Mark S. Kaiser

Moesin1 and Ve-cadherin are required in endothelial cells during in vivo tubulogenesis

Endothelial tubulogenesis is a crucial step in the formation of functional blood vessels during angiogenesis and vasculogenesis. Here, we use in vivo imaging of living zebrafish embryos expressing fluorescent fusion proteins of β-Actin, α-Catenin, and the ERM family member Moesin1 (Moesin a), to define a novel cord hollowing process that occurs during the initial stages of tubulogenesis in intersegmental vessels (ISVs) in the embryo. We show that the primary lumen elongates along cell junctions between at least two endothelial cells during embryonic angiogenesis. Moesin1-EGFP is enriched around structures that resemble intracellular vacuoles, which fuse with the luminal membrane during expansion of the primary lumen. Analysis of silent heart mutant embryos shows that initial lumen formation in the ISVs is not dependent on blood flow; however, stabilization of a newly formed lumen is dependent upon blood flow. Zebrafish moesin1 knockdown and cell transplantation experiments demonstrate that Moesin1 is required in the endothelial cells of the ISVs for in vivo lumen formation. Our analyses suggest that Moesin1 contributes to the maintenance of apical/basal cell polarity of the ISVs as defined by adherens junctions. Knockdown of the adherens junction protein Ve-cadherin disrupts formation of the apical membrane and lumen in a cell-autonomous manner. We suggest that Ve-cadherin and Moesin1 function to establish and maintain apical/basal polarity during multicellular lumen formation in the ISVs.

Modeling Poisson variables with positive spatial dependence

The Poisson auto-model is a natural vehicle for modeling data that consist of small counts and may exhibit dependence, frequently spatial dependence. Unfortunately, it is not possible to model positive dependence with a regular Poisson auto-model. We develop a model that allows positive dependencies in multivariate count data by specifying conditional distributions as Winsorized Poisson probability mass functions. This model may be used to incorporate either positive or negative dependencies among the variables.

Prediction of Spatial Cumulative Distribution Functions Using Subsampling

Abstract The spatial cumulative distribution function (SCDF) is a random function that provides a statistical summary of a random field over a spatial domain of interest. In this article we develop a spatial subsampling method for predicting an SCDF based on observations made on a hexagonal grid, similar to the one used in the Environmental Monitoring and Assessment Program of the U.S. Environmental Protection Agency. We show that under quite general conditions, the proposed subsampling method provides accurate data-based approximations to the sampling distributions of various functionals of the SCDF predictor. In particular, it produces estimators of different population characteristics, such as the quantiles and weighted mean integrated squared errors of the empirical predictor. As an illustration, we apply the subsampling method to construct large-sample prediction bands for the SCDF of an ecological index for foliage condition of red maple trees in the state of Maine.

Statistical Models for Limiting Nutrient Relations in Inland Waters

Abstract The ecological theory of limiting factors holds that the observed level of response in a biological process will be governed by the input factor in least supply—the limiting factor. This theory has formed the basis for numerous attempts by aquatic ecologists to describe the relation between the biological productivity of inland waters and the availability of plant nutrients required for algal growth. Regression analysis has been the primary statistical tool used in the development of such relations, yet any statistical model that represents the limiting effect of some explanatory factor as an expectation contradicts the substantive theory of limiting factors. Limnological data not resulting in an adequate regression of chlorophyll on phosphorus have been viewed as failing to support the limiting effect of this nutrient on algal biomass in lakes. But when represented by a more appropriate model, such data may be seen to provide similar evidence for the relation of chlorophyll to phosphorus as does...

Effects of Aggregation on Chlorophyll-Phosphorus Relations in Missouri Reservoirs

ABSTRACT Using chlorophyll and phosphorus data from 119 Missouri reservoirs we show how data aggregation-averaging data into seasonal means or long-term lake means – influences our ability to make inferences from large-scale statistical regression analyses. We demonstrate the most obvious phenomenon of data aggregation, that relations between variables estimated from aggregated data are generally stronger than the same relations estimated from unaggregated data. Averaging reduces the often large variation in the response of chlorophyll to phosphorus (Chl-TP) that characterizes measurements of these variables in lakes. We also demonstrate that inferences made from statistical regression analyses apply only to situations that match the level of aggregation used to produce the model. Using lake means we found a strong positive Chl-TP relation. This strong cross-sectional pattern among lakes in the study, however, did not always reflect the relation of these variables to one another in individual lakes. And t...

Spatial Analysis of Particulate Matter in an Urban Environment

Environmental regulations are usually set based on the effect of a pollutant on human health. In recent years, the U.S. Environmental Protection Agency has become aware of the potential harmful effects of particulate matter (PM) in ambient air; various epidemiological studies published in the mid 1990s show evidence of an association between daily PM measurements and nonaccidental deaths amongst the elderly. A common feature of these studies has been that they examine ambient air quality in a large city and its environs but for aggregated measurements, taken from the monitoring stations, over space. In this paper, we preserve the spatial component of the particulate-matter variable and carry out spatial statistical analyses that allow us to make maps of PM exposure with known confidence.

Transgenic Alfalfa That Accumulates Piceid (Trans-Resveratrol-3-O−β-D-glucopyranoside) Requires the Presence of β -Glucosidase to Inhibit the Formation of Aberrant Crypt Foci in the Colon of CF-1 Mice

Abstract: Plants have been genetically enhanced to produce a number of products for agricultural, industrial and pharmaceutical purposes. This technology could potentially be applied to providing chemoprevention strategies to the general population. Resveratrol (3,5,4′-trihydroxystilbene) is a compound that has been shown to have protective activity against a number of cancers and could be an ideal candidate for such an application. Alfalfa that was genetically modified to express resveratrol-synthase was used as a model in applying biotechnological approaches to cancer prevention. The transgenic alfalfa, which accumulates resveratrol as a glucoside (piceid = trans-resveratrol-3-O−β-D-glucopyranoside) (152 ± 17.5 μ g piceid/g dry weight), was incorporated into a standard mouse diet at 20% of the diet by weight and fed for 5 wk to 6-wk-old, female CF-1 mice (N = 17–30) that were injected with a single dose of azoxymethane (5 mg/kg body weight). While the addition of resveratrol-aglycone (20 mg/kg diet) to the basal diet reduced the number of aberrant crypt foci/mouse, the transgenic alfalfa did not inhibit the number, size, or multiplicity of aberrant crypt foci in the colon of the CF-1 mice relative to control alfalfa which does not accumulate resveratrol-glucoside. However, diets containing transgenic alfalfa with an exogenous β -glucosidase (860 U/kg diet) did significantly inhibit the number of aberrant crypt foci in the distal 2 cm of the colon of the mice relative to mice fed diets containing the transgenic alfalfa without the enzyme (P < 0.05; Fishers Combination of p-values). The β -glucosidase alone appeared to have no effect on the inhibition of aberrant crypt foci. These results suggest that piceid in transgenic piceid-accumulating alfalfa was not bioavailable.

Exploring dependence with data on spatial lattices.

The application of Markov random field models to problems involving spatial data on lattice systems requires decisions regarding a number of important aspects of model structure. Existing exploratory techniques appropriate for spatial data do not provide direct guidance to an investigator about these decisions. We introduce an exploratory quantity that is directly tied to the structure of Markov random field models based on one-parameter exponential family conditional distributions. This exploratory diagnostic is shown to be a meaningful statistic that can inform decisions involved in modeling spatial structure with statistical dependence terms. In this article, we develop the diagnostic, illustrate its use in guiding modeling decisions with simulated examples, and reexamine a previously published application.

Goodness of fit tests for a class of Markov random field models

This paper develops goodness of fit statistics that can be used to formally assess Markov random field models for spatial data, when the model distributions are discrete or continuous and potentially parametric. Test statistics are formed from generalized spatial residuals which are collected over groups of nonneighboring spatial observations, called concliques. Under a hypothesized Markov model structure, spatial residuals within each conclique are shown to be independent and identically distributed as uniform variables. The information from a series of concliques can be then pooled into goodness of fit statistics. Under some conditions, large sample distributions of these statistics are explicitly derived for testing both simple and composite hypotheses, where the latter involves additional parametric estimation steps. The distributional results are verified through simulation, and a data example illustrates the method for model assessment.

Inference for spatial processes using subsampling : A simulation study

Many environmental studies involve the measurement of ecological indices that yield spatially dependent data. One quantity that captures the empirical distribution of ecological measurements is the spatial cumulative distribution function (SCDF). Methods for making inferential statements about SCDFs have only recently been developed, one being that of spatial subsampling. While spatial subsampling produces inferential quantities with known asymptotic properties, the performance of this methodology in a finite-sample setting has not previously been investigated. In this article, we review the subsampling method and its theoretical justification, and investigate the performance of this method for finite samples with a simulation study involving several subsampling designs and types of spatial dependence. The subsampling methodology appears to give quite good results over a range of realistic spatial processes. For application to a set of spatially dependent data, an appropriate subsampling procedure may be designed on the basis of quantities contained in the (estimated) variogram.