An unsupervised approach to disjointness learning based on terminological cluster trees

In the context of the Semantic Web regarded as a Web of Data, research efforts have been devoted to improving the quality of the ontologies that are used as vocabularies to enable complex services based on automated reasoning. From various surveys it emerges that many domains would require better ontologies that include non-negligible constraints for properly conveying the intended semantics. In this respect, disjointness axioms are representative of this general problem: these axioms are essential for making the negative knowledge about the domain of interest explicit yet they are often overlooked during the modeling process (thus affecting the efficacy of the reasoning services). To tackle this problem, automated methods for discovering these axioms can be used as a tool for supporting knowledge engineers in modeling new ontologies or evolving existing ones. The current solutions, either based on statistical correlations or relying on external corpora, often do not fully exploit the terminology. Stemming from this consideration, we have been investigating on alternative methods to elicit disjointness axioms from existing ontologies based on the induction of terminological cluster trees, which are logic trees in which each node stands for a cluster of individuals which emerges as a sub-concept. The growth of such trees relies on a divide-and-conquer procedure that assigns, for the cluster representing the root node, one of the concept descriptions generated via a refinement operator and selected according to a heuristic based on the minimization of the risk of overlap between the candidate sub-clusters (quantified in terms of the distance between two prototypical individuals). Preliminary works have showed some shortcomings that are tackled in this paper. To tackle the task of disjointness axioms discovery we have extended the terminological cluster tree induction framework with various contributions: 1) the adoption of different distance measures for clustering the individuals of a knowledge base; 2) the adoption of different heuristics for selecting the most promising concept descriptions; 3) a modified version of the refinement operator to prevent the introduction of inconsistency during the elicitation of the new axioms. A wide empirical evaluation showed the feasibility of the proposed extensions and the improvement with respect to alternative approaches

Tissue distribution and acute toxicity of silver after single intravenous administration in mice: nano-specific and size-dependent effects

Background: Silver nanoparticles (AgNPs) are an important class of nanomaterials used as antimicrobial agents for a wide range of medical and industrial applications. However toxicity of AgNPs and impact of their physicochemical characteristics in in vivo models still need to be comprehensively characterized. The aim of this study was to investigate the effect of size and coating on tissue distribution and toxicity of AgNPs after intravenous administration in mice, and compare the results with those obtained after silver acetate administration. Methods: Male CD-1(ICR) mice were intravenously injected with AgNPs of different sizes (10 nm, 40 nm, 100 nm), citrate-or polyvinylpyrrolidone-coated, at a single dose of 10 mg/kg bw. An equivalent dose of silver ions was administered as silver acetate. Mice were euthanized 24 h after the treatment, and silver quantification by ICP-MS and histopathology were performed on spleen, liver, lungs, kidneys, brain, and blood. Results: For all particle sizes, regardless of their coating, the highest silver concentrations were found in the spleen and liver, followed by lung, kidney, and brain. Silver concentrations were significantly higher in the spleen, lung, kidney, brain, and blood of mice treated with 10 nm AgNPs than those treated with larger particles. Relevant toxic effects (midzonal hepatocellular necrosis, gall bladder hemorrhage) were found in mice treated with 10 nm AgNPs, while in mice treated with 40 nm and 100 nm AgNPs lesions were milder or negligible, respectively. In mice treated with silver acetate, silver concentrations were significantly lower in the spleen and lung, and higher in the kidney than in mice treated with 10 nm AgNPs, and a different target organ of toxicity was identified (kidney). Conclusions: Administration of the smallest (10 nm) nanoparticles resulted in enhanced silver tissue distribution and overt hepatobiliary toxicity compared to larger ones (40 and 100 nm), while coating had no relevant impact. Distinct patterns of tissue distribution and toxicity were observed after silver acetate administration. It is concluded that if AgNPs become systemically available, they behave differently from ionic silver, exerting distinct and size-dependent effects, strictly related to the nanoparticulate form

Heart failure pharmacological management. gaps and current perspectives

Proper therapeutic management of patients with heart failure (HF) is a major challenge for cardiologists. Current guidelines indicate to start therapy with angiotensin converting enzyme inhibitors/angiotensin receptor neprilysin inhibitors (ACEi/ARNI), beta blockers (BB), mineralocorticoid receptor antagonists (MRAs) and sodium glucose cotransporter 2 inhibitors (SGLT2i) to reduce the risk of death and hospitalization due to HF. However, certain aspects still need to be defined. Current guidelines propose therapeutic algorithms based on left ventricular ejection fraction values and clinical presentations. However, these last do not always reflect the precise hemodynamic status of patients and pathophysiological mechanisms involved, particularly in the acute setting. Even in the field of chronic management there are still some critical points to discuss. The guidelines do not specify which of the four pillar drugs to start first, nor at what dosage. Some authors suggest starting with SGLT2i and BB, others with ACEi or ARNI, while one of the most recent approach proposes to start with all four drugs together at low doses. The aim of this review is to revise current gaps and perspectives regarding pharmacological therapy management in HF patients, in both the acute and chronic phase

The Data Mining OPtimization Ontology

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner

KAFE: the Key-analysis Automated FITS-images Explorer

We present KAFE—the Key-analysis Automated FITS-images Explorer. KAFE is a web-based FITS image postprocessing analysis tool designed to be applicable in the radio to sub-mm wavelength domain. KAFE was developed to complement selected FITS files with metadata based on a uniform image analysis approach as well as to provide advanced image diagnostic plots. It is ideally suited for data mining purposes and multiwavelength/multi-instrument data samples that require uniform data diagnostic criteria. We present the code structure and interface, the keyword definitions, the products generated for selected users’ science cases, and application examples

Using stable isotopes to inform water resource management in forested and agricultural ecosystems

Present and future climatic trends are expected to markedly alter water fluxes and stores in the hydrologic cycle. In addition, water demand continues to grow due to increased human use and a growing population. Sustainably managing water resources requires a thorough understanding of water storage and flow in natural, agricultural, and urban ecosystems. Measurements of stable isotopes of water (hydrogen and oxygen) in the water cycle (atmosphere, soils, plants, surface water, and groundwater) can provide information on the transport pathways, sourcing, dynamics, ages, and storage pools of water that is difficult to obtain with other techniques. However, the potential of these techniques for practical questions has not been fully exploited yet. Here, we outline the benefits and limitations of potential applications of stable isotope methods useful to water managers, farmers, and other stakeholders. We also describe several case studies demonstrating how stable isotopes of water can support water management decision-making. Finally, we propose a workflow that guides users through a sequence of decisions required to apply stable isotope methods to examples of water management issues. We call for ongoing dialogue and a stronger connection between water management stakeholders and water stable isotope practitioners to identify the most pressing issues and develop best-practice guidelines to apply these techniques

Real-life effects of dupilumab in patients with severe type 2 asthma, according to atopic trait and presence of chronic rhinosinusitis with nasal polyps

BackgroundThe efficacy of dupilumab as biological treatment of severe asthma and chronic rhinosinusitis with nasal polyps (CRSwNP) depends on its ability to inhibit the pathophysiologic mechanisms involved in type 2 inflammation.ObjectiveTo assess in a large sample of subjects with severe asthma, the therapeutic impact of dupilumab in real-life, with regard to positive or negative skin prick test (SPT) and CRSwNP presence or absence.MethodsClinical, functional, and laboratory parameters were measured at baseline and 24 weeks after the first dupilumab administration. Moreover, a comparative evaluation was carried out in relation to the presence or absence of SPT positivity and CRSwNP.ResultsAmong the 127 recruited patients with severe asthma, 90 had positive SPT, while 78 reported CRSwNP. Compared with the 6 months preceding the first dupilumab injection, asthma exacerbations decreased from 4.0 (2.0-5.0) to 0.0 (0.0-0.0) (p < 0.0001), as well as the daily prednisone intake fell from 12.50 mg (0.00-25.00) to 0.00 mg (0.00-0.00) (p < 0.0001). In the same period, asthma control test (ACT) score increased from 14 (10-18) to 22 (20-24) (p < 0.0001), and sino-nasal outcome test (SNOT-22) score dropped from 55.84 ± 20.32 to 19.76 ± 12.76 (p < 0.0001). Moreover, we observed relevant increases in forced expiratory volume in one second (FEV1) from the baseline value of 2.13 L (1.62-2.81) to 2.39 L (1.89-3.06) (p < 0.0001). Fractional exhaled nitric oxide (FeNO) values decreased from 27.0 ppb (18.0-37.5) to 13.0 ppb (5.0-20.0) (p < 0.0001). These improvements were quite similar in subgroups of patients characterized by SPT negativity or positivity, and CRSwNP absence or presence. No statistically significant correlations were detected between serum IgE levels, baseline blood eosinophils or FeNO levels and dupilumab-induced changes, with the exception of FEV1 increase, which was shown to be positively correlated with FeNO values (r = 0.3147; p < 0.01).ConclusionOur results consolidate the strategic position of dupilumab in its role as an excellent therapeutic option currently available within the context of modern biological treatments of severe asthma and CRSwNP, frequently driven by type 2 airway inflammation

Semantic Knowledge Discovery from Heterogeneous Data Sources

Available domain ontologies are increasing over the time. However there is a huge amount of data stored and managed with RDBMS. We propose a method for learning association rules from both sources of knowledge in an integrated way. The extracted patterns can be used for performing: data analysis, knowledge completion, ontology refinement