Doubly Heavy Baryon Production at A High Luminosity e+e−e^+ e^- Collider

Within the framework of nonrelativistic QCD, we make a detailed discussion on the doubly heavy baryon production through the $e^+ e^-$ annihilation channel, $e^{+}e^{-}\rightarrow\gamma/Z^0 \rightarrow \Xi_{QQ^{\prime}} +\bar{Q} +\bar{Q^{\prime}}$, at a high luminosity $e^{+}e^{-}$ collider. Here $Q^{(\prime)}$ stands for the heavy $b$ or $c$ quark. In addition to the channel through the usually considered diquark state $(QQ^{\prime})[^3S_1]_{\bf\bar{3}}$, contributions from the channels through other same important diquark states such as $(QQ^{\prime})[^1S_0]_{\bf 6}$ have also been discussed. Uncertainties for the total cross sections are predicted by taking $m_c=1.80\pm0.30$ GeV and $m_b=5.10\pm0.40$ GeV. At a super $Z$-factory running around the $Z^0$ mass and with a high luminosity up to ${\cal L} \propto 10^{34}\sim 10^{36}{\rm cm}^{-2} {\rm s}^{-1}$, we estimate that about $1.1\times10^{5 \sim 7}$ $\Xi_{cc}$ events, $2.6\times10^{5 \sim 7}$ $\Xi_{bc}$ events and $1.2\times 10^{4 \sim 6}$ $\Xi_{bb}$ events can be generated in one operation year. Such a $Z$-factory, thus, will provide a good platform for studying the doubly heavy baryons in comparable to the CERN large hadronic collider.Comment: 9 pages, 4 figures. To be published in Phys.Rev.

catena-Poly[bis­(μ4-adipato-1:2:1′:2′κ4 O 1:O 1′:O 4:O 4′)bis­(N,N-dimethyl­formamide)-1κO,2κO-dicopper(II)]

In the title polymeric complex, [Cu2(C6H8O4)2(C3H7NO)2]n, the carboxyl­ate groups of the approximately U-shaped adipate dianion each bridge a pair of inversion-related, DMF-coordinated copper(II) atoms, generating a ribbon motif that runs along the b axis. The geometry of the copper(II) atom is distorted square-pyramidal; the apical site is occupied by the O atom of the DMF mol­ecule whereas the four basal sites are occupied by carboxyl­ate O atoms

Radio-wave communication with chaos

This research is supported in part by National Natural Science Foundation of China (61172070), Scientific and Technological Innovation Leading Talents Program of Shaanxi Province, The Key Basic Research Fund of Shaanxi Province (2016ZDJC-01).Peer reviewedPublisher PD

FastHiC: a fast and accurate algorithm to detect long-range chromosomal interactions from Hi-C data

Motivation: How chromatin folds in three-dimensional (3D) space is closely related to transcription regulation. As powerful tools to study such 3D chromatin conformation, the recently developed Hi-C technologies enable a genome-wide measurement of pair-wise chromatin interaction. However, methods for the detection of biologically meaningful chromatin interactions, i.e. peak calling, from Hi-C data, are still under development. In our previous work, we have developed a novel hidden Markov random field (HMRF) based Bayesian method, which through explicitly modeling the non-negligible spatial dependency among adjacent pairs of loci manifesting in high resolution Hi-C data, achieves substantially improved robustness and enhanced statistical power in peak calling. Superior to peak callers that ignore spatial dependency both methodologically and in performance, our previous Bayesian framework suffers from heavy computational costs due to intensive computation incurred by modeling the correlated peak status of neighboring loci pairs and the inference of hidden dependency structure

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

biological space

Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been proposed by us using a linear model framework which can handle continuous responses. In this paper, we extend our linear model-based framework to a generalized linear model-based framework so that the methods can handle other types of responses especially binary responses which is a common problem in association studies. To evaluate the performance of various estimators and compare them we performed simulation studies. We found that all methods have Type I errors controlled, and our NGS data-based methods have better performance than genotype-based methods for other types of responses, including binary responses (logistics regression) and count responses (Poisson regression), especially when sequencing depth is low. We have extended our previous linear model (LM) framework to a generalized linear model (GLM) framework and derived NGS data-based methods for a group of genetic variables. Compared with our previously proposed LM-based methods, the new GLM-based methods can handle more complex responses (for example, binary responses and count responses) in addition to continuous responses. Our methods have filled the literature gap and shown advantage over their corresponding genotype-based methods in the literature