Linking categorical models for prediction of pleasantness score using individual predictions of sweetness and creaminess: An advancement of categorical modeling

The aim of this work was to develop and evaluate approaches of linked categorical models using individual predictions of probability. A model was developed using data from a study which assessed the perception of sweetness, creaminess, and pleasantness in dairy solutions containing variable concentrations of sugar and fat. Ordered categorical models were used to predict the individual sweetness and creaminess scores and these individual predictions were used as covariates in the model of pleasantness response. The model using individual predictions was compared to a previously developed model using the amount of fat and sugar as covariates driving pleasantness score. The model using the individual prediction of odds of sweetness and creaminess had a lower variability of pleasantness than the model using the content of sugar and fat in the test solutions, which indicates that the individual odds explain part of the variability in pleasantness. Additionally, simultaneous and sequential modeling approaches were compared for the linked categorical model. Parameter estimation was similar, but precision was better with sequential modeling approaches compared to the simultaneous modeling approach. The previous model characterizing the pleasantness response was improved by using individual predictions of sweetness and creaminess rather than the amount of fat and sugar in the solution. The application of this approach provides an advancement within categorical modeling showing how categorical models can be linked to enable the utilization of individual prediction. This approach is aligned with biology of taste sensory which is reflective of the individual perception of sweetness and creaminess, rather than the amount of fat and sugar in the solution.

Bivariate Transmuted Burr Distribution: Properties and Application

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way of modeling complex joint phenomenon. In this article, a new bivariate distribution is proposed which is known as the bivariate transmuted Burr (BTB) distribution. This new bivariate distribution is extension of the univariate transmuted Burr (TB) distribution to two variables. The proposed BTB distribution is explored in detail and the marginal and conditional distributions for the distribution are obtained. Joint and conditional moments alongside hazard rate functions are obtained. The maximum likelihood estimation (MLE) for the parameters of the BTB distribution is also done. Finally, real data application of the BTB distribution is given. It is observed that the proposed BTB distribution is a suitable fit for the data used.

MethReg: estimating the regulatory potential of DNA methylation in gene transcription

Epigenome-wide association studies (EWAS) often detect a large number of differentially methylated sites or regions, many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here we present MethReg, an R/Bioconductor package that analyzes matched DNA-methylation and gene-expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize, and annotate CpG sites with high regulatory potential. By simultaneous modeling three key elements that contribute to gene transcription (CpG methylation, target gene expression and TF activity), MethReg identifies TF-target gene associations that are present only in a subset of samples with high (or low) methylation levels at the CpG that influences TF activities, which can be missed in analyses that use all samples. Using real colorectal cancer and Alzheimer’s disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

Influence of tourists’ sociodemographic characteristics on their perception of destination personality and emotional experience of a city break destination

Purpose The purpose of this paper is to investigate the influence of tourists’ sociodemographic characteristics on their perception of destination personality and emotional experience on the example of the city break tourism destination. Design/methodology/approach To examine this relationship, survey research was conducted on a sample of 203 national and international tourists who visited Novi Sad, the second-largest city in Serbia. Findings Research results confirmed the role of travelers’ sociodemographic variables in shaping their emotional experience and destination personality perception. The findings pointed out significant divergences in the perception of emotional experience in the case of respondents’ education level, previous visits to the city and travel companion. On the other hand, the analysis showed that repeat visitors significantly differed from first-time visitors regarding destination personality perception. In addition, differences in both destination personality and emotional experience assessment were found between national and international city break travelers. Originality/value The current study is first to focus on the role of travelers’ sociodemographic variables in simultaneous modeling of their perception of destination personality and emotional experience within the city break destination context. Besides, results revealed some new influencing factors of both destination personality and emotional experience perception, thus contributing to the existing tourism literature. In addition, this paper offers useful practical implications for city break marketers to adapt promotional activities, more effectively present the desired brand personality of the city to different sociodemographic categories of tourists and sustain repeat tourists’ perception of Positive surprise.