Conditional Generative Models for Simulation of EMG During Naturalistic Movements.

Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However, while modern biophysical simulations based on finite element methods (FEMs) are highly accurate, they are extremely computationally expensive and thus are generally limited to modeling static systems such as isometrically contracting limbs. As a solution to this problem, we propose to use a conditional generative model to mimic the output of an advanced numerical model.

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.

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.

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.

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

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.

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.

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.

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.

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.

[Effects of long-term fertilization on soil ammonia-oxidizing microorganisms].

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 (N), high level of inorganic N fertilizer (N), and organic manure combined with inorganic N fertilizer (MN), 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.

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.

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.

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.

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

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

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.