Developing Parsimonious and Efficient Algorithms for Water Resources Optimization Problems

In the current water resources scientific literature, a wide variety of engineering design problems are solved in a simulation-optimization framework. These problems can have single or multiple objective functions and their decision variables can have discrete or continuous values. The majority of current literature in the field of water resources systems optimization report using heuristic global optimization algorithms, including evolutionary algorithms, with great success. These algorithms have multiple parameters that control their behavior both in terms of computational efficiency and the ability to find near globally optimal solutions. Values of these parameters are generally obtained by trial and error and are case study dependent. On the other hand, water resources simulation-optimization problems often have computationally intensive simulation models that can require seconds to hours for a single simulation. Furthermore, analysts may have limited computational budget to solve these problems, as such, the analyst may not be able to spend some of the computational budget to fine-tune the algorithm settings and parameter values. So, in general, algorithm parsimony in the number of parameters is an important factor in the applicability and performance of optimization algorithms for solving computationally intensive problems. A major contribution of this thesis is the development of a highly efficient, single objective, parsimonious optimization algorithm for solving problems with discrete decision variables. The algorithm is called Hybrid Discrete Dynamically Dimensioned Search, HD-DDS, and is designed based on Dynamically Dimensioned Search (DDS) that was developed by Tolson and Shoemaker (2007) for solving single objective hydrologic model calibration problems with continuous decision variables. The motivation for developing HD-DDS comes from the parsimony and high performance of original version of DDS. Similar to DDS, HD-DDS has a single parameter with a robust default value. HD-DDS is successfully applied to several benchmark water distribution system design problems where decision variables are pipe sizes among the available pipe size options. Results show that HD-DDS exhibits superior performance in specific comparisons to state-of-the-art optimization algorithms. The parsimony and efficiency of the original and discrete versions of DDS and their successful application to single objective water resources optimization problems with discrete and continuous decision variables motivated the development of a multi-objective optimization algorithm based on DDS. This algorithm is called Pareto Archived Dynamically Dimensioned Search (PA-DDS). The algorithm parsimony is a major factor in the design of PA-DDS. PA-DDS has a single parameter from its search engine DDS. In each iteration, PA-DDS selects one archived non-dominated solution and perturbs it to search for new solutions. The solution perturbation scheme of PA-DDS is similar to the original and discrete versions of DDS depending on whether the decision variable is discrete or continuous. So, PA-DDS can handle both types of decision variables. PA-DDS is applied to several benchmark mathematical problems, water distribution system design problems, and water resources model calibration problems with great success. It is shown that hypervolume contribution, HVC1, as defined in Knowles et al. (2003) is the superior selection metric for PA-DDS when solving multi-objective optimization problems with Pareto fronts that have a general (unknown) shape. However, one of the main contributions of this thesis is the development of a selection metric specifically designed for solving multi-objective optimization problems with a known or expected convex Pareto front such as water resources model calibration problems. The selection metric is called convex hull contribution (CHC) and makes the optimization algorithm sample solely from a subset of archived solutions that form the convex approximation of the Pareto front. Although CHC is generally applicable to any stochastic search optimization algorithm, it is applied to PA-DDS for solving six water resources calibration case studies with two or three objective functions. These case studies are solved by PA-DDS with CHC and HVC1 selections using 1,000 solution evaluations and by PA-DDS with CHC selection and two popular multi-objective optimization algorithms, AMALGAM and ε-NSGAII, using 10,000 solution evaluations. Results are compared based on the best case and worst case performances (out of multiple optimization trials) from each algorithm to measure the expected performance range for each algorithm. Comparing the best case performance of these algorithms shows that, PA-DDS with CHC selection using 1,000 solution evaluations perform very well in five out of six case studies. Comparing the worst case performance of the algorithms shows that with 1,000 solution evaluations, PA-DDS with CHC selection perform well in four out of six case studies. Furthermore, PA-DDS with CHC selection using 10,000 solution evaluations perform comparable to AMALGAM and ε-NSGAII. Therefore, it is concluded that PA-DDS with CHC selection is a powerful optimization algorithm for finding high quality solutions of multi-objective water resources model calibration problems with convex Pareto front especially when the computational budget is limited

5-Azacytidine Enhancing Expression of E-cadherin in Adenocarcinoma Cell Line

Introduction: In this study, we assessed the expression of E-cadherin in HT29 cell line treated with 5-Azacytidine and colorectal cancer patient in an Iranian population. E-cadherin expression promotes metastasis and prognosis of colorectal cancer (CRC). 5-Azacytidine, a DNA methyl transferase inhibitor, is a clinically used epigenetic drug for treatment of cancer including colorectal cancer, leading to genes activation involved in tumor suppression, especially E-cadherin. Materials and Methods: HT29 cell line treated with 5-Azacitidine and 40 polyps, 20 tumors and 40 adjacent normal tissues samples were enrolled in this study. Using the real-time PCR method, the expression levels of E-cadherin were examined in treated cell line and colorectal cancer tissue. Results: This study proves that 5-Azacytidine induces over expression of E-cadherin in adenocarcinoma cell line, while the expression levels of E-cadherin were not different in tumor and polyp than adjacent normal tissue. Conclusion: To conclude, 5-Azacytidine induces re-expression of E-cadherin in adenocarcinoma cell line. Thus, 5-Azacytidine as demethylation drug activated tumor suppressor gene as E-cadherin

Relationship between ureB Sequence Diversity, Urease Activity and Genotypic Variations of Different Helicobacter pylori Strains in Patients with Gastric Disorders

Association of the severity of Helicobacter pylori induced diseases with virulence entity of the colonized strains was proven in some studies. Urease has been demonstrated as a potent virulence factor for H. pylori. The main aim of this study was investigation of the relationships of ureB sequence diversity, urease activity and virulence genotypes of different H. pylori strains with histopathological changes of gastric tissue in infected patients suffering from different gastric disorders. Analysis of the virulence genotypes in the isolated strains indicated significant associations between the presence of severe active gastritis and cagA+ (P = 0.039) or cagA/iceA1 genotypes (P = 0.026), and intestinal metaplasia and vacA m1 (P = 0.008) or vacA s1/m2 (P = 0.001) genotypes. Our results showed a 2.4-fold increased risk of peptic ulcer (95% CI: 0.483–11.93), compared with gastritis, in the infected patients who had dupA positive strains; however this association was not statistically significant. The results of urease activity showed a significant mean difference between the isolated strains from patients with PUD and NUD (P = 0.034). This activity was relatively higher among patients with intestinal metaplasia. Also a significant associa­tion was found between the lack of cagA and increased urease activity among the isolated strains (P = 0.036). While the greatest sequencevariation of ureB was detected in a strain from a patient with intestinal metaplasia, the sole determined amino acid change in UreB sequence (Ala201Thr, 30%), showed no influence on urease activity. In conclusion, the supposed role of H. pylori urease to form peptic ulcer and advancing of intestinal metaplasia was postulated in this study. Higher urease activity in the colonizing H. pylori strains that present specific virulence factors was indicated as a risk factor for promotion of histopathological changes of gastric tissue that advance gastric malignancy

Immune checkpoints in targeted-immunotherapy of pancreatic cancer: New hope for clinical development

Immunotherapy has been recently considered as a promising alternative for cancer treatment. Indeed, targeting of immune checkpoint (ICP) strategies have shown significant success in human malignancies. However, despite remarkable success of cancer immunotherapy in pancreatic cancer (PCa), many of the developed immunotherapy methods show poor therapeutic outcomes in PCa with no or few effective treatment options thus far. In this process, immunosuppression in the tumor microenvironment (TME) is found to be the main obstacle to the effectiveness of antitumor immune response induced by an immunotherapy method. In this paper, the latest findings on the ICPs, which mediate immunosuppression in the TME have been reviewed. In addition, different approaches for targeting ICPs in the TME of PCa have been discussed. This review has also synopsized the cutting-edge advances in the latest studies to clinical applications of ICP-targeted therapy in PCa

The future of sensitivity analysis: an essential discipline for systems modeling and policy support

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society

Process based calibration of a continental-scale hydrological model using soil moisture and streamflow data

Study region: Nelson Churchill River Basin (NCRB), Canada, and USA. Study Focus: Soil temperature and moisture are essential variables that fluctuate based on soil depth, controlling several sub-surface hydrologic processes. The Hydrological Predictions for the Environment (HYPE) model’s soil profile depth can vary up to four meters, discretized into three soil layers. Here, we further discretized the HYPE subsurface domain to accommodate up to seven soil layers to improve the representation of subsurface thermodynamics and water transfer more accurately. Soil moisture data from different locations across NCRB are collected from 2013 to 2017 for model calibration. We use multi-objective optimization (MOO) to account for streamflow and soil moisture variability and improve the model fidelity at a continental scale. New hydrological insights: Our study demonstrates that MOO significantly improves soil moisture simulation from the median Kling Gupta Efficiency (KGE) of 0.21–0.66 without deteriorating the streamflow performance. Streamflow and soil moisture simulation performance improvements are statistically insignificant between the original three-layer and seven-layer discretization of HYPE. However, the finer discretization model shows improved simulation in sub-surface components such as the evapotranspiration when verified against reanalysis products, indicating a 12 % underestimation of evapotranspiration from the three-layer HYPE model. The improvement of the discretized HYPE model and simulating the soil temperature at finer vertical resolution makes it a prospective model for permafrost identification and climate change analysis