Results confirm the recognized importance of age and upper and lower extremity strengths for walking after a SCL. They also highlight the role of 2 other factors, i.e., balance and spasticity, seldom considered as thoroughly in SCL.
On September 7, 2017, three potentially autochthonous cases of chikungunya were notified in the Lazio region. An Outbreak investigation based on established surveillance system data and molecular analysis of viral variant(s) were conducted. Epidemiological analysis suggested the occurrence of 3 main foci of local transmission. The major focus involved 317 cases with epidemiological link with the area of Anzio. The other two foci occurred in Rome (80 cases) and Latina (8 cases). Cumulative incidence in Anzio and Latina were 331.4 and 7.13 per 100,000 residents, respectively. Cumulative incidences ranged from 1.4 to 14.3/100,000 residents in Rome. This is the first report of a chikungunya outbreak involving a densely populated urban area in a western country. The outbreak probably started in Anzio, spread by continuity to neighbouring villages, and then to the metropolitan area of Rome and to the Latina area favoured by the touristic nature of the Anzio area.
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for the problem of classification and later generalized to various other situations. They are based on principles of statistical learning theory and convex optimization, and are currently used in various domains of application, including bioinformatics, text categorization, and computer vision.
S upport vector machines (SVMs), introduced byVapnik and coworkers in the 1990s, 1 are a family of algorithms for learning two-class discriminant functions from a set of training examples. They have been applied for a wide range of areas including text categorization, handwriting recognition, face detection, gene-expression data analysis, protein homology prediction, and many others. [2][3][4][5][6][7][8] The method combines ideas from statistical learning theory 9 and convex optimization, to find a suitable boundary in data space to separate the two classes of points. Extensions of the methods have been developed by a vast research community, and include methods for regression, clustering, factor analysis, based on the same design principles.
LINEAR SVMS FOR CLASSIFICATIONAs a family of algorithms, SVMs can address different learning tasks; here, we describe the case of two-class discrimination, while further extensions are discussed later on.Briefly, the aim of support vector classification is to search efficiently for a 'good' separating hyperplane in a high-dimensional feature space, 'good' in sense of some measure of generalization performance. The generalization performance of SVMs is discussed later on, but the most popular bound on error rates relies on the concept of margin that is the minimal distance between the hyperplane separating the two classes and the closest data points to the hyperplane. The optimal hyperplane is therefore defined as the one
BackgroundCountry-specific forecasts of the growing non-communicable disease (NCD) burden in ageing HIV-positive patients will be key to guide future HIV policies. We provided the first national forecasts for Italy and the Unites States of America (USA) and quantified direct cost of caring for these increasingly complex patients.Methods and SettingWe adapted an individual-based model of ageing HIV-positive patients to Italy and the USA, which followed patients on HIV-treatment as they aged and developed NCDs (chronic kidney disease, diabetes, dyslipidaemia, hypertension, non-AIDS malignancies, myocardial infarctions and strokes). The models were parameterised using data on 7,469 HIV-positive patients from the Italian Cohort Naïve to Antiretrovirals Foundation Study and 3,748 commercially-insured patients in the USA and extrapolated to national level using national surveillance data.ResultsThe model predicted that mean age of HIV-positive patients will increase from 46 to 59 in Italy and from 49 to 58 in the USA in 2015–2035. The proportion of patients in Italy and the USA diagnosed with ≥1 NCD is estimated to increase from 64% and 71% in 2015 to 89% and 89% by 2035, respectively, driven by moderate cardiovascular disease (CVD) (hypertension and dyslipidaemia), diabetes and malignancies in both countries. NCD treatment costs as a proportion of total direct HIV costs will increase from 11% to 23% in Italy and from 40% to 56% in the USA in 2015–2035.ConclusionsHIV patient profile in Italy and the USA is shifting to older patients diagnosed with multiple co-morbidity. This will increase NCD treatment costs and require multi-disciplinary patient management.
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