Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (M.Sc.)--Memorial University of Newfoundland, 1999. / Bibliography: leaves 52-61.

Thesis (B.S.)--University of Missouri, School of Mines and Metallurgy, 1906. / The entire thesis text is included in file. Typescript. Illustrated by authors. Title from title screen of thesis/dissertation PDF file (viewed December 5, 2008)

Photocopy of thesis. / Includes bibliographical references (p. 171-186).

Contexte: La microscopie joue un rôle important en biologie depuis plusieurs siècles, mais sa résolution a longtemps été limitée à environ 250 nm, de sorte que nombre de structures biologiques (virus, vésicules, pores nucléaires, synapses) ne pouvaient être résolues. Au cours de la dernière décennie, plusieurs méthodes de super-résolution ont été développées pour dépasser cette limite. Parmi ces techniques, les plus puissantes et les plus utilisées reposent sur la localisation de molécules uniques (microscopie à localisation de molécule unique, ou SMLM), comme PALM et STORM. En localisant précisément les positions de molécules fluorescentes isolées dans des milliers d'images de basse résolution acquises de manière séquentielle, la SMLM peut atteindre des résolutions de 20 à 50 nm voire mieux. Cependant, cette technique est intrinsèquement lente car elle nécessite l’accumulation d’un très grand nombre d’images et de localisations pour obtenir un échantillonnage super-résolutif des structures fluorescentes. Cette lenteur (typiquement ~ 30 minutes par image super-résolutive) rend difficile l'utilisation de la SMLM pour l'imagerie cellulaire à haut débit ou en cellules vivantes. De nombreuses méthodes ont été proposées pour pallier à ce problème, principalement en améliorant les algorithmes de localisation pour localiser des molécules proches, mais la plupart de ces méthodes compromettent la résolution spatiale et entraînent l’apparition d’artefacts. Méthodes et résultats: Nous avons adopté une stratégie de transformation d’image en image basée sur l'apprentissage profond dans le but de restaurer des images SMLM parcimonieuses et par là d’améliorer la vitesse d’acquisition et la qualité des images super-résolutives. Notre méthode, ANNA-PALM, s’appuie sur des développements récents en apprentissage profond, notamment l’architecture U-net et les modèles génératifs antagonistes (GANs). Nous montrons des validations de la méthode sur des images simulées et des images expérimentales de différentes structures cellulaires (microtubules, pores nucléaires et mitochondries). Ces résultats montrent qu’après un apprentissage sur moins de 10 images de haute qualité, ANNA-PALM permet de réduire le temps d’acquisition d’images SMLM, à qualité comparable, d’un facteur 10 à 100. Nous avons également montré que ANNA-PALM est robuste à des altérations de la structure biologique, ainsi qu’à des changements de paramètres de microscopie. Nous démontrons le potentiel applicatif d’ANNA-PALM pour la microscopie à haut débit en imageant ~ 1000 cellules à haute résolution en environ 3 heures. Enfin, nous avons conçu un outil pour estimer et réduire les artefacts de reconstruction en mesurant la cohérence entre l’image reconstruite et l’image en épi-fluorescence. Notre méthode permet une microscopie super-résolutive plus rapide et plus douce, compatible avec l’imagerie haut débit, et ouvre une nouvelle voie vers l'imagerie super-résolutive des cellules vivantes. La performance des méthodes d'apprentissage profond augmente avec la quantité des données d’entraînement. Le partage d’images au sein de la communauté de microscopie offre en principe un moyen peu coûteux d’augmenter ces données. Cependant, il est souvent difficile d'échanger ou de partager des données de SMLM, car les tables de localisation seules ont souvent une taille de plusieurs gigaoctets et il n'existe pas de plate-forme de visualisation dédiée aux données SMLM. Nous avons développé un format de fichier pour compresser sans perte des tables de localisation, ainsi qu’une plateforme web (https://shareloc.xyz) qui permet de visualiser et de partager facilement des données SMLM 2D ou 3D. A l’avenir, cette plate-forme pourrait grandement améliorer les performances des modèles d'apprentissage en profondeur, accélérer le développement des outils, faciliter la réanalyse des données et promouvoir la recherche reproductible et la science ouverte. / Background: Microscopy plays an important role in biology since several centuries, but its resolution has long been limited to ~250nm due to diffraction, leaving many important biological structures (e.g. viruses, vesicles, nuclear pores, synapses) unresolved. Over the last decade, several super-resolution methods have been developed that break this limit. Among the most powerful and popular super-resolution techniques are those based on single molecular localization (single molecule localization microscopy, or SMLM) such as PALM and STORM. By precisely localizing positions of isolated fluorescent molecules in thousands or more sequentially acquired diffraction limited images, SMLM can achieve resolutions of 20-50 nm or better. However, SMLM is inherently slow due to the necessity to accumulate enough localizations to achieve high resolution sampling of the fluorescent structures. The drawback in acquisition speed (typically ~30 minutes per super-resolution image) makes it difficult to use SMLM in high-throughput and live cell imaging. Many methods have been proposed to address this issue, mostly by improving the localization algorithms to localize overlapping spots, but most of them compromise spatial resolution and cause artifacts.Methods and results: In this work, we applied deep learning based image-to-image translation framework for improving imaging speed and quality by restoring information from rapidly acquired low quality SMLM images. By utilizing recent advances in deep learning including the U-net and Generative Adversarial Networks, we developed our method Artificial Neural Network Accelerated PALM (ANNA-PALM) which is capable of learning structural information from training images and using the trained model to accelerate SMLM imaging by tens to hundreds folds. With experimentally acquired images of different cellular structures (microtubules, nuclear pores and mitochondria), we demonstrated that deep learning can efficiently capture the structural information from less than 10 training samples and reconstruct high quality super-resolution images from sparse, noisy SMLM images obtained with much shorter acquisitions than usual for SMLM. We also showed that ANNA-PALM is robust to possible variations between training and testing conditions, due either to changes in the biological structure or to changes in imaging parameters. Furthermore, we take advantage of the acceleration provided by ANNA-PALM to perform high throughput experiments, showing acquisition of ~1000 cells at high resolution in ~3 hours. Additionally, we designed a tool to estimate and reduce possible artifacts is designed by measuring the consistency between the reconstructed image and the experimental wide-field image. Our method enables faster and gentler imaging which can be applied to high-throughput, and provides a novel avenue towards live cell high resolution imaging. Deep learning methods rely on training data and their performance can be improved even further with more training data. One cheap way to obtain more training data is through data sharing within the microscopy community. However, it often difficult to exchange or share localization microscopy data, because localization tables alone are typically several gigabytes in size, and there is no dedicated platform for localization microscopy data which provide features such as rendering, visualization and filtering. To address these issues, we developed a file format that can losslessly compress localization tables into smaller files, alongside with a web platform called ShareLoc (https://shareloc.xyz) that allows to easily visualize and share 2D or 3D SMLM data. We believe that this platform can greatly improve the performance of deep learning models, accelerate tool development, facilitate data re-analysis and further promote reproducible research and open science.

No description available.

No description available.

Idealized bathymetries were subjected to idealized cyclones in order to measure the storm surge response to a range of bathymetry features, under various storm conditions. Ten bathymetries were considered, including eight shoals, one pit, and a featureless reference domain. Six storms (two different sizes/intensities and three different landfall directions) were used as meteorological forcing. The bathymetry features influenced local surge response during pre- and post-peak surge conditions. However, peak surge and surge at the coast were not meaningfully affected by the presence of the bathymetry features considered. The effect of three bathymetry feature parameters on surge response was analyzed (i.e. depth below mean sea level, cross-shore width, and distance from shore). Of these parameters, feature depth below mean sea level was the most influential on surge generation.

Thesis (B.A.)--Bryn Mawr College, Dept. of Geology, 2007. / Includes bibliographical references.

Asian dust storms invaded Taiwan in springtime. During the Asian dust-storm periods, the dust particles suspended in the atmosphere could not only deteriorate the ambient air quality, mainly high particulate matter concentration and low visibility, but also cause severely adverse effects on human health. In this study, Asian dusts were sampled at Pencadores Islands and characterized the physical and chemical characteristics to investigate the influence of Asian dust storms. Due to its clean atmosphere, Pencadores Islands can be treated as one of the best air quality background sites in Taiwan. In this sampling campaign, five Asian dust storm episodes were observed at Pencadores Islands. Asian dusts transported to Taiwan along the east of China or the east ocean of China and invaded Taiwan from either the northeast or the northwest. The concentrations of atmospheric aerosols during Asian dust storm episodes were 2-3 times higher than the background level. The concentration of PM10 increased dramatically. The increase of PM10 concentration was mainly attributed to coarse particles. The ratio of coarse particles to fine particles for Asian dust storm periods was higher than those for non-Asian dust storm periods. From March to April, the concentration of PM10 increased due to sea-salt aerosol blow into atmosphere by strong eastwest monsoon. It suggested that, at Pencadores Islands, seawater was major chemical species of suspended particles. The concentration of F-, Cl-, Br-, NO3-, SO42-, Na+, Mg2+, and Ca2+ increased during Asian dust storm episodes indicated that pollutant would be transport by Asian dusts. The most possible chemical species in coarse particles would be MgSO4 and CaSO4. The carbon content of suspended particles increased dramatically. The increase of carbon content of coarse particles was mainly attributed to elemental carbon. The increase of carbon content of fine particles was mainly attributed to organic carbon from second reaction. The concentration of Al, K, Br-, Fe, and Ca increased during Asian dust storm episodes indicated that Asian dust storm would transport dusts to Pencadores Islands. The major pollution sources were mobile sources and dust sources at Pencadores Islands. During the Asian dust-storm periods, the percentages of industrial sources, seawater, and secondary aerosols increased dramatically.