Evaluation of risk attitude as a predictor of substance related risk taking

Decision making is a complex process influenced by a number of factors. Mathematical models of risk have been developed to deconstruct the decision making process into the components that are most influential. Risk attitude, or a person's preferred level of risk, has been identified as an important factor for decisions involving risk or risk taking. Risk attitude is related to a variety of risk taking behaviors, particularly those involving financial risk (e.g., investing in stocks). However, risk attitude has not been studied in the context of substance related risk taking behaviors. The goal of this study was to determine the utility of risk attitude as a predictor of substance related risk taking. Furthermore, this study was designed to test the associations between risk attitude and conceptually similar personality traits, such as impulsivity. The results of this study suggest that individuals who were risk seeking were more likely to experience negative consequences due to alcohol despite similar levels of alcohol consumption. Those who were risk seeking also engaged in risky sexual behaviors with new partners more frequently. Finally, those who were risk seeking scored higher on measures of impulsivity relative to those who were risk averse. These results provide initial evidence that risk attitude is related to substance related risk taking. Future research should examine the contextual variables that influence risk attitude (e.g., alcohol intoxication)

Nanoscale periodicity in stripe-forming systems at high temperature: Au/W(110)

We observe using low-energy electron microscopy the self-assembly of monolayer-thick stripes of Au on W(110) near the transition temperature between stripes and the non-patterned (homogeneous) phase. We demonstrate that the amplitude of this Au stripe phase decreases with increasing temperature and vanishes at the order-disorder transition (ODT). The wavelength varies much more slowly with temperature and coverage than theories of stress-domain patterns with sharp phase boundaries would predict, and maintains a finite value of about 100 nm at the ODT. We argue that such nanometer-scale stripes should often appear near the ODT.Comment: 5 page

A GENERALIZED APPROACH AND COMPUTER TOOL FOR QUANTITATIVE GENETICS STUDY

Quantitative genetics is one of the most important components to provide valuable genetic information for improving production and quality of plants and animals. The research history of quantitative genetics study could be traced back more than one hundred years. Since the Analysis of Variance (ANOVA) methods were proposed by Fisher in 1925, several useful genetic models have been proposed and have been widely applied in both plant and animal quantitative genetics studies. Useful examples included various North Carolina (NC) and diallel cross mating designs. However, many genetic models derived from these mating designs are ANOVA method based, so there are several major limitations. For example, ANOVA based methods are constricted to simple genetic models and specific mating designs and require balanced data structures. Though mixed linear model approaches were proposed in the 1960s, their applications in quantitative genetics study were limited until the early 1990s. The advantages of the mixed linear model approaches include the flexibility for unbalanced genetic data structures and complex genetic model systems. In the past years the mixed linear models have been applied to analyze various useful genetic models and a number of computer programs have been developed. In addition, researchers are not only interested in finding appropriate data structures needed for specific genetic models but also want to identify appropriate genetic models suitable for a specific data structure. Therefore, a generalized computer tool has been developed for both model evaluations and actual data analyses. In this paper, various genetic models will be detailed and generalized by mixed linear model approaches and the features of the new computer tool GenMod will be described

TESTING VARIANCE COMPONENTS BY TWO JACKKNIFE METHODS

The jackknife method, a resampling technique, has been widely used for statistical tests for years. The pseudo value based jackknife method (defined as pseudo jackknife method) is commonly used to reduce the bias for an estimate; however, sometimes it could result in large variation for an estimate and thus reduce the power for parameters of interest. In this study, a non-pseudo value based jackknife method (defined as non-pseudo jackknife method) was used for testing variance components under mixed linear models. We compared this non-pseudo value based jackknife method and the pseudo value based method by simulation regarding their biases, Type I errors, and powers. Our simulated results showed that biases obtained by the two jackknife methods are very similar; however, the non-pseudo value based method had higher testing powers than the pseudo value based method while the non-pseudo value based method had lower Type I error rates than the preset nomial probability values. Thus, we concluded that the non-pseudo value based jackknife method is superior to the pseudo value based method for testing variance components under a general mixed linear model

DISTRIBUTION OF BOLL NUMBER AND LINT YIELD BY TIME AND POSITION IN UPLAND COTTON CULTIVATORS

The time period and position which make the major contribution to total yield and to its variation is important for the field management and breeding for upland cotton, Gossypium hirsutum, L. Two-year end-of-season plant mapping data from 11 upland cotton cultivars were analyzed by position and by week. The data showed that the first position in the second and third weeks made the largest contribution to the total boll number and lint yield. The eleven cultivars differed with respect to the earliness but they had similar lint yield at harvest. The early season cultivars produce more yield and more bolls than late season cultivars in the first week of blooming, while the late season cultivars produce more yield and more bolls in the fourth week and later. The genotypic variance was the largest in week 5 and later for both lint yield and boll number. Thus, these results suggested that appropriate field management is required to maintain high yield in weeks 2 and 3 and to obtain maximum yield at late season, especially for late season cultivars. Breeders could be able to cross two cultivars which differ in earliness to obtain high yielding lines

VARIATION ANALYSIS FOR FIBER QUALITY TRAITS AMONG DIFFERENT POSITIONSIN EIGHT UPLAND COTTON CULTIVARS

Equivalencyof fiber quality within a plant of upland cotton, Gossypium hirsutum L., is very important. There are several traits within a plant that can be used to measure fiber quality and five of those traits will be investigated. Eight representative upland cultivars were grown at the Plant Science Research Farm at Mississippi State University in 1986 and five fiber traits: micronaire, fiber elongation, 2.5% and 50% span length, and fiber strength, were measured at different plant locations. The analysis of the study was modeled after a crop stability analysis with plant locations being treated as environments in the analysis. Three methodsof stability analyses were investigated:Francis and Kannenberg’s (F-K), Finlay and Wilkinson’s (F-W), and additive main effect and multiplication interaction (AMMI).The results showed that cultivar ST213 was stable for micronaire, MC235 for fiber span length, DPNSL and DES119 for fiber elongation, and CAMD-E for fiber strength

SEPARATION OF SINGLE GENE EFFECTS FROM ADDITIVE-DOMINANCE GENETIC MODELS

Separation of single gene and polygenic effects would be useful in crop improvement. In this study, additive-dominance model with a single qualitative gene based on diallel crosses of parents and progeny F1s (or F2s) was examined. The mixed linear model approach, minimum norm quadratic unbiased estimation (MINQUE), was used to estimate the variance and covariance components and single gene effects. Monte Carlo simulation was used to evaluate the efficiency of each parameter estimated from the MINQUE approach for this genetic model. The results of 200 simulations indicated that estimates of variance components and single gene effects were unbiased when setting different single gene effects for parents and F1s (or F2s). Results also indicated that estimates of variances and single gene effects were very similar for both genetic populations. Therefore, single gene effects could be effectively separated and estimated by this approach. This research should aid the extension of this model to cases that involve multiple linked or unlinked genes (or genetic markers) and other complex ploygenic models. For illustration, a real data set comprised of eight parents of upland cotton (Gossypium hirsutum L.) with normal leaf and one parent with okra leaf, and their 44 F2s were used to estimate the variance components and the genetic effects of the okra leaf gene on fiber traits

Public Knowledge and Trust of Agricultural and Natural Resources Organizations

The public lacks knowledge and connectedness to agriculture and natural resources in the United States, leading to a need for effective communications from agricultural and natural resources organizations. Trust is an integral component of communications, but it is not well understood how the public trusts the various organizations communicating agricultural and natural resources issues. The study evaluated non-profit, for-profit, and governmental organizations. A survey was conducted of a representative sample of the U.S. population to assess the public’s awareness, knowledge, and trust of organizations and their communications. The highest number of respondents was aware and knowledgeable of governmental organizations, except for Extension. Communications from non-profit organizations tended to be trusted the most compared to for-profit organizations and governmental organizations, except for Extension. Respondents’ trust of the non-profit organizations was typically higher than for-profit organizations and governmental organizations, except for Extension. The relationship between trust of an organization and trust of its communications were statistically significant for all organizations, while relationships between trust of an organization and knowledge of an organization were typically negligible and not statistically significant. For-profit organizations and governmental organizations should work to improve the public’s trust. Extension should seek to improve the public’s awareness and knowledge given the level of trust the knowledgeable respondents had for the organization. Future research should address what factors are influencing the public’s trust in organizations and organizations’ communications