Genetic redundancy among durum wheat accessions as assessed with SSRs and endosperm proteins

Reducing duplication in ex-situ collections is complicated and requires good quality genetic markers. This study was conducted to assess the value of endosperm proteins and SSRs for validation of potential duplicates and monitoring intra-accession variability. Fifty durum wheat (Triticum turgidum ssp. durum) accessions grouped in 23 potential duplicates, and previously characterised for 30 agro-morphological traits, were analysed for gliadin and high molecular weight glutenin (HMWG) subunit alleles, total protein, and 24 SSRs, covering a wide genome area. Similarity and dissimilarity matrices were generated based on protein and SSRs alleles. For heterogeneous accessions at gliadins the percent pattern homology (PH) between gliadin patterns and the Nei’s coefficient of genetic identity (I) were computed. Eighteen duplicates identical for proteins showed none or less than 3 unshared SSRs alleles. For heterogeneous accessions PH and I values lower than 80 identified clearly off-types with more than 3 SSRs unshared. Only those biotypes differing in no more than one protein-coding locus were confirmed with SSRs. A good concordance among proteins, morphological traits, and SSR were detected. However, the discrepancy in similarity detected in some cases showed that it is advisable to evaluate redundancy through distinct approaches. The analysis in proteins together with SSRs data are very useful to identify duplicates, biotypes, close related genotypes, and contaminations

Sharing, freeness, linearity, redundancy, widenings, and cliques

We discuss here different variants of the Sharing abstract domain, including the base domain that captures set-sharing, a variant to capture pairsharing, in which redundant sharing groups (w.r.t. the pair-sharing property) can be eliminated, and an alternative representation based on cliques. The original proposal for using cliques in the non-redundant version of the domain is reviewed, then extended to the base domain. Variants of all the domains including freeness alone, and freeness together with linearity are also studied.

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.

Desing and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications—it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ‘Activity Monitor’ has been designed and implemented: a personal health-persuasive application that provides feedback on the user’s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user’s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.d

Dealing with Semantic Heterogeneity Issues on the Web

The Semantic Web is an extension of the traditional Web in which meaning of information is well defined, thus allowing a better interaction between people and computers. To accomplish its goals, mechanisms are required to make explicit the semantics of Web resources, to be automatically processed by software agents (this semantics being described by means of online ontologies). Nevertheless, issues arise caused by the semantic heterogeneity that naturally happens on the Web, namely redundancy and ambiguity. For tackling these issues, we present an approach to discover and represent, in a non-redundant way, the intended meaning of words in Web applications, while taking into account the (often unstructured) context in which they appear. To that end, we have developed novel ontology matching, clustering, and disambiguation techniques. Our work is intended to help bridge the gap between syntax and semantics for the Semantic Web construction

A semantic scheduler architecture for federated hybrid clouds

Cloud computing is one the most relevant computing paradigms available nowadays. Its adoption has increased during last years due to the large investment and research from business enterprises and academia institutions. Among all the services cloud providers usually offer, Infrastructure as a Service has reached its momentum for solving HPC problems in a more dynamic way without the need of expensive investments. The integration of a large number of providers is a major goal as it enables the improvement of the quality of the selected resources in terms of pricing, speed, redundancy, etc. In this paper, we propose a system architecture, based on semantic solutions, to build an interoperable scheduler for federated clouds that works with several IaaS (Infrastructure as a Service) providers in a uniform way. Based on this architecture we implement a proof-of-concept prototype and test it with two different cloud solutions to provide some experimental results about the viability of our approach.

Semantic Heterogeneity Issues on the Web

The Semantic Web is an extension of the traditional Web in which meaning of information is well defined, thus allowing a better interaction between people and computers. To accomplish its goals, mechanisms are required to make explicit the semantics of Web resources, to be automatically processed by software agents (this semantics being described by means of online ontologies). Nevertheless, issues arise caused by the semantic heterogeneity that naturally happens on the Web, namely redundancy and ambiguity. For tackling these issues, we present an approach to discover and represent, in a non-redundant way, the intended meaning of words in Web applications, while taking into account the (often unstructured) context in which they appear. To that end, we have developed novel ontology matching, clustering, and disambiguation techniques. Our work is intended to help bridge the gap between syntax and semantics for the Semantic Web construction.

3D + t Morphological Processing: Applications to Embryogenesis Image Analysis

We propose to directly process 3D + t image sequences with mathematical morphology operators, using a new classi?cation of the 3D+t structuring elements. Several methods (?ltering, tracking, segmentation) dedicated to the analysis of 3D + t datasets of zebra?sh embryogenesis are introduced and validated through a synthetic dataset. Then, we illustrate the application of these methods to the analysis of datasets of zebra?sh early development acquired with various microscopy techniques. This processing paradigm produces spatio-temporal coherent results as it bene?ts from the intrinsic redundancy of the temporal dimension, and minimizes the needs for human intervention in semi-automatic algorithms.

Design and Validation of a Light Inference System to Support Embedded Context Reasoning

Embedded context management in resource-constrained devices (e.g. mobile phones, autonomous sensors or smart objects) imposes special requirements in terms of lightness for data modelling and reasoning. In this paper, we explore the state-of-the-art on data representation and reasoning tools for embedded mobile reasoning and propose a light inference system (LIS) aiming at simplifying embedded inference processes offering a set of functionalities to avoid redundancy in context management operations. The system is part of a service-oriented mobile software framework, conceived to facilitate the creation of context-aware applications?it decouples sensor data acquisition and context processing from the application logic. LIS, composed of several modules, encapsulates existing lightweight tools for ontology data management and rule-based reasoning, and it is ready to run on Java-enabled handheld devices. Data management and reasoning processes are designed to handle a general ontology that enables communication among framework components. Both the applications running on top of the framework and the framework components themselves can configure the rule and query sets in order to retrieve the information they need from LIS. In order to test LIS features in a real application scenario, an ?Activity Monitor? has been designed and implemented: a personal health-persuasive application that provides feedback on the user?s lifestyle, combining data from physical and virtual sensors. In this case of use, LIS is used to timely evaluate the user?s activity level, to decide on the convenience of triggering notifications and to determine the best interface or channel to deliver these context-aware alerts.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

Scalable Raid Storage Based on the Structure of Multimedia File.

In professional video production, users have to access to huge multimedia files simultaneously in an error-free environment, this restriction force the use of expensive disk architectures for video servers. Previous researches proposed different RAID systems for each specific task (ingest, editing, file, play-out, etc.). Video production companies have to acquire different servers with different RAIDs systems in order to support each task in the production workflow. The solution has multiples disadvantages, duplicated material in several RAIDs, duplicated material for different qualities, transfer and transcoding processes, etc. In this work, an architecture for video servers based on the spreading of JPEG200 data in different RAIDs is presented, each individual part of the data structure goes to a specific RAID type depending on the effect that produces the data on the overall image quality, the method provide a redundancy correlated with the data rank. The global storage can be used in all the different tasks of the production workflow saving disk space, redundant files and transfers procedures.

The NeOn Ontology Models

Interoperability on multiple levels, concerning both the ontologies themselves and their engineering activities, is a key requirement for ontology networks to be efficient, with minimal redundancy and high reuse. This requirement has a strict binding for software tools that can support some interoperability levels, yet they can be hindered by a lack of shared models and vocabularies describing the resources to be handled, as well as the ways of handling them. Here, three examples of metalevel vocabularies are proposed, each covering at least one peculiar interoperability aspect: OMV for modeling the artifacts themselves, LIR for managing a multilingual layer on top of them, and C-ODO Light for modeling collaboration-supportive life cycle management tasks and processes. All of these models lend themselves to handling by dedicated software tools and are all being employed within NeOn products.

Fault isolation schemes for a class of continuous-time stochastic dynamical systems

In this paper a new method for fault isolation in a class of continuous-time stochastic dynamical systems is proposed. The method is framed in the context of model-based analytical redundancy, consisting in the generation of a residual signal by means of a diagnostic observer, for its posterior analysis. Once a fault has been detected, and assuming some basic a priori knowledge about the set of possible failures in the plant, the isolation task is then formulated as a type of on-line statistical classification problem. The proposed isolation scheme employs in parallel different hypotheses tests on a statistic of the residual signal, one test for each possible fault. This isolation method is characterized by deriving for the unidimensional case, a sufficient isolability condition as well as an upperbound of the probability of missed isolation. Simulation examples illustrate the applicability of the proposed scheme.

Hardening digital systems with distributed functionality: robust networks

Collaborative hardening and hardware redundancy are nowadays the most interesting solutions in terms of fault tolerance achieved and low extra cost imposed to the project budget. Thanks to the powerful and cheap digital devices that are available in the market, extra processing capabilities can be used for redundant tasks, not only in early data processing (sensed data) but also in routing and interfacing1

Reproducing the Bolund experiment in wind tunnel

The Bolund experiment has been reproduced in a neutral boundary layer wind tunnel (WT) at scale 1:115 for two Reynolds numbers. All the results have been obtained for an incoming flow from the 270o wind direction (transect B in the Bolund experiment jargon). Vertical scans of the velocity field are obtained using non-time resolved two components particle image velocimetry. Time-resolved velocity time series with a three component hot-wire probe have been also measured for transects at 2 and 5 m height and in the vertical transects at met masts M6, M3 and M8 locations. Special attention has been devoted to the detailed characterization of the inflow in order to reduce uncertainties in future comparisons with other physical and numerical simulations. Emphasis is placed on the analysis of spectral functions of the undisturbed flow and those of the flow above the island. The result?s reproducibility and trustworthiness have been addressed through redundancy measurements using particle image velocimetry, two and three components hot-wire anemometry. The bias in the prediction of the mean speed is similar to the one reported during the Bolund experiment by the physical modellers. However, certain reduction of the bias in the estimation of the turbulent kinetic energy is achieved. TheWT results of spectra and cosprectra have revealed a behaviour similar to the full-scale measurements in some relevant locations, showing that WT modelling can contribute to provide valid information about these important structural loading factors.