High-Entropy Polymer Electrolytes Derived from Multivalent Polymeric Ligands for Solid-State Lithium Metal Batteries with Accelerated Li+ Transport.

Solid-state polymer-based electrolytes (SSPEs) exhibit great possibilities in realizing high-energy-density solid-state lithium metal batteries (SSLMBs). However, current SSPEs suffer from low ionic conductivity and unsatisfactory interfacial compatibility with metallic Li because of the high crystallinity of polymers and sluggish Li+ movement in SSPEs. Herein, differing from common strategies of copolymerization, a new strategy of constructing a high-entropy SSPE from multivariant polymeric ligands is proposed. As a protocol, poly(vinylidene fluoride-co-hexafluoropropylene) (PH) chains are grafted to the demoed polyethylene imine (PEI) with abundant -NH2 groups via a click-like reaction (HE-PEIgPHE). Compared to a PH-based SSPE, our HE-PEIgPHE shows a higher modulus (6.75 vs 5.18 MPa), a higher ionic conductivity (2.14 × 10-4 vs 1.03 × 10-4 S cm-1), and a higher Li+ transference number (0.55 vs 0.42). A Li|HE-PEIgPHE|Li cell exhibits a long lifetime (1500 h), and a Li|HE-PEIgPHE|LiFePO4 cell delivers an initial capacity of 160 mAh g-1 and a capacity retention of 98.7%, demonstrating the potential of our HE-PEIgPHE for the SSLMBs.

Multi-Dimensional Medical Image Fusion With Complex Sparse Representation.

In the field of medical imaging, the fusion of data from diverse modalities plays a pivotal role in advancing our understanding of pathological conditions. Sparse representation (SR), a robust signal modeling technique, has demonstrated noteworthy success in multi-dimensional (MD) medical image fusion. However, a fundamental limitation appearing in existing SR models is their lack of directionality, restricting their efficacy in extracting anatomical details from different imaging modalities. To tackle this issue, we propose a novel directional SR model, termed complex sparse representation (ComSR), specifically designed for medical image fusion. ComSR independently represents MD signals over directional dictionaries along specific directions, allowing precise analysis of intricate details of MD signals. Besides, current studies in medical image fusion mostly concentrate on addressing either 2D or 3D fusion problems. This work bridges this gap by proposing a MD medical image fusion method based on ComSR, presenting a unified framework for both 2D and 3D fusion tasks. Experimental results across six multi-modal medical image fusion tasks, involving 93 pairs of 2D source images and 20 pairs of 3D source images, substantiate the superiority of our proposed method over 11 state-of-the-art 2D fusion methods and 4 representative 3D fusion methods, in terms of both visual quality and objective evaluation. The source code of our fusion method is available at https://github.com/Imagefusions/imagefusions/tree/main.

Whole-genome sequencing of Sphingobium baderi SC-1 and identification of a crucial 3-phenoxybenzoic acid-degrading gene.

As an efficient degradation strain, Sphingobium baderi SC-1 can breakdown 3-phenoxybenzoic acid (3-PBA) with high proficiency. To investigate the internal factors that regulate this process, we conducted whole-genome sequencing and successfully identified the pivotal 3-PBA-degrading gene sca (1,230 bp). After sca was expressed in engineered bacteria, a remarkable degradation efficiency was observed, as 20 mg/L 3-PBA was almost completely decomposed within 24 h. The phenol was formed as one of the degradation products. Notably, in addition to their ability to degrade 3-PBA, the resting cells proficiently degraded 4'-HO-3-PBA and 3'-HO-4-PBA. In conclusion, we successfully identified and validated sca as the pivotal enzyme responsible for the efficient degradation of 3-PBA from Sphingomonas baderi, providing a crucial theoretical foundation for further explorations on the degradation potential of SC-1.

Lactiplantibacillus plantarum RS20D Alleviates Male Reproductive Toxicity Induced by Pubertal Exposure to Di-n-butyl Phthalate and Mono-n-butyl Phthalate.

Phthalate acid esters (PAEs) and their metabolites, such as di-n-butyl phthalate (DBP) and mono-n-butyl phthalate (MBP), are known to cause male reproductive damage. Lactiplantibacillus plantarum RS20D has demonstrated the ability to remove both DBP and MBP in vitro, suggesting its potential as a detoxifying agent against these compounds. This study aimed to investigate the protective effects of RS20D on DBP or MBP-induced male reproductive toxicity in adolescent rats. Oral administration of RS20D significantly mitigated the histological damage to the testes caused by MBP or DBP, restored sperm concentration, morphological abnormalities, and the proliferation index in MBP-exposed rats, and partially reversed spermatogenic damage in DBP-exposed rats. Furthermore, RS20D restored serum levels of estradiol (E2) and testosterone, and superoxide dismutase (SOD) activity in DBP-exposed rats, significantly increased testosterone levels in MBP-exposed rats, and restored copper (Cu) concentrations in the testes after exposure to DBP or MBP. Additionally, RS20D effectively modulated the intestinal microbiota in DBP-exposed rats and partially ameliorated dysbiosis induced by MBP, which may be associated with the alleviation of reproductive toxic effects induced by DBP or MBP. In conclusion, this study demonstrates that RS20D administration can alleviate male reproductive toxicity and gut dysbacteriosis induced by DBP or MBP exposure, providing a dietary strategy for the bioremediation of PAEs and their metabolites.

Enhancing the acid stability of the recombinant GH11 xylanase xynA through N-terminal substitution to facilitate its application in apple juice clarification.

The utilization of xylanase in juice clarification is contingent upon its stability within acidic environments. We generated a mutant xynA-1 by substituting the N-terminal segment of the recombinant xylanase xynA to investigate the correlation between the N-terminal region of xylanase and its acid stability. The enzymatic activity of xynA-1 was found to be superior under acidic conditions (pH 5.0). It exhibited enhanced acid stability, surpassing the residual enzyme activity values of xynA at pH 4.0 (53.07 %), pH 4.5 (69.8 %), and pH 5.0 (82.4 %), with values of 60.16 %, 77.74 %, and 87.3 %, respectively. Additionally, the catalytic efficiency of xynA was concurrently improved. Through molecular dynamics simulation, we observed that N-terminal shortening induced a reduction in motility across most regions of the protein structure while enhancing its stability, particularly Lys131-Phe146 and Leu176-Gly206. Furthermore, the application of treated xynA-1 in the process of apple juice clarification led to a significant increase in clarity within a short duration of 20 min at 35 °C while ensuring the quality of the apple juice. This study not only enhances the understanding of the N-terminal region of xylanase but also establishes a theoretical basis for augmenting xylanase resources employed in fruit juice clarification.

Occurrence of ochratoxin A in Sichuan bacon from different geographical regions and characterization and biocontrol of ochratoxigenic Aspergillus westerdijkiae strain 21G2-1A.

Sichuan bacon represents the most prevalent dry-cured meat product across Southwest China, but it is vulnerable to fungal spoilage. In the present study, a total of 47 Sichuan bacons were obtained from different regions of the Sichuan Province and analyzed for the presence of ochratoxin A (OTA), yielding a positive rate of 23.4 % (11/47). All the observed OTA concentrations exceeded the maximum admissible dose in meat products (1 µg/kg) established by some EU countries, with the highest OTA concentration being 250.75 µg/kg, which raises a food safety concern and reveals the need for a standardized scientific processing protocol. Then, an OTA-producing fungus named 21G2-1A was isolated from positive samples and found to be Aspergillus westerdijkiae. Further characterization suggested a positive correlation between fungal growth and OTA production. The optimal temperature for the former was 25 °C, while it was 20 °C for the latter. Although the A. westerdijkiae strain 21G2-1A demonstrated greater mycelium growth in the presence of NaCl, OTA production was significantly dismissed when the salinity was greater than 5 %. Four lactic acid bacteria (LAB) were screened out as antagonists against the ochratoxigenic fungus. In vitro evaluation of the antagonists revealed that live cells inhibited fungal growth, and adsorption also contributed to OTA removal at different levels. This study sheds some light on OTA control in Sichuan bacon through a biological approach.

SFE-Net: Spatial-Frequency Enhancement Network for robust nuclei segmentation in histopathology images.

Morphological features of individual nuclei serve as a dependable foundation for pathologists in making accurate diagnoses. Existing methods that rely on spatial information for feature extraction have achieved commendable results in nuclei segmentation tasks. However, these approaches are not sufficient to extract edge information of nuclei with small sizes and blurred outlines. Moreover, the lack of attention to the interior of the nuclei leads to significant internal inconsistencies. To address these challenges, we introduce a novel Spatial-Frequency Enhancement Network (SFE-Net) to incorporate spatial-frequency features and promote intra-nuclei consistency for robust nuclei segmentation. Specifically, SFE-Net incorporates a distinctive Spatial-Frequency Feature Extraction module and a Spatial-Guided Feature Enhancement module, which are designed to preserve spatial-frequency information and enhance feature representation respectively, to achieve comprehensive extraction of edge information. Furthermore, we introduce the Label-Guided Distillation method, which utilizes semantic features to guide the segmentation network in strengthening boundary constraints and learning the intra-nuclei consistency of individual nuclei, to improve the robustness of nuclei segmentation. Extensive experiments on three publicly available histopathology image datasets (MoNuSeg, TNBC and CryoNuSeg) demonstrate the superiority of our proposed method, which achieves 79.23%, 81.96% and 73.26% Aggregated Jaccard Index, respectively. The proposed model is available at https://github.com/jinshachen/SFE-Net.

Microbial inactivation of pressure spray combined with high-voltage electrospray and its application in honey raspberry juice.

Pressure spray combined with high-voltage electrospray (PS-ES) has garnered considerable interest as a novel, non-thermal approach for microbial inactivation and preservation of liquid food. This study compared PS-ES with heat treatment (HT) to understand its inactivation mechanism against E. coli and S. aureus in a simulated system. Microbial activity, cell permeability, membrane integrity, membrane potential, and cell membrane structure were assessed. Furthermore, the impact of PS-ES treatment on microbial activity and flavor in honey raspberry juice, was examined. The changes in microbial growth and color during storage were also discussed. The experimental findings revealed that PS-ES treatment effectively reduced the number of E. coli and S. aureus by 1.99 and 1.83 log colony-forming units (CFU/mL). Additionally, it disrupted the integrity of bacterial cell membranes increasing their permeability, which led to the release of cellular proteins and nucleic acids. PS-ES treatment lowered the membrane potential and altered the structure of bacterial proteins. Application of PS-ES in honey raspberry juice reduced bacterial counts from 4.48 log CFU/mL to 1.99 log CFU/mL, with less flavor deterioration compared to HT treatment. After 30 days of storage at 4 °C and room temperature, PS-ES effectively controlled the growth of microorganisms in raspberry juice and maintained the color of the juice.

A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI.

Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask encoding (EEG-ME) to mitigate overfitting. EEG-ME forces models to learn more robust features by masking partial EEG data, leading to enhanced generalization capabilities of models. Three different network architectures, including an architecture integrating convolutional neural networks (CNN) with Transformer (CNN-Former), time domain-based CNN (tCNN), and a lightweight architecture (EEGNet) are utilized to validate the effectiveness of EEG-ME on publicly available benchmark and BETA datasets. The results demonstrate that EEG-ME significantly enhances the average classification accuracy of various DL-based methods with different data lengths of time windows on two public datasets. Specifically, CNN-Former, tCNN, and EEGNet achieve respective improvements of 3.18%, 1.42%, and 3.06% on the benchmark dataset as well as 11.09%, 3.12%, and 2.81% on the BETA dataset, with the 1-second time window as an example. The enhanced performance of SSVEP classification with EEG-ME promotes the implementation of the asynchronous SSVEP-BCI system, leading to improved robustness and flexibility in human-machine interaction.

Microbial characterization of Sichuan Baoning vinegar: lactic acid bacteria, acetic acid bacteria and yeasts.

Sichuan Baoning vinegar, a typical representative of Sichuan bran vinegar, is a famous traditional fermented food made from cereals in China. At present, there are few studies on microbial characterization of culturable microorganisms in solid-state fermentation of Sichuan bran vinegar. To comprehensively understand the diversity of lactic acid bacteria, acetic acid bacteria and yeasts, which play an important role in the fermentation of Sichuan bran vinegar, traditional culture-dependent methods combined with morphological, biochemical, and molecular identification techniques were employed to screen and identify these isolates. A total of 34 lactic acid bacteria isolates, 39 acetic acid bacteria isolates, and 48 yeast isolates were obtained. Lactic acid bacteria were dominated by Enterococcus durans, Leuconostoc citreum, Lactococcus lactis, and Lactiplantibacillus plantarum, respectively. Latilactobacillus sakei was the first discovery in cereal vinegar. Acetic acid bacteria were mainly Acetobacter pomorum and A. pasteurianus. The dominant yeast isolates were Saccharomyces cerevisiae, in addition to four non-Saccharomyces yeasts. DNA fingerprinting revealed that isolates belonging to the same species exhibited intraspecific diversity, and there were differences between phenotypic and genotypic classification results. This study further enriches studies on cereal vinegar and lays a foundation for the development of vinegar starters.

Polarization-insensitive vortex beam generator by the holographic grating on an integrated multi-layer waveguide.

An integrated polarization-insensitive vortex beam generator is proposed in this study. It is composed of a holographic grating on a multi-layer waveguide, which enables conversion of Transverse Electric (TE) and Transverse Magnetic (TM) waveguide modes to y-polarized and x-polarized optical vortex beams, respectively. The conversion efficiency and the phase fidelity are numerically analyzed, and the working bandwidth is about 100 nm from 1500 nm to 1600 nm with a phase fidelity above 0.7. Moreover, the vortex beam with the superposition of the y-polarization and x-polarization states can be obtained with the incident of the superposition of TE and TM waveguide modes.

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network.

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.

Research of Multicopper Oxidase and Its Degradation of Histamine in Lactiplantibacillus plantarum LPZN19.

In order to explore the structural changes and products of histamine degradation by multicopper oxidase (MCO) in Lactiplantibacillus plantarum LPZN19, a 1500 bp MCO gene in L. plantarum LPZN19 was cloned, and the recombinant MCO was expressed in E. coli BL21 (DE3). After purification by Ni2+-NTA affinity chromatography, the obtained MCO has a molecular weight of 58 kDa, and it also has the highest enzyme activity at 50 °C and pH 3.5, with a relative enzyme activity of 100%, and it maintains 57.71% of the relative enzyme activity at 5% salt concentration. The secondary structure of MCO was determined by circular dichroism, in which the proportions of the α-helix, ß-sheet, ß-turn and random coil were 2.9%, 39.7%, 21.2% and 36.1%, respectively. The 6xj0.1.A with a credibility of 68.21% was selected as the template to predict the tertiary structure of MCO in L. plantarum LPZN19, and the results indicated that the main components of the tertiary structure of MCO were formed by the further coiling and folding of a random coil and ß-sheet. Histamine could change the spatial structure of MCO by increasing the content of the α-helix and ß-sheet. Finally, the LC-MS/MS identification results suggest that the histamine was degraded into imidazole acetaldehyde, hydrogen peroxide and ammonia.

Alignment-Enhanced Interactive Fusion Model for Complete and Incomplete Multimodal Hand Gesture Recognition.

Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless, these methods typically fuse sEMG and ACC either in the early or late stages, overlooking the integration of entire cross-modal hierarchical information within each individual hidden layer, thus inducing inefficient inter-modal fusion. To this end, we propose a novel Alignment-Enhanced Interactive Fusion (AiFusion) model, which achieves effective fusion via a progressive hierarchical fusion strategy. Notably, AiFusion can flexibly perform both complete and incomplete multimodal HGR. Specifically, AiFusion contains two unimodal branches and a cascaded transformer-based multimodal fusion branch. The fusion branch is first designed to adequately characterize modality-interactive knowledge by adaptively capturing inter-modal similarity and fusing hierarchical features from all branches layer by layer. Then, the modality-interactive knowledge is aligned with that of unimodality using cross-modal supervised contrastive learning and online distillation from embedding and probability spaces respectively. These alignments further promote fusion quality and refine modality-specific representations. Finally, the recognition outcomes are set to be determined by available modalities, thus contributing to handling the incomplete multimodal HGR problem, which is frequently encountered in real-world scenarios. Experimental results on five public datasets demonstrate that AiFusion outperforms most state-of-the-art benchmarks in complete multimodal HGR. Impressively, it also surpasses the unimodal baselines in the challenging incomplete multimodal HGR. The proposed AiFusion provides a promising solution to realize effective and robust multimodal HGR-based interfaces.

MGDUN: An interpretable network for multi-contrast MRI image super-resolution reconstruction.

Magnetic resonance imaging (MRI) Super-Resolution (SR) aims to obtain high resolution (HR) images with more detailed information for precise diagnosis and quantitative image analysis. Deep unfolding networks outperform general MRI SR reconstruction methods by providing better performance and improved interpretability, which enhance the trustworthiness required in clinical practice. Additionally, current SR reconstruction techniques often rely on a single contrast or a simple multi-contrast fusion mechanism, ignoring the complex relationships between different contrasts. To address these issues, in this paper, we propose a Model-Guided multi-contrast interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction, which explicitly incorporates the well-studied multi-contrast MRI observation model into an unfolding iterative network. Specifically, we manually design an objective function for MGDUN that can be iteratively computed by the half-quadratic splitting algorithm. The iterative MGDUN algorithm is unfolded into a special model-guided deep unfolding network that explicitly takes into account both the multi-contrast relationship matrix and the MRI observation matrix during the end-to-end optimization process. Extensive experimental results on the multi-contrast IXI dataset and the BraTs 2019 dataset demonstrate the superiority of our proposed model, with PSNR reaching 37.3366 and 35.9690 respectively. Our proposed MGDUN provides a promising solution for multi-contrast MR image super-resolution reconstruction. Code is available at https://github.com/yggame/MGDUN.

Sex, myelin, and clinical characteristics of Parkinson's disease.

Objective: To determine if there are sex differences in myelin in Parkinson's disease, and whether these explain some of the previously-described sex differences in clinical presentation. Methods: Thirty-three subjects (23 males, 10 females) with Parkinson's disease underwent myelin water fraction (MWF) imaging, an MRI scanning technique of in vivo myelin content. MWF of 20 white matter regions of interest (ROIs) were assessed. Motor symptoms were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS). Principal component analysis, logistic and multiple linear regressions, and t-tests were used to determine which white matter ROIs differed between sexes, the clinical features associated with these myelin changes, and if overall MWF and MWF laterality differed between males and females. Results: Consistent with prior reports, tremor and bradykinesia were more likely seen in females, whereas rigidity and axial symptoms were more likely seen in males in our cohort. MWF of the thalamic radiation, cingulum, cingulum hippocampus, inferior fronto-occipital fasciculi, inferior longitudinal fasciculi, and uncinate were significant in predicting sex. Overall MWF and asymmetry of MWF was greater in males. MWF differences between sexes were associated with tremor symptomatology and asymmetry of motor performance. Conclusion: Sex differences in myelin are associated with tremor and asymmetry of motor presentation. While preliminary, our results suggest that further investigation of the role of biological sex in myelin pathology and clinical presentation in Parkinson's disease is warranted.

Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective.

Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.

Homogeneous-Multiset-CCA-Based Brain Covariation and Contravariance Connectivity Network Modeling.

Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson's disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.

Cypermethrin adsorption by Lactiplantibacillus plantarum and its behavior in a simulated fecal fermentation model.

The presence of cypermethrin in the environment and food poses a significant threat to human health. Lactic acid bacteria have shown promise as effective absorbents for xenobiotics and well behaved in wide range of applications. This study aimed to characterize the biosorption behavior of cypermethrin by Lactiplantibacillus plantarum RS60, focusing on cellular components, functional groups, kinetics, and isotherms. Results indicated that RS60 exopolysaccharides played a crucial role removing cypermethrin, with the cell wall and protoplast contributing 71.50% and 30.29% to the overall removal, respectively. Notably, peptidoglycans exhibited a high affinity for cypermethrin binding. The presence of various cellular surface groups including -OH, -NH, -CH3, -CH2, -CH, -P = O, and -CO was responsible for the efficient removal of pollutants. Additionally, the biosorption process demonstrated a good fit with pseudo-second-order and Langmuir-Freundlich isotherm. The biosorption of cypermethrin by L. plantarum RS60 involved complex chemical and physical interactions, as well as intraparticle diffusion and film diffusion. RS60 also effectively reduced cypermethrin residues in a fecal fermentation model, highlighting its potential in mitigating cypermethrin exposure in humans and animals. These findings provided valuable insights into the mechanisms underlying cypermethrin biosorption by lactic acid bacteria and supported the advancement of their application in environmental and health-related contexts. KEY POINTS: • Cypermethrin adsorption by L. plantarum was clarified. • Cell wall and protoplast showed cypermethrin binding ability. • L. plantarum can reduce cypermethrin in a fecal fermentation model.

Cross-user gesture recognition from sEMG signals using an optimal transport assisted student-teacher framework.

The cross-user gesture recognition is a puzzle in the myoelectric control system, owing to great variability in muscle activities across different users. To address this problem, a novel optimal transport (OT) assisted student-teacher (ST) framework (termed OT-ST) was proposed in this paper to facilitate transfer across user domains in an unsupervised domain adaptation (UDA) manner. In this framework, the initial parameters of the ST models were trained with the labeled data from users in the source domain. In the model transfer stage for a new user in the target domain, the teacher model was utilized to generate pseudo labels for unlabeled testing samples, providing guidance to the adaptation of the student model. The OT algorithm was employed to optimize the pseudo labels generated from the teacher model, avoiding the model bias and further improving the effect of domain adaptation. The performance of the proposed OT-ST framework was evaluated via experiments of classifying seven hand gestures using high-density surface electromyogram (HD-sEMG) recordings from extensor digitorum muscles of eight intact-limbed subjects. The OT-ST framework yielded a high accuracy of 96.50 ± 2.88% for new users, and outperformed other common machine learning and UDA methods significantly (p < 0.01), demonstrating its effectiveness. The OT-ST framework does not require special repetitive training or any labeled data for calibration. In addition, it can incrementally learn from new testing samples and improve the recognition ability. This study provides a promising method for developing user-generic myoelectric pattern recognition, with wide applications in human-computer interaction, consumer electronics and prosthesis control.