59 resultados para errors-in-variables model


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Background and Aims: We have optimized the isolated perfused mouse kidney (IPMK) model for studying renal vascular and tubular function in vitro using 24-28 g C57BL6J mice; the wild type controls for many transgenic mice. Methods and Results: Buffer composition was optimized for bovine serum albumin concentration (BSA). The effect of adding erythrocytes on renal function and morphology was assessed. Autoregulation was investigated during stepped increases in perfusion pressure. Perfusion for 60 min at 90-110 mmHg with Krebs bicarbonate buffer containing 5.5% BSA, and amino acids produced functional parameters within the in vivo range. Erythrocytes increased renal vascular resistance (3.8 +/- 0.2 vs 2.4 +/- 0.1 mL/min.mmHg, P < 0.05), enhanced sodium reabsorption (FENa = 0.3 +/- 0.08 vs 1.5 +/- 0.7%, P < 0.05), produced equivalent glomerular filtration rates (GFR; 364 +/- 38 vs 400 +/- 9 muL/min per gkw) and reduced distal tubular cell injury in the inner stripe (5.8 +/- 1.7 vs 23.7 +/- 3.1%, P < 0.001) compared to cell free perfusion. The IPMK was responsive to vasoconstrictor (angiotensin II, EC50 100 pM) and vasodilator (methacholine, EC50 75 nM) mediators and showed partial autoregulation of perfusate flow under control conditions over 65-85 mmHg; autoregulatory index (ARI) of 0.66 +/- 0.11. Angiotensin II (100 pM) extended this range (to 65-120 mmHg) and enhanced efficiency (ARI 0.21 +/- 0.02, P < 0.05). Angiotensin II facilitation was antagonized by methacholine (ARI 0.76 +/- 0.08) and papaverine (ARI 0.91 +/- 0.13). Conclusion: The IPMK model is useful for studying renal physiology and pathophysiology without systemic neurohormonal influences.

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In this work we investigate the energy gap between the ground state and the first excited state in a model of two single-mode Bose-Einstein condensates coupled via Josephson tunnelling. The ene:rgy gap is never zero when the tunnelling interaction is non-zero. The gap exhibits no local minimum below a threshold coupling which separates a delocalized phase from a self-trapping phase that occurs in the absence of the external potential. Above this threshold point one minimum occurs close to the Josephson regime, and a set of minima and maxima appear in the Fock regime. Expressions for the position of these minima and maxima are obtained. The connection between these minima and maxima and the dynamics for the expectation value of the relative number of particles is analysed in detail. We find that the dynamics of the system changes as the coupling crosses these points.

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A calibration methodology based on an efficient and stable mathematical regularization scheme is described. This scheme is a variant of so-called Tikhonov regularization in which the parameter estimation process is formulated as a constrained minimization problem. Use of the methodology eliminates the need for a modeler to formulate a parsimonious inverse problem in which a handful of parameters are designated for estimation prior to initiating the calibration process. Instead, the level of parameter parsimony required to achieve a stable solution to the inverse problem is determined by the inversion algorithm itself. Where parameters, or combinations of parameters, cannot be uniquely estimated, they are provided with values, or assigned relationships with other parameters, that are decreed to be realistic by the modeler. Conversely, where the information content of a calibration dataset is sufficient to allow estimates to be made of the values of many parameters, the making of such estimates is not precluded by preemptive parsimonizing ahead of the calibration process. White Tikhonov schemes are very attractive and hence widely used, problems with numerical stability can sometimes arise because the strength with which regularization constraints are applied throughout the regularized inversion process cannot be guaranteed to exactly complement inadequacies in the information content of a given calibration dataset. A new technique overcomes this problem by allowing relative regularization weights to be estimated as parameters through the calibration process itself. The technique is applied to the simultaneous calibration of five subwatershed models, and it is demonstrated that the new scheme results in a more efficient inversion, and better enforcement of regularization constraints than traditional Tikhonov regularization methodologies. Moreover, it is argued that a joint calibration exercise of this type results in a more meaningful set of parameters than can be achieved by individual subwatershed model calibration. (c) 2005 Elsevier B.V. All rights reserved.

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The Gauss-Marquardt-Levenberg (GML) method of computer-based parameter estimation, in common with other gradient-based approaches, suffers from the drawback that it may become trapped in local objective function minima, and thus report optimized parameter values that are not, in fact, optimized at all. This can seriously degrade its utility in the calibration of watershed models where local optima abound. Nevertheless, the method also has advantages, chief among these being its model-run efficiency, and its ability to report useful information on parameter sensitivities and covariances as a by-product of its use. It is also easily adapted to maintain this efficiency in the face of potential numerical problems (that adversely affect all parameter estimation methodologies) caused by parameter insensitivity and/or parameter correlation. The present paper presents two algorithmic enhancements to the GML method that retain its strengths, but which overcome its weaknesses in the face of local optima. Using the first of these methods an intelligent search for better parameter sets is conducted in parameter subspaces of decreasing dimensionality when progress of the parameter estimation process is slowed either by numerical instability incurred through problem ill-posedness, or when a local objective function minimum is encountered. The second methodology minimizes the chance of successive GML parameter estimation runs finding the same objective function minimum by starting successive runs at points that are maximally removed from previous parameter trajectories. As well as enhancing the ability of a GML-based method to find the global objective function minimum, the latter technique can also be used to find the locations of many non-global optima (should they exist) in parameter space. This can provide a useful means of inquiring into the well-posedness of a parameter estimation problem, and for detecting the presence of bimodal parameter and predictive probability distributions. The new methodologies are demonstrated by calibrating a Hydrological Simulation Program-FORTRAN (HSPF) model against a time series of daily flows. Comparison with the SCE-UA method in this calibration context demonstrates a high level of comparative model run efficiency for the new method. (c) 2006 Elsevier B.V. All rights reserved.

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Objective This study aims to understand the pathophysiology of anaphylaxis in Dirofflaria immitis-sensitised cats by analysing objective physiological and haematological measurements after challenge. Design Nineteen healthy D immitis-naive cats were sensitised using weekly injections of aluminium hydroxide-adjuvanted D immitis antigen, administered subcutaneously over 6 weeks. After sensitisation, cats (n = 16) were anaesthetised and challenged with intravenous D immitis antigen. A control group (n = 3) was sham-challenged using intravenous sterile 0.9% saline. Systolic blood pressure (measured non-invasively/indirectly), respiratory rate, degree of dyspnoea, blood 0, saturation, expired CO2, and heart rate and were measured immediately before and at 10 to 15 min intervals after challenge until terminal apnoea occurred or euthanasia at 140 mins post-challenge. Blood was collected for complete blood count immediately before and at 10, 20 and 35 mins after challenge. Clinical observations were recorded as they occurred. Results Antigen-challenged cats were divided into two groups: acute (apnoea occurred within 25 mins of challenge) and subacute (breathing at 25 mins after challenge). In both groups, the degree of dyspnoea increased and blood O-2 saturation and blood pressure decreased. Respiratory rate increased in the subacute group. Expired CO2 decreased in both Ag-challenged and control groups. Haematocrit increased in the subacute group. Neutrophil count decreased in the acute group and platelet count decreased in the subacute group. Eosinophil count decreased in the subacute and control groups. Sustained dyspnoea and gastrointestinal signs were the most common clinical manifestations of anaphylaxis in the antigen-challenged cats. Conclusions Intravenous challenge with D immitis antigen in sensitised cats results in dyspnoea, hypoxaemia and systemic hypotension accompanied by haemoconcentration.

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Traditional measures of termite food preference assess consequences of foraging behavior such as wood consumption, aggregation and/or termite survivorship. Although studies have been done to investigate the specifics of foraging behavior this is not generally integrated into choice assay experiments. Here choice assays were conducted with small isolated (orphaned) groups of workers and compared with choice assays involving foragers from whole nests (non-orphaned) in the laboratory. Aggregation to two different wood types was used as a measure of preference. Specific worker caste and instars participating in initial exploration were compared between assay methods, with samples of termites taken from nest carton material and sites where termites were feeding. Aggregation results differ between choice assay techniques. Castes and instars responsible for initial exploration, as determined in whole nest trials, were not commonly found exploring in isolated group trials, nor were they numerous in termites taken from active feeding sites. Consequently the use of small groups of M. turneri worker termites extracted from active feeding sites may not be appropriate for use in choice assays.

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Fusarium oxysporum is a soilborne fungal pathogen that causes major economic losses by inducing necrosis and wilting symptoms in many crop plants. In this study, the interaction between F. oxysporum and the model plant Arabidopsis thaliana has been investigated to better understand the nature of host defences that are effective against the Fusarium wilt pathogen. The expression of salicylate- and jasmonate-responsive defence genes in F. oxysporum-challenged roots of A. thaliana plants as well as in the roots of plants whose leaves were treated with salicylate or jasmonate was analysed. Unexpectedly, genes (e.g. PR1, PDF1.2, and CHIB) encoding proteins with defensive functions or transcription factors (e.g. ERF1, AtERF2, AtERF4 and AtMYC2) known to positively or negatively regulate defences against F. oxysporum were not activated in F. oxysporum-inoculated roots. In contrast, the jasmonate-responsive defence gene PDF1.2 was induced in the leaves of plants whose roots were challenged with F. oxysporum, but the salicylate- responsive PR1 gene was not induced in the leaves of inoculated plants. Exogenous salicylic acid treatment prior to inoculation, however, activated PR1 and BGL2 defence gene expression in leaves and provided increased F. oxysporum resistance as evidenced by reduced foliar necrosis and plant death. Exogenous salicylic acid treatment of the foliar tissue did not activate defence gene expression in the roots of plants. This suggests that salicylate- dependent defences may function in foliar tissue to reduce the development of pathogen-induced wilting and necrosis. Despite the induction of defence gene expression in the leaves by jasmonate, this treatment did not lead to increased resistance to F. oxysporum. Overall, the results presented here suggest that the genetic manipulation of plant defence signalling pathways is a useful strategy to provide increased Fusarium wilt resistance.

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Calibration of a groundwater model requires that hydraulic properties be estimated throughout a model domain. This generally constitutes an underdetermined inverse problem, for which a Solution can only be found when some kind of regularization device is included in the inversion process. Inclusion of regularization in the calibration process can be implicit, for example through the use of zones of constant parameter value, or explicit, for example through solution of a constrained minimization problem in which parameters are made to respect preferred values, or preferred relationships, to the degree necessary for a unique solution to be obtained. The cost of uniqueness is this: no matter which regularization methodology is employed, the inevitable consequence of its use is a loss of detail in the calibrated field. This, ill turn, can lead to erroneous predictions made by a model that is ostensibly well calibrated. Information made available as a by-product of the regularized inversion process allows the reasons for this loss of detail to be better understood. In particular, it is easily demonstrated that the estimated value for an hydraulic property at any point within a model domain is, in fact, a weighted average of the true hydraulic property over a much larger area. This averaging process causes loss of resolution in the estimated field. Where hydraulic conductivity is the hydraulic property being estimated, high averaging weights exist in areas that are strategically disposed with respect to measurement wells, while other areas may contribute very little to the estimated hydraulic conductivity at any point within the model domain, this possibly making the detection of hydraulic conductivity anomalies in these latter areas almost impossible. A study of the post-calibration parameter field covariance matrix allows further insights into the loss of system detail incurred through the calibration process to be gained. A comparison of pre- and post-calibration parameter covariance matrices shows that the latter often possess a much smaller spectral bandwidth than the former. It is also demonstrated that, as all inevitable consequence of the fact that a calibrated model cannot replicate every detail of the true system, model-to-measurement residuals can show a high degree of spatial correlation, a fact which must be taken into account when assessing these residuals either qualitatively, or quantitatively in the exploration of model predictive uncertainty. These principles are demonstrated using a synthetic case in which spatial parameter definition is based oil pilot points, and calibration is Implemented using both zones of piecewise constancy and constrained minimization regularization. (C) 2005 Elsevier Ltd. All rights reserved.

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This paper investigates the performance of EASI algorithm and the proposed EKENS algorithm for linear and nonlinear mixtures. The proposed EKENS algorithm is based on the modified equivariant algorithm and kernel density estimation. Theory and characteristic of both the algorithms are discussed for blind source separation model. The separation structure of nonlinear mixtures is based on a nonlinear stage followed by a linear stage. Simulations with artificial and natural data demonstrate the feasibility and good performance of the proposed EKENS algorithm.