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.

A Novel Ground Fault Identification Method for 2 x 5 kV Railway Power Supply Systems

The location of ground faults in railway electric lines in 2 × 5 kV railway power supply systems is a difficult task. In both 1 × 25 kV and transmission power systems it is common practice to use distance protection relays to clear ground faults and localize their positions. However, in the particular case of this 2 × 25 kV system, due to the widespread use of autotransformers, the relation between the distance and the impedance seen by the distance protection relays is not linear and therefore the location is not accurate enough. This paper presents a simple and economical method to identify the subsection between autotransformers and the conductor (catenary or feeder) where the ground fault is happening. This method is based on the comparison of the angle between the current and the voltage of the positive terminal in each autotransformer. Consequently, after the identification of the subsection and the conductor with the ground defect, only the subsection where the ground fault is present will be quickly removed from service, with the minimum effect on rail traffic. This method has been validated through computer simulations and laboratory tests with positive results.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).

Identification of the block in targeted retroviral-mediated gene transfer

A chimeric retroviral vector (33E67) containing a CD33-specific single-chain antibody was generated in an attempt to target cells displaying the CD33 surface antigen. The chimeric envelope protein was translated, processed, and incorporated into viral particles as efficiently as wild-type envelope protein. The viral particles carrying the 33E67 envelope protein could bind efficiently to the CD33 receptor on target cells and were internalized, but no gene transfer occurred. A unique experimental approach was used to examine the basis for this postbinding block. Our data indicate that the chimeric envelope protein itself cannot participate in the fusion process, the most reasonable explanation being that this chimeric protein cannot undergo the appropriate conformational change that is thought to be triggered by receptor binding, a suggested prerequisite to subsequent fusion and core entry. These results indicate that the block to gene transfer in this system, and probably in most of the current chimeric retroviral vectors to date, is the inability of the chimeric envelope protein to undergo this obligatory conformational change.

Functional identification and reconstitution of an odorant receptor in single olfactory neurons

The olfactory system is remarkable in its capacity to discriminate a wide range of odorants through a series of transduction events initiated in olfactory receptor neurons. Each olfactory neuron is expected to express only a single odorant receptor gene that belongs to the G protein coupled receptor family. The ligand–receptor interaction, however, has not been clearly characterized. This study demonstrates the functional identification of olfactory receptor(s) for specific odorant(s) from single olfactory neurons by a combination of Ca2+-imaging and reverse transcription–coupled PCR analysis. First, a candidate odorant receptor was cloned from a single tissue-printed olfactory neuron that displayed odorant-induced Ca2+ increase. Next, recombinant adenovirus-mediated expression of the isolated receptor gene was established in the olfactory epithelium by using green fluorescent protein as a marker. The infected neurons elicited external Ca2+ entry when exposed to the odorant that originally was used to identify the receptor gene. Experiments performed to determine ligand specificity revealed that the odorant receptor recognized specific structural motifs within odorant molecules. The odorant receptor-mediated signal transduction appears to be reconstituted by this two-step approach: the receptor screening for given odorant(s) from single neurons and the functional expression of the receptor via recombinant adenovirus. The present approach should enable us to examine not only ligand specificity of an odorant receptor but also receptor specificity and diversity for a particular odorant of interest.

The identification of a 9-cis retinol dehydrogenase in the mouse embryo reveals a pathway for synthesis of 9-cis retinoic acid

The ligand-controlled retinoic acid (RA) receptors and retinoid X receptors are important for several physiological processes, including normal embryonic development, but little is known about how their ligands, all-trans and 9-cis RA, are generated. Here we report the identification of a stereo-specific 9-cis retinol dehydrogenase, which is abundantly expressed in embryonic tissues known to be targets in the retinoid signaling pathway. The membrane-bound enzyme is a member of the short-chain alcohol dehydrogenase/reductase superfamily, able to oxidize 9-cis retinol into 9-cis retinaldehyde, an intermediate in 9-cis RA biosynthesis. Analysis by nonradioactive in situ hybridization in mouse embryos shows that expression of the enzyme is temporally and spatially well controlled during embryogenesis with prominent expression in parts of the developing central nervous system, sensory organs, somites and myotomes, and several tissues of endodermal origin. The identification of this enzyme reveals a pathway in RA biosynthesis, where 9-cis retinol is generated for subsequent oxidation to 9-cis RA.

Computerized polymorphic marker identification: Experimental validation and a predicted human polymorphism catalog

A computational system for the prediction of polymorphic loci directly and efficiently from human genomic sequence was developed and verified. A suite of programs, collectively called pompous (polymorphic marker prediction of ubiquitous simple sequences) detects tandem repeats ranging from dinucleotides up to 250 mers, scores them according to predicted level of polymorphism, and designs appropriate flanking primers for PCR amplification. This approach was validated on an approximately 750-kilobase region of human chromosome 3p21.3, involved in lung and breast carcinoma homozygous deletions. Target DNA from 36 paired B lymphoblastoid and lung cancer lines was amplified and allelotyped for 33 loci predicted by pompous to be variable in repeat size. We found that among those 36 predominately Caucasian individuals 22 of the 33 (67%) predicted loci were polymorphic with an average heterozygosity of 0.42. Allele loss in this region was found in 27/36 (75%) of the tumor lines using these markers. pompous provides the genetic researcher with an additional tool for the rapid and efficient identification of polymorphic markers, and through a World Wide Web site, investigators can use pompous to identify polymorphic markers for their research. A catalog of 13,261 potential polymorphic markers and associated primer sets has been created from the analysis of 141,779,504 base pairs of human genomic sequence in GenBank. This data is available on our Web site (pompous.swmed.edu) and will be updated periodically as GenBank is expanded and algorithm accuracy is improved.

Identification and reconstitution of the origin recognition complex from Schizosaccharomyces pombe

The origin recognition complex (ORC), first identified in Saccharomyces cerevisiae (sc), is a six-subunit protein complex that binds to DNA origins. Here, we report the identification and cloning of cDNAs encoding the six subunits of the ORC of Schizosaccharomyces pombe (sp). Sequence analyses revealed that spOrc1, 2, and 5 subunits are highly conserved compared with their counterparts from S. cerevisiae, Xenopus, Drosophila, and human. In contrast, both spOrc3 and spOrc6 subunits are poorly conserved. As reported by Chuang and Kelly [(1999) Proc. Natl. Acad. Sci. USA 96, 2656–2661], the C-terminal region of spOrc4 is also conserved whereas the N terminus uniquely contains repeats of a sequence that binds strongly to AT-rich DNA regions. Consistent with this, extraction of S. pombe chromatin with 1 M NaCl, or after DNase I treatment, yielded the six-subunit ORC, whereas extraction with 0.3 M resulted in five-subunit ORC lacking spOrc4p. The spORC can be reconstituted in vitro with all six recombinant subunits expressed in the rabbit reticulocyte system. The association of spOrc4p with the other subunits required the removal of DNA from reaction mixture by DNase I. This suggests that a strong interaction between spOrc4p and DNA can prevent the isolation of the six-subunit ORC. The unique DNA-binding properties of the spORC may contribute to our understanding of the sequence-specific recognition required for the initiation of DNA replication in S. pombe.

Identification of the von Hippel–Lindau tumor-suppressor protein as part of an active E3 ubiquitin ligase complex

Mutations of von Hippel–Lindau disease (VHL) tumor-suppressor gene product (pVHL) are found in patients with dominant inherited VHL syndrome and in the vast majority of sporadic clear cell renal carcinomas. The function of the pVHL protein has not been clarified. pVHL has been shown to form a complex with elongin B and elongin C (VBC) and with cullin (CUL)-2. In light of the structural analogy of VBC-CUL-2 to SKP1-CUL-1-F-box ubiquitin ligases, the ubiquitin ligase activity of VBC-CUL-2 was examined in this study. We show that VBC-CUL-2 exhibits ubiquitin ligase activity, and we identified UbcH5a, b, and c, but not CDC34, as the ubiquitin-conjugating enzymes of the VBC-CUL-2 ubiquitin ligase. The protein Rbx1/ROC1 enhances ligase activity of VBC-CUL-2 as it does in the SKP1-CUL-1-F-box protein ligase complex. We also found that pVHL associates with two proteins, p100 and p220, which migrate at a similar molecular weight as two major bands in the ubiquitination assay. Furthermore, naturally occurring pVHL missense mutations, including mutants capable of forming a complex with elongin B–elongin C-CUL-2, fail to associate with p100 and p220 and cannot exhibit the E3 ligase activity. These results suggest that pVHL might be the substrate recognition subunit of the VBC-CUL-2 E3 ligase. This is also, to our knowledge, the first example of a human tumor-suppressor protein being directly involved in the ubiquitin conjugation system which leads to the targeted degradation of substrate proteins.

Identification by mass spectrometry of the phosphorylated residue responsible for activation of the catalytic domain of myosin I heavy chain kinase, a member of the PAK/STE20 family

Myosin I heavy chain kinase from Acanthamoeba castellanii is activated in vitro by autophosphorylation (8–10 mol of P per mol). The catalytically active C-terminal domain produced by trypsin cleavage of the phosphorylated kinase contains 2–3 mol of P per mol. However, the catalytic domain expressed in a baculovirus–insect cell system is fully active as isolated without autophosphorylation in vitro. We now show that the expressed catalytic domain is inactivated by incubation with acid phosphatase and regains activity upon autophosphorylation. The state of phosphorylation of all of the hydroxyamino acids in the catalytic domain were determined by mass spectrometry of unfractionated protease digests. Ser-627 was phosphorylated in the active, expressed catalytic domain, lost its phosphate when the protein was incubated with phosphatase, and was rephosphorylated when the dephosphorylated protein was incubated with ATP. No other residue was significantly phosphorylated in any of the three samples. Thus, phosphorylation of Ser-627, which is in the same position as the Ser and Thr residues that are phosphorylated in many other kinases, is necessary and sufficient for full activity of the catalytic domain. Ser-627 is also phosphorylated when full-length, native kinase is activated by autophosphorylation.

Identification of the endophilins (SH3p4/p8/p13) as novel binding partners for the β1-adrenergic receptor

Several G-protein coupled receptors, such as the β1-adrenergic receptor (β1-AR), contain polyproline motifs within their intracellular domains. Such motifs in other proteins are known to mediate protein–protein interactions such as with Src homology (SH)3 domains. Accordingly, we used the proline-rich third intracellular loop of the β1-AR either as a glutathione S-transferase fusion protein in biochemical “pull-down” assays or as bait in the yeast two-hybrid system to search for interacting proteins. Both approaches identified SH3p4/p8/p13 (also referred to as endophilin 1/2/3), a SH3 domain-containing protein family, as binding partners for the β1-AR. In vitro and in human embryonic kidney (HEK) 293 cells, SH3p4 specifically binds to the third intracellular loop of the β1-AR but not to that of the β2-AR. Moreover, this interaction is mediated by the C-terminal SH3 domain of SH3p4. Functionally, overexpression of SH3p4 promotes agonist-induced internalization and modestly decreases the Gs coupling efficacy of β1-ARs in HEK293 cells while having no effect on β2-ARs. Thus, our studies demonstrate a role of the SH3p4/p8/p13 protein family in β1-AR signaling and suggest that interaction between proline-rich motifs and SH3-containing proteins may represent a previously underappreciated aspect of G-protein coupled receptor signaling.

Identification and classification of 16 new kinesin superfamily (KIF) proteins in mouse genome

KIF (kinesin superfamily) proteins are microtubule-dependent molecular motors that play important roles in intracellular transport and cell division. The extent to which KIFs are involved in various transporting phenomena, as well as their regulation mechanism, are unknown. The identification of 16 new KIFs in this report doubles the existing number of KIFs known in the mouse. Conserved nucleotide sequences in the motor domain were amplified by PCR using cDNAs of mouse nervous tissue, kidney, and small intestine as templates. The new KIFs were studied with respect to their expression patterns in different tissues, chromosomal location, and molecular evolution. Our results suggest that (i) there is no apparent tendency among related subclasses of KIFs of cosegregation in chromosomal mapping, and (ii) according to their tissue distribution patterns, KIFs can be divided into two classes–i.e., ubiquitous and specific tissue-dominant. Further characterization of KIFs may elucidate unknown fundamental phenomena underlying intracellular transport. Finally, we propose a straightforward nomenclature system for the members of the mouse kinesin superfamily.

In situ investigation of rapid subsurface flow : Identification of relevant spatial structures beyond heterogeneity

Non peer reviewed

The identification of CD4+ T cell epitopes with dedicated synthetic peptide libraries

For a large number of T cell-mediated immunopathologies, the disease-related antigens are not yet identified. Identification of T cell epitopes is of crucial importance for the development of immune-intervention strategies. We show that CD4+ T cell epitopes can be defined by using a new system for synthesis and screening of synthetic peptide libraries. These libraries are designed to bind to the HLA class II restriction molecule of the CD4+ T cell clone of interest. The screening is based on three selection rounds using partial release of 14-mer peptides from synthesis beads and subsequent sequencing of the remaining peptide attached to the bead. With this approach, two peptides were identified that stimulate the β cell-reactive CD4+ T cell clone 1c10, which was isolated from a newly diagnosed insulin-dependent diabetes mellitus patient. After performing amino acid-substitution studies and protein database searches, a Haemophilus influenzae TonB-derived peptide was identified that stimulates clone 1c10. The relevance of this finding for the pathogenesis of insulin-dependent diabetes mellitus is currently under investigation. We conclude that this system is capable of determining epitopes for (autoreactive) CD4+ T cell clones with previously unknown peptide specificity. This offers the possibility to define (auto)antigens by searching protein databases and/or to induce tolerance by using the peptide sequences identified. In addition the peptides might be used as leads to develop T cell receptor antagonists or anergy-inducing compounds.

Identification and analysis of the plant peroxisomal targeting signal 1 receptor NtPEX5

Protein translocation into peroxisomes takes place via recognition of a peroxisomal targeting signal present at either the extreme C termini (PTS1) or N termini (PTS2) of matrix proteins. In mammals and yeast, the peroxisomal targeting signal receptor, Pex5p, recognizes the PTS1 consisting of -SKL or variants thereof. Although many plant peroxisomal matrix proteins are transported through the PTS1 pathway, little is known about the PTS1 receptor or any other peroxisome assembly protein from plants. We cloned tobacco (Nicotiana tabacum) cDNAs encoding Pex5p (NtPEX5) based on the protein’s interaction with a PTS1-containing protein in the yeast two-hybrid system. Nucleotide sequence analysis revealed that the tobacco Pex5p contains seven tetratricopeptide repeats and that NtPEX5 shares greater sequence similarity with its homolog from humans than from yeast. Expression of NtPEX5 fusion proteins, consisting of the N-terminal part of yeast Pex5p and the C-terminal region of NtPEX5, in a Saccharomyces cerevisiae pex5 mutant restored protein translocation into peroxisomes. These experiments confirmed the identity of the tobacco protein as a PTS1 receptor and indicated that components of the peroxisomal translocation apparatus are conserved functionally. Two-hybrid assays showed that NtPEX5 interacts with a wide range of PTS1 variants that also interact with the human Pex5p. Interestingly, the C-terminal residues of some of these peptides deviated from the established plant PTS1 consensus sequence. We conclude that there are significant sequence and functional similarities between the plant and human Pex5ps.