Critical evaluation of assessor difference correction approaches in sensory analysis

In sensory data analysis, assessor-dependent scaling effects may hinder the analysis of product differences. Romano et al. (2008) compared several approaches to reduce scaling differences between assessors by their ability to maximise the product effect F-values in a mixed ANOVA analysis. Their study on a sensory dataset of 14 cheese samples assessed by twelve assessors on a continuous scale showed that some of these approaches apparently improved the F-value of the product effect. However, this direct comparison is only legitimate if these F-values originate from the same null distribution. To obtain the null distributions of the different correction methods, we employed a permutation approach on the same cheese dataset also used by Romano et al. (2008) and a random noise simulation approach. Based on the empirically obtained null distributions, we calculated the corrected product effect significance to directly compare the performance of the preprocessing methods. Our results show that the null distributions of some preprocessing methods do not correspond to the expected F-distribution. In particular for the ten Berge method, the null distribution is shifted towards higher F-values. Therefore, an observed increase of the product effect F-value, as compared to the F-value on raw data, does not necessarily lead to increased product effect significance. If p-values are calculated based on such inflated F-values, significance may thus be overestimated. In contrast, calculation of p-values directly from the empirical null distributions obtained by permutation provides a common ground to properly compare method performance. Moreover, we show that differences in reproducibility between assessors, as they exist in real-world sensory datasets, may lead to overestimation of product effect significance by the mixed assessor model (MAM).publishedVersio

Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength

Anti-ferromagnetic ordering in arrays of superconducting pi-rings

We report experiments in which one dimensional (1D) and two dimensional (2D) arrays of YBa2Cu3O7-x-Nb pi-rings are cooled through the superconducting transition temperature of the Nb in various magnetic fields. These pi-rings have degenerate ground states with either clockwise or counter-clockwise spontaneous circulating supercurrents. The final flux state of each ring in the arrays was determined using scanning SQUID microscopy. In the 1D arrays, fabricated as a single junction with facets alternating between alignment parallel to a [100] axis of the YBCO and rotated 90 degrees to that axis, half-fluxon Josephson vortices order strongly into an arrangement with alternating signs of their magnetic flux. We demonstrate that this ordering is driven by phase coupling and model the cooling process with a numerical solution of the Sine-Gordon equation. The 2D ring arrays couple to each other through the magnetic flux generated by the spontaneous supercurrents. Using pi-rings for the 2D flux coupling experiments eliminates one source of disorder seen in similar experiments using conventional superconducting rings, since pi-rings have doubly degenerate ground states in the absence of an applied field. Although anti-ferromagnetic ordering occurs, with larger negative bond orders than previously reported for arrays of conventional rings, long-range order is never observed, even in geometries without geometric frustration. This may be due to dynamical effects. Monte-Carlo simulations of the 2D array cooling process are presented and compared with experiment.Comment: 10 pages, 15 figure

Divide et impera: How disentangling common and distinctive variability in multiset data analysis can aid industrial process troubleshooting and understanding

[EN] The possibility of addressing the problem of process troubleshooting and understanding by modelling common and distinctive sources of variation (factorsorcomponents) underlying two sets of measurements was explored in a real-world industrial case study. The used strategy includes a novel approach to systematically detect the number of common and distinctive components. An extension of this strategy for the analysis of a larger number of data blocks, which allows the comparison of data from multiple processing units, is also discussed.Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2017-82896-C2-1-RVitale, R.; Noord, OED.; Westerhuis, JA.; Smilde, AK.; Ferrer, A. (2021). Divide et impera: How disentangling common and distinctive variability in multiset data analysis can aid industrial process troubleshooting and understanding. Journal of Chemometrics. 35(2):1-12. https://doi.org/10.1002/cem.3266S11235

Heterofusion:Fusing genomics data of different measurement scales

In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant in the life sciences. With the availability of a multitude of measuring techniques, one of the central problems is the heterogeneity of the data. In this paper, we discuss a specific form of heterogeneity, namely, that of measurements obtained at different measurement scales, such as binary, ordinal, interval, and ratio‐scaled variables. Three generic fusion approaches are presented of which two are new to the systems biology community. The methods are presented, put in context, and illustrated with a real‐life genomics example

Superconducting gap of overdoped Tl2Ba2CuO6+d observed by Raman scattering

We report Raman scattering spectra for single crystals of overdoped Tl2Ba2CuO6+d (Tl-2201) at low temperatures. It was observed that the pair-breaking peaks in A1g and B1g spectra radically shift to lower energy with carrier doping. We interpret it as s-wave component mixing into d-wave, although the crystal structure is tetragonal. Since similar phenomena were observed also in YBa2Cu3Oy and Bi2Sr2CaCu2Oz, we conclude that s-wave mixing is a common property for overdoped high-Tc superconductors.Comment: 8 pages, 3 figures, proceedings of SNS200