Relationship between Destination Image and Loyalty: Developing Cooperative Branding for Rural Destinations

Abstract Destination image has been extensively studied, yet literature on the cooperative branding is limited. A cooperative branding model that represents the important determinants of destination loyalty was developed based on previous studies in a number of fields. Seven distinct image constructs were identified, three of which are affective based and four cognitive based. This study noted that idyllic (among affective image) and entertaining and quality service (among cognitive image) had a direct impact on overall destination image and indirect impact on destination loyalty through overall image. The results of this investigation provide important implications for strategic image management and can aid in designing and implementing marketing programs for creating and enhancing tourism destination images

IGSF4 is a novel TCR ζ-chain–interacting protein that enhances TCR-mediated signaling

The expression of cell surface molecule, IGSF4, on human and mouse T cells associate with the TCR ζ-chain and colocalizes to the c-SMAC to enhance cytokine production and adhesion to APCs

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data

Background Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimers disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The time orientation and 3-word recall score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.The publication costs, design of the study, data management and writing the manuscript for this article were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538), Korea Ministry of Health & Welfare, and from the Original Technology Research Program for Brain Science through the National Research Foundation of Korea funded by the Korean Government (MSIP; No. 2014M3C7A1064752)

Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population

Background: Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer's disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population. Methods: One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs. Results: In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10-8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74-0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain. Conclusion: The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations

Measuring Service Quality of Rural Accommodations

The aim of this study is to examine the most important service quality factors for rural accommodations in rural tourism villages in South Korea by simultaneously utilizing Importance–Performance analysis (IPA) and gap analysis. The tabulated results were presented in a two-dimensional grid t showing the strengths and weaknesses of the tourism attributes being studied. Among the 18 items measuring the service quality of rural accommodations, there are no items identified for the concentrate quadrant. Ten items fall in the “Keep up the good work” quadrant, which indicates high performance and importance values. It also includes “the attitude of local residents”, “the attitude of the accommodation’s owner”, and “the expertise of the owner”. Management of rural accommodations in South Korea must make special efforts to maintain and improve service quality for these ten high performance and importance attributes. Gap analysis is used to illustrate how tourism operators can improve their service quality

Factors influencing social capital in rural tourism communities

a b s t r a c t This study aimed to identify the factors influencing social capital as it affects community conflict management for community residents in rural tourism villages. An on-site survey consisting of selfadministered questionnaires was conducted with residents of rural tourism communities. These selfadministered surveys were obtained from 380 community residents in the study area. A factorclustering method identified distinct segments: high social capital (52%) and low social capital (47.7%). The estimation of a binary logistic regression model determined the characteristics of community residents who were most likely to be associated with each type of social capital. Results indicated that fruit, vegetable and rice farmers who also operated farm-stay businesses and rural activity programmes for tourists had the most social. We suggest that certain types of government policy programmes are helpful for increasing social capital and managing community conflicts by means of involvement in the tourism business