Ulrike Steiner

Bio: Ulrike Steiner is an academic researcher from University of Bonn. The author has contributed to research in topics: Fusarium & Convolvulaceae. The author has an hindex of 29, co-authored 79 publications receiving 4470 citations.

Size exclusion limits and lateral heterogeneity of the stomatal foliar uptake pathway for aqueous solutes and water-suspended nanoparticles

Abstract: Penetration rates of foliar-applied polar solutes are highly variable and the underlying mechanisms are not yet fully understood. The contribution of stomata especially, is still a matter of debate. Thus, the size exclusion limits of the stomatal foliar uptake pathway, its variability and its transport capacity have been investigated. The size exclusion limits were analyzed by studying the penetration of water-suspended hydrophilic particles of two different sizes (43 nm or 1.1 microm diameter) into leaves of Vicia faba (L.). To avoid agglutination of the particles, plants were kept in water-saturated atmosphere. Penetration of the larger particles was never detected, whereas after 2 to 9 days, the smaller particles occasionally penetrated the leaf interior through stomatal pores. Permeability of stomata to Na(2)-fluorescein along the leaf blade of Allium porrum (L.) was highly variable and not correlated with the position on the leaf. When evaporated residues of the foliar-applied solutions were rewetted repeatedly, approximately 60% of the previously penetrated stomata were penetrated again. The average rate constant of penetration of an individual stoma was in the same order of magnitude as typical rate constants reported for the cuticular pathway. The observed sparseness of stomatal penetration together with its high lateral variability but local and temporal persistency was taken as evidence that stomata contributing to uptake differ from non-penetrated ones in the wettability of their guard cell cuticle. These results show that the stomatal pathway is highly capacitive because of its large size exclusion limit above 10 nm and its high transport velocity, but at the same time the high variability renders this pathway largely unpredictable.

Recent advances in sensing plant diseases for precision crop protection

Abstract: Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach—including plant pathology, engineering, and informatics—is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.

Plant-Symbiotic Fungi as Chemical Engineers: Multi-Genome Analysis of the Clavicipitaceae Reveals Dynamics of Alkaloid Loci

Abstract: The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloe and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some—including the infamous ergot alkaloids—have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses.

Significant remote sensing vegetation indices: A review of developments and applications

Abstract: Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV) Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

Abstract: The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Hidden Fungi, Emergent Properties: Endophytes and Microbiomes

Abstract: Endophytes are microorganisms that live within plant tissues without causing symptoms of disease. They are important components of plant microbiomes. Endophytes interact with, and overlap in function with, other core microbial groups that colonize plant tissues, e.g., mycorrhizal fungi, pathogens, epiphytes, and saprotrophs. Some fungal endophytes affect plant growth and plant responses to pathogens, herbivores, and environmental change; others produce useful or interesting secondary metabolites. Here, we focus on new techniques and approaches that can provide an integrative understanding of the role of fungal endophytes in the plant microbiome. Clavicipitaceous endophytes of grasses are not considered because they have unique properties distinct from other endophytes. Hidden from view and often overlooked, endophytes are emerging as their diversity, importance for plant growth and survival, and interactions with other organisms are revealed.