911 resultados para Cleaning symbiosis
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Nodulation in legumes provides a major conduit of available nitrogen into the biosphere. The development of nitrogen-fixing nodules results from a symbiotic interaction between soil bacteria, commonly called rhizobia, and legume plants. Molecular genetic analysis in both model and agriculturally important legume species has resulted in the identification of a variety of genes that are essential for the establishment, maintenance and regulation of this symbiosis. Autoregulation of nodulation (AON) is a major internal process by which nodule numbers are controlled through prior nodulation events. Characterisation of AON-deficient mutants has revealed a novel systemic signal transduction pathway controlled by a receptor-like kinase. This review reports our present level of understanding on the short- and long-distance signalling networks controlling early nodulation events and AON.
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Marine invertebrates representing at least five phyla are symbiotic with dinoflagellates from the genus Symbiodinium. This group of single-celled protists was once considered to be a single pandemic species, Symbiodinium microadriaticum. Molecular investigations over the past 25 years have revealed, however, that Symbiodinium is a diverse group of organisms with at least eight (A-H) divergent clades that in turn contain multiple molecular subclade types. The diversity within this genus may subsequently determine the response of corals to normal and stressful conditions, leading to the proposal that the symbiosis may impart unusually rapid adaptation to environmental change by the metazoan host. These questions have added importance due to the critical challenges that corals and the reefs they build face as a consequence of current rapid climate change. This review outlines our current understanding of the diverse genus Symbiodinium and explores the ability of this genus and its symbioses to adapt to rapid environmental change. (c) 2006 Rubel Foundation, ETH Zurich. Published by Elsevier GmbH. All rights reserved.
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The EU intends to increase the fraction of fuels from biogenic energy sources from 2% in 2005 to 8% in 2020. This means a minimum of 30 million TOE/a of fuels from biomass. This makes technical-scale generation of syngas from high-grade biomass, e.g. straw, hay, bark, or paper/cardboard waste, and the production of synthetic fuels by Fischer-Tropsch (FT) synthesis highly attractive. The BTL concept (Biomass to Liquids) of the Karlsruhe Research Center, labeled bioliq, focuses on this challenge by locally concentrating the biomass energy content by fast pyrolysis in a coke/oil slurry followed by slurry conversion to syngas in a central entrained flow gasifier at 1200C and pressures above 4MPa. FT synthesis generates intermediate products for synthetic fuels. To prevent the sensitive catalysts from being poisoned the syngas must be free of tar and particulates. Trace concentrations of H2S, COS, CS2, HCl, NH3, and HCN must be on the order of a few ppb. Moreover, maximum conversion efficiency will be achieved by cleaning the gas above the synthesis conditions. (T>350C, P>4MPa). The concept of an innovative dry HTHP syngas cleaning process is presented. Based on HT particle filtration and suitable sorption and catalysis processes for the relevant contaminants, an overall concept will be derived, which leads to a syngas quality required for FT synthesis in only two combined stages. Results of filtration experiments on a pilot scale are presented. The influence of temperature on the separation and conversion, respectively, of particulates and gaseous contaminants is discussed on the basis of experimental results obtained on a laboratory and pilot scale. Extensive studies of this concept are performed in a scientific network comprising the Karlsruhe Research Center and five universities; funding is provided by the Helmholtz Association of National Research Centers in Germany.
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Biofuels and chemicals from biomass mean the gasification of biogenic feedstocks and the synthesis via methanol, dimethylester (DME) or Fischer-Tropsch products. To prevent the sensitive synthesis catalysts from poisoning the syngas must be free of tar and particulates. The trace concentrations of S-, C1-, N-species, alkali and heavy metals must be of the order of a few ppb. Moreover maximum conversion efficiency will be achieved performing the gas cleaning above the synthesis conditions. The concept of an innovative dry HTHP syngas cleaning is presented. Based on the HT particle filtration and suitable sorption and catalysis processes for the relevant contaminants a total concept will be derived, which leads to a syngas quality required for synthesis catalysts in only 2 combined stages. The experimental setup for the HT gas cleaning behind the 60 kWtherm entrained flow gasifier REGA of the institute is described. Results from HT filter experiments in pilot scale are presented. The performance of 2 natural minerals for HC1 and H2S sorption is discussed with respect to the parameters temperature, surface and residence time. Results from lab scale investigations on low temperature tar catalysts' performance (commercial and proprietary development) are discussed finally.
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Case law report - online
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A ground-based laser system for space-debris cleaning will use powerful laser pulses that can self-focus while propagating through the atmosphere. We demonstrate that for the relevant laser parameters, this self-focusing can noticeably decrease the laser intensity on the target. We show that the detrimental effect can be, to a great extent, compensated for by applying the optimal initial beam defocusing. The effect of laser elevation on the system performance is discussed.
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A tudásmenedzsment-rendszerek működtetése lassan elfogadottá, és a nagyobb vállalatok életében a mindennapok részévé vált az elmúlt években. A rendszer hordozta előnyök, lehetőségek teljes körű kiaknázása azonban közel sem mutat ilyen reményteli képet. Különösen igaz ez, ha a vállalati működés kulcsfolyamataival való kapcsolatát, egymásba épülését vizsgáljuk. E folyamatok közé tartozik az innováció is. Bár minden szakmabeli és laikus gondolkodás egyértelműen látja, hogy az innovációhoz tudás kell, és a tudásmenedzsment-rendszernek is a tudás az alapja, mégsem valósul meg e két terület szoros kapcsolata, együtt mozgása a siker érdekében. Különösen igaz ez a hiányosság a legújabb innovációs megoldásokban. A tanulmány a tudásmenedzsment-rendszer és a frugal innováció kapcsolatát, elvi és gyakorlati lehetőségeit mutatja be. ____ To operate a knowledge management system has become an accepted method and a part of everyday life in the biggest companies. The full circle exploitation of advantages and possibilities of this system does not show a hopeful picture. It is especially true when we examine relationships and constructions with other key processes in the operation of a company. Innovation belongs to above mentioned processes. Though every outsider and professional way of thinking sees clearly that knowledge is needed to innovate and knowledge is a basis of knowledge management, but the close connection of the two important processes has not been realized on behalf of success. Defectiveness is especially true in cases of the newest innovation methods. The paper shows the connection of frugal innovation and knowledge management, its theoretical and practical possibilities
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With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.
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Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.
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The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many research efforts attempt to improve the accuracy of the corrupted or "dirty" sensor data. The dirty data need to be cleaned or corrected. However, the developed data cleaning solutions restrict themselves to the scope of static WSNs where deployed sensors would rarely move during the operation. Nowadays, many emerging applications relying on WSNs need the sensor mobility to enhance the application efficiency and usage flexibility. The location of deployed sensors needs to be dynamic. Also, each sensor would independently function and contribute its resources. Sensors equipped with vehicles for monitoring the traffic condition could be depicted as one of the prospective examples. The sensor mobility causes a transient in network topology and correlation among sensor streams. Based on static relationships among sensors, the existing methods for cleaning sensor data in static WSNs are invalid in such mobile scenarios. Therefore, a solution of data cleaning that considers the sensor movements is actively needed. This dissertation aims to improve the quality of sensor data by considering the consequences of various trajectory relationships of autonomous mobile sensors in the system. First of all, we address the dynamic network topology due to sensor mobility. The concept of virtual sensor is presented and used for spatio-temporal selection of neighboring sensors to help in cleaning sensor data streams. This method is one of the first methods to clean data in mobile sensor environments. We also study the mobility pattern of moving sensors relative to boundaries of sub-areas of interest. We developed a belief-based analysis to determine the reliable sets of neighboring sensors to improve the cleaning performance, especially when node density is relatively low. Finally, we design a novel sketch-based technique to clean data from internal sensors where spatio-temporal relationships among sensors cannot lead to the data correlations among sensor streams.
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Wolbachia pipientis are bacterial endosymbionts carried by millions of invertebrate species, including ~40% of insect species and some filarial nematodes. In insects, basic Wolbachia research has potential applications in controlling vector borne disease. Conversely, Wolbachia of filarial nematodes are causative agents of neglected tropical diseases such as lymphatic filariasis and African river blindness. However, remarkably little is known about how Wolbachia interact with their hosts at the molecular level. Understanding this is important to inform the basis for symbiosis and help prevent human disease. I used a high-throughput proteomics approach to study how Drosophila host cells are modified by Wolbachia infection. This analysis identified 23 Drosophila proteins that significantly changed in amount as a result of Wolbachia infection. A subset of differentially abundant host proteins were consistent with Wolbachia-associated phenotypes reported previously. This study also provides the first ever discovery-based evidence for a Wolbachia-associated change in maternal germline histone loads, which has possible implications in Rescue of a common Wolbachia-induced reproductive manipulation known as Cytoplasmic Incompatibility.
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With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.
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Wolbachia pipientis are bacterial endosymbionts of arthropods and in some filarial nematodes. Wolbachia are of particular interest because nematodeWolbachia have been shown to cause the diseases African river blindness and Lymphatic Filariasis. Doxycycline can be used to eliminate nematode Wolbachia, however, more efficient treatments are needed. Ideally, we would like to repurpose another FDA approved drug that helps to shorten treatment duration. Vitamins are one of the best classes of FDA approved compounds, generally recognized as safe. Interestingly, prior work by Serbus and colleagues found that dietary yeast, which is highly enriched in vitamins, dramatically reducesWolbachia titer in Drosophila melanogaster ovarian tissue. Imaging data indicated that the Wolbachia nucleoids were disrupted in response to yeast. This raised the possibility that yeast cells contain a bio-reactive, anti-Wolbachiacompound. Our close examination of yeast nutritional information identified which vitamins are most highly enriched in yeast. We then administered several of these to D. melanogaster, and saw that two of these led to reduced ovarianWolbachia titers, analogous to yeast-fed flies. This was especially interesting, as both vitamins are critical for functioning of the same biochemical pathway. We used retested effect of one of these vitamins in oogenesis by performing a dilution series, and achieved positive correlation from this dilution series. This opens up the avenue for clarifying the mechanism of how vitamins suppressWolbachia titer, and for testing enhancement of Doxycycline, to hopefully provide faster, more affordable treatment for millions of patients.