3,153 research outputs found

    Modules for Experiments in Stellar Astrophysics (MESA): Giant Planets, Oscillations, Rotation, and Massive Stars

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    We substantially update the capabilities of the open source software package Modules for Experiments in Stellar Astrophysics (MESA), and its one-dimensional stellar evolution module, MESA Star. Improvements in MESA Star's ability to model the evolution of giant planets now extends its applicability down to masses as low as one-tenth that of Jupiter. The dramatic improvement in asteroseismology enabled by the space-based Kepler and CoRoT missions motivates our full coupling of the ADIPLS adiabatic pulsation code with MESA Star. This also motivates a numerical recasting of the Ledoux criterion that is more easily implemented when many nuclei are present at non-negligible abundances. This impacts the way in which MESA Star calculates semi-convective and thermohaline mixing. We exhibit the evolution of 3-8 Msun stars through the end of core He burning, the onset of He thermal pulses, and arrival on the white dwarf cooling sequence. We implement diffusion of angular momentum and chemical abundances that enable calculations of rotating-star models, which we compare thoroughly with earlier work. We introduce a new treatment of radiation-dominated envelopes that allows the uninterrupted evolution of massive stars to core collapse. This enables the generation of new sets of supernovae, long gamma-ray burst, and pair-instability progenitor models. We substantially modify the way in which MESA Star solves the fully coupled stellar structure and composition equations, and we show how this has improved MESA's performance scaling on multi-core processors. Updates to the modules for equation of state, opacity, nuclear reaction rates, and atmospheric boundary conditions are also provided. We describe the MESA Software Development Kit (SDK) that packages all the required components needed to form a unified and maintained build environment for MESA. [Abridged]Comment: Accepted for publication in The ApJ Supplement Series. Extra informations required to reproduce the calculations in this paper are available at http://mesastar.org/results/mesa

    Loss of the insulin receptor in murine megakaryocytes/platelets causes thrombocytosis and alterations in IGF signalling.

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    AIMS: Patients with conditions that are associated with insulin resistance such as obesity, type 2 diabetes mellitus, and polycystic ovary syndrome have an increased risk of thrombosis and a concurrent hyperactive platelet phenotype. Our aim was to determine whether insulin resistance of megakaryocytes/platelets promotes platelet hyperactivation. METHODS AND RESULTS: We generated a conditional mouse model where the insulin receptor (IR) was specifically knocked out in megakaryocytes/platelets and performed ex vivo platelet activation studies in wild-type (WT) and IR-deficient platelets by measuring aggregation, integrin αIIbβ3 activation, and dense and α-granule secretion. Deletion of IR resulted in an increase in platelet count and volume, and blocked the action of insulin on platelet signalling and function. Platelet aggregation, granule secretion, and integrin αIIbβ3 activation in response to the glycoprotein VI (GPVI) agonist collagen-related peptide (CRP) were significantly reduced in platelets lacking IR. This was accompanied by a reduction in the phosphorylation of effectors downstream of GPVI. Interestingly, loss of IR also resulted in a reduction in insulin-like growth factor-1 (IGF-1)- and insulin-like growth factor-2 (IGF-2)-mediated phosphorylation of IRS-1, Akt, and GSK3β and priming of CRP-mediated platelet activation. Pharmacological inhibition of IR and the IGF-1 receptor in WT platelets recapitulated the platelet phenotype of IR-deficient platelets. CONCLUSIONS: Deletion of IR (i) increases platelet count and volume, (ii) does not cause platelet hyperactivity, and (iii) reduces GPVI-mediated platelet function and platelet priming by IGF-1 and IGF-2

    Electronic monitoring for improved accountability in western Pacific tuna longline fisheries

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    The collection of accurate fisheries catch data is critical to ensuring sustainable management of tuna fisheries, mitigating their environmental impacts and for managing transboundary fish stocks. These challenges are exemplified by the western Pacific tuna longline fishery, who's management includes >26 nations, but is informed by critically low coverage of fishing activities by scientific observers. The gap in observer data could be filled by electronic monitoring (EM), but there are few trials that span multiple nations. A large-scale trial of EM systems on tuna longliners based in Palau, Federated States of Micronesia and the Republic of the Marshall Islands, is reported on. Comparisons are made of catch rates of market and bycatch species in corresponding EM, logbook and human observer data. Retained species were under-reported in logbooks by up to three times and discards of many species were not reported in logbooks. Discards identified in the EM data included threatened species such as marine turtles. Catch rate estimates from EM data were comparable to those estimated by human observers. EM data recorded a higher species diversity of catches than logbook data. Analysis of the EM data indicated clusters of bycatch that were associated with specific fishing practices. These results suggest further expansion of EM could inform improved management of both target and bycatch species. Ultimately greater coverage of EM data could contribute to reconciling debates in international stock allocation schemes and support actions to reduce the impacts of the fishery on threatened bycatch species

    Improving Thermospheric Density Predictions in Low‐Earth Orbit With Machine Learning

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    Thermospheric density is one of the main sources of uncertainty in the estimation of satellites' position and velocity in low-Earth orbit. This has negative consequences in several space domains, including space traffic management, collision avoidance, re-entry predictions, orbital lifetime analysis, and space object cataloging. In this paper, we investigate the prediction accuracy of empirical density models (e.g., NRLMSISE-00 and JB-08) against black-box machine learning (ML) models trained on precise orbit determination-derived thermospheric density data (from CHAMP, GOCE, GRACE, SWARM-A/B satellites). We show that by using the same inputs, the ML models we designed are capable of consistently improving the predictions with respect to state-of-the-art empirical models by reducing the mean absolute percentage error (MAPE) in the thermospheric density estimation from the range of 40%–60% to approximately 20%. As a result of this work, we introduce Karman: an open-source Python software package developed during this study. Karman provides functionalities to ingest and preprocess thermospheric density, solar irradiance, and geomagnetic input data for ML readiness. Additionally, it facilitates developing and training ML models on the aforementioned data and benchmarking their performance at different altitudes, geographic locations, times, and solar activity conditions. Through this contribution, we offer the scientific community a comprehensive tool for comparing and enhancing thermospheric density models using ML techniques

    TOP2A and EZH2 Provide Early Detection of an Aggressive Prostate Cancer Subgroup.

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    Purpose: Current clinical parameters do not stratify indolent from aggressive prostate cancer. Aggressive prostate cancer, defined by the progression from localized disease to metastasis, is responsible for the majority of prostate cancer–associated mortality. Recent gene expression profiling has proven successful in predicting the outcome of prostate cancer patients; however, they have yet to provide targeted therapy approaches that could inhibit a patient\u27s progression to metastatic disease. Experimental Design: We have interrogated a total of seven primary prostate cancer cohorts (n = 1,900), two metastatic castration-resistant prostate cancer datasets (n = 293), and one prospective cohort (n = 1,385) to assess the impact of TOP2A and EZH2 expression on prostate cancer cellular program and patient outcomes. We also performed IHC staining for TOP2A and EZH2 in a cohort of primary prostate cancer patients (n = 89) with known outcome. Finally, we explored the therapeutic potential of a combination therapy targeting both TOP2A and EZH2 using novel prostate cancer–derived murine cell lines. Results: We demonstrate by genome-wide analysis of independent primary and metastatic prostate cancer datasets that concurrent TOP2A and EZH2 mRNA and protein upregulation selected for a subgroup of primary and metastatic patients with more aggressive disease and notable overlap of genes involved in mitotic regulation. Importantly, TOP2A and EZH2 in prostate cancer cells act as key driving oncogenes, a fact highlighted by sensitivity to combination-targeted therapy. Conclusions: Overall, our data support further assessment of TOP2A and EZH2 as biomarkers for early identification of patients with increased metastatic potential that may benefit from adjuvant or neoadjuvant targeted therapy approaches. ©2017 AACR

    LMSD: LIPID MAPS structure database

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    The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available a

    Complications from Surgeries Related to Ovarian Cancer Screening

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    The aim of this study was to evaluate complications of surgical intervention for participants in the Kentucky Ovarian Cancer Screening Program and compare results to those of the Prostate, Lung, Colorectal and Ovarian Cancer Screening trial. A retrospective database review included 657 patients who underwent surgery for a positive screen in the Kentucky Ovarian Cancer Screening Program from 1988–2014. Data were abstracted from operative reports, discharge summaries, and office notes for 406 patients. Another 142 patients with incomplete records were interviewed by phone. Complete information was available for 548 patients. Complications were graded using the Clavien–Dindo (C–D) Classification of Surgical Complications and considered minor if assigned Grade I (any deviation from normal course, minor medications) or Grade II (other pharmacological treatment, blood transfusion). C–D Grade III complications (those requiring surgical, endoscopic, or radiologic intervention) and C–D Grade IV complications (those which are life threatening) were considered “major”. Statistical analysis was performed using SAS 9.4 software. Complications were documented in 54/548 (10%) subjects. For women with malignancy, 17/90 (19%) had complications compared to 37/458 (8%) with benign pathology (p \u3c 0.003). For non-cancer surgery, obesity was associated with increased complications (p = 0.0028). Fifty patients had minor complications classified as C–D Grade II or less. Three of 4 patients with Grade IV complications had malignancy (p \u3c 0.0004). In the Prostate, Lung, Colorectal and Ovarian Cancer Screening trial, 212 women had surgery for ovarian malignancy, and 95 had at least one complication (45%). Of the 1080 women with non-cancer surgery, 163 had at least one complication (15%). Compared to the Prostate, Lung, Colorectal and Ovarian Cancer Screening trial, the Kentucky Ovarian Cancer Screening Program had significantly fewer complications from both cancer and non-cancer surgery (p \u3c 0.0001 and p = 0.002, respectively). Complications resulting from surgery performed as a result of the Kentucky Ovarian Cancer Screening Program were infrequent and significantly fewer than reported in the Prostate, Lung, Colorectal and Ovarian Cancer Screening trial. Complications were mostly minor (93%) and were more common in cancer versus non-cancer surgery

    Lithium Depletion in Fully Convective Pre-Main Sequence Stars

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    We present an analytic calculation of the thermonuclear depletion of lithium in contracting, fully convective, pre-main sequence stars of mass M < 0.5 M_sun. Previous numerical work relies on still-uncertain physics (atmospheric opacities and convection, in particular) to calculate the effective temperature as a unique function of stellar mass. We assume that the star's effective temperature, T_eff, is fixed during Hayashi contraction and allow its actual value to be a free parameter constrained by observation. Using this approximation, we compute lithium burning analytically and explore the dependence of lithium depletion on T_eff, M, and composition. Our calculations yield the radius, age, and luminosity of a pre-main sequence star as a function of lithium depletion. This allows for more direct comparisons to observations of lithium depleted stars. Our results agree with those numerical calculations that explicitly determine stellar structure during Hayashi contraction. In agreement with Basri, Marcy, and Graham (1996), we show that the absence of lithium in the Pleiades star HHJ 3 implies that it is older than 100 Myr. We also suggest a generalized method for dating galactic clusters younger than 100 Myr (i.e., those with contracting stars of M > 0.08 M_sun) and for constraining the masses of lithium depleted stars.Comment: 13 pages, LaTex with 2 postscript figures, uses aaspp4.sty and epsfig.sty, to appear in the Astrophysical Journa

    Galaxy And Mass Assembly (GAMA) : stellar mass functions by Hubble type

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    This work was supported by the Austrian Science Foundation FWF under grant P23946. AWG was supported under the Australian Research Council's funding scheme FT110100263.We present an estimate of the galaxy stellar mass function and its division by morphological type in the local (0.025 < z < 0.06) Universe. Adopting robust morphological classifications as previously presented (Kelvin et al.) for a sample of 3727 galaxies taken from the Galaxy And Mass Assembly survey, we define a local volume and stellar mass limited sub-sample of 2711 galaxies to a lower stellar mass limit of M = 109.0 MΘ. We confirm that the galaxy stellar mass function is well described by a double-Schechter function given by Μ* = 1010.64 MΘ, α1 = 0.43, φ1* = 4.18 dex-1 Mpc-3, α2 = −1.50 and φ2* = 0.74 dex-1 Mpc-3. The constituent morphological-type stellar mass functions are well sampled above our lower stellar mass limit, excepting the faint little blue spheroid population of galaxies. We find approximately 71-4+3 per cent of the stellar mass in the local Universe is found within spheroid-dominated galaxies; ellipticals and S0-Sas. The remaining 29-3+4 per cent falls predominantly within late-type disc-dominated systems, Sab-Scds and Sd-Irrs. Adopting reasonable bulge-to-total ratios implies that approximately half the stellar mass today resides in spheroidal structures, and half in disc structures. Within this local sample, we find approximate stellar mass proportions for E : S0-Sa : Sab-Scd : Sd-Irr of 34 : 37 : 24 :5.Publisher PDFPeer reviewe
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