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In people suffering from schizophrenia, major areas of everyday life are impaired, including independent living, productive activities and social relationships. Enhanced understanding of factors that hinder real-life functioning is vital for treatments to translate into more positive outcomes. The goal of the present study was to identify predictors of real-life functioning in people with schizophrenia, and to assess their relative contribution. Based on previous literature and clinical experience, several factors were selected and grouped into three categories: illness-related variables, personal resources and context-related factors. Some of these variables were never investigated before in relationship with real-life functioning. In 921 patients with schizophrenia living in the community, we found that variables relevant to the disease, personal resources and social context explain 53.8% of real-life functioning variance in a structural equation model. Neurocognition exhibited the strongest, though indirect, association with real-life functioning. Positive symptoms and disorganization, as well as avolition, proved to have significant direct and indirect effects, while depression had no significant association and poor emotional expression was only indirectly and weakly related to real-life functioning. Availability of a disability pension and access to social and family incentives also showed a significant direct association with functioning. Social cognition, functional capacity, resilience, internalized stigma and engagement with mental health services served as mediators. The observed complex associations among investigated predictors, mediators and real-life functioning strongly suggest that integrated and personalized programs should be provided as standard treatment to people with schizophrenia. In people suffering from schizophrenia, major areas of everyday life are impaired, including independent living, productive activities and social relationships. Enhanced understanding of factors that hinder real-life functioning is vital for treatments to translate into more positive outcomes. The goal of the present study was to identify predictors of real-life functioning in people with schizophrenia, and to assess their relative contribution. Based on previous literature and clinical experience, several factors were selected and grouped into three categories: illness-related variables, personal resources and context-related factors. Some of these variables were never investigated before in relationship with real-life functioning. In 921 patients with schizophrenia living in the community, we found that variables relevant to the disease, personal resources and social context explain 53.8% of real-life functioning variance in a structural equation model. Neurocognition exhibited the strongest, though indirect, association with real-life functioning. Positive symptoms and disorganization, as well as avolition, proved to have significant direct and indirect effects, while depression had no significant association and poor emotional expression was only indirectly and weakly related to real-life functioning. Availability of a disability pension and access to social and family incentives also showed a significant direct association with functioning. Social cognition, functional capacity, resilience, internalized stigma and engagement with mental health services served as mediators. The observed complex associations among investigated predictors, mediators and real-life functioning strongly suggest that integrated and personalized programs should be provided as standard treatment to people with schizophrenia.

The analysis of large volumes of data is key for knowledge discovery in several scientific domains such as climate, astrophysics, life sciences among others. It requires a large set of computational and storage resources, as well as flexible and efficient software solutions able to dynamically exploit the available infrastructure and address issues related to data volume, distribution, velocity and heterogeneity. This paper presents a data-driven and cloud-based use case implemented in the context of the EUBrazilCC project for the analysis of climate change and biodiversity data. The use case architecture and main components, as well as a Platform as a Service (PaaS) framework for big data analytics named PDAS, together with its elastic deployment in the EUBrazilCC federated cloud infrastructure are presented and discussed in detail.

Thermal imaging (TI) allows the detection of thermal patterns emitted from objects as a function of their temperature in the long-infrared spectrum and produces visible images displaying temperature differences. The aim of this pilot study was to test TI to visualize the coronary circulation of swine hearts. Thirty swine hearts were prepared for ex situ coronarography, and thermal images were acquired through a FlirOne thermal camera (FLIR Systems®) paired with a Google Android Smartphone. Coronary arteries were cannulated, namely the anterior interventricular artery, the circumflex branch of the left coronary artery, and the right coronary artery. The heart was cooled, and contrast medium (CM) consisting of distilled water heated to 40°C was injected in a coronary vessel, while thermal images were captured. These steps were repeated for each coronary vessel and under experimentally simulated coronary heart disease. Thermal imaging coronarography (TIC) allowed a clear representation of the morphology and course of the coronary vessels and of experimentally simulated coronary heart disease, moreover, demonstrated to be easy to perform during or after autopsies on ex situ hearts, non-destructive, reproducible, and cheap. On the basis of these preliminary results, TIC might allow a subsequent more focused and comprehensive cardiopathological examination of the heart, which remains mandatory for the definitive diagnosis of coronary heart disease. Although these preliminary results seem encouraging, further systematic studies on human hearts, both normal and pathological, are necessary for estimating the sensitivity and specificity of the proposed method and to draw any definitive conclusion.

We are currently experiencing the fourth industrial revolution. This is what the German government initiative, first, has identified with ‘Industry 4.0’. The manufacturing future will be marked and will go through the new automation technologies that are being introduced with Industrial Internet of Things (I2oT). Industrial Control Systems (ICSs) are exploiting I2oT for reducing costs and improving efficiency. However, ICSs are already jeopardized by an increasingly large set of threat vectors. Those threats are used by malicious actors to misuse physical Critical Infrastructures that usually are vital services for well-being. I2oT implementation increases the threat surface, generating new possible vulnerabilities. Information Technology (IT) classical approaches to cyber attacks cannot be applied to ICS due to their extreme differences from main priorities to resource constrains. Therefore, innovative approaches and equipment must be developed to suit with ICS world. In this paper, a Smart Behavioural Filter (SBF) for the PLCs (Programmable Logic Controllers) is proposed aiming to secure the PLC itself against logic attacks, that are stealth for other more classical security approaches. An example of the considered logic attacks is many open and close commands towards a valve in a short time. Those logic attacks are usually a sequence of well-formed packets in which the content represents an anomalous and unpredicted behaviour. This smart field equipment can react in short time to cyber attacks isolating the PLC, communicate with other equipment like itself and increasing in general the resilience of the physical system. It can also generate alarms for the local Intrusion Detection System (IDS). The proposed equipment has been developed and validated in a real test-bed within the FP7 CockpitCI project and H2020 ATENA project.

The combined event of a suicide and the killing of a pet has been hardly explored in forensic literature, but it is not rare at all. In the reported case the dead corpse of a 60-year old mentally ill woman and the carcass of a dog were found on the bed of a private apartment. In light of death scene investigation, necroscopic examination and toxicological analyses death was attributed to licit drug intoxication and self-strangulation after lethal poisoning of the dog. Due to the presence of two lethal means (cervical noose and drugs), acting in chronological order, the event was classified as a secondary complex suicide. Moreover, the woman, although mentally ill, was a psychiatrist. Thus, her professional background, namely the easy access to psychiatric drugs together with her knowledge of drug composition and properties lead to suppose an occupation related suicide. The comprehensive analysis of all the available information, including death scene investigation, occupational, necroscopic and toxicological data, resulted of the utmost importance for a proper reconstruction of the events and are recommended in complex cases such as occupation related planned complex suicides combined to the killing of pets.

The continuous increase in the data produced by simulations, experiments and edge components in the last few years has forced a shift in the scientific research process, leading to the definition of a fourth paradigm in Science, concerning data-intensive computing. This data deluge, in fact, introduces various challenges related to big data volumes, formats heterogeneity and the speed in the data production and gathering that must be handled to effectively support scientific discovery. To this end, High Performance Computing (HPC) and data analytics are both considered as fundamental and complementary aspects of the scientific process and together contribute to a new paradigm encompassing the efforts from the two fields called High Performance Data Analytics (HPDA). In this context, the Ophidia project provides a HPDA framework which joins the HPC paradigm with scientific data analytics. This contribution presents some aspects regarding the Ophidia HPDA framework, such as the multidimensional storage model, its distributed and hierarchical implementation along with a benchmark of a parallel in-memory time series reduction operator.

Acute myocarditis is related to a significant number of sudden deaths among young adults and its diagnosis is often demanded to post-mortem investigations performed on a forensic setting. Eosinophilic myocarditis (EM) is a rare form of myocarditis that is pathologically characterized by myocardial inflammation with eosinophils, often in association with elevated levels of circulating blood eosinophils. The sudden death of a 19-year-old boy with no past medical history is reported. Diagnosis of fatal acute EM was performed after a comprehensive investigation including an in-depth analysis of anamnestic and circumstantial data, and complete autopsy followed by toxicologic and cardio-pathological investigations. Discussion focuses on the forensic issues related to diagnosis and therapy of this rare form of acute myocarditis. As acute EM may be patchy, extensive myocardial sampling is mandatory in order to recognize the extent and the phase of the disease. An early diagnosis is the basis for a timely therapy, which is the key-point for prevent extensive myocardial damage, allowing a better outcome, especially when EM is acute and necrotizing. However, as demonstrated from the case herein reported, the course of EM is sometimes fulminant and does not allow any therapy nor even clinical diagnosis. Finally, this paper serves as reminder to consider this infrequent disease in differential diagnosis when facing with a sudden death, even in a young subject and in the absence of any prodrome.

Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro-Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP). This paper reports about some ESiWACE WP3 activities described in the deliverable D3.10 "ESiWACE Scheduler development and support activities"

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

The availability of systems able to process and analyse big amount of data has boosted scientific advances in several fields. Workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area, the Ophidia project provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets. The framework exploits a server-side, declarative, parallel approach for data analysis and mining. It also features a complete workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operators dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators (both single and massive - parameter sweep) in an effective manner. For the definition of the big data analytics workflow, a JSON schema has been properly designed and implemented. To aid the definition of the workflows, a visual design language consisting of several symbols, named Data Analytics Workflow Modelling Language (DAWML), has been also defined.

Basing on the assumption that frontotemporal lobar degeneration (FTLD), schizophrenia and bipolar disorder (BPD) might share common aetiological mechanisms, we analyzed genetic variation in the FTLD risk gene progranulin (GRN) in a German population of patients with schizophrenia (n = 271) or BPD (n = 237) as compared with 574 age-, gender- and ethnicity-matched controls. Furthermore, we measured plasma progranulin levels in 26 German BPD patients as well as in 61 Italian BPD patients and 29 matched controls. A significantly decreased allelic frequency of the minor versus the wild-type allele was observed for rs2879096 (23.2 versus 34.2%, P

Recently, there has been much effort to integrate heterogeneous wireless technologies in order to make overlay networks able to provide mobile users with ubiquitous Internet access. Handover management is still one of the most challenging issues to be solved for seamless integration of wireless networks. In this paper, an algorithm for vertical handover with QoS guarantees between IEEE 802.16e and IEEE 802.11e networks, without computational complexity added on mobile nodes, has been proposed. It uses a decision mechanism based on the following metrics: the available bandwidth, the signal-to-noise ratio (SNR) and the access delay. Its characteristics are perfectly compatible with the guidelines specified in the IEEE 802.21 standard. The effectiveness of the defined algorithm has been evaluated, using the ns-2 tool, in terms of packet loss rate (PLR), satisfaction of minimum applications requirements and number of useless handovers. The performance of the defined algorithm has been compared with that obtained by using the algorithm suggested by IEEE 802.21 standard that exploits only the SNR in the decision procedure.

Objective To investigate the role in bipolar disorder of the C9ORF72 hexanucleotide repeat expansion responsible for frontotemporal lobe dementia and amyotrophic lateral sclerosis. Methods Eighty-nine subjects from a previously described panel of individuals with bipolar disorder ascertained for genetic studies were screened to detect expansion of the C9ORF72 repeat. One two-generation family with bipolar disorder and an expanded repeat was characterized in depth using molecular diagnostics, imaging, histopathology, and neurological and neuropsychological evaluation. Results One proband, with the typical clinical presentation of bipolar disorder, carried an expanded C9ORF72 allele of heterogeneous length between 14 and 20 kilobases (kb) as assessed by Southern blot. The expanded allele was inherited from a parent with atypical, late onset clinical features of bipolar disorder, who subsequently progressed to frontotemporal lobe dementia. The expansion in peripheral blood of the parent ranged from 8.5 to 20 kb. Cultured lymphoblastoid cells from this parent exhibited a homogeneous expansion of only 8.5 kb. Conclusions The disease course in the two generations described here demonstrates that expansion of the C9ORF72 may be associated with a form of bipolar disorder that presents clinically with classic phenomenology and progression to neurodegenerative disease. The frequency in our bipolar disorder cohort was only 1%, indicating that C9ORF72 is not a major contributor to bipolar disorder. DNA from cultured cells may be biased towards shorter repeats and nonrepresentative of the endogenous C9ORF72 expansion.

Telecommunication networks based on commonplace technologies (such as Ethernet) often constitute a vulnerable attack vector against modern critical infrastructures (CIs), particularly for supervisory control and data acquisition (SCADA) systems, which rely on them for monitoring and controlling physical components. This paper presents a unique platform that encompasses a range of capabilities, from cyber-attack detection to mitigation strategies, through interdependency and risk evaluation. The platform is made of two main components: a cyber-attack detection subsystem and a risk assessment framework. Both blocks are innovative from research point of view and they have been developed and customized to fit the CIs' features, that are completely different from telecommunication networks. This platform has been tested on a hybrid environment testbed, made of virtual and real components, within the scope of the EU FP7 CockpitCI and EU H2020 ATENA projects. The case study corresponds to a medium voltage power grid controlled by a SCADA control center, where the platform has been validated with optimal results in terms of detection capabilities and time response.