Do the photometric colors of Type II-P Supernovae allow accurate determination of host galaxy extinction?

We present infrared photometry of SN 1999em, plus optical photometry, infrared photometry, and optical spectroscopy of SN 2003hn. Both objects were Type II-P supernovae. The V-[RIJHK] color curves of these supernovae evolved in a very similar fashion until the end of plateau phase. This allows us to determine how much more extinction the light of SN 2003hn suffered compared to SN 1999em. Since we have an estimate of the total extinction suffered by SN 1999em from model fits of ground-based and space-based spectra as well as photometry of SN 1999em, we can estimate the total extinction and absolute magnitudes of SN 2003hn with reasonable accuracy. Since the host galaxy of SN 2003hn also produced the Type Ia SN 2001el, we can directly compare the absolute magnitudes of these two SNe of different types.Comment: 24 pages, 6 figure

The fast declining Type Ia supernova 2003gs, and evidence for a significant dispersion in near-infrared absolute magnitudes of fast decliners at maximum light

We obtained optical photometry of SN 2003gs on 49 nights, from 2 to 494 days after T(B_max). We also obtained near-IR photometry on 21 nights. SN 2003gs was the first fast declining Type Ia SN that has been well observed since SN 1999by. While it was subluminous in optical bands compared to more slowly declining Type Ia SNe, it was not subluminous at maximum light in the near-IR bands. There appears to be a bimodal distribution in the near-IR absolute magnitudes of Type Ia SNe at maximum light. Those that peak in the near-IR after T(B_max) are subluminous in the all bands. Those that peak in the near-IR prior to T(B_max), such as SN 2003gs, have effectively the same near-IR absolute magnitudes at maximum light regardless of the decline rate Delta m_15(B). Near-IR spectral evidence suggests that opacities in the outer layers of SN 2003gs are reduced much earlier than for normal Type Ia SNe. That may allow gamma rays that power the luminosity to escape more rapidly and accelerate the decline rate. This conclusion is consistent with the photometric behavior of SN 2003gs in the IR, which indicates a faster than normal decline from approximately normal peak brightness.Comment: 41 pages, 13 figures, to be published in the December, 2009, issue of the Astronomical Journa

The Atacama Cosmology Telescope: Sunyaev Zel'dovich Selected Galaxy Clusters at 148 GHz in the 2008 Survey

We report on twenty-three clusters detected blindly as Sunyaev-Zel'dovich (SZ) decrements in a 148 GHz, 455 square-degree map of the southern sky made with data from the Atacama Cosmology Telescope 2008 observing season. All SZ detections announced in this work have confirmed optical counterparts. Ten of the clusters are new discoveries. One newly discovered cluster, ACT-CL J0102-4915, with a redshift of 0.75 (photometric), has an SZ decrement comparable to the most massive systems at lower redshifts. Simulations of the cluster recovery method reproduce the sample purity measured by optical follow-up. In particular, for clusters detected with a signal-to-noise ratio greater than six, simulations are consistent with optical follow-up that demonstrated this subsample is 100% pure. The simulations further imply that the total sample is 80% complete for clusters with mass in excess of 6x10^14 solar masses referenced to the cluster volume characterized by five hundred times the critical density. The Compton y -- X-ray luminosity mass comparison for the eleven best detected clusters visually agrees with both self-similar and non-adiabatic, simulation-derived scaling laws.Comment: 13 pages, 7 figures, Accepted for publication in Ap

The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected via the Sunyaev-Zel'dovich Effect

We present constraints on cosmological parameters based on a sample of Sunyaev-Zel'dovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148 GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives sigma_8 = 0.851 +/- 0.115 and w = -1.14 +/- 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find sigma_8 = 0.821 +/- 0.044 and w = -1.05 +/- 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernoava which give sigma_8 = 0.802 +/- 0.038 and w = -0.98 +/- 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.Comment: 12 pages, 7 figures. Submitted to Ap

The Atacama Cosmology Telescope: a measurement of the primordial power spectrum

We present constraints on the primordial power spectrum of adiabatic fluctuations using data from the 2008 Southern Survey of the Atacama Cosmology Telescope (ACT). The angular resolution of ACT provides sensitivity to scales beyond \ell = 1000 for resolution of multiple peaks in the primordial temperature power spectrum, which enables us to probe the primordial power spectrum of adiabatic scalar perturbations with wavenumbers up to k \simeq 0.2 Mpc^{-1}. We find no evidence for deviation from power-law fluctuations over two decades in scale. Matter fluctuations inferred from the primordial temperature power spectrum evolve over cosmic time and can be used to predict the matter power spectrum at late times; we illustrate the overlap of the matter power inferred from CMB measurements (which probe the power spectrum in the linear regime) with existing probes of galaxy clustering, cluster abundances and weak lensing constraints on the primordial power. This highlights the range of scales probed by current measurements of the matter power spectrum.Comment: 10 pages, 5 figures, submitted to Ap

A CREDENCE Trial Substudy

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).proofepub_ahead_of_prin

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Publisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.publishersversionpublishe

Vaccine effectiveness against symptomatic SARS-CoV-2 infection in adults aged 65 years and older in primary care: I-MOVE-COVID-19 project, Europe, December 2020 to May 2021

I-MOVE-COVID-19 primary care study team (in addition to authors above): Nick Andrews, Jamie Lopez Bernal, Heather Whitaker, Caroline Guerrisi, Titouan Launay, Shirley Masse, Sylvie van der Werf, Vincent Enouf, John Cuddihy, Adele McKenna, Michael Joyce, Cillian de Gascun, Joanne Moran, Ana Miqueleiz, Ana Navascués, Camino Trobajo-Sanmartín, Carmen Ezpeleta, Paula López Moreno, Javier Gorricho, Eva Ardanaz, Fernando Baigorria, Aurelio Barricarte, Enrique de la Cruz, Nerea Egüés, Manuel García Cenoz, Marcela Guevara, Conchi Moreno-Iribas, Carmen Sayón, Verónica Gomez, Baltazar Nunes, Rita Roquete, Adriana Silva, Aryse Melo, Inês Costa, Nuno Verdasca, Patrícia Conde, Diogo FP Marques, Anna Molesworth, Leanne Quinn, Miranda Leyton, Selin Campbell, Janine Thoulass, Jim McMenamin, Ana Martínez Mateo, Luca Basile, Daniel Castrillejo, Carmen Quiñones Rubio, Concepción Delgado-Sanz, Jesús Oliva.The I-MOVE-COVID-19 network collates epidemiological and clinical information on patients with coronavirus disease (COVID-19), including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virological characterisation in 11 European countries [1]. One component of I-MOVE-COVID-19 is the multicentre vaccine effectiveness (VE) study at primary care/outpatient level in nine European study sites in eight countries. We measured overall and product-specific COVID-19 VE against symptomatic SARS-CoV-2 infection among those aged 65 years and older. We also measured VE by time since vaccination.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003673.info:eu-repo/semantics/publishedVersio

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Objectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm\u27s diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had \u3c50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (\u3c350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, \u3e400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient\u27s BMI or heart rate at time of scan affect the software\u27s diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters

Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. RESULTS: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. CONCLUSIONS: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies