Progress in superconducting performance of rolled multifilamentary Bi-2223 HTS composite conductors

Significant enhancements in critical current densities in rolled multifilamentary Bi-2223 HTS composite conductors have been achieved using the powder-in-tube technique. At 77 K and self field, oxide critical current densities (J{sub c})of 55 kA/cm{sup 2}, overall or engineering critical current densities of 15 kA/cm{sup 2}, and critical currents of 125 A have been achieved in different rolled multifilamentary composites. Progress in achieving such high electrical performance is believed to stem in part from an improvement of grain connectivity by reducing weak links. The J{sub c} dependence on magnetic field and the degree of c-axis texture of these high quality conductors have been investigated at various temperatures. Our results also demonstrate that the critical current retention in magnetic field can be independently controlled from the self field critical current density, suggesting that flux pinning improvements and weak link reductions can be separately engineered into Bi-2223 composites fabricated using manufacturable processes

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

Objectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. Results In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥ 0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Conclusion Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies

Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes.

The PROGRESS series (www.progress-partnership.org) sets out a framework of four interlinked prognosis research themes and provides examples from several disease fields to show why evidence from prognosis research is crucial to inform all points in the translation of biomedical and health related research into better patient outcomes. Recommendations are made in each of the four papers to improve current research standards What is prognosis research? Prognosis research seeks to understand and improve future outcomes in people with a given disease or health condition. However, there is increasing evidence that prognosis research standards need to be improved Why is prognosis research important? More people now live with disease and conditions that impair health than at any other time in history; prognosis research provides crucial evidence for translating findings from the laboratory to humans, and from clinical research to clinical practice This first article introduces the framework of four interlinked prognosis research themes and then focuses on the first of the themes - fundamental prognosis research, studies that aim to describe and explain future outcomes in relation to current diagnostic and treatment practices, often in relation to quality of care Fundamental prognosis research provides evidence informing healthcare and public health policy, the design and interpretation of randomised trials, and the impact of diagnostic tests on future outcome. It can inform new definitions of disease, may identify unanticipated benefits or harms of interventions, and clarify where new interventions are required to improve prognosis

A straw drift chamber spectrometer for studies of rare kaon decays

We describe the design, construction, readout, tests, and performance of planar drift chambers, based on 5 mm diameter copperized Mylar and Kapton straws, used in an experimental search for rare kaon decays. The experiment took place in the high-intensity neutral beam at the Alternating Gradient Synchrotron of Brookhaven National Laboratory, using a neutral beam stop, two analyzing dipoles, and redundant particle identification to remove backgrounds

Prognosis research strategy (PROGRESS) 4: Stratified medicine research

In patients with a particular disease or health condition, stratified medicine seeks to identify thosewho will have the most clinical benefit or least harm from a specific treatment. In this article, thefourth in the PROGRESS series, the authors discuss why prognosis research should form acornerstone of stratified medicine, especially in regard to the identification of factors that predictindividual treatment respons

Risk stratified monitoring for methotrexate toxicity in immune mediated inflammatory diseases: prognostic model development and validation using primary care data from the UK.

OBJECTIVE: To develop and validate a prognostic model to inform risk stratified decisions on frequency of monitoring blood tests during long term methotrexate treatment. DESIGN: Retrospective cohort study. SETTING: Electronic health records within the UK's Clinical Practice Research Datalink (CPRD) Gold and CPRD Aurum. PARTICIPANTS: Adults (≥18 years) with a diagnosis of an immune mediated inflammatory disease who were prescribed methotrexate by their general practitioner for six months or more during 2007-19. MAIN OUTCOME MEASURE: Discontinuation of methotrexate owing to abnormal monitoring blood test result. Patients were followed-up from six months after their first prescription for methotrexate in primary care to the earliest of outcome, drug discontinuation for any other reason, leaving the practice, last data collection from the practice, death, five years, or 31 December 2019. Cox regression was performed to develop the risk equation, with bootstrapping used to shrink predictor effects for optimism. Multiple imputation handled missing predictor data. Model performance was assessed in terms of calibration and discrimination. RESULTS: Data from 13 110 (854 events) and 23 999 (1486 events) participants were included in the development and validation cohorts, respectively. 11 candidate predictors (17 parameters) were included. In the development dataset, the optimism adjusted R2 was 0.13 and the optimism adjusted Royston D statistic was 0.79. The calibration slope and Royston D statistic in the validation dataset for the entire follow-up period was 0.94 (95% confidence interval 0.85 to 1.02) and 0.75 (95% confidence interval 0.67 to 0.83), respectively. The prognostic model performed well in predicting outcomes in clinically relevant subgroups defined by age group, type of immune mediated inflammatory disease, and methotrexate dose. CONCLUSION: A prognostic model was developed and validated that uses information collected during routine clinical care and may be used to risk stratify the frequency of monitoring blood test during long term methotrexate treatment

Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media

Numerical groundwater flow and dissolution models of physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input parameters. This uncertainty is propagated through the equations and needs to be quantified and characterised in order to rely on the model outputs. In this paper we present a Gaussian process emulation method as a tool for performing uncertainty quantification in mathematical models for convection and dissolution processes in porous media. One of the advantages of this method is its ability to significantly reduce the computational cost of an uncertainty analysis, while yielding accurate results, compared to classical Monte Carlo methods. We apply the methodology to a model of convectively-enhanced dissolution processes occurring during carbon capture and storage. In this model, the Gaussian process methodology fails due to the presence of multiple branches of solutions emanating from a bifurcation point, i.e., two equilibrium states exist rather than one. To overcome this issue we use a classifier as a precursor to the Gaussian process emulation, after which we are able to successfully perform a full uncertainty analysis in the vicinity of the bifurcation point

Search for the Decay τ−→4pi−3π+(π0)ντ\tau^{-}\to 4pi^{-}3\pi^{+}(\pi^{0})\nu_{\tau}

We have searched for the decay of the tau lepton into seven charged particles and zero or one pi0. The data used in the search were collected with the CLEO II detector at the Cornell Electron Storage Ring (CESR) and correspond to an integrated luminosity of 4.61 fb^(-1). No evidence for a signal is found. Assuming all the charged particles are pions, we set an upper limit on the branching fraction, B(tau- -> 4pi- 3pi+ (pi0) nu_tau) < 2.4 x 10^(-6) at the 90% confidence level. This limit represents a significant improvement over the previous limit.Comment: 9 page postscript file, postscript file also available through http://w4.lns.cornell.edu/public/CLN