Designing the ideal undergraduate program in tourism: expectations from industry and educators

Globally, there are numerous undergraduate programs available in tourism that differ in content aims and scope. It is hard to judge which programs are preferable and on what criteria to base choice. Thus, this article provides a content analysis of 64 bachelor programs taught in English. The actual programs are compared to indicators identified in literature review. Furthermore, an adaptive conjoint analysis identifies the importance and utilities of different subject areas and themes that can compose a tourism program. The population is specified in a broad manner, including academics as well as industry professionals from both the private and public sectors. Results show that there are discrepancies between existing programs and what academics and industry professionals perceive as being important. The research offers recommendations on which topics and issues should be included in a tourism curriculum and provokes discussion on adaptation of existing curricula for the future

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

© 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975)