A cross-cultural study on consumer preferences for olive oil

In this paper, we study consumer preferences for olive oil across four countries (Denmark, France, Tunisia, and the US). Based on a large-scale study with olive oil consumers (N = 3,462), we use the Best-Worst Scaling method to measure perceived importance for product attributes known to influence consumer choice. Our results show that consumers across all countries rate type, price, prior experience, and country of origin as important product attributes. On the other hand, packaging, label design, and brands are considered as less important product attributes. While the perceived importance for olive oil attributes differs across countries, the order of importance is almost similar for all countries. We further derive a three-segment solution and describe each segment based on its attitudinal beliefs, usage, and socio-demographic profile. We discuss implications for the study of consumer preferences for olive oil and provide managerial insights.© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Modelling prognostic factors in advanced pancreatic cancer

Pancreatic cancer is the fifth most common cause of cancer death. Identification of defined patient groups based on a prognostic index may improve the prediction of survival and selection of therapy. Many prognostic factors have been identified often based on retrospective, underpowered studies with unclear analyses. Data from 653 patients were analysed. Continuous variables are often simplified assuming a linear relationship with log hazard or introducing a step function (dichotomising). Misspecification may lead to inappropriate conclusions but has not been previously investigated in pancreatic cancer studies. Models based on standard assumptions were compared with a novel approach using nonlinear fractional polynomial (FP) transformations. The model based on FP-transformed covariates was most appropriate and confirmed five previously reported prognostic factors: albumin, CA19-9, alkaline phosphatase, LDH and metastases, and identified three additional factors not previously reported: WBC, AST and BUN. The effects of CA19-9, alkaline phosphatase, AST and BUN may go unrecognised due to simplistic assumptions made in statistical modelling. We advocate a multivariable approach that uses information contained within continuous variables appropriately. The functional form of the relationship between continuous covariates and survival should always be assessed. Our model should aid individual patient risk stratification and the design and analysis of future trials in pancreatic cancer

Multiscale imaging of plants: current approaches and challenges

International audienceWe review a set of recent multiscale imaging techniques, producing high-resolution images of interest for plant sciences. These techniques are promising because they match the multiscale structure of plants. However, the use of such high-resolution images is challenging in the perspective of their application to high-throughput phenotyping on large populations of plants, because of the memory cost for their data storage and the computational cost for their processing to extract information. We discuss how this renews the interest for multiscale image processing tools such as wavelets, fractals and recent variants to analyse such high-resolution images