A practical validation approach for virus titer testing of avian infectious bursal disease live vaccine according to current regulatory guidelines

The method for virus titer determination of avian infectious bursal disease (IBD) live vaccine, developed long before regulatory validation guidelines is a cell culture based biological assay intended for use in vaccine release testing.The aim of our study was to perform a validation, based on fit-for-purpose principle, of an old 50% tissue culture infectious dose (TCID₅₀) method according to Guidelines of the International Cooperation on Harmonization of Technical Requirements for Registration of Veterinary Medicinal Products (VICH).This paper addresses challenges and discusses some key aspects that should be considered when validating biological methods. A different statistical approach and non-parametric statistics was introduced in validation protocol in order to derive useful information from experimental data. This approach is applicable for a wide range of methods.In conclusion, the previous virus titration method had showed to be precise, accurate, linear, robust and in accordance with current regulatory standards, which indicates that there is no need for additional re-development or upgrades of the method for its suitability for intended use.     

Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics

Aim The study and prediction of species–environment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process‐based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process‐based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process‐based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.     

Causal inference in biomolecular pathways using a Bayesian network approach and an Implicit method

We introduce here the concept of Implicit networks which provide, like Bayesian networks, a graphical modelling framework that encodes the joint probability distribution for a set of random variables within a directed acyclic graph. We show that Implicit networks, when used in conjunction with appropriate statistical techniques, are very attractive for their ability to understand and analyze biological data. Particularly, we consider here the use of Implicit networks for causal inference in biomolecular pathways. In such pathways, an Implicit network encodes dependencies among variables (proteins, genes), can be trained to learn causal relationships (regulation, interaction) between them and then used to predict the biological response given the status of some key proteins or genes in the network. We show that Implicit networks offer efficient methodologies for learning from observations without prior knowledge and thus provide a good alternative to classical inference in Bayesian networks when priors are missing. We illustrate our approach by an application to simulated data for a simplified signal transduction pathway of the epidermal growth factor receptor (EGFR) protein.     

Summary statistics of neutral mutations in longitudinal DNA samples

Longitudinal samples of DNA sequences are the DNA sequences sampled from the same population at different time points. For fast evolving organisms, e.g. RNA virus, these kind of samples have increasingly been used to study the evolutionary process in action. Longitudinal samples provide some interesting new summary statistics of genetic variation, such as the frequency of mutation of size i in one sample and size j in another, the average number of mutations accumulated since the common ancestor of two sequences each from a different sample, and number of private, shared and fixed mutations within samples. To make the results more applicable, we used in this study a general two-sample model, which assumes two longitudinal samples were taken from the same measurably evolving population. Inspired by the HIV study, we also studied a two-sample-two-stage model, which is a special case of two-sample model and assumes a treatment after the first sampling instantaneously changes the population size. We derived the formulas for calculating statistical properties, e.g. expectations, variances and covariances, of these new summary statistics under the two models. Potential applications of these results were discussed.     

Individualism in plant populations: using stochastic differential equations to model individual neighbourhood-dependent plant growth

We study individual plant growth and size hierarchy formation in an experimental population of Arabidopsis thaliana, within an integrated analysis that explicitly accounts for size-dependent growth, size- and space-dependent competition, and environmental stochasticity. It is shown that a Gompertz-type stochastic differential equation (SDE) model, involving asymmetric competition kernels and a stochastic term which decreases with the logarithm of plant weight, efficiently describes individual plant growth, competition, and variability in the studied population. The model is evaluated within a Bayesian framework and compared to its deterministic counterpart, and to several simplified stochastic models, using distributional validation. We show that stochasticity is an important determinant of size hierarchy and that SDE models outperform the deterministic model if and only if structural components of competition (asymmetry; size- and space-dependence) are accounted for. Implications of these results are discussed in the context of plant ecology and in more general modelling situations.     

Insider Censoring: Distortion of Data with Nondetects

Environmental data often include low-level concentrations below reporting limits. These data may be reported as “< RL,” where RL is one of several types of reporting limits. Some values also may be reported as a single number, but flagged with a qualifier (J-values) to indicate a difference in precision as compared to values above the RL. A currently used method for reporting censored environmental data called “insider censoring” produces a strong upward bias, while also distorting the shape of the data distribution. This results in inaccurate estimates of summary statistics and regression coefficients, distorts evaluations of whether data follow a normal distribution, and introduces inaccuracies into risk assessments and models. Insider censoring occurs when data measured as below the detection limit (< DL) are reported as less than the higher quantitation limit (< QL), whereas values between the DL and QL are reported as individual numbers. Three unbiased alternatives to insider censoring are presented so that laboratories and their data users can recognize, and remedy, this problem.     

Bayesian Statistics-Based Procedure for Sampling of Contaminated Sites

A variable density sampling pattern based on Bayesian statistics is presented and compared to a uniform density statistical pattern and a judgmental approach in a real case study. The Bayesian statistics, supported by a software tool, supplied a soil sampling plan similar to the judgmental one, especially for the number of sampling points and their location. It allowed statistical goals to be set and expert judgment to be included in the sampling strategy in a transparent and systematic procedure. For these reasons, it appears quite suitable for inclusion into Quality Assurance Quality Control plans.     

浙江丽水生态示范区植物的分布特点

1999年底被国务院批准设立为全国第4个生态示范区的丽水市（包括莲都区,缙云县,云和县,松阳县,遂昌县,庆元县,青田县,龙泉市和景宁畲族自治县）,由于地形复杂,原生,次生林等保存较好,生态环境保持良好,植物资源极为丰富。笔者根据多年来对该地区的资源调查和标本采集统计,已知有维管束植物206科,94属,2881种,其中蕨类植物的种类和数量在中国东半部仅次于台湾和海南,尤其是鳞毛蕨属和复叶耳蕨属可以认为是在我国的一个分布中心,种子植物的种类和数量与邻近周边地相比也占有明显优势,区内珍衡濒危植物众多,区系起源古老,不乏古老的种,属及残遗种,且区系成分复杂,来源于多种地理成分。     

Identification of Horizontal Gene Transfer from Phylogenetic Gene Trees

We suggest a new procedure to search for the genes with horizontal transfer events in their evolutionary history. The search is based on analysis of topology difference between the phylogenetic trees of gene (protein) groups and the corresponding phylogenetic species trees. Numeric values are introduced to measure the discrepancy between the trees. This approach was applied to analyze 40 prokaryotic genomes classified into 132 classes of orthologs. This resulted in a list of the candidate genes for which the hypothesis of horizontal transfer in evolution looks true.