A Systematic Review and Meta-Analysis of Short-Term Ambient Ozone Exposure and COPD Hospitalizations

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally and ozone exposure is

P3‐104: Gene‐Brain Structure Networking Analysis In Alzheimer’S Disease Using The Pipeline Environment

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152823/1/alzjjalz2019063132.pd

Soft Phenotyping for Sepsis via EHR Time-aware Soft Clustering

Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories identifying sepsis sub-phenotypes is crucial to advance our understanding of sepsis characterization, identifying targeted treatments and optimal timing of interventions, and improving prognostication. Prior studies have described different sub-phenotypes of sepsis with organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships between the sub-phenotypes for clustering procedures. We develop a time-aware soft clustering algorithm guided by clinical context to identify sepsis sub-phenotypes using data from the EHR. We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more precise information on the recovery trajectory which can be important to inform management decisions and sepsis prognosis

Revised Stellar Properties of Kepler Targets for the Quarter 1-16 Transit Detection Run

We present revised properties for 196,468 stars observed by the NASA Kepler Mission and used in the analysis of Quarter 1-16 (Q1-Q16) data to detect and characterize transiting exoplanets. The catalog is based on a compilation of literature values for atmospheric properties (temperature, surface gravity, and metallicity) derived from different observational techniques (photometry, spectroscopy, asteroseismology, and exoplanet transits), which were then homogeneously fitted to a grid of Dartmouth stellar isochrones. We use broadband photometry and asteroseismology to characterize 11,532 Kepler targets which were previously unclassified in the Kepler Input Catalog (KIC). We report the detection of oscillations in 2,762 of these targets, classifying them as giant stars and increasing the number of known oscillating giant stars observed by Kepler by ~20% to a total of ~15,500 stars. Typical uncertainties in derived radii and masses are ~40% and ~20%, respectively, for stars with photometric constraints only, and 5-15% and ~10% for stars based on spectroscopy and/or asteroseismology, although these uncertainties vary strongly with spectral type and luminosity class. A comparison with the Q1-Q12 catalog shows a systematic decrease in radii for M dwarfs, while radii for K dwarfs decrease or increase depending on the Q1-Q12 provenance (KIC or Yonsei-Yale isochrones). Radii of F-G dwarfs are on average unchanged, with the exception of newly identified giants. The Q1-Q16 star properties catalog is a first step towards an improved characterization of all Kepler targets to support planet occurrence studies.Comment: 20 pages, 14 figures, 5 tables; accepted for publication in ApJS; electronic versions of Tables 4 and 5 are available as ancillary files (see sidebar on the right), and an interactive version of Table 5 is available at the NASA Exoplanet Archive (http://exoplanetarchive.ipac.caltech.edu/

Plasma exosome microRNAs are indicative of breast cancer

Table containing the clinicopathological features of the patient-derived xenograft (PDX) mice used in this study. (DOCX 13 kb

For Whom the Bell Tolls: Psychopathological and Neurobiological Correlates of the DNA Methylation Index of Time-To-Death

Psychopathology is a risk factor for accelerated biological aging and early mortality. We examined associations between broad underlying dimensions of psychopathology (reflecting internalizing and externalizing psychiatric symptoms), PTSD, and age-adjusted GrimAge (“GrimAge residuals”), a DNA methylation biomarker of mortality risk relative to age. We also examined neurobiological correlates of GrimAge residuals, including neurocognitive functioning, blood-based biomarkers (of inflammation, neuropathology, metabolic disease), and cortical thickness. Data from two independent trauma-exposed military cohorts (n = 647 [62.9% male, Mage = 52], n = 434 [90% male, Mage = 32]) were evaluated using linear regression models to test associations between GrimAge residuals, psychopathology, and health correlates. Externalizing psychopathology significantly predicted GrimAge residuals in both cohorts (ps \u3c 0.028). PTSD predicted GrimAge residuals in the younger (p = 0.001) but not the older cohort. GrimAge residuals were associated with several neurobiological variables available in the younger cohort, including cognitive disinhibition (padj = 0.021), poorer memory recall (padj = 0.023), cardiometabolic pathology (padj \u3c 0.001), oxidative stress (padj = 0.003), astrocyte damage (padj = 0.021), inflammation (C-reactive protein: padj \u3c 0.001; IL-6: padj \u3c 0.001), and immune functioning (padj \u3c 0.001). A subset of inflammatory and neuropathology analytes were available in the older cohort and showed associations with GrimAge residuals (IL-6: padj \u3c 0.001; TNF-α: padj \u3c 0.001). GrimAge residuals were also associated with reduced cortical thickness in right lateral orbitofrontal cortex (padj = 0.018) and left fusiform gyrus (padj = 0.030), which are related to emotion regulation and facial recognition, respectively. Psychopathology may be a common risk factor for elevated mortality risk. GrimAge could help identify those at risk for adverse health outcomes and allow for early disease identification and treatment

G359.97-0.038: A Hard X-Ray Filament Associated with a Supernova Shell-Molecular Cloud Interaction

We present the first high-energy X-ray (>10 keV) observations of the non-thermal filament G359.97-0.038 using the Nuclear Spectroscopic Telescope Array (NuSTAR). This filament is one of approximately 20 X-ray filaments of unknown origin located in the central 20 pc region in the Galactic Center near Sgr A^*. Its NuSTAR and Chandra broadband spectrum is characterized by a single power law with Γ = 1.3 ± 0.3 that extends from 2 to 50 keV, with an unabsorbed luminosity of 1.3 × 10^(33) erg s^(–1) (d/8 kpc)^2 in the 2-8 keV band. Despite possessing a cometary X-ray morphology that is typical of a pulsar wind nebula (PWN) in high-resolution Chandra imaging, our spatially resolved Chandra spectral analysis found no significant spectral softening along the filament as would be expected from particle synchrotron cooling. Coincident radio emission is detected using the Very Large Array at 5.5 and 8.3 GHz. We examine and subsequently discard a PWN or magnetic flux tube as the origin of G359.97-0.038. We use broadband spectral characteristics and a morphological analysis to show that G359.97-0.038 is likely an interaction site between the shell of Sgr A East and an adjacent molecular cloud. This is supported by CS molecular line spectroscopy and the presence of an OH maser

The role of input noise in transcriptional regulation

Even under constant external conditions, the expression levels of genes fluctuate. Much emphasis has been placed on the components of this noise that are due to randomness in transcription and translation; here we analyze the role of noise associated with the inputs to transcriptional regulation, the random arrival and binding of transcription factors to their target sites along the genome. This noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, global models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally.Comment: 11 pages, 5 figures minor correction