Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis

Today, there are many parameters used for cardiovascular risk quantification and to identify many of the high-risk subjects; however, many of them do not reflect reality. Modern personalized medicine is the key to fast and effective diagnostics and treatment of cardiovascular diseases. One step towards this goal is a better understanding of connections between numerous risk factors. We used Factor analysis to identify a suitable number of factors on observed data about patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Košice. The data describes 808 participants cross-identifying symptomatic and coronarography resulting characteristics. We created several clusters of factors. The most significant cluster of factors identified six factors: basic characteristics of the patient; renal parameters and fibrinogen; family predisposition to CVD; personal history of CVD; lifestyle of the patient; and echo and ECG examination results. The factor analysis results confirmed the known findings and recommendations related to CVD. The derivation of new facts concerning the risk factors of CVD will be of interest to further research, focusing, among other things, on explanatory methods.

Low Prevalence of Antibodies to SARS-CoV-2 and Undetectable Viral Load in Seropositive Blood Donors from South-Eastern Italy

Recently, a significant cluster of pneumonia caused by a novel betacoronavirus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) was described initially in China and then spread throughout the world. Like other coronaviridae, the viral transmission occurs mainly through droplets. In addition, the virus has been detected in different clinical specimens, suggesting a potential transmission by other routes, including blood transfusion. However, the potential risk of transmission of SARS-CoV-2 via blood products is still unclear. The aim of our study was to investigate the prevalence of antibodies against SARS-CoV-2 among blood donors from South-Eastern Italy. Moreover, in the seropositive donors, we searched for the presence of the virus in nasopharyngeal swabs and in plasma samples. Overall, 1,797 blood donors from the Apulia region were tested for anti-SARS-CoV-2 antibodies, using a commercially available assay. Only 18/1,797 donors (1.0%) tested positive for anti-SARS-CoV-2 antibodies; in none of them SARS-CoV-2 viral RNA was detected in nasopharyngeal swabs and in plasma samples. Our results indicate that most of the blood donors in Apulia remained uninfected during this wave of the pandemic; further, none had detectable virus both in nasopharyngeal swabs and in blood samples. The risk to carry and transmit the virus by healthy and asymptomatic blood donors is probably very low.

Searching for event clusters in frequency tables with applications in epidemiology and ecological monitoring

The article continues the series of publications developing the new general approach to data clustering. Here proposed method is applied for searching clusters of increased or decreased frequencies in two dimensional frequency tables (histograms) of population-based data. The observed frequency table can be compared either with the expected one (for instance uniform) or with some table corresponding to the previous moment of time. The regions with significantly changed frequencies are revealed. It allows performing statistically based control of the dynamic of epidemiological process or ecological monitoring. The groups of neighboring cells of frequency tables with the same direction of changes are unified in clusters which are checked to be statistically significant with account on multiple comparisons. Each group of cells is characterized with two parameters – its size (the number of cells) and the intensity of changing. If the size of group or (and) its intensity are too pronounced then such group is considered to be statistically significant cluster. No a priori suggestions concerning the number or shape of potentially existing clusters are made. Method can be generalized to multidimensional tables, not needs Monte Carlo simulations and can be used while comparing any frequency tables being supplemental to global nonparametric criteria such as Pearson chi-square criteria.

Searching for clusters in population data

The article continues the series of publications developing new statistically motivated approach to data clustering. Proposed method is applied for searching clusters of increased or decreased frequencies of some events in sets of neighboring cells in two dimensional tessellations of plane. Such cells may correspond to administrative regions, counties etc. The case of simple frequency tables (histograms) with rectangular cells was considered earlier. The observed distribution of event frequencies in cells can be compared either with expected one (for instance uniform) or with distribution corresponding to the previous moment of time. The groups of neighboring cells with the same direction of changes are unified in clusters which are checked to be statistically significant with account on multiple comparisons. Each group of cells is characterized with two parameters – its size (the number of cells) and the intensity of changing. If the size of group or (and) its intensity are too pronounced then such group is considered to be statistically significant cluster. There are no a priori suggestions concerning the number, size or shape of potentially existing clusters. Method can be used for clustering any multidimensional arrays of p-values which are independent and uniformly distributed according null hypothesis, while alternative is that there are sets of neighboring cells where p-values are close to 0 or to 1.

Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection

Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.

Accretion of galaxy groups into galaxy clusters

ABSTRACT We study the role of group infall in the assembly and dynamics of galaxy clusters in ΛCDM. We select 10 clusters with virial mass M200 ∼ 1014 $\rm M_\odot$ from the cosmological hydrodynamical simulation Illustris and follow their galaxies with stellar mass M⋆ ≥ 1.5 × 108 $\rm M_\odot$. A median of ${\sim}38{{\ \rm per\ cent}}$ of surviving galaxies at z = 0 is accreted as part of groups and did not infall directly from the field, albeit with significant cluster-to-cluster scatter. The evolution of these galaxy associations is quick, with observational signatures of their common origin eroding rapidly in 1–3 Gyr after infall. Substructure plays a dominant role in fostering the conditions for galaxy mergers to happen, even within the cluster environment. Integrated over time, we identify (per cluster) an average of 17 ± 9 mergers that occur in infalling galaxy associations, of which 7 ± 3 occur well within the virial radius of their cluster hosts. The number of mergers shows large dispersion from cluster to cluster, with our most massive system having 42 mergers above our mass cut-off. These mergers, which are typically gas rich for dwarfs and a combination of gas rich and gas poor for M⋆ ∼ 1011 $\rm M_\odot$, may contribute significantly within ΛCDM to the formation of specific morphologies, such as lenticulars (S0) and blue compact dwarfs in groups and clusters.

Applying a Prospective Space-Time Scan Statistic to Examine the Evolution of COVID-19 Clusters in the State of São Paulo, Brazil

We present the first geographic study that uses space-time statistics to monitor COVID-19 in Brazil. The first cases of COVID-19 were confirmed in December 2019 in Wuhan, China, caused by the contamination of the SARS-CoV-2 virus, and quickly turned into a pandemic. In Brazil, the first case occurred on January 23rd, 2020 but was officially reported by the Brazilian Ministry of Health on February 25th. Since then, the number of deaths and people infected by COVID-19 in Brazil have been steadily increasing. Despite the underreporting of coronavirus cases by government agencies across the country, the State of São Paulo has the highest rate among all Brazilian States. Thus, it is essential to detect which areas contain the highest concentration of COVID-19 to implement public policies, to mitigate the spread of the epidemic. To identify these critical areas, we utilized daily confirmed case data from the Brasil.IO website between February 25th, 2020 to May 5th, 2020; which were aggregated to the municipality level. A prospective space-time scan statistic was applied to evaluate possible active clusters in three different time periods. The results visualize the space-time evolution and dynamics of COVID-19 clusters in the State of São Paulo. Since the first study period, the results highlight approximately 4.6 times the number of municipalities belonging to a significant cluster with a RR>1 on May 5th. These results can inform health authorities and public management to take the necessary measures to minimize the transmission of COVID-19 and track the evolution of significant space-time clusters.HIGHLIGHTSProspective space-time statistics can improve COVID-19 surveillance in BrazilAll statistically significant clusters are located near São Paulo MunicipalityThere are municipalities with relative risk highest than one in the countryside4.6 times the number of municipalities belong to a significant cluster on May 5th

Interwar Imaginings of Collective Cognition

Scholarship on interwar understandings of ‘collective cognition’ – experiences of intellectual union with others – tends to focus on its capacity to threaten individuality. I counter this trend by investigating prose works by H.D., Olive Moore, Rebecca West, and H.G. Wells that champion collective cognition for its capacity to compose communities. I argue that these texts point to an underexplored strand that existed in and alongside modernism in which authors turned to collective cognition to imagine radically egalitarian communities that transcend hierarchies based on history, nationality, and species. After the Second World War, the cultural meanings of collective cognition narrowed, and ‘thinking together’ came to be strongly associated with loss of freedom and loss of self. This article shows that collective cognition emitted a powerfully hopeful potential for a significant cluster of interwar authors, who used it to imagine the peaceful and abundant possibilities of collectivity.

Enhancing Machine Learning Aptitude Using Significant Cluster Identification for Augmented Image Refining

Enhancing the image to remove noise, preserving the useful features and edges are the most important tasks in image analysis. In this paper, Significant Cluster Identification for Maximum Edge Preservation (SCI-MEP), which works in parallel with clustering algorithms and improved efficiency of the machine learning aptitude, is proposed. Affinity propagation (AP) is a base method to obtain clusters from a learnt dictionary, with an adaptive window selection, which are then refined using SCI-MEP to preserve the semantic components of the image. Since only the significant clusters are worked upon, the computational time drastically reduces. The flexibility of SCI-MEP allows it to be integrated with any clustering algorithm to improve its efficiency. The method is tested and verified to remove Gaussian noise, rain noise and speckle noise from images. Our results have shown that SCI-MEP considerably optimizes the existing algorithms in terms of performance evaluation metrics.

Green Procurement in Romanian Construction Projects. A Cluster Analysis of the Barriers and Enablers to Green Procurement in Construction Projects from the Bucharest-Ilfov Region of Romania

The research presented in this paper aimed at identifying the most significant green procurement barriers and enablers for construction projects in the Bucharest-Ilfov region and grouping them into clusters. For this purpose, 14 barriers and 14 enablers were selected on the basis of the literature review and a questionnaire-based survey was carried out with members of the construction projects’ teams from the analyzed region. The cluster analysis resulted in eight clusters for barriers and seven clusters for enablers. In the case of barriers, the most significant cluster was the one that included the barrier regarding technical and technological difficulties related to the use of green building materials. Another significant barrier was the increase of project execution costs. Enablers from the most significant cluster had higher energy efficiency and use of green building materials as a competitive advantage. Another significant enabler identified was regarding the pressure to implement environmental protection policies/legislation. To explain and detail the results of the cluster analysis, semi-structured interviews were carried out with experts involved in projects. They indicated, in most cases, the same barriers and enablers as those obtained from the cluster analysis.