Alternative PCR–RFLP methods for mussel Mytilus species identification

Mussels are the most important bivalves in both European aquaculture and market. Together with mussels produced in EU, the European market also allows the entrance of several mytilid species from up to 16 non-EU countries. Therefore, traceability methodologies are needed for the protection of consumers’ rights and for reinforce labeling regulations. The aim of this study was to establish a methodology that allows the accurate identification of 6 different mussel species, as in fresh as in processed forms. Amplification of mussel samples with primers Myti-F/R, which anneal in a region of the adhesive foot protein gene, yielded a 190-bp fragment in M. edulis, a 178-bp in M. trossulus, a 202-bp in M. californianus/M. coruscus, and a 136-bp in M. galloprovincialis/M. chilensis. Hence, differentiation among M. edulis, M. trossulus, and the rest of Mytilus species was achieved in a first step. Further digestion with Acl I in M. coruscus/M. californianus samples and with Aci I in M. galloprovincialis/M. chilensis allowed the accurate identification of these species. A second methodology based on amplification by PCR of mitochondrial cytochrome oxidase subunit I (COI) gene and digestion with Xba I was developed to further confirmation of M. chilensis samples. The current methodology was applied in cultured and natural samples and in commercial forms. The protocol described in this study provides an accurate, reliable, and a powerful tool for mussel species identification, traceability, and for reinforcement of labeling regulations.     

Effective Kawakita parameters for binary mixtures

Despite the fact that many industrial processes use mixtures of powders with different physical and mechanical properties, most fundamental studies of powder compression have focused on single-component systems. There is thus an obvious need for an improved understanding of the compression behaviour of mixtures of particulate and granular solids. In this contribution, we show that the Kawakita equation may be particularly useful in this regard. The reason for this is that the degree of compression (i.e., the ratio between the volume reduction and the initial powder volume) for sufficiently high compression pressures P is a linear function of 1 / P. For ideal mixtures, for which the volume of each component may be determined independently of the others, this linearity implies that effective Kawakita parameters for the mixture may be readily expressed in terms of the parameters of its components. In order to validate the proposed approach, binary mixtures of mm-sized spherical agglomerates prepared by wet granulation followed by extrusion and spheronization were investigated experimentally. Predicted and measured Kawakita parameters were generally in good agreement, indicating that the assumption of ideal mixing behaviour is valid for this type of systems.     

Wideband modeling of arbitrarily shaped E-plane waveguidecomponents by the “boundary integral-resonant mode expansionmethod”

The boundary integral-resonant mode expansion method is used for the solution of the eigenvalue problem involved in the determination of the poles and the residues of the Y-parameters of arbitrarily shaped E-plane waveguide junctions. Using this method, the frequency response and its sensitivity to deformations of the boundary can be calculated much faster than by other more conventional methods for arbitrary shapes. Therefore, the described algorithm is eligible for setting up very efficient CAD tools to produce optimized designs of complex E-plane components in reasonable times. Some examples demonstrate the efficiency of the method in the modeling of components of practical interest     

Lipid Composition,Natural Antioxidants and Physicochemical Characteristics in Liver Oil from Rajiforms from the Gulf of Mexico

Lipid composition by class, fatty acids, natural antioxidants (carotenes, tocopherols) and physicochemical characteristics of liver oil from three commercial rays, Rhinoptera bonasus (Chucha), Aetobatus narinari (Pinta) and Dasyatis americana (Bala) from the Gulf of Mexico were analyzed. Liver oil yield for R. bonasus, A. narinari and D. americana were of 43.04, 41.2 and 38.2% (wet weight), respectively. Triacylglycerols were the most abundant lipid by class in R. bonasus (68.9%), A. narinari (85.9%) and D. americana (81.6%), while sterols esters, sterols, di- and monoacylglycerides, polar lipids and wax esters were found in minor proportions. Species showed similar carotenes concentration, 8.7, 12.8 and 8.0 μg/g for R. bonasus, A. narinari and D. americana, respectively. α-tocopherol concentration was higher (p < 0.05) for A. narinari (46.7 mg/100 g) than for R. bonasus (21.0 mg/100 g) and D. americana (13.7 mg/100 g). Omega-3 polyunsaturated fatty acids in R. bonasus were high with docosahexaenoic acid (12.1%) in a higher proportion than eicosapentaenoic acid (7%).     

On Approximating the Stationary Distribution of Time-Reversible Markov Chains

Approximating the stationary probability of a state in a Markov chain through Markov chain Monte Carlo techniques is, in general, inefficient. Standard random walk approaches require $\tilde {O}(\tau /\pi (v))$ operations to approximate the probability π(v) of a state v in a chain with mixing time τ, and even the best available techniques still have complexity $\tilde {O}(\tau ^{1.5}/\pi (v)^{0.5})$; and since these complexities depend inversely on π(v), they can grow beyond any bound in the size of the chain or in its mixing time. In this paper we show that, for time-reversible Markov chains, there exists a simple randomized approximation algorithm that breaks this “small-π(v) barrier”.     

Querying high-dimensional data in single-dimensional space

In this paper, we propose a new tunable index scheme, called iMinMax( ), that maps points in high-dimensional spaces to single-dimensional values determined by their maximum or minimum values among all dimensions. By varying the tuning knob, , we can obtain different families of iMinMax structures that are optimized for different distributions of data sets. The transformed data can then be indexed using existing single-dimensional indexing structures such as the B ⁺ -trees. Queries in the high-dimensional space have to be transformed into queries in the single-dimensional space and evaluated there. We present efficient algorithms for evaluating window queries as range queries on the single-dimensional space. We conducted an extensive performance study to evaluate the effectiveness of the proposed schemes. Our results show that iMinMax( ) outperforms existing techniques, including the Pyramid scheme and VA-file, by a wide margin. We then describe how iMinMax could be used in approximate K-nearest neighbor (KNN) search, and we present a comparative study against the recently proposed iDistance, a specialized KNN indexing method.Received: 21 May 2000, Revised: 14 March 2002, Published online: 8 April 2004Edited by: M. Kitsuregawa.     

Emotion regulation as a mediator of associations between mother–child attachment and peer relationships in middle childhood.

Although a link between attachment and peer relationships has been established, the mechanisms that account for this link have not been identified. The 1st goal of this study was to test emotion regulation as a mediator of this link in middle childhood. The 2nd goal was to examine how different aspects of emotion regulation relate to peer competence. Fifth graders completed self-report and semiprojective measures to index mother–child attachment, mothers reported on children's emotionality and coping strategies, and teachers reported on children's peer competence. Constructive coping was related to both attachment and peer competence, and mediated the association between attachment and peer competence, suggesting that emotion regulation is one of the mechanisms accounting for attachment-peer links. Constructive coping was more strongly associated with peer competence for children high on negative emotionality than for children low on negative emotionality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed–crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed–crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naïve Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed.     

Current approaches to XML management

The Extensible Markup Language has become the standard for information interchange on the Web. We study the data- and document-centric uses of XML management systems (XMLMS). We want to provide XML data users with a guideline for choosing the data management system that best meets their needs. Because the systems we test are first-generation approaches, we suggest a hypothetical design for a useful XML database that could use all the expressive power of XML and XML query languages.     

An implicit/explicit spectral method for Burgers' equation

Chebyshev spectral collocation methods for approximating the solution of Burgers' equation are defined and analyzed. Discretization in time by an implicit/explicit single step method is discussed. This method is shown to be stable under a very weak condition on the time step, for the (linear) diffusive part is dealt with implicitly. Besides, fast transform methods can be used to compute the explicit (non linear) convective term. Optimal order error estimates are established in the weighted L²-norm. The research of this author has been partially supported by the U.S. Army through its European Research Office under contract No. DAJA-84-C-0035.