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.

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.

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.

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.

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.

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.

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.

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.

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, β-sheet, β-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 β-sheet. Histamine could change the spatial structure of MCO by increasing the content of the α-helix and β-sheet. Finally, the LC-MS/MS identification results suggest that the histamine was degraded into imidazole acetaldehyde, hydrogen peroxide and ammonia.

Class 1 integron carrying qacEΔ1 gene confers resistance to disinfectant and antibiotics in Salmonella

Salmonella has presented increasingly alarming rates of antimicrobial resistance believed to be a result of a high prevalence of integrons. It is speculated that disinfectant-resistant isolates are due to the expression of qacEΔ1, an efflux pump located in the 3' conserved sequence (3'CS) of class 1 integrons. With this concern, we tested the antibiotic and disinfectant resistance of 581 Salmonella strains collected from different sources, and characterized their integron structures. Gene expression and induction experiments were also performed. Results showed that Salmonella have high resistance to antimicrobials, especially to sulfonamides (SAs, 78.83 %), tetracyclines (TCs, 75.04 %) and benzalkonium chloride (BC, 87.26 %). The multi-drug resistance (MDR) frequency reached up to 63.17 %, and the prevalence of intI1 was 45.78 %. Molecular characterization of class 1 integrons exhibited nine different gene cassette arrays, of these, dfrA12-orf-aadA2 (n = 75), EstX (n = 25) and aadA2 (n = 14) were the most frequent. Importantly, 74.06 % of intI1-positive isolates were carrying qacEΔ1-sul1 genes in the 3'CS. This study also demonstrated that phenotypic resistance to both antibiotics and disinfectants was significantly correlated with the emergence of intI1 (p < 0.05). 91.37 % of qacEΔ1-sul1 positive Salmonella were found with disinfectant resistance. Additionally, expression of qacEΔ1 gene in Escherichia coli confirmed qacEΔ1 is predominantly involved in conferring disinfectant resistance. Disinfectant induction experiments further implicated qacEΔ1 in disinfectant resistance. RT-qPCR revealed a disinfectant-mediated increase in the relative expression of antibiotic-resistant genes (ARGs), aadA2 and dfrA12 on the integron, and efflux pump genes (mdtH and acrD) indicating that disinfectant could trigger co or cross-resistance. Therefore, our study confirmed that using disinfectant could provide selection pressure for strains with acquired resistance to antibiotics, providing new insights into the public health impact of Salmonella and guide continued efforts in antimicrobial stewardship and prevention of antibiotic resistance.

Image De-Raining Transformer

Existing deep learning based de-raining approaches have resorted to the convolutional architectures. However, the intrinsic limitations of convolution, including local receptive fields and independence of input content, hinder the model's ability to capture long-range and complicated rainy artifacts. To overcome these limitations, we propose an effective and efficient transformer-based architecture for the image de-raining. First, we introduce general priors of vision tasks, i.e., locality and hierarchy, into the network architecture so that our model can achieve excellent de-raining performance without costly pre-training. Second, since the geometric appearance of rainy artifacts is complicated and of significant variance in space, it is essential for de-raining models to extract both local and non-local features. Therefore, we design the complementary window-based transformer and spatial transformer to enhance locality while capturing long-range dependencies. Besides, to compensate for the positional blindness of self-attention, we establish a separate representative space for modeling positional relationship, and design a new relative position enhanced multi-head self-attention. In this way, our model enjoys powerful abilities to capture dependencies from both content and position, so as to achieve better image content recovery while removing rainy artifacts. Experiments substantiate that our approach attains more appealing results than state-of-the-art methods quantitatively and qualitatively.

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.

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.

Oriented artificial niche provides physical-biochemical stimulations for rapid nerve regeneration

Skin wound is always accompanied with nerve damage, leading to significant sensory function loss. Currently, the functional matrix material based stem cell transplantation and in situ nerve regeneration are thought to be effective strategies, of which, how to recruit stem cells, retard senescence, and promote neural differentiation has been obstacle to be overcome. However, the therapeutic efficiency of the reported systems has yet to be improved and side effect reduced. Herein, a conduit matrix with three-dimensional ordered porous structures, regular porosity, appropriate mechanical strength, and conductive features was prepared by orienting the freezing technique, which was further filled with neural-directing exosomes to form a neural-stimulating matrix for providing hybrid physical-biochemical stimulations. This neural-stimulating matrix was then compacted with methacrylate gelatin (GelMA) hydrogel thin coat that loaded with chemokines and anti-senescence drugs, forming a multi-functional artificial niche (termed as GCr-CSL) that promotes MSCs recruitment, anti-senescence, and neural differentiation. GCr-CSL was shown to rapidly enhances in situ nerve regeneration in skin wound therapy, and with great potential in promoting sensory function recovery. This study demonstrates proof-of-concept in building a biomimetic niche to organize endogenous MSCs recruitment, differentiation, and functionalization for fast neurological and sensory recovery.

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.

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.

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.

Photodynamic antibacterial chitosan/nitrogen-doped carbon dots composite packaging film for food preservation applications

In this study, we synthesized nitrogen-doped carbon dots (N-CDs) with remarkable photodynamic antibacterial properties by a hydrothermal method. The composite film was prepared by solvent casting method, compounding N-CDs with chitosan (CS). The morphology and structure of the films were analyzed by Fourier-transformed infrared spectroscopy (FTIR), scanning electron microscope (SEM), atomic force microscope (AFM), and transmission electron microscope (TEM) techniques. The films' mechanical, barrier, thermal stability, and antibacterial properties were analyzed. A preservation test of the films was studied on the samples of pork, volatile base nitrogen (TVB-N), total viable count (TVC), and pH were determined. Besides, the effect of film on the preservation of blueberries was observed. The study found that, compared with the CS film, the CS/N-CDs composite film is strong and flexible, with good UV light barrier performance. The prepared CS/7 % N-CDs composites showed high photodynamic antibacterial rates of 91.2 % and 99.9 % for E. coli and S. aureus, respectively. In the preservation of pork, it was found that its pH, TVB-N, and TVC indicators were significantly lower. The extent of mold contamination and anthocyanin loss was less in the CS/3 % N-CDs composite film-coated group, which could greatly extend the shelf life of food.

Insight into the acid tolerance mechanism of Acetilactobacillus jinshanensis subsp. aerogenes Z-1

Several lactic acid bacteria (LAB) are double-edged swords in the production of Sichuan bran vinegar; on the one hand, they are important for the flavour of the vinegar, but on the other hand, they result in vinegar deterioration because of their gas-producing features and their acid resistance. These characteristics intensify the difficulty in managing the safe production of vinegar using strains such as Acetilactobacillus jinshanensis subsp. aerogenes Z-1. Therefore, it is necessary to characterize the mechanisms underlying their acid tolerance. The results of this study showed a survival rate of 77.2% for Z-1 when exposed to pH 3.0 stress for 1 h. This strain could survive for approximately 15 days in a vinegar solution with 4% or 6% total acid content, and its growth was effectively enhanced by the addition of 10 mM of arginine (Arg). Under acidic stress, the relative content of the unsaturated fatty acid C18:1 (n-11) increased, and eight amino acids accumulated in the cells. Meanwhile, based on a transcriptome analysis, the genes glnA, carA/B, arcA, murE/F/G, fabD/H/G, DnaK, uvrA, opuA/C, fliy, ecfA2, dnaA and LuxS, mainly enriched in amino acid transport and metabolism, protein folding, DNA repair, and cell wall/membrane metabolism processes, were hypothesized to be acid resistance-related genes in Z-1. This work paves the way for further clarifying the acid tolerance mechanism of Z-1 and shares applicable perspectives for vinegar brewing.

Bidirectional association between depressive symptoms and cardiovascular disease in the middle-aged and elderly Chinese: a 5-year longitudinal study

OBJECTIVE

Depressive symptoms and cardiovascular diseases (CVDs) are important issues affecting the health of the middle-aged and elderly population in China. This study aimed to investigate the bidirectional association between depressive symptoms and CVD in middle-aged and elderly people in China.

DESIGN

A 5-year longitudinal study.

SETTING AND PARTICIPANTS

We included 6702 middle-aged and elderly participants from China Health and Retirement Longitudinal Study (CHARLS), which is a nationwide longitudinal household survey that started in 2011 (T1) and followed up every 2 years in 2013 (T2) and 2015 (T3).

OUTCOME MEASURES

Depressive symptoms were measured by the Center for Epidemiological Studies Depression Scale. Binary logistic regression was used to identify the influencing factors of depressive symptoms and CVD at T1. The cross-lagged panel model was used to analyse the association between depressive symptoms and CVD at T1, T2 and T3.

RESULTS

The CHARLS is a representative longitudinal survey of people aged ≥45 years. Using data extracted from the CHARLS, overall, at T1, 2621 (39.10%) participants had depressive symptoms and 432 (6.4%) had CVD, and at T3, 2423 (36.2%) had depressive symptoms and 760 (11.3%) had CVD, respectively. Depressive symptoms at T1 had a effect on CVD at T2 (β=0.015, p=0.009), and depressive symptoms at T2 had an effect on CVD at T3 (β=0.015, p=0.034). CVD at T1 predicted depressive symptoms at T2 (β=0.036, p=0.002).

CONCLUSIONS

There is a bidirectional predictive effect between depressive symptoms and CVD. The effect of depressive symptoms on CVD is stable, and CVD has an effect on depressive symptoms in a short period of time.

[Joint association of depression symptoms and 10-year risk of ischemic cardiovascular disease with the cardiovascular disease in middle-aged and elderly people in China]

OBJECTIVE

To explore joint association of depression symptoms and 10-year risk of ischemic cardiovascular disease (CVD) in middle-aged and elderly people in China.

METHODS

Based on China Health and Retirement Longitudinal Study(CHARLS)database using 2011 baseline data and the follow-up cohort data of 2013, 2015 and 2018, describe the distribution characteristics of baseline depressive symptoms and 10-year risk of ischemic cardiovascular disease in 2011. Cox survival analysis model was used to analyze the individual, independent and joint association of depression symptoms and 10-year risk of ischemic cardiovascular disease with cardiovascular disease.

RESULTS

A total of 9 412 subjects were enrolled. The detection rate of depressive symptoms at baseline was 44.7%, and the 10-year middle and high risk of ischemic cardiovascular disease was 13.62%. During an average follow-up of 6.19 (6.19±1.66) years, 1 401 cases of cardiovascular disease were diagnosed in 58 258 person-years, revealing an overall incidence density of 24.048/1 000 person-years. After adjusting the factors, in terms of individual impact, the participants with depressive symptoms had a higher risk of developing CVD (HR=1.263, 95%CI: 1.133-1.408), while medium to high risk of ischemic cardiovascular disease had a higher risk of developing CVD (HR=1.892, 95%CI: 1.662-2.154). Among independent influences, participants with depressive symptoms had a higher risk of developing CVD (HR=1.269, 95% CI: 1.138-1.415), while medium to high risk of 10-year risk of ischemic cardiovascular disease had a higher risk of developing CVD (HR=1.898, 95%CI: 1.668-2.160). Joint impact result showed the incidence of cardiovascular disease in the low risk of 10-year risk of ischemic cardiovascular disease with depressive symptoms group, middle and high risk of 10-year risk of ischemic cardiovascular disease without depressive symptoms group, and 10-year middle and high risk of ischemic cardiovascular disease with depressive symptoms group were 1.390, 2.149, and 2.339 times higher than that of low risk of 10-year risk of ischemic cardiovascular disease without depressive symptoms (P < 0.001).

CONCLUSION

The superimposed depression symptoms of the middle and high-risk population at the 10-year risk of ischemic cardiovascular disease will aggravate the risk of cardiovascular disease in middle-aged and elderly people. In combination with the actual lifestyle intervention and physical index health management, attention should be paid to mental health intervention.

Hue Guidance Network for Single Image Reflection Removal

Reflection from glasses is ubiquitous in daily life, but it is usually undesirable in photographs. To remove these unwanted noises, existing methods utilize either correlative auxiliary information or handcrafted priors to constrain this ill-posed problem. However, due to their limited capability to describe the properties of reflections, these methods are unable to handle strong and complex reflection scenes. In this article, we propose a hue guidance network (HGNet) with two branches for single image reflection removal (SIRR) by integrating image information and corresponding hue information. The complementarity between image information and hue information has not been noticed. The key to this idea is that we found that hue information can describe reflections well and thus can be used as a superior constraint for the specific SIRR task. Accordingly, the first branch extracts the salient reflection features by directly estimating the hue map. The second branch leverages these effective features, which can help locate salient reflection regions to obtain a high-quality restored image. Furthermore, we design a new cyclic hue loss to provide a more accurate optimization direction for the network training. Experiments substantiate the superiority of our network, especially its excellent generalization ability to various reflection scenes, as compared with state-of-the-arts both qualitatively and quantitatively. Source codes are available at https://github.com/zhuyr97/HGRR.

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising

Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts is an essential step in practice. As of now, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. However, they still suffer from the following limitations. The existing structure designs have not fully taken into account the temporal characteristics of artifacts. Meanwhile, the existing training strategies usually ignore the holistic consistency between denoised EEG signals and authentic clean ones. To address these issues, we propose a GAN guided parallel CNN and transformer network, named GCTNet. The generator contains parallel CNN blocks and transformer blocks to respectively capture local and global temporal dependencies. Then, a discriminator is employed to detect and correct the holistic inconsistencies between clean and denoised EEG signals. We evaluate the proposed network on both semi-simulated and real data. Extensive experimental results demonstrate that GCTNet significantly outperforms state-of-the-art networks in various artifact removal tasks, as evidenced by its superior objective evaluation metrics. For example, in the task of removing electromyography artifacts, GCTNet achieves 11.15% reduction in RRMSE and 9.81% improvement in SNR over other methods, highlighting the potential of the proposed method as a promising solution for EEG signals in practical applications.

Preparation and characterization of inclusion complex of Myristica fragrans Houtt. (nutmeg) essential oil with 2-hydroxypropyl-β-cyclodextrin

Nutmeg essential oil (NEO) is a natural condimentwith versatile biological activities. However, the application of NEO in food has several limitations due to its poor stability and low aqueous solubility. To overcome the shortcomings, this paper focused on the preparation of the inclusion complex (IC) of NEO with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) by the coprecipitation method. The optimal condition was inclusion temperature 36 ℃, time 247 min, stirring speed 520 r/min, and wall-core ratio 12:1, resulting in a recovery of 80.63%. The formation of IC was verified by various methods such as scanning electron microscopy, Fourier transform infrared spectroscopy and nuclear magnetic resonance. The improvement of thermal stability, antioxidant, and nitrite scavenging activities of NEO after encapsulation was proven. Moreover, the controlled release of NEO from IC can be implemented by regulating the temperature and relative humidity. Overall, NEO/HP-β-CD IC has great application potential in food industries.

Efficacy and safety of carnitine supplementation on NAFLD: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

The efficacy and safety of L-carnitine supplementation on non-alcoholic fatty liver disease (NAFLD) are unclear. This systematic review and meta-analysis aimed to assess the efficacy and safety of L-carnitine supplementation on NAFLD.

METHODS

We searched in four databases (PubMed, Embase, Cochrane Library, and Web of Science) from inception to 1 November 2022 (updated on March 20, 2023) for potentially relevant records without language restrictions. We collected information on the first author, publication year, country, setting, study design, population characteristics, duration of follow-up, outcome variables of interest, and sources of funding. We used a modified Cochrane risk of bias tool to assess the risk of bias, used GRADE to assess the certainty of evidence, and used the Credibility of Effect Modification Analyses (ICEMAN) tool to assess the credibility of any apparent subgroup effect.

RESULTS

This systematic review and meta-analysis included eight eligible randomized controlled trials (RCTs). Compared to placebo, low certainty evidence show that L-carnitine supplementation significantly changes (reduced) more in AST levels and ALT levels (MD: - 26.38, 95%CI: - 45.46 to - 7.30), and moderate certainty evidence show that L-carnitine supplementation significantly changes (reduced) more in HDL cholesterol levels (MD: 1.14, 95%CI: 0.21 to 2.07) and triglyceride levels (MD: - 6.92, 95%CI: - 13.82 to - 0.03). Moderate credibility of ICEMAN results shows that L-carnitine supplementation has no difference in changes of AST and ALT levels in younger ones (MD: 0.5, 95%CI: - 0.70 to 1.70) but has significant changes (reduced) in adults (MD: - 20.3, 95%CI: - 28.62 to - 12.28) compared to placebo.

CONCLUSION

L-carnitine supplementation may improve liver function and regulate triglyceride metabolism in patients with NAFLD, and with no significant adverse effects.

Research into microfinance and ICTs: A bibliometric analysis

Information and communication technologies (ICTs) play an ever-increasing role in improving the efficiency, profitability, and sustainability of microfinance institutions. This paper aims to assess the role of ICTs in the microfinance industry by systematically reviewing the literature with bibliometric methods. In this research, a total of 347 samples (from 1998 to 2021) were selected from the Web of Science database according to the guideline of the systematic review. By performing descriptive statistical analysis, the contributing institutions, countries, journals, authors, as well as influential publications were identified. In the co-citation and co-word analysis section, three primary types of visualization-cluster views, timezone views and timeline viewswere all presented through CiteSpace. It turns out that crowdfunding, P2P lending and mobile banking have been the favorite topics. A central issue is the role of these platforms in entrepreneurship. We also proposed that applying fintech, especially blockchain and other emerging technologies, to promote financial inclusion is one of the future research trends. The findings of this study will be of interest to researchers, managers, policymakers, and evaluators and facilitate them to make well-informed decisions in their respective domains.

Antibiotic and heavy metal resistance genes in sewage sludge survive during aerobic composting

Municipal sewage sludge has been generated in increasing amounts with the acceleration of urbanization and economic development. The nutrient rich sewage sludge can be recycled by composting that has a great potential to produce stabilized organic fertilizer and substrate for plant cultivation. However, little is known about the metals, pathogens and antibiotic resistance transfer risks involved in applying the composted sludge in agriculture. We studied changes in and relationships between heavy metal contents, microbial communities, and antibiotic resistance genes (ARGs), heavy metal resistance genes (HMRGs) and mobile genetic elements (MGEs) in aerobic composting of sewage sludge. The contents of most of the analyzed heavy metals were not lower after composting. The bacterial α-diversity was lower, and the community composition was different after composting. Firmicutes were enriched, and Proteobacteria and potential pathogens in the genera Arcobacter and Acinetobacter were depleted in the composted sludge. The differences in bacteria were possibly due to the high temperature phase during the composting which was likely to affect temperature-sensitive bacteria. The number of detected ARGs, HMRGs and MGEs was lower, and the relative abundances of several resistance genes were lower after composting. However, the abundance of seven ARGs and six HMRGs remained on the same level after composting. Co-occurrence analysis of bacterial taxa and the genes suggested that the ARGs may spread via horizontal gene transfer during composting. In summary, even though aerobic composting is effective for managing sewage sludge and to decrease the relative abundance of potential pathogens, ARGs and HMRGs, it might include a potential risk for the dissemination of ARGs in the environment.

Laser-Cutted Epidermal Microfluidic Patch with Capillary Bursting Valves for Chronological Capture, Storage, and Colorimetric Sensing of Sweat

Flexible wearable microfluidic devices show great feasibility and potential development in the collection and analysis of sweat due to their convenience and non-invasive characteristics in health-level feedback and disease prediction. However, the traditional production process of microfluidic patches relies on resource-intensive laboratory and high-cost facilities. In this paper, a low-cost laser-cutting technology is proposed to fabricate epidermal microfluidic patches for the collection, storage and colorimetric analysis of sweat. Two different types of capillary bursting valves are designed and integrated into microchannel layers to produce two-stage bursting pressure for the reliable routing of sweat into microreservoirs in sequential fashion, avoiding the mixing of old and new sweat. Additionally, an enzyme-based reagent is embedded into the microreservoirs to quantify the glucose level in sweat by using colorimetric methods, demonstrating a high detection sensitivity at the glucose concentration from 0.1 mM to 1 mM in sweat and an excellent anti-interference performance that prevents interference from substances probably existent in sweat. In vitro and on-body experiments demonstrate the validity of the low-cost, laser-cut epidermal microfluidic patch for the chronological analysis of sweat glucose concentration and its potential application in the monitoring of human physiological information.

Discovery of HNPC-A9229: A Novel Pyridin-2-yloxy-Based Pyrimidin-4-amine Fungicide

To search for novel pesticides, the synthesis around commercialized insecticide tebufenpyrad accidentally led us to the discovery of the fungicidal lead compound, 3-ethyl-1-methyl-N-((2-phenylthiazol-4-yl)methyl)-1H-pyrazole-5-carboxamide (1a) and its pyrimidin-4-amine-based optimization derivative 5-chloro-2,6-dimethyl-N-(1-(2-(p-tolyl)thiazol-4-yl)ethyl)pyrimidin-4-amine (2a). Compound 2a not only demonstrates fungicidal activity superior to commercial fungicides such as diflumetorim but also exhibits the good features that come with pyrimidin-4-amines, such as unique modes of action and no cross-resistance to other pesticide classes. However, 2a is highly toxic to rats. Further optimization of 2a by introducing pyridin-2-yloxy substructure finally led to the discovery of 5b5-6 (HNPC-A9229) (5-chloro-N-(1-((3-chloropyridin-2-yl)oxy)propan-2-yl)-6-(difluoromethyl)pyrimidin-4-amine). HNPC-A9229 exhibits excellent fungicidal activities with EC₅₀ values of 0.16 mg/L against Puccinia sorghi and 1.14 mg/L against Erysiphe graminis, respectively. Not only that its fungicidal potency is significantly superior to or comparable to commercial fungicides including diflumetorim, tebuconazole, flusilazole, and isopyrazam, HNPC-A9229 possesses low toxicity to rats.

Effect of exercise therapy on PICC-associated venous thromboembolism: A meta-analysis

BACKGROUND

Peripherally inserted central catheter (PICC) is widely used in clinical practice because of its long retention time and easy maintenance. However, PICC-associated venous thromboembolism (VTE) is the most serious complication of PICC. Guidelines recommend exercise therapy to prevent PICC-associated VTE. However, inconsistent findings have been reported across the literature. This study conducted a meta-analysis to further evaluate the effect of exercise therapy on PICC-associated VTE.

METHODS

We searched CNKI, Wanfang database, Chinese Science and Technology Journal Full Text Database, PubMed, Embase, Web of Science and Cochrane Library databases and included all randomized controlled trials (RCTs) of exercise therapy for the prevention of PICC-associated VTE. Two investigators independently screened the literature, extracted information, and evaluated the risk of bias for eligible RCTs. Meta-analysis was conducted by RevMan5.4 software.

RESULTS

Eleven RCTs were included, including 1919 patients. Meta-analysis showed that the incidence of PICC-associated VTE was lower in the exercise therapy group than in the usual care group (RR = 0.30, 95% CI: 0.22-0.41, p < 0.00001).Exercise therapy increased the axillary vein maximum velocity (SMD = 0.93, 95% CI: 0.58-1.28, p < 0.00001) and the axillary vein time-mean flow velocity (SMD = 0.86, 95% CI: 0.53-1.20, p < 0.00001). Subgroup analysis showed statistically significant differences for the incidence of PICC-associated VTE for intervention times<4 weeks (RR = 0.26, 95% CI: 0.17-0.40, p < 0.00001) and intervention times≥4 weeks (RR = 0.35, 95% CI: 0.22-0.54, p < 0.00001). For axillary vein maximum velocity, the difference was statistically significant for both intervention time <4 weeks (SMD = 0.73, 95% CI: 0.55-0.91, p < 0.00001) and intervention time ≥4 weeks (SMD = 1.18, 95% CI: 0.18-2.19, p = 0.02). For axillary vein time-mean flow velocity, the intervention time <4 weeks (SMD = 0.75, 95% CI: 0.46-1.04, p < 0.00001), and the difference was statistically significant; while ≥4 weeks, the difference was not statistically significant (SMD = 1.14, 95% CI: -0.07 to 2.35, p = 0.06).

CONCLUSIONS

Exercise therapy improved venous blood flow velocity and effectively reduced the incidence of PICC-associated VTE. However, RCTs with large samples and high quality are needed to further evaluate the effectiveness of exercise therapy in PICC patients.

Serum hsCRP in early pregnancy and preterm delivery in twin gestations: a prospective cohort study

BACKGROUND

Systemic inflammation during pregnancy may be associated with preterm delivery (PTD), but data for twin gestations are lacking. The aim of this study was to examine the association of serum high-sensitivity C-reactive protein (hsCRP), a marker of inflammation, in early pregnancy of twin gestations with risk of PTD, including spontaneous (sPTD) and medical-induced preterm delivery (mPTD).

METHODS

A prospective cohort study involved 618 twin gestations was conducted in a tertiary hospital in Beijing, from 2017 to 2020. Serum samples collected in early pregnancy were analyzed for hsCRP using particle-enhanced immunoturbidimetric method. Unadjusted and adjusted geometric means (GM) of hsCRP were estimated using linear regression, and compared between PTD before 37 weeks of gestation and term delivery at 37 or more weeks of gestation using Mann-Whitney rank sum test. The association between hsCRP tertiles and PTDs was estimated using logistic regression, and further converted overestimated odds ratios into relative risks (RR).

RESULTS

A total of 302 (48.87%) women were classified as PTD, with 166 sPTD and 136 mPTD. The adjusted GM of serum hsCRP was higher in PTDs (2.13 mg/L, 95% confidence interval [CI] 2.09 -2.16) compared to term deliveries (1.84 mg/L, 95% CI 1.80 -1.88) (P < 0.001). Compared with the lowest tertile of hsCRP, the highest tertile was associated with increased risk of PTD (adjusted relative risks [ARR] 1.42; 95% CI: 1.08-1.78). Among twin pregnancies, the adjusted association between high values of serum hsCRP in early pregnancy and preterm delivery was only observed in the subgroup of spontaneous preterm deliveries (ARR 1.49, 95%CI:1.08-1.93).

CONCLUSIONS

Elevated hsCRP in early pregnancy was associated with increased risk of PTD, particular the risk of sPTD in twin gestations.

Continual Image Deraining with Hypergraph Convolutional Networks

Image deraining is a challenging task since rain streaks have the characteristics of spatially long structure and complex diversity. Existing deep learning-based methods mainly construct the deraining networks by stacking vanilla convolutional layers with local relations, and can only handle a single dataset due to the catastrophic forgetting, resulting in a limited performance and insufficient adaptability. To address these issues, we propose a new image deraining framework to effectively explore nonlocal similarity, and to continuously learn on multiple datasets. Specifically, we first design a patchwise hypergraph convolutional module, which aims to better extract the nonlocal properties with higher-order constraints on the data, to construct a new backbone and to improve the deraining performance. Then, to achieve better generalizability and adaptability in real-world scenarios, we propose a biological brain-inspired continual learning algorithm. By imitating the plasticity mechanism of brain synapses during the learning and memory process, our continual learning process allows the network to achieve a subtle stability-plasticity tradeoff. This it can effectively alleviate catastrophic forgetting and enables a single network to handle multiple datasets. Compared with the competitors, our new deraining network with unified parameters attains a state-of-the-art performance on seen synthetic datasets and has a significantly improved generalizability on unseen real rainy images.

Effect of Alloying Powders on Microstructure and Mechanical Properties of Aluminum Alloy Arc Additive Manufacturing

Cold metal transfer arc additive manufacturing technique was used to produce 5356 aluminum alloy by adding refining agents to solve the problems of coarse grains and poor performance. Metallic powders (Ti, TiH, and Ti+B4C) were used to refine the grain size and promote the mechanical properties of the alloy. The effects of refining agents on the microstructure and mechanical properties of straight wall samples (SWSs) were studied. Samples with Ti+B4C addition had a profound impact on morphology. However, the TiH added sample revealed uneven transition between sediment layers, unstable precipitation process, unstable wall height and wall width, poor morphology, and defects. All SWSs with powder addition revealed the formation of the Al3Ti phase. Moreover, the columnar grains between the layers were transformed into equiaxed grains and finer grains at the center of the layers. There was a significant effect of TiH on the grain refinement. The samples with Ti demonstrated superior mechanical properties. The tensile strength and elongation of the SWSs increased by 28 MPa and 4.6% in the parallel additive direction and by 37 MPa and 8.9% in the vertical direction. The addition of Ti also contributed to the even distribution of the mechanical properties in both directions.

Somatic-to-primordial germ cell-like transformation is critical in tumor initiation of mouse breast tumor 4T1 cells

It has been proposed that tumorigenicity was an intrinsic feature of embryonic/germ cell developmental axis as well as embryonic/germ cell-related genes play a crucial role in tumorigenicity. Our previous studies indicated that primordial germ cell (PGC)-like potential could be reactivated in tumorigenesis. In this study, 4T1, 168FARN and 67NR cells which originated from the same mouse breast cancer were studied and the results indicated that the acquisition of embryonic/germ cell-like state is essential for tumorigenicity. We further demonstrated that somatic to PGC-like transformation (SPLT) was activated in 4T1 cells and that inhibition of PGC-like cell formation by depleting pluripotency and/or PGC specification-related genes markedly repressed SPLT and the tumorigenicity. Collectively, our findings reveal that tumorigenicity is linked to the acquisition of PGC-like state through SPLT in 4T1 cells, providing new insight into deeper understanding the biological nature of tumors and novel therapeutical strategies for cancer targeting.

A Self-Interpretable Deep Learning Model for Seizure Prediction Using a Multi-Scale Prototypical Part Network

The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients' quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significant progress in recent years. However, the "black-box" nature of deep learning models makes the clinician mistrust the prediction results, severely limiting its clinical application. For this purpose, in this study, we propose a self-interpretable deep learning model for patient-specific epileptic seizure prediction: Multi-Scale Prototypical Part Network (MSPPNet). This model attempts to measure the similarity between the inputs and prototypes (learned during training) as evidence to make final predictions, which could provide a transparent reasoning process and decision basis (e.g., significant prototypes for inputs and corresponding similarity score). Furthermore, we assign different sizes to the prototypes in latent space to capture the multi-scale features of EEG signals. To the best of our knowledge, this is the first study that develops a self-interpretable deep learning model for seizure prediction, other than the existing post hoc interpretation studies. Our proposed model is evaluated on two public epileptic EEG datasets (CHB-MIT: 16 patients with a total of 85 seizures, Kaggle: 5 dogs with a total of 42 seizures), with a sensitivity of 93.8% and a false prediction rate of 0.054/h in the CHB-MIT dataset and a sensitivity of 88.6% and a false prediction rate of 0.146/h in the Kaggle dataset, achieving the current state-of-the-art performance with self-interpretable evidence.

Reduce the User Burden of Multiuser Myoelectric Interface via Few-Shot Domain Adaptation

Due to physiological and anatomical variations across users, myoelectric interfaces trained by multiple users cannot be adapted to the unique hand movement patterns of the new user. Most current work requires the new user to provide one or more trials per gesture (dozens to hundreds of samples), applying domain adaptation methods to calibrate the model and achieve promising movement recognition performance. However, the user burden associated with time-consuming electromyography signal acquisition and annotation is a key factor hindering the practical application of myoelectric control. As shown in this work, once the number of calibration samples is reduced, the performance of previous cross-user myoelectric interfaces will degrade due to the lack of enough statistics to characterize the distributions. In this paper, a few-shot supervised domain adaptation (FSSDA) framework is proposed to address this issue. It aligns the distributions of different domains by calculating the distribution distances of point-wise surrogates. Specifically, we introduce a positive-negative pair distance loss to find a shared embedding subspace where each scarce sample from the new user will be closer to the positive samples and away from the negative samples of multiple users. Thus, FSSDA allows every target domain sample to be paired with all source domain samples and optimizes the feature distance between each target domain sample and the source domain samples within the same batch, instead of direct estimation of the data distribution of the target domain. The proposed method is validated on two high-density EMG datasets, which achieves the averaged recognition accuracies of 97.59% and 82.78% with only 5 samples per gesture. In addition, FSSDA is also effective even when only one sample per gesture is provided. The experimental results show that FSSDA greatly reduces the user burden and further facilitates the development of myoelectric pattern recognition techniques.

Gpx4, Selenov, and Txnrd3 Are Three Most Testis-Abundant Selenogenes Resistant to Dietary Selenium Concentrations and Actively Expressed During Reproductive Ages in Rats

Almost all selenogenes are expressed in the testis, and those have the highest and constant expressions will be the primary candidates for functional analysis of selenium (Se) in male reproduction. This study aimed to profile the mRNA expressions of the testis-abundant selenogenes of rat models in responses to growth and dietary Se concentrations. Forty-eight weaning SD male rats were fed Se deficient basal diet (BD) for 5 weeks and then randomly grouped (n = 12/group) for being fed BD or BD plus 0.25, 3, or 5 mg Se/kg for 4 more weeks before sacrifice. Abundances of selenogenomic mRNAs in the liver and testis were determined with relative qPCR and those of the testis-abundant selenogenes in 13 kinds of tissues were assayed with a molecular beacon-based qPCR. Spatiotemporal expressions of rat selenogenome were also analyzed with the RNA-Seq transcriptomic data published by NCBI. mRNA abundances of glutathione peroxidase 4 (Gpx4), nuclear Gpx4 (nGpx4), selenoprotein V (Selenov), and thioredoxin reductase 3 (Txnrd3) in the testis were significantly higher than that in any other tissues (P < 0.05). Moreover, testicular mRNA abundances of Gpx4, Selenov, and Txnrd3 were not affected by levels of dietary Se supplementation (P > 0.05), and much higher at 6-21 weeks old than at 2 and 104 weeks old (P < 0.05). The result showed that Gpx4, Selenov, and Txnrd3 were most highly expressed in the testis of rats especially at reproductive ages and resistant to the impact of dietary Se levels, which suggested their specific importance in male reproduction.

A Generalized Zero-Shot Learning Scheme for SSVEP-Based BCI System

The steady-state visual evoked potential (SSVEP) has been widely used in building multi-target brain-computer interfaces (BCIs) based on electroencephalogram (EEG). However, methods for high-accuracy SSVEP systems require training data for each target, which needs significant calibration time. This study aimed to use the data of only part of the targets for training while achieving high classification accuracy on all targets. In this work, we proposed a generalized zero-shot learning (GZSL) scheme for SSVEP classification. We divided the target classes into seen and unseen classes and trained the classifier only using the seen classes. During the test time, the search space contained both seen classes and unseen classes. In the proposed scheme, the EEG data and the sine waves are embedded into the same latent space using convolutional neural networks (CNN). We use the correlation coefficient of the two outputs in the latent space for classification. Our method was tested on two public datasets and reached 89.9% of the classification accuracy of the state-of-the-art (SOTA) data-driven method, which needs the training data of all targets. Compared to the SOTA training-free method, our method achieved a multifold improvement. This work shows that it is promising to build an SSVEP classification system that does not need the training data of all targets.

Exploring the role of Sichuan Baoning vinegar microbiota and the association with volatile flavor compounds at different fermentation depths

Cereal vinegar is usually produced through solid-state fermentation, and the microbial community plays an important role in fermentation. In this study, the composition and function of Sichuan Baoning vinegar microbiota at different fermentation depths were evaluated by high-throughput sequencing combined with PICRUSt and FUNGuild analysis, and variations in volatile flavor compounds were also determined. The results revealed that no significant differences (p > 0.05) were found in both total acid content and pH of vinegar Pei collected on the same day with different depths. There were significant differences between the bacterial community of samples from the same day with different depths at both phylum and genus levels (p < 0.05), however, no obvious difference (p > 0.05) was observed in the fungal community. PICRUSt analysis indicated that fermentation depth affected the function of microbiota, meanwhile, FUNGuild analysis showed that there were variations in the abundance of trophic mode. Additionally, differences in volatile flavor compounds were observed in samples from the same day with different depths, and significant correlations between microbial community and volatile flavor compounds were observed. The present study provides insights into the composition and function of microbiota at different depths in cereal vinegar fermentation and quality control of vinegar products.

Full-Cut Manufacture of Skin-Interfaced Microfluidic Patch with Copper Electrode for In Situ Admittance Sensing of Sweat Rate

Sweat-rate measurement has received more and more attention, especially for specific groups, such as athletes, soldiers and manual workers, due to their excessive sweat loss under prolonged intense heat stress, which increases the risk of dehydration and electrolyte imbalance. The highly effective manufacture of a sweat-sensing device is essential to its wide range of applications in perspiration-related physiological information detection. In this work, we propose a simple and cost-effective strategy for the manufacture of a microfluidic sweat-rate-sensing patch via laser cutting and transfer printing technology. A copper foil tape is used as the electrode for in situ admittance based sweat-rate-sensing. The detection circuits and measurement conditions are optimized to prevent the negative effect of an electrochemical reaction between a copper electrode and sweat for precise admittance measurement. In vitro and on-body experiments demonstrate that the copper electrode is applicable for admittance-based sweat sensing and is capable of achieving equivalent sensing accuracy as a gold electrode and that the proposed sensor structure can perform consecutive and accurate sweat-rate-sensing and facilitates a significant increase in manufacturing efficiency.