A Framework for Reasoning on Probabilistic Description Logics

While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets

Handling large data sets for high-performance embedded applications in heterogeneous systems-on-chip

Local memory is a key factor for the performance of accelerators in SoCs. Despite technology scaling, the gap between on-chip storage and memory footprint of embedded applications keeps widening. We present a solution to preserve the speedup of accelerators when scaling from small to large data sets. Combining specialized DMA and address translation with a software layer in Linux, our design is transparent to user applications and broadly applicable to any class of SoCs hosting high-throughput accelerators. We demonstrate the robustness of our design across many heterogeneous workload scenarios and memory allocation policies with FPGA-based SoC prototypes featuring twelve concurrent accelerators accessing up to 768MB out of 1GB-addressable DRAM

Drug-eluting balloons for the treatment of the superficial femoral artery in-stent restenosis: 2-year follow-up.

OBJECTIVES: The aim of this prospective registry was to evaluate the safety and efficacy at 2-year follow-up of the use of drug-eluting balloons (DEBs) for the treatment of superficial femoral artery (SFA) in-stent restenosis (ISR). BACKGROUND: The use of DEBs for the treatment of SFA ISR is associated with a satisfactory primary patency rate at 1 year, but no data are available for longer follow-up. Unfortunately, when DEBs were used to treat SFA de novo lesions, the occurrence of restenosis increased by 50% between the first and the second years of follow-up. METHODS: From December 2009 to December 2010, 39 consecutive patients underwent percutaneous transluminal angioplasty of SFA ISR at our institution (Clinica Montevergine, Mercogliano, Italy). All patients underwent conventional SFA percutaneous transluminal angioplasty and final post-dilation with paclitaxel-eluting balloons (IN.PACT, Medtronic Inc., Minneapolis, Minnesota). Patients were evaluated for up to 24 months. RESULTS: During follow-up, 1 patient died of heart failure and another of sudden death, for a 2-years rate of cardiovascular mortality rate of 5.12 %. The primary patency rate at 2 years was 70.3% (11 of 37 patients experienced restenosis recurrence at 2-year follow-up). The treatment of complex ISR lesions (classes II and III) was associated with an increased rate of recurrent restenosis compared with class I (33.3 % and 36.3 % vs. 12.5%; p = 0.05). CONCLUSIONS: The data suggest that adjunctive use of DEBs for the treatment of SFA ISR is a safe and effective therapeutic strategy up to 2 years of follow-up

An FPGA-based infrastructure for fine-grained DVFS analysis in high-performance embedded systems

Emerging technologies provide SoCs with fine-grained DVFS capabilities both in space (number of domains) and time (transients in the order of tens of nanoseconds). Analyzing these systems requires cycle-accurate accounting of rapidly-changing dynamics and complex interactions among accelerators, interconnect, memory, and OS. We present an FPGA-based infrastructure that facilitates such analyses for high-performance embedded systems. We show how our infrastructure can be used to first generate SoCs with loosely-coupled accelerators, and then perform design-space exploration considering several DVFS policies under full-system workload scenarios, sweeping spatial and temporal domain granularity

Predictors of carotid occlusion intolerance?during proximal protected?carotid artery?stenting.

OBJECTIVES: The aim of this study was to identify predictors of occlusion intolerance (OI) developing during proximal protected carotid artery stenting (CAS). BACKGROUND: The use of proximal embolic protection devices, such as endovascular occlusion, during CAS has been demonstrated to be particularly safe and effective. However, endovascular occlusion can expose the ipsilateral hemisphere to hypoperfusion and produce transient neurological symptoms (OI). METHODS: From March 2010 to March 2012, 605 consecutive patients underwent proximal protected CAS at our institution. To identify independent predictors of OI, a multivariate logistic regression model was developed that included all patients' clinical/angiographic and procedural characteristics. RESULTS: OI developed in a total of 184 patients (30.4%). Compared with patients in whom OI did not develop, those who experienced OI had lower occlusion pressure (OP) (42.3 ± 12.7 mm Hg vs. 61.9 ± 15.4 mm Hg, p < 0.001). Receiver-operating characteristic curve analysis demonstrated that OP was the most consistent predictor of OI with a C-statistic of 0.85 (95% confidence interval [CI]: 0.82 to 0.88) with best cutoff being ≤40 mm Hg (sensitivity, 68.5%; specificity, 93.3%). By logistic regression analysis, the most powerful independent predictor of OI developing was an OP ≤40 mm Hg (odds ratio: 33.2, 95% CI: 19.1 to 57.7) and the most powerful clinical predictor of such OP was the presence of contralateral internal carotid artery occlusion (odds ratio: 3.1, 95% CI: 1.5 to 6.2). CONCLUSIONS: OI may occur in as many as one-third of the patients undergoing proximal protected CAS. This event is more common in those patients with an OP ≤40 mm Hg. Patients presenting with concomitant occlusion of the contralateral internal carotid artery more frequently have an OP ≤40 mm Hg

Nat Metab.

Bile acids (BAs) are signalling molecules that mediate various cellular responses in both physiological and pathological processes. Several studies report that BAs can be detected in the brain1, yet their physiological role in the central nervous system is still largely unknown. Here we show that postprandial BAs can reach the brain and activate a negative-feedback loop controlling satiety in response to physiological feeding via TGR5, a G-protein-coupled receptor activated by multiple conjugated and unconjugated BAs2 and an established regulator of peripheral metabolism3,4,5,6,7,8. Notably, peripheral or central administration of a BA mix or a TGR5-specific BA mimetic (INT-777) exerted an anorexigenic effect in wild-type mice, while whole-body, neuron-specific or agouti-related peptide neuronal TGR5 deletion caused a significant increase in food intake. Accordingly, orexigenic peptide expression and secretion were reduced after short-term TGR5 activation. In vitro studies demonstrated that activation of the Rho–ROCK–actin-remodelling pathway decreases orexigenic agouti-related peptide/neuropeptide Y (AgRP/NPY) release in a TGR5-dependent manner. Taken together, these data identify a signalling cascade by which BAs exert acute effects at the transition between fasting and feeding and prime the switch towards satiety, unveiling a previously unrecognized role of physiological feedback mediated by BAs in the central nervous system.Développment d'une infrastructure française distribuée coordonnéeLa signalisation des acides biliaires dans le cerveau et son rôle dans le contrôle métaboliqueInnovations instrumentales et procédurales en psychopathologie expérimentale chez le rongeu

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

Inference and Learning Systems for Uncertain Relational Data

With the advent of the Semantic Web, which makes use of formalisms based on Description Logics (DLs) for knowledge representation, it has become increasingly important to tackle the problem of managing uncertain information. The main goal of this book is to propose inference and (distributed) learning algorithms for Probabilistic Logic Programs (PLPs) and Probabilistic Description Logics (PDLs). Moreover two web applications are presented: cplint on SWISH (http://www.cplint.eu/) and TRILL on SWISH (http://trill-sw.eu/) that allow, with just a web browser, to perform inference over PLPs and PDLs respectively. The book provides guidelines for using all these systems. The book is self-contained and progresses from the most basic concepts of First-Order Logic to the most advanced issues of Statistical Relational Learning. It is structured in such a way that it will be of interest to both beginners and experts who want to learn about the state-of-the-art of inference and learning systems for probabilistic logics

Systems and Learning Algorithms for Probabilistic Logical Knowledge Bases

In real world domains the information is often uncertain, hence it is of foremost importance to be able to model uncertainty and to reason over it. In this paper we show tools and learning systems under development for probabilistic structured data. Four systems will be considered and an overview of the related issues and of future work will be given. The first described system is cplint on SWISH, a web application that allows the user to write Probabilistic Logic Programs and submit the computation of the probability of queries with a web browser. Then two distributed structure learning algorithm are illustrated: SEMPRE (“distributed Structure lEarning by MaPREduce”) and LEAP^MR (“LEArning Probabilistic description logics by MapReduce”), the former learns new clauses of Probabilistic Logic Programs, the latter is used in the context of Probabilistic Description Logics. The last system taken into account is SML-Bench, developed by the research group AKSW of Leipzig, a benchmarking tool for structured data that has been extended to deal with algorithms for probabilistic structured data