An Upper-Limb Rehabilitation Exoskeleton System Controlled by MI Recognition Model With Deep Emphasized Informative Features in a VR Scene.

The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49% ± 3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82% ± 4.66% and 88.48% ± 5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.

Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation.

Functional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and frequency. This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted to evaluate the feasibility of the proposed BILSTM model in predicting the sEMG values. The experimental results showed that the [Formula: see text] value of the best-obtained prediction result reached 0.85 using the BILSTM model, which was significantly higher than that using traditional prediction methods. Moreover, two patients after stroke were recruited in the online assisted-walking experiment to verify the effectiveness of the proposed walking-assistance system. The experimental results showed that the activation of the target muscles of the patients was higher after FES, and the gait movement data were significantly different before and after FES. The proposed system can be effectively applied to walking assistance for stroke patients, and the experimental results can provide new ideas and methods for sEMG-controlled FES rehabilitation applications.

Nociceptor Neurons are Involved in the Host Response to Escherichia coli Urinary Tract Infections.

Purpose: Urinary tract infections (UTIs) can evoke a rapid host immune response leading to bladder inflammation and epithelial damage. Neuroimmune interactions are critical for regulating immune function in mucosal tissues. Yet the role of nociceptor neurons in bladder host defense has not been well defined. This study aimed to explore the interaction between nociceptor neurons and bladder immune system during UTIs. Methods: In this study, whether uropathogenic Escherichia coli (UPEC) and lipopolysaccharide (LPS) can directly stimulate nociceptor neurons was detected. Female C57BL/6J mice were treated with high dose of capsaicin, a high-affinity TRPV1 agonist, to ablate nociceptor neurons. Bladder inflammation, barrier epithelial function and bladder immune cell infiltration were assessed after UPEC infection. The level of neuropeptide calcitonin gene-related peptide (CGRP) in infected bladder was detected. Furthermore, the effects of CGRP on neutrophils and macrophages were evaluated both in vitro and in vivo. Results: We found that UPEC and its pathogenic factor LPS could directly excite nociceptor neurons, releasing CGRP into infected bladder, which suppressed the recruitment of neutrophils, the polarization of macrophages and the killing function of UPEC. Both Botulinum neurotoxin A (BoNT/A) and BIBN4096 (CGRP antagonism) blocked neuronal inhibition and prevented against UPEC infection. Conclusion: The present study showed a novel mechanism by which UPEC stimulated the secretion of CGRP from nociceptor neurons to suppress innate immunity.

Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control.

Most studies on estimating user's joint angles to control upper-limb exoskeleton have focused on using surface electromyogram (sEMG) signals. However, the variations in limb velocity and acceleration can affect the sEMG data and decrease the angle estimation performance in the practical use of the exoskeleton. This paper demonstrated that the variations in elbow angular velocity (EAV) and elbow angular acceleration (EAA) associated with normal use led to a large effect on the elbow joint angle estimation. To minimize this effect, we proposed two methods: (1) collecting sEMG data of multiple EAVs and EAAs as training data and (2) measuring the values of EAV and EAA with a gyroscope. A self-developed upper-limb exoskeleton with pneumatic muscles was used in the online control phase to verify our methods' effectiveness. The predicted elbow angle from the sEMG-angle models which were trained in the offline estimation phase was transferred to control signal of the pneumatic muscles to actuate the exoskeleton to move to the same angle. In the offline estimation phase, the average root mean square error (RMSE) between predicted elbow angle and actual elbow angle was reduced from 22.54° to 10.01° (using method one) and to 6.45° (using method two), respectively; in the online control phase, method two achieved a best control performance (average RMSE = 6.87°). The results showed that using multi-sensor fusion (sEMG sensors and gyroscope) achieved a better estimation performance than using only sEMG sensor, which was helpful to eliminate the velocity and acceleration effect in real-time joint angle estimation for upper-limb exoskeleton control.

Prognostic Role of the Ubiquitin Proteasome System in Clear Cell Renal Cell Carcinoma: A Bioinformatic Perspective.

Background: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urinary system. The ubiquitin proteasome system (UPS) plays an important role in the generation, metabolism and survival of tumor. We are aimed to make a comprehensive exploration of the UPS's role in ccRCC with bioinformatic tools, which may contribute to the understanding of UPS in ccRCC, and give insight for further research. Methods: The UPS-related genes (UPSs) were collected by an integrative approach. The expression and clinical data were downloaded from TCGA database. R soft was used to perform the differentially expressed UPSs analysis, functional enrichment analysis. We also estimated prognostic value of each UPS with the help of GEPIA database. Two predicting models were constructed with the differentially expressed UPSs and prognosis-related genes, respectively. The correlations of risk score with clinical characteristics were also evaluated. Data of GSE29609 cohort were obtained from GEO database to validate the prognostic models. Results: We finally identified 91 differentially expressed UPSs, 48 prognosis related genes among them, and constructed a prognostic model with 18 UPSs successfully, the AUC was 0.760. With the help of GEPIA, we found 391 prognosis-related UPSs, accounting for 57.84% of all UPSs. Another prognostic model was constructed with 28 prognosis-related genes of them, and with a better AUC of 0.825. Additionally, our models can also stratify patients into high and low risk groups accurately in GSE29609 cohort. Similar prognostic values of our models were observed in the validated GSE29609 cohort. Conclusions: UPS is dysregulated in ccRCC. UPS related genes have significant prognostic value in ccRCC. Models constructed with UPSs are effective and applicable. An abnormal ubiquitin proteasome system should play an important role in ccRCC and be worthy of further study.

Metformin ameliorates bladder dysfunction in a rat model of partial bladder outlet obstruction.

Alteration of bladder morphology and function was the most important consequence of bladder outlet obstruction (BOO). Using a rat model of partial BOO (pBOO), we found that rats treated with metformin showed lower baseline pressures with a reduced inflammatory reaction in the early phase (2 wk) after pBOO. The NLR family pyrin domain containing 3 inflammasome pathway was inhibited in pBOO rat bladders with treatment of metformin in the early phase. Metformin reduced the activity of NLR family pyrin domain containing 3 in primary urothelial cells. In the chronic phase (9 wk after pBOO), metformin treatment ameliorated bladder fibrosis and improved the reduced compliance. Treatment with metformin suppressed the activation of Smad3 and compensated the diminished autophagy in 9-wk pBOO rat bladders. Autophagy was inhibited with upregulation of profibrotic proteins in primary fibroblasts from chronic pBOO bladders, which could be restored by administration of metformin. The antifibrotic effects of metformin on fibroblasts were diminished after silencing of AMP-activated protein kinase or light chain 3B. In summary, this study elucidates that oral administration of metformin relieves inflammation in the bladder during the early phase of pBOO. Long-term oral administration of metformin can prevent functional and histological changes in the pBOO rat bladder. The current study suggests that metformin might be used to prevent the development of bladder dysfunction secondary to BOO.NEW & NOTEWORTHY The present study in a rat model showed that oral administration of metformin alleviated inflammation following partial bladder outlet obstruction in the early phase and ameliorated bladder fibrosis as well as bladder dysfunction by long-term treatment. Our study indicated that metformin is a potential drug to inhibit bladder remodeling and alleviate bladder dysfunction. Clinical trials are needed to validate the effect of metformin on the bladder dysfunction and bladder fibrosis in the future.

Single-cell transcriptomes of mouse bladder urothelium uncover novel cell type markers and urothelial differentiation characteristics.

OBJECTIVES: Much of the information to date in terms of subtypes and function of bladder urothelial cells were derived from anatomical location or by the expression of a small number of marker genes. To have a comprehensive map of the cellular anatomy of bladder urothelial cells, we performed single-cell RNA sequencing to thoroughly characterize mouse bladder urothelium. MATERIALS AND METHODS: A total of 18,917 single cells from mouse bladder urothelium were analysed by unbiased single-cell RNA sequencing. The expression of the novel cell marker was confirmed by immunofluorescence using urinary tract infection models. RESULTS: Unsupervised clustering analysis identified 8 transcriptionally distinct cell subpopulations from mouse bladder urothelial cells. We discovered a novel type of bladder urothelial cells marked by Plxna4 that may be involved with host response and wound healing. We also found a group of basal-like cells labelled by ASPM that could be the progenitor cells of adult bladder urothelium. ASPM+ urothelial cells are significantly increased after injury by UPEC. In addition, specific transcription factors were found to be associated with urothelial cell differentiation. At the last, a number of interstitial cystitis/bladder pain syndrome-regulating genes were found differentially expressed among different urothelial cell subpopulations. CONCLUSIONS: Our study provides a comprehensive characterization of bladder urothelial cells, which is fundamental to understanding the biology of bladder urothelium and associated bladder disease.

Motion Imagery-BCI Based on EEG and Eye Movement Data Fusion.

Existing studies have demonstrated that eye tracking can be a complementary approach to Electroencephalogram (EEG) based brain-computer interaction (BCI), especially in improving BCI performance in visual perception and cognition. In this paper, we proposed a method to fuse EEG and eye movement data extracted from motor imagery (MI) tasks. The results of the tests showed that on the feature layer, the average MI classification accuracy from the fusion of EEG and eye movement data was higher than that of pure EEG data or pure eye movement data, respectively. Besides, we also found that the average classification accuracy from the fusion on the decision layer was higher than that from the feature layer. Additionally, when EEG data were not available for the shifting of parts of electrodes, we combined EEG data collected from the rest of the electrodes (only 50% of the original) with the eye movement data, and the average MI classification accuracy was only 1.07% lower than that from all available electrodes. This result indicated that eye movement data was feasible to compensate for the loss of the EEG data in the MI scenario. Overall our approach was proved valuable and useful for augmenting MI based BCI applications.

And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model.

Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers' ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.

Study on the Recognition of Exercise Intensity and Fatigue on Runners Based on Subjective and Objective Information.

A running exhaustion experiment was used to explore the correlations between the time-frequency domain indexes extracted from the surface electromyography (EMG) signals of targeted muscles, heart rate and exercise intensity, and subjective fatigue. The study made further inquiry into the feasibility of reflecting and evaluating the exercise intensity and fatigue effectively during running using physiological indexes，thus providing individualized guidance for running fitness. Twelve healthy men participated in a running exhaustion experiment with an incremental and constant load. The percentage of heart rate reserve (%HRR), mean power frequency (MPF) and root mean square (RMS) from surface EMG (sEMG) signals of the rectus femoris (RF), biceps femoris (BF), tibialis anterior muscle (TA), and the lateral head of gastrocnemius (GAL) were obtained in real-time. The data were processed and analyzed with the rating of perceived exertion (RPE) scale. The experimental results show that the MPF on all the muscles increased with time, but there was no significant correlation between MPF and RPE in both experiments. Additionally, there was no significant correlation between RMS and RPE of GAL and BF, but there was a negative correlation between RMS and RPE of RF. The correlation coefficient was lower in the constant load mode, with the value of only -0.301. The correlation between RMS and RPE of TA was opposite in both experiments. There was a significant linear correlation between %HRR and exercise intensity (r = 0.943). In the experiment, %HRR was significantly correlated with subjective exercise fatigue (r = 0.954). Based on the above results，the MPF and RMS indicators on the four targeted muscles could not conclusively identify fatigue of lower extremities during running. The %HRR could be used to identify exercise intensity and human fatigue during running and could be used as an indicator of recognizing fatigue and exercise intensity in runners.

An upper-limb power-assist exoskeleton using proportional myoelectric control.

We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user's motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme's reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user's motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury.