No description available.

No description available.

No description available.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23785号 / 医博第4831号 / 新制||医||1057(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 伊達 洋至, 教授 中本 裕士, 教授 鈴木 実 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

No description available.

With the recent increase in the threat of biological terrorism as well as the continual risk of other diseases, the research in public health surveillance and disease monitoring has grown tremendously. There is an abundance of data available in all sorts of forms. Hospitals, federal and local governments, and industries are all collecting data and developing new methods to be used in the detection of anomalies. Many of these methods are developed, applied to a real data set, and incorporated into software. This research, however, takes a different view of the evaluation of these methods. We feel that there needs to be solid statistical evaluation of proposed methods no matter the intended area of application. Using proof-by-example does not seem reasonable as the sole evaluation criteria especially concerning methods that have the potential to have a great impact in our lives. For this reason, this research focuses on determining the properties of some of the most common anomaly detection methods. A distinction is made between metrics used for retrospective historical monitoring and those used for prospective on-going monitoring with the focus on the latter situation. Metrics such as the recurrence interval and time-to-signal measures are therefore the most applicable. These metrics, in conjunction with control charts such as exponentially weighted moving average (EWMA) charts and cumulative sum (CUSUM) charts, are examined. Two new time-to-signal measures, the average time-between-signal events and the average signal event length, are introduced to better compare the recurrence interval with the time-to-signal properties of surveillance schemes. The relationship commonly thought to exist between the recurrence interval and the average time to signal is shown to not exist once autocorrelation is present in the statistics used for monitoring. This means that closer consideration needs to be paid to the selection of which of these metrics to report. The properties of a commonly applied scan method are also studied carefully in the strictly temporal setting. The counts of incidences are assumed to occur independently over time and follow a Poisson distribution. Simulations are used to evaluate the method under changes in various parameters. In addition, there are two methods proposed in the literature for the calculation of the p-value, an adjustment based on the tests for previous time periods and the use of the recurrence interval with no adjustment for previous tests. The difference in these two methods is also considered. The quickness of the scan method in detecting an increase in the incidence rate as well as the number of false alarm events that occur and how long the method signals after the increase threat has passed are all of interest. These estimates from the scan method are compared to other attribute monitoring methods, mainly the Poisson CUSUM chart. It is shown that the Poisson CUSUM chart is typically faster in the detection of the increased incidence rate. / Ph. D.

We examine some of the methodologies implemented by the Centers for Disease Control and Prevention's (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS method W2 allows one to monitor syndrome counts (W2count) from each source and the proportion of counts of a particular syndrome relative to the total number of visits (W2rate). We investigate the performance of the W2r method designed using an empiric recurrence interval (RI) in this dissertation research. An adaptive threshold monitoring method is introduced based on fitting sample data to the underlying distributions, then converting the current value to a Z-score through a p-value. We compare the upper thresholds on the Z-scores required to obtain given values of the recurrence interval for different sets of parameter values. We then simulate one-week outbreaks in our data and calculate the proportion of times these methods correctly signal an outbreak using Shewhart and exponentially weighted moving average (EWMA) charts. Our results indicate the adaptive threshold method gives more consistent statistical performance across different parameter sets and amounts of baseline historical data used for computing the statistics. For the power analysis, the EWMA chart is superior to its Shewhart counterpart in nearly all cases, and the adaptive threshold method tends to outperform the W2 rate method. Two modified W2r methods proposed in the dissertation also tend to outperform the W2r method in terms of the RI threshold functions and in the power analysis. / Ph. D.

<p dir="ltr">Proper development of hypersonic vehicles requires knowledge of thermal loads, which are primarily dictated by the turbulent kinetic energy [1]. Accurate measurements of these values require measurements of velocity fluctuations, which are difficult to obtain using conventional seeded velocimetry methods [2]. However, molecular tagging velocimetry methods such as femtosecond laser electronic excitation tagging (FLEET) have been shown to be capable of measuring mean velocity within highly varying flows [3].</p><p dir="ltr">This work extends the available measurements provided by FLEET, through combination with optical thermometry and novel analysis methods of the signal. By performing FLEET velocimetry alongside thermometry, this shows capability to make instantaneous measurements of Mach number within supersonic flows [4]. Additionally, by tracking multiple characteristics of FLEET image signals, the ability to both capture instantaneous velocity fluctuations and improve measurement of mean velocity are demonstrated.</p><p dir="ltr">Furthermore, the uncertainty intrinsic to the analysis of FLEET signals is investigated. This is done using a combination of both classical statistical methods and uncertainty calculation methods commonly used in particle imaging velocimetry [5]. This is necessary to provide the best possible estimate of velocity fluctuations for the validation of computational fluid dynamic (CFD) models of boundary layer heat transfer.</p><p dir="ltr">Beyond simply improving the quality of velocity measurements, frequency analysis tools are developed and extended to analysis of fluid dynamic problems. These tools have been used prior for detecting extreme transitions within a signal [6], but are applied here to demonstrate their ability to detect physics captured within flow fields. These tools show promise in the ability to detect frequency couplings in time and can potentially be implemented to improve current control strategies in the field of fluid dynamics.</p>

Cancer remains the second leading cause of death in the United States and the world, despite years of research and the development of different treatments. One reason for this is cancer cells are able to survive through adaptation to their environment and aberrantly activated growth signaling. As such, developing new therapies that overcome these hurdles are necessary to combat cancer. Previous work in our laboratory using RNA interference screening identified genes that regulate the survival of glioblastoma (GBM) or autophagy in chronic myelogenous leukemia (CML) cancer cells. One screen identified Phosphatidylinositol-4,5-bisophosphate 3-kinase catalytic subunit beta (PIK3CB) in the family of Phosphatidylinositol 3-kinases (PI3K) as a survival kinase gene in GBM. Work contained in this dissertation set out to study PIK3CB mediated GBM cell survival. We report that only PIK3CB, in its family of other PI3K genes, is a biomarker for GBM recurrence and is selectively important for GBM cell survival. Another screen identified the long non-coding RNA, Linc00467, as a gene that regulates autophagy in CML. Autophagy is a dynamic survival process used by all cells, benign and cancerous, where cellular components are broken down and re-assimilated to sustain survival. Work contained in this dissertation set out to characterize the role that Linc00467 serves in regulating autophagy in a myriad of cancers. Collectively our data have showed Linc00467 to actively repress levels of autophagy in cancer cells. Further, our data revealed an important role for Linc00467 in regulating the stability of the autophagy regulating protein serine-threonine kinase 11 (STK11). Because of the unique role that Linc00467 serves in regulating autophagy we renamed it as, autophagy regulating long intergenic noncoding RNA or ARLINC. Taken together the work in this dissertation unveils the inner-workings of two important cancer cell survival pathways and shows their potential for development into therapeutic targets to treat cancer. / Ph. D. / Cancer remains the second leading cause of death in the United States and the world, despite years of research and the development of different treatments. One reason for this is cancer cells are able to survive through adaptation to their environment and aberrantly activated growth signaling. As such, developing new therapies that overcome these hurdles are necessary to combat cancer. Previous work in our laboratory using high throughput genetic screens identified genes that regulate the survival of cancer cells from a deadly type of brain cancer called glioblastoma (GBM). Another screen revealed genes that regulate a process called autophagy in cancer cells from a type of leukemia called chronic myelogenous leukemia (CML). Autophagy is a process that cancer cells can use to survive through chemotherapy. One screen identified the gene Phosphatidylinositol-4,5-bisophosphate 3-kinase catalytic subunit β (PIK3CB) in the family of Phosphatidylinositol 3-kinases (PI3K) as a survival gene in GBM. Work contained in this dissertation set out to study PIK3CB mediated GBM cell survival. We report that only PIK3CB, in its family of other PI3K genes, is a biomarker for GBM recurrence and is selectively important for GBM cell survival. Another screen identified the long non-coding RNA, Linc00467, as a gene that regulates autophagy in CML. Autophagy is a dynamic survival process used by all cells, normal and cancerous, where cellular components are broken down and reassimilated to sustain survival. Work contained in this dissertation set out to characterize the role that Linc00467 serves in regulating autophagy in different types of cancer. Collectively our data have showed Linc00467 to actively repress levels of autophagy in cancer cells. Further, our data revealed an important role for Linc00467 in regulating the stability of the autophagy regulating protein serine-threonine kinase 11 (STK11). Because of the unique role that Linc00467 serves in regulating autophagy we renamed it as, autophagy regulating long intergenic noncoding RNA or ARLINC. Taken together the work in this dissertation unveils the inner-workings of two important cancer cell survival pathways and shows their potential for development into therapeutic targets to treat cancer.

La déformation des signaux au long de leur trajet de propagation est un des plus importants facteurs qui doivent être considérés à la réception. Ces effets sont dus à des phénomènes comme l’atténuation, la réflexion, la dispersion et le bruit. Alors que les premiers deux phénomènes sont assez facile à surveiller, parce qu’elles affectent l’amplitude, respectivement le retard des signaux, les deux derniers phénomènes sont plus difficiles à contrôler, parce qu’elles changent les paramètres du signal (amplitude, fréquence et phase) de manière totalement dépendante de l’environnement.Dans cette thèse, l’objectif principal est de contribuer à l’analyse des signaux liés aux différents phénomènes physiques, en visant une meilleure compréhension de ces phénomènes, ainsi que l’estimation de leurs paramètres qui sont intéressants de point de vue applicatif. Plusieurs contextes applicatifs ont été investigués dans deux configurations de : active et passive.Pour la configuration active, le premier contexte applicatif consiste en l’étude du phénomène de cavitation dans le domaine de la surveillance de systèmes hydrauliques. La deuxième application de la configuration active est la détection et le suivi des objets immergés sans synchronisation entre les capteurs d’émission et de réception.Pour la configuration passive, nous nous concentrons sur l’analyse des transitoires de pression dans les conduites d’eau en utilisant une méthode non-intrusive ainsi que sur la surveillance des réseaux d’énergie électrique en présence des phénomènes transitoires comme les arcs électriques.Malgré les différences entre les considérations physiques spécifiques à ces applications, nous proposons un modèle mathématique unique pour les signaux issus des deux types de configurations. Le modèle est basé sur l’analyse des récurrences. Avec ce concept, nous proposons une nouvelle approche pour les ondes basées sur l’espace des phases. Cette technique de construction des formes d’ondes présente l’intérêt de conduire à des méthodes de d’investigation active à haut cadence, très utiles pour la surveillance des phénomènes dynamiques.En plus, nous proposons des approches nouvelles pour l’investigation des caractéristiques des signaux. La première est la mesure TDR* (Time Distributed Recurrences) qui quantifie la matrice des récurrences/ distances et qui est utilisée pour la détection des signaux transitoires. La deuxième approche est l’analyse des phases à plusieurs retards et elle est utilisée pour la discrimination entre des signaux avec des paramètres très proches. Finalement, la quantification des lignes diagonales de la matrice des récurrences est proposée comme alternative pour l’analyse des signaux modulés en fréquence.Les travaux présentent les résultats expérimentaux en utilisant les méthodes théorétiques proposées dans cette thèse. Les résultats sont comparés avec des techniques classiques.Des perspectives de ces travaux, tant dans les domaines théorique et qu’applicatif, sont discutés à la fin du mémoire. / Signals’ deformation along their propagation path is among the most important aspect which has to be taken into account at reception. These effects are caused by phenomena like attenuation, reflection, dispersion and noise. Whereas the first two are rather easy to monitor, because they affect the amplitude, respectively the delay, the latter two are more difficult to control, because they change signals’ parameters (amplitude, frequency and phase) in an environment-dependent manner.In this thesis, the main objective is to contribute to the analysis of signals related to different physical phenomena, aiming to better understand them as well as to estimate their parameters that are interesting from application point of view. Different applicative contexts have been investigated in active and passive sensing configurations. For the active part, we mention the monitoring of cavitation phenomena and its characterization for hydraulic system surveillance. The second application of the active sensing is the underwater object detection and tracking without synchronization between sensors. For the passive configuration, we focus on the pressure transient analysis in water pipes investigation with a non-intrusive method and on the surveillance of electrical power systems in the presence of transient phenomena such as electrical arcs.Despite the differences between the physical considerations, we propose a unique mathematical model of the signals issued from the active/passive sensing system used to analyze the considered phenomena. This model is based on the Recurrence Plot Analysis (RPA) method. With this concept, we propose the phase-space based waveform design. This waveform design technique presents the interest to conduct to a high speed sensing methods, very useful to monitor dynamic phenomena.Moreover, we propose new tools for the investigation of the signals characteristics. The first one is the TDR* measure (Time Distributed Recurrences) that quantifies the recurrence/ distance matrix and it is used for the detection of transient signals. The second one is the multi-lag phase analysis using multiple lags and it is successfully used to discriminate between signals with close parameters. Finally, the diagonal lines quantification of RPA matrix is proposed as an alternative for the analysis of modulated signals.Our work presents the experimental results using the proposed theoretical methods introduced by this thesis. The results are compared with classical techniques.The perspectives of this thesis are presented at the end of this paper.