Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Mass spectrometry-based analytical tools for the molecular protein characterization of human plasma lipoproteins

Lipoproteins are a heterogeneous population of blood plasma particles composed of apolipoproteins and lipids. Lipoproteins transport exogenous and endogenous triglycerides and cholesterol from sites of absorption and formation to sites of storage and usage. Three major classes of lipoproteins are distinguished according to their density: high-density (HDL), low-density (LDL) and very low-density lipoproteins (VLDL). While HDLs contain mainly apolipoproteins of lower molecular weight, the two other classes contain apolipoprotein B and apolipoprotein (a) together with triglycerides and cholesterol. HDL concentrations were found to be inversely related to coronary heart disease and LDL/VLDL concentrations directly related. Although many studies have been published in this area, few have concentrated on the exact protein composition of lipoprotein particles. Lipoproteins were separated by density gradient ultracentrifugation into different subclasses. Native gel electrophoresis revealed different gel migration behaviour of the particles, with less dense particles having higher apparent hydrodynamic radii than denser particles. Apolipoprotein composition profiles were measured by matrix-assisted laser desorption/ionization-mass spectrometry on a macromizer instrument, equipped with the recently introduced cryodetector technology, and revealed differences in apolipoprotein composition between HDL subclasses. By combining these profiles with protein identifications from native and denaturing polyacrylamide gels by liquid chromatography-tandem mass spectrometry, we characterized comprehensively the exact protein composition of different lipoprotein particles. We concluded that the differential display of protein weight information acquired by macromizer mass spectrometry is an excellent tool for revealing structural variations of different lipoprotein particles, and hence the foundation is laid for the screening of cardiovascular disease risk factors associated with lipoproteins.

Matrix models for quantifying competitive intransitivity from species abundance data

In a network of competing species, a competitive intransitivity occurs when the ranking of competitive abilities does not follow a linear hierarchy (A > B > C but C > A). A variety of mathematical models suggests that intransitive networks can prevent or slow down competitive exclusion and maintain biodiversity by enhancing species coexistence. However, it has been difficult to assess empirically the relative importance of intransitive competition because a large number of pairwise species competition experiments are needed to construct a competition matrix that is used to parameterize existing models. Here we introduce a statistical framework for evaluating the contribution of intransitivity to community structure using species abundance matrices that are commonly generated from replicated sampling of species assemblages. We provide metrics and analytical methods for using abundance matrices to estimate species competition and patch transition matrices by using reverse-engineering and a colonization-competition model. These matrices provide complementary metrics to estimate the degree of intransitivity in the competition network of the sampled communities. Benchmark tests reveal that the proposed methods could successfully detect intransitive competition networks, even in the absence of direct measures of pairwise competitive strength. To illustrate the approach, we analyzed patterns of abundance and biomass of five species of necrophagous Diptera and eight species of their hymenopteran parasitoids that co-occur in beech forests in Germany. We found evidence for a strong competitive hierarchy within communities of flies and parasitoids. However, for parasitoids, there was a tendency towards increasing intransitivity in higher weight classes, which represented larger resource patches. These tests provide novel methods for empirically estimating the degree of intransitivity in competitive networks from observational datasets. They can be applied to experimental measures of pairwise species interactions, as well as to spatio-temporal samples of assemblages in homogenous environments or environmental gradients.