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The Galactic center is an interesting region for high-energy (0.1-100 GeV) and very-high-energy (E > 100 GeV) gamma-ray observations. Potential sources of GeV/TeV gamma-ray emission have been suggested, e.g., the accretion of matter onto the supermassive black hole, cosmic rays from a nearby supernova remnant (e.g., Sgr A East), particle acceleration in a plerion, or the annihilation of dark matter particles. The Galactic center has been detected by EGRET and by Fermi/LAT in the MeV/GeV energy band. At TeV energies, the Galactic center was detected with moderate significance by the CANGAROO and Whipple 10 m telescopes and with high significance by H.E.S.S., MAGIC, and VERITAS. We present the results from three years of VERITAS observations conducted at large zenith angles resulting in a detection of the Galactic center on the level of 18 standard deviations at energies above similar to 2.5 TeV. The energy spectrum is derived and is found to be compatible with hadronic, leptonic, and hybrid emission models discussed in the literature. Future, more detailed measurements of the high-energy cutoff and better constraints on the high-energy flux variability will help to refine and/or disentangle the individual models.
Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.