The Role of TLR4 on B Cell Activation and Anti- β

High titer of anti-β2-glycoprotein I antibodies (anti-β2GPI Ab) plays a pathogenic role in antiphospholipid syndrome (APS). Numerous studies have focused on the pathological mechanism in APS; however, little attention is paid to the immune mechanism of production of anti-β2GPI antibodies in APS. Our previous study demonstrated that Toll-like receptor 4 (TLR4) plays a vital role in the maturation of bone marrow-derived dendritic cells (BMDCs) from the mice immunized with human β2-glycoprotein I (β2GPI). TLR4 is required for the activation of B cells and the production of autoantibody in mice treated with β2GPI. However, TLR4 provides a third signal for B cell activation and then promotes B cells better receiving signals from both B cell antigen receptor (BCR) and CD40, thus promoting B cell activation, surface molecules expression, anti-β2GPI Ab production, and cytokines secretion and making B cell functioning like an antigen presenting cell (APC). At the same time, TLR4 also promotes B cells producing antibodies by upregulating the expression of B-cell activating factor (BAFF). In this paper, we aim to review the functions of TLR4 in B cell immune response and antibody production in autoimmune disease APS and try to find a new way for the prevention and treatment of APS

Cyclist’s Intention Identification on Pedestrian-Bicycle Mixed Sections Based on Phase-Field Coupling Theory

Bicycle is one of the main factors that affects the traffic safety and capacity on pedestrian-bicycle mixed traffic sections. It is important for implementing the warning of bicycle safety and improving the active safety to identify the cyclists’ intention in the mixed traffic environments under the condition of the “Internet of Things”. The phase-field coupling theory has been developed in this paper to comprehensively analyse the generation, spring up, increase, transfer, regression and reduction method of the traffic phase. The adaptive genetic algorithm based on the information entropy has been used to extract feature vectors of different types of cyclists for intention identification from the reduced pedestrian-bicycle traffic phase, and the theory of evidence has been provided here to build the identification model. The experimental verification shows that the extraction method of cyclists’ intention feature vector and identification model are scientific and reasonable. The theoretical basis can be applied to establishing the pedestrian-bicycle interactive security system.</p

Analysis of Dynamic Characteristics of Pilots Under Different Intentions in Complex Flight Environment

Intention is the main embodiment of human cerebral conscious activities, which has an important influence on guiding the realization of human behaviour. It is a vital prerequisite for analysing the dynamic characteristics of pilots with different intentions. Considering the intention law of the generation, transfer and reduction, this paper analyses dynamic characteristics of pilots with different intentions, starting from the factors of effect on the intention. Taking airfield traffic pattern as an example for simulating flight experiments, the pilot’s multi-source dynamic data of human – aircraft – environment system under different intentions and their psycho-physiological-physical characteristics were recorded. Based on Matlab, one-way analysis of variance was used to extract variables with significant changes, and the variables under different intentions were compared and analysed. The results show that the conventional pilots are more conducive to control the aircraft to keep a stable flight attitude. This study is of great significance for perfecting the warning system of flight safety and improving the pilot’s micro-behaviour assessment system.</p

A Cross-Tissue Transcriptome-Wide Association Study Identifies Novel Susceptibility Genes for Juvenile Idiopathic Arthritis in Asia and Europe

BackgroundJuvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children, and its pathogenesis is still unclear. Genome-wide association studies (GWASs) of JIA have identified hundreds of risk factors, but few of them implicated specific biological mechanisms.MethodsA cross-tissue transcriptome-wide association study (TWAS) was performed with the functional summary-based imputation software (FUSION) tool based on GWAS summary datasets (898 JIA patients and 346,102 controls from BioBank Japan (BBJ)/FinnGen). The gene expression reference weights of skeletal muscle and the whole blood were obtained from the Genotype-Tissue Expression (GTExv8) project. JIA-related genes identified by TWAS findings genes were further compared with the differentially expressed genes (DEGs) identified by the mRNA expression profile of JIA from the Gene Expression Omnibus (GEO) database (accession number: GSE1402). Last, candidate genes were analyzed using functional enrichment and annotation analysis by Metascape to examine JIA-related gene sets.ResultsThe TWAS identified 535 significant genes with P &lt; 0.05 and contains 350 for Asian and 195 for European (including 10 genes both expressed in Asian and European), such as CDC16 (P = 1.72E-03) and PSMD5-AS1 (P = 3.65E-02). Eight overlapping genes were identified based on TWAS results and DEGs of JIA patients, such as SIRPB1 (PTWAS = 4.21E-03, PDEG = 1.50E-04) and FRAT2 (PTWAS = 2.82E-02, PDEG = 1.43E-02). Pathway enrichment analysis of TWAS identified 183 pathways such as cytokine signaling in the immune system and cell adhesion molecules. By integrating the results of DEGs pathway and process enrichment analyses, 19 terms were identified such as positive regulation of T-cell activation.ConclusionBy conducting two populations TWAS, we identified a group of JIA-associated genes and pathways, which may provide novel clues to uncover the pathogenesis of JIA

No genetic causal association between iron status and osteoporosis: A two-sample Mendelian randomization

ObjectiveTo explore the genetic causal association between osteoporosis (OP) and iron status through Mendelian randomization (MR).MethodsPublicly available genome-wide association study (GWAS) summary data were used for MR analysis with four iron status-related indicators (ferritin, iron, total iron binding capacity, and transferrin saturation) as exposures and three different types of OP (OP, OP with pathological fracture, and postmenopausal OP with pathological fracture) as outcomes. The inverse-variance weighted (IVW) method was used to analyze the genetic causal association between the four indicators of iron status and OP. The heterogeneity of MR results was determined using IVW and MR–Egger methods. The pleiotropy of MR results was determined using MR–Egger regression. A leave-one-SNP-out test was performed to determine whether the MR results were affected by a single nucleotide polymorphism (SNP). The weighted median method was conducted to further validate our results.ResultsBased on IVW, MR–Egger and weighted median models, we found no causal association between iron status (ferritin, iron, total iron binding capacity, or transferrin saturation) and OP (Pbeta &gt; 0.05 in all models). IVW and MR–Egger analysis of OP with pathological fracture and iron status indicators showed no potential genetic causal association (Pbeta&gt; 0.05 in the two analyses). The results of the weighted median were consistent with those of IVW (Pbeta&gt; 0.05 in all analyses). There was no potential genetic causal association between iron status and postmenopausal OP with pathological fracture based on serum iron (Pbeta&gt;0.05 in all models). No heterogeneity or horizontal pleiotropy was found in any of the analyses. None of the leave-one-out tests in the analyses found any SNP that could affect the results of MR.ConclusionOur results demonstrate that there is no genetic causal association between OP and iron status, but the effects of other factors were not excluded

The associations of maternal liver biomarkers in early pregnancy with the risk of gestational diabetes mellitus: a prospective cohort study and Mendelian randomization analysis

BackgroundAssociations of liver function with the risk of gestational diabetes mellitus (GDM) remain unclear. This study aimed to examine the relationship and the potential causality between maternal liver biomarkers and the risk of subsequent GDM, as well as to evaluate the interaction between liver biomarkers and lipids on GDM risk.MethodsIn an ongoing Zhoushan Pregnant Women Cohort, pregnant women who finished the first prenatal follow-up record, underwent liver function tests in early pregnancy, and completed the GDM screening were included in this study. Logistic regression models were used to investigate the association, and the inverse-variance weighted method supplemented with other methods of two-sample Mendelian randomization (MR) analysis was applied to deduce the causality.ResultsAmong 9,148 pregnant women, 1,668 (18.2%) developed GDM. In general, the highest quartile of liver function index (LFI), including ALT, AST, GGT, ALP, and hepatic steatosis index, was significantly associated with an increased risk of GDM (OR ranging from 1.29 to 3.15), especially an elevated risk of abnormal postprandial blood glucose level. Moreover, the causal link between ALT and GDM was confirmed by the MR analysis (OR=1.28, 95%CI:1.05-1.54). A significant interaction between AST/ALT and TG on GDM risk was observed (Pinteraction = 0.026).ConclusionElevated levels of LFI in early pregnancy were remarkably associated with an increased risk of GDM in our prospective cohort. Besides, a positive causal link between ALT and GDM was suggested

Pseudonocardians A–C, New Diazaanthraquinone Derivatives from a Deap-Sea Actinomycete Pseudonocardia sp. SCSIO 01299

Pseudonocardians A–C (2–4), three new diazaanthraquinone derivatives, along with a previously synthesized compound deoxynyboquinone (1), were produced by the strain SCSIO 01299, a marine actinomycete member of the genus Pseudonocardia, isolated from deep-sea sediment of the South China Sea. The structures of compounds 1–4 were determined by mass spectrometry and NMR experiments (1H, 13C, HSQC, and HMBC). The structure of compound 1, which was obtained for the first time from a natural source, was confirmed by X-ray analysis. Compounds 1–3 exhibited potent cytotoxic activities against three tumor cell lines of SF-268, MCF-7 and NCI-H460 with IC50 values between 0.01 and 0.21 μm, and also showed antibacterial activities on Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212 and Bacillus thuringensis SCSIO BT01, with MIC values of 1–4 μg mL−1

Research on the Identification of Pilots&rsquo; Fatigue Status Based on Functional Near-Infrared Spectroscopy

Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of pilots during flight missions. The data was pre-processed, and 1080 valid samples were determined. Then, mean value, variance, standard deviation, kurtosis, skewness, coefficient of variation, peak value, and range of oxyhemoglobin (HbO2) in each channel were extracted. These indexes were regarded as the input of a stacked denoising autoencoder (SDAE) and were used to train the identification model of pilots&rsquo; fatigue state. The identification model of pilots&rsquo; fatigue status was established. The identification accuracy of the SDAE model was 91.32%, which was 23.26% and 15.97% higher than that of linear discriminant analysis (LDA) models and support vector machines (SVM) models, respectively. Results show that the SDAE model established in our study has high identification accuracy, which can accurately identify different fatigue states of pilots. Identification of pilots&rsquo; fatigue status based on fNIRS has important practical significance for reducing flight accidents caused by pilot fatigue

Research on the Identification of Pilots’ Fatigue Status Based on Functional Near-Infrared Spectroscopy

Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of pilots during flight missions. The data was pre-processed, and 1080 valid samples were determined. Then, mean value, variance, standard deviation, kurtosis, skewness, coefficient of variation, peak value, and range of oxyhemoglobin (HbO2) in each channel were extracted. These indexes were regarded as the input of a stacked denoising autoencoder (SDAE) and were used to train the identification model of pilots’ fatigue state. The identification model of pilots’ fatigue status was established. The identification accuracy of the SDAE model was 91.32%, which was 23.26% and 15.97% higher than that of linear discriminant analysis (LDA) models and support vector machines (SVM) models, respectively. Results show that the SDAE model established in our study has high identification accuracy, which can accurately identify different fatigue states of pilots. Identification of pilots’ fatigue status based on fNIRS has important practical significance for reducing flight accidents caused by pilot fatigue

Time-Varying Pilot’s Intention Identification Based on ESAX-CSA-ELM Classification Method in Complex Environment

Dynamic and accurate identification of pilot intention is an important prerequisite for more accurate identification of control behavior, automatic flight early warning, and human–aircraft shared autonomy. Meanwhile, it is also the basic requirement of microscopic research on flight safety. In response to these demands, the airfield traffic pattern flight simulation experiment was carried out to obtain the pilot’s physiological data, such as electrocardiogram, respiration, and skin electricity, under different intentions. The extended symbol aggregation approximation theory (ESAX) and the intelligent icon method were utilized to analyze and extract the characteristics of the pilot’s intention. Furthermore, combined with the crow search algorithm (CSA) and extreme learning machine (ELM), a CSA-ELM pilot intention identification model was constructed and it is applied to climb, descend, level flight, and other situations in airfield traffic pattern missions to effectively identify whether the pilot has an intention. The rationality and validity of the identification model were verified through experiments with interactive computer simulations. In addition, compared with the traditional machine learning method, the accuracy of the identification method proposed in this paper is improved by about 10%. The above shows that the research results in this paper can provide support for improving the flight safety early-warning system and the pilot’s micro-behavior evaluation system