XMM-Newton observation of the eclipsing binary Algol

We present an {\sl XMM-Newton} observation of the eclipsing binary Algol which contains an X-ray dark B8V primary and an X-ray bright K2IV secondary. The observation covered the optical secondary eclipse and captured an X-ray flare that was eclipsed by the B star. The EPIC and RGS spectra of Algol in its quiescent state are described by a two-temperature plasma model. The cool component has a temperature around 6.4$\times 10^{6}$ K while that of the hot component ranges from 2 to 4.0$\times 10^{7}$ K. Coronal abundances of C, N, O, Ne, Mg, Si and Fe were obtained for each component for both the quiescent and the flare phases, with generally upper limits for S and Ar, and C, N, and O for the hot component. F-tests show that the abundances need not to be different between the cool and the hot component and between the quiescent and the flare phase with the exception of Fe. Whereas the Fe abundance of the cool component remains constant at $\sim$0.14, the hot component shows an Fe abundance of $\sim$0.28, which increases to $\sim$0.44 during the flare. This increase is expected from the chromospheric evaporation model. The absorbing column density $N_H$ of the quiescent emission is 2.5$\times10^{20}$ cm$^{-2}$, while that of the flare-only emission is significantly lower and consistent with the column density of the interstellar medium. This observation substantiates earlier suggestions of the presence of X-ray absorbing material in the Algol system.Comment: 15 pages, 9 figures, Accpted by RA

SMA observations of C2H in High-Mass Star Forming Regions

C$_2$H is a representative hydrocarbon that is abundant and ubiquitous in the interstellar medium (ISM). To study its chemical properties, we present Submillimeter Array (SMA) observations of the C$_2$H $N=3-2$ and HC$_3$N $J=30-29$ transitions and the 1.1 mm continuum emission toward four OB cluster-forming regions, AFGL 490, ON 1, W33 Main, and G10.6-0.4, which cover a bolometric luminosity range of $\sim$10$^3$--10$^6$ $L_{\odot}$. We found that on large scales, the C$_2$H emission traces the dense molecular envelope. However, for all observed sources, the peaks of C$_2$H emission are offset by several times times 10$^4$ AU from the peaks of 1.1 mm continuum emission, where the most luminous stars are located. By comparing the distribution and profiles of C$_2$H hyperfine lines and the 1.1 mm continuum emission, we find that the C$_2$H column density (and abundance) around the 1.1 mm continuum peaks is lower than those in the ambient gas envelope. Chemical models suggest that C$_2$H might be transformed to other species owing to increased temperature and density; thus, its reduced abundance could be the signpost of the heated molecular gas in the $\sim$10$^4$ AU vicinity around the embedded high-mass stars. Our results support such theoretical prediction for centrally embedded $\sim10^3$--$10^6L_{\odot}$ OB star-forming cores, while future higher-resolution observations are required to examine the C$_2$H transformation around the localized sites of high-mass star formation.Comment: 10 pages, 6 figures. ApJ accepted. Comments welcom

Mutation of SLC35D3 causes metabolic syndrome by impairing dopamine signaling in striatal D1 neurons

We thank Dr. Ya-Qin Feng from Shanxi Medical University, Dr. Tian-Yun Gao from Nanjing University and Dr. Yan-Hong Xue from Institute of Biophysics (CAS) for technical assistance in this study. We are very thankful to Drs. Richard T. Swank and Xiao-Jiang Li for their critical reading of this manuscript and invaluable advice. Funding: This work was partially supported by grants from National Basic Research Program of China (2013CB530605; 2014CB942803), from National Natural Science Foundation of China 1230046; 31071252; 81101182) and from Chinese Academy of Sciences (KSCX2-EW-R-05, KJZD-EW-L08). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

The human disease network in terms of dysfunctional regulatory mechanisms

BACKGROUND: Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. In pioneer studies, disease similarity was commonly estimated according to clinical manifestation. Subsequently, scientists started to investigate disease similarity based on gene-phenotype knowledge, which were inevitably biased to well-studied diseases. In recent years, estimating disease similarity according to transcriptomic behavior significantly enhances the probability of finding novel disease relationships, while the currently available studies usually mine expression data through differential expression analysis that has been considered to have little chance of unraveling dysfunctional regulatory relationships, the causal pathogenesis of diseases. METHODS: We developed a computational approach to measure human disease similarity based on expression data. Differential coexpression analysis, instead of differential expression analysis, was employed to calculate differential coexpression level of every gene for each disease, which was then summarized to the pathway level. Disease similarity was eventually calculated as the partial correlation coefficients of pathways’ differential coexpression values between any two diseases. The significance of disease relationships were evaluated by permutation test. RESULTS: Based on mRNA expression data and a differential coexpression analysis based method, we built a human disease network involving 1326 significant Disease-Disease links among 108 diseases. Compared with disease relationships captured by differential expression analysis based method, our disease links shared known disease genes and drugs more significantly. Some novel disease relationships were discovered, for example, Obesity and cancer, Obesity and Psoriasis, lung adenocarcinoma and S. pneumonia, which had been commonly regarded as unrelated to each other, but recently found to share similar molecular mechanisms. Additionally, it was found that both the type of disease and the type of affected tissue influenced the degree of disease similarity. A sub-network including Allergic asthma, Type 2 diabetes and Chronic kidney disease was extracted to demonstrate the exploration of their common pathogenesis. CONCLUSION: The present study produces a global view of human diseasome for the first time from the viewpoint of regulation mechanisms, which therefore could provide insightful clues to etiology and pathogenesis, and help to perform drug repositioning and design novel therapeutic interventions. REVIEWERS: This article was reviewed by Limsoon Wong, Rui Wang-Sattler, and Andrey Rzhetsky. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-015-0088-z) contains supplementary material, which is available to authorized users

Changes in lymphocyte subsets in patients with Guillain-Barré syndrome treated with immunoglobulin

BACKGROUND: Guillain-Barré syndrome (GBS) is an autoimmune condition characterized by peripheral neuropathy. The pathogenesis of GBS is not fully understood, and the mechanism of how intravenous immunoglobulin (IVIG) cures GBS is ambiguous. Herein, we investigated lymphocyte subsets in patients with two major subtypes of GBS (acute inflammatory demyelinating polyneuropathy, AIDP, and acute motor axonal neuropathy, AMAN) before and after treatment with IVIG, and explored the possible mechanism of IVIG action. METHODS: Sixty-four patients with GBS were selected for our study and divided into two groups: AIDP (n = 38) and AMAN (n = 26). Thirty healthy individuals were chosen as the control group. Relative counts of peripheral blood T and B lymphocyte subsets were detected by flow cytometry analysis. RESULTS: In the AIDP group, the percentage of CD4(+)CD45RO(+) T cells was significantly higher, while the percentage of CD4(+)CD45RA(+) T cells was notably lower, than in the control group. After treatment with IVIG, the ratio of CD4(+)/CD8(+) T cells and the percentage of CD4(+)CD45RA(+) T cells increased, while the percentages of CD8(+) T cells and CD4(+)CD45RO(+) T cells decreased significantly, along with the number of CD19(+) B cells. However, there were not such obvious changes in the AMAN group. The Hughes scores were significantly lower in both the AIDP and AMAN groups following treatment with IVIG, but the changes in Hughes scores showed no significant difference between the two groups. CONCLUSIONS: This study suggested that the changes in T and B-lymphocyte subsets, especially in CD4(+)T-lymphocyte subsets, might play an important role in the pathogenesis of AIDP, and in the mechanism of IVIG action against AIDP

Distribution of Spectral Lags in Gamma Ray Bursts

Using the data acquired in the Time To Spill (TTS) mode for long gamma-ray bursts (GRBs) collected by the Burst and Transient Source Experiment on board the Compton Gamma Ray Observatory (BATSE/CGRO), we have carefully measured spectral lags in time between the low (25-55 keV) and high (110-320 keV) energy bands of individual pulses contained in 64 multi-peak GRBs. We find that the temporal lead by higher-energy gamma-ray photons (i.e., positive lags) is the norm in this selected sample set of long GRBs. While relatively few in number, some pulses of several long GRBs do show negative lags. This distribution of spectral lags in long GRBs is in contrast to that in short GRBs. This apparent difference poses challenges and constraints on the physical mechanism(s) of producing long and short GRBs. The relation between the pulse peak count rates and the spectral lags is also examined. Observationally, there seems to be no clear evidence for systematic spectral lag-luminosity connection for pulses within a given long GRB.Comment: 20 pages, 4 figure

DNetDB: The human disease network database based on dysfunctional regulation mechanism

Additional analysis and concepts explanation. This file contains 1) comparison of DNetDB and the results of differential expression analysis (DEA-) based method ; 2) comparison of DNetDB and traditional disease classification; 3) negative disease relationships and 4) DCp and DCe. (DOCX 6926 kb