A Compact Beam Stop for a Rare Kaon Decay Experiment

We describe the development and testing of a novel beam stop for use in a rare kaon decay experiment at the Brookhaven AGS. The beam stop is located inside a dipole spectrometer magnet in close proximity to straw drift chambers and intercepts a high-intensity neutral hadron beam. The design process, involving both Monte Carlo simulations and beam tests of alternative beam-stop shielding arrangements, had the goal of minimizing the leakage of particles from the beam stop and the resulting hit rates in detectors, while preserving maximum acceptance for events of interest. The beam tests consisted of measurements of rates in drift chambers, scintilation counter hodoscopes, a gas threshold Cherenkov counter, and a lead glass array. Measurements were also made with a set of specialized detectors which were sensitive to low-energy neutrons, photons, and charged particles. Comparisons are made between these measurements and a detailed Monte Carlo simulation.Comment: 39 pages, 14 figures, submitted to Nuclear Instruments and Method

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Background and ObjectivesWe sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.MethodsWe search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes.ResultsWe included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]).ConclusionOur review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models

Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population

External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, case-mix) and the generalizability of predictor coefficients, but most discrimination indices do not provide any insight into their respective contributions. To disentangle differences in discriminative ability across external validation samples due to a lack of model generalizability from differences in sample characteristics, we propose propensity-weighted measures of discrimination. These weighted metrics, which are derived from propensity scores for sample membership, are standardized for case-mix differences between the model development and validation samples, allowing for a fair comparison of discriminative ability in terms of model characteristics in a target population of interest. We illustrate our methods with the validation of eight prediction models for deep vein thrombosis in 12 external validation data sets and assess our methods in a simulation study. In the illustrative example, propensity score standardization reduced between-study heterogeneity of discrimination, indicating that between-study variability was partially attributable to case-mix. The simulation study showed that only flexible propensity-score methods (allowing for non-linear effects) produced unbiased estimates of model discrimination in the target population, and only when the positivity assumption was met. Propensity score-based standardization may facilitate the interpretation of (heterogeneity in) discriminative ability of a prediction model as observed across multiple studies, and may guide model updating strategies for a particular target population. Careful propensity score modeling with attention for non-linear relations is recommended

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

Origins of the Ambient Solar Wind: Implications for Space Weather

The Sun's outer atmosphere is heated to temperatures of millions of degrees, and solar plasma flows out into interplanetary space at supersonic speeds. This paper reviews our current understanding of these interrelated problems: coronal heating and the acceleration of the ambient solar wind. We also discuss where the community stands in its ability to forecast how variations in the solar wind (i.e., fast and slow wind streams) impact the Earth. Although the last few decades have seen significant progress in observations and modeling, we still do not have a complete understanding of the relevant physical processes, nor do we have a quantitatively precise census of which coronal structures contribute to specific types of solar wind. Fast streams are known to be connected to the central regions of large coronal holes. Slow streams, however, appear to come from a wide range of sources, including streamers, pseudostreamers, coronal loops, active regions, and coronal hole boundaries. Complicating our understanding even more is the fact that processes such as turbulence, stream-stream interactions, and Coulomb collisions can make it difficult to unambiguously map a parcel measured at 1 AU back down to its coronal source. We also review recent progress -- in theoretical modeling, observational data analysis, and forecasting techniques that sit at the interface between data and theory -- that gives us hope that the above problems are indeed solvable.Comment: Accepted for publication in Space Science Reviews. Special issue connected with a 2016 ISSI workshop on "The Scientific Foundations of Space Weather." 44 pages, 9 figure

4pi Models of CMEs and ICMEs

Coronal mass ejections (CMEs), which dynamically connect the solar surface to the far reaches of interplanetary space, represent a major anifestation of solar activity. They are not only of principal interest but also play a pivotal role in the context of space weather predictions. The steady improvement of both numerical methods and computational resources during recent years has allowed for the creation of increasingly realistic models of interplanetary CMEs (ICMEs), which can now be compared to high-quality observational data from various space-bound missions. This review discusses existing models of CMEs, characterizing them by scientific aim and scope, CME initiation method, and physical effects included, thereby stressing the importance of fully 3-D ('4pi') spatial coverage.Comment: 14 pages plus references. Comments welcome. Accepted for publication in Solar Physics (SUN-360 topical issue

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Most clinical specialties have a plethora of studies that develop or validate one or more prediction models, for example, to inform diagnosis or prognosis. Having many prediction model studies in a particular clinical field motivates the need for systematic reviews and meta-analyses, to evaluate and summarise the overall evidence available from prediction model studies, in particular about the predictive performance of existing models. Such reviews are fast emerging, and should be reported completely, transparently, and accurately. To help ensure this type of reporting, this article describes a new reporting guideline for systematic reviews and meta-analyses of prediction model research

Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models

Objectives: We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques. Study Design and Setting: We systematically searched PubMed from 01/2018 to 12/2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning. No restrictions were placed on data source, outcome, or clinical specialty. Results: We included 152 studies: 38% reported diagnostic models and 62% prognostic models. When reported, discrimination was described without precision estimates in 53/71 abstracts (74.6% [95% CI 63.4–83.3]) and 53/81 main texts (65.4% [95% CI 54.6–74.9]). Of the 21 abstracts that recommended the model to be used in daily practice, 20 (95.2% [95% CI 77.3–99.8]) lacked any external validation of the developed models. Likewise, 74/133 (55.6% [95% CI 47.2–63.8]) studies made recommendations for clinical use in their main text without any external validation. Reporting guidelines were cited in 13/152 (8.6% [95% CI 5.1–14.1]) studies. Conclusion: Spin practices and poor reporting standards are also present in studies on prediction models using machine learning techniques. A tailored framework for the identification of spin will enhance the sound reporting of prediction model studies

Evaluation of clinical prediction models (part 1): from development to external validation

Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance

Insights into malaria susceptibility using genome-wide data on 17,000 individuals from Africa, Asia and Oceania

The human genetic factors that affect resistance to infectious disease are poorly understood. Here we report a genome-wide association study in 17,000 severe malaria cases and population controls from 11 countries, informed by sequencing of family trios and by direct typing of candidate loci in an additional 15,000 samples. We identify five replicable associations with genome-wide levels of evidence including a newly implicated variant on chromosome 6. Jointly, these variants account for around one-tenth of the heritability of severe malaria, which we estimate as -23% using genome-wide genotypes. We interrogate available functional data and discover an erythroid-specific transcription start site underlying the known association in ATP2B4, but are unable to identify a likely causal mechanism at the chromosome 6 locus. Previously reported HLA associations do not replicate in these samples. This large dataset will provide a foundation for further research on thegenetic determinants of malaria resistance in diverse populations.Peer reviewe