A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

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.

Enhanced reproducibility of SADI web service workflows with Galaxy and Docke

Background: Semantic Web technologies have been widely applied in the life sciences, for example by data providers such as OpenLifeData and through web services frameworks such as SADI. The recently reported OpenLifeData2SADI project offers access to the vast OpenLifeData data store through SADI services. Findings: This article describes how to merge data retrieved from OpenLifeData2SADI with other SADI services using the Galaxy bioinformatics analysis platform, thus making this semantic data more amenable to complex analyses. This is demonstrated using a working example, which is made distributable and reproducible through a Docker image that includes SADI tools, along with the data and workflows that constitute the demonstration. Conclusions: The combination of Galaxy and Docker offers a solution for faithfully reproducing and sharing complex data retrieval and analysis workflows based on the SADI Semantic web service design patterns.

ESPIRITUALIDADE NO MUNDO CORPORATIVO: APROXIMAÇÕES ENTRE PRÁTICA RELIGIOSA E VIDA PROFISSIONAL

O tema Espiritualidade no mundo corporativo: aproximações entre a práxis religiosa e a vida profissional, é compreendido no campo das Ciências da Religião, especificamente na área de Práxis religiosa na sociedade, e foi pesquisado de acordo com o método histórico crítico, tendo como metodologia para a coleta de dados a pesquisa bibliográfica e documental. O estudo do tema buscou verificar duas hipóteses, a saber: a espiritualidade no mundo corporativo como resposta às necessidades espirituais da sociedade contemporânea, em substituição às expressões religiosas institucionalizadas, e a espiritualidade no mundo corporativo como conceito solidário à práxis religiosa para a promoção humana no mercado e na sociedade. Os conceitos de espiritualidade em Paul Tillich e práxis em Casiano Floristán foram utilizados como referencias teóricos para estudo do tema. A dissertação foi estruturada em três capítulos. No primeiro, há definições e relações dos termos que determinam o campo semântico da espiritualidade, que são: espiritualidade e religião, religioso e secular, sagrado e profano. No segundo, há o registro das origens do movimento, identificando seu período de florescimento, seus principais atores, seu contexto científico, ideológico, religioso, econômico e filosófico, e principalmente lista os diferentes conceitos que o termo espiritualidade adquire no mundo corporativo. Esse capítulo esclarece os fatores sociais e históricos que possibilitaram a inserção da espiritualidade no local de trabalho e a inclusão do tema no mundo corporativo. Finalmente, o terceiro e último capítulo analisa três possíveis utilizações do conceito de espiritualidade no mundo corporativo: a espiritualidade a serviço da religião (proselitismo), do capital (performance) e do ser humano (práxis). Possibilitando assim ao leitor ampliar seus conhecimentos sobre o tema que é o objeto desta dissertação.(AU)

ESPIRITUALIDADE NO MUNDO CORPORATIVO: APROXIMAÇÕES ENTRE PRÁTICA RELIGIOSA E VIDA PROFISSIONAL

O tema Espiritualidade no mundo corporativo: aproximações entre a práxis religiosa e a vida profissional, é compreendido no campo das Ciências da Religião, especificamente na área de Práxis religiosa na sociedade, e foi pesquisado de acordo com o método histórico crítico, tendo como metodologia para a coleta de dados a pesquisa bibliográfica e documental. O estudo do tema buscou verificar duas hipóteses, a saber: a espiritualidade no mundo corporativo como resposta às necessidades espirituais da sociedade contemporânea, em substituição às expressões religiosas institucionalizadas, e a espiritualidade no mundo corporativo como conceito solidário à práxis religiosa para a promoção humana no mercado e na sociedade. Os conceitos de espiritualidade em Paul Tillich e práxis em Casiano Floristán foram utilizados como referencias teóricos para estudo do tema. A dissertação foi estruturada em três capítulos. No primeiro, há definições e relações dos termos que determinam o campo semântico da espiritualidade, que são: espiritualidade e religião, religioso e secular, sagrado e profano. No segundo, há o registro das origens do movimento, identificando seu período de florescimento, seus principais atores, seu contexto científico, ideológico, religioso, econômico e filosófico, e principalmente lista os diferentes conceitos que o termo espiritualidade adquire no mundo corporativo. Esse capítulo esclarece os fatores sociais e históricos que possibilitaram a inserção da espiritualidade no local de trabalho e a inclusão do tema no mundo corporativo. Finalmente, o terceiro e último capítulo analisa três possíveis utilizações do conceito de espiritualidade no mundo corporativo: a espiritualidade a serviço da religião (proselitismo), do capital (performance) e do ser humano (práxis). Possibilitando assim ao leitor ampliar seus conhecimentos sobre o tema que é o objeto desta dissertação.(AU)

The EMBL nucleotide sequence database

The EMBL Nucleotide Sequence Database (http://www.ebi.ac.uk/embl/) is maintained at the European Bioinformatics Institute (EBI) in an international collaboration with the DNA Data Bank of Japan (DDBJ) and GenBank at the NCBI (USA). Data is exchanged amongst the collaborating databases on a daily basis. The major contributors to the EMBL database are individual authors and genome project groups. Webin is the preferred web-based submission system for individual submitters, whilst automatic procedures allow incorporation of sequence data from large-scale genome sequencing centres and from the European Patent Office (EPO). Database releases are produced quarterly. Network services allow free access to the most up-to-date data collection via ftp, email and World Wide Web interfaces. EBI’s Sequence Retrieval System (SRS), a network browser for databanks in molecular biology, integrates and links the main nucleotide and protein databases plus many specialized databases. For sequence similarity searching a variety of tools (e.g. Blitz, Fasta, BLAST) are available which allow external users to compare their own sequences against the latest data in the EMBL Nucleotide Sequence Database and SWISS-PROT.

IMGT/HLA Database—a sequence database for the human major histocompatibility complex

The IMGT/HLA Database (www.ebi.ac.uk/imgt/hla/) specialises in sequences of polymorphic genes of the HLA system, the human major histocompatibility complex (MHC). The HLA complex is located within the 6p21.3 region on the short arm of human chromosome 6 and contains more than 220 genes of diverse function. Many of the genes encode proteins of the immune system and these include the 21 highly polymorphic HLA genes, which influence the outcome of clinical transplantation and confer susceptibility to a wide range of non-infectious diseases. The database contains sequences for all HLA alleles officially recognised by the WHO Nomenclature Committee for Factors of the HLA System and provides users with online tools and facilities for their retrieval and analysis. These include allele reports, alignment tools and detailed descriptions of the source cells. The online IMGT/HLA submission tool allows both new and confirmatory sequences to be submitted directly to the WHO Nomenclature Committee. The latest version (release 1.7.0 July 2000) contains 1220 HLA alleles derived from over 2700 component sequences from the EMBL/GenBank/DDBJ databases. The HLA database provides a model which will be extended to provide specialist databases for polymorphic MHC genes of other species.

Database resources of the National Center for Biotechnology Information

In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources that operate on the data in GenBank and a variety of other biological data made available through NCBI’s Web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing, Human MapViewer, GeneMap’99, Human–Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, Cancer Genome Anatomy Project (CGAP), SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheri­tance in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved Domain Database (CDD). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.

CluSTr: a database of clusters of SWISS-PROT+TrEMBL proteins

The CluSTr (Clusters of SWISS-PROT and TrEMBL proteins) database offers an automatic classification of SWISS-PROT and TrEMBL proteins into groups of related proteins. The clustering is based on analysis of all pairwise comparisons between protein sequences. Analysis has been carried out for different levels of protein similarity, yielding a hierarchical organisation of clusters. The database provides links to InterPro, which integrates information on protein families, domains and functional sites from PROSITE, PRINTS, Pfam and ProDom. Links to the InterPro graphical interface allow users to see at a glance whether proteins from the cluster share particular functional sites. CluSTr also provides cross-references to HSSP and PDB. The database is available for querying and browsing at http://www.ebi.ac.uk/clustr.

CKAAPs DB: a conserved key amino acid positions database

The Conserved Key Amino Acid Positions DataBase (CKAAPs DB) provides access to an analysis of structurally similar proteins with dissimilar sequences where key residues within a common fold are identified. The derivation and significance of CKAAPs starting from pairwise structure alignments is described fully in Reddy et al. [Reddy,B.V.B., Li,W.W., Shindyalov,I.N. and Bourne,P.E. (2000) Proteins, in press]. The CKAAPs identified from this theoretical analysis are provided to experimentalists and theoreticians for potential use in protein engineering and modeling. It has been suggested that CKAAPs may be crucial features for protein folding, structural stability and function. Over 170 substructures, as defined by the Combinatorial Extension (CE) database, which are found in approximately 3000 representative polypeptide chains have been analyzed and are available in the CKAAPs DB. CKAAPs DB also provides CKAAPs of the representative set of proteins derived from the CE and FSSP databases. Thus the database contains over 5000 representative poly­peptide chains, covering all known structures in the PDB. A web interface to a relational database permits fast retrieval of structure-sequence alignments, CKAAPs and associated statistics. Users may query by PDB ID, protein name, function and Enzyme Classification number. Users may also submit protein alignments of their own to obtain CKAAPs. An interface to display CKAAPs on each structure from a web browser is also being implemented. CKAAPs DB is maintained by the San Diego Supercomputer Center and accessible at the URL http://ckaaps.sdsc.edu.

VIDA: a virus database system for the organization of animal virus genome open reading frames

VIDA is a new virus database that organizes open reading frames (ORFs) from partial and complete genomic sequences from animal viruses. Currently VIDA includes all sequences from GenBank for Herpesviridae, Coronaviridae and Arteriviridae. The ORFs are organized into homologous protein families, which are identified on the basis of sequence similarity relationships. Conserved sequence regions of potential functional importance are identified and can be retrieved as sequence alignments. We use a controlled taxonomical and functional classification for all the proteins and protein families in the database. When available, protein structures that are related to the families have also been included. The database is available for online search and sequence information retrieval at http://www.biochem.ucl.ac.uk/bsm/virus_database/VIDA.html.

iProClass: an integrated, comprehensive and annotated protein classification database

The iProClass database is an integrated resource that provides comprehensive family relationships and structural and functional features of proteins, with rich links to various databases. It is extended from ProClass, a protein family database that integrates PIR superfamilies and PROSITE motifs. The iProClass currently consists of more than 200 000 non-redundant PIR and SWISS-PROT proteins organized with more than 28 000 superfamilies, 2600 domains, 1300 motifs, 280 post-translational modification sites and links to more than 30 databases of protein families, structures, functions, genes, genomes, literature and taxonomy. Protein and family summary reports provide rich annotations, including membership information with length, taxonomy and keyword statistics, full family relationships, comprehensive enzyme and PDB cross-references and graphical feature display. The database facilitates classification-driven annotation for protein sequence databases and complete genomes, and supports structural and functional genomic research. The iProClass is implemented in Oracle 8i object-relational system and available for sequence search and report retrieval at http://pir.georgetow n.edu/iproclass/.

PlasmoDB: An integrative database of the Plasmodium falciparum genome. Tools for accessing and analyzing finished and unfinished sequence data

The Plasmodium falciparum Genome Database (http://PlasmoDB.org) integrates sequence information, automated analyses and annotation data emerging from the P.falciparum genome sequencing consortium. To date, raw sequence coverage is available for >90% of the genome, and two chromosomes have been finished and annotated. Data in PlasmoDB are organized by chromosome (1–14), and can be accessed using a variety of tools for graphical and text-based browsing or downloaded in various file formats. The GUS (Genomics Unified Schema) implementation of PlasmoDB provides a multi-species genomic relational database, incorporating data from human and mouse, as well as P.falciparum. The relational schema uses a highly structured format to accommodate diverse data sets related to genomic sequence and gene expression. Tools have been designed to facilitate complex biological queries, including many that are specific to Plasmodium parasites and malaria as a disease. Additional projects seek to integrate genomic information with the rich data sets now becoming available for RNA transcription, protein expression, metabolic pathways, genetic and physical mapping, antigenic and population diversity, and phylogenetic relationships with other apicomplexan parasites. The overall goal of PlasmoDB is to facilitate Internet- and CD-ROM-based access to both finished and unfinished sequence information by the global malaria research community.

General and specific brain regions involved in encoding and retrieval of events: what, where, and when.

Remembering an event involves not only what happened, but also where and when it occurred. We measured regional cerebral blood flow by positron emission tomography during initial encoding and subsequent retrieval of item, location, and time information. Multivariate image analysis showed that left frontal brain regions were always activated during encoding, and right superior frontal regions were always activated at retrieval. Pairwise image subtraction analyses revealed information-specific activations at (i) encoding, item information in left hippocampal, location information in right parietal, and time information in left fusiform regions; and (ii) retrieval, item in right inferior frontal and temporal, location in left frontal, and time in anterior cingulate cortices. These results point to the existence of general encoding and retrieval networks of episodic memory whose operations are augmented by unique brain areas recruited for processing specific aspects of remembered events.

Casamento de modelos baseado em projeções radiais e circulares invariante a pontos de vista.

Este trabalho aborda o problema de casamento entre duas imagens. Casamento de imagens pode ser do tipo casamento de modelos (template matching) ou casamento de pontos-chaves (keypoint matching). Estes algoritmos localizam uma região da primeira imagem numa segunda imagem. Nosso grupo desenvolveu dois algoritmos de casamento de modelos invariante por rotação, escala e translação denominados Ciratefi (Circula, radial and template matchings filter) e Forapro (Fourier coefficients of radial and circular projection). As características positivas destes algoritmos são a invariância a mudanças de brilho/contraste e robustez a padrões repetitivos. Na primeira parte desta tese, tornamos Ciratefi invariante a transformações afins, obtendo Aciratefi (Affine-ciratefi). Construímos um banco de imagens para comparar este algoritmo com Asift (Affine-scale invariant feature transform) e Aforapro (Affine-forapro). Asift é considerado atualmente o melhor algoritmo de casamento de imagens invariante afim, e Aforapro foi proposto em nossa dissertação de mestrado. Nossos resultados sugerem que Aciratefi supera Asift na presença combinada de padrões repetitivos, mudanças de brilho/contraste e mudanças de pontos de vista. Na segunda parte desta tese, construímos um algoritmo para filtrar casamentos de pontos-chaves, baseado num conceito que denominamos de coerência geométrica. Aplicamos esta filtragem no bem-conhecido algoritmo Sift (scale invariant feature transform), base do Asift. Avaliamos a nossa proposta no banco de imagens de Mikolajczyk. As taxas de erro obtidas são significativamente menores que as do Sift original.