Preliminary Study on Pulse Wave Changes in Patients with Inflammatory Arthropathies Treated with bDMARDs.

Background: Patients with inflammatory arthropathies exhibit an increased cardiovascular disease (CVD) risk as compared to the general population, which is not fully quantified by the conventional CVD risk scores. Biotechnological disease-modifying drugs (bDMARDs) have proved beneficial to reduce the overall CVD risk in these patients, although CVD remains a major cause of increased mortality. Since it has been shown that pulse wave parameters and in particular carotid-femoral pulse wave velocity (cfPWV) are predictors of CVD risk, the aim of this study was to evaluate their changes in patients with inflammatory arthropathies before and after bDMARD therapy. Methods: Pulse wave parameters were evaluated with applanation tonometry in patients with ankylosing spondylitis (AS), psoriatic arthritis (PsA), and rheumatoid arthritis (RA), before and after two years of bDMARD therapy. Results: At baseline, cfPWV was significantly associated with age (p < 0.001) and, among pulse wave parameters, the subendocardial viability ratio was negatively associated with C-reactive protein (CRP) (p = 0.04) and the HAQ-disability index (p = 0.03). At baseline, PsA patients showed a higher percentage of male subjects, higher CRP, and the highest cfPWV values (p = 0.048). After two years, pulse wave parameters improved in the AS and RA groups, but not in the PsA group. Conclusions: Our data confirm that pulse wave parameters are potentially reversible after bDMARD therapy, as they improved in AS and RA patients. In PsA patients, there were no changes, which may be due to the higher percentage of male subjects and higher baseline cfPWV values.

Faster algorithms for counting subgraphs in sparse graphs.

Given a k-node pattern graph H and an n-node host graph G, the subgraph counting problem asks to compute the number of copies of H in G. In this work we address the following question: can we count the copies of H faster if G is sparse? We answer in the affirmative by introducing a novel tree-like decomposition for directed acyclic graphs, inspired by the classic tree decomposition for undirected graphs. This decomposition gives a dynamic program for counting the homomorphisms of H in G by exploiting the degeneracy of G, which allows us to beat the state-of-the-art subgraph counting algorithms when G is sparse enough. For example, we can count the induced copies of any k-node pattern H in time 2 O ( k 2 ) O ( n 0.25 k + 2 log n ) if G has bounded degeneracy, and in time 2 O ( k 2 ) O ( n 0.625 k + 2 log n ) if G has bounded average degree. These bounds are instantiations of a more general result, parameterized by the degeneracy of G and the structure of H, which generalizes classic bounds on counting cliques and complete bipartite graphs. We also give lower bounds based on the Exponential Time Hypothesis, showing that our results are actually a characterization of the complexity of subgraph counting in bounded-degeneracy graphs.

Socio-Emotional Competencies and School Performance in Adolescence: What Role for School Adjustment?

There is growing evidence in the literature of positive relationships between socio-emotional competencies and school performance. Several hypotheses have been used to explain how these variables may be related to school performance. In this paper, we explored the role of various school adjustment variables in the relationship between interpersonal socio-emotional competencies and school grades, using a weighted network approach. This network approach allowed us to analyze the structure of interrelations between each variable, pointing to both central and mediatory school and socio-emotional variables within the network. Self-reported data from around 3,400 French vocational high school students were examined. This data included a set of interpersonal socio-emotional competencies (cognitive and affective empathy, socio-emotional behaviors and collective orientation), school adjustment measures (adaptation to the institution, school anxiety, self-regulation at school, and self-perceived competence at school) as well as grades in mathematics and French language. The results showed that self-regulation at school weighted the most strongly on the whole network, and was the most important mediatory pathway. More specifically, self-regulation mediated the relationships between interpersonal socio-emotional competencies and school grades.

The mediational effect of social support between organizational identification and employees' health: a three-wave study on the social cure model.

BACKGROUND: Recent research postulated that organizational identification plays an important role in employees' health and well-being. Building on the Social Identity Approach as a framework, we test the so-called social cure hypothesis, according to which group-based processes of social support should reduce employees' psychological distress. DESIGN AND METHODS: While there is a considerable amount of cross-sectional evidence concerning the positive role played by organizational identification in this dynamic, there is a lack of full panel studies. This study tries to fill this gap by using data from a sample of technical and administrative staff of a University in Italy at three time points (N = 96). Data were analyzed using Autoregressive Cross-Lagged Panel models. RESULTS: We found support for the hypothesized longitudinal mediational model. Specifically, strongly identified employees tend to receive more social support, and this in turn reduces psychological distress over time. CONCLUSIONS: This study is the first test of the social cure hypothesis in an organizational context that uses a panel study design. We discuss the theoretical and practical implications for management.

The Effect of Faking on the Correlation Between Two Ordinal Variables: Some Population and Monte Carlo Results.

Correlational measures are probably the most spread statistical tools in psychological research. They are used by researchers to investigate, for example, relations between self-report measures usually collected using paper-pencil or online questionnaires. Like many other statistical analysis, also correlational measures can be seriously affected by specific sources of bias which constitute serious threats to the final observed results. In this contribution, we will focus on the impact of the fake data threat on the interpretation of statistical results for two well-know correlational measures (the Pearson product-moment correlation and the Spearman rank-order correlation). By using the Sample Generation by Replacement (SGR) approach, we analyze uncertainty in inferences based on possible fake data and evaluate the implications of fake data for correlational results. A population-level analysis and a Monte Carlo simulation are performed to study different modulations of faking on bivariate discrete variables with finite supports and varying sample sizes. We show that by using our paradigm it is always possible, under specific faking conditions, to increase (resp. decrease) the original correlation between two discrete variables in a predictable and systematic manner.