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Introducing the CTA concept
(2013)
The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project.
Ground-based gamma-ray astronomy has had a major breakthrough with the impressive results obtained using systems of imaging atmospheric Cherenkov telescopes. Ground-based gamma-ray astronomy has a huge potential in astrophysics, particle physics and cosmology. CTA is an international initiative to build the next generation instrument, with a factor of 5-10 improvement in sensitivity in the 100 GeV-10 TeV range and the extension to energies well below 100 GeV and above 100 TeV. CTA will consist of two arrays (one in the north, one in the south) for full sky coverage and will be operated as open observatory. The design of CTA is based on currently available technology. This document reports on the status and presents the major design concepts of CTA.
Background:
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.
Objective:
The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.
Methods:
We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.
Results:
Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.
Conclusions:
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.
Context. The TESS satellite was launched in 2018 to perform high-precision photometry from space over almost the whole sky in a search for exoplanets orbiting bright stars. This instrument has opened new opportunities to study variable hot subdwarfs, white dwarfs, and related compact objects. Targets of interest include white dwarf and hot subdwarf pulsators, both carrying high potential for asteroseismology. Aims. We present the discovery and detailed asteroseismic analysis of a new g-mode hot B subdwarf (sdB) pulsator, EC 21494-7018 (TIC 278659026), monitored in TESS first sector using 120-s cadence. Methods. The TESS light curve was analyzed with standard prewhitening techniques, followed by forward modeling using our latest generation of sdB models developed for asteroseismic investigations. By simultaneously best-matching all the observed frequencies with those computed from models, we identified the pulsation modes detected and, more importantly, we determined the global parameters and structural configuration of the star. Results. The light curve analysis reveals that EC 21494-7018 is a sdB pulsator counting up to 20 frequencies associated with independent g-modes. The seismic analysis singles out an optimal model solution in full agreement with independent measurements provided by spectroscopy (atmospheric parameters derived from model atmospheres) and astrometry (distance evaluated from Gaia DR2 trigonometric parallax). Several key parameters of the star are derived. Its mass (0.391 +/- 0.009x2006;M-circle dot) is significantly lower than the typical mass of sdB stars and suggests that its progenitor has not undergone the He-core flash; therefore this progenitor could originate from a massive (greater than or similar to 2;M-circle dot) red giant, which is an alternative channel for the formation of sdBs. Other derived parameters include the H-rich envelope mass (0.0037 +/- 0.0010;M-circle dot), radius (0.1694 +/- 0.0081;R-circle dot), and luminosity (8.2 +/- 1.1;L-circle dot). The optimal model fit has a double-layered He+H composition profile, which we interpret as an incomplete but ongoing process of gravitational settling of helium at the bottom of a thick H-rich envelope. Moreover, the derived properties of the core indicate that EC 21494-7018 has burnt similar to 43% (in mass) of its central helium and possesses a relatively large mixed core (M-core;=;0.198 +/- 0.010;M-circle dot), in line with trends already uncovered from other g-mode sdB pulsators analyzed with asteroseismology. Finally, we obtain for the first time an estimate of the amount of oxygen (in mass; X(O)(core) = 0.16(-0.05)(+0.13)X(O)core=0.16-0.05+0.13$ X(mathrm{O})_{mathrm{core}}=0.16_{-0.05}<^>{+0.13} $) produced at this stage of evolution by an helium-burning core. This result, along with the core-size estimate, is an interesting constraint that may help to narrow down the still uncertain C-12(alpha,;gamma)O-16 nuclear reaction rate.
We present an accurate analysis of the H-2 absorption lines from the z(abs) similar to 2.4018 damped Ly alpha system towards HE 0027-1836 observed with the Very Large Telescope Ultraviolet and Visual Echelle Spectrograph (VLT/UVES) as a part of the European Southern Observatory Large Programme 'The UVES large programme for testing fundamental physics' to constrain the variation of proton-to-electron mass ratio, mu m(p)/m(e). We perform cross-correlation analysis between 19 individual exposures taken over three years and the combined spectrum to check the wavelength calibration stability. We notice the presence of a possible wavelength-dependent velocity drift especially in the data taken in 2012. We use available asteroids spectra taken with UVES close to our observations to confirm and quantify this effect. We consider single-and two-component Voigt profiles to model the observed H-2 absorption profiles. We use both linear regression analysis and Voigt profile fitting where Delta mu/mu is explicitly considered as an additional fitting parameter. The two-component model is marginally favoured by the statistical indicators and we get Delta mu/mu = -2.5 +/- 8.1(stat) +/- 6.2(sys) ppm. When we apply the correction to the wavelength-dependent velocity drift, we find Delta mu/mu = -7.6 +/- 8.1(stat) +/- 6.3(sys) ppm. It will be important to check the extent to which the velocity drift we notice in this study is present in UVES data used for previous Delta mu/mu measurements.
Context. Absorption-line systems detected in quasar spectra can be used to compare the value of the fine-structure constant, alpha, measured today on Earth with its value in distant galaxies. In recent years, some evidence has emerged of small temporal and also spatial variations in alpha on cosmological scales. These variations may reach a fractional level of approximate to 10 ppm (parts per million).
Aims. To test these claims we are conducting a Large Program of observations with the Very Large Telescope's Ultraviolet and Visual Echelle Spectrograph (UVES), and are obtaining high-resolution (R approximate to 60 000) and high signal-to-noise ratio (S/N approximate to 100) UVES spectra calibrated specifically for this purpose. Here we analyse the first complete quasar spectrum from this programme, that of HE 2217-2818.
Methods. We applied the many multiplet method to measure alpha in five absorption systems towards this quasar: z(abs) = 0.7866, 0.9424, 1.5558, 1.6279, and 1.6919.
Results. The most precise result is obtained for the absorber at z(abs) = 1.6919 where 3 Fe II transitions and Al II lambda 1670 have high S/N and provide a wide range of sensitivities to alpha. The absorption profile is complex with several very narrow features, and it requires 32 velocity components to be fitted to the data. We also conducted a range of tests to estimate the systematic error budget. Our final result for the relative variation in alpha in this system is Delta alpha/alpha = +1.3 +/- 2.4(stat) +/- 1.0(sys) ppm. This is one of the tightest current bounds on alpha-variation from an individual absorber. A second, separate approach to the data reduction, calibration, and analysis of this system yielded a slightly different result of -3.8 +/- 2.1(stat) ppm, possibly suggesting a larger systematic error component than our tests indicated. This approach used an additional 3 Fe II transitions, parts of which were masked due to contamination by telluric features. Restricting this analysis to the Fe II transitions alone and using a modified absorption profile model gave a result that is consistent with the first approach, Delta alpha/alpha = +1.1 +/- 2.6(stat) ppm. The four other absorbers have simpler absorption profiles, with fewer and broader features, and offer transitions with a narrower range of sensitivities to alpha. They therefore provide looser bounds on Delta alpha/alpha at the greater than or similar to 10 ppm precision level.
Conclusions. The absorbers towards quasar HE 2217-2818 reveal no evidence of any variation in alpha at the 3-ppm precision level (1 sigma confidence). If the recently reported 10-ppm dipolar variation in alpha across the sky is correct, the expectation at this sky position is (3.2-5.4) +/- 1.7 ppm depending on dipole model used. Our constraint of Delta alpha/alpha = +1.3 +/- 2.4(stat) +/- 1.0(sys) ppm is not inconsistent with this expectation.
Metabolic signatures of healthy lifestyle patterns and colorectal cancer risk in a European cohort
(2020)
BACKGROUND & AIMS: Colorectal cancer risk can be lowered by adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines. We derived metabolic signatures of adherence to these guidelines and tested their associations with colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort.
METHODS: Scores reflecting adherence to the WCRF/AICR recommendations (scale, 1-5) were calculated from participant data on weight maintenance, physical activity, diet, and alcohol among a discovery set of 5738 cancer-free European Prospective Investigation into Cancer and Nutrition participants with metabolomics data. Partial least-squares regression was used to derive fatty acid and endogenous metabolite signatures of the WCRF/AICR score in this group. In an independent set of 1608 colorectal cancer cases and matched controls, odds ratios (ORs) and 95% CIs were calculated for colorectal cancer risk per unit increase in WCRF/AICR score and per the corresponding change in metabolic signatures using multivariable conditional logistic regression.
RESULTS: Higher WCRF/AICR scores were characterized by metabolic signatures of increased odd-chain fatty acids, serine, glycine, and specific phosphatidylcholines. Signatures were inversely associated more strongly with colorectal cancer risk (fatty acids: OR, 0.51 per unit increase; 95% CI, 0.29-0.90; endogenous metabolites: OR, 0.62 per unit change; 95% CI, 0.50-0.78) than the WCRF/AICR score (OR, 0.93 per unit change; 95% CI, 0.86-1.00) overall. Signature associations were stronger in male compared with female participants.
CONCLUSIONS: Metabolite profiles reflecting adherence to WCRF/AICR guidelines and additional lifestyle or biological risk factors were associated with colorectal cancer. Measuring a specific panel of metabolites representative of a healthy or unhealthy lifestyle may identify strata of the population at higher risk of colorectal cancer.