The language of others mirrored in thy face: The role of political affiliation in automatic facial effects of language

People tend to automatically mimic facial expressions of others. If clear evidence exists on the effect of non-verbal behavior (emotion faces) on automatic facial mimicry, little is known about the role of verbal behavior (emotion language) in triggering such effects. Whereas it is well-established that political affiliation modulates facial mimicry, no evidence exists on whether this modulation passes also through verbal means. This research addressed the role of verbal behavior in triggering automatic facial effects depending on whether verbal stimuli are attributed to leaders of different political parties. Study 1 investigated the role of interpersonal verbs, referring to positive and negative emotion expressions and encoding them at different levels of abstraction, in triggering corresponding facial muscle activation in a reader. Study 2 examined the role of verbs expressing positive and negative emotional behaviors of political leaders in modulating automatic facial effects depending on the matched or mismatched political affiliation of participants and politicians of left-and right-wing. Study 3 examined whether verbs expressing happiness displays of ingroup politicians induce a more sincere smile (Duchenne) pattern among readers of same political affiliation relative to happiness expressions of outgroup politicians. Results showed that verbs encoding facial actions at different levels of abstraction elicited differential facial muscle activity (Study 1). Furthermore, political affiliation significantly modulated facial activation triggered by emotion verbs as participants showed more congruent and enhanced facial activity towards ingroup politicians’ smiles and frowns compared to those of outgroup politicians (Study 2). Participants facially responded with a more sincere smile pattern towards verbs expressing smiles of ingroup compared to outgroup politicians (Study 3). Altogether, results showed that the role of political affiliation in modulating automatic facial effects passes also through verbal channels and is revealed at a fine-grained level by inducing quantitative and qualitative differences in automatic facial reactions of readers.

Scene di violenza, esperienze emotive e condotte aggressive

The aim of this research is to estimate the impact of violent film excerpts on university students (30 f, 30 m) in two different sequences, a “justified” violent scene followed by an “unjustified” one, or vice versa, as follows: 1) before-after sequences, using Aggressive behaviour I-R Questionnaire, Self Depression Scale and ASQ-IPAT Anxiety SCALE; 2) after every excerpt, using a self-report to evaluate the intensity and hedonic tone of emotions and the violence justification level. Emotion regulation processes (suppression, reappraisal, self-efficacy) were considered. In contrast with the “unjustified” violent scene, during the “justified” one, the justification level was higher; intensity and unpleasantness of negative emotions were lower. Anxiety (total and latent) and rumination diminished after both types of sequences. Rumination decreases less after the JV-UV sequence than after the UV-JV sequence. Self-efficacy in controlling negative emotions reduced rumination, whereas suppression reduced irritability. Reappraisal, self-efficacy in positive emotion expression and perceived emphatic selfefficacy did not have any effects.

Outcome of HIV-infected patients with hepatocellular carcinoma: a comparison with HIV negative controls

Background and rationale for the study. This study investigated whether human immunodeficiency virus (HIV) infection adversely affects the prognosis of patients diagnosed with hepatocellular carcinoma (HCC).Thirty-four HIV-positive patients with chronic liver disease, consecutively diagnosed with HCC from 1998 to 2007 were one-to-one matched with 34 HIV negative controls for: sex, liver function (Child-Turcotte-Pugh class [CTP]), cancer stage (BCLC model) and, whenever possible, age, etiology of liver disease and modality of cancer diagnosis. Survival in the two groups and independent prognostic predictors were assessed. Results. Among HIV patients 88% were receiving HAART. HIV-RNA was undetectable in 65% of cases; median lymphocyte CD4+ count was 368.5/mmc. Etiology of liver disease was mostly related to HCV infection. CTP class was: A in 38%, B in 41%, C in 21% of cases. BCLC cancer stage was: early in 50%, intermediate in 23.5%, advanced in 5.9%, end-stage in 20.6% of cases. HCC treatments and death causes did not differ between the two groups. Median survival did not differ, being 16 months (95% CI: 6-26) in HIV positive and 23 months (95% CI: 5-41) in HIV negative patients (P=0.391). BCLC cancer stage and HCC treatment proved to be independent predictors of survival both in the whole population and in HIV patients. Conclusions. Survival of HIV infected patients receiving antiretroviral therapy and diagnosed with HCC is similar to that of HIV negative patients bearing this tumor. Prognosis is determined by the cancer bulk and its treatment.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.