A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients

Support Vector Machines (SVMs) represent a powerful learning paradigm able to provide accurate and reliable decision functions in several application fields. In particular, they are really attractive for application in medical domain, where often a lack of knowledge exists. Kernel trick, on which SVMs are based, allows to map non-linearly separable data into potentially linearly separable one, according to the kernel function and its internal parameters value. During recent years non-parametric approaches have also been proposed for learning the most appropriate kernel, such as linear combination of basic kernels. Thus, SVMs classifiers may have several parameters to be tuned and their optimal values are usually difficult to be identified a-priori. Furthermore, combining different classifiers may reduce risk to perform errors on new unseen data. For such reasons, we present an hyper-solution framework for SVM classification, based on meta-heuristics, that searches for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration. We have applied the proposed framework on a critical and quite complex issue for the management of Chronic Heart Failure patient: the early detection of decompensation conditions. In fact, predicting new destabilizations in advance may reduce the burden of heart failure on the healthcare systems while improving quality of life of affected patients. Promising reliability has been obtained on 10-fold cross validation, proving our approach to be efficient and effective for an high-level analysis of clinical data

A novel similarity-measure for the analysis of genetic data in complex phenotypes

BACKGROUND: Recent technological advances in DNA sequencing and genotyping have led to the accumulation of a remarkable quantity of data on genetic polymorphisms. However, the development of new statistical and computational tools for effective processing of these data has not been equally as fast. In particular, Machine Learning literature is limited to relatively few papers which are focused on the development and application of data mining methods for the analysis of genetic variability. On the other hand, these papers apply to genetic data procedures which had been developed for a different kind of analysis and do not take into account the peculiarities of population genetics. The aim of our study was to define a new similarity measure, specifically conceived for measuring the similarity between the genetic profiles of two groups of subjects (i.e., cases and controls) taking into account that genetic profiles are usually distributed in a population group according to the Hardy Weinberg equilibrium. RESULTS: We set up a new kernel function consisting of a similarity measure between groups of subjects genotyped for numerous genetic loci. This measure weighs different genetic profiles according to the estimates of gene frequencies at Hardy-Weinberg equilibrium in the population. We named this function the "Hardy-Weinberg kernel". The effectiveness of the Hardy-Weinberg kernel was compared to the performance of the well established linear kernel. We found that the Hardy-Weinberg kernel significantly outperformed the linear kernel in a number of experiments where we used either simulated data or real data. CONCLUSION: The "Hardy-Weinberg kernel" reported here represents one of the first attempts at incorporating genetic knowledge into the definition of a kernel function designed for the analysis of genetic data. We show that the best performance of the "Hardy-Weinberg kernel" is observed when rare genotypes have different frequencies in cases and controls. The ability to capture the effect of rare genotypes on phenotypic traits might be a very important and useful feature, as most of the current statistical tools loose most of their statistical power when rare genotypes are involved in the susceptibility to the trait under study

The On-Site Analysis of the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) observatory will be one of the largest ground-based very high-energy gamma-ray observatories. The On-Site Analysis will be the first CTA scientific analysis of data acquired from the array of telescopes, in both northern and southern sites. The On-Site Analysis will have two pipelines: the Level-A pipeline (also known as Real-Time Analysis, RTA) and the level-B one. The RTA performs data quality monitoring and must be able to issue automated alerts on variable and transient astrophysical sources within 30 seconds from the last acquired Cherenkov event that contributes to the alert, with a sensitivity not worse than the one achieved by the final pipeline by more than a factor of 3. The Level-B Analysis has a better sensitivity (not be worse than the final one by a factor of 2) and the results should be available within 10 hours from the acquisition of the data: for this reason this analysis could be performed at the end of an observation or next morning. The latency (in particular for the RTA) and the sensitivity requirements are challenging because of the large data rate, a few GByte/s. The remote connection to the CTA candidate site with a rather limited network bandwidth makes the issue of the exported data size extremely critical and prevents any kind of processing in real-time of the data outside the site of the telescopes. For these reasons the analysis will be performed on-site with infrastructures co-located with the telescopes, with limited electrical power availability and with a reduced possibility of human intervention. This means, for example, that the on-site hardware infrastructure should have low-power consumption. A substantial effort towards the optimization of high-throughput computing service is envisioned to provide hardware and software solutions with high-throughput, low-power consumption at a low-cost.Comment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

First-episode psychosis and migration in Italy (PEP-Ita migration): a study in the Italian mental health services

BACKGROUND: It has been frequently reported a higher incidence of psychotic disorders in immigrants than in native populations. There is, however, a lack of knowledge about risk factors which may explain this phenomenon. A better understanding of the causes of psychosis among first-generation migrants is highly needed, particularly in Italy, a country with a recent massive migration. METHODS/DESIGN: The "Italian study on first-episode psychosis and migration (PEP-Ita)" is a prospective observational study over a two-year period (1 January 2012-31 December 2013) which will be carried out in 11 Italian mental health centres. All participating centres will collect data about all new cases of migrants with first-episode psychosis. The general purpose ("core") of the PEP-Ita study is to explore the socio-demographic and clinical characteristics, and the pathways to care of a population of first-episode psychosis migrants in Italy. Secondary aims of the study will be: 1) to understand risk and protective factors for the development of psychotic disorders in migrants; 2) to evaluate the correlations between psychopathology of psychotic disorders in migrants and socio-demographic characteristics, migration history, life experiences; 3) to evaluate the clinical and social outcomes of first-episode psychoses in migrants. DISCUSSION: The results of the PEP-Ita study will allow a better understanding of risk factors for psychosis in first-generation migrants in Italy. Moreover, our results will contribute to the development of prevention programmes for psychosis and to the improvement of early intervention treatments for the migrant population in Italy

A prototype for the real-time analysis of the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) observatory will be one of the biggest ground-based very-high-energy (VHE) γ- ray observatory. CTA will achieve a factor of 10 improvement in sensitivity from some tens of GeV to beyond 100 TeV with respect to existing telescopes. The CTA observatory will be capable of issuing alerts on variable and transient sources to maximize the scientific return. To capture these phenomena during their evolution and for effective communication to the astrophysical community, speed is crucial. This requires a system with a reliable automated trigger that can issue alerts immediately upon detection of γ-ray flares. This will be accomplished by means of a Real-Time Analysis (RTA) pipeline, a key system of the CTA observatory. The latency and sensitivity requirements of the alarm system impose a challenge because of the anticipated large data rate, between 0.5 and 8 GB/s. As a consequence, substantial efforts toward the optimization of highthroughput computing service are envisioned. For these reasons our working group has started the development of a prototype of the Real-Time Analysis pipeline. The main goals of this prototype are to test: (i) a set of frameworks and design patterns useful for the inter-process communication between software processes running on memory; (ii) the sustainability of the foreseen CTA data rate in terms of data throughput with different hardware (e.g. accelerators) and software configurations, (iii) the reuse of nonreal- time algorithms or how much we need to simplify algorithms to be compliant with CTA requirements, (iv) interface issues between the different CTA systems. In this work we focus on goals (i) and (ii)

The innovative Cherenkov camera based on SiPM sensors of the ASTRI-Horn telescope: from the T/M and electrical design to the full assembly and testing in a harsh environment

ASTRI-Horn is a prototypal telescope of an imaging atmospheric Cherenkov telescope developed by the Italian National Institute of Astrophysics (INAF), proposed for the Cherenkov Telescope Array (CTA) Observatory. The CTA Observatory represents the next generation of imaging atmospheric Cherenkov telescopes and will explore the very highenergy domain from a few tens of GeV up to few hundreds of TeV. It will be composed of large-, medium-, and small sized telescopes; ASTRI-Horn is an end-to-end prototype proposed for the Small Sized array. The main scientific instrument of the ASTRI-Horn telescope is an innovative and compact Camera with Silicon- Photomultiplier based detectors and a specifically designed fast read-out electronics based on a custom peak-detector mode. The thermo-mechanical assembly is designed to host both the entire electronics chain, from the sensors to the raw data transmission system and the calibration system, and the complete thermoregulation system. This contribution gives a high level description of the T/M and electrical design of the Cherenkov Camera, it describes the assembling procedure of its different subsystems and their integration into the complete camera system. A discussion about possible design improvements coming from the problems/difficulties encountered during assembly is also presented. Finally, results from engineering tests conducted in-field are also presented

The ASTRI camera for the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) foresees, in its southern site (Chile), the implementation of up to 70 small-sized telescopes (SSTs), which will extend the energy coverage up to hundreds of TeV. It has been proposed that one of the first set of CTA SSTs will be represented by the ASTRI mini-array, which includes (at least) nine ASTRI telescopes. The endto-end prototype of such telescopes, named the ASTRI SST-2M, is installed in Italy and it is now completing the overall commissioning and entering the science verification phase. ASTRI telescopes are characterized by an optical system based on a dual-mirror Schwarzschild-Couder design and a camera at the focal plane composed of silicon photomultiplier sensors managed by a fast read-out electronics specifically designed. Based on a custom peak-detector mode, the ASTRI camera electronics is designed to perform Cherenkov signal detection, trigger generation, digital conversion of the signals and data transmission to the camera server. In this contribution we will describe the main features of the ASTRI camera, its performance and results obtained during the commissioning phase of the ASTRI SST-2M prototype in view of the ASTRI mini-array implementation

Clinical and laboratory features associated with macrophage activation syndrome in Still's disease: data from the international AIDA Network Still's Disease Registry

: To characterize clinical and laboratory signs of patients with still's disease experiencing macrophage activation syndrome (MAS) and identify factors associated with MAS development. patients with still's disease classified according to internationally accepted criteria were enrolled in the autoInflammatory disease alliance (AIDA) still's disease registry. clinical and laboratory features observed during the inflammatory attack complicated by MAS were included in univariate and multivariate logistic regression analysis to identify factors associated to MAS development. A total of 414 patients with Still's disease were included; 39 (9.4%) of them developed MAS during clinical history. At univariate analyses, the following variables were significantly associated with MAS: classification of arthritis based on the number of joints involved (p = 0.003), liver involvement (p = 0.04), hepatomegaly (p = 0.02), hepatic failure (p = 0.01), axillary lymphadenopathy (p = 0.04), pneumonia (p = 0.03), acute respiratory distress syndrome (p < 0.001), platelet abnormalities (p < 0.001), high serum ferritin levels (p = 0.009), abnormal liver function tests (p = 0.009), hypoalbuminemia (p = 0.002), increased LDH (p = 0.001), and LDH serum levels (p < 0.001). at multivariate analysis, hepatomegaly (OR 8.7, 95% CI 1.9-52.6, p = 0.007) and monoarthritis (OR 15.8, 95% CI 2.9-97.1, p = 0.001), were directly associated with MAS, while the decade of life at Still's disease onset (OR 0.6, 95% CI 0.4-0.9, p = 0.045), a normal platelet count (OR 0.1, 95% CI 0.01-0.8, p = 0.034) or thrombocytosis (OR 0.01, 95% CI 0.0-0.2, p = 0.008) resulted to be protective. clinical and laboratory factors associated with MAS development have been identified in a large cohort of patients based on real-life data