Although psychopathic personality traits are widely reported to be related to reduced reactivity to emotion-eliciting situations, findings are not consistent. It has been argued that these differences could be related to variations in the way psychopathy is measured. To examine whether measurement variance resulting from the use of clinical assessment versus self-report assessment could be driving such differences, this systematic review and meta-analysis investigated the comparability of relations between psychopathic traits and responsiveness to emotion-inducing tasks for clinical versus self-report measures. The systematic review resulted in eight studies and 131 effect sizes, which included studies of emotion categorization, emotion regulation, decision-making, and executive functioning tasks. Robust Variance Estimation correlated effects models revealed no significant differences between effect sizes for clinical (PCL-R) versus self-report (PPI, SRP, and LSRP) assessment-based psychopathic traits and emotion tasks. Despite the small number of studies that included both clinical and self-report assessments of psychopathy, these results do not provide any evidence for an assessment-based difference in correlations with emotional responsiveness across tasks. The findings also show no associations between scores on emotional responsiveness and indices of psychopathy. Future research on emotional responsiveness in psychopathy should include both assessment types to be able to increase the research basis for the comparison.

Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.

Little is known about the tooth wear of South American theropod dinosaurs. This paper describes wear facets in Abelisauridae, Carcharodontosauridae and some indeterminate theropods teeth, from the Marília Formation. Four types of wear facets are proposed: vertically-oriented attritional striations; perpendicular attritional surfaces; oval wear facets; and apical grooves. All these worn surfaces were produced by dental occlusion, except the apical grooves, which are produced by the contact between predator teeth and the prey bone during predator–prey interaction. More detailed biomechanical and hardness testing of teeth and bone may further elucidate the pattern of tooth wear in theropods.