University of Central Florida

About: University of Central Florida is a education organization based out in Orlando, Florida, United States. It is known for research contribution in the topics: Laser & Population. The organization has 18822 authors who have published 48679 publications receiving 1234422 citations. The organization is also known as: UCF.

A solution to minimum sample size for regressions

Abstract: Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. What is the minimum N to identify the most plausible data pattern using regressions? Statistical power analysis is often used to answer that question, but it has its own problems and logically should follow model selection to first identify the most plausible model. Here we make null, simple linear and quadratic data with different variances and effect sizes. We then sample and use information theoretic model selection to evaluate minimum N for regression models. We also evaluate the use of coefficient of determination (R2) for this purpose; it is widely used but not recommended. With very low variance, both false positives and false negatives occurred at N < 8, but data shape was always clearly identified at N ≥ 8. With high variance, accurate inference was stable at N ≥ 25. Those outcomes were consistent at different effect sizes. Akaike Information Criterion weights (AICc wi) were essential to clearly identify patterns (e.g., simple linear vs. null); R2 or adjusted R2 values were not useful. We conclude that a minimum N = 8 is informative given very little variance, but minimum N ≥ 25 is required for more variance. Alternative models are better compared using information theory indices such as AIC but not R2 or adjusted R2. Insufficient N and R2-based model selection apparently contribute to confusion and low reproducibility in various disciplines. To avoid those problems, we recommend that research based on regressions or meta-regressions use N ≥ 25.

Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural networks, and genetic algorithm: a case study in romantic decision support

Abstract: The 21st century is seeing technological advances that make it possible to build more robust and sophisticated decision support systems than ever before. But the effectiveness of these systems may be limited if we do not consider more eclectic (or romantic) options. This paper exemplifies the potential that lies in the novel application and combination of methods, in this case to evaluating stock market purchasing opportunities using the "technical analysis" school of stock market prediction. Members of the technical analysis school predict market prices and movements based on the dynamics of market price and volume, rather than on economic fundamentals such as earnings and market share. The results of this paper support the effectiveness of the technical analysis approach through use of the "bull flag" price and volume pattern heuristic. The romantic approach to decision support exemplified in this paper is made possible by the recent development of: (1) high-performance desktop computing, (2) the methods and techniques of machine learning and soft computing, including neural networks and genetic algorithms, and (3) approaches recently developed that combine diverse classification and forecasting systems. The contribution of this paper lies in the novel application and combination of the decision-making methods and in the nature and superior quality of the results achieved.

MCP-induced protein 1 deubiquitinates TRAF proteins and negatively regulates JNK and NF-κB signaling

Abstract: The intensity and duration of macrophage-mediated inflammatory responses are controlled by proteins that modulate inflammatory signaling pathways. MCPIP1 (monocyte chemotactic protein-induced protein 1), a recently identified CCCH Zn finger-containing protein, plays an essential role in controlling macrophage-mediated inflammatory responses. However, its mechanism of action is poorly understood. In this study, we show that MCPIP1 negatively regulates c-Jun N-terminal kinase (JNK) and NF-κB activity by removing ubiquitin moieties from proteins, including TRAF2, TRAF3, and TRAF6. MCPIP1-deficient mice spontaneously developed fatal inflammatory syndrome. Macrophages and splenocytes from MCPIP1(-/-) mice showed elevated expression of inflammatory gene expression, increased JNK and IκB kinase activation, and increased polyubiquitination of TNF receptor-associated factors. In vitro assays directly demonstrated the deubiquitinating activity of purified MCPIP1. Sequence analysis together with serial mutagenesis defined a deubiquitinating enzyme domain and a ubiquitin association domain in MCPIP1. Our results indicate that MCPIP1 is a critical modulator of inflammatory signaling.