High-quality semen is an essential factor for the success of artificial insemination, and revealing the genetic structure of pig semen traits helps improve semen quality. This study aimed to identify candidate genes associated with semen traits in three pig breeds (Duroc, Landrace, and Yorkshire) through weighted GWAS and multi-tissue transcriptome analysis. In this study, to identify candidate genes associated with semen traits in Duroc, Landrace, and Yorkshire, we performed weighted GWAS in four traits (sperm motility, sperm progressive motility, sperm abnormality rate, and total sperm count) using 936 pigs and multi-tissue transcriptome analysis using 34 tissues RNA-seq data of 5457 pigs from FarmGTEx. It was found that 16, 9, and 12 significant SNPs associated with semen traits were identified in Duroc, Landrace, and Yorkshire, with corresponding 7, 5, and 7 candidate genes in these three breeds, respectively, which may be involved in mammal spermatogenesis, testicular function, and male fertility. Moreover, we not only found the same candidate gene DNAI2 as in previous studies but also found two new candidate genes PNLDC1 and RSPH3, which were identified simultaneously in both Landrace and Yorkshire. By integrating the GWAS and multi-tissue transcriptome analysis results, we found that candidate genes associated with semen traits of three pig breeds were highly expressed in the testis tissue. The three genotypes of rs320928244 had significant effects on the expression of the DYNLT1 gene in the testis tissue of Landrace. These results together showed that these candidate genes were mainly related to sperm motility defects. This study helps deepen the understanding of the genetic basis of semen traits and provides a theoretical foundation for improving the semen quality of Duroc, Landrace, and Yorkshire breeds.
We report results from searches for new physics with low-energy electronic recoil data recorded with the XENON1T detector, with an exposure of 0.65 t-y and an unprecedentedly low background rate of $76 \pm 2_{stat}$ events/(t y keV) between 1-30 keV. An excess over known backgrounds is observed below 7 keV, rising towards lower energies and prominent between 2-3 keV. The solar axion model has a 3.5$\sigma$ significance, and a three-dimensional 90% confidence surface is reported for axion couplings to electrons, photons, and nucleons. This surface is inscribed in the cuboid defined by $g_{ae} < 3.7 \times 10^{-12}$, $g_{ae}g_{an}^{eff} < 4.6 \times 10^{-18}$, and $g_{ae}g_{a\gamma} < 7.6\times10^{-22}~{GeV}^{-1}$, and excludes either $g_{ae}=0$ or $g_{ae}g_{a\gamma}=g_{ae}g_{an}^{eff}=0$. The neutrino magnetic moment signal is similarly favored over background at 3.2$\sigma$ and a confidence interval of $\mu_{\nu} \in (1.4,~2.9)\times10^{-11}\mu_B$ (90% C.L.) is reported. Both results are in strong tension with stellar constraints. The excess can also be explained by $\beta$ decays of tritium at 3.2$\sigma$ significance with a corresponding tritium concentration in xenon of $(6.2 \pm 2.0) \times 10^{-25}$ mol/mol. Such a trace amount can be neither confirmed nor excluded with current knowledge of production and reduction mechanisms. The significances of the solar axion and neutrino magnetic moment hypotheses are decreased to 2.1$\sigma$ and 0.9$\sigma$, respectively, if an unconstrained tritium component is included in the fitting. With respect to bosonic dark matter, the excess favors a monoenergetic peak at ($2.3 \pm 0.2$) keV (68% C.L.) with a 3.0$\sigma$ global (4.0$\sigma$ local) significance over background. This analysis sets the most restrictive direct constraints to date on pseudoscalar and vector bosonic dark matter for most masses between 1 and 210 keV/c$^2$.
Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. In these images, the clustering of distinct particles into superstructures is of central importance to the current and future neutrino physics program. Electromagnetic (EM) activity typically exhibits spatially detached fragments of varying morphology and orientation that are challenging to efficiently assemble using traditional algorithms. Similarly, particles that are spatially removed from each other in the detector may originate from a common interaction. Graph Neural Networks (GNNs) were developed in recent years to find correlations between objects embedded in an arbitrary space. The Graph Particle Aggregator (GrapPA) first leverages GNNs to predict the adjacency matrix of EM shower fragments and to identify the origin of showers, i.e. primary fragments. On the PILArNet public LArTPC simulation dataset, the algorithm achieves achieves a shower clustering accuracy characterized by a mean adjusted Rand index (ARI) of 97.8 % and a primary identification accuracy of 99.8 %. It yields a relative shower energy resolution of $(4.1+1.4/\sqrt{E (\text{GeV})})\,\%$ and a shower direction resolution of $(2.1/\sqrt{E(\text{GeV})})^{\circ}$. The optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean ARI of 99.2 % for an interaction density of $\sim\mathcal{O}(1)\,m^{-3}$.
Liquid Argon Time Projection Chambers are used as precision detectors in ongoing and upcoming neutrino experiments. In this paper the authors adapt machine learning techniques to better identify the beginning- and end-points of particle tracks in LarTPCs, offering significant improvement over traditional methods in reconstructing the true event.
Abstract The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.
Abstract 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 m 3 . 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.
PandaX is a large upgradable liquid-xenon detector system that can be used for both direct dark-matter detection and $^{136}$Xe double-beta decay search. It is located in the Jinping Deep-Underground Laboratory in Sichuan, China. The detector operates in dual-phase mode, allowing detection of both prompt scintillation, and ionization charge through proportional scintillation. The central time projection chamber will be staged, with the first stage accommodating a target mass of about 120\,kg. In stage II, the target mass will be increased to about 0.5\,ton. In the final stage, the detector can be upgraded to a multi-ton target mass. In this paper a detailed description of the stage-I detector design and performance results established during the commissioning phase is presented.
