986 resultados para Applied Behavioral Analysis


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Trees are a great bank of data, named sometimes for this reason as the "silentwitnesses" of the past. Due to annual formation of rings, which is normally influenced directly by of climate parameters (generally changes in temperature and moisture or precipitation) and other environmental factors; these changes, occurred in the past, are"written" in the tree "archives" and can be "decoded" in order to interpret what hadhappened before, mainly applied for the past climate reconstruction.Using dendrochronological methods for obtaining samples of Pinus nigra fromthe Catalonian PrePirineous region, the cores of 15 trees with total time spine of about 100 - 250 years were analyzed for the tree ring width (TRW) patterns and had quite high correlation between them (0.71 ¿ 0.84), corresponding to a common behaviour for the environmental changes in their annual growth.After different trials with raw TRW data for standardization in order to take outthe negative exponential growth curve dependency, the best method of doubledetrending (power transformation and smoothing line of 32 years) were selected for obtaining the indexes for further analysis.Analyzing the cross-correlations between obtained tree ring width indexes andclimate data, significant correlations (p<0.05) were observed in some lags, as forexample, annual precipitation in lag -1 (previous year) had negative correlation with TRW growth in the Pallars region. Significant correlation coefficients are between 0.27- 0.51 (with positive or negative signs) for many cases; as for recent (but very short period) climate data of Seu d¿Urgell meteorological station, some significant correlation coefficients were observed, of the order of 0.9.These results confirm the hypothesis of using dendrochronological data as aclimate signal for further analysis, such as reconstruction of climate in the past orprediction in the future for the same locality.

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Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

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Blood doping involves the use of products that enhance the uptake, transport, or delivery of oxygen to the blood. One approach uses artificial oxygen carriers, known as hemoglobin-based oxygen carriers (HBOCs). This study describes an analytical strategy based on CE for detecting intact HBOCs in plasma samples collected for doping control. On-capillary detection was performed by UV/Vis at 415 nm, which offered detection selectivity for hemoproteins (such as hemoglobin and HBOCs). On-line ESI-MS detection with a TOF analyzer was further used to provide accurate masses on CE peaks and to confirm the presence of HBOCs. An immunodepletion sample preparation step was mandatory prior to analysis, in order to remove most abundant proteins that interfered with CE separation and altered the ESI process. This analytical method was successfully applied to plasma samples enriched with Oxyglobin, a commercially available HBOC used for veterinary purposes. Detection limits of 0.20 and 0.45 g/dL were achieved in plasma for CE-UV/Vis at 415 nm and CE-ESI-TOF/MS, respectively.

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Interaction analysis is not a prerogative of any discipline in social sciences. It has its own history within each disciplinary field and is related to specific research objects. From the standpoint of psychology, this article first draws upon a distinction between factorial and dialogical conceptions of interaction. It then briefly presents the basis of a dialogical approach in psychology and focuses upon four basic assumptions. Each of them is examined on a theoretical and on a methodological level with a leading question: to what extent is it possible to develop analytical tools that are fully coherent with dialogical assumptions? The conclusion stresses the difficulty of developing methodological tools that are fully consistent with dialogical assumptions and argues that there is an unavoidable tension between accounting for the complexity of an interaction and using methodological tools which necessarily "monologise" this complexity.

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The modeling and estimation of the parameters that define the spatial dependence structure of a regionalized variable by geostatistical methods are fundamental, since these parameters, underlying the kriging of unsampled points, allow the construction of thematic maps. One or more atypical observations in the sample data can affect the estimation of these parameters. Thus, the assessment of the combined influence of these observations by the analysis of Local Influence is essential. The purpose of this paper was to propose local influence analysis methods for the regionalized variable, given that it has n-variate Student's t-distribution, and compare it with the analysis of local influence when the same regionalized variable has n-variate normal distribution. These local influence analysis methods were applied to soil physical properties and soybean yield data of an experiment carried out in a 56.68 ha commercial field in western Paraná, Brazil. Results showed that influential values are efficiently determined with n-variate Student's t-distribution.

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The rate of carbon dioxide production is commonly used as a measure of microbial activity in the soil. The traditional method of CO2 determination involves trapping CO2 in an alkali solution and then determining CO2 concentration indirectly by titration of the remaining alkali in the solution. This method is still commonly employed in laboratories throughout the world due to its relative simplicity and the fact that it does not require expensive, specific equipment. However, there are several drawbacks: the method is time-consuming, requires large amounts of chemicals and the consistency of results depends on the operator's skills. With this in mind, an improved method was developed to analyze CO2 captured in alkali traps, which is cheap and relatively simple, with a substantially shorter sample handling time and reproducibility equivalent to the traditional titration method. A comparison of the concentration values determined by gas phase flow injection analysis (GPFIA) and titration showed no significant difference (p > 0.05), but GPFIA has the advantage that only a tenth of the sample volume of the titration method is required. The GPFIA system does not require the purchase of new, costly equipment but the device was constructed from items commonly found in laboratories, with suggestions for alternative configurations for other detection units. Furthermore, GPFIA for CO2 analysis can be equally applied to samples obtained from either the headspace of microcosms or from a sampling chamber that allows CO2 to be released from alkali trapping solutions. The optimised GPFIA method was applied to analyse CO2 released from degrading hydrocarbons from a site contaminated by diesel spillage.

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In order to select soil management practices that increase the nitrogen-use efficiency (NUE) in agro-ecosystems, the different indices of agronomic fertilizer efficiency must be evaluated under varied weather conditions. This study assessed the NUE indices in no-till corn in southern Paraguay. Nitrogen fertilizer rates from 0 to 180 kg ha-1 were applied in a single application at corn sowing and the crop response investigated in two growing seasons (2010 and 2011). The experimental design was a randomized block with three replications. Based on the data of grain yield, dry matter, and N uptake, the following fertilizer indices were assessed: agronomic N-use efficiency (ANE), apparent N recovery efficiency (NRE), N physiological efficiency (NPE), partial factor productivity (PFP), and partial nutrient balance (PNB). The weather conditions varied largely during the experimental period; the rainfall distribution was favorable for crop growth in the first season and unfavorable in the second. The PFP and ANE indices, as expected, decreased with increasing N fertilizer rates. A general analysis of the N fertilizer indices in the first season showed that the maximum rate (180 kg ha-1) obtained the highest corn yield and also optimized the efficiency of NPE, NRE and ANE. In the second season, under water stress, the most efficient N fertilizer rate (60 kg ha-1) was three times lower than in the first season, indicating a strong influence of weather conditions on NUE. Considering that weather instability is typical for southern Paraguay, anticipated full N fertilization at corn sowing is not recommended due the temporal variability of the optimum N fertilizer rate needed to achieve high ANE.

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THESIS ABSTRACTThis thesis project was aimed at studying the molecular mechanisms underlying learning and memory formation, in particular as they relate to the metabolic coupling between astrocytes and neurons. For that, changes in the metabolic activity of different mice brain regions after 1 or 9 days of training in an eight-arm radial maze were assessed by (14C) 2-deoxyglucose (2DG) autoradiography. Significant differences in the areas engaged during the behavioral task at day 1 (when animals are confronted for the first time to the learning task) and at day 9 (when animals are highly performing) have been identified. These areas include the hippocampus, the fornix, the parietal cortex, the laterodorsal thalamic nucleus and the mammillary bodies at day 1 ; and the anterior cingulate, the retrosplenial cortex and the dorsal striatum at day 9. Two of these cerebral regions (those presenting the greatest changes at day 1 and day 9: the hippocampus and the retrosplenial cortex, respectively) were microdissected by laser capture microscopy and selected genes related to neuron-glia metabolic coupling, glucose metabolism and synaptic plasticity were analyzed by RT-PCR. 2DG and gene expression analysis were performed at three different times: 1) immediately after the end of the behavioral paradigm, 2) 45 minutes and 3) 6 hours after training. The main goal of this study was the identification of the metabolic adaptations following the learning task. Gene expression results demonstrate that the learning task profoundly modulates the pattern of gene expression in time, meaning that these two cerebral regions with high 2DG signal (hippocampus and retrosplenial cortex) have adapted their metabolic molecular machinery in consequence. Almost all studied genes show a higher expression in the hippocampus at day 1 compared to day 9, while an increased expression was found in the retrosplenial cortex at day 9. We can observe these molecular adaptations with a short delay of 45 minutes after the end of the task. However, 6 hours after training a high gene expression was found at day 9 (compared to day 1) in both regions, suggesting that only one day of training is not sufficient to detect transcriptional modifications several hours after the task. Thus, gene expression data match 2DG results indicating a transfer of information in time (from day 1 to day 9) and in space (from the hippocampus to the retrosplenial cortex), and this at a cellular and a molecular level. Moreover, learning seems to modify the neuron-glia metabolic coupling, since several genes involved in this coupling are induced. These results also suggest a role of glia in neuronal plasticity.RESUME DU TRAVAIL DE THESECe projet de thèse a eu pour but l'étude des mécanismes moléculaires qui sont impliqués dans l'apprentissage et la mémoire et, en particulier, à les mettre en rapport avec le couplage métabolique existant entre les astrocytes et les neurones. Pour cela, des changements de l'activité métabolique dans différentes régions du cerveau des souris après 1 ou 9 jours d'entraînement dans un labyrinthe radial à huit-bras ont été évalués par autoradiographie au 2-désoxyglucose (2DG). Des différences significatives dans les régions engagées pendant la tâche comportementale au jour 1 (quand les animaux sont confrontés pour la première fois à la tâche) et au jour 9 (quand les animaux ont déjà appris) ont été identifiés. Ces régions incluent, au jour 1, l'hippocampe, le fornix, le cortex pariétal, le noyau thalamic laterodorsal et les corps mamillaires; et, au jour 9, le cingulaire antérieur, le cortex retrosplenial et le striatum dorsal. Deux de ces régions cérébrales (celles présentant les plus grands changements à jour 1 et à jour 9: l'hippocampe et le cortex retrosplenial, respectivement) ont été découpées par microdissection au laser et quelques gènes liés au couplage métabolique neurone-glie, au métabolisme du glucose et à la plasticité synaptique ont été analysées par RT-PCR. L'étude 2DG et l'analyse de l'expression de gènes ont été exécutés à trois temps différents: 1) juste après entraînement, 2) 45 minutes et 3) 6 heures après la fin de la tâche. L'objectif principal de cette étude était l'identification des adaptations métaboliques suivant la tâche d'apprentissage. Les résultats de l'expression de gènes démontrent que la tâche d'apprentissage module profondément le profile d'expression des gènes dans le temps, signifiant que ces deux régions cérébrales avec un signal 2DG élevé (l'hippocampe et le cortex retrosplenial) ont adapté leurs « machines moléculaires » en conséquence. Presque tous les gènes étudiés montrent une expression plus élevée dans l'hippocampe au jour 1 comparé au jour 9, alors qu'une expression accrue a été trouvée dans le cortex retrosplenial au jour 9. Nous pouvons observer ces adaptations moléculaires avec un retard court de 45 minutes après la fin de la tâche. Cependant, 6 heures après l'entraînement, une expression de gènes élevée a été trouvée au jour 9 (comparé à jour 1) dans les deux régions, suggérant que seulement un jour d'entraînement ne suffit pas pour détecter des modifications transcriptionelles plusieurs heures après la tâche. Ainsi, les données d'expression de gènes corroborent les résultats 2DG indiquant un transfert d'information dans le temps (de jour 1 à jour 9) et dans l'espace (de l'hippocampe au cortex retrosplenial), et ceci à un niveau cellulaire et moléculaire. D'ailleurs, la tâche d'apprentissage semble modifier le couplage métabolique neurone-glie, puisque de nombreux gènes impliqués dans ce couplage sont induits. Ces observations suggèrent un rôle important de la glie dans les mécanismes de plasticité du système nerveux.

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Millions of blood products are transfused every year; many lives are thus directly concerned by transfusion. The three main labile blood products used in transfusion are erythrocyte concentrates, platelet concentrates and fresh frozen plasma. Each of these products has to be stored according to its particular components. However, during storage, modifications or degradation of those components may occur, and are known as storage lesions. Thus, biomarker discovery of in vivo blood aging as well as in vitro labile blood products storage lesions is of high interest for the transfusion medicine community. Pre-analytical issues are of major importance in analyzing the various blood products during storage conditions as well as according to various protocols that are currently used in blood banks for their preparations. This paper will review key elements that have to be taken into account in the context of proteomic-based biomarker discovery applied to blood banking.

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The quadrennial need study was developed to assist in identifying county highway financial needs (construction, rehabilitation, maintenance, and administration) and in the distribution of the road use tax fund (RUTF) among the counties in the state. During the period since the need study was first conducted using HWYNEEDS software, between 1982 and 1998, there have been large fluctuations in the level of funds distributed to individual counties. A recent study performed by Jim Cable (HR-363, 1993), found that one of the major factors affecting the volatility in the level of fluctuations is the quality of the pavement condition data collected and the accuracy of these data. In 1998, the Center for Transportation Research and Education researchers (Maze and Smadi) completed a project to study the feasibility of using automated pavement condition data collected for the Iowa Pavement Management Program (IPMP) for the paved county roads to be used in the HWYNEEDS software (TR-418). The automated condition data are objective and also more current since they are collected in a two year cycle compared to the 10-year cycle used by HWYNEEDS right now. The study proved the use of the automated condition data in HWYNEEDS would be feasible and beneficial in educing fluctuations when applied to a pilot study area. In another recommendation from TR-418, the researchers recommended a full analysis and investigation of HWYNEEDS methodology and parameters (for more information on the project, please review the TR-418 project report). The study reported in this document builds on the previous study on using the automated condition data in HWYNEEDS and covers the analysis and investigation of the HWYNEEDS computer program methodology and parameters. The underlying hypothesis for this study is thatalong with the IPMP automated condition data, some changes need to be made to HWYNEEDS parameters to accommodate the use of the new data, which will stabilize the process of allocating resources and reduce fluctuations from one quadrennial need study to another. Another objective of this research is to investigate the gravel roads needs and study the feasibility of developing a more objective approach to determining needs on the counties gravel road network. This study identifies new procedures by which the HWYNEEDS computer program is used to conduct the quadrennial needs study on paved roads. Also, a new procedure will be developed to determine gravel roads needs outside of the HWYNEED program. Recommendations are identified for the new procedures and also in terms of making changes to the current quadrennial need study. Future research areas are also identified.

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A major issue in the application of waveform inversion methods to crosshole georadar data is the accurate estimation of the source wavelet. Here, we explore the viability and robustness of incorporating this step into a time-domain waveform inversion procedure through an iterative deconvolution approach. Our results indicate that, at least in non-dispersive electrical environments, such an approach provides remarkably accurate and robust estimates of the source wavelet even in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity. Our results also indicate that the proposed source wavelet estimation approach is relatively insensitive to ambient noise and to the phase characteristics of the starting wavelet. Finally, there appears to be little-to-no trade-off between the wavelet estimation and the tomographic imaging procedures.

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CE is a powerful analytical tool used to separate intact biomolecules such as proteins. The coupling of CE with TOF/MS produces a very promising method that can be used to detect and identify proteins in different matrices. This paper describes an efficient, rapid, and simple CE-ESI-TOF/MS procedure for the analysis of endogenous human growth hormone and recombinant human growth hormone without sample preparation. Operational factors were optimized using an experimental design, and the method was successfully applied to distinguish human growth hormone and recombinant human growth hormone in unknown samples.

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BACKGROUND: Clinical results of total ankle arthroplasty with early designs were disappointing. Recently-developed ankle prostheses have good mid-term results; however, limited information is available regarding effects of total ankle arthroplasty on ankle laxity. METHODS: Eight cadaveric lower extremities were tested with a custom device which enabled measurement of multi-axial forces, moments, and displacement during applied axial, shear, and rotational loading. Tests consisted of anterior-posterior and medial-lateral translation and internal-external rotation of the talus relative to the tibia during axial loads on the tibia simulating body weight (700 N) and an unloaded condition (5 N). Tests were performed in neutral, dorsiflexion, and plantarflexion. Laxity was determined for the intact ankle, and following insertion of an unconstrained total ankle implant, comparing load-displacement curve. FINDINGS: Laxity after total ankle arthroplasty did not approximate the normal ankle in most conditions tested. Displacement was significantly greater for total ankle arthroplasty in both posterior and lateral translation, and internal rotation, with 5 N axial loading, and anterior-posterior, medial-lateral translation, and internal-external rotation for 700 N axial loading. For the 700 N axial load condition, in the neutral ankle position, total anterior-posterior translation averaged 0.4 mm (SD 0.2 mm), but 6.0 mm (SD 1.5 mm) after total ankle arthroplasty (P<0.01). This study demonstrated more laxity in the replaced ankle than normal ankle for both unloaded and 700 N axially loaded conditions. INTERPRETATION: These data indicate the increased responsibility of the ligaments for ankle laxity after total ankle arthroplasty and suggest the importance of meticulous ligament reconstruction with total ankle arthroplasty operations.