An Ultra Deep Field survey with WFIRST

Studying the formation and evolution of galaxies at the earliest cosmic times, and their role in reionization, requires the deepest imaging possible. Ultra-deep surveys like the HUDF and HFF have pushed to mag \mAB$\,\sim\,$30, revealing galaxies at the faint end of the LF to $z$$\,\sim\,$9$\,-\,$11 and constraining their role in reionization. However, a key limitation of these fields is their size, only a few arcminutes (less than a Mpc at these redshifts), too small to probe large-scale environments or clustering properties of these galaxies, crucial for advancing our understanding of reionization. Achieving HUDF-quality depth over areas $\sim$100 times larger becomes possible with a mission like the Wide Field Infrared Survey Telescope (WFIRST), a 2.4-m telescope with similar optical properties to HST, with a field of view of $\sim$1000 arcmin$^2$, $\sim$100$\times$ the area of the HST/ACS HUDF. This whitepaper motivates an Ultra-Deep Field survey with WFIRST, covering $\sim$100$\,-\,$300$\times$ the area of the HUDF, or up to $\sim$1 deg$^2$, to \mAB$\,\sim\,$30, potentially revealing thousands of galaxies and AGN at the faint end of the LF, at or beyond $z$\,$\sim$\,9$\,-\,$10 in the epoch of reionization, and tracing their LSS environments, dramatically increasing the discovery potential at these redshifts. (Note: This paper is a somewhat expanded version of one that was submitted as input to the Astro2020 Decadal Survey, with this version including an Appendix (which exceeded the Astro2020 page limits), describing how the science drivers for a WFIRST Ultra Deep Field might map into a notional observing program, including the filters used and exposure times needed to achieve these depths.

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p

Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration

The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies

Cyclic alkenylsulfonyl fluorides: palladium‐catalyzed synthesis and functionalization of compact multifunctional reagents

A series of low‐molecular‐weight, compact, and multifunctional cyclic alkenylsulfonyl fluorides were efficiently prepared from the corresponding alkenyl triflates. Palladium‐catalyzed sulfur dioxide insertion using the surrogate reagent DABSO effects sulfinate formation, before trapping with an F electrophile delivers the sulfonyl fluorides. A broad range of functional groups are tolerated, and a correspondingly large collection of derivatization reactions are possible on the products, including substitution at sulfur, conjugate addition, and N‐functionalization. Together, these attributes suggest that this method could find new applications in chemical biology

Cyclic alkenylsulfonyl fluorides: palladium-catalyzed synthesis and functionalization of compact multi-functional reagents

A series of low‐molecular‐weight, compact, and multifunctional cyclic alkenylsulfonyl fluorides were efficiently prepared from the corresponding alkenyl triflates. Palladium‐catalyzed sulfur dioxide insertion using the surrogate reagent DABSO effects sulfinate formation, before trapping with an F electrophile delivers the sulfonyl fluorides. A broad range of functional groups are tolerated, and a correspondingly large collection of derivatization reactions are possible on the products, including substitution at sulfur, conjugate addition, and N‐functionalization. Together, these attributes suggest that this method could find new applications in chemical biology

One-pot palladium-catalyzed synthesis of sulfonyl fluorides from aryl bromides

A mild, efficient synthesis of sulfonyl fluorides from aryl and heteroaryl bromides utilizing palladium catalysis is described. The process involves the initial palladium catalyzed sulfonylation of aryl bromides using DABSO as an SO2 source, followed by in situ treatment of the resultant sulfinate with the electrophilic fluorine source NFSI. This sequence represents the first general method for the sulfonylation of aryl bromides, and offers a practical, one-pot alternative to previously described syntheses of sulfonyl fluorides, allowing rapid access to these biologically important molecules. Excellent functional group tolerance is demonstrated, with the transformation succesfully achieved on a number of active pharmaceutical ingredients, and their precursors. The preparation of peptide-derived sulfonyl fluorides is also demonstrated