Methods of tropical optimization in rating alternatives based on pairwise comparisons

We apply methods of tropical optimization to handle problems of rating alternatives on the basis of the log-Chebyshev approximation of pairwise comparison matrices. We derive a direct solution in a closed form, and investigate the obtained solution when it is not unique. Provided the approximation problem yields a set of score vectors, rather than a unique (up to a constant factor) one, we find those vectors in the set, which least and most differentiate between the alternatives with the highest and lowest scores, and thus can be representative of the entire solution.Comment: 9 pages, presented at the Annual Intern. Conf. of the German Operations Research Society (GOR), Helmut Schmidt University Hamburg, Germany, August 30 - September 2, 201

Atherosclerosis of the ascending aorta is an independent predictor of long-term neurologic events and mortality

AbstractOBJECTIVESThis study was undertaken to determine whether atherosclerosis of the ascending aorta is a predictor of long-term neurologic events and mortality.BACKGROUNDAtherosclerosis of the thoracic aorta has been recently considered a significant predictor of neurologic events and peripheral embolism, but not of long-term mortality.METHODSLong-term follow-up (a total of 5,859 person-years) was conducted of 1,957 consecutive patients ≥50 years old who underwent cardiac surgery. Atherosclerosis of the ascending aorta was assessed intraoperatively (epiaortic ultrasound) and patients were divided into four groups according to severity (normal, mild, moderate or severe). Carotid artery disease was evaluated (carotid ultrasound) in 1,467 (75%) patients. Cox proportional-hazards regression analysis was performed to assess the independent effect of predictors on neurologic events and mortality.RESULTSA total of 491 events occurred in 472 patients (neurologic events 92, all-cause mortality 399). Independent predictors of long-term neurologic events were: hypertension (p = 0.009), ascending aorta atherosclerosis (p = 0.011) and diabetes mellitus (p = 0.015). The independent predictors of mortality were advanced age (p < 0.0001), left ventricular dysfunction (p < 0.0001), ascending aorta atherosclerosis (p < 0.0001), hypertension (p = 0.0001) and diabetes mellitus (p = 0.0002). There was >1.5-fold increase in the incidence of both neurologic events and mortality as the severity of atherosclerosis increased from normal-mild to moderate, and a greater than threefold increase in the incidence of both as the severity of atherosclerosis increased from normal-mild to severe.CONCLUSIONSAtherosclerosis of the ascending aorta is an independent predictor of long-term neurologic events and mortality. These results provide additional evidence that in addition to being a direct cause of cerebral atheroembolism, an atherosclerotic ascending aorta may be a marker of generalized atherosclerosis and thus of increased morbidity and mortality

Developing a multiple-document-processing performance assessment for epistemic literacy

The LAK15 theme “shifts the focus from data to impact”, noting the potential for Learning Analytics based on existing technologies to have scalable impact on learning for people of all ages. For such demand and potential in scalability to be met the challenges of addressing higher-order thinking skills should be addressed. This paper discuses one such approach – the creation of an analytic and task model to probe epistemic cognition in complex literacy tasks. The research uses existing technologies in novel ways to build a conceptually grounded model of trace-indicators for epistemic-commitments in information seeking behaviors. We argue that such an evidence centered approach is fundamental to realizing the potential of analytics, which should maintain a strong association with learning theory

Cytosine Methylation Dysregulation in Neonates Following Intrauterine Growth Restriction

Perturbations of the intrauterine environment can affect fetal development during critical periods of plasticity, and can increase susceptibility to a number of age-related diseases (e.g., type 2 diabetes mellitus; T2DM), manifesting as late as decades later. We hypothesized that this biological memory is mediated by permanent alterations of the epigenome in stem cell populations, and focused our studies specifically on DNA methylation in CD34+ hematopoietic stem and progenitor cells from cord blood from neonates with intrauterine growth restriction (IUGR) and control subjects.Our epigenomic assays utilized a two-stage design involving genome-wide discovery followed by quantitative, single-locus validation. We found that changes in cytosine methylation occur in response to IUGR of moderate degree and involving a restricted number of loci. We also identify specific loci that are targeted for dysregulation of DNA methylation, in particular the hepatocyte nuclear factor 4alpha (HNF4A) gene, a well-known diabetes candidate gene not previously associated with growth restriction in utero, and other loci encoding HNF4A-interacting proteins.Our results give insights into the potential contribution of epigenomic dysregulation in mediating the long-term consequences of IUGR, and demonstrate the value of this approach to studies of the fetal origin of adult disease

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.Comment: 14 pages, 8 figure

Implementation of an Optimal First-Order Method for Strongly Convex Total Variation Regularization

We present a practical implementation of an optimal first-order method, due to Nesterov, for large-scale total variation regularization in tomographic reconstruction, image deblurring, etc. The algorithm applies to $\mu$-strongly convex objective functions with $L$-Lipschitz continuous gradient. In the framework of Nesterov both $\mu$ and $L$ are assumed known -- an assumption that is seldom satisfied in practice. We propose to incorporate mechanisms to estimate locally sufficient $\mu$ and $L$ during the iterations. The mechanisms also allow for the application to non-strongly convex functions. We discuss the iteration complexity of several first-order methods, including the proposed algorithm, and we use a 3D tomography problem to compare the performance of these methods. The results show that for ill-conditioned problems solved to high accuracy, the proposed method significantly outperforms state-of-the-art first-order methods, as also suggested by theoretical results.Comment: 23 pages, 4 figure

A simple formula to find the closest consistent matrix to a reciprocal matrix

Achieving consistency in pair-wise comparisons between decision elements given by experts or stakeholders is of paramount importance in decision-making based on the AHP methodology. Several alternatives to improve consistency have been proposed in the literature. The linearization method (Benitez et al., 2011 [10]), derives a consistent matrix based on an original matrix of comparisons through a suitable orthogonal projection expressed in terms of a Fourier-like expansion. We propose a formula that provides in a very simple manner the consistent matrix closest to a reciprocal (inconsistent) matrix. In addition, this formula is computationally efficient since it only uses sums to perform the calculations. A corollary of the main result shows that the normalized vector of the vector, whose components are the geometric means of the rows of a comparison matrix, gives the priority vector only for consistent matrices. (C) 2014 Elsevier Inc. All rights reserved.This work has been performed with the support of the project IDAWAS, DPI2009-11591 of the Direccion General de Investigacion del Ministerio de Ciencia e Innovacion (Spain), with the supplementary support of ACOMP/2010/146 of the Conselleria d'Educacio of the Generalitat Valenciana, and the support given to the first author by the Spanish project MTM2010-18539. The use of English in this paper was revised by John Rawlins; and the revision was funded by the Universitat Politecnica de Valencia, Spain.Benítez López, J.; Izquierdo Sebastián, J.; Pérez García, R.; Ramos Martínez, E. (2014). A simple formula to find the closest consistent matrix to a reciprocal matrix. Applied Mathematical Modelling. 38(15-16):3968-3974. https://doi.org/10.1016/j.apm.2014.01.007S396839743815-1

Statin Therapy and Risk of Developing Type 2 Diabetes: A Meta-Analysis

OBJECTIVE: Although statin therapy reduces cardiovascular risk, its relationship with the development of diabetes is controversial. The first study (West of Scotland Coronary Prevention Study [WOSCOPS]) that evaluated this association reported a small protective effect but used nonstandardized criteria for diabetes diagnosis. However, results from subsequent hypothesis-testing trials have been inconsistent. The aim of this meta-analysis is to evaluate the possible effect of statin therapy on incident diabetes. RESEARCH DESIGN AND METHODS: A systematic literature search for randomized statin trials that reported data on diabetes through February 2009 was conducted using specific search terms. In addition to the hypothesis-generating data from WOSCOPS, hypothesis-testing data were available from the Heart Protection Study (HPS), the Long-Term Intervention with Pravastatin in Ischemic Disease (LIPID) Study, the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT), the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER), and the Controlled Rosuvastatin Multinational Study in Heart Failure (CORONA), together including 57,593 patients with mean follow-up of 3.9 years during which 2,082 incident diabetes cases accrued. Weighted averages were reported as risk ratios (RRs) with 95% CIs using a random-effects model. Statistical heterogeneity scores were assessed with the Q and I2 statistic.RESULTS In the meta-analysis of the hypothesis-testing trials, we observed a small increase in diabetes risk (RR 1.13 [95% CI 1.03–1.23]) with no evidence of heterogeneity across trials. However, this estimate was attenuated and no longer significant when the hypothesis-generating trial WOSCOPS was included (1.06 [0.93–1.25]) and also resulted in significant heterogeneity (Q 11.8 [5 d.f.], P = 0.03, I2 = 57.7%). CONCLUSIONS: Although statin therapy greatly lowers vascular risk, including among those with and at risk for diabetes, the relationship of statin therapy to incident diabetes remains uncertain. Future statin trials should be designed to formally address this issue

Gene-Gene Interactions Lead to Higher Risk for Development of Type 2 Diabetes in an Ashkenazi Jewish Population

Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7-5.3]; P<or=0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7-3.4; P<or=0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4-2.8; P<or=0.0001] and 2.3 [95% CI = 1.2-4.4; P<or=0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P<or=0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk