Precise Throughput Determination of the PanSTARRS Telescope and the Gigapixel Imager using a Calibrated Silicon Photodiode and a Tunable Laser: Initial Results

We have used a precision calibrated photodiode as the fundamental metrology reference in order to determine the relative throughput of the PanSTARRS telescope and the Gigapixel imager, from 400 nm to 1050 nm. Our technique uses a tunable laser as a source of illumination on a transmissive flat-field screen. We determine the full-aperture system throughput as a function of wavelength, including (in a single integral measurement) the mirror reflectivity, the transmission functions of the filters and the corrector optics, and the detector quantum efficiency, by comparing the light seen by each pixel in the CCD array to that measured by a precision-calibrated silicon photodiode. This method allows us to determine the relative throughput of the entire system as a function of wavelength, for each pixel in the instrument, without observations of celestial standards. We present promising initial results from this characterization of the PanSTARRS system, and we use synthetic photometry to assess the photometric perturbations due to throughput variation across the field of view.Physic

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

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

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

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.

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

Complications from Surgeries Related to Ovarian Cancer Screening

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

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

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

Pelagic Functional Group Modeling: Progress, Challenges and Prospects

In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochernical cycles in the ocean will respond to global warming. We define the term biogeochemical functional group to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, functional groups have no phylogenetic meaning-these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. One obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our tendency to model the organisms for which we have the most validation data (e.g., E huxleyi and Trichodesmium) even when they may represent only a fraction of the biogeochemical functional group we are trying to represent. When we step back and look at the paleo-oceanographic record, it suggests that oxygen concentrations have played a central role in the evolution and emergence of many of the key functional groups that influence biogeochemical cycles in the present-day ocean. However, more subtle effects are likely to be important over the next century like changes in silicate supply or turbulence that can influence the relative success of diatoms versus dinoflagellates, coccolithophorids and diazotrophs. In general, inferences drawn from the paleo-oceanographic record and theoretical work suggest that global warming will tend to favor the latter because it will give rise to increased stratification. However, decreases in pH and Fe supply could adversely impact coccolithophorids and diazotrophs in the future. It may be necessary to include explicit dynamic representations of nitrogen fixation, denitrification, silicification and calcification in our models if our goal is predicting the oceanic carbon cycle in the future, because these processes appear to play a very significant role in the carbon cycle of the present-day ocean and they are sensitive to climate change. Observations and models suggest that it may also be necessary to include the DMS cycle to predict future climate, though the effects are still highly uncertain. We have learned a tremendous amount about the distributions and biogeochemical impact of bacteria in the ocean in recent years, yet this improved understanding has not yet been incorporated into many of our models. All of these considerations lead us toward the development of increasingly complex models. However, recent quantitative model intercomparison studies suggest that continuing to add complexity and more functional groups to our ecosystem models may lead to decreases in predictive ability if the models are not properly constrained with available data. We also caution that capturing the present-day variability tells us little about how well a particular model can predict the future. If our goal is to develop models that can be used to predict how the oceans will respond to global warming, then we need to make more rigorous assessments of predictive skill using the available data

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort with External Validation

Background: Acute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration. Methods and Results: We collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end‐stage renal disease, renal transplant, and missing pre‐ and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator (LASSO) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross‐validated C‐statistics of 0.74 (95% CI: 0.74–0.75) for AKI, 0.83 (95% CI: 0.82–0.84) for AKIN2, 0.74 (95% CI: 0.74–0.75) for contrast‐induced nephropathy, and 0.89 (95% CI: 0.87–0.90) for dialysis. Conclusions: We developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high‐risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice