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Over the past few decades, scientific collaboration has been widely considered an important driver of research innovation. By collaborating together, scientists can benefit from both methodological and technological complementarities and synergy, improving the quality and quantity of their research outputs. As evidence of this, collaboration among scientists is increasing in all disciplines and government policies in international exchange programs are aimed at promoting collaboration among researchers. Collaboration among scientists can be represented as a network, usually adopting co-authorship as linkages. In this view, Social Network Analysis provides a useful theoretical and methodological approach because collaboration features can be related to the topological characteristics of the network. Recently, several empirical studies have found positive associations between researchers’ position in the co-authorship network and their productivity, although the results can be different depending on the discipline, scientific performance measure, and data source retrieved to construct the co-authorship networks. In this contribution, we propose the use of SNA tools for scientific evaluation purposes. Network indices at the individual and subgroup levels will be introduced to analyze the relation with both the individual research productivity and scientific output quality measure provided by the Italian academic researchers involved in VQR from the period 2011–2014.

Background Acute chest pain with mild troponin rise and inconclusive diagnosis after clinical evaluation represents a diagnostic challenge. Triple-rule-out (TRO) CT may exclude coronary artery disease (CAD), as well as acute aortic syndrome and pulmonary embolism, but cannot help identify other causes of myocardial injury. Purpose To investigate the diagnostic value of a comprehensive CT protocol including both an angiographic and a late contrast enhancement (LCE) scan in participants with troponin-positive acute chest pain. Materials and Methods In this prospective study, consecutive patients with troponin-positive acute chest pain or anginal equivalent and inconclusive diagnosis after clinical evaluation (symptoms, markers, electrocardiography, and echocardiography) who underwent TRO CT between June 2018 and September 2020 were enrolled. TRO CT was performed to evaluate the presence of obstructive CAD (stenosis ≥50%), acute aortic syndrome, and pulmonary embolism. If the findings on the TRO CT scan were negative, an LCE CT scan was acquired after 10 minutes to assess the presence and pattern of scar and quantify the myocardial extracellular volume fraction. CT-based diagnoses were compared with diagnoses obtained with reference standard methods, including invasive coronary angiography, cardiac MRI, and endomyocardial biopsy. Results Eighty-four patients (median age, 69 years [interquartile range, 50-77 years]; 45 men) were enrolled. TRO CT helped identify obstructive CAD in 35 participants (42%), acute aortic syndrome in one (1.2%), and pulmonary embolism in six (7.1%). LCE CT scans were acquired in the remaining 42 participants. The following diagnoses were reached with use of LCE CT: myocarditis (22 of 42 participants [52%]), takotsubo cardiomyopathy (four of 42 [10%]), amyloidosis (three of 42 [7.1%]), myocardial infarction with nonobstructed coronary arteries (three of 42 [7.1%]), dilated cardiomyopathy (two of 42 [4.8%]), and negative or inconclusive findings (eight of 42 [19%]). The addition of LCE CT improved the diagnostic rate of TRO CT from 42 of 84 participants (50% [95% CI: 38.9, 61.1]) to 76 of 84 (90% [95% CI: 82.1, 95.8]) (P < .001). Conclusion A CT protocol including triple-rule-out and late contrast enhancement CT scans improved diagnostic rate in participants presenting with acute chest pain syndrome. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Nagpal and Bluemke in this issue.

In this work, we reconstruct the tweet-retweet and tweet-reply relations of opinions about a trending topic on the Twitter platform. We propose a multi-steps approach to derive a signed network expressing the spread of contents and opinions. The first step consists in reducing data dimensionality by means of a clustering procedure on tweets able to identify the concepts they convey. In the second step, focusing on message contents, we adapt different sentiment analysis algorithms in order to determine the sign of both the original tweet (with respect to the trending topic) and the sign of the edge connecting the original tweet to the replies, conditional on the replied tweet. Each tweet will spread its concepts by means of signed retweet and reply relations. The aim is to study the different structure, in terms of both network structure and sentiment, of the signed network related to each concept. A comparative analysis will be possible as well among the various identified signed networks.

Aims Anatomic knowledge of the tricuspid valve (TV) is the first step in the management of patients with tricuspid regurgitation (TR) who are candidates for transcatheter tricuspid valve intervention (TTVI). Echocardiography is undoubtedly the first approach in assessing the aetiology and severity of TR and the size and function of the right chambers. Computed tomography (CT) provides a detailed morphological visualization of the cardiac structures owing to acquisition of 3D data with high spatial resolution. These findings may undoubtedly help in decision-making progress for novel transcatheter therapies. The purpose of the present study was to assess the geometrical changes of the TV complex using CT images, in patients suffering from functional TR and lead-induced TR. Methods The study population consisted of 21 consecutive patients with symptomatic severe TR referred to Policlinico Universitario Campus Biomedico between November 2020 and October 2021. Patients were prospectively included in the study only if they presented severe TR, diagnosed by echocardiography and underwent cardiac CT study dedicated to the right-chambers. The reconstructions were transferred to an external workstation for off-line image analysis. The following measurements were reported: tricuspid annulus area, perimeter, septal–lateral and antero-posterior diameters. Commissures were identified as antero-septal (AS), postero-septal (PS) and anteroposterior (AP). Were measured the inferior vena cava ostium to tricuspid valve centroid distance, anatomic regurgitant orifice area (AROA) and its position respect to the centroid, and the right chambers. Results All 21 patients underwent CT scan using Siemens SOMATOM Definition AS 128 Slice CT Machine. The measurements were calculated off-line using the 3mensio workstation. In our study population, the annulus resulted enlarged in the annulus area, perimeter, septal-lateral and anterior-posterior dimensions. Measurements did not differ significantly, except for the septal-lateral diameter that was smaller in systole (52.80 ± 7.28 mm vs. 47.83 ± 6.83 mm (P=0.027). Also, distances between the commissures were similar except for the AP-AS distance that was shorter in systole (45.26 ± 3.48 mm vs. 42.13 ± 3.73, P=0.007). The AROA resulted to be central in 7 patients, the IVC ostium to TV centroid distance was 23±3 mm. Right chambers and IVC resulted very enlarged in all patients. Conclusions CT provides a complete morphologic imaging of the heart structures, thanks to a high spatial resolution with excellent capacity to define the endocardial border and allows acquisition of three-dimensional data with high spatial resolution of the TV and provides valuable information about the geometric variations of the tricuspid complex in patients with TR. Image quality for analysis should be optimized with specific CT acquisition protocols that focus on the right ventricles.

Background A systematic examination of low-dose CT (LDCT) scan, beside lung nodules, may disclose the presence of undiagnosed diseases, improving the efficacy and the cost/efficacy of these programs. The study was aimed at evaluating the association between LDCT scan findings and non-oncologic and oncologic diseases. Methods The LDCT scan of participants to the “Un Respiro per la vita”® lung cancer screening program were checked and abnormal findings, beside lung nodules, recorded. First admission to the acute care because of cardiovascular (CD), respiratory (RD) and oncological diseases (OD) in the following three years were retrieved. The association of LDCT scan abnormal findings with CD, RD and OD was assessed through univariable and multivariable logistic regression models. Results Mean age of 746 participants was 62 years (SD:5), 62% were male. 11 (1.5%) received a diagnosis of lung cancer. 16.1% participants were admitted to the acute care in the following three years: 8.6% for CD, 4.3% for RD and 5.2% for OD. Valve calcification (OR 2.02, p:0.02) and mucus plugs (OR 3.37, p:0.04) were positively associated with CD, while sub-pleural fibrosis had a protective role (OR 0.47, p:0.01). Lung nodules > 8 mm (OR 5.54, p: Conclusions The observed association between selected LDCT findings and ensuing CD, RD and OD suggests that the information potential of LCDT goes beyond the screening of lung cancer.

In this work, we reconstruct the tweet-retweet and tweet-reply relations of opinions about a trending topic on the Twitter platform. We propose a multi-steps approach to derive a signed network expressing the spread of contents and opinions. The first step consists in reducing data dimensionality by means of a clustering procedure on tweets able to identify the concepts they express. In the second step, focusing on message contents, we adapt different sentiment analysis algorithms in order to determine the sign of both the original tweet (with respect to the trending topic) and the sign of the edge connecting the original tweet to the replies, conditional on the replied tweet. Each tweet will spread its concepts by means of signed retweet and reply relations. The aim is to study the different structure, in terms of both network structure and sentiment, of the signed network related to each concept. A comparative analysis will be possible as well among the various identified signed networks.

Community detection is a very appealing topic in network analysis. A precise definition of community is still lacking, so the comparison of different methods is not a simple task. This paper shows exploratory results by adopting two well-known community detection methods and a new proposal to discover groups of scientists in the co-authorship network of Italian academic statisticians.

La disponibilita di cibo e l’esposizione ad agenti patogeni hanno condizionato l’evoluzione della specie umana favorendo l’acquisizione di un’elevata efficienza nell’accumulare energia e lo sviluppo di un efficace apparato di difesa, rappresentato dal sistema immunitario. Il network che connette funzioni metaboliche e immunitarie si e strutturato in un contesto diverso da quello odierno, cosicche l’attuale l’overload metabolico induce uno stato infiammatorio cronico di basso grado.

Empirical evidence shows that knowledge transfer from research institutions and universities contributes to local innovation processes in a variety of ways. Several studies have also shown that the extent of innovation diffusion is greatly affected by the structure of the network in which innovation processes take place. In this contribution, we aim to identify the complex structure of relationships at the basis of knowledge and innovation diffusion among actors from various organizations (firms, academic and research institutions) in a given territory. A multiplex approach is proposed to explain co-authorship and co-inventions interactions among Author-Inventors community in a specific geographic area. To this end, we carry out a case study of the Trieste area (North-East Italy), characterized by a very high concentration of research organizations and by the emergence of a lively sector of small firms working in the field of R&D.

In this paper we discuss the use of Multiple Correspondence Analysis to analyze and graphically represent two-mode networks, and we propose to apply it in a Greenacre's doubling perspective. We discuss how Multiple Correspondence Analysis: (i) properly takes into account the nature of relational data and the intrinsic asymmetry of actors/events in two-mode networks; (ii) allows a proper graphical appraisal of the underlying relational structure of actors or events; (iii) makes it possible to add actor and event attributes to the analysis in order to improve results interpretation; and (iv) gives different results with respect to the usual Simple Correspondence Analysis. © 2014 Elsevier B.V.

The present paper aims at describing the scientific collaboration patterns of the Italian academic statisticians by merging bibliographic data from heterogenous sources–ISI-WoS, Current Index to Statistics, and the database of nationally funded research projects, PRIN. To obtain a unified database, containing both top international as well as nationally oriented production, information were com- bined by identifying and linking duplicate records, i.e. record linkage. The unique co-authorship network was then used as basis for network analysis.

This paper aims to explore the effects of collaborative behaviour on scholar scientific performance. Individual network measures related to scholar centrality as well as attitude to collaborate with others are derived from co-authorship networks in a given scientific community (i.e. Italian academic statisticians). Co-authorship information have been collected from three data sources of national-based, discipline-based, and international-based high-impact publications. Both network and individual covariates are used to model individual h-index by generalized extreme value distribution. Results show a positive association between performance and actors' central position in the network. Having a large number of co-authors and occupying central positions are likely to positively affect scientific performance.

In the present work we propose to analyze, through Multiple Factorial Analysis (MFA), the relational structure embedded in collaboration networks observed across time occasions. We show, through a case study, how the solutions provided by the MFA can be interpreted in a suitable way in the relational setting which arises in complex and heterogeneous networks. Valuable information about the strength and typology of the collaboration structure and its evolution can be obtained. As case study, we analyze a Technological District located in South Italy.

The role of academics in patenting activity is not limited to patents owned by universities (university-owned patents) as academics often contribute to inventions patented by other organizations (university-invented patents). Moreover, it has been shown that academics play central roles inside the patenting network and that appropriability rules can interfere with the pattern of knowledge diffusion. In this study, we use social network analysis to analyze university-owned and university-invented patents in two Italian universities. We investigate the quality of the ties and the reasons why academics patent with their universities or with external organizations. We identify three subnetwork typologies. Two out of three subnetworks well exemplify the conditions for university ownership and for professor privilege, but the most complex structure, stemming from academic gatekeepers, need more flexible property attribution arrangements. In this contexts, aggressive policies toward university ownership can damage the networks with the highest grade of science-industry cross-fertilisation.

The aim of the present contribution is to merge bibliographic data for members of a bounded scientific community in order to derive a complete unified archive, with top-international and nationally oriented production, as a new basis to carry out network analysis on a unified co-authorship network. A two-step procedure is used to deal with the identification of duplicate records and the author name disambiguation. Specifically, for the second step we strongly drew inspiration from a well-established unsupervised disambiguation method proposed in the literature following a network-based approach and requiring a restricted set of record attributes. Evidences from Italian academic statisticians were provided by merging data from three bibliographic archives. Non-negligible differences were observed in network results in the comparison of disambiguated and not disambiguated data sets, especially in network measures at individual level.

In this paper, we present the results of a network analysis applied to academic patent data in a subsector of the chemical field in Italy in the period 2000–2011. In particular, we analyse the micro-level interactions to point out the different network structures shaped by university-owned and university-invented patents. We detected three subnetwork typologies (labelled type A, B, and C) that exemplify different qualitative relational structures as well as different attributions to propriety rights. Type A (open science) exemplifies the typical owned patent; type B (multiple ties) represents the hybrid structure with multiple ties and involvement of academics as individuals and of universities as organisation; type C (disconnected subnetworks) represents the typical invented patent with no role of universities as organisation. The whole network seems to show a breaking point in terms of connectivity around 2005, a year that marks a change in policy rule and strategic orientation of Italian universities towards patenting. After 2005, the number of actors grew disproportionally and the network appears disconnected in several comparable components. Also, the composition in terms of subnetwork types changed. The overall picture seems to underline a big structural change dominated by the important increase of academic patenting both direct (university ownership) and indirect (increasing academic patenting).

Evidence from economic literature suggests that innovative activities based on extensive interactions between industry, universities and local government can yield high levels of economic performance. In many countries, therefore, steps have been taken at an institutional level to set up innovation networks and, in particular, regional technological districts. Our paper deals with Italian Technological Districts: we aim to analyse the network additionality for territorial innovation determined by district policy. The analysis is based on a priori structural regional characteristics and on Social Network Analysis techniques.

Most social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes.

The present paper deals with network analysis of collaboration patterns in Statistics focusing on co-authorship relations. Attention to this discipline derives from several motivations. Unlike other fields, co-authorship behavior in Statistics has not yet been investigated. This discipline presents some characteristics common to natural sciences as well as social sciences, and it plays a central role in addressing problems in everyday applications. Therefore, it is of interest to examine what are the network properties and the emerging patterns characterizing this discipline as well as the effects collaboration has on individual performance. To this purpose, we collected co-authorship data on the 792 academic statisticians in Italy as recorded in the Italian Ministry of University and Research database in March 2010. We used three bibliographic archives including both top-international as well as nationally oriented publications: ISI-WoS, Current Index to Statistics, and bibliographic information related to nationally funded research projects. Given that each data source showed peculiar characteristics affecting network results (as reported in De Stefano et al. 2013), in this paper we aim at merging the three databases to obtain an unified archive and to use it as a new basis for network analysis. Specifically, two main challenges are managed to obtain the unified co-authorship network: how to combine information from heterogeneous sources by identifying duplicate records, and how to deal with issues related to authors synonyms and homonymies (i.e. name disambiguation) to guarantee the data quality. The merged co-authorship network will be used to describe collaborative behaviour among Italian statisticians in a comparative way with respect to the previous findings based on network analysis of each data sources.

The present paper aims at describing the scientific collaboration patterns of the Italian academic statisticians by exploiting the information retrieved through different bibliographic archives (ISI-WoS, Current Index to Statistics), and the bibliographic information of the database of nationally funded research projects. A data merging procedure is adopted to obtain a unified dataset containing both topinternational as well as nationally oriented production. The unified dataset will be used as basis for network analysis.

Factorial techniques are widely used in Social Network Analysis to analyze and visualize networks. When the purpose is to represent the relational similarities, simple correspondence analysis is the most frequent used technique. However, in the case of affiliation networks, its use can be criticized because the involved χ 2 distance does not adequately reflect the actual relational patterns. In this paper we perform a simulation study to compare the metric involved in Correspondence Analysis with respect to the one in Multiple Correspondence Analysis. Analytical results and simulation outcomes show that Multiple Correspondence Analysis allows a proper graphical appraisal of the underlying two-mode relational structure.

This paper analyses the relationships between firms involved in a pilot project to promote the creation of a multifunctional local system, a case of collective action. The project area is a rural alpine space in the Friuli Venezia Giulia region (NE Italy). The analysis, which is part of the pilot project, used Social Network Analysis tools to explore the structure and dynamics of firm relationships in terms of mutual awareness, types and strength of business ties, and extension and location of economic and social interactions. These characteristics are described at both complete and ego network levels. The findings can be used to design policies and rural development agendas. The analysis also revealed the potentials of Social Network Analysis in assessing networking projects and enhancing rural actors’ awareness of their relational system.

Scientific collaboration is usually derived from archival co-authorship data. Several data sources may be examined, but they all have advantages and disadvantages, especially when a specific discipline or community is of interest. The aim of this paper is to explore the effect of the use of three data sources – Web of Science, Current Index to Statistics and nationally funded research projects – on the analysis of co-authorship networks among Italian academic statisticians. Results provide evidence of our hypotheses on distinct collaboration patterns among statisticians, as well as distinct effects of scientist network positions on scientific performance, by both Statistics subfield and data source.

Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relationships. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: (i) the use of local archives versus international bibliographic databases; (ii) the use of different approaches for setting boundaries in a whole-network; (iii) the definition of a co-authorship data matrix (binary and weighted ties) and (iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases within an illustrative example on real data which is referred to scientific collaboration among researchers affiliated to an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted co-authorship networks in different disciplines.

Empirical evidence from economic literature suggests that innovative activities based on extensive interactions between industry, university and local government have shown high developing performances. In many countries, institutional arrangements have been designed to start regional technological districts. In this paper we aim to analyze a nationwide systems of public grants to joint R&D projects realized by organizations involved in seven Italian Technological Districts. In this framework the main purpose is to explore how network structures influence network efficiency in terms of knowledge exchange and innovation diffusion by means of Social Network Analysis tools, graph comparison methods and suitable models.

The problem faced in this paper is related to the comparison between two undirected networks on n actors. Actors are in two different configurations G k (k=1,2). Comparison is based on the evaluation of the how the relational node distances evolve in the passage from the first net (G 1 ) to the second net (G 2 ). The procedure consists of two steps: (i) define an appropriate relational distance among nodes of the two networks; (ii) compare the corresponding distance matrices. The first step is based on the so-called Euclidean Commute-Time Distance among the n nodes computed from a random walk on the graph and Laplacian matrix. The second step concerns the comparison between the obtained distance matrices by using Multidimensional Scaling techniques. The procedure has a wide range of application, especially for experimental purposes in social network applications where this issue has not been treated systematically.

Lo scritto si propone di analizzare il tema della adeguatezza della tutela giurisdizionale in materia di energia ed ambiente. I temi affrontati sono due. Il primo tema pone il quesito se l’inefficienza del mercato sia dovuta non solo al fallimento della regolazione ma anche al fallimento del processo. Il secondo tema individua gli interessi che sono oggetto di tutela giurisdizionale. L’analisi di questi due temi parte dalla ricostruzione di una serie di elementi. Il primo riguarda il significato giuridico della nozione di ambiente. Il secondo riguarda il rapporto tra energia e ambiente. Il terzo aspetto analizza il rapporto tra energia, ambiente e mercato. Il quarto ricostruisce il sistema di tutela giurisdizionale italiano. Il quinto analizza il funzionamento dei tribunali specializzati. La conclusione è che giudicare per il mercato non significa ancora soddisfare gli interessi della collettività. Pertanto si rende necessario un maggiore confronto dialettico e un potenziamento di sedi procedimentali volte a prevenire il contenzioso The paper aims to analyse the issue of the adequacy of judicial protection in the field of energy and the environment. There are two themes addressed. The first topic raises the question of whether the market inefficiency is due not only to the failure of regulation but also to the failure of the process. The second theme identifies the interests that are the subject of judicial protection. The analysis of these two themes starts from the reconstruction of a series of elements. The first concerns the legal meaning of the notion of environment. The second concerns the relationship between energy and the environment. The third aspect analyses the relationship between energy, environment and the market. The fourth reconstructs the Italian judicial protection system. The fifth analyses the functioning of specialized courts. The conclusion is that judging by the market does not yet mean meeting the interests of the community. However, a greater dialectical confrontation and a strengthening of procedural offices are needed to prevent litigation.