29 research outputs found
Gene expression profiling reveals a close relationship between follicular lymphoma grade 3A and 3B, but distinct profiles of follicular lymphoma grade 1 and 2
No full textA linear progression model of follicular lymphomas (FL) FL1, FL2 and FL3A has been favored, since FL3A often co-exist with an FL1/2 component. FL3B, in contrast, is thought to be more closely related to diffuse large B-cell lymphoma (DLBCL), and both are often simultaneously present in one tumor (DLBCL/FL3B). To obtain more detailed insights into follicular lymphoma progression, a comprehensive analysis of a well-defined set of FL1/2 (n=22), FL3A (n=16), FL3B (n=6), DLBCL/FL3B (n=9), and germinal center B-cell-type diffuse large B-cell lymphoma (n=45) was undertaken using gene expression profiling, immunohistochemical stainings and genetic analyses by fluorescence in situ hybridization. While immunohistochemical (CD10, IRF4/MUM1, Ki67, BCL2, BCL6) and genetic profiles (translocations of BCL2, BCL6 and MYC) delineate FL1-3A from FL3B and DLBCL/FL3B, significant differences were observed between FL1/2 and FL3A upon gene expression profiling. Interestingly, FL3B turned out to be closely related to FL3A, not categorizing within a separate gene expression cluster, and both FL3A and FL3B showed overlapping profiles in between FL1/2 and diffuse large B-cell lymphoma. Finally, based upon their gene expression pattern, DLBCL/FL3B represent a composite form of FL3B and DLBCL, with the majority of samples more closely resembling the latter. The fact that gene expression profiling clearly separated FL1/2 from both FL3A and FL3B suggests a closer biological relationship between the latter. This notion, however, is in contrast to immunohistochemical and genetic profiles of the different histological FL subtypes that point to a closer relationship between FL1/2 and FL3A, and separates them from FL3B
Identification and characterization of progression factors in follicular Non-Hodgkin’s lymphomas in a collective of the project Molecular Mechanisms of Malignant Lymphomas
No full textDas follikuläre Lymphom (FL) wird nach der aktuellen Klassifikation der WHO (World Health Organization Classification of Lymphoid Tumours) anhand der Zahl der Zentroblasten in drei Grade und der Grad 3 weiter in 3A und 3B eingeteilt. Bis heute ist die Rolle der FL3B aufgrund der morphologischen und genetischen Unterschiede zu den anderen FL umstritten, es wird eine eigene Entität und Pathogenese des FL3B diskutiert. Durch das Verbundprojekt „Molekulare Mechanismen in malignen Lymphomen“ (MMML) Daten zu FISH-, Genexpressionsanalysen und immunhistochemischen Färbungen bearbeitet werden.
Diesen Daten zufolge sind FL3B in ihrer Genexpression nicht von FL3A trennbar. Es konnte jedoch eine Abgrenzung der FL1/2 zu den FL3A/B durch die erhöhte Expression von 13 Genen in den FL3A/B gefunden werden, von denen Homolog, double strand break repair nuclease (MRE11A), Topoisomerase II alpha (TOP2A) und Thioredoxin (TXN) schon zuvor im Rahmen von FL und NHL diskutiert wurden.According to the WHO classification (World Health Organization Classification of Lymphoid Tumours) follicular lymphomas (FL) are classified into three grades of which FL3 is further categorized into FL3A and FL3B, according to their histology. Until now, the role of FL3B is debated, due to morphologic and genetic differences compared to other FL. The data of the project Molecular Mechanisms in Malignant Lymphomas (MMML) was used to compare FISH, gene expression, and immunohistochemistry between those grades.
Comparing gene expression, FL3B is not distinguishable from FL3A. However, a distinction can be seen in a higher expression of 13 genes in FL3A/B compared to FL1/2. Of these genes, Homolog double strand break repair nuclease (MRE11A), Topoisomerase II alpha (TOP2A), and Thioredoxin (TXN) have been previously found to play a role in the etiology and treatment in Non-Hodgkin’s lymphomas
A comparison of the recycle and no-recycle options in light water reactors
No full textThe author wishes to express his appreciation to Dr. Forrest J. Remick for the professional guidance and encouragement he provided during this study, and also to Dr. Warren F. Witzig for his assistance in the preparation of this work. This work and the author's course of study were made possible by the U. S. Naval Postgraduate Educational Program.http://archive.org/details/acomparisonofrec109451804
The sweet corn industry of Iroquois and Vermilion Counties
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Previous issue date: 1951Embargo set by: Seth Robbins for item 99247
Lift date: 2019-05-18T15:22:37Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 99247 on 2019-05-19T09:15:16Z.Thesis (M.S.)--University of Illinois at Urbana-Champaign, 1951.Includes bibliographical references
Reoptimization Techniques in MIP Solvers
No full textMany optimization problems can be modeled as Mixed Integer Programs (MIPs). In general, MIPs cannot be solved efficiently, since solving MIPs is NP-hard, see, e.g., Schrijver, 2003. Common methods for solving NP-hard problems are branch-and-bound and column generation. In the case of column generation, the original problem
becomes decomposed or re-formulated into one ore more smaller subproblems, which are easier to solve. Each of these subproblems is solved separately and recurrently, which can be interpreted as solving a sequence of optimization problems.
In this thesis, we consider a sequence of MIPs which only differ in the respective objective functions. Furthermore, we assume each of these MIPs get solved with a branch-and-bound algorithm. This thesis aims to figure out whether the solving process of a given sequence of MIPs can be accelerated by reoptimization. As reoptimization we understand starting the solving process
of a MIP of this sequence at a given frontier of a search tree corresponding to another MIP of this sequence.
At the beginning we introduce an LP-based branch-and-bound algorithm. This algorithm is inspired by the reoptimizing algorithm of Hiller, Klug, and the author of this
thesis, 2013. Since most of the state-of-the-art MIP
solvers come to decisions based on dual information, which leads to the loss of feasible solutions after changing the objective function, we present a technique to guarantee optimality despite using these information. A decision is based on a dual information if this decision is valid for at least one feasible solution, whereas a decision is based on a primal information if this decision is valid for all feasible solutions. Afterwards, we consider representing the search frontier of the tree by a set of nodes of a given size. We call this the Tree Compression Problem. Moreover, we present a criterion characterizing the similarity of two objective functions. To evaluate our approach of reoptimization we extend the well-known and well-maintained MIP solver SCIP to an LP-based branch-and-bound framework, introduce two heuristics for solving the Tree Compression Problem, and a primal heuristic which is especially fitted to column generation. Finally, we present computational experiments on several problem classes, e.g., the Vertex Coloring and k-Constrained Shortest Path. Our experiments show, that a straightforward reoptimization, i.e., without additional heuristics, provides no benefit in general. However, in combination with the techniques and methods presented in this thesis, we can accelerate the solving of a given sequence up to the factor 14. For this purpose it is essential to take the differences of the objective functions into account and to restart the reoptimization, i.e., solve the subproblem from scratch, if the objective functions are not similar enough. Finally, we discuss the possibility to parallelize the solving process of the search frontier at the beginning of each solving process
The 5E model as a framework for facilitating multiple teacher education outcomes : a secondary science methods course in Australia
No full textAustralia is a large country geographically with a relatively small population of approximately twenty four million people. Like many countries, the health of the economy fluctuates over time, but Australia has enjoyed a mostly healthy and stable trajectory of economic output over the last forty years. There is now a strong push to develop STEM education throughout the country, not only due to the need for more students to enter into STEM professions, but also due to concerns about science and mathematics literacy (Australian Council of Learned Academies, 2013). This chapter describes preparing grade 7–10 science teachers at Western Sydney University in Australia using an approach based on the 5E learning cycle (Bybee et al., 2006). The author focuses on teacher discourse practices as his signature lesson, and has students develop their own 5E lesson plan as a summative assessment in the course
Maximum power point tracking under realistic operating conditions
No full textThe process of tracking the Maximum Power Point (MPP), known as MPPT, becomes problematic under realistic operating conditions due to the potential for there to be more than one local maxima. A very detailed physics based model has been developed for a PV module (in this application a PV roof tile) using the Orcad platform for PSpice. This model is unusual in that it properly represents partial module shading and cell temperature variation. The PV roof tile, based on polycrystalline silicon cells, comprises 18 series-connected cells. In the model, each cell is represented by a standard two-diode sub-model, for which different levels of radiation and cell temperature can be simulated to obtain a realistic overall I-V characteristic for the module. The model can be extended to model any reasonable number of PV roof tiles wired in series and parallel to form a roof array. The IV characteristics calculated in this way using PSpice will be validated using an outdoor PV roof test system located at the University of Strathclyde, Glasgow
