Establishing and Characterizing Patient-Derived Breast Cancer Cell Lines

Commercial cancer cell lines have long been extensively used as an important platform to study cancer. They have contributed to a plethora of discoveries in the field of cancer research. However, there are limitations with using these cell lines, such as induced mutations over the long-term in vitro culture. These mutations cause incorrect exhibition of the in vivo characteristics of the cancer cells. Here, we focused on establishing Patient-derived breast cancer cell lines and attempted to characterize them in terms of several biomarkers that are shown to be overexpressed in breast cancer cells. Patient-derived breast cancer cell lines are more reliable tools to study the molecular and cellular processes taking place in vivo, since they are freshly isolated from the tumor biopsy and do not undergo induced immortalization. We explored the CK19, Ki67, vimentin, EpCAM, E-cadherin, and N-cadherin expression in three successfully established patient-derived breast cancer cell lines

Bayesian Model Selection for hydro-morphodynamic models

A good grasp of hydro-morphodynamic processes plays a major role in modern river management to accommodate its often-conflicting functions. In the last century, a variety of models has been developed to improve our perception of sediment transport and the resulting changes in river bed topography, using several empirical formulations. Therefore, there is a demonstrated need to establish a framework that helps the river engineer to select the closest model to the measurements. This study suggested a Bayesian Model Selection (BMS) framework to direct the modeler towards the most robust and sensible representation of the hydro-morphodynamic conditions of the river under investigation. The proposed framework employs Bayesian Model Evidence (BME) resulting from Bayesian Model Averaging (BMA) as a model evaluation yardstick for ranking competing models. BMA performs a compromise between bias and variance, i.e. it blends a measure for goodness of fit with a penalty for unacceptable model complexity. This approach requires many model simulations, which are computationally expensive. However, this issue can be diminished by a mathematically optimal response surface via the aPC technique projects the original model. This response surface, also known as a reduced (surrogate) model can exhibit the reliance of the model on all relevant parameters for calibration at high order accuracy. The proposed framework was implemented in the model selection of two test cases; namely a test case model, based on an experiment done by Yen and Lee (1995) and a river model of a 10-km stretch of the lower Rhine, provided by the FederalWaterways Research Institute (BAW) in Karlsruhe. The results demonstrated that the proposed framework was acceptably able to detect the most desirable model in which a good agreement existed between the simulation results and measurement data when the complete knowledge of initial parameters lacked. Further, the BMS framework could direct us to the most probable parameter regions for the task of optimization via probability density distributions of uncertain variables. Overall, this research fills a void in the literature with respect to the selection of sediment transport equation for representation of hydro-morphodynamics of natural rivers. The suggested approach provides an objective guidance in the model selection to assist even less experienced users by reducing the professional expertise required for further optimization tasks

A surrogate-assisted Bayesian framework for uncertainty-aware validation benchmarks

Over the last century, computational modeling in geoscience, especially in porous media research, has witnessed tremendous improvement. After decades of development, the state-of-the-art simulators can now solve coupled partial differential equations governing the complex subsurface multiphase flow system within a practically large spatial and temporal domain. Given the importance of computational modeling, quality assessment of these models in light of the purpose of a given simulation is of paramount importance to engineering designers and managers, public officials, and those affected by the decisions based on the predictions. Users and developers of computational simulations deal with a challenging question: How should confidence in modeling and simulation be critically assessed? Validation is one of the primary methods for building and quantifying confidence in modeling and simulation. It investigates the degree to which a model accurately represents reality from the perspective of the intended application of the model. Usually, this comparison between model outputs and experimental data constitutes plotting the model results against data on the same axes to provide a visual assessment of agreement or lack thereof. While comparisons between model and data are at the heart of any validation procedure, there are several concerns with such naive comparisons. First, these comparisons tend to provide qualitative rather than quantitative assessments and are clearly insufficient as a basis for making decisions regarding model validity. Second, naive comparisons often disregard or only partly account for existing uncertainties in the experimental observations or the model input parameters. Third, such comparisons can not reveal whether the model is appropriate for the intended purposes, as they mainly focus on the agreement in the observable quantities. These pitfalls give rise to the need for an uncertainty-aware framework that includes a validation metric. This metric shall provide a measure for comparison of the system response quantities of an experiment with the ones from a computational model while accounting for uncertainties in both in a rigorous way. To address this need, we developed a statistical framework incorporating a probabilistic modeling technique using a fully Bayesian approach. The dissertation aims to help modelers perform uncertainty aware model validation benchmarks. A two-stage Bayesian multi-model framework is discussed for modeling tasks where a set of models are at hand. To make this framework applicable for computationally demanding models, it is extended to a surrogate-assisted framework, keeping the computational costs at a reasonable level. Moreover, correction factors were introduced to compensate for the surrogate error in the Bayesian hypothesis testing and Bayesian model selection, as using surrogate representations instead of the full-fidelity computational models introduces additional errors to the validation metrics. In this dissertation, I show how the Bayesian formalism could be materialized by employing the concept of polynomial chaos expansion to achieve more accurate surrogates with a sparse representation and account for the uncertainty in the surrogate’s predictions. I also highlight how such surrogate models could be constructed with as few simulations as the computational budget allows. To this end, sequential adaptive sampling strategies are discussed, in which one attempts to augment the initial design iteratively. By doing so, informative regions in the parameter space are adequately explored. These regions are more likely to provide valuable information on the behavior of the original model responses. Using a sequential sampling strategy avoids the waste of computational resources, as opposed to the so-called one-shot designs. A series of benchmark studies are conducted to investigate the predictive capabilities of different sparsity and sequential adaptive sampling methods. Moreover, I introduce BayesValidRox, an open-source, object-oriented Python package that provides an automated workflow for surrogate-based sensitivity analysis, Bayesian calibration, and validation of computational models with a modular structure. The uncertainty-aware validation framework was applied to a range of cases in the field of subsurface hydro-system modeling, mainly to flow and transport in porous media, such as flow simulation models in fractured porous media, coupling free flow and porous medium flow, and microbially induced calcite precipitation. However, this validation framework can be transferred to other disciplines in which models are used for prediction

The first eigenvalue and eigenfunction of a nonlinear elliptic system

In this paper, we study the first eigenvalue of a nonlinear elliptic system involving p-Laplacian as the differential operator. The principal eigenvalue of the system and the corresponding eigenfunction are investigated both analytically and numerically. An alternative proof to show the simplicity of the first eigenvalue is given. In addition, the upper and lower bounds of the first eigenvalue are provided. Then, a numerical algorithm is developed to approximate the principal eigenvalue. This algorithm generates a decreasing sequence of positive numbers and various examples numerically indicate its convergence. Further, the algorithm is generalized to a class of gradient quasilinear elliptic systems

Comparison of urinary and plasma ketone using urinary nitroprusside strip in patients with diabetic ketoacidosis

Background: Diabetic ketoacidosis is one of the most important and serious acute complications of diabetes and one of the medical emergencies that has been the most common cause of death in patients with diabetes. Prompt diagnosis and therapeutic intervention play an important role in reducing complications and mortality. The aim of this study was to compare urinary and plasma ketones using urinary nitroprusside strip in patients with diabetic ketoacidosis. Methods: In this cross-sectional study, 38 diabetic ketoacidosis patients were included in this study during the years 2017 and 2018 in the emergency department of Imam Khomeini hospital in Ardabil city. To test for plasma ketones, 2 cc of venous blood samples were taken and transferred to the laboratory for plasma isolation. The resulting plasma was examined with a urine dipstick and the discoloration was recorded. This was repeated at 0, 6 and 12 o'clock for serum ketones. All patients received their treatment according to the treatment protocol of diabetic ketoacidosis and urine ketone, PH and bicarbonate and BE patients were measured routinely. Results: Serum ketones were positive in all patients and 34 patients had positive urinary ketones. In this study, serum ketone levels were significantly correlated with blood acidity at baseline and with bicarbonate and basal arterial gas deficit at all three stages. However, urinary ketones had a significant correlation with blood acidity at baseline and at 12 hours, with bicarbonate at baseline and with arterial gas deficiency at 12 hours. Conclusions: The results showed that examination of plasma ketones with dipstick can be a useful, rapid and accurate clinical trial for the diagnosis of diabetic ketoacidosis in patients with diabetes

Implementing the Modern Management Systems in TQM Companies and the Surveillance Companies Performance

The purpose of the present study is to investigate the issues associated to the modern management systems in the performance of TQM companies and the surveillance companies. This research also discusses the mediators’ roles existed between the relationship of TQM and surveillance companies. This qualitative research aims at investigating the previous works done completely on the TQM methods in learning and the surveillance companies’ performances. This research anticipates that TQM supports both aspects of learning and surveillance companies’ performances. The further researches should be guided in a way that validates the empirical analysis or change the proposed suggestions of this research