SARS-CoV-2 Infection Increases High Risk Rate of Down Syndrome Screening Test by Reducing Alpha-Fetoprotein.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a significant global health issue in recent years. Numerous studies indicate that COVID-19 during pregnancy is associated with an increased likelihood of pregnancy complications. Additionally, pregnancy itself is known to elevate the risk of severe SARS-CoV-2 infection. To explore the potential impact of SARS-CoV-2 infection on the probability of Down syndrome in fetuses, we conducted serological testing of Down syndrome markers in pregnant women who had contracted the virus. METHODS: Serological experiments were conducted utilizing a particle chemiluminescence test. The cohort of pregnant women was categorized into three groups: a control group with no infection, a group infected with SARS-CoV-2 Omicron within the first six weeks of gestation, and a group infected beyond the sixth week of gestation. RESULTS: In the group of individuals infected within 6 gestational weeks, the infection resulted in a decrease in alpha-fetoprotein (AFP) levels and a higher positive rate of Down syndrome screening tests (p ˂ 0.05). However, in this study, SARS-CoV-2 infection did not lead to an increase in the occurrence of Down syndrome in the fetus. The positive rate of women infected beyond 6 gestational weeks was slightly higher than the non-infected group (6.2% vs. 5.7%), but these differences were not statistically significant (p > 0.05). Within the group infected beyond 6 gestational weeks, there was, compared to the control group, a decrease in free beta human chorionic gonadotropin (ß-hCG) levels (p < 0.05). CONCLUSIONS: This study presents a novel investigation into the impact of SARS-CoV-2 infection on AFP and ß-hCG levels. It has been observed that pregnant women who contract SARS-CoV-2 may exhibit an increased likelihood of positive results in serum tests conducted for Down syndrome screening. However, it is important to note that the occurrence of Down syndrome in the developing fetus does not appear to be elevated. To validate these findings, additional research involving larger and diverse cohorts is necessary.

A Simple-Structured Perovskite Wavelength Sensor for Full-Color Imaging Application.

In this study, simple-structured wavelength sensors were developed by depositing two back-to-back Au/MAPbI3/Au photodetectors on an MAPbI3 single crystal. This sensor could quantitatively distinguish wavelengths. Further device analysis showed that both photodetectors possess entirely disparate optoelectronic properties. Consequently, the as-developed wavelength sensor could accurately distinguish incident-light wavelengths ranging from 265 to 860 nm with a resolution of less than 1.5 nm based on the relation between the photocurrent ratios of both photodetectors and the incident light wavelengths. Notably, a high resolution and wide detection range are among the optimum reported values for such sensors and enable full-color imaging. Furthermore, technology computer-aided design (TCAD) simulations showed that a mechanism involved in distinguishing wavelengths is attributed to the wavelength-dependent photon generation rate in MAPbI3 single crystals. The high-performance MAPbI3 wavelength sensor can potentially drive the research progress of perovskites in wavelength recognition and full-color imaging.

Effects of Environmental and Spatial Variables on Bacteria in Zhanjiang Mangrove Sediments.

The bottom mud of mangroves contains numerous microbial groups that play an important role in the main ecological functions of the mangrove ecosystem. The diversity and functional and environmental factors related to microbial communities, in terms of the assembly process and in environmental adaptation of the abundance and rare bacterial communities in the mangrove ecosystem, have not been fully explored. We used 16S high-throughput sequencing and operational taxonomic unit analysis to compare the diversity and composition of bacterial communities in different tidal zones in the sediments of the Zhanjiang Gaoqiao Mangrove Nature Reserve, compare the ecological adaptation thresholds and phylogenetic signals of bacterial communities under different environmental gradients, and examine the factors affecting the composition of the bacterial community. The diversity of microbial species and structure and function of the mangrove sediments were affected by the environment, showing the trend: mid tide zone > climax zone > low tide zone. Organic matter content, oxygen content, pH, and total phosphorus were identified as important environmental factors determining the functional diversity of bacterial communities and survival, while pH influences species evolution. The abundant taxa showed a wider response threshold and stronger phylogenetic signals of ecological preference across environmental gradients compared to rare taxa. The abundant bacterial groups have broader environmental adaptability than rare bacterial groups, and different environmental factors affect different communities and functions in the mangrove ecological environment. These results elucidate the mechanism underlying the generation and maintenance of bacterial diversity in response to global environmental changes.

Effects of Microplastics on Microbial Community in Zhanjiang Mangrove Sediments.

Microplastics are easily consumed by marine animals, thereby entering the food chain and endangering animal health. However, there are few studies focusing on the effects of microplastics in mangrove sediments on microbial communities. In order to study the influence of microplastics on microorganisms, microplastics and microorganisms were extracted from Zhanjiang (Guangdong Province, China) mangrove sediments and analyzed. The results showed that there were differences in Shannon and Simpson indices of the microbial community in microplastics (p < 0.05), and there were also differences between JG30_KF_CM45 and Natranaerovirga at the genus level, indicating that microplastics may affect the diversity and composition of microorganisms in sediments. In addition, FAPROTAX function prediction analysis showed that microplastics may affect the nitrification of microbial communities. The results from this study indicate that microplastics affected the diversity and richness of microorganisms in mangrove sediments, which provides an experimental basis for the relationship between microplastics and microorganisms.

First report and genetic diversity of porcine bufavirus in China.

BACKGROUND: Bufavirus is a newly discovered zoonotic virus reported in numerous mammals and humans. However, the epidemiological and genetic characteristics of porcine bufaviruses (PBuVs) in China remain unclear. METHODS: To detect PBuVs in China, 384 samples (92 fecal and 292 serum specimens) were collected from 2017 to 2018, covering six provinces in China, and were evaluated by nested PCR. Further, the positive samples from different provinces were selected to obtain the complete genome of Chinese PBuVs. RESULTS: The prevalence rate of PBuV was 16.7% in Chinese domestic pigs in the Guangdong, Guangxi, Fujian, Jiangxi, Anhui, and Henan provinces. Additionally, the positive rate of fecal specimens was higher than that of the serum samples. Next, we sequenced nine near-complete genomes of Chinese field PBuV strains from different provinces. Homology and phylogenetic analyses indicated that Chinese PBuVs have high genetic variation (93.3-99.2%), showed higher nucleotide identity with an Austrian PBuV strain (KU867071.1), and developed into different branches within the same cluster. CONCLUSION: To our knowledge, this is the first report on PBuV in China, expanding the geographic boundaries of PBuV circulation. Our data demonstrate that PBuVs are widely distributed in the six Chinese provinces. Moreover, these Chinese PBuVs exhibit genetic variation and continuous evolution characteristics. Taken together, our findings provide a foundation for future studies on bufaviruses.

White matter microstructure disruptions mediate the adverse relationships between hypertension and multiple cognitive functions in cognitively intact older adults.

Although hypertension is a prominent vascular risk factor for late-life cognitive decline, the underlying pathophysiological mechanism remains unclear. Accordingly, the aim of this study was to examine the role of white matter microstructural integrity in hypertension-related cognitive detriments. We recruited 66 cognitively normal older adults, comprising 41 hypertensive patients and 25 normotensive controls. All participants underwent a comprehensive neuropsychological battery. White matter microstructural integrity was assessed using a tract-based automatic analysis approach derived from diffusion spectrum imaging. Mediating effects of white matter integrity were evaluated using structural equation modeling analyses. The results revealed that hypertensive older adults displayed poorer processing speed, executive function, and memory encoding. Lower white matter microstructural integrity was observed in the hypertensive elderly patients, primarily in long-range association fiber bundles. In particular, low microstructural integrity in specific tract bundles connecting frontal and posterior cerebral regions was found to underlie the adverse relationships between hypertension and multiple cognitive domains, including processing speed, executive function, memory encoding, and memory retention. Our findings suggest that hypertension may impair multiple cognitive functions by undermining white matter microstructures, even in cognitively intact older adults, thus further highlighting the necessity of monitoring vascular health to prevent cognitive decline.

Genetic polymorphisms associated with nonalcoholic fatty liver disease in Uyghur population: a case-control study and meta-analysis.

BACKGROUND: Polymorphisms have been identified to predispose to NAFLD. Here, we accessed the seven polymorphisms of rs1260326, rs780094 in GCKR, rs2954021 near TRIB1, rs2228603 in NCAN, rs58542926 in TM6SF2, rs12137855 near LYPLAL1, and rs10883437 near CPN1 on NAFLD susceptibility in the Uygur population. MATERIAL AND METHODS: We collected 620 samples (317 NAFLD and 303 controls) for this case-control study. Meta-analysis was performed using Stata Software. RESULTS: Our data detected that the rs1260326 (T vs. C: OR = 1.27, 95% CI = 1.01-1.59) and rs780094 (T vs. C: OR = 1.30, 95% CI = 1.04-1.63) were significantly associated with the susceptibility to NAFLD in Uygur population. The rs1260326 and rs780094 T/T genotype are significantly associated with soda, egg, and soybean intakes in the consumption group with twice or more in a week. Furthermore, a significant haplotype effect of rs1260326/T- rs780094/T was found (OR = 1.29, 95% CI: 1.03-1.62) compared with CC haplotype. An additional meta-analysis using 4352 cases and 10,168 controls established that rs780094 (OR = 1.21, 95%CI: 1.14-1.28) is significantly associated with NAFLD. Finally, among the 4 case-control studies on rs1260326, including 712 NAFLD and 658 controls, significant associations were found in Asian, liver biopsy, adult and pediatric groups. CONCLUSION: Collectively, both our case-control study and meta-analysis confirm a significant association between rs780094 and NAFLD. Additionally, our results suggest Asian-specific, liver biopsy-specific, adult-specific and pediatric-specific associations between the rs1260326 and NAFLD. Moreover, the rs1260326 and rs780094 T/T genotype are significantly associated with food habits, such as soda, egg, and soybean.

Perceived physical and mental fatigability in older adults with and without multiple sclerosis.

BACKGROUND: Fatigue stands out as a prevalent and debilitating symptom in both Multiple Sclerosis (MS) and the aging population. Traditional methods for measuring perceived fatigue may not adequately account for individual activity differences, leading to varied prevalence rates. Perceived fatigability anchors fatigue to specific activities with predetermined intensity and duration, thereby mitigating self-pacing bias. Despite its potential, perceived fatigability is poorly understood in older adults, particularly those with neurological conditions, including MS. This study thus aimed to (1) investigate whether, among older adults, MS was associated with worse perceived physical and mental fatigability; (2) evaluate whether, among older adults with MS (OAMS), greater patient-reported disease-related disability was associated with worse perceived physical and mental fatigability. METHODS: Participants were 96 older adults with a physician-confirmed diagnosis of MS (mean age: 64.6 ± 4.2) and 110 healthy controls (mean age: 68.2 ± 7.2), all confirmed to be dementia-free through established case conference procedures. Physical and mental fatigability were measured using the Pittsburgh Fatigability Scale, a 10-item questionnaire (score range: 0 to 50) designed to assess fatigue levels that individuals expect to feel after engaging in a range of typical activities for older adults. MS disease-related disability was assessed with the Patient Determined Disease Steps scale, which ranges from 0 (normal) to 8 (bedridden), with scores ≥ 2 indicating worse MS-related disability after a median split. Separate linear regression models were performed to investigate associations between group status (MS vs. Control) as the predictor and perceived physical and mental fatigability scores as the outcome variables. Within the MS group, additional linear regression models were performed to explore the relationship between disease-related disability and fatigability levels. All models adjusted for age, sex, race, education, global health, general cognitive function, and depressive symptoms levels. RESULTS: The fully adjusted models yielded the following key findings: OAMS reported significantly higher levels of perceived physical fatigability (M = 25.11 ± 9.67) compared to controls (M = 17.95 ± 8.35) (p = 0.003). Similarly, the perceived mental fatigability in OAMS (M = 16.82 ± 11.79) was significantly greater than that in controls (M = 9.15 ± 7.12) (p = 0.003). Within the MS group, individuals with greater disease-related disability reported significantly greater levels of both physical (M = 30.13 ± 7.71 vs. 18.67 ± 8.00, p < 0.001) and mental fatigability (M = 20.31 ± 12.18 vs. 12.33 ± 9.69, p = 0.009) compared to those with lower MS-related disability. Of note, the significance of these findings persisted in models that adjusted for depressive symptoms. CONCLUSION: Our study provides compelling evidence that OAMS exhibit significantly higher perceived physical and mental fatigability compared to healthy controls. Additionally, worse MS-related disability correlates with worse physical and mental fatigability. These results persist after adjusting for confounders including depressive symptoms. Our findings underscore the necessity of holistic management strategies that cater to both physical and psychological aspects of MS, laying a foundation for future studies to uncover the pathophysiological mechanisms of fatigability in older adults with and without MS.

An Efficient Robotic Pushing and Grasping Method in Cluttered Scene.

Pushing and grasping (PG) are crucial skills for intelligent robots. These skills enable robots to perform complex grasping tasks in various scenarios. These PG methods can be categorized into single-stage and multistage approaches. Single-stage methods are faster but less accurate, while multistage methods offer high accuracy at the expense of time efficiency. To address this issue, a novel end-to-end PG method called efficient PG network (EPGNet) is proposed in this article. EPGNet achieves both high accuracy and efficiency simultaneously. To optimize performance with fewer parameters, EfficientNet-B0 is used as the backbone of EPGNet. Additionally, a novel cross-fusion module is introduced to enhance network performance in robotic PG tasks. This module fuses and utilizes local and global features, aiding the network in handling objects of varying sizes in different scenes. EPGNet consists of two branches dedicated to predicting PG actions, respectively. Both branches are trained simultaneously within a Q-learning framework. Training data is collected through trial and error, involving the robot performing PG actions. To bridge the gap between simulation and reality, a unique PG dataset is proposed. Additionally, a YOLACT network is trained on the PG dataset to facilitate object detection and segmentation. A comprehensive set of experiments is conducted in simulated environments and real-world scenarios. The results demonstrate that EPGNet outperforms single-stage methods and offers competitive performance compared to multistage methods, all while utilizing fewer parameters. A video is available at https://youtu.be/HNKJjQH0MPc.

A Study on the Protective Impact of Resveratrol on Liver Damage in Rats with Obstructive Jaundice.

BACKGROUND: Obstructive Jaundice (OJ) is a common clinical condition with potential outcomes, including hepatocyte necrosis, bile duct hyperplasia, significant cholestatic liver fibrosis, and, in severe cases, liver failure. Resveratrol (RES), a polyphenol present in grapes and berries, has demonstrated efficacy in improving OJ. However, the precise mechanism of its action remains unclear. METHODS: In this study, we employed network pharmacology to investigate the underlying molecular mechanism of RES in the treatment of OJ. The targets of RES were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), SuperPred, and SwissTargetPrediction database. The targets related to OJ were gathered from the DisGeNET, GeneCards, DrugBank, and Online Mendelian Inheritance in Man (OMIM) databases, and the intersection of these targets was determined using Venny2.1.0. Subsequently, an active component-target network was constructed using Cytoscape software. The Protein-Protein Interaction (PPI) network was generated using the String database and Cytoscape software. Following this, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Bioconductor platform. Finally, quantitative Real-Time PCR (qRT-PCR), Western Blotting (WB), and Enzyme-Linked Immunosorbent Assay (ELISA) were employed to assess RNA and protein expression levels in related pathways. RESULTS: The findings revealed a selection of 56 potential targets for RES, and a search through the online database identified 2,742 OJ-related targets with overlapping in 27 targets. In the PPI network, mTOR, CYP2C9, CYP1A1, CYP3A4, AHR, ESR1, and HSD17B1 emerged as core targets. KEGG analyses demonstrated that the primary pathways of RES in treating OJ, particularly those related to lipid metabolism, include linoleic acid metabolism, arachidonic acid metabolism, metabolism of xenobiotics by cytochrome P450, lipid and atherosclerosis, tyrosine metabolism, steroid hormone biosynthesis, and pentose and glucuronate interconversions signaling pathways. Furthermore, in vivo experiments indicated that RES significantly ameliorated liver injury induced by Common Bile Duct Ligation (CBDL) in rats with OJ. It lowered serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, reduced liver tissue MDA levels, increased glutathione (GSH) content, and enhanced activity of superoxide dismutase (SOD), alleviating liver damage. Metabolomics analysis revealed that the therapeutic effect of RES in OJ involved alterations in lipid metabolic pathways, hinting at the potential mechanism of RES in treating OJ. ELISA, qRTPCR, and WB analyses confirmed lower expression levels of mTOR, CYP1A1, and CYP2C9 in the RES group compared to the model group, validating their involvement in the lipid metabolism pathway. CONCLUSION: In conclusion, RES exhibited a protective effect on liver function in rats with OJ. The underlying mechanism appears to be linked to antioxidant activity and modulation of lipid metabolism pathways.

6-D Object Pose Estimation Based on Point Pair Matching for Robotic Grasp Detection.

The 6-D pose estimation is a critical work essential to achieve reliable robotic grasping. Currently, the prevalent method is reliant on keypoint correspondence. However, this approach hinges on the determination of object keypoint locations, alongside their detection and localization in real scenes. It also employs the random sample consensus (RANSAC)-based perspective-n-point (PnP) algorithm to solve the pose. Yet, it is nondifferentiable and incapable of backpropagation with loss during the training phase. Alternatively, the direct regression method, while speedy and differentiable, falls short in terms of pose estimation performance, and thus needs enhancement. In view of these gaps, we investigate PPM6D, a new method for 6-D object pose estimation based on regression and point pair matching. Our methodology begins with a proposed cross-fusion module, designed to achieve the fusion and complementation of RGB features and point cloud features. Subsequently, an attention module adjusts the features of the object's 3-D model. Finally, we design a point pair matching module for effective matching of points and characteristics, resulting in an integral matching and fusion. PPM6D is extensively trained and tested utilizing benchmark datasets like LINEMOD, occlusion LINEMOD (LINEMOD-occ), YCB-Video, and T-LESS dataset. Experimental results prove that PPM6D can outperform many keypoint-based pose estimation methods, given its relatively rapid speed, thereby offering novel regression-based pose estimation ideas. When applied to real-world scenarios of object pose estimation tasks and grasp tasks of an actual Baxter robot, PPM6D demonstrates superior performance as compared to most alternatives.

Cooperative Online Learning for Multiagent System Control via Gaussian Processes With Event-Triggered Mechanism.

In the realm of the cooperative control of multiagent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data into GP models, promises to improve control performance by enhancing predictions during the operation. Therefore, this article investigates the online cooperative learning algorithm for MAS control. Moreover, an event-triggered data selection mechanism, inspired by the analysis of a centralized event-trigger (CET), is introduced to reduce the model update frequency and enhance the data efficiency. With the proposed learning-based control, the practical convergence of the MAS is validated with guaranteed tracking performance via the Lyapunov theory. Furthermore, the exclusion of the Zeno behavior for individual agents is shown. Finally, the effectiveness of the proposed event-triggered online learning method is demonstrated in simulations.

New method for calculating the windward area of irregular fragments.

Average windward area is an important index for calculating the trajectory, velocity attenuation and terminal effect of explosive fragments. In order to solve the problems that existing theoretical method cannot calculate windward area of irregular fragment and experiment method is not convenient for automatic calculation and has low accuracy, a Monte Carlo subdivision projection simulation algorithm is proposed. The average windward area of arbitrary shaped fragments can be obtained with coordinate translation, random rotation, plane projection, convex-hull triangulation, concave boundary searching and sorting with maximum edge length constraint, subdivision area calculation, and averaging by thousands of cycles. Results show that projection area obtained by the subdivision projection algorithm is basically the same as that obtained by software method of computer aided design. Moreover, the maximum calculation error of the algorithm is less than 7%, and its accuracy is much higher than that of the equivalent ellipsoid method. The average windward area calculated by the Monte Carlo subdivision projection simulation algorithm is consistent with theoretical formula for prefabricated fragments, and the error is less than 3%. The convergence and accuracy of the Monte Carlo subdivision projection algorithm are better than those of the icosahedral uniform orientation method.

Category-Level 6-D Object Pose Estimation With Shape Deformation for Robotic Grasp Detection.

Category-level 6-D object pose estimation plays a crucial role in achieving reliable robotic grasp detection. However, the disparity between synthetic and real datasets hinders the direct transfer of models trained on synthetic data to real-world scenarios, leading to ineffective results. Additionally, creating large-scale real datasets is a time-consuming and labor-intensive task. To overcome these challenges, we propose CatDeform, a novel category-level object pose estimation network trained on synthetic data but capable of delivering good performance on real datasets. In our approach, we introduce a transformer-based fusion module that enables the network to leverage multiple sources of information and enhance prediction accuracy through feature fusion. To ensure proper deformation of the prior point cloud to align with scene objects, we propose a transformer-based attention module that deforms the prior point cloud from both geometric and feature perspectives. Building upon CatDeform, we design a two-branch network for supervised learning, bridging the gap between synthetic and real datasets and achieving high-precision pose estimation in real-world scenes using predominantly synthetic data supplemented with a small amount of real data. To minimize reliance on large-scale real datasets, we train the network in a self-supervised manner by estimating object poses in real scenes based on the synthetic dataset without manual annotation. We conduct training and testing on CAMERA25 and REAL275 datasets, and our experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) techniques in both self-supervised and supervised training paradigms. Finally, we apply CatDeform to object pose estimation and robotic grasp experiments in real-world scenarios, showcasing a higher grasp success rate.

Discrete-Time Control Barrier Function: High-Order Case and Adaptive Case.

This article proposes the novel concepts of the high-order discrete-time control barrier function (CBF) and adaptive discrete-time CBF. The high-order discrete-time CBF is used to guarantee forward invariance of a safe set for discrete-time systems of high relative degree. An optimization problem is then established unifying high-order discrete-time CBFs with discrete-time control Lyapunov functions to yield a safe controller. To improve the feasibility of such optimization problems, the adaptive discrete-time CBF is designed, which can relax constraints on system control input through time-varying penalty functions. The effectiveness of the proposed methods in dealing with high relative degree constraints and improving feasibility is verified on the discrete-time system of a three-link manipulator.

A Novel Robotic Pushing and Grasping Method Based on Vision Transformer and Convolution.

Robotic grasping techniques have been widely studied in recent years. However, it is always a challenging problem for robots to grasp in cluttered scenes. In this issue, objects are placed close to each other, and there is no space around for the robot to place the gripper, making it difficult to find a suitable grasping position. To solve this problem, this article proposes to use the combination of pushing and grasping (PG) actions to help grasp pose detection and robot grasping. We propose a pushing-grasping combined grasping network (GN), PG method based on transformer and convolution (PGTC). For the pushing action, we propose a vision transformer (ViT)-based object position prediction network pushing transformer network (PTNet), which can well capture the global and temporal features and can better predict the position of objects after pushing. To perform the grasping detection, we propose a cross dense fusion network (CDFNet), which can make full use of the RGB image and depth image, and fuse and refine them several times. Compared with previous networks, CDFNet is able to detect the optimal grasping position more accurately. Finally, we use the network for both simulation and actual UR3 robot grasping experiments and achieve SOTA performance. Video and dataset are available at https://youtu.be/Q58YE-Cc250.

Isolation and genomic characterization of Klebsiella Lw3 with polychlorinated biphenyl degradability.

Bioremediation of sediment organic pollution has been intensely investigated, but the degradation of complex organic compounds, pesticide residues, and polychlorinated biphenyls (PCBs) remains poorly studied. In this study, sediments were collected from Zhanjiang Mangrove Reserve and inoculated in an inorganic salt medium using only biphenyl (BP) and PCBs as the carbon sources to obtain a PCB-degrading strain. A gram-negative bacterium that metabolized PCBs was isolated and identified as Klebsiella Lw3 by 16S rDNA phylogenetic analysis. Genomic sequencing showed that this bacterium possessed genes related to BP/PCB degradation, and its GC content was 58.2%; we identified 3326 cellular pathways. Gas chromatography-mass spectrometry was employed to test the PCB degrading ability; the results showed that the strain had a good degradation effect on PCB3 at concentrations of 5, 10, 20, 40, and 60 mg/L and that the final degradation rate was higher than 97% after 96 h. Interestingly, this strain showed good biodegradability of PCBs despite having no classical PCB degradation pathway, providing a new direction for Klebsiella research with practical significance for in situ bioremediation of PCB contamination. Overall, this study provides valuable insights into the genetic structure of PCB-degrading strains as well as eco-friendly and low-cost PCB degradation and lays a foundation for the discovery of new degradation pathways.

Chemosensory Protein 2 of Male Athetis lepigone Is Involved in the Perception of Sex Pheromones and Maize Volatiles.

In moths, the interactions between chemosensory proteins (CSPs) and sex pheromones have yet to be comprehensively investigated. Here, we examined the function of AlepCSP2 in male Athetis lepigone based on protein expression, molecular docking, site-directed mutagenesis, fluorescence competitive binding analyses, and RNA interference (RNAi) experiments. We found that AlepCSP2 showed strong binding affinity for two sex pheromones and five maize volatiles and that binding was optimal under neutral conditions. Furthermore, we identified six amino acids as being key residues involved in the interaction between AlepCSP2 and multiple ligands. Further RNAi showed that siCSP2 males displayed consistently lower electroantennography responses to two sex pheromones and three maize volatiles at different dosages tested, and the mating rate also decreased significantly by 37.50%. These findings will contribute to characterizing the binding mechanisms of moth CSPs to sex pheromones and host volatiles and also identify unique targets for developing novel pest behavior disruptors.

Pediatric Tuina for functional constipation in children: study protocol for a randomized controlled trail.

BACKGROUND: Functional constipation (FC) is a common functional gastrointestinal disorder, which brings many negative impacts to the children's daily life. Pediatric Tuina has been proved to be a potential therapy for FC. However, the evidence for its effectiveness and safety is insufficient due to the lack of high-quality study. This study aims to evaluate the efficacy and safety of pediatric Tuina for children with FC. METHODS/DESIGN: This study is a randomized, controlled, multicentre, clinical trial. We will include 176 children with FC from five hospitals. The participants will be randomly allocated into two groups: the pediatric Tuina group and the Medilac-Vita group. This study will include a 1-week actual treatment period and a 2-week follow-up period. Primary outcomes are weekly spontaneous bowel movements and weekly complete spontaneous bowel movements. The secondary outcomes are effective rate, stool form, distress sensation, and glycerine enema rate. The assessment will be performed each week. Adverse event will be monitored in the treatment period and follow-up period. DISCUSSION: This study is designed to evaluate the efficacy and safety of pediatric Tuina for children with FC, and we hypothesize that pediatric Tuina is more effective than probiotics. It will provide reliable evidence and support for the treatment of FC by pediatric Tuina. TRIAL REGISTRATION: This protocol was registered in the Chinese Clinical Trial Registry (ChiCTR2100046485). .

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI) system. By combining the advantages of multivariate variational mode decomposition (MVMD) and canonical correlation analysis (CCA), an MVMD-CCA algorithm is investigated to improve the detection ability of SSVEP electroencephalogram (EEG) signals. In comparison with the classical filter bank canonical correlation analysis (FBCCA), the nonlinear and non-stationary EEG signals are decomposed into a fixed number of sub-bands by MVMD, which can enhance the effect of SSVEP-related sub-bands. The experimental results show that MVMD-CCA can effectively reduce the influence of noise and EEG artifacts and improve the performance of SSVEP-based BCI. The offline experiments show that the average accuracies of MVMD-CCA in the training dataset and testing dataset are improved by 3.08% and 1.67%, respectively. In the SSVEP-based online robotic manipulator grasping experiment, the recognition accuracies of the four subjects are 92.5%, 93.33%, 90.83%, and 91.67%, respectively.