YODA MAPK kinase kinase regulates a novel immunity pathway conferring broad-spectrum resistance to pathogens

Plant mitogen-activated protein kinase (MAPK) casca des transduce environmental molecular signals and developmental cues into cellular responses. Among these signals are the pathogen-associated molecular patterns (PAMPs) that upon recognition by plant pattern recognition receptors (PRR), including Receptor-Like Kinases (RLKs), activate MAPK cascades that regulate PAMP-triggered immunity responses (PTI).

Debonding identification in FRP-plated RC structures using PZT sensors

The application of the Electro-Mechanical Impedance (EMI) method for damage detection in Structural Health Monitoring has noticeable increased in recent years. EMI method utilizes piezoelectric transducers for directly measuring the mechanical properties of the host structure, obtaining the so called impedance measurement, highly influenced by the variations of dynamic parameters of the structure. These measurements usually contain a large number of frequency points, as well as a high number of dimensions, since each frequency range swept can be considered as an independent variable. That makes this kind of data hard to handle, increasing the computational costs and being substantially time-consuming. In that sense, the Principal Component Analysis (PCA)-based data compression has been employed in this work, in order to enhance the analysis capability of the raw data. Furthermore, a Support Vector Machine (SVM), which has been widespread used in machine learning and pattern recognition fields, has been applied in this study in order to model any possible existing pattern in the PCAcompress data, using for that just the first two Principal Components. Different known non-damaged and damaged measurements of an experimental tested beam were used as training input data for the SVM algorithm, using as test input data the same amount of cases measured in beams with unknown structural health conditions. Thus, the purpose of this work is to demonstrate how, with a few impedance measurements of a beam as raw data, its healthy status can be determined based on pattern recognition procedures.

Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.

Functional characterization of YODA, a mitogen-activated protein kinase kinase kinase (MAP3K) that regulates a novel innate immunity pathway in Arabidopsis thaliana

Las cascadas de señalización mediadas por proteína quinasas activadas por mitógeno (MAP quinasas) son capaces de integrar y transducir señales ambientales en respuestas celulares. Entre estas señales se encuentran los PAMPs/MAMPs (Pathogen/Microbe-Associated Molecular Patterns), que son moléculas de patógenos o microorganismos, o los DAMPs (Damaged-Associated Molecular Patterns), que son moléculas derivadas de las plantas producidas en respuesta a daño celular. Tras el reconocimiento de los PAMPs/DAMPs por receptores de membrana denominados PRRs (Pattern Recognition Receptors), como los receptores con dominio quinasa (RLKs) o los receptores sin dominio quinasa (RLPs), se activan respuestas moleculares, incluidas cascadas de MAP quinasas, que regulan la puesta en marcha de la inmunidad activada por PAMPs (PTI). Esta Tesis describe la caracterización funcional de la MAP quinasa quinasa quinasa (MAP3K) YODA (YDA), que actúa como un regulador clave de la PTI en Arabidopsis. Se ha descrito previamente que YDA controla varios procesos de desarrollo, como la regulación del patrón estomático, la elongación del zigoto y la arquitectura floral. Hemos caracterizado un alelo mutante hipomórfico de YDA (elk2 o yda11) que presenta una elevada susceptibilidad a patógenos biótrofos y necrótrofos. Notablemente, plantas que expresan una forma constitutivamente activa de YDA (CA-YDA), con una deleción en el dominio N-terminal, presentan una resistencia de amplio espectro frente a diferentes tipos de patógenos, incluyendo hongos, oomicetos y bacterias, lo que indica que YDA juega un papel importante en la regulación de la resistencia de las plantas a patógenos. Nuestros datos indican que esta función es independiente de las respuestas inmunes mediadas por los receptores previamente caracterizados FLS2 y CERK1, que reconocen los PAMPs flg22 y quitina, respectivamente, y que están implicados en la resistencia de Arabidopsis frente a bacterias y hongos. Hemos demostrado que YDA controla la resistencia frente al hongo necrótrofo Plectosphaerella cucumerina y el patrón estomático mediante su interacción genética con la RLK ERECTA (ER), un PRR implicado en la regulación de estos procesos. Por el contrario, la interacción genética entre ER y YDA en la regulación de otros procesos de desarrollo es aditiva en lugar de epistática. Análisis genéticos indicaron que MPK3, una MAP quinasa que funciona aguas abajo de YDA en el desarrollo estomático, es un componente de la ruta de señalización mediada por YDA para la resistencia frente a P. cucumerina, lo que sugiere que el desarrollo de las plantas y la PTI comparten el módulo de transducción de MAP quinasas asociado a YDA. Nuestros experimentos han revelado que la resistencia mediada por YDA es independiente de las rutas de señalización reguladas por las hormonas de defensa ácido salicílico, ácido jasmónico, ácido abscísico o etileno, y también es independiente de la ruta de metabolitos secundarios derivados del triptófano, que están implicados en inmunidad vegetal. Además, hemos demostrado que respuestas asociadas a PTI, como el aumento en la concentración de calcio citoplásmico, la producción de especies reactivas de oxígeno, la fosforilación de MAP quinasas y la expresión de genes de defensa, no están afectadas en el mutante yda11. La expresión constitutiva de la proteína CA-YDA en plantas de Arabidopsis no provoca un aumento de las respuestas PTI, lo que sugiere la existencia de mecanismos de resistencia adicionales regulados por YDA que son diferentes de los regulados por FLS2 y CERK1. En línea con estos resultados, nuestros datos transcriptómicos revelan una sobre-representación en plantas CA-YDA de genes de defensa que codifican, por ejemplo, péptidos antimicrobianos o reguladores de muerte celular, o proteínas implicadas en la biogénesis de la pared celular, lo que sugiere una conexión potencial entre la composición e integridad de la pared celular y la resistencia de amplio espectro mediada por YDA. Además, análisis de fosfoproteómica indican la fosforilación diferencial de proteínas relacionadas con la pared celular en plantas CA-YDA en comparación con plantas silvestres. El posible papel de la ruta ER-YDA en la regulación de la integridad de la pared celular está apoyado por análisis bioquímicos y glicómicos de las paredes celulares de plantas er, yda11 y CA-YDA, que revelaron cambios significativos en la composición de la pared celular de estos genotipos en comparación con la de plantas silvestres. En resumen, nuestros datos indican que ER y YDA forman parte de una nueva ruta de inmunidad que regula la integridad de la pared celular y respuestas defensivas, confiriendo una resistencia de amplio espectro frente a patógenos. ABSTRACT Plant mitogen-activated protein kinase (MAPK) cascades transduce environmental signals and developmental cues into cellular responses. Among these signals are the pathogen- or microbe-associated molecular patterns (PAMPs or MAMPs) and the damage-associated molecular patterns (DAMPs). These PAMPs/DAMPs, upon recognition by plant pattern recognition receptors (PRRs), such as Receptor-Like Kinases (RLKs) and Receptor-Like Proteins (RLPs), activate molecular responses, including MAPK cascades, which regulate the onset of PAMP-triggered immunity (PTI). This Thesis describes the functional characterization of the MAPK kinase kinase (MAP3K) YODA (YDA) as a key regulator of Arabidopsis PTI. YDA has been previously described to control several developmental processes, such as stomatal patterning, zygote elongation and inflorescence architecture. We characterized a hypomorphic, non-embryo lethal mutant allele of YDA (elk2 or yda11) that was found to be highly susceptible to biotrophic and necrotrophic pathogens. Remarkably, plants expressing a constitutive active form of YDA (CA-YDA), with a deletion in the N-terminal domain, showed broad-spectrum resistance to different types of pathogens, including fungi, oomycetes and bacteria, indicating that YDA plays a relevant function in plant resistance to pathogens. Our data indicated that this function is independent of the immune responses regulated by the well characterized FLS2 and CERK1 RLKs, which are the PRRs recognizing flg22 and chitin PAMPs, respectively, and are required for Arabidopsis resistance to bacteria and fungi. We demonstrate that YDA controls resistance to the necrotrophic fungus Plectosphaerella cucumerina and stomatal patterning by genetically interacting with ERECTA (ER) RLK, a PRR involved in regulating these processes. In contrast, the genetic interaction between ER and YDA in the regulation of other ER-associated developmental processes was additive, rather than epistatic. Genetic analyses indicated that MPK3, a MAP kinase that functions downstream of YDA in stomatal development, also regulates plant resistance to P. cucumerina in a YDA-dependent manner, suggesting that the YDA-associated MAPK transduction module is shared in plant development and PTI. Our experiments revealed that YDA-mediated resistance was independent of signalling pathways regulated by defensive hormones like salicylic acid, jasmonic acid, abscisic acid or ethylene, and of the tryptophan-derived metabolites pathway, which are involved in plant immunity. In addition, we showed that PAMP-mediated PTI responses, such as the increase of cytoplasmic Ca2+ concentration, reactive oxygen species (ROS) burst, MAPK phosphorylation, and expression of defense-related genes are not impaired in the yda11 mutant. Furthermore, the expression of CA-YDA protein does not result in enhanced PTI responses, further suggesting the existence of additional mechanisms of resistance regulated by YDA that differ from those regulated by the PTI receptors FLS2 and CERK1. In line with these observations, our transcriptomic data revealed the over-representation in CA-YDA plants of defensive genes, such as those encoding antimicrobial peptides and cell death regulators, and genes encoding cell wall-related proteins, suggesting a potential link between plant cell wall composition and integrity and broad spectrum resistance mediated by YDA. In addition, phosphoproteomic data revealed an over-representation of genes encoding wall-related proteins in CA-YDA plants in comparison with wild-type plants. The putative role of the ER-YDA pathway in regulating cell wall integrity was further supported by biochemical and glycomics analyses of er, yda11 and CA-YDA cell walls, which revealed significant changes in the cell wall composition of these genotypes compared with that of wild-type plants. In summary, our data indicate that ER and YDA are components of a novel immune pathway that regulates cell wall integrity and defensive responses, which confer broad-spectrum resistance to pathogens.

Cognitive Surveillance Architecture for Scenario Understanding

Uno de los mayores retos para la comunidad científica es conseguir que las máquinas posean en un futuro la capacidad del sistema visual y cognitivo humanos, de forma que, por ejemplo, en entornos de video vigilancia, puedan llegar a proporcionar de manera automática una descripción fiable de lo que está ocurriendo en la escena. En la presente tesis, mediante la propuesta de un marco de trabajo de referencia, se discuten y plantean los pasos necesarios para el desarrollo de sistemas más inteligentes capaces de extraer y analizar, a diferentes niveles de abstracción y mediante distintos módulos de procesamiento independientes, la información necesaria para comprender qué está sucediendo en un conjunto amplio de escenarios de distinta naturaleza. Se parte de un análisis de requisitos y se identifican los retos para este tipo de sistemas en la actualidad, lo que constituye en sí mismo los objetivos de esta tesis, contribuyendo así a un modelo de datos basado en el conocimiento que permitirá analizar distintas situaciones en las que personas y vehículos son los actores principales, dejando no obstante la puerta abierta a la adaptación a otros dominios. Así mismo, se estudian los distintos procesos que se pueden lanzar a nivel interno así como la necesidad de integrar mecanismos de realimentación a distintos niveles que permitan al sistema adaptarse mejor a cambios en el entorno. Como resultado, se propone un marco de referencia jerárquico que integra las capacidades de percepción, interpretación y aprendizaje para superar los retos identificados en este ámbito; y así poder desarrollar sistemas de vigilancia más robustos, flexibles e inteligentes, capaces de operar en una variedad de entornos. Resultados experimentales ejecutados sobre distintas muestras de datos (secuencias de vídeo principalmente) demuestran la efectividad del marco de trabajo propuesto respecto a otros propuestos en el pasado. Un primer caso de estudio, permite demostrar la creación de un sistema de monitorización de entornos de parking en exteriores para la detección de vehículos y el análisis de plazas libres de aparcamiento. Un segundo caso de estudio, permite demostrar la flexibilidad del marco de referencia propuesto para adaptarse a los requisitos de un entorno de vigilancia completamente distinto, como es un hogar inteligente donde el análisis automático de actividades de la vida cotidiana centra la atención del estudio. ABSTRACT One of the most ambitious objectives for the Computer Vision and Pattern Recognition research community is that machines can achieve similar capacities to the human's visual and cognitive system, and thus provide a trustworthy description of what is happening in the scene under surveillance. Thus, a number of well-established scenario understanding architectural frameworks to develop applications working on a variety of environments can be found in the literature. In this Thesis, a highly descriptive methodology for the development of scene understanding applications is presented. It consists of a set of formal guidelines to let machines extract and analyse, at different levels of abstraction and by means of independent processing modules that interact with each other, the necessary information to understand a broad set of different real World surveillance scenarios. Taking into account the challenges that working at both low and high levels offer, we contribute with a highly descriptive knowledge-based data model for the analysis of different situations in which people and vehicles are the main actors, leaving the door open for the development of interesting applications in diverse smart domains. Recommendations to let systems achieve high-level behaviour understanding will be also provided. Furthermore, feedback mechanisms are proposed to be integrated in order to let any system to understand better the environment and the logical context around, reducing thus the uncertainty and noise, and increasing its robustness and precision in front of low-level or high-level errors. As a result, a hierarchical cognitive architecture of reference which integrates the necessary perception, interpretation, attention and learning capabilities to overcome main challenges identified in this area of research is proposed; thus allowing to develop more robust, flexible and smart surveillance systems to cope with the different requirements of a variety of environments. Once crucial issues that should be treated explicitly in the design of this kind of systems have been formulated and discussed, experimental results shows the effectiveness of the proposed framework compared with other proposed in the past. Two case studies were implemented to test the capabilities of the framework. The first case study presents how the proposed framework can be used to create intelligent parking monitoring systems. The second case study demonstrates the flexibility of the system to cope with the requirements of a completely different environment, a smart home where activities of daily living are performed. Finally, general conclusions and future work lines to further enhancing the capabilities of the proposed framework are presented.

The SBASE protein domain library, release 8.0: a collection of annotated protein sequence segments

SBASE 8.0 is the eighth release of the SBASE library of protein domain sequences that contains 294 898 annotated structural, functional, ligand-binding and topogenic segments of proteins, cross-referenced to most major sequence databases and sequence pattern collections. The entries are clustered into over 2005 statistically validated domain groups (SBASE-A) and 595 non-validated groups (SBASE-B), provided with several WWW-based search and browsing facilities for online use. A domain-search facility was developed, based on non-parametric pattern recognition methods, including artificial neural networks. SBASE 8.0 is freely available by anonymous ‘ftp’ file transfer from ftp.icgeb.trieste.it. Automated searching of SBASE can be carried out with the WWW servers http://www.icgeb.trieste.it/sbase/ and http://sbase.abc.hu/sbase/.

Long-term modifications of synaptic efficacy in the human inferior and middle temporal cortex.

The primate temporal cortex has been demonstrated to play an important role in visual memory and pattern recognition. It is of particular interest to investigate whether activity-dependent modification of synaptic efficacy, a presumptive mechanism for learning and memory, is present in this cortical region. Here we address this issue by examining the induction of synaptic plasticity in surgically resected human inferior and middle temporal cortex. The results show that synaptic strength in the human temporal cortex could undergo bidirectional modifications, depending on the pattern of conditioning stimulation. High frequency stimulation (100 or 40 Hz) in layer IV induced long-term potentiation (LTP) of both intracellular excitatory postsynaptic potentials and evoked field potentials in layers II/III. The LTP induced by 100 Hz tetanus was blocked by 50-100 microM DL-2-amino-5-phosphonovaleric acid, suggesting that N-methyl-D-aspartate receptors were responsible for its induction. Long-term depression (LTD) was elicited by prolonged low frequency stimulation (1 Hz, 15 min). It was reduced, but not completely blocked, by DL-2-amino-5-phosphonovaleric acid, implying that some other mechanisms in addition to N-methyl-DL-aspartate receptors were involved in LTD induction. LTD was input-specific, i.e., low frequency stimulation of one pathway produced LTD of synaptic transmission in that pathway only. Finally, the LTP and LTD could reverse each other, suggesting that they can act cooperatively to modify the functional state of cortical network. These results suggest that LTP and LTD are possible mechanisms for the visual memory and pattern recognition functions performed in the human temporal cortex.

Expression cloning of dSR-CI, a class C macrophage-specific scavenger receptor from Drosophila melanogaster.

Mammalian class A macrophage-specific scavenger receptors (SR-A) exhibit unusually broad binding specificity for a wide variety of polyanionic ligands. The properties of these receptors suggest that they may be involved in atherosclerosis and host defense. We have previously observed a similar receptor activity in Drosophila melanogaster embryonic macrophages and in the Drosophila macrophage-like Schneider L2 cell line. Expression cloning was used to isolate from L2 cells a cDNA that encodes a third class (class C) of scavenger receptor, Drosophila SR-CI (dSR-CI). dSR-CI expression was restricted to macrophages/hemocytes during embryonic development. When expressed in mammalian cells, dSR-CI exhibited high affinity and saturable binding of 125I-labeled acetylated low density lipoprotein and mediated its chloroquine-dependent, presumably lysosomal, degradation. Although the broad polyanionic ligand-binding specificity of dSR-CI was similar to that of SR-A, their predicted protein sequences are not similar. dSR-CI is a 609-residue type I integral membrane protein containing several well-known sequence motifs, including two complement control protein (CCP) domains and somatomedin B, MAM, and mucin-like domains. Macrophage scavenger receptors apparently mediate important, well-conserved functions and may be pattern-recognition receptors that arose early in the evolution of host-defense mechanisms. Genetic and physiologic analysis of dSR-CI function in Drosophila should provide further insights into the roles played by scavenger receptors in host defense and development.

A methodology for improving computed individual regressions predictions.

This research proposes a methodology to improve computed individual prediction values provided by an existing regression model without having to change either its parameters or its architecture. In other words, we are interested in achieving more accurate results by adjusting the calculated regression prediction values, without modifying or rebuilding the original regression model. Our proposition is to adjust the regression prediction values using individual reliability estimates that indicate if a single regression prediction is likely to produce an error considered critical by the user of the regression. The proposed method was tested in three sets of experiments using three different types of data. The first set of experiments worked with synthetically produced data, the second with cross sectional data from the public data source UCI Machine Learning Repository and the third with time series data from ISO-NE (Independent System Operator in New England). The experiments with synthetic data were performed to verify how the method behaves in controlled situations. In this case, the outcomes of the experiments produced superior results with respect to predictions improvement for artificially produced cleaner datasets with progressive worsening with the addition of increased random elements. The experiments with real data extracted from UCI and ISO-NE were done to investigate the applicability of the methodology in the real world. The proposed method was able to improve regression prediction values by about 95% of the experiments with real data.

Aplicação de técnicas inteligentes com análise no domínio do tempo para reconhecimento de defeitos em motores de indução trifásicos

Os motores de indução trifásicos são os principais elementos de conversão de energia elétrica em mecânica motriz aplicados em vários setores produtivos. Identificar um defeito no motor em operação pode fornecer, antes que ele falhe, maior segurança no processo de tomada de decisão sobre a manutenção da máquina, redução de custos e aumento de disponibilidade. Nesta tese são apresentas inicialmente uma revisão bibliográfica e a metodologia geral para a reprodução dos defeitos nos motores e a aplicação da técnica de discretização dos sinais de correntes e tensões no domínio do tempo. É também desenvolvido um estudo comparativo entre métodos de classificação de padrões para a identificação de defeitos nestas máquinas, tais como: Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Rede Neural Artificial (Perceptron Multicamadas), Repeated Incremental Pruning to Produce Error Reduction e C4.5 Decision Tree. Também aplicou-se o conceito de Sistemas Multiagentes (SMA) para suportar a utilização de múltiplos métodos concorrentes de forma distribuída para reconhecimento de padrões de defeitos em rolamentos defeituosos, quebras nas barras da gaiola de esquilo do rotor e curto-circuito entre as bobinas do enrolamento do estator de motores de indução trifásicos. Complementarmente, algumas estratégias para a definição da severidade dos defeitos supracitados em motores foram exploradas, fazendo inclusive uma averiguação da influência do desequilíbrio de tensão na alimentação da máquina para a determinação destas anomalias. Os dados experimentais foram adquiridos por meio de uma bancada experimental em laboratório com motores de potência de 1 e 2 cv acionados diretamente na rede elétrica, operando em várias condições de desequilíbrio das tensões e variações da carga mecânica aplicada ao eixo do motor.

Metodologia para reconhecimento de perfil cognitivo: uma abordagem computacional pautada pela teoria das inteligências múltiplas.

A área da Tecnologia da Informação no Brasil sofre um problema latente com a falta de planejamento e atrasos constantes em projetos, determinando para os profissionais vinculados a ela um ambiente altamente desmotivador para a condução de seus trabalhos. Supõe-se que o que possa corroborar para tal problema seja a formação educacional deficitária dos indivíduos que atuam neste segmento, principalmente aqueles relacionados a cargos executivos e que estejam exercendo atividades de gestão. De acordo com teóricos como Edgard Morin (2004), em se tratando de educação fundamental, média ou superior os aspectos educacionais podem ser considerados deficitários justamente porque, ao segmentar o conhecimento, eles promovem uma alienação do indivíduo, eliminando sua capacidade criativa e reflexiva. Seria interessante, portanto, que ao avaliar a capacidade cognitiva de uma pessoa, a inteligência a ser mensurada não seja abordada através de um único espectro de conhecimento, mas através de muitos deles. A teoria das Inteligências Múltiplas, desenvolvida por Howard Gardner vem de encontro a essa necessidade, pois de acordo com o autor, a inteligência de um indivíduo deve ser mensurada através de uma gama de nove espectros: Linguística, Musical, Lógico-Matemática, Espacial, Corporal Cinestésica, Interpessoal, Intrapessoal, Naturalista e Existencial. Isto posto, este trabalho aborda uma metodologia computacional para classificação e descoberta de padrões em indivíduos, sejam esses alunos ou profissionais graduados, de uma determinada área. Além da metodologia, foi proposto um estudo de caso, considerando cursos superiores relacionados à área de Computação no Brasil.

Deformable templates for tracking and analysis of intravascular ultrasound sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

Constellations and the unsupervised learning of graphs

In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of fast matching with graph transformations. Our experiments, both with random graphs and in realistic situations (visual localization), show that our prototypes improve the set median graphs and also the prototypes derived from our previous incremental method. We also discuss how the method scales with a growing number of images.

Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

Representation of 2D objects with a topology preserving network

Comunicación presentada en el 2nd International Workshop on Pattern Recognition in Information Systems, Alicante, April, 2002.