| 1. |
- Raices, M., et al.
(författare)
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Cellobiose quinone oxidoreductase from the white rot fungus Phanerochaete chrysosporium is produced by intracellular proteolysis of cellobiose dehydrogenase
- 2002
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Ingår i: Biochimica et Biophysica Acta, Gene Structure and Expression. - 0167-4781 .- 1879-2634. ; 1576:02-jan, s. 15-22
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Tidskriftsartikel (refereegranskat)abstract
- The fungus Phanerochaete chrysosporium was grown in a 10-1 automatic fermenter using cellobiose us carbon source to monitor the induction of cellobiose dehydrogenase (CDH) and cellobiose quinone oxidoreductase (CBQ) enzymes, and to search for tentative cbq and cdh genes and their transcriptional products. After 24 h of induction, CDH was detected in the culture supernatant and a protein was recognized by a specific anti-CDH polyclonal antibody in the sonicated biomass. Northern blot experiments performed with several fungal RNA samples showed, after 24 h of induction. only one single species of an mRNA transcript corresponding in size to the cdh gene (2.5 kb) The relative amount of this transcript decreased as a function of time. Southern blot experiments done with genomic DNA and database search in the recently available genome information also ruled out the presence in this strain of a separate cbq gene distinct from the cdh gene, Taken together, these results demonstrated that CBQ originates from the cdh gene. Furthermore, it is not produced by differential splicing but by a posttranslational, predominantly intracellular, proteolytic cleavage,
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| 2. |
- Raices Cruz, Ivette, et al.
(författare)
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A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
- 2022
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Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:17, s. 3365-3379
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Tidskriftsartikel (refereegranskat)abstract
- Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.
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| 3. |
- Raices Cruz, Ivette, et al.
(författare)
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A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments
- 2022
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Ingår i: Risk Analysis. - : Wiley. - 0272-4332 .- 1539-6924. ; 42:2, s. 239-253
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Tidskriftsartikel (refereegranskat)abstract
- An honest communication of uncertainty about quantities of interest enhances transparency in scientific assessments. To support this communication, risk assessors should choose appropriate ways to evaluate and characterize epistemic uncertainty. A full treatment of uncertainty requires methods that distinguish aleatory from epistemic uncertainty. Quantitative expressions for epistemic uncertainty are advantageous in scientific assessments because they are nonambiguous and enable individual uncertainties to be characterized and combined in a systematic way. Since 2019, the European Food Safety Authority (EFSA) recommends assessors to express epistemic uncertainty in conclusions of scientific assessments quantitatively by subjective probability. A subjective probability can be used to represent an expert judgment, which may or may not be updated using Bayes's rule to integrate evidence available for the assessment and could be either precise or approximate. Approximate (or bounded) probabilities may be enough for decision making and allow experts to reach agreement on certainty when they struggle to specify precise subjective probabilities. The difference between the lower and upper bound on a subjective probability can also be used to reflect someone's strength of knowledge. In this article, we demonstrate how to quantify uncertainty by bounded probability, and explicitly distinguish between epistemic and aleatory uncertainty, by means of robust Bayesian analysis, including standard Bayesian analysis through precise probability as a special case. For illustration, the two analyses are applied to an intake assessment.
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| 4. |
- Raices Cruz, Ivette, et al.
(författare)
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Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis
- 2022
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Ingår i: Computational Statistics and Data Analysis. - : Elsevier BV. - 0167-9473. ; 176
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Tidskriftsartikel (refereegranskat)abstract
- Bayesian inference under a set of priors, called robust Bayesian analysis, allows for estimation of parameters within a model and quantification of epistemic uncertainty in quantities of interest by bounded (or imprecise) probability. Iterative importance sampling can be used to estimate bounds on the quantity of interest by optimizing over the set of priors. A method for iterative importance sampling when the robust Bayesian inference relies on Markov chain Monte Carlo (MCMC) sampling is proposed. To accommodate the MCMC sampling in iterative importance sampling, a new expression for the effective sample size of the importance sampling is derived, which accounts for the correlation in the MCMC samples. To illustrate the proposed method for robust Bayesian analysis, iterative importance sampling with MCMC sampling is applied to estimate the lower bound of the overall effect in a previously published meta-analysis with a random effects model. The performance of the method compared to a grid search method and under different degrees of prior-data conflict is also explored.
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| 5. |
- RAICES, M, et al.
(författare)
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Cloning and characterization of a cDNA encoding a cellobiose dehydrogenase from the white rot fungus Phanerochaete chrysosporium
- 1995
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Ingår i: FEBS Letters. - : Wiley. - 0014-5793 .- 1873-3468. ; 369:2-3, s. 233-238
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Tidskriftsartikel (refereegranskat)abstract
- The cDNA of cellobiose dehydrogenase (CDH) from Phanerochaete chrysosporium has been cloned and sequenced. The 5′ end was obtained by PCR amplification. The cDNA contains 2310 translated bases excluding the poly(A) tail. The deduced mature protein contains 770 amino acid residues and is preceded by a 18 residue long signal peptide. The regions of the amino acid sequence corresponding to the heme and FAD domains of CDH were identified as well as the nucleotide-binding motif, the disulfide pairing and a methionine residue chelating the heme iron. No homologous sequences were found for the heme domain, however, the FAD domain appears to be distantly related to the GMC oxidoreductase family.
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| 6. |
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