6 resultados para state estimation

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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This study provides further developments of the evaluation procedure for J and CTOD in SE(T) fracture specimens based on plastic eta-factors and load separation analysis. Non-linear finite element analyses for plane-strain and 3-D models provide the relationship between plastic work and crack driving forces which define the eta-values. Further analyses based on the load separation method define alternative eta-values for the analyzed specimen configurations. Overall, the present results provide improved estimation equations for J and CTOD as a function of loading condition (pin load vs. clamp ends), crack geometry and strain hardening properties. (C) 2011 Elsevier Ltd. All rights reserved.

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Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

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Background: Few equations have been developed in veterinary medicine compared to human medicine to predict body composition. The present study was done to evaluate the influence of weight loss on biometry (BIO), bioimpedance analysis (BIA) and ultrasonography (US) in cats, proposing equations to estimate fat (FM) and lean (LM) body mass, as compared to dual energy x-ray absorptiometry (DXA) as the referenced method. For this were used 16 gonadectomized obese cats (8 males and 8 females) in a weight loss program. DXA, BIO, BIA and US were performed in the obese state (T0; obese animals), after 10% of weight loss (T1) and after 20% of weight loss (T2). Stepwise regression was used to analyze the relationship between the dependent variables (FM, LM) determined by DXA and the independent variables obtained by BIO, BIA and US. The better models chosen were evaluated by a simple regression analysis and means predicted vs. determined by DXA were compared to verify the accuracy of the equations. Results: The independent variables determined by BIO, BIA and US that best correlated (p < 0.005) with the dependent variables (FM and LM) were BW (body weight), TC (thoracic circumference), PC (pelvic circumference), R (resistance) and SFLT (subcutaneous fat layer thickness). Using Mallows'Cp statistics, p value and r(2), 19 equations were selected (12 for FM, 7 for LM); however, only 7 equations accurately predicted FM and one LM of cats. Conclusions: The equations with two variables are better to use because they are effective and will be an alternative method to estimate body composition in the clinical routine. For estimated lean mass the equations using body weight associated with biometrics measures can be proposed. For estimated fat mass the equations using body weight associated with bioimpedance analysis can be proposed.

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The estimation of reference evapotranspiration (ETo), used in water balance, allows to determine soil water content, assisting on irrigation management. The present study aimed to compare simple ETo estimating methods with the Penman-Monteith (FAO), in the folowing time scales: daily, 5, 10, 15 and 30 days and monthly in the counties of Frederico Westphalen and Palmeira das Missoes, in the Rio Grande do Sul state, Brazil. The methods tested had their efficiency improved by increasing the time scale of analysis, keeping the same performance for both locations. The highest and lowest ETo values occurred in December and June, respectively. Most methods underestimated ETo. For any of the time scales Makking and Radiaton FAO24 methods can replace the Penman-Monteith for estimating ETo.

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Abstract Background The study of the distribution and ecology of sandfly species is essential for epidemiological surveillance and estimation of the transmission risk of Leishmania spp. infection. Findings In the present study, sandflies were captured in native fragmented forest areas in Rubião Júnior district, Botucatu municipality, São Paulo state, Brazil, between September 2001 and January 2005. A minimum of two automatic light traps were installed per night from 6 pm to 8 am, in different months, resulting in approximately 900 collecting hours. During this period, 216 sandfly specimens of sixteen species were captured. Pintomyia monticola and Brumptomyia guimaraesi were the most abundant with 56 specimens (25.93%) captured per species, followed by Pintomyia fischeri 28 (12.96%) and Psathyromyia pascalei 18 (8.33%). Other captured species were Lutzomyia amarali, Sciopemyia sordellii, Psathyromyia aragaoi, Nyssomyia whitmani, Migonemyia migonei, Pintomyia bianchigalatiae, Pintomyia misionensis, Brumptomyia carvalheiroi, Brumptomyia cardosoi, Brumptomyia cunhai, Brumptomyia nitzulescui, Brumptomyia brumpti and Brumptomyia spp. represented by 58 (26.85%) specimens. Conclusions Although less frequently found, the presence of Pintomyia fischeri, Nyssomyia whitmani and Migonemyia migonei, known vectors of Leishmania braziliensis, indicates risk of American cutaneous leishmaniasis occurrence. Moreover, the absence of Lutzomyia longipalpis-the main vector of Leishmania infantum chagasi, which is the agent of American visceral leishmaniasis-suggests that there is no risk of introduction and establishment of this disease in the studied area.

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Despite the great importance of soybeans in Brazil, there have been few applications of soybean crop modeling on Brazilian conditions. Thus, the objective of this study was to use modified crop models to estimate the depleted and potential soybean crop yield in Brazil. The climatic variable data used in the modified simulation of the soybean crop models were temperature, insolation and rainfall. The data set was taken from 33 counties (28 Sao Paulo state counties, and 5 counties from other states that neighbor São Paulo). Among the models, modifications in the estimation of the leaf area of the soybean crop, which includes corrections for the temperature, shading, senescence, CO2, and biomass partition were proposed; also, the methods of input for the model's simulation of the climatic variables were reconsidered. The depleted yields were estimated through a water balance, from which the depletion coefficient was estimated. It can be concluded that the adaptation soybean growth crop model might be used to predict the results of the depleted and potential yield of soybeans, and it can also be used to indicate better locations and periods of tillage.