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Understanding the association between autonomic nervous system [ANS] function and brain morphology across the lifespan provides important insights into neurovisceral mechanisms underlying health and disease. Resting-state ANS activity, indexed by measures of heart rate [HR] and its variability [HRV] has been associated with brain morphology, particularly cortical thickness [CT]. While findings have been mixed regarding the anatomical distribution and direction of the associations, these inconsistencies may be due to sex and age differences in HR/HRV and CT. Previous studies have been limited by small sample sizes, which impede the assessment of sex differences and aging effects on the association between ANS function and CT. To overcome these limitations, 20 groups worldwide contributed data collected under similar protocols of CT assessment and HR/HRV recording to be pooled in a mega-analysis (N = 1,218 (50.5% female), mean age 36.7 years (range: 12-87)). Findings suggest a decline in HRV as well as CT with increasing age. CT, particularly in the orbitofrontal cortex, explained additional variance in HRV, beyond the effects of aging. This pattern of results may suggest that the decline in HRV with increasing age is related to a decline in orbitofrontal CT. These effects were independent of sex and specific to HRV; with no significant association between CT and HR. Greater CT across the adult lifespan may be vital for the maintenance of healthy cardiac regulation via the ANS-or greater cardiac vagal activity as indirectly reflected in HRV may slow brain atrophy. Findings reveal an important association between CT and cardiac parasympathetic activity with implications for healthy aging and longevity that should be studied further in longitudinal research.
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.
Estimating global mean sea-level rise and its uncertainties by 2100 and 2300 from an expert survey
(2020)
Sea-level rise projections and knowledge of their uncertainties are vital to make informed mitigation and adaptation decisions. To elicit projections from members of the scientific community regarding future global mean sea-level (GMSL) rise, we repeated a survey originally conducted five years ago. Under Representative Concentration Pathway (RCP) 2.6, 106 experts projected a likely (central 66% probability) GMSL rise of 0.30-0.65 m by 2100, and 0.54-2.15 m by 2300, relative to 1986-2005. Under RCP 8.5, the same experts projected a likely GMSL rise of 0.63-1.32 m by 2100, and 1.67-5.61 m by 2300. Expert projections for 2100 are similar to those from the original survey, although the projection for 2300 has extended tails and is higher than the original survey. Experts give a likelihood of 42% (original survey) and 45% (current survey) that under the high-emissions scenario GMSL rise will exceed the upper bound (0.98 m) of the likely range estimated by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, which is considered to have an exceedance likelihood of 17%. Responses to open-ended questions suggest that the increases in upper-end estimates and uncertainties arose from recent influential studies about the impact of marine ice cliff instability on the meltwater contribution to GMSL rise from the Antarctic Ice Sheet.
Estimating global mean sea-level rise and its uncertainties by 2100 and 2300 from an expert survey
(2020)
Sea-level rise projections and knowledge of their uncertainties are vital to make informed mitigation and adaptation decisions. To elicit projections from members of the scientific community regarding future global mean sea-level (GMSL) rise, we repeated a survey originally conducted five years ago. Under Representative Concentration Pathway (RCP) 2.6, 106 experts projected a likely (central 66% probability) GMSL rise of 0.30-0.65 m by 2100, and 0.54-2.15 m by 2300, relative to 1986-2005. Under RCP 8.5, the same experts projected a likely GMSL rise of 0.63-1.32 m by 2100, and 1.67-5.61 m by 2300. Expert projections for 2100 are similar to those from the original survey, although the projection for 2300 has extended tails and is higher than the original survey. Experts give a likelihood of 42% (original survey) and 45% (current survey) that under the high-emissions scenario GMSL rise will exceed the upper bound (0.98 m) of the likely range estimated by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, which is considered to have an exceedance likelihood of 17%. Responses to open-ended questions suggest that the increases in upper-end estimates and uncertainties arose from recent influential studies about the impact of marine ice cliff instability on the meltwater contribution to GMSL rise from the Antarctic Ice Sheet.
River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth’s biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented “next-generation biomonitoring” by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.
A catalog of genetic loci associated with kidney function from analyses of a million individuals
(2019)
Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
VERITAS and Fermi-LAT Observations of TeV Gamma-Ray Sources Discovered by HAWC in the 2HWC Catalog
(2018)
The High Altitude Water Cherenkov (HAWC) collaboration recently published their 2HWC catalog, listing 39 very high energy (VHE; >100 GeV) gamma-ray sources based on 507 days of observation. Among these, 19 sources are not associated with previously known teraelectronvolt (TeV) gamma-ray sources. We have studied 14 of these sources without known counterparts with VERITAS and Fermi-LAT. VERITAS detected weak gamma-ray emission in the 1 TeV-30 TeV band in the region of DA 495, a pulsar wind nebula coinciding with 2HWC J1953+294, confirming the discovery of the source by HAWC. We did not find any counterpart for the selected 14 new HAWC sources from our analysis of Fermi-LAT data for energies higher than 10 GeV. During the search, we detected gigaelectronvolt (GeV) gamma-ray emission coincident with a known TeV pulsar wind nebula, SNR G54.1+0.3 (VER J1930+188), and a 2HWC source, 2HWC J1930+188. The fluxes for isolated, steady sources in the 2HWC catalog are generally in good agreement with those measured by imaging atmospheric Cherenkov telescopes. However, the VERITAS fluxes for SNR G54.1+0.3, DA 495, and TeV J2032+4130 are lower than those measured by HAWC, and several new HAWC sources are not detected by VERITAS. This is likely due to a change in spectral shape, source extension, or the influence of diffuse emission in the source region.
The "Lomonosov" space project is lead by Lomonosov Moscow State University in collaboration with the following key partners: Joint Institute for Nuclear Research, Russia, University of California, Los Angeles (USA), University of Pueblo (Mexico), Sungkyunkwan University (Republic of Korea) and with Russian space industry organi-zations to study some of extreme phenomena in space related to astrophysics, astroparticle physics, space physics, and space biology. The primary goals of this experiment are to study:
-Ultra-high energy cosmic rays (UHECR) in the energy range of the Greizen-ZatsepinKuzmin (GZK) cutoff;
-Ultraviolet (UV) transient luminous events in the upper atmosphere;
-Multi-wavelength study of gamma-ray bursts in visible, UV, gamma, and X-rays;
-Energetic trapped and precipitated radiation (electrons and protons) at low-Earth orbit (LEO) in connection with global geomagnetic disturbances;
-Multicomponent radiation doses along the orbit of spacecraft under different geomagnetic conditions and testing of space segments of optical observations of space-debris and other space objects;
-Instrumental vestibular-sensor conflict of zero-gravity phenomena during space flight.
This paper is directed towards the general description of both scientific goals of the project and scientific equipment on board the satellite. The following papers of this issue are devoted to detailed descriptions of scientific instruments.
The "Lomonosov" space project is lead by Lomonosov Moscow State University in collaboration with the following key partners: Joint Institute for Nuclear Research, Russia, University of California, Los Angeles (USA), University of Pueblo (Mexico), Sungkyunkwan University (Republic of Korea) and with Russian space industry organi-zations to study some of extreme phenomena in space related to astrophysics, astroparticle physics, space physics, and space biology. The primary goals of this experiment are to study: -Ultra-high energy cosmic rays (UHECR) in the energy range of the Greizen-ZatsepinKuzmin (GZK) cutoff; -Ultraviolet (UV) transient luminous events in the upper atmosphere; -Multi-wavelength study of gamma-ray bursts in visible, UV, gamma, and X-rays; -Energetic trapped and precipitated radiation (electrons and protons) at low-Earth orbit (LEO) in connection with global geomagnetic disturbances; -Multicomponent radiation doses along the orbit of spacecraft under different geomagnetic conditions and testing of space segments of optical observations of space-debris and other space objects; -Instrumental vestibular-sensor conflict of zero-gravity phenomena during space flight. This paper is directed towards the general description of both scientific goals of the project and scientific equipment on board the satellite. The following papers of this issue are devoted to detailed descriptions of scientific instruments.
Prospects for Cherenkov Telescope Array Observations of the Young Supernova Remnant RX J1713.7-3946
(2017)
We perform simulations for future Cherenkov Telescope Array (CTA) observations of RX J1713.7-3946, a young supernova remnant (SNR) and one of the brightest sources ever discovered in very high energy (VHE) gamma rays. Special attention is paid to exploring possible spatial (anti) correlations of gamma rays with emission at other wavelengths, in particular X-rays and CO/H I emission. We present a series of simulated images of RX J1713.7-3946 for CTA based on a set of observationally motivated models for the gamma-ray emission. In these models, VHE gamma rays produced by high-energy electrons are assumed to trace the nonthermal X-ray emission observed by XMM-Newton, whereas those originating from relativistic protons delineate the local gas distributions. The local atomic and molecular gas distributions are deduced by the NANTEN team from CO and H I observations. Our primary goal is to show how one can distinguish the emission mechanism(s) of the gamma rays (i.e., hadronic versus leptonic, or a mixture of the two) through information provided by their spatial distribution, spectra, and time variation. This work is the first attempt to quantitatively evaluate the capabilities of CTA to achieve various proposed scientific goals by observing this important cosmic particle accelerator.