Steps Ahead in the Few-Group CRoss-Section Library Generation at the Pin Level

There exists an interest in performing pin-by-pin calculations coupled with thermal hydraulics so as to improve the accuracy of nuclear reactor analysis. In the framework of the EU NURISP project, INRNE and UPM have generated an experimental version of a few group diffusion cross sections library with discontinuity factors intended for VVER analysis at the pin level with the COBAYA3 code. The transport code APOLLO2 was used to perform the branching calculations. As a first proof of principle the library was created for fresh fuel and covers almost the full parameter space of steady state and transient conditions. The main objective is to test the calculation schemes and post-processing procedures, including multi-pin branching calculations. Two library options are being studied: one based on linear table interpolation and another one using a functional fitting of the cross sections. The libraries generated with APOLLO2 have been tested with the pin-by-pin diffusion model in COBAYA3 including discontinuity factors; first comparing 2D results against the APOLLO2 reference solutions and afterwards using the libraries to compute a 3D assembly problem coupled with a simplified thermal-hydraulic model.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ?traditional? set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Distinct and conserved transcriptomic changes during nematode-induced giant cell development in tomato compared with Arabidopsis: a functional role for gene repression

Root-knot nematodes (RKNs) induce giant cells (GCs) from root vascular cells inside the galls. Accompanying molecular changes as a function of infection time and across different species, and their functional impact, are still poorly understood. Thus, the transcriptomes of tomato galls and laser capture microdissected (LCM) GCs over the course of parasitism were compared with those of Arabidopsis, and functional analysis of a repressed gene was performed. Microarray hybridization with RNA from galls and LCM GCs, infection-reproduction tests and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) transcriptional profiles in susceptible and resistant (Mi-1) lines were performed in tomato. Tomato GC-induced genes include some possibly contributing to the epigenetic control of GC identity. GC-repressed genes are conserved between tomato and Arabidopsis, notably those involved in lignin deposition. However, genes related to the regulation of gene expression diverge, suggesting that diverse transcriptional regulators mediate common responses leading to GC formation in different plant species. TPX1, a cell wall peroxidase specifically involved in lignification, was strongly repressed in GCs/galls, but induced in a nearly isogenic Mi-1 resistant line on nematode infection. TPX1 overexpression in susceptible plants hindered nematode reproduction and GC expansion. Time-course and cross-species comparisons of gall and GC transcriptomes provide novel insights pointing to the relevance of gene repression during RKN establishment.

Large-scale identification of gibberellin-related transcription factor defines group VII ERFs as functional of DELLA partners

DELLA proteins are the master negative regulators in gibberellin (GA) signaling acting in the nucleus as transcriptional regulators. The current view of DELLA action indicates that their activity relies on the physical interaction with transcription factors (TFs). Therefore, the identification of TFs through which DELLAs regulate GA responses is key to understanding these responses from a mechanistic point of view. Here, we have determined the TF interactome of the Arabidopsis (Arabidopsis thaliana) DELLA protein GIBBERELLIN INSENSITIVE and screened a collection of conditional TF overexpressors in search of those that alter GA sensitivity. As a result, we have found RELATED TO APETALA2.3, an ethylene-induced TF belonging to the group VII ETHYLENE RESPONSE FACTOR of the APETALA2/ethylene responsive element binding protein superfamily, as a DELLA interactor with physiological relevance in the context of apical hook development. The combination of transactivation assays and chromatin immunoprecipitation indicates that the interaction with GIBBERELLIN INSENSITIVE impairs the activity of RELATED TO APETALA2.3 on the target promoters. This mechanism represents a unique node in the cross regulation between the GA and ethylene signaling pathways controlling differential growth during apical hook development.

Research challenges for cross-cloud application

Federated clouds can expose the Internet as a homogeneous compute fabric. There is an opportunity for developing cross-cloud applications that can be deployed pervasively over the Internet, dynamically adapting their internal topology to their needs. In this paper we explore the main challenges for fully realizing the potential of cross-cloud applications. First, we focus on the networking dimension of these applications. We evaluate what support is needed from the infrastructure, and what are the further implications of opening the networking side. On a second part, we examine the impact of a distributed deployment for applications, assessing the implications from a management perspective, and how it affects the delivery of quality of service and non-functional requirements.

Efficient chemo-enzymatic gluten detoxification: reducing toxic epitopes for celiac patients improving functional properties

Protein engineering of gluten, the exogenous effector in celiac disease, seeking its detoxification by selective chemical modification of toxic epitopes is a very attractive strategy and promising technology when compared to pharmacological treatment or genetic engineering of wheat. Here we present a simple and efficient chemo-enzymatic methodology that decreases celiac disease toxic epitopes of gluten proteins improving its technological value through microbial transglutaminase-mediated transamidation of glutamine with n-butylamine under reducing conditions. First, we found that using low concentrations of amine-nucleophile under non-reducing conditions, the decrease in toxic epitopes is mainly due to transglutaminase-mediated cross-linking. Second, using high amine nucleophile concentrations protein cross-linking is substantially reduced. Third, reducing conditions increase 7-fold the transamidation reaction further decreasing toxic epitopes amount. Fourth, using n-butylamine improves gluten hydrophobicity that strengthens the gluten network. These results open the possibility of tailoring gluten for producing hypoallergenic flours while still taking advantage of the unique viscoelastic properties of gluten.