Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of "basis" series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs

a multi view sentiment corpus

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twine a real time system for tweet analysis via information extraction

In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information enclosed in them. In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at http://twine-mind.cloudapp.net/streamin

Risk of classical Kaposi sarcoma by plasma levels of Epstein-Barr virus antibodies, sCD26, sCD23 and sCD30

<p>Abstract</p> <p>Background</p> <p>To clarify the immunological alterations leading to classical Kaposi sarcoma (cKS) among people infected with KS-associated herpesvirus (KSHV).</p> <p>Methods</p> <p>In a population-based study of 119 cKS cases, 105 KSHV-seropositive controls, and 155 KSHV-seronegative controls, we quantified plasma soluble cluster of differentiation (sCD) levels and antibodies against Epstein-Barr virus nuclear antigen-1 (anti-EBNA-1) and viral capsid antigen (anti-VCA). Differences between groups in prevalence of low-tertile anti-EBNA-1 and high-tertile anti-VCA were compared by logistic regression. Continuous levels between groups and by presence of cKS co-factors among controls were compared by linear regression and Mann-Whitney-Wilcoxon methods.</p> <p>Results</p> <p>Comparisons of cKS cases to seropositive controls and of seropositive to seronegative controls revealed no significant differences. However, controls with known cKS cofactors (male sex, nonsmoking, diabetes and cortisone use) had significantly lower levels of anti-EBNA (<it>P </it>= 0.0001 - 0.07) and anti-VCA (<it>P </it>= 0.0001 - 0.03). Levels of sCD26 were significantly lower for male and non-smoking controls (<it>P</it>_adj≤ 0.03), and they were marginally lower with older age and cortisone use (<it>P</it>_adj≤ 0.09).</p> <p>Conclusions</p> <p>Anti-EBV and sCD26 levels were associated with cofactors for cKS, but they did not differ between cKS cases and matched controls. Novel approaches and broader panels of assays are needed to investigate immunological contributions to cKS.</p

A telerehabilitation platform for cognitive, physical and behavioural rehabilitation in elderly patients affected by dementia

Dementia is one of the main causes of disability in elderly people and its treatment becomes, year after year, an increasingly compelling priority for the public health system. In the last years, home assistance and telemedicine have paved the way to decrease the treatments’ costs and to improve the patients and caregivers quality of life quality. In this framework, the aim of ABILITY project is to design, develop and validate an integrated platform of services aimed at supporting and enhancing the rehabilitation process for patients with dementia at their homes. ABILITY platform allows the clinician to assign rehabilitation plans with a strong compliance monitoring, enabled by the technological solutions integrated, and the holistic approach to rehabilitation, as the plan includes physical, cognitive and behavioral therapies/exercises. The ABILITY platform will be assessed through a set of validation activities, involving a small group of pilot patients, and a Randomized Control Trial. In conclusion, the ABILITY project generates a series of assistive services inside a modular and flexible platform, adaptable to the single patient and his/her needs, increasing the treatment efficiency and efficacy with respect to the state of the art

Efficacy of a new technique - INtubate-RECruit-SURfactant-Extubate - "IN-REC-SUR-E" - in preterm neonates with respiratory distress syndrome: Study protocol for a randomized controlled trial

Background: Although beneficial in clinical practice, the INtubate-SURfactant-Extubate (IN-SUR-E) method is not successful in all preterm neonates with respiratory distress syndrome, with a reported failure rate ranging from 19 to 69&nbsp;%. One of the possible mechanisms responsible for the unsuccessful IN-SUR-E method, requiring subsequent re-intubation and mechanical ventilation, is the inability of the preterm lung to achieve and maintain an "optimal" functional residual capacity. The importance of lung recruitment before surfactant administration has been demonstrated in animal studies showing that recruitment leads to a more homogeneous surfactant distribution within the lungs. Therefore, the aim of this study is to compare the application of a recruitment maneuver using the high-frequency oscillatory ventilation (HFOV) modality just before the surfactant administration followed by rapid extubation (INtubate-RECruit-SURfactant-Extubate: IN-REC-SUR-E) with IN-SUR-E alone in spontaneously breathing preterm infants requiring nasal continuous positive airway pressure (nCPAP) as initial respiratory support and reaching pre-defined CPAP failure criteria. Methods/design: In this study, 206 spontaneously breathing infants born at 24+0-27+6 weeks' gestation and failing nCPAP during the first 24&nbsp;h of life, will be randomized to receive an HFOV recruitment maneuver (IN-REC-SUR-E) or no recruitment maneuver (IN-SUR-E) just prior to surfactant administration followed by prompt extubation. The primary outcome is the need for mechanical ventilation within the first 3&nbsp;days of life. Infants in both groups will be considered to have reached the primary outcome when they are not extubated within 30&nbsp;min after surfactant administration or when they meet the nCPAP failure criteria after extubation. Discussion: From all available data no definitive evidence exists about a positive effect of recruitment before surfactant instillation, but a rationale exists for testing the following hypothesis: a lung recruitment maneuver performed with a step-by-step Continuous Distending Pressure increase during High-Frequency Oscillatory Ventilation (and not with a sustained inflation) could have a positive effects in terms of improved surfactant distribution and consequent its major efficacy in preterm newborns with respiratory distress syndrome. This represents our challenge. Trial registration: ClinicalTrials.gov identifier: NCT02482766. Registered on 1 June 2015

Improving leakage management in urban water distribution networks through data analytics and hydraulic simulation

Worldwide, water utilities are finding it increasingly difficult to meet the growing water demand. The problem, already acute in view of the urbanization trends, is compounded by the age of the infrastructure: one third of water utilities have 20% or more of their pipelines nearing the end of their useful life. This paper outlines an innovative approach for improving leakage management processes through the adoption of data analytics techniques and hydraulic simulation. More in detail, the aim is to provide analytical leaks localization and correspondent severity estimation in order to reduce time and costs for interventions and rehabilitation while improving asset management. The technical solution has been developed as a set of web services able to interoperate with other technological systems usually adopted by urban water distribution utilities, such as Supervisory Control And Data Acquisition (SCADA) system, Customer Information System (CIS), Geographical Information System (GIS) and Hydraulic Simulation tools. This paper presents some results obtained by the application of the approach to a real case study and also proves how its effectiveness may be improved in a sectorized water distribution network