Modelamiento de diversos factores que inciden en el rendimiento académico de los estudiantes de física de la Universidad de Nariño en el semestre A-2017

Este trabajo tiene como objetivo identificar factores relacionados con el desempeño académico de los estudiantes de Física de la Universidad de Nariño, y hacer uso de estos, mediante métodos estadísticos para buscar relaciones entre dichas variables y así proponer un modelo que explique en buena medida el desempeño académico de la población mencionada. El estudio incluye breves antecedentes tomados de anteriores investigaciones afines; algunas de las cuales fueron realizadas también en la Universidad de Nariño, otros trabajos realizados en el exterior y dan una idea de la problemática que guarda esta situación en varias naciones, posteriormente se presenta la metodología seguida en la investigación, los resultados estadísticos obtenidos, su discusión y las conclusiones del presente, para finalmente listar las referencias bibliográficas utilizadas en el estudio

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRxResearch; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Effectiveness of the psychological and pharmacological treatment of catastrophization in patients with fibromyalgia: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Fibromyalgia is a prevalent and disabling disorder characterized by widespread pain and other symptoms such as insomnia, fatigue or depression. Catastrophization is considered a key clinical symptom in fibromyalgia; however, there are no studies on the pharmacological or psychological treatment of catastrophizing. The general aim of this study is to assess the effectiveness of cognitive-behaviour therapy and recommended pharmacological treatment for fibromyalgia (pregabalin, with duloxetine added where there is a comorbid depression), compared with usual treatment at primary care level.</p> <p>Method/design</p> <p><it>Design</it>: A multi-centre, randomized controlled trial involving three groups: the control group, consisting of usual treatment at primary care level, and two intervention groups, one consisting of cognitive-behaviour therapy, and the other consisting of the recommended pharmacological treatment for fibromyalgia.</p> <p><it>Setting</it>: 29 primary care health centres in the city of Zaragoza, Spain.</p> <p><it>Sample</it>: 180 patients, aged 18–65 years, able to understand and read Spanish, who fulfil criteria for primary fibromyalgia, with no previous psychological treatment, and no pharmacological treatment or their acceptance to discontinue it two weeks before the onset of the study.</p> <p><it>Intervention</it>: Psychological treatment is based on the manualized protocol developed by Prof. Escobar et al, from the University of New Jersey, for the treatment of somatoform disorders, which has been adapted by our group for the treatment of fibromyalgia. It includes 10 weekly sessions of cognitive-behaviour therapy. Pharmacological therapy consists of the recommended pharmacological treatment for fibromyalgia: pregabalin (300–600 mg/day), with duloxetine (60–120 mg/day) added where there is a comorbid depression).</p> <p><it>Measurements</it>: The following socio-demographic data will be collected: sex, age, marital status, education, occupation and social class. The diagnosis of psychiatric disorders will be made with the Structured Polyvalent Psychiatric Interview. Other instruments to be administered are the Pain Catastrophizing Scale, the Hamilton tests for Anxiety and for Depression, the Fibromyalgia Impact Questionnaire (FIQ), the EuroQuol-5 domains (EQ-5D), and the use of health and social services (CSRI). Assessments will be carried out at baseline, 1, 3, and 6 months.</p> <p><it>Main variable</it>: Pain catastrophizing.</p> <p><it>Analysis</it>: The analysis will be per intent to treat. We will use the general linear models of the SPSS version 15 statistical package, to analyse the effect of the treatment on the result variable (pain catastrophizing).</p> <p>Discussion</p> <p>It is necessary to assess the effectiveness of pharmacological and psychological treatments for pain catastrophizing in fibromyalgia. This randomized clinical trial will determine whether both treatments are effective for this important prognostic variable in patients with fibromyalgia.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN10804772</p

Search for narrow resonances in dilepton mass spectra in proton-proton collisions at root s=13 TeV and combination with 8 TeV data

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Search for top squark pair production in pp collisions at root s=13 TeV using single lepton events

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Search for heavy gauge W ' bosons in events with an energetic lepton and large missing transverse momentum at root s=13TeV

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Search for anomalous Wtb couplings and flavour-changing neutral currents in t-channel single top quark production in pp collisions at root s=7 and 8 TeV

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Search for massive resonances decaying in to WW,WZ or ZZ bosons in proton-proton collisions at root s=13 TeV

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Measurement of the production cross section of a W boson in association with two b jets in pp collisions at root s=8TeV

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