Spitzer Survey of the Large Magellanic Cloud, Surveying the Agents of a Galaxy's Evolution (SAGE) I: Overview and Initial Results

We are performing a uniform and unbiased, ~7x7 degrees imaging survey of the Large Magellanic Cloud (LMC), using the IRAC and MIPS instruments on board the Spitzer Space Telescope in order to survey the agents of a galaxy's evolution (SAGE), the interstellar medium (ISM) and stars in the LMC. The detection of diffuse ISM with column densities >1.2x10^21 H cm^-2 permits detailed studies of dust processes in the ISM. SAGE's point source sensitivity enables a complete census of newly formed stars with masses >3 solar masses that will determine the current star formation rate in the LMC. SAGE's detection of evolved stars with mass loss rates >1x10^-8 solar masses per year will quantify the rate at which evolved stars inject mass into the ISM of the LMC. The observing strategy includes two epochs in 2005, separated by three months, that both mitigate instrumental artifacts and constrain source variability. The SAGE data are non-proprietary. The data processing includes IRAC and MIPS pipelines and a database for mining the point source catalogs, which will be released to the community in support of Spitzer proposal cycles 4 and 5. We present initial results on the epoch 1 data with a special focus on the N79 and N83 region. The SAGE epoch 1 point source catalog has ~4 million sources. The point source counts are highest for the IRAC 3.6 microns band and decrease dramatically towards longer wavelengths consistent with the fact that stars dominate the point source catalogs and that the dusty objects, e.g. young stellar objects and dusty evolved stars that detected at the longer wavelengths, are rare in comparison. We outline a strategy for identifying foreground MW stars, that may comprise as much as 18% of the source list, and background galaxies, that may comprise ~12% of the source list.Comment: Accepted by the Astronomical Journa

Spitzer survey of the Large Magellanic Cloud, surveying the agents of a galaxy's evolution (SAGE). IV. Dust properties in the interstellar medium

The goal of this paper is to present the results of a preliminary analysis of the extended infrared (IR) emission by dust in the interstellar medium (ISM) of the Large Magellanic Cloud (LMC). We combine Spitzer Surveying the Agents of Galaxy Evolution (SAGE) and Infrared Astronomical Satellite (IRAS) data and correlate the infrared emission with gas tracers of H I, CO, and Hα. We present a global analysis of the infrared emission as well as detailed modeling of the spectral energy distribution (SED) of a few selected regions. Extended emission by dust associated with the neutral, molecular, and diffuse ionized phases of the ISM is detected at all IR bands from 3.6 μm to 160 μm. The relative abundance of the various dust species appears quite similar to that in the Milky Way (MW) in all the regions we have modeled. We construct maps of the temperature of large dust grains. The temperature map shows variations in the range 12.1-34.7 K, with a systematic gradient from the inner to outer regions, tracing the general distribution of massive stars and individual H II regions as well as showing warmer dust in the stellar bar. This map is used to derive the far-infrared (FIR) optical depth of large dust grains. We find two main departures in the LMC with respect to expectations based on the MW: (1) excess mid-infrared (MIR) emission near 70 μm, referred to as the 70 μm excess, and (2) departures from linear correlation between the FIR optical depth and the gas column density, which we refer to as FIR excess emission. The 70 μm excess increases gradually from the MW to the LMC to the Small Magellanic Cloud (SMC), suggesting evolution with decreasing metallicity. The excess is associated with the neutral and diffuse ionized gas, with the strongest excess region located in a loop structure next to 30 Dor. We show that the 70 μm excess can be explained by a modification of the size distribution of very small grains with respect to that in the MW, and a corresponding mass increase of ≃13% of the total dust mass in selected regions. The most likely explanation is that the 70 μm excess is due to the production of large very small grains (VSG) through erosion of larger grains in the diffuse medium. This FIR excess could be due to intrinsic variations of the dust/gas ratio, which would then vary from 4.6 to 2.3 times lower than the MW values across the LMC, but X_(CO) values derived from the IR emission would then be about three times lower than those derived from the Virial analysis of the CO data. We also investigate the possibility that the FIR excess is associated with an additional gas component undetected in the available gas tracers. Assuming a constant dust abundance in all ISM phases, the additional gas component would have twice the known H I mass. We show that it is plausible that the FIR excess is due to cold atomic gas that is optically thick in the 21 cm line, while the contribution by a pure H_2 phase with no CO emission remains a possible explanation

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Opportunities for Examining Child Health Impacts of Early-Life Nutrition in the ECHO Program: Maternal and Child Dietary Intake Data from Pregnancy to Adolescence

Background Longitudinal measures of diet spanning pregnancy through adolescence are needed from a large, diverse sample to advance research on the effect of early-life nutrition on child health. The Environmental influences on Child Health Outcomes (ECHO) Program, which includes 69 cohorts, >33,000 pregnancies, and >31,000 children in its first 7-y cycle, provides such data, now publicly available. Objectives This study aimed to describe dietary intake data available in the ECHO Program as of 31 August, 2022 (end of year 6 of Cycle 1) from pregnancy through adolescence, including estimated sample sizes, and to highlight the potential for future analyses of nutrition and child health. Methods We identified and categorized ECHO Program dietary intake data, by assessment method, participant (pregnant person or child), and life stage of data collection. We calculated the number of maternal-child dyads with dietary data and the number of participants with repeated measures. We identified diet-related variables derived from raw dietary intake data and nutrient biomarkers measured from biospecimens. Results Overall, 66 cohorts (26,941 pregnancies, 27,103 children, including 22,712 dyads) across 34 US states/territories provided dietary intake data. Dietary intake assessments included 24-h recalls (1548 pregnancies and 1457 children), food frequency questionnaires (4902 and 4117), dietary screeners (8816 and 23,626), and dietary supplement use questionnaires (24,798 and 26,513). Repeated measures were available for ∼70%, ∼30%, and ∼15% of participants with 24-h recalls, food frequency questionnaires, and dietary screeners, respectively. The available diet-related variables describe nutrient and food intake, diet patterns, and breastfeeding practices. Overall, 17% of participants with dietary intake data had measured nutrient biomarkers. Conclusions ECHO cohorts have collected longitudinal dietary intake data spanning pregnancy through adolescence from a geographically, socioeconomically, and ethnically diverse US sample. As data collection continues in Cycle 2, these data present an opportunity to advance the field of nutrition and child health

Breaking the photoswitch speed limit

The forthcoming generation of materials, including artificial muscles, recyclable and healable systems, photochromic heterogeneous catalysts, or tailorable supercapacitors, relies on the fundamental concept of rapid switching between two or more discrete forms in the solid state. Herein, we report a breakthrough in the “speed limit” of photochromic molecules on the example of sterically-demanding spiropyran derivatives through their integration within solvent-free confined space, allowing for engineering of the photoresponsive moiety environment and tailoring their photoisomerization rates. The presented conceptual approach realized through construction of the spiropyran environment results in ~1000 times switching enhancement even in the solid state compared to its behavior in solution, setting a record in the field of photochromic compounds. Moreover, integration of two distinct photochromic moieties in the same framework provided access to a dynamic range of rates as well as complementary switching in the material’s optical profile, uncovering a previously inaccessible pathway for interstate rapid photoisomerization.</p

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment