Applying mathematical models to dynamic contrast enhanced MRI (DCE MRI) data to perform quantitative tracer kinetic analysis enables the estimation of tissue characteristics such as vascular permeability and the fractional volume of plasma in a tissue. However, it is unclear to what extent modeling assumptions, particularly regarding water exchange between tissue compartments, impacts parameter estimates derived from clinical DCE MRI data. In this work, a new model is developed which includes water exchange effects, termed the water exchange modified two compartment exchange model (WX-2CXM). Two boundaries of this model (the fast and no exchange limits) were used to analyse a clinical DCE MRI bladder cancer dataset. Comparisons with DCE CT, which is not affected by water exchange, suggested that water exchange may have affected estimates of vp, the fractional volume of plasma. Further investigation and simulations led to the development of a DCE MRI protocol which was sensitised to water exchange, in order to further evaluate the water exchange effects found in the bladder cancer dataset. This protocol was tested by imaging the parotid glands in eight healthy volunteers, and confirmed evidence of water exchange effects on vp, as well as flow Fp and the fractional volume of extravascular extracellular space ve. This protocol also enabled preliminary estimates of the water residence times in parotid tissue, however, these estimates had a large variability and require further validation. The work presented in this thesis suggests that, although water exchange effects do not have a large effect on clinical data, the effect is measurable, and may lead to the ability to estimate of tissue water residence times. Results do not support a change in the current practise of neglecting water exchange effects in clinical DCEMRI acquisitions.

Background: In addition to conventional chemotherapeutics, nucleic acid-based therapeutics like antisense oligodeoxynucleotides (AS-ODN) represent a novel approach for the treatment of bladder cancer (BCa). An efcient delivery of AS-ODN to the urothelium and then into cancer cells might be achieved by the local application of multiwalled carbon nanotubes (MWCNT). In the present study, pristine MWCNT and MWCNT functionalized with hydrophilic moieties were synthesized and then investigated regarding their physicochemical characteristics, dispersibility, biocompatibility, cellular uptake and mucoadhesive properties. Finally, their binding capacity for AS-ODN via hybridization to carrier strand oligodeoxynucleotides (CS-ODN), which were either non-covalently adsorbed or covalently bound to the diferent MWCNT types, was evaluated. Results: Pristine MWCNT were successfully functionalized with hydrophilic moieties (MWCNT-OH, -COOH, -NH2, -SH), which led to an improved dispersibility and an enhanced dispersion stability. A viability assay revealed that MWCNTOH, MWCNT-NH2 and MWCNT-SH were most biocompatible. All MWCNT were internalized by BCa cells, whereupon the highest uptake was observed for MWCNT-OH with 40% of the cells showing an engulfment. Furthermore, all types of MWCNT could adhere to the urothelium of explanted mouse bladders, but the amount of the covered urothelial area was with 2–7% rather low. As indicated by fuorescence measurements, it was possible to attach CS-ODN by adsorption and covalent binding to functionalized MWCNT. Adsorption of CS-ODN to pristine MWCNT, MWCNT-COOH and MWCNT-NH2 as well as covalent coupling to MWCNT-NH2 and MWCNT-SH resulted in the best binding capacity and stability. Subsequently, therapeutic AS-ODN could be hybridized to and reversibly released from the CS-ODN coupled via both strategies to the functionalized MWCNT. The release of AS-ODN at experimental conditions (80 °C, bufer) was most efective from CS-ODN adsorbed to MWCNT-OH and MWCNT-NH2 as well as from CS-ODN covalently attached to MWCNT-COOH, MWCNT-NH2 and MWCNT-SH. Furthermore, we could exemplarily demonstrate that AS-ODN could be released following hybridization to CS-ODN adsorbed to MWCNT-OH at physiological settings (37 °C, urine). Conclusions: In conclusion, functionalized MWCNT might be used as nanotransporters in antisense therapy for the local treatment of BCa.

<p>Noble metal nanostructures display collective oscillation of the surface conduction electrons upon light irradiation as a form of localized surface plasmon resonance (LSPR) properties. Size, shape and the refractive index of surrounding environment are the key features that controls the LSPR properties. Surface passivating ligands have the ability to modify the charge density of nanostructures to allow resonant wavelength to match that of the incident light, a phenomenon called “plasmoelectric effect,”. According to the drude model Red and blue shifts of LSPR peak of nanostructures are observed in the event of reducing and increasing charge density, respectively. However, herein we report unusual LSPR properties of gold triangular nanoprisms (Au TNPs) upon functionalization with para-substituted thiophenols (X-Ph-SH, X = -NH₂, -OCH₃, -CH₃, -H, -Cl, -CF₃, and -NO₂). Accordingly, we hypothesized that an appropriate energy level alignment between the Au Fermi energy and the HOMO or LUMO of ligands allows delocalization of surface plasmon excitation at the hybrid inorganic-organic interface, and thus provides a thermodynamically driven plasmoelectric effect. We further validated our hypothesis by calculating the HOMO and LUMO levels and also work function changes of Au TNPs upon functionalization with para substituted thiol. We further utilized our unique finding to design ultrasensitive plasmonic substrate for biosensing of cancer microRNA in bladder cancer and owe to unpresidential sensitivity of the developed Au TNPs based LSPR sensor, for the first time we have been utilized to analysis the tumor suppressor microRNA for more accurate diagnosis of BC. Additionally, we have been advancing our sensing platform to mitigate the false positive and negative responses of the sensing platform using surface enhanced fluorescence technique. This noninvasive, highly sensitive, highly specific, also does not have false positives technique provide strong key to detect cancer at very early stage, hence increase the cancer survival rate. Moreover, the electromagnetic field enhancement of Surface-Enhanced Raman Scattering (SERS) and other related surface-enhanced spectroscopic processes resulted from the LSPR property. This dissertation describes the design and development of entirely new SERS nanosensors using flexible SERS substrate based on unique LSPR property of Au TNPs and developed sensors shows excellent SERS activity (enhancement factor = ~6.0 x 106) and limit of detection (as low as 56 parts-per-quadrillions) with high selectivity by chemometric analyses among three commonly used explosives (TNT, RDX, and PETN). Further we achieved the programable self-assembly of Au TNPs using molecular tailoring to form a 3D supper lattice array based on the substrate effect. Here we achieved the highest reported sensitivity for potent drug analysis, including opioids and synthetic cannabinoids from human plasma obtained from the emergency room. This exquisite sensitivity is mainly due to the two reasons, including molecular resonance of the adsorbate molecules and the plasmonic coupling among the nanoparticles. Altogether we are highly optimistic that our research will not only increase the patient survival rate through early detection of cancer but also help to battle the “war against drugs” that together is expected to enhance the quality of human life. </p> <p> </p>

This diploma thesis includes an experimental and a bioinformatic part. The two parts are linked together through the subject of transcription factors of 'forkhead box O' (FOXO) family. FOXO transcription factors have a key role in many cellular processes including cell cycle regulation, apoptosis and metabolism. For a long time, they have been considered strictly as the tumor-suppressors yet a growing number of evidence is pointing out to their pro-tumorigenic role. In consequence FOXO transcription factors are studied intensively as potential therapeutic targets in cancer. In the past decade, in silico prediction of protein-protein interactions has become popular in basic research as well as in drug development. Nonetheless, the predicted structures are still far from fitting to the expected behavior of the respective biomolecules. In the experimental part of this thesis, I verified the interaction of four in silico predicted protein binders based on naturally occurring PDZ domain with FOXO4 using microscale thermophoresis. Non-invasive bladder tumors represent a heterogeneous disease where reliable prediction of tumor aggressiveness is still lacking despite an intensive research. In the bioinformatic part of this thesis, I described the cellular composition of the tumor microenvironment and demonstrated...

During recent years, significant advances have been made in the field of molecular therapy in urologic oncology, mainly for advanced renal cell carcinoma. In this hitherto largely treatment-refractory disease, several agents have been developed targeting the von Hippel-Lindau metabolic pathway which is involved in carcinogenesis and progression of the majority of renal cell carcinomas. Although cure may not be expected, new drugs, such as the multikinase inhibitors sorafenib and sunitinib and the mammalian target of rapamycine inhibitor temsirolimus, frequently stabilize the disease course and may improve survival. Fewer data are available supporting molecular therapies in prostate, bladder, and testicular cancers. Preliminary data suggest a potential role of high-dose calcitriol and thalidomide in hormone-refractory prostate cancer, whereas targeted therapies in bladder and testicular cancers are still more or less limited to single-case experiences. The great theoretical potential and the multitude of possible targets and drug combinations, however, support further research into this exciting field of medical treatment of urologic malignancies. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Das Harnblasenkarzinom (BCa) ist die zweithäufigste bösartige urologische Tumorerkrankung sowie die siebthäufigste tumorbedingte Todesursache bei Männern. Zur Senkung des erheblichen Rezidiv- und Progressionsrisikos oberflächlicher BCa kommen lokale Immun- oder Chemotherapeutika zum Einsatz, die jedoch starke Nebenwirkungen verursachen können bzw. ungenügende langfristige Effekte bewirken. Eine neuartige Therapieoption besteht in der gezielten Expressionshemmung von Genen, die den Tumorzellen einen Wachstumsvorteil vermitteln. Hierfür eignen sich besonders synthetische Nukleinsäuren wie Antisense-Oligodesoxynukleotide (AS-ODN) und small interfering RNAs (siRNAs). In der vorliegenden Arbeit wurde die Expressionshemmung des potenziellen Targetgens hTERT (humane Telomerase Reverse Transkriptase) mit AS-ODN und siRNAs in BCa-Zellen untersucht. Die Tumorspezifität der hTERT-mRNA-Expression konnte zunächst an tumor- und tumorfreien Gewebeproben von BCa-Patienten gezeigt werden. Die verwendeten AS-ODN reduzierten die hTERT-mRNA-Expression auf bis zu 40%, womit eine Verringerung der Telomeraseaktivität einherging. Die AS-ODN-Behandlung bewirkte des Weiteren eine konzentrationsabhängige Viabilitätsreduktion verschiedener BCa-Zelllinien sowie eine verminderte Zellkoloniebildungsrate. Diese antiproliferativen Effekte waren auf eine Apoptoseinduktion zurückzuführen. Durch eine Vorbehandlung von vier BCa-Zelllinien mit hTERT-AS-ODN konnten die zytotoxischen Effekte der für das BCa relevanten Chemotherapeutika Cisplatin, Mitomycin C und Gemcitabin signifikant verstärkt werden. Nach Untersuchung der AS-ODN-Wirkung in vitro erfolgte die Etablierung eines subkutanen Xenotransplantantmodells der Nacktmaus. Die Eignung einer intraperitonealen Applikation wurde mit fluoreszenzmarkierten AS-ODN belegt. In weiteren Zellkulturexperimenten kamen hTERT-siRNAs, als alternative Methode der Geninhibition, zum Einsatz. Die Reduktion der hTERT-mRNA-Expression auf 50% war mit der durch AS-ODN bewirkten Inhibition vergleichbar. Im Gegensatz zur AS-ODN-Behandlung induzierten siRNAs keine unmittelbare Apoptose. Eine Kombination der siRNAs mit Cisplatin und Mitomycin C bewirkte jedoch eine Verdopplung der Apoptoserate. Um die molekularen Mechanismen der Wirkung der nukleinsäurebasierten hTERT-Inhibitoren und den Einfluss targetunabhängiger Effekte zu untersuchen, wurden transkriptomweite Expressionsanalysen mittels Oligonukleotid-Microarrays durchgeführt. Hierbei zeigte sich, dass die AS-ODN-Behandlung vorwiegend zu einer gesteigerten Expression von Genen führte, die mit einer zellulären Stressantwort assoziiert sind (u.a. ATF3, EGR1, GADD45). Diese Expressionsmuster stimmten in hohem Maße mit denen überein, die durch Transfektion mit AS-ODN gegen andere Targets erhalten wurden. Diese Ergebnisse deuten auf eine, zumindest teilweise, durch off-Targeteffekte ausgelöste Wachstumshemmung hin. Die siRNA-Behandlungen gegen unterschiedliche Targets zeigten relativ geringe Übereinstimmungen in den Expressionsmustern und somit eine höhere Spezifität. Außerdem wurde erstmalig gezeigt, dass eine hTERT-Inhibition mit siRNAs zur trankriptionellen Hemmung der Onkogene EGFR und FOSL1 führt. Diese Daten sowie die Ergebnisse anderer Arbeitsgruppen deuten auf einen wechselseitigen Zusammenhang zwischen hTERT und EGFR in der Regulation der EGFR-stimulierten Proliferation von BCa-Zellen hin. Zusammenfassend lässt sich feststellen, dass hTERT als tumorspezifisch exprimierter und funktionell relevanter Faktor ein hervorragendes Target für eine nukleinsäurebasierte BCa-Therapieoption darstellt. Im Vergleich zu AS-ODN wirken siRNAs grundsätzlich targetspezifischer. Die therapeutische Wertigkeit der lokal applizierten Inhibitoren, insbesondere in Kombination mit herkömmlichen Chemotherapeutika, sollte in nachfolgenden Experimenten im Rahmen eines orthotopen BCa-Xenotransplantatmodells untersucht werden.

Tesis por compendio / [ES] Esta tesis presenta soluciones de vanguardia basadas en algoritmos de computer vision (CV) y machine learning (ML) para ayudar a los expertos en el diagnóstico clínico. Se centra en dos áreas relevantes en el campo de la imagen médica: la patología digital y la oftalmología. Este trabajo propone diferentes paradigmas de machine learning y deep learning para abordar diversos escenarios de supervisión en el estudio del cáncer de próstata, el cáncer de vejiga y el glaucoma. En particular, se consideran métodos supervisados convencionales para segmentar y clasificar estructuras específicas de la próstata en imágenes histológicas digitalizadas. Para el reconocimiento de patrones específicos de la vejiga, se llevan a cabo enfoques totalmente no supervisados basados en técnicas de deep-clustering. Con respecto a la detección del glaucoma, se aplican algoritmos de memoria a corto plazo (LSTMs) que permiten llevar a cabo un aprendizaje recurrente a partir de volúmenes de tomografía por coherencia óptica en el dominio espectral (SD-OCT). Finalmente, se propone el uso de redes neuronales prototípicas (PNN) en un marco de few-shot learning para determinar el nivel de gravedad del glaucoma a partir de imágenes OCT circumpapilares. Los métodos de inteligencia artificial (IA) que se detallan en esta tesis proporcionan una valiosa herramienta de ayuda al diagnóstico por imagen, ya sea para el diagnóstico histológico del cáncer de próstata y vejiga o para la evaluación del glaucoma a partir de datos de OCT. / [CA] Aquesta tesi presenta solucions d'avantguarda basades en algorismes de *computer *vision (CV) i *machine *learning (ML) per a ajudar als experts en el diagnòstic clínic. Se centra en dues àrees rellevants en el camp de la imatge mèdica: la patologia digital i l'oftalmologia. Aquest treball proposa diferents paradigmes de *machine *learning i *deep *learning per a abordar diversos escenaris de supervisió en l'estudi del càncer de pròstata, el càncer de bufeta i el glaucoma. En particular, es consideren mètodes supervisats convencionals per a segmentar i classificar estructures específiques de la pròstata en imatges histològiques digitalitzades. Per al reconeixement de patrons específics de la bufeta, es duen a terme enfocaments totalment no supervisats basats en tècniques de *deep-*clustering. Respecte a la detecció del glaucoma, s'apliquen algorismes de memòria a curt termini (*LSTMs) que permeten dur a terme un aprenentatge recurrent a partir de volums de tomografia per coherència òptica en el domini espectral (SD-*OCT). Finalment, es proposa l'ús de xarxes neuronals *prototípicas (*PNN) en un marc de *few-*shot *learning per a determinar el nivell de gravetat del glaucoma a partir d'imatges *OCT *circumpapilares. Els mètodes d'intel·ligència artificial (*IA) que es detallen en aquesta tesi proporcionen una valuosa eina d'ajuda al diagnòstic per imatge, ja siga per al diagnòstic histològic del càncer de pròstata i bufeta o per a l'avaluació del glaucoma a partir de dades d'OCT. / [EN] This thesis presents cutting-edge solutions based on computer vision (CV) and machine learning (ML) algorithms to assist experts in clinical diagnosis. It focuses on two relevant areas at the forefront of medical imaging: digital pathology and ophthalmology. This work proposes different machine learning and deep learning paradigms to address various supervisory scenarios in the study of prostate cancer, bladder cancer and glaucoma. In particular, conventional supervised methods are considered for segmenting and classifying prostate-specific structures in digitised histological images. For bladder-specific pattern recognition, fully unsupervised approaches based on deep-clustering techniques are carried out. Regarding glaucoma detection, long-short term memory algorithms (LSTMs) are applied to perform recurrent learning from spectral-domain optical coherence tomography (SD-OCT) volumes. Finally, the use of prototypical neural networks (PNNs) in a few-shot learning framework is proposed to determine the severity level of glaucoma from circumpapillary OCT images. The artificial intelligence (AI) methods detailed in this thesis provide a valuable tool to aid diagnostic imaging, whether for the histological diagnosis of prostate and bladder cancer or glaucoma assessment from OCT data. / García Pardo, JG. (2022). Machine learning strategies for diagnostic imaging support on histopathology and optical coherence tomography [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182400 / TESIS / Compendio

Le cancer de la vessie (BCa), est une tumeur maligne de l’urothélium, fréquente dans le monde entier, dont le traitement particulièrement coûteux ne permet cependant pas d’éviter les récidives et les progressions. FGFR3 est l'un des gènes les plus fréquemment mutés dans le BCa et les cellules tumorales sont dépendantes de son expression pour leur prolifération. La mutation FGFR3 S249C est fortement surreprésentée (62% des mutations récurrentes de FGFR3). Dans la première partie de ma thèse, en réalisant une étude de la signature de mutation, nous avons montré que cette surreprésentation de la mutation FGFR3 était liée à une pression sélective induite par la mutagenèse APOBEC et non due à un gain de fonction plus important induit par cette mutation. En plus de FGFR3 S249C, 44 mutations fréquentes (représentant près de la moitié des mutations fréquentes du BCa) ont été identifiées comme étant associées à la signature mutationnelle APOBEC et la plupart d'entre elles étaient surreprésentées par rapport à d'autres mutations au sein du même gène. Il est intéressant de noter que ces mutations associées à APOBEC incluaient à la fois de nouveaux ‘conducteurs’ et des ‘passagers’ fréquents potentiels et qu’elles pouvaient potentiellement prédire la réponse à l’immunothérapie et à un traitement anti-ATR (pas anti-ATM). Dans la deuxième partie de cette thèse, nous nous sommes intéressés aux effets fonctionnels du gène FGFR3 dans le BCa. En utilisant un modèle de souris transgénique, nous avons apporté la première preuve in vivo selon laquelle cette mutation FGFR3 S249C conférait un pouvoir de transformation maligne. Ce processus était associé à une instabilité accrue du génome, activation de MYC et à une angiogenèse accrue, probablement induites par le facteur induisant l'hypoxie (HIF1A). En outre, nous avons caractérisé le réseau de régulation contrôlé par FGFR3 en analysant des données protéomiques obtenues par spectrométrie de masse à partir d'une lignée de cellules cancéreuses du cancer de la vessie portant la mutation FGFR3 S249C - UMUC14. Plusieurs voies de signalisation bien connues comme étant régulées par FGFR3 ont été identifiées. Nous avons également mis en évidence de nouvelles cascades de signalisation suite à l'activation de FGFR3 pouvant être jouer un rôle dans la progression tumorale, notamment un axe FGFR3 / HIF1A / angiogenèse qui a été validé dans certains modèles de BCa in vitro et in vivo. / Bladder cancer (BCa) is a worldwide frequent and costly urothelial malignancy. FGFR3 is one of the most frequently mutated genes in BCa and a driver of an oncogenic dependency. Here, we systematically catalogued the FGFR3 point mutation spectrum in BCa and identified 14 recurrent residues (frequency ≥ 2). One hotspot mutation - FGFR3 S249C - was strongly over-represented compared to other recurrent FGFR3 mutations (62% of all recurrent mutations). Based on in-depth investigation of mutational signature, we revealed that this over-representation of FGFR3 S249C mutation was merely favoured by APOBEC mutagenesis rather than a stronger functional selection compared to other oncodriver mutations on FGFR3. Similarly, together with FGFR3 S249C, 44 frequent mutations (accounts for nearly half of all frequent mutations in BCa) were pinpointed to be associated with APOBEC mutational signature and most of them were over-represented compared to other mutations within the same gene. Interestingly, these APOBEC-associated mutations included both novel potential ‘drivers’ as well as ‘frequent passengers’, and had a potential to predict responders for immunotherapy and anti-ATR but not anti-ATM treatment. On the other hand, we were interested in functional effects of FGFR3 activation in BCa. We provided the first in vivo evidence that FGFR3 S249C mutation conferred potency to BCa transformation using a transgenic mice model. This process was associated with increased genome instability, MYC activation and enhanced angiogenesis probably mediated by hypoxia-inducing factor (HIF1A). Further, we tried to characterize FGFR3-driven regulatory network through mass spectrometry based proteomic data generated in a BCa cell line bearing FGFR3 S249C mutation – UMUC14. As expected, several well-known FGFR3 regulated signaling pathways could be identified. Of note, we also highlighted some novel signaling cascades that may be relevant to FGFR3 activation, including a FGFR3/HIF1A/angiogenesis signaling axis that we validated in several in vitro and in vivo BCa models.

[ES] La búsqueda novedosa es un nuevo paradigma de los algoritmos de optimización, evolucionarios y bioinspirados, que está basado en la idea de forzar la búsqueda del óptimo global en aquellas partes inexploradas del dominio de la función que no son atractivas para el algoritmo, con la intención de evitar estancamientos en óptimos locales. La búsqueda novedosa se ha aplicado al algoritmo de optimización de enjambre de partículas, obteniendo un nuevo algoritmo denominado algoritmo de enjambre novedoso (NS). NS se ha aplicado al conjunto de pruebas sintéticas CEC2005, comparando los resultados con los obtenidos por otros algoritmos del estado del arte. Los resultados muestran un mejor comportamiento de NS en funciones altamente no lineales, a cambio de un aumento en la complejidad computacional. En lo que resta de trabajo, el algoritmo NS se ha aplicado en diferentes modelos, específicamente en el diseño de un motor de combustión interna, en la estimación de demanda de energía mediante gramáticas de enjambre, en la evolución del cáncer de vejiga de un paciente concreto y en la evolución del COVID-19. Cabe remarcar que, en el estudio de los modelos de COVID-19, se ha tenido en cuenta la incertidumbre, tanto de los datos como de la evolución de la enfermedad. / [CA] La cerca nova és un nou paradigma dels algoritmes d'optimització, evolucionaris i bioinspirats, que està basat en la idea de forçar la cerca de l'òptim global en les parts inexplorades del domini de la funció que no són atractives per a l'algoritme, amb la intenció d'evitar estancaments en òptims locals. La cerca nova s'ha aplicat a l'algoritme d'optimització d'eixam de partícules, obtenint un nou algoritme denominat algoritme d'eixam nou (NS). NS s'ha aplicat al conjunt de proves sintètiques CEC2005, comparant els resultats amb els obtinguts per altres algoritmes de l'estat de l'art. Els resultats mostren un millor comportament de NS en funcions altament no lineals, a canvi d'un augment en la complexitat computacional. En el que resta de treball, l'algoritme NS s'ha aplicat en diferents models, específicament en el disseny d'un motor de combustió interna, en l'estimació de demanda d'energia mitjançant gramàtiques d'eixam, en l'evolució del càncer de bufeta d'un pacient concret i en l'evolució del COVID-19. Cal remarcar que, en l'estudi dels models de COVID-19, s'ha tingut en compte la incertesa, tant de les dades com de l'evolució de la malaltia. / [EN] Novelty Search is a recent paradigm in evolutionary and bio-inspired optimization algorithms, based on the idea of forcing to look for those unexplored parts of the domain of the function that might be unattractive for the algorithm, with the aim of avoiding stagnation in local optima. Novelty Search has been applied to the Particle Swarm Optimization algorithm, obtaining a new algorithm named Novelty Swarm (NS). NS has been applied to the CEC2005 benchmark, comparing its results with other state of the art algorithms. The results show better behaviour in high nonlinear functions at the cost of increasing the computational complexity. During the rest of the thesis, the NS algorithm has been used in different models, specifically the design of an Internal Combustion Engine, the prediction of energy demand estimation with Grammatical Swarm, the evolution of the bladder cancer of a specific patient and the evolution of COVID-19. It is also remarkable that, in the study of COVID-19 models, uncertainty of the data and the evolution of the disease has been taken in account. / Martínez Rodríguez, D. (2021). Optimization Algorithm Based on Novelty Search Applied to the Treatment of Uncertainty in Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178994 / TESIS

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