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Summary Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive, and the nonuniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler’s model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many steam-assisted gravity drainage (SAGD) well pairs tends to deviate from a set of predefined type curves. Historical well data is a digital asset that can be utilized to develop machine learning (ML) or data-driven models for production forecasting. These models involve lower computational effort than numerical simulators and can offer better accuracy compared to proxy models based on Butler’s equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and “What If” scenario analysis due to their lower computational cost. This paper presents a novel ML workflow that includes a predictive model development using the random forest algorithm, clustering (to group well pairs by geological properties), Bayesian updating, and Monte Carlo sampling (for uncertainty quatification) for the forecasting of real-world SAGD injection and production data. The training data set consists of field data from 152 well pairs, including approximately 3 years of operational data. Each well pair’s data set involves data that are typically available for an SAGD well pair (e.g., operational data, geological, and well design parameters). This ML workflow can update predictions in real time and be applied for quantifying the uncertainties associated with the forecasts, making it an important step for development planning. To the best of the author’s knowledge, this is the first time ML algorithms have been applied to an SAGD field data set of this size.

Injection or co-injection of solvents has been proposed as an in-situ recovery mechanism for bitumen (Gupta et al., 2002). Solvent-based recovery schemes have several potential advantages over steam-based schemes such as SAGD. These benefits include lower GHG emissions and water requirements, lower capital intensity, and lower operating costs. Additionally, solvent-based recovery schemes have been proposed to improve the quality of produced bitumen (Jossy et al., 2008). The primary mechanism for in-situ upgrading during a solvent injection process is solvent de-asphalting. Solvent de-asphalting is a proven commercial process for processing and upgrading oil in surface facilities such as refineries and upgraders. Several laboratory studies have shown that solvent de-asphalting can also occur in situ, resulting in API improvement in the produced bitumen (AITF report, 2017 and 2018, Brons and Yu, 1995). In this work the economic benefits of upgrading bitumen in-situ will be studied. It will be shown that, at a given price environment, there exists an optimum level of upgrading (as measured by °API). Generally, too little upgrading reduces the value of bitumen due to blending and transportation costs, while too much upgrading reduces bitumen yield and ultimate recovery.

Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type-curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive and the non-uniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler's model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many SAGD well-pairs tends to deviate from a set of pre-defined type curves. Historical well data is a digital asset that can be utilized to develop machine learning or data-driven models for the purpose of production forecasting. These models involve lower computational effort compared to numerical simulators and can offer better accuracy compared to proxy models based on Butler's equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and "What If" scenario analysis due to its lower computational cost. This paper presents a novel machine learning workflow that includes a predictive model development using the random forest algorithm, clustering (to group wells by geological properties), Bayesian updating, Monte Carlo sampling (for uncertainty quatification), and genetic algorithm for the forecasting of real-world SAGD injection and production data, and optimization. The training dataset involves field data that is typically available for a SAGD well-pair (e.g., operational data, geological, and well design parameters). Just as importantly, this machine learning workflow can update predictions in real-time, be applied for the quantification of the uncertainties associated with the forecasts, and optimize steam allocation, making it an important step for development planning and field-wide optimization. To the best of the author's knowledge, this is the first time that machine learning algorithms have been applied to a SAGD data set of this size.

In SAGD operations, the steam chamber temperature can be as high as 250°C. Foam formulations that can withstand such high temperature are required for field deployment of foam as conformance control technique. An evaluation of 12 surfactant formulations for their viability as steam foam candidate at chamber temperatures as high as 250°C is presented. Unlike other studies in the literature that used nitrogen for foam generation, methane was employed in this project. This project reveals that strong foam can be formed at 250°C. Also, foam is only generated within a specific range of methane mass quality. Stronger foam was formed when steam and methane were injected simultaneously into the core. Lastly, the existence of residual oil in the core reduced the foam strength. We analyzed the effect of a few parameters on foam strength. Parameters considered include concentration of surfactant, presence of steam as well as methane, mass quality of methane, and residual oil saturation. Laboratory analyses comprising thermal stability, solubility, foam height, adsorption, and coreflood, were conducted. The learnings from this project has the ability to accelerate the application of foam systems in high-temperature SAGD operations to increase conformance, maximize steam usage, and ultimately lead to overall reduction in GHG emissions from SAGD projects.

Solvent Aided Process (SAP) combines benefit of using steam with solvents and has the potential to substantially improve SAGD performance with lower energy intensity and impact on the environment. Cenovus has been developing a SAP since 1996 and has conducted a few successful pilots (Senlac 2002, Christina Lake A0101 2004-2005, Christina Lake A0202 2009-2016). Cenovus has planned to implement a SAP in the Narrows Lake development when that project is sanctioned. The general practice for SAP is to co-inject solvent with steam after the SAGD peak rate is reached as steam is most effective for the vertical growth of steam chamber. However, by injecting solvent during the rise rate phase, there could be a large facility saving by not requiring an additional solvent line to each wellhead. Rather, solvent can be added to the steam header immediately after steam leaves the steam generation plant and distributed to all wells, regardless of their vintage. To de-risk the addition of solvent at the steam header, Cenovus implemented an early rise rate phase SAP pilot at Christina Lake A0201 well pair. The objective of this field pilot was to investigate the effects of butane injection during the SAGD early rise rate phase. This paper describes the implementation and results of the Christina Lake A0201 early rise rate SAP pilot. Presented in this paper are the results of this field test showing that no adverse effects on SAGD performance were observed when injecting solvent during the rise rate phase. This paper also shows the history match of field production data using CMG CMOST simulation. The findings of this investigation add to the knowledge base of information related to the optimal solvent injection timing in a SAP process. Insights into performing SAP history matches are also presented based on the simulation study that was undertaken.

A two-level full factorial design and/or a central composite design are used to optimize the operation of a distillation column by manipulating three variables: the feed flow rate, the reflux ratio and the steam flow to the reboiler. The response variable of interest is a profit function where the objective of this experiment is to examine the effect of each of the three factors and their interactions on this profit function. This is accomplished in a sieve tray type continuous distillation column operated at ambient pressure to separate a methanol–isopropanol mixture. Students were asked to determine the significant variables and the optimal operating conditions for steam rate, reflux ratio and feed rate such that the profit is maximized. As a result, this experiment not only exposes students to an industrially relevant unit operation but it also helps complement the classroom teaching on design of experiment concepts.

Fast and accurate determination of lipid content in microalgal cultures is a required step for algal bioprocess development and optimization. An improved high-throughput Nile Red staining protocol using a microplate fluorescence reader is proposed, resulting in an increased correlation coefficient and a reduction in the relative standard deviation of 84% when compared to previously reported staining protocols. Differences in the staining efficacy of Nile Red among different algal strains were observed, even though the performance of the proposed method was found to be satisfactory for the different Trebouxiophyceae and Chlorophyceae microalgal cultures evaluated. Therefore, the proposed Nile Red method should only be used for evaluating lipid content variations in previously standardized strains. BODIPY 505/515 was evaluated as a potential substitute for Nile Red, but was found to be unsuitable as a quantitative stain for algal lipids in the microplate-based staining technique.

A dynamic systems model was used to generate parameters describing a phenotype of Hypothalamic-Pituitary-Adrenal (HPA) behavior in a sample of 36 patients with chronic fatigue syndrome (CFS) and/or fibromyalgia (FM) and 36 case-matched healthy controls. Altered neuroendocrine function, particularly in relation to somatic symptoms and poor sleep quality, may contribute to the pathophysiology of these disorders. Blood plasma was assayed for cortisol and ACTH every 10 min for 24h. The dynamic model was specified with an ordinary differential equation using three parameters: (1) ACTH-adrenal signaling, (2) inhibitory feedback, and (3) non-ACTH influences. The model was "personalized" by estimating an individualized set of parameters from each participant's data. Day and nighttime parameters were assessed separately. Two nocturnal parameters (ACTH-adrenal signaling and inhibitory feedback) significantly differentiated the two patient subgroups ("fatigue-predominant" patients with CFS only versus "pain-predominant" patients with FM and comorbid chronic fatigue) from controls (all p's

In this work, Raman spectroscopy and a machine learning technique known as support vector regression (SVR) are used for building an online sensor to monitor the heterotrophic algal culture conditions in a computer-interfaced bench-scale microalgal bioreactor system, for the production of bio-oil. Monitoring of process conditions in algal cultures is required to enable the use of different control strategies to maximize oil productivity. In order to correlate the Raman spectra with culture conditions, three independent experimental datasets are used. The effect of several preprocessing techniques, including Savitzky–Golay filtering, baseline correction, and standard normal variate transformation, on the goodness of fit is evaluated. A multivariate sensor for real time online monitoring of the concentrations of biomass, glucose and percentage oil content is successfully built and validated. The advantages of using the proposed real-time on-line sensor are illustrated in an experimental microalgal bioreactor system.

Biological processes are often characterised by significant nonlinearities, noisy measurements and hidden process variables. The dynamic behaviour of such processes can be represented by stochastic differential equations obtained from physical laws. We propose a Bayesian algorithm for parameter estimation in stochastic nonlinear biological processes with unmeasured (or hidden) variables. The proposed algorithm, involves drawing random samples iteratively from a posterior density functions of the parameters and the hidden variables. A Bayesian sampling techniques is used to approximate these posterior density functions. Both Metropolis–Hastings algorithm and Gibbs sampling are used for sample generation. The algorithm is extended to handle multiple data sets and missing observations. The algorithm is applied to an experimental data set collected from an algal bioreactor system. © 2011 Canadian Society for Chemical Engineering

Microalgal feedstocks have shown potential for the production of biofuels and fine chemicals. Recently, an optimal experimental input profile for the identification of parameters of a microalgal bioreactor, containing 6 states and 12 unknown parameters has been proposed. In this work, the proposed design is implemented and parameters are estimated. It was found that the parameter estimation procedure can be made more computational efficient by the use of a novel iterative non-linear model reparameterization algorithm. By applying the proposed algorithm to experimental data, a good degree of output prediction was achieved along with bounds on the parameter values. The final, validated, model can be used for optimal control and process simulation.

Microalgal bioprocesses are of increasing interest due to the possibility of producing fine chemicals, pharmaceuticals, and biofuels. In this work, the parameter estimability of a first principles ODE model of a microalgal bioreactor, containing 6 states and 12 unknown parameters, is investigated. For this purpose, the system input trajectories are computed using the D-optimality criterion. Even by using a D-optimal input, not all parameters were found to have a significant effect on model predictions. Linear and non-linear transformations are used to partition the parameter space into estimable and inestimable subspaces. For the linear re-parameterization, a set of four directions in the twelve dimensional parameter space, along which a significant change in the output occurs, is identified using singular value decomposition of the parameter covariance matrix. The non-linear re-parameterization utilizes the three system rate functions as pseudo-outputs in order to perform a non-linear transformation which reduces the dimension of the parameter space from twelve to three. Both the proposed re-parameterization methods achieve a good degree of output prediction at a greatly decreased computational cost.

A non-invasive digital imaging procedure has been successfully developed to quantify the strain history during electric resistance welded (ERW) pipe forming. A curve fitting method was used to reconstruct a continuous and smooth pipe profile from digital image data. Three B-spline approximation methods (one-dimensional, two-dimensional, and trigonometric B-spline) were evaluated using the same set of digital image data, and validated against the strain gauge measurements. Trigonometric B-spline provides superior curve fitting and strain prediction. Using the trigonometric B-spline approximation method, a 3D strain history contour plot was generated to optimize the mill setup and identify the strain reversal.

Reverse engineering of complex gene networks is a significant step towards understanding various biological processes. In this work, a novel algorithm for reverse engineering gene networks on a genome-wide scale using a noisy gene expression data is proposed. Under the proposed scheme, the challenges in reverse engineering of gene networks from these gene expression datasets are highlighted. Also, the parameter space describing gene interaction is partitioned into estimable and inestimable linear subspaces. The estimable linear subspace is obtained by using principal components analysis (PCA), the Akaike information criterion (AIC), and jackknifing. Furthermore, the approach is tested and validated using a simulated gene network model.

In this work the problem of optimal input design (OID) in a receding-horizon framework for online parameter estimation is solved. The designed optimum input is used for dynamic experiment and subsequent estimation of parameters. A fuel cell experiment design and parameter estimation problem is investigated through the proposed approach. Some of the issues related to the application of the proposed method are examined and guidelines for selecting appropriate experimental settings are provided.

An understanding of local strain behaviour during a metal forming operation can be important but maybe difficult to measure. A non-invasive digital imaging procedure has been developed to quantify local strain behaviour in metal forming. From digital images obtained during the forming process, discrete spatial data of the object profile was acquired. B-spline curve fitting was then applied to obtain a continuous profile. The deformation strains were calculated from the curvature of the continuous profile. The procedure was applied to an electric resistance welded pipe forming process. Strain gauge data was used to validate the proposed procedure.

The hypothalamic-pituitary-adrenal (HPA) axis is a neuroendocrine control system regulating the stress response of the human body as well as affecting mood and cognition. HPA dysfunction has been associated with a variety of disorders including Fibromyalgia, and chronic fatigue syndrome (CFS). Recently, a lumped parameter model of the HPA axis which includes the effect of pharmacological agents has been proposed. In this work, an analysis of the HPA axis dynamics near rest points is used to develop an approach for robust control of the HPA system.

A system is identifiable if there exists a unique relationship between its input-output behaviour and the parameter values. Differential-Algebraic Equation (DAE) systems have an input-output behaviour that is described by a parameterized set of ordinary differential and algebraic equations. Methods proposed in the literature to test the identifiability of DAE systems are based on the tools of differential algebra and rely on time-differentiation of model equations. As a result, even when dealing with a few states and parameters, the calculations required for these methods may become intractable. An alternative, which we propose, is to linearize the non-linear DAE system about some rest point and then test the identifiability properties of the linearized system. In this work, we show that strong local identifiability of the linearized DAE system provides a sufficient condition for the strong local identifiability of the original non-linear DAE system. Un systeme est identifiable s'il existe une relation unique entre son comportement entree-sortie et les valeurs de parametres. Les systemes d'equations differentielles-algebriques (DAE) ont un comportement entree-sortie qui est decrit par un ensemble parametrise d'equations differentielles et algebriques ordinaires. Les methodes proposees dans la litterature scientifique pour tester l'identifiabilite des systemes DAE sont basees sur les outils de l'algebre differentielle et reposent sur la differentiation en temps des equations de modele. Ainsi, meme lorsqu'il s'agit de quelques etats et parametres, les calculs requis pour ces methodes peuvent devenir insolubles. Une autre facon de proceder, que nous proposons, consiste a lineariser le systeme DAE non lineaire vers un certain point de repos, puis de tester les proprietes d'identifiabilite du systeme linearise. Dans ce travail, nous montrons qu'une forte identifiabilite locale du systeme DAE linearise fournit une condition suffisante pour la forte identifiabilite locale du systeme DAE non lineaire original.

Mathematical models of physical systems often have parameters that must be estimated from measured data. Inestimable models have more parameters than can be estimated from available data. In this work, a method for identifying inestimable parameters or parameter combinations is proposed. The method is based on partitioning the parameter space into estimable and inestimable subspaces. Parameter combinations in the inestimable subspace have little effect on measured values and can therefore be fixed at a nominal value. The number of effective parameters is thereby reduced to the dimension of the estimable subspace. The proposed method is applicable over a range of experimental conditions. Detailed examples, including a batch bioreactor and a three-phase reactor system, are included for illustration. © 2008 American Institute of Chemical Engineers AIChE J, 2008

The objective of the investigation was to establish from a field trial feasibility of employing fewer wells for SAGD projects using solvent co-injection. An implicit issue in in situ recovery process of bitumen or extra-heavy is the requirement of heat which in the case of SAGD translates into the use of expensive steam. Gupta and Gittins (2006) have previously described a solvent co-injection based improvement to SAGD called Solvent Aided Process (SAP) which combines the benefit of using steam with solvents. As Edmunds et al. (2010) describe, over the years, significant work has been done to show the effectiveness of SAP in improving the energy efficiency of SAGD. SAGD is carried out in repeated patterns of side-by-side horizontal well-pairs, injector and producer lying approximately in a vertical plane, with these planes spaced from each other at a distance known as well-spacing. An optimal well spacing balances well capital cost against steam related capital and operating costs. SAP, as previously postulated (Gupta et al., 2006), allows this spacing to be larger (as much as by 100%, depending on reservoir conditions) requiring fewer well pairs to drain a given reservoir pool. To test the hypothesis Cenovus Energy conducted a field test at its Christina Lake thermal project, starting in 2009. The test location was specifically chosen to be in an isolated region which allowed for a larger lateral development of the vapor chamber without being intersected by any of the neighboring SAGD patterns. Presented in this paper are the results of this field test showing the expected production enhancement and reduction of steam to oil ratio. Furthermore, the results are interpreted in light of the allowance of a larger well spacing. The field results presented in this work confirm the feasibility of employing wider well spacing with SAP compared with steam-alone process or SAGD. This finding will be of interest to the wider industrial community as it holds the promise to improve the economics of the oil sands and similar projects. The findings of this investigation add to the knowledge base information related to well-spacing that has a significant impact on the economics of SAGD/SAP based projects.

Identifiability is a structural property of mathematical models. A model is identifiable if the model parameters can be uniquely identified from input-output behaviour. This work focuses on the application of a novel methodology for testing differential algebraic equation systems for identifiability. In this work, a non-linear, differential algebraic model of a liquid-liquid phase-transfer reactor is tested for identifiability using a linearization-based approach. This approach is shown to be useful for the testing of identifiability even for inherently non-linear models. Furthermore, this approach allows the effect of system parameters on the non-linear system to be qualitatively analyzed.

A mathematical model is identifiable if and only if there is a unique relationship between each parameter value and the input−output behavior of the model. If a model is not identifiable, there is ...

A mathematical model is identifiable if and only if there is a unique relationship between each parameter value and the input−output behavior of the model. If a model is not identifiable, there is no unique solution to the parameter estimation problem, regardless of the number and types of experiments that are performed. A new method for testing the identifiability of linear time-invariant (LTI) differential-algebraic systems is presented. This method is an extension of the Markov parameter method, proposed by Grewal and Glover (IEEE Trans. Autom. Control 1976, 21, 833) and Vajda (Sci. Pap. Inst. Tech. Cybernetics Tech. Univ. Wroclaw 1985, 29, 228) for LTI ordinary differential equation systems. In the proposed method, the differential-algebraic system is partitioned into an ordinary differential equation subsystem and an algebraic subsystem. This allows for the computation of structural invariants called the generalized Markov parameters (GMPs). A system is identifiable if and only if the dependence of t...

Background Adverse reactions to local anesthetics (LAs) are frequently reported. Although most of these reactions are not immune mediated, many patients are referred to allergy clinics and undergo extensive evaluation. Objective To determine the prevalence of true LA allergy among the patients referred for suspected hypersensitivity and to evaluate the usefulness of the currently used evaluation protocol. Methods A total of 236 patients referred to our allergy clinic for investigation of LA hypersensitivity were included in this study. The evaluation protocol was composed of skin prick and intradermal tests, followed by subcutaneous challenge with unrelated LA preparations that contained preservatives. Results Skin prick and intradermal test results were negative for all subjects. No objective adverse reactions were observed during the challenge in all but 1 patient, who developed local erythema at the site of injection and later underwent an uneventful challenge with a different LA. Conclusions Allergic reactions were not reproduced during testing and challenge with LA preparations that contained preservatives or preservatives with adrenaline in our large group of patients with suspected LA allergy. Since both prick and intradermal skin test results were negative in all the patients and did not provide us with useful information, we propose to modify the standard protocol by omitting intradermal tests and shortening the challenge. We also suggest that the whole procedure be performed with LAs that contain preservatives, which are usually the preferred preparations widely used in daily practice.

The optimization of biomass and oil productivities in heterotrophic cultures of Auxenochlorella protothecoides was achieved using a non-linear model-based approach. A 10-fold increase in the average biomass productivity, and a 16-fold increase in the maximum productivity, was observed with respect to batch cultures as a result of the proposed optimization strategy. Final cell density in the optimized culture was 144 g/L (dry weight), with 49.4%w/w oil content. Maximum lipid productivity was 20.16 g/L d, achieved during the exponential growth phase at an average cell density of 86 g/L. Lipid productivity in the optimized microalgal culture was higher than previously reported values for other oleaginous microorganisms. Oil composition analysis showed that the oil has a high quality as biodiesel precursor. The higher productivity and excellent lipid profile of the optimized microalgal culture make A. protothecoides an exceptional source for biodiesel production and a potential source of single cell oil for other applications.

Inference of gene networks is an important step in understanding cellular dynamics. In this work, a novel algorithm is proposed for inferring gene networks from gene expression data using linear ordinary differential equations. Under the proposed method, a combination of known statistical tools including partial least squares (PLS), leave-one-out jackknifing, and the Akaike information criterion (AIC) are used for robust estimation of gene connectivity matrix. The proposed approach is tested and validated using a computer simulated gene network model and an experimental data on a nine gene network in Eschericia coli.

In this work, the time varying characteristics of microalgal cultures are investigated. Microalgae are a promising source of biofuels and other valuable chemicals; a better understanding of their dynamic behavior is, however, required to facilitate process scale-up, optimization and control. Growth and oil production rates are evaluated as a function of carbon and nitrogen sources concentration. It is found that nitrogen has a major role in controlling the productivity of microalgae. Moreover, it is shown that there exists a nitrogen source concentration at which biomass and oil production can be maximized. A mathematical model that describes the effect of nitrogen and carbon source on growth and oil production is proposed. The model considers the uncoupling between nutrient uptake and growth, a characteristic of algal cells. Validity of the proposed model is tested on fed-batch cultures.

As mathematical models are increasingly available for biological/biomedical systems, dynamic optimization can be a useful tool for manipulating systems. Dynamic optimization is a computational tool for finding a sequence of optimal actions to attain desired outcomes from the system. This chapter discusses two dynamic optimization algorithms, model predictive control and dynamic programming, in the context of finding optimal treatment strategy for correcting hypothalamic-pituitary-adrenal (HPA) axis dysfunction. It is shown that dynamic programming approach has the advantage over the model predictive control (MPC) methodology in terms of robustness to error in parameter estimates and flexibility of accommodating clinically relevant objective function.

As mathematical models are increasingly available for biological/biomedical systems, dynamic optimization can be a useful tool for manipulating systems. Dynamic optimization is a computational tool for finding a sequence of optimal actions to attain desired outcomes from the system. This chapter discusses two dynamic optimization algorithms, model predictive control and dynamic programming, in the context of finding optimal treatment strategy for correcting hypothalamic–pituitary–adrenal (HPA) axis dysfunction. It is shown that dynamic programming approach has the advantage over the model predictive control (MPC) methodology in terms of robustness to error in parameter estimates and flexibility of accommodating clinically relevant objective function.

The hypothalamic-pituitary-adrenal (HPA) axis is a major system maintaining body homeostasis by regulating the neuroendocrine and sympathetic nervous systems as well modulating immune function. Recent work has shown that the complex dynamics of this system accommodate several stable steady states, one of which corresponds to the hypocortisol state observed in patients with chronic fatigue syndrome (CFS). At present these dynamics are not formally considered in the development of treatment strategies. Here we use model-based predictive control (MPC) methodology to estimate robust treatment courses for displacing the HPA axis from an abnormal hypocortisol steady state back to a healthy cortisol level. This approach was applied to a recent model of HPA axis dynamics incorporating glucocorticoid receptor kinetics. A candidate treatment that displays robust properties in the face of significant biological variability and measurement uncertainty requires that cortisol be further suppressed for a short period until adrenocorticotropic hormone levels exceed 30% of baseline. Treatment may then be discontinued, and the HPA axis will naturally progress to a stable attractor defined by normal hormone levels. Suppression of biologically available cortisol may be achieved through the use of binding proteins such as CBG and certain metabolizing enzymes, thus offering possible avenues for deployment in a clinical setting. Treatment strategies can therefore be designed that maximally exploit system dynamics to provide a robust response to treatment and ensure a positive outcome over a wide range of conditions. Perhaps most importantly, a treatment course involving further reduction in cortisol, even transient, is quite counterintuitive and challenges the conventional strategy of supplementing cortisol levels, an approach based on steady-state reasoning., Author Summary The hypothalamic-pituitary-adrenal (HPA) axis is one of the body's major control systems helping to regulate functions ranging from digestion to immune response to metabolism. Dysregulation of the HPA axis is associated with a number of neuroimmune disorders including chronic fatigue syndrome (CFS), depression, Gulf War illness (GWI), and posttraumatic stress disorder (PTSD). Objective diagnosis and targeted treatments of these disorders have proven challenging because they present no obvious lesion. However, the body's various components do not work in isolation, and it is important to consider exactly how their interactions might be altered by disease. Using a relatively simple mathematical description of the HPA axis, we show how the complex dynamical behavior of this system will readily accommodate multiple stable resting states, some of which may correspond to chronic loss of function. We propose that a well-directed push given at the right moment may encourage the axis to reset under its own volition. We use model-based predictive control theory to compute such a push. The result is counterintuitive and challenges the conventional time-invariant approach to disease and therapy. Indeed we demonstrate that in some cases it might be possible to exploit the natural dynamics of these physiological systems to stimulate recovery.

A deformation analysis of an ERW forming operation for a 114.3 mm (NPS 4) diameter steel pipe was undertaken using digital imaging technology. Digitals images taken of the inside profile of the pipe were calibrated and converted into Cartesian coordinates and a profile map along length of the forming operation was generated. A numerical curve fitting technique was applied to each individual profile to calculate the circumferential strain around the inside diameter. The circumferential strains calculated using the digital imaging technique corresponded well with strain gauge measurements. The calculated strains were combined into a 3D contour plot of strain along the length of the ERW process.

A system is identifiable if and only if the relationship between the parameters and the input-output behaviour of the system is unique. If a system is not identifiable, then accurate parameter estimation is not possible because identical input-output behaviour can be obtained for several values of the parameters. Most identifiability work in the literature has focused on ordinary differential equation (ODE) models. In this work we propose a method for testing linear time-invariant (LTI) differential-algebraic (DAE) systems for identifiability. Our method is computationally efficient, allows the treatment of systems that are nonlinear in the parameters, and allows the construction of an identifiable realization of the system.

Background The ability to diagnose and monitor asthma on the basis of noninvasive measurements of airway cellular dysfunction is difficult in the typical clinical setting. Objective Metabolomics is the study of molecules created by cellular metabolic pathways. We hypothesized that the metabolic activity of children with asthma would differ from healthy children without asthma. Furthermore, children having an asthma exacerbation would be different compared with children with stable asthma in outpatient clinics. Finally, we hypothesized that 1 H-nuclear magnetic resonance (NMR) would measure such differences using urine samples, one of the least invasive forms of biofluid sampling. Methods Children (135 total, ages 4-16 years) were enrolled, having met the criteria of healthy controls (C), stable asthma in the outpatient clinic (AO), or unstable asthma in the emergency department (AED). Partial least squares discriminant analysis was performed on the NMR data to create models of separation (70 metabolites were measured/urine sample). Some NMR data were withheld from modeling to be run blindly to determine possible diagnostic accuracy. Results On the basis of the model of AO versus C, 31 of 33 AO samples were correctly diagnosed with asthma (94% accuracy). Only 1 of 20 C samples was incorrectly labeled as asthma (5% misclassification). On the basis of the AO versus AED model, 31 of the 33 AO samples were correctly diagnosed as outpatient asthma (94% accurate). Conclusion This is the first report suggesting that 1 H-NMR analysis of human urine samples has the potential to be a useful clinical tool for physicians treating asthma.

The effect of parameter perturbations on the trajectories of dynamical systems has been extensively studied in the literature. The effect of parameter values on the number and type of rest-points in a dynamical system has received less attention. For systems that exhibit input multiplicities, the sensitivity of system trajectories to parameter values can be discontinuous when state trajectories are near the boundary between the basins of different attractors. In this work, a computational scheme for conducting a single and joint parameter sensitivity analysis for systems with multiple steady states is presented. The proposed approach is computationally efficient and relies on algebraic geometric tools to obtain a simplified polynomial representation of the system at steady state. The proposed approach is applied to a model of the human hypothalamic-pituitary-adrenal axis and is shown to be relevant to the development of mechanistic models of chronic diseases.

Age-associated changes in the embryo occur from the moment of conception as a part of development. The cornea is no exception in reflecting the developmental changes in the process of growth and the wear and tear of the aging process. It is often possible to estimate the age of the cornea by histological study including electron microscopy, or by the newly developed immunohistochemical methods of monoclonal antibodies against developmentally regulated corneal antigens, using the immunofluorescent or immunoperoxidase staining. The morphologic age-related manifestations in the eye are well known, but details of the use of monoclonal antibodies as a tool detecting specific antigens of the cornea including the corneal extracellular matrix will be presented here.

We have previously shown that a basic 64-kilodalton (no. 3 in the catalog of Moll et al.) and an acidic 55-ki-lodalton (no. 12) keratin are characteristic of suprabasal cell layers in cultured rabbit corneal epithelial colonies, and therefore may be regarded as markers for an advanced stage of corneal epithelial differentiation. Moreover, using an AE5 mouse monoclonal antibody, we showed that the 64-kilodalton keratin marker is expressed suprabasally in limbal epithelium but uniformly (basal layer included) in central corneal epithelium, suggesting that corneal basal cells are in a more differentiated state than limbal basal cells. In conjunction with previous data implicating the centripetal migration of corneal epithelial cells, our data support a model of corneal epithelial maturation in which corneal epithelial stem cells are located in the limbus, the transitional zone between the cornea and conjunctiva. In the present study, we analyzed the expression of the 64-kilodalton keratin in developing human corneal epithelium by immunohistochemical staining. At 8 weeks of gestation, the presumptive corneal epithelium is composed of a single layer of cuboidal cells with an overlying periderm; neither of these cell layers is AE5 positive. At 12–13 weeks of gestation, some superficial cells of the three- to four-layered epithelium become AE5 positive, providing the earliest sign of overt corneal epithelial differentiation. At 36 weeks, although the epithelium is morphologically mature (four to six layers), AE5 produces a suprabasal staining pattern, this being in contrast to the adult epithelium which exhibits uniform staining. Such a suprabasal AE5 staining pattern may be explained by two mechanisms which need not be mutually exclusive: a ‘hyperproliferative’ mode of corneal epithelial growth, or the influence of an immature corneal basement membrane. Additional data are required to test these hypotheses.

The ability to form 37°C stable rosettes with sheep red blood cells is a property of activated human peripheral blood T lymphocytes. The frequency of stable E-rosette-forming T cells is markedly increased in a number of disease states. In the present study, we have investigated the role of interleukin 1, a key mediator of macrophage-dependent T-cell activation, in the generation of stable E-rosette-forming human T cells. When human T cells were incubated with either normal human monocyte or murine macrophage tumor cell line-derived interleukin 1 (IL 1), the frequency of stable E-rosette-forming T cells increased dramatically. This effect of IL 1 was both concentration and time dependent. Enhanced stable E-rosette-forming activity was detected within 6 hr after addition of IL 1 and reached a plateau after 60–70 hr. The stimulatory effect of IL 1 was markedly inhibited by sodium azide. Mild trypsin treatment of T lymphocytes revealed that the effect of IL 1 on rosette formation was dependent on the appearance of new cell surface receptors for the sheep red blood cells. Our results support the hypothesis that the macrophage, via the release of IL 1, may play a pivotal role in the generation of stable E-rosette-forming T cells in a number of disease states. The effect of IL 1 on the appearance of stable E-rosette receptors on T lymphocytes is consistent with its general role in increasing the functional activity of different cell types involved in immune and inflammatory responses.

Collagen, fibronectin and laminin are important components of the extracellular matrix of the human cornea. We used the immunofluorescence technique with polyclonal antibodies directed against these proteins and to bullous pemphigoid antigen (BPA), in order to study their distribution in human corneas from 8 weeks of gestation to term and in adult corneas. Immunoreactivity was observed with antibodies to type I collagen in the limbus and the corneal stroma at 8 weeks of gestation. At 11 weeks of gestation it was found in epithelial basement membrane (EBM) and Descemet's membrane (DM) and continued thus throughout fetal and adult life. Type II collagen was not detected in fetal or adult cornea. Type III collagen was detected during 8-20th weeks of gestation in the EBM, DM and stroma. After 27th weeks of gestation, type III collagen could no longer be detected in the central cornea. Type IV collagen was detected in the EBM as early as 8 weeks of gestation and remained positive throughout fetal and adult life. Descemet's membrane was negative for type IV collagen at 8 weeks of gestation and became positive thereafter. Immunostaining for fibronectin in DM was negative at 8 weeks of gestation, followed by patchy staining of corneal stroma and EBM up to the age of 37 weeks of gestation. Staining in the EBM was negative or variable up to 70 years of age, and then became positive again in a 77 year old individual. Staining for LN was positive in the EBM after 8 weeks of gestation. Staining was negative in DM at that age, but became positive after 9 weeks of gestation. Staining for BPA was negative at 8-9 weeks of gestation, then gradually became positive.

The occurrence of Zollinger-Ellison syndrome and neurofibromatosis in the same patient has theoretic and practical implications. It was believed that the overlap between MEN-I and MEN-2 was restricted to the parathyroid glands. Recently, however, several reports have described patients in whom characteristics of both MEN conditions existed ..-o The findings in our patient support the concept of a relationship between MEN-I and MEN-2. Adult patients with neurofibromatosis and hypertension are likely to have pheochromocytoma, but renal artery stenosis is a more common cause for hypertension in young patients with neurofibromatosis." We were unable to demonstrate a pheochromocytoma or renal artery stenosis in our patient. After total gastrectomy, the urinary vanillylmandelic acid content decreased abruptly, and the blood pressure and heart rate returned to normal limits. We cannot explain these changes. A similar return to normal of the blood pressure was described by Tomlinson," who reported that a hypertensive patient with the Zollinger-Ellison syndrome, a pituitary tumor, and hyperparathyroidism became normotensive after total gastrectomy and parathyroid resection.

The in vitro proliferation of primed lymph node lymphocytes (LNL) in response to the soluble antigen ovalbumin (OVA) was dependent upon the presence of adherent cells. Restoration of OVA-induced LNL proliferation could be achieved by addition of highly purified lymphocyte-activating factor (LAF; Interleukin 1, IL 1): LAF (IL 1) did not stimulate LNL proliferation in the absence of the priming antigen or T lymphocytes. Furthermore, treatment of the LNL with antimacrophage serum completely blocked the ability of the LNL to respond to OVA and LAF (IL 1), suggesting that the residual macrophages in the LNL population were necessary to provide an additional function or signal, possibly antigen presentation, in conjunction with LAF (IL 1). These data therefore support the two signal hypothesis of macrophage-mediated lymphocyte activation and demonstrate the ability of LAF (IL 1) to provide one of these signals.

A case report of a 4-yr-old child who developed an anaphylactic reaction to parenteral nutrition is presented. Dermal allergy tests demonstrated a sensitivity to Travasol solution and Armour multivitamin 2 solution. This is the first reported case known to us of such a response to elemental parenteral nutrition.

The development of lymphocytic leukemia with a rapidly fatal clinical course is reported in a patient with kappa light-chain multiple myeloma treated with alkeran. The leukemic cells lacked the ultrastructural features of plasma cells but bore readily detectable B-cell markers and resembled lymphocytes under the light, transmission, and scanning electron microscopes. The leukemic phase is perhaps best defined as lymphocytic and probably represents a variant of plasma cell leukemia, in which the cells showed a degree of dedifferentiation from plasma cells to B lymphocytes. The possible relation between these 2 proliferative processes is discussed and the nature of leukemias developing in cases of plasma cell myeloma is briefly reviewed.