A New Code for Nonlinear Force-Free Field Extrapolation of the Global Corona

Reliable measurements of the solar magnetic field are still restricted to the photosphere, and our present knowledge of the three-dimensional coronal magnetic field is largely based on extrapolation from photospheric magnetogram using physical models, e.g., the nonlinear force-free field (NLFFF) model as usually adopted. Most of the currently available NLFFF codes have been developed with computational volume like Cartesian box or spherical wedge while a global full-sphere extrapolation is still under developing. A high-performance global extrapolation code is in particular urgently needed considering that Solar Dynamics Observatory (SDO) can provide full-disk magnetogram with resolution up to $4096\times 4096$. In this work, we present a new parallelized code for global NLFFF extrapolation with the photosphere magnetogram as input. The method is based on magnetohydrodynamics relaxation approach, the CESE-MHD numerical scheme and a Yin-Yang spherical grid that is used to overcome the polar problems of the standard spherical grid. The code is validated by two full-sphere force-free solutions from Low & Lou's semi-analytic force-free field model. The code shows high accuracy and fast convergence, and can be ready for future practical application if combined with an adaptive mesh refinement technique.Comment: Accepted by ApJ, 26 pages, 10 figure

Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

In this paper, a resource-efficient FPGA-based neural network regression model is developed for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned upgrades, the entirely new FPGA-based hardware muon trigger system will be installed in 2025-2026 that will process full muon detector data within a 10 ${\mu}s$ latency window. The planned large FPGA devices should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our model promises to improve the rejection of the dominant source of background events in the central detector region, which are due to muon candidates with low transverse momenta. This neural network was implemented in the hardware description language using 65 digital signal processors and about 10,000 lookup tables. The simulated network latency and deadtime are 245 and 60 ns, respectively, when implemented in the FPGA device using a 400 MHz clock frequency. These results are well within the requirements of the future ATLAS muon trigger system, therefore opening a possibility for deploying machine learning methods for data taking by the ATLAS experiment at the High Luminosity LHC.Comment: 12 pages, 17 figure

Haplo2Ped: a tool using haplotypes as markers for linkage analysis

<p>Abstract</p> <p>Background</p> <p>Generally, SNPs are abundant in the genome; however, they display low power in linkage analysis because of their limited heterozygosity. Haplotype markers, on the other hand, which are composed of many SNPs, greatly increase heterozygosity and have superiority in linkage statistics.</p> <p>Results</p> <p>Here we developed Haplo2Ped to automatically transform SNP data into haplotype markers and then to compute the logarithm (base 10) of odds (LOD) scores of regional haplotypes that are homozygous within the disease co-segregation haploid group. The results are reported as a hypertext file and a 3D figure to help users to obtain the candidate linkage regions. The hypertext file contains parameters of the disease linked regions, candidate genes, and their links to public databases. The 3D figure clearly displays the linkage signals in each chromosome. We tested Haplo2Ped in a simulated SNP dataset and also applied it to data from a real study. It successfully and accurately located the causative genomic regions. Comparison of Haplo2Ped with other existing software for linkage analysis further indicated the high effectiveness of this software.</p> <p>Conclusions</p> <p>Haplo2Ped uses haplotype fragments as mapping markers in whole genome linkage analysis. The advantages of Haplo2Ped over other existing software include straightforward output files, increased accuracy and superior ability to deal with pedigrees showing incomplete penetrance. Haplo2Ped is freely available at: <url>http://bighapmap.big.ac.cn/software.html</url>.</p

SNP@Evolution: a hierarchical database of positive selection on the human genome

<p>Abstract</p> <p>Background</p> <p>Positive selection is a driving force that has shaped the modern human. Recent developments in high throughput technologies and corresponding statistics tools have made it possible to conduct whole genome surveys at a population scale, and a variety of measurements, such as heterozygosity (HET), <it>F</it>_<it>ST</it>, and Tajima's D, have been applied to multiple datasets to identify signals of positive selection. However, great effort has been required to combine various types of data from individual sources, and incompatibility among datasets has been a common problem. SNP@Evolution, a new database which integrates multiple datasets, will greatly assist future work in this area.</p> <p>Description</p> <p>As part of our research scanning for evolutionary signals in HapMap Phase II and Phase III datasets, we built SNP@Evolution as a multi-aspect database focused on positive selection. Among its many features, SNP@Evolution provides computed <it>F</it>_{<it>ST </it>}and HET of all HapMap SNPs, 5+ HapMap SNPs per qualified gene, and all autosome regions detected from whole genome window scanning. In an attempt to capture multiple selection signals across the genome, selection-signal enrichment strength (E_S) values of HET, <it>F</it>_<it>ST</it>, and <it>P</it>-values of iHS of most annotated genes have been calculated and integrated within one frame for users to search for outliers. Genes with significant E_Sor <it>P</it>-values (with thresholds of 0.95 and 0.05, respectively) have been highlighted in color. Low diversity chromosome regions have been detected by sliding a 100 kb window in a 10 kb step. To allow this information to be easily disseminated, a graphical user interface (GBrowser) was constructed with the Generic Model Organism Database toolkit.</p> <p>Conclusion</p> <p>Available at <url>http://bighapmap.big.ac.cn</url>, SNP@Evolution is a hierarchical database focused on positive selection of the human genome. Based on HapMap Phase II and III data, SNP@Evolution includes 3,619,226/1,389,498 SNPs with their computed HET and <it>F</it>_<it>ST</it>, as well as qualified genes of 21,859/21,099 with E_Svalues of HET and <it>F</it>_<it>ST</it>. In at least one HapMap population group, window scanning for selection signals has resulted in 1,606/10,138 large low HET regions. Among Phase II and III geographical groups, 660 and 464 regions show strong differentiation.</p