Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human-machine-environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions.

Effects of Combined Use of Intermittent Theta Burst Stimulation and Cognitive Training on Poststroke Cognitive Impairment: A Single-Blind Randomized Controlled Trial

OBJECTIVE

Poststroke cognitive impairment substantially affects patients' quality of life. This study explored the therapeutic efficacy of intermittent theta burst stimulation combined with cognitive training for poststroke cognitive impairment.

DESIGN

The experimental group received intermittent theta burst stimulation and cognitive training, whereas the control group only received cognitive training, both for 6 wks. The outcome measures were the Loewenstein Occupational Therapy Cognitive Assessment, modified Barthel Index, transcranial Doppler ultrasonography, and functional near-infrared spectroscopy.

RESULTS

After therapy, between-group comparisons revealed a substantial difference in the Loewenstein Occupational Therapy Cognitive Assessment scores ( P = 0.024). Improvements in visuomotor organization and thinking operations were more noticeable in the experimental group than in the other groups ( P = 0.017 and P = 0.044, respectively). After treatment, the resistance index of the experimental group differed from that of the control group; channels 29, 37, and 41 were activated ( P < 0.05). The active locations were the left dorsolateral prefrontal cortex, prefrontal polar cortex, and left Broca's region.

CONCLUSIONS

Intermittent theta burst stimulation combined with cognitive training had a superior effect on improving cognitive function and everyday activities compared with cognitive training alone, notably in visuomotor organization and thinking operations. Intermittent theta burst stimulation may enhance cognitive performance by improving network connectivity.

Boosting lower-limb motor imagery performance through an ensemble method for gait rehabilitation

Lower-limb exoskeletons have been used extensively in many rehabilitation applications to assist disabled people with their therapies. Brain-machine interfaces (BMIs) further provide effective and natural control schemes. However, the limited performance of brain signal decoding from lower-limb kinematics restricts the broad growth of both BMI and rehabilitation industry. To address these challenges, we propose an ensemble method for lower-limb motor imagery (MI) classification. The proposed model employs multiple techniques to boost performance, including deep and shallow parts. Traditional wavelet transformation followed by filter-bank common spatial pattern (CSP) employs neurophysiologically reasonable patterns, while multi-head self-attention (MSA) followed by temporal convolutional network (TCN) extracts deeper encoded generalized patterns. Experimental results in a customized lower-limb exoskeleton on 8 subjects in 3 consecutive sessions showed that the proposed method achieved 60.27% and 64.20% for three (MI of left leg, MI of right leg, and rest) and two classes (lower-limb MI vs. rest), respectively. Besides, the proposed model achieves improvements of up to 4% and 2% accuracy for the subject-specific and subject-independent modes compared to the current state-of-the-art (SOTA) techniques, respectively. Finally, feature analysis was conducted to show discriminative brain patterns in each MI task and sessions with different feedback modalities. The proposed models integrated in the brain-actuated lower-limb exoskeleton established a potential BMI for gait training and neuroprosthesis.

Lower Limb Activity Recognition Based on sEMG Using Stacked Weighted Random Forest

The existing surface electromyography-based pattern recognition system (sEMG-PRS) exhibits limited generalizability in practical applications. In this paper, we propose a stacked weighted random forest (SWRF) algorithm to enhance the long-term usability and user adaptability of sEMG-PRS. First, the weighted random forest (WRF) is proposed to address the issue of imbalanced performance in standard random forests (RF) caused by randomness in sampling and feature selection. Then, the stacking is employed to further enhance the generalizability of WRF. Specifically, RF is utilized as the base learner, while WRF serves as the meta-leaning layer algorithm. The SWRF is evaluated against classical classification algorithms in both online experiments and offline datasets. The offline experiments indicate that the SWRF achieves an average classification accuracy of 89.06%, outperforming RF, WRF, long short-term memory (LSTM), and support vector machine (SVM). The online experiments indicate that SWRF outperforms the aforementioned algorithms regarding long-term usability and user adaptability. We believe that our method has significant potential for practical application in sEMG-PRS.

Depth-aware pose estimation using deep learning for exoskeleton gait analysis

In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human-machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human-machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients' walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.

A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding

Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.

Tracking Control for a Lower Extremity Exoskeleton Based on Adaptive Dynamic Programing

The utilization of lower extremity exoskeletons has witnessed a growing presence across diverse domains such as the military, medical treatment, and rehabilitation. This paper introduces a novel design of a lower extremity exoskeleton specifically tailored for individuals engaged in heavy object carrying tasks. The exoskeleton incorporates an impressive 12 degrees of freedom (DOF), with four of them being effectively controlled through hydraulic cylinders. To achieve optimal control of this intricate lower extremity exoskeleton system, the authors propose an adaptive dynamic programming (ADP) algorithm. Several crucial components are established to implement this control scheme. These include the formulation of the state equation for the lower extremity exoskeleton system, which is well-suited for the ADP algorithm. Additionally, a corresponding performance index function based on the tracking error is devised, along with the game algebraic Riccati equation. By employing the value iteration ADP scheme, the lower extremity exoskeleton demonstrates highly effective tracking control. This research not only highlights the potential of the proposed control approach but also showcases its ability to enhance the overall performance and functionality of lower extremity exoskeletons, particularly in scenarios involving heavy object carrying. Overall, this study contributes to the advancement of lower extremity exoskeleton technology and offers valuable insights into the application of ADP algorithms for achieving precise and efficient control in demanding tasks.

RGB-D-Based Stair Detection and Estimation Using Deep Learning

Stairs are common vertical traffic structures in buildings, and stair detection tasks are important in environmental perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a stair detection network with red-green-blue (RGB) and depth inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB feature maps and the depth feature maps and fuse the features effectively in different scenes. In addition, we propose several postprocessing algorithms, including a stair line clustering algorithm and a coordinate transformation algorithm, to obtain the stair geometric parameters. Experiments show that our method has better performance than existing the state-of-the-art deep learning method, and the accuracy, recall, and runtime are improved by 5.64%, 7.97%, and 3.81 ms, respectively. The improved indexes show the effectiveness of the multimodal inputs and the selective module. The estimation values of stair geometric parameters have root mean square errors within 15 mm when ascending stairs and 25 mm when descending stairs. Our method also has extremely fast detection speed, which can meet the requirements of most real-time applications.

ECGNet: An Efficient Network for Detecting Premature Ventricular Complexes Based on ECG Images

BACKGROUND

Preoperative prediction of the origin site of premature ventricular complexes (PVCs) is critical for the success of operations. However, current methods are not efficient or accurate enough. In addition, among the proposed strategies, there are few good prediction methods for electrocardiogram (ECG) images combined with deep learning aspects.

METHODS

We propose ECGNet, a new neural network for the classification of 12-lead ECG images. In ECGNet, 609 ECG images from 310 patients who had undergone successful surgery in the Division of Cardiology, the First Affiliated Hospital of Soochow University, are utilized to construct the dataset. We adopt dense blocks, special convolution kernels and divergent paths to improve the performance of ECGNet. In addition, a new loss function is designed to address the sample imbalance situation, whose cause is the uneven distribution of cases themselves, which often occurs in the medical field. We also conduct extensive experiments in terms of network prediction accuracy to compare ECGNet with other networks, such as ResNet and DarkNet.

RESULTS

Our ECGNet achieves extremely high prediction accuracy (91.74%) and efficiency with very small datasets. Our newly proposed loss function can solve the problem of sample imbalance during the training process.

CONCLUSION

The proposed ECGNet can quickly and accurately realize the multiclassification of PVCs after training with little data. Our network has the potential to be helpful to doctors with a preoperative diagnosis of PVCs. We will continue to collect similar cases and perfect our network structure to further improve the accuracy of our network's prediction.

Three-Dimensional Gait Analysis and sEMG Measures for Robotic-Assisted Gait Training in Subacute Stroke: A Randomized Controlled Trial

Background

The efficacy of robotic-assisted gait training (RAGT) should be considered versatilely; among which, gait assessment is one of the most important measures; observational gait assessment is the most commonly used method in clinical practice, but it has certain limitations due to the deviation of subjectivity; instrumental assessments such as three-dimensional gait analysis (3DGA) and surface electromyography (sEMG) can be used to obtain gait data and muscle activation during walking in stroke patients with hemiplegia, so as to better evaluate the rehabilitation effect of RAGT.

Objective

This single-blind randomized controlled trial is aimed at analyzing the impact of RAGT on the 3DGA parameters and muscle activation in patients with subacute stroke and evaluating the clinical effect of improving walking function of RAGT.

Methods

This randomized controlled trial evaluated the improvement of 4-week RAGT on patients with subacute stroke by 3DGA and surface electromyography (sEMG), combined with clinical scales: experimental group (n = 18, 20 sessions of RAGT) or control group (n = 16, 20 sessions of conventional gait training). Gait performance was evaluated by the 3DGA, and clinical evaluations based on Fugl-Meyer assessment for lower extremity (FMA-LE), functional ambulation category (FAC), and 6-minute walk test (6MWT) were used. Of these patients, 30 patients underwent sEMG measurement synchronized with 3DGA; the cocontraction index in swing phase of the knee and ankle of the affected side was calculated.

Results

After 4 weeks of intervention, intragroup comparison showed that walking speed, temporal symmetry, bilateral stride length, range of motion (ROM) of the bilateral hip, flexion angle of the affected knee, ROM of the affected ankle, FMA-LE, FAC, and 6MWT in the experimental group were significantly improved (p < 0.05), and in the control group, significant improvements were observed in walking speed, temporal symmetry, stride length of the affected side, ROM of the affected hip, FMA-LE, FAC, and 6MWT (p < 0.05). Intergroup comparison showed that the experimental group significantly outperformed the control group in walking speed, temporal symmetry of the spatiotemporal parameters, ROM of the affected hip and peak flexion of the knee in the kinematic parameters, and the FMA-LE and FAC in the clinical scale (p < 0.05). In patients evaluated by sEMG, the experimental group showed a noticeable improvement in the cocontraction index of the knee (p = 0.042), while no significant improvement was observed in the control group (p = 0.196), and the experimental group was better than the control group (p = 0.020). No noticeable changes were observed in the cocontraction index of the ankle in both groups (p > 0.05).

Conclusions

Compared with conventional gait training, RAGT successfully improved part of the spatiotemporal parameters of patients and optimized the motion of the affected lower limb joints and muscle activation patterns during walking, which is crucial for further rehabilitation of walking ability in patients with subacute stroke. This trial is registered with ChiCTR2200066402.

Synchronous analyses between electroencephalogram and surface electromyogram based on motor imagery and motor execution

The functional coupling of the cerebral cortex and muscle contraction indicates that electroencephalogram (EEG) and surface electromyogram (sEMG) signals are coherent. The objective of this study is to clearly describe the coupling relationship between EEG and sEMG through a variety of analysis methods. We collected the EEG and sEMG data of left- or right-hand motor imagery and motor execution from six healthy subjects and six stroke patients. To enhance the coherence coefficient between EEG and sEMG signals, the algorithm of EEG modification based on the peak position of sEMG signals is proposed. Through analyzing a variety of signal synchronization analysis methods, the most suitable coherence analysis algorithm is selected. In addition, the wavelet coherence analysis method based on time spectrum estimation was used to study the linear correlation characteristics of the frequency domain components of EEG and sEMG signals, which verified that wavelet coherence analysis can effectively describe the temporal variation characteristics of EEG-sEMG coherence. In the task of motor imagery, the significant EEG-sEMG coherence is mainly in the imagination process with the frequency distribution of the alpha and beta frequency bands; in the task of motor execution, the significant EEG-sEMG coherence mainly concentrates before and during the task with the frequency distribution of the alpha, beta, and gamma frequency bands. The results of this study may provide a theoretical basis for the cooperative working mode of neurorehabilitation training and introduce a new method for evaluating the functional state of neural rehabilitation movement.

Deep leaning-based ultra-fast stair detection

Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarse-grained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve 81.49\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% accuracy, 81.91\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% recall and 12.48 ms runtime, and our method has higher performance in terms of both speed and accuracy than previous methods. A lightweight version can even achieve 300+ frames per second with the same resolution.

Deep Convolutional Neural Network for EEG-Based Motor Decoding

Brain-machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI. This study presents a deep convolutional neural network (CNN) for EEG-based motor decoding. Both upper-limb and lower-limb motor imagery were detected from this end-to-end learning with four datasets. An average classification accuracy of 93.36 ± 1.68% was yielded on the four datasets. We compared the proposed approach with two other models, i.e., multilayer perceptron and the state-of-the-art framework with common spatial patterns and support vector machine. We observed that the performance of the CNN-based framework was significantly better than the other two models. Feature visualization was further conducted to evaluate the discriminative channels employed for the decoding. We showed the feasibility of the proposed architecture to decode motor imagery from raw EEG data without manually designed features. With the advances in the fields of computer vision and speech recognition, deep learning can not only boost the EEG decoding performance but also help us gain more insight from the data, which may further broaden the knowledge of neuroscience for brain mapping.

Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of depth and ego-motion self-supervised learning is that the image brightness remains constant within nearby frames. Unfortunately, the endoscopic scene does not meet this assumption because there are severe brightness fluctuations induced by illumination variations, non-Lambertian reflections and interreflections during data collection, and these brightness fluctuations inevitably deteriorate the depth and ego-motion estimation accuracy. In this work, we introduce a novel concept referred to as appearance flow to address the brightness inconsistency problem. The appearance flow takes into consideration any variations in the brightness pattern and enables us to develop a generalized dynamic image constraint. Furthermore, we build a unified self-supervised framework to estimate monocular depth and ego-motion simultaneously in endoscopic scenes, which comprises a structure module, a motion module, an appearance module and a correspondence module, to accurately reconstruct the appearance and calibrate the image brightness. Extensive experiments are conducted on the SCARED dataset and EndoSLAM dataset, and the proposed unified framework exceeds other self-supervised approaches by a large margin. To validate our framework's generalization ability on different patients and cameras, we train our model on SCARED but test it on the SERV-CT and Hamlyn datasets without any fine-tuning, and the superior results reveal its strong generalization ability. Code is available at: https://github.com/ShuweiShao/AF-SfMLearner.

A multi-scale unsupervised learning for deformable image registration

PURPOSE

Image registration is a fundamental task in the area of image processing, and it is critical to many clinical applications, e.g., computer-assisted surgery. In this work, we attempt to design an effective framework that gains higher accuracy at a minimal cost of the invertibility of registration field.

METHODS

A hierarchically aggregated transformation (HAT) module is proposed. Within each HAT module, we connect multiple convolutions in a hierarchical manner to capture the multi-scale context, enabling small and large displacements between a pair of images to be taken into account simultaneously during the registration process. Besides, an adaptive feature scaling (AFS) mechanism is presented to refine the multi-scale feature maps derived from the HAT module by rescaling channel-wise features in the global receptive field. Based on the HAT module and AFS mechanism, we establish an efficacious and efficient unsupervised deformable registration framework.

RESULTS

The devised framework is validated on the dataset of SCARED and MICCAI Instrument Segmentation and Tracking Challenge 2015, and the experimental results demonstrate that our method achieves better registration accuracy with fewer number of folding pixels than three widely used baseline approaches of SyN, NiftyReg and VoxelMorph.

CONCLUSION

We develop a novel method for unsupervised deformable image registration by incorporating the HAT module and AFS mechanism into the framework, which provides a new way to obtain a desirable registration field between a pair of images.

[Clinical features of 123 patients with hyperinsulinemic hypoglycemia auxiliarily diagnosed by 18F-DOPA-PET CT scanning]

Objective: To summarize the clinical features and therapeutic outcomes of patients with hyperinsulinemic hypoglycemia (HH) auxiliarily diagnosed by ¹⁸F-DOPA positron emission tomography (PET) CT scanning. Methods: The clinical data of 123 patients who were diagnosed with hyperinsulinemic hypoglycemia by comprehensive clinical diagnostic procedures in the Department of Pediatric Endocrinology and Inherited Metabolic Diseases, Children's Hospital of Fudan University between January 2016 and December 2020 were retrospectively analyzed. Clinical data such as gender, age of onset, province, concurrent serum insulin level measured during hypoglycemia, lesion type of pancreas by ¹⁸F-DOPA-PET CT scanning, genetic test results, and treatment were collected successively. The clinical features and therapeutic outcomes were compared between patients with focal and diffuse pancreatic lesions. T test, Rank sum test, and χ² test were used for comparison between groups. Results: A total of 123 patients with hyperinsulinemic hypoglycemia (72 males and 51 females), whose average age of onset was 3 days (ranging from 1 day to 4 860 days), were recruited from 24 provinces. The concurrent serum insulin level was 7.1 (0.4-303.0) mU/L during hypoglycemia. ¹⁸F-DOPA-PET CT scanning identified focal lesions in 25.2% (31/123) and diffuse lesions in 74.8% (92/123) of the patients; 64.2% (79/123) of the HH cases were found to have pathogenic gene variants, in which 88.6% (70/79) were found to have K_ATP channel related genes (61 in ABCC8 and 9 in KCNJ11 mutations). Thirty-seven patients (17 focal and 20 diffuse) received surgical treatment with a success rate of 67.6% (25/37). The effective rate of diazoxide for children with diffuse type was significantly higher than that of children with focal group (28.3% (26/92) vs. 9.7% (3/31), χ²=10.31, P=0.001). Conclusions: ¹⁸F-DOPA-PET CT scan can improve the success rate of surgery. Comprehensive diagnosis of the etiology of hyperinsulinemic hypoglycemia by genetic analysis and ¹⁸F-DOPA-PET CT scanning can result in better treatment and prognosis.

Technical note: Understanding the effect of COVID-19 on particle pollution using a low-cost sensor network

The 2020 coronavirus pandemic and the following quarantine measures have led to significant changes in daily life worldwide. Preliminary research indicates that air quality has improved in many urban areas as a result of these measures. This study takes a neighborhood-scale approach to quantifying this change in pollution. Using data from a network of citizen-hosted, low-cost particulate matter (PM) sensors, called Air Quality & yoU (AQ&U), we obtained high-spatial resolution measurements compared to the relatively sparse state monitoring stations. We compared monthly average estimated PM_2.5 concentrations from February 11 to May 11, 2019 at 71 unique locations in Salt Lake County, UT, USA with the same (71) sensors' measurements during the same timeframe in 2020. A paired t-test showed significant reductions (71.1% and 21.3%) in estimated monthly PM_2.5 concentrations from 2019 to 2020 for the periods from March 11-April 10 and April 11-May 10, respectively. The March time period corresponded to the most stringent COVID-19 related restrictions in this region. Significant decreases in PM_2.5 were also reported by state monitoring sites during March (p < 0.001 compared to the previous 5-year average). While we observed decreases in PM_2.5 concentrations across the valley in 2020, it is important to note that the PM_2.5 concentrations did not improve equally in all locations. We observed the greatest reductions at lower elevation, more urbanized areas, likely because of the already low levels of PM_2.5 at the higher elevation, more residential areas, which were generally below 2 μg/m³ in both 2019 and 2020. Although many of measurements during March and April were near or below the estimated detection limit of the low-cost PM sensors and the federal equivalent measurements, every low-cost sensor (51) showed a reduction in PM_2.5 concentration in March of 2020 compared to 2019. These results suggest that the air quality improvement seen after March 11, 2020 is due to quarantine measures reducing traffic and decreasing pollutant emissions in the region.

The clinical value of serum sirtuin-1 in the diagnosis of rheumatoid arthritis: a pilot study

Introduction: Cell biology studies, animal models and other data suggest a role for sirtuin-1 in the pathogenesis of rheumatoid arthritis (RA). We hypothesized the clinical significance of serum sirtuin-1 in this disease.Methods: Serum was obtained from 141 RA patients, 144 non-RA patients and 88 healthy controls. Sirtuin-1, anti-mutant citrulline vimentin antibody (anti-MCV), anti-cyclic citrulline polypeptide antibody (anti-CCP), rheumatoid factor and C-reactive protein were measured by immunological methods, and erythrocyte sedimentation rate was determined by the Westergren method.Results: All markers were higher in the RA group than in the non-RA group and the healthy control group (P < 0.01). The specificity of sirtuin-1 for the diagnosis of RA was 97% (the highest among all markers), sensitivity was 71%. In ROC curve analysis, the AUCs (95% CI) of sirtuin-1, anti-CCP and anti-MCV were 0.87 (0.82-0.91), 0.91 (0.88-0.94) and 0.92 (0.89-0.95) respectively (all p < 0.01). The combination of sirtuin-1and anti-MCV gave the highest Youden index of 0.79, whilst Cox regression showed sirtuin-1 and rheumatoid factor were the strongest independent predictors of RA.Conclusions: Serum sirtuin-1 is increased in RA, and may have a place is the diagnosis of this disease when combined with other markers.

Numerical and Theoretical Analysis of the Inertia Effects and Interfacial Friction in SHPB Test Systems

The dynamic properties of materials should be analyzed for the material selection and safety design of robots used in the army and other protective structural applications. Split Hopkinson pressure bars (SHPB) is a widely used system for measuring the dynamic behavior of materials between 10² and 10⁴ s⁻¹ strain rates. In order to obtain accurate dynamic parameters of materials, the influences of friction and inertia should be considered in the SHPB tests. In this study, the effects of the friction conditions, specimen shape, and specimen configuration on the SHPB results are numerically investigated for rate-independent material, rate-dependent elastic-plastic material, and rate-dependent visco-elastic material. High-strength steel DP500 and polymethylmethacrylate are the representative materials for the latter two materials. The rate-independent material used the same elastic modulus and hardening modulus as the rate-dependent visco-elastic material but without strain rate effects for comparison. The impact velocities were 3 and 10 m/s. The results show that friction and inertia can produce a significant increase in the flow stress, and their effects are affected by impact velocities. Rate-dependent visco-elasticity material specimen is the most sensitive material to friction and inertia effects among these three materials (rate-independent material, rate-dependent elastic-plastic material, and rate-dependent visco-elastic material). A theoretical analysis based on the conservation of energy is conducted to quantitatively analyze the relationship between the stress measured in the specimen and friction as well as inertia effects. Furthermore, the methods to reduce the influence of friction and inertia effects on the experimental results are further analyzed.

iTRAQ-based quantitative proteomics analysis reveals inhibitory mechanismsof the antimicrobial peptide MDAP-2 against Salmonella gallinarum

MDAP-2 is a new AMP with high inhibitory activity on Salmonella gallinarum, which may be developed as an antimicrobial agent in the agricultural industry and food preservation. To investigate the underlying the action mechanism of MDAP-2 on Salmonella gallinarum, impacts of MDAP-2 on the growth curve and bacterial morphology of Salmonella gallinarum were studied. iTRAQ-based proteomics analysis was also performed on proteins extracted from treated and untreated Salmonella gallinarum cells. The differentially expressed proteins were then analyzed using the KEGG and GO databases. Finally, the function of some differentially expressed proteins was verified. The results showed that 150 proteins (41 up-regulated and 109 down-regulated) were found differentially expressed (fold > 1.8, p⟨0.05). The results indi- cate that MDAP-2 kills Salmonella gallinarum mainly through two mechanisms: (i) direct inhibi- tion of cell wall/ membrane/ envelope biogenesis, energy production/ conversion, carbohydrate transport/ metabolism, and DNA transcription/ translation through regulation of special protein levels; (ii) indirect effects on the same pathway through the accumulation of Reactive oxygen species (O2 ▪-, H2O2 and OH▪-).

dl-3n-butylphthalide reduces oxygen-glucose deprivation-induced endothelial cell damage by increasing PGC-1α

OBJECTIVE

Animal experiments verified that dl-3-n-butylphthalide (NBP) can protect vascular endothelial cells from ischemic damage and promote vascular proliferation in ischemic stroke treatment, but the underlying mechanism has not been fully clarified. This study aimed to investigate the effects of NBP on peroxisome proliferators-activated receptor-γ coactivator-1α (PGC-1α) expression in endothelial cells exposed to oxygen-glucose deprivation (OGD) and to clarify the related molecular mechanism.

MATERIALS AND METHODS

SV40-transformed aortic rat endothelial cell line was cultured and subjected to OGD in the presence or absence of NBP. The cell viability was evaluated by using thiazolyl blue tetrazolium bromide (MTT) method. The cellular endothelial nitric oxide synthase (eNOS) activity was measured by using eNOS activity assay. The nuclear changes were assessed with Hoechst 33342 fluorescent dye. The immunofluorescence analysis and Western blotting assay were conducted to evaluate the protein expression.

RESULTS

We found that NBP could significantly prevent endothelial cells from OGD-induced injuries, in terms of cell morphology and cell viability. Both immunofluorescence analysis and Western blot findings confirmed that the NBP treatment further enhanced PGC-1α expression during OGD, which was prevented in the presence of selective endothelial nitric oxide synthetase (eNOS) inhibitor N5-(1-Iminoethyl)-L-ornithine-HCL (L-NIO). Furthermore, we found that NBP could protect the eNOS activity about by 40% during OGD and did not influence the eNOS protein level in the spectrophotometric-based analysis.

CONCLUSIONS

NBP maintained the endothelial PGC-1α expression via regulating eNOS activity during the exposure to OGD; therefore, it presented its protective function to cell viability and vascular proliferation.

Value of serum collagen triple helix repeat containing-1(CTHRC1) and 14-3-3η protein compared to anti-CCP antibodies and anti-MCV antibodies in the diagnosis of rheumatoid arthritis

INTRODUCTION

Serological markers are important in the diagnosis of rheumatoid arthritis (RA) and other connective tissues diseases This study explored the clinical value of collagen triple helix repeat containing-1 (CTHRC1) and 14-3-3η protein, compared to routine markers, in the diagnosis of RA.

METHODS

We recruited 103 RA patients, 105 non-RA patients (osteoarthritis, ankylosing spondylitis, systemic lupus erythematosus) and 59 healthy controls. CTHRC1, 14-3-3η, anti-cyclic citrullinated peptide antibody (anti-CCP), anti-mutated citrullinated vimentin antibody (anti-MCV), rheumatoid factor and erythrocyte sedimentation rate (ESR) levels were measured, and their diagnostic value for RA evaluated and compared.

RESULTS

All laboratory indices were elevated in RA (P < 0.05). Of these, anti-MCV had the highest sensitivity (86.4%) and anti-CCP the highest specificity (94.5%). The areas under the curve (AUC) of CTHRC1, 14-3-3η, anti-CCP, anti-MCV, rheumatoid factor and ESR were 0.84, 0.81, 0.89, 0.91, 0.85 and 0.77 respectively (all P < 0.01). Anti-CCP and anti-MCV were the most valuable in the diagnosis of RA. The combination of anti-CCP and anti-MCV had the maximum Youden index, followed by the combination of anti-CCP and 14-3-3η. Binary logistic regression analysis showed that 14-3-3η had the largest odds ratio value (95% CI) at 5.1 (2.1-12.5) for RA.

CONCLUSION

CTHRC1 and 14-3-3η are promising serological indicators of RA, and when combined with anti-CCP, anti-MCV and ESR, can improve the diagnosis of this disease.

[Comparison of the early efficacy of unicompartmental and total knee arthroplasty in patients with medial compartmental osteoarthritis of the knee: a propensity score matching study]

Objective: To compare the short-term efficacy of unicompartmental knee arthroplasty (UKA) and total knee arthroplasty(TKA) in the treatment of medial compartmental knee osteoarthritis. Methods: A retrospective analysis was performed on 197 patients with medial compartment osteoarthritis of the knee treated by the same group of doctors from January 2015 to December 2018.There were 86 males and 111 females, aged (67.7±10.5) years (range: 46 to 92 years), among which 101 cases received UKA and 96 cases received TKA.The UKA and TKA patients were matched by the propensity score matching method, and a total of 41 pairs of patients were successfully matched.The difference of short-term outcomes between the two groups were compared by t test, χ(2) test or Fisher exact probability methods. Results: Compared with TKA group, the postoperative reduction of hemogloblin in the UKA group was lower ((15.3±6.4) g/L vs. (20.1±7.5) g/L, t=-3.117, P<0.01), opioid dosage was lower ((160.5±29.3) mg vs. (186.1±46.8) mg, t=-2.969, P<0.01), and the length of hospital stay was shorter ((7.0±2.0)d vs. (10.0±2.5)d, t=-6.000, P<0.01). Forgotten joint score of UKA group was higher ( (65.1±7.6) vs. (58.3±13.9) , t=2.732, P<0.01), the incidence of knee clunk or crepitus was lower (P=0.03) . There was no significant difference in the time of surgical tourniquet, range of motion, American knee society clinical score and incidence of deep vein thrombosis in lower extremities between the two groups.No complications such as surgical site infection, prosthesis loosening and dislocation occurred in the two groups. Conclusion: The early effect of UKA is similar to that of TKA, and it is better than TKA in the aspects of knee clunk or crepitus, forgotten joint score, blood loss, opioid dosage and postoperative hospital stay.

PTI-125 Reduces Biomarkers of Alzheimer's Disease in Patients

BACKGROUND

The most common dementia worldwide, Alzheimer's disease is often diagnosed via biomarkers in cerebrospinal fluid, including reduced levels of Aβ1-42, and increases in total tau and phosphorylated tau-181. Here we describe results of a Phase 2a study of a promising new drug candidate that significantly reversed all measured biomarkers of Alzheimer's disease, neurodegeneration and neuroinflammation. PTI-125 is an oral small molecule drug candidate that binds and reverses an altered conformation of the scaffolding protein filamin A found in Alzheimer's disease brain. Altered filamin A links to the α7-nicotinic acetylcholine receptor to allow Aβ42's toxic signaling through this receptor to hyperphosphorylate tau. Altered filamin A also links to toll-like receptor 4 to enable Aβ-induced persistent activation of this receptor and inflammatory cytokine release. Restoring the native shape of filamin A prevents or reverses filamin A's linkages to the α7-nicotinic acetylcholine receptor and toll-like receptor 4, thereby blocking Aβ42's activation of these receptors. The result is reduced tau hyperphosphorylation and neuroinflammation, with multiple functional improvements demonstrated in transgenic mice and postmortem Alzheimer's disease brain.

OBJECTIVES

Safety, pharmacokinetics, and cerebrospinal fluid and plasma biomarkers were assessed following treatment with PTI-125 for 28 days. Target engagement and mechanism of action were assessed in patient lymphocytes by measuring 1) the reversal of filamin A's altered conformation, 2) linkages of filamin A with α7-nicotinic acetylcholine receptor or toll-like receptor 4, and 3) levels of Aβ42 bound to α7-nicotinic acetylcholine receptor or CD14, the co-receptor for toll-like receptor 4.

DESIGN

This was a first-in-patient, open-label Phase 2a safety, pharmacokinetics and biomarker study.

SETTING

Five clinical trial sites in the U.S. under an Investigational New Drug application.

PARTICIPANTS

This study included 13 mild-to-moderate Alzheimer's disease patients, age 50-85, Mini Mental State Exam ≥16 and ≤24 with a cerebrospinal fluid total tau/Aβ42 ratio ≥0.30.

INTERVENTION

PTI-125 oral tablets (100 mg) were administered twice daily for 28 consecutive days.

MEASUREMENTS

Safety was assessed by electrocardiograms, clinical laboratory analyses and adverse event monitoring. Plasma levels of PTI-125 were measured in blood samples taken over 12 h after the first and last doses; cerebrospinal fluid levels were measured after the last dose. Commercial enzyme linked immunosorbent assays assessed levels of biomarkers of Alzheimer's disease in cerebrospinal fluid and plasma before and after treatment with PTI-125. The study measured biomarkers of pathology (pT181 tau, total tau and Aβ42), neurodegeneration (neurofilament light chain and neurogranin) and neuroinflammation (YKL-40, interleukin-6, interleukin-1β and tumor necrosis factor α). Plasma levels of phosphorylated and nitrated tau were assessed by immunoprecipitation of tau followed by immunoblotting of three different phospho-epitopes elevated in AD (pT181-tau, pS202-tau and pT231-tau) and nY29-tau. Changes in conformation of filamin A in lymphocytes were measured by isoelectric focusing point. Filamin A linkages to α7-nicotinic acetylcholine receptor and toll-like receptor 4 were assessed by immunoblot detection of α7-nicotinic acetylcholine receptor and toll-like receptor 4 in anti-filamin A immunoprecipitates from lymphocytes. Aβ42 complexed with α7-nicotinic acetylcholine receptor or CD14 in lymphocytes was also measured by co-immunoprecipitation. The trial did not measure cognition.

RESULTS

Consistent with the drug's mechanism of action and preclinical data, PTI-125 reduced cerebrospinal fluid biomarkers of Alzheimer's disease pathology, neurodegeneration and neuroinflammation from baseline to Day 28. All patients showed a biomarker response to PTI-125. Total tau, neurogranin, and neurofilament light chain decreased by 20%, 32% and 22%, respectively. Phospho-tau (pT181) decreased 34%, evidence that PTI-125 suppresses tau hyperphosphorylation induced by Aβ42's signaling through α7-nicotinic acetylcholine receptor. Cerebrospinal fluid biomarkers of neuroinflammation (YKL-40 and inflammatory cytokines) decreased by 5-14%. Biomarker effects were similar in plasma. Aβ42 increased slightly - a desirable result because low Aβ42 indicates Alzheimer's disease. This increase is consistent with PTI-125's 1,000-fold reduction of Aβ42's femtomolar binding affinity to α7-nicotinic acetylcholine receptor. Biomarker reductions were at least p ≤ 0.001 by paired t test. Target engagement was shown in lymphocytes by a shift in filamin A's conformation from aberrant to native: 93% was aberrant on Day 1 vs. 40% on Day 28. As a result, filamin A linkages with α7-nicotinic acetylcholine receptor and toll-like receptor 4, and Aβ42 complexes with α7-nicotinic acetylcholine receptor and CD14, were all significantly reduced by PTI-125. PTI-125 was safe and well-tolerated in all patients. Plasma half-life was 4.5 h and approximately 30% drug accumulation was observed on Day 28 vs. Day 1.

CONCLUSIONS

PTI-125 significantly reduced biomarkers of Alzheimer's disease pathology, neurodegeneration, and neuroinflammation in both cerebrospinal fluid and plasma. All patients responded to treatment. The magnitude and consistency of reductions in established, objective biomarkers imply that PTI-125 treatment counteracted disease processes and reduced the rate of neurodegeneration. Based on encouraging biomarker data and safety profile, approximately 60 patients with mild-to-moderate AD are currently being enrolled in a Phase 2b randomized, placebo-controlled confirmatory study to assess the safety, tolerability and efficacy of PTI-125.

Clinical diagnostic significance of 14-3-3η protein, high-mobility group box-1, anti-cyclic citrullinated peptide antibodies, anti-mutated citrullinated vimentin antibodies and rheumatoid factor in rheumatoid arthritis

Introduction: Circulating markers of rheumatoid arthritis (RA) include the 14-3-3η protein, high-mobility group box-1 (HMGB1), anti-cyclic citrullinated peptide (anti-CCP) antibodies, anti-mutated citrullinated vimentin (anti-MCV) antibodies and rheumatoid factor (RF). We set out to determine which two markers in combination provided best discriminatory power for this disease.Methods: We recruited 108 RA patients, 102 non-RA patients (SLE, AS, Sjogren's syndrome, MCTD) and 90 healthy controls. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio and the Youden index of each analyte were calculated and binary logistic regression analysis and receiver operating characteristic (ROC) curve were performed to evaluate their diagnostic value for RA alone and in paired combination.Results: As expected, all markers were elevated in RA patients (P < 0.05). Binary logistic regression analysis showed that 14-3-3η had the highest odds ratio (95% CI) at 2.4 (1.9-2.8). Anti-CCP and anti-MCV had the highest areas under the curves [AUC (95% CI)] at 0.85 (0.78-0.90) and 0.85 (0.78-0.91) respectively (both P < 0.001). In serial detection (one marker followed by another), no combination had a Youden index >0.6. In parallel analysis (both considered together) several combinations had a Youden index >0.7, of which the highest (0.78) was anti-CCP with anti-MCV, with a sensitivity of 93.3% and specificity of 84.7%.Conclusions: Despite individual increases in serum 14-3-3η, HMGB1, anti-CCP, anti-MCV and RF, the combination of anti-CCP and anti-MCV might be of great help for diagnostic in RA, and so should be considered as routine tests for this disease.

Development of an EMG-Controlled Knee Exoskeleton to Assist Home Rehabilitation in a Game Context

As a leading cause of loss of functional movement, stroke often makes it difficult for patients to walk. Interventions to aid motor recovery in stroke patients should be carried out as a matter of urgency. However, muscle activity in the knee is usually too weak to generate overt movements, which poses a challenge for early post-stroke rehabilitation training. Although electromyography (EMG)-controlled exoskeletons have the potential to solve this problem, most existing robotic devices in rehabilitation centers are expensive, technologically complex, and allow only low training intensity. To address these problems, we have developed an EMG-controlled knee exoskeleton for use at home to assist stroke patients in their rehabilitation. EMG signals of the subject are acquired by an easy-to-don EMG sensor and then processed by a Kalman filter to control the exoskeleton autonomously. A newly-designed game is introduced to improve rehabilitation by encouraging patients' involvement in the training process. Six healthy subjects took part in an initial test of this new training tool. The test showed that subjects could use their EMG signals to control the exoskeleton to assist them in playing the game. Subjects found the rehabilitation process interesting, and they improved their control performance through 20-block training, with game scores increasing from 41.3 ± 15.19 to 78.5 ± 25.2. The setup process was simplified compared to traditional studies and took only 72 s according to test on one healthy subject. The time lag of EMG signal processing, which is an important aspect for real-time control, was significantly reduced to about 64 ms by employing a Kalman filter, while the delay caused by the exoskeleton was about 110 ms. This easy-to-use rehabilitation tool has a greatly simplified training process and allows patients to undergo rehabilitation in a home environment without the need for a therapist to be present. It has the potential to improve the intensity of rehabilitation and the outcomes for stroke patients in the initial phase of rehabilitation.

Biological characteristics of a new antibacterial peptide and its antibacterial mechanisms against Gram-negative bacteria

MDAP-2 is a new antibacterial peptide with a unique structure that was isolated from house- flies. However, its biological characteristics and antibacterial mechanisms against bacteria are still poorly understood. To study the biological characteristics, antibacterial activity, hemolytic activi- ty, cytotoxicity to mammalian cells, and the secondary structure of MDAP-2 were detected; the results showed that MDAP-2 displayed high antibacterial activity against all of the tested Gram-negative bacteria. MDAP-2 had lower hemolytic activity to rabbit red blood cells; only 3.4% hemolytic activity was observed at a concentration of 800μg/ml. MDAP-2 also had lower cytotoxicity to mammalian cells; IC50 values for HEK-293 cells, VERO cells, and IPEC-J2 cells were greater than 1000 μg/ml. The circular dichroism (CD) spectra showed that the peptide most- ly has α-helical properties and some β-fold structure in water and in membrane-like conditions. MDAP-2 is therefore a promising antibacterial agent against Gram-negative bacteria. To deter- mine the antibacterial mechanism(s) of action, fluorescent probes, flow cytometry, and transmis- sion electron microscopy (TEM) were used to study the effects of MDAP-2 on membrane perme- ability, polarization ability, and integrity of Gram-negative bacteria. The results indicated that the peptide caused membrane depolarization, increased membrane permeability, and destroyed membrane integrity. In conclusion, MDAP-2 is a broad-spectrum, lower hemolytic activity, and lower cytotoxicity antibacterial peptide, which is mainly effective on Gram-negative bacteria. It exerts its antimicrobial effects by causing bacterial cytoplasm membrane depolarization, increas- ing cell membrane permeability and disturbing the membrane integrity of Gram-negative bacte- ria. MDAP-2 may offer a new strategy to for defense against Gram-negative bacteria.

EEG-Based Lower-Limb Movement Onset Decoding: Continuous Classification and Asynchronous Detection

Brain-machine interfaces have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography. Active neural participation is crucial to promote brain plasticity thus to enhance the opportunity of motor recovery. This paper presents the decoding of lower-limb movement-related cortical potentials with continuous classification and asynchronous detection. We executed experiments in a customized gait trainer, where 10 healthy subjects performed self-initiated ankle plantar flexion. We further analyzed the features, evaluated the impact of the limb side, and compared the proposed framework with other typical decoding methods. No significant differences were observed between the left and right legs in terms of neural signatures of movement and classification performance. We obtained a higher true positive rate, lower false positives, and comparable latencies with respect to the existing online detection methods. This paper demonstrates the feasibility of the proposed framework to build a closed-loop gait trainer. Potential applications include gait training neurorehabilitation in clinical trials.

Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors

Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was -0.334 ± 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 ± 3.5% and 68.2 ± 4.6% was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here might be further exploited in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices.

A brain-controlled lower-limb exoskeleton for human gait training

Brain-computer interfaces have been a novel approach to translate human intentions into movement commands in robotic systems. This paper describes an electroencephalogram-based brain-controlled lower-limb exoskeleton for gait training, as a proof of concept towards rehabilitation with human-in-the-loop. Instead of using conventional single electroencephalography correlates, e.g., evoked P300 or spontaneous motor imagery, we propose a novel framework integrated two asynchronous signal modalities, i.e., sensorimotor rhythms (SMRs) and movement-related cortical potentials (MRCPs). We executed experiments in a biologically inspired and customized lower-limb exoskeleton where subjects (N = 6) actively controlled the robot using their brain signals. Each subject performed three consecutive sessions composed of offline training, online visual feedback testing, and online robot-control recordings. Post hoc evaluations were conducted including mental workload assessment, feature analysis, and statistics test. An average robot-control accuracy of 80.16% ± 5.44% was obtained with the SMR-based method, while estimation using the MRCP-based method yielded an average performance of 68.62% ± 8.55%. The experimental results showed the feasibility of the proposed framework with all subjects successfully controlled the exoskeleton. The current paradigm could be further extended to paraplegic patients in clinical trials.

A rare-earth free magnesium alloy with improved intrinsic ductility

Metals are the backbone of manufacturing owing to their strength and formability. Compared to polymers they have high mass density. There is, however, one exception: magnesium. It has a density of only 1.7 g/cm³, making it the lightest structural material, 4.5 times lighter than steels, 1.7 times lighter than aluminum, and even slightly lighter than carbon fibers. Yet, the widespread use of magnesium is hampered by its intrinsic brittleness. While other metallic alloys have multiple dislocation slip systems, enabling their well-known ductility, the hexagonal lattice of magnesium offers insufficient modes of deformation, rendering it intrinsically brittle. We have developed a quantum-mechanically derived treasure map which screens solid solution combinations with electronic bonding, structure and volume descriptors for similarity to the ductile magnesium-rare earth alloys. Using this insight we synthesized a surprisingly simple, compositionally lean, low-cost and industry-compatible new alloy which is over 4 times more ductile and 40% stronger than pure magnesium. The alloy contains 1 wt.% aluminum and 0.1 wt.% calcium, two inexpensive elements which are compatible with downstream recycling constraints.

Brain-actuated gait trainer with visual and proprioceptive feedback

OBJECTIVE

Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension.

APPROACH

We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects.

MAIN RESULTS

(i) For real-time classification, the average accuracy was [Formula: see text]% and [Formula: see text]% for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback ([Formula: see text]%) was significantly better than with visual feedback ([Formula: see text]%), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn.

SIGNIFICANCE

The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.

Paravascular pathways contribute to vasculitis and neuroinflammation after subarachnoid hemorrhage independently of glymphatic control

Subarachnoid hemorrhage (SAH) is a devastating disease with high mortality. The mechanisms underlying its pathological complications have not been fully identified. Here, we investigate the potential involvement of the glymphatic system in the neuropathology of SAH. We demonstrate that blood components rapidly enter the paravascular space following SAH and penetrate into the perivascular parenchyma throughout the brain, causing disastrous events such as cerebral vasospasm, delayed cerebral ischemia, microcirculation dysfunction and widespread perivascular neuroinflammation. Clearance of the paravascular pathway with tissue-type plasminogen activator ameliorates the behavioral deficits and alleviates histological injury of SAH. Interestingly, AQP4^−/− mice showed no improvements in neurological deficits and neuroinflammation at day 7 after SAH compared with WT control mice. In conclusion, our study proves that the paravascular pathway dynamically mediates the pathological complications following acute SAH independently of glymphatic control.

Preferable sites and orientations of transgene inserted in the adenovirus vector genome: The E3 site may be unfavorable for transgene position

The adenovirus vector (AdV) can carry two transgenes in its genome, the therapeutic gene and a reporter gene, for example. The E3 insertion site has often been used for the expression of the second transgene. A transgene can be inserted at six different sites/orientations: E1, E3 and E4 sites, and right and left orientations. However, the best combination of the insertion sites and orientations as for the titers and the expression levels has not sufficiently been studied. We attempted to construct 18 AdVs producing GFP or LacZ gene driven by the EF1α promoter and Cre gene driven by the α-fetoprotein promoter. The AdV containing GFP gene at E3 in the rightward orientation (GFP-E3R) was not available. The LacZ-E3R AdV showed 20-fold lower titer and 50-fold lower level of fiber mRNA than the control E1L AdV. Notably, we found four aberrantly spliced mRNAs in the LacZ-E3L/R AdVs, probably explaining their very low titers. Although the transgene expression levels in the E4R AdVs were about threefold lower than those in the E1L AdVs, their titers are comparable with that of E1L AdVs. We concluded that E1L and E4R sites/orientations are preferable for expressing the main target gene and a second gene, respectively.

Valproic acid attenuates the suppression of acetyl histone H3 and CREB activity in an inducible cell model of Machado-Joseph disease

Machado-Joseph disease (MJD) is caused by a (CAG)n trinucleotide repeat expansion that is translated into an abnormally long polyglutamine tract. This disease is considered the most common form of spinocerebellar ataxia (SCA). In the present study, we developed stable inducible cell lines (PC12Tet-On-Ataxin-3-Q28/84) expressing ataxin-3 with either normal or abnormal CAG repeats under doxycycline control. The expression of acetyl histone H3 and the induction of c-Fos in response to cAMP were strongly suppressed in cells expressing the protein with the expanded polyglutamine tract. Treatment with valproic acid, a histone deacetylase inhibitor (HDACi), attenuated mutant ataxin-3-induced cell toxicity and suppression of acetyl histone H3, phosphorylated cAMP-responsive element binding protein (p-CREB) as well as c-Fos expression. These results indicate that VPA can stimulate the up-regulation of gene transcription through hyperacetylation. Thus, VPA might have a therapeutic effect on MJD.

Mother's body mass index and food intake in school-aged children: results of the GINIplus and the LISAplus studies

BACKGROUND/OBJECTIVES

Mother's body mass index (BMI) is a strong predictor of child BMI. Whether mother's BMI correlates with child's food intake is unclear. We investigated associations between mother's BMI/overweight and child's food intake using data from two German birth cohorts.

SUBJECTS/METHODS

Food intakes from 3230 participants were derived from parent-completed food frequency questionnaires. Intakes of 11 food groups were categorized into three levels using group- and sex-specific tertile cutoffs. Mother's BMI and overweight were calculated on the basis of questionnaire data. Multinomial regression models assessed associations between a child's food intake and mother's BMI/overweight. Linear regression models assessed associations between a child's total energy intake and mother's BMI. Models were adjusted for study region, maternal education, child's age, sex, pubertal status and energy intake and the BMIs of the child and father.

RESULTS

Mothers' BMI was associated with high meat intake in children (adjusted relative risk ratio (RRR (95% confidence interval))=1.06 (1.03; 1.09)). Mothers' overweight was associated with the meat intake (medium versus low RRR=1.30 (1.07; 1.59); high versus low RRR=1.50 (1.19; 1.89)) and egg intake (medium versus low RRR=1.24 (1.02; 1.50); high versus low RRR=1.30 (1.07; 1.60)) of children. There were no consistent associations for rest of the food groups. For every one-unit increase in mothers' BMI, the total energy intake in children increased by 9.2 kcal (3.7; 14.7). However, this effect was not significant after adjusting for children's BMI.

CONCLUSIONS

Our results suggest that mother's BMI and mother's overweight are important correlates of a child's intake of energy, meat and eggs.

The expression and roles of Nde1 and Ndel1 in the adult mammalian central nervous system

Mental and neurological illnesses affect one in four people. While genetic linkage analyses have shown an association of nuclear distribution factor E (NDE1, or NudE) and its ohnolog NDE-like 1 (NDEL1, or Nudel) with mental disorders, the cellular mechanisms remain unclear. In the present study, we have demonstrated that Nde1 and Ndel1 are differentially localised in the subventricular zone (SVZ) of the forebrain and the subgranular zone (SGZ) of the hippocampus, two regions where neurogenesis actively occurs in the adult brain. Nde1, but not Ndel1, is localized to putative SVZ stem cells, and to actively dividing progenitors of the SGZ. The influence of these proteins on neural stem cell differentiation was investigated by overexpression in a hippocampal neural stem cell line, HCN-A94. Increasing Nde1 expression in this neural stem cell line led to increased neuronal differentiation while decreasing levels of astroglial differentiation. In primary cultured neurons and astrocytes, Nde1 and Ndel1 were found to have different but comparable subcellular localizations. In addition, we have shown for the first time that Nde1 is heterogeneously distributed in cortical astrocytes of human brains. Our data indicate that Nde1 and Ndel1 have distinct but overlapping distribution patterns in mouse brain and cultured nerve cells. They may function differently and therefore their dosage changes may contribute to some aspects of mental disorders.

A novel single nucleotide polymorphism in the protein tyrosine phosphatase N22 gene (PTPN22) is associated with Type 1 diabetes in a Chinese population

AIMS

To examine single nucleotide polymorphisms in the protein tyrosine phosphatase N22 gene (PTPN22) and to study their association with Type 1 diabetes in a Chinese cohort.

METHODS

Three hundred and sixty-four young patients with Type 1 diabetes and 719 healthy children were included in this case-controlled study. The genotypes of rs1217385, rs2488457 (-1123C>G), rs1217414, rs1217419, rs3765598 and rs2476601 (1858C>T) in the PTPN22 gene were determined using the SNaPshot method. Alleles, genotypes and haplotype frequencies were compared between patients with Type 1 diabetes and healthy control subjects. The association between single nucleotide polymorphisms and clinical traits/autoantibody status was also analysed.

RESULTS

The single nucleotide polymorphism, rs1217419, located in the second intron of the PTPN22 gene was associated with Type 1 diabetes (odds ratio 1.5, 95% CI 1.14-1.97, P = 0.003). An additional single nucleotide polymorphism, rs1217385, was also associated with Type 1 diabetes; however, the association was secondary to that of rs1217419. The previously reported single nucleotide polymorphism that is associated with Type 1 diabetes (-1123G>C) had only marginal association with Type 1 diabetes in our study. A marginal association was also identified between -1123G>C and glutamic acid decarboxylase autoantibody positivity in patients with Type 1 diabetes. There was no association between the single nucleotide polymorphism 1858C>T and Type 1 diabetes in our studied cohort.

CONCLUSIONS

Our study confirmed that PTPN22 is a gene that contributes to Type 1 diabetes susceptibility. The primary association occurs with single nucleotide polymorphism rs1217419 and there is clear heterogeneity of the association between PTPTN22 polymorphisms and Type 1 diabetes in a Chinese population compared with other populations.