University of Nevada, Reno

About: University of Nevada, Reno is a education organization based out in Reno, Nevada, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13561 authors who have published 28217 publications receiving 882002 citations. The organization is also known as: University of Nevada & Nevada State University.

An investigation of experienced secondary science teachers' beliefs about inquiry: An examination of competing belief sets

Abstract: The purpose of this study was to investigate the beliefs of six experienced high school science teachers about (1) what is successful science learning; (2) what are the purposes of laboratory in science teaching; and (3) how inquiry is implemented in the classroom. An interpretive multiple case study with an ethnographic orientation was used. The teachers' beliefs about successful science learning were substantively linked to their beliefs about laboratory and inquiry implementation. For example, two teachers who believed that successful science learning was deep conceptual understanding, used verification labs primarily to illustrate these concepts and used inquiry as a type of isolated problem-solving experience. Another teacher who believed that successful science learning was enculturation into scientific practices used inquiry-based labs extensively to teach the practices of science. Tension in competing beliefs sets and implications for reform are discussed. ? 2004 Wiley Periodicals, Inc. J Res Sci Teach 41: 936-960, 2004.

Artificial neural networks improve the accuracy of cancer survival prediction

Abstract: BACKGROUND The TNM staging system originated as a response to the need for an accurate, consistent, universal cancer outcome prediction system. Since the TNM staging system was introduced in the 1950s, new prognostic factors have been identified and new methods for integrating prognostic factors have been developed. This study compares the prediction accuracy of the TNM staging system with that of artificial neural network statistical models. METHODS For 5-year survival of patients with breast or colorectal carcinoma, the authors compared the TNM staging system's predictive accuracy with that of artificial neural networks (ANN). The area under the receiver operating characteristic curve, as applied to an independent validation data set, was the measure of accuracy. RESULTS For the American College of Surgeons' Patient Care Evaluation (PCE) data set, using only the TNM variables (tumor size, number of positive regional lymph nodes, and distant metastasis), the artificial neural network's predictions of the 5-year survival of patients with breast carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.720; ANN, 0.770; P < 0.001). For the National Cancer Institute's Surveillance, Epidemiology, and End Results breast carcinoma data set, using only the TNM variables, the artificial neural network's predictions of 10-year survival were significantly more accurate than those of the TNM staging system (TNM, 0.692; ANN, 0.730; P < 0.01). For the PCE colorectal data set, using only the TNM variables, the artificial neural network's predictions of the 5-year survival of patients with colorectal carcinoma were significantly more accurate than those of the TNM staging system (TNM, 0.737; ANN, 0.815; P < 0.001). Adding commonly collected demographic and anatomic variables to the TNM variables further increased the accuracy of the artificial neural network's predictions of breast carcinoma survival (0.784) and colorectal carcinoma survival (0.869). CONCLUSIONS Artificial neural networks are significantly more accurate than the TNM staging system when both use the TNM prognostic factors alone. New prognostic factors can be added to artificial neural networks to increase prognostic accuracy further. These results are robust across different data sets and cancer sites. Cancer 1997; 79:857-62. © 1997 American Cancer Society.

A review of feedforward control approaches in nanopositioning for high-speed spm

Abstract: Control can enable high-bandwidth nanopositioning needed to increase the operating speed of scanning probe microscopes (SPMs). High-speed SPMs can substantially impact the throughput of a wide range of emerging nanosciences and nanotechnologies. In particular, inversion-based control can find the feedforward input needed to account for the positioning dynamics and, thus, achieve the required precision and bandwidth. This article reviews inversion-based feedforward approaches used for high-speed SPMs such as optimal inversion that accounts for model uncertainty and inversion-based iterative control for repetitive applications. The article establishes connections to other existing methods such as zero-phase-error-tracking feedforward and robust feedforward. Additionally the article reviews the use of feedforward in emerging applications such as SPM-based nanoscale combinatorial-science studies, image-based control for subnanometer-scale studies, and imaging of large soft biosamples with SPMs.

Randomized Clinical Trial of Cognitive Behavioral Therapy (CBT) versus Acceptance and Commitment Therapy (ACT) for Mixed Anxiety Disorders

Abstract: Objective: Randomized comparisons of acceptance-based treatments with traditional cognitive behavioral therapy (CBT) for anxiety disorders are lacking. To address this gap, we compared acceptance and commitment therapy (ACT) to CBT for heterogeneous anxiety disorders. Method: One hundred twenty-eight individuals (52% female, mean age 38, 33% minority) with 1 or more DSM–IV anxiety disorders began treatment following randomization to CBT or ACT; both treatments included behavioral exposure. Assessments at pre-treatment, post-treatment, and 6- and 12-month follow-up measured anxiety-specific (principal disorder Clinical Severity Ratings [CSRs], Anxiety Sensitivity Index, Penn State Worry Questionnaire, Fear Questionnaire avoidance) and non-anxiety-specific (Quality of Life Index [QOLI], Acceptance and Action Questionnaire–16 [AAQ]) outcomes. Treatment adherence, therapist competency ratings, treatment credibility, and co-occurring mood and anxiety disorders were investigated. Results: CBT and ACT improved similarly across all outcomes from pre- to post-treatment. During follow-up, ACT showed steeper linear CSR improvements than CBT (p .05, d 1.26), and at 12-month follow-up, ACT showed lower CSRs than CBT among completers (p .05, d 1.10). At 12-month follow-up, ACT reported higher AAQ than CBT (p .08, d 0.42; completers: p .05, d 0.56), whereas CBT reported higher QOLI than ACT (p .05, d 0.42). Attrition and comorbidity improvements were similar; ACT used more non-study psychotherapy at 6-month follow-up. Therapist adherence and competency were good; treatment credibility was higher in CBT. Conclusions: Overall improvement was similar between ACT and CBT, indicating that ACT is a highly viable treatment for anxiety disorders.