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

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

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

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

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

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

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images.

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Thermographic imaging: assessment of drought and heat tolerance in Spanish germplasm of Brachypodium distachyon.

The annual grass Brachypodium distachyon has been recently recognized as the model plant for functional genomics of temperate grasses, including cereals of economic relevance like wheat and barley. Sixty-two lines of B. distachyon were assessed for response to drought stress and heat tolerance. All these lines, except the reference genotype BD21, derive from specimens collected in 32 distinct locations of the Iberian Peninsula, covering a wide range of geo- climatic conditions. Sixteen lines of Brachypodium hybridum, an allotetraploid closely related to B. distachyon were used as reference of abiotic-stress well-adapted genotypes. Drought tolerance was assessed in a green-house trial. At the rosette-stage, no irrigation was applied to treated plants whereas their replicates at the control were maintained well watered during all the experiment. Thermographic images of treated and control plants were taken after 2 and 3 weeks of drought treatment, when stressed plants showed medium and extreme wilting symptoms. The mean leaf temperature of stressed (LTs) and control (LTc) plants was estimated based upon thermographic records from selected pixels (183 per image) that strictly correspond to leaf tissue. The response to drought was based on the analysis of two parameters: LTs and the thermal difference (TD) between stressed and control plants (LTs – LTc). The response to heat stress was based on LTc. Comparison of the mean values of these parameters showed that: 1) Genotypes better adapted to drought (B. hybridum lines) presented a higher LTs and TD than B. distachyon lines. 2) Under high temperature conditions, watered plants of B. hybridum lines maintained lower LTc than those of B. distachyon. Those results suggest that in these species adaptation to drought is linked to a more efficient stomata regulation: under water stress stomata are closed, increasing foliar temperature but also water use efficiency by reducing transpiration. With high temperature and water availability the results are less definite, but still seems that opening stomata allow plants to increase transpiration and therefore to diminish foliar temperature.

Combining Text and Graphics for Interactive Exploration of Behavior Datasets

Modern sensor technologies and simulators applied to large and complex dynamic systems (such as road traffic networks, sets of river channels, etc.) produce large amounts of behavior data that are difficult for users to interpret and analyze. Software tools that generate presentations combining text and graphics can help users understand this data. In this paper we describe the results of our research on automatic multimedia presentation generation (including text, graphics, maps, images, etc.) for interactive exploration of behavior datasets. We designed a novel user interface that combines automatically generated text and graphical resources. We describe the general knowledge-based design of our presentation generation tool. We also present applications that we developed to validate the method, and a comparison with related work.

Robust thermal face recognition using region classifiers

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 different region classifiers. The region classifiers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classifier is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identification process difficult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.

A Ground-Truth Video Dataset for the Development and Evaluation of Vision-based Sense-and-Avoid systems

The importance of vision-based systems for Sense-and-Avoid is increasing nowadays as remotely piloted and autonomous UAVs become part of the non-segregated airspace. The development and evaluation of these systems demand flight scenario images which are expensive and risky to obtain. Currently Augmented Reality techniques allow the compositing of real flight scenario images with 3D aircraft models to produce useful realistic images for system development and benchmarking purposes at a much lower cost and risk. With the techniques presented in this paper, 3D aircraft models are positioned firstly in a simulated 3D scene with controlled illumination and rendering parameters. Realistic simulated images are then obtained using an image processing algorithm which fuses the images obtained from the 3D scene with images from real UAV flights taking into account on board camera vibrations. Since the intruder and camera poses are user-defined, ground truth data is available. These ground truth annotations allow to develop and quantitatively evaluate aircraft detection and tracking algorithms. This paper presents the software developed to create a public dataset of 24 videos together with their annotations and some tracking application results.

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.

The small ribosomal subunit from Thermus thermophilus at 4.5 Å resolution: Pattern fittings and the identification of a functional site

The electron density map of the small ribosomal subunit from Thermus thermophilus, constructed at 4.5 Å resolution, shows the recognizable morphology of this particle, as well as structural features that were interpreted as ribosomal RNA and proteins. Unbiased assignments, carried out by quantitative covalent binding of heavy atom compounds at predetermined sites, led to the localization of the surface of the ribosomal protein S13 at a position compatible with previous assignments, whereas the surface of S11 was localized at a distance of about twice its diameter from the site suggested for its center by neutron scattering. Proteins S5 and S7, whose structures have been determined crystallographically, were visually placed in the map with no alterations in their conformations. Regions suitable to host the fold of protein S15 were detected in several positions, all at a significant distance from the location of this protein in the neutron scattering map. Targeting the 16S RNA region, where mRNA docks to allow the formation of the initiation complex by a mercurated mRNA analog, led to the characterization of its vicinity.

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

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

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

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

Large vortex-like structure of dipole field in computer models of liquid water and dipole-bridge between biomolecules

We propose a framework to describe the cooperative orientational motions of water molecules in liquid water and around solute molecules in water solutions. From molecular dynamics (MD) simulation a new quantity “site-dipole field” is defined as the averaged orientation of water molecules that pass through each spatial position. In the site-dipole field of bulk water we found large vortex-like structures of more than 10 Å in size. Such coherent patterns persist more than 300 ps although the orientational memory of individual molecules is quickly lost. A 1-ns MD simulation of systems consisting of two amino acids shows that the fluctuations of site-dipole field of solvent are pinned around the amino acids, resulting in a stable dipole-bridge between side-chains of amino acids. The dipole-bridge is significantly formed even for the side-chain separation of 14 Å, which corresponds to five layers of water. The way that dipole-bridge forms sensitively depends on the side-chain orientations and thereby explains the specificity in the solvent-mediated interactions between biomolecules.