A Semantic Similarity Measure for Expressive Description Logics

A totally semantic measure is presented which is able to calculate a similarity value between concept descriptions and also between concept description and individual or between individuals expressed in an expressive description logic. It is applicable on symbolic descriptions although it uses a numeric approach for the calculus. Considering that Description Logics stand as the theoretic framework for the ontological knowledge representation and reasoning, the proposed measure can be effectively used for agglomerative and divisional clustering task applied to the semantic web domain.Comment: 13 pages, Appeared at CILC 2005, Convegno Italiano di Logica Computazionale also available at http://www.disp.uniroma2.it/CILC2005/downloads/papers/15.dAmato_CILC05.pd

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

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used

Synthesis, characterisation and photochemistry of PtIV pyridyl azido acetato complexes

PtII azido complexes [Pt(bpy)(N3)2] (1), [Pt(phen)(N3)2] (2) and trans-[Pt(N3)2(py)2] (3) incorporating the bidentate diimine ligands 2,2′-bipyridine (bpy), 1,10-phenanthroline (phen) or the monodentate pyridine (py) respectively, have been synthesised from their chlorido precursors and characterised by X-ray crystallography; complex 3 shows significant deviation from square-planar geometry (N3–Pt–N3 angle 146.7°) as a result of steric congestion at the Pt centre. The novel PtIV complexes trans, cis-[Pt(bpy)(OAc)2(N3)2] (4), trans, cis-[Pt(phen)(OAc)2(N3)2] (5), trans, trans, trans-[Pt(OAc)2(N3)2(py)2] (6), were obtained from 1–3via oxidation with H2O2 in acetic acid followed by reaction of the intermediate with acetic anhydride. Complexes 4–6 exhibit interesting structural and photochemical properties that were studied by X-ray, NMR and UV-vis spectroscopy and TD-DFT (time-dependent density functional theory). These PtIV complexes exhibit greater absorption at longer wavelengths (ε = 9756 M−1 cm−1 at 315 nm for 4; ε = 796 M−1 cm−1 at 352 nm for 5; ε = 16900 M−1 cm−1 at 307 nm for 6, in aqueous solution) than previously reported PtIV azide complexes, due to the presence of aromatic amines, and 4–6 undergo photoactivation with both UVA (365 nm) and visible green light (514 nm). The UV-vis spectra of complexes 4–6 were calculated using TD-DFT; the nature of the transitions contributing to the UV-vis bands provide insight into the mechanism of production of the observed photoproducts. The UV-vis spectra of 1–3 were also simulated by computational methods and comparison between PtII and PtIV electronic and structural properties allowed further elucidation of the photochemistry of 4–6

A zinc, copper and citric acid biocomplex shows promise for control of Xylella fastidiosa subsp. pauca in olive trees in Apulia region (southern Italy)

The bacterium Xylella fastidiosa subsp. pauca is associated with the “olive quick decline syndrome” in the Apulia region of southern Italy. To investigate control of this phytopathogen, a compound containing zinc and copper complexed with citric-acid hydracids (Dentamet®) was evaluated for in vitro and in planta bactericidal activity. Confocal laser scanning microscopy, fluorescent quantification and atomic emission spectroscopy were then used to determine if the compound reached the xylem networks of leaves, twigs and branches of olive, to release zinc and copper within the xylem. A 3-year field trial in an olive orchard containing mature Cellina di Nardò and Ogliarola salentina olive trees, and officially declared infected by X. fastidiosa subsp. pauca,was also carried out o to determine if the compound affected severity of the disease. Each year, from early April to October (excluding July and August), six spray treatments of 0.5% (v:v) Dentamet® were applied on the olive tree crowns. The compound reduced severity of symptoms in both cultivars. Most untreated trees died by the end of the trial, whereas all treated trees survived with good vegetative status as assessed by a normalized difference vegetation index. Quantitative real-time PCR was performed from June 2016 to September 2017, following the official procedures established by the European and Mediterranean Plant Protection Organization. The analysis revealed a statistically significant reduction of X. fastidiosa cell densities within the leaves of treated trees. These promising results suggest that integrated management to reduce severity of X. fastidiosa that includes regular pruning and soil harrowing with spring and summer spray treatments with Dentamet®, is likely to effectively control the disease.

Performance Assessment in Fingerprinting and Multi Component Quantitative NMR Analyses

An interlaboratory comparison (ILC) was organized with the aim to set up quality control indicators suitable for multicomponent quantitative analysis by nuclear magnetic resonance (NMR) spectroscopy. A total of 36 NMR data sets (corresponding to 1260 NMR spectra) were produced by 30 participants using 34 NMR spectrometers. The calibration line method was chosen for the quantification of a five-component model mixture. Results show that quantitative NMR is a robust quantification tool and that 26 out of 36 data sets resulted in statistically equivalent calibration lines for all considered NMR signals. The performance of each laboratory was assessed by means of a new performance index (named Qp-score) which is related to the difference between the experimental and the consensus values of the slope of the calibration lines. Laboratories endowed with a Qp-score falling within the suitable acceptability range are qualified to produce NMR spectra that can be considered statistically equivalent in terms of relative intensities of the signals. In addition, the specific response of nuclei to the experimental excitation/relaxation conditions was addressed by means of the parameter named NR. NR is related to the difference between the theoretical and the consensus slopes of the calibration lines and is specific for each signal produced by a well-defined set of acquisition parameters