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1.
Mycotoxin Res ; 18 Suppl 2: 188-92, 2002 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-23606160

RESUMO

The mycotoxin aflatoxin M1 (AfM1) is a serious food safety hazard for which the European Commission has already established a maximum permissible level of 0.05 µg/kg AfM1 in milk and products thereof. For control analysis laboratories are increasingly asked to submit full uncertainties of their analytical results.The evaluation of measurement uncertainties of an analytical method for the determination of AfM1 in milk and milk powder on the basis of 'in-house' validation data in compliance with the 'Guide to the Expression of Uncertainty in Measurement (GUM)' [1] and the 'EURACHEM Guide' [2] is described. A similar approach will be used to assess the performance of methods employed by laboratories participating in the certification of reference materials for AfM1 in milk powder.

2.
Chemosphere ; 44(4): 529-37, 2001 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-11482639

RESUMO

In this article, the production and validation of a new certified reference material "PCBs in animal fat" for the control of the maximum level of 200 ng/g setup by the European Communities for veterinary products from Belgium is described. Three materials are established: a blank, one material with about 100 ng/g and one with about 200 ng/g (sum of seven PCBs). Data on the production and certification are given. Additionally, this material was used as an unknown test material in the quality assurance program of the Belgium meat monitoring system (before the certification of the material). While the certification was performed with an uncertainty of less than 10%, the round robin exhibited larger deviations. However, these deviations were less than 20% for most of the 30 participating laboratories. Only two had significantly higher deviations.


Assuntos
Tecido Adiposo/química , Contaminação de Alimentos , Bifenilos Policlorados/farmacocinética , Ração Animal , Animais , Carne , Bifenilos Policlorados/análise , Valores de Referência , Suínos , Distribuição Tecidual
3.
Pac Symp Biocomput ; : 380-91, 2000.
Artigo em Inglês | MEDLINE | ID: mdl-10902186

RESUMO

We present a new statistical approach for eukaryotic polymerase II promoter recognition. We apply stochastic segment models in which each state represents a functional part of the promoter. The segments are trained in an unsupervised way. We compare segment models with three and five states with our previous system which modeled the promoters as a whole, i.e. as a single state. Results on the classification of a representative collection of human and D. melanogaster promoter and non-promoter sequences show great improvements. The practical importance is demonstrated on the mining of large contiguous sequences.


Assuntos
Modelos Genéticos , Regiões Promotoras Genéticas , Algoritmos , Animais , Simulação por Computador , DNA Polimerase II/genética , Bases de Dados Factuais , Drosophila melanogaster/genética , Genoma , Humanos , Processos Estocásticos
4.
Bioinformatics ; 15(5): 362-9, 1999 May.
Artigo em Inglês | MEDLINE | ID: mdl-10366656

RESUMO

MOTIVATION: We describe a new content-based approach for the detection of promoter regions of eukaryotic protein encoding genes. Our system is based on three interpolated Markov chains (IMCs) of different order which are trained on coding, non-coding and promoter sequences. It was recently shown that the interpolation of Markov chains leads to stable parameters and improves on the results in microbial gene finding (Salzberg et al., Nucleic Acids Res., 26, 544-548, 1998). Here, we present new methods for an automated estimation of optimal interpolation parameters and show how the IMCs can be applied to detect promoters in contiguous DNA sequences. Our interpolation approach can also be employed to obtain a reliable scoring function for human coding DNA regions, and the trained models can easily be incorporated in the general framework for gene recognition systems. RESULTS: A 5-fold cross-validation evaluation of our IMC approach on a representative sequence set yielded a mean correlation coefficient of 0.84 (promoter versus coding sequences) and 0.53 (promoter versus non-coding sequences). Applied to the task of eukaryotic promoter region identification in genomic DNA sequences, our classifier identifies 50% of the promoter regions in the sequences used in the most recent review and comparison by Fickett and Hatzigeorgiou ( Genome Res., 7, 861-878, 1997), while having a false-positive rate of 1/849 bp.


Assuntos
DNA/análise , Cadeias de Markov , Regiões Promotoras Genéticas , Algoritmos , Animais , Drosophila melanogaster/genética , Processamento Eletrônico de Dados , Células Eucarióticas , Humanos
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