Variation of the chemical composition of tomato cultivars (Lycopersicon esculentum Mill.) according to resistance against the tomato yellow leaf curl virus (TYLCV)

BACKGROUND: Tomato producers are looking for alternatives to protect their crops against the tomato yellow leaf curl virus (TYLCV), including replacement of cultivars with other more resistant ones. Major chemical components, sugars, mineral composition, organic acids, lycopene, total phenols and hydroxycinnamic acids were determined in six tomato cultivars, three of them (Boludo, Dorothy and Tyna) resistant, and the other three (Daniela, Dominique and Thomas), non‐resistant against TYLCV. RESULTS: The Daniela cultivar showed the greatest difference with respect to the others, mostly due to the higher content of soluble solids. The major significant differences between the mean values according to the cultivar and resistance against the TYLCV were observed for total soluble solids, pH, ascorbic acid, total phenols and hydroxycinnamic acids. High coefficients of correlation were found between glucose and fructose, and among ferulic, caffeic and p‐coumaric acids. CONCLUSION: Linear discriminant analysis made it possible to differentiate tomato samples according to the cultivar. A clear varietal influence on the chemical composition was observed; however, resistance or non‐resistance against TYLCV does not seem to influence the chemical composition. Copyright © 2008 Society of Chemical Industry     

Quo vadis source tracking? Towards a strategic framework for environmental monitoring of fecal pollution

Advances in microbial source tracking (MST) have largely been driven by the need to comply with water quality standards based on traditional indicator bacteria. Recently, a number of culture-independent, and library-independent methods based on polymerase chain reaction (PCR) have been gaining popularity among source trackers. However, only a limited number of these methods have been successfully used in field applications, primarily due to the fact that many of them are still being developed. In this critical outlook, we examine different viewpoints associated with the practical use of MST to identify critical research gaps, propose a priority-based timeline to address them, and outline emerging technologies that will likely impact the future of source tracking. We propose that it is necessary to consider each of these aspects in order to advance towards a unifying framework in source identification, so that fecal pollution monitoring can be reliably used for comprehensive environmental microbial monitoring, to develop risk assessment models, and to implement and validate adequate management practices.     

Identification of chicken-specific fecal microbial sequences using a metagenomic approach

In this study, we applied a genome fragment enrichment (GFE) method to select for genomic regions that differ among different fecal metagenomes. Competitive DNA hybridizations were performed between chicken fecal DNA and pig fecal DNA (CP) and between chicken fecal DNA and an avian DNA composite consisting of turkey, goose, and seagull fecal DNA extracts (CB) to enrich for chicken-specific DNA fragments. A total of 471 non-redundant chicken metagenomic sequences were retrieved and analyzed. All of the clone sequences were similar to prokaryotic genes, of which more than 60% could not be assigned to previously characterized functional roles. In general terms, sequences assigned characterized functional roles were associated with cellular processes (11.7%), metabolism (11.0%) and information storage and processing (13.4%). Approximately 53% of the non-redundant sequences are similar to genes present in intestinal bacteria belonging to Clostridia (20.9%), Bacteroidetes (15.0%), and Bacilli (17.3%). Twenty-five sequences from the CP and CB clone libraries were selected to develop chicken fecal-specific PCR assays. These assays were challenged against fecal DNA extracted from 21 different animal species, including mammals and birds. The results from the host-specificity studies showed that 12 of the assays had a high degree of specificity to chicken feces. In addition, three assays were specific to chicken and turkey while another four assays tested positive to more than two avian species, suggesting a broader distribution of some of the enriched gene fragments among different avian fecal microbial communities. Fecal pollution signals were detected using chicken-specific assays in contaminated water samples, although the PCR assays showed different detection limits. These results indicate the need for multiple assays to detect poultry fecal sources of pollution. The competitive DNA hybridization approach used in this study can rapidly select for numerous chicken fecal metagenomic regions that can be used as potential genetic markers for fecal source tracking.     

New smooth test statistics of goodness-of-fit for categorized composite null hypotheses

Testing for goodness-of-fit is possible in a parametric setup by using smooth test statistics. This approach relies on embedding the probability model to be selected, in a larger parametric model. This reduces the problem of testing goodness-of-fit to the simpler problem of testing whether a parameter is zero. In this paper, new classes of smooth test statistics are introduced for categorized data and their asymptotic distributions are obtained under composite null hypotheses. Supported by the grants DGES PB-96 0635 and GV99-159-1-01     

A Matrix Formulation of Discrete Chirp Fourier Transform Algorithms

This work presents a computational matrix framework in terms of tensor signal algebra for the formulation of discrete chirp Fourier transform algorithms.These algorithms are used in this work to estimate the point target functions(impulse response functions)of multiple-input multiple-output(MIMO)synthetic aperture radar(SAR)systems.This estimation technique is being studied as an alternative to the estimation of point target functions using the discrete cross-ambiguity function for certain types of environmental surveillance applications.The tensor signal algebra is presented as a mathematics environment composed of signal spaces,finite dimensional linear operators,and special matrices where algebraic methods are used to generate these signal transforms as computational estimators.Also,the tensor signal algebra contributes to analysis,design,and implementation of parallel algorithms.An instantiation of the framework was performed by using the MATLAB Parallel Computing Toolbox,where all the algorithms presented in this paper were implemented.     

A Distributed Energy-Aware Routing Protocol for Underwater Wireless Sensor Networks

The design of routing protocols for Underwater Wireless Sensor Networks (UWSNs) poses many challenges due to the intrinsic properties of underwater environments. In this paper we present DUCS (Distributed Underwater Clustering Scheme), a new GPS-free routing protocol that does not use flooding techniques, minimizes the proactive routing message exchange and uses data aggregation to eliminate redundant information. Besides, DUCS assumes random node mobility and compensates the high propagation delays of the underwater medium using a continually adjusted timing advance combined with guard time values to minimize data loss. The theoretical and simulation studies carried out demonstrate its effectiveness.     

Operational flexibility quantification in a make-to-order assembly system

Manufacturing flexibility is becoming a fundamental production objective, along with cost, quality, and delivery time. Current production systems face quick changes in market conditions and they need to adapt in this environment. The supply chain and industrial globalization give an important role for assembly systems. Placed at the end of the value chain, assembly systems must face those quick changes successfully to reach the expected performance. The key performance indicators are normally based on cost, quality, and delivery time objectives. Reducing costs and improving quality are almost universal goals. Delivery time is typically determined by customer demand in the supply chain, planning from make-to-stock to make-to-order, and aspiring to reach a just-in-time manufacturing system. In this context, flexibility could be the differential advantage to tackle uncertainty. Closely related to the rest of production objectives and the overall performance of the system, flexibility must be integrated in the system for successful decision-making in operations. This work presents this approach of flexibility. A brief review of flexibility concepts and measurements in the literature precedes an introduction to flexibility, defined based on the function of utility. This function represents the expectations of system performance. This approach allows the formulation of the taxonomy of operational flexibility in agreement with the classical types identified in former works. Next, an integer model is programmed to simulate the basic behavior of task planning in a make-to-order assembly system. This first application illustrates flexibility quantification based on utility evolution. The use of common industrial parameters to quantify operational flexibility will finally facilitate an integrated interpretation of system performance trends.     

Determination of metals in wine with atomic spectroscopy (flame-AAS, GF-AAS and ICP-AES); a review

Metals in wine occur at the mg l 1 level or less and, though not directly related to the taste of the final product, their content should be determined because excess is undesirable, and in some cases prohibited, due to potential toxicity. Lead content in wine, for example, is restricted in several states by legislation to guarantee consumer health protection. Of several methods for metal determination, techniques of atomic spectroscopy are the most sensitive and rapid. Most of the elements present in wine can be determined with these techniques, at concentrations ranging from the mg l -1 to the μg l -1 level. Here, inductively coupled plasma-atomic emission spectrometry (ICP-AES), flame atomic absorption spectrometry (flame-AAS) and graphite furnace-atomic absorption spectrometry (GF-AAS) are compared for their characteristics as employed in metal determination in wine.