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.

Module optical analyzer: Identification of defects on the production line

The usefulness of the module optical analyzer when identifying module defects on production line is presented in this paper. Two different case studies performed with two different kind of CPV modules are presented to show the use of MOA both in IES-UPM and Daido Steel facilities.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.

Crowdsourcing without a crowd : Reliable online species identification using Bayesian models to minimize crowd size

Acknowledgment This research is supported by an award made by the RCUK Digital Economy program to the University of Aberdeen’s dot.rural Digital Economy Hub (ref. EP/G066051/1).

Identification of an ATP-binding cassette transporter involved in bicarbonate uptake in the cyanobacterium Synechococcus sp. strain PCC 7942

Exposure of cells of cyanobacteria (blue–green algae) grown under high-CO2 conditions to inorganic C-limitation induces transcription of particular genes and expression of high-affinity CO2 and HCO3− transport systems. Among the low-CO2-inducible transcription units of Synechococcus sp. strain PCC 7942 is the cmpABCD operon, encoding an ATP-binding cassette transporter similar to the nitrate/nitrite transporter of the same cyanobacterium. A nitrogen-regulated promoter was used to selectively induce expression of the cmpABCD genes by growth of transgenic cells on nitrate under high CO2 conditions. Measurements of the initial rate of HCO3− uptake after onset of light, and of the steady-state rate of HCO3− uptake in the light, showed that the controlled induction of the cmp genes resulted in selective expression of high-affinity HCO3− transport activity. The forced expression of cmpABCD did not significantly increase the CO2 uptake capabilities of the cells. These findings demonstrated that the cmpABCD genes encode a high-affinity HCO3− transporter. A deletion mutant of cmpAB (M42) retained low CO2-inducible activity of HCO3− transport, indicating the occurrence of HCO3− transporter(s) distinct from the one encoded by cmpABCD. HCO3− uptake by low-CO2-induced M42 cells showed lower affinity for external HCO3− than for wild-type cells under the same conditions, showing that the HCO3− transporter encoded by cmpABCD has the highest affinity for HCO3− among the HCO3− transporters present in the cyanobacterium. This appears to be the first unambiguous identification and description of a primary active HCO3− transporter.

Identification of a third distinct estrogen receptor and reclassification of estrogen receptors in teleosts

This paper describes three distinct estrogen receptor (ER) subtypes: ERα, ERβ, and a unique type, ERγ, cloned from a teleost fish, the Atlantic croaker Micropogonias undulatus; the first identification of a third type of classical ER in vertebrate species. Phylogenetic analysis shows that ERγ arose through gene duplication from ERβ early in the teleost lineage and indicates that ERγ is present in other teleosts, although it has not been recognized as such. The Atlantic croaker ERγ shows amino acid differences in regions important for ligand binding and receptor activation that are conserved in all other ERγs. The three ER subtypes are genetically distinct and have different distribution patterns in Atlantic croaker tissues. In addition, ERβ and ERγ fusion proteins can each bind estradiol-17β with high affinity. The presence of three functional ERs in one species expands the role of ER multiplicity in estrogen signaling systems and provides a unique opportunity to investigate the dynamics and mechanisms of ER evolution.

A novel approach for the identification of protein–protein interaction with integral membrane proteins

Protein–protein interaction plays a major role in all biological processes. The currently available genetic methods such as the two-hybrid system and the protein recruitment system are relatively limited in their ability to identify interactions with integral membrane proteins. Here we describe the development of a reverse Ras recruitment system (reverse RRS), in which the bait used encodes a membrane protein. The bait is expressed in its natural environment, the membrane, whereas the protein partner (the prey) is fused to a cytoplasmic Ras mutant. Protein–protein interaction between the proteins encoded by the prey and the bait results in Ras membrane translocation and activation of a viability pathway in yeast. We devised the expression of the bait and prey proteins under the control of dual distinct inducible promoters, thus enabling a rapid selection of transformants in which growth is attributed solely to specific protein–protein interaction. The reverse RRS approach greatly extends the usefulness of the protein recruitment systems and the use of integral membrane proteins as baits. The system serves as an attractive approach to explore novel protein–protein interactions with high specificity and selectivity, where other methods fail.

Phylogeny of genes for secretion NTPases: Identification of the widespread tadA subfamily and development of a diagnostic key for gene classification

Macromolecular transport systems in bacteria currently are classified by function and sequence comparisons into five basic types. In this classification system, type II and type IV secretion systems both possess members of a superfamily of genes for putative NTP hydrolase (NTPase) proteins that are strikingly similar in structure, function, and sequence. These include VirB11, TrbB, TraG, GspE, PilB, PilT, and ComG1. The predicted protein product of tadA, a recently discovered gene required for tenacious adherence of Actinobacillus actinomycetemcomitans, also has significant sequence similarity to members of this superfamily and to several unclassified and uncharacterized gene products of both Archaea and Bacteria. To understand the relationship of tadA and tadA-like genes to those encoding the putative NTPases of type II/IV secretion, we used a phylogenetic approach to obtain a genealogy of 148 NTPase genes and reconstruct a scenario of gene superfamily evolution. In this phylogeny, clear distinctions can be made between type II and type IV families and their constituent subfamilies. In addition, the subgroup containing tadA constitutes a novel and extremely widespread subfamily of the family encompassing all putative NTPases of type IV secretion systems. We report diagnostic amino acid residue positions for each major monophyletic family and subfamily in the phylogenetic tree, and we propose an easy method for precisely classifying and naming putative NTPase genes based on phylogeny. This molecular key-based method can be applied to other gene superfamilies and represents a valuable tool for genome analysis.

A strategy for the identification of proteins targeted by thioredoxin

Thioredoxins are 12-kDa proteins functional in the regulation of cellular processes throughout the animal, plant, and microbial kingdoms. Growing evidence with seeds suggests that an h-type of thioredoxin, reduced by NADPH via NADP-thioredoxin reductase, reduces disulfide bonds of target proteins and thereby acts as a wakeup call in germination. A better understanding of the role of thioredoxin in seeds as well as other systems could be achieved if more were known about the target proteins. To this end, we have devised a strategy for the comprehensive identification of proteins targeted by thioredoxin. Tissue extracts incubated with reduced thioredoxin are treated with a fluorescent probe (monobromobimane) to label sulfhydryl groups. The newly labeled proteins are isolated by conventional two-dimensional electrophoresis: (i) nonreducing/reducing or (ii) isoelectric focusing/reducing SDS/PAGE. The isolated proteins are identified by amino acid sequencing. Each electrophoresis system offers an advantage: the first method reveals the specificity of thioredoxin in the reduction of intramolecular vs. intermolecular disulfide bonds, whereas the second method improves the separation of the labeled proteins. By application of both methods to peanut seed extracts, we isolated at least 20 thioredoxin targets and identified 5—three allergens (Ara h2, Ara h3, and Ara h6) and two proteins not known to occur in peanut (desiccation-related and seed maturation protein). These findings open the door to the identification of proteins targeted by thioredoxin in a wide range of systems, thereby enhancing our understanding of its function and extending its technological and medical applications.

The chaperone/usher pathways of Pseudomonas aeruginosa: Identification of fimbrial gene clusters (cup) and their involvement in biofilm formation

Pseudomonas aeruginosa, an important opportunistic human pathogen, persists in certain tissues in the form of specialized bacterial communities, referred to as biofilm. The biofilm is formed through series of interactions between cells and adherence to surfaces, resulting in an organized structure. By screening a library of Tn5 insertions in a nonpiliated P. aeruginosa strain, we identified genes involved in early stages of biofilm formation. One class of mutations identified in this study mapped in a cluster of genes specifying the components of a chaperone/usher pathway that is involved in assembly of fimbrial subunits in other microorganisms. These genes, not previously described in P. aeruginosa, were named cupA1–A5. Additional chaperone/usher systems (CupB and CupC) have been also identified in the genome of P. aeruginosa PAO1; however, they do not appear to play a role in adhesion under the conditions where the CupA system is expressed and functions in surface adherence. The identification of these putative adhesins on the cell surface of P. aeruginosa suggests that this organism possess a wide range of factors that function in biofilm formation. These structures appear to be differentially regulated and may function at distinct stages of biofilm formation, or in specific environments colonized by this organism.

Identification and characterization of a second melanin-concentrating hormone receptor, MCH-2R

Melanin-concentrating hormone (MCH) is a 19-aa cyclic neuropeptide originally isolated from chum salmon pituitaries. Besides its effects on the aggregation of melanophores in fish several lines of evidence suggest that in mammals MCH functions as a regulator of energy homeostasis. Recently, several groups reported the identification of an orphan G protein-coupled receptor as a receptor for MCH (MCH-1R). We hereby report the identification of a second human MCH receptor termed MCH-2R, which shares about 38% amino acid identity with MCH-1R. MCH-2R displayed high-affinity MCH binding, resulting in inositol phosphate turnover and release of intracellular calcium in mammalian cells. In contrast to MCH-1R, MCH-2R signaling is not sensitive to pertussis toxin and MCH-2R cannot reduce forskolin-stimulated cAMP production, suggesting an exclusive Gαq coupling of the MCH-2R in cell-based systems. Northern blot and in situ hybridization analysis of human and monkey tissue shows that expression of MCH-2R mRNA is restricted to several regions of the brain, including the arcuate nucleus and the ventral medial hypothalamus, areas implicated in regulation of body weight. In addition, the human MCH-2R gene was mapped to the long arm of chromosome 6 at band 6q16.2–16.3, a region reported to be associated with cytogenetic abnormalities of obese patients. The characterization of a second mammalian G protein-coupled receptor for MCH potentially indicates that the control of energy homeostasis in mammals by the MCH neuropeptide system may be more complex than initially anticipated.

Identification and characterization of a melanin-concentrating hormone receptor

Melanin-concentrating hormone (MCH), a neuropeptide expressed in central and peripheral nervous systems, plays an important role in the control of feeding behaviors and energy metabolism. An orphan G protein-coupled receptor (SLC-1/GPR24) has recently been identified as a receptor for MCH (MCHR1). We report here the identification and characterization of a G protein-coupled receptor as the MCH receptor subtype 2 (MCHR2). MCHR2 has higher protein sequence homology to MCHR1 than any other G protein-coupled receptor. The expression of MCHR2 has been detected in many regions of the brain. In contrast to MCHR1, which is intronless in the coding region and is located at the chromosomal locus 22q13.3, the MCHR2 gene has multiple exons and is mapped to locus 6q21. MCHR2 is specifically activated by nanomolar concentrations of MCH, binds to MCH with high affinity, and signals through Gq protein. This discovery is important for a full understanding of MCH biology and the development of potential therapeutics for diseases involving MCH, including obesity.

Genetic identification of a gene involved in constitutive, high-affinity nitrate transport in higher plants.

Two mutations have been found in a gene (NRT2) of Arabidopsis thaliana that specifically impair constitutive, high-affinity nitrate uptake. These mutants were selected for resistance to 0.1 mM chlorate in the absence of nitrate. Progency from one of the backcrossed mutants showed no constitutive uptake of nitrate below 0.5 mM at pH 7.0 in liquid culture (that is, within 30 min of initial exposure to nitrate). All other uptake activities measured (high-affinity phosphate and sulfate uptake, inducible high-affinity nitrate uptake, and constitutive low-affinity nitrate uptake) were present or nearly normal in the backcrossed mutant. Electrophysiological analysis of individual root cells showed that the nrt2 mutant showed little response to 0.25 mM of nitrate, whereas NRT2 wild-type cells showed an initial depolarization followed by recovery. At 10 mM of nitrate both the mutant and wild-type cells displayed similar, strong electrical responses. These results indicate that NRT2 is a critical and perhaps necessary gene for constitutive, high-affinity nitrate uptake in Arabidopsis, but not for inducible, high-affinity nor constitutive, low-affinity nitrate uptake. Thus, these systems are genetically distinct.

Identification of a pathogenicity island required for Salmonella survival in host cells.

We have identified a region unique to the Salmonella typhimurium chromosome that is essential for virulence in mice. This region harbors at least three genes: two (spiA and spiB) encode products that are similar to proteins found in type III secretion systems, and a third (spiR) encodes a putative regulator. A strain with a mutation in spiA was unable to survive within macrophages but displayed wild-type levels of epithelial cell invasion. The culture supernatants of the spi mutants lacked a modified form of flagellin, which was present in the supernatant of the wild-type strain. This suggests that the Spi secretory apparatus exports a protease, or a protein that can alter the activity of a secreted protease. The "pathogenicity island" harboring the spi genes may encode the virulence determinants that set Salmonella apart from other enteric pathogens.