Creating a honey bee consensus gene set

BACKGROUND: We wished to produce a single reference gene set for honey bee (Apis mellifera). Our motivation was twofold. First, we wished to obtain an improved set of gene models with increased coverage of known genes, while maintaining gene model quality. Second, we wished to provide a single official gene list that the research community could further utilize for consistent and comparable analyses and functional annotation. RESULTS: We created a consensus gene set for honey bee (Apis mellifera) using GLEAN, a new algorithm that uses latent class analysis to automatically combine disparate gene prediction evidence in the absence of known genes. The consensus gene models had increased representation of honey bee genes without sacrificing quality compared with any one of the input gene predictions. When compared with manually annotated gold standards, the consensus set of gene models was similar or superior in quality to each of the input sets. CONCLUSION: Most eukaryotic genome projects produce multiple gene sets because of the variety of gene prediction programs. Each of the gene prediction programs has strengths and weaknesses, and so the multiplicity of gene sets offers users a more comprehensive collection of genes to use than is available from a single program. On the other hand, the availability of multiple gene sets is also a cause for uncertainty among users as regards which set they should use. GLEAN proved to be an effective method to combine gene lists into a single reference set

A Deep Chandra Observation of the Distant Galaxy Cluster MS1137.5+6625

We present results from a deep Chandra observation of MS1137.5+66, a distant (z=0.783) and massive cluster of galaxies. Only a few similarly massive clusters are currently known at such high redshifts; accordingly, this observation provides much-needed information on the dynamical state of these rare systems. The cluster appears both regular and symmetric in the X-ray image. However, our analysis of the spectral and spatial X-ray data in conjunction with interferometric Sunyaev-Zel'dovich effect data and published deep optical imaging suggests the cluster has a fairly complex structure. The angular diameter distance we calculate from the Chandra and Sunyaev-Zel'dovich effect data assuming an isothermal, spherically symmetric cluster implies a low value for the Hubble constant for which we explore possible explanations.Comment: 16 pages, 6 figures, submitted to Ap

Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction

Diagnostic Doses and Times for Phlebotomus papatasi and Lutzomiya longipalpis Sand Flies (Diptera: Psychodidae: Phleboominae) Using the CDC Bottle Bioassay to Assess Insecticide Resistance

Background: Insecticide resistance to synthetic chemical insecticides is a worldwide concern in phlebotomine sand flies (Diptera: Psychodidae), the vectors of Leishmania spp. parasites. The CDC bottle bioassay assesses resistance by testing populations against verified diagnostic doses and diagnostic times for an insecticide, but the assay has been used limitedly with sand flies. The objective of this study was to determine diagnostic doses and diagnostic times for laboratory Lutzomyia longipalpis (Lutz & Nieva) and Phlebotomus papatasi (Scopoli) to ten insecticides, including pyrethroids, organophosphates, carbamates, and DDT, that are used worldwide to control vectors. Methods: Bioassays were conducted in 1,000-ml glass bottles each containing 10–25 sand flies from laboratory colonies of L. longipalpis or P. papatasi. Four pyrethroids, three organophosphates, two carbamates and one organochlorine, were evaluated. A series of concentrations were tested for each insecticide, and four replicates were conducted for each concentration. Diagnostic doses were determined only during the exposure bioassay for the organophosphates and carbamates. For the pyrethroids and DDT, diagnostic doses were determined for both the exposure bioassay and after a 24-hour recovery period. Results: Both species are highly susceptible to the carbamates as their diagnostic doses are under 7.0 μg/ml. Both species are also highly susceptible to DDT during the exposure assay as their diagnostic doses are 7.5 μg/ml, yet their diagnostic doses for the 24-h recovery period are 650.0 μg/ml for Lu. longipalpis and 470.0 μg/ml for P. papatasi. Conclusions: Diagnostic doses and diagnostic times can now be incorporated into vector management programs that use the CDC bottle bioassay to assess insecticide resistance in field populations of Lu. longipalpis and P. papatasi. These findings provide initial starting points for determining diagnostic doses and diagnostic times for other sand fly vector species and wild populations using the CDC bottle bioassay

Diagnostic Doses and Times for Phlebotomus papatasi and Lutzomiya longipalpis Sand Flies (Diptera: Psychodidae: Phleboominae) Using the CDC Bottle Bioassay to Assess Insecticide Resistance

Background: Insecticide resistance to synthetic chemical insecticides is a worldwide concern in phlebotomine sand flies (Diptera: Psychodidae), the vectors of Leishmania spp. parasites. The CDC bottle bioassay assesses resistance by testing populations against verified diagnostic doses and diagnostic times for an insecticide, but the assay has been used limitedly with sand flies. The objective of this study was to determine diagnostic doses and diagnostic times for laboratory Lutzomyia longipalpis (Lutz & Nieva) and Phlebotomus papatasi (Scopoli) to ten insecticides, including pyrethroids, organophosphates, carbamates, and DDT, that are used worldwide to control vectors. Methods: Bioassays were conducted in 1,000-ml glass bottles each containing 10–25 sand flies from laboratory colonies of L. longipalpis or P. papatasi. Four pyrethroids, three organophosphates, two carbamates and one organochlorine, were evaluated. A series of concentrations were tested for each insecticide, and four replicates were conducted for each concentration. Diagnostic doses were determined only during the exposure bioassay for the organophosphates and carbamates. For the pyrethroids and DDT, diagnostic doses were determined for both the exposure bioassay and after a 24-hour recovery period. Results: Both species are highly susceptible to the carbamates as their diagnostic doses are under 7.0 μg/ml. Both species are also highly susceptible to DDT during the exposure assay as their diagnostic doses are 7.5 μg/ml, yet their diagnostic doses for the 24-h recovery period are 650.0 μg/ml for Lu. longipalpis and 470.0 μg/ml for P. papatasi. Conclusions: Diagnostic doses and diagnostic times can now be incorporated into vector management programs that use the CDC bottle bioassay to assess insecticide resistance in field populations of Lu. longipalpis and P. papatasi. These findings provide initial starting points for determining diagnostic doses and diagnostic times for other sand fly vector species and wild populations using the CDC bottle bioassay

The Shape of Galaxy Cluster Dark Matter Haloes: Systematics of Its Imprint on Cluster Gas, and Comparison to Observations

(Abridged) We study predictions for galaxy cluster observables that can test the statistics of dark matter halo shapes expected in a flat LCDM universe. We present a simple analytical model for the prediction of cluster-scale X-ray observations, approximating clusters as isothermal systems in hydrostatic equilibrium, and dark matter haloes as ellipsoids with uniform axial ratios. We test the model against high-resolution, hydrodynamic cluster simulations to gauge its reliability. We find that this simple prescription does a good job of predicting the distribution of cluster X-ray ellipticities compared to the simulations as long as one focuses on cluster regions that are less sensitive to recent mergers. Based on this simple model, the distribution of cluster-size halo shapes expected in the concordance LCDM cosmology implies an X-ray ellipticity distribution with a mean of 0.32 +- 0.01 and a scatter of 0.14 +- 0.01 for the mass range (1-4)x10^{14} Msun/h. We find it important to include the mass dependence of halo shape to make comparisons to observational samples that contain many, very massive clusters. We analyse the systematics of four observational samples of cluster ellipticities and find that our results are statistically compatible with observations. In particular, we find remarkably good agreement between two recent ROSAT samples and LCDM predictions that DO NOT include gas cooling. We also test how well our analytical model can predict Sunyaev-Zel'dovich decrement maps and find that it is less successful although still useful; the model does not perform as well as a function of flux level in this case because of the changing triaxiality of dark matter haloes as a function of radial distance. Both this effect and the changing alignment of isodensity shells of dark matter haloes leave an imprint on cluster gas...Comment: 16 pages, 9 figures; corrected typo (no result affected) submitted to MNRA

ERβ-mediated induction of cystatins results in suppression of TGFβ signaling and inhibition of triple-negative breast cancer metastasis.

Triple-negative breast cancer (TNBC) accounts for a disproportionately high number of deaths due to a lack of targeted therapies and an increased likelihood of distant recurrence. Estrogen receptor beta (ERβ), a well-characterized tumor suppressor, is expressed in 30% of TNBCs, and its expression is associated with improved patient outcomes. We demonstrate that therapeutic activation of ERβ elicits potent anticancer effects in TNBC through the induction of a family of secreted proteins known as the cystatins, which function to inhibit canonical TGFβ signaling and suppress metastatic phenotypes both in vitro and in vivo. These data reveal the involvement of cystatins in suppressing breast cancer progression and highlight the value of ERβ-targeted therapies for the treatment of TNBC patients

Self-similar scaling and evolution in the galaxy cluster X-ray Luminosity-Temperature relation

We investigate the form and evolution of the X-ray luminosity-temperature (LT) relation of a sample of 114 galaxy clusters observed with Chandra at 0.1<z<1.3. The clusters were divided into subsamples based on their X-ray morphology or whether they host strong cool cores. We find that when the core regions are excluded, the most relaxed clusters (or those with the strongest cool cores) follow an LT relation with a slope that agrees well with simple self-similar expectations. This is supported by an analysis of the gas density profiles of the systems, which shows self-similar behaviour of the gas profiles of the relaxed clusters outside the core regions. By comparing our data with clusters in the REXCESS sample, which extends to lower masses, we find evidence that the self-similar behaviour of even the most relaxed clusters breaks at around 3.5keV. By contrast, the LT slopes of the subsamples of unrelaxed systems (or those without strong cool cores) are significantly steeper than the self-similar model, with lower mass systems appearing less luminous and higher mass systems appearing more luminous than the self-similar relation. We argue that these results are consistent with a model of non-gravitational energy input in clusters that combines central heating with entropy enhancements from merger shocks. Such enhancements could extend the impact of central energy input to larger radii in unrelaxed clusters, as suggested by our data. We also examine the evolution of the LT relation, and find that while the data appear inconsistent with simple self-similar evolution, the differences can be plausibly explained by selection bias, and thus we find no reason to rule out self-similar evolution. We show that the fraction of cool core clusters in our (non-representative) sample decreases at z>0.5 and discuss the effect of this on measurements of the evolution in the LT relation.Comment: 21 pages, 15 figures. Submitted to MNRAS. Comments welcom

Apoptosis Regulates ipRGC Spacing Necessary for Rods and Cones to Drive Circadian Photoentrainment

SummaryThe retina consists of ordered arrays of individual types of neurons for processing vision. Here, we show that such order is necessary for intrinsically photosensitive retinal ganglion cells (ipRGCs) to function as irradiance detectors. We found that during development, ipRGCs undergo proximity-dependent Bax-mediated apoptosis. Bax mutant mice exhibit disrupted ipRGC spacing and dendritic stratification with an increase in abnormally localized synapses. ipRGCs are the sole conduit for light input to circadian photoentrainment, and either their melanopsin-based photosensitivity or ability to relay rod/cone input is sufficient for circadian photoentrainment. Remarkably, the disrupted ipRGC spacing does not affect melanopsin-based circadian photoentrainment but severely impairs rod/cone-driven photoentrainment. We demonstrate reduced rod/cone-driven cFos activation and electrophysiological responses in ipRGCs, suggesting that impaired synaptic input to ipRGCs underlies the photoentrainment deficits. Thus, for irradiance detection, developmental apoptosis is necessary for the spacing and connectivity of ipRGCs that underlie their functioning within a neural network