1,721,063 research outputs found
Using dual-network-analyser for communities detecting in dual networks
No full textBackground: Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges. Results: We focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data. Conclusion: The proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs
Spatio-temporal resource mapping for intensive care units at regional level for COVID-19 emergency in Italy
No full textCOVID-19 is a worldwide emergency since it has rapidly spread from China to almost all the countries worldwide. Italy has been one of the most affected countries after China. North Italian regions, such as Lombardia and Veneto, had an abnormally large number of cases. COVID-19 patients management requires availability of sufficiently large number of Intensive Care Units (ICUs) beds. Resources shortening is a critical issue when the number of COVID-19 severe cases are higher than the available resources. This is also the case at a regional scale. We analysed Italian data at regional level with the aim to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, we retain that the here proposed model can be also used in other countries
Sharing mass spectrometry data in a grid-based distributed proteomics laboratory
No full textData produced by mass spectrometry (MS) have been using in proteomics experiments to identify proteins or patterns in clinical samples that may be responsible for human diseases. MS-based proteomics is becoming a powerful, widely used technique to identify different molecular targets in different pathological contexts. Moreover, MS samples contain a huge amount of data; retrieving such information requires accessing to large volumes of data, thus an efficient organization is necessary both to reduce access time and to allow efficient knowledge extraction. Bioinformatics laboratories have been using more than one mass spectrometer to improve efficiency, largely increasing the volume of data obtained by experiments. Moreover, experimental data is enriched by observations and descriptions added by specialists through metadata. Thus, information retrieval of spectra data (and metadata describing them) inside a laboratory and among different laboratories requires large and scalable storage solutions, and high performance computational platforms. We present a software system for managing, sharing, and querying MS data in a distributed laboratory, using a spectra data management system, called SpecDB, where information retrieval is performed by using computational grid facilities. Information retrieval can be conducted either locally, by considering portions of spectra data, or in a distributed scenario, exploiting metadata and annotations about spectra datasets stored on the grid
A Novel Algorithm for Local Network Alignment Based on Network Embedding
No full textNetworks are widely used in bioinformatics and biomedicine to represent associations across a large class of biological entities. Network alignment refers to the set of approaches that aim to reveal similarities among networks. Local Network Alignment (LNA) algorithms find (relatively small) local regions of similarity between two or more networks. Such algorithms are in general based on a set of seed nodes that are used to build the alignment incrementally. A large fraction of LNA algorithms uses a set of vertices based on context information as seed nodes, even if this may cause a bias or a data-circularity problem. Moreover, using topology information to choose seed nodes improves overall alignment. Finally, similarities among nodes can be identified by network embedding methods (or representation learning). Given there are two networks, we propose to use network embedding to capture structural similarity among nodes, which can also be used to improve LNA effectiveness. We present an algorithm and experimental tests on real and syntactic graph data to find LNAs
On the reliability of measurements for a stent positioning simulation system
No full textBackground and objective: Computer aided simulations are useful to support the physician in many steps of the surgical activity, but also in pre-surgical patient classification and in post-surgical diagnosis and treatment decisions. At a broader level, computerized technologies and infrastructures permeate every aspect of the medical activity, from patient management to surgery and patients' follow up with outcomes analyses. Radiography assisted surgery is often used in hemodynamic surgery to study and support cardio-circulatory stents positioning with the use of radioscopy coupled with contrast liquid injected into the vessels. Computer based surgery instruments (both software and hardware) are used to support clinicians during interventions, e.g., to reduce radioscopy time exposure, to minimize errors and to estimate tissues and organs dimension. In this paper we present the use of a newly developed system which supports physicians during transcatheter percutaneous coronary interventions.
Methods: This paper presents a Java-based tool which acquires images from angiographic equipment during surgery procedures. An high performance image acquisition module has been used and a stent simulation environment module is available to simulate stent positioning and to measure vessels. Operators may acquire images, perform measurements and simulations on DICOM images. We performed tests off-line on images to validate the reliability of the tool. Real cases and on line tests have been performed by operators showing the robustness of the system to be used in surgery room. The system has been integrated in the surgery room control panel and allows (i) vascular images acquisition, (ii) vessels and coronary measurement and (iii) stent positioning simulations. The tool is an aid for the physician for both measuring tissues or lesions and for defining the stent's geometry and position before its deployment in the patient's vessels.
Results: Experiments have been performed on lesions and vessels by different operators using the system and an available commercial system, on both real patient cases and synthetic images designed with a CAD. It has been tested on 76 images extracted from real angiography cases and on 11 synthetic images created by using CAD. Five different operators performed 2128 measurements for the real cases images (for both Cartesio and CAAS tools) and 112 for the synthetic dataset. Results show the efficacy of the system compared with the commercial one by means of several statistical tests.
Conclusions: The proposed system is a reliable tool for hemodynamic surgery and can be used both for decision support in stent positioning procedures and for didactic training of new physicians
A framework for the atrial fibrillation prediction in electrophysiological studies
No full textBackground and objective
Cardiac arrhythmias are disorders in terms of speed or rhythm in the heart's electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures.
Methods
We gathered data signals collected by a General Electric Healthcare's CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation.
Results
A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS
Extracting Dense and Connected Communities in Dual Networks: An Alignment Based Algorithm
No full textNetworks-based models have been used to represent and analyse datasets in many fields such as
computational biology, medical informatics and social networks. Nevertheless, it has been recently shown
that, in their standard form, they are unable to capture some aspects of the investigated scenarios. Thus,
more complex and enriched models, such as heterogeneous networks or dual networks, have been proposed.
We focus on the latter model, which consists of a pair of networks having the same nodes but different
edges. In dual networks, one network, called physical, has unweighted edges representing binary associations
among nodes. The other is an edge-weighted one where weights represent the strength of the associations
among nodes. Dual networks capture in a single model some aspects that cannot be described by using a
standard model. Dual networks can be used, for instance, to capture a co-authorships network, where physical
network represents co-authors. In contrast, the conceptual network is used to model topics sharing among
a couple of authors by means of edge connections. This allows capturing similar interests among authors
even though they are not co-authors. We propose an innovative algorithm to find the Densest Connected
Subgraph (DCS) in dual networks. DCS is the largest density subgraph in the conceptual network, which is
also connected in the physical network. A DCS represents a set of highly similar nodes. Moreover, since DCS
is a computationally hard problem, we propose novel heuristics to solve it. We tested the proposed algorithm
on social, biological, and co-authorship networks. Results demonstrate that our approach is efficient and is
able to extract meaningful information from dual networks
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