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.

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Ubiquitin-dependent degradation of IκBα is mediated by a ubiquitin ligase Skp1/Cul 1/F-box protein FWD1

Activation of the transcription factor nuclear factor kappa B (NF-κB) is controlled by proteolysis of its inhibitory subunit (IκB) via the ubiquitin-proteasome pathway. Signal-induced phosphorylation of IκBα by a large multisubunit complex containing IκB kinases is a prerequisite for ubiquitination. Here, we show that FWD1 (a mouse homologue of Slimb/βTrCP), a member of the F-box/WD40-repeat proteins, is associated specifically with IκBα only when IκBα is phosphorylated. The introduction of FWD1 into cells significantly promotes ubiquitination and degradation of IκBα in concert with IκB kinases, resulting in nuclear translocation of NF-κB. In addition, FWD1 strikingly evoked the ubiquitination of IκBα in the in vitro system. In contrast, a dominant-negative form of FWD1 inhibits the ubiquitination, leading to stabilization of IκBα. These results suggest that the substrate-specific degradation of IκBα is mediated by a Skp1/Cull 1/F-box protein (SCF) FWD1 ubiquitin-ligase complex and that FWD1 serves as an intracellular receptor for phosphorylated IκBα. Skp1/Cullin/F-box protein FWD1 might play a critical role in transcriptional regulation of NF-κB through control of IκB protein stability.

The Ets transcription factor ERM is Th1-specific and induced by IL-12 through a Stat4-dependent pathway

Interleukin 12 (IL-12)-induced T helper 1 (Th1) development requires Stat4 activation. However, antigen-activated Th1 cells can produce interferon γ (IFN-γ) independently of IL-12 and Stat4 activation. Thus, in differentiated Th1 cells, factors regulated by IL-12 and Stat4 may be involved in IFN-γ production. Using subtractive cloning, we identified ERM, an Ets transcription factor, to be a Th1-specific, IL-12-induced gene. IL-12-induction of ERM occurred in wild-type and Stat1-deficient, but not Stat4-deficient, T cells, suggesting ERM is Stat4-inducible. Retroviral expression of ERM did not restore IFN-γ production in Stat4-deficient T cells, but augmented IFN-γ expression in Stat4-heterozygous T cells. Ets factors frequently regulate transcription via cooperative interactions with other transcription factors, and ERM has been reported to cooperate with c-Jun. However, in the absence of other transcription factors, ERM augmented expression of an IFN-γ reporter by only 2-fold. Thus, determining the requirement for ERM in Th1 development likely will require gene targeting.

Induction of ARF tumor suppressor gene expression and cell cycle arrest by transcription factor DMP1

Expression of the DMP1 transcription factor, a cyclin D-binding Myb-like protein, induces growth arrest in mouse embryo fibroblast strains but is devoid of antiproliferative activity in primary diploid fibroblasts that lack the ARF tumor suppressor gene. DMP1 binds to a single canonical recognition site in the ARF promoter to activate gene expression, and in turn, p19ARF synthesis causes p53-dependent cell cycle arrest. Unlike genes such as Myc, adenovirus E1A, and E2F-1, which, when overexpressed, activate the ARF-p53 pathway and trigger apoptosis, DMP1, like ARF itself, does not induce programmed cell death. Therefore, apart from its recently recognized role in protecting cells from potentially oncogenic signals, ARF can be induced in response to antiproliferative stimuli that do not obligatorily lead to apoptosis.

Ephrin-dependent growth and pruning of hippocampal axons

Neuronal connections are arranged topographically such that the spatial organization of neurons is preserved by their termini in the targets. During the development of topographic projections, axons initially explore areas much wider than the final targets, and mistargeted axons are pruned later. The molecules regulating these processes are not known. We report here that the ligands of the Eph family tyrosine kinase receptors may regulate both the initial outgrowth and the subsequent pruning of axons. In the presence of ephrins, the outgrowth and branching of the receptor-positive hippocampal axons are enhanced. However, these axons are induced later to degenerate. These observations suggest that the ephrins and their receptors may regulate topographic map formation by stimulating axonal arborization and by pruning mistargeted axons.

Neurotrophic factors [activity-dependent neurotrophic factor (ADNF) and basic fibroblast growth factor (bFGF)] interrupt excitotoxic neurodegenerative cascades promoted by a PS1 mutation

Although an excitotoxic mechanism of neuronal injury has been proposed to play a role in chronic neurodegenerative disorders such as Alzheimer’s disease, and neurotrophic factors have been put forward as potential therapeutic agents, direct evidence is lacking. Taking advantage of the fact that mutations in the presenilin-1 (PS1) gene are causally linked to many cases of early-onset inherited Alzheimer’s disease, we generated PS1 mutant knock-in mice and directly tested the excitotoxic and neurotrophic hypotheses of Alzheimer’s disease. Primary hippocampal neurons from PS1 mutant knock-in mice exhibited increased production of amyloid β-peptide 42/43 and increased vulnerability to excitotoxicity, which occurred in a gene dosage-dependent manner. Neurons expressing mutant PS1 exhibited enhanced calcium responses to glutamate and increased oxyradical production and mitochondrial dysfunction. Pretreatment with either basic fibroblast growth factor or activity-dependent neurotrophic factor protected neurons expressing mutant PS1 against excitotoxicity. Both basic fibroblast growth factor and activity-dependent neurotrophic factor stabilized intracellular calcium levels and abrogated the increased oxyradical production and mitochondrial dysfunction otherwise caused by the PS1 mutation. Our data indicate that neurotrophic factors can interrupt excitotoxic neurodegenerative cascades promoted by PS1 mutations.

Manipulation of the membrane binding site of vitamin K-dependent proteins: Enhanced biological function of human factor VII

Recent studies suggested that modification of the membrane contact site of vitamin K-dependent proteins may enhance the membrane affinity and function of members of this protein family. The properties of a factor VII mutant, factor VII-Q10E32, relative to wild-type factor VII (VII, containing P10K32), have been compared. Membrane affinity of VII-Q10E32 was about 20-fold higher than that of wild-type factor VII. The rate of autoactivation VII-Q10E32 with soluble tissue factor was 100-fold faster than wild-type VII and its rate of activation by factor Xa was 30 times greater than that of wild-type factor VII. When combined with soluble tissue factor and phospholipid, activated factor VII-Q10E32 displayed increased activation of factor X. Its coagulant activity was enhanced in all types of plasma and with all sources of tissue factor tested. This difference in activity (maximum 50-fold) was greatest when coagulation conditions were minimal, such as limiting levels of tissue factor and/or phospholipid. Because of its enhanced activity, factor VII-Q10E32 and its derivatives may provide important reagents for research and may be more effective in treatment of bleeding and/or clotting disorders.

Polymerase recognition of synthetic oligodeoxyribonucleotides incorporating degenerate pyrimidine and purine bases

A universal base that is capable of substituting for any of the four natural bases in DNA would be of great utility in both mutagenesis and recombinant DNA experiments. This paper describes the properties of oligonucleotides incorporating two degenerate bases, the pyrimidine base 6H,8H-3,4-dihydropyrimido[4,5-c][1,2]oxazin-7-one and the purine base N6-methoxy-2,6-diaminopurine, designated P and K, respectively. An equimolar mixture of the analogues P and K (called M) acts, in primers, as a universal base. The thermal stability of oligonucleotide duplexes were only slightly reduced when natural bases were replaced by P or K. Templates containing the modified bases were copied by Taq polymerase; P behaved as thymine in 60% of copying events and as cytosine in 40%, whereas K behaved as if it were guanine (13%) or adenine (87%). The dUTPase gene of Caenorhabditis elegans, which we have found to contain three nonidentical homologous repeats, was used as a model system to test the use of these bases in primers for DNA synthesis. A pair of oligodeoxyribonucleotides, each 20 residues long and containing an equimolar mixture of P and K at six positions, primed with high specificity both T7 DNA polymerase in sequencing reactions and Taq polymerase in PCRs; no nonspecific amplification was obtained on genomic DNA of C. elegans. Use of P and K can significantly reduce the complexity of degenerate oligonucleotide mixtures, and when used together, P and K can act as a universal base.

Bcl-XL interacts with Apaf-1 and inhibits Apaf-1-dependent caspase-9 activation

Recent studies indicate that Caenorhabditis elegans CED-4 interacts with and promotes the activation of the death protease CED-3, and that this activation is inhibited by CED-9. Here we show that a mammalian homolog of CED-4, Apaf-1, can associate with several death proteases, including caspase-4, caspase-8, caspase-9, and nematode CED-3 in mammalian cells. The interaction with caspase-9 was mediated by the N-terminal CED-4-like domain of Apaf-1. Expression of Apaf-1 enhanced the killing activity of caspase-9 that required the CED-4-like domain of Apaf-1. Furthermore, Apaf-1 promoted the processing and activation of caspase-9 in vivo. Bcl-XL, an antiapoptotic member of the Bcl-2 family, was shown to physically interact with Apaf-1 and caspase-9 in mammalian cells. The association of Apaf-1 with Bcl-XL was mediated through both its CED-4-like domain and the C-terminal domain containing WD-40 repeats. Expression of Bcl-XL inhibited the association of Apaf-1 with caspase-9 in mammalian cells. Significantly, recombinant Bcl-XL purified from Escherichia coli or insect cells inhibited Apaf-1-dependent processing of caspase-9. Furthermore, Bcl-XL failed to inhibit caspase-9 processing mediated by a constitutively active Apaf-1 mutant, suggesting that Bcl-XL regulates caspase-9 through Apaf-1. These experiments demonstrate that Bcl-XL associates with caspase-9 and Apaf-1, and show that Bcl-XL inhibits the maturation of caspase-9 mediated by Apaf-1, a process that is evolutionarily conserved from nematodes to humans.

NF-κB-dependent inhibition of apoptosis is essential for host cell survival during Rickettsia rickettsii infection

The possibility that bacteria may have evolved strategies to overcome host cell apoptosis was explored by using Rickettsia rickettsii, an obligate intracellular Gram-negative bacteria that is the etiologic agent of Rocky Mountain spotted fever. The vascular endothelial cell, the primary target cell during in vivo infection, exhibits no evidence of apoptosis during natural infection and is maintained for a sufficient time to allow replication and cell-to-cell spread prior to eventual death due to necrotic damage. Prior work in our laboratory demonstrated that R. rickettsii infection activates the transcription factor NF-κB and alters expression of several genes under its control. However, when R. rickettsii-induced activation of NF-κB was inhibited, apoptosis of infected but not uninfected endothelial cells rapidly ensued. In addition, human embryonic fibroblasts stably transfected with a superrepressor mutant inhibitory subunit IκB that rendered NF-κB inactivatable also underwent apoptosis when infected, whereas infected wild-type human embryonic fibroblasts survived. R. rickettsii, therefore, appeared to inhibit host cell apoptosis via a mechanism dependent on NF-κB activation. Apoptotic nuclear changes correlated with presence of intracellular organisms and thus this previously unrecognized proapoptotic signal, masked by concomitant NF-κB activation, likely required intracellular infection. Our studies demonstrate that a bacterial organism can exert an antiapoptotic effect, thus modulating the host cell’s apoptotic response to its own advantage by potentially allowing the host cell to remain as a site of infection.

Region-dependent dynamics of cAMP response element-binding protein phosphorylation in the basal ganglia

The cAMP response element-binding protein (CREB) is an activity-dependent transcription factor that is involved in neural plasticity. The kinetics of CREB phosphorylation have been suggested to be important for gene activation, with sustained phosphorylation being associated with downstream gene expression. If so, the duration of CREB phosphorylation might serve as an indicator for time-sensitive plastic changes in neurons. To screen for regions potentially involved in dopamine-mediated plasticity in the basal ganglia, we used organotypic slice cultures to study the patterns of dopamine- and calcium-mediated CREB phosphorylation in the major subdivisions of the striatum. Different durations of CREB phosphorylation were evoked in the dorsal and ventral striatum by activation of dopamine D1-class receptors. The same D1 stimulus elicited (i) transient phosphorylation (≤15 min) in the matrix of the dorsal striatum; (ii) sustained phosphorylation (≤2 hr) in limbic-related structures including striosomes, the nucleus accumbens, the fundus striati, and the bed nucleus of the stria terminalis; and (iii) prolonged phosphorylation (up to 4 hr or more) in cellular islands in the olfactory tubercle. Elevation of Ca2+ influx by stimulation of L-type Ca2+ channels, NMDA, or KCl induced strong CREB phosphorylation in the dorsal striatum but not in the olfactory tubercle. These findings differentiate the response of CREB to dopamine and calcium signals in different striatal regions and suggest that dopamine-mediated CREB phosphorylation is persistent in limbic-related regions of the neonatal basal ganglia. The downstream effects activated by persistent CREB phosphorylation may include time-sensitive neuroplasticity modulated by dopamine.

Lignin monomer composition is determined by the expression of a cytochrome P450-dependent monooxygenase in Arabidopsis

The phenylpropanoid pathway provides precursors for the biosynthesis of soluble secondary metabolites and lignin in plants. Ferulate-5-hydroxylase (F5H) catalyzes an irreversible hydroxylation step in this pathway that diverts ferulic acid away from guaiacyl lignin biosynthesis and toward sinapic acid and syringyl lignin. This fact led us to postulate that F5H was a potential regulatory step in the determination of lignin monomer composition. To test this hypothesis, we have used Arabidopsis to examine the impact of F5H overexpression. Arabidopsis is a useful model system in which to study lignification because in wild-type plants, guaiacyl and syringyl lignins are deposited in a tissue-specific fashion, while the F5H-deficient fah1 mutant accumulates only guaiacyl lignin. Here we show that ectopic overexpression of F5H in Arabidopsis abolishes tissue-specific lignin monomer accumulation. Surprisingly, overexpression of F5H under the control of the lignification-associated cinnamate-4-hydroxylase promoter, but not the commonly employed cauliflower mosaic virus 35S promoter, generates a lignin that is almost entirely comprised of syringylpropane units. These experiments demonstrate that modification of F5H expression may enable engineering of lignin monomer composition in agronomically important plant species.

A model for the structure of the P domains in the subtilisin-like prohormone convertases

The proprotein convertases are a family of at least seven calcium-dependent endoproteases that process a wide variety of precursor proteins in the secretory pathway. All members of this family possess an N-terminal proregion, a subtilisin-like catalytic module, and an additional downstream well-conserved region of ≈150 amino acid residues, the P domain, which is not found in any other subtilase. The pro and catalytic domains cannot be expressed in the absence of the P domains; their thermodynamic instability may be attributable to the presence of large numbers of negatively charged Glu and Asp side chains in the substrate binding region for recognition of multibasic residue cleavage sites. Based on secondary structure predictions, we here propose that the P domains consist of 8-stranded β-barrels with well-organized inner hydrophobic cores, and therefore are independently folded components of the proprotein convertases. We hypothesize further that the P domains are integrated through strong hydrophobic interactions with the catalytic domains, conferring structural stability and regulating the properties and activity of the convertases. A molecular model of these interdomain interactions is proposed in this report.