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AIM: Patients with hypertension may exhibit abnormal vasodilator capacity during pharmacological vasodilatation. We assessed coronary flow reserve (CFR) by sestamibi imaging in hypertensive patients with normal coronary vessels. METHODS: Twenty-five patients with untreated mild essential hypertension and normal coronary vessels and 10 control subjects underwent dipyridamole-rest Tc-99m sestamibi imaging. Myocardial blood flow (MBF) was estimated by measuring first transit counts in pulmonary artery and myocardial counts from tomograhic images. CFR was expressed as the ratio of stress to rest MBF. Coronary vascular resistances (CVR) were computed as the ratio between mean arterial pressure and MBF. RESULTS: Estimated MBF at rest was not different in patients and controls (1.11 ± 0.59 vs. 1.14 ± 0.28 counts/pixel/s; P=0.87). Conversely, stress MBF was lower in patients than in controls (1.55 ± 0.47 vs. 2.68 ± 0.53 counts/pixel/s; P

Different animal models have been used to reproduce coronary heart disease but in the last years mice became the animals of choice, because of their short life cycle and the possibility of genetic manipulation. Various techniques are currently used for cardiovascular imaging in mice, including high-resolution ultrasound, X-ray computed tomography (CT), magnetic resonance imaging and nuclear medicine procedures. In particular, molecular imaging with cardiac positron emission tomography (PET) allows to evaluate noninvasively changes in myocardial perfusion, metabolism, apoptosis, inflammation, and gene expression or to measure changes in left ventricular functional parameters. With technological advancements, dedicated small laboratory PET/CT imaging has emerged in cardiovascular research, providing in vivo a non-invasive, serial and quantitative assessment of left ventricular function, myocardial perfusion and metabolism at a molecular level. This non-invasive methodology might be useful in longitudinal studies monitoring cardiac biochemical parameters and might facilitate studies to assess the effect of different interventions after acute myocardial ischemia.

In the fight against the racketeering and terrorism, knowledge about the structure and the organization of criminal networks is of fundamental importance for both the investigations and the development of efficient strategies to prevent and restrain crimes. Intelligence agencies exploit information obtained from the analysis of large amounts of heterogeneous data deriving from various informative sources including the records of phone traffic, the social networks, surveillance data, interview data, experiential police data, and police intelligence files, to acquire knowledge about criminal networks and initiate accurate and destabilizing actions. In this context, visual representation techniques coordinate the exploration of the structure of the network together with the metrics of social network analysis. Nevertheless, the utility of visualization tools may become limited when the dimension and the complexity of the system under analysis grow beyond certain terms. In this paper we show how we employ some interactive visualization techniques to represent criminal and terrorist networks reconstructed from phone traffic data, namely foci, fisheye and geo-mapping network layouts. These methods allow the exploration of the network through animated transitions among visualization models and local enlargement techniques in order to improve the comprehension of interesting areas. By combining the features of the various visualization models it is possible to gain substantial enhancements with respect to classic visualization models, often unreadable in those cases of great complexity of the network., 6 pages, 4 figures, DataWiz 2014 (held in conjunction with ACM Hypertext 2014)

Online Social Networks (OSN) during last years acquired a huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a significant sample of data reflecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities between the developing of OSN and real-life social networks., Comment: 6 pages, 1 figure; MIFI '10: Proceedings of the 1st International Workshop on Mining the Future Internet

As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Then, the quadratic form of the error function is minimized. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real networks. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and PisCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. It is proven that the proposed PPPM method is robust, reasonable, and effective. This method can also be applied to the general social networking community discovery field.

The role of an actor in a social network is identified through a set of measures called centrality. Degree centrality, betweenness centrality, closeness centrality, and clustering coefficient are the most frequently used metrics to compute the node centrality. Their computational complexity in some cases makes unfeasible, when not practically impossible, their computations. For this reason, we focused on two alternative measures, WERW-Kpath and Game of Thieves, which are at the same time highly descriptive and computationally affordable. Our experiments show that a strong correlation exists between WERW-Kpath and Game of Thieves and the classical centrality measures. This may suggest the possibility of using them as useful and more economic replacements of the classical centrality measures.

Data collected in criminal investigations may suffer from: (i) incompleteness, due to the covert nature of criminal organisations; (ii) incorrectness, caused by either unintentional data collection errors and intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyse nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data and to determine which network type is most affected by it. The networks are firstly pruned following two specific methods: (i) random edges removal, simulating the scenario in which the Law Enforcement Agencies (LEAs) fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) nodes removal, that catches the hypothesis in which some suspects cannot be intercepted or investigated. Finally we compute spectral (i.e., Adjacency, Laplacian and Normalised Laplacian Spectral Distances) and matrix (i.e., Root Euclidean Distance) distances between the complete and pruned networks, which we compare using statistical analysis. Our investigation identified two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., 10% removed edges); second, removing even a small fraction of suspects not investigated (i.e., 2% removed nodes) may lead to significant misinterpretation of the overall network., Comment: 18 pages, 4 figures, submitted to PLoS ONE Journal

Network Science is an active research field, with numerous applications in areas like computer science, economics, or sociology. Criminal networks, in particular, possess specific topologies which allow them to exhibit strong resilience to disruption. Starting from a dataset related to meetings between members of a Mafia organization which operated in Sicily during 2000s, we here aim to create artificial models with similar properties. To this end, we use specific tools of Social Network Analysis, including network models (Barabási-Albert identified to be the most promising) and metrics which allow us to quantify the similarity between two networks. To the best of our knowledge, the DeltaCon and spectral distances have never been applied in this context. The construction of artificial, but realistic models can be a very useful tool for Law Enforcement Agencies, who could reconstruct and simulate the evolution and structure of criminal networks based on the information available.

Social Network Analysis (SNA) is used to study the exchange of resources among individuals, groups, or organizations. The role of individuals or connections in a network is described by a set of centrality metrics which represent one of the most important results of SNA. Degree, closeness, betweenness and clustering coefficient are the most used centrality measures. Their use is, however, severely hampered by their computation cost. This issue can be overcome by an algorithm called Game of Thieves (GoT). Thanks to this new algorithm, we can compute the importance of all elements in a network (i.e. vertices and edges), compared to the total number of vertices. This calculation is done not in a quadratic time, as when we use the classical methods, but in polylogarithmic time. Starting from this we present our results on the correlation existing between GoT and the most widely used centrality measures. From our experiments emerge that a strong correlation exists, which makes GoT eligible as a centrality measure for large scale complex networks.

Compared to other types of social networks, criminal networks present hard challenges, due to their strong resilience to disruption, which poses severe hurdles to law-enforcement agencies. Herein, we borrow methods and tools from Social Network Analysis to (i) unveil the structure of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently disrupt them. Mafia networks have peculiar features, due to the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts are also faced with the difficulty in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data derived from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our network disruption analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). We measured the effectiveness of each approach through a number of network centrality metrics. We found Betweeness Centrality to be the most effective metric, showing how, by neutralizing only the 5% of the affiliates, network connectivity dropped by 70%. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions frequency) no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for tackling criminal and terrorist networks., 12 pages, 4 figures, paper submitted to PLOS ONE Journal

In this paper, we focus on the study of Sicilian Mafia organizations through Social Network Analysis. We analyse datasets reflecting two different Mafia Families, based on examinations of digital trails and judicial documents, respectively. The first dataset includes the phone calls logs among suspected individuals. The second one is based on police traces of meeting that have taken place among different types of criminals. Our breakthrough is twofold. First in the method followed to generate these new datasets. Second, in the method used to carry out a quantitative phenomena investigation that are hard to evaluate. Our networks are weighted ones, with each weight catching the frequency of interactions between criminals. Therefore, our analysis focuses on weight and shortest paths distributions in both networks. We identify new types of unusual interactions in the Mafia networks, leading to substantial differences between Mafia networks and other types of criminal or terrorist networks.

Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analysis relies on an original dataset manually extracted from the judicial documents of operation “Montagna”, conducted by the Italian law enforcement agencies against individuals affiliated with the Sicilian Mafia. To run our analysis, we extracted two networks: one including meetings and one recording telephone calls among suspects, respectively. We conducted two experiments on these networks. First, we applied several link prediction algorithms and observed that link prediction algorithms leveraging the full graph topology (such as the Katz score) provide very accurate results even on very sparse networks. Second, we carried out extensive simulations to investigate how the noisy and incomplete nature of criminal networks may affect the accuracy of link prediction algorithms. The experimental findings suggest the soundness of link predictions is relatively high provided that only a limited amount of knowledge about connections is hidden or missing, and the unobserved edges follow some kind of generative law. The different results on the meeting and telephone call networks indicate that the specific features of a network should be taken into careful consideration.

Diabetes mellitus is a complex clinical entity that will grow in importance in the future. The complications of diabetes have a significant impact on patient survival and quality of life, particularly with respect to coronary artery disease (CAD). Appropriate screening and aggressive intervention can significantly benefit many patients with diabetes. In addition, it is important to consider strategies useful not only in the diagnosis of CAD but also in the prognostic evaluation of diabetic patients with coronary disease. Prognostic data are essential in defining risk categories and to apply appropriate treatment for the degree of risk. Therefore, accurate cardiovascular risk stratification of patients with type 2 diabetes is required. However, this can be a problematic issue because the clinical presentation and progression of CAD differs between diabetic and nondiabetic subjects. In addition to a higher prevalence of CAD, patients with diabetes experience more diffuse and extensive coronary artery involvement, more often have left ventricular dysfunction, a more advanced coronary disease at the time of diagnosis, and more often experience silent ischemia. Furthermore, diabetic patients have frequently a less favorable response to revascularization procedures and a poorer long-term outcome. The purpose of this review is to discuss the relative role of various procedures for diagnosis of CAD and for cardiac risk stratification in patients with diabetes.

Detecting communities in graphs is a fundamental tool to understand the structure of Web-based systems and predict their evolution. Many community detection algorithms are designed to process undirected graphs (i.e., graphs with bidirectional edges) but many graphs on the Web-e.g., microblogging Web sites, trust networks or the Web graph itself-are often directed . Few community detection algorithms deal with directed graphs but we lack their experimental comparison. In this paper we evaluated some community detection algorithms across accuracy and scalability. A first group of algorithms (Label Propagation and Infomap) are explicitly designed to manage directed graphs while a second group (e.g., WalkTrap) simply ignores edge directionality; finally, a third group of algorithms (e.g., Eigenvector) maps input graphs onto undirected ones and extracts communities from the symmetrized version of the input graph. We ran our tests on both artificial and real graphs and, on artificial graphs, WalkTrap achieved the highest accuracy, closely followed by other algorithms; Label Propagation has outstanding performance in scalability on both artificial and real graphs. The Infomap algorithm showcased the best trade-off between accuracy and computational performance and, therefore, it has to be considered as a promising tool for Web Data Analytics purposes.

Left ventricular (LV) function is one of the most powerful predictors of prognosis in the setting of heart failure (HF) or following myocardial infarction. It is important to differentiate patients in whom LV dysfunction is caused by necrosis and scar tissue formation from those with LV dysfunction because of ischemia or hibernating, but viable myocardium. Previous studies showed that identification of the latter group of patients predicts substantial survival benefit, symptomatic improvement, and improved LV function with revascularization. On the other hand, more recent investigations, such as the Surgical Treatment for Ischemic Heart Failure (STICH) trial [3], did not find a significant interaction between myocardial-viability status and medical versus surgical treatment with respect to the rates of death from any cause or from cardiovascular causes or the rate of death or hospitalization for cardiovascular causes.

Silent myocardial ischemia is defined as objective documentation of myocardial ischemia in the absence of angina or anginal equivalents. The first description of silent ischemia date to the 1970s, but despite intensive investigation, its pathophysiology, detection, prevalence, and clinical significance are still debated. The need and the benefit of treating silent ischemia, particularly in patients without history of coronary artery disease (CAD) and who are asymptomatic, remain questionable. Asymptomatic (silent) myocardial ischemia may be identified during routine daily activities or during stress testing in patients without or with known CAD. One of the earliest investigations addressing the prevalence and significance of silent myocardial ischemia in asymptomatic subjects is the United States Air Force study, conducted in 1,390 men, 111 of whom had positive exercise tests. Thirty-four of these 111 (about 2.5% of the total) had coronary artery lesions of at least 50% stenosis. Thaulow et al studied 2014 Norwegian male office workers between 40 and 59 years of age (mean age 50 year). Coronary angiography in men with positive exercise tests demonstrated that 69 had stenoses of 50% or greater in one coronary artery; 50 of these (2.7% of the total) patients were completely asymptomatic, a percentage similar to that found in the United States Air Force study. He et al reported in nearly 4,000 subjects studied, that the severity of coronary artery calcification by electron-beam computed tomography (CT) can predict silent myocardial ischemia on stress single-photon emission computed tomography (MPS). Silent myocardial ischemia was present in 2.6% of patients with coronary calcium score between 11 and 100, 11.3% of patients with scores between 101 and 399, and 46% of patients with scores above 400. In this issue of the Journal, Malhotra et al evaluated the relationship between silent myocardial ischemia and CAD risk factors in a retrospective analysis of 1,354 asymptomatic patients without known CAD referred for stress MPS. The most common reasons for referral of these patients without chest pain or dyspnea were CAD risk factors, atrial fibrillation, and pre-operative evaluation. As expected, the prevalence of silent myocardial ischemia in these asymptomatic subjects was very low (7.2%). The authors identified a positive relationship between the number of CAD risk factors and the prevalence of silent myocardial ischemia. However in older patients ([74 years), who had a significantly greater prevalence of silent ischemia, the presence and number of risk factors were not predictive. The positive predictive value of an abnormal MPS for predicting angiographic CAD was only 53% and, among all asymptomatic patients referred for stress MPS, significant anatomical CAD was found in only 31 of 1,354 (2%) patients. Malhotra et al concluded that their findings support current guidelines, which generally advocate against routine testing in asymptomatic patients. Unfortunately, they did not provide outcome information for these patients, in particular for the 60 patients (4.4% of overall study population) defined to have ‘‘prognostically significant ischemia’’ (i.e., [10% of left ventricular myocardium). Previous studies have found that silent myocardial ischemia detected in healthy individuals without known CAD has been shown to predict adverse events. The Lipid Research Clinic’s Coronary Primary Prevention Trial (LRCPPT) and the Multiple Risk Factor Intervention Trial (MRFIT) showed that the presence of asymptomatic myocardial ischemia detected during baseline treadmill exercise testing in subjects without known CAD predicted a more than fivefold increased risk of coronary events and cardiac death during the 7to 10-year follow-up period. Other studies evaluated From the Department of Translational Medical Sciences, University Federico II, Institute of Diagnostic and Nuclear Development, SDN Foundation; and Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy. Reprint requests: Alberto Cuocolo, MD, Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131 Naples, Italy; cuocolo@unina.it. J Nucl Cardiol 2013;20:707–10. 1071-3581/$34.00 Copyright 2013 American Society of Nuclear Cardiology. doi:10.1007/s12350-013-9725-z