Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks

Physiological response of two different Chlamydomonas reinhardtii strains to light-dark rhythms

Cells of a cell wall deficient line (cw15-type) of Chlamydomonas reinhardtii and of the corresponding wild type were grown during repetitive light-dark cycles. In a direct comparison, both lines showed approximately the same relative biomass increase during light phase but the cw-line produced significantly more and smaller daughter cells. Throughout the light period the average cellular starch content, the cellular chlorophyll content, the cellular rate of dark respiration and cellular rate of photosynthesis of the cw-line was lower. Despite this, several non-cell volume related parameters like the development of starch content per cell volume were clearly different over time between the strains. Additionally, the chlorophyll-based photosynthesis rates were two fold higher in the mutant than in the wild type cells, the chlorophyll a to b ratio and the light-saturation index were also consistently higher in the mutant cells. Differences in the starch content were also confirmed by single cell analyses using a sensitive SHG-based microscopy approach. In summary, the cw15-type mutant deviates from its genetic background in the entire cell physiology. Both lines should be used in further studies in comparative systems biology with focus on the detailed relation between cell volume increase, photosynthesis, starch metabolism, and daughter cell productivity.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Simple and fast geometrical descriptors for writer identification

Recent advances in writer identification push the limits by using increasingly complex methods relying on sophisticated preprocessing, or the combination of already complex descriptors. In this paper, we pursue a simpler and faster approach to writer identification, introducing novel descriptors computed from the geometrical arrangement of interest points at different scales. They capture orientation distributions and geometrical relationships of script parts such as strokes, junctions, endings, and loops. Thus, we avoid a fixed set of character appearances as in standard codebook-based methods. The proposed descriptors significantly cut down processing time compared to existing methods, are simple and efficient, and can be applied out-of-the-box to an unseen dataset. Evaluations on widely-used datasets show their potential when applied by themselves, and in combination with other descriptors. Limitations of our method relate to the amount of data needed to obtain reliable models

Creating ground truth for historical manuscripts with document graphs and scribbling interaction

Ground truth is both – indispensable for training and evaluating document analysis methods, and yet very tedious to create manually. This especially holds true for complex historical manuscripts that exhibit challenging layouts with interfering and overlapping handwriting. In this paper, we propose a novel semi-automatic system to support layout annotations in such a scenario based on document graphs and a pen-based scribbling interaction. On the one hand, document graphs provide a sparse page representation that is already close to the desired ground truth and on the other hand, scribbling facilitates an efficient and convenient pen-based interaction with the graph. The performance of the system is demonstrated in the context of a newly introduced database of historical manuscripts with complex layouts

Polycomb protein RING1A limits hematopoietic differentiation in myelodysplastic syndromes

Altres ajuts: This project was supported by grants from Deutsche José Carreras Leukaemie Stiftung DJCLS R 14/16 (KSG and MB), Radiumhemmets forskningsfonder, the Swedish Cancer foundation and the Swedish Research council (AL), German Cancer Consortium DKTK (AKG), the German Research Council DFG FOR2033 Go 713/2-1 and SFB 1243 A09 (KSG). Research in the Buschbeck lab is further supported by AFM-Téléthon (AFM-18738), the Marie Skłodowska Curie Training network 'ChroMe' (H2020-MSCAITN-2015-675610), and AGAUR (2014-SGR-35). Research at the IJC is supported by the 'La Caixa' Foundation, the Fundació Internacional Josep Carreras, Celgene Spain and the CERCA Programme / Generalitat de Catalunya.Genetic lesions affecting epigenetic regulators are frequent in myelodysplastic syndromes (MDS). Polycomb proteins are key epigenetic regulators of differentiation and stemness that act as two multimeric complexes termed polycomb repressive complexes 1 and 2, PRC1 and PRC2, respectively. While components and regulators of PRC2 such as ASXL1 and EZH2 are frequently mutated in MDS and AML, little is known about the role of PRC1. To analyze the role of PRC1, we have taken a functional approach testing PRC1 components in loss- and gain-of-function experiments that we found overexpressed in advanced MDS patients or dynamically expressed during normal hematopoiesis. This approach allowed us to identify the enzymatically active component RING1A as the key PRC1 component in hematopoietic stem cells and MDS. Specifically, we found that RING1A is expressed in CD34 + bone marrow progenitor cells and further overexpressed in high-risk MDS patients. Knockdown of RING1A in an MDS-derived AML cell line facilitated spontaneous and retinoic acid-induced differentiation. Similarly, inactivation of RING1A in primary CD34 + cells augmented erythroid differentiation. Treatment with a small compound RING1 inhibitor reduced the colony forming capacity of CD34 + cells from MDS patients and healthy controls. In MDS patients higher RING1A expression associated with an increased number of dysplastic lineages and blasts. Our data suggests that RING1A is deregulated in MDS and plays a role in the erythroid development defect

Nautilus ::real-time interaction between dancers and augmented reality with pixel-cloud avatars

Real-time interaction with augmented reality is a novel material for dancers and choreographers to work with on stage. Rather than focusing on a perfect synchronization between dance and music, it allows the dancers to affect their audiovisual environment and react to the change. In this paper, we report the process and outcome of a col-laborative effort between art and technology that has explored this new material and resulted in the dance performance Nautilus. We suggest an interaction method based on a depth sensor and pixel-cloud avatars that allows the dancers to interact reliably with an augmented reality while moving freely on stage.L'interaction en temps réel avec la réalité augmentée re-présente un nouveau matériel avec lequel les danseurs et chorégraphes peuvent travailler pour leurs spectacles. Cela permet aux danseurs d'aller au-delà de la seule syn-chronisation entre musique et mouvement et amène de nouvelles opportunités comme modifier l'environnement audio-visuel et de réagir à ses changements. Dans cet article, nous présentons le processus et le résultat d'un travail collaboratif entre art et technologie, lequel a per- mis d’explorer ce nouveau matériel dans le cadre du spec- tacle Nautilus. Nous suggérons une approche basée sur le tracking des corps par caméra 3D et sur des avatars com- posés de nuages de pixels ; cette approche permet aux danseurs d’interagir de manière fiable avec la réalité augmentée en gardant la liberté de mouvements