A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests

A Graph-Kernel Method for Re-identification

Re-identification, that is recognizing that an object appearing in a scene is a reoccurrence of an object seen previously by the system (by the same camera or possibly by a different one) is a challenging problem in video surveillance. In this paper, the problem is addressed using a structural, graph-based representation of the objects of interest. A recently proposed graph kernel is adopted for extending to this representation the Principal Component Analyisis (PCA) technique. An experimental evaluation of the method has been performed on two video sequences from the publicly available PETS2009 database

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Understanding the role played by genetic variations in diseases, exploring genomic variants, and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, the authors propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis

Smart Metering Projects: An Interpretive Framework for Successful Implementation

Purpose – The purpose of this paper is to analyze a set of smart meters implementation projects and provide insights and recommendations to facilitate smart metering deployment strategies. Design/methodology/approach – Several significant projects are analyzed on different fronts: scale, technology, economics and regulation, using a common methodology to unfold patterns that constitute key components of successful smart meters diffusion. Findings – Key elements and controllable enabling patterns from Europe-wide SM implementation projects are identified together with drivers and barriers for patterns replication. Research limitations/implications – The authors provide a framework considering different stakeholders that will help distribution systemoperators to accelerate and extend smart meters’ penetration. Originality/value – Based on the Meter-ON project (supported by the 7th Framework Program of the European Commission), we provide valuable information aimed at facilitating the large-scale deployment of smart meters

Mn-Containing Bioactive Glass-Ceramics: BMP-2-Mimetic Peptide Covalent Grafting Boosts Human-Osteoblast Proliferation and Mineral Deposition

The addition of Mn in bioceramic formulation is gaining interest in the field of bone implants. Mn activates human osteoblast (h-osteoblast) integrins, enhancing cell proliferation with a dose-dependent effect, whereas Mn-enriched glasses induce inhibition of Gram-negative or Gram-positive bacteria and fungi. In an effort to further optimize Mn-containing scaffolds' beneficial interaction with h-osteoblasts, a selective and specific covalent functionalization with a bioactive peptide was carried out. The anchoring of a peptide, mapped on the BMP-2 wrist epitope, to the scaffold was performed by a reaction between an aldehyde group of the peptide and the aminic groups of silanized Mn-containing bioceramic. SEM-EDX, FT-IR, and Raman studies confirmed the presence of the peptide grafted onto the scaffold. In in vitro assays, a significant improvement in h-osteoblast proliferation, gene expression, and calcium salt deposition after 7 days was detected in the functionalized Mn-containing bioceramic compared to the controls

Design of technology-based rehabilitation pathways: the experience of Santobono-Pausilipon Hospital

The fields of rehabilitation robotics and virtual reality (VR) are becoming a growing area in the clinical rehabilitation of people with motion impairments. These systems have the potential to assess abilities through physiological measurements and modelling activities such as posture, gait, and balance. They can be used as rehabilitative tools by providing patients with task-specific training in a motivating and engaging way too. Although the potential advantages of such systems, until now there is a general limitation of their use in rehabilitative practice. Robotics and VR systems can be challenging, engaging and fun, particularly for children with disabilities, since they are often not very motivated to comply with conventional therapy. The aim of this work is to accurately describe the clinical use of innovative rehabilitative technologies and their use for the development of two technology-based rehabilitation pathways for the treatment of gait disorders following obesity and neurological diseases in treatment of pediatric patients

Generalised median of a set of correspondences based on the hamming distance.

A correspondence is a set of mappings that establishes a relation between the elements of two data structures (i.e. sets of points, strings, trees or graphs). If we consider several correspondences between the same two structures, one option to define a representative of them is through the generalised median correspondence. In general, the computation of the generalised median is an NP-complete task. In this paper, we present two methods to calculate the generalised median correspondence of multiple correspondences. The first one obtains the optimal solution in cubic time, but it is restricted to the Hamming distance. The second one obtains a sub-optimal solution through an iterative approach, but does not have any restrictions with respect to the used distance. We compare both proposals in terms of the distance to the true generalised median and runtime

GraphFind: enhancing graph searching by low support data mining techniques

<p>Abstract</p> <p>Background</p> <p>Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed.</p> <p>Results</p> <p>This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining.</p> <p>Conclusions</p> <p>GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction.</p

Graph edit distance or graph edit pseudo-distance?

Graph Edit Distance has been intensively used since its appearance in 1983. This distance is very appropriate if we want to compare a pair of attributed graphs from any domain and obtain not only a distance, but also the best correspondence between nodes of the involved graphs. In this paper, we want to analyse if the Graph Edit Distance can be really considered a distance or a pseudo-distance, since some restrictions of the distance function are not fulfilled. Distinguishing between both cases is important because the use of a distance is a restriction in some methods to return exact instead of approximate results. This occurs, for instance, in some graph retrieval techniques. Experimental validation shows that in most of the cases, it is not appropriate to denominate the Graph Edit Distance as a distance, but a pseudo-distance instead, since the triangle inequality is not fulfilled. Therefore, in these cases, the graph retrieval techniques not always return the optimal graph

Clinical features and comorbidity pattern of HCV infected migrants compared to native patients in care in Italy: A real-life evaluation of the PITER cohort

Background: Direct-acting antivirals are highly effective for the treatment of hepatitis C virus (HCV) infection, regardless race/ethnicity. We aimed to evaluate demographic, virological and clinical data of HCV-infected migrants vs. natives consecutively enrolled in the PITER cohort. Methods: Migrants were defined by country of birth and nationality that was different from Italy. Mann-Whitney U test, Chi-squared test and multiple logistic regression were used. Results: Of 10,669 enrolled patients, 301 (2.8%) were migrants: median age 47 vs. 62 years, (p &lt; 0.001), females 56.5% vs. 45.3%, (p &lt; 0.001), HBsAg positivity 3.8% vs. 1.4%, (p &lt; 0.05). Genotype 1b was prevalent in both groups, whereas genotype 4 was more prevalent in migrants (p &lt; 0.05). Liver disease severity and sustained virologic response (SVR) were similar. A higher prevalence of comorbidities was reported for natives compared to migrants (p &lt; 0.05). Liver disease progression cofactors (HBsAg, HIV coinfection, alcohol abuse, potential metabolic syndrome) were present in 39.1% and 47.1% (p &gt; 0.05) of migrants and natives who eradicated HCV, respectively. Conclusion: Compared to natives, HCV-infected migrants in care have different demographics, HCV genotypes, viral coinfections and comorbidities and similar disease severity, SVR and cofactors for disease progression after HCV eradication. A periodic clinical assessment after HCV eradication in Italians and migrants with cofactors for disease progression is warranted