Quality test of clamping connection of transmission lines across tensile line

This paper develops a new technology for the quality inspection of the transmission line that is important across the tensile clamp. The new technology mainly based on the ultrasonic pulse echo thickness measurement mechanism tests the thickness of the aluminum sleeve after crimping the tensile clamp to reflect the relative position of the aluminum sleeve and the steel anchor after the crimping, thereby judging whether there is a crimping positioning defect. At the same time, it is supplemented by steel anchor model comparison, crimping position length comparison, and crimping to margin detection to determine whether the transmission line crimping quality is qualified

A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome Studies

Researchers have increasingly employed family-based or longitudinal study designs to survey the roles of the human microbiota on diverse host traits of interest (e. g., health/disease status, medical intervention, behavioral/environmental factor). Such study designs are useful to properly control for potential confounders or the sensitive changes in microbial composition and host traits. However, downstream data analysis is challenging because the measurements within clusters (e.g., families, subjects including repeated measures) tend to be correlated so that statistical methods based on the independence assumption cannot be used. For the correlated microbiome studies, a distance-based kernel association test based on the linear mixed model, namely, correlated sequence kernel association test (cSKAT), has recently been introduced. cSKAT models the microbial community using an ecological distance (e.g., Jaccard/Bray-Curtis dissimilarity, unique fraction distance), and then tests its association with a host trait. Similar to prior distance-based kernel association tests (e.g., microbiome regression-based kernel association test), the use of ecological distances gives a high power to cSKAT. However, cSKAT is limited to handling Gaussian traits [e.g., body mass index (BMI)] and a single chosen distance measure at a time. The power of cSKAT differs a lot by which distance measure is used. However, choosing an optimal distance measure is challenging because of the unknown nature of the true association. Here, we introduce a distance-based kernel association test based on the generalized linear mixed model (GLMM), namely, GLMM-MiRKAT, to handle diverse types of traits, such as Gaussian (e.g., BMI), Binomial (e.g., disease status, treatment/placebo) or Poisson (e.g., number of tumors/treatments) traits. We further propose a data-driven adaptive test of GLMM-MiRKAT, namely, aGLMM-MiRKAT, so as to avoid the need to choose the optimal distance measure. Our extensive simulations demonstrate that aGLMM-MiRKAT is robustly powerful while correctly controlling type I error rates. We apply aGLMM-MiRKAT to real familial and longitudinal microbiome data, where we discover significant disparity in microbial community composition by BMI status and the frequency of antibiotic use. In summary, aGLMM-MiRKAT is a useful analytical tool with its broad applicability to diverse types of traits, robust power and valid statistical inference

MiRKAT-MC: A Distance-Based Microbiome Kernel Association Test With Multi-Categorical Outcomes

Increasing evidence has elucidated that the microbiome plays a critical role in many human diseases. Apart from continuous and binary traits that measure the extent or presence of a disease, multi-categorical outcomes including variations/subtypes of a disease or ordinal levels of disease severity are commonly seen in clinical studies. On top of that, studies with clustered design (i.e., family-based and longitudinal studies) are popular alternatives to population-based ones as they are able to identify characteristics on both individual and population levels and to investigate the trajectory of traits of interest over time. However, existing methods for microbiome association analysis are inadequate to handle multi-categorical outcomes, neither independent nor clustered data. We propose a microbiome kernel association test with multi-categorical outcomes (MiRKAT-MC). Our method is versatile to deal with both nominal and ordinal outcomes for independent and clustered data. In addition, it incorporates multiple ecological distances to allow for different association patterns between outcomes and microbiome compositions to be incorporated. A computationally efficient pseudo-permutation strategy is used to evaluate the statistical significance. Comprehensive simulations show that MiRKAT-MC preserves the nominal type I error and increases statistical powers under various scenarios and data types. We also apply MiRKAT-MC to real data sets with nominal and ordinal outcomes to gain biological insights. MiRKAT-MC is easy to implement, and freely available via an R package at https://github.com/Zhiwen-Owen-Jiang/MiRKATMC with a Graphical User Interface through R Shinny also available

Poly[[aqua­(μ5-3,4,5,6-tetra­carb­oxy­cyclo­hexane-1,2-dicarboxyl­ato)strontium] monohydrate]

In the title compound, {[Sr(C12H10O12)(H2O)]·H2O}n, the SrII ion is coordinated by six O atoms of five symmetry-related 3,4,5,6-tetra­carb­oxy­cyclo­hexane-1,2-dicarboxyl­ate ligands and one water mol­ecule in a slightly distorted monocapped trigonal–prismatic environment. The ligands bridge the SrII ions, forming a two-dimensional structure. In the crystal, O—H⋯O hydrogen bonds further connect the structure into a three-dimensional network. The H atoms of two of the carboxyl groups were refined as half-occupancy

Geometry and optics calibration of WFCTA prototype telescopes using star light

The Large High Altitude Air Shower Observatory project is proposed to study high energy gamma ray astronomy ( 40 GeV-1 PeV ) and cosmic ray physics ( 20 TeV-1 EeV ). The wide field of view Cherenkov telescope array, as a component of the LHAASO project, will be used to study energy spectrum and compositions of cosmic ray by measuring the total Cherenkov light generated by air showers and shower maximum depth. Two prototype telescopes have been in operation since 2008. The pointing accuracy of each telescope is crucial to the direction reconstruction of the primary particles. On the other hand the primary energy reconstruction relies on the shape of the Cherenkov image on the camera and the unrecorded photons due to the imperfect connections between photomultiplier tubes. UV bright stars are used as point-like objects to calibrate the pointing and to study the optical properties of the camera, the spot size and the fractions of unrecorded photons in the insensitive areas of the camera.Comment: 5 pages, 6 figures, submitted to Chinese Physics

Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis

ObjectivesThe most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate-related biomarkers for LN and analyze the underlying mechanism.MethodsTo identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA.ResultsA sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways ‘antigen processing and presentation’ and ‘NOD-like receptor signaling pathway’. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well.ConclusionOverall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN