Applying a Goal-Directed Behavior Model to Determine Risk Perception of COVID-19 and War on Potential Travelers' Behavioral Intentions.

The purpose of this study is to verify the influence of the relationship between risk perception of COVID-19 and the war-applied Model of Goal-directed Behavior (MGB) based on stimulus-organism-response (SOR) and potential travelers' behavioral intention. In addition, this study attempted to verify the relationship among uncertainty toward international travel, mental well-being toward international travel, and desire toward travelers' behavioral intention. Moreover, we examined the moderating effect of gender (female vs. male) among all variables for dependents. The survey was conducted on potential travelers in Korea. As for the survey period, a survey was conducted for one month beginning on 2 September 2022. Of the total 413 surveys, 361 surveys were used for the final analysis, and 52 unfaithful surveys were excluded. In addition, demographic, CFA, correlation analysis, structural equation modeling, and moderation effect analysis were verified using SPSS and AMOS. For the data analysis, we used SPSS 18.0 and Amos 20.0 to perform factor analysis and SEM. Significant effects were found in support for Hypotheses 1-5. Further, when it comes to the difference of gender on the relationship between all the variables, while no significant effect was found for Hypotheses 6a,c,e,g, a significant effect was found for Hypotheses 6b,d,f. Thus, H6a,c,e were rejected and H6b,d,f were supported. It was found that females had a greater influence on mental health and desire for overseas travel than males, but it was found that there was no difference between females and males in the relationship between desire and behavioral intention. Therefore, it was possible to verify that the MGB desire is an important psychological variable for both females and males. Furthermore, these findings offer academic practical implications to travel and tourism companies by presenting basic data based on the results of empirical research analysis in the context of the current dangerous situation.

An Investigation of Customer Psychological Perceptions of Green Consciousness in a Green Hotel Context: Applying a Extended Theory of Planned Behavior.

We investigated the relationship between green consciousness and green behavior, and the relationship between psychological state, attitude, and behavior of green hotel customers by applying variables suitable for an expanded theory of planned behavior. The purpose of the study was to predict green behavior based on the theory of planned behavior. Together with preceding research including the correlation between customers' image perception of green corporate social responsibility (CSR), green psychological benefit, and green consciousness, we added willingness to sacrifice for the environment to define the relationship with green consciousness and green behavior. A survey was conducted with 410 customers of green hotels in Seoul, Korea more than twice over a period of over 6~12 months. Vague and insincere answers were removed. SPSS 18.0 and Amos 20.0 were used to conduct factor and SEM data analysis. Our theory was verified and adopted following validation from our analysis. The results have important theoretical and practical implications for the environment by providing primary data on customers' perceptions of eco friendliness to support the establishment of corporate management strategies. Moreover, they may encourage green hotels to participate in preventing environmental problems.

Genome-Wide Identification of Discriminative Genetic Variations in Beef and Dairy Cattle via an Information-Theoretic Approach.

Analyzing the associations between genotypic changes and phenotypic traits on a genome-wide scale can contribute to understanding the functional roles of distinct genetic variations during breed development. We performed a whole-genome analysis of Angus and Jersey cattle breeds using conditional mutual information, which is an information-theoretic method estimating the conditional independency among multiple factor variables. The proposed conditional mutual information-based approach allows breed-discriminative genetic variations to be explicitly identified from tens of millions of SNP (single nucleotide polymorphism) positions on a genome-wide scale while minimizing the usage of prior knowledge. Using this data-driven approach, we identified biologically relevant functional genes, including breed-specific variants for cattle traits such as beef and dairy production. The identified lipid-related genes were shown to be significantly associated with lipid and triglyceride metabolism, fat cell differentiation, and muscle development. In addition, we confirmed that milk-related genes are involved in mammary gland development, lactation, and mastitis-associated processes. Our results provide the distinct properties of Angus and Jersey cattle at a genome-wide level. Moreover, this study offers important insights into discovering unrevealed genetic variants for breed-specific traits and the identification of genetic signatures of diverse cattle breeds with respect to target breed-specific properties.

Cattle genome-wide analysis reveals genetic signatures in trypanotolerant N'Dama.

BACKGROUND: Indigenous cattle in Africa have adapted to various local environments to acquire superior phenotypes that enhance their survival under harsh conditions. While many studies investigated the adaptation of overall African cattle, genetic characteristics of each breed have been poorly studied. RESULTS: We performed the comparative genome-wide analysis to assess evidence for subspeciation within species at the genetic level in trypanotolerant N'Dama cattle. We analysed genetic variation patterns in N'Dama from the genomes of 101 cattle breeds including 48 samples of five indigenous African cattle breeds and 53 samples of various commercial breeds. Analysis of SNP variances between cattle breeds using wMI, XP-CLR, and XP-EHH detected genes containing N'Dama-specific genetic variants and their potential associations. Functional annotation analysis revealed that these genes are associated with ossification, neurological and immune system. Particularly, the genes involved in bone formation indicate that local adaptation of N'Dama may engage in skeletal growth as well as immune systems. CONCLUSIONS: Our results imply that N'Dama might have acquired distinct genotypes associated with growth and regulation of regional diseases including trypanosomiasis. Moreover, this study offers significant insights into identifying genetic signatures for natural and artificial selection of diverse African cattle breeds.

Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors.

Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting. Here we propose a deep-learning neural network architecture that resolves the catastrophic forgetting problem. Based on the neurocognitive theory of the complementary learning systems of the neocortex and hippocampus, we introduce a dual memory architecture (DMA) that, on one hand, slowly acquires the structured knowledge representations and, on the other hand, rapidly learns the specifics of individual experiences. The DMA system learns continuously through incremental feature adaptation and weight transfer. We evaluate the performance on two real-life datasets, the CIFAR-10 image-stream dataset and the 46-day Lifelog dataset collected from Google Glass, showing that the proposed model outperforms other online learning methods.

Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes.

Predicting the clinical outcomes of cancer patients is a challenging task in biomedicine. A personalized and refined therapy based on predicting prognostic outcomes of cancer patients has been actively sought in the past decade. Accurate prognostic prediction requires higher-order representations of complex dependencies among genetic factors. However, identifying the co-regulatory roles and functional effects of genetic interactions on cancer prognosis is hindered by the complexity of the interactions. Here we propose a prognostic prediction model based on evolutionary learning that identifies higher-order prognostic biomarkers of cancer clinical outcomes. The proposed model represents the interactions of prognostic genes as a combinatorial space. It adopts a flexible hypergraph structure composed of a large population of hyperedges that encode higher-order relationships among many genetic factors. The hyperedge population is optimized by an evolutionary learning method based on sequential Bayesian sampling. The proposed learning approach effectively balances performance and parsimony of the model using information-theoretic dependency and complexity-theoretic regularization priors. Using MAQC-II project data, we demonstrate that our model can handle high-dimensional data more effectively than state-of-the-art classification models. We also identify potential gene interactions characterizing prognosis and recurrence risk in cancer.

Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning.

BACKGROUND: Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes. RESULTS: We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits. CONCLUSIONS: Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.