Nonrandom Territory Occupancy by Nesting Gyrfalcons (\u3ci\u3eFalco rusticolus\u3c/i\u3e)

We know little regarding how specific aspects of habitat influence spatial variation in site occupancy by Arctic wildlife, yet this information is fundamental to effective conservation. To address this information gap, we assessed occupancy of 84 Gyrfalcon (Falco rusticolus Linnaeus, 1758) breeding territories observed annually between 2004 and 2013 in western Alaska. In line with the theory of population regulation by site dependence, we asked whether Gyrfalcons exhibited a nonrandom pattern of site selection and if heterogeneous landscape attributes correlated with observed occupancy patterns. We characterized high- and low-occupancy breeding territories as those occupied more or less often than expected by chance, and we evaluated land cover at 1 and 15 km circles centered around nesting territories to identify habitat variables associated with observed occupancy patterns. We tested 15 competing models to rank hypotheses reflecting prey and habitat variables important to nesting Gyrfalcons. We confirmed a nonrandom pattern of site selection but found only weak evidence that the distribution of prey habitat was responsible for this pattern. We reason that preferential habitat use by nesting Gyrfalcons may be determined by spatial scales other than those we measured or may be driven by landscape-level attributes at time periods other than during the brood rearing period

Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model

We use an ensemble of surface (EPA CSN, IMPROVE, SEARCH, AERONET), aircraft (SEAC4RS), and satellite (MODIS, MISR) observations over the southeast US during the summer–fall of 2013 to better understand aerosol sources in the region and the relationship between surface particulate matter (PM) and aerosol optical depth (AOD). The GEOS-Chem global chemical transport model (CTM) with 25 × 25 km2 resolution over North America is used as a common platform to interpret measurements of different aerosol variables made at different times and locations. Sulfate and organic aerosol (OA) are the main contributors to surface PM2.5 (mass concentration of PM finer than 2.5 μm aerodynamic diameter) and AOD over the southeast US. OA is simulated successfully with a simple parameterization, assuming irreversible uptake of low-volatility products of hydrocarbon oxidation. Biogenic isoprene and monoterpenes account for 60 % of OA, anthropogenic sources for 30 %, and open fires for 10 %. 60 % of total aerosol mass is in the mixed layer below 1.5 km, 25 % in the cloud convective layer at 1.5–3 km, and 15 % in the free troposphere above 3 km. This vertical profile is well captured by GEOS-Chem, arguing against a high-altitude source of OA. The extent of sulfate neutralization (f = [NH4+]/(2[SO42−] + [NO3−]) is only 0.5–0.7 mol mol−1 in the observations, despite an excess of ammonia present, which could reflect suppression of ammonia uptake by OA. This would explain the long-term decline of ammonium aerosol in the southeast US, paralleling that of sulfate. The vertical profile of aerosol extinction over the southeast US follows closely that of aerosol mass. GEOS-Chem reproduces observed total column aerosol mass over the southeast US within 6 %, column aerosol extinction within 16 %, and space-based AOD within 8–28 % (consistently biased low). The large AOD decline observed from summer to winter is driven by sharp declines in both sulfate and OA from August to October. These declines are due to shutdowns in both biogenic emissions and UV-driven photochemistry. Surface PM2.5 shows far less summer-to-winter decrease than AOD and we attribute this in part to the offsetting effect of weaker boundary layer ventilation. The SEAC4RS aircraft data demonstrate that AODs measured from space are consistent with surface PM2.5. This implies that satellites can be used reliably to infer surface PM2.5 over monthly timescales if a good CTM representation of the aerosol vertical profile is available

Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): Insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations

Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM2.5) air quality, but this requires better understanding of the AOD–PM2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM2.5 composition, PM10, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions

The anti-inflammatory cytokine interleukin-37 is an inhibitor of trained immunity.

Summary Trained immunity (TI) is a de facto innate immune memory program induced in monocytes/macrophages by exposure to pathogens or vaccines, which evolved as protection against infections. TI is characterized by immunometabolic changes and histone post-translational modifications, which enhance production of pro-inflammatory cytokines. As aberrant activation of TI is implicated in inflammatory diseases, tight regulation is critical; however, the mechanisms responsible for this modulation remain elusive. Interleukin-37 (IL-37) is an anti-inflammatory cytokine that curbs inflammation and modulates metabolic pathways. In this study, we show that administration of recombinant IL-37 abrogates the protective effects of TI in vivo, as revealed by reduced host pro-inflammatory responses and survival to disseminated candidiasis. Mechanistically, IL-37 reverses the immunometabolic changes and histone post-translational modifications characteristic of TI in monocytes, thus suppressing cytokine production in response to infection. IL-37 thereby emerges as an inhibitor of TI and as a potential therapeutic target in immune-mediated pathologies

Prediction of survival among patients receiving transarterial chemoembolization for hepatocellular carcinoma: A response-based approach

Background and aims: The heterogeneity of intermediate-stage hepatocellular carcinoma (HCC) and the widespread use of transarterial chemoembolization (TACE) outside recommended guidelines have encouraged the development of scoring systems that predict patient survival. The aim of this study was to build and validate statistical models that offer individualized patient survival prediction using response to TACE as a variable. Approach and results: Clinically relevant baseline parameters were collected for 4,621 patients with HCC treated with TACE at 19 centers in 11 countries. In some of the centers, radiological responses (as assessed by modified Response Evaluation Criteria in Solid Tumors [mRECIST]) were also accrued. The data set was divided into a training set, an internal validation set, and two external validation sets. A pre-TACE model ("Pre-TACE-Predict") and a post-TACE model ("Post-TACE-Predict") that included response were built. The performance of the models in predicting overall survival (OS) was compared with existing ones. The median OS was 19.9 months. The factors influencing survival were tumor number and size, alpha-fetoprotein, albumin, bilirubin, vascular invasion, cause, and response as assessed by mRECIST. The proposed models showed superior predictive accuracy compared with existing models (the hepatoma arterial embolization prognostic score and its various modifications) and allowed for patient stratification into four distinct risk categories whose median OS ranged from 7 months to more than 4 years. Conclusions: A TACE-specific and extensively validated model based on routinely available clinical features and response after first TACE permitted patient-level prognosticatio

OMICmAge : an integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe