975 resultados para differential observing response


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A previous seroepidemiological study in the rural zone of Vargem Alta (ES) SouthEast of Brazil, showed a prevalence of up to 9% of hepatitis B surface antigen (HBsAg) in some areas. One hundred susceptible children aging 1 to 5 years old were selected and immunized with a recombinant DNA hepatitis B vaccine (Smith-Kline 20 mcg) using the 0-1-6 months vaccination schedule. Blood samples were collected at the time of the first vaccine dose (month 0) in order to confirm susceptible individuals and 1,3,6 and 8 months after the first dose , to evaluate the antibody response. Our results showed that two and five months after the second dose, 79% and 88% of children seroconverted respectively, reaching 97% after the third dose. The levels of anti-HBs were calculated in milli International Units/ml (mIU/ml) and demonstrated the markedly increase of protective levels of antibodies after the third dose. These data showed a good immunogenicity of the DNA recombinant hepatitis B vaccine when administered in children of endemic areas.

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Activity rhythms in animal groups arise both from external changes in the environment, as well as from internal group dynamics. These cycles are reminiscent of physical and chemical systems with quasiperiodic and even chaotic behavior resulting from “autocatalytic” mechanisms. We use nonlinear differential equations to model how the coupling between the self-excitatory interactions of individuals and external forcing can produce four different types of activity rhythms: quasiperiodic, chaotic, phase locked, and displaying over or under shooting. At the transition between quasiperiodic and chaotic regimes, activity cycles are asymmetrical, with rapid activity increases and slower decreases and a phase shift between external forcing and activity. We find similar activity patterns in ant colonies in response to varying temperature during the day. Thus foraging ants operate in a region of quasiperiodicity close to a cascade of transitions leading to chaos. The model suggests that a wide range of temporal structures and irregularities seen in the activity of animal and human groups might be accounted for by the coupling between collectively generated internal clocks and external forcings.

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OBJECTIVE: To evaluate the predictive value of genetic polymorphisms in the context of BCG immunotherapy outcome and create a predictive profile that may allow discriminating the risk of recurrence. MATERIAL AND METHODS: In a dataset of 204 patients treated with BCG, we evaluate 42 genetic polymorphisms in 38 genes involved in the BCG mechanism of action, using Sequenom MassARRAY technology. Stepwise multivariate Cox Regression was used for data mining. RESULTS: In agreement with previous studies we observed that gender, age, tumor multiplicity and treatment scheme were associated with BCG failure. Using stepwise multivariate Cox Regression analysis we propose the first predictive profile of BCG immunotherapy outcome and a risk score based on polymorphisms in immune system molecules (SNPs in TNFA-1031T/C (rs1799964), IL2RA rs2104286 T/C, IL17A-197G/A (rs2275913), IL17RA-809A/G (rs4819554), IL18R1 rs3771171 T/C, ICAM1 K469E (rs5498), FASL-844T/C (rs763110) and TRAILR1-397T/G (rs79037040) in association with clinicopathological variables. This risk score allows the categorization of patients into risk groups: patients within the Low Risk group have a 90% chance of successful treatment, whereas patients in the High Risk group present 75% chance of recurrence after BCG treatment. CONCLUSION: We have established the first predictive score of BCG immunotherapy outcome combining clinicopathological characteristics and a panel of genetic polymorphisms. Further studies using an independent cohort are warranted. Moreover, the inclusion of other biomarkers may help to improve the proposed model.

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The objective of lhe present study was to determine the stimulatory response to antirabies vaccination promoted by glucan in mice. Glucan increased both resistance to infection and antibody titres and this effect was more evident when glucan was used at dose of 0.5 mg, administered intraperitoneally before, during and after immunization and when the challenge virus was applied to the foot-pad.

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In today’s healthcare paradigm, optimal sedation during anesthesia plays an important role both in patient welfare and in the socio-economic context. For the closed-loop control of general anesthesia, two drugs have proven to have stable, rapid onset times: propofol and remifentanil. These drugs are related to their effect in the bispectral index, a measure of EEG signal. In this paper wavelet time–frequency analysis is used to extract useful information from the clinical signals, since they are time-varying and mark important changes in patient’s response to drug dose. Model based predictive control algorithms are employed to regulate the depth of sedation by manipulating these two drugs. The results of identification from real data and the simulation of the closed loop control performance suggest that the proposed approach can bring an improvement of 9% in overall robustness and may be suitable for clinical practice.

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As it is widely known, in structural dynamic applications, ranging from structural coupling to model updating, the incompatibility between measured and simulated data is inevitable, due to the problem of coordinate incompleteness. Usually, the experimental data from conventional vibration testing is collected at a few translational degrees of freedom (DOF) due to applied forces, using hammer or shaker exciters, over a limited frequency range. Hence, one can only measure a portion of the receptance matrix, few columns, related to the forced DOFs, and rows, related to the measured DOFs. In contrast, by finite element modeling, one can obtain a full data set, both in terms of DOFs and identified modes. Over the years, several model reduction techniques have been proposed, as well as data expansion ones. However, the latter are significantly fewer and the demand for efficient techniques is still an issue. In this work, one proposes a technique for expanding measured frequency response functions (FRF) over the entire set of DOFs. This technique is based upon a modified Kidder's method and the principle of reciprocity, and it avoids the need for modal identification, as it uses the measured FRFs directly. In order to illustrate the performance of the proposed technique, a set of simulated experimental translational FRFs is taken as reference to estimate rotational FRFs, including those that are due to applied moments.

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Anti-Toxocara antibody production and persistence were studied in experimental infections of BALB/c mice, according to three different schedules: Group I (GI) - 25 mice infected with 200 T. canis eggs in a single dose; Group II (GII) 25 mice infected with 150 T. canis eggs given in three occasions, 50 in the 1st, 50 in the 5th and 50 in the 8th days; Group III (GIII) - 25 mice also infected with 150 T. canis eggs, in three 50 eggs portions given in the 1st, 14th and 28th days. A 15 mice control group (GIV) was maintained without infection. In the 30th, 50th, 60th, 75th, 105th and 180th post-infection days three mice of the GI, GII and GIII groups and two mice of the control group had been sacrificed and exsanguinated for sera obtention. In the 360th day the remainder mice of the four groups were, in the same way, killed and processed. The obtained sera were searched for the presence of anti-Toxocara antibodies by an ELISA technique, using T. canis larvae excretion-secretion antigen. In the GI and GII, but not in the GIII, anti-Toxocara antibodies had been found, at least, up to the 180th post-infection day. The GIII only showed anti-Toxocara antibodies, at significant level, in the 30th post-infection day.

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The response to interferon treatment in chronic hepatitis NANB/C has usually been classified as complete, partial or absent, according to the behavior of serum alanine aminotransferase (ALT). However, a more detailed observation of the enzymatic activity has shown that the patterns may be more complex. The aim of this study was to describe the long term follow-up and patterns of ALT response in patients with chronic hepatitis NANB/C treated with recombinant interferon-alpha. A follow-up of 6 months or more after interferon-a was achieved in 44 patients. We have classified the serum ALT responses into six patterns and the observed frequencies were as follows: I. Long term response = 9 (20.5%); II. Normalization followed by persistent relapse after IFN = 7 (15.9%); III. Normalization with transient relapse = 5 (11.9%); IV. Temporary normalization and relapse during IFN = 4 (9.1%); V. Partial response (more than 50% of ALT decrease) = 7 (15.9%); VI. No response = 12 (27.3%). In conclusion, ALT patterns vary widely during and after IFN treatment and can be classified in at least 6 types.

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Demand response is assumed as an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets and of the increasing use of renewable-based energy sources. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed in this paper aims the minimization of the operation costs in a distribution network operated by a virtual power player that manages the available energy resources focusing on hour ahead re-scheduling. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs. Real time pricing is also applied. The proposed model is especially useful when actual and day ahead wind forecast differ significantly. Its application is illustrated in this paper implementing the characteristics of a real resources conditions scenario in a 33 bus distribution network with 32 consumers and 66 distributed generators.

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Demand response has gain increasing importance in the context of competitive electricity markets environment. The use of demand resources is also advantageous in the context of smart grid operation. In addition to the need of new business models for integrating demand response, adequate methods are necessary for an accurate determination of the consumers’ performance evaluation after the participation in a demand response event. The present paper makes a comparison between some of the existing baseline methods related to the consumers’ performance evaluation, comparing the results obtained with these methods and also with a method proposed by the authors of the paper. A case study demonstrates the application of the referred methods to real consumption data belonging to a consumer connected to a distribution network.

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The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results.