Modulating Inter-Muscular Coordination Patterns in the Upper Extremity Induces Changes to Inter-Muscular, Cortico-Muscular, and Cortico-Cortical Connectivity.

OBJECTIVE: The changes in neural drive to muscles associated with modulation of inter-muscular coordination in the upper extremity have not yet been investigated. Such information could help elucidate the neural mechanisms behind motor skill learning. METHODS: Six young, neurologically healthy participants underwent a six-week training protocol to decouple two synergist elbow flexor muscles as a newly learned motor skill in the isometric force generation in upward and medial directions. Concurrent electroencephalography and surface electromyography from twelve upper extremity muscles were recorded in two conditions (As-Trained & Habitual) across two assessments (Week 0 vs. Week 6). Changes to inter-muscular connectivity (IMC), functional muscle networks, cortico-muscular connectivity (CMC), cortico-cortical connectivity (CCC) as well as functional brain network controllability (FBNC) associated with the modulation of inter-muscular coordination patterns were assessed to provide a perspective on the neural mechanisms for the newly learned motor skills. RESULTS: Significant decreases in elbow flexor IMC, CMC, and increases in CCC were observed. No significant changes were observed for FBNC. CONCLUSION: The results of this study suggest that modulating the inter-muscular coordination of the elbow flexor muscle synergy during isometric force generation is associated with multiple yet distinct changes in functional connectivity across the central and peripheral perspectives. SIGNIFICANCE: Understanding the neural mechanisms of modulating inter-muscular coordination patterns can help inform motor rehabilitation regimens.

Corticospinal and corticoreticulospinal projections benefit motor behaviors in chronic stroke.

After corticospinal tract (CST) stroke, several motor deficits in the upper extremity (UE) emerge, including diminished muscle strength, motor control, and muscle individuation. Both the ipsilesional CST and contralesional corticoreticulospinal tract (CReST) innervate the paretic UE and may have different innervation patterns for the proximal and distal UE segments. These patterns may underpin distinct pathway relationships to separable motor behaviors. In this cross-sectional study of 15 chronic stroke patients and 28 healthy subjects, we examined two key questions: (1) whether segmental motor behaviors differentially relate to ipsilesional CST and contralesional CReST projection strengths, and (2) whether motor behaviors segmentally differ in the paretic UE. We measured strength, motor control, and muscle individuation in a proximal (biceps, BIC) and distal muscle (first dorsal interosseous, FDI) of the paretic UE. We measured the projection strengths of the ipsilesional CST and contralesional CReST to these muscles using transcranial magnetic stimulation (TMS). Stroke subjects had abnormal motor control and muscle individuation despite strength comparable to healthy subjects. In stroke subjects, stronger ipsilesional CST projections were linked to superior motor control in both UE segments, whereas stronger contralesional CReST projections were linked to superior muscle strength and individuation in both UE segments. Notably, both pathways also shared associations with behaviors in the proximal segment. Motor control deficits were segmentally comparable, but muscle individuation was worse for distal motor performance. These results suggest that each pathway has specialized contributions to chronic motor behaviors but also work together, with varying levels of success in supporting chronic deficits. Key points summary: Individuals with chronic stroke typically have deficits in strength, motor control, and muscle individuation in their paretic upper extremity (UE). It remains unclear how these altered behaviors relate to descending motor pathways and whether they differ by proximal and distal UE segment.In this study, we used transcranial magnetic stimulation (TMS) to examine projection strengths of the ipsilesional corticospinal tract (CST) and contralesional corticoreticulospinal tract (CReST) with respect to quantitated motor behaviors in chronic stroke.We found that stronger ipsilesional CST projections were associated with better motor control in both UE segments, whereas stronger contralesional CReST projections were associated with better strength and individuation in both UE segments. In addition, projections of both pathways shared associations with motor behaviors in the proximal UE segment.We also found that deficits in strength and motor control were comparable across UE segments, but muscle individuation was worse with controlled movement in the distal UE segment.These results suggest that the CST and CReST have specialized contributions to chronic motor behaviors and also work together, although with different degrees of efficacy.

Exploring the Prospects of Transcranial Electrical Stimulation (tES) as a Therapeutic Intervention for Post-Stroke Motor Recovery: A Narrative Review.

INTRODUCTION: Stroke survivors often have motor impairments and related functional deficits. Transcranial Electrical Stimulation (tES) is a rapidly evolving field that offers a wide range of capabilities for modulating brain function, and it is safe and inexpensive. It has the potential for widespread use for post-stroke motor recovery. Transcranial Direct Current Stimulation (tDCS), Transcranial Alternating Current Stimulation (tACS), and Transcranial Random Noise Stimulation (tRNS) are three recognized tES techniques that have gained substantial attention in recent years but have different mechanisms of action. tDCS has been widely used in stroke motor rehabilitation, while applications of tACS and tRNS are very limited. The tDCS protocols could vary significantly, and outcomes are heterogeneous. PURPOSE: the current review attempted to explore the mechanisms underlying commonly employed tES techniques and evaluate their prospective advantages and challenges for their applications in motor recovery after stroke. CONCLUSION: tDCS could depolarize and hyperpolarize the potentials of cortical motor neurons, while tACS and tRNS could target specific brain rhythms and entrain neural networks. Despite the extensive use of tDCS, the complexity of neural networks calls for more sophisticated modifications like tACS and tRNS.

Analysis of Multiscale Corticomuscular Coupling Networks Based on Ordinal Patterns.

The coupled analysis of corticomuscular function based on physiological electrical signals can identify differences in causal relationships between electroencephalogram (EEG) and surface electromyogram (sEMG) in different motor states. The existing methods are mainly devoted to the analysis in the same frequency band, while ignoring the cross-band coupling, which plays an active role in motion control. Considering the inherent multiscale characteristics of physiological signals, a method combining Ordinal Partition Transition Networks (OPTNs) and Multivariate Variational Modal Decomposition (MVMD) was proposed in this paper. The EEG and sEMG were firstly decomposed on a time-frequency scale using MVMD, and then the coupling strength was calculated by the OPTNs to construct a corticomuscular coupling network, which was analyzed with complex network parameters. Experimental data were obtained from a self-acquired dataset consisting of EEG and sEMG of 16 healthy subjects at different sizes of constant grip force. The results showed that the method was superior in representing changes in the causal link among multichannel signals characterized by different frequency bands and grip strength patterns. Complex information transfer between the cerebral cortex and the corresponding muscle groups during constant grip force output from the human upper limb. Furthermore, the sEMG of the flexor digitorum superficialis (FDS) in the low frequency band is the hub in the effective information transmission between the cortex and the muscle, while the importance of each frequency component in this transmission network becomes more dispersed as the grip strength grows, and the increase in coupling strength and node status is mainly in the γ band (30~60Hz). This study provides new ideas for deconstructing the mechanisms of neural control of muscle movements.

Expanding the repertoire of intermuscular coordination patterns and modulating intermuscular connectivity in stroke-affected upper extremity through electromyogram-guided training: a pilot study.

Abnormal intermuscular coordination is a major stroke-induced functional motor impairment in the upper extremity (UE). Previous studies have computationally identified the abnormalities in the intermuscular coordination in the stroke-affected UE and their negative impacts on motor outputs. Therefore, targeting the aberrant muscle synergies has the potential as an effective approach for stroke rehabilitation. Recently, we verified the modifiability of the naturally expressed muscle synergies of young able-bodied adults in UE through an electromyographic (EMG) signal-guided exercise protocol. This study tested if an EMG-guided exercise will induce new muscle synergies, alter the associated intermuscular connectivity, and improve UE motor outcome in stroke-affected UE with moderate-to-severe motor impairment. The study used the six-week isometric EMG signal-guided exercise protocol that focused on independently activating two specific muscles, the biceps and brachioradialis, to develop new muscle activation groups. The study found that both the stroke and age-matched, able-bodied groups were able to develop new muscle coordination patterns through the exercise while habitual muscle activation was still available, which led to improvements in the motor control of the trained arm. In addition, the results provided preliminary evidence of increased intermuscular connectivity between targeted muscles in the beta-band frequencies for stroke patients after training, suggesting a modulation of the common neural drive. These findings suggest that our isometric exercise protocol has the potential to improve stroke survivors' performance of UE in their activities in daily lives (ADLs) and, ultimately, their quality of life through expanding their repertoire of intermuscular coordination.Clinical Relevance- This study shows the feasibility of expanding the intermuscular coordination pattern in stroke-affected UE through an isometric EMG-guided exercise which positively affects task performance and intermuscular connectivity.

Image Segmentation for High-Density Surface Electromyography Mappings of Pelvic Floor Muscle Activity of Women with Interstitial Cystitis/Bladder Pain Syndrome.

Interstitial cystitis/bladder pain syndrome (IC/BPS) can result in pelvic floor muscle (PFM) overactivity. Current clinical assessment protocols include basic electromyographic assessment of PFM activation; however, they do not provide a comprehensive assessment localized to each region of the PFM. We examined the ability of high-definition features from intravaginal high-density surface electromyography (HD-sEMG) to assess the severity of PFM overactivity in female IC/BPS patients. HD-sEMG was collected from fifteen female IC/BPS patients and fifteen urologically healthy female controls. The 2D mappings of root mean squared amplitude (RMS) at rest normalized by maximal voluntary contraction (resting RMS ratios) were segmented via k-means to identify areas of peak activity and surrounding activity. Female IC/BPS patients exhibited significantly greater resting RMS ratios for peak activity (p=0.0096), surrounding activity (p=0.0003), and average activity (p=0.0016) compared to healthy female controls. Furthermore, the area of peak activity was significantly larger for female IC/BPS patients than for healthy female controls (p=0.0063). Image segmentation of intravaginal HD-sEMG provides a more robust biomarker of PFM as compared to current methods.

Alterations of Motor Unit Characteristics Associated With Muscle Fatigue.

This study aims to characterize motor unit (MU) features associated with muscle fatigue, using high-density surface electromyography (HD-sEMG). The same MUs recruited before/after, and during muscle fatigue were identified for analysis. The surface location of the innervation zones (IZs) of the MUs was identified from the HD-sEMG bipolar motor unit action potential (MUAP) map. The depth of the MU was also identified from the decay pattern of the MUAP along the muscle fiber transverse direction. Both the surface IZ location and the MU depth information were utilized to ensure the same MU was examined during the contraction before/after muscle fatigue. The MUAP similarity, defined as the correlation coefficient between MUAP morphology, was adopted to reveal the alterations in MU characteristics under the condition of fatigue. The biomarkers of the same MUs were compared before/after fatigue (task 1) at 5%, 10%, and 15% maximal voluntary contraction (MVC) and in the process of continuous fatigue (task 2) at 20% MVC. Our results indicate that the MUAP morphology similarity of the same MUs was 0.91 ± 0.06 (task 1) and 0.93 ± 0.04 (task 2). The results showed that MUAP morphology maintained good stability before/after, and during muscle fatigue. The findings of this study may advance our understanding of the mechanism of MU neuromuscular fatigue.

Upper Limb Cortical-Muscular Coupling Analysis Based on Time-Delayed Back Maximum Information Coefficient Model.

In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short time series. However, it cannot identify the direction of information transmission. This paper proposes an effective time-delayed back maximum information coefficient (TDBackMIC) analysis method by introducing a time delay parameter to measure the causal coupling. Firstly, the effectiveness of TDBackMIC is verified on simulations, and then it is applied to the analysis of functional cortical-muscular coupling and intermuscular coupling networks to explore the difference of coupling characteristics under different grip force intensities. Experimental results show that functional cortical-muscular coupling and intermuscular coupling are bidirectional. The average coupling strength of EEG → EMG and EMG → EEG in beta band is 0.86 ± 0.04 and 0.81 ± 0.05 at 10% maximum voluntary contraction (MVC) condition, 0.83 ± 0.05 and 0.76 ± 0.04 at 20% MVC, and 0.76 ± 0.03 and 0.73 ± 0.04 at 30% MVC. With the increase of grip strength, the strength of functional cortical-muscular coupling in beta frequency band decreases, the intermuscular coupling network exhibits enhanced connectivity, and the information exchange is closer. The results demonstrate that TDBackMIC can accurately judge the causal coupling relationship, and functional cortical-muscular coupling and intermuscular coupling network under different grip forces are different, which provides a certain theoretical basis for sports rehabilitation.

High-Density Surface EMG Decomposition by Combining Iterative Convolution Kernel Compensation With an Energy-Specific Peel-off Strategy.

Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel 'peel-off' strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed 'peel-off' strategy estimates more accurate MUAP waveforms at six noise levels than the 'peel-off' strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ±3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.

Gamma-band Intermuscular Connectivity Is Associated With Increased Neural Drive to Pelvic Floor Muscles in Women With Interstitial Cystitis/Bladder Pain Syndrome.

PURPOSE: Interstitial cystitis/bladder pain syndrome patients can experience overactive pelvic floor muscle activity at rest. While the frequency power spectrum of pelvic floor muscle has briefly been explored, intermuscular connectivity of the pelvic floor muscle has yet to be studied, which may provide useful insight into the neurological component, ie, neural drive to muscles, in interstitial cystitis/bladder pain syndrome. MATERIALS AND METHODS: High-density surface electromyography was collected from 15 female interstitial cystitis/bladder pain syndrome patients with pelvic floor tenderness and 15 urologically healthy female controls. Intermuscular connectivity was calculated across the maximally active locations of the left and right sides of the pelvic floor muscle as identified from the root mean squared amplitude at rest and compared with Student t tests for common sensorimotor rhythms involved in motor control: alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-70 Hz) frequency bands. The root mean squared amplitudes at rest were also compared across groups. RESULTS: The resting root mean squared amplitude of the pelvic floor muscle was significantly greater in female interstitial cystitis/bladder pain syndrome patients compared to healthy female controls (P = .0046). The gamma-band intermuscular connectivity was significantly different between rest and pelvic floor muscle contraction (P = .0001) for healthy female controls, but not for female patients with interstitial cystitis/bladder pain syndrome (P = .1214). Both results indicate an elevated neural drive to pelvic floor muscle at rest in female interstitial cystitis/bladder pain syndrome patients. CONCLUSIONS: Gamma-band pelvic floor muscle connectivity in female interstitial cystitis/bladder pain syndrome patients is increased at rest. The results of this study may provide insight into the impaired neural drive to pelvic floor muscle implicated with interstitial cystitis/bladder pain syndrome.

Joint Filter-Band-Combination and Multi-View CNN for Electroencephalogram Decoding.

Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications. However, when EEG data is taken as input in raw time series, traditional 1D-CNNs are unable to acquire both frequency domain and channel association information. To solve this problem, this study proposes a novel algorithm by inserting two modules into CNN. One is the Filter Band Combination (FBC) Module, which preserves as many frequency domain features as possible while maintaining the time domain characteristics of EEG. Another module is Multi-View structure that can extract features from the output of FBC module. To prevent over fitting, we used a cosine annealing algorithm with restart strategy to update the learning rate. The proposed algorithm was validated on the BCI competition dataset and the experiment dataset, using accuracy, standard deviation, and kappa coefficient. Compared with traditional decoding algorithms, our proposed algorithm achieved an improvement of the maximum average correct rate of 6.6% on the motion imagery 4-classes recognition mission and 11.3% on the 2-classes classification task.

Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays.

Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.

Platelet-rich Plasma Promotes Restoration of the Anterior Vaginal Wall for the Treatment of Pelvic Floor Dysfunction in Rats.

STUDY OBJECTIVE: To determine the efficacy of using platelet-rich plasma (PRP) for vaginal wall repair in rats with vaginal wall impairment induced by vaginal distension (VD). DESIGN: A single-blind, randomized study. SETTING: A certified animal research facility. ANIMALS: Twenty-four female Sprague Dawley rats. INTERVENTIONS: Female Sprague Dawley rats were divided into sham (n = 8), VD (n = 8), and VD + PRP (n = 8) groups. Vaginal tissues from the VD group were dissected at 28-day post injury. VD + PRP rats received vaginal PRP injections on the 1st, 7th, 14th, and 21st day after VD and sacrificed on the 28th day. MEASUREMENTS AND MAIN RESULTS: Urodynamic tests were performed in all rats. Immunohistochemistry was used to evaluate matrix metalloprotease-2 (MMP-2) and matrix metalloprotease-9 (MMP-9). Masson's staining was used to evaluate collagen fibers and calculate collagen volume fraction. Collagen fiber damage was confirmed in the VD group, evidenced by thinner and sparse distribution of collagen fibers, with significantly higher MMP-2 and MMP-9 expression than the sham group (p <.05). The collagen fiber damage in the vaginal wall likely led to pelvic floor dysfunction (PFD), evidenced by significantly decreased bladder leak-point pressure (p <.01) and abdominal leak-point pressure (p <.01) in the VD group compared with the sham group. After completion of the PRP treatment, a significantly higher collagen volume fraction (p <.01) and significantly increased bladder leak-point pressure (p <.05) and abdominal leak-point pressure (p <.01) were achieved in the VD + PRP compared with the VD group, thus indicating repair of the vaginal wall and improvement of PFD. CONCLUSION: PRP injections facilitate the regeneration of vaginal wall tissue, particularly collagen fiber, after VD, leading to functional improvement of PFD. Findings support the feasibility of using PRP as a novel treatment for PFD.

Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network.

Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after the movement onset. The MECN method achieved statistically significant improvement on the state-of-the-art methods. The results showed that the algorithm proposed in this study can effectively decode four kinds of hand movements based on EEG signals.

Large-scale evaluation of physical drift and volatility of 2,4-D choline in cotton: a four-year field study.

BACKGROUND: Cotton with resistance to 2,4-d choline, glufosinate and glyphosate allows over-the-top use of these herbicides for postemergence weed control. Nontransgenic cotton is highly responsive to low rates of 2,4-d, causing concern among farmers when the herbicide is applied near the crop. Injury to nearby cotton following application of 2,4-d choline is sometimes blamed on volatilization of the herbicide. RESULTS: A large-scale experiment was conducted in 2018-2021 to better understand causes of damage to nearby sensitive cotton following an application of 2,4-d choline plus glufosinate-ammonium. The herbicides were applied to 0.4 ha in the center of a 4-ha non-Enlist cotton field. At 30 min after application, air samplers were established in the treated center and outside the treated area in cardinal directions. The 72-h cumulative air concentration of 2,4-d in the swath ranged from 3.3 to 7.1 ng m-3 , and most volatile residues (5.0-25.5 ng m-3 ) were detected in samplers established in the downwind direction directly adjacent to the treated field. Cotton plants in three downwind transects that were covered for 30 min after application were not damaged by 2,4-d, whereas noncovered plants along the downwind transects were injured. No cotton injury occurred outside the treated area, except in the downwind direction during application even though wind direction changed after application. CONCLUSION: 2,4-d choline volatilizes, but findings show that the volatilization is not sufficient to damage cotton in the neighboring area following applications, pointing to the importance of applicators understanding wind direction/shift during the application along with proximity of sensitive crops in the downwind direction. © 2022 Society of Chemical Industry.

Inter-muscular coherence and functional coordination in the human upper extremity after stroke.

Muscle coordination and motor function of stroke patients are weakened by stroke-related motor impairments. Our earlier studies have determined alterations in inter-muscular coordination patterns (muscle synergies). However, the functional connectivity of these synergistically paired or unpaired muscles is still unclear in stroke patients. The goal of this study is to quantify the alterations of inter-muscular coherence (IMC) among upper extremity muscles that have been shown to be synergistically or non-synergistically activated in stroke survivors. In a three-dimensional isometric force matching task, surface EMG signals are collected from 6 age-matched, neurologically intact healthy subjects and 10 stroke patients, while the target force space is divided into 8 subspaces. According to the results of muscle synergy identification with non-negative matrix factorization algorithm, muscle pairs are classified as synergistic and non-synergistic. In both control and stroke groups, IMC is then calculated for all available muscle pairs. The results show that synergistic muscle pairs have higher coherence in both groups. Furthermore, anterior and middle deltoids, identified as synergistic muscles in both groups, exhibited significantly weaker IMC at alpha band in stroke patients. The anterior and posterior deltoids, identified as synergistic muscles only in stroke patients, revealed significantly higher IMC in stroke group at low gamma band. On the contrary, anterior deltoid and pectoralis major, identified as synergistic muscles in control group only, revealed significantly higher IMC in control group in alpha band. The results of muscle synergy and IMC analyses provide congruent and complementary information for investigating the mechanism that underlies post-stroke motor recovery.

Optimizing Input for Gesture Recognition using Convolutional Networks on HD-sEMG Instantaneous Images.

Hand gesture recognition using high-density surface electromyography (HD-sEMG) has gained increasing attention recently due its advantages of high spatio-temporal resolution. Convolutional neural networks (CNN) have also recently been implemented to learn the spatio-temporal features from the instantaneous samples of HD-sEMG signals. While the CNN itself learns the features from the input signal it has not been considered whether certain pre-processing techniques can further improve the classification accuracies established by previous studies. Therefore, common pre-processing techniques were applied to a benchmark HD-sEMG dataset (CapgMyo DB-a) and their validation accuracies were compared. Monopolar, bipolar, rectified, common-average referenced, and Laplacian spatial filtered configurations of the HD-sEMG signals were evaluated. Results showed that the baseline monopolar HD-sEMG signals maintained higher prediction accuracies versus the other signal configurations. The results of this study discourage the use of extra pre-processing steps when using convolutional networks to classify the instantaneous samples of HD-sEMG for gesture recognition.

Alterations in Muscle Networks in the Upper Extremity of Chronic Stroke Survivors.

Muscle networks describe the functional connectivity between muscles quantified through the decomposition of intermuscular coherence (IMC) to identify shared frequencies at which certain muscles are co-modulated by common neural input. Efforts have been devoted to characterizing muscle networks in healthy subjects but stroke-linked alterations to muscle networks remain unexplored. Muscle networks were assessed for eight key upper extremity muscles during isometric force generation in stroke survivors with mild, moderate, and severe impairment and compared against healthy controls to identify stroke-specificalterations in muscle connectivity. Coherence matrices were decomposed using non-negative matrix factorization. The variance accounted for thresholding was then assessed to identify the number of muscle networks. Results showed that the number of muscle networks decreased in stroke survivors compared to age-matched healthy controls (four networks in the healthy control group) as the severity of post-stroke motor impairment increased (three in the mild- and two in the moderate- and severe-strokegroups). Statistically significant reductions of IMC in the synergistic deltoid muscles in the alpha-band in stroke patients versus healthy controls ( p < 0.05) were identified. This study represents the first effort, to the best of our knowledge, to assess stroke-linked alterations in functional intermuscular connectivity using muscle network analysis. The findings revealed a pattern of alterations to muscle networks in stroke survivors compared to healthy controls, as a result of the loss of brain function associated with the stroke. These alterations in muscle networks reflected underlying pathophysiology. These findings can help better understand the motor impairment and motor control in stroke and may advance rehabilitation efforts for stroke by identifying the impaired neuromuscular coordination among multiple muscles in the frequency domain.

Cap 1 Messenger RNA Synthesis with Co-transcriptional CleanCap® Analog by In Vitro Transcription.

Synthetic messenger RNA (mRNA)-based therapeutics are an increasingly popular approach to gene and cell therapies, genome engineering, enzyme replacement therapy, and now, during the global SARS-CoV-2 pandemic, vaccine development. mRNA for such purposes can be synthesized through an enzymatic in vitro transcription (IVT) reaction and formulated for in vivo delivery. Mature mRNA requires a 5'-cap for gene expression and mRNA stability. There are two methods to add a cap in vitro: via a two-step multi-enzymatic reaction or co-transcriptionally. Co-transcriptional methods minimize reaction steps and enzymes needed to make mRNA when compared to enzymatic capping. CleanCap® AG co-transcriptional capping results in 5 mg/ml of IVT with 94% 5'-cap 1 structure. This is highly efficient compared to first-generation cap analogs, such as mCap and ARCA, that incorporate cap 0 structures at lower efficiency and reaction yield. This article describes co-transcriptional capping using TriLink Biotechnology's CleanCap® AG in IVT. © 2021 Wiley Periodicals LLC. Basic Protocol 1: IVT with CleanCap Basic Protocol 2: mRNA purification and analysis.