An incident at sea: The historic combat between U.S. Navy Blimp K-74 and U-Boat 134

This thesis studies the historic encounter between United States Navy airship K-74 and Nazi submarine U-134 in World War II. The Battle of the Atlantic is examined through case study of this one U-boat and its voyage. In all things except her fight with the American blimp, the patrol was perfectly typical. Looked at from start to finish, both her reports and the reports of the Allies encountered, many realities of the war can be studied. U-134 sailed to attack shipping between Florida and Cuba. She was challenged by the attack of United States Navy airship K-74 over the Florida Straits. It is the only documented instance of battle between two such combatants in history. That merits attention. Thesis finding disprove historian William Eliot Morison’s contention that the K-74 airship bombs were not dropped and did not damage the U-boat. Study of this U-boat and its antagonist broadens our understanding of the Battle of the Atlantic. It is a contribution to our knowledge of military, naval, aviation, and local history.

Annotated record of the detailed examination of Mn micronodules from stations over George Bank, Atlantic Ocean

Ferromanganese micronodules have been found on Georges Bank, off the U.S. northeast coast, distributed throughout the surficial sediments within an area about 125 km long and at least 12 km wide. These coarse, sand-sized concretions have precipitated from metal-rich interstitial waters and contain many of the textural and structural features common to other neritic nodules. Most of the nodules have accreted around detrital grains, and X-ray powder diffraction analyses indicate the presence of geothite and vernadite ( delta -MnO sub(2)) in the ferromanganese layers. Chemical analyses of the micronodules, when compared with similar data on deep-sea manganese nodules, reveal lower Mn/Fe ratios, significantly higher concentrations of V and As, comparable values of Mo, and an order of magnitude less of Co, Ni, Ce and most other, metals.

Understanding the role of transforming growth factor beta signalling and epigenomics in osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis with a high socioeconomic burden, with an incompletely understood etiology. Evidence suggests a role for the transforming growth factor beta (TGF-ß) signalling pathway and epigenomics in OA. The aim of this thesis was to understand the involvement of the TGF-ß pathway in OA and to determine the DNA methylation patterns of OA-affected cartilage as compared to the OA-free cartilage. First, I found that a common SNP in the BMP2 gene, a ligand in the Bone morphogenetic protein (BMP) subunit of TGF-ß pathway, was associated with OA in the Newfoundland population. I also showed a genetic association between SMAD3 - a signal transducer in the TGF-ß subunit of the TGF-ß signalling pathway - and the total radiographic burden of OA. I further demonstrated that SMAD3 is over-expressed in OA cartilage, suggesting an over activation of the TGF-ß signalling in OA. Next, I examined the connection of these genes in the regulation of matrix metallopeptidase 13 (MMP13) - an enzyme known to destroy extracellular matrix in OA cartilage - in the context of the TGF-ß signalling. The analyses showed that TGF-ß, MMP13, and SMAD3 were overexpressed in OA cartilage, whereas the expression of BMP2 was significantly reduced. The expression of TGF-ß was positively correlated with that of SMAD3 and MMP13, suggesting that TGF-ß signalling is involved in up-regulation of MMP13. This regulation, however, appears not to be controlled by SMAD3 signals, possibly due to the involvement of collateral signalling, and to be suppressed by BMP regulation in healthy cartilage, whose levels were reduced in end-stage OA. In a genome-wide DNA methylation analysis, I reported CpG sites differentially methylated in OA and showed that the cartilage methylome has a potential to distinguish between OA-affected and non-OA cartilage. Functional clustering analysis of the genes harbouring differentially methylated loci revealed that they are enriched in the skeletal system morphogenesis pathway, which could be a potential candidate for further OA studies. Overall, the findings from the present thesis provide evidence that the TGF-ß signalling pathway is associated with the development of OA, and epigenomics might be involved as a potential mechanism in OA.

Single particle characterization using the soot particle aerosol mass spectrometer (SP-AMS)

Understanding the impact of atmospheric black carbon (BC) containing particles on human health and radiative forcing requires knowledge of the mixing state of BC, including the characteristics of the materials with which it is internally mixed. In this study, we demonstrate for the first time the capabilities of the Aerodyne Soot-Particle Aerosol Mass Spectrometer equipped with a light scattering module (LS-SP-AMS) to examine the mixing state of refractory BC (rBC) and other aerosol components in an urban environment (downtown Toronto). K-means clustering analysis was used to classify single particle mass spectra into chemically distinct groups. One resultant cluster is dominated by rBC mass spectral signals (C+1 to C+5) while the organic signals fall into a few major clusters, identified as hydrocarbon-like organic aerosol (HOA), oxygenated organic aerosol (OOA), and cooking emission organic aerosol (COA). A nearly external mixing is observed with small BC particles only thinly coated by HOA ( 28% by mass on average), while over 90% of the HOA-rich particles did not contain detectable amounts of rBC. Most of the particles classified into other inorganic and organic clusters were not significantly associated with BC. The single particle results also suggest that HOA and COA emitted from anthropogenic sources were likely major contributors to organic-rich particles with low to mid-range aerodynamic diameter (dva). The similar temporal profiles and mass spectral features of the organic clusters and the factors from a positive matrix factorization (PMF) analysis of the ensemble aerosol dataset validate the conventional interpretation of the PMF results.

(Table 2) U-channel paleomagnetic record of ODP Site 178-1098

Thick Holocene sedimentary sections (>45 m) cored in the Palmer Deep by the United States Antarctic Program (USAP) and during Ocean Drilling Program (ODP) Leg 178 provide the first opportunity to examine past geomagnetic field behavior at high southern latitudes. After removal of a low-coercivity drilling overprint the sediments display a stable, single-component remanent magnetization. Two short cores that recovered the uppermost 2.6 m of sediment have inclinations that fluctuate about the present day inclination (-57°) measured at Faraday Station, and several features with wavelengths of 10 to 20 cm appear to be correlative. However, shipboard measurements of inclination fluctuations on split-core samples from three holes drilled at ODP Site 1098 do not correlate well with each other, even though the intensity and susceptibility data correlate very well and the overall mean inclination for cores from each hole is consistent with the expected geocentric axial dipole (GAD) inclination. The correlation is improved dramatically by using inclinations measured on u-channels taken from the pristine center of a split core. Consequently, the anomalous directions and the resulting poor between-hole correlation of inclinations obtained from shipboard data can be attributed to coring-induced deformation, which is common on the outer edge of ODP piston cores, and/or measurement artifacts in the split-core data. Our preferred inclination record is thus derived from u-channel results. The upper ~25 m represents continuous sedimentation over the past 9000 yr, with an average sedimentation rate exceeding 250 cm/kyr (0.25 cm/yr). Given that remanence measurements on u-channels average over an interval <7 cm long, we obtained independent measurements of the paleo-geomagnetic field that average over only ~30 yr. This high-resolution record is characterized by an inclination that fluctuates within +/-15° of the current GAD inclination.

Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Matrix and reservoir-type multipurpose vaginal rings for controlled release of dapivirine and levonorgestrel

A matrix-type silicone elastomer vaginal ring providing 28-day continuous release of dapivirine (DPV) - a lead candidate human immunodeficiency virus type 1 (HIV-1) microbicide compound - has recently demonstrated moderate levels of protection in two Phase III clinical studies. Here, next-generation matrix and reservoir-type silicone elastomer vaginal rings are reported for the first time offering simultaneous and continuous in vitro release of DPV and the contraceptive progestin levonorgestrel (LNG) over a period of between 60 and 180days. For matrix-type vaginal rings comprising initial drug loadings of 100, 150 or 200mg DPV and 0, 16 or 32mg LNG, Day 1 daily DPV release values were between 4132 and 6113μg while Day 60 values ranged from 284 to 454μg. Daily LNG release ranged from 129 to 684μg on Day 1 and 2-91μg on Day 60. Core-type rings comprising one or two drug-loaded cores provided extended duration of in vitro release out to 180days, and maintained daily drug release rates within much narrower windows (either 75-131μg/day or 37-66μg/day for DPV, and either 96-150μg/day or 37-57μg/day for LNG, depending on core ring configuration and ignoring initial lag release effect for LNG) compared with matrix-type rings. The data support the continued development of these devices as multi-purpose prevention technologies (MPTs) for HIV prevention and long-acting contraception.

A New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

Manipulation in U.S. REIT Investment Performance Evaluation: Empirical Evidence

We investigate whether Real Estate Investment Trust (REIT) managers actively manipulate performance measures in spite of the strict regulation under the REIT regime. We provide empirical evidence that is consistent with this hypothesis. Specifically, manipulation strategies may rely on the opportunistic use of leverage. However, manipulation does not appear to be uniform across REIT sectors and seems to become more common as the level of competition in the underlying property sector increases. We employ a set of commonly used traditional performance measures and a recently developed manipulation-proof measure (MPPM, Goetzmann, Ingersoll, Spiegel, and Welch (2007)) to evaluate the performance of 147 REITs from seven different property sectors over the period 1991-2009. Our findings suggest that the existing REIT regulation may fail to mitigate a substantial agency conflict and that investors can benefit from evaluating return information carefully in order to avoid potentially manipulative funds.

The High-Redshift Quasar Luminosity Function from Multi-Epoch Imaging Surveys

Thesis (Ph.D.)--University of Washington, 2016-08

Hallux rigidus: Arthroplasty or arthrodesis. What technique produces better results?

Hallux rígidus (HR) affects the first metatarsophalangeal joint (MTPJ) between 35% and 60% of the population over 65 years and there are multiple ways of treatment. Depending on the radiological stage where you find the deformity determines the procedure to be performed; in the early stages cheilectomy techniques and corrective osteotomy is performed while the more advanced ratings, the surgeon chooses destructive techniques considered as arthrodesis and arthroplasty. This final of degree project aims to focus on 1 MTPJ destructive techniques to clarify which of the procedures generates better results by a number of parameters; outcomes of the American Orthopaedic Foot scale and Ankle Society Hallux metatarsophalangeal Interphalangeal-scale (AOFAS), range of motion (ROM) of the 1ºAMTF, radiological classification. As for the implant arthroplasty technique, this article offers information on material and design that generates better relating to patient characteristics such as age, inflammatory joint diseases, viability and durability of the implant results. The conclusion from this review is that the values obtained in the arthrodesis according AOFAS decrease due to loss of mobility, but both techniques have similar values of effectiveness and concludes with the decision that the technique used is determined taking into account various factors and patient characteristics. Keywords: Hallux rígidus; (Hallux Rígidus) and surgery treatment; Hallux Rígidus arthrodesis; Hallux Rígidus arthroplasty; Hallux Rígidus (arthroplasty and arthrodesis).

Convective Cold Pools over the Atlas Mountains and Their Influence on the Saharan Heat Low

The West African Monsoon (WAM) and its representation in numerical models are strongly influenced by the Saharan Heat Low (SHL), a low-pressure system driven by radiative heating over the central Sahara and ventilated by the cold and moist inflow from adjacent oceans. It has recently been shown that a significant part of the southerly moisture flux into the SHL originates from convective cold pools over the Sahel. These density currents driven by evaporation of rain are largely absent in models with parameterized convection. This crucial issue has been hypothesized to contribute to the inability of many climate models to reproduce the variability of the WAM. Here, the role of convective cold pools approaching the SHL from the Atlas Mountains, which are a strong orographic trigger for deep convection in Northwest Africa, is analyzed. Knowledge about the frequency of these events, as well as their impact on large-scale dynamics, is required to understand their contribution to the variability of the SHL and to known model uncertainties. The first aspect is addressed through the development of an objective and automated method for the generation of multi-year climatologies not available before. The algorithm combines freely available standard surface observations with satellite microwave data. Representativeness of stations and influence of their spatial density are addressed by comparison to a satellite-only climatology. Applying this algorithm to data from automated weather stations and manned synoptic stations in and south of the Atlas Mountains reveals the frequent occurrence. On the order of 6 events per month are detected from May to September when the SHL is in its northernmost position. The events tend to cluster into several-days long convectively active periods, often with strong events on consecutive days. This study is the first to diagnose dynamical impacts of such periods on the SHL, based on simulations of two example cases using the Weather Research and Forecast (WRF) model at convection-permitting resolution. Sensitivity experiments with artificially removed cold pools as well as different resolutions and parameterizations are conducted. Results indicate increases in surface pressure of more than 1 hPa and significant moisture transports into the desert over several days. This moisture affects radiative heating and thus the energy balance of the SHL. Even though cold pool events north of the SHL are less frequent when compared to their Sahelian counterparts, it is shown that they gain importance due to their temporal clustering on synoptic timescale. Together with studies focusing on the Sahel, this work emphasizes the need for improved parameterization schemes for deep convection in order to produce more reliable climate projections for the WAM.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.