Immune pathways and defence mechanisms in honey bees Apis mellifera

Social insects are able to mount both group-level and individual defences against pathogens. Here we focus on individual defences, by presenting a genome-wide analysis of immunity in a social insect, the honey bee Apis mellifera. We present honey bee models for each of four signalling pathways associated with immunity, identifying plausible orthologues for nearly all predicted pathway members. When compared to the sequenced Drosophila and Anopheles genomes, honey bees possess roughly one-third as many genes in 17 gene families implicated in insect immunity. We suggest that an implied reduction in immune flexibility in bees reflects either the strength of social barriers to disease, or a tendency for bees to be attacked by a limited set of highly coevolved pathogens

Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3147 of these likely SNe and estimate that ~1000 are Type Ia SNe (SNe Ia) with light-curve quality sufficient for a cosmological analysis. We use these data with simulations to determine the impact of core-collapse SN (CC SN) contamination on measurements of the dark energy equation of state parameter, w. Using the method of Bayesian Estimation Applied to Multiple Species (BEAMS), distances to SNe Ia and the contaminating CC SN distribution are simultaneously determined. We test light-curve-based SN classification priors for BEAMS as well as a new classification method that relies upon host galaxy spectra and the association of SN type with host type. By testing several SN classification methods and CC SN parameterizations on large SN simulations, we estimate that CC SN contamination gives a systematic error on w (${\sigma }_{w}^{{CC}}$) of 0.014, 29% of the statistical uncertainty. Our best method gives ${\sigma }_{w}^{{CC}}=0.004$, just 8% of the statistical uncertainty, but could be affected by incomplete knowledge of the CC SN distribution. This method determines the SALT2 color and shape coefficients, α and β, with ~3% bias. However, we find that some variants require α and β to be fixed to known values for BEAMS to yield accurate measurements of w. Finally, the inferred abundance of bright CC SNe in our sample is greater than expected based on measured CC SN rates and luminosity functions

Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters

We use 1169 Pan-STARRS supernovae (SNe) and 195 low-z (z < 0.1) SNe Ia to measure cosmological parameters. Though most Pan-STARRS SNe lack spectroscopic classifications, in a previous paper we demonstrated that photometrically classified SNe can be used to infer unbiased cosmological parameters by using a Bayesian methodology that marginalizes over core-collapse (CC) SN contamination. Our sample contains nearly twice as many SNe as the largest previous SN Ia compilation. Combining SNe with cosmic microwave background (CMB) constraints from Planck, we measure the dark energy equation-of-state parameter w to be −0.989 ± 0.057 (stat+sys). If w evolves with redshift as w(a) = w 0 + w a (1 − a), we find w 0 = −0.912 ± 0.149 and w a = −0.513 ± 0.826. These results are consistent with cosmological parameters from the Joint Light-curve Analysis and the Pantheon sample. We try four different photometric classification priors for Pan-STARRS SNe and two alternate ways of modeling CC SN contamination, finding that no variant gives a w differing by more than 2% from the baseline measurement. The systematic uncertainty on w due to marginalizing over CC SN contamination, ${\sigma }_{w}^{\mathrm{CC}}=0.012$, is the third-smallest source of systematic uncertainty in this work. We find limited (1.6σ) evidence for evolution of the SN color-luminosity relation with redshift, a possible systematic that could constitute a significant uncertainty in future high-z analyses. Our data provide one of the best current constraints on w, demonstrating that samples with ~5% CC SN contamination can give competitive cosmological constraints when the contaminating distribution is marginalized over in a Bayesian framework

The Complete Light-curve Sample of Spectroscopically Confirmed SNe Ia from Pan-STARRS1 and Cosmological Constraints from the Combined Pantheon Sample

We present optical light curves, redshifts, and classifications for $365$ spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry, astrometry, and calibration that reduce the systematic uncertainties in the PS1 SN Ia distances. We combine the subset of $279$ PS1 SNe Ia (0.03 < z < 0.68) with useful distance estimates of SNe Ia from the Sloan Digital Sky Survey (SDSS), SNLS, and various low-z and Hubble Space Telescope samples to form the largest combined sample of SNe Ia, consisting of a total of $1048$ SNe Ia in the range of 0.01 < z < 2.3, which we call the "Pantheon Sample." When combining Planck 2015 cosmic microwave background (CMB) measurements with the Pantheon SN sample, we find ${{\rm{\Omega }}}_{m}=0.307\pm 0.012$ and $w=-1.026\pm 0.041$ for the wCDM model. When the SN and CMB constraints are combined with constraints from BAO and local H 0 measurements, the analysis yields the most precise measurement of dark energy to date: ${w}_{0}=-1.007\pm 0.089$ and ${w}_{a}=-0.222\pm 0.407$ for the ${w}_{0}{w}_{a}$CDM model. Tension with a cosmological constant previously seen in an analysis of PS1 and low-z SNe has diminished after an increase of 2× in the statistics of the PS1 sample, improved calibration and photometry, and stricter light-curve quality cuts. We find that the systematic uncertainties in our measurements of dark energy are almost as large as the statistical uncertainties, primarily due to limitations of modeling the low-redshift sample. This must be addressed for future progress in using SNe Ia to measure dark energy