51 research outputs found

    Theorem-Proving Analysis of Digital Control Logic Interacting with Continuous Dynamics

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    AbstractThis work outlines an equation-based formulation of a digital control program and transducer interacting with a continuous physical process, and an approach using the Coq theorem prover for verifying the performance of the combined hybrid system. Considering thermal dynamics with linear dissipation for simplicity, we focus on a generalizable, physically consistent description of the interaction of the real-valued temperature and the digital program acting as a thermostat. Of interest in this work is the discovery and formal proof of bounds on the temperature, the degree of variation, and other performance characteristics. Our approach explicitly addresses the need to mathematically represent the decision problem inherent in an analog-to-digital converter, which for rare values can take an arbitrarily long time to produce a digital answer (the so-called Buridan's Principle); this constraint ineluctably manifests itself in the verification of thermostat performance. Furthermore, the temporal causality constraints in the thermal physics must be made explicit to obtain a consistent model for analysis. We discuss the significance of these findings toward the verification of digital control for more complex physical variables and fields

    COMPOSE-HPC: A Transformational Approach to Exascale

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    The goal of the COMPOSE-HPC project is to 'democratize' tools for automatic transformation of program source code so that it becomes tractable for the developers of scientific applications to create and use their own transformations reliably and safely. This paper describes our approach to this challenge, the creation of the KNOT tool chain, which includes tools for the creation of annotation languages to control the transformations (PAUL), to perform the transformations (ROTE), and optimization and code generation (BRAID), which can be used individually and in combination. We also provide examples of current and future uses of the KNOT tools, which include transforming code to use different programming models and environments, providing tests that can be used to detect errors in software or its execution, as well as composition of software written in different programming languages, or with different threading patterns

    Multidrug resistant Acinetobacter baumannii: a descriptive study in a city hospital

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    <p>Abstract</p> <p>Background</p> <p>Multidrug resistant <it>Acinetobacter baumannii</it>, (MRAB) is an important cause of hospital acquired infection. The purpose of this study is to determine the risk factors for MRAB in a city hospital patient population.</p> <p>Methods</p> <p>This study is a retrospective review of a city hospital epidemiology data base and includes 247 isolates of Acinetobacter baumannii (AB) from 164 patients. Multidrug resistant <it>Acinetobacter baumannii </it>was defined as resistance to more than three classes of antibiotics. Using the non-MRAB isolates as the control group, the risk factors for the acquisition of MRAB were determined.</p> <p>Results</p> <p>Of the 247 AB isolates 72% (177) were multidrug resistant. Fifty-eight percent (143/247) of isolates were highly resistant (resistant to imipenem, amikacin, and ampicillin-sulbactam). Of the 37 patients who died with Acinetobacter colonization/infection, 32 (86%) patients had the organism recovered from the respiratory tract. The factors which were found to be significantly associated (p ≤ 0.05) with multidrug resistance include the recovery of AB from multiple sites, mechanical ventilation, previous antibiotic exposure, and the presence of neurologic impairment. Multidrug resistant Acinetobacter was associated with significant mortality when compared with sensitive strains (p ≤ 0.01). When surgical patients (N = 75) were considered separately, mechanical ventilation and multiple isolates remained the factors significantly associated with the development of multidrug resistant Acinetobacter. Among surgical patients 46/75 (61%) grew a multidrug resistant strain of AB and 37/75 (40%) were resistant to all commonly used antibiotics including aminoglycosides, cephalosporins, carbepenems, extended spectrum penicillins, and quinolones. Thirty-five percent of the surgical patients had AB cultured from multiple sites and 57% of the Acinetobacter isolates were associated with a co-infecting organism, usually a Staphylococcus or Pseudomonas. As in medical patients, the isolation of Acinetobacter from multiple sites and the need for mechanical ventilation were significantly associated with the development of MRAB.</p> <p>Conclusions</p> <p>The factors significantly associated with MRAB in both the general patient population and surgical patients were mechanical ventilation and the recovery of Acinetobacter from multiple anatomic sites. Previous antibiotic use and neurologic impairment were significant factors in medical patients. Colonization or infection with MRAB is associated with increased mortality.</p

    Clinical field-strength MRI of amyloid plaques induced by low-level cholesterol feeding in rabbits

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    Two significant barriers have limited the development of effective treatment of Alzheimer's disease. First, for many cases the aetiology is unknown and likely multi-factorial. Among these factors, hypercholesterolemia is a known risk predictor and has been linked to the formation of β-amyloid plaques, a pathological hallmark this disease. Second, standardized diagnostic tools are unable to definitively diagnose this disease prior to death; hence new diagnostic tools are urgently needed. Magnetic resonance imaging (MRI) using high field-strength scanners has shown promise for direct visualization of β-amyloid plaques, allowing in vivo longitudinal tracking of disease progression in mouse models. Here, we present a new rabbit model for studying the relationship between cholesterol and Alzheimer's disease development and new tools for direct visualization of β-amyloid plaques using clinical field-strength MRI. New Zealand white rabbits were fed either a low-level (0.125–0.25% w/w) cholesterol diet (n = 5) or normal chow (n = 4) for 27 months. High-resolution (66 × 66 × 100 µm3; scan time = 96 min) ex vivo MRI of brains was performed using a 3-Tesla (T) MR scanner interfaced with customized gradient and radiofrequency coils. β-Amyloid-42 immunostaining and Prussian blue iron staining were performed on brain sections and MR and histological images were manually registered. MRI revealed distinct signal voids throughout the brains of cholesterol-fed rabbits, whereas minimal voids were seen in control rabbit brains. These voids corresponded directly to small clusters of extracellular β-amyloid-positive plaques, which were consistently identified as iron-loaded (the presumed source of MR contrast). Plaques were typically located in the hippocampus, parahippocampal gyrus, striatum, hypothalamus and thalamus. Quantitative analysis of the number of histologically positive β-amyloid plaques (P < 0.0001) and MR-positive signal voids (P < 0.05) found in cholesterol-fed and control rabbit brains corroborated our qualitative observations. In conclusion, long-term, low-level cholesterol feeding was sufficient to promote the formation of extracellular β-amyloid plaque formation in rabbits, supporting the integral role of cholesterol in the aetiology of Alzheimer's disease. We also present the first evidence that MRI is capable of detecting iron-associated β-amyloid plaques in a rabbit model of Alzheimer's disease and have advanced the sensitivity of MRI for plaque detection to a new level, allowing clinical field-strength scanners to be employed. We believe extension of these technologies to an in vivo setting in rabbits is feasible and that our results support future work exploring the role of MRI as a leading imaging tool for this debilitating and life-threatening disease

    The human brainome: network analysis identifies \u3ci\u3eHSPA2\u3c/i\u3e as a novel Alzheimer’s disease target

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    Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-B40 and amyloid-B42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes

    Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

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    We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

    Analysis of shared heritability in common disorders of the brain

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    ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders
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