Phylogenetic structure of communities between temperate and tropical regions: Exploring patterns through literature datasets

Latitudinal patterns of diversity are one of the most striking large-scale biological phenomena and several hypotheses have been proposed to explain them. Using data from literature-surveys we investigated how phylogenetic patterns in microorganisms, plants, and, metazoans communities differ between the tropical and temperate regions and then explored possible ecological and evolutionary process that could shape such patterns. Using the Net Relatedness Index, we analyzed data from 1486 biological communities, collected in 32 articles that considered the phylogenetic structure of biological communities. We found a pattern of phylogenetic clustering in both regions for microorganisms, while for plants we found phylogenetic clustering in temperate regions and phylogenetic overdispersion in the tropics. We did not detect a clear pattern of clustering or overdispersion in tropical or temperate regions in metazoans. From these patterns we explore different ecological and evolutionary processes that have shaped these communities over space and time.     

A model with space-varying regression coefficients for clustering multivariate spatial count data

Multivariate spatial count data are often segmented by unobserved space-varying factors that vary across space. In this setting, regression models that assume space-constant covariate effects could be too restrictive. Motivated by the analysis of cause-specific mortality data, we propose to estimate space-varying effects by exploiting a multivariate hidden Markov field. It models the data by a battery of Poisson regressions with spatially correlated regression coefficients, which are driven by an unobserved spatial multinomial process. It parsimoniously describes multivariate count data by means of a finite number of latent classes. Parameter estimation is carried out by composite likelihood methods, that we specifically develop for the proposed model. In a case study of cause-specific mortality data in Italy, the model was capable to capture the spatial variation of gender differences and age effects.     

Propensity score methods for time-dependent cluster confounding

In observational studies, subjects are often nested within clusters. In medical studies, patients are often treated by doctors and therefore patients are regarded as nested or clustered within doctors. A concern that arises with clustered data is that cluster-level characteristics (e.g., characteristics of the doctor) are associated with both treatment selection and patient outcomes, resulting in cluster-level confounding. Measuring and modeling cluster attributes can be difficult and statistical methods exist to control for all unmeasured cluster characteristics. An assumption of these methods however is that characteristics of the cluster and the effects of those characteristics on the outcome (as well as probability of treatment assignment when using covariate balancing methods) are constant over time. In this paper, we consider methods that relax this assumption and allow for estimation of treatment effects in the presence of unmeasured time-dependent cluster confounding. The methods are based on matching with the propensity score and incorporate unmeasured time-specific cluster effects by performing matching within clusters or using fixed- or random-cluster effects in the propensity score model. The methods are illustrated using data to compare the effectiveness of two total hip devices with respect to survival of the device and a simulation study is performed that compares the proposed methods. One method that was found to perform well is matching within surgeon clusters partitioned by time. Considerations in implementing the proposed methods are discussed.     

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants

The evolution of “informatics” technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high‐dimensionality of omics‐type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.     

The dopamine neuron synaptic map in the striatum

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Hierarchical Classification II: Complete-Linkage Method

The second part of our survey of hierarchical joining methods deals with the Maximum Method. Both the Maximum Method and the Minimum Method lie at the extremes of PEAY'S r-clique methods.     

Discontinuous Genetic Variation Among Mesophilic Naegleria Isolates: Further Evidence that N. gruberi Is Not a Single Species

Naegleria isolates which are currently placed in the type species N. gruberi display great genetic, physiological and morphological heterogeneity. There are two possible interpretations of the nature of this species–that N. gruberi is a species complex or that it is a single continuously variable species. To distinguish between these alternatives, allelic states were determined for 33 loci in 74 new isolates selected to represent wide geographic sources and diverse temperature limits for growth. The results were compared with data for culture collection strains of N. gruberi and other species in the genus. The isolates formed a discontinuous series of clusters, separated by genetic distances similar to those separating the better-characterised taxa N. fowleri, N. lovaniensis, N. jadini, N. australiensis australiensis and N. australiensis italica . Culture collection strains assigned to N. gruberi fell into six distinct clusters, while other clusters were not represented by reference strains. The data are most consistent with the interpretation that N. gruberi is a group of several distinct species, each equivalent to the recently described species in the genus. Naegleria andersoni andersoni and N. andersoni jamiesoni also formed two distinct clusters, equivalent to species. Characteristic temperature limits for growth show that the mesophilic species are ecological as well as genetic entities.     

五个杨树品种对锦纹吉丁虫的抗性

调查5个杨树品种对杨锦纹吉丁虫的抗性。用模糊类聚分析法可把它们分为4类。白城杨2号抗性最强，小黑杨抗性较强，小叶杨和加青杨抗性较差，中东杨抗性最差。