Fall Fertilization Timing Effects on Nitrate Leaching and Turfgrass Color and Growth

Fall season fertilization is a widely recommended practice for turfgrass. Fertilizer applied in the fall, however, may be subject to substantial leaching losses. A field study was conducted in Connecticut to determine the timing effects of fall fertilization on nitrate N (NO3-N) leaching, turf color, shoot density, and root mass of a 90% Kentucky bluegrass (Poa pratensis L.), 10% creeping red fescue (Festuca rubra L.) lawn. Treatments consisted of the date of fall fertilization: 15 September, 15 October, 15 November, 15 December, or control which received no fall fertilizer. Percolate water was collected weekly with soil monolith lysimeters. Mean log10 NO3-N concentrations in percolate were higher for fall fertilized treatments than for the control. Mean NO3-N mass collected in percolate water was linearly related to the date of fertilizer application, with higher NO3-N loss for later application dates. Applying fall fertilizer improved turf color and density but there were no differences in color or density among applications made between 15 October and 15 December. These findings suggest that the current recommendation of applying N in mid- to late November in southern New England may not be compatible with water quality goals.

Technology Creation, Diffusion, and Growth Cycles

Standard macroeconomic models that assume an exogenous stochastic process for multifactor productivity offer the interpretation that recessions are the result of ''bad news'' (technological regress) and expansions are the result of ''good news'' (technological advancement). The view taken here is that both expansions and recessions are the result of ''good news'' in the sense that in both cases, aggregate production possibilities have increased. Recessions can be thought of as the transition from one technological frontier to the next.

THE BACON CHOW STUDY: THE RELATIONSHIP BETWEEN INFECTIOUS ILLNESS AND GROWTH OF INFANTS

Multiple dietary deficiencies and high rates of infectious illness are major health problems leading to malnutrition and limitation of growth of children in developing countries. Longitudinal studies which provide information on illness incidence and growth velocity are needed in order to untangle the complex interrelationship between nutrition, illness and growth. From 1967 to 1973, researchers led by Dr. Bacon Chow of the Johns Hopkins University School of Hygiene undertook a quasi-experimental prospective study in Suilin Township, Taiwan to determine the effects of a nutritional supplement to the diets of pregnant and lactating women on the growth, development and resistance to disease of their offspring. This dissertation presents results from the analysis of infant morbidity and postnatal growth.^ Maternal nutritional supplementation has no apparent effect on the postnatal growth or morbidity of infants. Significant sex differences exist in growth response to illness and in illness susceptibility. Male infants have more diarrhea and upper respiratory illness. Respiratory illness is positively associated with growth rate in weight in the first semester of life. Diarrhea is significantly negatively associated with growth in length in the second semester. Small-for-date infants are more susceptible to illness in general and have a different pattern of growth response than large-for-date infants.^ Principal components analysis of illness data is shown to be an effective technique for making more precise use of ambiguous morbidity data. Multiple regression with component scores is an accurate method for estimating variance in growth rate predicted by indepenent illness variables. A model is advanced in which initial postnatal growth rate determines subsequent susceptibility to nutritional stress and infection. Initial growth rate is a function of prenatal nutrition, but is not significantly affected by maternal supplementation during gestation or lactation. Critical evaluation is made of nutritional supplementation programs which do not afford disease control.^

RUNOFF FROM COAL PILES: EFFECTS ON THE ENVIRONMENT AND THE PUBLIC'S HEALTH

The use of coal for fuel in place of oil and natural gas has been increasing in the United States. Typically, users store their reserves of coal outdoors in large piles and rainfall on the coal creates runoffs which may contain materials hazardous to the environment and the public's health. To study this hazard, rainfall on model coal piles was simulated, using deionized water and four coals of varying sulfur content. The simulated surface runoffs were collected during 9 rainfall simulations spaced 15 days apart. The runoffs were analyzed for 13 standard water quality parameters, extracted with organic solvents and then analyzed with capillary column GC/MS, and the extracts were tested for mutagenicity with the Ames Salmonella microsomal assay and for clastogenicity with Chinese hamster ovary cells.^ The runoffs from the high-sulfur coals and the lignite exhibited extremes of pH (acidity), specific conductance, chemical oxygen demand, and total suspended solids; the low-sulfur coal runoffs did not exhibit these extremes. Without treatment, effluents from these high-sulfur coals and lignite would not comply with federal water quality guidelines.^ Most extracts of the simulated surface runoffs contained at least 10 organic compounds including polycyclic aromatic hydrocarbons, their methyl and ethyl homologs, olefins, paraffins, and some terpenes. The concentrations of these compounds were generally less than 50 (mu)g/l in most extracts.^ Some of the extracts were weakly mutagenic and affected both a DNA-repair proficient and deficient Salmonella strain. The addition of S9 decreased the effect significantly. Extracts of runoffs from the low-sulfur coal were not mutagenic.^ All extracts were clastogenic. Extracts of runoffs from the high-sulfur coals were both clastogenic and cytotoxic; those from the low-sulfur coal and the lignite were less clastogenic and not cytotoxic. Clastogenicity occurred with and without S9 activation. Chromosomal lesions included gaps, breaks and exchanges. These data suggest a relationship between the sulfur content of a coal, its mutagenicity and also its clastogenicity.^ The runoffs from actual coal piles should be investigated for possible genotoxic effects in view of the data presented in this study.^

Objective assessment of the built environment and its relationship to physical activity and obesity

The role of physical activity in the promotion of individual and population health has been well documented in research and policy publications. Significant research activities have produced compelling evidence for the support of the positive association between physical activity and improved health. Despite the knowledge about these public health benefits of physical activity, over half of US adults do not engage in physical activity at levels consistent with public health recommendations. Just as physical inactivity is of significant public health concern in the US, the prevalence of obesity (and its attendant co-morbidities) is also increasing among US adults.^ Research suggests racial and ethnic disparities relevant to physical inactivity and obesity in the US. Various studies have shown more favorable outcomes among non-Hispanic whites when compared to other minority groups as far as physical activity and obesity are concerned. The health disparity issue is especially important because Mexican-Americans who are the fastest growing segment of the US population are disproportionately affected by physical inactivity and obesity by a significant margin (when compared to non-Hispanic whites), so addressing the physical inactivity and obesity issues in this group is of significant public health concern. ^ Although the evidence for health benefits of physical activity is substantial, various research questions remain on the potential motivators for engaging in physical activity. One area of emerging interest is the potential role that the built environment may play in facilitating or inhibiting physical activity.^ In this study, based on an ongoing research project of the Department of Epidemiology at the University of Texas M. D. Anderson Cancer Center, we examined the built environment, measured objectively through the use of geographical information systems (GIS), and its association with physical activity and obesity among a cohort of Mexican- Americans living in Harris County, Texas. The overall study hypothesis was that residing in dense and highly connected neighborhoods with mixed land-use is associated with residents’ increased participation in physical activity and lowered prevalence of obesity. We completed the following specific aims: (1) to generate a land-use profile of the study area and create a “walkability index” measure for each block group within the study area; (2) to compare the level of engagement in physical activity between study participants that reside in high walkability index block groups and those from low walkability block groups; (3) to compare the prevalence of obesity between study participants that reside in high walkability index block groups and those from low walkability block groups. ^ We successfully created the walkability index as a form of objective measure of the built environment for portions of Harris County, Texas. We used a variety of spatial and non-spatial dataset to generate the so called walkability index. We are not aware of previous scholastic work of this kind (construction of walkability index) in the Houston area. Our findings from the assessment of relationships among walkability index, physical activity and obesity suggest the following, that: (1) that attempts to convert people to being walkers through health promotion activities may be much easier in high-walkability neighborhoods, and very hard in low-walkability neighborhoods. Therefore, health promotion activities to get people to be active may require supportive environment, walkable in this case, and may not succeed otherwise; and (2) Overall, among individuals with less education, those in the high walkability index areas may be less obese (extreme) than those in the low walkability area. To the extent that this association can be substantiated, we – public health practitioners, urban designers, and policy experts – we may need to start thinking about ways to “retrofit” existing urban forms to conform to more walkable neighborhoods. Also, in this population especially, there may be the need to focus special attention on those with lower educational attainment.^

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Average nutrient concentration, and growth characteristics of bacteria during USCGC Healy cruises in 2002 and 2004

Standing stocks and production rates for phytoplankton and heterotrophic bacteria were examined during four expeditions in the western Arctic Ocean (Chukchi Sea and Canada Basin) in the spring and summer of 2002 and 2004. Rates of primary production (PP) and bacterial production (BP) were higher in the summer than in spring and in shelf waters than in the basin. Most surprisingly, PP was 3-fold higher in 2004 than in 2002; ice-corrected rates were 1581 and 458 mg C/m**2/d respectively, for the entire region. The difference between years was mainly due to low ice coverage in the summer of 2004. The spatial and temporal variation in PP led to comparable variation in BP. Although temperature explained as much variability in BP as did PP or phytoplankton biomass, there was no relationship between temperature and bacterial growth rates above about 0°C. The average ratio of BP to PP was 0.06 and 0.79 when ice-corrected PP rates were greater than and less than 100 mg C/m**2/d, respectively; the overall average was 0.34. Bacteria accounted for a highly variable fraction of total respiration, from 3% to over 60% with a mean of 25%. Likewise, the fraction of PP consumed by bacterial respiration, when calculated from growth efficiency (average of 6.9%) and BP estimates, varied greatly over time and space (7% to >500%). The apparent uncoupling between respiration and PP has several implications for carbon export and storage in the western Arctic Ocean.

Fruit yield and growth parameters of several Carica papaya L. genotypes in a temperate climate

The aim of this work was to evaluate changes in growth and productivity parameters of different precocious hybrids and a naturalized variety of papaya under both greenhouse and field cultivation in a temperate climate (the center of the province of Santa Fe, Argentina). In view of the aforesaid, the purpose of our research was to identify further genotypes better suited for the cultivation of this species in temperate climates and demonstrate the need for the use of semi-controlled systems to make possible the cultivation of these promising genotypes in middle latitudes. The average yield was 291% higher in greenhouse than in the field. The average productivity for hybrid genotypes compared with the naturalized variety more than doubled in both environments. Considering behavior in height, leaf area index and yield parameters, hybrids H2 (principally), and H4 showed a great adaptation for use in semi-forced systems. The use of greenhouse and short stature papaya hybrids allows its feasible and surely profitable cultivation in non- tropical climates.

Environment and habitat characteristics of wintering lemmings in the Arctic

Snow cover has dramatic effects on the structure and functioning of Arctic ecosystems in winter. In the tundra, the subnivean space is the primary habitat of wintering small mammals and may be critical for their survival and reproduction. We have investigated the effects of snow cover and habitat features on the distributions of collared lemming (Dicrostonyx groenlandicus) and brown lemming (Lemmus trimucronatus) winter nests, as well as on their probabilities of reproduction and predation by stoats (Mustela erminea) and arctic foxes (Vulpes lagopus). We sampled 193 lemming winter nests and measured habitat features at all of these nests and at random sites at two spatial scales. We also monitored overwinter ground temperature at a subsample of nest and random sites. Our results demonstrate that nests were primarily located in areas with high micro-topography heterogeneity, steep slopes, deep snow cover providing thermal protection (reduced daily temperature fluctuations) and a high abundance of mosses. The probability of reproduction increased in collared lemming nests at low elevation and in brown lemming nests with high availability of some graminoid species. The probability of predation by stoats was density dependent and was higher in nests used by collared lemmings. Snow cover did not affect the probability of predation of lemming nests by stoats, but deep snow cover limited predation attempts by arctic foxes. We conclude that snow cover plays a key role in the spatial structure of wintering lemming populations and potentially in their population dynamics in the Arctic.