Characterisation and modulation of drug resistance in lung cancer cell lines

Chemotherapy drug resistance is a major obstacle in the treatment of cancer. It can result from an increase in levels of cellular drug efflux pumps such as P-glycoprotein (P-gp). Using cellular models, this thesis aimed to investigate resistance in lung cancer cells while developing siRNA and membrane proteomic techniques and to increase our knowledge of the effect of lapatinib, a newly developed targeted therapy, in these resistant cells. Lapatinib, a growth factor receptor tyrosine kinase inhibitor synergised with P-gp substrate cytotoxics in P-gp over-expressing resistant cells. However, lapatinib treatment, at clinically relevant concentrations, also increased levels of the P-gp drug transporter in a dose-responsive manner. Conversely, exposure to the epidermal growth factor (EGF), an endogenous growth factor receptor ligand, resulted in a decrease in P-gp expression. Using drug accumulation, efflux and toxicity assays we determined that alteration in P-gp levels by either lapatinib or EGF had little functional significance. P-gp is not the only resistance mechanism so siRNA-mediated gene silencing was exploited to investigate the role of additional proteins with potential roles in resistance. Firstly, P-gp knockdown by siRNA was coupled with toxicity and accumulation assays to determine the impact of silencing this protein in the chosen resistant lung cells. Additional putative targets were chosen from microarray data identifying genes associated with the development of paclitaxel resistance. Of the three genes investigated, ID3, CRYZ and CRIP1, ID3 emerged as having a potential role in contributing to resistance in one of the resistant lung carcinoma cell lines investigated. Many of the proteins important in resistance are membrane expressed but due to their size and hydrophobic nature, can be difficult to characterise. A 2D-LC-MS method was designed and employed to examine membrane proteins from the resistant lung cell models. Suitable parameters important in optimal identification of the proteins were determined. Large numbers of proteins were identified and comparisons made, highlighting those that were differentially expressed

Effective techniques for handling incomplete data using decision trees

Decision Trees (DTs) have been recognized as one of the most successful formalisms for knowledge representation and reasoning and are currently applied to a variety of data mining or knowledge discovery applications, particularly for classification problems. There are several efficient methods to learn a DT from data. However, these methods are often limited to the assumption that data are complete. In this thesis, some contributions to the field of machine learning and statistics that solve the problem of extracting DTs for learning and classification tasks from incomplete databases are presented. The methodology underlying the thesis blends together well-established statistical theories with the most advanced techniques for machine learning and automated reasoning with uncertainty. The first contribution is the extensive simulations which study the impact of missing data on predictive accuracy of existing DTs which can cope with missing values, when missing values are in both the training and test sets or when they are in either of the two sets. All simulations are performed under missing completely at random, missing at random and informatively missing mechanisms and for different missing data patterns and proportions. The proposal of a simple, novel, yet effective proposed procedure for training and testing using decision trees in the presence of missing data is the next contribution. Original and simple splitting criteria for attribute selection in tree building are put forward. The proposed technique is evaluated and validated in empirical tests over many real world application domains. In this work, the proposed algorithm maintains (sometimes exceeds) the outstanding accuracy of multiple imputation, especially on datasets containing mixed attributes and purely nominal attributes. Also, the proposed algorithm greatly improves in accuracy for IM data. Another major advantage of this method over multiple imputation is the important saving in computational resources due to it simplicity. The next contribution is the proposal of three versions of simple probabilistic techniques that could be used for classifying incomplete vectors using decision trees based on complete data. The proposed procedure is superficially similar to that of fractional cases but more effective. The experimental results demonstrate that these approaches can achieve comparative quality to sophisticated algorithms like multiple imputation and therefore are applicable to all kinds of datasets. Finally, novel uses of two proposed ensemble procedures for handling incomplete training and test data are proposed and discussed. The algorithms combine the two best approaches either with resampling (REMIMIA) or without resampling (EMIMIA) of the training data before growing the decision trees. Experiments are used to evaluate and validate the success of the proposed ensemble methods with respect to individual missing data techniques in the form of empirical tests. EMIMIA attains the highest overall level of prediction accuracy

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Molecular dissection of pericyte-to-neuron reprogramming reveals cellular identity safeguarding mechanisms

Neurodegenerative diseases, strokes, and injuries affect millions of people worldwide and current treatment options are insufficient. Since death of neurons in the brain is a common feature of all these disorders, a potential therapy could replace the lost neurons by newly generated ones to restore brain function. Natural adult neurogenesis in humans has been proven inadequate to deal with a major loss of brain cells. Therefore, for many years, transplantation of fetal tissue or stem cell-derived neural progenitors have been the focus of investigations regarding new treatments. More recently, new methods and insights have rendered brain-resident cells a promising means of an alternative therapeutic approach. While cellular identity was believed to be irreversible once differentiated for a long time, this view has changed gradually over the last decades. Among other cells, it has been shown for human brain pericytes that retroviral expression of the transcription factors (TFs) Ascl1 and Sox2 (AS) is sufficient to generate functional induced neurons (iNs) by direct reprogramming, and that this process is accompanied by a neural stem cell (NSC)-like state. While it is clear now that even a terminal cellular identity can be changed, the exact mechanisms remain elusive. Therefore, in this study we aimed at (i) identifying barriers and molecular mechanisms involved in cellular identity conversion from somatic cells into induced neurons, (ii) improving the efficiency of pericyte-to-neuron reprogramming, and (iii) directing the reprogramming process towards the desired cell types. By single cell RNA sequencing, we generated a high-resolution dataset of cells during pericyte-to-iN conversion. Using RNA velocity analysis, we were able to predict the progression of cells towards the neuronal fate and could identify blocker and facilitator genes that obstruct or enable cells to pass past a designated decision point. Among the facilitator genes, we identified several chromatin remodelers and cytoskeleton genes, and revealed a temporal heterogeneity regarding their expression pattern. Interestingly, we show that the blocker genes are part of a cellular identity safeguarding mechanism triggered by AS reprogramming. We demonstrate that the metabolic transition from glycolysis to oxidative phosphorylation is an essential barrier cells must overcome to transit from a pericyte towards a neuronal identity. Our findings suggest that any failure to meet metabolic requirements results in cells being either unable to change their identity or adopting a confused fate. To impact on the NSC-like state, we used either modulation of NOTCH signaling or TGF-β signaling by inhibition of the γ-secretase or dual SMAD inhibition, respectively, via small molecules. Strikingly, both treatments counteracted pericyte identity safeguarding mechanisms and significantly lowered reprogramming barriers. Consequently, our results show a strong increase in the number of generated iNs. Interestingly, we demonstrate that TGF-β signaling inhibition is more potent in lowering these metabolic barriers than NOTCH signaling inhibition, re-routing cells onto an entirely different route towards neurons. Additionally, TGF-β signaling inhibition almost completely suppresses the generation of undesired off-target cells without a clear identity, likely due to antioxidant regulon activity, which supports the metabolic transition. Remarkably, we illustrate that despite different treatments, iNs are transcriptionally similar and that both neuronal subtypes can be mapped to developing human brain regions. Finally, we used a different approach and reprogrammed pericytes into TUBB3+ cells using Neurog2/Sox2 (NS). We show that NS generated cells have a distinct transcriptomic identity from AS generated ones: While they are more likely to lose their original identity, the NS-generated iNs exhibit more progenitor-like properties, pointing at the different reprogramming capacities of proneural TFs. Altogether, this thesis emphasizes not only that cellular identity even in terminally differentiated cells can still be altered without returning to a pluripotent state. It further illustrates several previously unknown mechanisms during direct pericyte-to-iN reprogramming and opens new ways to improve its efficiency. Every new insight into cross-lineage cellular identity conversion paves the way for future neuronal replacement therapies

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

DATA-DRIVEN ANALYSIS AND MAPPING OF THE POTENTIAL DISTRIBUTION OF MOUNTAIN PERMAFROST

In alpine environments, mountain permafrost is defined as a thermal state of the ground and it corresponds to any lithosphere material that is at or below 0°C for at least two years. Its degradation is potentially leading to an increasing rock fall activity and sediment transfer rates. During the last 20 years, knowledge on this phenomenon has significantly improved thanks to many studies and monitoring projects, revealing an extremely discontinuous and complex spatial distribution, especially at the micro scale (scale of a specific landform; tens to several hundreds of metres). The objective of this thesis was the systematic and detailed investigation of the potential of data-driven techniques for mountain permafrost distribution modelling. Machine learning (ML) algorithms are able to consider a greater number of pa- rameters compared to classic approaches. Not only can permafrost distribution be modelled by using topo-climatic parameters as a proxy, but also by taking into ac- count known field permafrost evidences. These latter were collected in a sector of the Western Swiss Alps and they were mapped from field data (thermal and geoelectrical data) and ortho-image interpretations (rock glacier inventorying). A permafrost dataset was built from these evidences and completed with environmental and mor- phological predictors. Data were firstly analysed with feature relevance techniques in order to identify the statistical contribution of each controlling factor and to exclude non-relevant or redundant predictors. Five classification algorithms, belonging to statistics and machine learning, were then applied to the dataset and tested: Logistic regression (LR), linear and non-linear Support Vector Machines (SVM), Multilayer perceptrons (MLP) and Random forests (RF). These techniques inferred a classifica- tion function from labelled training data (pixels of permafrost absence and presence) to predict the permafrost occurrence where this was unknown. Classification performances, assessed with AUROC curves, ranged between 0.75 (linear SVM) and 0.88 (RF). These values are generally indicative of good model performances. Besides these statistical measures, a qualitative evaluation was performed by using field expert knowledge. Both quantitative and qualitative evaluation approaches suggested to employ the RF algorithm to obtain the best model. As machine learning is a non-deterministic approach, an overview of the model uncertainties is also offered. It informs about the location of most uncertain sectors where further field investigations are required to be carried out to improve the reliability of permafrost maps. RF demonstrated to be efficient for permafrost distribution modelling thanks to consistent results that are comparable to the field observations. The employment of environmental variables illustrating the micro-topography and the ground charac- teristics (such as curvature indices, NDVI or grain size) favoured the prediction of the permafrost distribution at the micro scale. These maps presented variations of probability of permafrost occurrence within distances of few tens of metres. In some talus slopes, for example, a lower probability of occurrence in the mid-upper part of the slope was predicted. In addition, permafrost lower limits were automatically recognized from permafrost evidences. Lastly, the high resolution of the input dataset (10 metres) allowed elaborating maps at the micro scale with a modelled permafrost spatial distribution, which was less optimistic than traditional spatial models. The permafrost prediction was indeed computed without recurring to altitude thresh- olds (above which permafrost may be found) and the representation of the strong discontinuity of mountain permafrost at the micro scale was better respected. -- Dans les environnements alpins, le pergélisol de montagne est défini comme un état thermique du sol et correspond à tout matériau de la lithosphère qui maintient une température égale ou inférieure à 0°C pendant au moins deux ans. Sa dégradation peut conduire à une activité croissante de chutes de blocs et à une augmentation des taux de transfert de sédiments. Au cours des 20 dernières années, les connaissances sur ce phénomène ont considérablement augmenté grâce à de nombreuses études et projets de suivi, qui ont révélé une distribution spatiale extrêmement discontinue et complexe du phénomène, en particulier à la micro-échelle (échelle d’une forme géomorphologique; dizaines à plusieurs centaines de mètres). L’objectif de cette recherche était l’étude systématique et détaillée des potentialités offertes par une approche axée sur les données dans le cadre de la modélisation de la distribution du pergélisol de montagne. Les algorithmes d’apprentissage au- tomatique (machine learning) sont capables de considérer un plus grand nombre de variables que les approches classiques. La distribution du pergélisol peut être modélisée non seulement en utilisant des paramètres topo-climatiques (altitude, radiation solaire, etc.), mais aussi en tenant compte de la présence et de l’absence connues du pergélisol (observations de terrain). Collectées dans un secteur des Alpes occidentales suisses, ces dernières ont été cartographiées sur la base d’investigations de terrain (données thermiques et géoélectriques), d’interprétation d’orthophotos et d’inventaires de glaciers rocheux. Un jeu de données a été construit à partir de ces évidences de terrain et complété par des prédicteurs environnementaux et morphologiques. Les données ont d’abord été analysées avec des techniques mon- trant la pertinence des variables permettant d’identifier la contribution statistique de chaque facteur de contrôle et d’exclure les prédicteurs non pertinents ou redondants. Cinq algorithmes de classification appartenant aux domaines des statistiques et de l’apprentissage automatique ont ensuite été appliqués et testés : Logistic regression (LR), la version linéaire et non-linéaire de Support Vector Machines (SVM), Mul- tilayer perceptrons (MLP) et Random forests (RF). Ces techniques déduisent une fonction de classification à partir des données dites d’entraînement représentant l’absence et la présence certaine du pergélisol. Elles permettent ensuite de prédire l’occurrence du phénomène là où elle est inconnue. Les performances de classification, évaluées avec des courbes AUROC, variaient entre 0.75 (SVM linéaire) et 0.88 (RF). Ces valeurs sont généralement indicatives de bonnes performances. En plus de ces mesures statistiques, une évaluation qualitative a été réalisée et se base sur l’expertise géomorphologique. Les RF se sont révélées être la technique produisant le meilleur modèle. Comme l’apprentissage automatique est une approche non déterministe, il a également offert un aperçu des incertitudes de la modélisation, qui informent sur la localisation des secteurs les plus incertains dans lesquels des futures campagnes de terrain méritent d’être menées afin d’améliorer la fiabilité des cartes produites. Finalement, RF ont démontré leur efficacité dans le cadre de la modélisation de la distribution du pergélisol grâce à des résultats comparables aux observations de terrain. L’emploi de variables environnementales illustrant la micro-topographie du relief et les caractéristiques du sol (tels que les indices de courbure, le NDVI et la granulométrie) favorise la prédiction de la distribution du pergélisol à la micro- échelle, avec des cartes présentant des variations de la probabilité d’occurrence du pergélisol sur des distances de quelques dizaines de mètres. Par exemple, dans cer- tains éboulis, les cartes illustrent une probabilité plus faible dans la partie amont de la pente, ce qui s’avère cohérent avec les observations de terrain. La limite inférieure du pergélisol a ainsi été automatiquement reconnue à partir des évidences de terrain fournies à l’algorithme. Enfin, la haute résolution du jeu de données (10 mètres) a permis d’élaborer des cartes présentant une distribution spatiale du pergélisol moins optimiste que celle offerte par les modèles spatiaux classiques. La prédiction du pergélisol a en effet été calculée sans utiliser des seuils d’altitude (au-dessus desquels on peut trouver du pergélisol) et respecte ainsi mieux la représentation de la forte discontinuité du pergélisol de montagne à la micro-échelle. -- Negli ambienti alpini, il permafrost di montagna è definito come uno stato termico del suolo e corrisponde a qualsiasi materiale nella litosfera che mantiene una temper- atura uguale o inferiore a 0° C per almeno due anni. La sua degradazione può portare ad una crescente attività di caduta di blocchi e ad un aumento dei tassi di trasferi- mento dei sedimenti. Negli ultimi 20 anni, le conoscenze riguardanti il permafrost di montagna sono aumentate considerevolmente grazie ai numerosi studi e progetti di monitoraggio che hanno rivelato una distribuzione spaziale fortemente discontinua e complessa del fenomeno, in particolare alla scala della forma geomorfologica (definita come la micro scala, da decine a diverse centinaia di metri). L’obiettivo di questa ricerca é lo studio sistematico e dettagliato delle potenzialità offerte da un approccio basato sui dati, nell’ottica di una modellizzazione della distribuzione del permafrost di montagna. Gli algoritmi di apprendimento auto- matico (machine learning) sono in grado di considerare più variabili rispetto agli approcci classici. La distribuzione del permafrost può essere modellizzata non solo utilizzando i parametri topo-climatici classici (altitudine, radiazione solare, ecc.), ma anche considerando esempi di presenza e assenza del permafrost (osservazioni sul campo). Raccolti in un’area delle Alpi occidentali svizzere, questi ultimi sono stati mappati sulla base di indagini di terreno (dati termici e geoelettrici), interpretazione di ortofoto e inventari di ghiacciai rocciosi. A partire dalle evidenze di terreno, è stato creato un set di dati, al quale sono stati integrati diversi predittori ambien- tali e morfologici. I dati sono stati dapprima analizzati con tecniche di indagine della rilevanza delle variabili; tali tecniche sono capaci di identificare il contributo statistico di ciascun fattore di controllo del permafrost e sono in grado di escludere i predittori non pertinenti o ridondanti. Sono stati, quindi, applicati e testati cinque al- goritmi di classificazione appartenenti ai campi della statistica e dell’apprendimento automatico: Logistic regression (LR), la versione lineare e non lineare di Support Vector Machines (SVM), Multilayer Perceptron (MLP) e Random forest (RF). Queste tecniche deducono una funzione di classificazione dai cosiddetti dati di allenamento, che rappresentano l’assenza e la presenza certa del permafrost, e permettono in seguito di predire il fenomeno laddove è sconosciuto. Le prestazioni di classificazione, valutate con le curve AUROC, variavano da 0.75 (SVM lineare) a 0.88 (RF). Questi valori sono generalmente indicativi di buone prestazioni. Oltre a queste misure statistiche, è stata effettuata una valutazione qualitativa. RF si é rivelata essere la tecnica che produce il modello migliore. Poiché l’apprendimento automatico è un approccio non deterministico, é stato possibile ottenere informazioni sulle incertezze della modellizzazione. Quest’ultime indicano in quali aree il modello é più incerto e, dunque, dove occorre pianificare nuove campagne di terreno per migliorare l’affidabilità delle mappe prodotte. RF ha dimostrato la sua efficacia nella modellizzazione della distribuzione del per- mafrost con risultati paragonabili alle osservazioni sul campo. L’uso di variabili ambientali che illustrano la topografia e le caratteristiche del suolo (come indici di curvatura, NDVI e granulometria) aiuta a predire la distribuzione del permafrost alla micro scala, con mappe che mostrano variazioni spaziali importanti della probabilità del permafrost su distanze di poche decine di metri. In alcune falde di detrito le mappe mostrano una probabilità inferiore nella parte a monte, risultato coerente con le osservazioni sul campo. Il limite inferiore del permafrost è stato inoltre riconosci- uto automaticamente dagli esempi forniti all’algoritmo. Infine, l’alta risoluzione del set di dati (10 metri) ha permesso una simulazione della distribuzione spaziale del fenomeno meno ottimistica rispetto a quella fornita dai modelli classici. La previsione del permafrost è stata, infatti, calcolata senza utilizzare delle soglie di altitudine e quindi rispetta meglio la rappresentazione dell’alta discontinuità del permafrost di montagna alla micro scala

Machine Learning Approaches for Improving Prediction Performance of Structure-Activity Relationship Models

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies. First, to improve the prediction accuracy of learning from imbalanced data, Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms combined with bagging as an ensemble strategy was evaluated. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that this method significantly outperformed other conventional methods. SMOTEENN with bagging became less effective when IR exceeded a certain threshold (e.g., \u3e40). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p \u3c 0.001, ANOVA) by 22-27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Lastly, current features used for QSAR based machine learning are often very sparse and limited by the logic and mathematical processes used to compute them. Transformer embedding features (TEF) were developed as new continuous vector descriptors/features using the latent space embedding from a multi-head self-attention. The significance of TEF as new descriptors was evaluated by applying them to tasks such as predictive modeling, clustering, and similarity search. An accuracy of 84% on the Ames mutagenicity test indicates that these new features has a correlation to biological activity. Overall, the findings in this study can be applied to improve the performance of machine learning based Quantitative Structure-Activity/Property Relationship (QSAR) efforts for enhanced drug discovery and toxicology assessments

Mechanical characterisation of Nb3Sn Rutherford cable stacks

Nb3Sn Rutherford cables are used in CERN’s superconducting 11 T dipole and MQXF quadrupole magnets, which are proposed for the instantaneous luminosity (rate of particle collisions) upgrade of the Large Hadron Collider (LHC) by a factor of five to a High Luminosity-Large Hadron Collider (HL-LHC). Nb3Sn-based conductors are the key technology for the envisioned Future Circular Collider (FCC) with an operating magnetic dipole field of 16 T. The baseline superconductor of the LHC dipole magnets is Nb–Ti, whereas an operation above 10 T is not possible due to the current carrying performance limitations of this superconductor at higher magnetic fields. Therefore, a superconducting material such as Nb3Sn has to be used with proven performance capabilities of 10 T and above. The conductor choice towards Nb3Sn-based cables affects the magnet manufacturing process, as it requires a heat treatment up to 650°C, an epoxy resin impregnation and introduces mechanical diffculties as the superconducting filaments are brittle and strain sensitive. A mechanical over loading of the filaments lead to irreversible conductor damage. The designs of 11 and 16 T magnets are supposed to push the conductor towards its mechanical and electrical performance limitations. The magnetic field induced forces on the current carrying conductor are balanced by mechanical pre-loading of the magnet. Thereby the highest controlled mechanical pre-load for the 11 T dipole magnet is set at ambient temperature. The mechanical stress limits of Nb3Sn-based cables have been investigated at cryogenic temperatures. The material strength and stiffness of the cable insulation system, formed by glass-fibre-reinforced resin, is increased at low temperatures. The ultimate stress values, determined at cryogenic temperature, are therefore not conservative. The ultimate stress limitation of the insulated conductor is assumed to be lower at ambient temperature. The cable limitations at ambient temperature need to be known for the ongoing magnet manufacturing process and also for future design approaches. Furthermore, the compressive stress–strain behaviour of a coil conductor block at ambient temperature is the key material characteristic, in order to recalculate the stress level in the coil during the assembly process. Existing approaches using an indirect strain measurement method provide uncertainties in the low-strain regime, which is the essential strain range for a material compound consisting of major fractions composed of heat-annealed copper and epoxy resin. Compressive stress–strain data of an impregnated conductor block are required, based on a direct strain measurement system, as available data have been collected on samples based on a different strand type and insulation system. The elaborated direct strain measurements can be correlated to strain gauge data, measured directly on a coil. The stress distribution in a Nb3Sn Rutherford cable need to be understood and validated to understand strain-induced degradation effects in the insulated conductor. This knowledge can also help to optimise the stress distribution envisioned magnet designs. The stress–strain state in the copper and Nb3Sn phase of a loaded conductor block has to be determined experimentally. This dissertation describes a test protocol and first elaborated results on the investigated stress limitations of a Nb3Sn Rutherford cable under homogeneous load applied in transversal direction. The compressive stress–strain behaviour of impregnated Nb3Sn Rutherford cable stacks was investigated experimentally. This includes a detailed report on the sample manufacturing process, measurements performed and validation of results through a comparison with the elaborated data of cable stacks extracted from a coil. The presented results from neutron diffraction measurements of loaded cable stacks allow the determination of the stress–strain level of the copper and Nb3Sn phase in the impregnated conductor. The relevant measured results have been recalculated with numerical calculations based on the Finite Element Method (FEM).:1. Introduction 1 1.1. The LHC and the HL-LHC project 1.2. The FCC study 1.3. Superconducting materials for accelerator magnets 1.4. Multi-filamentary wires and Rutherford cables 1.5. Coil manufacturing process 1.6. Magnet coil assembly 1.7. Objectives of this thesis 2. Theory: fundamental principles 17 2.1. Analytical calculation: sector coil dipole 2.2. Mechanical behaviour of composite materials 2.3. Failure criteria and strength hypotheses for materials 2.4. Compressive tests 2.5. Fundamental principles of Neutron scattering 2.5.1. Test apparatus and measurement method 2.5.2. Lattice plane and Miller indices 2.5.3. Bragg diffraction and interference 2.5.4. Diffraction-based strain calculation 2.5.5. Diffraction-based stress calculation 2.6. Fundamental principles of FEM 3. Homogeneous transversal compression of Nb3Sn Rutherford cables 3.1. Superconducting cable test stations 3.2. The FRESCA test facility and specific sample holder 3.3. The sample description 3.4. Experimental procedure 3.5. Review of existing contact pressure measurement system 3.6. Compressive test station 3.7. Validation of the pressure-sensitive films 3.8. Press punch 3.9. Improvement of the contact stress distribution 3.9.1. First test: cable pressed between the bare tools 3.9.2. Second test: tool shimmed with a soft Sn96Ag4 3.9.3. Third test: tool shimmed with a soft Sn60Pb40 3.9.4. Fourth test: tool shimmed with a soft indium 3.9.5. Fifth test: tool shimmed with a polyimide film 3.10. Test results 3.11. Conclusion 4. Material characterisation by a compression test 4.1. Test set-ups for compressive tests and validation 4.2. Sample preparation 4.3. Compressive stress–strain measurement 4.4. Ten-stack sample stiffness estimation-based composite theories 4.5. Dye penetration test on loaded and unloaded samples 4.6. Conclusion 5. Neutron diffraction measurements 80 5.1. Test set-up for neutron diffraction measurement 5.2. The samples 5.3. Experiment: lattice stress–strain measurements 5.4. Conclusion 6. Simulation and modelling of Nb3Sn cables 6.1. The models 6.2. The 2D simulation results 6.3. The 3D simulation results 6.4. Conclusion 7. Comprehensive summary 7.1. Summary 7.2. Critical review 7.3. Next steps Appendix 113 A. Calculation of the magnetic field components in a sector coil without iron B. Approaches for the determination of diffraction elastic constants C. Manufacturing drawings D. FEM calculation results of the 2D model E. FEM calculation results of the 3D model F. Source Codes Bibliograph

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining