Unlocking Enhanced Ionic Transport: A Machine Learning-Driven AIMD Study on Doping, Defects, and Strain in Antiperovskite Solid-State Electrolytes.

Ab initio molecular dynamics (AIMD) simulations have been employed to investigate doped antiperovskite solid-state electrolyte (AP SSE) structures, specifically formulated as LiOClBr ( = 0, 0.25, 0.50, 0.

Continuous Estimation of Hand Kinematics from Electromyographic Signals based on Power-and Time-Efficient Transformer Deep Learning Network.

Surface Electromyographic (sEMG) signals contain motor-related information and therefore can be used for human-machine interaction (HMI). Deep learning plays an important role in extracting motor-related information from sEMG signals. However, most studies prioritize model accuracy without sufficient consideration of model efficiency, including the model size, power consumption, and the computational speed of the model.

Multi-scale attention patching encoder network: a deployable model for continuous estimation of hand kinematics from surface electromyographic signals.

Background: Simultaneous and proportional control (SPC) based on surface electromyographic (sEMG) signals has emerged as a research hotspot in the field of human-machine interaction (HMI). However, the existing continuous motion estimation methods mostly have an average Pearson coefficient (CC) of less than 0.85, while high-precision methods suffer from the problem of long inference time (> 200 ms) and can only estimate SPC of less than 15 hand movements, which limits their applications in HMI.

A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.

This study proposed a U-Net based partial convolutional time-domain model for a real-time high-density surface electromyography (HD-sEMG) decomposition. The model combines U-Net and a separation block containing partial convolution, aiming to efficiently identify motor units (MUs) without preprocessing. The proposed U-Net based network was trained by the HD-sEMG signals with innervation pulse trains (IPTs) labels, and the results are compared between different step sizes, noises, and model structures under the sliding time window with 120 sampling points.

Comprehensive analysis of housekeeping genes, tissue-specific genes, and dynamic regulation across developmental stages in pearl millet.

Background: Pearl millet (Pennisetum glaucum (L.) R. Br.

Evaluation of an Active Disturbance Rejection Controller for Ophthalmic Robots with Piezo-Driven Injector.

Retinal vein cannulation involves puncturing an occluded vessel on the micron scale. Even single millinewton force can cause permanent damage. An ophthalmic robot with a piezo-driven injector is precise enough to perform this delicate procedure, but the uncertain viscoelastic characteristics of the vessel make it difficult to achieve the desired contact force without harming the retina.

Robust gesture recognition based on attention-deep fast convolutional neural network and surface electromyographic signals.

The surface electromyographic (sEMG) signals reflect human motor intention and can be utilized for human-machine interfaces (HMI). Comparing to the sparse multi-channel (SMC) electrodes, the high-density (HD) electrodes have a large number of electrodes and compact space between electrodes, which can achieve more sEMG information and have the potential to achieve higher performance in myocontrol. However, when the HD electrodes grid shift or damage, it will affect gesture recognition and reduce recognition accuracy.

Fusion inception and transformer network for continuous estimation of finger kinematics from surface electromyography.

Decoding surface electromyography (sEMG) to recognize human movement intentions enables us to achieve stable, natural and consistent control in the field of human computer interaction (HCI). In this paper, we present a novel deep learning (DL) model, named fusion inception and transformer network (FIT), which effectively models both local and global information on sequence data by fully leveraging the capabilities of Inception and Transformer networks. In the publicly available Ninapro dataset, we selected surface EMG signals from six typical hand grasping maneuvers in 10 subjects for predicting the values of the 10 most important joint angles in the hand.

Cross-Subject Lifelong Learning for Continuous Estimation From Surface Electromyographic Signal.

The employment of surface electromyographic (sEMG) signals in the estimation of hand kinematics represents a promising non-invasive methodology for the advancement of human-machine interfaces. However, the limitations of existing subject-specific methods are obvious as they confine the application to individual models that are custom-tailored for specific subjects, thereby reducing the potential for broader applicability. In addition, current cross-subject methods are challenged in their ability to simultaneously cater to the needs of both new and existing users effectively.

A rotary transformer cross-subject model for continuous estimation of finger joints kinematics and a transfer learning approach for new subjects.

Introduction: Surface Electromyographic (sEMG) signals are widely utilized for estimating finger kinematics continuously in human-machine interfaces (HMI), and deep learning approaches are crucial in constructing the models. At present, most models are extracted on specific subjects and do not have cross-subject generalizability. Considering the erratic nature of sEMG signals, a model trained on a specific subject cannot be directly applied to other subjects.

A Bi-GRU-attention neural network to identify motor units from high-density surface electromyographic signals in real time.

To utilize surface electromyographics (sEMG) for control purposes, it is necessary to perform real-time estimation of the neural drive to the muscles, which involves real-time decomposition of the EMG signals. In this paper, we propose a Bidirectional Gate Recurrent Unit (Bi-GRU) network with attention to perform online decomposition of high-density sEMG signals. The model can give different levels of attention to different parts of the sEMG signal according to their importance using the attention mechanism.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition.

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm.

Milletdb: a multi-omics database to accelerate the research of functional genomics and molecular breeding of millets.

Millets are a class of nutrient-rich coarse cereals with high resistance to abiotic stress; thus, they guarantee food security for people living in areas with extreme climatic conditions and provide stress-related genetic resources for other crops. However, no platform is available to provide a comprehensive and systematic multi-omics analysis for millets, which seriously hinders the mining of stress-related genes and the molecular breeding of millets. Here, a free, web-accessible, user-friendly millets multi-omics database platform (Milletdb, http://milletdb.

Evaluation of continuous curvilinear capsulorhexis based on a neural-network.

Purpose: Continuous curvilinear capsulorhexis (CCC), as a prerequisite for successful cataract surgery, is one of the most important and difficult steps in phacoemulsification. In clinical practice, the size and circularity of the capsular tear and eccentricity with the lens are often employed as indicators to evaluate the effect of CCC.

Methods: We present a neural network-based model to improve the efficiency and accuracy of evaluation for capsulorhexis results.

Evaluation of the Hand Motion and Peeling Force in Inner Limiting Membrane Peeling.

Purpose: Robot assistance in membrane peeling may improve precision and dexterity or prevent complications by task automation. To design robotic devices, surgical instruments' velocity, acceptable position/pose error, and load ability need to be precisely quantified.

Methods: A fiber Bragg grating and inertial sensors are attached to forceps.

Pangenomic analysis identifies structural variation associated with heat tolerance in pearl millet.

Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance.

A BERT Based Method for Continuous Estimation of Cross-Subject Hand Kinematics From Surface Electromyographic Signals.

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following μ -law normalization.

Metabolic Dysfunction in the Regulation of the NLRP3 Inflammasome Activation: A Potential Target for Diabetic Nephropathy.

Metabolic dysfunction plays a key role in the development of diabetic nephropathy (DN). However, the exact effects and mechanisms are still unclear. The pyrin domain-containing protein 3 (NLRP3) inflammasome, a member of the nod-like receptor family, is considered a crucial inflammatory regulator and plays important roles in the progress of DN.

Precision forceps tracking and localisation using a Kalman filter for continuous curvilinear capsulorhexis.

Background: Continuous curvilinear capsulorhexis (CCC) requires surgeons to manipulate fragile eye tissue at the microscale. The limited perceptual accuracy of surgeons makes it difficult to precisely position the forceps. Robot technology provides a feasible way to improve the performance of CCC.

Operation behaviours of surgical forceps in continuous curvilinear capsulorhexis.

Background: Continuous curvilinear capsulorhexis (CCC) is a delicate ophthalmic procedure which may benefit from robot technology. Measuring the behaviours (physiological tremor, operation force) of surgeons provides baseline data to develop assistive CCC robot.

Methods: A forceps with fibre bragg grating and inertial sensors is used to measure the surgeons' behaviours while experts/novices perform CCC on ex-vivo pig eyes, in-vivo rabbit eyes and ex-vivo human lens.

Transcriptional Changes in Pearl Millet Leaves under Heat Stress.

High-temperature stress negatively affects the growth and development of plants, and therefore threatens global agricultural safety. Cultivating stress-tolerant plants is the current objective of plant breeding programs. Pearl millet is a multi-purpose plant, commonly used as a forage but also an important food staple.

Research on the drought tolerance mechanism of Pennisetum glaucum (L.) in the root during the seedling stage.

Background: Drought is one of the major environmental stresses resulting in a huge reduction in crop growth and biomass production. Pearl millet (Pennisetum glaucum L.) has excellent drought tolerance, and it could be used as a model plant to study drought resistance.

Prrx1 promotes stemness and angiogenesis via activating TGF-β/smad pathway and upregulating proangiogenic factors in glioma.

Glioma is one of the most lethal cancers with highly vascularized networks and growing evidences have identified glioma stem cells (GSCs) to account for excessive angiogenesis in glioma. Aberrant expression of paired-related homeobox1 (Prrx1) has been functionally associated with cancer stem cells including GSCs. In this study, Prrx1 was found to be markedly upregulated in glioma specimens and elevated Prrx1 expression was inversely correlated with prognosis of glioma patients.

An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks.

Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy.