<p>Within the field of immunology, the existence of autoimmune diseases presents a unique set of challenges. The immune system typically protects the host by identifying foreign pathogens and mounting an appropriate response to eliminate them. Great strides have been made in understanding how foreign pathogens are identified and responded to, leading to the development of powerful immunological tools, such as vaccines and a myriad of models used to study infectious diseases and processes. However, it is occasionally possible for host tissues themselves to be inappropriately identified as foreign, prompting an immune response that attempts to eliminate the host tissue. The immune system has processes in place, referred to as selection, designed to prevent the development of cells capable of recognizing the self as foreign. While a great deal of work has been invested in understanding these processes, many concrete answers remain elusive. </p><p>Our laboratory, which focuses on understanding the roles of E and Id proteins in lymphocyte development, has established the Id3 knockout mouse as a model of autoimmune disease. Id3 knockout mice develop a disease reminiscent of human Sj&#1255;gren's Syndrome, an autoimmune disease that progressively damages the salivary and lachrymal glands. Continued study of this model has yielded interesting results. These include the identification of CD4+ T cells as initiators of disease as well as the identification of the cytokine Interleukin 13 (IL-13) as a potential causative agent. However, the source of IL-13, its true role as a causative agent of disease, as well as the developmental basis for its elevated expression remained elusive. </p><p>To this end, I utilized a reporter gene that enabled me to detect cells producing IL-13 as well as test the effects of IL-13 deletion on disease progression. Using this system, I was able to identify both CD4+ T cells and &#947;&#948; T cells as major sources of IL-13. I was also able to determine that elimination of IL-13 in Id3 knockout mice was sufficient to block the development of disease symptoms, reinforcing the hypothesis that IL-13 is a causative agent in disease initiation. Finally, I attempted to better characterize the phenotype of cells producing IL-13. These experiments indicated that the T cell receptor (TCR) repertoire of Id3 knockout mice is markedly different than that of wild-type (WT) mice. Furthermore, cells bearing certain TCRs appeared to express IL-13 at dramatically different rates, indicating that certain TCRs may be predisposed to IL-13 particular effector fates.</p> / Dissertation

AbstractMachine learning is a subfield within artificial intelligence that revolves around constructingalgorithms that can learn from, and make predictions on data. Instead of following strict andstatic instruction, the system operates by adapting and learning from input data in order tomake predictions and decisions. This work will focus on a subcategory of machine learningcalled “MultilabelClassification”, which is the concept of where items introduced to thesystem is categorized by an analytical model, learned through supervised learning, whereeach instance of the dataset can belong to multiple labels, or classes.This paper presents the task of implementing a multilabelclassifier based on the ID3algorithm, which we call MLID (MultilabelIterative Dichotomiser). The solution is presentedboth in a sequentially executed version as well as an parallelized one.We also presents acomparison based on accuracy and execution time, that is performed against algorithms of asimilar nature in order to evaluate the viability of using ID3 as a base to further expand andbuild upon in regards of multi label classification.In order to evaluate the performance of the MLID algorithm, we have measured theexecution time, accuracy, and made a summarization of precision and recall into what iscalled Fmeasure,which is the harmonic mean of both precision and sensitivity of thealgorithm. These results are then compared to already defined and established algorithms,on a range of datasets of varying sizes, in order to assess the viability of the MLID algorithm.The results produced when comparing MLID against other multilabelalgorithms such asBinary relevance, Classifier Chains and Random Trees shows that MLID can compete withother classifiers in term of accuracy and Fmeasure,but in terms of training the algorithm,the time required is proven inferior. Through these results, we can conclude that MLID is aviable option to use as a multilabelclassifier. Although, some constraints inherited from theoriginal ID3 algorithm does impede the full utility of the algorithm, we are certain thatfollowing the same path of development and improvement as ID3 experienced would allowMLID to develop towards a suitable choice of algorithm for a diverse range of multilabelclassification problems.

La crête neurale (CN) est une structure transitoire apparaissant en bordure de la plaque neurale chez les embryons de vertébrés. Au cours du développement embryonnaire, les cellules de la CN prolifèrent, subissent une transition épithélio-mésenchymateuse, migrent et se différencient en de nombreux types cellulaires tels que des neurones et cellules gliales du système nerveux périphérique, des mélanocytes, des cellules musculaires lisses ou des élements du squelette cranio-facial. Afin de mieux comprendre les mécanismes moléculaires contrôlant la prolifération et la spécification des cellules de la CN, nous avons étudié le rôle de deux facteurs de transcription, Hairy2 et Stat3, via des expériences de perte et gain de fonction chez l’embryon de xénope. Le gène Hairy2 code pour un facteur de transcription bHLH-O répresseur. Il est exprimé précocement au niveau de la bordure de la plaque neurale incluant la CN présomptive. Nous avons montré que Hairy2 est requis pour la prolifération des cellules de la CN en aval de signaux FGFs et qu’il maintient les cellules dans un état indifférencié en réprimant l’expression précoce des gènes spécifiques de la CN. Hairy2 réprime aussi la transcription du gène Id3 codant pour un facteur HLH essentiel à la prolifération des cellules de la CN. Id3 affecte également Hairy2. Nous avons observé que la protéine Id3 interagit physiquement avec Hairy2 et bloque son activité, démontrant que les interactions entre Hairy2 et Id3 jouent un rôle important dans la prolifération et la spécification des cellules de la CN. Afin de comprendre le mode d’action de Hairy2 dans la CN, nous avons comparé les propriétés de la protéine Hairy2 sauvage à celle d’une version mutée de la protéine incapable de lier l’ADN. Nos résultats ont montré que Hairy2 fonctionne selon deux mécanismes distincts. La capacité de Hairy2 à promouvoir la survie et la maintenance des cellules progénitrices de la CN dans un état non spécifié et indifférencié est dépendante de sa liaison à l’ADN. A l’inverse, sa capacité à stimuler la prolifération cellulaire et l’expression des gènes spécifiques de la CN est indépendante de sa liaison à l’ADN mais nécessite l’activation du ligand du récepteur Notch, Delta1. De plus, nous avons également montré que la capacité de Hairy2 d’induire Delta1 dans la CN requiert Stat3. Le gène Stat3 code pour un facteur de transcription latent dans le cytoplasme pouvant être activé par de nombreux signaux extracellulaires. Nos résultats ont montré que Stat3 joue un rôle crucial dans la prolifération cellulaire et dans l’expression des gènes de la bordure de la plaque neurale et de la CN. Stat3 est phosphorylé directement par la voie de signalisation FGF via FGFR4 et est requis in vivo en aval de FGFR4. Nous avons aussi montré que Hairy2 et Id3 sont des régulateurs positifs et négatifs de l’activité de Stat3 qui facilite et inhibe la formation du complexe Stat3-FGFR4, respectivement. De plus, Stat3 contrôle la transcription des gènes Hairy2 et Id3 de manière dose dépendante. Nous avons observé que Hairy2 est activé à faible dose et Id3 à forte dose de Stat3, suggérant que Stat3 s’auto-régule de manière indirecte via l’activation d’une boucle de rétro-contrôle positive (Hairy2) et une négative (Id3). Stat3 régule également de manière dose dépendante la prolifération et la différenciation des cellules de la CN. Une faible activité de Stat3 stimule la prolifération cellulaire et l’expression des gènes spécifiques de la CN tandis qu’une forte activité de Stat3 ralentit le cycle cellulaire, inhibe l’expression des gènes de la CN et maintient les cellules de l’ectoderme dans un état non spécifié et indifférencié. En conclusion, nous montrons pour la première fois que Stat3, en aval des FGFs et sous le rétro-contrôle de Hairy2 et Id3, joue un rôle essentiel dans la coordination de la progression du cycle cellulaire et de la spécification de la CN au cours du développement embryonnaire du xénope.

Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and tools exist. However, existing tools are slow, produce a large number of rules and/or lack the support for automatic fuzzification of input data. These limitations make those tools unsuitable for a variety of applications including those with many features and real time ones such as intrusion detection. In addition, the large number of rules produced by these tools renders the generated decision model un-interpretable. In this research work, we proposed an improved version of the fuzzy ID3 algorithm. We also introduced a new method for reducing the number of fuzzy rules generated by Fuzzy ID3. In addition we applied fuzzy decision trees to the classification of real and pseudo microRNA precursors. Our experimental results showed that our improved fuzzy ID3 can achieve better classification accuracy and is more efficient than the original fuzzy ID3 algorithm, and that fuzzy decision trees can outperform several existing machine learning algorithms on a wide variety of datasets. In addition our experiments showed that our developed fuzzy rule reduction method resulted in a significant reduction in the number of produced rules, consequently, improving the produced decision model comprehensibility and reducing the fuzzy decision tree execution time. This reduction in the number of rules was accompanied with a slight improvement in the classification accuracy of the resulting fuzzy decision tree. In addition, when applied to the microRNA prediction problem, fuzzy decision tree achieved better results than other machine learning approaches applied to the same problem including Random Forest, C4.5, SVM and Knn.

Data classification is a novel data analysis technique that involves extracting valuable information with potential utility from databases. It has found extensive applications in various domains, including finance, insurance, government, education, transportation, and defense. There are several methods available for data classification, with decision tree algorithms being one of the most widely used. These algorithms are based on instance-based inductive learning and offer advantages such as rule extraction, low computational complexity, and the ability to highlight important decision attributes, leading to high classification accuracy. According to statistics, decision tree algorithms[1] are among the most widely utilized data mining algorithms. To address these challenges, a decision tree algorithm is employed to solve classification problems. However, the existing decision tree algorithm exhibits limitations such as low calculation efficiency and multi-valued[2] bias. Therefore, a data classification system based on an optimized decision tree algorithm written in Python and a data storage system based on PostgreSQL were developed. The proposed algorithm surpasses traditional classification algorithms in terms of dimensionality reduction, attribute selection, and scalability. Ultimately, a combined optimization decision tree classifier system is introduced, which exhibits superior performance compared to the widely used ID3[3] algorithm. The improved decision tree algorithm has both theoretical and practical significance for data mining applications. / Dataklassificering är en ny dataanalysteknik som innebär att man extraherar värdefull information med potentiell nytta från databaser. Den har hittat omfattande tillämpningar inom olika domäner, inklusive finans, försäkring, regering, utbildning, transport och försvar. Det finns flera metoder tillgängliga för dataklassificering, där beslutsträdsalgoritmer är en av de mest använda. Dessa algoritmer är baserade på instansbaserad induktiv inlärning och erbjuder fördelar som regelextraktion, låg beräkningskomplexitet och förmågan att lyfta fram viktiga beslutsattribut, vilket leder till hög klassificeringsnoggrannhet. Enligt statistik är beslutsträdsalgoritmer bland de mest använda datautvinningsalgoritmerna. För att hantera dessa utmaningar används en beslutsträdsalgoritm för att lösa klassificeringsproblem. Den befintliga beslutsträds-algoritmen uppvisar dock begränsningar såsom låg beräkningseffektivitet och flervärdig bias. Därför utvecklades ett dataklassificeringssystem baserat på en optimerad beslutsträdsalgoritm skriven i Python och ett datalagringssystem baserat på PostgreSQL. Den föreslagna algoritmen överträffar traditionella klassificeringsalgoritmer när det gäller dimensionsreduktion, attributval och skalbarhet. I slutändan introduceras ett kombinerat optimeringsbeslutsträd-klassificeringssystem, som uppvisar överlägsen prestanda jämfört med den allmänt använda ID3-algoritmen. Den förbättrade beslutsträdsalgoritmen har både teoretisk och praktisk betydelse för datautvinningstillämpningar.

<p>E and Id proteins are members of the basic helix-loop-helix (bHLH) transcription regulator family. These proteins control a broad range of lymphocyte biology, from the development of multiple lineages to execution of their effector functions. With the development of new experiment models, novel functions of E and Id proteins continued to be discovered. In this thesis, I focused my study on the role of Id2 in gamma delta T cells and CD4<super>+</super> alpha beta T cells, as well as the role of Id3 in B cells.</p><p> Id proteins have been shown to control gamma delta T cell development. Id3 knockout mice demonstrate a dramatic expansion of innate-like Vgamma1.1<super>+</super> Vdelta6.3<super>+</super> T cells in the neonatal stage, suggesting that Id3 is an inhibitor of their development. Interestingly, Id3 knockout mice with a B6/129 mix background have much less expansion of the Vgamma1.1<super>+</super> Vdelta6.3<super>+</super> T cells compared to mice with pure B6 background. Genetic studies showed that this difference is strongly influences by a chromosome region very close to the Id2 locus. Using the Id2<super>f/f</super> CD4Cre<super>+</super> mice, I found that Id2 is also an inhibitor of gamma delta T cell development. Deletion of Id2 alone is sufficient to enhance the maturation of these cells in the thymus and induce a moderate expansion of gamma delta T cells in the periphery. This study demonstrated the delicate balance of transcription control in cells of the immune system.</p><p> The Id2<super>f/f</super> CD4Cre<super>+</super> mice also enabled me to study the role of Id2 in peripheral CD4<super>+</super> alpha beta T cell functions, which was difficult in the past because Id2 knockout mice lack lymph node development. I found that CD4 T cells in these mice have a profound defect in mounting immune responses, demonstrated by a complete resistance to induction of experimental autoimmune encephalomyelitis (EAE). I found that Id2-deficient CD4 T cells fail to infiltrate the central nervous system, and the effector CD4 T cell population is smaller compared to that in control mice. Id2 is important for the survival and proliferation of effector CD4 T cells, and this phenotype was correlated with an increased expression of <italic>Bim</italic> and <italic>SOCS3</italic>. This study revealed a novel role of Id2 in the functioning of CD4<super>+ </super>alpha beta T cells.</p><p> Switching my focus to B cells, recent next generation sequencing of human Burkitt lymphoma samples revealed that a significant proportion of them have mutations of Id3. This finding suggests that Id3 may be a tumor suppressor gene in the lymphoid system. Utilizing various Id3 knockout and conditional knockout mouse models, I showed that Id3 deficiency can accelerate lymphoid tumor genesis driven by the over-expression of oncogene c-Myc. This work may lead to development of a more realistic mouse model of human Burkitt lymphoma, allowing more mechanistic studies and perhaps preclinical tests of new therapies.</p> / Dissertation

This diploma thesis presents description on several algorithms for decision trees induction and software RapidMiner. The first part of the thesis deals with partition and terminology of decision trees. There’re described all algorithms for decision tree construction in RapidMiner. The second part deals with implementation and comparison of chosen algorithms. The application was developed in C++. Based on the real datesets the comparisson of different algorithms was realized using Rapid Miner 4.0.

Für die Progression des Wachstums maligner Tumoren und ihre Metastasierung ist die Angiogenese, die Bildung neuer Blutgefäße aus bereits existierenden, eine essentielle Voraussetzung. In dieser Arbeit konnte gezeigt werden, dass die proangiogenen Transkriptionsfaktoren c-Jun, Id1 und Id3 in ihrer Stabilität gegenüber dem Ubiquitin/26S-Proteasom-System durch das COP9-Signalosom (CSN) kontrolliert werden. Dieses bildet einen multimeren Proteinkomplex, der deutliche Homologien mit dem Lid-Subkomplex des 26S-Proteasoms aufweist. Sowohl c-Jun als auch Id3 binden an die Untereinheit CSN5. Id3 interagiert zusätzlich mit CSN7. Rekombinantes c-Jun, ein bekanntes Substrat der CSN-assoziierten Kinasen CK2 und PKD, wird durch Curcumin, einen Hemmstoff dieser Kinasen, deutlich destabilisiert. Daneben induziert Curcumin hochmolekulare Formen von c-Jun, bei denen es sich höchstwahrscheinlich um Ubiquitin-Konjugate handelt. Ferner beschleunigt Curcumin, ebenso wie die CK2- und PKD-Inhibitoren Emdodin, DRB und Resveratrol, in HeLa-Zellen den proteasomalen Abbau von c-Jun. Die c-Jun-abhängige Produktion von VEGF wird durch alle vier Kinase-Hemmstoffe signifikant reduziert. Verstärkt wird dieser Effekt noch durch den proteasomalen Inhibitor MG-132. Id3 wird nicht von den CSN-assoziierten Kinasen phosphoryliert. Allerdings hemmt es in einem Kinase-Assay die Phosphorylierung von c-Jun, ICSBP und CSN2. Curcumin und Emodin regen in HeLa-Zellen die Ubiquitinierung und den proteasomalen Abbau von Id3 an. Die Proteolyse von Id1 wird in HeLa-Zellen ebenfalls in Anwesenheit dieser beiden Hemmstoffe stimuliert. Mittels Kotransfektion von Id3 und His-markiertem Ubiquitin konnte eine verstärkte Ubiquitinierung von Id3 in Gegenwart von Curcumin direkt nachgewiesen werden. Außerdem wird Id3 durch die Überexpression von CSN2 stabilisiert. Auf diesen Daten basiert die Schlussfolgerung, dass die CSN-abhängige Phosphorylierung den Abbau von c-Jun und der beiden Id-Proteine über das Ubiquitin/26S-Proteasom-System inhibiert und dadurch ein interessantes neues Ziel einer antiangiogenen Tumortherapie repräsentiert. / Angiogenesis, the formation of new blood vessels from the existing vasculature, is a prerequisite for the progression of solid tumor growth and metastasis. In this study it is shown that the COP9 signalosome (CSN) regulates the stability of the angiogenic transcription factors c-Jun, Id1 and Id3 towards the ubiquitin/26S proteasome system. The COP9 signalosome constitutes a multimeric protein complex that shares sequence homology with the 26S proteasome lid complex. Both c-Jun and Id3 physically interact with the CSN subunit CSN5. In addition, Id3 can bind to CSN7. Recombinant c-Jun, a substrate of the CSN-associated kinases CK2 und PKD, is destabilized by curcumin, an inhibitor of these two kinases. Furthermore, curcumin induces high molecular weight c-Jun species, most likely ubiquitin conjugates. All tested inhibitors of the CK2 and PKD, emodin, DRB, resveratrol, as well as curcumin accelerate the degradation of c-Jun by the 26S proteasome in HeLa cells. The c-Jun-dependent expression of VEGF, the most potent angiogenic factor, is significantly reduced by the four kinase inhibitors. MG-132, an inhibitor of the 26S proteasome, also diminishes the production of VEGF. Id3 is not phosphorylated by the CSN-associated kinases. However, it inhibits c-Jun, ICSBP and CSN2 phosphorylation. Curcumin and emodin significantly induce ubiquitination and proteasome-dependent degradation of Id3 in HeLa cells. Proteasome-dependent degradation Id1 in HeLa cells is also stimulated by treatment with curcumin or emodin. Ubiquitination of Id3 is shown directly by cotransfection of HeLa cells with Id3 and His-tagged ubiquitin. Curcumin increases Id3-ubiquitin conjugate formation. In addition, overexpression of CSN2 leads to stabilization of Id3 protein. On the basis of these data it is concluded that CSN-mediated phosphorylation inhibits ubiquitination and proteasome-dependent degradation of c-Jun, Id1 and Id3. The COP9 signalosome thus represents an interesting new target for antiangiogenic tumor therapy.

<p>This thesis discusses the possibility of combining music and images through the use of metadata. Test subjects from different usability tests say they are interested in seeing images of the band or artist they are listening too. Lyrics matching the actual song are also something they would like to see. As a result an application for cellphones is created with Flash Lite which shows that it is possible to listen to music and automatically get images from Flickr and lyrics from Lyrictracker which match the music and show them on a cellphone.</p>

The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same class as the PDB structures (correct or incorrect protein models). Different subsets of PDB (protein data bank) are considered for evaluating the prediction potential of the machine learning methods. Here we show two such machines, one using SVM (support vector machines) and another using fuzzy decision trees (FDT). First using a preliminary encoding style SVM could get around 70% in protein model quality assessment accuracy, and improved Fuzzy Decision Tree (IFDT) could reach above 80% accuracy. For the purpose of reducing computational overhead multiprocessor environment and basic feature selection method is used in machine learning algorithm using SVM. Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment. Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same.