Patient rule induction method for subgroup identification given censored data.

The identification of subgroups in clinical studies is an important aspect of personalized medicine. In order to develop tailored therapeutics, the factors that characterize subgroups with differential prognosis, response to treatment, and incidence of adverse events or toxicities must be elucidated. We present a generalization of a statistical learning algorithm, Patient Rule Induction Method (PRIM), that is well suited for this task given a right-censored time-to-event outcome measure. This algorithm works to recursively partition a covariate space into mutually exclusive boxes that can be utilized to define subgroups. Conceptually the algorithm is similar to classification and regression trees but rather than satisfying the goal of minimizing overall prediction error, PRIM works to find the extrema of the response surface. The algorithm\u27s performance in prognostic subgroup identification is demonstrated with simulation studies and a case study using data from the Framingham Heart Study. We find that the algorithm has much utility as it provides a set of easy to interpret rules that define subgroups with maximal (minimal) survival or differential response to an intervention as measured by a survival outcome

Bayesian analytical approaches for metabolomics : a novel method for molecular structure-informed metabolite interaction modeling, a novel diagnostic model for differentiating myocardial infarction type, and approaches for compound identification given mass spectrometry data.

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, which we define as the testing of hypotheses that are more complex than single metabolite hypothesis tests. This methodology utilizes informative priors that are generated via the analysis of molecular structure similarity to enable the estimation of metabolite interactomes (or probabilistic models) which are organism-, sample media-, and condition-specific as well as comprehensive; and that can serve as reference models for studying perturbations in metabolic systems. After discussing the development of our methodology, we present an evaluation of its performance conducted using simulation studies, and we use the methodology for estimating a plasma metabolite interactome for stable heart disease. This interactome may serve as a reference model for evaluating systems-level changes that occur with acute disease events such as myocardial infarction (MI) or unstable angina. In the second part of this work, we present the challenge of developing diagnostic classification models which utilize metabolite abundances and that do not overfit relatively small sample sizes, especially given the high dimensionality of metabolite data acquired using platforms such as liquid chromatography-mass spectrometry. We use a Bayesian methodology for estimating a multinomial logistic regression classifier for the detection and discrimination of the subtype of acute myocardial infarction utilizing metabolite abundance data quantified from blood plasma. As heart disease is the leading cause of global mortality, a blood-based and non-invasive diagnostic test that could differentiate between MI types at the time of the event would have great utility. In the final part of this dissertation we review Bayesian approaches for compound identification in metabolomics experiments that utilize liquid chromatography-mass spectrometry which remains a challenging problem

Coding information into all infinite subsets of a dense set

Suppose you have an uncomputable set $X$ and you want to find a set $A$, all of whose infinite subsets compute $X$. There are several ways to do this, but all of them seem to produce a set $A$ which is fairly sparse. We show that this is necessary in the following technical sense: if $X$ is uncomputable and $A$ is a set of positive lower density then $A$ has an infinite subset which does not compute $X$. We will show that this theorem is sharp in certain senses and also prove a quantitative version formulated in terms of Kolmogorov complexity. Our results use a modified version of Mathias forcing and build on work by Seetapun and others on the reverse math of Ramsey's theorem for pairs.Comment: 30 page

Wisdom of artificial crowds feature selection in untargeted metabolomics: An application to the development of a blood-based diagnostic test for thrombotic myocardial infarction

Introduction: Heart disease remains a leading cause of global mortality. While acute myocardial infarction (colloquially: heart attack), has multiple proximate causes, proximate etiology cannot be determined by a blood-based diagnostic test. We enrolled a suitable patient cohort and conducted a non-targeted quantification of plasma metabolites by mass spectrometry for developing a test that can differentiate between thrombotic MI, non-thrombotic MI, and stable disease. A significant challenge in developing such a diagnostic test is solving the NP-hard problem of feature selection for constructing an optimal statistical classifier. Objective: We employed a Wisdom of Artificial Crowds (WoAC) strategy for solving the feature selection problem and evaluated the accuracy and parsimony of downstream classifiers in comparison with traditional feature selection techniques including the Lasso and selection using Random Forest variable importance criteria. Materials and methods: Artificial Crowd Wisdom was generated via aggregation of the best solutions from independent and diverse genetic algorithm populations that were initialized with bootstrapping and a random subspaces constraint. Results/Conclusions: Strong evidence was observed that a statistical classifier utilizing WoAC feature selection can discriminate between human subjects presenting with thrombotic MI, non-thrombotic MI, and stable Coronary Artery Disease given abundances of selected plasma metabolites. Utilizing the abundances of twenty selected metabolites, a leave-one-out cross-validation estimated misclassification rate of 2.6% was observed. However, the WoAC feature selection strategy did not perform better than the Lasso over the current study

System for energy harvesting and/or generation, storage, and delivery

A device and method for harvesting, generating, storing, and delivering energy to a load, particularly for remote or inaccessible applications. The device preferably comprises one or more energy sources, at least one supercapacitor, at least one rechargeable battery, and a controller. The charging of the energy storage devices and the delivery of power to the load is preferably dynamically varied to maximize efficiency. A low power consumption charge pump circuit is preferably employed to collect power from low power energy sources while also enabling the delivery of higher voltage power to the load. The charging voltage is preferably programmable, enabling one device to be used for a wide range of specific applications

A NEWLY FORMING COLD FLOW PROTOGALACTIC DISK, A SIGNATURE of COLD ACCRETION from the COSMIC WEB

How galaxies form from, and are fueled by, gas from the intergalactic medium (IGM) remains one of the major unsolved problems in galaxy formation. While the classical Cold Dark Matter paradigm posits galaxies forming from cooling virialized gas, recent theory and numerical simulations have highlighted the importance of cold accretion flows - relatively cool (T ∼ few × 104 K) unshocked gas streaming along filaments into dark matter halos, including hot, massive, high-redshift halos. These flows are thought to deposit gas and angular momentum into the circumgalactic medium resulting in disk- or ring-like structures, eventually coalescing into galaxies forming at filamentary intersections. We earlier reported a bright, Lyα emitting filament near the QSO HS1549+19 at redshift z = 2.843 discovered with the Palomar Cosmic Web Imager. We now report that the bright part of this filament is an enormous (R > 100 kpc) rotating structure of hydrogen gas with a disk-like velocity profile consistent with a 4 × 1012 M o halo. The orbital time of the outer part of the what we term a "protodisk" is comparable to the virialization time and the age of the universe at this redshift. We propose that this protodisk can only have recently formed from cold gas flowing directly from the cosmic we

Presence of multiple coronary angiographic characteristics for the diagnosis of acute coronary thrombus

Background: Coronary angiography is frequently employed to aid in the diagnosis of acute coronary thrombosis, but there is limited data to support its efficacy. The aim of the study was to evaluate sensitivity and specificity of five commonly used angiographic characteristics for diagnosis of acute coronary thrombosis: Ambrose complex lesion morphology; spherical, ovoid, or irregular filling defect; abrupt vessel cutoff; intraluminal staining; and any coronary filling defect. Methods: Coronary angiography of 80 acute myocardial infarction or stable coronary artery disease subjects were assessed in blinded fashion, for the presence or absence of five angiographic characteristics. Only lesions of ≥ 10% stenosis were included in the analysis. Presence or absence of each angiographic characteristic was compared between lesions with or without the following study defined outcomes: 1) histologically confirmed thrombus, 2) highly probable thrombus, and 3) highly unlikely thrombus. Results: A total of 323 lesions were evaluated. All studied angiographic characteristics were associated with histologically confirmed and highly probable thrombotic lesions vs. lesions not meeting criteria for these outcomes (p < 0.03), except for complex Ambrose morphology which was not associated with any of the study outcomes (p > 0.05). Specificity for identifying histologically confirmed or highly probable thrombotic lesion was high (92–100%), especially for spherical, ovoid, or irregular filling defect (99–100%) and intraluminal staining (99%). Sensitivity for identification of histologically confirmed or highly probable thrombotic lesions was low for all tested angiographic characteristics (17–60%). Conclusions: The presence of spherical, ovoid, or irregular filling defect or intraluminal staining was highly suggestive of coronary thrombus. However, none of the evaluated angiographic characteristics were useful for ruling out the presence of coronary thrombus. If confirmed in an independent cohort, these angiographic characteristic will be of significant value in confirming the diagnosis of acute coronary thrombosis.

A Newly Forming Cold Flow Protogalactic Disk, a Signature of Cold Accretion from the Cosmic Web

How galaxies form from, and are fueled by, gas from the intergalactic medium (IGM) remains one of the major unsolved problems in galaxy formation. While the classical Cold Dark Matter paradigm posits galaxies forming from cooling virialized gas, recent theory and numerical simulations have highlighted the importance of cold accretion flows—relatively cool (T ~ few × 104 K) unshocked gas streaming along filaments into dark matter halos, including hot, massive, high-redshift halos. These flows are thought to deposit gas and angular momentum into the circumgalactic medium resulting in disk- or ring-like structures, eventually coalescing into galaxies forming at filamentary intersections. We earlier reported a bright, Lyα emitting filament near the QSO HS1549+19 at redshift z = 2.843 discovered with the Palomar Cosmic Web Imager. We now report that the bright part of this filament is an enormous (R > 100 kpc) rotating structure of hydrogen gas with a disk-like velocity profile consistent with a 4 × 10^(12) M_⊙ halo. The orbital time of the outer part of the what we term a "protodisk" is comparable to the virialization time and the age of the universe at this redshift. We propose that this protodisk can only have recently formed from cold gas flowing directly from the cosmic web

The role and function of IκKα/β in monocyte impairment

Following major trauma, sepsis or surgery, some patients exhibit an impaired monocyte inflammatory response that is characterized by a decreased response to a subsequent bacterial challenge. To investigate this poorly understood phenomenon, we adopted an in-vitro model of endotoxin tolerance utilising primary human CD14 + monocytes to focus on the effect of impairment on IκKα/β, a critical part of the NFκB pathway. Impaired monocytes had decreased IκKα mRNA and protein expression and decreased phosphorylation of the IκKα/β complex. The impaired monocyte secretome demonstrated a distinct cytokine/chemokine footprint from the naïve monocyte, and that TNF-α was the most sensitive cytokine or chemokine in this setting of impairment. Inhibition of IκKα/β with a novel selective inhibitor reproduced the impaired monocyte phenotype with decreased production of TNF-α, IL-6, IL-12p70, IL-10, GM-CSF, VEGF, MIP-1β, TNF-β, IFN-α2 and IL-7 in response to an LPS challenge. Surgical patients with infection also exhibited an impaired monocyte phenotype and had decreased SITPEC, TAK1 and MEKK gene expression, which are important for IκKα/β activation. Our results emphasize that impaired monocyte function is, at least in part, related to dysregulated IκKα/β activation, and that IκKα/β is likely involved in mounting a sufficient monocyte inflammatory response. Future studies may wish to focus on adjuvant therapies that augment IκKα/β function to restore monocyte function in this clinically important problem