非人灵长类局部脑缺血动物模型研究现状

非人灵长类动物在种系发生上较啮齿类更接近于人类,用来制备局部脑缺血模型可以更好的拟合临床症状和机理。通过对国内外非人灵长类动物局部脑缺血模型的制备方法和应用现状进行收集、分类和述评,展望非人灵长类动物模型的应用前景,尤其是利用低等灵长类动物树鼩研究缺血性中风的优势。     

Statistical quality control of variety trial data

I propose detection criteria for identifying an abnormal or erroneous data vector provided by a single variety trial in a longer series of variety trials. The test criteria are based on the linear effects estimated separately for each studied trial using global variance components estimated from the whole series of variety trials. The criteria comprise three mutually independent test statistics. The first one is a quadratic form in the estimated fixed effects, the second one is a quadratic form in the estimated realized linear random effects not including the residual effects, and the third one is a quadratic form in the estimated realized residual effects. Under the null hypothesis defining a valid data vector, the three quadratics have independent chi2 distributions. Under natural alternative hypotheses, they have noncentral chi2 distributions. Decomposing the total variation of the data vector studied into quadratic forms due to different types of the realized linear effects intuitively justifies the resulting test criteria. The decomposition may also be used to show that the resulting tests are likelihood ratio tests. I further present computational procedures that allow us to dispense with the need for prior estimation of the linear effects.     

Placing empirical limits on metapopulation models for terrestrial plants

Both island-biogeographic (dynamic) and niche-based (static) metapopulation models make predictions about the distribution and abundance of species assemblages. We tested the utility of these models concerning such predictions for terrestrial vascular plants using data from 74 landscapes across the globe. We examined correlations between species frequency and local abundance and shapes of the species frequency distribution. No data set met all of the predictions of any single island-biogeographic metapopulation model. In contrast, all data sets met the predictions of the niche-based model. We conclude that in predicting the distribution of species assemblages of plants over scales greater than 10–1 km, niche-based models are robust while current metapopulation models are insufficient. We discuss limitations in the assumptions of the various models and the types of empirical observations that they will each have to deal with in further developments.     

Spread of North American wind-dispersed trees in future environments

Despite ample research, understanding plant spread and predicting their ability to track projected climate changes remain a formidable challenge to be confronted. We modelled the spread of North American wind-dispersed trees in current and future (c. 2060) conditions, accounting for variation in 10 key dispersal, demographic and environmental factors affecting population spread. Predicted spread rates vary substantially among 12 study species, primarily due to inter-specific variation in maturation age, fecundity and seed terminal velocity. Future spread is predicted to be faster if atmospheric CO(2) enrichment would increase fecundity and advance maturation, irrespective of the projected changes in mean surface windspeed. Yet, for only a few species, predicted wind-driven spread will match future climate changes, conditioned on seed abscission occurring only in strong winds and environmental conditions favouring high survival of the farthest-dispersed seeds. Because such conditions are unlikely, North American wind-dispersed trees are expected to lag behind the projected climate range shift.     

Additive mixed effect model for clustered failure time data

We propose an additive mixed effect model to analyze clustered failure time data. The proposed model assumes an additive structure and includes a random effect as an additional component. Our model imitates the commonly used mixed effect models in repeated measurement analysis but under the context of hazards regression; our model can also be considered as a parallel development of the gamma-frailty model in additive model structures. We develop estimating equations for parameter estimation and propose a way of assessing the distribution of the latent random effect in the presence of large clusters. We establish the asymptotic properties of the proposed estimator. The small sample performance of our method is demonstrated via a large number of simulation studies. Finally, we apply the proposed model to analyze data from a diabetic study and a treatment trial for congestive heart failure.     

Thermal performance curves of Paramecium caudatum: a model selection approach

The ongoing climate change has motivated numerous studies investigating the temperature response of various organisms, especially that of ectotherms. To correctly describe the thermal performance of these organisms, functions are needed which sufficiently fit to the complete optimum curve. Surprisingly, model-comparisons for the temperature-dependence of population growth rates of an important ectothermic group, the protozoa, are still missing. In this study, temperature reaction norms of natural isolates of the freshwater protist Paramecium caudatum were investigated, considering nearly the entire temperature range. These reaction norms were used to estimate thermal performance curves by applying a set of commonly used model functions. An information theory approach was used to compare models and to identify the best ones for describing these data. Our results indicate that the models which can describe negative growth at the high- and low-temperature branch of an optimum curve are preferable. This is a prerequisite for accurately calculating the critical upper and lower thermal limits. While we detected a temperature optimum of around 29 °C for all investigated clonal strains, the critical thermal limits were considerably different between individual clones. Here, the tropical clone showed the narrowest thermal tolerance, with a shift of its critical thermal limits to higher temperatures.     

BIOVOLUME CALCULATION FOR PELAGIC AND BENTHIC MICROALGAE

Microalgal biovolume is commonly calculated to assess the relative abundance (as biomass or carbon) of co-occurring algae varying in shape and/or size. However, a standardized set of equations for biovolume calculations from microscopically measured linear dimensions that includes the entire range of microalgal shapes is not available yet. In comparison with automated methods, the use of microscopical measurements allows high taxonomic resolution, up to the species level, and has fewer sources of error. We present a set of geometric shapes and mathematical equations for calculating biovolumes of >850 pelagic and benthic marine and freshwater microalgal genera. The equations are designed to minimize the effort of microscopic measurement. The similarities and differences between our proposal for standardization and previously published proposals are discussed and recommendations for quality standards given.     

The Tyrosine Phosphatase STEP Is Involved in Age-Related Memory Decline

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Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.