IMPROVING POPULATION-SPECIFIC ALLELE FREQUENCY ESTIMATES BY ADAPTING SUPPLEMENTAL DATA: AN EMPIRICAL BAYES APPROACH

Estimation of the allele frequency at genetic markers is a key ingredient in biological and biomedical research, such as studies of human genetic variation or of the genetic etiology of heritable traits. As genetic data becomes increasingly available, investigators face a dilemma: when should data from other studies and population subgroups be pooled with the primary data? Pooling additional samples will generally reduce the variance of the frequency estimates; however, used inappropriately, pooled estimates can be severely biased due to population stratification. Because of this potential bias, most investigators avoid pooling, even for samples with the same ethnic background and residing on the same continent. Here, we propose an empirical Bayes approach for estimating allele frequencies of single nucleotide polymorphisms. This procedure adaptively incorporates genotypes from related samples, so that more similar samples have a greater influence on the estimates. In every example we have considered, our estimator achieves a mean squared error (MSE) that is smaller than either pooling or not, and sometimes substantially improves over both extremes. The bias introduced is small, as is shown by a simulation study that is carefully matched to a real data example. Our method is particularly useful when small groups of individuals are genotyped at a large number of markers, a situation we are likely to encounter in a genome-wide association study.     

Three-level main-effects designs exploiting prior information about model uncertainty

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467–475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters’ prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters’ prior beliefs.     

接触式低温固定中样品内部传热过程的分析

建立了用于电子显微镜生物样品制备的接触式低温固定法中的传热过程模型,并求得了一维常物性条件下的精确解。讨论了样品内部冷却速率的一些变化规律及其与样品初始温度和固化温度的关系,为实际的低温固定操作提供了理论指导。     

The robustness of the quasilikelihood estimator

The quasilikelihood estimator is widely used in data analysis where a likelihood is not available. We illustrate that with a given variance function it is not only conservative, in minimizing a maximum risk, but also robust against a possible misspecification of either the likelihood or cumulants of the model. In examples it is compared with estimators based on maximum likelihood and quadratic estimating functions.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

Acceptance of automation manufacturing technology in China: an examination of perceived norm and organizational efficacy

Abstract

With a shift to more automation technology, social acceptance of technology plays an important role in the manufacturing sector. To what extent this occurs, and affects the adoption of technology, has been less researched, but is important in deciding how such technology is introduced, and the nature of the shift from labour-intensive manufacturing to automation. This research applies the revised technology acceptance model (TAM) to examine the impact of social and individual antecedents on the acceptance of automation manufacturing technology. Survey data are collected from 258 Chinese manufacturers. Results suggest that perceived norms significantly affect organizational intention to use automation manufacturing technology both directly and via perceived usefulness; organizational efficacy explains the intention to use via mediating effect of perceived usefulness and perceived ease of use. This research is one of the first extending and applying TAM from individuals to organizations.     

Stop the Beat: Quiet Regulation and Cultural Conflict1

This article explores a recent conflict over the youth phenomenon known as “raving” in the City of Chicago. By interviewing participants involved in the conflict, I set out to understand the extent to which the crackdown on raves in Chicago was similar to earlier social reactions to jazz, comic books, rock and roll, and Dead Head culture, as well as to more recent conflicts over punk, rap music, and raves in other cities. While most previous research on cultural conflict has focused on moral crusades, campaigns, and panics, the Chicago conflict represents an example of “quiet regulation.” Opponents refrained from highly visible, morally charged attacks; instead, in the absence of media hype and visible public discourse, public officials justified the crackdown in highly bureaucratic terms—avoiding risk, collecting taxes, enforcing codes. Nonetheless, they also drew on cultural schemas that linked raves and raving with drugs, sex, and deviance. As a result, officials selected a course of regulation that criminalized DJs and discredited the artform. In the absence of a highly visible moral campaign, the rave participants were unable to mobilize and resist the regulation and defend their lifestyle. Sociologists must move beyond highly visible campaigns and crusades and pay greater attention to quiet regulation, both because such regulation is likely to increase in the future and because it has significant consequences for power, cultural expression, and identity.     

Adaptable Careers: Maximizing Less and Exploring More

Today, young adults are expected to decide between educational, vocational, and job options and to make the best choice possible. Career literatures emphasize the importance of young adults' career decision making but also acknowledge the problems related to making these decisions. The authors argue that career counselors could support clients' intuitive processing of career information and help their clients to develop a positive and flexible view of the self and the environment while diminishing concerns about accountability for and irreversibility of career decisions. The authors argue that career adaptability rather than decision making should become the focal concept of career theory and practice.