The present study examined the occurrence of a novel behavior pattern with respect to a novel configuration of stimuli enabled by the participation of those stimuli in equivalence classes. In Experiment 1, functional substitutabilities were established via equivalence between two independent sets of musical stimuli. Aspects of stimuli from the two sets were then compounded to produce novel stimulus configurations. Behavioral components enabled by each separate class combined to produce novel musical performances and accurate descriptions of them. In Experiment 2, the impact of experimenter-provided names for equivalence classes on the musical performances was investigated in naive subjects by establishing similar classes without experimenter-provided names. The results indicated few differences in the playing performances under these conditions. These experiments demonstrated a possible method for the analysis of rule following.

OBJECTIVES-Develop a fully automated, objective method for evaluating morphology on breast MR and evaluate effectiveness of the new morphological method for detecting breast cancers. SUBJECTS AND METHODS-We present a new automated method (Morphological Blooming) for identifying and classifying breast lesions on MR which measures margin sharpness, a characteristic related to blooming, defined as rapid enhancement, with a border that is initially sharp but becomes unsharp after seven minutes. Independent training sets (98 biopsy-proven lesions) and testing sets (179 breasts, 127 patients, acquired at 5 institutions) were used. Morphological Blooming was evaluated as a stand-alone feature and as an adjunct to kinetics using FROC (free-response ROC)Correspondence to: Alan Penn, apenn@ONCAD.com. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. CONCLUSION-We present a new, fully automated method of identifying and classifying margin sharpness of breast lesions on MR that can be used to direct radiologists' attention to lesions with suspicious morphologies. Morphological Blooming may have important utility for assisting radiologists in identifying cancers with benign-like kinetics and discriminating normal tissues that exhibit cancer-like enhancement curves and for improving the performance of CAD systems. NIH Public AccessStudies have demonstrated the effectiveness of breast MRI for improving breast cancer detection and diagnosis [1,2,3,4,5,6,7]. Breast MRI presents the breast imager with two challenges: low specificity, which can result in a clinically unacceptable number of false positives [8,9], and a large number of images that must be interpreted. Recent screening studies in Netherlands, Canada, and United Kingdom have reported breast cancer sensitivities using MRI ranging from 71%-77% [3]. A follow-up of the MARIBS study in the United Kingdom found that independent double-reading of breast MRI improved sensitivity by 7% [10]. To address these challenges, breast MRI computer-aided-detection (CAD) systems have been developed to help radiologists more easily sort through images and focus on suspicious areas of enhancement to improve efficiencies in interpreting breast MRI.Interpretive features that are used to discriminate malignant from benign lesions on breast MRI fall into two general categories: kinetics, based on the rate and degree an enhancing agent washes into and out of a region-of-interest, and morphology, based on the shape and texture of the enhancement pattern [11]. Some cancers, particularly invasive lobular [7,12], DCIS [12,13,14], and scirrhous ductal ...

Rapid and effective medical intervention in response to civil and military-related disasters is crucial for saving lives and limiting long-term disability. Inexperienced providers may suffer in performance when faced with limited supplies and the demands of stabilizing casualties not generally encountered in the comparatively resource-rich hospital setting. Head trauma and multiple injury cases are particularly complex to diagnose and treat, requiring the integration and processing of complex multimodal data. In this project, collaborators adapted and merged existing technologies to produce a flexible, modular patient simulation system with both three-dimensional virtual reality and two-dimensional flat screen user interfaces for teaching cognitive assessment and treatment skills. This experiential, problem-based training approach engages the user in a stress-filled, high fidelity world, providing multiple learning opportunities within a compressed period of time and without risk. The system simulates both the dynamic state of the patient and the results of user intervention, enabling trainees to watch the virtual patient deteriorate or stabilize as a result of their decision-making speed and accuracy. Systems can be deployed to the field enabling trainees to practice repeatedly until their skills are mastered and to maintain those skills once acquired. This paper describes the technologies and the process used to develop the trainers, the clinical algorithms, and the incorporation of teaching points. We also characterize aspects of the actual simulation exercise through the lens of the trainee.

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