Genetic analysis of risk in clonal populations of forest trees

Summary A major concern arising from the culture of clonally propagated crops of forest trees is risk of catastrophic loss due to an agent or event not anticipated at the time of population establishment. Since danger of such a catastrophe depends to some degree on the genetic variability within clonal mixtures, attention has been focused on the number of clones needed to keep the risk of catastrophic loss below specified levels. In this paper, we describe a genetical analysis of susceptibility to a destructive agent and the effect that frequency of genes for susceptibility have on the number of clones needed to effectively manage this risk. As a part of the analysis, parameters representing the minimum unacceptable mortality rates in plantations () and acceptable levels of risk () are defined, and their effects on the number of single-pair matings needed for the production of clonal stock are evaluated. Dominance and recessive gene action models for a single two-allele genetic locus are investigated. Probabilities for plantation failure are functions of the gene frequency for the allele conferring susceptibility. These functions converge to zero for allele frequencies less than but to one for frequencies greater than or equal to . This convergence is periodic rather than monotonie, since probabilities for plantation failure increase rather than decrease over restricted ranges of increasing numbers of clones. Recessive and dominance gene actions are found to have different effects on the minimum number of clones needed to attain acceptable risk levels. For conditions in which substantial numbers of clones are required, selecting multiple clones per mating is an effective method for reducing the number of matings necessary to achieve acceptable risks.Paper No. 12480 of the Journal Series of the North Carolina Agriculture Research Service, Raleigh, NC 27695-7643, USA     

Capture–recapture analysis with a latent class model allowing for local dependence and observed heterogeneity

In epidemiology, capture–recapture models are commonly used to estimate the size of an unknown population based on several incomplete lists of individuals. The method operates under two main assumptions: independence between the lists (local independence) and homogeneity of capture probabilities of individuals. In practice, these assumptions are rarely satisfied. We introduce a multinomial latent class model that can account for both list dependence and heterogeneity. Parameter estimation is performed by maximizing the conditional likelihood function with the use of the EM algorithm. In addition, a new approach for evaluating the standard errors of the parameter estimates is discussed, which considerably reduces the computational burden associated with the evaluation of the variance of the population size estimate.     

The log multinomial regression model for nominal outcomes with more than two attributes

An estimate of the risk or prevalence ratio, adjusted for confounders, can be obtained from a log binomial model (binomial errors, log link) fitted to binary outcome data. We propose a modification of the log binomial model to obtain relative risk estimates for nominal outcomes with more than two attributes (the "log multinomial model"). Extensive data simulations were undertaken to compare the performance of the log multinomial model with that of an expanded data multinomial logistic regression method based on the approach proposed by Schouten et al. (1993) for binary data, and with that of separate fits of a Poisson regression model based on the approach proposed by Zou (2004) and Carter, Lipsitz and Tilley (2005) for binary data. Log multinomial regression resulted in "inadmissable" solutions (out-of-bounds probabilities) exceeding 50% in some data settings. Coefficient estimates by the alternative methods produced out-of-bounds probabilities for the log multinomial model in up to 27% of samples to which a log multinomial model had been successfully fitted. The log multinomial coefficient estimates generally had lesser relative bias and mean squared error than the alternative methods. The practical utility of the log multinomial regression model was demonstrated with a real data example. The log multinomial model offers a practical solution to the problem of obtaining adjusted estimates of the risk ratio in the multinomial setting, but must be used with some care and attention to detail.     

Using Bayesian multinomial classifier to predict whether a given protein sequence is intrinsically disordered

Intrinsically disordered proteins (IDPs) lack a well-defined three-dimensional structure under physiological conditions. Intrinsic disorder is a common phenomenon, particularly in multicellular eukaryotes, and is responsible for important protein functions including regulation and signaling. Many disease-related proteins are likely to be intrinsically disordered or to have disordered regions. In this paper, a new predictor model based on the Bayesian classification methodology is introduced to predict for a given protein or protein region if it is intrinsically disordered or ordered using only its primary sequence. The method allows to incorporate length-dependent amino acid compositional differences of disordered regions by including separate statistical representations for short, middle and long disordered regions. The predictor was trained on the constructed data set of protein regions with known structural properties. In a Jack-knife test, the predictor achieved the sensitivity of 89.2% for disordered and 81.4% for ordered regions. Our method outperformed several reported predictors when evaluated on the previously published data set of Prilusky et al. [2005. FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics 21 (16), 3435-3438]. Further strength of our approach is the ease of implementation.     

A continuous time version and a generalization of a Markov-recapture model for trapping experiments

Wileyto et al. [E.P. Wileyto, W.J. Ewens, M.A. Mullen, Markov-recapture population estimates: a tool for improving interpretation of trapping experiments, Ecology 75 (1994) 1109] propose a four-state discrete time Markov process, which describes the structure of a marking-capture experiment as a method of population estimation. They propose this method primarily for estimation of closed insect populations. Their method provides a mark-recapture estimate from a single trap observation by allowing subjects to mark themselves. The estimate of the unknown population size is based on the assumption of a closed population and a simple Markov model in which the rates of marking, capture, and recapture are assumed to be equal. Using the one step transition probability matrix of their model, we illustrate how to go from an embedded discrete time Markov process to a continuous time Markov process assuming exponentially distributed holding times. We also compute the transition probabilities after time t for the continuous time case and compare the limiting behavior of the continuous and discrete time processes. Finally, we generalize their model by relaxing the assumption of equal per capita rates for marking, capture, and recapture. Other questions about how their results change when using a continuous time Markov process are examined.     

Factors affecting the health of residents in China: A perspective based on the living environment

Residents’ health is an important factor affecting social development and harmony. Based on 2010 China Family Panel Studies data of the Institute of Social Science Survey, Peking University and using a multi-classification logit regression model, we analyze the factors that affect the health status of residents in China. These factors include environmental pollution, which is a particularly important factor. Our study found that the impacts of residents’ characteristic variables, external living environment, and living habits vary. As residents age, their health status deteriorates. For the General, Less healthy, and Unhealthy groups, an income of less than CNY 10,000 significantly affects health status; however, when their income is greater than CNY 10,000, it no longer has a significant effect. For the Very unhealthy group, this particular threshold value is CNY 3000. At least one of urban–rural classification and residence registration status is significant, indicating that the urban–rural dual structure as well as the household registration system significantly affects residents’ health status. However, the direction of this effect is uncertain. Cooking water significantly affects the Less healthy and Unhealthy groups, and tap water is more conducive to health. Polluting enterprises within a radius of five kilometers mainly affect the Unhealthy group, but the direction of its impact is contrary to expectations. Smoking and drinking significantly affect the health status of the General, Less healthy, and Unhealthy groups. However, the direction of their impact was contrary to expectations. For the Very unhealthy group, drinking has a significant impact on residents’ health status, but the direction of the impact was again the opposite of what we expected. Smoking has no significant impact on the health status of this group. Exercise significantly affects the Less healthy and Unhealthy groups, but its influence has no obvious trend. Our study shows that living habits have a smaller influence on residents’ health status.     

CO2激光对西瓜种子最佳辐射剂量的筛选及其数学模拟

采用功率密度为８９８ｍ ｗ ／ｃｍ ２ 的ＣＯ２ 激光对西瓜种子进行不同时间的辐射处理,筛选出了最佳刺激剂量,并应用多项式回归进行数学模拟,得到了比较理想的结论。