Robust Principal Component Analysis for Compositional Tables

A data table which is arranged according to two factors can often be considered as a compositional table. An example is the number of unemployed people, split according to gender and age classes. Analyzed as compositions, the relevant information would consist of ratios between different cells of such a table. This is particularly useful when analyzing several compositional tables jointly, where the absolute numbers are in very different ranges, e.g. if unemployment data are considered from different countries. Within the framework of the logratio methodology, compositional tables can be decomposed into independent and interactive parts, and orthonormal coordinates can be assigned to these parts. However, these coordinates usually require some prior knowledge about the data, and they are not easy to handle for exploring the relationships between the given factors. Here we propose a special choice of coordinates with a direct relation to centered logratio (clr) coefficients, which are particularly useful for an interpretation in terms of the original cells of the tables. With these coordinates, robust principal component analysis (PCA) is performed for dimension reduction, allowing to investigate the relationships between the factors. The link between orthonormal coordinates and clr coefficients enables to apply robust PCA, which would otherwise suffer from the singularity of clr coefficients.Comment: 20 pages, 2 figure

Differential coupling of gibberellin responses by Rht-B1c suppressor alleles and Rht-B1b in wheat highlights a unique role for the DELLA N-terminus in dormancy

During the Green Revolution, substantial increases in wheat (Triticum aestivum) yields were realized, at least in part, through the introduction of the Reduced height (Rht)-B1b and Rht-D1b semi-dwarfing alleles. In contrast to Rht-B1b and Rht-D1b, the Rht-B1c allele is characterized by extreme dwarfism and exceptionally strong dormancy. Recently, 35 intragenic Rht-B1c suppressor alleles were created in the spring wheat cultivar Maringa, and termed overgrowth (ovg) alleles. Here, 14 ovg alleles with agronomically relevant plant heights were reproducibly classified into nine tall and five semi-dwarf alleles. These alleles differentially affected grain dormancy, internode elongation rate, and coleoptile and leaf lengths. The stability of these ovg effects was demonstrated for three ovg alleles in different genetic backgrounds and environments. Importantly, two semi-dwarf ovg alleles increased dormancy, which correlated with improved pre-harvest sprouting (PHS) resistance. Since no negative effects on grain yield or quality were observed, these semi-dwarf ovg alleles are valuable for breeding to achieve adequate height reduction and protection of grain quality in regions prone to PHS. Furthermore, this research highlights a unique role for the first 70 amino acids of the DELLA protein, encoded by the Rht-1 genes, in grain dormancy

Bouw een intelligente robot in de klas met Dwengo

Elektronica is niet meer weg te denken uit onze samenleving: niet enkel computers en spelconsoles, maar ook alledaagse huishoudtoestellen of auto’s zitten vandaag vol met micro-controllers. We staan er niet bij stil dat in elk van die micro-controllers een programmaatje loopt dat er voor zorgt dat alles correct werkt in interactie met de omgeving. Informatica in actie, dus. Dwengo vzw brengt micro-controllers naar het onderwijs. Leerlingen uit het voortgezet onderwijs bouwen zelf een intelligente robot in de klas. Daarbij ervaren ze hoe software bijzonder praktische en fysieke impact kan hebben. Al doende passen de leerlingen alle concepten toe die ze in de inforamticales hebben geleerd. Uit onze ervaringen blijkt dat het werken met robots leerlingen motiveert om het beste van zichzelf te geven