Andreas Backhaus

LEAFPROCESSOR: a new leaf phenotyping tool using contour bending energy and shape cluster analysis

*Significant progress has been made in the identification of the genetic factors controlling leaf shape. However, no integrated solution for the quantification and categorization of leaf form has been developed. In particular, the analysis of local changes in margin growth, which define many of the differences in shape, remains problematical. *Here, we report on a software package (LEAFPROCESSOR) which provides a semi-automatic and landmark-free method for the analysis of a range of leaf-shape parameters, combining both single metrics and principal component analysis. In particular, we explore the use of bending energy as a tool for the analysis of global and local leaf perimeter deformation. *As a test case for the implementation of the LEAFPROCESSOR program, we show that this integrated analysis leads to deeper insights into the morphogenic changes underpinning a series of previously identified Arabidopsis leaf-shape mutants. Our analysis reveals that many of these mutants which, at first sight, show similar leaf morphology, can be distinguished via our shape analysis. *The LEAFPROCESSOR program provides a novel integrated tool for the analysis of leaf shape.

Phased Control of Expansin Activity during Leaf Development Identifies a Sensitivity Window for Expansin-Mediated Induction of Leaf Growth

Expansins are cell wall proteins associated with the process of plant growth. However, investigations in which expansin gene expression has been manipulated throughout the plant have often led to inconclusive results. In this article, we report on a series of experiments in which overexpression of expansin was targeted to specific phases of leaf growth using an inducible promoter system. The data indicate that there is a restricted window of sensitivity when increased expansin gene expression leads to increased endogenous expansin activity and an increase in leaf growth. This phase of maximum expansin efficacy corresponds to the mid phase of leaf growth. We propose that the effectiveness of expansin action depends on the presence of other modulating factors in the leaf and we suggest that it is the control of expression of these factors (in conjunction with expansin gene expression) that defines the extent of leaf growth. These data help to explain some of the previously observed variation in growth response following manipulation of expansin gene expression and highlight a potential linkage of the expression of modifiers of expansin activity with the process of exit from cell division.

A shift towards smaller cell size via manipulation of cell cycle gene expression acts to smoothen Arabidopsis leaf shape

Understanding the relationship of the size and shape of an organism to the size, shape, and number of its constituent cells is a basic problem in biology; however, numerous studies indicate that the relationship is complex and often nonintuitive. To investigate this problem, we used a system for the inducible expression of genes involved in the G1/S transition of the plant cell cycle and analyzed the outcome on leaf shape. By combining a careful developmental staging with a quantitative analysis of the temporal and spatial response of cell division pattern and leaf shape to these manipulations, we found that changes in cell division frequency occurred much later than the observed changes in leaf shape. These data indicate that altered cell division frequency cannot be causally involved in the observed change of shape. Rather, a shift to a smaller cell size as a result of the genetic manipulations performed correlated with the formation of a smoother leaf perimeter, i.e. appeared to be the primary cellular driver influencing form. These data are discussed in the context of the relationship of cell division, growth, and leaf size and shape.

Non-invasive Presymptomatic Detection of Cercospora beticola Infection and Identification of Early Metabolic Responses in Sugar Beet

Cercospora beticola is an economically significant fungal pathogen of sugar beet, and is the causative pathogen of Cercospora leaf spot. Selected host genotypes with contrasting degree of susceptibility to the disease have been exploited to characterize the patterns of metabolite responses to fungal infection, and to devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen. Sugar beet genotypes were analyzed for metabolite profiles and hyperspectral signatures. Correlation of data matrices from both approaches facilitated identification of candidates for metabolic markers. Hyperspectral imaging was highly predictive with a classification accuracy of 98.5–99.9% in detecting C. beticola. Metabolite analysis revealed metabolites altered by the host as part of a successful defense response: these were L-DOPA, 12-hydroxyjasmonic acid 12-O-β-D-glucoside, pantothenic acid, and 5-O-feruloylquinic acid. The accumulation of glucosylvitexin in the resistant cultivar suggests it acts as a constitutively produced protectant. The study establishes a proof-of-concept for an unbiased, presymptomatic and non-invasive detection system for the presence of C. beticola. The test needs to be validated with a larger set of genotypes, to be scalable to the level of a crop improvement program, aiming to speed up the selection for resistant cultivars of sugar beet. Untargeted metabolic profiling is a valuable tool to identify metabolites which correlate with hyperspectral data.

Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks

Hyperspectral imaging of crop plants offers the means for a non-invasive, precise and high-throughput plant-phenotyping in plant research and precision agriculture. We already reported the successful separation of spectral signatures by means of unsupervised learning (e.g. clustering) of tobacco leaves grown from different genetic background and under different nutritional conditions [1,2]. In this contribution we evaluate supervised methods to predict the plants nutrition state by classification and whether they are robust towards dominant sources of data variation like leaf age or intra-leaf pixel position which are irrelevant for the task at hand. Support Vector Machine (SVM)[3], Supervised Relevance Neural Gas (SRNG) [4], Generalized Relevance Learning Vector Quantization (GRLVQ) [5] and a Radial Basis Function (RBF) Network [6] adopted to perform relevance learning as well were tested. Leaf age snowed the largest impact on classification performance, where SVM and RBF produced robust results while SRNG and GRLVQ methods were reduced to near guessing level. Three cameras covering the VIS/SWIR range were tested and relevance of spectral bands towards nutrition prediction were calculated.

Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based RBF networks

Ensembles of RBF networks trained with

Quantitative Measurements of model interpretability for the analysis of spectral data

\gamma

A multivariate wavelet-PCA denoising-filter for hyperspectral images

γ-divergence-based similarity measures can improve classification accuracy of hyperspectral imaging data significantly compared to any single RBF network as well as to RBF ensembles based on the Euclidian distance. So far, the drawback of using classifier ensembles is the need to compute the results of a typically large number of RBF networks prior to combination. In this paper, a modified approach to the fusion of classifier outputs is proposed which is based on decision trees. It is shown for several real-world datasets that a small subset of the RBF networks contributes to the decisions in the average case. Hence, for any decision, a conditional computation of required RBF network outputs yields a significant decrease in the computational costs. Additionally, a selection scheme for subsets of RBF classifiers based on their relevance in the fusion process is proposed. This alternative approach can be used, if analysis requires fixed settings, e.g., to meet time constraints.