University of Central Arkansas

About: University of Central Arkansas is a education organization based out in Conway, Arkansas, United States. It is known for research contribution in the topics: Population & Anxiety. The organization has 1495 authors who have published 2459 publications receiving 51283 citations.

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Abstract: This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.

Routine Activities and Individual Deviant Behavior

Abstract: We extend the routine activity perspective's situational analysis of crime to individual offending and to a broad range of deviant behaviors. In this view, unstructured socializing with peers in the absence of authority figures presents opportunities for deviance: In the presence of peers, deviant acts will be easier and more rewarding; the absence of authority figures reduces the potential for social control responses to deviance; and the lack of structure leaves time available for deviant behavior. To determine whether individuals who spend more time in unstructured socializing activities engage in deviant behaviors more frequently, we analyzed within-individual changes in routine activities and deviance across five waves of data for a national sample of more than 1,700 18- to 26-year-olds. Participation in these routine activities was strongly associated with criminal behavior, heavy alcohol use, use of marijuana and other illicit drugs, and dangerous driving. Furthermore, routine activities accounted for a substantial portion of the association between these deviant behaviors and age, sex, and socioeconomic status.

Development and validation of a revised short version of the working alliance inventory

Abstract: The Working Alliance Inventory (WAI; Horvath & Greenberg, 1989) and the Working Alliance Inventory–Short (WAI-S; Tracey & Kokotovic, 1989) are widely used measures of alliance in therapy. This study evaluated the factor structure of the WAI and WAI-S with confirmatory factor analysis in two relatively large samples (Ns = 231 and 235). The hypothesized structures were not confirmed. An alternative 12-item WAI (WAI-SR), consistent with Bordin's (1979) model of alliance, was developed in one sample and cross-validated in the other. The WAI-SR better differentiated Goal, Task, and Bond alliance dimensions and correlated well with other alliance measures. The Task dimension was particularly salient, as expected based on Bordin's original theory. Additional psychometric properties and item response theory analysis of the WAI-SR are presented. Zusammenfassung Eine Revision der Kurzform des Working Alliance Inventars (WAI-SR) Das Arbeitsbeziehungsinventar (Working Alliance Inventory, WAI; Horvath & Green...

Extrapolating brain development from experimental species to humans.

Abstract: To better understand the neurotoxic effects of diverse hazards on the developing human nervous system, researchers and clinicians rely on data collected from a number of model species that develop and mature at varying rates. We review the methods commonly used to extrapolate the timing of brain development from experimental mammalian species to humans, including morphological comparisons, ‘‘rules of thumb’’ and ‘‘event-based’’ analyses. Most are unavoidably limited in range or detail, many are necessarily restricted to rat/human comparisons, and few can identify brain regions that develop at different rates. We suggest this issue is best addressed using ‘‘neuroinformatics’’, an analysis that combines neuroscience, evolutionary science, statistical modeling and computer science. A current use of this approach relates numeric values assigned to 10 mammalian species and hundreds of empirically derived developing neural events, including specific evolutionary advances in primates. The result is an accessible, online resource (http://www.translatingtime.net/) that can be used to equate dates in the neurodevelopmental literature across laboratory species to humans, predict neurodevelopmental events for which data are lacking in humans, and help to develop clinically relevant experimental models.