Comparison of the defensive elicitation activity of cerato-populin and cerato-platanin: a structural model

Plants can defend themselves from potential pathogenic microorganisms relying on a complex interplay of signaling pathways: activation of the MAPK cascade, transcription of defense related genes, production of reactive oxygen species, nitric oxide and synthesis of other defensive compounds such as phytoalexins. These events are triggered by the recognition of pathogen’s effectors (effector-triggered immunity) or PAMPs (PAMP-triggered immunity). The Cerato Platanin Family (CPF) members are Cys-rich proteins secreted and localized on fungal cell walls, involved in several aspects of fungal development and pathogen-host interactions. Although more than hundred genes of the CPF have been identified and analyzed, the structural and functional characterization of the expressed proteins has been restricted only to few members of the family. Interestingly, those proteins have been shown to bind chitin with diverse affinity and after foliar treatment they elicit defensive mechanisms in host and non-host plants. This property turns cerato platanins into interesting candidates, worth to be studied to develop new fungal elicitors with applications in sustainable agriculture. This study focus on cerato-platanin (CP), core member of the family and on the orthologous cerato-populin (Pop1). The latter shows an identity of 62% and an overall homology of 73% with respect to CP. Both proteins are able to induce MAPKs phosphorylation, production of reactive oxygen species and nitric oxide, overexpression of defense’s related genes, programmed cell death and synthesis of phytoalexins. CP, however, when compared to Pop1, induces a faster response and, in some cases, a stronger activity on plane leaves. Aim of the present research is to verify if the dissimilarities observed in the defense elicitation activity of these proteins can be associated to their structural and dynamic features. Taking advantage of the available CP NMR structure, Pop1’s 3D one was obtained by homology modeling. Experimental residual dipolar couplings and 1H, 15N, 13C resonance assignments were used to validate the model. Previous works on CPF members, addressed the highly conserved random coil regions (loops b1-b2 and b2-b3) as sufficient and necessary to induce necrosis in plants’ leaves: that region was investigated in both Pop1 and CP. In the two proteins the loops differ, in their primary sequence, for few mutations and an insertion with a consequent diversification of the proteins’ electrostatic surface. A set of 2D and 3D NMR experiments was performed to characterize both the spatial arrangement and the dynamic features of the loops. NOE data revealed a more extended network of interactions between the loops in Pop1 than in CP. In addition, in Pop1 we identified a salt bridge Lys25/Asp52 and a strong hydrophobic interaction between Phe26/Trp53. These structural features were expected not only to affect the loops’ spatial arrangement, but also to reduce the degree of their conformational freedom. Relaxation data and the order parameter S2 indeed highlighted reduced flexibility, in particular for loop b1-b2 of Pop1. In vitro NMR experiments, where Pop1 and CP were titrated with oligosaccharides, supported the hypothesis that the loops structural and dynamic differences may be responsible for the different chitin-binding properties of the two proteins: CP selectively binds tetramers of chitin in a shallow groove on one side of the barrel defined by loops b1-b2, b2-b3 and b4-b5, Pop1, instead, interacts in a non-specific fashion with oligosaccharides. Because the region involved in chitin-binding is also responsible for the defense elicitation activity, possibly being recognized by plant's receptors, it is reasonable to expect that those structural and dynamic modifications may also justify the different extent of defense elicitation. To test that hypothesis, the initial steps of a protocol aimed to the identify a receptor for CP, in silico, are presented.

Mapeamento associativo para múltiplos ambientes e múltiplos locos, visando tolerância à seca em milho

A seca é um dos estresses abióticos mais importantes na cultura do milho, o qual ocasiona reduções significativas na produção de grãos. A arquitetura genética da tolerância à seca é complexa, fazendo-se necessária a melhor compreensão desse caráter. Estudos envolvendo mapeamento associativo são úteis por explorarem a variação genética de caracteres quantitativos e, adicionalmente, levam em conta informações acerca de genótipos, ambientes e interações genótipo por ambiente (G × E). Ao considerar efeitos de G × E em modelos de mapeamento associativo há possibilidade de identificar regiões no genoma associadas à condições e ambientes específicos. Este trabalho teve como objetivo detectar associações relacionadas à tolerância à seca em milho por meio de um modelo de mapeamento associativo para múltiplos ambientes e múltiplos locos, o qual permitiu distinguir associações com efeitos ambiente-específico daquelas com efeitos principais e de interação associação por ambiente (QEI). O painel associativo foi composto por 190 linhagens, classificadas de acordo com os grupos heteróticos quanto ao tipo de grão. Marcadores SNPs (∼500k) foram utilizados para a genotipagem do painel associativo. Duas linhagens (L228-3 e L3) foram usadas como testadores comuns e os híbridos obtidos foram avaliados em duas localidades (Janaúba-MG e Teresina-PI), dois anos agrícolas (2010 e 2011), sob duas condições de tratamento (irrigado e não irrigado). Ao total, consideraram-se seis caracteres: peso de grãos, intervalo de florescimento, florescimento feminino e masculino, altura de planta e de espiga. Consideraram-se dois grupos de mapeamento, agrupados de acordo com os testadores utilizados. SNPs foram úteis para testar associações ao longo do genoma do milho e investigar o relacionamento genético entre indivíduos. O modelo de mapeamento associativo, com inclusão de informações sobre interação G × E, detectou o total de 179 associações, e o maior número de associações foram relacionadas aos caracteres de florescimento. A maioria das associações (168) apresentaram QEI significativo, sendo que o tamanho e a magnitude desses efeitos distinguiram-se de acordo com o ambiente em avaliação. Apenas o caráter florescimento feminino não apresentou associações com efeitos estáveis ao longo dos ambientes em estudo. A detecção de algumas associações em posições próximas do genoma evidenciam possíveis efeitos de pleiotropia. Algumas associações foram co-localizadas em regiões do genoma do milho relacionadas à tolerância à seca, sendo que algumas dessas associações estavam envolvidas a fatores pertencentes à vias metabólicas de interesse. O presente estudo forneceu informações úteis para a compreensão da base genética da tolerância à seca em milho sob os ambientes específicos em avaliação.

Architecture of a Bench-Type Carbonate Lake Margin and Its Relation to Fluvially Dominated Deltas, Las Minas Basin, Upper Miocene, Spain

The Upper Miocene stratigraphic succession of the Las Minas Basin, located at the external zone of the Betic Chain in SE Spain, preserves several examples of lake carbonate bench deposits. Excellent exposures of the carbonate benches allow detailed observation of the architecture of these sediments and provide new insights for the ‘‘steep-gradient bench margin–low energy’’ model proposed by Platt and Wright (1991). The lake carbonate benches developed in close association with fluvially dominated shallow deltas that exhibit typical Gilbert-type profiles. The delta sequences comprise bottomset prodelta marl facies, distal to proximal foreset facies, deposited mainly in a delta-front environment, and topset facies, the latter reflecting both subaqueous delta-front and subaerial delta-plain environments. The development of the carbonate benches was constrained by the convexupward morphology of the deltaic deposits, which led to the available accommodation space for the growth of the steep-gradient platforms. The benches display a progradational pattern characterized by sigmoid-oblique internal geometries and offlap upper boundary relationships, which suggests that the carbonate benches developed under slow though continuous lake-level rise. Both the dimensions of the benches and the dominant carbonate components (i.e., encrusted charophyte stems and calcified cyanobaterial remains), allow comparisons with the progradational marl benches recognized in modern temperate hardwater lakes. Accordingly, the case study presented here provides a good ancient sedimentary analog for low-energy lake carbonate benches. Moreover, the evolutionary trend inferred from the fossil example offers new insights into the depositional conditions of this type of sediment and allows recognition of the transitional pattern from bench to ramp carbonate lake margins.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Fénix: a flexible information exchange data model for natural language processing

In this paper we describe Fénix, a data model for exchanging information between Natural Language Processing applications. The format proposed is intended to be flexible enough to cover both current and future data structures employed in the field of Computational Linguistics. The Fénix architecture is divided into four separate layers: conceptual, logical, persistence and physical. This division provides a simple interface to abstract the users from low-level implementation details, such as programming languages and data storage employed, allowing them to focus in the concepts and processes to be modelled. The Fénix architecture is accompanied by a set of programming libraries to facilitate the access and manipulation of the structures created in this framework. We will also show how this architecture has been already successfully applied in different research projects.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Feedbacks between vegetation pattern and resource loss dramatically decrease ecosystem resilience and restoration potential in a simple dryland model

Conceptual frameworks of dryland degradation commonly include ecohydrological feedbacks between landscape spatial organization and resource loss, so that decreasing cover and size of vegetation patches result in higher water and soil losses, which lead to further vegetation loss. However, the impacts of these feedbacks on dryland dynamics in response to external stress have barely been tested. Using a spatially-explicit model, we represented feedbacks between vegetation pattern and landscape resource loss by establishing a negative dependence of plant establishment on the connectivity of runoff-source areas (e.g., bare soils). We assessed the impact of various feedback strengths on the response of dryland ecosystems to changing external conditions. In general, for a given external pressure, these connectivity-mediated feedbacks decrease vegetation cover at equilibrium, which indicates a decrease in ecosystem resistance. Along a gradient of gradual increase of environmental pressure (e.g., aridity), the connectivity-mediated feedbacks decrease the amount of pressure required to cause a critical shift to a degraded state (ecosystem resilience). If environmental conditions improve, these feedbacks increase the pressure release needed to achieve the ecosystem recovery (restoration potential). The impact of these feedbacks on dryland response to external stress is markedly non-linear, which relies on the non-linear negative relationship between bare-soil connectivity and vegetation cover. Modelling studies on dryland vegetation dynamics not accounting for the connectivity-mediated feedbacks studied here may overestimate the resistance, resilience and restoration potential of drylands in response to environmental and human pressures. Our results also suggest that changes in vegetation pattern and associated hydrological connectivity may be more informative early-warning indicators of dryland degradation than changes in vegetation cover.

SOA-Based Model for Value-Added ITS Services Delivery

Integration is currently a key factor in intelligent transportation systems (ITS), especially because of the ever increasing service demands originating from the ITS industry and ITS users. The current ITS landscape is made up of multiple technologies that are tightly coupled, and its interoperability is extremely low, which limits ITS services generation. Given this fact, novel information technologies (IT) based on the service-oriented architecture (SOA) paradigm have begun to introduce new ways to address this problem. The SOA paradigm allows the construction of loosely coupled distributed systems that can help to integrate the heterogeneous systems that are part of ITS. In this paper, we focus on developing an SOA-based model for integrating information technologies (IT) into ITS to achieve ITS service delivery. To develop our model, the ITS technologies and services involved were identified, catalogued, and decoupled. In doing so, we applied our SOA-based model to integrate all of the ITS technologies and services, ranging from the lowest-level technical components, such as roadside unit as a service (RS S), to the most abstract ITS services that will be offered to ITS users (value-added services). To validate our model, a functionality case study that included all of the components of our model was designed.

An Electronic Reconfigurable Neural Architecture for Intrusion Detection

The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.

Large scale optimization of a sour water stripping plant using surrogate models

In this work, we propose a new methodology for the large scale optimization and process integration of complex chemical processes that have been simulated using modular chemical process simulators. Units with significant numerical noise or large CPU times are substituted by surrogate models based on Kriging interpolation. Using a degree of freedom analysis, some of those units can be aggregated into a single unit to reduce the complexity of the resulting model. As a result, we solve a hybrid simulation-optimization model formed by units in the original flowsheet, Kriging models, and explicit equations. We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA). The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.

Patterns of Stem Cell Divisions Contribute to Plant Longevity

The lifespan of plants ranges from a few weeks in annuals to thousands of years in trees. It is hard to explain such extreme longevity considering that DNA replication errors inevitably cause mutations. Without purging through meiotic recombination, the accumulation of somatic mutations will eventually result in mutational meltdown, a phenomenon known as Muller’s ratchet. Nevertheless, the lifespan of trees is limited more often by incidental disease or structural damage than by genetic aging. The key determinants of tree architecture are the axillary meristems, which form in the axils of leaves and grow out to form branches. The number of branches is low in annual plants, but in perennial plants iterative branching can result in thousands of terminal branches. Here, we use stem cell ablation and quantitative cell-lineage analysis to show that axillary meristems are set aside early, analogous to the metazoan germline. While neighboring cells divide vigorously, axillary meristem precursors maintain a quiescent state, with only 7–9 cell divisions occurring between the apical and axillary meristem. During iterative branching, the number of branches increases exponentially, while the number of cell divisions increases linearly. Moreover, computational modeling shows that stem cell arrangement and positioning of axillary meristems distribute somatic mutations around the main shoot, preventing their fixation and maximizing genetic heterogeneity. These features slow down Muller’s ratchet and thereby extend lifespan.

Tropical climate and vegetation simulations during the Heinrich event 1 using an Earth System Model of Intermediate Complexity (EMIC) - the University of Victoria Earth System-Climate Model (UVic ESCM)

Abrupt climate changes from 18 to 15 thousand years before present (kyr BP) associated with Heinrich Event 1 (HE1) had a strong impact on vegetation patterns not only at high latitudes of the Northern Hemisphere, but also in the tropical regions around the Atlantic Ocean. To gain a better understanding of the linkage between high and low latitudes, we used the University of Victoria (UVic) Earth System-Climate Model (ESCM) with dynamical vegetation and land surface components to simulate four scenarios of climate-vegetation interaction: the pre-industrial era, the Last Glacial Maximum (LGM), and a Heinrich-like event with two different climate backgrounds (interglacial and glacial). We calculated mega-biomes from the plant-functional types (PFTs) generated by the model to allow for a direct comparison between model results and palynological vegetation reconstructions. Our calculated mega-biomes for the pre-industrial period and the LGM corresponded well with biome reconstructions of the modern and LGM time slices, respectively, except that our pre-industrial simulation predicted the dominance of grassland in southern Europe and our LGM simulation resulted in more forest cover in tropical and sub-tropical South America. The HE1-like simulation with a glacial climate background produced sea-surface temperature patterns and enhanced inter-hemispheric thermal gradients in accordance with the "bipolar seesaw" hypothesis. We found that the cooling of the Northern Hemisphere caused a southward shift of those PFTs that are indicative of an increased desertification and a retreat of broadleaf forests in West Africa and northern South America. The mega-biomes from our HE1 simulation agreed well with paleovegetation data from tropical Africa and northern South America. Thus, according to our model-data comparison, the reconstructed vegetation changes for the tropical regions around the Atlantic Ocean were physically consistent with the remote effects of a Heinrich event under a glacial climate background.