Learning Logic Rules for Disease Classification: With an Application to Developing Criteria Sets for the Diagnostic and Statistical Manual of Mental Disorders

This dissertation develops several new statistical methods for disease classification that directly account for the unique logic structure of criteria sets found in the Diagnostic and Statistical Manual of Mental Disorders. For psychiatric disorders, a clinically significant anatomical or physiological deviation cannot be used to determine disease status. Instead, clinicians rely on criteria sets from the Diagnostic and Statistical Manual of Mental Disorders to make diagnoses. Each criteria set is comprised of several symptom domains, with the domains determined by expert opinion or psychometric analyses. In order to be diagnosed, an individual must meet the minimum number of symptoms, or threshold, required for each domain. If both the overall number of domains and the number of symptoms within each domain are small, an exhaustive search to determine these thresholds is feasible, with the thresholds chosen to minimize the overall misclassification rate. However, for more complicated scenarios, such as incorporating a continuous biomarker into the diagnostic criteria, a novel technique is necessary. In this dissertation, we propose several novel approaches to empirically determine these thresholds. Within each domain, we start by fitting a linear discriminant function based upon a sample of individuals in which disease status and the number of symptoms present in that domain are both known. Since one must meet the criteria for all domains, an overall positive diagnosis is only issued if the prediction in each domain is positive. Therefore, the overall decision rule is the intersection of all the domain specific rules. We fit this model using several approaches. In the first approach, we directly apply the framework of the support vector machine (SVM). This results in a non-convex minimization problem, which we can approximate by an iterative algorithm based on the Difference of Convex functions algorithm. In the second approach, we recognize that the expected population loss function can be re-expressed in an alternative form. Based on this alternative form, we propose two more iterative algorithms, SVM Iterative and Logistic Iterative. Although the number of symptoms per domain for the current clinical application is small, the proposed iterative methods are general and flexible enough to be adapted to complicated settings such as using continuous biomarker data, high-dimensional data (for example, imaging markers or genetic markers), other logic structures, or non-linear discriminant functions to assist in disease diagnosis. Under varying simulation scenarios, the Exhaustive Search and both proposed methods, SVM Iterative and Logistic Iterative, have good performance characteristics when compared with the oracle decision rule. We also examine one simulation in which the Exhaustive Search is not feasible and find that SVM Iterative and Logistic Iterative perform quite well. Each of these methods is then applied to a real data set in order to construct a criteria set for Complicated Grief, a new psychiatric disorder of interest. As the domain structure is currently unknown, both a two domain and three domain structure is considered. For both domain structures, all three methods choose the same thresholds. The resulting criteria sets are then evaluated on an independent data set of cases and shown to have high sensitivities. Using this same data, we also evaluate the sensitivity of three previously published criteria sets for Complicated Grief. Two of the three published criteria sets show poor sensitivity, while the sensitivity of the third is quite good. To fully evaluate our proposed criteria sets, as well as the previously published sets, a sample of controls is necessary so that specificity can also be assessed. The collection of this data is currently ongoing. We conclude the dissertation by considering the influence of study design on criteria set development and its evaluation. We also discuss future extensions of this work such as handling complex logic structures and simultaneously discovering both the domain structure and domain thresholds

Biodegradable scaffolds

In some embodiments, the present invention provides compositions that comprise: (1) a biodegradable polymer matrix; and (2) at least one biodegradable reinforcing particle that is dispersed in the matrix. In some embodiments, the biodegradable reinforcing particle is selected from the group consisting of porous oxide particles and porous semiconductor particles. In additional embodiments, the compositions of the present invention further comprise a (3) porogen particle that is also dispersed in the matrix. In further embodiments, the compositions of the present invention are also associated with one or more active agents. In various embodiments, the active agents are associated with the biodegradable polymer matrix, the biodegradable reinforcing particle, and/or the porogen particle. In various embodiments, the compositions of the present invention may be utilized as scaffolds, such as scaffolds for treating bone defects. Further embodiments of the present invention pertain to methods of making the compositions of the present invention.Board of Regents, University of Texas Syste

Identification of ä-Spectrin Domains Susceptible to Ubiquitination

Previously, we demonstrated that alpha-spectrin is a substrate for the ubiquitin system and that this conjugation is a dynamic process (Corsi, D., Galluzzi, L., Crinelli, R., and Magnani, M. (1995) J. Biol. Chem. 270, 8928-8935). In this study, we mapped the sites of ubiquitination on erythrocyte alpha-spectrin. A peptide map of digested alpha-spectrin, previously submitted to in vitro 125I-ubiquitin conjugation, revealed the presence of four distinct labeled bands with Mr 40,000, 36,000, 29,000, and 25,500. Western blotting experiments using antibodies against each alpha-spectrin domain revealed that only IgG anti-alphaIII domain recognized the 125I-labeled ubiquitin peptide of 29 kDa, whereas the IgG anti-alphaV domain recognized the Mr 40,000 125I-ubiquitin-labeled peptide. The other two labeled bands of Mr 36,000 and Mr 25,500 were identified as tetra and tri multiubiquitin chains. Ubiquitination of the alphaIII and alphaV domains was further confirmed by anti-alpha-spectrin domain immunoaffinity chromatography. Endoprotease Lys C-digested spectrin conjugated previously to 125I-ubiquitin was incubated with antibodies against each trypsin-resistant domain of alpha-spectrin. Gamma counting of the radiolabeled antigen-antibody complexes purified by protein A chromatography showed labeling in the IgG anti-alphaIII and anti-alphaV complexes alone. Domain alphaIII is not associated with any known function, whereas domain alphaV contains the nucleation site for the association of the alpha and beta chains. Ubiquitination of the latter domain suggests a role for ubiquitin in the modulation of the stability, deformability, and viscoelastic properties of the erythrocyte membrane

The Evolutionary Debunker Meets Sentimental Realism

In this paper, we propose a defence of Value Realism that relies on the unusual combination of Values Realism with Sentimentalism. What this account, which we call “Sentimental Realism”, holds, in a nutshell, is that what makes evaluative facts special is their relationship to emotions. More precisely, Sentimental Realism claims that evaluative facts are fully objective facts, but that such facts are picked out by concepts that are response-dependent, in the sense that they are essentially tied to emotions. Our plan is as follows. We shall start with a presentation of Sentimental Realism and a discussion of its main virtues. On the basis of this, we shall discuss an objection to Value Realism that draws on evolutionary considerations, the Evolutionary Debunking Argument. We shall argue that Sentimental Realism safely escapes from this dilemma

U.S. pregnant women's knowledge and attitudes about behavioral strategies and vaccines to prevent Zika acquisition

INTRODUCTION: Zika virus infection during pregnancy can cause significant infant morbidity. Little is known about pregnant women's attitudes regarding behavioral strategies and hypothetical vaccination to prevent Zika infections and sequelae. METHODS: Pregnant women across the United States (N = 362) completed an online questionnaire regarding attitudes about Zika, including six behavioral prevention strategies (i.e., abstaining from sex, using condoms, not traveling to an area with Zika, their partner not traveling into an area with Zika, using mosquito repellant, wearing long pants and sleeves) and vaccination. RESULTS: Most women (91%) were married/living with the baby's father, 65% were non-Hispanic White, and 71% had been pregnant. Seventy-four percent were worried about Zika, while 30% thought they were knowledgeable about Zika. The mean knowledge score was 5.0 out of 8 (SD = 2.09), and the mean behavioral strategies score was 4.9 out of 12 (SD = 3.7) with a range of 0 (none would be hard to do) to 12 (all would be hard to do). In a multivariable model, having had a sexually transmitted infection, living/traveling in an area with Zika, and worrying about Zika were significantly related to reporting behavioral strategies as hard to do. Seventy-two percent would be willing to be vaccinated. In the multivariable model, living/traveling in an area with Zika, believing they knew a lot about Zika, worrying about Zika, and considering Zika vaccine development as important were significantly associated with willingness to get vaccinated. CONCLUSIONS: Pregnant women were worried about Zika, yet had gaps in their factual knowledge. Most women reported they would get vaccinated if a vaccine was available. Pregnant women who reported themselves as vulnerable (being worried, having lived in or traveled to a Zika area) were more likely to view behavioral strategies as hard to do and to accept vaccination

Prevalence of marijuana use does not differentially increase among youth after states pass medical marijuana laws: Commentary on Stolzenberg et al. (2015) and reanalysis of US National Survey on Drug Use in Households data 2002–2011

There is considerable interest in the effects of medical marijuana laws (MML) on marijuana use in the USA, particularly among youth. The article by Stolzenberg et al. (2015) “The effect of medical cannabis laws on juvenile cannabis use” concludes that “implementation of medical cannabis laws increase juvenile cannabis use”. This result is opposite to the findings of other studies that analysed the same US National Survey on Drug Use in Households data as well as opposite to studies analysing other national data which show no increase or even a decrease in youth marijuana use after the passage of MML. We provide a replication of the Stolzenberg et al. results and demonstrate how the comparison they are making is actually driven by differences between states with and without MML rather than being driven by pre and post-MML changes within states. We show that Stolzenberg et al. do not properly control for the fact that states that pass MML during 2002–2011 tend to already have higher past-month marijuana use before passing the MML in the first place. We further show that when within-state changes are properly considered and pre-MML prevalence is properly controlled, there is no evidence of a differential increase in past-month marijuana use in youth that can be attributed to state MML

Confluence via strong normalisation in an algebraic \lambda-calculus with rewriting

The linear-algebraic lambda-calculus and the algebraic lambda-calculus are untyped lambda-calculi extended with arbitrary linear combinations of terms. The former presents the axioms of linear algebra in the form of a rewrite system, while the latter uses equalities. When given by rewrites, algebraic lambda-calculi are not confluent unless further restrictions are added. We provide a type system for the linear-algebraic lambda-calculus enforcing strong normalisation, which gives back confluence. The type system allows an abstract interpretation in System F.Comment: In Proceedings LSFA 2011, arXiv:1203.542

State Medical Marijuana Laws and the Prevalence of Opioids Detected Among Fatally Injured Drivers

Objectives. To assess the association between medical marijuana laws (MMLs) and the odds of a positive opioid test, an indicator for prior use. Methods. We analyzed 1999–2013 Fatality Analysis Reporting System (FARS) data from 18 states that tested for alcohol and other drugs in at least 80% of drivers who died within 1 hour of crashing (n = 68 394). Within-state and between-state comparisons assessed opioid positivity among drivers crashing in states with an operational MML (i.e., allowances for home cultivation or active dispensaries) versus drivers crashing in states before a future MML was operational. Results. State-specific estimates indicated a reduction in opioid positivity for most states after implementation of an operational MML, although none of these estimates were significant. When we combined states, we observed no significant overall association (odds ratio [OR] = 0.79; 95% confidence interval [CI] = 0.61, 1.03). However, age-stratified analyses indicated a significant reduction in opioid positivity for drivers aged 21 to 40 years (OR = 0.50; 95% CI = 0.37, 0.67; interaction P < .001). Conclusions. Operational MMLs are associated with reductions in opioid positivity among 21- to 40-year-old fatally injured drivers and may reduce opioid use and overdose