Search

The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].

Objective: To evaluate the susceptibility of pulmonary tuberculosis based on the single nucleotide polymorphism (SNP) of Toll like receptor 4 (TLR4) gene. Methods: We searched PubMed, Web of science, EMBASE, and Chinese National Knowledge Infrastructure (CNKI) databases using mesh words: “tuberculosis”, “pulmonary”, “TLR4”, “SNP”, “Toll like receptor 4”, “nucleotide polymorphism” for studies on the relations between TLR4 SNP polymorphism and the risk of pulmonary tuberculosis that were published before September 1(st), 2021. Papers were selected according to the inclusion and exclusion criteria established in advance. The allele and genotype data of the four most widely studied SNP loci (rs4986791, rs4986790, rs11536889, rs10759932) in TLR4 gene were extracted and analyzed by Review Manager 5.3 software. Results: 20 studies including a total of 24727 patients were included in the final meta-analysis. Results of the meta-analysis showed that the C allele of rs10759932 increased the risk of pulmonary tuberculosis (odds ratio - OR: 1.144; 95% confidence interval (CI) 1.043-1.254, P = 0.004). Compared with TT genotype, CC+CT genotype of rs10759932 and CT alone genotype significantly increased the risk of pulmonary tuberculosis (OR = 1.218, 95% CI 1.084-1.369, P = 0.001; OR = 1.227, 95% CI 1.085-1.387, P = 0.001). However, rs4986790, rs4986791 and rs11536889 had no significant correlation with the susceptibility of pulmonary tuberculosis (P > 0.05). Conclusion: G allele GG+GA genotype, and the GA genotype of rs4986790; C allele, CC+CT genotype, and the CC genotype of rs10759932 increased the risk of pulmonary tuberculosis, and may potentially be used as a marker for pulmonary tuberculosis diagnosis and monitoring.

Additional file 1: Fig. S1. EAE mice were induced by MOG35-55. a Clinical scores of naïve and MOG-immunized mice. Data represent the mean clinical scores of 10 mice per group ± SEM. b Histopathological analysis of neuroinflammation and demyelination in spinal cords of naïve and EAE mice. Sections were stained by the use of H&E staining and LFP staining. Fig. S2. The auditory brainstem response (ABR) of LRRC4−/− mice compared with that of the WT control. a Schemeatic diagrams showing construction of LRRC4−/− mice. b Real-time PCR analysis of LRRC4 mRNA levels in brains and spinal cords of WT or LRRC4−/− mice. c Western blotting analysis of LRRC4 protein levels in brains and spinal cords of WT or LRRC4−/− mice. d Representative diagrams of ABR of WT or LRRC4−/− mice showing waves at different decibel levels of click stimuli. e The threshold of ABR of WT or LRRC4−/− mice. Results are shown as means ± SEM (n=8). **p < 0.01. Fig. S3. Analysis of differentially expressed genes between EAE-WT and EAE-LRRC4−/− mice by RNA-seq. a A heatmap showing DEGs in spinal cord of EAE-WT versus EAE-LRRC4−/− mice. b The top 10 GO terms of up-regulated and down-regulated DEGs between EAE-WT and EAE-LRRC4−/− mice. c The top 10 KEGG pathways of up-regulated and down-regulated DEGs between EAE-WT and EAE-LRRC4−/− mice. d The comparison of GO terms between up-regulated DEGs and down-regulated DEGs. e Comparison of KEGG pathways between up-regulated DEGs and down-regulated DEGs.

Antibody-secreting cells (ASCs) play a fundamental role in humoral immunity. The aberrant function of ASCs is related to a number of disease states, including autoimmune diseases and cancer. Recent insights into activated B cell subsets, including naïve B cell to ASC stages and their resultant cellular disturbances, suggest that aberrant ASC differentiation occurs during autoimmune diseases and is closely related to disease severity. However, the mechanisms underlying highly active ASC differentiation and the B cell subsets in autoimmune patients remain undefined. Here, we first review the processes of ASC generation. From the perspective of novel therapeutic target discovery, prediction of disease progression, and current clinical challenges, we further summarize the aberrant activity of B cell subsets including specialized memory CD11chiT-bet+ B cells that participate in the maintenance of autoreactive ASC populations. An improved understanding of subgroups may also enhance the knowledge of antigen-specific B cell differentiation. We further discuss the influence of current B cell therapies on B cell subsets, specifically focusing on systemic lupus erythematosus, rheumatoid arthritis, and myasthenia gravis.