The Galactic Isotropic γ\gamma-ray Background and Implications for Dark Matter

We present an analysis of the radial angular profile of the galacto-isotropic (GI) $\gamma$-ray flux--the statistically uniform flux in circular annuli about the Galactic center. Two different approaches are used to measure the GI flux profile in 85 months of Fermi-LAT data: the BDS statistic method which identifies spatial correlations, and a new Poisson ordered-pixel method which identifies non-Poisson contributions. Both methods produce similar GI flux profiles. The GI flux profile is well-described by an existing model of bremsstrahlung, $\pi^0$ production, inverse Compton scattering, and the isotropic background. Discrepancies with data in our full-sky model are not present in the GI component, and are therefore due to mis-modeling of the non-GI emission. Dark matter annihilation constraints based solely on the observed GI profile are close to the thermal WIMP cross section below 100 GeV, for fixed models of the dark matter density profile and astrophysical $\gamma$-ray foregrounds. Refined measurements of the GI profile are expected to improve these constraints by a factor of a few.Comment: 20 pages, 15 figures, references adde

Determination of Angle of Light Deflection in Higher-Derivative Gravity Theories

Gravitational light deflection is known as one of three classical tests of general relativity and the angle of deflection may be computed explicitly using approximate or exact solutions describing the gravitational force generated from a point mass. In various generalized gravity theories, however, such explicit determination is often impossible due to the difficulty with obtaining an exact expression for the deflection angle. In this work, we present some highly effective globally convergent iterative methods to determine the angle of semiclassical gravitational deflection in higher- and infinite-derivative formalisms of quantum gravity theories. We also establish the universal properties that the deflection angle always stays below the classical Einstein angle and is a strictly decreasing function of the incident photon energy, in these formalisms.Comment: 32 pages, 8 figure

Uncovering seasonal patterns of 56 pesticides in surface coastal waters of the Ria Formosa lagoon (Portugal), using a GC-MS method

<div><p>ABSTRACT</p><p>This study describes the simultaneous quantification of 56 pesticides in surface coastal water, supported by the development and validation of a gas chromatography (GC)–ion trap (IT) mass spectrometry (MS) method. Samples (500 mL) were pre-concentrated 2500 times by solid phase extraction (OASISTMHLB). The compounds were identified and quantified, within 35 minutes, by GC tandem mass spectrometry (GC-MS/MS) and GC-MS, respectively. The methodology proved to be highly specific for all target pesticides, with an average linearity of 0.99. Detection limits and recovery rates ranged from 0.4 to 1.3 ng L⁻¹ and 71% to 120%, respectively. The performance of the method was checked using water samples collected from nine sampling sites along the Ria Formosa Lagoon Natural Park (south of Portugal, n = 54) in each season (2010). The total annual concentrations of all pesticides in each category (fungicides, herbicides and insecticides) were 1.4, 0.6 and 9.0 µg L⁻¹, respectively. Moreover, 89% of the pesticides tested for were detected, 84% could be quantified and 25% had concentrations above the European recommended levels (2013/39/EU). The highest total loads of pesticides were found in the spring, which is in agreement with their seasonal application. Physicochemical parameters such as, nitrites, nitrates, ammonia and phosphates, also indicate poor water quality, supporting the fact that the Ria Formosa lagoon actually needs an effective monitoring programme for effective preservation of its natural reserve status.</p></div

Additional file 1: Figure S1. of A widespread family of polymorphic toxins encoded by temperate phages

Relationships between MuF families. Network association of the four HMM profiles of MuF proteins. Each node corresponds to a MuF HMM profile, and each edge width is proportional to the probability of homologous relationship computed by HHsearch for pairwise comparison of HMM profiles using HH-suite (see Methods). (PDF 381 kb

Additional file 8: of A widespread family of polymorphic toxins encoded by temperate phages

HMMER3 hidden Markov model for MuF4. (HMM 107 kb

Additional file 2: Table S1. of A widespread family of polymorphic toxins encoded by temperate phages

General characteristics of bacteriophages, results of the prediction of lifestyle, and of the detection of MuF. Table S2. General characteristics of bacterial genomes, prophages and information on whether they encode a MuF protein. Table S3. General characteristics of the HMM protein profiles. Table S4. General characteristics of the 614 MuF proteins from bacteriophages. Table S5. General characteristics of the 901 MuF proteins identified in bacterial genomes. Table S6. Information on the bacterial prophages where muf genes could be identified. Table S7. General characteristics of toxin domains. Table S8. List of the MuF proteins identified in the integrated reference catalog of the human gut microbiome [41]. (XLSX 1074 kb

Additional file 4: Table S2. of Comparative genomic analysis of Acinetobacter strains isolated from murine colonic crypts

Distribution of the genes of A. modestus CM11G and A. radioresistens CM38.2 following the functional categories obtained following RAST annotations. (DOCX 73 kb

Additional file 1: Table S1. of Comparative genomic analysis of Acinetobacter strains isolated from murine colonic crypts

Sequence of primers used for identification of the strains based on the sequences of 16S rRNA and recA. (DOCX 33 kb

Additional file 2: Figure S1. of Comparative genomic analysis of Acinetobacter strains isolated from murine colonic crypts

Whole genome comparative alignment of A. radioresistens CM38.2. The genome sequence is presented horizontally with the scale showing the sequence coordinates and the conserved shared synteny represented as the colored blocks which are connected across genomes. Upper panel: PacBio sequencing; lower panel: Illumina paired-end sequencing. (PDF 80 kb

Schematic representation of two evolutionary scenarios of <i>Cpt1</i> genes.

<p>Model 1 derived from Morash et al. 2010 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138447#pone.0138447.ref009" target="_blank">9</a>], and Model 2 derived from Ka et al. 2013 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0138447#pone.0138447.ref008" target="_blank">8</a>].</p