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.