Potential on Using Cultural Syndromes for Explaining Differences in Attitudes in Northern and Southern EU Countries

Nowadays, it is more realistic to view the development of a new technology as a result of a complex social system of interactions and decisions. Understanding the public's range of views on biotechnology is important for decision makers, in order to be able to anticipate potential acceptance problems or, one step further, to take consumer or public desires and concerns into account so that desirable applications can be developed. Previous work from the same research group, using data from Eurobarometer surveys, was trying to explore the attitudes of the European consumers towards genetic modification of food. Emerging differences in attitudes towards genetically modified food have not been explained adequately in most cases using only sociodemographic variables. In addition strong national differences lead to the idea that cultural differences should also be taken into account, despite the difficulties in formulating specific hypotheses that can be tested empirically. In this paper, in an effort to approach culture in a more clear way, we try to track down and analyse the specific units (customs, traditions, beliefs, and other social norms) that comprise cultures. The notion of cultural syndromes as approached by Triandis is tackled. Furthermore applying data from the European Social Survey (ESS) to Schwartz's value system, our objective is to validate empirically the potential utilisation of Schwartz values to further explain existing differences in attitudes towards GM food among European countries. Further research can lead to a deeper and more precise understanding of cultural differentiation as well as to a more valid cross-cultural theory of attitude formation.attitudes towards genetically modified food, attitude formation, cultural differentiation, cultural syndromes, Consumer/Household Economics,

Prostate cancer biomarkers: a practical review based on different clinical scenarios

Traditionally, diagnosis and staging of prostate cancer (PCa) have been based on prostate-specific antigen (PSA) level, digital rectal examination (DRE), and transrectal ultrasound (TRUS) guided prostate biopsy. Biomarkers have been introduced into clinical practice to reduce the overdiagnosis and overtreatment of low-grade PCa and increase the success of personalized therapies for high-grade and high-stage PCa. The purpose of this review was to describe available PCa biomarkers and examine their use in clinical practice. A nonsystematic literature review was performed using PubMed and Scopus to retrieve papers related to PCa biomarkers. In addition, we manually searched websites of major urological associations for PCa guidelines to evaluate available evidence and recommendations on the role of biomarkers and their potential contribution to PCa decision-making. In addition to PSA and its derivates, thirteen blood, urine, and tissue biomarkers are mentioned in various PCa guidelines. Retrospective studies have shown their utility in three main clinical scenarios: (1) deciding whether to perform a biopsy, (2) distinguishing patients who require active treatment from those who can benefit from active surveillance, and (3) defining a subset of high-risk PCa patients who can benefit from additional therapies after RP. Several validated PCa biomarkers have become commercially available in recent years. Guidelines now recommend offering these tests in situations in which the assay result, when considered in combination with routine clinical factors, is likely to affect management. However, the lack of direct comparisons and the unproven benefits, in terms of long-term survival and cost-effectiveness, prevent these biomarkers from being integrated into routine clinical use

Value, but high costs in post-deposition data Curation

© The Author(s) 2016. Published by Oxford University Press. Discoverability of sequence data in primary data archives is proportional to the richness of contextual information associated with the data. Here, we describe an exercise in the improvement of contextual information surrounding sample records associated with metagenomics sequence reads available in the European Nucleotide Archive. We outline the annotation process and summarize findings of this effort aimed at increasing usability of publicly available environmental data. Furthermore, we emphasize the benefits of such an exercise and detail its costs. We conclude that such a third party annotation approach is expensive and has value as an element of curation, but should form only part of a more sustainable submitter-driven approach

Value, but high costs in post-deposition data curation

Discoverability of sequence data in primary data archives is proportional to the richness of contextual information associated with the data. Here, we describe an exercise in the improvement of contextual information surrounding sample records associated with metagenomics sequence reads available in the European Nucleotide Archive. We outline the annotation process and summarize findings of this effort aimed at increasing usability of publicly available environmental data. Furthermore, we emphasize the benefits of such an exercise and detail its costs. We conclude that such a third party annotation approach is expensive and has value as an element of curation, but should form only part of a more sustainable submitter-driven approach

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model- data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries

WOSMIP II- Workshop on Signatures of Medical and Industrial Isotope Production

Medical and industrial fadioisotopes are fundamental tools used in science, medicine and industry with an ever expanding usage in medical practice where their availability is vital. Very sensitive environmental radionuclide monitoring networks have been developed for nuclear-security-related monitoring [particularly Comprehensive Test-Ban-Treaty (CTBT) compliance verification] and are now operational