Testing the correlation for clustered categorical and censored discrete time-to-event data when covariates are measured without/with errors

In the analysis of clustered categorical data, it is of common interest to test for the correlation within clusters, and the heterogeneity across different clusters. We address this problem by proposing a class of score tests for the null hypothesis that the variance components are zero in random effects models, for clustered nominal and ordinal categorical responses. We extend the results to accommodate clustered censored discrete time-to-event data. We next consider such tests in the situation where covariates are measured with errors. We propose using the SIMEX method to construct the score tests for the null hypothesis that the variance components are zero. Key advantages of the proposed score tests are that they can be easily implemented by fitting standard polytomous regression models and discrete failure time models, and that they are robust in the sense that no assumptions need to be made regarding the distributions of the random effects and the unobserved covariates. The asymptotic properties of the proposed tests are studied. We illustrate these tests by analyzing two data sets and evaluate their performance with simulations.     

Benchmark Dose Profiles for Joint‐Action Quantal Data in Quantitative Risk Assessment

Summary Benchmark analysis is a widely used tool in public health risk analysis. Therein, estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a prespecified Benchmark Response (BMR) is well understood for the case of an adverse response to a single stimulus. For cases where two agents are studied in tandem, however, the benchmark approach is far less developed. This article demonstrates how the benchmark modeling paradigm can be expanded from the single‐dose setting to joint‐action, two‐agent studies. Focus is on response outcomes expressed as proportions. Extending the single‐exposure setting, representations of risk are based on a joint‐action dose–response model involving both agents. Based on such a model, the concept of a benchmark profile (BMP) – a two‐dimensional analog of the single‐dose BMD at which both agents achieve the specified BMR – is defined for use in quantitative risk characterization and assessment. The resulting, joint, low‐dose guidelines can improve public health planning and risk regulation when dealing with low‐level exposures to combinations of hazardous agents.     

应用化学基因组信息预测小分子化合物的潜在生物靶标的理论方法

在后基因组时代,化学基因组技术在药物作用靶点的确认、小分子化合物对通路的作用,以及小分子先导化合物的识别等方面都有着广泛的应用,为新药研发提供了新的技术方法。本文主要介绍了当前几种基于化学基因组信息来预测小分子化合物潜在生物靶标的理论方法（包括化学相似性搜索方法、反向分子对接方法、数据挖掘方法以及生物活性谱图分析方法）,并分析了这些方法的优缺点以及应用前景。     

Estimating number of families for an urban fox population by using two public data sets

The number of families in the urban fox population of Sapporo, Japan, was estimated from two sets of data reported by the public to government: records of road-killed foxes (information-A) and records of complaints about foxes (information-B). We assumed that fox populations consist of families that have exclusive home ranges, i.e., territories, during the period between gestation and dispersal. The urban area was then divided into hexagons that correspond to the territories. The locations from the two sets of records during the territorial period were plotted on the map. The number of fox families for which information-A and/or B was reported was estimated by counting the number of hexagons that include the record. The total number of families was estimated by using a double-observation method. We adopted Chapman’s unbiased estimator which is based on the hypergeometric distribution that corresponds to the conditional likelihood. We demonstrated the possibility of estimating the abundance of animals from government data such as road kill and complaints if the animals have territories. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Current activities of the national LCA project in Japan

The Ministry of International Trade and Industry (MITI) has launched a national project, ‘Development of Assessment Technology of Life Cycle Environmental Impacts of Products’ (commonly known as the LCA Project). The activities of this project will be continued for 5 yeas since fiscal 1998 with an overall budget of total 850 million yen. The LCA Project aims to develop a highly reliable LCA database and LCA methodology which can be readily used throughout Japan. In this paper, the overall plans and current activities of project are indicated.     

Mutational analysis of the stability of the H2A and H2B histone monomers

The eukaryotic histone heterodimer H2A-H2B folds through an obligatory dimeric intermediate that forms in a nearly diffusion-limited association reaction in the stopped-flow dead time. It is unclear whether there is partial folding of the isolated monomers before association. To address the possible contributions of structure in the monomers to the rapid association, we characterized H2A and H2B monomers in the absence of their heterodimeric partner. By far-UV circular dichroism, the H2A and H2B monomers are 15% and 31% helical, respectively—significantly less than observed in X-ray crystal structures. Acrylamide quenching of the intrinsic Tyr fluorescence was indicative of tertiary structure. The H2A and H2B monomers exhibit free energies of unfolding of 2.5 and 2.9 kcal mol^− 1, respectively; at 10 μM, the sum of the stability of the monomers is ∼ 60% of the stability of the native dimer. The helical content, stability, and m values indicate that H2B has a more stable, compact structure than H2A. The monomer m values are larger than expected for the extended histone fold motif, suggesting that the monomers adopt an overly collapsed structure. Stopped-flow refolding—initiated from urea-denatured monomers or the partially folded monomers populated at low denaturant concentrations—yielded essentially identical rates, indicating that monomer folding is productive in the rapid association and folding of the heterodimer. A series of Ala and Gly mutations were introduced into H2A and H2B to probe the importance of helix propensity on the structure and stability of the monomers. The mutational studies show that the central α-helix of the histone fold, which makes extensive intermonomer contacts, is structured in H2B but only partially folded in H2A.     

Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision‐making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision‐making and planning.     

Shifts in the psychophysical function in rats

The primary goal was to compare results from a free-operant procedure with pigeons [Machado, A., Guilhardi, P., 2000. Shifts in the psychometric function and their implications for models of timing. J. Exp. Anal. Behav. 74, 25-54, Experiment 2] with new results obtained with rats. The secondary goal was to compare the results of both experiments with dependent variables that were not used in the original publication. As in the original study with pigeons, rats were trained on a two-alternative free-operant psychophysical procedure in which left lever press responses were reinforced during the first and second quarters of a 60-s trial, and right lever press responses were reinforced during the third and fourth quarters of the trial. The quarters were reinforced according to four independent variable interval (VI) schedules of reinforcement. The VI duration was manipulated in each quarter, and shifts in the psychophysical functions that relate response rate with time since trial onset were measured. The results obtained with rats were consistent with those previously obtained with pigeons. In addition, results not originally reported were also consistent between rats and pigeons, and provided insights into the perception, memory, and decision processes in Scalar Expectancy Theory and Learning-to-Time Theory.     

Impact of nonignorable coarsening on Bayesian inference

The coarse data model of Heitjan and Rubin (1991) generalizes the missing data model of Rubin (1976) to cover other forms of incompleteness such as censoring and grouping. The model has 2 components: an ideal data model describing the distribution of the quantity of interest and a coarsening mechanism that describes a distribution over degrees of coarsening given the ideal data. The coarsening mechanism is said to be nonignorable when the degree of coarsening depends on an incompletely observed ideal outcome, in which case failure to properly account for it can spoil inferences. A theme in recent research is to measure sensitivity to nonignorability by evaluating the effect of a small departure from ignorability on the maximum likelihood estimate (MLE) of a parameter of the ideal data model. One such construct is the "index of local sensitivity to nonignorability" (ISNI) (Troxel and others, 2004), which is the derivative of the MLE with respect to a nonignorability parameter evaluated at the ignorable model. In this paper, we adapt ISNI to Bayesian modeling by instead defining it as the derivative of the posterior expectation. We propose the application of ISNI as a first step in judging the robustness of a Bayesian analysis to nonignorable coarsening. We derive formulas for a range of models and apply the method to evaluate sensitivity to nonignorable coarsening in 2 real data examples, one involving missing CD4 counts in an HIV trial and the other involving potentially informatively censored relapse times in a leukemia trial.