A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control.

An exoskeleton, a wearable device, was designed based on the user's physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals.

An Instrumented Glove-Controlled Portable Hand-Exoskeleton for Bilateral Hand Rehabilitation.

Effective bilateral hand training is desired in rehabilitation programs to restore hand function for people with unilateral hemiplegia, so that they can perform daily activities independently. However, owing to limited human resources, the hand function training available in current clinical settings is significantly less than the adequate amount needed to drive optimal neural reorganization. In this study, we designed a lightweight and portable hand exoskeleton with a hand-sensing glove for bilateral hand training and home-based rehabilitation. The hand-sensing glove measures the hand movement of the less-affected hand using a flex sensor. Thereafter, the affected hand is driven by the hand exoskeleton using the measured hand movements. Compared with the existing hand exoskeletons, our hand exoskeleton improves the flexible mechanism for the back of the hand for better wearing experience and the thumb mechanism to make the pinch gesture possible. We designed a virtual reality game to increase the willingness of repeated movement practice for rehabilitation. Our system not only facilitates bilateral hand training but also assists in activities of daily living. This system could be beneficial for patients with hemiplegia for starting correct and sufficient hand function training in the early stages to optimize their recovery.

Microecology research: a new target for the prevention of asthma.

The incidence and prevalence of asthma have increased remarkably in recent years. There are lots of factors contributing to the occurrence and development of asthma. With the improvement of sequencing technology, it has been found that the microbiome plays an important role in the formation of asthma in early life. The roles of the microbial environment and human microbiome in the occurrence and development of asthma have attracted more and more attention. The environmental microbiome influences the occurrence of asthma by shaping the human microbiome. The specific mechanism may be related to the immune regulation of Toll-like receptors and T cells (special Tregs). Intestinal microbiome is formed and changed by regulating diet and lifestyle in early life, which may affect the development and maturation of the pulmonary immune system through the intestinal-pulmonary axis. It is well-recognized that both environmental microbiomes and human microbiomes can influence the onset of asthma. This review aims to summarize the recent advances in the research of microbiome, its relationship with asthma, and the possible mechanism of the microbiome in the occurrence and development of asthma. The research of the microbial environment and human microbiome may provide a new target for the prevention of asthma in children who have high-risk factors to allergy. However, further study of "when and how" to regulate microbiome is still needed.

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning.

Objective: In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained decoders with several days of training data to make them inherently robust to changes in neural recording conditions. However, these decoders might not be robust to changes in neural recording conditions when only a few days of training data are available. In time-series prediction and feedback control system, an error feedback was commonly adopted to reduce the effects of model uncertainty. This motivated us to introduce an error feedback to a neural decoder for dealing with the variability in neural recording conditions. Approach: We proposed an evolutionary constructive and pruning neural network with error feedback (ECPNN-EF) as a neural decoder. The ECPNN-EF with partially connected topology decoded the instantaneous firing rates of each sorted unit into forelimb movement of a rat. Furthermore, an error feedback was adopted as an additional input to provide kinematic information and thus compensate for changes in functional mapping. The proposed neural decoder was trained on data collected from a water reward-related lever-pressing task for a rat. The first 2 days of data were used to train the decoder, and the subsequent 10 days of data were used to test the decoder. Main Results: The ECPNN-EF under different settings was evaluated to better understand the impact of the error feedback and partially connected topology. The experimental results demonstrated that the ECPNN-EF achieved significantly higher daily decoding performance with smaller daily variability when using the error feedback and partially connected topology. Significance: These results suggested that the ECPNN-EF with partially connected topology could cope with both within- and across-day changes in neural recording conditions. The error feedback in the ECPNN-EF compensated for decreases in decoding performance when neural recording conditions changed. This mechanism made the ECPNN-EF robust against changes in functional mappings and thus improved the long-term decoding stability when only a few days of training data were available.

In Silico Identification of Three Types of Integrative and Conjugative Elements in Elizabethkingia anophelis Strains Isolated from around the World.

Elizabethkingia anophelis is an emerging global multidrug-resistant opportunistic pathogen. We assessed the diversity among 13 complete genomes and 23 draft genomes of E. anophelis strains derived from various environmental settings and human infections from different geographic regions around the world from 1950s to the present. Putative integrative and conjugative elements (ICEs) were identified in 31/36 (86.1%) strains in the study. A total of 52 putative ICEs (including eight degenerated elements lacking integrases) were identified and categorized into three types based on the architecture of the conjugation module and the phylogeny of the relaxase, coupling protein, TraG, and TraJ protein sequences. The type II and III ICEs were found to integrate adjacent to tRNA genes, while type I ICEs integrate into intergenic regions or into a gene. The ICEs carry various cargo genes, including transcription regulator genes and genes conferring antibiotic resistance. The adaptive immune CRISPR-Cas system was found in nine strains, including five strains in which CRISPR-Cas machinery and ICEs coexist at different locations on the same chromosome. One ICE-derived spacer was present in the CRISPR locus in one strain. ICE distribution in the strains showed no geographic or temporal patterns. The ICEs in E. anophelis differ in architecture and sequence from CTnDOT, a well-studied ICE prevalent in Bacteroides spp. The categorization of ICEs will facilitate further investigations of the impact of ICE on virulence, genome epidemiology, and adaptive genomics of E. anophelisIMPORTANCEElizabethkingia anophelis is an opportunistic human pathogen, and the genetic diversity between strains from around the world becomes apparent as more genomes are sequenced. Genome comparison identified three types of putative ICEs in 31 of 36 strains. The diversity of ICEs suggests that they had different origins. One of the ICEs was discovered previously from a large E. anophelis outbreak in Wisconsin in the United States; this ICE has integrated into the mutY gene of the outbreak strain, creating a mutator phenotype. Similar to ICEs found in many bacterial species, ICEs in E. anophelis carry various cargo genes that enable recipients to resist antibiotics and adapt to various ecological niches. The adaptive immune CRISPR-Cas system is present in nine of 36 strains. An ICE-derived spacer was found in the CRISPR locus in a strain that has no ICE, suggesting a past encounter and effective defense against ICE.

Complete Circularized Genome Sequences of Four Strains of Elizabethkingia anophelis, Including Two Novel Strains Isolated from Wild-Caught Anopheles sinensis.

We provide complete circularized genome sequences of two mosquito-derived Elizabethkingia anophelis strains with draft sequences currently in the public domain (R26 and Ag1), and two novel E. anophelis strains derived from a different mosquito species, Anopheles sinensis (AR4-6 and AR6-8). The genetic similarity of all four mosquito-derived strains is remarkable.