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Conformational changes associated with the assembly of recombinant ;2-microglobulin in vitro under acidic conditions were investigated using infrared spectroscopy and static and dynamic light scattering. In parallel, the morphology of the different aggregated species obtained under defined conditions was characterized by electron microscopy. The initial salt-induced aggregate form of ;2-microglobulin, composed of small oligomers (dimers to tetramers), revealed the presence of ;-strands organized in an intramolecular-like fashion. Further particle growth was accompanied by the formation of intermolecular ;-sheet structure and led to short curved forms. An increase in temperature by only 25 °C was able to disaggregate these assemblies, followed by the formation of longer filamentous structures. In contrast, a rise in temperature up to 100 °C was associated with a reorganization of the short curved forms at the level of secondary structure and the state of assembly, leading to a species with a characteristic infrared spectrum different from those of all the other aggregates observed before, suggesting a unique overall structure. The infrared spectral features of this species were nearly identical to those of ;2-microglobulin assemblies formed at low ionic strength with agitation, indicating the presence of fibrils, which was confirmed by electron microscopy. The observed spectroscopic changes suggest that the heat-triggered conversion of the short curved assemblies into fibrils involves a reorganization of the ;-strands from an antiparallel arrangement to a parallel arrangement, with the latter being characteristic of amyloid fibrils of ;2-microglobulin.
beta(2)-microglobulin (beta(2)m) is known to be the major component of fibrillar deposits in the joints of patients suffering from dialysis-related amyloidosis. We have developed a simplified procedure to convert monomeric recombinant beta(2)m into amyloid fibrils at physiological pH by a combination of stirring and heating, enabling us to follow conformational changes associated with the assembly by infrared spectroscopy and electron microscopy. Our studies reveal that fibrillogenesis begins with the formation of relatively large aggregates, with secondary structure not significantly altered by the stirring-induced association. In contrast, the conversion of the amorphous aggregates into amyloid fibrils is associated with a profound re-organization at the level of the secondary and tertiary structures, leading to non-native like parallel arrangements of the beta-strands in the fully formed amyloid structure of beta(2)m. This study highlights the power of an approach to investigate the formation of beta(2)m fibrils by a combination of biophysical techniques including IR spectroscopy.
For several applications it is very useful to classify the linear or non-linear mappings by their summability properties. Absolutely summing operators and polynomials are prominent and classical examples of such setting. Here we are interested in the larger class of almost summing polynomials and we investigate their connections to other related notions of summability.
Approximation numbers of linear operators are a very useful tool in order to understand the structure and the numerical behaviour of the operators. In this paper, this concept is extended to polynomials on Banach spaces and the approximation numbers of diagonal polynomials are estimated. As a main tool the rank of polynomials as a graduation of finite type polynomials is introduced and studied.
Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers. (c) 2006 Elsevier B.V. All rights reserved.
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 x 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.
The Low Earth Orbit (LEO) experiment Biology and Mars Experiment (BIOMEX) is an interdisciplinary and international space research project selected by ESA. The experiment will be accommodated on the space exposure facility EXPOSE-R2 on the International Space Station (ISS) and is foreseen to be launched in 2013. The prime objective of BIOMEX is to measure to what extent biomolecules, such as pigments and cellular components, are resistant to and able to maintain their stability under space and Mars-like conditions. The results of BIOMEX will be relevant for space proven biosignature definition and for building a biosignature data base (e.g. the proposed creation of an international Raman library). The library will be highly relevant for future space missions such as the search for life on Mars. The secondary scientific objective is to analyze to what extent terrestrial extremophiles are able to survive in space and to determine which interactions between biological samples and selected minerals (including terrestrial, Moon- and Mars analogs) can be observed under space and Mars-like conditions. In this context, the Moon will be an additional platform for performing similar experiments with negligible magnetic shielding and higher solar and galactic irradiation compared to LEO. Using the Moon as an additional astrobiological exposure platform to complement ongoing astrobiological LEO investigations could thus enhance the chances of detecting organic traces of life on Mars. We present a lunar lander mission with two related objectives: a lunar lander equipped with Raman and PanCam instruments which can analyze the lunar surface and survey an astrobiological exposure platform. This dual use of testing mission technology together with geo- and astrobiological analyses will significantly increase the science return, and support the human preparation objectives. It will provide knowledge about the Moon's surface itself and, in addition, monitor the stability of life-markers, such as cells, cell components and pigments, in an extraterrestrial environment with much closer radiation properties to the surface of Mars. The combination of a Raman data base of these data together with data from LEO and space simulation experiments, will lead to further progress on the analysis and interpretation of data that we will obtain from future Moon and Mars exploration missions.