Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Macroevolution of insect–plant associations: The relevance of host biogeography to host affiliation

Identifying the factors that have promoted host shifts by phytophagous insects at a macroevolutionary scale is critical to understanding the associations between plants and insects. We used molecular phylogenies of the beetle genus Blepharida and its host genus Bursera to test whether these insects have been using hosts with widely overlapping ranges over evolutionary time. We also quantified the importance of host range coincidence relative to host chemistry and host phylogenetic relatedness. Overall, the evolution of host use of these insects has not been among hosts that are geographically similar. Host chemistry is the factor that best explains their macroevolutionary patterns of host use. Interestingly, one exceptional polyphagous species has shifted among geographically close chemically dissimilar plants.

Protective immunity to HIV-1 in SCID/beige mice reconstituted with peripheral blood lymphocytes of exposed but uninfected individuals

Immunodeficiency typically appears many years after initial HIV infection. This long, essentially asymptomatic period contributes to the transmission of HIV in human populations. In rare instances, clearance of HIV-1 infection has been observed, particularly in infants. There are also reports of individuals who have been frequently exposed to HIV-1 but remain seronegative for the virus, and it has been hypothesized that these individuals are resistant to infection by HIV-1. However, little is known about the mechanism of immune clearance or protection against HIV-1 in these high-risk individuals because it is difficult to directly demonstrate in vivo protective immunity. Although most of these high-risk individuals show an HIV-1-specific cell-mediated immune response using in vitro assays, their peripheral blood lymphocytes (PBLs) are still susceptible to HIV infection in tissue culture. To study this further in vivo, we have established a humanized SCID mouse infection model whereby T-, B-, and natural killer-cell defective SCID/beige mice that have been reconstituted with normal human PBLs can be infected with HIV-1. When the SCID/beige mice were reconstituted with PBLs from two different multiply exposed HIV-1 seronegative individuals, the mice showed resistance to infection by two strains of HIV-1 (macrophage tropic and T cell tropic), although the same PBLs were easily infected in vitro. Mice reconstituted with PBLs from non-HIV-exposed controls were readily infected. When the same reconstituted mice were depleted of human CD8 T cells, however, they became susceptible to HIV-1 infection, indicating that the in vivo protection required CD8 T cells. This provides clear experimental evidence that some multiply exposed, HIV-1-negative individuals have in vivo protective immunity that is CD8 T cell-dependent. Understanding the mechanism of such protective immunity is critical to the design and testing of effective prophylactic vaccines and immunotherapeutic regimens.

Myeloblastin is a granulocyte colony-stimulating factor-responsive gene conferring factor-independent growth to hematopoietic cells

Hematopoiesis depends on a pool of quiescent hematopoietic stem/progenitor cells. When exposed to specific cytokines, a portion of these cells enters the cell cycle to generate an amplified progeny. Myeloblastin (MBN) initially was described as involved in proliferation of human leukemia cells. The granulocyte colony-stimulating factor (G-CSF), which stimulates the proliferation of granulocytic precursors, up-regulates MBN expression. Here we show that constitutive overexpression of MBN confers factor-independent growth to murine bone marrow-derived Ba/F3/G-CSFR cells. Our results point to MBN as a G-CSF responsive gene critical to factor-independent growth and indicate that expression of the G-CSF receptor is a prerequisite to this process. A 91-bp MBN promoter region containing PU.1, C/EBP, and c-Myb binding sites is responsive to G-CSF treatment. Although PU.1, C/EBP, and c-Myb transcription factors all were critical for expression of MBN, its up-regulation by G-CSF was associated mainly with PU.1. These findings suggest that MBN is an important target of PU.1 and a key protease for factor-independent growth of hematopoietic cells.

Neonatal gene transfer leads to widespread correction of pathology in a murine model of lysosomal storage disease

For many inborn errors of metabolism, early treatment is critical to prevent long-term developmental sequelae. We have used a gene-therapy approach to demonstrate this concept in a murine model of mucopolysaccharidosis type VII (MPS VII). Newborn MPS VII mice received a single intravenous injection with 5.4 × 106 infectious units of recombinant adeno-associated virus encoding the human β-glucuronidase (GUSB) cDNA. Therapeutic levels of GUSB expression were achieved by 1 week of age in liver, heart, lung, spleen, kidney, brain, and retina. GUSB expression persisted in most organs for the 16-week duration of the study at levels sufficient to either reduce or prevent completely lysosomal storage. Of particular significance, neurons, microglia, and meninges of the central nervous system were virtually cleared of disease. In addition, neonatal treatment of MPS VII mice provided access to the central nervous system via an intravenous route, avoiding a more invasive procedure later in life. These data suggest that gene transfer mediated by adeno-associated virus can achieve therapeutically relevant levels of enzyme very early in life and that the rapid growth and differentiation of tissues does not limit long-term expression.

Exchanges are not equally able to enhance meiotic chromosome segregation in yeast.

Homologous chromosomes pair, and then migrate to opposite poles of the spindle at meiosis I. In most eukaryotic organisms, reciprocal recombinations (crossovers) between the homologs are critical to the success of this process. Individuals with defects in meiotic recombination typically produce high levels of aneuploid gametes and exhibit low fertility or are sterile. The experiments described here were designed to test whether different crossovers are equally able to contribute to the fidelity of meiotic chromosome segregation in yeast. These experiments were performed with model chromosomes with which it was possible to control and measure the distributions of meiotic crossovers in wild-type cells. Physical and genetic approaches were used to map crossover positions on model chromosomes and to correlate crossover position with meiotic segregation behavior. The results show that crossovers at different chromosomal positions have different abilities to enhance the fidelity of meiotic segregation.

Examining the relationship among genes, attention bias to emotion, and depression in youth

The investigation of biologically initiated pathways to psychological disorder is critical to advance our understanding of mental illness. Research has suggested that attention bias to emotion may be an intermediate trait for depression associated with biologically plausible candidate genes, such as the serotonin transporter (5-HTTLPR) and catechol-o-methyl-transferase (COMT) genes, yet there have been mixed findings in regards to the precise direction of effects. The experience of recent stressful life events (SLEs) may be an important, yet currently unstudied, moderator of the relationship between genes and attention bias as SLEs have been associated with both gene expression and attention to emotion. Additionally, although attention biases to emotion have been studied as a possible intermediate trait associated with depression, no study has examined whether attention biases within the context of measured genetic risk lead to increased risk for clinical depressive episodes over time. Therefore, this research investigated both whether SLEs moderate the link between genetic risk (5-HTTLPR and COMT) and attention bias to emotion and whether 5-HTTLPR and COMT moderated the relationship between attention biases to emotional faces and clinical depression onset prospectively across 18 months within a large community sample of youth (n= 467). Analyses revealed a differential effect of gene. Youth who were homozygous for the low expressing allele of 5-HTTLPR (S/S) and had experienced more recent SLEs within the last three months demonstrated preferential attention toward negative emotional faces (angry and sad). However, youth who were homozygous for the high expressing COMT genotype (Val/Val) and had experienced more recent SLEs showed attentional avoidance of positive facial expressions (happy). Additionally, youth who avoided negative emotion (i.e., anger) and were homozygous for the S allele of the 5-HTTLPR gene were at greater risk for prospective depressive episode onset. Increased risk for depression onset was specific to the 5-HTTLPR gene and was not found when examining moderation by COMT. These findings highlight the importance of examining risk for depression across multiple levels of analysis, such as combined genetic, environmental, and cognitive risk, and is the first study to demonstrate clear evidence of attention biases to emotion functioning as an intermediate trait predicting depression.

Plant regeneration functional groups modulate the response to fire of soil enzyme activities in a Mediterranean shrubland

Soil enzymes are critical to soil nutrient cycling function but knowledge on the factors that control their response to major disturbances such as wildfires remains very limited. We evaluated the effect of fire-related plant functional traits (resprouting and seeding) on the resistance and resilience to fire of two soil enzyme activities involved in phosphorus and carbon cycling (acid phosphatase and β-glucosidase) in a Mediterranean shrublands in SE Spain. Using experimental fires, we compared four types of shrubland microsites: SS (vegetation patches dominated by seeder species), RR (patches dominated by resprouter species), SR (patches co-dominated by seeder and resprouter species), and IP (shrub interpatches). We assessed pre- and post-fire activities of the target soil enzymes, available P, soil organic C, and plant cover dynamics over three years after the fire. Post-fire regeneration functional groups (resprouter, seeder) modulated both pre- and post-fire activity of acid phosphatase and β-glucosidase, with higher activity in RR and SR patches than in SS patches and IP. However, we found no major differences in enzyme resistance and resilience between microsite types, except for a trend towards less resilience in SS patches. Fire similarly reduced the activity of both enzymes. However, acid phosphatase and β-glucosidase showed contrasting post-fire dynamics. While β-glucosidase proved to be rather resilient to fire, fully recovering three years after fire, acid phosphatase showed no signs of recovery in that period. Overall, the results indicate a positive influence of resprouter species on soil enzyme activity that is very resistant to fire. Long-lasting decrease in acid phosphatase activity probably resulted from the combined effect of P availability and post-fire drought. Our results provide insights on how plant functional traits modulate soil biochemical and microbiological response to fire in Mediterranean fire-prone shrublands.

Reducing task’s redundancy – the strategy of Faculty of Pharmacy Library to centralize all scientific research

Introduction – Based on a previous project of University of Lisbon (UL) – a Bibliometric Benchmarking Analysis of University of Lisbon, for the period of 2000-2009 – a database was created to support research information (ULSR). However this system was not integrated with other existing systems at University, as the UL Libraries Integrated System (SIBUL) and the Repository of University of Lisbon (Repositório.UL). Since libraries were called to be part of the process, the Faculty of Pharmacy Library’ team felt that it was very important to get all systems connected or, at least, to use that data in the library systems. Objectives – The main goals were to centralize all the scientific research produced at Faculty of Pharmacy, made it available to the entire Faculty, involve researchers and library team, capitalize and reinforce team work with the integration of several distinct projects and reducing tasks’ redundancy. Methods – Our basis was the imported data collection from the ISI Web of Science (WoS), for the period of 2000-2009, into ULSR. All the researchers and indexed publications at WoS, were identified. A first validation to identify all the researchers and their affiliation (university, faculty, department and unit) was done. The final validation was done by each researcher. In a second round, concerning the same period, all Pharmacy Faculty researchers identified their published scientific work in other databases/resources (NOT WoS). To our strategy, it was important to get all the references and essential/critical to relate them with the correspondent digital objects. To each researcher previously identified, was requested to register all their references of the ‘NOT WoS’ published works, at ULSR. At the same time, they should submit all PDF files (for both WoS and NOT WoS works) in a personal area of the Web server. This effort enabled us to do a more reliable validation and prepare the data and metadata to be imported to Repository and to Library Catalogue. Results – 558 documents related with 122 researchers, were added into ULSR. 1378 bibliographic records (WoS + NOT WoS) were converted into UNIMARC and Dublin Core formats. All records were integrated in the catalogue and repository. Conclusions – Although different strategies could be adopted, according to each library team, we intend to share this experience and give some tips of what could be done and how Faculty of Pharmacy created and implemented her strategy.

Immigrant's Sense of Belonging to Chinatown-International District, the Changing Neighborhood

Thesis (Master's)--University of Washington, 2016-06

Alternative harvest strategies to alleviate predation bottlenecks: a bioeconomic modeling approach to inform adaptive management

Thesis (Ph.D.)--University of Washington, 2016-06

Factors to consider in Micronesian food-based interventions: a case study of preventing Vitamin A deficiency

Background: Many factors need to be considered in a food-based intervention. Vitamin A deficiency and chronic diseases, such as diabetes, heart disease and cancer, have become serious problems in the Federated States of Micronesia (FSM) following the decreased production and consumption of locally grown foods. However, agricultural and social conditions are still favourable for local food production. Aim: To identify key factors to consider in a Micronesian food-based intervention focusing on increased production and consumption of four major Micronesian staple foods: banana, breadfruit, giant swamp taro and pandanus. Methods: Ethnographic methods including key informant interviews and a literature review. Results: Pacific and Micronesian values, concepts of food and disease, and food classifications differ sharply from Western concepts. There are few FSM professionals with nutrition expertise. Traditional foods and food cultivars vary in nutrient content, consumption level, cost, availability, status, convenience in growing, storing and cooking, and organoleptic factors. Conclusions: A systematic consideration of the factors that relate to a food-based intervention is critical to its success. The evaluation of which food and cultivar of that food that might be most effectively promoted is also critical. Regional differences, for example FSM inter-island differences between the staple foods and cultivars, must be considered carefully. The evaluation framework presented here may be relevant to Pacific island and other countries with similar foods where food-based interventions are being planned. An ethnographic approach was found to be essential in understanding the cultural context and in data collection and analysis.

Distance measures to compare real and ideal quantum processes

With growing success in experimental implementations it is critical to identify a gold standard for quantum information processing, a single measure of distance that can be used to compare and contrast different experiments. We enumerate a set of criteria that such a distance measure must satisfy to be both experimentally and theoretically meaningful. We then assess a wide range of possible measures against these criteria, before making a recommendation as to the best measures to use in characterizing quantum information processing.

A visiting occupational therapy service to indigenous children in school: Results of a pilot project

The need for additional support for Indigenous children at school is well documented. Occupational therapists are well positioned to form part of this support. However, many occupational therapists report that Indigenous families do not access their services and when they do, the occupational therapist feels uncertain about how best to meet their needs. This article documents a pilot project which delivered occupational therapy services within several schools and preschools in Brisbane which had significant numbers of Indigenous students. The project was evaluated using a qualitative methodology and included focus groups and interviews with teachers and parents. The results indicated that in general the service provided valuable support to students, teachers and parents. In particular, providing the service within the school context was seen as critical to its success. Suggestions for improvements in future support services are also provided.

SARS coronavirus nucleocapsid immunodominant T-cell epitope cluster is common to both exogenous recombinant and endogenous DNA-encoded immunogens

Correspondence between the T-cell epitope responses of vaccine immunogens and those of pathogen antigens is critical to vaccine efficacy. In the present study, we analyzed the spectrum of immune responses of mice to three different forms of the SARS coronavirus nucleocapsid (N): (1) exogenous recombinant protein (N-GST) with Freund's adjuvant; (2) DNA encoding unmodified N as an endogenous cytoplasmic protein (pN); and (3) DNA encoding N as a LAMP-I chimera targeted to the lysosomal MHC II compartment (p-LAMP-N). Lysosomal trafficking of the LAMP/N chimera in transfected cells was documented by both confocal and immunoelectron microscopy. The responses of the immunized mice differed markedly. The strongest T-cell IFN-gamma and CTL responses were to the LAMP-N chimera followed by the pN immunogen. In contrast, N-GST elicited strong T cell IL-4 but minimal IFN-gamma responses and a much greater antibody response. Despite these differences, however, the immunodominant T-cell ELISpot responses to each of the three immunogens were elicited by the same N peptides, with the greatest responses being generated by a cluster of five overlapping peptides, N76-114, each of which contained nonameric H2(d) binding domains with high binding scores for both class I and, except for N76-93, class II alleles. These results demonstrate that processing and presentation of N, whether exogenously or endogenously derived, resulted in common immunodominant epitopes, supporting the usefulness of modified antigen delivery and trafficking forms and, in particular, LAMP chimeras as vaccine candidates. Nevertheless, the profiles of T-cell responses were distinctly different. The pronounced Th-2 and humoral response to N protein plus adjuvant are in contrast to the balanced IFN-gamma and IL-4 responses and strong memory CTL responses to the LAMP-N chimera. (C) 2005 Elsevier Inc. All rights reserved.