SDSS-II SUPERNOVA SURVEY: AN ANALYSIS of the LARGEST SAMPLE of TYPE IA SUPERNOVAE and CORRELATIONS with HOST-GALAXY SPECTRAL PROPERTIES

This is the author accepted manuscript. The final version is available from the Institute of Physics via http://dx.doi.org/10.3847/0004-637X/821/2/115Using the largest single-survey sample of Type Ia supernovae (SNe Ia) to date, we study the relationship between properties of SNe Ia and those of their host galaxies, focusing primarily on correlations with Hubble residuals (HRs). Our sample consists of 345 photometrically classified or spectroscopically confirmed SNe Ia discovered as part of the SDSS-II Supernova Survey (SDSS-SNS). This analysis utilizes host-galaxy spectroscopy obtained during the SDSS-I/II spectroscopic survey and from an ancillary program on the SDSS-III Baryon Oscillation Spectroscopic Survey that obtained spectra for nearly all host galaxies of SDSS-II SN candidates. In addition, we use photometric host-galaxy properties from the SDSS-SNS data release such as host stellar mass and star formation rate. We confirm the well-known relation between HR and host-galaxy mass and find a 3.6σ significance of a nonzero linear slope. We also recover correlations between HR and host-galaxy gas-phase metallicity and specific star formation rate as they are reported in the literature. With our large data set, we examine correlations between HR and multiple host-galaxy properties simultaneously and find no evidence of a significant correlation. We also independently analyze our spectroscopically confirmed and photometrically classified SNe Ia and comment on the significance of similar combined data sets for future surveys.This material is based on work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1321851. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. M.S. and J.A.F. are supported by the Department of Energy grant DE-SC-0009890. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England ... Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation ..

HOST GALAXY IDENTIFICATION FOR SUPERNOVA SURVEYS

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope, which will discover SNe by the thousands. Spectroscopic resources are limited, and so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey

Imprint of DES super-structures on the Cosmic Microwave Background

Small temperature anisotropies in the Cosmic Microwave Background can be sourced by density perturbations via the late-time integrated Sachs-Wolfe effect. Large voids and superclusters are excellent environments to make a localized measurement of this tiny imprint. In some cases excess signals have been reported. We probed these claims with an independent data set, using the first year data of the Dark Energy Survey in a different footprint, and using a different super-structure finding strategy. We identified 52 large voids and 102 superclusters at redshifts $0.2 < z < 0.65$. We used the Jubilee simulation to a priori evaluate the optimal ISW measurement configuration for our compensated top-hat filtering technique, and then performed a stacking measurement of the CMB temperature field based on the DES data. For optimal configurations, we detected a cumulative cold imprint of voids with $\Delta T_{f} \approx -5.0\pm3.7~\mu K$ and a hot imprint of superclusters $\Delta T_{f} \approx 5.1\pm3.2~\mu K$ ; this is $\sim1.2\sigma$ higher than the expected $|\Delta T_{f}| \approx 0.6~\mu K$ imprint of such super-structures in $\Lambda$CDM. If we instead use an a posteriori selected filter size ($R/R_{v}=0.6$), we can find a temperature decrement as large as $\Delta T_{f} \approx -9.8\pm4.7~\mu K$ for voids, which is $\sim2\sigma$ above $\Lambda$CDM expectations and is comparable to previous measurements made using SDSS super-structure data

Differential Analysis of Ovarian and Endometrial Cancers Identifies a Methylator Phenotype

Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model

Background To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development

The Human TPR Protein TTC4 Is a Putative Hsp90 Co-Chaperone Which Interacts with CDC6 and Shows Alterations in Transformed Cells

BACKGROUND: The human TTC4 protein is a TPR (tetratricopeptide repeat) motif-containing protein. The gene was originally identified as being localized in a genomic region linked to breast cancer and subsequent studies on melanoma cell lines revealed point mutations in the TTC4 protein that may be associated with the progression of malignant melanoma. METHODOLOGY/PRINCIPLE FINDINGS: Here we show that TTC4 is a nucleoplasmic protein which interacts with HSP90 and HSP70, and also with the replication protein CDC6. It has significant structural and functional similarities with a previously characterised Drosophila protein Dpit47. We show that TTC4 protein levels are raised in malignant melanoma cell lines compared to melanocytes. We also see increased TTC4 expression in a variety of tumour lines derived from other tissues. In addition we show that TTC4 proteins bearing some of the mutations previously identified from patient samples lose their interaction with the CDC6 protein. CONCLUSIONS/SIGNIFICANCE: Based on these results and our previous work with the Drosophila Dpit47 protein we suggest that TTC4 is an HSP90 co-chaperone protein which forms a link between HSP90 chaperone activity and DNA replication. We further suggest that the loss of the interaction with CDC6 or with additional client proteins could provide one route through which TTC4 could influence malignant development of cells

Wirelessly controlled, bioresorbable drug delivery device with active valves that exploit electrochemically triggered crevice corrosion.

Implantable drug release platforms that offer wirelessly programmable control over pharmacokinetics have potential in advanced treatment protocols for hormone imbalances, malignant cancers, diabetic conditions, and others. We present a system with this type of functionality in which the constituent materials undergo complete bioresorption to eliminate device load from the patient after completing the final stage of the release process. Here, bioresorbable polyanhydride reservoirs store drugs in defined reservoirs without leakage until wirelessly triggered valve structures open to allow release. These valves operate through an electrochemical mechanism of geometrically accelerated corrosion induced by passage of electrical current from a wireless, bioresorbable power-harvesting unit. Evaluations in cell cultures demonstrate the efficacy of this technology for the treatment of cancerous tissues by release of the drug doxorubicin. Complete in vivo studies of platforms with multiple, independently controlled release events in live-animal models illustrate capabilities for control of blood glucose levels by timed delivery of insulin