Role of electrostatic interactions for ligand recognition and specificity of peptide transporters.

BACKGROUND Peptide transporters are membrane proteins that mediate the cellular uptake of di- and tripeptides, and of peptidomimetic drugs such as β-lactam antibiotics, antiviral drugs and antineoplastic agents. In spite of their high physiological and pharmaceutical importance, the molecular recognition by these transporters of the amino acid side chains of short peptides and thus the mechanisms for substrate binding and specificity are far from being understood. RESULTS The X-ray crystal structure of the peptide transporter YePEPT from the bacterium Yersinia enterocolitica together with functional studies have unveiled the molecular bases for recognition, binding and specificity of dipeptides with a charged amino acid residue at the N-terminal position. In wild-type YePEPT, the significant specificity for the dipeptides Asp-Ala and Glu-Ala is defined by electrostatic interaction between the in the structure identified positively charged Lys314 and the negatively charged amino acid side chain of these dipeptides. Mutagenesis of Lys314 into the negatively charged residue Glu allowed tuning of the substrate specificity of YePEPT for the positively charged dipeptide Lys-Ala. Importantly, molecular insights acquired from the prokaryotic peptide transporter YePEPT combined with mutagenesis and functional uptake studies with human PEPT1 expressed in Xenopus oocytes also allowed tuning of human PEPT1's substrate specificity, thus improving our understanding of substrate recognition and specificity of this physiologically and pharmaceutically important peptide transporter. CONCLUSION This study provides the molecular bases for recognition, binding and specificity of peptide transporters for dipeptides with a charged amino acid residue at the N-terminal position.

RNAs and ribonucleoproteins in recognition and catalysis.

CONTENTS. 1. Did life begin with catalytic RNA?–2. Self-splicing and self-cleaving RNAs–2.1 Self-splicing of group I introns – 2.2 Self-splicing of group II introns – 2.3 Self-cleaving RNAs–3. Splicing mediated by trans-acting factors–3.1 Group III introns – 3.2 Splicing of nuclear pre-mRNAs – 3.3 Trans-splicing – 3.4 Is nuclear pre-mRNA splicing evolutionarily related to group I and group II self-splicing?– 3.5 Non-RNA mediated splicing of tRNAs–4. Processing of ribosomal precursor RNAs–5. Processing of pre-mRNA 3′ ends–5.1 Polyadenylation – 5.2 Histone pre-mRNA 3′ processing–6. Other RNPs involved in metabolic mechanisms–6.1 5′ end processing of pre-tRNAs by RNase P – 6.2 The signal recognition particle – 6.3 Telomerase – 6.4 RNA editing in trypanosomatid mitochondria–7. Why RNA?

Roles of mismatch repair proteins in the recognition and processing of DNA interstrand crosslinks in human cells

DNA interstrand crosslinks (ICLs) are among the most toxic type of damage to a cell. Many ICL-inducing agents are widely used as therapeutic agents, e.g. cisplatin, psoralen. A bettor understanding of the cellular mechanism that eliminates ICLs is important for the improvement of human health. However, ICL repair is still poorly understood in mammals. Using a triplex-directed site-specific ICL model, we studied the roles of mismatch repair (MMR) proteins in ICL repair in human cells. We are also interested in using psoralen-conjugated triplex-forming oligonucleotides (TFOs) to direct ICLs to a specific site in targeted DNA and in the mammalian genomes. ^ MSH2 protein is the common subunit of two MMR recognition complexes, and MutSα and MutSβ. We showed that MSH2 deficiency renders human cell hypersensitive to psoralen ICLs. MMR recognition complexes bind specifically to triplex-directed psoralen ICLs in vitro. Together with the fact that psoralen ICL-induced repair synthesis is dramatically decreased in MSH2 deficient cell extracts, we demonstrated that MSH2 function is critical for the recognition and processing of psoralen ICLs in human cells. Interestingly, lack of MSH2 does not reduce the level of psoralen ICL-induced mutagenesis in human cells, suggesting that MSH2 does not contribute to error-generating repair of psoralen ICLs, and therefore, may represent a novel error-free mechanism for repairing ICLs. We also studied the role of MLH1, anther key protein in MMR, in the processing of psoralen ICLs. MLH1-deficient human cells are more resistant to psoralen plus UVA treatment. Importantly, MLH1 function is not required for the mutagenic repair of psoralen ICLs, suggesting that it is not involved in the error-generating repair of this type of DNA damage in human cells. ^ These are the first data indicating mismatch repair proteins may participate in a relatively error-free mechanism for processing psoralen ICL in human cells. Enhancement of MMR protein function relative to nucleotide excision repair proteins may reduce the mutagenesis caused by DNA ICLs in humans. ^ In order to specifically target ICLs to mammalian genes, we identified novel TFO target sequences in mouse and human genomes. Using this information, many critical mammalian genes can now be targeted by TFOs.^

Turbidite recognition and radiocarbon ages of marine sediment cores along the continental margin of Chile

Much progress has been made in estimating recurrence intervals of great and giant subduction earthquakes using terrestrial, lacustrine, and marine paleoseismic archives. Recent detailed records suggest these earthquakes may have variable recurrence periods and magnitudes forming supercycles. Understanding seismic supercycles requires long paleoseismic archives that record timing and magnitude of such events. Turbidite paleoseismic archives may potentially extend past earthquake records to the Pleistocene and can thus complement commonly shorter-term terrestrial archives. However, in order to unambiguously establish recurring seismicity as a trigger mechanism for turbidity currents, synchronous deposition of turbidites in widely spaced, isolated depocenters has to be ascertained. Furthermore, characteristics that predispose a seismically active continental margin to turbidite paleoseismology and the correct sample site selection have to be taken into account. Here we analyze 8 marine sediment cores along 950 km of the Chile margin to test for the feasibility of compiling detailed and continuous paleoseismic records based on turbidites. Our results suggest that the deposition of areally widespread, synchronous turbidites triggered by seismicity is largely controlled by sediment supply and, hence, the climatic and geomorphic conditions of the adjacent subaerial setting. The feasibility of compiling a turbidite paleoseismic record depends on the delicate balance between sufficient sediment supply providing material to fail frequently during seismic shaking and sufficiently low sedimentation rates to allow for coeval accumulation of planktonic foraminifera for high-resolution radiocarbon dating. We conclude that offshore northern central Chile (29-32.5°S) Holocene turbidite paleoseismology is not feasible, because sediment supply from the semi-arid mainland is low and almost no Holocene turbidity-current deposits are found in the cores. In contrast, in the humid region between 36 and 38°S frequent Holocene turbidite deposition may generally correspond to paleoseismic events. However, high terrigenous sedimentation rates prevent high-resolution radiocarbon dating. The climatic transition region between 32.5 and 36°S appears to be best suited for turbidite paleoseismology.

The gap between recognition and the 'compensation business' : the claim against Britain for compensation by Kenya's former Mau Mau fighters

During the first Kibaki administration (2002-2007), a movement by the former Mau Mau fighters demanded recognition for the role that they had played in the achievement of independence. They began to demand, also, monetary compensation for past injustices. Why had it taken over 40 years (from independence in 1963) for the former Mau Mau fighters to initiate this movement? What can be observed as the outcome of their movement? To answer these questions, three different historical currents need to be taken into account. These were, respectively, changing trends in the government of Kenya, progress in historical research into the actual circumstances of colonial control, and a realization, based on mounting experience, that launching a legal action against Britain could turn out to be a lucrative initiative. This paper concludes that, regardless of the actual purpose of the legal case, neither of their objectives was certain to be achieved. Two inescapable realities remain: the doubts cast on the reputation of the government by its decision to lift the Mau Mau‟s outlaw status – a decision that was widely seen as a latter-day example of the „Kikuyu favouritism‟ policy followed by the first Kibaki administration – and the popular interpretation of the involvement of Leigh Day, well known in Kenya ever since the unexploded bombs case for its success in obtaining substantial compensation payments, as a vehicle for squeezing large amounts of money from the British government for the benefit of the Kikuyu people.

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Automatic physical activity pattern recognition and meal intake modelling in type 1 diabetes for their inclusion in artificial pancreas systems

La diabetes comprende un conjunto de enfermedades metabólicas que se caracterizan por concentraciones de glucosa en sangre anormalmente altas. En el caso de la diabetes tipo 1 (T1D, por sus siglas en inglés), esta situación es debida a una ausencia total de secreción endógena de insulina, lo que impide a la mayoría de tejidos usar la glucosa. En tales circunstancias, se hace necesario el suministro exógeno de insulina para preservar la vida del paciente; no obstante, siempre con la precaución de evitar caídas agudas de la glucemia por debajo de los niveles recomendados de seguridad. Además de la administración de insulina, las ingestas y la actividad física son factores fundamentales que influyen en la homeostasis de la glucosa. En consecuencia, una gestión apropiada de la T1D debería incorporar estos dos fenómenos fisiológicos, en base a una identificación y un modelado apropiado de los mismos y de sus sorrespondientes efectos en el balance glucosa-insulina. En particular, los sistemas de páncreas artificial –ideados para llevar a cabo un control automático de los niveles de glucemia del paciente– podrían beneficiarse de la integración de esta clase de información. La primera parte de esta tesis doctoral cubre la caracterización del efecto agudo de la actividad física en los perfiles de glucosa. Con este objetivo se ha llevado a cabo una revisión sistemática de la literatura y meta-análisis que determinen las respuestas ante varias modalidades de ejercicio para pacientes con T1D, abordando esta caracterización mediante unas magnitudes que cuantifican las tasas de cambio en la glucemia a lo largo del tiempo. Por otro lado, una identificación fiable de los periodos con actividad física es un requisito imprescindible para poder proveer de esa información a los sistemas de páncreas artificial en condiciones libres y ambulatorias. Por esta razón, la segunda parte de esta tesis está enfocada a la propuesta y evaluación de un sistema automático diseñado para reconocer periodos de actividad física, clasificando su nivel de intensidad (ligera, moderada o vigorosa); así como, en el caso de periodos vigorosos, identificando también la modalidad de ejercicio (aeróbica, mixta o de fuerza). En este sentido, ambos aspectos tienen una influencia específica en el mecanismo metabólico que suministra la energía para llevar a cabo el ejercicio y, por tanto, en las respuestas glucémicas en T1D. En este trabajo se aplican varias combinaciones de técnicas de aprendizaje máquina y reconocimiento de patrones sobre la fusión multimodal de señales de acelerometría y ritmo cardíaco, las cuales describen tanto aspectos mecánicos del movimiento como la respuesta fisiológica del sistema cardiovascular ante el ejercicio. Después del reconocimiento de patrones se incorpora también un módulo de filtrado temporal para sacar partido a la considerable coherencia temporal presente en los datos, una redundancia que se origina en el hecho de que en la práctica, las tendencias en cuanto a actividad física suelen mantenerse estables a lo largo de cierto tiempo, sin fluctuaciones rápidas y repetitivas. El tercer bloque de esta tesis doctoral aborda el tema de las ingestas en el ámbito de la T1D. En concreto, se propone una serie de modelos compartimentales y se evalúan éstos en función de su capacidad para describir matemáticamente el efecto remoto de las concetraciones plasmáticas de insulina exógena sobre las tasas de eleiminación de la glucosa atribuible a la ingesta; un aspecto hasta ahora no incorporado en los principales modelos de paciente para T1D existentes en la literatura. Los datos aquí utilizados se obtuvieron gracias a un experimento realizado por el Institute of Metabolic Science (Universidad de Cambridge, Reino Unido) con 16 pacientes jóvenes. En el experimento, de tipo ‘clamp’ con objetivo variable, se replicaron los perfiles individuales de glucosa, según lo observado durante una visita preliminar tras la ingesta de una cena con o bien alta carga glucémica, o bien baja. Los seis modelos mecanísticos evaluados constaban de: a) submodelos de doble compartimento para las masas de trazadores de glucosa, b) un submodelo de único compartimento para reflejar el efecto remoto de la insulina, c) dos tipos de activación de este mismo efecto remoto (bien lineal, bien con un punto de corte), y d) diversas condiciones iniciales. ABSTRACT Diabetes encompasses a series of metabolic diseases characterized by abnormally high blood glucose concentrations. In the case of type 1 diabetes (T1D), this situation is caused by a total absence of endogenous insulin secretion, which impedes the use of glucose by most tissues. In these circumstances, exogenous insulin supplies are necessary to maintain patient’s life; although caution is always needed to avoid acute decays in glycaemia below safe levels. In addition to insulin administrations, meal intakes and physical activity are fundamental factors influencing glucose homoeostasis. Consequently, a successful management of T1D should incorporate these two physiological phenomena, based on an appropriate identification and modelling of these events and their corresponding effect on the glucose-insulin balance. In particular, artificial pancreas systems –designed to perform an automated control of patient’s glycaemia levels– may benefit from the integration of this type of information. The first part of this PhD thesis covers the characterization of the acute effect of physical activity on glucose profiles. With this aim, a systematic review of literature and metaanalyses are conduced to determine responses to various exercise modalities in patients with T1D, assessed via rates-of-change magnitudes to quantify temporal variations in glycaemia. On the other hand, a reliable identification of physical activity periods is an essential prerequisite to feed artificial pancreas systems with information concerning exercise in ambulatory, free-living conditions. For this reason, the second part of this thesis focuses on the proposal and evaluation of an automatic system devised to recognize physical activity, classifying its intensity level (light, moderate or vigorous) and for vigorous periods, identifying also its exercise modality (aerobic, mixed or resistance); since both aspects have a distinctive influence on the predominant metabolic pathway involved in fuelling exercise, and therefore, in the glycaemic responses in T1D. Various combinations of machine learning and pattern recognition techniques are applied on the fusion of multi-modal signal sources, namely: accelerometry and heart rate measurements, which describe both mechanical aspects of movement and the physiological response of the cardiovascular system to exercise. An additional temporal filtering module is incorporated after recognition in order to exploit the considerable temporal coherence (i.e. redundancy) present in data, which stems from the fact that in practice, physical activity trends are often maintained stable along time, instead of fluctuating rapid and repeatedly. The third block of this PhD thesis addresses meal intakes in the context of T1D. In particular, a number of compartmental models are proposed and compared in terms of their ability to describe mathematically the remote effect of exogenous plasma insulin concentrations on the disposal rates of meal-attributable glucose, an aspect which had not yet been incorporated to the prevailing T1D patient models in literature. Data were acquired in an experiment conduced at the Institute of Metabolic Science (University of Cambridge, UK) on 16 young patients. A variable-target glucose clamp replicated their individual glucose profiles, observed during a preliminary visit after ingesting either a high glycaemic-load or a low glycaemic-load evening meal. The six mechanistic models under evaluation here comprised: a) two-compartmental submodels for glucose tracer masses, b) a single-compartmental submodel for insulin’s remote effect, c) two types of activations for this remote effect (either linear or with a ‘cut-off’ point), and d) diverse forms of initial conditions.

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.

Dual role of the nuclear factor of activated T cells insert region in DNA recognition and cooperative contacts to activator protein 1

The transcription factors nuclear factor of activated T cells (NFAT) and activator protein 1 (AP-1) coordinately regulate cytokine gene expression in activated T-cells by binding to closely juxtaposed sites in cytokine promoters. The structural basis for cooperative binding of NFAT and AP-1 to these sites, and indeed for the cooperative binding of transcription factors to composite regulatory elements in general, is not well understood. Mutagenesis studies have identified a segment of AP-1, which lies at the junction of its DNA-binding and dimerization domains (basic region and leucine zipper, respectively), as being essential for protein–protein interactions with NFAT in the ternary NFAT/AP-1/DNA complex. In a model of the ternary complex, the segment of NFAT nearest AP-1 is the Rel insert region (RIR), a feature that is notable for its hypervariability in size and in sequence amongst members of the Rel transcription factor family. Here we have used mutational analysis to study the role of the NFAT RIR in binding to DNA and AP-1. Parallel yeast one-hybrid screening assays in combination with alanine-scanning mutagenesis led to the identification of four amino acid residues in the RIR of NFAT2 (also known as NFATC1 or NFATc) that are essential for cooperativity with AP-1 (Ile-544, Glu-545, Thr-551, and Ile-553), and three residues that are involved in interactions with DNA (Lys-538, Arg-540, and Asn-541). These results were confirmed and extended through in vitro binding assays. We thus conclude that the NFAT RIR plays an essential dual role in DNA recognition and cooperative binding to AP-1 family transcription factors.

Differences in antigen recognition and cytolytic activity of CD8+ and CD8− T cells that express the same antigen-specific receptor

CD8+ and CD8− T cell lines expressing the same antigen-specific receptor [the 2C T cell receptor (TCR)] were compared for ability to bind soluble peptide-MHC and to lyse target cells. The 2C TCR on CD8− cells bound a syngeneic MHC (Kb+)-peptide complex 10–100 times less well than the same TCR on CD8+ cells, and the CD8− 2C cells lysed target cells presenting this complex very poorly. Surprisingly, however, the CD8− cells differed little from CD8+ cells in ability to bind an allogeneic MHC (Ld+)-peptide complex and to lyse target cells presenting this complex. The CD8+/CD8− difference provided an opportunity to estimate how long TCR engagements with peptide-MHC have to persist to initiate the cytolytic T cell response.

Image recognition: Visual grouping, recognition, and learning

Vision extracts useful information from images. Reconstructing the three-dimensional structure of our environment and recognizing the objects that populate it are among the most important functions of our visual system. Computer vision researchers study the computational principles of vision and aim at designing algorithms that reproduce these functions. Vision is difficult: the same scene may give rise to very different images depending on illumination and viewpoint. Typically, an astronomical number of hypotheses exist that in principle have to be analyzed to infer a correct scene description. Moreover, image information might be extracted at different levels of spatial and logical resolution dependent on the image processing task. Knowledge of the world allows the visual system to limit the amount of ambiguity and to greatly simplify visual computations. We discuss how simple properties of the world are captured by the Gestalt rules of grouping, how the visual system may learn and organize models of objects for recognition, and how one may control the complexity of the description that the visual system computes.

Involvement of Asn-293 in stereospecific agonist recognition and in activation of the beta 2-adrenergic receptor.

To investigate the molecular mechanism for stereospecific binding of agonists to beta 2-adrenergic receptors we used receptor models to identify potential binding sites for the beta-OH-group of the ligand, which defines the chiral center. Ser-165, located in transmembrane helix IV, and Asn-293, situated in the upper half of transmembrane helix VI, were identified as potential binding sites. Mutation of Ser-165 to Ala did not change the binding of either isoproterenol isomer as revealed after transient expression in human embryonic kidney (HEK)-293 cells. In contrast, a receptor mutant in which Asn-293 was replaced by Leu showed substantial loss of stereospecific isoproterenol binding. Adenylyl cyclase stimulation by this mutant after stable expression in CHO cells confirmed the substantial loss of stereospecificity for isoproterenol. In a series of agonists the loss of affinity in the Leu-293 mutant receptor was strongly correlated with the intrinsic activity of the compounds. Full agonists showed a 10-30-fold affinity loss, whereas partial agonists had almost the same affinity for both receptors. Stereospecific recognition of antagonists was unaltered in the Leu-293 mutant receptor. These data indicate a relationship between stereospecificity and intrinsic activity of agonists and suggest that Asn-293 is important for both properties of the agonist-receptor interaction.

Self-recognition and abstraction abilities in the common chimpanzee studied with distorting mirrors.

The reactions of chimpanzees to regular mirrors and the results of the standard Gallup mark test have been well documented. In addition to using the mark test to demonstrate self-recognition in a regular mirror, we exposed six female chimpanzees to mirrors that produced distorted or multiplied self-images. Their reactions to their self-images, in terms of mirror-guided self-referenced behaviors, indicated that correct assessment of the source of the mirror image was made by each subject in each of the mirrors. Recognition of a distorted self-image implies an ability for abstraction in the subjects in that the distortion must be rationalized before self-recognition occurs. The implications of these results in terms of illuminating the relative importance of feature and contingency of movement cues to self-recognition are discussed.

Cue recognition and cue elaboration in learning from examples.

This paper describes the processes used by students to learn from worked-out examples and by working through problems. Evidence is derived from protocols of students learning secondary school mathematics and physics. The students acquired knowledge from the examples in the form of productions (condition-->action): first discovering conditions under which the actions are appropriate and then elaborating the conditions to enhance efficiency. Students devoted most of their attention to the condition side of the productions. Subsequently, they generalized the productions for broader application and acquired specialized productions for special problem classes.

A cooperative model for receptor recognition and cell adhesion: evidence from the molecular packing in the 1.6-A crystal structure of the pheromone Er-1 from the ciliated protozoan Euplotes raikovi.

The crystal structure of the pheromone Er-1 from the unicellular eukaryotic organism Euplotes raikovi was determined at 1.6 A resolution and refined to a crystallographic R factor of 19.9%. In the tightly packed crystal, two extensive intermolecular helix-helix interactions arrange the Er-1 molecules into layers. Since the putative receptor of the pheromone is a membrane-bound protein, whose extracellular C-terminal domain is identical in amino acid sequence to the soluble pheromone, the interactions found in the crystal may mimic the pheromone-receptor interactions as they occur on a cell surface. Based on this, we propose a model for the interaction between soluble pheromone molecules and their receptors. In this model, strong pheromone-receptor binding emerges as a consequence of the cooperative utilization of several weak interactions. The model offers an explanation for the results of binding studies and may also explain the adhesion between cells that occurs during mating.