DEVELOPMENT IMPACT FEE ADOPTION AND ITS EFFECTS IN TEXAS

The purpose of my thesis is to study what factors affect the adoption of impact fees in Texas and what effects impact fees have on city budgets. This research was done using two models. The first model looked at the adoption of impact fees as the dependent variable and the second model looked at the total impact fee assed on new residential units as the dependent variable. Both models used the gross tax rate, debt per capita, change in city population as a percentage, city population, average price of a new home in 2007, number of building permits issued in 2007, and the average household income as independent variables. The most significant independent variable found for the assessment of impact fees is change in population as a percentage. The total impact fee charged per city was driven by several factors. These included change in population as a percentage, population, and gross tax rate

Macrophage-dependent nitric oxide expression regulates tumor cell detachment and metastasis after IL-2/anti-CD40 immunotherapy

Immunotherapy with IL-2 and anti-CD40 induces the expression of NOS2 in tumor-associated macrophages, and its expression is required for the inhibition of tumor metastasis

Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10−8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (<5%). In the trans-ethnic fine mapping of 47 BMI loci and 27 WHRadjBMI loci that were locus-wide significant (P < 0.05 adjusted for effective number of variants per locus) from the African ancestry sex-combined and sex-stratified analyses, 26 BMI loci and 17 WHRadjBMI loci contained ≤ 20 variants in the credible sets that jointly account for 99% posterior probability of driving the associations. The lead variants in 13 of these loci had a high probability of being causal. As compared to our previous HapMap imputed GWAS for BMI and WHRadjBMI including up to 71,412 and 27,350 African ancestry individuals, respectively, our results suggest that 1000 Genomes imputation showed modest improvement in identifying GWAS loci including low frequency variants. Trans-ethnic meta-analyses further improved fine mapping of putative causal variants in loci shared between the African and European ancestry populations

Can water evaporation rates be enhanced by employing a different polar ambient?

Enhancing evaporation rates are of great practical interest in many technological applications such as water desalination or drying in industry. Since the conventional methods of increasing evaporation viz. increasing temperature, surface area etc. may not be always practically feasible or economical, novel methods of evaporation enhancement are necessary. The current study explores the possibility of evaporation enhancement by introducing a Tetrafluoroethane gas ambient under different conditions of heating and circulation. Depending upon the temperature and circulation conditions, as enhancement of 58 to 375% in evaporation rate has been measured and physical explanations into the underlying mechanism have been suggested

Influence of lifestyle and genetic variants in the <i>aldo-keto reductase 1C3</i> rs12529 polymorphism in high-risk prostate cancer detection variability assessed between US and New Zealand cohorts

<div><p>Introduction</p><p>The prostate-specific antigen (PSA) based prostate cancer (PC) screening is currently being debated. The current assessment is to understand the variability of detecting high-risk PC in a NZ cohort in comparison to a US cohort with better PSA screening facilities. Aldo-keto reductase 1C3 (AKR1C3) is known for multiple functions with a potential to regulate subsequent PSA levels. Therefore, we wish to understand the influence of tobacco smoking and the AKR1C3 rs12529 gene polymorphism in this variability.</p><p>Method</p><p>NZ cohort (n = 376) consisted of 94% Caucasians while the US cohort consisted of African Americans (AA), n = 202, and European Americans (EA), n = 232. PSA level, PC grade and stage at diagnosis were collected from hospital databases for assigning high-risk PC status. Tobacco smoking status and the AKR1C3 rs12529 SNP genotype were considered as confounding variables. Variation of the cumulative % high-risk PC (outcome variable) with increasing PSA intervals (exposure factor) was compared between the cohorts using the Kolmogorov-Smirnov test. Comparisons were carried out with and without stratifications made using confounding variables.</p><p>Results</p><p>NZ cohort has been diagnosed at a significantly higher mean age (66.67± (8.08) y) compared to both AA (62.65±8.17y) and EA (64.83+8.56y); median PSA (NZ 8.90ng/ml compared to AA 6.86ng/ml and EA 5.80ng/ml); and Gleason sum (NZ (7) compared EA (6)) (p<0.05). The cumulative % high-risk PC detection shows NZ cohort with a significantly lower diagnosis rates at PSA levels between >6 - <10ng/ml compared to both US groups (p<0.05). These were further compounded significantly by smoking status and genetics.</p><p>Conclusions</p><p>High-risk PCs recorded at higher PSA levels in NZ could be due to factors including lower levels of PSA screening and subsequent specialist referrals for biopsies. These consequences could be pronounced among NZ ever smokers carrying the <i>AKR1C3</i> rs12529 G alleles making them a group that requires increased PSA screening attention.</p></div

Genetic and ethnic stratification of high-risk PCs as fractions of all high-risk PCs in each PSA interval.

<p>Genetic and ethnic stratification of high-risk PCs as fractions of all high-risk PCs in each PSA interval.</p

Cumulative % high-risk PC as a fraction of all PCs by PSA groups separated by smoking status.

<p>(NZ = New Zealanders, AA = African Americans, EA = European Americans. S = ever smokers NS = never smokers. (Points of significant differences (p<0.05) are shown according to the Kolmogorov-Smirnov test results. Full double headed arrow = maximum difference between EA ever smokers and NZ ever smokers. Dashed double headed arrow = maximum difference between AA ever smokers and NZ ever smokers).</p

High-risk PC statistics against genotype, smoking status and PSA interval among study groups.

<p>High-risk PC statistics against genotype, smoking status and PSA interval among study groups.</p

Cumulative frequency of high-risk PCs as a fraction of all high-risk PCs by PSA groups, genotype and tobacco smoking status.

<p>(AA = African American EA = Caucasian American NZ = New Zealanders S = ever smokers NS = never smokers. Points of significant differences (p<0.05) are shown according to the Kolmogorov-Smirnov test results. Full double headed arrow = maximum difference between EA and NZ. Dashed double headed arrow = maximum difference between AA and NZ).</p

Patient characteristics compared between the study cohorts.

<p>Patient characteristics compared between the study cohorts.</p