Negative urgency as a state-level process.

OBJECTIVE: Test whether global self-reports of urgency moderated the within-person associations of affect and impulsive behaviors. BACKGROUND: Negative urgency is a personality trait that is a risk factor for a range of psychopathology. Although it is assumed that global self-reports of urgency measure individual tendencies to act more impulsively in the face of negative emotions, evidence from ecological momentary assessment studies is mixed. METHOD: In this Registered Report, we used ecological momentary assessment data from a large sample of young adults (n = 496, age 18-22, 5 surveys per day for 40 days). RESULTS: All forms of momentary impulsivity were impaired in moments when people reported more intense negative emotions, but global self-reports of urgency did not explain individual differences in this association. Moreover, averaged affective states, rather than specific dimensions, affective circumplex, or appraisals, best predicted impulsive states. CONCLUSIONS: Results suggest that face-valid interpretations of global self-report of urgency are inaccurate, and it may be important to understand how some people come to understand themselves as high on urgency rather than assuming that people's self-reports of their motivations are accurate. Momentary experiences of emotions globally impact multiple weakly to moderately associated impulsive behaviors, and future research should seek to understand both when and for whom these associations are strongest.

A new computational approach, based on images trajectories, to identify the subjacent heterogeneity of sperm to the effects of ketanserin.

The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer-assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots. Semen samples of boar sperm were treated with distinct concentrations of ketanserin (an antagonist of the 5-HT2 receptor of serotonin) and untreated samples were used as a control. The motility of sperm in each sample was analyzed at 0 and 30 min of incubation. Six subpopulations were found. The subpopulation 2 presented the highest values of velocities at 0 or 30 min. After 30 min of incubation, the ketanserin increased the values of the curvilinear velocity at high concentrations, whereas the linearity and the straight velocity decreased. Our computational model permits better identification of the kinematic subpopulations than the traditional approach and provides insights onto the heterogeneity of the response to ketanserin; thus, it could significantly impact the research on the relationship between sperm heterogeneity-fertility.