NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

Observation of the Elevation of Cholinesterase Activity in Brain Glioma by a Near-Infrared Emission Chemsensor.

The excessive expression of cholinesterases (ChEs) directly disturbs the metabolism of acetylcholine (ACh), causing disordering neurotransmission in the brain or even Alzheimer's disease and cancer. However, the variation of ChEs including acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in brain glioma has not yet been investigated. Therefore, the development of a suitable method for in situ imaging ChEs in brain tissues to understand the physiological functions of ChEs in depth is very important. Herein, a new near-infrared emission fluorescent probe (IPAN) for visualization of ChE activity was developed. IPAN exhibits ultrafast response to ChEs, low detection limit for AChE (0.127 U/mL) and BChE (0.0117 U/mL), and a large Stokes shift with near-infrared emission. Based on these excellent attributes, the IPAN was effectively utilized for imaging the fluctuations of ChE activity in the apoptosis cells and zebrafish. Notably, by utilizing the unique probe IPAN, we observed a significant enhancement of ChE activity in the tumor cells and brain glioma, for the first time. We believe that this interesting finding could provide a powerful guidance for tumor resection in the future.

Highly regio- and stereoselective trans-iodofluorination of ynamides enabling the synthesis of (E)-α-fluoro-ß-iodoenamides.

A highly regio- and stereoselective trans-iodofluorination reaction of ynamides with NIS and Et3N·3HF has been achieved, affording (E)-α-fluoro-ß-iodoenamides in moderate to good yields. The reaction proceeds under mild reaction conditions and exhibits good functional group compatibility.

Optimized uniform sampling and validation of fiber tracts from magnetic resonance tractography for in vivo architectural measurement of human forearm muscles.

In vivo muscle architectural parameters can be calculated from the fiber tracts using magnetic resonance (MR) tractography. However, the reconstructed tracts may be unevenly distributed within the muscle volume and there lacks commonly used metric to quantitatively evaluate the validity of the tracts. Our objective is to measure forearm muscle architecture by uniformly sampling fiber tracts from the candidate streamlines in MR tractography and validate the reconstructed fiber tracts qualitatively and quantitatively. We proposed farthest streamline sampling (FSS) to uniformly sample fiber tracts from the candidate streamlines. The method was evaluated on the MR data acquired from 12 healthy subjects for 17 forearm muscles and was compared with two conventional methods through uniform coverage performance. Anatomical correctness was verified by: 1. visually assessing fiber orientation, 2. checking whether architectural parameters were within physiological ranges and 3. classifying architectural types. The proposed FSS yielded optimal uniform coverage performance among the three methods (P<0.05). FSS reduced the sampling of long tracts (10% fiber length reduction, P<0.05), and the estimated architectural parameters were within the physiological ranges (P<0.05). The tractography visually matched cadaveric specimens. The architectural types of 16 muscles were correctly classified except for the palmaris longus, which exhibited a linear arrangement of fiber endpoints (R2 = 0.95±0.02, P<0.001). The proposed FSS method reconstructed uniformly distributed fiber tracts and the anatomical correctness of the reconstructed tracts was verified. The novel methods allow for accurate in vivo muscle architectural measurement, which was demonstrated through the characterization of architectural properties in human forearm muscles.

Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations.

OBJECTIVE: The surface electromyography (EMG) decomposition techniques have shown promising results in neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, current decomposition methods could only decode a limited number of motor units (MUs) because of the local convergence. The number of identified MUs remains similar even though more muscles or movements are involved, where multiple motor neuron populations are activated. The objective of this study was to develop a segment-wise decomposition strategy to increase the number of MU decoded from multiple motor neuron populations. METHODS: The EMG signals were divided into several segments depending on the number of involved movements. The motor neurons, activated during each movement, were regarded as a population. The convolution kernel compensation (CKC) method was applied individually for each segment to decode the motor unit discharges from each motor neuron population. The MU filters were obtained in each segment and filtrated to estimate the MU spike trains (MUSTs) from the global EMG signals. The decomposition performance was validated on synthetic and experimental EMG signals. MAIN RESULTS: From synthetic EMG signals generated by two motor neuron populations, the proposed segment-wise CKC (swCKC) decoded significantly more MUs during low and medium excitation levels, with an increased rate of 16.3% to 75.4% compared with the conventional CKC. From experimental signals recorded during ten motor tasks, 133±24 MUs with the pulse-to-noise ratio of 36.6±6.5 dB were identified for each subject by swCKC, whereas the conventional CKC identified only 43±12 MUs. CONCLUSION AND SIGNIFICANCE: These results indicate the feasibility and superiority of the proposed swCKC to decode MU activities across motor neuron populations, extending the potential applications of EMG decomposition for neural decoding during multiple motor tasks.

A musculoskeletal model driven by muscle synergy-derived excitations for hand and wrist movements.

Objective.Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements.Approach.Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted.Main results.The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes.Significance.The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.

Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers With Approximate Conductivity Tensor.

OBJECTIVE: Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific arrangements. However, the fiber orientation may change along the fiber paths and differ from fiber to fiber. Here we propose a subject-specific EMG model that simulates the fiber trajectories in muscles of the upper arm and analytically derives the action potentials assuming an approximate conductivity tensor. METHODS: Magnetic Resonance (MR) images were acquired to generate muscle fiber paths and to build the volume conductor. While the action potentials propagated along the identified curvilinear fibers, the conductivity tensor was approximated to be cylindrically anisotropic. Single fiber action potentials (SFAPs) were computed by simulating the generation, propagation, and extinction of membrane current sources. To validate the assumption of the approximate conductivity tensor, two numerical models with approximate or exact conductivity tensors were implemented. RESULTS: The motor unit action potentials generated by the proposed analytical model and the two numerical models were highly similar (cross-correlation 0.98, normalized root mean square error, nRMSE ≤ 0.04, relative error in the median frequency of the simulated waveforms of approximately 3%). The proposed analytical model was also evaluated by comparing simulated and experimentally recorded compound muscle action potentials (CMAPs). Finally, the proposed model was used to test the accuracy of an EMG decomposition algorithm, providing a realistic benchmark. CONCLUSIONS AND SIGNIFICANCE: The proposed analytical model generates action potentials that reflect the spatial distributions of muscle fibers with curvilinear paths.

EMG Signal Filtering Based on Variational Mode Decomposition and Sub-Band Thresholding.

Surface electromyography (EMG) signals are inevitably contaminated by various noise components, including powerline interference (PLI), baseline wandering (BW), and white Gaussian noise (WGN). These noises directly degrade the efficiency of EMG processing and affect the accuracy and robustness of further applications. Currently, most of the EMG filters only target one category of noise. Here, we propose a novel filter to remove all three types of noise. The noisy EMG signal is first decomposed into an ensemble of band-limited modes using variational mode decomposition (VMD). Each category of noise is located within specific modes and is separately removed in sub-bands. In particular, WGN is suppressed by soft thresholding with a noise level-dependent threshold. The denoising performance was assessed from simulated and experimental signals using three performance metrics: the root mean square error ([Formula: see text]), the improvement in signal-to-noise ratio ([Formula: see text]), and the percentage reduction in the correlation coefficient ( η). Other methods, including traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method, were examined for comparison. The proposed method achieved the best performance to remove BW or WGN. It also effectively reduced PLI noise when the signal-to-noise ratio (SNR) was low. The SNR was improved by 18.6, 19.2, and 8.0 dB for EMG signals corrupted with PLI, BW, and WGN at -6 dB SNR, respectively. The experimental results illustrated that noise was completely removed from resting states, and obvious spikes were distinguished from action states. For two of the ten subjects, the improved SNR reached 20 dB. This study explores the special characteristics of VMD and demonstrates the feasibility of using the VMD-based filter to denoise EMG signals. The proposed filter is efficient at removing three categories of noise and can be used for any application that requires EMG signal filtering at the preprocessing stage, such as gesture recognition and EMG decomposition.

Electrodes Adaptive Model in Estimating the Depth of Motor Unit: A Motor Unit Action Potential Based Approach.

High-density surface electromyography (EMG) has been proposed to overcome the lower selectivity with respect to needle EMG and to provide information on a wide area over the considered muscle. Motor units decomposed from surface EMG signal of different depths differ in the distribution of action potentials detected in the skin surface. We propose a noninvasive model for estimating the depth of motor unit. We find that the depth of motor unit is linearly related to the Gaussian RMS width fitted by data points extracted from motor unit action potential. Simulated and experimental signals are used to evaluate the model performance. The correlation coefficient between reference depth and estimated depth is 0.92 ± 0.01 for simulated motor unit action potentials. Due to the symmetric nature of our model, no significant decrease is detected during the electrode selection procedure. We further checked the estimation results from decomposed motor units, the correlation coefficient between reference depth and estimated depth is 0.82 ± 0.07. For experimental signals, high discrimination of estimated depth vector is detected across gestures among trials. These results show the potential for a straightforward assessment of depth of motor units inside muscles. We discuss the potential of a non-invasive way for the location of decomposed motor units.

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.

OBJECTIVE: Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. METHODS: A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). MAIN RESULTS: The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was 80% for most conditions. From experimental EMG signals, on average, 12 ± 2 MUSTs were identified from each electrode grid with PNR of 25.0 ± 1.8 dB, corresponding to an estimated decomposition accuracy 75%. CONCLUSION AND SIGNIFICANCE: These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.

Subject-Specific EMG Modeling with Multiple Muscles: A Preliminary Study.

Modeling of surface electromyographic (EMG) signal has been proven valuable for signal interpretation and algorithm validation. However, most EMG models are currently limited to single muscle, either with numerical or analytical approaches. Here, we present a preliminary study of a subject-specific EMG model with multiple muscles. Magnetic resonance (MR) technique is used to acquire accurate cross section of the upper limb and contours of five muscle heads (biceps brachii, brachialis, lateral head, medial head, and long head of triceps brachii). The MR image is adjusted to an idealized cylindrical volume conductor model by image registration. High-density surface EMG signals are generated for two movements - elbow flexion and elbow extension. The simulated and experimental potentials were compared using activation maps. Similar activation zones were observed for each movement. These preliminary results indicate the feasibility of the multi-muscle model to generate EMG signals for complex movements, thus providing reliable data for algorithm validation.

[Effects of long-term fertilization on soil ammonia-oxidizing microorganisms]. / 长期施肥对土壤氨氧化微生物的影响.

Long-term fertilization can change the supply of soil carbon and nitrogen (N), with consequences on the abundance and community structure of soil microorganisms. Based on the long-term fertilization positioning experiment station of brown earth, we analyzed the dynamics of soil ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) under different fertilization treatments, including no fertilization (CK), low level of inorganic N fertilizer (N2), high level of inorganic N fertilizer (N4), and organic manure combined with inorganic N fertilizer (M2N2), aiming to provide a basis for microbiological mechanism of soil N transformation and improvement of soil fertility. The results showed that the ratio of AOA to AOB abundance was 2.28-61.95 under different fertilization treatments. Compared with that in CK, the AOA abundance was reduced by 1.6%-13.6% after long-term fertilization. The abundance of AOB in N4 treatment decreased first and then increased with soil depths, but with contrary results in other treatments. The Shannon diversity index (H), evenness index (J), and Simpson index (S) of AOB were higher than those of AOA. The AOB diversity was increased at 0-20 cm soil layer in M2N2 treatment, while that of AOA was decreased. Soil AOB clustered with soil depths, and neither AOA nor AOB community clustered with fertilization treatments. In summary, long-term fertilization altered the composition of AOA and AOB. AOA was sensitive to environment, whereas AOB was more abundant and stable.

Estimation of Motor Unit Global Firing Rate by Maximum Power Amplitude.

Motor unit (MU) global firing rate is widely applied in physiological and clinical investigation. Currently it still remains difficult to measure the MU global firing rate from sEMG. In this study, we propose a new feature of maximum power amplitude (MPA) from sEMG power spectrum. Based on an analysis of mathematical model and simulated signals, MPA was demonstrated to be highly correlated with the MU global firing rate. The performance of MPA was comparable with features based on sEMG amplitude in the time domain. Moreover, the simulation results showed that the square of MPA changed accordingly with the output force, indicating potential application estimating force using MPA2.

Structure-based bioisosterism design, synthesis, insecticidal activity and structure-activity relationship (SAR) of anthranilic diamide analogues containing 1,2,4-oxadiazole rings.

BACKGROUND: Anthranilic diamide derivatives are among the most important classes of synthetic insecticides. Moreover, the 1,2,4-oxadiazole heterocycle, a bioisostere of amide, has been extensively used in pesticides. In order to discover novel molecules with high insecticidal activities, a series of anthranilic diamide analogues containing 1,2,4-oxadiazole rings were designed and synthesised. RESULTS: A series of novel anthranilic diamide derivatives containing 1,2,4-oxadiazole were obtained, and confirmed by 1 H and 13 C nuclear magnetic resonance and high-resolution mass spectrometry. The structure of 3-bromo-N-(4-chloro-2-methyl-6-{3-[(methylsulphonyl)methyl]-1,2,4-oxadiazol-5-yl}phenyl)-1-(3-chloropyridin-2-yl)-1H-pyrazole-5-carboxamide was further characterised by X-ray diffraction analysis. In addition, bioassays showed that most of the newly synthesised compounds displayed 100% mortality against Plutella xylostella at 100 mg L-1 , and compound 3IIl showed 90% larvicidal activities at a concentration of 0.5 mg L-1 . The LC50 value of 3IIl was 0.20 mg L-1 , which indicated that it may be used as a potential leading compound for further structural optimisation. Furthermore, brief comparative molecular field analysis (CoMFA) models were established to study the structure-activity relationships (SARs) of the title compounds. CONCLUSION: Compound 3IIl may be used as a potential leading compound for further structural optimisation, and SARs and CoMFA models could provide reliable clues for further structural optimisation. © 2016 Society of Chemical Industry.