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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.

This study presents two cases of type II mixed cryoglobulinemia. One case is essential, while the other is presumably associated with hepatitis B virus (HBV) infection. Both patients tested positive for monoclonal IgMκ, but negative for MyD88 mutation. They showed resistance to rituximab combined with a glucosteroid regimen, but responded positively to BTK inhibitors. These cases highlight the remarkable effectiveness of BTK inhibitors in treating refractory type II cryoglobulinemia without MyD88 mutation. The first patient achieved rapid complete remission of nephrotic syndrome within one month of starting ibrutinib, along with a significant reduction in cryoglobulin levels and abnormal clonal cells. The second patient had a rapid disappearance of rash within three days and accelerated wound healing within one week of initiating orelabrutinib, accompanied by a reduction in C-reactive protein. However, there was no reduction in cryoglobulin levels during the 12-month follow-up. These findings suggest varied mechanisms of action of BTK inhibitors in type II cryoglobulinemia through different mechanisms.

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.

A microfluidic sweat monitoring patch that collects human sweat for a long time is designed to achieve the effect of detecting the rise and fall of human sweat glucose over a long period of time by increasing the use time of a single patch. Five collection pools, four serpentine channels, and two different valves are provided. Among them, the three-dimensional valve has a large burst pressure as a balance between the internal and external air pressures of the patch. The bursting pressure of the two-dimensional diverter valve is smaller than that of the three-dimensional gas valve, and its role is to control the flow direction of the liquid. Through plasma hydrophilic treatment of different durations, the optimal hydrophilic duration is obtained. The embedded chromogenic disc detects the sweat glucose value at two adjacent time intervals and compares the information of the human body to increase or reduce glucose. The patch has good flexibility and can fit well with human skin, and because polydimethylsiloxane (PDMS) has good light transmission, it reduces the measurement error caused by the color-taking process and makes the detection results more accurate.

Abstract 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.

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.

ObjectiveTo 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.MethodsThirty-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.ResultsConsistent 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.ConclusionSex 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.

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.

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.

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.

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.

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.

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.

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.

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.

Brain-computer interface (BCI) based on Steady-State Visual Evoked Potentials (SSVEP) provides an effective method for human-computer communication. In practical application scenarios, SSVEP-BCI systems are easily interfered by physiological noises such as electromyography (EMG) and electrooculography (EOG). The performance of traditional SSVEP recognition methods will degrade in such a noisy environment, which limits their real-world applications. To alleviate the interference of noise, existing works either require additional reference electrodes or are designed for removing background noise such as trend terms rather than physiological noises. In this study, we utilize adversarial training (AT) and neural networks (NNs) to construct a robust recognition method for SSVEP contaminated by physiological noise. During model training, we generate adversarial noises which are most harmful to the current model according to gradients and enforce the model to overcome them. In this way, we strengthen the robustness of the model to potential noises, such as physiological noises. In this study, we recorded a real-world speaking SSVEP dataset and simulated various noisy datasets to conducted comparison experiments on two benchmark models named EEGNet and DeepConvNet. The experimental results demonstrated that AT strategies can help the neural networks get better performance on SSVEP data contaminated by EMG and EOG. We also verified that introducing AT can slightly improve the performance of models under a cross-subject scenario. Our method can be integrated into existing deep learning methods efficiently and will contribute to the real-world applications of SSVEP.

Chinese traditional fermented seasonings, essential to the culinary heritage of China, are produced through fermentation, resulting in a diverse range of unique flavors and aromas. The microorganisms involved in fermentation play significant roles in shaping the quality of these traditional fermented seasonings. The production of traditional fermented seasonings is affected by various biological and abiotic factors, presenting challenges concerning product quality and safety. This review investigates the impact of bioaugmentation technology on key Chinese traditional fermented seasonings, such as vinegar, soy sauce, sufu, doubanjiang, dajiang, and douchi. Additionally, the challenges and constraints linked to the implementation of bioaugmentation technology are discussed. The potential of bioaugmentation is highlighted by its ability to shorten the fermentation time, optimize raw material utilization, improve nutritional value, and enhance the quality parameters of these seasonings. This paper demonstrates an interesting convergence of traditional culinary heritage and contemporary technological advancements.

Abstract Tactile sensing is essentially required for dexterous manipulation in robotic applications. Mimicking human perception of softness identification in a non-invasive fashion, thus achieving satisfactory interaction with fragile objects remains a grand challenge. Here, a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported. This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm, contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback. The integration of multifunctional sensing system allows the manipulator to hold capabilities of self-sensing and adaptive grasping motility in response to objects with the various softness (i.e., kPa-MPa). As a proof-of-concept demonstration, the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness, paving the way for the development of microsurgery robots, human-machine interfacing, and advanced prosthetics.

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.

Background: Electroencephalography (EEG) has gained popularity in various types of biomedical applications as a signal source that can be easily acquired and conveniently analyzed. However, owing to a complex scalp electrical environment, EEG is often polluted by diverse artifacts, with electromyography artifacts being the most difficult to remove. In particular, for ambulatory EEG devices with a restricted number of channels, dealing with muscle artifacts is a challenge. Methods: In this study, we propose a simple but effective novel scheme that combines singular spectrum analysis (SSA) and canonical correlation analysis (CCA) algorithms for single-channel problems and then extend it to a fewchannel case by adding additional combining and dividing operations to channels. Results: We evaluated our proposed framework on both semi-simulated and real-life data and compared it with some state-of-theart methods. The results demonstrate this novel framework's superior performance in both single-channel and few-channel cases. Conclusions: This promising approach, based on its effectiveness and low time cost, is suitable for real-world biomedical signal processing applications.

Objective: Epileptic seizure prediction based on scalp electroencephalogram (EEG) is of great significance for improving the quality of life of patients with epilepsy. In recent years, a number of studies based on deep learning methods have been proposed to address this issue and achieve excellent performance. However, most studies on epileptic seizure prediction by EEG fail to take full advantage of temporal-spatial multi-scale features of EEG signals, while EEG signals carry information in multiple temporal and spatial scales. To this end, in this study, we proposed an end-to-end framework by using a temporal-spatial multi-scale convolutional neural network with dilated convolutions for patient-specific seizure prediction. Methods: Specifically, the model divides the EEG processing pipeline into two stages: the temporal multi-scale stage and the spatial multi-scale stage. In each stage, we firstly extract the multi-scale features along the corresponding dimension. A dilated convolution block is then conducted on these features to expand our model’s receptive fields further and systematically aggregate global information. Furthermore, we adopt a feature-weighted fusion strategy based on an attention mechanism to achieve better feature fusion and eliminate redundancy in the dilated convolution block. Results: The proposed model obtains an average sensitivity of 93.3%, an average false prediction rate of 0.007 per hour, and an average proportion of time-in-warning of 6.3% testing in 16 patients from the CHB-MIT dataset with the leave-one-out method. Conclusion: Our model achieves superior performance in comparison to state-of-the-art methods, providing a promising solution for EEG-based seizure prediction.

The quality and safety of wheat flour are of public concern since they are related to the quality of flour products and human health. Therefore, efficient and convenient analytical techniques are needed for the quality and safety controls of wheat flour. Near-infrared (NIR) spectroscopy has become an ideal technique for assessing the quality and safety of wheat flour, as it is a rapid, efficient and nondestructive method. The application of NIR spectroscopy in the quality and safety analysis of wheat flour is addressed in this review. First, we briefly summarize the basic knowledge of NIR spectroscopy and chemometrics. Then, recent advances in the application of NIR spectroscopy for chemical composition, technological parameters, and safety analysis are presented. Finally, the potential of NIR spectroscopy is discussed. Combined with chemometric methods, NIR spectroscopy has been used to detect chemical composition, technological parameters, deoxynivalenol, adulterants and additives of wheat flour. Furthermore, NIR spectroscopy has shown great potential for the rapid and online analysis of the quality and safety of wheat flour. It is anticipated that the current review will serve as a reference for the future analysis of wheat flour by NIR spectroscopy to ensure the quality and safety of flour products.

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) 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.

We investigated the impact of seasons of the year on microbiota and physicochemical indices in industrial-scale fermentation of Sichuan Baoning vinegar. Illumina HiSeq sequencing results showed significant differences (P 0.05), the increase in total acidity was highest in the autumn. Acetic acid content in raw vinegar was highest in the autumn (3472.42 mg/100 mL), and lowest in the summer (2304.01 mg/100 mL). This study provides a theoretical basis for the production of Sichuan bran vinegar with consistent quality and provides insights into the quality control of traditional fermented foods.

Abstract Background Electrostimulation (ES) therapy for wound healing is limited in clinical use due to barriers such as cumbersome equipment and intermittent delivery of therapy. Methods We adapted a human skin xenograft model that can be used to directly examine the nanogenerator-driven ES (NG-ES) effects on human skin in vivo—an essential translational step toward clinical application of the NG-ES technique for wound healing. Results We show that NG-ES leads to rapid wound closure with complete restoration of normal skin architecture within 7 days compared to more than 30 days in the literature. NG-ES accelerates the inflammatory phase of wound healing with more rapid resolution of neutrophils and macrophages and enhances wound bed perfusion with more robust neovascularization. Conclusion Our results support the translational evaluation and optimization of the NG-ES technology to deliver convenient, efficient wound healing therapy for use in human wounds. Graphic abstract

BackgroundGait disturbances are critical motor symptoms in Parkinson’s disease (PD). The mechanisms of gait impairment in PD are not entirely understood but likely involve changes in the Pedunculopontine Nucleus (PPN), a critical locomotion center, and its associated connections. Exercise is universally accepted as helpful in PD, but the extent and intensity of exercise required for plastic changes are unclear.MethodsTwenty-seven PD subjects participated in a 3-month gait training intervention. Clinical assessments and resting-state functional magnetic resonance imaging were performed at baseline and 3 months after exercise. Functional connectivity of PPN was assessed by combining the methods of partial least squares, conditional dependence and partial correlation. In addition, paired t-tests were used to examine the effect of exercise on PPN functional connectivity and clinical measures, and Pearson’s correlation was used to assess the association between altered PPN functional connectivity and clinical measures.ResultsExercise significantly improved Unified Parkinson’s Disease Rating Scale-III (UPDRS-III). A significant increase in right PPN functional connectivity was observed after exercise, which did not correlate with motor improvement. However, the decrease in left PPN functional connectivity significantly correlated with the improvement in UPDRS-III and was linearly related to both number of walks and the duration of walks. In addition, exercise induced a significant increase in the laterality of PPN connectivity strength, which correlated with motor improvement.ConclusionPPN functional connectivity is modifiable by walking exercise in both a dose-independent (right PPN and laterality of PPN connectivity strength) and dose-dependent (left PPN) manner. The PPN may contribute to pathological and compensatory processes in PD gait control. The observed gait improvement by walking exercise is most likely due to the reversal of the maladaptive compensatory mechanism. Altered PPN functional connectivity can be a marker for exercise-induced motor improvement in PD.

Bakery products are nutritious, but they are susceptible to fungal contamination, which leads to a decline in quality and safety. Chemical preservatives are often used to extend the shelf-life of bakery products, but long-term consumption of these preservatives may increase the risk of chronic diseases. Consumers increasingly demand food with fewer chemical preservatives. The application of lactic acid bacteria (LAB) as a novel biological preservative not only prolongs the shelf-life of bakery products but also improves the baking properties of bakery products. This review summarizes different types and action mechanisms of antifungal compounds produced by LAB, factors affecting the production of antifungal compounds, and the effects of antifungal LAB on bakery products, providing a reference for future applications of antifungal LAB in bakery products.

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.

Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. However, the diverse lead-specific features (known as diversity) among 12 leads were neglected, causing inadequate information learning for 12-lead ECG. To maximize the information learning of multi-lead ECG, the information fusion of comprehensive features with integrity and lead-specific features with diversity should be taken into account. In this paper, we propose a novel Multi-Lead-Branch Fusion Network (MLBF-Net) architecture for arrhythmia classification by integrating multi-loss optimization to jointly learning diversity and integrity of multi-lead ECG. MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network. We demonstrate our MLBF-Net on China Physiological Signal Challenge 2018 which is an open 12-lead ECG dataset. The experimental results show that MLBF-Net obtains an average $F_{1}$ score of 0.855, reaching the highest arrhythmia classification performance. The proposed method provides a promising solution for multi-lead ECG analysis from an information fusion perspective.

Autism Spectrum Disorder (ASD) is one common developmental disorder with great variations in symptoms and severity, making the diagnosis of ASD a challenging task. Existing deep learning models using brain connectivity features to classify ASD still suffer from degraded performance for multi-center data due to limited feature representation ability and insufficient interpretability. Given that Graph Convolutional Network (GCN) has demonstrated superiority in learning discriminative representations of brain connectivity networks, in this paper, we propose an invertible dynamic GCN model to identify ASD and investigate the alterations of connectivity patterns associated with the disease. In order to select explainable features from the model, invertible blocks are introduced in the whole network, and we are able to reconstruct the input dynamic features from the network's output. A pre-screening of connectivity features is adopted to reduce the redundancy of the input information, and a fully-connected layer is added to perform classification. The experimental results on 867 subjects show that our proposed method achieves superior disease classification performance. It provides an interpretable deep learning model for brain connectivity analysis and is of great potential in studying brain-related disorders.

Norfloxacin (NOR) is a common antibiotic used in humans and animals, and its high levels can cause intolerance or poisoning. Therefore, NOR levels in animal-derived foods must be monitored due to potential side effects and illegal use phenomena. This research centered on the development of an environmentally friendly electrochemical sensor for NOR detection. Potassium carbonate activated tea branch biochar (K-TBC) as an efficient use of waste was coated on the surface of glassy carbon electrode (GCE), and a molecular-imprinted polymer (MIP) layer was subsequently electropolymerized onto the modified electrode. NOR was used as template molecule and o-phenylenediamine (o-PD) and o-aminophenol (o-AP) were used as bifunctional monomers. The electrochemical sensor was built and its electrochemical behavior on NOR was investigated. The sensor demonstrated an excellent linear current response to NOR concentrations in the ranges of 0.1–0.5 nM and 0.5–100 nM under ideal experimental circumstances, with a detection limit of 0.028 nM (S/N = 3). With recoveries ranging from 85.90% to 101.71%, the designed sensor was effectively used to detect NOR in actual samples of milk, honey, and pork. Besides, the fabricated sensor had low price, short detection time, good selectivity and stability, which can provide a theoretical and practical basis for the actual monitoring of NOR residues.

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.

Autism spectrum disorder (ASD) affects around 1% of children with no effective blood test or cure. Recent studies have suggested that these are neurological disorders with a strong genetic basis and that they are associated with the abnormal formation of dendritic spines. Chromosome microarray (CMA) together with high-throughput sequencing technology has been used as a powerful tool to identify new candidate genes for ASD. In the present study, CMA was first used to scan for genome-wide copy number variants in a proband, and no clinically significant copy number variants were found. Whole-exome sequencing (WES) was used further for genetic testing of the whole quad family affected by ASD, including the proband, his non-autistic sister, and his parents. Sanger sequencing and MassARRAY-based validation were used to identify and confirm variants associated with ASD. WES yielded a 151-fold coverage depth for each sample. A total of 98.65% of the targeted whole-exome region was covered at >20-fold depth. A de novo variant in CTTNBP2, p.M115T, was identified. The CTTNBP2 gene belongs to a family of ankyrin repeat domain-containing proteins associated with dendritic spine formation. Although CTTNBP2 has been associated with ASD, limited studies have been developed to identify clinically relevant de novo mutations of CTTNBP2 in children with ASD; family-based WES successfully identified a clinically relevant mutation in the CTTNBP2 gene in a quad family affected by ASD. Considering the neuron-specific expression of CTTNBP2 and its role in dendritic spine formation, our results suggest a correlation between the CTTNBP2 mutation and ASD, providing genetic evidence for ASD spine pathology. Although the present study is currently insufficient to support the assertion that the de novo mutation M115T in CTTNBP2 directly causes the autism phenotype, our study provides support for the assertion that this mutation is a candidate clinically relevant variant in autism.

Smart hydrogel actuators with programmable anisotropic structures present fascinating prospects considering their distinctive shape transformation and controllable environmental responsiveness under external stimuli. However, the design of anisotropic hydrogels with simple and universal fabrication and programmable functionality is challenging for their valuable applications in smart actuators and soft robots. Herein, a simple, green, and devisable strategy is proposed to construct a heterogeneous porous hydrogel system by the different liquid diffusion (such as amyl alcohol, water, and ethanol) into a monomeric precursor solution of thermosensitive hydrogels. The well‐defined micro/nanoporous gradient and patterned structures related to selective liquid stratification and interfacial diffusion favor the fast response and accurate programmable deformation of hydrogels under temperature stimuli. Inspiringly, this simple diffusion‐driven tactic can be perfectly applicable for different responsive hydrogels with programmable multifunctionality by adding functional nanomaterials into the diffusible liquid. This green, general, and facile diffusion‐driven strategy provides significant guidance for fabricating environmentally responsive hydrogels with tailorable functionality for their multipurpose applications in drug delivery, bioengineering, smart actuators, and soft robots.

Metals are widely used in animal feed for their growth-stimulating and antimicrobial effects, yet their use may potentially promote the proliferation of antibiotic resistance through co-selection. We studied the prevalence and associations of metal, antibiotic, and disinfectant resistances of 300 Salmonella Typhimurium isolates from pig meat, pig manure, chicken meat, poultry manure, and human stool from Sichuan, China. Seventy four percent of the 300 Salmonella Typhimurium isolates were considered resistant to Cu, almost 50% to Zn and Cr, over 25% to Mn and Cd, and almost 10% to Co. Most of the isolates carried at least one heavy metal resistance gene (HMRG). The Cr-Zn-Cd-resistance gene czcD was carried by 254 isolates and the Cu-resistance genes pcoR and pcoC by 196 and 179 isolates, respectively. Most of the isolates were resistant to at least one antibiotic and almost 80% were multidrug-resistant. The prevalence of resistance to six antibiotics was higher among the pig meat and manure isolates than among other isolates, and that of streptomycin and ampicillin were highest among the pig meat isolates and that of ciprofloxacin and ofloxacin among the pig manure isolates. From 55 to 79% of the isolates were considered resistant to disinfectants triclosan, trichloroisocyanuric acid, or benzalkonium chloride. The metal resistances and HMRGs were associated with resistance to antibiotics and disinfectants. Especially, Cu-resistance genes were associated with resistance to several antibiotics and disinfectants. The transfer of the Cr-Zn-Cd-resistance gene czcD, Cu-resistance gene pcoC, and Co-Ni-resistance gene cnrA into Escherichia coli and the increased Cu-resistance of the transconjugants implied that the resistance genes were located on conjugative plasmids. Thus, the excessive use of metals and disinfectants as feed additives and in animal care may have the potential to promote antibiotic resistance through co-selection and maintain and promote antibiotic resistance even in the absence of antibiotics.

Electroencephalogram (EEG) signals are often contaminated with diverse artifacts, such as electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. These artifacts make subsequent EEG analysis inaccurate and prevent practical usage. Recently, the use of wearable EEG devices in ambulatory systems has been developed. For practical reasons, these systems usually contain a single EEG channel. Several studies have proposed to combine single-channel decomposition methods with blind source separation (BSS) methods to denoise the single-channel EEG. However, the existing methods have their own limitations since most of them only focus on removing one single kind of artifacts. Unfortunately, the EEG is prone to be contaminated by various kinds of artifacts simultaneously. Yet to our knowledge, there are no existing methods to remove diverse artifacts simultaneously from the single-channel EEG. To address this issue, we propose an effective method to remove diverse artifacts simultaneously for the single-channel EEG case. This method is a combination of singular spectrum analysis (SSA) and second-order blind identification (SOBI) method. We conduct a semi-simulated study to investigate all possible cases of the single-channel EEG been contaminated by EMG, EOG, and ECG artifacts. The results show that the proposed method can successfully remove diverse artifacts from the single-channel EEG. It is a promising tool for biomedical signal processing applications.

BackgroundThe aim of this study was to explore potential risk factors for cytomegalovirus (CMV) reactivation and their impact on liver failure patient outcomes.MethodsA 10-year retrospective case–control study was conducted in adult participants, who were diagnosed with liver failure and had undergone CMV DNA tests. CMV reactivation cases were matched with controls at a 2:1 ratio based on age, sex, and year of admission. Univariate and multivariate analyses were used to explore risk factors for CMV reactivation.ResultsBetween January 2011 and April 2020, 198 adult patients with liver failure and available CMV DNA test results were enrolled into the study. Among them, 33 patients had detectable CMV DNA in their plasma (16.7%). Clinical manifestations and liver function were comparable between the CMV reactivation and non-reactivation groups. However, CMV reactivation may triple mortality in patients with liver failure. We found that nearly 50% of patients in the CMV-positive group received glucocorticoids, compared to 13.6% in the CMV-negative group (P=0.000). The median total glucocorticoid dose included 836.5 mg of methylprednisolone (IQR 308.7-1259.0 mg) in the CMV-positive group, which was significantly higher than that in the CMV-negative group. A multivariate analysis revealed that glucocorticoid use significantly increased the risk of CMV reactivation (adjusted OR, 4.84; 95% CI, 1.61–14.49; P=0.005). Patients with CMV reactivation tended to be associated with higher white cell counts (adjusted OR, 1.21; 95% CI, 1.08–1.36; P=0.002).ConclusionsHigh intravenous glucocorticoid doses may be the most important risk factor for CMV reactivation in liver failure.

This study investigated the effects of xyloglucan (XG) compounded inulin and XG compounded arabinoxylan on the hypoglycemic activity of mice fed with high-fat diet (HFD). Forty male ICR/KM mice were randomly divided into four groups, namely, control group, high-fat group, XG and 4% inulin supplemented in a HFD group (HFXI), and XG and 4% arabinoxylan supplemented in a HFD group (HFXA). Results showed that XI and XA supplementation decreased serum glucose level and intestinal disaccharidase activity. XI and XA decreased the level of total bile acid (TBA) in the serum and liver. Fecal TBA excretion was increased in the HFXI group, and XA treatment decreased colon digesta TBA excretion. XI treatment activated liver farnesoid X receptor (FXR) and G protein-coupled receptor 5 (TGR5). Intestinal FXR and TGR5 were activated after XA supplementation. XA increased the abundance of Verrucomicrobia, promoting Akkermansia growth, whereas XI treatment increased the abundance of gut Bifidobacterium.

ObjectiveAnti-DFS70 antibodies correlating with the nuclear dense fine speckled (DFS) pattern in the HEp-2 indirect immunofluorescence assay (IFA) are less common in patients with systemic autoimmune rheumatic disease (SARD) than in healthy subjects and their clinical associations remain elusive. We hosted a multi-center HEp-2 IFA training program to improve the ability of clinical laboratories to recognize the DFS pattern and to investigate the prevalence and relevance of anti-DFS70 antibodies.MethodsDFS pattern sera identified by HEp-2 IFA in 29 centers in China were redirected to a central laboratory for anti-DFS70 testing by line immunoblot assay (LIA), enzyme-linked immunosorbent assay (ELISA), and IFA with HEp-2 ELITE/DFS70-KO substrate. Anti-extractable nuclear antigen antibodies were measured by LIA and the clinical relevance was examined in adult and pediatric patients.ResultsHEp-2 IFA positive rate and DFS pattern in positive sera were 36.2% (34,417/95,131) and 1.7% (582/34,417) in the patient cohort, and 10.0% (423/4,234) and 7.8% (33/423) in a healthy population, respectively. Anti-DFS70 prevalence among sera presenting the DFS pattern was 96.0, 93.7, and 49.6% by ELISA, LIA, and HEp-2 ELITE, respectively. 15.5% (52/336) of adult and 50.0% (20/40) of pediatric anti-DFS70 positive patients were diagnosed with SARD. Diseases most common in anti-DFS70 positive patients were spontaneous abortion (28.0%) in adults and juvenile idiopathic arthritis (22.5%) in pediatric patients.ConclusionAccurate DFS pattern identification increased the detection rate of anti-DFS70 antibodies by ELISA and LIA. Anti-DFS70 antibodies are remarkably high in cases of spontaneous abortion and in pediatric SARD patients, but not prevalent in adult SARD patients.

Although functional connectivity has been extensively studied in MS, robust estimates of both stationary (static connectivity at the time) and dynamic (connectivity variation across time) functional connectivity has not been commonly evaluated and neither has its association to cognition. In this study, we focused on interhemispheric connections as previous research has shown links between anatomical homologous connections and cognition. We examined functional interhemispheric connectivity (IC) in MS during resting-state functional MRI using both stationary and dynamic strategies and related connectivity measures to processing speed performance. Twenty-five patients with relapsing-remitting MS and 41 controls were recruited. Stationary functional IC was assessed between homologous Regions of Interest (ROIs) using correlation. For dynamic IC, a sliding window approach was used to quantify changes between homologous ROIs across time. We related IC measures to cognitive performance with correlation and regression. Compared to control subjects, MS demonstrated increased IC across homologous regions, which accurately predicted performance on the symbol digit modalities test (SDMT) (R2 = 0.96) and paced auditory serial addition test (PASAT) (R2 = 0.59). Dynamic measures were not different between the 2 groups, but dynamic IC was related to PASAT scores. The associations between stationary/dynamic connectivity and cognitive tests demonstrated that different aspects of functional IC were associated with cognitive processes. Processing speed measured in SDMT was associated with static interhemispheric connections and better PASAT performance, which requires working memory, sustain attention, and processing speed, was more related to rigid IC, underlining the neurophysiological mechanism of cognition in MS.

“Yibin Baijiu” (YB) is a special Chinese strong-aroma Baijiu (CSAB) that originated in Yibin, a city in western China. YB is fermented in cellars lined with pit mud (PM), the microbiota in which may affect YB quality. In this study, high throughput sequencing of the 16S rRNA gene was used to demonstrate the bacterial community structure and diversity in PM of YB. In addition, the physicochemical characteristics of PM were also analyzed, including moisture content, pH, and available phosphorous, ammonia nitrogen, and humic acid levels. Results showed that Firmicutes was the dominant phylum in all PM samples with abundance > 70.0%, followed by Euryarchaeota (11.3%), Bacteroidetes (6.5%), Synergistetes (3.0%), Actinobacteria (1.4%), and Proteobacteria (1.2%). Furthermore, 14 different genera with average relative abundance of > 1% were detected. The Chao1 and Shannon indexes did not vary significantly between the sub-layer and middle-layer PM (P > 0.05). However, Linear discriminant analysis Effect Size (LEfSe) analysis showed that the relative abundance of Lactobacillus in the sub-layer PM was significantly higher than in middle-layer PM. pH differed significantly (P

Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences.HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features.Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders. Keywords: fMRI, Dynamic parcellation, Dynamic connectivity, Temporal homogeneous subunits, Parkinson's Disease, Putamen

Sichuan bacon is one of the most popular types of Chinese bacon in the domestic market. High-throughput sequencing was used to characterize the bacterial diversity of 39 Sichuan bacon samples collected from 3 geographical regions. The results showed that the bacterial diversity of Sichuan bacon in different regions demonstrated certain specificity as well as similarity, and the shared OTUs were close to 81% of the total number in the basin group, mountain group, and plateau group. At the genus level, Staphylococcus is the most dominant genus among the three groups, covering 26.7%, 20.6%, and 22.7%, respectively. Beta diversity shows significant differences in bacterial compositions in different geographic regions, especially for Pseudomonas, Brochothrix, Lactobacillus, Lactococcus, and Enterococcus. Meanwhile, some physicochemical characteristics were analyzed, and a significant difference (p < 0.05) among the three regions was shown in the Aw, pH, and nitrite content, which were significantly correlated with undesired bacteria. This study provides insights into the understanding of the role of bacterial communities in the microbial safety and quality improvement of Sichuan bacon.

The mycelial polysaccharides from Catathelasma ventricosum (mCVP-1Ss) were modified by carboxymethylation with different reaction conditions (reaction time, temperature and chloroacetic acid dosage) to obtain carboxymethylated mCVP-1Ss (cmCVP-1Ss), and their primary structures were characterized. NMR results indicated that substitution of carboxylate groups appeared primarily at C-6 position. Carboxymethylation significantly improved the antioxidant and antibacterial activities of mCVP-1S, while cmCVP-1Ss with medium degree of substitution (DS) and stable triple-helical structurepossessed the greatest DPPH radical scavenging activity, reducing power, metal chelating activity andantibacterial activity. When the concentration of cmCVP-1S3/8/13 reached 7.5 mg/mL, it showed excellent inhibitory effect on Escherichia coli, Salmonella Typhimurium, Staphylococcus aureus and Bacillus subtilis. However, excessive DS might destroy the triple helical structure of cmCVP-1S, thus significantly weaken the biological activities of cmCVP-1S·cmCVP-1Ss possess excellent antioxidant and antibacterial activities and have potential to be used as new types of antioxidant and antibiotic, which are inartificial and safer.

Based on the tectonic evolution and depositional characteristics of Central–West African Rift System and regional geologic background, the characteristics of oil and gas accumulation in the Termit rift superimposed basin were examined, and the factors controlling oil and gas accumulation there were discussed. The Termit basin is a rift superimposed basin formed by two stages of rifting in the early Cretaceous and Paleogene, where the Yogou Formation marine source rock is widespread. The two sets of reservoirs, the first member of the Paleogene Sokor Formation and the Cretaceous Yogou Formation, dominated by quartz sandstone, have relatively good porosity-permeability characteristics. They can form multiple sets of favorable caprock-reservoir assemblage below the regional caprock of the second member of Paleogene Sokor Formation. It is found that fault pattern and oil and gas migration pattern control the oil and gas accumulation patterns in the basin, including “Y-shaped” hydrocarbon migration and accumulation in the Araga graben, “multi-step” vertical hydrocarbon migration and accumulation in the Dinga faulted terrace, and the “composite” hydrocarbon migration and accumulation in the Fana transfer zone. Hydrocarbon enrichment in this basin was controlled by tectonic evolution of rift basins, that is, fault belts formed during the two stages of rifting controlled the horizontal distribution of oil and gas, while favorable reservoir-caprock assemblages in the sequences formed in the late rifting stage controlled the vertical hydrocarbon enrichment. Key words: Niger, Termit basin, rift superimposed basin, hydrocarbon accumulation, controlling factor

Abstract Beta‐cypermethrin (β‐CY) and its major metabolite 3‐phenoxybenzoic acid (3‐PBA) spread extensively in the environment because of utilization in agricultural and home formulations, exerting negative impact on environment as well as human health. Several golden flower fungi were isolated from fu brick tea, by which the biodegradation of β‐CY and 3‐PBA was evaluated, turning out strain Eurotium cristatum ET1 had the highest capacity. Furthermore, β‐CY and 3‐PBA degradation rates were positively correlated with biomass of E. cristatum ET1, and the processes of degradation fitted well with a first‐order kinetic equation. The half‐lives of β‐CY and 3‐PBA ranged from 3.382 to 11.517 days and 1.749 to 3.194 days, respectively, under different substrate concentrations, incubation temperatures, and pH values. The degraded products were analyzed using gas chromatography‐mass spectrometry and liquid chromatography‐mass spectrometry, and results showed that E. cristatum ET1 degrades β‐CY by transforming it into 3‐PBA, which is then gradually metabolized into phenol and catechol. Moreover, E. cristatum ET1 showed efficiency in degrading these metabolites. Our results suggest that this strain is a potential microorganism for bioremediation of pesticide‐contaminated environments and fermented foods.

The human brain is a highly dynamic structure requiring dynamic coordination between different neural systems to perform numerous cognitive and behavioral tasks. Emerging perspectives on basal ganglia (BG) and thalamic functions have highlighted their role in facilitating and mediating information transmission among cortical regions. Thus, changes in BG and thalamic structures can induce aberrant modulation of cortico-cortical interactions. Recent work in deep brain stimulation (DBS) has demonstrated that externally applied electrical current to BG structures can have multiple downstream effects in large-scale brain networks. In this work, we identified EEG-based altered resting-state cortical functional connectivity in Parkinson’s disease (PD) and examined effects of dopaminergic medication and electrical vestibular stimulation (EVS), a non-invasive brain stimulation (NIBS) technique capable of stimulating the BG and thalamus through vestibular pathways. Resting EEG was collected from 16 PD subjects and 18 age-matched, healthy controls (HC) in four conditions: sham (no stimulation), EVS1 (4–8 Hz multisine), EVS2 (50–100 Hz multisine) and EVS3 (100–150 Hz multisine). The mean, variability, and entropy were extracted from time-varying phase locking value (PLV), a non-linear measure of pairwise functional connectivity, to probe abnormal cortical couplings in the PD subjects. We found the mean PLV of Cz and C3 electrodes were important for discrimination between PD and HC subjects. In addition, the PD subjects exhibited lower variability and entropy of PLV (mostly in theta and alpha bands) compared to the controls, which were correlated with their clinical characteristics. While levodopa medication was effective in normalizing the mean PLV only, all EVS stimuli normalized the mean, variability and entropy of PLV in the PD subject, with the exact extent and duration of improvement a function of stimulus type. These findings provide evidence demonstrating both low- and high-frequency EVS exert widespread influences on cortico-cortical connectivity, likely via subcortical activation. The improvement observed in PD in a stimulus-dependent manner suggests that EVS with optimized parameters may provide a new non-invasive means for neuromodulation of functional brain networks.

Autism spectrum disorders (ASD) affect 1% of children. Although there is no cure, early diagnosis and behavioral intervention can relieve the symptoms. The clinical heterogeneity of ASD has created a need for improved sensitive and specific laboratory diagnostic methods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analysis of the metabolome has shown great potential to uncover biomarkers for complex diseases such as ASD. Here, we used a two-step discovery–validation approach to identify potential novel metabolic biomarkers for ASD. Urine samples from 57 children with ASD and 81 matched children with typical development (TD) were analyzed by LS-MS/MS to assess differences in urinary amino acids and their metabolites (referred to as UAA indicators). A total of 63 UAA indicators were identified, of which 21 were present at significantly different levels in the urine of ASD children compared with TD children. Of these 21, the concentrations of 19 and 10 were higher and lower, respectively, in the urine of ASD children compared with TD children. Using support vector machine modeling and receiver operating characteristic curve analysis, we identified a panel of 7 UAA indicators that discriminated between the samples from ASD and TD children (lysine, 2-aminoisobutyric acid, 5-hydroxytryptamine, proline, aspartate, arginine/ornithine, and 4-hydroxyproline). Among the significantly changed pathways in ASD children were the ornithine/urea cycle (decreased levels of the excitatory amino acid aspartate [p = 2.15 × 10-10] and increased arginine/ornithine [p = 5.21 × 10-9]), tryptophan metabolism (increased levels of inhibitory 5-hydroxytryptamine p = 3.62 × 10-9), the methionine cycle (increased methionine sulfoxide [p = 1.46 × 10-10] and decreased homocysteine [p = 2.73 × 10-7]), and lysine metabolism (reduced lysine [p = 7.8 × 10-9], α-aminoadipic acid [p = 1.16 × 10-9], and 5-aminovaleric acid [p = 1.05 × 10-5]). Collectively, the data presented here identify a possible imbalance between excitatory and inhibitory amino acid metabolism in ASD children. The significantly altered UAA indicators could therefore be potential diagnostic biomarkers for ASD.

Falls and balance difficulties remain a major source of morbidity in Parkinson's Disease (PD) and are stubbornly resistant to therapeutic interventions. The mechanisms of gait impairment in PD are incompletely understood but may involve changes in the Pedunculopontine Nucleus (PPN) and its associated connections. We utilized fMRI to explore the modulation of PPN connectivity by Galvanic Vestibular Stimulation (GVS) in healthy controls (n = 12) and PD subjects even without overt evidence of Freezing of Gait (FOG) while on medication (n = 23). We also investigated if the type of GVS stimuli (i.e., sinusoidal or stochastic) differentially affected connectivity. Approximate PPN regions were manually drawn on T1 weighted images and 58 other cortical and subcortical Regions of Interest (ROI) were obtained by automatic segmentation. All analyses were done in the native subject's space without spatial transformation to a common template. We first used Partial Least Squares (PLS) on a subject-by-subject basis to determine ROIs across subjects that covaried significantly with the voxels within the PPN ROI. We then performed functional connectivity analysis on the PPN-ROI connections. In control subjects, GVS did not have a significant effect on PPN connectivity. In PD subjects, baseline overall magnitude of PPN connectivity was negatively correlated with UPDRS scores (p < 0.05). Both noisy and sinusoidal GVS increased the overall magnitude of PPN connectivity (p = 6 × 10−5, 3 × 10−4, respectively) in PD, and increased connectivity with the left inferior parietal region, but had opposite effects on amygdala connectivity. Noisy stimuli selectively decreased connectivity with basal ganglia and cerebellar regions. Our results suggest that GVS can enhance deficient PPN connectivity seen in PD in a stimulus-dependent manner. This may provide a mechanism through which GVS assists balance in PD, and may provide a biomarker to develop individualized stimulus parameters.

A ‘sandwich'-structured TiO2NR/HGN/CdS photoanode was successfully fabricated by the electrophoretic deposition of hollow gold nanospheres (HGNs) on the surface of TiO2 nanorods (NRs). The HGNs presented a wide surface plasmon resonance character in the visible region from 540 to 630 nm, and further acted as the scatter elements and light energy ‘antennas' to trap the local-field light near the TiO2NR/CdS layer, resulting in the increase of the light harvesting. An outstanding enhancement in the photochemical behaviour of TiO2NR/HGN/CdS photoanodes was attained by the contribution of HGNs in increasing the light absorption and the number of electron-hole pairs of photosensitive semiconductors. The optimized photochemical performance of TiO2NR/HGN/CdS photoanodes by using plasmonic HGNs demonstrated their potential application in energy conversion devices.