Somatic hypermutation of the new antigen receptor gene (NAR) in the nurse shark does not generate the repertoire: Possible role in antigen-driven reactions in the absence of germinal centers

The new antigen receptor (NAR) gene in the nurse shark diversifies extensively by somatic hypermutation. It is not known, however, whether NAR somatic hypermutation generates the primary repertoire (like in the sheep) or rather is used in antigen-driven immune responses. To address this issue, the sequences of NAR transmembrane (Tm) and secretory (Sec) forms, presumed to represent the primary and secondary repertoires, respectively, were examined from the peripheral blood lymphocytes of three adult nurse sharks. More than 40% of the Sec clones but fewer than 11% of Tm clones contained five mutations or more. Furthermore, more than 75% of the Tm clones had few or no mutations. Mutations in the Sec clones occurred mostly in the complementarity-determining regions (CDR) with a significant bias toward replacement substitutions in CDR1; in Tm clones there was no significant bias toward replacements and only a low level of targeting to the CDRs. Unlike the Tm clones where the replacement mutational pattern was similar to that seen for synonymous changes, Sec replacements displayed a distinct pattern of mutations. The types of mutations in NAR were similar to those found in mouse Ig genes rather than to the unusual pattern reported for shark and Xenopus Ig. Finally, an oligoclonal family of Sec clones revealed a striking trend toward acquisition of glutamic/aspartic acid, suggesting some degree of selection. These data strongly suggest that hypermutation of NAR does not generate the repertoire, but instead is involved in antigen-driven immune responses.

Antigen-driven CD4+ T cell and HIV-1 dynamics: Residual viral replication under highly active antiretroviral therapy

Antigen-induced stimulation of the immune system can generate heterogeneity in CD4+ T cell division rates capable of explaining the temporal patterns seen in the decay of HIV-1 plasma RNA levels during highly active antiretroviral therapy. Posttreatment increases in peripheral CD4+ T cell counts are consistent with a mathematical model in which host cell redistribution between lymph nodes and peripheral blood is a function of viral burden. Model fits to patient data suggest that, although therapy reduces HIV replication below replacement levels, substantial residual replication continues. This residual replication has important consequences for long-term therapy and the evolution of drug resistance and represents a challenge for future treatment strategies.

Collagenase-3 Induction in Rat Lung Fibroblasts Requires the Combined Effects of Tumor Necrosis Factor-α and 12-Lipoxygenase Metabolites: A Model of Macrophage-induced, Fibroblast-driven Extracellular Matrix Remodeling during Inflammatory Lung Injury

The mechanisms responsible for the induction of matrix-degrading proteases during lung injury are ill defined. Macrophage-derived mediators are believed to play a role in regulating synthesis and turnover of extracellular matrix at sites of inflammation. We find a localized increase in the expression of the rat interstitial collagenase (MMP-13; collagenase-3) gene from fibroblastic cells directly adjacent to macrophages within silicotic rat lung granulomas. Conditioned medium from macrophages isolated from silicotic rat lungs was found to induce rat lung fibroblast interstitial collagenase gene expression. Conditioned medium from primary rat lung macrophages or J774 monocytic cells activated by particulates in vitro also induced interstitial collagenase gene expression. Tumor necrosis factor-α (TNF-α) alone did not induce interstitial collagenase expression in rat lung fibroblasts but did in rat skin fibroblasts, revealing tissue specificity in the regulation of this gene. The activity of the conditioned medium was found to be dependent on the combined effects of TNF-α and 12-lipoxygenase-derived arachidonic acid metabolites. The fibroblast response to this conditioned medium was dependent on de novo protein synthesis and involved the induction of nuclear activator protein-1 activity. These data reveal a novel requirement for macrophage-derived 12-lipoxygenase metabolites in lung fibroblast MMP induction and provide a mechanism for the induction of resident cell MMP gene expression during inflammatory lung processes.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Unusual 1H NMR chemical shifts support (His) Cɛ1—H⋅⋅⋅O⩵C H-bond: Proposal for reaction-driven ring flip mechanism in serine protease catalysis

13C-selective NMR, combined with inhibitor perturbation experiments, shows that the Cɛ1—H proton of the catalytic histidine in resting α-lytic protease and subtilisin BPN′ resonates, when protonated, at 9.22 ppm and 9.18 ppm, respectively, which is outside the normal range for such protons and ≈0.6 to 0.8 ppm further downfield than previously reported. They also show that the previous α-lytic protease assignments [Markley, J. L., Neves, D. E., Westler, W. M., Ibanez, I. B., Porubcan, M. A. & Baillargeon, M. W. (1980) Front. Protein Chem. 10, 31–61] were to signals from inactive or denatured protein. Simulations of linewidth vs. pH demonstrate that the true signal is more difficult to detect than corresponding signals from inactive derivatives, owing to higher imidazole pKa values and larger chemical shift differences between protonated and neutral forms. A compilation and analysis of available NMR data indicates that the true Cɛ1—H signals from other serine proteases are similarly displaced downfield, with past assignments to more upfield signals probably in error. The downfield displacement of these proton resonances is shown to be consistent with an H-bond involving the histidine Cɛ1—H as donor, confirming the original hypothesis of Derewenda et al. [Derewenda, Z. S., Derewenda, U. & Kobos, P. M. (1994) J. Mol. Biol. 241, 83–93], which was based on an analysis of literature x-ray crystal structures of serine hydrolases. The invariability of this H-bond among enzymes containing Asp-His-Ser triads indicates functional importance. Here, we propose that it enables a reaction-driven imidazole ring flip mechanism, overcoming a major dilemma inherent in all previous mechanisms, namely how these enzymes catalyze both the formation and productive breakdown of tetrahedral intermediates.

Protein Information Resource: a community resource for expert annotation of protein data

The Protein Information Resource, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the most comprehensive and expertly annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database. To provide timely and high quality annotation and promote database interoperability, the PIR-International employs rule-based and classification-driven procedures based on controlled vocabulary and standard nomenclature and includes status tags to distinguish experimentally determined from predicted protein features. The database contains about 200 000 non-redundant protein sequences, which are classified into families and superfamilies and their domains and motifs identified. Entries are extensively cross-referenced to other sequence, classification, genome, structure and activity databases. The PIR web site features search engines that use sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. The PIR-Inter­national databases and search tools are accessible on the PIR web site at http://pir.georgetown.edu/ and at the MIPS web site at http://www.mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.

The acquisition of skilled motor performance: Fast and slow experience-driven changes in primary motor cortex

Behavioral and neurophysiological studies suggest that skill learning can be mediated by discrete, experience-driven changes within specific neural representations subserving the performance of the trained task. We have shown that a few minutes of daily practice on a sequential finger opposition task induced large, incremental performance gains over a few weeks of training. These gains did not generalize to the contralateral hand nor to a matched sequence of identical component movements, suggesting that a lateralized representation of the learned sequence of movements evolved through practice. This interpretation was supported by functional MRI data showing that a more extensive representation of the trained sequence emerged in primary motor cortex after 3 weeks of training. The imaging data, however, also indicated important changes occurring in primary motor cortex during the initial scanning sessions, which we proposed may reflect the setting up of a task-specific motor processing routine. Here we provide behavioral and functional MRI data on experience-dependent changes induced by a limited amount of repetitions within the first imaging session. We show that this limited training experience can be sufficient to trigger performance gains that require time to become evident. We propose that skilled motor performance is acquired in several stages: “fast” learning, an initial, within-session improvement phase, followed by a period of consolidation of several hours duration, and then “slow” learning, consisting of delayed, incremental gains in performance emerging after continued practice. This time course may reflect basic mechanisms of neuronal plasticity in the adult brain that subserve the acquisition and retention of many different skills.

Binding of the cellulose-binding domain of exoglucanase Cex from Cellulomonas fimi to insoluble microcrystalline cellulose is entropically driven.

Isothermal titration microcalorimetry is combined with solution-depletion isotherm data to analyze the thermodynamics of binding of the cellulose-binding domain (CBD) from the beta-1,4-(exo)glucanase Cex of Cellulomonas fimi to insoluble bacterial microcrystalline cellulose. Analysis of isothermal titration microcalorimetry data against two putative binding models indicates that the bacterial microcrystalline cellulose surface presents two independent classes of binding sites, with the predominant high-affinity site being characterized by a Langmuir-type Ka of 6.3 (+/-1.4) x 10(7) M-1 and the low-affinity site by a Ka of 1.1 (+/-0.6) x 10(6) M-1. CBDCex binding to either site is exothermic, but is mainly driven by a large positive change in entropy. This differs from protein binding to soluble carbohydrates, which is usually driven by a relatively large exothermic standard enthalpy change for binding. Differential heat capacity changes are large and negative, indicating that sorbent and protein dehydration effects make a dominant contribution to the driving force for binding.

Temperature chaos in 3D Ising spin glasses is driven by rare events

Temperature chaos has often been reported in the literature as a rare-event–driven phenomenon. However, this fact has always been ignored in the data analysis, thus erasing the signal of the chaotic behavior (still rare in the sizes achieved) and leading to an overall picture of a weak and gradual phenomenon. On the contrary, our analysis relies on a largedeviations functional that allows to discuss the size dependences. In addition, we had at our disposal unprecedentedly large configurations equilibrated at low temperatures, thanks to the Janus computer. According to our results, when temperature chaos occurs its effects are strong and can be felt even at short distances.

Uma nova arquitetura para Internet das Coisas com análise e reconhecimento de padrões e processamento com Big Data.

A Internet das Coisas é um novo paradigma de comunicação que estende o mundo virtual (Internet) para o mundo real com a interface e interação entre objetos. Ela possuirá um grande número de dispositivos heteregôneos interconectados, que deverá gerar um grande volume de dados. Um dos importantes desafios para seu desenvolvimento é se guardar e processar esse grande volume de dados em aceitáveis intervalos de tempo. Esta pesquisa endereça esse desafio, com a introdução de serviços de análise e reconhecimento de padrões nas camadas inferiores do modelo de para Internet das Coisas, que procura reduzir o processamento nas camadas superiores. Na pesquisa foram analisados os modelos de referência para Internet das Coisas e plataformas para desenvolvimento de aplicações nesse contexto. A nova arquitetura de implementada estende o LinkSmart Middeware pela introdução de um módulo para reconhecimento de padrões, implementa algoritmos para estimação de valores, detecção de outliers e descoberta de grupos nos dados brutos, oriundos de origens de dados. O novo módulo foi integrado à plataforma para Big Data Hadoop e usa as implementações algorítmicas do framework Mahout. Este trabalho destaca a importância da comunicação cross layer integrada à essa nova arquitetura. Nos experimentos desenvolvidos na pesquisa foram utilizadas bases de dados reais, provenientes do projeto Smart Santander, de modo a validar da nova arquitetura de IoT integrada aos serviços de análise e reconhecimento de padrões e a comunicação cross-layer.

Large-scale clustering measurements with photometric redshifts: comparing the dark matter haloes of X-ray AGN, star-forming and passive galaxies at z ≈ 1

We combine multi-wavelength data in the AEGIS-XD and C-COSMOS surveys to measure the typical dark matter halo mass of X-ray selected active galactic nuclei (AGN) [L_X(2–10 keV) > 10^42 erg s^− 1] in comparison with far-infrared selected star-forming galaxies detected in the Herschel/PEP survey (PACS Evolutionary Probe; L_IR > 10^11 L_⊙) and quiescent systems at z ≈ 1. We develop a novel method to measure the clustering of extragalactic populations that uses photometric redshift probability distribution functions in addition to any spectroscopy. This is advantageous in that all sources in the sample are used in the clustering analysis, not just the subset with secure spectroscopy. The method works best for large samples. The loss of accuracy because of the lack of spectroscopy is balanced by increasing the number of sources used to measure the clustering. We find that X-ray AGN, far-infrared selected star-forming galaxies and passive systems in the redshift interval 0.6 < z < 1.4 are found in haloes of similar mass, log M_DMH/(M_⊙ h^−1) ≈ 13.0. We argue that this is because the galaxies in all three samples (AGN, star-forming, passive) have similar stellar mass distributions, approximated by the J-band luminosity. Therefore, all galaxies that can potentially host X-ray AGN, because they have stellar masses in the appropriate range, live in dark matter haloes of log M_DMH/(M_⊙ h^−1) ≈ 13.0 independent of their star formation rates. This suggests that the stellar mass of X-ray AGN hosts is driving the observed clustering properties of this population. We also speculate that trends between AGN properties (e.g. luminosity, level of obscuration) and large-scale environment may be related to differences in the stellar mass of the host galaxies.

Diseño y desarrollo de un servicio big data en la nube para búsqueda, compartición de ficheros y data mining

En esta memoria se presenta el diseño y desarrollo de una aplicación en la nube destinada a la compartición de objetos y servicios. El desarrollo de esta aplicación surge dentro del proyecto de I+D+i, SITAC: Social Internet of Things – Apps by and for the Crowd ITEA 2 11020, que trata de crear una arquitectura integradora y un “ecosistema” que incluya plataformas, herramientas y metodologías para facilitar la conexión y cooperación de entidades de distinto tipo conectadas a la red bien sean sistemas, máquinas, dispositivos o personas con dispositivos móviles personales como tabletas o teléfonos móviles. El proyecto innovará mediante la utilización de un modelo inspirado en las redes sociales para facilitar y unificar las interacciones tanto entre personas como entre personas y dispositivos. En este contexto surge la necesidad de desarrollar una aplicación destinada a la compartición de recursos en la nube que pueden ser tanto lógicos como físicos, y que esté orientada al big data. Ésta será la aplicación presentada en este trabajo, el “Resource Sharing Center”, que ofrece un servicio web para el intercambio y compartición de contenido, y un motor de recomendaciones basado en las preferencias de los usuarios. Con este objetivo, se han usado tecnologías de despliegue en la nube, como Elastic Beanstalk (el PaaS de Amazon Web Services), S3 (el sistema de almacenamiento de Amazon Web Services), SimpleDB (base de datos NoSQL) y HTML5 con JavaScript y Twitter Bootstrap para el desarrollo del front-end, siendo Python y Node.js las tecnologías usadas en el back end, y habiendo contribuido a la mejora de herramientas de clustering sobre big data. Por último, y de cara a realizar el estudio sobre las pruebas de carga de la aplicación se ha usado la herramienta ApacheJMeter.

A model driven framework for geographic knowledge discovery

Geographic knowledge discovery (GKD) is the process of extracting information and knowledge from massive georeferenced databases. Usually the process is accomplished by two different systems, the Geographic Information Systems (GIS) and the data mining engines. However, the development of those systems is a complex task due to it does not follow a systematic, integrated and standard methodology. To overcome these pitfalls, in this paper, we propose a modeling framework that addresses the development of the different parts of a multilayer GKD process. The main advantages of our framework are that: (i) it reduces the design effort, (ii) it improves quality systems obtained, (iii) it is independent of platforms, (iv) it facilitates the use of data mining techniques on geo-referenced data, and finally, (v) it ameliorates the communication between different users.

Applying model-driven engineering to the development of Rich Internet Applications for Business Intelligence

Business Intelligence (BI) applications have been gradually ported to the Web in search of a global platform for the consumption and publication of data and services. On the Internet, apart from techniques for data/knowledge management, BI Web applications need interfaces with a high level of interoperability (similar to the traditional desktop interfaces) for the visualisation of data/knowledge. In some cases, this has been provided by Rich Internet Applications (RIA). The development of these BI RIAs is a process traditionally performed manually and, given the complexity of the final application, it is a process which might be prone to errors. The application of model-driven engineering techniques can reduce the cost of development and maintenance (in terms of time and resources) of these applications, as they demonstrated by other types of Web applications. In the light of these issues, the paper introduces the Sm4RIA-B methodology, i.e., a model-driven methodology for the development of RIA as BI Web applications. In order to overcome the limitations of RIA regarding knowledge management from the Web, this paper also presents a new RIA platform for BI, called RI@BI, which extends the functionalities of traditional RIAs by means of Semantic Web technologies and B2B techniques. Finally, we evaluate the whole approach on a case study—the development of a social network site for an enterprise project manager.

Estadística Aplicada a los Recursos Marinos. Clustering. Análisis de segmentación

Introducción al análisis con Clustering