Point subtraction aggression paradigm: validity of a brief schedule of use

The Point Subtraction Aggression Paradigm (PSAP) is a validated behavioral measure of aggression in response to provocation. Canonically, multiple sessions continue until performance asymptotes, requiring significant time. We sought to examine whether use of the PSAP-first-session ("PSAP-FS") had acceptable construct validity for group data, potentially expanding the settings in which the PSAP may be useful. One hundred male and female young adult subjects completed the PSAP-FS and additional behavioral measures of aggression, and provided information related to risk factors. The PSAP-FS correlated significantly with known risk factors of aggression. The PSAP-FS, which measures aggression in the present, correlated well with measures of recent aggression and less strongly with life history of aggression. The PSAP-FS had satisfactory validity properties for use where group-level data are of interest.     

Correlates of gay and lesbian couples' relationship satisfaction and relationship dissolution

A sample of committed gay and lesbian cohabiting couples engaged in two conversations after being apart for at least 8 hours: (a) an events of the day conversation and (b) a conflict resolution conversation. Physiological data were collected during the conversations and a videotape record was made. Couples viewed the videotapes and rated their affect during the interaction. The video records were coded with a system that categorized specific affects displayed. Models derived from physiology, from the perception of interaction, and from specific affective behavior were related to relationship satisfaction, and to the prediction of relationship dissolution over a 12-year period. Results supported previous findings that satisfaction and stability in gay and lesbian relationships are related to similar emotional qualities as in heterosexual relationships.     

The maximum cardinality cut problem in co-bipartite chain graphs

A co-bipartite chain graph is a co-bipartite graph in which the neighborhoods of the vertices in each clique can be linearly ordered with respect to inclusion. It is known that the maximum cardinality cut problem (\({\textsc {MaxCut}}\)) is \({\textsc {NP}}{\text {-hard}}\) in co-bipartite graphs (Bodlaender and Jansen, Nordic J Comput 7(2000):14–31, 2000). We consider \({\textsc {MaxCut}}\) in co-bipartite chain graphs. We first consider the twin-free case and present an explicit solution. We then show that \({\textsc {MaxCut}}\) is polynomial time solvable in this graph class.     

If Love Then Justice

Love belongs to the personal realm, while justice belongs to the public. Love originated evolutionarily in the subjective parent-child relationship. Justice originated in the objective perception of our social relationships. Both concepts have deep roots in our feelings. Although justice and love are interdependent, love is a prerequisite to justice. Most of those who love would behave justly to their loved ones, but not vice versa. Laws and norms try unsuccessfully to enhance love among us. However, they would hardly attain justice. With love we do not need laws or armies. Justice would necessarily prevail, and much more. Intuitively and psychologically, love is more basic than justice for our existence. Research shows that in our pursuit of happiness, love plays a central role, while justice does not. For many, love is also a goal of our existence.
"The long demand for justice exhausts the love which nevertheless gave it birth." (source unknown)     

How Cox models react to a study-specific confounder in a patient-level pooled dataset: random effects better cope with an imbalanced covariate across trials unless baseline hazards differ

Combining patient-level data from clinical trials can connect rare phenomena with clinical endpoints, but statistical techniques applied to a single trial may become problematical when trials are pooled. Estimating the hazard of a binary variable unevenly distributed across trials showcases a common pooled database issue. We studied how an unevenly distributed binary variable can compromise the integrity of fixed and random effects Cox proportional hazards (cph) models. We compared fixed effect and random effects cph models on a set of simulated datasets inspired by a 17-trial pooled database of patients presenting with ST segment elevation myocardial infarction (STEMI) and non-STEMI undergoing percutaneous coronary intervention. An unevenly distributed covariate can bias hazard ratio estimates, inflate standard errors, raise type I error, and reduce power. While uneveness causes problems for all cph models, random effects suffer least. Compared to fixed effect models, random effects suffer lower bias and trade inflated type I errors for improved power. Contrasting hazard rates between trials prevent accurate estimates from both fixed and random effects models.