Advances in water resources modeling are improving the information that can be supplied to support decisions that affect the safety and sustainability of society, but these advances result in models being more computationally demanding. To facilitate the use of cost- effective computing resources to meet the increased demand through high-throughput computing (HTC) and cloud computing in modeling workflows and web applications, I developed a comprehensive Python toolkit that provides the following features: (1) programmatic access to diverse, dynamically scalable computing resources; (2) a batch scheduling system to queue and dispatch the jobs to the computing resources; (3) data management for job inputs and outputs; and (4) the ability for jobs to be dynamically created, submitted, and monitored from the scripting environment. To compose this comprehensive computing toolkit, I created two Python libraries (TethysCluster and CondorPy) that leverage two existing software tools (StarCluster and HTCondor). I further facilitated access to HTC in web applications by using these libraries to create powerful and flexible computing tools for Tethys Platform, a development and hosting platform for web-based water resources applications. I tested this toolkit while collaborating with other researchers to perform several modeling applications that required scalable computing. These applications included a parameter sweep with 57,600 realizations of a distributed, hydrologic model; a set of web applications for retrieving and formatting data; a web application for evaluating the hydrologic impact of land-use change; and an operational, national-scale, high- resolution, ensemble streamflow forecasting tool. In each of these applications the toolkit was successful in automating the process of running the large-scale modeling computations in an HTC environment.

To identify novel therapeutic targets for basal-like breast cancer (BLBC) subtype, we investigated several DNA repair mechanisms associated with maintenance of high genomic instability for cell survival in cancer cells. We identified that the mismatch repair proteins, MSH2 and MSH6 (referred to as MSH2/6 hereafter), are highly elevated across BLBC samples. High expression level of MSH2/6 in BLBC is associated with worse prognosis and survivability for patients. Therefore, we knocked out MSH2 in BLBC cell lines and performed in vivo xenograft and syngeneic mice model studies to find significant attenuation of tumor growth in MSH2 KO group. Also, MSH2-deficient BLBC cells have increased rate of new mutations. Additionally, we tested the efficacy of conventional chemotherapeutics and radiation treatment that would further tip the genomic instability in MSH2-deficient BLBC cells towards cell death, but found them to be ineffective. Next, we performed high-throughput screening of 1280 FDA-approved compounds to discover that calcium channel blockers preferentially kill MSH2-deficient BLBC cells. This was likely due to association of significantly mutated pathways that involved calcium ion binding and calmodulin binding sites. Here we provide evidence of an alternative therapeutic strategy targeting DNA repair genes in BLBC patients utilizing bioinformatics analysis, high-throughput drug screening, in vitro,and vivoexperimentalmodels.

Regulator of G-protein Signaling (RGS) proteins temporally regulate the G protein signaling cascades initiated by GPCR activation. Reports have established dysregulation of RGS expression in a variety of disease states including several cancers. Additionally, use of genetic ablation techniques has implicated RGS proteins in a variety of other disease states through the native action of the RGS i.e. not a consequence of dysregulation of RGS expression. Therefore identification and optimization of small molecule lead compounds that alter RGS protein function has emerged as a promising therapeutic strategy. In this thesis, we use high throughput screening to interrogate small molecule libraries targeting two RGS proteins, RGS6 and RGS17. RGS6 has been reported as an essential mediator of doxorubicin induced cardiotoxicity, alcohol induced cardio and hepatotoxicity, anxiety, depression, and alcohol dependence. RGS17 has largely been implicated in a variety of cancer pathogenesis, with reported over expression in prostate, lung, breast, and hepatocellular carcinomas. Chapter 2 of this work focuses on the screening efforts targeting RGS6. Three separate screening campaigns interrogating over 20K compounds led to the identification of 3 small molecules that inhibit the RGS6: Gαo protein protein interaction with appreciable selectivity over control assays. The development of a cell based protein interaction assay is discussed, and the compounds were investigated using this system. All compounds tested did not appreciably alter signal over control, meaning that the cellular activity of these compounds remains ambiguous. Chapter 3 details the screening and follow up efforts targeting RGS17. The primary screening and/or follow up of four separate screening campaigns interrogating over 110K compounds is discussed. In total, 10 identified leads and a panel of analogs were subjected to significant follow up evaluation. All compounds were found to be cysteine dependent. The second generation RGS17 inhibitors (UI series) were determined to be both cytostatic and cytotoxic against lung and prostate cancer cell lines in culture, although whether this is due to RGS17 dependent mechanisms or due to general promiscuity of the compounds remains to be determined. Lead compounds from a library provided by the NCI were found to have cellular activity and were subjected to an investigation of structure activity relationships via commercially available compounds. The active form of three of these compounds was found to be a degradation product, which is likely due to decomposition of furan or methyl furan moieties that these compounds shared. One compound demonstrated robust SAR which allowed for the generation of schemes detailing putative inhibitory mechanisms. Finally, the role of RGS17 in the transition from epithelial to mesenchymal phenotypes is investigated. RGS17 was found to cause a sub population of PC3 cells to shift to mesenchymal phenotype, indicating that RGS17 may indeed play a role in this transition. Chapter 4 focuses on efforts to investigate variable potencies of published RGS4 inhibitors against a panel of RGS proteins, with the goal of gleaning insight in to structural characteristics that influence the inhibitability of RGS proteins. Most compounds tested were found to be more potent inhibitors of RGS14 rather than RGS4 in biochemical assays. We developed the NanoBit protein complementation assay to assess the interaction of RGS proteins with either Gαi1 or Gαq in a cellular context, and used this system to investigate compound selectivity in a cellular context. The compounds tested showed selectivity for RGS2, RGS4, and RGS14 over the other RGS proteins tested. The structural differences between the RGS proteins is discussed. Chapter 5 focuses on the future directions the lab may take with respect to the projects outlined in the previous chapters. This includes the screening of more targeted libraries or even virtual screening for RGS6, the development of in vivo assessment tools for RGS17, and an expanded structural examination of RGS proteins including NMR and crystal structure analysis. Additionally, the development of the NanoBit system to interrogate RGS protein interactions that are not RGS: Gα interactions is discussed.

L'évolution des télécommunications amené aujourd'hui à un foisonnement des appareils connectés et une massification des services multimédias. Face à cette demande accrue de service, les opérateurs ont besoin d'adapter le fonctionnement de leurs réseaux, afin de continuer à garantir un certain niveau de qualité d'expérience à leurs utilisateurs. Pour ce faire, les réseaux d'opérateur tendent vers un fonctionnement plus cognitif voire autonomique. Il s'agit de doter les réseaux de moyens d'exploiter toutes les informations ou données à leur disposition, les aidant à prendre eux-mêmes les meilleures décisions sur leurs services et leur fonctionnement, voire s'autogérer. Il s'agit donc d'introduire de l'intelligence artificielle dans les réseaux. Cela nécessite la mise en place de moyens d'exploiter les données, d'effectuer surelles de l'apprentissage automatique de modèles généralisables, apportant l’information qui permet d'optimiser les décisions. L'ensemble de ces moyens constituent aujourd'hui une discipline scientifique appelée science des données. Cette thèse s'insère dans une volonté globale de montrer l'intérêt de l'introduction de la science des données dans différents processus d'exploitation des réseaux. Elle comporte deux contributions algorithmiques correspondant à des cas d'utilisation de la science des données pour les réseaux d'opérateur, et deux contributions logicielles, visant à faciliter, d'une part l'analyse, et d'autre part le déploiement des algorithmes issus de la science des données. Les résultats concluants de ces différents travaux ont démontré l'intérêt et la faisabilité de l'utilisation de la science des données pour l'exploitation des réseaux d'opérateur. Ces résultats ont aussi fait l'objet de plusieurs utilisations par des projets connexes. / The evolution of telecommunications has led today to a proliferation of connected devices and a massification of multimedia services. Faced with this increased demand for service, operators need to adapt the operation of their networks, in order to continue to guarantee a certain level of quality of experience to their users. To do this, operator networks tend towards a more cognitive or autonomic functioning. It is about giving the networks the means to exploit all the information or data at their disposal, helping them to make the best decisions about their services and operations,and even self-manage. It is therefore a questionof introducing artificial intelligence into networks. This requires setting up means to exploit the data, to carry out on them the automatic learning of generalizable models, providing information that can optimize decisions. All these means today constitute a scientific discipline called data science. This thesis fits into a global desire to show the interest of the introduction of data science in different network operating processes. It inlcudes two algorithmic contributions corresponding to use cases of data science for the operator networks, and two software contributions, aiming to facilitate,on the one hand, the analysis, and on the other hand the deployment of the algorithms produced through data science. The conclusive results of these various studies have demonstrated the interest and the feasibility of using data science for the exploitation of operator networks. These results have also been used by related projects.

Le niveau médiocre des taux de réponses et des améliorations de survie lorsque des stratégies conventionnelles sont appliquées souligne la nécessité de développer des outils prédictifs performants, robustes et applicables en clinique. La démocratisation des technologies d’analyses à haut-débit est le substrat de la médecine de précision permettant le développement de modèles prédictifs capables d’orienter les stratégies thérapeutiques et la définition d’une nouvelle taxonomie des cancers par l’intégration de données moléculaires de haute dimension. A travers cette thèse, nous avons d’abord analysé des données publiques d’expression génique de cancer bronchique non à petites cellules dans le but de prédire la probabilité de survie à trois ans. Le fort pouvoir prédictif de la TNM seule et la faible taille des cohortes de validation ont malheureusement limité la possibilité de traduire nos résultats en clinique. Nous avons ensuite développé un prédicteur du phénotype « KRAS muté » spécifique du cancer colorectal, permettant d’identifier de nouveaux traits moléculaires responsables de ce phénotype et d’améliorer la prédiction de la réponse au cetuximab chez les patients KRAS sauvage. Enfin, nous avons combiné les données moléculaires des panels de lignées cellulaires CCLE et Sanger avec les données des cohortes du TCGA pour produire des prédicteurs performants de la sensibilité aux drogues. Ces modèles sont concordants avec des screens produits par interférence RNA et permettent d’expliquer la réponse extrême de patients sectionnés dans le programme de screening moléculaire MOSCATO.Les défis spécifiques posés par les données moléculaires de haute dimension dans le développement d’outils prédictifs applicables en clinique sont discutés dans cette thèse. / The mediocre level of the rates of answers and the improvements of survival when conventional strategies are applied underlines the necessity of developing successful, strong and applicable predictive tools in private hospital. The democratization of the technologies of analyses with top-debit(-flow) is the substratum of the medicine of precision allowing the development of predictive models capable of directing the therapeutic strategies and the definition of a new taxonomy of cancers by the integration of molecular data of high dimension(size).Through this thesis(theory), we analyzed at first public data of genic expression of bronchial cancer not in small cells(units) with the aim of predicting the probability of survival in three years. The strong predictive power of the only TNM and

Les aptamères sont des oligomères d'ADN ou d'ARN simple brin qui, en se repliant sous forme de structures tridimensionnelles peuvent avoir des interactions fortes et spécifiques envers un certain nombre de cibles. L'objectif de cette thèse a été de compléter les études existantes sur l'utilisation de l'électrophorèse capillaire (CE) et les aptamères afin de mettre au point une méthode de sélection d'aptamères par CE couplée à la fluorescence induite par laser et le séquençage haut-débit Illumina. Dans un premier temps, nous avons mis au point une méthode de détection et de séparation par électrophorèse capillaire couplée à la double détection UV-LEDIF d'une banque d'ADN en interaction avec une cible : la thrombine. C'est un modèle déjà étudié pour lequel deux aptamères ont fait l'objet de publications. Nous avons utilisé l'aptamère T29 dans le cadre de notre étude car c'est celui qui présente la meilleure affinité. L'électrophorèse capillaire est un puissant outil analytique qui facilite l'efficacité de sélection des aptamères et précise la détermination des paramètres d'interactions. Nous avons ainsi pu déterminer la constante d'affinité KD par CE-UV-LEDIF sur le modèle de base : la thrombine. Par ailleurs, nous montrons également comment l'utilisation du tampon Tris peut dégrader un ADN simple brin en électrophorèse capillaire et nous proposons comme alternative l'utilisation d'un tampon sodium phosphate dibasique qui évite ce phénomène de dégradation. Enfin, nous expliquons la difficulté d'amplification par qPCR et PCR d'un aptamère comme le T29 ayant une structure en G-quadruplex. Nous avons montré que le séquençage haut-débit Illumina nous a permis de trouver une corrélation entre le nombre de molécules séquencées et le nombre de séquences obtenues. L'analyse des séquences obtenues montre une quantité importante (20%) de séquences de T29 qui ne correspondent pas à la séquence de cet aptamère. Cela prouve que les étapes de PCR et de séquençage haut débit pour la détection de G-quadruplex peuvent induire un biais dans l'identification de ces molécules. / Aptamers are oligomers of small single-stranded DNA or RNA which can have strong and specific interactions with some targets when they fold into three-dimensional structures. The objective of this thesis was to complete existing studies on the use of capillary electrophoresis in order to develop a method for the selection of aptamers by CE coupled to laser induced fluorescence and Illumina high-throughput sequencing. In a first step, we developed a method of detection and separation by capillary electrophoresis coupled with the double detection UV-LEDIF of a DNA library interacting with a target: thrombin. It is a model already studied and for which two aptamers have been published. We used aptamer T29 as part of our study because it has the best affinity. Capillary Electrophoresis is a powerful analytical tool that facilitates the selection efficiency of aptamers and specifies the determination of the interaction parameters. We thus were able to determine the affinity constant KD by CE-UV-LEDIF on the basic model: thrombin. Moreover, we also show how the use of Tris buffer can degrade single-stranded DNA during capillary electrophoresis and we propose as an alternative the use of a dibasic sodium phosphate buffer which avoids the phenomenon of degradation. Finally, we explain the difficulty of amplification by qPCR and PCR of an aptamer such as T29 with a G-quadruplex structure. We showed that the Illumina high-throughput sequencing allowed us to find a correlation between the number of sequenced molecules and the number of sequences obtained. Analysis of the sequences obtained shows a significant amount (20%) of T29 sequences which do not correspond to the sequence of this aptamer. This shows that the PCR and high-throughput sequencing steps for the detection of G-quadruplex can induce bias in the identification of these molecules.

The active human retrotransposon L1 is the most prevalent human retroelement, constituting 17% of the mass of the human genome and contributing significantly to mutagenesis. L1 mutagenizes human genomes in a number of ways including insertional mutagenesis of itself and other retrotransposons, creating of DNA double strand breaks, and induction of non-allelic homologous recombination. Through these processes, the activity of L1 is responsible for approximately 0.5% of all new genetic diseases. All L1-derived mutagenesis stems from the activity of a small number of intact full-length L1 loci that remain capable of mobilization. A smaller subset of these active L1s are called hot L1s and are responsible for the vast majority of all L1 activity. Hot L1s are polymorphic in the population and represent evolutionarily recent L1 insertion events. Here, we show that potentially active full length L1 elements are more prevalent in individual genomes than previously believed. We find that the typical individual likely harbors approximately 60 active and 50 hot L1s. However, we also find that there is significant variation between individuals in numbers of potentially active L1s. As a result, the mutagenic burden associated with L1 likely varies between individuals. / acase@tulane.edu

The modern VLSI circuit designs manufactured with advanced technology nodes of 65nm or below exhibit an increasing sensitivity to the variations of manufacturing process. New design-specific and feature-sensitive failure mechanisms are on the rise. Systematic yield issues can be severe due to the complex variability involved in process and layout features. Without improved yield analysis methods, time-to-market is delayed, mature yield is suboptimal, and product quality may suffer, thereby undermining the profitability of the semiconductor company. Diagnosis-driven yield improvement is a methodology that leverages production test results, diagnosis results, and statistical analysis to identify the root cause of yield loss and fix the yield limiters to improve the yield. To fully leverage fault diagnosis, the diagnosis-driven yield analysis requires that the diagnosis tool should provide high-quality diagnosis results in terms of accuracy and resolution. In other words, the diagnosis tool should report the real defect location without too much ambiguity. The second requirement for fast diagnosis-driven yield improvement is that the diagnosis tool should have the capability of processing a volume of failing dies within a reasonable time so that the statistical analysis can have enough information to identify the systematic yield issues. In this dissertation, we first propose a method to accurately diagnose the defects inside the library cells when multi-cycle test patterns are used. The methods to diagnose the interconnect defect have been well studied for many years and are successfully practiced in industry. However, for process technology at 90nm or 65nm or below, there is a significant number of manufacturing defects and systematic yield limiters lie inside library cells. The existing cell internal diagnosis methods work well when only combinational test patterns are used, while the accuracy drops dramatically with multi-cycle test patterns. A method to accurately identify the defective cell as well as the failing conditions is presented. The accuracy can be improved up to 94% compared with about 75% accuracy for previous proposed cell internal diagnosis methods. The next part of this dissertation addresses the throughput problem for diagnosing a volume of failing chips with high transistor counts. We first propose a static design partitioning method to reduce the memory footprint of volume diagnosis. A design is statically partitioned into several smaller sub-circuits, and then the diagnosis is performed only on the smaller sub-circuits. By doing this, the memory usage for processing the smaller sub-circuit can be reduced and the throughput can be improved. We next present a dynamic design partitioning method to improve the throughput and minimize the impact on diagnosis accuracy and resolution. The proposed dynamic design partitioning method is failure dependent, in other words, each failure file has its own design partition. Extensive experiments have been designed to demonstrate the efficiency of the proposed dynamic partitioning method.

G–Protein Coupled Receptors are one of the most important targets in drug development, making up over 60% of drug targets. Recent studies have implicated a role of Regulator of G–Protein Signaling (RGS) proteins in the development and progression of pathologies, including some cancers. RGS17, the most–recently identified family member of the RZ family of RGS proteins, has been implicated in the growth, proliferation, metastasis and migration of prostate tumors as well as small–cell and non–small cell lung cancers. In neoplastic tumor tissues RGS17 is up–regulated 13 fold over patient–matched normal tissues in prostate cancer. Studies have shown that RGS17 RNAi knockdown inhibits colony formation and decreases tumorigenesis in nude mice. Based on these findings, this thesis explores the research undertaken to develop small molecule inhibitors of the RGS17: Gαo protein: protein interaction. In this thesis, we implemented AlphaScreen® technology to develop a high–throughput screening method for interrogating small molecule libraries for inhibitors of RGS17. Chapter 3 focuses on the initial results of the AlphaScreen® in 384–well format. The screen utilizes a measurement of the Gα: RGS17 protein: protein interaction (PPI) and with an excellent Z–score exceeding 0.73, a signal to noise ratio >70 and a screening time of 1,100 compounds per hour. Chapter 3 presents the development, validation and initial high–throughput screening for inhibitors of Gα: RGS17 interaction as well as preliminary characterization of the RL series of hits. In this pilot screen the NCI Diversity Set II was interrogated, yielding 35 initial hits of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 ΜM in dose–response experiments for inhibiting the Gα: RGS17 interaction. Four exhibited IC50 values <6 ΜM while inhibiting the Gα: RGS17 interaction >50% when compared to a biotinylated GST control (TrueHits). Compounds RL–1 and RL–2 were confirmed by flow cytometry protein interaction assay (FCPIA) while RL–3 and RL–4 were unable to disrupt this PPI in FCPIA. All four compounds were tested using the differential scanning fluorimetry (DSF) method, which is based on energetic coupling between ligand binding and protein unfolding and found compounds RL–1 to RL–4 all slightly increased protein stability upon ligand binding. Chapter 4 focuses on the miniaturization and optimization of AlphaScreen® to a 1536–well format and screening of the MicroSource SPECTRUM and NDL3000 small molecule libraries. This increased throughput 11–fold and decreased our working volumes from 45 ΜL to 10 ΜL, which reduced reagent cost. After optimization, we retained in an excellent Z–factor ≥0.70 with S/N>5.77 and increased the screening rate to more than 12,000 compounds per hour. In this format, the initial screening of the SPECTRUM and NDL3000 libraries was completed and filtered the initial hits by counter screening and PAINs filtering as well as developing four powerful orthogonal assays for the characterization of potential lead molecules. Chapter 6 focuses on the future directions, which include the screening the in–house 50,000 compound library in the University of Iowa HTS Core facility as well as the development of cell based assays to determine the activity of these leads in the cellular milieu. These screens are the first step to developing novel pharmacophores for further optimization of structure with the focus on RGS17 activity in enzymatic, whole cell, xenograft and whole animal models as well as providing new avenues for the development of anticancer therapies.

Ultra Wideband (UWB) is an emerging technology for use in the indoor wireless personal area networks and ad hoc networks. The more common form of UWB which uses sub-nanosecond pulses without any form of carrier signal is considered in this research. UWB signals have a large bandwidth with allocated frequency spectrum from 3.1 GHz to 10.6 GHz and maximum power restricted to -41dBm/MHz. The IEEE 802.11a is a popular standard for high data rate wireless local area networks (WLANs). The operating frequency of the IEEE 802.11a WLAN is 5 GHz which is right inside the allocated UWB frequency spectrum. One of the main obstacles facing the implementation of UWB devices is the challenge of reducing interference caused by UWB to other systems and vice versa. The potential operating areas/frequencies of the IEEE 802.11a WLAN and UWB systems overlap and therefore the problem of UWB interference to the IEEE 802.11a WLANs and vice versa becomes significant. In this research we have focused on studying the effect of UWB interference on IEEE 802.11a WLANs. The different UWB parameters that affect the interference caused by UWB to IEEE 802.11a WLAN have been considered for determining their effect on the performance of the IEEE 802.11a WLAN. The effect of UWB multipath on the performance of the IEEE 802.11a WLAN has been observed. The UWB parameters have also been compared based on their effect on the performance of the IEEE 802.11a system in the presence of UWB multipath. Additionally, two different interference mitigation techniques that reduce UWB interference to the IEEE 802.11a WLANs have been studied.