An Unbiassed Census of Active Galactic Nuclei in the Two Micron All Sky Survey

(Abridged) We present an unbiassed near-IR selected AGN sample, covering 12.56 square degrees down to K ~ 15.5, selected from the Two Micron All Sky Survey (2MASS). Our only selection effect is a moderate color cut (J-K>1.2) designed to reduce contamination from galactic stars. We observed both point-like and extended sources. Using the brute-force capabilities of the 2dF multi-fiber spectrograph on the Anglo-Australian Telescope, we obtained spectra of 65% of the target list: an unbiassed sub-sample of 1526 sources. 80% of the 2MASS sources in our fields are galaxies, with a median redshift of 0.15. The remainder are K- and M-dwarf stars. Seyfert-2 Galaxies are roughly three times more common in this sample than in optically selected galaxy samples (once corrections have been made for the equivalent width limit and for different aperture sizes). We find 14 broad-line (Type-1) AGNs, giving a surface density down to K<15 comparable to that of optical samples down to B=18.5. Half of our Type-1 AGNs could not have been found by normal color selection techniques. In all cases this was due host galaxy light contamination rather than intrinsically red colors. We conclude that the Type-1 AGN population found in the near-IR is not dramatically different from that found in optical samples. There is no evidence for a large population of AGNs that could not be found at optical wavelengths, though we can only place very weak constraints on any population of dusty high-redshift QSOs.Comment: AJ in pres

An Unbiassed Census of Active Galactic Nuclei in the Two Micron All Sky Survey

(Abridged) We present an unbiassed near-IR selected AGN sample, covering 12.56 square degrees down to K ~ 15.5, selected from the Two Micron All Sky Survey (2MASS). Our only selection effect is a moderate color cut (J-K>1.2) designed to reduce contamination from galactic stars. We observed both point-like and extended sources. Using the brute-force capabilities of the 2dF multi-fiber spectrograph on the Anglo-Australian Telescope, we obtained spectra of 65% of the target list: an unbiassed sub-sample of 1526 sources. 80% of the 2MASS sources in our fields are galaxies, with a median redshift of 0.15. The remainder are K- and M-dwarf stars. Seyfert-2 Galaxies are roughly three times more common in this sample than in optically selected galaxy samples (once corrections have been made for the equivalent width limit and for different aperture sizes). We find 14 broad-line (Type-1) AGNs, giving a surface density down to K<15 comparable to that of optical samples down to B=18.5. Half of our Type-1 AGNs could not have been found by normal color selection techniques. In all cases this was due host galaxy light contamination rather than intrinsically red colors. We conclude that the Type-1 AGN population found in the near-IR is not dramatically different from that found in optical samples. There is no evidence for a large population of AGNs that could not be found at optical wavelengths, though we can only place very weak constraints on any population of dusty high-redshift QSOs.Comment: AJ in pres

The Southern 2MASS AGN Survey: spectroscopic follow-up with 6dF

The Two Micron All-Sky Survey (2MASS) has provided a uniform photometric catalog to search for previously unknown red AGN and QSOs. We have extended the search to the southern equatorial sky by obtaining spectra for 1182 AGN candidates using the 6dF multifibre spectrograph on the UK Schmidt Telescope. These were scheduled as auxiliary targets for the 6dF Galaxy Redshift Survey. The candidates were selected using a single color cut of J - Ks > 2 to Ks ~ 15.5 and a galactic latitude of |b|>30 deg. 432 spectra were of sufficient quality to enable a reliable classification. 116 sources (or ~27%) were securely classified as type 1 AGN, 20 as probable type 1s, and 57 as probable type 2 AGN. Most of them span the redshift range 0.05<z<0.5 and only 8 (or ~6%) were previously identified as AGN or QSOs. Our selection leads to a significantly higher AGN identification rate amongst local galaxies (>20%) than in any previous galaxy survey. A small fraction of the type 1 AGN could have their optical colors reddened by optically thin dust with A_V<2 mag relative to optically selected QSOs. A handful show evidence for excess far-IR emission. The equivalent width (EW) and color distributions of the type 1 and 2 AGN are consistent with AGN unified models. In particular, the EW of the [OIII] emission line weakly correlates with optical--near-IR color in each class of AGN, suggesting anisotropic obscuration of the AGN continuum. Overall, the optical properties of the 2MASS red AGN are not dramatically different from those of optically-selected QSOs. Our near-IR selection appears to detect the most near-IR luminous QSOs in the local universe to z~0.6 and provides incentive to extend the search to deeper near-IR surveys.Comment: 57 pages, 12 figures, 4 tables, to appear in vol.27/4 of Publications of the Astronomical Society of Australia (PASA

An HST/WFPC2 Snapshot Survey of 2MASS-Selected Red QSOs

Using simple infrared color selection, 2MASS has found a large number of red, previously unidentified, radio-quiet QSOs. Although missed by UV/optical surveys, the 2MASS QSOs have K-band luminosities that are comparable to "classical" QSOs. This suggests the possible discovery of a previously predicted large population of dust-obscured radio-quiet QSOs. We present the results of an imaging survey of 29 2MASS QSOs observed with WFPC2 onboard the Hubble Space Telescope. I-band images, which benefit from the relative faintness of the nuclei at optical wavelengths, are used to characterize the host galaxies, measure the nuclear contribution to the total observed I-band emission, and to survey the surrounding environments. The 2MASS QSOs are found to lie in galaxies with a variety of morphologies, luminosities, and dynamical states, not unlike those hosting radio-quiet PG QSOs. Our analysis suggests that the extraordinary red colors of the 2MASS QSOs are caused by extinction of an otherwise typical QSO spectrum due to dust near the nucleus.Comment: 23 pages including 9 figures and 7 tables, accepted for publication in ApJ, higher resolution HST images at: http://shapley.as.arizona.edu/~amarble/papers/twomq

Observing with the infrared array camera (IRAC) on the Spitzer Space Telescope

We describe the astronomical observation template (AOT) for the Infrared Array Camera (IRAC) on the Spitzer Space Telescope (formerly SIRTF, hereafter Spitzer). Commissioning of the AOTs was carried out in the first three months of the Spitzer mission. Strategies for observing fixed and moving targets are described, along with the performance of the AOT in flight. We also outline the operation of the IRAC data reduction pipeline at the Spitzer Science Center (SSC) and describe residual effects in the data due to electronic and optical anomalies in the instrument

Observing with the infrared array camera (IRAC) on the Spitzer Space Telescope

We describe the astronomical observation template (AOT) for the Infrared Array Camera (IRAC) on the Spitzer Space Telescope (formerly SIRTF, hereafter Spitzer). Commissioning of the AOTs was carried out in the first three months of the Spitzer mission. Strategies for observing fixed and moving targets are described, along with the performance of the AOT in flight. We also outline the operation of the IRAC data reduction pipeline at the Spitzer Science Center (SSC) and describe residual effects in the data due to electronic and optical anomalies in the instrument

Rationale and design of the Exercise Intensity Trial (EXCITE): A randomized trial comparing the effects of moderate versus moderate to high-intensity aerobic training in women with operable breast cancer

<p>Abstract</p> <p>Background</p> <p>The Exercise Intensity Trial (EXcITe) is a randomized trial to compare the efficacy of supervised moderate-intensity aerobic training to moderate to high-intensity aerobic training, relative to attention control, on aerobic capacity, physiologic mechanisms, patient-reported outcomes, and biomarkers in women with operable breast cancer following the completion of definitive adjuvant therapy.</p> <p>Methods/Design</p> <p>Using a single-center, randomized design, 174 postmenopausal women (58 patients/study arm) with histologically confirmed, operable breast cancer presenting to Duke University Medical Center (DUMC) will be enrolled in this trial following completion of primary therapy (including surgery, radiation therapy, and chemotherapy). After baseline assessments, eligible participants will be randomized to one of two supervised aerobic training interventions (moderate-intensity or moderate/high-intensity aerobic training) or an attention-control group (progressive stretching). The aerobic training interventions will include 150 mins.wk^-1of supervised treadmill walking per week at an intensity of 60%-70% (moderate-intensity) or 60% to 100% (moderate to high-intensity) of the individually determined peak oxygen consumption (VO_2peak) between 20-45 minutes/session for 16 weeks. The progressive stretching program will be consistent with the exercise interventions in terms of program length (16 weeks), social interaction (participants will receive one-on-one instruction), and duration (20-45 mins/session). The primary study endpoint is VO_2peak, as measured by an incremental cardiopulmonary exercise test. Secondary endpoints include physiologic determinants that govern VO_2peak, patient-reported outcomes, and biomarkers associated with breast cancer recurrence/mortality. All endpoints will be assessed at baseline and after the intervention (16 weeks).</p> <p>Discussion</p> <p>EXCITE is designed to investigate the intensity of aerobic training required to induce optimal improvements in VO_2peakand other pertinent outcomes in women who have completed definitive adjuvant therapy for operable breast cancer. Overall, this trial will inform and refine exercise guidelines to optimize recovery in breast and other cancer survivors following the completion of primary cytotoxic therapy.</p> <p>Trial Registration</p> <p>NCT01186367</p

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODErn), to generate cause fractions and cause specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NC Ds) comprised the greatest fraction of deaths, contributing to 73.4% (95% uncertainty interval [UI] 72.5-74.1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 186% (17.9-19.6), and injuries 8.0% (7.7-8.2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22.7% (21.5-23.9), representing an additional 7.61 million (7. 20-8.01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7.9% (7.08.8). The number of deaths for CMNN causes decreased by 222% (20.0-24.0) and the death rate by 31.8% (30.1-33.3). Total deaths from injuries increased by 2.3% (0-5-4-0) between 2007 and 2017, and the death rate from injuries decreased by 13.7% (12.2-15.1) to 57.9 deaths (55.9-59.2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118.0% (88.8-148.6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36.4% (32.2-40.6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33.6% (31.2-36.1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respirator}, infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990 neonatal disorders, lower respiratory infections, and diarrhoeal diseases were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade. Copyright (C) 2018 The Author(s). Published by Elsevier Ltd.Peer reviewe

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2\ub75th percentile and 100 as the 97\ub75th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59\ub74 (IQR 35\ub74–67\ub73), ranging from a low of 11\ub76 (95% uncertainty interval 9\ub76–14\ub70) to a high of 84\ub79 (83\ub71–86\ub77). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030