Tourism destination competitiveness: second thoughts on the world economic forum reports

The Travel and Tourism Competitiveness Reports of the World Economic Forum elaborate the Travel and Tourism Competitiveness Index (TTCI) as an overall measure of destination competitiveness for 130 economies worldwide. From a tourism management point of view, a measure such as the TTCI is expected to be instrumental in explaining and predicting the tourism performance of receiving countries. This study explores several ways to transform the TTCI into a formative structural model. Partial least squares path modelling, PLS regression, mixture modelling and non-linear covariance-based structural equation modelling are applied to examine the TTCI's predictive power. The analysis probes possible measures for improvement. The destination countries may be subject to unobserved heterogeneity with regard to how the various constituents of competitiveness act on tourism performance. Interaction phenomena seem to prohibit a simple cause-effect pattern and non-linear relationships show encouraging results

Improved Heterosis Prediction by Combining Information on DNA- and Metabolic Markers

Background: Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress. Dominance, overdominance or epistasis based models are commonly used explanations. Conclusion/Significance: The heterosis level is clearly a function of the combination of the parents used for offspring production. This results in a major challenge for plant breeders, as usually several thousand combinations of parents have to be tested for identifying the best combinations. Thus, any approach to reliably predict heterosis levels based on properties of the parental lines would be highly beneficial for plant breeding. Methodology/Principal Findings: Recently, genetic data have been used to predict heterosis. Here we show that a combination of parental genetic and metabolic markers, identified via feature selection and minimum-description-length based regression methods, significantly improves the prediction of biomass heterosis in resulting offspring. These findings will help furthering our understanding of the molecular basis of heterosis, revealing, for instance, the presence of nonlinear genotype-phenotype relationships. In addition, we describe a possible approach for accelerated selection in plant breeding

Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection

Phylogenies of highly genetically variable viruses such as HIV-1 are potentially informative of epidemiological dynamics. Several studies have demonstrated the presence of clusters of highly related HIV-1 sequences, particularly among recently HIV-infected individuals, which have been used to argue for a high transmission rate during acute infection. Using a large set of HIV-1 subtype B pol sequences collected from men who have sex with men, we demonstrate that virus from recent infections tend to be phylogenetically clustered at a greater rate than virus from patients with chronic infection (‘excess clustering’) and also tend to cluster with other recent HIV infections rather than chronic, established infections (‘excess co-clustering’), consistent with previous reports. To determine the role that a higher infectivity during acute infection may play in excess clustering and co-clustering, we developed a simple model of HIV infection that incorporates an early period of intensified transmission, and explicitly considers the dynamics of phylogenetic clusters alongside the dynamics of acute and chronic infected cases. We explored the potential for clustering statistics to be used for inference of acute stage transmission rates and found that no single statistic explains very much variance in parameters controlling acute stage transmission rates. We demonstrate that high transmission rates during the acute stage is not the main cause of excess clustering of virus from patients with early/acute infection compared to chronic infection, which may simply reflect the shorter time since transmission in acute infection. Higher transmission during acute infection can result in excess co-clustering of sequences, while the extent of clustering observed is most sensitive to the fraction of infections sampled

Predicting Peptide Binding Affinities to MHC Molecules Using a Modified Semi-Empirical Scoring Function

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods

A novel approach to investigate tissue-specific trinucleotide repeat instability

Abstract Background In Huntington's disease (HD), an expanded CAG repeat produces characteristic striatal neurodegeneration. Interestingly, the HD CAG repeat, whose length determines age at onset, undergoes tissue-specific somatic instability, predominant in the striatum, suggesting that tissue-specific CAG length changes could modify the disease process. Therefore, understanding the mechanisms underlying the tissue specificity of somatic instability may provide novel routes to therapies. However progress in this area has been hampered by the lack of sensitive high-throughput instability quantification methods and global approaches to identify the underlying factors. Results Here we describe a novel approach to gain insight into the factors responsible for the tissue specificity of somatic instability. Using accurate genetic knock-in mouse models of HD, we developed a reliable, high-throughput method to quantify tissue HD CAG repeat instability and integrated this with genome-wide bioinformatic approaches. Using tissue instability quantified in 16 tissues as a phenotype and tissue microarray gene expression as a predictor, we built a mathematical model and identified a gene expression signature that accurately predicted tissue instability. Using the predictive ability of this signature we found that somatic instability was not a consequence of pathogenesis. In support of this, genetic crosses with models of accelerated neuropathology failed to induce somatic instability. In addition, we searched for genes and pathways that correlated with tissue instability. We found that expression levels of DNA repair genes did not explain the tissue specificity of somatic instability. Instead, our data implicate other pathways, particularly cell cycle, metabolism and neurotransmitter pathways, acting in combination to generate tissue-specific patterns of instability. Conclusion Our study clearly demonstrates that multiple tissue factors reflect the level of somatic instability in different tissues. In addition, our quantitative, genome-wide approach is readily applicable to high-throughput assays and opens the door to widespread applications with the potential to accelerate the discovery of drugs that alter tissue instability

The pls Package: Principal Component and Partial Least Squares Regression in R

Contains fulltext : 36604.pdf (publisher's version ) (Open Access

Closely related tree species support distinct communities of seed‐associated fungi in a lowland tropical forest

Previous theoretical work has highlighted the potential for natural enemies to mediate the coexistence of species with similar life histories via density-dependent effects on survivorship. For plant pathogens to play this role, they must differ in their ability to infect or induce disease in different host plant species. In tropical forests characterized by high diversity, these effects must extend to phylogenetically closely related species pairs. Mortality at the seed and seedling stage strongly influences the abundance and distribution of tropical tree species, but the host preferences and spatial distributions of fungi are rarely determined. We examined how host species identity, relatedness and seed viability influence the composition of fungal communities associated with seeds of four co-occurring pioneer trees (Cecropia insignis, C. longipes, C. peltata and Jacaranda copaia). Seeds were buried in mesh bags in five common gardens in the understorey of a lowland tropical forest in Panama and retrieved at intervals from 1 to 30 months. A subset of the seeds in each bag was used to determine germination success. One half of each remaining seed was tested for viability; and the other half was used to culture and identify seed-infecting fungi. Seeds were infected by fungi after burial. Although fungal communities differed in viable versus dead seeds, and across burial locations, community composition primarily varied as a function of plant species identity (30.7% of variation in community composition vs. 4.5% for viability and location together), even for congeneric Cecropia species. Phylogenetic reconstruction showed that relatedness of fungi mostly reflected differences between Jacaranda (Bignoniaceae) and Cecropia (Urticaceae). Although the proportion of germinable seeds decreased gradually over time for all species, intraspecific variation in survival was high at the same location (e.g. ranging from 0% to 100% for C. peltata) suggesting variable exposure or susceptibility to seed pathogens. Synthesis. Our study provides evidence under field conditions that congeneric tree species with similar life history traits differ markedly in seed-associated fungal communities when exposed to the same soil-borne fungi. This is a critical first step supporting pathogen-mediated coexistence of closely related tree species. © 2021 British Ecological SocietyNational Science Foundation12 month embargo; first published online 7 February 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]