Generating realizations of the permeability field drawn from a probability density function conditioned Oll inaccurate pressure or saturation data is difficult, even if the unconditional realizations are Gaussian random fields, because the problem is highly nonlinear. Inefficient methods that generate large numbers of rejected images, such as simulated annealing, must be ruled out as impractical because of the repeated need for reservoir flow simulation. In this paper, we present a two-step Markov chain Monte Carlo method of proposing transitions in the Metropolis-Hastings algorithm such th at the resulting state bas a high probability of acceptance. The first step is to propose an unconditional realization from a known probability distribution. This step could be carried out using any unconditional simulation technique so it is not limited to simple stochastic modeIs. The secend part of the proposed transition involves "history matching" of the unconditional simulation to production data th at has noise added. The decision to accept or reject the resulting "history matched" realization is made on the basis of the Metropolis-Hastings algorithm. Using this procedure on a simpie, but highly nonlinear, problem results in a suite of realizations whose distribution is nearly indistinquishable from the analytical distribution. Because, however, calculation of the acceptance criterion for calibrated models is very difficult, we propose an approximate acceptance criterion in which al! proposed transitions are accepted. This method is shown to work wel! on a smal! nonlinear problem for which the approximate distribution can be compared to the desired distribution.

Recently, we have shown that reservoir descriptions conditioned to multiwell pressure data and univariate and bivariate statistics for permeability and porosity can be obtained by techniques developed from inverse problem theory. The techniques yield estimates of well skin factors and porosity and permeability fields which honor both the spatial statistics and the pressure data. Imbedded in the methodology is the application of the Gauss-Newton method to construct the maximum a posteriori estimate of the reservoir parameters. If one wishes to determine permeability and porosity values at thousands of grid-blocks for use in a reservoir simulator, then inversion of the Hessian matrix-at each iteration of the Gauss-Newton procedure becomes computationally expensive. In this work, we present two methods to reparameterize the reservoir model to improve the computational efficiency. The first method uses spectral (eigenvalue/eigenvector) decomposition of the prior model. The second method uses a subspace method to reduce the size of the matrix problem that must be solved at each iteration of the Gauss-Newton method. It is shown that proper implementation of the reparameterization techniques significantly decreases the computational time required to generate realizations of the reservoir model, i.e., the porosity and permeability fields and well skin factors, conditioned to prior information on porosity and permeability and multiwell pressure data. Introduction Proper integration of static data (core, log, seismic, and geologic information) with dynamic data (production and well tests) is critical for reservoir characterization. It is known that ignoring prior information obtained from static data when history matching production data yields nonunique solutions, i.e., widely different estimates of the set of reservoir parameters may all yield an acceptable match of the production history. As early as 1976, Gavalas et al. recognized that incorporating prior data when history matching production data would reduce the variation in the estimates of gridblock values of porosity and permeability. Inverse problem theory provides a methodology to incorporate prior information when history matching production data. The standard application of inverse problem theory depends on the assumption that prior information on the model (set of reservoir parameters to be estimated) satisfies a multinormal distribution and that measurement errors in production data can be considered as Gaussian random variables with zero mean and known variance. Under these assumptions, the most probable model (the maximum a posteriori estimate) conditioned to both prior information and production data can be obtained by minimizing an objective function derived directly from the a posteriori probability density function. Since the a posteriori probability density function is derived from Bayes's theorem, this approach is often referred to as Bayesian estimation. It is convenient to minimize the objective function by a gradient method to obtain an approximation to the most probable model which is referred to as the maximum a posteriori estimate. Gavalas et al. used Gaussian type expressions for the covariance functions of porosity and permeability, the cross covariance between them, and the prior estimates of the means of porosity and permeability to incorporate prior information in the objective function when history matching multiwell pressure data obtained in a synthetic one-dimensional reservoir under single-phase flow conditions. They showed that incorporating the prior information reduced the errors in the estimates of permeability and porosity and also improved the convergence properties of the minimization algorithms considered.

Colorectal cancer is one of the leading causes of cancer deaths. It correlates to a high fat diet, which causes an increase of the secondary bile acids including deoxycholate (DOC) in the intestine. We aimed to determine the effects of DOC on intestinal carcinogenesis in Apc (min/+) mice, a model of spontaneous intestinal adenomas. Four-week old Apc (min/+) mice were treated with 0.2 % DOC in drinking water for 12 weeks. The number and size of tumors were measured, and tissue sections were prepared for the evaluation of intestinal carcinogenesis, cell proliferation, and apoptosis. The activation of Wnt signaling was detected in the intestinal tumor cells of the Apc (min/+) mice, and also in the human colon samples. DOC increased the number of intestine tumors by 165.1 % compared with that in untreated Apc (min/+) mice mainly in the middle and distal segments of the small intestine and colon. The numbers of all sizes of tumors in the small intestine were increased. Intestinal carcinogenesis was confirmed in 75 % mice in DOC treated-Apc (min/+) mice compared with 0 % in untreated mice. This was accompanied by promoting tumor cell proliferation and decreasing apoptosis, and increasing the percentage of β-catenin positive cells and its nuclear expression in intestinal tumor cells of Apc (min/+) mice, and also up-regulating the expression of cyclin D1. In addition, the activation of Wnt signaling also played in modulating human colon carcinogenesis. Our studies suggest that DOC enhances the multiplicity of intestinal tumor, and accelerates intestinal adenoma-adenocarcinoma sequence in Apc (min/+) mice mediated by stimulating tumor cell proliferation and decreasing apoptosis through enhancing Wnt signaling.

With the advent of population aging, aging-related diseases have become a challenge for governments worldwide. Sarcopenia has defined as a clinical syndrome associated with age-related loss such as skeletal muscle mass, strength, function, and physical performance. It is commonly seen in elderly patients with chronic diseases. Changes in lean mass are common critical determinants in the pathophysiology and progression of cardiovascular diseases (CVDs). Sarcopenia may be one of the most important causes of poor physical function and decreased cardiopulmonary function in elderly patients with CVDs. Sarcopenia may induce CVDs through common pathogenic pathways such as malnutrition, physical inactivity, insulin resistance, inflammation; these mechanisms interact. In this study, we aimed to investigate the relationship between sarcopenia and CVDs in the elderly. Further research is urgently needed to understand better the relationship, pathophysiology, clinical presentation, diagnostic criteria, and mechanisms of sarcopenia and CVDs, which may shed light on potential interventions to improve clinical outcomes and provide greater insight into the disorders above.