Impact of residual antibiotics on microbial decomposition of livestock manures in Eutric Regosol: Implications for sustainable nutrient recycling and soil carbon sequestration.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, ß-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

Quality control for serological testing.

OBJECTIVES: The quality control of serological assays remains controversial. The aim of this project was to describe the problems associated with a working model for controlling these assays and solutions, including using a source of well-defined targets and acceptable limits, a process to identify lot-to-lot reagent variation and an interpretation of the result that accounted for the clinical situation. False-negative results are problematic but can be reduced by identifying and comparing reagent lot variation with previous results. METHODS: The components of the Quality Assurance strategy are the following: Lot-to-lot reagent and calibrator variation assessment; dynamic, big-data approach to determine accurate targets and acceptable limits for manufacturer-provided QC material; negative QC monitoring process; use of commutable EQA with a sufficient method subgroup size to assess bias; clinical assessment of any statistically flagged error; and provision of support to the clinician for the interpretation of results. RESULTS: The model described has been used for twelve months, and acceptable variation has been maintained. CONCLUSIONS: The paper presents a solution that emphasizes the early detection of reagent lot variation and patient risk rather than instrument control. Reducing the risk of a false result to patients requires optimal assay quality control and an effective mechanism to support the clinician's use of these results in diagnosis and monitoring. The problems of serological assays are well-known, but there remain few integrated solutions in the literature.

Learning site-invariant features of connectomes to harmonize complex network measures.

Multi-site diffusion MRI data is often acquired on different scanners and with distinct protocols. Differences in hardware and acquisition result in data that contains site dependent information, which confounds connectome analyses aiming to combine such multi-site data. We propose a data-driven solution that isolates site-invariant information whilst maintaining relevant features of the connectome. We construct a latent space that is uncorrelated with the imaging site and highly correlated with patient age and a connectome summary measure. Here, we focus on network modularity. The proposed model is a conditional, variational autoencoder with three additional prediction tasks: one for patient age, and two for modularity trained exclusively on data from each site. This model enables us to 1) isolate site-invariant biological features, 2) learn site context, and 3) re-inject site context and project biological features to desired site domains. We tested these hypotheses by projecting 77 connectomes from two studies and protocols (Vanderbilt Memory and Aging Project (VMAP) and Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) to a common site. We find that the resulting dataset of modularity has statistically similar means (p-value <0.05) across sites. In addition, we fit a linear model to the joint dataset and find that positive correlations between age and modularity were preserved.

Deep learning models to predict primary open-angle glaucoma.

Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).

Rapid Tissue Perfusion Using Sacrificial Percolation of Anisotropic Networks.

Tissue engineering has long sought to rapidly generate perfusable vascularized tissues with vessel sizes spanning those seen in humans. Current techniques such as biological 3D printing (top-down) and cellular self-assembly (bottom-up) are resource intensive and have not overcome the inherent tradeoff between vessel resolution and assembly time, limiting their utility and scalability for engineering tissues. We present a flexible and scalable technique termed SPAN - Sacrificial Percolation of Anisotropic Networks, where a network of perfusable channels is created throughout a tissue in minutes, irrespective of its size. Conduits with length scales spanning arterioles to capillaries are generated using pipettable alginate fibers that interconnect above a percolation density threshold and are then degraded within constructs of arbitrary size and shape. SPAN is readily used within common tissue engineering processes, can be used to generate endothelial cell-lined vasculature in a multi-cell type construct, and paves the way for rapid assembly of perfusable tissues.

Myricetin alleviates renal tubular epithelial-mesenchymal transition via NOX4/NF-κB/snail axis in diabetic nephropathy based on network pharmacology analysis.

Diabetic nephropathy (DN), a leading cause of end-stage renal disease, remains a formidable challenge in diabetes management due to the complex nature of its pathogenesis, particularly the epithelial-mesenchymal transition (EMT) process. Our innovative study leverages network pharmacology to explore the therapeutic potentials of Myricetin, a natural flavonoid, focusing on its effects against NOX4, a critical mediator in DN progression. This investigation marks a pioneering approach by integrating network pharmacology to predict and elucidate the inhibitory relationship between Myricetin and NOX4. Utilizing a high-fat diet/streptozotocin (HFD/STZ) induced DN mouse model, we delved into the effects of Myricetin on renal EMT processes. Through network pharmacology analyses coupled with molecular docking studies, we identified and confirmed Myricetin's binding efficacy to NOX4. Extensive in vitro and in vivo experiments further established Myricetin's significant impact on mitigating EMT by modulating the NOX4-NF-κB-Snail signaling pathway. Results from our research demonstrated notable improvements in renal function and reductions in tissue fibrosis among treated HFD/STZ mice. By curtailing NOX4 expression, Myricetin effectively reduced reactive oxygen species (ROS) production, thereby inhibiting NF-κB activation and subsequent Snail expression, crucial steps in the EMT pathway. Supported by both theoretical predictions and empirical validations, this study unveils the mechanism underlying Myricetin's modulation of EMT in DN through disrupting the NOX4-NF-κB-Snail axis. These findings not only contribute a new therapeutic avenue for DN treatment but also underscore the utility of network pharmacology in advancing drug discovery processes.

FedETC: Encrypted traffic classification based on federated learning.

The current popular traffic classification methods based on feature engineering and machine learning are difficult to obtain suitable traffic feature sets for multiple traffic classification tasks. Besides, data privacy policies prohibit network operators from collecting and sharing traffic data that might compromise user privacy. To address these challenges, we propose FedETC, a federated learning framework that allows multiple participants to learn global traffic classifiers, while keeping locally encrypted traffic invisible to other participants. In addition, FedETC adopts one-dimensional convolutional neural network as the base model, which avoids manual traffic feature design. In the experiments, we evaluate the FedETC framework for the tasks of both application identification and traffic characterization in a publicly available real-world dataset. The results show that FedETC can achieve promising accuracy rates that are close to centralized learning schemes.

Shensong yangxin, a multi-functional traditional Chinese medicine for arrhythmia: A review of components, pharmacological mechanisms, and clinical applications.

As a common cardiovascular disease (CVD), Arrhythmia refers to any abnormality in the origin, frequency, rhythm, conduction velocity, timing, pathway, sequence, or other aspect of cardiac impulses, and it is one of the common cardiovascular diseases in clinical practice. At present, various ion channel blockers are used for treatment of arrhythmia that include Na+ ion channel blockers, K+ ion channel blockers and Ca2+ ion channel blockers. While these drugs offer benefits, they have led to a gradual increase in drug-related adverse reactions across various systems. As a result, the quest for safe and effective antiarrhythmic drugs is pressing. Recent years have seen some advancements in the treatment of ventricular arrhythmias using traditional Chinese medicine(TCM). The theory of Luobing in TCM has proposed a new drug intervention strategy of "fast and slow treatment, integrated regulation" leading to a shift in mindset from "antiarrhythmic" to "rhythm-regulating". Guided by this theory, the development of Shen Song Yang Xin Capsules (SSYX) has involved various Chinese medicinal ingredients that comprehensively regulate the myocardial electrophysiological mechanism, exerting antiarrhythmic effects on multiple ion channels and non-ion channels. Similarly, in clinical studies, evidence-based research has confirmed that SSYX combined with conventional antiarrhythmic drugs can more effectively reduce the occurrence of arrhythmias. Therefore, this article provides a comprehensive review of the composition and mechanisms of action, pharmacological components, network pharmacology analysis, and clinical applications of SSYX guided by the theory of Luobing, aiming to offer valuable insights for improved clinical management of arrhythmias and related research.

Exploring the mechanism of action of Vanda tessellata extract for the treatment of osteoarthritis through network pharmacology, molecular modelling and experimental assays.

The present study employed a comprehensive approach of network pharmacology, molecular dynamic simulation and in-vitro assays to investigate the underlying mechanism of the anti-osteoarthritic potential of Vanda tessellata extract (VTE). Thirteen active compounds of VTE were retrieved from the literature and the IMPPAT database. All of these passed the drug likeness and oral bioavailability parameters. A total of 535 VTE targets and 2577 osteoarthritis related targets were obtained. The compound-target-disease network analysis revealed vanillin, daucosterol, gigantol and syringaldehyde as the core key components. Protein-protein interaction analysis revealed BCL2, FGF2, ICAM 1, MAPK1, MMP1, MMP2, MMP9, COX2, STAT3 and ESR1 as the hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed AGE-RAGE signalling pathway, HIF-1 signalling pathway and ESR signalling pathway as the major signalling pathway of VTE involved in treating osteoarthritis. Molecular docking analysis showed daucosterol and gigantol to have good binding affinity with BCL2, ESR1 and MMP9, and the results were further confirmed through molecular dynamics simulation analysis. The mechanism predicted by network pharmacology was validated in vitro on IL-1ß-induced SW982 synovial cells. VTE did not show any cytotoxicity and inhibited the migration of SW982 cells. VTE inhibited the expression level of IL-6, IL-8, TNF-α, PGE-2, MMP-2 and MMP-9 in a dose-dependent manner. VTE inhibited nuclear translocation of NF- κß and suppressed phosphorylation of p38, extracellular signal-regulated kinase (ERK), and c-Jun NH2-terminal kinase (JNK) of the mitogen-activated protein kinase (MAPK) signalling pathway. The results showed that VTE exerted an anti-osteoarthritic effect by a multi-target, multi-component and multi-signalling pathway approach.

Efficacy and safety of combined Chinese and western medicine in the treatment of metabolic syndrome: A network meta-analysis of randomized controlled trials.

Objectives: To comprehensively analyze the randomized controlled clinical trials of integrated traditional Chinese medicine (TCM) and western medicine in the treatment of metabolic syndrome (MetS), and to explore the clinical efficacy and safety of different TCM combined with western medicine for MetS. The purpose of this study is to provide specific suggestions for clinical guidance in the treatment of MetS. Methods: A comprehensive literature review was conducted across several databases, including China Knowledge Network, Wanfang Data, VIP Information, China Biomedical Literature Service System, Embase, PubMed, and Web of Science, up to October 2023. The scope of this review was confined to RCTs focusing on the treatment of metabolic syndrome through an integrated approach of TCM and Western medicine. The primary efficacy endpoints analyzed were clinical efficacy, fasting blood glucose (FBG), triglyceride (TG), and high-density lipoprotein (HDL). Data synthesis and analysis were performed using Stata 16 and RevMan 5.4 for both traditional and network meta-analyses. Results: The findings from both traditional and network meta-analyses reveal that the combination of JiangZhiHuoXue pills (JZHX) + Conventional Western Medicine (CWM) significantly reduces FBG levels. Similarly, the AnShenNingXin capsules (ASNX) + CWM combination markedly lowers TG levels, while the FuFangQiMa capsules (FFQM) + CWM combination shows enhanced efficacy in elevating HDL levels. Notably, the combination of KangNing capsules (KNJN) + CWM demonstrates a more pronounced clinical effect compared to CWM/placebo alone. Conclusions: The study concludes that the synergistic combination of TCM and Western medicine exhibits superior therapeutic benefits in treating MetS compared to CWM/Placebo treatments alone. The combinations of JZHX, AXNX, FFQM, and KNJN with CWM emerge as potentially effective treatments.

Integrated network pharmacology and metabolomics to study the potential mechanism of Jiawei Yinchenhao decoction in chronic hepatitis B.

Chronic hepatitis B infection (CHB) is a major risk factor for the development of hepatocellular carcinoma (HCC) globally and continues to pose a significant global health challenge. Jiawei Yinchenhao decoction (JWYCH) is a modified version of Yinchenhao decoction (YCHD), which is widely used to treat liver diseases including icteric hepatitis, cholelithiasis, and hepatic ascites. However, the effectiveness and underlying mechanism of JWYCH on CHB are still unclear. This study aimed to investigate the impact of JWYCH on CHB and explore the underlying mechanism via network pharmacology and metabolomics. C57BL/6 mice were administered rAAV-HBV1.3 via hydrodynamic injection (HDI) to establish the CHB model. The infected mice were orally administered JWYCH for 4 weeks. HBsAg, HBeAg, HBV DNA, the serum liver function index, and histopathology were detected. In addition, network pharmacology was used to investigate potential targets, whereas untargeted metabolomics analysis was employed to explore the hepatic metabolic changes in JWYCH in CHB mice and identify relevant biomarkers and metabolic pathways. JWYCH was able to reduce HBeAg levels and improve liver pathological changes in mice with CHB. Additionally, metabolomics analysis indicated that JWYCH can influence 105 metabolites, including pipecolic acid, alpha-terpinene, adenosine, and L-phenylalanine, among others. Bile acid metabolism, arachidonic acid metabolism, and retinol metabolism are suggested to be potential targets of JWYCH in CHB. In conclusion, JWYCH demonstrated a hepatoprotective effect on a mouse model of CHB, suggesting a potential alternative therapeutic strategy for CHB. The effect of JWYCH is associated mainly with regulating the metabolism of bile acid, arachidonic acid, and retinol. These differentially abundant metabolites may serve as potential biomarkers and therapeutic targets for CHB.

Preliminary investigation on the mechanism of anti-periodontitis effect of Scutellariae Radix based on bioinformatics analysis and in vitro verification.

Objective: To investigate the material basis, targets and molecular mechanism of Scutellariae Radix against periodontitis to provide theoretical basis for clinical applications. Materials and methods: The active compounds and targets of Scutellariae Radix were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, and the periodontitis-related targets were collected by integrating Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), Genecards and Gene Expression Omnibus (GEO) database together. The potential targets of Scutellariae Radix against periodontitis were obtained from the intersection of two target sets. Metascape database was used for Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Discovery Studio software was used for molecular docking between key targets and compounds to evaluate their binding affinity. Western blot was used to check the expression of PTGS2 and MMP9 to verify the regulatory effects of baicalein, the main active compound of Scutellariae Radix, on human periodontal ligament stem cells (hPDLSCs) cultured under inflammatory environment which induced by lipopolysaccharide (LPS). Results: 15 active compounds of Scutellariae Radix and 53 common targets for periodontitis treatment were identified. Among these targets, the 10 core targets were AKT1, IL-6, TNF, VEGFA, TP53, PTGS2, CASP3, JUN, MMP9 and HIF1A. GO and KEGG analysis mainly focused on response to LPS and pathways in cancer. Molecular docking showed that the main active compounds had good binding affinity with key targets. Cell experiments confirmed that baicalein can interfere the expression of pro-inflammatory factors PTGS2 and MMP9 proteins and exert anti-inflammatory effects. Conclusion: Current study preliminarily analyzed the mechanism of Scutellariae Radix against periodontitis, which provide a new idea for the utilization of Scutellariae Radix and the development of novel medicine for the clinical treatment of periodontitis.

The analysis of teaching quality evaluation for the college sports dance by convolutional neural network model and deep learning.

This study aims to comprehensively analyze and evaluate the quality of college physical dance education using Convolutional Neural Network (CNN) models and deep learning methods. The study introduces a teaching quality evaluation (TQE) model based on one-dimensional CNN, addressing issues such as subjectivity and inconsistent evaluation criteria in traditional assessment methods. By constructing a comprehensive TQE system comprising 24 evaluation indicators, this study innovatively applies deep learning technology to quantitatively assess the quality of physical dance education. This TQE model processes one-dimensional evaluation data by extracting local features through convolutional layers, reducing dimensions via pooling layers, and feeding feature vectors into a classifier through fully connected layers to achieve an overall assessment of teaching quality. Experimental results demonstrate that after 150 iterations of training and validation on the TQE model, convergence is achieved, with mean squared error (MSE) decreasing to 0.0015 and 0.0216 on the training and validation sets, respectively. Comparatively, the TQE model exhibits significantly lower MSE on the training, validation, and test sets compared to the Back-Propagation Neural Network, accompanied by a higher R2 value, indicating superior accuracy and performance in data fitting. Further analysis on robustness, parameter sensitivity, multi-scenario adaptability, and long-term learning capabilities reveals the TQE model's strong resilience and stability in managing noisy data, varying parameter configurations, diverse teaching contexts, and extended time-series data. In practical applications, the TQE model is implemented in physical dance courses at X College to evaluate teaching quality and guide improvement strategies for instructors, resulting in notable enhancements in teaching quality and student satisfaction. In conclusion, this study offers a comprehensive evaluation of university physical dance education quality through a multidimensional assessment system and the application of the 1D-CNN model. It introduces a novel and effective approach to assessing teaching quality, providing a scientific foundation and practical guidance for future educational advancements.

A Systems Biology Approach to the Pathogenesis of Celiac Disease: Identification of Potential Protective and Promoting Mechanisms.

Background: Celiac disease (CeD) is an autoimmune enteropathy triggered by dietary gluten. Almost 90% of CeD patients have HLA-DQ2 or -DQ8 haplotypes. As a high proportion of first-degree relatives (FDRs) of CeD patients have the same haplotype, it is assumed that they are at a higher risk of disease development than the general population. Nevertheless, the prevalence of CeD among FDRs is considerably low (7.5%). Materials and Methods: In order to figure out this discrepancy, a microarray dataset of intestinal mucosal biopsies of CeD patients, FDRs, and control groups was reanalyzed, and a protein-protein interaction network was constructed. Results: Principal component analysis showed that CeD and FDR groups are far away in terms of gene expression. Comparing differentially expressed genes of both networks demonstrated inverse expression of some genes mainly related to cell cycle mechanisms. Moreover, analysis of the modular structures of up- and downregulated gene networks determined activation of protein degradation mechanisms and inhibition of ribosome-related protein synthesis in celiac patients with an upside-down pattern in FDRs. Conclusions: The top-down systems biology approach determined some regulatory pathways with inverse function in CeD and FDR groups. These genes and molecular mechanisms could be a matter of investigation as potential druggable targets or prognostic markers in CeD.

Deciphering the molecular mechanisms underlying anti-pathogenic potential of a polyherbal formulation Enteropan® against multidrug-resistant Pseudomonas aeruginosa.

Objective: Anti-pathogenic potential of a polyherbal formulation Enteropan® was investigated against a multidrug-resistant strain of the bacterium Pseudomonas aeruginosa. Methods: Growth, pigment production, antibiotic susceptibility, etc., were assessed through appropriate in vitro assays. Virulence of the test pathogen was assessed employing the nematode worm Caenorhabditis elegans as a model host. Molecular mechanisms underlining the anti-pathogenic activity of the test formulation were elucidated through whole transcriptome analysis of the extract-exposed bacterial culture. Results: Enteropan-pre-exposed P. aeruginosa displayed reduced (~70%↓) virulence towards the model host C. elegans. Enteropan affected various traits like biofilm formation, protein synthesis and secretion, quorum-modulated pigment production, antibiotic susceptibility, nitrogen metabolism, etc., in this pathogen. P. aeruginosa could not develop complete resistance to the virulence-attenuating activity of Enteropan even after repeated exposure to this polyherbal formulation. Whole transcriptome analysis showed 17% of P. aeruginosa genome to get differentially expressed under influence of Enteropan. Major mechanisms through which Enteropan exerted its anti-virulence activity were found to be generation of nitrosative stress, oxidative stress, envelop stress, quorum modulation, disturbance of protein homeostasis and metal homeostasis. Network analysis of the differently expressed genes resulted in identification of 10 proteins with high network centrality as potential targets from among the downregulated genes. Differential expression of genes coding for five (rpoA, tig, rpsB, rpsL, and rpsJ) of these targets was validated through real-time polymerase chain reaction too, and they can further be pursued as potential targets by various drug discovery programmes.

Ferulic Acid Methyl Ester Attenuates Cerebral Ischemia-Reperfusion Injury in Rats by Modulating PI3K/HIF-1α/VEGF Signaling Pathway.

Background: Cerebral ischaemia-reperfusion injury (CIRI) could worsen the inflammatory response and oxidative stress in brain tissue. According to previous studies, ferulic acid methyl ester (FAME), as the extract with the strongest comprehensive activity in the traditional Chinese medicine Huang Hua oil dot herb, has significant anti-oxidative stress and neuroprotective functions, and can effectively alleviate CIRI, but its mechanism of action is still unclear. Methods: Firstly, the pharmacological effects of FAME were investigated by in vitro oxidative stress and inflammatory experiments. Secondly, evaluate the therapeutic effects of FAME in the treatment of CIRI by brain histopathological staining and cerebral infarct area by replicating the in vivo MACO model. Thirdly, RNA-Seq and network pharmacology were utilized to predict the possible targets and mechanisms of FAME for CIRI at the molecular level. Finally, the expression of key target proteins, as well as the key regulatory relationships were verified by molecular docking visualization, Western Blotting and immunohistochemistry. Results: The results of in vitro experiments concluded that FAME could significantly reduce the content of TNF-α, IL-1ß and ROS, inhibiting COX-2 and iNOS protein expression in cells(p<0.01). FAME was demonstrated to have anti-oxidative stress and anti-inflammatory effects. The results of in vivo experiments showed that after the administration of FAME, the area of cerebral infarction in rats with CIRI was reduced, the content of Bcl-2 and VEGF was increased(p<0.05). Network pharmacology and RNA-Seq showed that the alleviation of CIRI by FAME may be through PI3K-AKT and HIF-1 signaling pathway. Enhanced expression of HIF-1α, VEGF, p-PI3K, p-AKT proteins in the brain tissues of rats in the FAME group was verified by molecular docking and Western Blotting. Conclusion: FAME possesses significant anti-inflammatory and anti-oxidative stress activities and alleviates CIRI through the PI3K/HIF-1α/VEGF signaling pathway.

Functional genomics identifies a small secreted protein that plays a role during the biotrophic to necrotrophic shift in the root rot pathogen Phytophthora medicaginis.

Introduction: Hemibiotrophic Phytophthora are a group of agriculturally and ecologically important pathogenic oomycetes causing severe decline in plant growth and fitness. The lifestyle of these pathogens consists of an initial biotrophic phase followed by a switch to a necrotrophic phase in the latter stages of infection. Between these two phases is the biotrophic to necrotrophic switch (BNS) phase, the timing and controls of which are not well understood particularly in Phytophthora spp. where host resistance has a purely quantitative genetic basis. Methods: To investigate this we sequenced and annotated the genome of Phytophthora medicaginis, causal agent of root rot and substantial yield losses to Fabaceae hosts. We analyzed the transcriptome of P. medicaginis across three phases of colonization of a susceptible chickpea host (Cicer arietinum) and performed co-regulatory analysis to identify putative small secreted protein (SSP) effectors that influence timing of the BNS in a quantitative pathosystem. Results: The genome of P. medicaginis is ~78 Mb, comparable to P. fragariae and P. rubi which also cause root rot. Despite this, it encodes the second smallest number of RxLR (arginine-any amino acid-leucine-arginine) containing proteins of currently sequenced Phytophthora species. Only quantitative resistance is known in chickpea to P. medicaginis, however, we found that many RxLR, Crinkler (CRN), and Nep1-like protein (NLP) proteins and carbohydrate active enzymes (CAZymes) were regulated during infection. Characterization of one of these, Phytmed_10271, which encodes an RxLR effector demonstrates that it plays a role in the timing of the BNS phase and root cell death. Discussion: These findings provide an important framework and resource for understanding the role of pathogenicity factors in purely quantitative Phytophthora pathosystems and their implications to the timing of the BNS phase.

Recurrent neural network for trajectory tracking control of manipulator with unknown mass matrix.

Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.

Trends and multi-model prediction of hepatitis B incidence in Xiamen.

Background: This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022, and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027. Methods: Data were obtained from the China Information System for Disease Control and Prevention (CISDCP). The Joinpoint Regression Model analyzed temporal trends, while the Age-Period-Cohort (APC) model assessed the effects of age, period, and cohort on hepatitis B incidence rates. We also compared the predictive performance of the Neural Network Autoregressive (NNAR) Model, Bayesian Structural Time Series (BSTS) Model, Prophet, Exponential Smoothing (ETS) Model, Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Hybrid Model, selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years. Results: Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend, with rates higher in men than in women. Higher incidence rates were observed in adults, particularly in the 30-39 age group. Moreover, the period and cohort effects on incidence showed a declining trend. Furthermore, in the best-performing NNAR(10, 1, 6)[12] model, the number of new cases is predicted to be 4271 in 2023, increasing to 5314 by 2027. Conclusions: Hepatitis B remains a significant issue in Xiamen, necessitating further optimization of hepatitis B prevention and control measures. Moreover, targeted interventions are essential for adults with higher incidence rates.