Hybrid Rehabilitation System with Motion Estimation Based on EMG Signals.

Patients with upper limb paralysis undergo various types of rehabilitation to reconstruct upper limb functions necessary for their return to daily life and social activities. Therefore, it is necessary to develop an effective rehabilitation support system using robotic technologies. In this study, we propose an EMG-driven hybrid rehabilitation system based on the estimation of intended motion using a probabilistic neural network. In the proposed system, the developed robot and functional electrical stimulation are controlled by estimating the patient's intention, which enables the intuitive learning of the appropriate control abilities of joint motions and muscle contraction patterns. In the experiments, hybrid and visual feedback training were conducted for pointing movements of the wrist joint of the non-dominant hand. The results confirmed that the proposed method provides effective training and has great potential for use in rehabilitation.

[A case of neuromyelitis optica spectrum disorder after human immunodeficiency virus infection treated with rituximab].

The patient was a 58-year-old Japanese man. At age 52 years, he was diagnosed with human immunodeficiency virus (HIV) infection and had been receiving highly active antiretroviral therapy (HAART). He presented to the emergency department with acute upper left and right lower extremity paralysis. Spinal cord magnetic resonance imaging showed intramedullary signal changes over three vertebral bodies in the cervical spinal cord. Anti-aquaporin-4 antibody was positive in serum, and neuromyelitis optica spectrum disorder (NMOSD) was diagnosed. Steroid pulse therapy and plasma exchange were performed but had limited effects. In Japan, there is no report of NMOSD after HIV infection treated with rituximab. Rituximab administration should be considered in refractory cases of NMOSD after HIV infection.

Effects of neurofeedback on standing postural control task with combined imagined and executed movements.

Introduction: Motor imagery (MI) is a method of imagining movement without actual movement, and its use in combination with motor execution (ME) enhances the effects of motor learning. Neurofeedback (NFB) is another method that promotes the effects of MI. This study aimed to investigate the effects of NFB on combined MI and ME (MIME) training in a standing postural control task. Methods: Sixteen participants were randomly divided into MIME and MIME + NFB groups and performed 10 trials of a postural control task on an unstable board, with nine trials of MI in between. Electroencephalogram was assessed during MI, and the MIME + NFB group received neurofeedback on the degree of MI via auditory stimulation. A postural control task using an unstable board was performed before and after the MIME task, during which postural instability was evaluated. Results: Postural instability was reduced after the MIME task in both groups. In addition, the root mean square, which indicates the sway of the unstable board, was significantly reduced in the MIME + NFB group compared to that in the MIME group. Conclusion: Our results indicate that MIME training is effective for motor learning of standing postural control. Furthermore, when MI and ME are combined, the feedback on the degree of MI enhances the learning effect.

Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial.

Introduction: Neurofeedback (NFB) training and transcranial direct current stimulation (tDCS) have been shown to individually improve motor imagery (MI) abilities. However, the effect of combining both of them with MI has not been verified. Therefore, the aim of this study was to examine the effect of applying tDCS directly before MI with NFB. Methods: Participants were divided into an NFB group (n = 10) that performed MI with NFB and an NFB + tDCS group (n = 10) that received tDCS for 10 min before MI with NFB. Both groups performed 60 MI trials with NFB. The MI task was performed 20 times without NFB before and after training, and µ-event-related desynchronization (ERD) and vividness MI were evaluated. Results: µ-ERD increased significantly in the NFB + tDCS group compared to the NFB group. MI vividness significantly increased before and after training. Discussion: Transcranial direct current stimulation and NFB modulate different processes with respect to MI ability improvement; hence, their combination might further improve MI performance. The results of this study indicate that the combination of NFB and tDCS for MI is more effective in improving MI abilities than applying them individually.

Rapid diagnosis of papillary thyroid carcinoma with machine learning and probe electrospray ionization mass spectrometry.

Frozen section examination could provide pathological diagnosis for surgery of thyroid nodules, which is time-consuming, skill- and experience-dependent. This study developed a rapid classification method for thyroid nodules and machine learning. Total 69 tissues were collected including 43 nodules and 26 nodule-adjacent tissues. Intraoperative frozen section was first performed to give accurate diagnosis, and the rest frozen specimen were pretreated for probe electrospray ionization mass measurement. By multivariate analysis of mass scan data, a series compounds were found downregulated in the extraction solution of papillary thyroid carcinoma (PTC), but some were found upregulated by mass spectrometry imaging. m/z 758.5713 ([PC[34:2] + H]+ ), m/z 772.5845 ([PC[32:0] + K]+ ), and m/z 786.6037 ([PC[36:2] + H]+ ) were firstly identified as potential biomarkers for nodular goiter (NG). Machine learning was employed by means of support vector machine (SVM) and random forest (RF) algorithms. For classification of PTC from NG, SVM and RF algorithms exhibited the same performance and the concordance was 94.2% and 94.4% between prediction and pathological diagnosis with positive and negative mass dataset, respectively. For the classification of PTC from PTC adjacent tissues, SVM was better than RF and the concordance was 93.8% and 83.3% with positive and negative mass dataset, respectively. With the identified compounds as training features, the sensitivity and specificity are 87.5% and 88.9% for the test set. The developed method could also correctly predict the malignancy of one medullary thyroid carcinoma and one adenomatous goiter (benign). The diagnosis time is about 10 min for one specimen, and it is very promising for the intraoperative diagnosis of papillary thyroid carcinoma.

A deep neural network model for multi-view human activity recognition.

Multiple cameras are used to resolve occlusion problem that often occur in single-view human activity recognition. Based on the success of learning representation with deep neural networks (DNNs), recent works have proposed DNNs models to estimate human activity from multi-view inputs. However, currently available datasets are inadequate in training DNNs model to obtain high accuracy rate. Against such an issue, this study presents a DNNs model, trained by employing transfer learning and shared-weight techniques, to classify human activity from multiple cameras. The model comprised pre-trained convolutional neural networks (CNNs), attention layers, long short-term memory networks with residual learning (LSTMRes), and Softmax layers. The experimental results suggested that the proposed model could achieve a promising performance on challenging MVHAR datasets: IXMAS (97.27%) and i3DPost (96.87%). A competitive recognition rate was also observed in online classification.

A System for Wound Evaluation Support Using Depth and Image Sensors.

This paper proposes an evaluation/treatment sup-port system enabling automatic determination of wound evaluation indices from RGB-depth images and fully convolutional networks (FCNs). Segmentation experiments based on wound images and surface area determination experiments based on artificial images showed reduced errors and smaller parameters/higher levels of tissue classification than with previous approaches (proposed: 65.8 %; conventional: 60.2 %), thereby demonstrating the effectiveness of the technique.

Identifying autism spectrum disorder symptoms using response and gaze behavior during the Go/NoGo game CatChicken.

Previous studies have found that Autism Spectrum Disorder (ASD) children scored lower during a Go/No-Go task and faced difficulty focusing their gaze on the speaker's face during a conversation. To date, however, there has not been an adequate study examining children's response and gaze during the Go/No-Go task to distinguish ASD from typical children. We investigated typical and ASD children's gaze modulation when they played a version of the Go/No-Go game. The proposed system represents the Go and the No-Go stimuli as chicken and cat characters, respectively. It tracks children's gaze using an eye tracker mounted on the monitor. Statistically significant between-group differences in spatial and auto-regressive temporal gaze-related features for 21 ASD and 31 typical children suggest that ASD children had more unstable gaze modulation during the test. Using the features that differ significantly as inputs, the AdaBoost meta-learning algorithm attained an accuracy rate of 88.6% in differentiating the ASD subjects from the typical ones.

Control of anisotropic conduction of carbon nanotube sheets and their use as planar-type thermoelectric conversion materials.

The large anisotropic thermal conduction of a carbon nanotube (CNT) sheet that originates from the in-plane orientation of one-dimensional CNTs is disadvantageous for thermoelectric conversion using the Seebeck effect since the temperature gradient is difficult to maintain in the current flow direction. To control the orientation of the CNTs, polymer particles are introduced as orientation aligners upon sheet formation by vacuum filtration. The thermal conductivities in the in-plane direction decrease as the number of polymer particles in the sheet increases, while that in the through-plane direction increases. Consequently, a greater temperature gradient is observed for the anisotropy-controlled CNT sheet as compared to that detected for the CNT sheet without anisotropy control when a part of the sheet is heated, which results in a higher power density for the planar-type thermoelectric device. These findings are quite useful for the development of flexible and wearable thermoelectric batteries using CNT sheets.

A wearable light-touch contact device for human balance support.

There is an urgent need for the development of ways to address the major issue of falls among today's globally aging population. The authors here outline a new approach referred to as virtual light-touch contact to mitigate postural sway during ambulatory and stationary periods, and propose a wearable light-touch (WLT) system featuring a virtual partition around the user that allows determination of virtual forces resulting from related contact. The data produced are used to create vibrotactile fingertip feedback, which supports comprehensive perception of the partition. Non-impaired subjects were recruited to support evaluation of the prototype system (incorporating tactile stimulation and motion-capture technology), with outcomes showing successful mitigation of postural sway in a heel-to-toe tandem stance. Research performed with 150 able-bodied volunteers to validate the performance of the new set-up (incorporating an acceleration sensor and a voice coil motor to render the light-touch effect) suggested that the proposed WLT approach supports human balance on a level comparable to that of the light-touch effect.

Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies.

BACKGROUND AND OBJECTIVES: The number of preterm babies is steadily growing world-wide and these neonates are at risk of neuro-motor-cognitive deficits. The observation of spontaneous movements in the first three months of age is known to predict such risk. However, the analysis by specifically trained physiotherapists is not suited for the clinical routine, motivating the development of simple computerized video analysis systems, integrated with a well-structured Biobank to make available for preterm babies a growing service with diagnostic, prognostic and epidemiological purposes. METHODS: MIMAS (Markerless Infant Movement Analysis System) is a simple, low-cost system of video analysis of spontaneous movements of newborns in their natural environment, based on a single standard RGB camera, without markers attached to the body. The original videos are transformed into binarized sequences highlighting the silhouette of the baby, in order to minimize the illumination effects and increase the robustness of the analysis; such sequences are then coded by a large set of parameters (39) related to the spatial and spectral changes of the silhouette. The parameter vectors of each baby were stored in the Biobank together with related clinical information. RESULTS: The preliminary test of the system was carried out at the Gaslini Pediatric Hospital in Genoa, where 46 preterm (PT) and 21 full-term (FT) babies (as controls) were recorded at birth (T0) and 8-12 weeks thereafter (T1). A simple statistical analysis of the data showed that the coded parameters are sensitive to the degree of maturation of the newborns (comparing T0 with T1, for both PT and FT babies), and to the conditions at birth (PT vs. FT at T0), whereas this difference tends to vanish at T1. Moreover, the coding method seems also able to detect the few 'abnormal' preterm babies in the PT populations that were analyzed as specific case studies. CONCLUSIONS: Preliminary results motivate the adoption of this tool in clinical practice allowing for a systematic accumulation of cases in the Biobank, thus for improving the accuracy of data analysis performed by MIMAS and ultimately allowing the adoption of data mining techniques.

[A case of anti-MuSK positive MG with recurrent right-sided heart failure as the initial manifestation].

We report a case of anti-MuSK antibody (Ab)-positive myasthenia gravis (MG) in a patient who developed recurrent right-sided congestive heart failure. The patient presented with right-sided congestive heart failure of unknown etiology, necessitating hospitalization on three occasions over a 6-month period. During the third episode of hospitalization, she developed disturbance of consciousness, and heart failure was attributed to carbon dioxide narcosis. We performed various investigations including an anti-MuSK Ab assay, which showed positive results, and she was diagnosed with MG based solely on anti-MuSK Ab positivity. Selective plasma exchange did not produce a satisfactory therapeutic effect, and she received additional intravenous immunoglobulin, plasmapheresis, and oral immunosuppressive therapy after which she was successfully weaned off the ventilator. This case report highlights the following points: (a) Recurrent right-sided congestive heart failure may be the first manifestation of anti-MuSK Ab-positive MG and, (b) detection of the anti-MuSK Ab alone is a convincing rationale to diagnose patients with MG.

PEPPI-MS: Polyacrylamide-Gel-Based Prefractionation for Analysis of Intact Proteoforms and Protein Complexes by Mass Spectrometry.

Prefractionation of complex mixtures of proteins derived from biological samples is indispensable for proteome analysis via top-down mass spectrometry (MS). Polyacrylamide gel electrophoresis (PAGE), which enables high-resolution protein separation based on molecular size, is a widely used technique in biochemical experiments and has the potential to be useful in sample fractionation for top-down MS analysis. However, the lack of a means to efficiently recover the separated proteins in-gel has always been a barrier to its use in sample prefractionation. In this study, we present a novel experimental workflow, called Passively Eluting Proteins from Polyacrylamide gels as Intact species for MS ("PEPPI-MS"), which allows top-down MS of PAGE-separated proteins. The optimization of Coomassie brilliant blue staining followed by the passive extraction step in the PEPPI-MS workflow enabled the efficient recovery of proteins, separated on commercial precast gels, from a wide range of molecular weight regions in under 10 min. Two-dimensional separation combining offline PEPPI-MS with online reversed-phase liquid chromatographic separation resulted in identification of over 1000 proteoforms recovered from the target region of the gel (≤50 kDa). Given the widespread availability and relatively low cost of traditional sodium dodecyl sulfate (SDS)-PAGE equipment, the PEPPI-MS workflow will be a powerful prefractionation strategy for top-down proteomics.

Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours.

Imaging mass spectrometry (IMS) has been rarely used to examine specimens of human brain tumours. In the current study, high quality brain tumour samples were selected by tissue observation. Further, IMS analysis was combined with a new hierarchical cluster analysis (IMS-HCA) and region of interest analysis (IMS-ROI). IMS-HCA was successful in creating groups consisting of similar signal distribution images of glial fibrillary acidic protein (GFAP) and related multiple proteins in primary brain tumours. This clustering data suggested the relation of GFAP and these identified proteins in the brain tumorigenesis. Also, high levels of histone proteins, haemoglobin subunit α, tubulins, and GFAP were identified in a metastatic brain tumour using IMS-ROI. Our results show that IMS-HCA and IMS-ROI are promising techniques for identifying biomarkers using brain tumour samples.

Markerless Measurement and Evaluation of General Movements in Infants.

General movements (GMs), a type of spontaneous movement, have been used for the early diagnosis of infant disorders. In clinical practice, GMs are visually assessed by qualified licensees; however, this presents a difficulty in terms of quantitative evaluation. Various measurement systems for the quantitative evaluation of GMs track target markers attached to infants; however, these markers may disturb infants' spontaneous movements. This paper proposes a markerless movement measurement and evaluation system for GMs in infants. The proposed system calculates 25 indices related to GMs, including the magnitude and rhythm of movements, by video analysis, that is, by calculating background subtractions and frame differences. Movement classification is performed based on the clinical definition of GMs by using an artificial neural network with a stochastic structure. This supports the assessment of GMs and early diagnoses of disabilities in infants. In a series of experiments, the proposed system is applied to movement evaluation and classification in full-term infants and low-birth-weight infants. The experimental results confirm that the average agreement between four GMs classified by the proposed system and those identified by a licensee reaches up to 83.1 ± 1.84%. In addition, the classification accuracy of normal and abnormal movements reaches 90.2 ± 0.94%.

Improved MALDI-MS method for the highly sensitive and reproducible detection of biomarker peaks for the proteotyping of Salmonella serotypes.

The rapid identification and classification of pathogenic microorganisms, including Salmonella enterica, is important for the surveillance and prevention of foodborne diseases. Matrix-assisted laser desorption\ionization time-of-flight mass spectrometry (MALDI-TOFMS) has been shown to be an effective tool for the rapid identification of microorganisms. In a previous report, a mass database consisting of 12 biomarker proteins, S8, L15, L17, L21, L25, S7, superoxide dismutase (SodA), peptidylprolyl cis-trans isomerase C, Gns, YibT, YaiA, and YciF, was introduced for the serotyping of S. enterica via MALDI-MS (Applied Microbiology and Biotechnology, 2017, 101, 8557-8569). However, the reproducibility of peak detection of biomarkers such as SodA at m\z 23 000 was poor. We report here an optimized MALDI-MS method for detecting these biomarkers with high sensitivity and reproducibility. The issue was solved by controlling the bacterial concentration at 1 × 10 to 1 × 102 MFU (3 × 106 to 3 × 107 CFU\µL, as calculated from the MFU), using the colony suspension supernatant obtained by centrifugation, and using matrix additives such as methylenediphosphonic acid and N-decyl-ß-D-maltopyranoside. We propose that the method including the above steps is one of the best for detecting biomarkers with high sensitivity and reproducibility.

Effects of somatosensory information provision to fingertips for mitigation of postural sway and promotion of muscle coactivation in an upright posture.

This study was conducted to investigate the effects of somatosensory information provision to the fingertips using a device proposed by the authors for mitigation of postural sway and muscle co-contraction in an upright posture, which both increase with aging and result in inefficient postural control. In the research, center of pressure (CoP) fluctuation index values and muscle co-contraction for ankle joint movement were monitored with healthy young adults in a standing position. The results showed that the proposed device helped to reduce the root mean square (RMS) of the CoP and muscle co-contraction in the right ankle joint, thereby suggesting its potential for contribution to the assistance of efficient postural control.

A Standing Function Evaluation System Based on Virtual Light Touch Contact and Factor Analysis.

This paper proposes a standing function evaluation system on the basis of factor analysis for indices related to standing characteristics. For this approach, five standing function factors were checked in a large-scale experiment using the results of extraction from 48 indices based on factor analysis. Five factors were extracted from these indices, and the related score radar charts indicated that most factors changed with age. The evaluation results suggest that the system facilitates intuitive comprehension of standing-related factors based on these charts.