Simultaneous Modelling of the Thermal Degradation Kinetics of Pectin Methylesterase in Lettuce (<i>Lactuca sativa</i>L.) and Carrot (<i>Daucus carota</i>L.) Extracts: Analysis of Seasonal Variation and Tissue Type

Rico, Daniel; Martín-Diana, Ana Belén; Barry‐Ryan, Catherine; Henehan, Gary; Frias, Jesus Maria

doi:10.1271/bbb.60484

Cited by 3 publications

(2 citation statements)

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“…Others had optimal temperatures around 55°C (Castro et al 2004;Van Den Broek et al 2000). In contrast, losses of activity at 55°C, as observed for AtPME31, were reported for highly puriWed lettuce and carrot PMEs (Rico et al 2007). The enhancing eVects of NaCl and temperature on PME activities may have been overestimated in some studies.…”

Section: Discussionmentioning

confidence: 90%

Activity of an atypical Arabidopsis thaliana pectin methylesterase

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Mareck

et al. 2008

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An Arabidopsis thaliana pectin methylesterase that was not predicted to contain any signaling sequence was produced in E. coli and purified using a His tag added at its N-terminus. The enzyme demethylesterified Citrus pectin with a Km of 0.86 mg/ml. The enzyme did not require salt for activity and was found to be relatively temperature-sensitive. The precipitation of enzyme-treated pectin by CaCl2 suggested that the enzyme had a blockwise mode of pectin demethylesterification. A purified kiwi (Actinidia chinensis) pectin methylesterase inhibitor had no effect on the activity of the enzyme whereas it strongly inhibited a flax pectin methylesterase. A model of the protein structure revealed that an extra amino acid sequence in this particular Arabidopsis pectin methylesterase could form a ss-strand outside the core structure, which might be preventing the inhibitor from binding the protein.

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Section: Discussionmentioning

confidence: 90%

Activity of an atypical Arabidopsis thaliana pectin methylesterase

Dedeurwaerder

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Mareck

et al. 2008

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“…Linear mixed effects models (MEM) are widely used in many fields; for instance, biology (Rico et al, 2007), ecology (Stevens et al, 2007), linguistics (Nooteboom and Quené, 2008) and social sciences (Kliegl et al, 2009). They extend upon classical linear regression techniques to support data that have some form of grouping.…”

Section: Comparison With Conventional Methodsmentioning

confidence: 99%

Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space

et al. 2017

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Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in the treatment of ADHD (Reeves and Schweitzer, 2004; Dopheide and Pliszka, 2009) and are prescribed to an increasing number of children and adolescents in the US and the UK every year (Safer et al., 1996; McCarthy et al., 2009), though recent studies suggest that this is tailing off, e.g., Holden et al. (2013). Around 70% of children demonstrate a clinically significant treatment response to stimulant medication (Spencer et al., 1996; Schachter et al., 2001; Swanson et al., 2001; Barbaresi et al., 2006). However, it is unclear which patient characteristics may moderate treatment effectiveness. As such, most existing research has focused on investigating univariate or multivariate correlations between a set of patient characteristics and the treatment outcome, with respect to dosage of one or several types of medication. The results of such studies are often contradictory and inconclusive due to a combination of small sample sizes, low-quality data, or a lack of available information on covariates. In this paper, feature extraction techniques such as latent trait analysis were applied to reduce the dimension of on a large dataset of patient characteristics, including the responses to symptom-based questionnaires, developmental health factors, demographic variables such as age and gender, and socioeconomic factors such as parental income. We introduce a Bayesian modeling approach in a “learning in the model space” framework that combines existing knowledge in the literature on factors that may potentially affect treatment response, with constraints imposed by a treatment response model. The model is personalized such that the variability among subjects is accounted for by a set of subject-specific parameters. For remission classification, this approach compares favorably with conventional methods such as support vector machines and mixed effect models on a range of performance measures. For instance, the proposed approach achieved an area under receiver operator characteristic curve of 82–84%, compared to 75–77% obtained from conventional regression or machine learning (“learning in the data space”) methods.

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