University of Alabama

About: University of Alabama is a education organization based out in Tuscaloosa, Alabama, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 27323 authors who have published 48609 publications receiving 1565337 citations. The organization is also known as: Alabama & Bama.

Efficacy of psychosocial treatments for geriatric depression: A quantitative review.

Abstract: A meta-analysis of 17 studies examined the efficacy of psychosocial treatments for depression among older adults. Studies were included only if a comparison was made to a control condition (no-, delayed-, or placebo-treatment) or another psychosocial intervention. Results indicated that treatments were reliably more effective than no-treatment on self-rated and clinician-rated measures of depression. Effect sizes for studies involving participants with major depression disorder were also reliably different from zero, as were effect sizes from studies involving participants with less severe levels of depression. These findings compare favorably with several other quantitative reviews of treatments for depression. Results suggest more balanced presentations of the potential benefits of psychosocial interventions are warranted.

Bug localization using latent Dirichlet allocation

Abstract: Context: Some recent static techniques for automatic bug localization have been built around modern information retrieval (IR) models such as latent semantic indexing (LSI). Latent Dirichlet allocation (LDA) is a generative statistical model that has significant advantages, in modularity and extensibility, over both LSI and probabilistic LSI (pLSI). Moreover, LDA has been shown effective in topic model based information retrieval. In this paper, we present a static LDA-based technique for automatic bug localization and evaluate its effectiveness. Objective: We evaluate the accuracy and scalability of the LDA-based technique and investigate whether it is suitable for use with open-source software systems of varying size, including those developed using agile methods. Method: We present five case studies designed to determine the accuracy and scalability of the LDA-based technique, as well as its relationships to software system size and to source code stability. The studies examine over 300 bugs across more than 25 iterations of three software systems. Results: The results of the studies show that the LDA-based technique maintains sufficient accuracy across all bugs in a single iteration of a software system and is scalable to a large number of bugs across multiple revisions of two software systems. The results of the studies also indicate that the accuracy of the LDA-based technique is not affected by the size of the subject software system or by the stability of its source code base. Conclusion: We conclude that an effective static technique for automatic bug localization can be built around LDA. We also conclude that there is no significant relationship between the accuracy of the LDA-based technique and the size of the subject software system or the stability of its source code base. Thus, the LDA-based technique is widely applicable.

Genetic/familial high-risk assessment: Breast and ovarian, version 2.2015 Featured updates to the NCCN guidelines

Abstract: The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast and Ovarian provide recommendations for genetic testing and counseling and risk assessment and management for hereditary cancer syndromes. Guidelines focus on syndromes associated with an increased risk of breast and/or ovarian cancer and are intended to assist with clinical and shared decision-making. These NCCN Guidelines Insights summarize major discussion points of the 2015 NCCN Genetic/Familial High-Risk Assessment: Breast and Ovarian panel meeting. Major discussion topics this year included multigene testing, risk management recommendations for less common genetic mutations, and salpingectomy for ovarian cancer risk reduction. The panel also discussed revisions to genetic testing criteria that take into account ovarian cancer histology and personal history of pancreatic cancer.