A Calculus for Dynamic Loading

We present the load-calculus, used to model dynamic loading, and prove it sound. The calculus extends the polymorphic λ-calculus with a load primitive that dynamically loads terms that are closed, with respect to values. The calculus is meant to approximate the process of dynamic loading in TAL/Load [4], a version of Typed Assembly Language [7] extending with dynamic linking. To model the key aspects of TAL, the calculus contains references and facilities for named types. Loadable programs may refer to named types defined by the running program, and may export new types to code loaded later. Our approach follows the framework initially outlined by Glew et. al [3]. This calculus has been implemented in the TALx86 [6] version of Typed Assembly Language, and is used to implement a full-featured dynamic linking library, DLpop [4]

Modeling Data Analytic Iteration With Probabilistic Outcome Sets

In 1977 John Tukey described how in exploratory data analysis, data analysts use tools, such as data visualizations, to separate their expectations from what they observe. In contrast to statistical theory, an underappreciated aspect of data analysis is that a data analyst must make decisions by comparing the observed data or output from a statistical tool to what the analyst previously expected from the data. However, there is little formal guidance for how to make these data analytic decisions as statistical theory generally omits a discussion of who is using these statistical methods. In this paper, we propose a model for the iterative process of data analysis based on the analyst's expectations, using what we refer to as expected and anomaly probabilistic outcome sets, and the concept of statistical information gain. Here, we extend the basic idea of comparing an analyst's expectations to what is observed in a data visualization to more general analytic situations. Our model posits that the analyst's goal is to increase the amount of information the analyst has relative to what the analyst already knows, through successive analytic iterations. We introduce two criteria--expected information gain and anomaly information gain--to provide guidance about analytic decision-making and ultimately to improve the practice of data analysis. Finally, we show how our framework can be used to characterize common situations in practical data analysis.Comment: 30 page

Design Principles for Data Analysis

The data science revolution has led to an increased interest in the practice of data analysis. While much has been written about statistical thinking, a complementary form of thinking that appears in the practice of data analysis is design thinking -- the problem-solving process to understand the people for whom a product is being designed. For a given problem, there can be significant or subtle differences in how a data analyst (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. Here, we introduce design principles for data analysis and describe how they can be mapped to data analyses in a quantitative, objective and informative manner. We also provide empirical evidence of variation of principles within and between both producers and consumers of data analyses. Our work leads to two insights: it suggests a formal mechanism to describe data analyses based on the design principles for data analysis, and it provides a framework to teach students how to build data analyses using formal design principles.Comment: arXiv admin note: text overlap with arXiv:1903.0763

Evaluating the Alignment of a Data Analysis between Analyst and Audience

A challenge that data analysts face is building a data analysis that is useful for a given consumer. Previously, we defined a set of principles for describing data analyses that can be used to create a data analysis and to characterize the variation between analyses. Here, we introduce a concept that we call the alignment of a data analysis between the data analyst and a consumer. We define a successfully aligned data analysis as the matching of principles between the analyst and the consumer for whom the analysis is developed. In this paper, we propose a statistical model for evaluating the alignment of a data analysis and describe some of its properties. We argue that this framework provides a language for characterizing alignment and can be used as a guide for practicing data scientists and students in data science courses for how to build better data analyses

miQC : An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC. Author summary We developed the miQC package to predict the low-quality cells in a given scRNA-seq dataset by jointly modeling both the proportion of reads mapping to mitochondrial DNA (mtDNA) genes and the number of detected genes using mixture models in a probabilistic framework. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses.Peer reviewe

Acute glycogen synthase kinase-3 inhibition modulates human cardiac conduction

Glycogen synthase kinase 3 (GSK-3) inhibition has emerged as a potential therapeutic target for several diseases, including cancer. However, the role for GSK-3 regulation of human cardiac electrophysiology remains ill-defined. We demonstrate that SB216763, a GSK-3 inhibitor, can acutely reduce conduction velocity in human cardiac slices. Combined computational modeling and experimental approaches provided mechanistic insight into GSK-3 inhibition-mediated changes, revealing that decreased sodium-channel conductance and tissue conductivity may underlie the observed phenotypes. Our study demonstrates that GSK-3 inhibition in human myocardium alters electrophysiology and may predispose to an arrhythmogenic substrate; therefore, monitoring for adverse arrhythmogenic events could be considered

Effect of a Motivational Interviewing–Based Intervention on Initiation of Mental Health Treatment and Mental Health After an Emergency Department Visit Among Suicidal Adolescents

Abstract IMPORTANCE Emergency department (ED) visits present opportunities to identify and refer suicidal youth for outpatient mental health care, although this practice is not routine. OBJECTIVE To examine whether a motivational interviewing–based intervention increases linkage of adolescents to outpatient mental health services and reduces depression symptoms and suicidal ideation in adolescents seeking emergency care for non–mental health–related concerns who screen positive for suicide risk. DESIGN, SETTING, AND PARTICIPANTS In this randomized clinical trial, adolescents aged 12 to 17 years who screened positive on the Ask Suicide Screening Questions (ASQ) during a nonpsychiatric ED visit at 2 academic pediatric EDs in Ohio were recruited from April 2013 to July 2015. Intention-totreat analyses were performed from September 2018 to October 2019. INTERVENTIONS The Suicidal Teens Accessing Treatment After an Emergency Department Visit (STAT-ED) intervention included motivational interviewing to target family engagement, problem solving, referral assistance, and limited case management. The enhanced usual care (EUC) intervention consisted of brief mental health care consultation and referral. MAIN OUTCOMES AND MEASURES Primary outcomes were mental health treatment initiation and attendance within 2 months of ED discharge and suicidal ideation (assessed by the Suicidal Ideation Questionnaire JR) and depression symptoms (assessed by the Center for Epidemiologic Studies– Depression scale) at 2 and 6 months. Exploratory outcomes included treatment initiation and attendance and suicide attempts at 6 months. RESULTS A total of 168 participants were randomized and 159 included in the intention-to-treat analyses (mean [SD] age, 15.0 [1.5] years; 126 [79.2%] female; and 80 [50.3%] white). Seventy-nine participants were randomized to receive the STAT-ED intervention and 80 to receive EUC. At 2 months, youth in the STAT-ED group had similar rates of mental health treatment initiation compared with youth in the EUC group as assessed by parent report (29 [50.9%] vs 22 [34.9%]; adjusted odds ratio [OR], 2.08; 95% CI, 0.97-4.45) and administrative data from mental health care agencies (19 [29.7%] vs 11 [19.3%]; adjusted OR, 1.77; 95% CI, 0.76-4.15). At 2 months, youth in the STAT-ED group and the EUC group had similar rates of treatment attendance (1 appointment: 6 [9.7%] vs 2 [3.6%]; adjusted OR, 2.97; 95% CI, 0.56-15.73; 2 appointments: 10 [16.1%] vs 7 [12.7%]; adjusted OR, 1.43; 95% CI, 0.50-4.11). There were no significant group × time differences in suicidal ideation (F = 0.28; P = .72) and depression symptoms (F = 0.49; P = .60) during the 6-month follow-up period. In exploratory analyses, at 6 months, STAT-ED participants had significantly higher rates of agencyreported mental health treatment initiation (adjusted OR, 2.48; 95% CI, 1.16-5.28) and more completed appointments (t99.7 = 2.58; P = .01). CONCLUSIONS AND RELEVANCE This study’s findings indicate that no differences were found on any primary outcome by study condition. However, STAT-ED was more efficacious than EUC at increasing mental health treatment initiation and attendance at 6 months. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01779414 JAMA Network Open. 2019;2(12):e1917941. doi:10.1001/jamanetworkopen.2019.1794