Phosphorylation of transitory starch by α-glucan, water dikinase during starch turnover affects the surface properties and morphology of starch granules.

Glucan, water dikinase (GWD) is a key enzyme of starch metabolism but the physico-chemical properties of starches isolated from GWD-deficient plants and their implications for starch metabolism have so far not been described. Transgenic Arabidopsis thaliana plants with reduced or no GWD activity were used to investigate the properties of starch granules. In addition, using various in vitro assays, the action of recombinant GWD, ß-amylase, isoamylase and starch synthase 1 on the surface of native starch granules was analysed. The internal structure of granules isolated from GWD mutant plants is unaffected, as thermal stability, allomorph, chain length distribution and density of starch granules were similar to wild-type. However, short glucan chain residues located at the granule surface dominate in starches of transgenic plants and impede GWD activity. A similarly reduced rate of phosphorylation by GWD was also observed in potato tuber starch fractions that differ in the proportion of accessible glucan chain residues at the granule surface. A model is proposed to explain the characteristic morphology of starch granules observed in GWD transgenic plants. The model postulates that the occupancy rate of single glucan chains at the granule surface limits accessibility to starch-related enzymes.

Cell-to-cell diversity in a synchronized Chlamydomonas culture as revealed by single-cell analyses.

In a synchronized photoautotrophic culture of Chlamydomonas reinhardtii, cell size, cell number, and the averaged starch content were determined throughout the light-dark cycle. For single-cell analyses, the relative cellular starch was quantified by measuring the second harmonic generation (SHG). In destained cells, amylopectin essentially represents the only biophotonic structure. As revealed by various validation procedures, SHG signal intensities are a reliable relative measure of the cellular starch content. During photosynthesis-driven starch biosynthesis, synchronized Chlamydomonas cells possess an unexpected cell-to-cell diversity both in size and starch content, but the starch-related heterogeneity largely exceeds that of size. The cellular volume, starch content, and amount of starch/cell volume obey lognormal distributions. Starch degradation was initiated by inhibiting the photosynthetic electron transport in illuminated cells or by darkening. Under both conditions, the averaged rate of starch degradation is almost constant, but it is higher in illuminated than in darkened cells. At the single-cell level, rates of starch degradation largely differ but are unrelated to the initial cellular starch content. A rate equation describing the cellular starch degradation is presented. SHG-based three-dimensional reconstructions of Chlamydomonas cells containing starch granules are shown.

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.

Sensor-based outdoor monitoring of insects in arable crops for their precise control.

The implementation of precision farming technologies into agricultural practice requires, among other things, precise determination of the extent and intensity of insect infestation in the farmer' fields. Manual insect identification is time-consuming and has low efficiency, especially for large fields. Therefore, scientists and practitioners devote much effort to the automatization of this process. There are two complementary approaches to insect identification: (i) direct, in which the insect (ultimately the species) is determined, and (ii) indirect, in which the damage caused by the insects is monitored and forms the basis on which to formulate the information about insect infestation. A mini-review of both approaches is presented in this work. Additionally, the advantages and disadvantages of each are briefly described. Methods of insect identification are still characterized by relatively small selectivity and efficiency, therefore it is necessary to keep searching for new methods and improve the development of existing ones. The goal of such systems should be to work in real time and be inexpensive to run, enabling widespread use amongst farmers. A possible solution seems to be integrating various techniques (sensor fusion) into a single measurement system. © 2020 Society of Chemical Industry.