203 research outputs found
Detection of genetically modified plant products by protein strip testing: an evaluation of real-life samples
The determination of the presence of genetically modified plant material by the detection of expressed genetically engineered proteins using lateral flow protein strip tests has been evaluated in different matrices. The presence of five major genetically engineered proteins (CP4-EPSPS, CryIAb, Cry9C, PAT/pat and PAT/bar protein) was detected at low levels in seeds, seed/leaf powder and leaf tissue from genetically modified soy, maize or oilseed rape. A comparison between "protein strip test" (PST) and "polymerase chain reaction" (PCR) analysis of genetically modified food/feed samples demonstrates complementarities of both techniques. -® Springer-Verlag 2007</p
Validation of the performance of a GMO multiplex screening assay based on microarray detection
A new screening method for the detection and identification of GMO, based on the use of multiplex PCR followed by microarray, has been developed and is presented. The technology is based on the identification of quite ubiquitous GMO genetic target elements first amplified by PCR, followed by direct hybridisation of the amplicons on a predefined microarray (DualChip® GMO, Eppendorf, Germany). The validation was performed within the framework of a European project (Co-Extra, contract no 007158) and in collaboration with 12 laboratories specialised in GMO detection. The present study reports the strategy and the results of an ISO complying validation of the method carried out through an inter-laboratory study. Sets of blind samples were provided consisting of DNA reference materials covering all the elements detectable by specific probes present on the array. The GMO concentrations varied from 1% down to 0.045%. In addition, a mixture of two GMO events (0.1% RRS diluted in 100% TOPAS19/2) was incorporated in the study to test the robustness of the assay in extreme conditions. Data were processed according to ISO 5725 standard. The method was evaluated with predefined performance criteria with respect to the EC CRL method acceptance criteria. The overall method performance met the acceptance criteria; in particular, the results showed that the method is suitable for the detection of the different target elements at 0.1% concentration of GMO with a 95% accuracy rate. This collaborative trial showed that the method can be considered as fit for the purpose of screening with respect to its intra- and inter-laboratory accuracy. The results demonstrated the validity of combining multiplex PCR with array detection as provided by the DualChip® GMO (Eppendorf, Germany) for the screening of GMO. The results showed that the technology is robust, practical and suitable as a screening too
Confluence Modulo Equivalence in Constraint Handling Rules
Previous results on proving confluence for Constraint Handling Rules are
extended in two ways in order to allow a larger and more realistic class of CHR
programs to be considered confluent. Firstly, we introduce the relaxed notion
of confluence modulo equivalence into the context of CHR: while confluence for
a terminating program means that all alternative derivations for a query lead
to the exact same final state, confluence modulo equivalence only requires the
final states to be equivalent with respect to an equivalence relation tailored
for the given program. Secondly, we allow non-logical built-in predicates such
as var/1 and incomplete ones such as is/2, that are ignored in previous work on
confluence.
To this end, a new operational semantics for CHR is developed which includes
such predicates. In addition, this semantics differs from earlier approaches by
its simplicity without loss of generality, and it may also be recommended for
future studies of CHR.
For the purely logical subset of CHR, proofs can be expressed in first-order
logic, that we show is not sufficient in the present case. We have introduced a
formal meta-language that allows reasoning about abstract states and
derivations with meta-level restrictions that reflect the non-logical and
incomplete predicates. This language represents subproofs as diagrams, which
facilitates a systematic enumeration of proof cases, pointing forward to a
mechanical support for such proofs
A theoretical introduction to “Combinatory SYBR®Green qPCR Screening”, a matrix-based approach for the detection of materials derived from genetically modified plants
The detection of genetically modified (GM) materials in food and feed products is a complex multi-step analytical process invoking screening, identification, and often quantification of the genetically modified organisms (GMO) present in a sample. “Combinatory qPCR SYBR®Green screening” (CoSYPS) is a matrix-based approach for determining the presence of GM plant materials in products. The CoSYPS decision-support system (DSS) interprets the analytical results of SYBR®GREEN qPCR analysis based on four values: the Ct- and Tm values and the LOD and LOQ for each method. A theoretical explanation of the different concepts applied in CoSYPS analysis is given (GMO Universe, “Prime number tracing”, matrix/combinatory approach) and documented using the RoundUp Ready soy GTS40-3-2 as an example. By applying a limited set of SYBR®GREEN qPCR methods and through application of a newly developed “prime number”-based algorithm, the nature of subsets of corresponding GMO in a sample can be determined. Together, these analyses provide guidance for semi-quantitative estimation of GMO presence in a food and feed product
SYBR®Green qPCR methods for detection of endogenous reference genes in commodity crops: a step ahead in combinatory screening of genetically modified crops in food and feed products
Lightweight String Reasoning for OCL
International audienceModels play a key role in assuring software quality in the modeldriven approach. Precise models usually require the definition of OCL expressions to specify model constraints that cannot be expressed graphically. Techniques that check the satisfiability of such models and find corresponding instances of them are important in various activities, such as model-based testing and validation. Several tools to check model satisfiability have been developed but to our knowledge, none of them yet supports the analysis of OCL expressions including operations on Strings in general terms. As, in contrast, many industrial models do contain such operations, there is evidently a gap. There has been much research on formal reasoning on strings in general, but so far the results could not be included into model finding approaches. For model finding, string reasoning only contributes a sub-problem, therefore, a string reasoning approach for model finding should not add up front too much computational complexity to the global model finding problem. We present such a lightweight approach based on constraint satisfaction problems and constraint rewriting. Our approach efficiently solves several common kinds of string constraints and it is integrated into the EMFtoCSP model finder
Probabilistic (logic) programming concepts
A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years
Impact of random safety analyses on structure, process and outcome indicators: multicentre study
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