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The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene–steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P < 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10−3); while the next best signal was rs951613 (P = 7.46 × 10−3). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene–steroid interaction effects (OR = 2.18, 95% CI = 1.31−3.63 with P = 2.9 × 10−3 for rs6532496 and OR = 2.07, 95% CI = 1.24 −3.45 with P = 5.43 × 10−3 for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR = 2.26, 95% CI = 1.2 −3.38 for rs6532496 and OR = 2.14, 95% CI = 1.14 −3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene–steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene–steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene–steroid interaction effect (OR = 2.49, 95% CI = 1.5 −4.13 with P = 4.0 × 10−4 based on the classic logistic regression and OR = 2.59, 95% CI = 1.4 −3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Background: Oral health disparities exist in the USA. However, little is known of the relationship between oral health disparity and citizenship. The aims of this study were: (i) to describe the differences in self-rated oral health (SROH) between adult American citizens and non-citizens (>20 years of age); and (ii) to test whether factors such as frequency of dentist visits and socio-economic status (SES) are differently associated with SROH in these two groups. Methods: The data used in this study were drawn from the National Health and Nutrition Examination Survey conducted in 2011–2012. Weighted logistic regression models were used to detect the strengths of the association between a series of predictors and SROH. Results: More non-citizens (59.54%) than their citizen peers (26.24%) rated their oral health as fair/bad. All factors analysed in this study were differently associated with SROH based on citizenship. More specifically, natural characteristics, such as ethnicity and age, were significantly associated with SROH among non-citizens, and SES was significantly associated with American citizens. Among non-citizens, Hispanic, Non-Hispanic Black and Asian subjects were more likely than Non-Hispanic White subjects to report their oral health as being ‘good’. Family poverty level, education and the frequency of dentist visits were significantly associated with SROH among citizens. Conclusion: The findings of this study indicate that American immigrants report their oral health across most dimensions as being worse than do American citizens. Each explanatory factor may have a different strength of association with SROH in immigrants and citizens, which implies that different steps should be taken within these groups to reduce disparities in oral health.

This article examines predictors of the future market value of microcap pharmaceutical companies. This is problematic since the large majority of these firms seldom report positive net income. Their value comes from the potential of a liquidity event such as occurs when a key drug is approved by the FDA. The typical scenario is one in which the company is either acquired by a larger pharmaceutical firm or enters into a joint venture with another pharmaceutical firm. Binary logistic regression is used to determine the impact of the firm's drug treatment pipeline and its investment in research and development on the firm's market cap. Using annual financial data from 2007 through 2010, this study finds that the status of the firm's drug treatment pipeline and its research and development expenses are significant predictors of the firm's future stock value relative to other microcap pharmaceutical firms.

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.

Although data is often used to estimate parameters for models in calculus and differential equations, the models themselves are seldom justified. In this paper, the data itself is used to motivate mathematical models in introductory mathematics courses. In doing so, various regression and optimization techniques are illustrated.

<p>Razvijen je model za procenu rizika amonijaka u logistiĉkim podsistemima. Navedene su osnovne parametarske i neparametarske statistiĉke karakteristike akcidenata sa amonijakom po logistiĉkim podsistemima. Raspodele hospitalizovanih, nehospitalizovanih i nastradalih uĉesnika akcidenata imaju homogene zakone, zasnovane na Vejbulovoj raspodeli. Hospitalizovani preminuli uĉesnici su raspodeljeni po Binomnoj raspodeli. Najveći broj uĉesnika u akcidentu amonijaka je ustanovljen u logistiĉkom podsistemu proizvodnje, proseĉno 8,8750. Kritiĉan rizik amonijaka je ustanovljen u logistiĉkom podsistemu pretovara.</p> / <p>Ammonia risk assessment model in logistical subsystems has been developed. Basic parametric and non-parametric statistical characteristics of ammonia accidents by logistic subsystems are given. Distributions of hospitalized, non-hospitalized, and injured accident participants have homogeneous laws, based on the Weibull distribution. Hospitalized deceased participants were distributed by Binomial distribution. The largest number of participants in the ammonia incident was established in the logistics subsystem of production, with an average of 8.8750. Critical risk of ammonia is established in the logistic subsystem of reloading.</p>

A geographic information system (GIS) was used to create landform measurements and maps for elevation, slope, aspect, landform index, relative phenologic change, and slope position for 3 topographic quadrangles in Virginia. A set of known observation points of the Northern dusky flying squirrel (Glaucomys sabrinus) was used to build 3 models to delineate sites with landform characteristics equivalent to those known points. All models were built using squirrel observation points from 2 topographic quadrangles. The first model, called "exclusionary", excluded those pixels with landform characteristics different from the known squirrel pixels based on histogram analyses. Logistic regression was used to create the other 2 models. Each model resulted in an image of pixels considered equivalent to the known squirrel pixels. Each model excluded approximately 65% of the Highland study area, but the exclusionary model excluded the fewest known squirrel pixels (12.62%). Both logistic regression models excluded approximately 10% more known squirrel pixels than the exclusionary approach. The models were tested in the area of a third quadrangle with points known to be occupied by squirrels. After the model was applied to the third topographic quadrangle, the exclusionary model excluded the least amount of full-area pixels (79.30%) and only 14.81% of the known squirrel pixels. The second logistic regression excluded 81.16 % of the full area and no known squirrel pixels. All models proved useful in quickly delineating pixels equivalent to areas where wildlife were known to occur. / Master of Science

This thesis describes a computer simulation model for determining effective spares stock levels for recoverable items at Air Force bases and depots. The simulation model is based on the following fundamental inventory theory; whenever a demand arises, it is satisfied from stock on hand, and the quantity equal to that demand is recorded immediately; when a demand exceeds stock on hand, the excess demand is backordered immediately and when item life expires procurement action is initiated at depot level. The resulting product of the model cam be used as a guide for the optimum distribution of available spares or as a computation of the necessary spares which will meet a desired percent fill rate. Outputs from the simulation model will also enable evaluation of the spares level effects as a result of change in other logistic parameters. The purpose of this thesis is two-fold to the extent that it presents: (a) A computer simulation model of an Air Force logistic system; and (b) A discussion of compound Monte-Carlo demand generation involving various analytic failure distributions. The specific nature of the problem to which the simulation model is applied is described and the model construction and output are discussed in detail.

Georeferenced field data were used to develop logistic regression models of the geographic distribution of 38 frequently common plant species throughout Rich County, Utah, to assist in the future correlation of Natural Resources Conservation Service Ecological Site Descriptions to soil map units. Field data were collected primarily during the summer of 2007, and augmented with previously existing data collected in 2001 and 2006. Several abiotic parameters and Landsat Thematic Mapper imagery were used to stratify the study area into sampling units prior to the 2007 field season. Models were initially evaluated using an independent dataset extracted from data collected by the Bureau of Land Management and by another research project conducted in Rich County by Utah State University. By using this independent dataset, model accuracy statistics widely varied across individual species, but the average model sensitivity (modeling a species as common where it was common in the independent dataset) was 0.626, and the average overall correct classification rate was 0.683. Because of concerns pertaining to the appropriateness of the independent dataset for evaluation, models were also evaluated using an internal cross-validation procedure. Model accuracy statistics computed by this procedure averaged 0.734 for sensitivity and 0.813 for overall correct classification rate. There was less variability in accuracy statistics across species using the internal cross-validation procedure. Despite concerns with the independent dataset, we wanted to determine if models would be improved, based on internal cross-validation accuracy statistics, by adding these data to the original training data. Results indicated that the original training data, collected with this modeling effort in mind, were better for choosing model parameters, but sometimes model coefficients were better when computed using the combined dataset.