997 resultados para sequential niche technique


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Between 1985 and 1990 we treated 11 large segmental bone defects (average 6.7 cm) in ten patients with the Ilizarov technique. Open fractures, type III according to Gustilo, represented the largest group (8 of 11 cases). The average delay before the Ilizarov technique was initiated was 8.9 months. The external fixator was usually maintained for 1 year. Bone regeneration was obtained in every case. Consolidation was not fulfilled with this technique in three cases. The complications observed were one refracture, four leg-length discrepancies (average 1.5 cm), and five axial deformities exceeding 5 degrees. No pin-track infection was observed. In our limited series of four type IIIC open fractures treated by the Ilizarov technique, no patients required amputation. The Ilizarov technique is particularly useful in the treatment of large bone defects, without major complications, especially if there is an adequate initial debridement.

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Biomphalaria tenagophila, one of the intermediate hosts of the trematoda Schistosoma mansoni, is a simultaneous hermafrodite snail species. In order to analyse the genetic structure of these populations, we performed a double-stringency PCR technique to obtain genetic markers with microsatellites and arbitrary primers in a single reaction.

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Biomphalaria glabrata, highly susceptible to Schistosoma mansoni, were seen to shed less and less cercariae along the time of infection. Histological examination kept a close correlation with this changing pattern of cercarial shedding, turning an initial picture of no-reaction (tolerance) gradually into one of hemocyte proliferation with formation of focal encapsulating lesions around disintegrating sporocysts and cercariae, a change that became disseminated toward the 142nd day post miracidial exposure. Findings were suggestive of a gradual installation of acquired immunity in snails infected with S. mansoni.

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Des progrès significatifs ont été réalisés dans le domaine de l'intégration quantitative des données géophysique et hydrologique l'échelle locale. Cependant, l'extension à de plus grandes échelles des approches correspondantes constitue encore un défi majeur. Il est néanmoins extrêmement important de relever ce défi pour développer des modèles fiables de flux des eaux souterraines et de transport de contaminant. Pour résoudre ce problème, j'ai développé une technique d'intégration des données hydrogéophysiques basée sur une procédure bayésienne de simulation séquentielle en deux étapes. Cette procédure vise des problèmes à plus grande échelle. L'objectif est de simuler la distribution d'un paramètre hydraulique cible à partir, d'une part, de mesures d'un paramètre géophysique pertinent qui couvrent l'espace de manière exhaustive, mais avec une faible résolution (spatiale) et, d'autre part, de mesures locales de très haute résolution des mêmes paramètres géophysique et hydraulique. Pour cela, mon algorithme lie dans un premier temps les données géophysiques de faible et de haute résolution à travers une procédure de réduction déchelle. Les données géophysiques régionales réduites sont ensuite reliées au champ du paramètre hydraulique à haute résolution. J'illustre d'abord l'application de cette nouvelle approche dintégration des données à une base de données synthétiques réaliste. Celle-ci est constituée de mesures de conductivité hydraulique et électrique de haute résolution réalisées dans les mêmes forages ainsi que destimations des conductivités électriques obtenues à partir de mesures de tomographic de résistivité électrique (ERT) sur l'ensemble de l'espace. Ces dernières mesures ont une faible résolution spatiale. La viabilité globale de cette méthode est testée en effectuant les simulations de flux et de transport au travers du modèle original du champ de conductivité hydraulique ainsi que du modèle simulé. Les simulations sont alors comparées. Les résultats obtenus indiquent que la procédure dintégration des données proposée permet d'obtenir des estimations de la conductivité en adéquation avec la structure à grande échelle ainsi que des predictions fiables des caractéristiques de transports sur des distances de moyenne à grande échelle. Les résultats correspondant au scénario de terrain indiquent que l'approche d'intégration des données nouvellement mise au point est capable d'appréhender correctement les hétérogénéitées à petite échelle aussi bien que les tendances à gande échelle du champ hydraulique prévalent. Les résultats montrent également une flexibilté remarquable et une robustesse de cette nouvelle approche dintégration des données. De ce fait, elle est susceptible d'être appliquée à un large éventail de données géophysiques et hydrologiques, à toutes les gammes déchelles. Dans la deuxième partie de ma thèse, j'évalue en détail la viabilité du réechantillonnage geostatique séquentiel comme mécanisme de proposition pour les méthodes Markov Chain Monte Carlo (MCMC) appliquées à des probmes inverses géophysiques et hydrologiques de grande dimension . L'objectif est de permettre une quantification plus précise et plus réaliste des incertitudes associées aux modèles obtenus. En considérant une série dexemples de tomographic radar puits à puits, j'étudie deux classes de stratégies de rééchantillonnage spatial en considérant leur habilité à générer efficacement et précisément des réalisations de la distribution postérieure bayésienne. Les résultats obtenus montrent que, malgré sa popularité, le réechantillonnage séquentiel est plutôt inefficace à générer des échantillons postérieurs indépendants pour des études de cas synthétiques réalistes, notamment pour le cas assez communs et importants où il existe de fortes corrélations spatiales entre le modèle et les paramètres. Pour résoudre ce problème, j'ai développé un nouvelle approche de perturbation basée sur une déformation progressive. Cette approche est flexible en ce qui concerne le nombre de paramètres du modèle et lintensité de la perturbation. Par rapport au rééchantillonage séquentiel, cette nouvelle approche s'avère être très efficace pour diminuer le nombre requis d'itérations pour générer des échantillons indépendants à partir de la distribution postérieure bayésienne. - Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending corresponding approaches beyond the local scale still represents a major challenge, yet is critically important for the development of reliable groundwater flow and contaminant transport models. To address this issue, I have developed a hydrogeophysical data integration technique based on a two-step Bayesian sequential simulation procedure that is specifically targeted towards larger-scale problems. The objective is to simulate the distribution of a target hydraulic parameter based on spatially exhaustive, but poorly resolved, measurements of a pertinent geophysical parameter and locally highly resolved, but spatially sparse, measurements of the considered geophysical and hydraulic parameters. To this end, my algorithm links the low- and high-resolution geophysical data via a downscaling procedure before relating the downscaled regional-scale geophysical data to the high-resolution hydraulic parameter field. I first illustrate the application of this novel data integration approach to a realistic synthetic database consisting of collocated high-resolution borehole measurements of the hydraulic and electrical conductivities and spatially exhaustive, low-resolution electrical conductivity estimates obtained from electrical resistivity tomography (ERT). The overall viability of this method is tested and verified by performing and comparing flow and transport simulations through the original and simulated hydraulic conductivity fields. The corresponding results indicate that the proposed data integration procedure does indeed allow for obtaining faithful estimates of the larger-scale hydraulic conductivity structure and reliable predictions of the transport characteristics over medium- to regional-scale distances. The approach is then applied to a corresponding field scenario consisting of collocated high- resolution measurements of the electrical conductivity, as measured using a cone penetrometer testing (CPT) system, and the hydraulic conductivity, as estimated from electromagnetic flowmeter and slug test measurements, in combination with spatially exhaustive low-resolution electrical conductivity estimates obtained from surface-based electrical resistivity tomography (ERT). The corresponding results indicate that the newly developed data integration approach is indeed capable of adequately capturing both the small-scale heterogeneity as well as the larger-scale trend of the prevailing hydraulic conductivity field. The results also indicate that this novel data integration approach is remarkably flexible and robust and hence can be expected to be applicable to a wide range of geophysical and hydrological data at all scale ranges. In the second part of my thesis, I evaluate in detail the viability of sequential geostatistical resampling as a proposal mechanism for Markov Chain Monte Carlo (MCMC) methods applied to high-dimensional geophysical and hydrological inverse problems in order to allow for a more accurate and realistic quantification of the uncertainty associated with the thus inferred models. Focusing on a series of pertinent crosshole georadar tomographic examples, I investigated two classes of geostatistical resampling strategies with regard to their ability to efficiently and accurately generate independent realizations from the Bayesian posterior distribution. The corresponding results indicate that, despite its popularity, sequential resampling is rather inefficient at drawing independent posterior samples for realistic synthetic case studies, notably for the practically common and important scenario of pronounced spatial correlation between model parameters. To address this issue, I have developed a new gradual-deformation-based perturbation approach, which is flexible with regard to the number of model parameters as well as the perturbation strength. Compared to sequential resampling, this newly proposed approach was proven to be highly effective in decreasing the number of iterations required for drawing independent samples from the Bayesian posterior distribution.

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Why generalist and specialist species coexist in nature is a question that has interested evolutionary biologists for a long time. While the coexistence of specialists and generalists exploiting resources on a single ecological dimension has been theoretically and empirically explored, biological systems with multiple resource dimensions (e.g. trophic, ecological) are less well understood. Yet, such systems may provide an alternative to the classical theory of stable evolutionary coexistence of generalist and specialist species on a single resource dimension. We explore such systems and the potential trade-offs between different resource dimensions in clownfishes. All species of this iconic clade are obligate mutualists with sea anemones yet show interspecific variation in anemone host specificity. Moreover, clownfishes developed variable environmental specialization across their distribution. In this study, we test for the existence of a relationship between host-specificity (number of anemones associated with a clownfish species) and environmental-specificity (expressed as the size of the ecological niche breadth across climatic gradients). We find a negative correlation between host range and environmental specificities in temperature, salinity and pH, probably indicating a trade-off between both types of specialization forcing species to specialize only in a single direction. Trade-offs in a multi-dimensional resource space could be a novel way of explaining the coexistence of generalist and specialists.

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There has been an ardent interest in herbivore saliva due to its roles in inducing plant defenses and its impact on herbivore fitness. Two techniques are described that inhibit the secretion of labial saliva from the caterpillar, Helicoverpa zea, during feeding. The methods rely on cauterizing the caterpillar's spinneret, the principal secretory structure of the labial glands, or surgically removing the labial salivary gland. Both methods successfully inhibit secretion of saliva and the principal salivary enzyme glucose oxidase. Caterpillars with inhibited saliva production feed at similar rates as the untreated caterpillars, pupate, and emerge as adults. Glucose oxidase has been suggested to increase the caterpillar's survival through the suppression of inducible anti-herbivore defenses in plants. Tobacco (Nicotiana tabacum) leaves fed on by caterpillars with ablated salivary glands had significantly higher levels of nicotine, an inducible anti-herbivore defense compound of tobacco, than leaves fed upon by caterpillars with intact labial salivary glands. Tomato (Lycopersicon esculentum) leaves fed upon by caterpillars with suppressed salivary secretions showed greatly reduced evidence of hydrogen peroxide formation compared to leaves fed upon by intact caterpillars. These two methods are useful techniques for determining the role that saliva plays in manipulating plant anti-herbivore defenses.

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Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.

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Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

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Solid phase microextraction (SPME) has been widely used for many years in various applications, such as environmental and water samples, food and fragrance analysis, or biological fluids. The aim of this study was to suggest the SPME method as an alternative to conventional techniques used in the evaluation of worker exposure to benzene, toluene, ethylbenzene, and xylene (BTEX). Polymethylsiloxane-carboxen (PDMS/CAR) showed as the most effective stationary phase material for sorbing BTEX among other materials (polyacrylate, PDMS, PDMS/divinylbenzene, Carbowax/divinylbenzene). Various experimental conditions were studied to apply SPME to BTEX quantitation in field situations. The uptake rate of the selected fiber (75 microm PDMS/CAR) was determined for each analyte at various concentrations, relative humidities, and airflow velocities from static (calm air) to dynamic (> 200 cm/s) conditions. The SPME method also was compared with the National Institute of Occupational Safety and Health method 1501. Unlike the latter, the SPME approach fulfills the new requirement for the threshold limit value-short term exposure limit (TLV-STEL) of 2.5 ppm for benzene (8 mg/m(3))

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The survival and molting incidence in Triatoma infestans, a vector of Chagas disease, were investigated following sequential shocks at 0ºC in fifth instar nymphs under moderate fasting and full nutritional conditions. The shocks were separated by intervals of 8 h and 24 h at 30ºC. The results indicated that in terms of insect survival, T. infestans is tolerant to a single cold shock at 0ºC even for 12 h, or to sequential cold shocks, regardless of the nutritional state of the specimens. In terms of molting rate, fasting enhanced the tolerance to sequential cold shocks, but did not exceed the tolerance acquired by fully-nourished specimens, except when cold shocks were separated by an 8 h interval at 30ºC. The protective action elicited by fasting was assumed to be additive to that induced by a single mild cold shock or sequential cold shocks. The cold-tolerance response of T. infestans may have favoured its survival in areas of South America with low temperatures, even considering that this species is predominantly associated with human habitats.

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The nuclear phenotypes of Malpighian tubule cells in fifth instar nymphs of Triatoma infestans, one of the most important vectors of Chagas disease, were studied following sequential shocks at 0ºC, separated by intervals of 8 h and 24 h at 30ºC, under conditions of moderate fasting and full nourishment. The insects pertained to colonies reared in the laboratory and originated from domestic specimens collected in the Brazilian states of São Paulo (north) and Minas Gerais (south). Since nuclear phenotypes in this species are affected by single cold shocks, it was expected that these phenotypes could also be changed by sequential shocks. Nuclear phenotypes indicative of mechanisms of cell survival (nuclear fusion and heterochromatin decondensation) and cell death (apoptosis and necrosis) were observed concomitantly in all the conditions tested. Nuclear fusion and heterochromatin decondensation were not found relevant for the presumed acquisition of the cold-hardening response in T. infestans. The decreased frequency of apoptosis and necrosis following sequential cold shocks including under fasting conditions, indicated that tolerance to sequential cold shocks occurred in T. infestans of the mentioned origin.