IMPACT OF EPA AND DHA SUPPLEMENTATION AND 15-LOX-1 EXPRESSION ON COLITIS AND COLITIS-ASSOCIATED COLORECTAL CANCER

Inflammatory bowel disease (IBD) patients not only suffer from colitis but also from increased morbidity and mortality of colitis-associated colorectal cancer (CAC). The enzyme 15-lipoxygenase-1 (15-LOX-1) is crucial to converting omega-3 fatty acid derivatives eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) to resolvins, potent anti-inflammatory products. 15-LOX-1 effects on the conversion of EPA and DHA to resolvins that subsequently exert anti-inflammatory and anti-tumorigenic effects have received little attention. To address this knowledge gap, we hypothesize that 15-LOX-1 expression in colonic epithelial cells is essential for resolvin biosynthesis from EPA and DHA to modulate immunophenotype, limit inflammation, promote resolution, and help prevent colitis and CAC. Mice were treated with dextran sodium sulfate (DSS) alone only to induce acute and chronic colitis, or with DSS following an azoxymethane injection to induce CAC. In the chronic colitis model, DHA diet and/or expression of 15-LOX-1 reduced inflammation and altered immune cell populations. In the CAC model, DHA reduces tumor numbers by 54% in the WT/DHA group and by 52% in the 15-LOX-1/DHA group. Increased levels of 17-HDHA, RvD1, RvD4, and RvD5 in the 15-LOX-1/DHA group were inversely correlated to tumor number. EPA diet with and without expression of 15-LOX-1 reduced tumors by 47-48%. In conclusion, our study strongly supports the critical role of 15-LOX-1 for RvD production from DHA. CAC suppression occurred with DHA supplementation with and without 15-LOX-1 transgenic expression and seems to be less dependent on the production of RvDs. Further in-depth mechanistic studies are therefore needed to be better define the role of resolvins in CAC and colonic tumorigenesis in general

Analytic Prognostic in the Linear Damage Case Applied to Buried Petrochemical Pipelines and the Complex Probability Paradigm

In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers by adding a supplementary three original axioms. Therefore, any experiment can be performed in the set C of complex probabilities which is the summation of the set R of real probabilities and the set M of imaginary probabilities. The purpose here is to include additional imaginary dimensions to the experiment taking place in the “real” laboratory in R and hence to evaluate all the probabilities. Consequently, the probability in the entire set C = R + M is permanently equal to one no matter what the stochastic distribution of the input random variable in R is; therefore the outcome of the probabilistic experiment in C can be determined perfectly. This is due to the fact that the probability in C is calculated after subtracting from the degree of our knowledge the chaotic factor of the random experiment. Consequently, the purpose in this chapter is to join my complex probability paradigm to the analytic prognostic of buried petrochemical pipelines in the case of linear damage accumulation. Accordingly, after the calculation of the novel prognostic model parameters, we will be able to evaluate the degree of knowledge, the magnitude of the chaotic factor, the complex probability, the probabilities of the system failure and survival, and the probability of the remaining useful lifetime; after that a pressure time t has been applied to the pipeline, which are all functions of the system degradation subject to random and stochastic influences

The Monte Carlo Techniques and the Complex Probability Paradigm

The concept of mathematical probability was established in 1933 by Andrey Nikolaevich Kolmogorov by defining a system of five axioms. This system can be enhanced to encompass the imaginary numbers set after the addition of three novel axioms. As a result, any random experiment can be executed in the complex probabilities set C which is the sum of the real probabilities set R and the imaginary probabilities set M. We aim here to incorporate supplementary imaginary dimensions to the random experiment occurring in the “real” laboratory in R and therefore to compute all the probabilities in the sets R, M, and C. Accordingly, the probability in the whole set C=R+M is constantly equivalent to one independently of the distribution of the input random variable in R, and subsequently the output of the stochastic experiment in R can be determined absolutely in C. This is the consequence of the fact that the probability in C is computed after the subtraction of the chaotic factor from the degree of our knowledge of the nondeterministic experiment. We will apply this innovative paradigm to the well-known Monte Carlo techniques and to their random algorithms and procedures in a novel way

The Paradigm of Complex Probability and Quantum Mechanics: The Infinite Potential Well Problem - The Position Wave Function

The system of axioms for probability theory laid in 1933 by Andrey Nikolaevich Kolmogorov can be extended to encompass the imaginary set of numbers and this by adding to his original five axioms an additional three axioms. Therefore, we create the complex probability set C, which is the sum of the real setR with its corresponding real probability, and the imaginary setM with its corresponding imaginary probability. Hence, all stochastic experiments are performed now in the complex setC instead of the real setR. The objective is then to evaluate the complex probabilities by considering supplementary new imaginary dimensions to the event occurring in the “real” laboratory. Consequently, the corresponding probability in the whole set C is always equal to one and the outcome of the random experiments that follow any probability distribution in R is now predicted totally inC. Subsequently, it follows that chance and luck in R is replaced by total determinism in C. Consequently, by subtracting the chaotic factor from the degree of our knowledge of the stochastic system, we evaluate the probability of any random phenomenon in C. My innovative complex probability paradigm (CPP) will be applied to the established theory of quantum mechanics in order to express it completely deterministically in the universe C=R+M

Limit States Design of Deep Foundations

Load and Resistance Factor Design (LRFD) shows promise as a viable alternative to the present working stress design (WSD) approach to foundation design. The key improvements of LRFD over the traditional Working Stress Design (WSD) are the ability to provide a more consistent level of reliability and the possibility of accounting for load and resistance uncertainties separately. In order for foundation design to be consistent with current structural design practice, the use of the same loads, load factors and load combinations would be required. In this study, we review the load factors presented in various LRFD Codes from the US, Canada and Europe. A simple firstorder second moment (FOSM) reliability analysis is presented to determine appropriate ranges for the values of the load factors. These values are compared with those proposed in the Codes. The comparisons between the analysis and the Codes show that the values of load factors given in the Codes generally fall within ranges consistent with the results of the FOSM analysis. For LRFD to gain acceptance in geotechnical engineering, a framework for the objective assessment of resistance factors is needed. Such a framework, based on reliability analysis is proposed in this study. Probability Density Functions (PDFs), representing design variable uncertainties, are required for analysis. A systematic approach to the selection of PDFs is presented. Such a procedure is a critical prerequisite to a rational probabilistic analysis in the development of LRFD methods in geotechnical engineering. Additionally, in order for LRFD to fulfill its promise for designs with more consistent reliability, the methods used to execute a design must be consistent with the methods assumed in the development of the LRFD factors. In this study, a methodology for the estimation of soil parameters for use in design equations is proposed that should allow for more statistical consistency in design inputs than is possible in traditional methods. Resistance factor values are dependent upon the values of load factors used. Thus, a method to adjust the resistance factors to account for code-specified load factors is also presented. Resistance factors for ultimate bearing capacity are computed using reliability analysis for shallow and deep foundations both in sand and in clay, for use with both ASCE-7 (1996) and AASHTO (1998) load factors. The various considered methods obtain their input parameters from the CPT, the SPT, or laboratory testing. Designers may wish to use design methods that are not considered in this study. As such, the designer needs the capability to select resistance factors that reflect the uncertainty of the design method chosen. A methodology is proposed in this study to accomplish this task, in a way that is consistent with the framework

Réutilisation des processus d'affaires pour le développement de systèmes d'information

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal