The conveyor belt hypothesis for thymocyte migration: participation of adhesion and de-adhesion molecules

Thymocyte differentiation is the process by which bone marrow-derived precursors enter the thymus, proliferate, rearrange the genes and express the corresponding T cell receptors, and undergo positive and/or negative selection, ultimately yielding mature T cells that will represent the so-called T cell repertoire. This process occurs in the context of cell migration, whose cellular and molecular basis is still poorly understood. Kinetic studies favor the idea that these cells leave the organ in an ordered pattern, as if they were moving on a conveyor belt. We have recently proposed that extracellular matrix glycoproteins, such as fibronectin, laminin and type IV collagen, among others, produced by non-lymphoid cells both in the cortex and in the medulla, would constitute a macromolecular arrangement allowing differentiating thymocytes to migrate. Here we discuss the participation of both molecules with adhesive and de-adhesive properties in the intrathymic T cell migration. Functional experiments demonstrated that galectin-3, a soluble ß-galactoside-binding lectin secreted by thymic microenvironmental cells, is a likely candidate for de-adhesion proteins by decreasing thymocyte interaction with the thymic microenvironment.

Working fluid selection and design of small-scale waste heat recovery systems based on organic Rankine cycles

Demand for the use of energy systems, entailing high efficiency as well as availability to harness renewable energy sources, is a key issue in order to tackling the threat of global warming and saving natural resources. Organic Rankine cycle (ORC) technology has been identified as one of the most promising technologies in recovering low-grade heat sources and in harnessing renewable energy sources that cannot be efficiently utilized by means of more conventional power systems. The ORC is based on the working principle of Rankine process, but an organic working fluid is adopted in the cycle instead of steam. This thesis presents numerical and experimental results of the study on the design of small-scale ORCs. Two main applications were selected for the thesis: waste heat re- covery from small-scale diesel engines concentrating on the utilization of the exhaust gas heat and waste heat recovery in large industrial-scale engine power plants considering the utilization of both the high and low temperature heat sources. The main objective of this work was to identify suitable working fluid candidates and to study the process and turbine design methods that can be applied when power plants based on the use of non-conventional working fluids are considered. The computational work included the use of thermodynamic analysis methods and turbine design methods that were based on the use of highly accurate fluid properties. In addition, the design and loss mechanisms in supersonic ORC turbines were studied by means of computational fluid dynamics. The results indicated that the design of ORC is highly influenced by the selection of the working fluid and cycle operational conditions. The results for the turbine designs in- dicated that the working fluid selection should not be based only on the thermodynamic analysis, but requires also considerations on the turbine design. The turbines tend to be fast rotating, entailing small blade heights at the turbine rotor inlet and highly supersonic flow in the turbine flow passages, especially when power systems with low power outputs are designed. The results indicated that the ORC is a potential solution in utilizing waste heat streams both at high and low temperatures and both in micro and larger scale appli- cations.

Portfolio Selection via Composite Measure Analysis: Evidence from the U.S. Stock Market

The aim of this research is to examine the pricing anomalies existing in the U.S. market during 1986 to 2011. The sample of stocks is divided into decile portfolios based on seven individual valuation ratios (E/P, B/P, S/P, EBIT/EV, EVITDA/EV, D/P, and CE/P) and price momentum to investigate the efficiency of individual valuation ratio and their combinations as portfolio formation criteria. This is the first time in financial literature when CE/P is employed as a constituent of composite value measure. The combinations are based on median scaled composite value measures and TOPSIS method. During the sample period value portfolios significantly outperform both the market portfolio and comparable glamour portfolios. The results show the highest return for the value portfolio that was based on the combination of S/P & CE/P ratios. The outcome of this research will increase the understanding on the suitability of different methodologies for portfolio selection. It will help managers to take advantage of the results of different methodologies in order to gain returns above the market.

Green supplier selection via Multiple Criteria Data Envelopment Analysis

An appropriate supplier selection and its profound effects on increasing the competitive advantage of companies has been widely discussed in supply chain management (SCM) literature. By raising environmental awareness among companies and industries they attach more importance to sustainable and green activities in selection procedures of raw material providers. The current thesis benefits from data envelopment analysis (DEA) technique to evaluate the relative efficiency of suppliers in the presence of carbon dioxide (CO2) emission for green supplier selection. We incorporate the pollution of suppliers as an undesirable output into DEA. However, to do so, two conventional DEA model problems arise: the lack of the discrimination power among decision making units (DMUs) and flexibility of the inputs and outputs weights. To overcome these limitations, we use multiple criteria DEA (MCDEA) as one alternative. By applying MCDEA the number of suppliers which are identified as efficient will be decreased and will lead to a better ranking and selection of the suppliers. Besides, in order to compare the performance of the suppliers with an ideal supplier, a “virtual” best practice supplier is introduced. The presence of the ideal virtual supplier will also increase the discrimination power of the model for a better ranking of the suppliers. Therefore, a new MCDEA model is proposed to simultaneously handle undesirable outputs and virtual DMU. The developed model is applied for green supplier selection problem. A numerical example illustrates the applicability of the proposed model.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Service product internationalisation : market selection for new service products in a multinational financial service firm

The significance and impact of services in the modern global economy has become greater and there has been more demand for decades in the academic community of international business for further research into better understanding internationalisation of services. Theories based on the internationalisation of manufacturing firms have been long questioned for their applicability to services. This study aims at contributing to understanding internationalisation of services by examining how market selection decisions are made for new service products within the existing markets of a multinational financial service provider. The study focused on the factors influencing market selection and the study was conducted as a case study on a multinational financial service firm and two of its new service products. Two directors responsible for the development and internationalisation of the case service products were interviewed in guided semi-structured interviews based on themes adopted from the literature review and the outcome theoretical framework. The main empirical findings of the study suggest that the most significant factors influencing the market selection for new service products within a multinational financial service firm’s existing markets are: commitment to the new service products by both the management and the rest of the product related organisation; capability and competence by the local country organisations to adopt new services; market potential which combines market size, market structure and competitive environment; product fit to the market requirements; and enabling partnerships. Based on the empirical findings, this study suggests a framework of factors influencing market selection for new service products, and proposes further research issues and methods to test and extend the findings of this research.

Candidate gene linkage analysis indicates genetic heterogeneity in Marfan syndrome

Marfan syndrome (MFS) is an autosomal dominant disease of the connective tissue that affects the ocular, skeletal and cardiovascular systems, with a wide clinical variability. Although mutations in the FBN1 gene have been recognized as the cause of the disease, more recently other loci have been associated with MFS, indicating the genetic heterogeneity of this disease. We addressed the issue of genetic heterogeneity in MFS by performing linkage analysis of the FBN1 and TGFBR2 genes in 34 families (345 subjects) who met the clinical diagnostic criteria for the disease according to Ghent. Using a total of six microsatellite markers, we found that linkage with the FBN1 gene was observed or not excluded in 70.6% (24/34) of the families, and in 1 family the MFS phenotype segregated with the TGFBR2 gene. Moreover, in 4 families linkage with the FBN1 and TGFBR2 genes was excluded, and no mutations were identified in the coding region of TGFBR1, indicating the existence of other genes involved in MFS. Our results suggest that the genetic heterogeneity of MFS may be greater that previously reported.

REGγ is a strong candidate for the regulation of cell cycle, proliferation and the invasion by poorly differentiated thyroid carcinoma cells

REGγ is a proteasome activator that facilitates the degradation of small peptides. Abnormally high expression of REGγ has been observed in thyroid carcinomas. The purpose of the present study was to explore the role of REGγ in poorly differentiated thyroid carcinoma (PDTC). For this purpose, small interfering RNA (siRNA) was introduced to down-regulate the level of REGγ in the PDTC cell line SW579. Down-regulation of REGγ at the mRNA and protein levels was confirmed by RT-PCR and Western blot analyses. FACS analysis revealed cell cycle arrest at the G1/S transition, the MTT assay showed inhibition of cell proliferation, and the Transwell assay showed restricted cell invasion. Furthermore, the expression of the p21 protein was increased, the expression of proliferating cell nuclear antigen (PCNA) protein decreased, and the expression of the p27 protein was unchanged as shown by Western blot analyses. REGγ plays a critical role in the cell cycle, proliferation and invasion of SW579 cells. The alteration of p21 and PCNA proteins related to the down-regulation of REGγ suggests that p21 and PCNA participate in the process of REGγ regulation of cell cycle progression and cell proliferation. Thus, targeting REGγ has a therapeutic potential in the management of PDTC patients.

DNA methylation patterns of candidate genes regulated by thymine DNA glycosylase in patients with TP53 germline mutations

Li-Fraumeni syndrome (LFS) is a rare, autosomal dominant, hereditary cancer predisposition disorder. In Brazil, the p.R337H TP53 founder mutation causes the variant form of LFS, Li-Fraumeni-like syndrome. The occurrence of cancer and age of disease onset are known to vary, even in patients carrying the same mutation, and several mechanisms such as genetic and epigenetic alterations may be involved in this variability. However, the extent of involvement of such events has not been clarified. It is well established that p53 regulates several pathways, including the thymine DNA glycosylase (TDG) pathway, which regulates the DNA methylation of several genes. This study aimed to identify the DNA methylation pattern of genes potentially related to the TDG pathway (CDKN2A, FOXA1, HOXD8, OCT4, SOX2, and SOX17) in 30 patients with germline TP53mutations, 10 patients with wild-type TP53, and 10 healthy individuals. We also evaluated TDG expression in patients with adrenocortical tumors (ADR) with and without the p.R337H TP53 mutation. Gene methylation patterns of peripheral blood DNA samples assessed by pyrosequencing revealed no significant differences between the three groups. However, increased TDG expression was observed by quantitative reverse transcription PCR in p.R337H carriers with ADR. Considering the rarity of this phenotype and the relevance of these findings, further studies using a larger sample set are necessary to confirm our results.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.