GLAST: Understanding the High Energy Gamma-Ray Sky

We discuss the ability of the GLAST Large Area Telescope (LAT) to identify, resolve, and study the high energy gamma-ray sky. Compared to previous instruments the telescope will have greatly improved sensitivity and ability to localize gamma-ray point sources. The ability to resolve the location and identity of EGRET unidentified sources is described. We summarize the current knowledge of the high energy gamma-ray sky and discuss the astrophysics of known and some prospective classes of gamma-ray emitters. In addition, we also describe the potential of GLAST to resolve old puzzles and to discover new classes of sources.Comment: To appear in Cosmic Gamma Ray Sources, Kluwer ASSL Series, Edited by K.S. Cheng and G.E. Romer

Galactic and Extragalactic Samples of Supernova Remnants: How They Are Identified and What They Tell Us

Supernova remnants (SNRs) arise from the interaction between the ejecta of a supernova (SN) explosion and the surrounding circumstellar and interstellar medium. Some SNRs, mostly nearby SNRs, can be studied in great detail. However, to understand SNRs as a whole, large samples of SNRs must be assembled and studied. Here, we describe the radio, optical, and X-ray techniques which have been used to identify and characterize almost 300 Galactic SNRs and more than 1200 extragalactic SNRs. We then discuss which types of SNRs are being found and which are not. We examine the degree to which the luminosity functions, surface-brightness distributions and multi-wavelength comparisons of the samples can be interpreted to determine the class properties of SNRs and describe efforts to establish the type of SN explosion associated with a SNR. We conclude that in order to better understand the class properties of SNRs, it is more important to study (and obtain additional data on) the SNRs in galaxies with extant samples at multiple wavelength bands than it is to obtain samples of SNRs in other galaxiesComment: Final 2016 draft of a chapter in "Handbook of Supernovae" edited by Athem W. Alsabti and Paul Murdin. Final version available at https://doi.org/10.1007/978-3-319-20794-0_90-

Radio emission from Supernova Remnants

The explosion of a supernova releases almost instantaneously about 10^51 ergs of mechanic energy, changing irreversibly the physical and chemical properties of large regions in the galaxies. The stellar ejecta, the nebula resulting from the powerful shock waves, and sometimes a compact stellar remnant, constitute a supernova remnant (SNR). They can radiate their energy across the whole electromagnetic spectrum, but the great majority are radio sources. Almost 70 years after the first detection of radio emission coming from a SNR, great progress has been achieved in the comprehension of their physical characteristics and evolution. We review the present knowledge of different aspects of radio remnants, focusing on sources of the Milky Way and the Magellanic Clouds, where the SNRs can be spatially resolved. We present a brief overview of theoretical background, analyze morphology and polarization properties, and review and critical discuss different methods applied to determine the radio spectrum and distances. The consequences of the interaction between the SNR shocks and the surrounding medium are examined, including the question of whether SNRs can trigger the formation of new stars. Cases of multispectral comparison are presented. A section is devoted to reviewing recent results of radio SNRs in the Magellanic Clouds, with particular emphasis on the radio properties of SN 1987A, an ideal laboratory to investigate dynamical evolution of an SNR in near real time. The review concludes with a summary of issues on radio SNRs that deserve further study, and analyzing the prospects for future research with the latest generation radio telescopes.Comment: Revised version. 48 pages, 15 figure

Estimates of live-tree carbon stores in the Pacific Northwest are sensitive to model selection

<p>Abstract</p> <p>Background</p> <p>Estimates of live-tree carbon stores are influenced by numerous uncertainties. One of them is model-selection uncertainty: one has to choose among multiple empirical equations and conversion factors that can be plausibly justified as locally applicable to calculate the carbon store from inventory measurements such as tree height and diameter at breast height (DBH). Here we quantify the model-selection uncertainty for the five most numerous tree species in six counties of northwest Oregon, USA.</p> <p>Results</p> <p>The results of our study demonstrate that model-selection error may introduce 20 to 40% uncertainty into a live-tree carbon estimate, possibly making this form of error the largest source of uncertainty in estimation of live-tree carbon stores. The effect of model selection could be even greater if models are applied beyond the height and DBH ranges for which they were developed.</p> <p>Conclusions</p> <p>Model-selection uncertainty is potentially large enough that it could limit the ability to track forest carbon with the precision and accuracy required by carbon accounting protocols. Without local validation based on detailed measurements of usually destructively sampled trees, it is very difficult to choose the best model when there are several available. Our analysis suggests that considering tree form in equation selection may better match trees to existing equations and that substantial gaps exist, in terms of both species and diameter ranges, that are ripe for new model-building effort.</p

Accurate Genome Relative Abundance Estimation Based on Shotgun Metagenomic Reads

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data- sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes

Circulating CD133+VEGFR2+ and CD34+VEGFR2+ cells and arterial function in patients with beta-thalassaemia major

Arterial dysfunction has been documented in patients with beta-thalassaemia major. This study aimed to determine the quantity and proliferative capacity of circulating CD133+VEGFR2+ and CD34+VEGFR2+ cells in patients with beta-thalassaemia major and those after haematopoietic stem cell transplantation (HSCT), and their relationships with arterial function. Brachial arterial flow-mediated dilation (FMD), carotid arterial stiffness, the quantity of these circulating cells and their number of colony-forming units (CFUs) were determined in 17 transfusion-dependent thalassaemia patients, 14 patients after HSCT and 11 controls. Compared with controls, both patient groups had significantly lower FMD and greater arterial stiffness. Despite having increased CD133+VEGFR2+ and CD34+VEGFR2+ cells, transfusion-dependent patients had significantly reduced CFUs compared with controls (p = 0.002). There was a trend of increasing CFUs across the three groups with decreasing iron load (p = 0.011). The CFUs correlated with brachial FMD (p = 0.029) and arterial stiffness (p = 0.02), but not with serum ferritin level. Multiple linear regression showed that CFU was a significant determinant of FMD (p = 0.043) and arterial stiffness (p = 0.02) after adjustment of age, sex, body mass index, blood pressure and serum ferritin level. In conclusion, arterial dysfunction found in patients with beta-thalassaemia major before and after HSCT may be related to impaired proliferation of CD133+VEGFR2+ and CD34+VEGFR2+ cells

Mutation analysis of the MDM4 gene in German breast cancer patients

<p>Abstract</p> <p>Background</p> <p>MDM4 is a negative regulator of p53 and cooperates with MDM2 in the cellular response to DNA damage. It is unknown, however, whether <it>MDM4 </it>gene alterations play some role in the inherited component of breast cancer susceptibility.</p> <p>Methods</p> <p>We sequenced the whole <it>MDM4 </it>coding region and flanking untranslated regions in genomic DNA samples obtained from 40 German patients with familial breast cancer. Selected variants were subsequently screened by RFLP-based assays in an extended set of breast cancer cases and controls.</p> <p>Results</p> <p>Our resequencing study uncovered two <it>MDM4 </it>coding variants in 4/40 patients. Three patients carried a silent substitution at codon 74 that was linked with another rare variant in the 5'UTR. No association of this allele with breast cancer was found in a subsequent screening of 133 patients with bilateral breast cancer and 136 controls. The fourth patient was heterozygous for the missense substitution D153G which is located in a less conserved region of the MDM4 protein but may affect a predicted phosphorylation site. The D153G substitution only partially segregated with breast cancer in the family and was not identified on additional 680 chromosomes screened.</p> <p>Conclusion</p> <p>This study did not reveal clearly pathogenic mutations although it uncovered two new unclassified variants at a low frequency. We conclude that there is no evidence for a major role of <it>MDM4 </it>coding variants in the inherited susceptibility towards breast cancer in German patients.</p

HTR1A a Novel Type 1 Diabetes Susceptibility Gene on Chromosome 5p13-q13

Background: We have previously performed a genome-wide linkage study in Scandinavian Type 1 diabetes (T1D) families. In the Swedish families, we detected suggestive linkage (LOD less than= 2.2) to the chromosome 5p13-q13 region. The aim of our study was to investigate the linked region in search for possible T1D susceptibility genes. Methodology/Principal Findings: Microsatellites were genotyped in the Scandinavian families to fine-map the previously linked region. Further, SNPs were genotyped in Swedish and Danish families as well as Swedish sporadic cases. In the Swedish families we detected genome-wide significant linkage to the 5-hydroxytryptamine receptor 1A (HTR1A) gene (LOD 3.98, pless than9.8x10(-6)). Markers tagging two separate genes; the ring finger protein 180 (RNF180) and HTR1A showed association to T1D in the Swedish and Danish families (pless than0.002, pless than0.001 respectively). The association was not confirmed in sporadic cases. Conditional analysis indicates that the primary association was to HTR1A. Quantitative PCR show that transcripts of both HTR1A and RNF180 are present in human islets of Langerhans. Moreover, immunohistochemical analysis confirmed the presence of the 5-HTR1A protein in isolated human islets of Langerhans as well as in sections of human pancreas. Conclusions: We have identified and confirmed the association of both HTR1A and RFN180, two genes in high linkage disequilibrium (LD) to T1D in two separate family materials. As both HTR1A and RFN180 were expressed at the mRNA level and HTR1A as protein in human islets of Langerhans, we suggest that HTR1A may affect T1D susceptibility by modulating the initial autoimmune attack or either islet regeneration, insulin release, or both