Abstract. We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edgebased pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages when the object sizes are known a priori, as demonstrated in an ellipse detection application. The method outperforms the best-performing algorithm on the CVPR 2013 Symmetry Detection Competition Database in the single-symmetry case. Code and a new database for 2D symmetry detection is available.

For practical purposes the casein content of milk is often estimated from the crude protein content using a general conversion factor. This is done even though it is known that several factors can influence the percentage of casein nitrogen in the total nitrogen (casein number). The uncontrolled variation in casein number affects optimization of cheese production because casein is a limiting factor for cheese yield. Fourier transform infrared spectrometry has recently been introduced for casein determination. Using this technique we obtained a standard error of prediction (SEP) of 0·033% for casein concentrations in the range 2·1–4·0%. The true prediction error was estimated as 0·025%. The correlation between casein numbers obtained by reference analysis and infrared spectrometry could be expressed by an R2 of 0·73 and an SEP of 0·89 for casein numbers in the range 70·7–81·0.

Microbes are associated with nearly all surfaces of plants and are even present within multiple cell types (Compant et al., 2019). The rhizosphere is arguably the most important sites for plant-microbe interactions, with the fungi and bacteria within the rhizosphere directly linked to host vitality. Consequently, understanding the regulation of the rhizosphere microbiome is of considerable importance,

No abstract

The plant-associated microbiome has been shown to vary considerably between species and across environmental gradients. The effects of genomic variation on the microbiome within single species are less clearly understood, with results often confounded by the larger effects of climatic and edaphic variation. In this study, the effect of genomic variation on the rhizosphere bacterial communities of maize was investigated by comparing different genotypes grown within controlled environments. Rhizosphere bacterial communities were profiled by metabarcoding the universal bacterial 16S rRNA v3-v4 region. Initially, plants from the inbred B73 line and the Ancho - More 10 landrace were grown for 12-weeks and compared. The experiment was then repeated with an additional four Mexican landraces (Apachito - Chih 172, Tehua - Chis 204, Serrano - Pueb 180 and Hairnoso de Ocho) that were grown alongside additional B73 and Ancho - More 10 genotypes. In both experiments there were significant genotypic differences in the rhizosphere bacteria. Additionally, the bacterial communities were significantly correlated with genomic distance between genotypes, with the more closely related landraces being more similar in rhizosphere bacterial communities. Despite limited sampling numbers, here we confirm that genomic variation in maize landraces is associated with differences in the rhizosphere bacterial communities. Further studies that go beyond correlations to identify the mechanisms that determine the genotypic variation of the rhizosphere microbiome are required.