An e-class in action: Experiences with ICT-intensive teaching and learning of discrete dynamical models at secondary school

In 2007, a small team of university and secondary school mathematics teachers jointly developed and piloted an e-class for 4th and 5th grade students (age: 16-17yrs) at both pre-university and general vocational level. The goal was to develop and try out innovative ways of teaching mathematics that would enable schools to offer optional courses for small numbers of students. The e-class can be summarized as web-supported instruction in a blended learning approach. The instructional material consisted of the chapter on discrete dynamical models from a brand-new mathematics textbook, supplemented by investigative activities. Students could build and simulate dynamical models with the computer learning environment Coach. Instructions for learning to work with software were given through screen casts created by the teacher to gear with students' needs and made available in the Sakai-based virtual learning environment. Students got weekly on-line assignments, which they submitted digitally. At home they could get assistance from peers and the teacher in a chat room. The authors discuss some of the e-ingredients of the e-class and their potential for teaching and learning mathematics and science in terms of principled design approaches to multimedia learning and pedagogical arrangements. The authors report the experiences of the participants of the project and present the future plans based on this work

On estimation of covariance function for functional data with detection limits

In many studies on disease progression, biomarkers are restricted by detection limits, hence informatively missing. Current approaches ignore the problem by just filling in the value of the detection limit for the missing observations for the estimation of the mean and covariance function, which yield inaccurate estimation. Inspired by our recent work [Liu and Houwing-Duistermaat (2022), ‘Fast Estimators for the Mean Function for Functional Data with Detection Limits’, Stat, e467.] in which novel estimators for mean function for data subject to detection limit are proposed, in this paper, we will propose a novel estimator for the covariance function for sparse and dense data subject to a detection limit. We will derive the asymptotic properties of the estimator. We will compare our method to the standard method, which ignores the detection limit, via simulations. We will illustrate the new approach by analysing biomarker data subject to a detection limit. In contrast to the standard method, our method appeared to provide more accurate estimates of the covariance. Moreover its computation time is small

A randomised trial of honey barrier cream versus zinc oxide ointment

In this single-blind multicentre, intervention study, 31 patients with symmetrical intertrigo in large skin folds were included to study the clinical effect of two topical treatments, i.e. standard therapy with zinc oxide ointment versus honey barrier cream. Patients were treated twice daily for 21 days, and the severity of intertrigo was scored in an observation period of 21 days. Patients were used as their own controls by treating symmetrical skin folds, on the left and right side. There was no significant difference in treatment effect between intervention groups. For the majority of patients, both treatments were effective. However, the use of honey barrier cream showed lower pruritus complaints (12.9% versus 29.0%). Honey barrier cream is a suitable alternative in the treatment of intertrigo, and promotes patient comfort

Integrating omics datasets with the OmicsPLS package

Background: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. Results: We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. Conclusions: We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLSand can be installed in R via install.packages("OmicsPLS")

On Estimation of the Effect Lag of Predictors and Prediction in a Functional Linear Model

We propose a functional linear model to predict a functional response using multiple functional and longitudinal predictors and to estimate the effect lags of predictors. The coefficient functions are written as the expansion of a basis system (e.g. functional principal components, splines), and the coefficients of the basis functions are estimated via optimizing a penalization criterion. Then effect lags are determined by simultaneously searching on a prior designed grid mesh based on minimization of a proposed prediction error criterion. Mathematical properties of the estimated regression functions and predicted responses are studied. The performance of the method is evaluated by extensive simulations and a real data analysis application on chronic obstructive pulmonary disease (COPD)

The mixed model for the analysis of a repeated‐measurement multivariate count data

Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less interest. Here, we focus on the correlation between time points. In addition, the effects of covariates on the multivariate counts distribution need to be assessed. To fulfill these requirements, a regression model based on the Dirichlet‐multinomial distribution for association between covariates and the categorical counts is extended by using random effects to deal with the additional clustering. This model is the Dirichlet‐multinomial mixed regression model. Alternatively, a negative binomial regression mixed model can be deployed where the corresponding likelihood is conditioned on the total count. It appears that these two approaches are equivalent when the total count is fixed and independent of the random effects. We consider both subject‐specific and categorical‐specific random effects. However, the latter has a larger computational burden when the number of categories increases. Our work is motivated by microbiome data sets obtained by sequencing of the amplicon of the bacterial 16S rRNA gene. These data have a compositional structure and are typically overdispersed. The microbiome data set is from an epidemiological study carried out in a helminth‐endemic area in Indonesia. The conclusions are as follows: time has no statistically significant effect on microbiome composition, the correlation between subjects is statistically significant, and treatment has a significant effect on the microbiome composition only in infected subjects who remained infected