The history of botanical pesticides reveals that their study did mainly focus on the determination of acute median lethal dose or concentration. In the current situation, it is the dire need to understand the sublethal eVects of the botanical extracts along with the traditional studies of lethal concentrations in order to comprehensively investigate the future role of the botanical extracts as pesticides. This study reveals the eVects of traditionally used medicinal plant extracts harmaline (H) and ricinine (R) either individually or in combination with Bacillus thuringiensis (Bt) on the acute toxicity and sublethal eVects on the nutrition and enzyme system of Spodoptera exigua. Harmaline and ricinine caused reduction in the growth of neonate larvae up to 93.12 and 84.31%. The EC 50 values of harmaline against fourth and Wfth instars were 0.24 and 0.27 mg/ml, but these values remained 0.49 and 0.54 mg/ml against fourth and Wfth instars after being treated with ricinine. The combination of harmaline and ricinine with Bt resulted in the increased eYciency of these chemicals as the mortality percentages signiWcantly increased up to 96 and 87.82% in signiWcantly less exposure time in case of H + Bt and R + Bt respectively, as compared to individual treatments. The nutritional analysis revealed the increased toxicity of harmaline and ricinine in combination with Bt, but H + Bt2 showed the higher eYciency with minimal relative consumption rate 2.50 mg/mg/day, relative growth rate 1.16 mg/mg/day and eYciency of conversion of ingested food 29.66% of control, respectively. Changes in antioxidant enzymes such as superoxide dismutase (SOD) and catalases (CAT) were noticed to some extent over diVerent exposure times at all the treatments. The highest SOD (+37.29%) and CAT (+29.27%) activity was observed at the 6th day of treatment with H + R + Bt2. The study clearly shows the signiWcantly increased eYciency of harmaline and ricinine in combination with Bt against S. exigua. This phenomenon can be helpful in order to develop better control strategies against diVerent notorious pests.

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The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.

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With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of ‘marker density’ seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of ‘marker density’ based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large .σd2., which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20–30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

Background Hashimoto’s thyroiditis (HT) is an autoimmune disease. Recent studies have found that the gut microbiota may play an important role in inducing HT, but there are no systematic studies on the changes in the gut microbiota during the development of HT. Methods In this study, 16S rDNA high-throughput sequencing technology in combination with the Kruskal–Wallis test, CCA/RDA analysis, Spearman correlation analysis, and other statistical methods were used to analyze the effects of age, gender, hormones, and other environmental factors on gut microbiota by comparing the differences in the microbiota at different stages of HT development. Results The results showed that there were differences in the gut microbiota composition between healthy people (HCA) and in patients with HT. Lachnoclostridium, Bilophila, and Klebsiella were enriched in the HCA group, while Akkermansia, Lachnospiraceae, Bifidobacterium, Shuttleia, and Clostriworthdia were enriched in the HT group. Environmental factors analysis revealed that the Bifidobacterium and Klebsiella were two groups of bacteria that have undergone dramatic changes in HCA and HT, and mainly affected by gender. Romboutsia and Haemophilus regulated by the hormone of free triiodothyronine (FT3) may promote the development of HT, while Faecalibacterium and Lachnospiraceae regulated by free thyroxine (FT4) may protect the host. Conclusions Comprehensive studies have shown that gender is an important factor affecting gut microbial composition, but with the development of HT, hormones, age, and TSH begin to become dominant factors.

Objective This study screened out the key genes associated with the occurrence and development of lupus nephritis (LN) using bioinformatics methods, and then explored the expression of key genes in LN and the inhibitory effect of triptolide. Methods The GEO2R online tool in the GEO database was used to perform differential analysis of gene expression in LN tissues and normal kidney tissues. The GO function and KEGG pathway enrichment analysis of differentially expressed genes (DEGs), STRING, and Cytoscape software were used to build a protein–protein interaction network (PPI) to screen out the Hub gene. Mouse glomerular mesangial cells (MMC) were randomly divided into a control group, an interferon-γ (IFN-γ) stimulation group, and a triptolide intervention group. The relative expression of CXCL10 mRNA in each group was detected by real-time fluorescent quantitative PCR (RT-PCR). CXCL10 secretion was detected by enzyme-linked immunosorbent assay (ELISA), and Western blot was used to detect the expression of the JAK/STAT1 signaling pathway–related proteins STAT1 and p-STAT1 in each group. Results Bioinformatics showed that there were 22 DEGs expression differences in the GEO database. The GO enrichment analysis showed that biological process (BP) such as the type I interferon signaling pathway, innate immune response, IFN-γ-mediated signaling pathway, virus defense response, and immune response were significantly regulated by DEGs. Through the combination of String database analysis and cytoscape software, it was found that STAT1 and CXCL10 are closely related to LN. Experimental results showed that IFN-γ induces the expression of CXCL10 mRNA and protein by activating the JAK/STAT1 signaling pathway, while triptolide inhibits the expression of CXCL10 mRNA and protein by inhibiting the JAK/STAT1 signaling pathway. Conclusion STAT1 and CXCL10 are the key genes in the occurrence and development of LN. IFN-γ induces the expression of CXCL10 by activating the JAK/STAT1 signaling pathway, while triptolide inhibits the expression of CXCL10 by blocking the JAK/STAT1 signaling pathway. Inhibition of the JAK/STAT1 signaling pathway and CXCL10 expression is expected to become a potential target for the treatment of LN. Key Points• Bioinformatics showed that there were 22 DEGs expression differences in the GEO database.• Through the combination of String database analysis and Cytoscape software, it was found that STAT1 and CXCL10 are closely related to LN.• Experimental results showed that IFN-γ induces the expression of CXCL10 mRNA and protein by activating the JAK/STAT1 signaling pathway, while triptolide inhibits the expression of CXCL10 mRNA and protein by inhibiting the JAK/STAT1 signaling pathway.

Animal breed identification has wide and important application prospects in the field of genetic breeding. It not only provides effective genetic information for the selection and breeding of superior animals (Behl et al., 2006), but also provides new methods for the traceability of animal products (Dalvit et al., 2007). Meanwhile, it plays a vital role in biological science research (Yaro et al., 2017), pedigree identification (Dreger et al., 2016) and breed resource conservation (Weigend et al., 2004).The earliest breed identification was mainly carried out in a morphological manner (Ceccobelli et al., 2016).