Density Distribution Sunflower Plots

Density distribution sunflower plots are used to display high-density bivariate data. They are useful for data where a conventional scatter plot is difficult to read due to overstriking of the plot symbol. The x-y plane is subdivided into a lattice of regular hexagonal bins of width w specified by the user. The user also specifies the values of l, d, and k that affect the plot as follows. Individual observations are plotted when there are less than l observations per bin as in a conventional scatter plot. Each bin with from l to d observations contains a light sunflower. Other bins contain a dark sunflower. In a light sunflower each petal represents one observation. In a dark sunflower, each petal represents k observations. (A dark sunflower with p petals represents between /2-pk k and /2+pk k observations.) The user can control the sizes and colors of the sunflowers. By selecting appropriate colors and sizes for the light and dark sunflowers, plots can be obtained that give both the overall sense of the data density distribution as well as the number of data points in any given region. The use of this graphic is illustrated with data from the Framingham Heart Study. A documented Stata program, called sunflower, is available to draw these graphs. It can be downloaded from the Statistical Software Components archive at http://ideas.repec.org/c/boc/bocode/s430201.html . (Journal of Statistical Software 2003; 8 (3): 1-5. Posted at http://www.jstatsoft.org/index.php?vol=8 .)

Effect of CPOE User Interface Design on User-Initiated Access to Educational and Patient Information during Clinical Care

Objective: Authors evaluated whether displaying context sensitive links to infrequently accessed educational materials and patient information via the user interface of an inpatient computerized care provider order entry (CPOE) system would affect access rates to the materials. Design: The CPOE of Vanderbilt University Hospital (VUH) included "baseline” clinical decision support advice for safety and quality. Authors augmented this with seven new primarily educational decision support features. A prospective, randomized, controlled trial compared clinicians' utilization rates for the new materials via two interfaces. Control subjects could access study-related decision support from a menu in the standard CPOE interface. Intervention subjects received active notification when study-related decision support was available through context sensitive, visibly highlighted, selectable hyperlinks. Measurements: Rates of opportunities to access and utilization of study-related decision support materials from April 1999 through March 2000 on seven VUH Internal Medicine wards. Results: During 4,466 intervention subject-days, there were 240,504 (53.9/subject-day) opportunities for study-related decision support, while during 3,397 control subject-days, there were 178,235 (52.5/subject-day) opportunities for such decision support, respectively (p = 0.11). Individual intervention subjects accessed the decision support features at least once on 3.8% of subject-days logged on (278 responses); controls accessed it at least once on 0.6% of subject-days (18 responses), with a response rate ratio adjusted for decision support frequency of 9.17 (95% confidence interval 4.6-18, p < 0.0005). On average, intervention subjects accessed study-related decision support materials once every 16 days individually and once every 1.26 days in aggregate. Conclusion: Highlighting availability of context-sensitive educational materials and patient information through visible hyperlinks significantly increased utilization rates for study-related decision support when compared to "standard” VUH CPOE methods, although absolute response rates were lo

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P \u3c .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P \u3c .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention

Copy Number Variants Are Ovarian Cancer Risk Alleles at Known and Novel Risk Loci

BACKGROUND: Known risk alleles for epithelial ovarian cancer (EOC) account for approximately 40% of the heritability for EOC. Copy number variants (CNVs) have not been investigated as EOC risk alleles in a large population cohort. METHODS: Single nucleotide polymorphism array data from 13 071 EOC cases and 17 306 controls of White European ancestry were used to identify CNVs associated with EOC risk using a rare admixture maximum likelihood test for gene burden and a by-probe ratio test. We performed enrichment analysis of CNVs at known EOC risk loci and functional biofeatures in ovarian cancer-related cell types. RESULTS: We identified statistically significant risk associations with CNVs at known EOC risk genes; BRCA1 (PEOC = 1.60E-21; OREOC = 8.24), RAD51C (Phigh-grade serous ovarian cancer [HGSOC] = 5.5E-4; odds ratio [OR]HGSOC = 5.74 del), and BRCA2 (PHGSOC = 7.0E-4; ORHGSOC = 3.31 deletion). Four suggestive associations (P < .001) were identified for rare CNVs. Risk-associated CNVs were enriched (P < .05) at known EOC risk loci identified by genome-wide association study. Noncoding CNVs were enriched in active promoters and insulators in EOC-related cell types. CONCLUSIONS: CNVs in BRCA1 have been previously reported in smaller studies, but their observed frequency in this large population-based cohort, along with the CNVs observed at BRCA2 and RAD51C gene loci in EOC cases, suggests that these CNVs are potentially pathogenic and may contribute to the spectrum of disease-causing mutations in these genes. CNVs are likely to occur in a wider set of susceptibility regions, with potential implications for clinical genetic testing and disease prevention

Perspectives and Integration in SOLAS Science

Why a chapter on Perspectives and Integration in SOLAS Science in this book? SOLAS science by its nature deals with interactions that occur: across a wide spectrum of time and space scales, involve gases and particles, between the ocean and the atmosphere, across many disciplines including chemistry, biology, optics, physics, mathematics, computing, socio-economics and consequently interactions between many different scientists and across scientific generations. This chapter provides a guide through the remarkable diversity of cross-cutting approaches and tools in the gigantic puzzle of the SOLAS realm. Here we overview the existing prime components of atmospheric and oceanic observing systems, with the acquisition of ocean–atmosphere observables either from in situ or from satellites, the rich hierarchy of models to test our knowledge of Earth System functioning, and the tremendous efforts accomplished over the last decade within the COST Action 735 and SOLAS Integration project frameworks to understand, as best we can, the current physical and biogeochemical state of the atmosphere and ocean commons. A few SOLAS integrative studies illustrate the full meaning of interactions, paving the way for even tighter connections between thematic fields. Ultimately, SOLAS research will also develop with an enhanced consideration of societal demand while preserving fundamental research coherency. The exchange of energy, gases and particles across the air-sea interface is controlled by a variety of biological, chemical and physical processes that operate across broad spatial and temporal scales. These processes influence the composition, biogeochemical and chemical properties of both the oceanic and atmospheric boundary layers and ultimately shape the Earth system response to climate and environmental change, as detailed in the previous four chapters. In this cross-cutting chapter we present some of the SOLAS achievements over the last decade in terms of integration, upscaling observational information from process-oriented studies and expeditionary research with key tools such as remote sensing and modelling. Here we do not pretend to encompass the entire legacy of SOLAS efforts but rather offer a selective view of some of the major integrative SOLAS studies that combined available pieces of the immense jigsaw puzzle. These include, for instance, COST efforts to build up global climatologies of SOLAS relevant parameters such as dimethyl sulphide, interconnection between volcanic ash and ecosystem response in the eastern subarctic North Pacific, optimal strategy to derive basin-scale CO2 uptake with good precision, or significant reduction of the uncertainties in sea-salt aerosol source functions. Predicting the future trajectory of Earth’s climate and habitability is the main task ahead. Some possible routes for the SOLAS scientific community to reach this overarching goal conclude the chapter

The Physics of Star Cluster Formation and Evolution

© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/s11214-020-00689-4.Star clusters form in dense, hierarchically collapsing gas clouds. Bulk kinetic energy is transformed to turbulence with stars forming from cores fed by filaments. In the most compact regions, stellar feedback is least effective in removing the gas and stars may form very efficiently. These are also the regions where, in high-mass clusters, ejecta from some kind of high-mass stars are effectively captured during the formation phase of some of the low mass stars and effectively channeled into the latter to form multiple populations. Star formation epochs in star clusters are generally set by gas flows that determine the abundance of gas in the cluster. We argue that there is likely only one star formation epoch after which clusters remain essentially clear of gas by cluster winds. Collisional dynamics is important in this phase leading to core collapse, expansion and eventual dispersion of every cluster. We review recent developments in the field with a focus on theoretical work.Peer reviewe

Relativistic Binaries in Globular Clusters

Galactic globular clusters are old, dense star systems typically containing 10\super{4}--10\super{7} stars. As an old population of stars, globular clusters contain many collapsed and degenerate objects. As a dense population of stars, globular clusters are the scene of many interesting close dynamical interactions between stars. These dynamical interactions can alter the evolution of individual stars and can produce tight binary systems containing one or two compact objects. In this review, we discuss theoretical models of globular cluster evolution and binary evolution, techniques for simulating this evolution that leads to relativistic binaries, and current and possible future observational evidence for this population. Our discussion of globular cluster evolution will focus on the processes that boost the production of hard binary systems and the subsequent interaction of these binaries that can alter the properties of both bodies and can lead to exotic objects. Direct {\it N}-body integrations and Fokker--Planck simulations of the evolution of globular clusters that incorporate tidal interactions and lead to predictions of relativistic binary populations are also discussed. We discuss the current observational evidence for cataclysmic variables, millisecond pulsars, and low-mass X-ray binaries as well as possible future detection of relativistic binaries with gravitational radiation.Comment: 88 pages, 13 figures. Submitted update of Living Reviews articl

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570