OLIgo mass profiling (OLIMP) of extracellular polysaccharides.

The direct contact of cells to the environment is mediated in many organisms by an extracellular matrix. One common aspect of extracellular matrices is that they contain complex sugar moieties in form of glycoproteins, proteoglycans, and/or polysaccharides. Examples include the extracellular matrix of humans and animal cells consisting mainly of fibrillar proteins and proteoglycans or the polysaccharide based cell walls of plants and fungi, and the proteoglycan/glycolipid based cell walls of bacteria. All these glycostructures play vital roles in cell-to-cell and cell-to-environment communication and signalling. An extraordinary complex example of an extracellular matrix is present in the walls of higher plant cells. Their wall is made almost entirely of sugars, up to 75% dry weight, and consists of the most abundant biopolymers present on this planet. Therefore, research is conducted how to utilize these materials best as a carbon-neutral renewable resource to replace petrochemicals derived from fossil fuel. The main challenge for fuel conversion remains the recalcitrance of walls to enzymatic or chemical degradation due to the unique glycostructures present in this unique biocomposite. Here, we present a method for the rapid and sensitive analysis of plant cell wall glycostructures. This method OLIgo Mass Profiling (OLIMP) is based the enzymatic release of oligosaccharides from wall materials facilitating specific glycosylhydrolases and subsequent analysis of the solubilized oligosaccharide mixtures using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS)(1) (Figure 1). OLIMP requires walls of only 5000 cells for a complete analysis, can be performed on the tissue itself(2), and is amenable to high-throughput analyses(3). While the absolute amount of the solubilized oligosaccharides cannot be determined by OLIMP the relative abundance of the various oligosaccharide ions can be delineated from the mass spectra giving insights about the substitution-pattern of the native polysaccharide present in the wall. OLIMP can be used to analyze a wide variety of wall polymers, limited only by the availability of specific enzymes(4). For example, for the analysis of polymers present in the plant cell wall enzymes are available to analyse the hemicelluloses xyloglucan using a xyloglucanase(5, 11, 12, 13), xylan using an endo-beta-(1-4)-xylanase (6,7), or for pectic polysaccharides using a combination of a polygalacturonase and a methylesterase (8). Furthermore, using the same principles of OLIMP glycosylhydrolase and even glycosyltransferase activities can be monitored and determined (9)

How to Bootstrap Aalen-Johansen Processes for Competing Risks? Handicaps, Solutions and Limitations

Statistical inference in competing risks models is often based on the famous Aalen-Johansen estimator. Since the corresponding limit process lacks independent increments, it is typically applied together with Lin's (1997) resampling technique involving standard normal multipliers. Recently, it has been seen that this approach can be interpreted as a wild bootstrap technique and that other multipliers, as e.g. centered Poissons, may lead to better finite sample performances, see Beyersmann et al. (2013). Since the latter is closely related to Efron's classical bootstrap, the question arises whether this or more general weighted bootstrap versions of Aalen-Johansen processes lead to valid results. Here we analyze their asymptotic behaviour and it turns out that such weighted bootstrap versions in general possess the wrong covariance structure in the limit. However, we explain that the weighted bootstrap can nevertheless be applied for specific null hypotheses of interest and also discuss its limitations for statistical inference. To this end, we introduce different consistent weighted bootstrap tests for the null hypothesis of stochastically ordered cumulative incidence functions and compare their finite sample performance in a simulation study.Comment: Keywords: Aalen-Johansen Estimator; Bootstrap; Competing risk; Counting processes; Cumulative incidence function; Left-truncation; Right-censoring; Weighted Bootstra

MATS: Inference for potentially Singular and Heteroscedastic MANOVA

In many experiments in the life sciences, several endpoints are recorded per subject. The analysis of such multivariate data is usually based on MANOVA models assuming multivariate normality and covariance homogeneity. These assumptions, however, are often not met in practice. Furthermore, test statistics should be invariant under scale transformations of the data, since the endpoints may be measured on different scales. In the context of high-dimensional data, Srivastava and Kubokawa (2013) proposed such a test statistic for a specific one-way model, which, however, relies on the assumption of a common non-singular covariance matrix. We modify and extend this test statistic to factorial MANOVA designs, incorporating general heteroscedastic models. In particular, our only distributional assumption is the existence of the group-wise covariance matrices, which may even be singular. We base inference on quantiles of resampling distributions, and derive confidence regions and ellipsoids based on these quantiles. In a simulation study, we extensively analyze the behavior of these procedures. Finally, the methods are applied to a data set containing information on the 2016 presidential elections in the USA with unequal and singular empirical covariance matrices