Displaying the Heterogeneity of the SN 2002cx-like Subclass of Type Ia Supernovae with Observations of the Pan-STARRS-1 Discovered SN2009ku

SN2009ku, discovered by Pan-STARRS-1, is a Type Ia supernova (SNIa), and a member of the distinct SN2002cx-like class of SNeIa. Its light curves are similar to the prototypical SN2002cx, but are slightly broader and have a later rise to maximum in g. SN2009ku is brighter (~0.6 mag) than other SN2002cx-like objects, peaking at M_V = -18.4 mag - which is still significantly fainter than typical SNeIa. SN2009ku, which had an ejecta velocity of ~2000 kms^-1 at 18 days after maximum brightness is spectroscopically most similar to SN2008ha, which also had extremely low-velocity ejecta. However, SN2008ha had an exceedingly low luminosity, peaking at M_V = -14.2 mag, ~4 mag fainter than SN2009ku. The contrast of high luminosity and low ejecta velocity for SN2009ku is contrary to an emerging trend seen for the SN2002cx class. SN2009ku is a counter-example of a previously held belief that the class was more homogeneous than typical SNeIa, indicating that the class has a diverse progenitor population and/or complicated explosion physics. As the first example of a member of this class of objects from the new generation of transient surveys, SN2009ku is an indication of the potential for these surveys to find rare and interesting objects.Comment: 7 pages, 3 figure

Selection of Burst-like Transients and Stochastic Variables Using Multi-band Image Differencing in the PAN-STARRS1 Medium-deep Survey

We present a novel method for the light-curve characterization of Pan-STARRS1 Medium Deep Survey (PS1 MDS) extragalactic sources into stochastic variables (SVs) and burst-like (BL) transients, using multi-band image-differencing time-series data. We select detections in difference images associated with galaxy hosts using a star/galaxy catalog extracted from the deep PS1 MDS stacked images, and adopt a maximum a posteriori formulation to model their difference-flux time-series in four Pan-STARRS1 photometric bands g P1, r P1, i P1, and z P1. We use three deterministic light-curve models to fit BL transients; a Gaussian, a Gamma distribution, and an analytic supernova (SN) model, and one stochastic light-curve model, the Ornstein-Uhlenbeck process, in order to fit variability that is characteristic of active galactic nuclei (AGNs). We assess the quality of fit of the models band-wise and source-wise, using their estimated leave-out-one cross-validation likelihoods and corrected Akaike information criteria. We then apply a K-means clustering algorithm on these statistics, to determine the source classification in each band. The final source classification is derived as a combination of the individual filter classifications, resulting in two measures of classification quality, from the averages across the photometric filters of (1) the classifications determined from the closest K-means cluster centers, and (2) the square distances from the clustering centers in the K-means clustering spaces. For a verification set of AGNs and SNe, we show that SV and BL occupy distinct regions in the plane constituted by these measures. We use our clustering method to characterize 4361 extragalactic image difference detected sources, in the first 2.5 yr of the PS1 MDS, into 1529 BL, and 2262 SV, with a purity of 95.00% for AGNs, and 90.97% for SN based on our verification sets. We combine our light-curve classifications with their nuclear or off-nuclear host galaxy offsets, to define a robust photometric sample of 1233 AGNs and 812 SNe. With these two samples, we characterize their variability and host galaxy properties, and identify simple photometric priors that would enable their real-time identification in future wide-field synoptic survey

Selecting superluminous supernovae in faint galaxies from the first year of the Pan-STARRS1 Medium Deep Survey

The Pan-STARRS1 (PS1) survey has obtained imaging in five bands (griz yP1) over 10 Medium Deep Survey (MDS) fields covering a total of 70 square degrees. This paper describes the search for apparently hostless supernovae (SNe) within the first year of PS1 MDS data with an aim of discovering superluminous supernovae (SLSNe). A total of 249 hostless transients were discovered down to a limiting magnitude of MAB ∼ 23.5, of which 76 were classified as Type Ia supernovae (SNe Ia). There were 57 SNe with complete light curves that are likely core-collapse SNe (CCSNe) or type Ic SLSNe and 12 of these have had spectra taken. Of these 12 hostless, non-Type Ia SNe, 7 were SLSNe of type Ic at redshifts between 0.5 and 1.4. This illustrates that the discovery rate of type Ic SLSNe can be maximized by concentrating on hostless transients and removing normal SNe Ia. We present data for two possible SLSNe; PS1-10pm (z = 1.206) and PS1-10ahf (z = 1.1), and estimate the rate of type Ic SLSNe to be between 3+3−2×10−5 and 8+2−1×10−5 that of the CCSN rate within 0.3 ≤ z ≤ 1.4 by applying a Monte Carlo technique. The rate of slowly evolving, type Ic SLSNe (such as SN2007bi) is estimated as a factor of 10 lower than this range

Cosmological Constraints from Measurements of Type Ia Supernovae Discovered during the First 1.5 yr of the Pan-STARRS1 Survey

We present griz P1 light curves of 146 spectroscopically confirmed Type Ia supernovae (SNe Ia; 0.03 < z < 0.65) discovered during the first 1.5 yr of the Pan-STARRS1 Medium Deep Survey. The Pan-STARRS1 natural photometric system is determined by a combination of on-site measurements of the instrument response function and observations of spectrophotometric standard stars. We find that the systematic uncertainties in the photometric system are currently 1.2% without accounting for the uncertainty in the Hubble Space Telescope Calspec definition of the AB system. A Hubble diagram is constructed with a subset of 113 out of 146 SNe Ia that pass our light curve quality cuts. The cosmological fit to 310 SNe Ia (113 PS1 SNe Ia + 222 light curves from 197 low-z SNe Ia), using only supernovae (SNe) and assuming a constant dark energy equation of state and flatness, yields $w=-1.120^{+0.360}_{-0.206}\hbox{(Stat)} ^{+0.269}_{-0.291}\hbox{(Sys)}$. When combined with BAO+CMB(Planck)+H 0, the analysis yields $\Omega _{\rm M}=0.280^{+0.013}_{-0.012}$ and $w=-1.166^{+0.072}_{-0.069}$ including all identified systematics. The value of w is inconsistent with the cosmological constant value of –1 at the 2.3σ level. Tension endures after removing either the baryon acoustic oscillation (BAO) or the H 0 constraint, though it is strongest when including the H 0 constraint. If we include WMAP9 cosmic microwave background (CMB) constraints instead of those from Planck, we find $w=-1.124^{+0.083}_{-0.065}$, which diminishes the discord to <2σ. We cannot conclude whether the tension with flat ΛCDM is a feature of dark energy, new physics, or a combination of chance and systematic errors. The full Pan-STARRS1 SN sample with ~three times as many SNe should provide more conclusive results

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo