Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

A novel and ubiquitous miRNA-involved regulatory module ensures precise phosphorylation of RNA polymerase II and proper transcription.

Proper transcription orchestrated by RNA polymerase II (RNPII) is crucial for cellular development, which is rely on the phosphorylation state of RNPII's carboxyl-terminal domain (CTD). Sporangia, developed from mycelia, are essential for the destructive oomycetes Phytophthora, remarkable transcriptional changes are observed during the morphological transition. However, how these changes are rapidly triggered and their relationship with the versatile RNPII-CTD phosphorylation remain enigmatic. Herein, we found that Phytophthora capsici undergone an elevation of Ser5-phosphorylation in its uncanonical heptapeptide repeats of RNPII-CTD during sporangia development, which subsequently changed the chromosomal occupation of RNPII and primarily activated transcription of certain genes. A cyclin-dependent kinase, PcCDK7, was highly induced and phosphorylated RNPII-CTD during this morphological transition. Mechanistically, a novel DCL1-dependent microRNA, pcamiR1, was found to be a feedback modulator for the precise phosphorylation of RNPII-CTD by complexing with PcAGO1 and regulating the accumulation of PcCDK7. Moreover, this study revealed that the pcamiR1-CDK7-RNPII regulatory module is evolutionarily conserved and the impairment of the balance between pcamiR1 and PcCDK7 could efficiently reduce growth and virulence of P. capsici. Collectively, this study uncovers a novel and evolutionary conserved mechanism of transcription regulation which could facilitate correct development and identifies pcamiR1 as a promising target for disease control.

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed.

N6-methyloxyadenine-mediated detoxification and ferroptosis confer a trade-off between multi-fungicide resistance and fitness.

Multi-fungicide resistance (MFR) is a serious environmental problem, which results in the excessive use of fungicides. Fitness penalty, as a common phenomenon in MFR, can partially counteract the issue of resistance due to the weakened vigor of MFR pathogens. Their underlying mechanism and relationship remain unexplained. By Oxford Nanopore Technologies sequencing and dot blot, we found that N6-methyloxyadenine (6mA) modification, the dominate epigenetic marker in Phytophthora capsici, was significantly altered after MFR emerged. Among the differently methylated genes, PcGSTZ1 could efficiently detoxify SYP-14288, a novel uncoupler, through complexing the fungicide with glutathione and induce MFR. Interestingly, PcGSTZ1 overexpression was induced by elevated 6mA levels and chromatin accessibility to its genomic loci. Moreover, the overexpression led to reactive oxygen species burst and ferroptosis in SYP-14288-resistant mutants, which enhanced the resistance and induced fitness penalty in P. capsici through triggering low energy shock adaptive response. Furthermore, this study revealed that the 6mA-PcGSTZ1-ferroptosis axis could mediate intergenerational resistance memory transmission and enabled adaptive advantage to P. capsici. In conclusion, the findings provide new insights into the biological role of 6mA as well as the mechanisms underlying the trade-off between MFR and fitness. These could also benefit disease control through the blockade of the epigenetic axis to resensitize resistant isolates.IMPORTANCEN6-methyloxyadenine (6mA) modification on DNA is correlated with tolerance under different stress in prokaryotes. However, the role of 6mA in eukaryotes remains poorly understood. Our current study reveals that DNA adenine methyltransferase 1 (DAMT1)-mediated 6mA modification at the upstream region of GST zeta 1 (GSTZ1) is elevated in the resistant strain. This elevation promotes the detoxification uncoupler and induces multifungicide resistance (MFR). Moreover, the overexpression led to reactive oxygen species burst and ferroptosis in SYP-14288-resistant mutants, which enhanced the resistance and induced fitness penalty in Phytophthora capsici through triggering low energy shock adaptive response. Furthermore, this study revealed that the 6mA-PcGSTZ1-ferroptosis axis could mediate intergenerational resistance memory transmission and enabled adaptive advantage to P. capsici. Overall, our findings uncover an innovative mechanism underlying 6mA modification in regulating PcGSTZ1 transcription and the ferroptosis pathway in P. capsici.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection.

OBJECTIVE: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. METHODS: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. RESULTS: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. CONCLUSION: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SIGNIFICANCE: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.

Introduction: In recent years, more and more attention has been paid to the visual fatigue caused by steady state visual evoked potential (SSVEP) paradigm. It is well known that the large-scale application of brain-computer interface is closely related to SSVEP, and the fatigue caused by SSVEP paradigm leads to the reduction of application effect. At present, the mainstream method of objectively quantifying visual fatigue in SSVEP paradigm is based on traditional canonical correlation analysis (CCA). Methods: In this paper, we propose a new SSVEP paradigm visual fatigue quantification algorithm based on underdamped second-order stochastic resonance (USSR) to accurately quantify visual fatigue caused by SSVEP paradigm in different working modes using single-channel electroencephalogram (EEG) signals. This scheme uses the fixed-step energy parameter optimization algorithm we designed, combined with the USSR model, to significantly improve the signal-to-noise ratio of the processed signal at the target characteristic frequency. We not only compared the new algorithm with CCA, but also with the traditional subjective quantitative visual fatigue gold standard Likert fatigue scale. Results: There was no significant difference (p = 0.090) between the quantitative value of paradigm fatigue obtained by the single channel SSVEP processed by the new algorithm and the gold standard of subjective fatigue quantification, while there was a significant difference (p < 0.001***) between the quantitative value of paradigm fatigue obtained by the traditional multi-channel CCA algorithm and the gold standard of subjective fatigue quantification. Discussion: The conclusion shows that the quantization value obtained by the new algorithm can better match the subjective gold standard score, which also shows that the new algorithm is more reliable, which reflects the superiority of the new algorithm.

Bionic intelligent ankle-foot prosthesis based on the conjugate curved surface.

Given the poor biomimetic motion of traditional ankle-foot prostheses, it is of great significance to develop an intelligent prosthesis that can realize the biomimetic mechanism of human feet and ankles. To this end, we presented a bionic intelligent ankle-foot prosthesis based on the complex conjugate curved surface. The proposed prosthesis is mainly composed of the rolling conjugated joints with a bionic design and the carbon fiber energy-storage foot. We investigated the flexibility of the prosthetic ankle joint movement, and the ability of the prosthetic foot to absorb ground impact during the gait cycle. Experimental results showed the matching of the ankle/toe position relationship of the human foot during simulated walking, which is helpful to realize the biomimetic motion of the human foot and ankle. It can also help therapists and clinicians provide better rehabilitation for lower-limb amputees.

Development and validation of a novel nomogram predicting clinically significant prostate cancer in biopsy-naive men based on multi-institutional analysis.

BACKGROUND: Prediction of clinically significant prostate cancer (csPCa) is essential to select biopsy-naive patients for prostate biopsy. This study was to develop and validate a nomogram based on clinicodemographic parameters and exclude csPCa using prostate-specific antigen density (PSAD) stratification. METHODS: Independent predictors were determined via univariate and multivariate logistic analysis and adopted for developing a predictive nomogram, which was assessed in terms of discrimination, calibration, and net benefit. Different PSAD thresholds were used for deciding immediate biopsies in patients with Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions. RESULTS: A total of 932 consecutive patients who underwent ultrasound-guided transperineal cognitive biopsy were enrolled in our study. In the development cohort, age (odds ratio [OR], 1.075; 95% confidence interval [CI], 1.036-1.114), PSAD (OR, 6.003; 95% CI, 2.826-12.751), and PI-RADS (OR, 3.419; 95% CI, 2.453-4.766) were significant predictors for csPCa. On internal and external validation, this nomogram showed high areas under the curve of 0.943, 0.922, and 0.897, and low Brier scores of 0.092, 0.102, and 0.133 and insignificant unreliability tests of 0.713, 0.490, and 0.859, respectively. Decision curve analysis revealed this model could markedly improve clinical net benefit. The probability of excluding csPCa was 98.51% in patients with PI-RADS 3 lesions and PSAD <0.2 ng/ml2 . CONCLUSION: This novel nomogram including age, PSAD, and PI-RADS could be applied to accurately predict csPCa, and 44.08% of patients with equivocal imaging findings plus PSAD <0.2 ng/ml2 could safely forgo biopsy.

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study.

Objective.The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control.Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure.Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R2= 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R2= 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability.Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.

Objective: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR). Methods: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features. Results: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components. Conclusion: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness. Significance: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

The CAP superfamily protein PsCAP1 secreted by Phytophthora triggers immune responses in Nicotiana benthamiana through a leucine-rich repeat receptor-like protein.

The role of cysteine-rich secretory proteins, antigen 5, and pathogenesis-related 1 (CAP) superfamily proteins in the innate immune responses of mammals is well characterized. However, the biological function of CAP superfamily proteins in plant-microbe interactions is poorly understood. We used proteomics and transcriptome analyses to dissect the apoplastic effectors secreted by the oomycete Phytophthora sojae during early infection of soybean leaves. By transiently expressing these effectors in Nicotiana benthamiana, we identified PsCAP1, a novel type of secreted CAP protein that triggers immune responses in multiple solanaceous plants including N. benthamiana. This secreted CAP protein is conserved among oomycetes, and multiple PsCAP1 homologs can be recognized by N. benthamiana. PsCAP1-triggered immune responses depend on the N-terminal immunogenic fragment (aa 27-151). Pretreatment of N. benthamiana with PsCAP1 or the immunogenic fragment increases plant resistance against Phytophthora. The recognition of PsCAP1 and different homologs requires the leucine-rich repeat receptor-like protein RCAP1, which associates with two central receptor-like kinases BRI1-associated receptor kinase 1 (BAK1) and suppressor of BIR1-1 (SOBIR1) in planta. These findings suggest that the CAP-type apoplastic effectors act as an important player in plant-microbe interactions that can be perceived by plant membrane-localized receptor to activate plant resistance.

A Phytophthora receptor-like kinase regulates oospore development and can activate pattern-triggered plant immunity.

Plant cell-surface leucine-rich repeat receptor-like kinases (LRR-RLKs) and receptor-like proteins (LRR-RLPs) form dynamic complexes to receive a variety of extracellular signals. LRR-RLKs are also widespread in oomycete pathogens, whereas it remains enigmatic whether plant and oomycete LRR-RLKs could mediate cell-to-cell communications between pathogen and host. Here, we report that an LRR-RLK from the soybean root and stem rot pathogen Phytophthora sojae, PsRLK6, can activate typical pattern-triggered immunity in host soybean and nonhost tomato and Nicotiana benthamiana plants. PsRLK6 homologs are conserved in oomycetes and also exhibit immunity-inducing activity. A small region (LRR5-6) in the extracellular domain of PsRLK6 is sufficient to activate BAK1- and SOBIR1-dependent immune responses, suggesting that PsRLK6 is likely recognized by a plant LRR-RLP. Moreover, PsRLK6 is shown to be up-regulated during oospore maturation and essential for the oospore development of P. sojae. Our data provide a novel type of microbe-associated molecular pattern that functions in the sexual reproduction of oomycete, and a scenario in which a pathogen LRR-RLK could be sensed by a plant LRR-RLP to mount plant immunity.

Case report: High-frequency repetitive transcranial magnetic stimulation for treatment of hereditary spastic paraplegia type 11.

Hereditary spastic paraplegia (HSP) is a heterogeneous group of inherited neurodegenerative disorders that currently have no cure. HSP type 11 (SPG11-HSP) is a complex form carrying mutations in the SPG11 gene. Neuropathological studies demonstrate that motor deficits in these patients are mainly attributed to axonal degeneration of the corticospinal tract (CST). Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique that can induce central nervous system plasticity and promote neurological recovery by modulating the excitability of cortical neuronal cells. Although rTMS is expected to be a therapeutic tool for neurodegenerative diseases, no previous studies have applied rTMS to treat motor symptoms in SPG11-HSP. Here, we report a case of SPG11-HSP with lower extremity spasticity and gait instability, which were improved by applying high-frequency rTMS (HF-rTMS) at the primary motor cortex (M1). Clinical and physiological features were measured throughout the treatment, including the Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), the timed up and go (TUG) test and the 10-meter walk test time (10 MWT). The structure and excitability of the CST were assessed by diffusion tensor imaging (DTI) and transcranial magnetic stimulation (TMS), respectively. After treatment, the patient gained 17 points of BBS, along with a gradual decrease in MAS scores of the bilateral lower extremity. In addition, the TUG test and 10 MWT improved to varying degrees. TMS assessment showed increased motor evoked potential (MEP) amplitude, decreased resting motor threshold (RMT), decreased central motor conduction time (CMCT), and decreased difference in the cortical silent period (CSP) between bilateral hemispheres. Using the DTI technique, we observed increased fractional anisotropy (FA) values and decreased mean diffusivity (MD) and radial diffusivity (RD) values in the CST. It suggests that applying HF-rTMS over the bilateral leg area of M1 (M1-LEG) is beneficial for SPG11-HSP. In this study, we demonstrate the potential of rTMS to promote neurological recovery from both functional and structural perspectives. It may provide a clinical rationale for using rTMS in the rehabilitation of HSP patients.

Capturing synchronization with complexity measure of ordinal pattern transition network constructed by crossplot.

To evaluate the synchronization of bivariate time series has been a hot topic, and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network into the crossplot, a new method for measuring the synchronization of bivariate time series is proposed. After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitability for short time series. Finally, electroencephalogram (EEG) data from auditory-evoked potential EEG-biometric dataset are investigated, and some useful and interesting results are obtained.

A Precise Frequency Recognition Method of Short-Time SSVEP Signals Based on Signal Extension.

OBJECTIVE: Improving the Information Transfer Rate (ITR) is a popular research topic in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The higher recognition accuracy of short-time SSVEP signal is critical to improving ITR and achieving high-speed SSVEP-BCIs. However, the existing algorithms have unsatisfactory performance on recognizing short-time SSVEP signals, especially for calibration-free methods. METHOD: This study for the first time proposed improving the recognition accuracy of short-time SSVEP signals based on the calibration-free method by extending the SSVEP signal length. A signal extension model based on Multi-channel adaptive Fourier decomposition with different Phase (DP-MAFD) is proposed to achieve signal extension. Then the Canonical Correlation Analysis based on signal extension (SE-CCA) is proposed to complete the recognition and classification of SSVEP signals after extension. RESULT: The similarity study and SNR comparison analysis on public SSVEP datasets demonstrate that the proposed signal extension model has the ability to extend SSVEP signals. The classification results show that the proposed method outperforms Canonical Correlation Analysis (CCA) and Filter Bank Canonical Correlation Analysis (FBCCA) significantly in the measure of classification accuracy and information transmission rate (ITR), especially for short-time signals. The highest ITR of SE-CCA is improved to 175.61 bits/min at around 1s, while CCA is 100.55 bits/min at 1.75s and FBCCA is 141.76 bits/min at 1.25s. CONCLUSION: The signal extension method can improve the recognition accuracy of short-time SSVEP signals and further improve the ITR of SSVEP-BCIs.

Efficacy of peripheral electrical nerve stimulation on improvements of urodynamics and voiding diary in patients with neurogenic lower urinary tract dysfunction: a systematic review and meta-analysis.

BACKGROUND: Peripheral electrical nerve stimulation is a routinely recommended treatment for non-neurogenic overactive bladder but has not been approved for patients with neurogenic lower urinary tract dysfunction (NLUTD). This systematic review and meta-analysis was to elucidate the efficacy and safety of electrostimulation and thus provide firm evidence for treating NLUTD. MATERIALS AND METHODS: We systematically performed the literature search through PubMed, Web of Science, and Cochrane Library databases in March 2022. The eligible studies were identified across the inclusion criteria and the data on urodynamic outcomes, voiding diary parameters, and safety was collected to quantitatively synthesize the pooled mean differences (MDs) with 95% CIs. Subgroup analyses and sensitivity analyses were subsequently used to investigate the possible heterogeneity. This report was achieved in accordance with the preferred reporting items for systematic reviews and meta-analyses statement. RESULTS: A total of 10 studies involving 464 subjects and 8 studies with 400 patients were included for systematic review and meta-analysis, respectively. The pooled effect estimates indicated that electrostimulation could significantly improve urodynamic outcomes, including maximum cystometric capacity (MD=55.72, 95% CI 15.73, 95.72), maximum flow rate (MD=4.71, 95% CI 1.78, 7.65), maximal detrusor pressure (MD=-10.59, 95% CI -11.45, -9.73), voided volume (MD=58.14, 95% CI 42.97, 73.31), and post-void residual (MD=-32.46, 95% CI -46.63, -18.29); for voiding diary parameters, patients undergoing electrostimulation showed lower MDs of incontinence episodes per 24 h (MD=-2.45, 95% CI -4.69, -0.20) and overactive bladder symptom score (MD=-4.46, 95% CI -6.00, -2.91). In addition to surface redness and swelling, no stimulation-related severe adverse events were reported else. CONCLUSIONS: The current evidence demonstrated that peripheral electrical nerve stimulation might be effective and safe for managing NLUTD, whereas more reliable data from large-scale randomized controlled trials are necessary to strengthen this concept.

Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis.

Background: There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies. Methods: Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays. Result: Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis. Conclusion: Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.

Effects of integrated action and sensory observation therapy based on mirror neuron and embodied cognition theory on upper limb sensorimotor function in chronic stroke: a study protocol for a randomised controlled trial.

INTRODUCTION: This study protocol aims to explore the effectiveness and neural mechanism of the integration of action observation therapy (AOT) and sensory observation therapy (SOT) for post-stroke patients on upper limb sensorimotor function. METHODS AND ANALYSIS: This is a single-centre, single-blind, randomised controlled trial. A total of 69 patients with upper extremity hemiparesis after stroke will be recruited and randomly divided into an AOT group, a combined action observation and somatosensory stimulation therapy (AOT+SST) group, and a combined AOT and SOT (AOT+SOT) group in a 1:1:1 ratio. Each group will receive 30 min of daily treatment, five times weekly for 4 weeks. The primary clinical outcome will be the Fugl-Meyer Assessment for Upper Extremity. Secondary clinical outcomes will include the Box and Blocks Test, modified Barthel Index and sensory assessment. All clinical assessments and resting-state functional MRI and diffusion tensor imaging data will be obtained at pre-intervention (T1), post-intervention (T2) and 8 weeks of follow-up (T3). ETHICS AND DISSEMINATION: The trial was approved by the Ethics Committee of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Chinese Traditional Medicine (Grant No. 2020-178). The results will be submitted to a peer-review journal or at a conference. TRIAL REGISTRATION NUMBER: ChiCTR2000040568.

CRISPR/Cas9-mediated editing of GmTAP1 confers enhanced resistance to Phytophthora sojae in soybean.

Soybean root rot disease caused by Phytophthora sojae seriously constrains soybean yield. Knocking out the susceptibility gene GmTAP1 in soybean created new soybean lines resistant to several P. sojae strains and these lines showed no agronomic penalties in the field.

Neural signatures for the n-back task with different loads: An event-related potential study.

The n-back task is widely used in working memory (WM) research. However, it remains unclear how the electrophysiological correlates of WM processes, the P2, N2, P300, and negative slow wave (NSW), are affected by differences in load. Specifically, while previous work has examined the P300, less attention has been paid to the other components assessing the load of the n-back paradigm. The present study aims to investigate whether other sub-processes in WM (such as inhibitory control) are as sensitive to n-back load changes as the update process by observing changes in the above event-related potential (ERP) components. The results showed poorer behavioral performance with increasing WM load. Greater NSW and smaller P300 amplitudes were elicited by n-back task with a higher load compared to that with lower load. In contrast, there was no significant effect of the n-back load on the amplitudes of P2 and N2. These findings suggest that the updating process and the maintenance process are sensitive to the n-back load change. Therefore, changes in the updating and maintenance processes should be considered when using the n-back task to manipulate the WM load in experiments. The present study may contribute to the understanding of the complexity of WM loads. Additionally, a theoretical basis for follow-up research to explore ways of improving WM performance with high load is provided.