A new approach for the glotis segmentation using Snakes

The present work describes a new methodology for the automatic detection of the glottal space from laryngeal images based on active contour models (snakes). In order to obtain an appropriate image for the use of snakes based techniques, the proposed algorithm combines a pre-processing stage including some traditional techniques (thresholding and median filter) with more sophisticated ones such as anisotropic filtering. The value selected for the thresholding was fixed to the 85% of the maximum peak of the image histogram, and the anisotropic filter permits to distinguish two intensity levels, one corresponding to the background and the other one to the foreground (glottis). The initialization carried out is based on the magnitude obtained using the Gradient Vector Flow field, ensuring an automatic process for the selection of the initial contour. The performance of the algorithm is tested using the Pratt coefficient and compared against a manual segmentation. The results obtained suggest that this method provided results comparable with other techniques such as the proposed in (Osma-Ruiz et al., 2008).

Video of the labelling of projected Placido rings through the scanning annulus

The Elliptical Scanning Algorithm is an effective method to individually detect and label the projected rings. It consecutively defines an elliptical annulus of one pixel wide which grows pixel by pixel and sweeps the image, from centre to periphery, until it detects and labels each whole ring. In a way, it works like a snake-annealing algorithm. Active contour models (snakes) are energy-minimising curves that deform to fit image features. Elliptical Scanning Algorithm changes its geometry in order to label reflected rings.

Evaluation of an inactivated Ross River virus vaccine in active and passive mouse immunization models and establishment of a correlate of protection

Ross River Virus has caused reported outbreaks of epidemic polyarthritis, a chronic debilitating disease associated with significant long-term morbidity in Australia and the Pacific region since the 1920s. To address this public health concern, a formalin- and UV-inactivated whole virus vaccine grown in animal protein-free cell culture was developed and tested in preclinical studies to evaluate immunogenicity and efficacy in animal models. After active immunizations, the vaccine dose-dependently induced antibodies and protected adult mice from viremia and interferon α/β receptor knock-out (IFN-α/βR(-/-)) mice from death and disease. In passive transfer studies, administration of human vaccinee sera followed by RRV challenge protected adult mice from viremia and young mice from development of arthritic signs similar to human RRV-induced disease. Based on the good correlation between antibody titers in human sera and protection of animals, a correlate of protection was defined. This is of particular importance for the evaluation of the vaccine because of the comparatively low annual incidence of RRV disease, which renders a classical efficacy trial impractical. Antibody-dependent enhancement of infection, did not occur in mice even at low to undetectable concentrations of vaccine-induced antibodies. Also, RRV vaccine-induced antibodies were partially cross-protective against infection with a related alphavirus, Chikungunya virus, and did not enhance infection. Based on these findings, the inactivated RRV vaccine is expected to be efficacious and protect humans from RRV disease

Synthetic peptide models for the redox-active disulfide loop of glutaredoxin. Conformational studies

Two cyclic peptide disulfides Boc-Cys-Pro-X-Cys-NHMe (X = L-Tyr or L-Phe) have been synthesized as models for the 14-membered redox-active disulfide loop of glutaredoxin. 'H NMR studies at 270 MHz in chloroform solutions establish a type I 0-turn conformation for the Pro-X segment in both peptides, stabilized by a 4-1 hydrogen bond between the Cys(1) CO and Cys(4) NH groups. Nuclear Overhauser effects establish that the aromatic ring in the X = Phe peptide is oriented over the central peptide unit. In dimethyl sulfoxide solutions two conformational species are observed in slow exchange on the NMR time scale, for both peptides. These are assigned to type I and type I1 p-turn structures with -Pro-Tyr(Phe)-as the corner residues. The structural assignments are based on correlation of NMR parameters with model 14-membered cyclic cystine peptides with Pro-X spacers. Circular dichroism studies based on the -S-Sn- u* transition suggest a structural change in the disulfide bridge with changing solvent polarity, establishing conformational coupling between the peptide backbone and the disulfide linkage in these systems.

Identification of Active Sources in Single-Channel Convolutive Mixtures Using Known Source Models

We address the problem of identifying the constituent sources in a single-sensor mixture signal consisting of contributions from multiple simultaneously active sources. We propose a generic framework for mixture signal analysis based on a latent variable approach. The basic idea of the approach is to detect known sources represented as stochastic models, in a single-channel mixture signal without performing signal separation. A given mixture signal is modeled as a convex combination of known source models and the weights of the models are estimated using the mixture signal. We show experimentally that these weights indicate the presence/absence of the respective sources. The performance of the proposed approach is illustrated through mixture speech data in a reverberant enclosure. For the task of identifying the constituent speakers using data from a single microphone, the proposed approach is able to identify the dominant source with up to 8 simultaneously active background sources in a room with RT60 = 250 ms, using models obtained from clean speech data for a Source to Interference Ratio (SIR) greater than 2 dB.

Transition metal clusters supported by multinucleating ligand frameworks as models of biological active sites

This dissertation describes efforts to model biological active sites with small molecule clusters. The approach used took advantage of a multinucleating ligand to control the structure and nuclearity of the product complexes, allowing the study of many different homo- and heterometallic clusters. Chapter 2 describes the synthesis of the multinucleating hexapyridyl trialkoxy ligand used throughout this thesis and the synthesis of trinuclear first row transition metal complexes supported by this framework, with an emphasis on tricopper systems as models of biological multicopper oxidases. The magnetic susceptibility of these complexes were studied, and a linear relation was found between the Cu-O(alkoxide)-Cu angles and the antiferromagnetic coupling between copper centers. The triiron(II) and trizinc(II) complexes of the ligand were also isolated and structurally characterized.

Chapter 3 describes the synthesis of a series of heterometallic tetranuclear manganese dioxido complexes with various incorporated apical redox-inactive metal cations (M = Na⁺, Ca²⁺, Sr²⁺, Zn²⁺, Y³⁺). Chapter 4 presents the synthesis of heterometallic trimanganese(IV) tetraoxido complexes structurally related to the CaMn₃ subsite of the oxygen-evolving complex (OEC) of Photosystem II. The reduction potentials of these complexes were studied, and it was found that each isostructural series displays a linear correlation between the reduction potentials and the Lewis acidities of the incorporated redox-inactive metals. The slopes of the plotted lines for both the dioxido and tetraoxido clusters are the same, suggesting a more general relationship between the electrochemical potentials of heterometallic manganese oxido clusters and their “spectator” cations. Additionally, these studies suggest that Ca²⁺ plays a role in modulating the redox potential of the OEC for water oxidation.

Chapter 5 presents studies of the effects of the redox-inactive metals on the reactivities of the heterometallic manganese complexes discussed in Chapters 3 and 4. Oxygen atom transfer from the clusters to phosphines is studied; although the reactivity is kinetically controlled in the tetraoxido clusters, the dioxido clusters with more Lewis acidic metal ions (Y³⁺ vs. Ca²⁺) appear to be more reactive. Investigations of hydrogen atom transfer and electron transfer rates are also discussed.

Appendix A describes the synthesis, and metallation reactions of a new dinucleating bis(N-heterocyclic carbene)ligand framework. Dicopper(I) and dicobalt(II) complexes of this ligand were prepared and structurally characterized. A dinickel(I) dichloride complex was synthesized, reduced, and found to activate carbon dioxide. Appendix B describes preliminary efforts to desymmetrize the manganese oxido clusters via functionalization of the basal multinucleating ligand used in the preceding sections of this dissertation. Finally, Appendix C presents some partially characterized side products and unexpected structures that were isolated throughout the course of these studies.

Heterometallic Complexes as Models of Enzymatic Active Sites

This dissertation describes studies on two multinucleating ligand architectures: the first scaffold was designed to support tricopper complexes, while the second platform was developed to support tri- and tetrametallic clusters.

In Chapter 2, the synthesis of yttrium (and lanthanide) complexes supported by a tripodal ligand framework designed to bind three copper centers in close proximity is described. Tricopper complexes were shown to react with dioxygen in a 1:1 [Cu₃]/O₂ stoichiometry to form intermediates in which the O–O bond was fully cleaved, as characterized via UV-Vis spectroscopy and determination of the reaction stoichiometry. Pre-arrangement of the three Cu centers was pivotal to cooperative O₂ activation, as mono-copper complexes reacted differently with dioxgyen. The reactivity of the observed intermediates was studied with various substrates (reductants, O-atom acceptors, H-atom donors, Brønsted acids) to determine their properties. In Chapter 3, the reactivity of the same yttrium-tricopper complex with nitric oxide was explored. Reductive coupling to form a trans-hyponitrite complex (characterized by X-ray crystallography) was observed via cooperative reactivity by an yttrium and a copper center on two distinct tetrametallic units. The hyponitrite complex was observed to release nitrous oxide upon treatment with a Brønsted acid, supporting its viability as an intermediate in nitric oxide reduction to nitrous oxide.

In Chapter 4, a different multinucleating ligand scaffold was employed to synthesize heterometallic triiron clusters containing one oxide and one hydroxide bridges. The effects of the redox-inactive, Lewis acidic heterometals on redox potential was studied by cyclic voltammetry, unveiling a linear correlation between redox potential and heterometal Lewis acidity. Further studies on these complexes showed that the Lewis acidity of the redox-inactive metals also affected the oxygen-atom transfer reactivity of these clusters. Comparisons of this reactivity with manganese systems, collaborative efforts to reassign the structures of related manganese oxo-hydroxo clusters, and synthetic attempts to access related dioxo clusters are also described.

In Appendix A, ongoing efforts to synthesize new clusters supported by the same multinucleating ligand platform are described. Studies of novel approaches towards ligand exchange in tetrametallic clusters and incorporation of new supporting and bridging ligand motifs in trinuclear complexes are presented.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Active Learning with Statistical Models

For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.