Gene therapy for cystic fibrosis: Will it affect the uptake of prenatal carrier screening?

We report a study which examined whether the decision of 135 couples to accept prenatal cystic fibrosis (CF) carrier screening would be influenced by the advent of gene therapy. A majority (91 couples; 67 per cent) felt that gene therapy for CF would not influence their decision to be screened. Twenty-two couples (16 per cent) stated that they would decline to be screened and an equal number felt ambivalent. Even if the life expectancy of a CF sufferer were increased by gene therapy to normal, 78 per cent of couples would still wish to avail themselves of prenatal carrier screening. A majority of women who decline screening do so because they are opposed to termination of pregnancy. The availability of gene therapy could increase the proportion of couples who accept screening.     

试论教师体态语言对课堂教学效果的影响

文章在肯定教学中教师的有声语言作用的基础上,通过作者在教学过程中对体态语言运用的探索和体会,着重从情绪、着装、眼神和行为举止等方面,论述了教师体态语言对教学效果的影响。     

Participatory Bayesian network modeling to understand driving factors of land-use change decisions: insights from two case studies in northeast Madagascar

ABSTRACT

Forest frontiers worldwide reveal trade-offs that are key in mitigating global change. In the forest frontiers of northeast Madagascar, land-use changes result from decisions made by smallholder farmers. In the past, subsistence needs led to increasing shifting cultivation, resulting in forest degradation and deforestation. This study focuses on investigating the role of locally determined factors in land-use change decisions in the forest frontier context. Therefore, we developed a Bayesian network-based land-use decision model that represents the causalities between factors influencing land-use decisions and takes into account local decision-makers’ knowledge. The approach is applied in two comparative case studies in northeast Madagascar. Results show that farmers mostly aim at extending the cultivation of cash crops. These results and the causal mechanisms disentangled for the forest frontier of northeast Madagascar help understand change mechanisms and hence, support decision-making to attain the Sustainable Development Goals.     

增强UV-B辐射对植物的影响

综述了大气臭氧层的减少是影响地面UV-B辐射增强的主要因素,并进一步简述了增强UV-B辐射对陆生植物影响的最新研究进展。UV-B辐射一方面在分子水平上直接或间接地损害植物的DNA分子和蛋白质的结构,从而影响植物的各种生理生化过程影响;另一方面对植物的形态学特征(如叶表皮结构和花的形态结构等)产生广泛的影响。过强的UV-B辐射穿过叶表皮后直接对植物的光合系统、膜系统和植物生长调控激素等方面产生影响,从而会在植株个体、种群和生态系统等层次上表现出来。增强UV-B辐射作为全球变化因子,可导致敏感植物种类的死亡或退化,较能忍耐UV-B辐射的耐荫树种比阳生树种的适应性更强,能竞争到更多的利用资源,因此,在全球UV-B辐射增强下阳生树种可能会提早退出植物群落,这对森林群落的演替更新会起着重要的作用。文章还综述了UV-B辐射与其它污染胁迫因子和环境因子的复合作用共同对植物产生的影响,以及植物对UV-B辐射的防御和修复等防护对策,提出了今后要加强对生态系统的大尺度、长时间的宏观研究,以及开展增强UV-B辐射对地下生态学,对DNA的修复与表达机理,对植物信号接收和传导的新途径等方面的研究。     

Estimating Population Size with Noninvasive Capture-Mark-Recapture Data

Abstract:  Estimating population size of elusive and rare species is challenging. The difficulties in catching such species has triggered the use of samples collected noninvasively, such as feces or hair, from which genetic analysis yields data similar to capture-mark-recapture (CMR) data. There are, however, two differences between classical CMR and noninvasive CMR. First, capture and recapture data are gathered over multiple sampling sessions in classical CMR, whereas in noninvasive CMR they can be obtained from a single sampling session. Second, because of genotyping errors and unlike classical CMR, there is no simple relationship between (genetic) marks and individuals in noninvasive CMR. We evaluated, through simulations, the reliability of population size estimates based on noninvasive CMR. For equal sampling efforts, we compared estimates of population size N obtained from accumulation curves, a maximum likelihood, and a Bayesian estimator. For a closed population and without sampling heterogeneity, estimates obtained from noninvasive CMR were as reliable as estimates from classical CMR. The sampling structure (single or multiple session) did not alter the results, the Bayesian estimator in the case of a single sampling session presented the best compromise between low mean squared error and a 95% confidence interval encompassing the parametric value of N in most simulations. Finally, when suitable field and lab protocols were used, genotyping errors did not substantially bias population size estimates (bias < 3.5% in all simulations). The ability to reliably estimate population size from noninvasive samples taken during a single session offers a new and useful technique for the management and conservation of elusive and rare species.     

Dynamic multi-resolution spatial models

Data from remote-sensing platforms play an important role in monitoring environmental processes, such as the distribution of stratospheric ozone. Remote-sense data are typically spatial, temporal, and massive. Existing prediction methods such as kriging are computationally infeasible. The multi-resolution spatial model (MRSM) captures nonstationary spatial dependence and produces fast optimal estimates using a change-of-resolution Kalman filter. However, past data can provide valuable information about the current status of the process being investigated. In this article, we incorporate the temporal dependence into the process by developing a dynamic MRSM. An application of the dynamic MRSM to a month of daily total column ozone data is presented, and on a given day the results of posterior inference are compared to those for the spatial-only MRSM. It is apparent that there are advantages to using the dynamic MRSM in regions where data are missing, such as when a whole swath of satellite data is missing.     

A spatial zero-inflated poisson regression model for oak regeneration

Ecological counts data are often characterized by an excess of zeros and spatial dependence. Excess zeros can occur in regions outside the range of the distribution of a given species. A zero-inflated Poisson regression model is developed, under which the species range is determined by a spatial probit model, including physical variables as covariates. Within that range, species counts are independently drawn from a Poisson distribution whose mean depends on biotic variables. Bayesian inference for this model is illustrated using data on oak seedling counts. Received: May 2004 / Revised: December 2004     

Multiple indicators, partially ordered sets, and linear extensions: Multi-criterion ranking and prioritization

This paper is concerned with the question of ranking a finite collection of objects when a suite of indicator values is available for each member of the collection. The objects can be represented as a cloud of points in indicator space, but the different indicators (coordinate axes) typically convey different comparative messages and there is no unique way to rank the objects while taking all indicators into account. A conventional solution is to assign a composite numerical score to each object by combining the indicator information in some fashion. Consciously or otherwise, every such composite involves judgments (often arbitrary or controversial) about tradeoffs or substitutability among indicators. Rather than trying to combine indicators, we take the view that the relative positions in indicator space determine only a partial ordering and that a given pair of objects may not be inherently comparable. Working with Hasse diagrams of the partial order, we study the collection of all rankings that are compatible with the partial order (linear extensions). In this way, an interval of possible ranks is assigned to each object. The intervals can be very wide, however. Noting that ranks near the ends of each interval are usually infrequent under linear extensions, a probability distribution is obtained over the interval of possible ranks. This distribution, called the rank-frequency distribution, turns out to be unimodal (in fact, log-concave) and represents the degree of ambiguity involved in attempting to assign a rank to the corresponding object. Stochastic ordering of probability distributions imposes a partial order on the collection of rank-frequency distributions. This collection of distributions is in one-to-one correspondence with the original collection of objects and the induced ordering on these objects is called the cumulative rank-frequency (CRF) ordering; it extends the original partial order. Although the CRF ordering need not be linear, it can be iterated to yield a fixed point of the CRF operator. We hypothesize that the fixed points of the CRF operator are exactly the linear orderings. The CRF operator treats each linear extension as an equal voter in determining the CRF ranking. It is possible to generalize to a weighted CRF operator by giving linear extensions differential weights either on mathematical grounds (e.g., number of jumps) or empirical grounds (e.g., indicator concordance). Explicit enumeration of all possible linear extensions is computationally impractical unless the number of objects is quite small. In such cases, the rank-frequencies can be estimated using discrete Markov chain Monte Carlo (MCMC) methods.     

Some Interpolation Estimators in Environmental Risk Assessment for Spatially Misaligned Health Data

Ecological regression studies are widely used in geographical epidemiology to assess the relationships between health hazard and putative risk factors. Very often, health data are measured at an aggregate level because of confidentiality restrictions, while putative risk factors are measured on a different grid, i.e., independent (exposure) variable and response (counts) variable are spatially misaligned. To perform a regression of risk on exposure, one needs to realign the spatial support of the variables. Bayesian hierarchical models constitute a natural approach to the problem because of their ability to model the exposure field and the relationship between exposure and relative risk at different levels of the hierarchy, taking proper account of the variability induced by the covariate estimation. In the current paper, we propose two fully Bayesian solutions to the problem. The first one is based on the kernel-smoothing technique, while the second one is built on the tessellation of the study region. We illustrate our methods by assessing the relationship between exposure to uranium in drinkable waters and cancer incidence, in South Carolina (USA).     

基于贝叶斯网络的事故多级多米诺效应计算方法研究

为了更合理地分析事故风险,提出了基于贝叶斯网络的多级多米诺效应计算方法及其计算步骤,并从个人风险和社会风险2个角度,定量分析了生产安全事故的多级多米诺效应。同时以某企业的2个汽油罐区为例,运用上述方法对其生产安全事故的多米诺效应进行定量计算,并将计算结果与未考虑多米诺效应、仅考虑一级多米诺效应时的计算结果进行比较。研究结果表明:基于贝叶斯网络的计算方法,同时考虑了多级多米诺效应和事故的协同效应,可以使计算的个人风险和社会风险更接近于实际。