevention and treatment of AD.

Stroke patients need rehabilitation to recover their abilities of moving, and the underwater rehabilitation can reduce the possibility of secondary injury during the rehabilitation process. Collecting surface electromyography (sEMG) signals underwater can provide better rehabilitation guidance to the medical doctors. However, the current sEMG electrodes cannot be applied to collect sEMG signals underwater. To solve this problem, we propose a soft sEMG electrode based on conductive silicon. The time domain and frequency domain features of sEMG signals are extracted. The sEMG signals are identified by Back Propagation Neural Network (BPNN) under the dry, simulated sweating and water environments respectively. Under the dry environment, there is no significant difference in the recognition accuracy of sEMG signals between the conductive silicon electrode and the Ag/AgCl electrode. Under the water environment, the recognition accuracy of sEMG signals acquired by the conductive silicon electrode is 95.47% by employing time domain features.

The development of traditional SLAM technology has gradually encountered bottlenecks in recent years. With deep learning, especially vision-based machine learning, a new and improved way of development has been found for SLAM named Semantic SLAM. The study of semantic SLAM has been a very hot topic in recent years. However, the related research is still in its infancy and is not systematic. In this paper, we will discuss the existing SLAM systems, semantic segmentation networks and semantic SLAM systems, introduce secondly the multiple effects of semantic network on localization, mapping and their applications, and finally put forward an idea of semantic Fusion.

In this paper, an optimization algorithm is proposed to achieve efficient global positioning and recovery from kidnap in open environment. Due to the ability of some sensors to achieve global localization efficiently, such as Ultra-Wideband (UWB), Wi-Fi, and camera, we take the UWB sensor to improve AMCL. By comparing various ranging and positioning schemes, we propose a specific analysis of UWB ranging and positioning methods, as well as an observation model for integrating UWB information. Finally, the efficiency of this method is proved by comparison with AMCL. Thus, the time to locate globally is about 3 seconds, while more than 100 seconds using AMCL.

With the tendency of aging society, the assistant robot for the aged has become a research hotspot, such as exoskeleton robot. Surface electromyography (sEMG) is a kind of biological signals generated by muscle, and the joint angle estimated by sEMG can be used to construct efficient control strategy for the assistant robot. Estimating joint angle requires large amount of sEMG training data; however, due to the muscle fatigue, it is inapplicable to collect sEMG signals from each subject for a long time. To improve the angle estimation accuracy based on less data, this paper proposes a novel joint angle estimation method with empirical mode decomposition (EMD), and selects the elbow joint as the research joint. This estimation method utilizes extra intrinsic mode functions (IMF) feature extracted from IMF and conventional sEMG features including auto-regressive model (AR model) and root mean square (RMS). This research adopts support vector regression (SVR) to estimate elbow joint angle by IMF features and conventional sEMG features. The experimental results show that with the help of extra IMF features the accuracy of estimation result is substantially improved. To be specific, the average regression mean square error (MSE) is reduced from 0.2459 to 0.1712 and the average R2 is improved from 0.7546 to 0.8242. According to the experimental results, it can be concluded that the estimation method with extra IMF feature can estimate the elbow joint angle efficiently, which paves a solid foundation for the future control strategy of the assistant robots.

With complex functions, hand is an important organ for human. Unfortunately, many people in China are suffering from hand losing. Therefore, the effective hand motion recognition system is required to help the amputees live or work normally. The surface electromyography (sEMG) signal can represent the hand motion effectively, and many studies about sEMG-based prosthetic hands have been investigated. However, some prosthetic hands use on-off switch control command, which limits the intelligence and flexibility of the prosthetic hands. Some intelligent recognition systems require too many sensors, which is unrealistic for amputees with limited residual muscles. In addition, some algorithms are too complicated, which brings difficulties for practical applications. To solve these problems, we attempted to recognize six commonly used handgestures with two-channel sensors, and the classification performance and calculation time of different algorithms are compared. Finally, we achieved the recognition accuracy of 91.93% by three time domain features and back propagation neural network (BPNN) classifier, which balances the accuracy and computation time. In future work, the proposed method will be applied to real-time prosthetic hands to improve the amputee’s quality of life.

In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model’s reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature., Author summary MiRNAs are known as gene regulators and play critical roles in various biological processes. Many associations between miRNAs and human diseases have been identified, which promotes the understanding towards the molecular mechanisms of diseases and contributes to prevention and treatment of diseases. Computational methods of predicting potential miRNA-disease associations make the discovery more efficient and experiments more productive. We developed EDTMDA by constructing a computational framework integrating ensemble learning and dimensionality reduction. We performed global LOOCV, local LOOCV and 5-fold cross validation to evaluate performance of EDTMDA, which outperformed many classic methods. In addition, we carried out three types of case studies on important diseases, which were used to evaluate performance of model based on known associations in HMDD V2.0, for new diseases without known associations and based on known associations in HMDD V1.0. As a result, most predicted miRNAs in top 50 predictions were confirmed by experimental evidences in literature. So, we believe that EDTMDA can make reliable predictions and guide experiments to uncover more miRNA-disease associations.

Several epidemiological investigations indicate that cancer survivors have a lower risk for Alzheimer's disease (AD) and vice versa. However, the associations between plasma amyloid-beta (Aβ) levels with cancer remain largely unknown. In this case-control study, 110 cancer patients, 70 AD patients, and 70 age- and gender-matched normal controls were recruited. The cancer types include esophagus cancer, colorectal cancer, hepatic cancer, and lung cancer, all of which were reported to be associated with a lower risk for AD. Plasma levels of Aβ40, Aβ42, common pro-inflammatory cytokines, IL-1β, IL-6, TNF-α, IFN-γ, anti-inflammatory IL-4, chemokines, and cytokines MCP-1 were measured with enzyme-linked immunosorbent assay (ELISA) kits. Plasma levels of Aβ40 and Aβ42 in all cancer patients were higher than that in normal controls. More specifically, hepatic cancer patients exhibited significantly higher plasma Aβ levels. No significant difference in plasma Aβ levels was found between chemotherapy and no chemotherapy subgroups. Plasma Aβ levels were not significantly correlated with pro-inflammatory cytokines, anti-inflammatory, chemokines, and cytokines. Peripheral Aβ levels increased in cancer patients, especially in patients with hepatic cancer, independent of chemotherapy and inflammation. Further verification is required for the association between plasma Aβ and cancer.

Surface electromyogram (sEMG) signals can be applied in medical, rehabilitation, robotic, and industrial fields. As a typical application, a myoelectric prosthetic hand is controlled by the sEMG signals of the amputee’s residual muscles. To improve the dexterity of the myoelectric prosthetic hand, additional hand motion commands need to be classified. The more sEMG sensors are used, the more hand motion commands can be classified. However, the amputee’s residual muscles are limited. In order to improve the practicability of the myoelectric prosthetic hand, it is critical to investigate the effective pattern recognition algorithms to deal with the sEMG signals detected by fewer sensors, while identifying as many hand motion commands as possible. Current pattern recognition algorithms for sEMG signals are challenged by limited recognition patterns and unsteady classification accuracy rates. To solve these dilemmas, we employed discrete wavelet transform (DWT) and wavelet neural network (WNN) algorithms to improve the pattern recognition effects of sEMG signals. In addition, the back propagation and gradient descent algorithms were utilized to train WNN. In this work, we only used three sEMG sensors to classify and recognize six kinds of hand motion commands. The maximum identification accuracy rate is 100%, and an average classification accuracy rate of the proposed WNN is 94.67%, which is substantially better than the artificial neural network (ANN) algorithm.

Lvzi Xu,1 Xiao Qi,2 Chi Zhu,3 Lihua Wan1 1Department of Forensic Medicine, Chongqing Medical University, Chongqing, China; 2Department of Rehabilitation, Xinqiao Hospital, Third Military Medical University, Chongqing, China; 3Department of Neurology, Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, China Abstract: To investigate the autoimmune mechanisms of schizophrenia, we explored the relationship between schizophrenia and cancer using gene expression data of peripheral blood mononuclear cells from GSE27383 datasets. Gene screening and enrichment analysis using Gene Set Enrichment Analysis were applied to identify possible connections between schizophrenia and cancer. Real-time PCR (quantitative PCR), Western blotting and immunohistochemistry were performed on the brain tissue from both schizophrenia patients and normal controls. The genes for IL-8, as well as PTGS2, TPR, JUN, CXCL1, CXCL3, CXCL5 and PARD3 were highly expressed in schizophrenia patients. Cancer and chemokine signaling pathways were enriched in the schizophrenic group, related to the high expression of IL-8. Increased expression of IL-8 was further confirmed by quantitative PCR, Western blotting and immunohistochemistry results. Our results suggest that IL-8 may participate specifically in the pathophysiological changes that occur in schizophrenia. Additionally, our findings provide novel evidence supporting the autoimmune hypothesis of schizophrenia. Keywords: bioinformatics, multiple primary cancers, autoimmune disease, cancer pathway, GSEA

Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals&rsquo, daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals&rsquo, features of the shoulder joint, which is the most complex joint in the human&rsquo, s body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300&ndash, 400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously.

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people. In this work, we propose a motion estimation method using electroencephalography (EEG) signals to augment human performance. Because the EEG signal occurs before the actual motion is executed, there is a time lag between motion and EEG signals. In this paper, we introduce this time lag to construct a linear model that correlates the electromyography (EMG) signal to the EEG signals based on motion-related features extracted from multi-location EEG signal measurements by Independent Component Analysis (ICA). The constructed model is used to estimate the human muscular activity of shoulder joint from EEG signals. Furthermore, we also discuss the effect on the estimation results with different training data and overlap rates for the model, and finally we know how to select the optimal values of parameters for proposed method. The proposed approach is experimentally verified. Our results suggest that the estimation of EMG signal based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities.

Alzheimer’s disease (AD), the most common type of dementia, is becoming a major challenge for global health and social care. However, the current understanding of AD pathogenesis is limited, and no early diagnosis and disease-modifying therapy are currently available. During the past year, significant progress has been made in clinical research on the diagnosis, prevention, and treatment of AD. In this review, we summarize the latest achievements, including diagnostic biomarkers, polygenic hazard score, amyloid and tau PET imaging, clinical trials targeting amyloid-beta (Aβ), tau, and neurotransmitters, early intervention, and primary prevention and systemic intervention approaches, and provide novel perspectives for further efforts to understand and cure the disease.

Purpose. Recent study shows that blood-derived amyloid-beta (Aβ) can induce cerebral amyloidosis and is involved in the pathogenesis of Alzheimer’s disease (AD). The vast majority of blood Aβ is generated from platelet. Whether blood Aβ levels are associated with the count of platelets remains unknown. Methods. 58 clinically diagnosed AD patients, 18 11C-PIB-PET diagnosed AD patients, and 61 age- and gender-matched cognitively normal controls were included to analyze the correlation of plasma Aβ levels with platelet count. 13 AD patients and 40 controls with cerebrospinal fluid (CSF) samples were included to further analyze the correlation of CSF Aβ levels with platelet count. Aβ40 and Aβ42 levels in plasma and CSF were measured by ELISA kits. Results. The plasma Aβ42 level was positively correlated with platelet count in both AD patients and control group, especially in AD patients with positive PIB-PET, while there was no correlation as to Aβ40. The CSF Aβ levels also had no significant correlation with platelet count. Conclusion. It suggests that platelets may be involved in the pathogenesis of AD and become a potential peripheral biomarker for AD.

Objective: This meta-analysis included papers which evaluated the effects of physical activity on the health-related quality of life (HRQoL) of osteoporotic and osteopenic postmenopausal women. Methods: Four English databases were searched for relevant randomized clinical trials (RTCs) published from 1970 to June, 2014. Eligible RCTs which used the Quality of Life Questionnaire of the European Foundation for Osteoporosis (QUALEFFO) as their outcome measure were selected for inclusion. Results: Five RCTs were included in the final meta-analysis. The results showed that physical activity produced favorable effects in the HRQoL domains of physical function (p = 0.001) and pain (p = 0.01), but not in other domains. Compared with a single exercise, combined exercise produced more favorable effects on both physical function (p = 0.0004) and pain (p = 0.02). Short-term physical activity produced significant favorable results in all general health domains of HRQoL (p = 0.01), whereas middle-term physical activity produced significant improvements only in the physical function (p

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. In this work, we propose a motion estimation method based on electroencephalography (EEG) signals to realize the power assistance. In order to improve the accuracy of online estimation, time lag is introduced, and in particular, a linear model that correlates the EMG signal to the EEG signals is constructed based on motion-related features extracted from multi-location EEG signal measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG signals. The proposed approach is experimentally verified. Our results suggest that the estimation of EMG signal based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities.

Cloud robotic system needs to learn real time data stream in order to adapt to the new environment for better performance. Incremental learning as a kind of dynamics classification method can avoid sample repeating learning, and develop model for updating itself. In this paper, we proposed an incremental learning-based cloud robot object recognition mechanism. It can learn new objects through human-robot interaction by speech recognition. The proposed incremental learning method was tested with small scale data in domestic indoor environment application, and the cloud robotic system was validated by basic conversation with different testing groups. The experimental results verify the feasibility of the incrementally learning new information algorithm and the proposed cloud robotic system.

Parkinson’s disease (PD) and Parkinsonism are common neurodegenerative disorders with continuously increasing prevalence, causing high global burdens. However, data concerning the comorbidity burden of patients with PD or Parkinsonism in China are lacking. To investigate the health condition and comorbidity burden, a total of 3367 PD and 823 Parkinsonism patients were included from seven tertiary hospitals in seven cities across China from 2003 to 2012. Their comorbidity burden was collected and quantified by the Elixhauser Comorbidity Index (ECI) and Charlson Comorbidity Index (CCI). The comorbidity spectra differed between PD and Parkinsonism patients. Compared with PD patients, Parkinsonism patients were older (69.8 ± 11.5 vs. 67.9 ± 11.4, P P P P P

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. Up so far, no studies have succeeded in the power augmentation for the multi-DOFs robot based on EEG signals, especially for the complex shoulder joint. In this work, we propose an electromyography (EMG) estimation method based on electroencephalography (EEG) signals to realize the power assistance. The positions of the electrodes where the motion information of shoulder joint is effectively and exactly extracted are discussed, and a linear model that correlates the EMG to the EEG signal is constructed utilizing motion-related features extracted from multi-location EEG measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG using Principal Component Analysis (PCA) method. The proposed approach is experimentally verified, and an average correlation coefficients are as high as about 0.90 for different subjects are obtained between the estimated and the actually measured EMG signal. Our results suggest that the estimation of EMG based on EEG is feasible. This demonstrates the potential of using EEG signals to support human activities via brain-machine interface.

Although a cellular manufacturing system can produce diversified products flexibly, its assembly efficiency is mainly limited by the operators' assembly performance. To improve the operators' assembly performance without longtime training, an assembly-skill-transferring method was proposed to realize the assembly skill transfer process from skilled operators to novice operators. In the cellular manufacturing system, the assembly skills consist of cognition skills and motor skills. Taking a “Peg-in-Hole Task” as an example, the effect of the proposed assembly transfer method was verified for motor skills. The results show that the proposed assembly-skill-transferring system can improve the novice operators' assembly performance greatly.

In this paper, we design a human computer interaction (HCI) system for the locked-in syndrome (LIS) patients which combines the eye movement signals and Electroencephalogram (EEG) signals. The LIS describes patients who are faced with the quadriplegia, aphonia and facial paralysis problems. However, they are still conscious, have the ability to hear and give a hint with the blink or eye movements. Thus, the LIS patients suffer great miseries due to the difficulty to communicate with the outside. This HCI system can help the LIS patients effectively communicate with the outside. Firstly, the system gets the subject's gaze point on the screen through the acquisition data of eye movement information, and obtains the button that the subject is focusing on. Then, the system confirms or cancels the button according to the classification results of the EEG signals. The computer can execute the commands of the corresponding button to bridge the communication between the subjects and the outside. When the subject stares at the screen unconsciously, the button will not respond because there is no confirmation of the EEG signals. The subject's eyes can be relaxed. The EEG signals are divided into two categories, and the classification results can be trained to reach a high classification accuracy (more than 90%). In the test, the average accuracy is about 80% in the case of using eye tracker alone. In the case of EEG, the average accuracy can reach more than 85%. The HCI system can realize some hardware control, simple voice interaction and typing with virtual keyboard. The system can make subjects communicate with the outside through the eye movement and EEG signals with high accuracy.

The health care is in increasingly high demand with the aging population. In this paper, we present a novel health care platform for elderly people and patients who need to be taken care. The platform consists of a walk-aid unit, medical apparatus for health monitoring, data storage unit and remote monitoring unit. The medical devices are used for the elderly or patients who are undergoing a rehabilitative treatment, to determine the body's vital physiological indexes like pulse rate, heart rate, blood pressure level, body temperature and the inertial navigation system (INS). The inertial navigation system (INS) is employed to capture people's abnormal action like tumble. The walk-aid platform is designed to enhance people's mobility and collect position information of the elderly in case of an emergency. It is also equipped with a camera to capture a picture of the elderly and the environment. The data storage unit can collect and store the data from the medical devices and the walk-aid unit. The remote monitoring unit like pad or smart phone can be connected with the platform to check the situation of users. And in turn, if an emergency situation happened, the server computer can also send a message to the mobile device. The case study shows that it is an efficient way to keep the user under observation.

Brain-Machine Interface (BMI), has been considered as an effective way to help and support the disabled's daily lives and rehabilitation to use their brain activity information instead of their bodies. As an extension, it is also expected to be used for healthy individuals. In our study, we try to estimate the necessary force/torque information for a subject from his/her EEG signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external force sensors and EMG sensors. For real time measurement and control of the system, it is necessary to reduce the number of electrodes and signal processing burden. Therefore, it is important to know the EEG distribution on the scalp, and hence extract the motion-related information from multiple measurement locations, effectively and exactly. In this paper, at first, the contribution of each electrode is analyzed by PCA(Principal Component Analysis). Then, ICA (Independent Components Analysis) is used to extract the source information of neural components to obtain the location of each source information by plotting the components at the scalp to determine which measurement location should be measured to obtain effective signals related to motion. At last, the transition of the EEG signals with time is analyzed, and the results are graphically plotted. The results show that the changes of EEG signals, related to the motion of shoulder joint, are distributed throughout the brain cortex, and the locations of electrodes at C 3 , C 4 , C z , P 3 , P 4 are the best ones for obtaining useful and effective information of motion consciousness. This discovery brings great benefit in the use of EEG signals for supporting activity for both healthy and disabled individuals by controlling a brain-machine interface.

the principle and control method of torso orientation in dynamic biped walking is addressed. First, the role of torso motion in walking performance, especially at the landing moment, is investigated by using a simplified model. Second, a control strategy of torso orientation is proposed, including the algorithms in the push-off and the single support phases (SSP). Furthermore, the module of torso control is integrated into the control method of dynamic biped walking, which was proposed in our previous research. The paper ends with the simulation of a humanoid robot with 21 internal degrees of freedom (DOF), and shows the feasibility of the proposed method.

BACKGROUND Capsaicin-rich diets are common worldwide. Capsaicin has been shown to have favorable effects on various diseases including atherosclerosis, cardiovascular diseases, stroke, obesity, hypertension, cancer, and gastrointestinal and inflammatory diseases. The impact of capsaicin on Alzheimer's disease (AD), which is the most common form of dementia in the elderly, remains unknown. OBJECTIVE To investigate the correlations of capsaicin intake with cognition and blood markers of AD. METHODS A total of 338 participants aged 40 years or older were enrolled from communities. Dietary habits regarding chili pepper consumption were collected using a Food Frequency Questionnaire (FFQ). Cognitive function was measured using the Chinese version of the Mini-Mental State Examination (MMSE). Blood amyloid-β (Aβ)40 and Aβ42 were measured with ELISA kits. RESULTS In univariate analysis, MMSE scores (r = 0.209, p

In this study, we are developing a power add-on unit PAU for attendant propelled wheelchairs with sensorless speed control and power assistance to help an attendant who are pushing a conventional commercialized manual wheelchair in which a patient is sitting in the wheelchair. This development is based on the following three concept: to make our PAU 1 easy to be attached to and detached from the wheelchair; 2 able to be used to the most of the conventional commercialized wheelchairs; 3 low cost. Further, in order to make the attendant more easily and comfortably propel the wheelchair, a sensorless power assistance approach is proposed. The experimental results verify the effectiveness of the proposed approaches.

Efficient walking is one of the main goals of researches on biped robots. A feasible way is to translate the understanding from human walking into robot walking, for example, an artificial control approach on a human like walking structure. In this paper, a walking pattern based on Center of Pressure (COP) switched and modeled after human walking is introduced firstly. Then, a parameterization method for the proposed walking gait is presented. In view of the complication, a multi-space planning method which divides the whole planning task into three sub-spaces, including simplified model space, work space and joint space, is proposed. Furthermore, a finite-state-based control method is also developed to implement the proposed walking pattern. The state switches of this method are driven by sensor events. For convincing verification, a 2D simulation system with a 9-link planar biped robot is developed. The simulation results exhibit an efficient walking gait.

Rationale: Clinically mild encephalitis/encephalopathy with a reversible splenial lesion of the corpus callosum (MERS) is a recently identified clinically and radiologically distinct syndrome. Clinical symptoms and lesions on the magnetic resonance imaging (MRI) often disappear in 1 week or a few weeks. However, MERS manifesting as a severe clinical course with significant sequela has not yet been reported. Patient concerns: A 42-year-old male presented with a 3-day history of headache, fever, and irrational speech. Physical examination showed a body temperature of 39.5°C, dysarthria, dyscalculia, recent memory disturbance, and otherwise normal vital signs. The patient developed status epilepticus and progressive consciousness disturbance. MRI showed abnormal patchy signals in the splenium of the corpus callosum. Diagnosis: The clinical feature and the characteristic of MRI are mostly consistent with MERS. At the same time, we made a differential diagnosis by testing the NMDARAb, AMPA1Ab, AMPA2Ab, LG1Ab, CASPR2Ab, GABABRAb in CSF and serum. Interventions: The subject was treated with ganciclovir, antiepileptic, and antipyretic therapy. Outcomes: The subject was living a virtually normal life with persistent mild memory disturbance. MRI showed that the abnormal signals in the splenium of the corpus callosum had disappeared, but hyperintensity on T2-weighted and FLAIR imaging was noted in the centrum semiovale. Lessons: MERS is a rare clinicoradiological syndrome, which can manifest as severe symptoms as well. Early diagnosis and treatment should be emphasized, and the diagnostic value of MRI is highlighted. Clinicians should be alert to the potential sequela.

A biped robot, MARI-3, for jumping is developed, of which the ultimate objective is fast walking and running. Its mechanical structure including the joint configuration and specification, the knee joint, and the speed reduction mechanism are described in detail. A specific control system RON (RObot Network) for MARI-3 that is a serial and distributed network and consists of a microcontroller, host unit, servo units, sensor units and servo amplifiers is presented as well as the sensor system. With the developed biped robot MARI-3, one-leg jumping of 110 ms jumping time and 4.0 cm jumping height was implemented as initiative and verification experiments. Furthermore, by comparison of MARI-3 with other jumping or running robots, MARI-3's potential ability for fast walking, jumping and running becomes clear.

Aiming to improve positioning precision of stop posture (position and orientation) of an object and decrease trial numbers in our proposed releasing manipulation, two iterative learning control (ILC) schemes, learning control based on convergent condition (LCBCC), and learning control based on optimal principle (LCBOP) are designed in an experimental-oriented way. The experimental results show that these methods are effective. By discussing the characteristics of these control methods, we conclude that when there is no enough system knowledge, LCBCC is the only choice and to be used to learn system knowledge; after enough experience has been acquired, LCBOP is better than LCBCC in the view of precision.

The objective of this study was to evaluate the feasibility and safety of lung volume reduction by transbronchial alcohol and lipiodol suspension infusion with the aid of balloon-tipped catheter occlusion. Twenty-six healthy adult rabbits were divided into four treatment groups: alcohol and lipiodol suspension infusion (n = 8), lipiodol infusion (n = 8), alcohol infusion (n = 5), or bronchial lumen occlusion (n = 5). After selective lobar or segmental bronchial catheterization using a balloon-tipped occlusion catheter, the corresponding drug infusion was performed. Bone cement was used to occlude the bronchial lumen in the occlusion group. The animals were followed up for 10 weeks by chest X-ray and computed tomography (CT), and then the whole lungs were harvested for histological examination. Alcohol and lipiodol suspension or lipiodol could be stably retained in alveoli in the first two groups based on chest X-ray and CT, but obvious collapse only occurred in the group receiving alcohol and lipiodol suspension or the bronchial lumen occlusion group. Histological examination revealed damage and disruption of the alveolar epithelium and fibrosis in related lung tissue in the group receiving alcohol and lipiodol suspension. Similar changes were seen in the bronchial lumen occlusion group, apart from obvious marginal emphysema of the target areas in two animals. Interstitial pneumonia and dilated alveoli existed in some tissue in target areas in the lipiodol group, in which pulmonary fibrosis obliterating alveoli also occurred. Chronic alveolitis and pleural adhesion in target areas occurred in the group infused with alcohol alone, whereas visceral pleura of the other three groups was regular and no pleural effusion or adhesion was found. Alcohol and lipiodol suspension that is stably retained in alveoli can result in significant lung volume reduction. Through alcohol and lipiodol suspension infusion, obstructive emphysema or pneumonia arising from bronchial lumen occlusion could be avoided.

PurposeTo develop an in‐pipe robot to be used for inspecting pipes that are laid underneath a waste disposal site and for sampling sewage water leaking from holes around the pipe.Design/methodology/approachThe paper presents a compact design of the robot's mechanical and electronic systems, and develops a simple and practical method for determining the hole position using some characteristics of this in‐pipe robot.FindingsDevelopment of a multi‐functional in‐pipe robot with elaborate design is feasible, and multiple trapezoidal‐shaped wheels have good capabilities of providing the robot large traction force and keeping the robot horizontal in pipeline. A good mechanical structure design can greatly reduce control and computation cost by using some specific features of the object system.Research limitations/implicationsThis study provides guidance for future design of multi‐functional in‐pipe robots.Practical implicationsThe combination of a vision system and a manipulator can perform in‐pipe inspection and other different tasks. The developed robot has already been used practically by a large Japanese construction company for pipe inspection and sewage sampling.Originality/valueAn in‐pipe robot is developed that cannot only implement pipe inspection, but which can also take sewage water samples. Most of such multiple‐functional small robots are still in development and rarely reported.

This paper proposes verification for the feasibility of human-robot coexistence and interaction simulation in SIGVerse environment. SIGVerse is a human-robot interaction environment, which can be also applied for virtual reality. It solves the problem for lacking of human-robot interaction in simulation software previously. In this paper, a robot following human task in the domestic indoor environment is presented to test the software. In the experiment, binocular visual servo planar mobile robot is used for completing two confusion challenges, binocular vision distance measurement and extended PD controller are used to complete the task, which are set to test the universal of the algorithm and the feasibility of the simulation. Then the measuring and following algorithm is tested in the real environment as well. The experimental result shows that it is possible for same algorithm using for both simulation and real environment.