Evolving MCDM Applications Using Hybrid Expert-Based ISM and DEMATEL Models: An Example of Sustainable Ecotourism

Ecological degradation is an escalating global threat. Increasingly, people are expressing awareness and priority for concerns about environmental problems surrounding them. Environmental protection issues are highlighted. An appropriate information technology tool, the growing popular social network system (virtual community, VC), facilitates public education and engagement with applications for existent problems effectively. Particularly, the exploration of related involvement behavior of VC member engagement is an interesting topic. Nevertheless, member engagement processes comprise interrelated sub-processes that reflect an interactive experience within VCs as well as the value co-creation model. To address the top-focused ecotourism VCs, this study presents an application of a hybrid expert-based ISM model and DEMATEL model based on multi-criteria decision making tools to investigate the complex multidimensional and dynamic nature of member engagement. Our research findings provide insightful managerial implications and suggest that the viral marketing of ecotourism protection is concerned with practitioners and academicians alike

Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Macrophage Migration Inhibitory Factor Induces Autophagy via Reactive Oxygen Species Generation

Autophagy is an evolutionarily conserved catabolic process that maintains cellular homeostasis under stress conditions such as starvation and pathogen infection. Macrophage migration inhibitory factor (MIF) is a multifunctional cytokine that plays important roles in inflammation and tumorigenesis. Cytokines such as IL-1β and TNF-α that are induced by MIF have been shown to be involved in the induction of autophagy. However, the actual role of MIF in autophagy remains unclear. Here, we have demonstrated that incubation of human hepatoma cell line HuH-7 cells with recombinant MIF (rMIF) induced reactive oxygen species (ROS) production and autophagy formation, including LC3-II expression, LC3 punctae formation, autophagic flux, and mitochondria membrane potential loss. The autophagy induced by rMIF was inhibited in the presence of MIF inhibitor, ISO-1 as well as ROS scavenger N-acetyl-L-cysteine (NAC). In addition, serum starvation-induced MIF release and autophagy of HuH-7 cells were partly blocked in the presence of NAC. Moreover, diminished MIF expression by shRNA transfection or inhibition of MIF by ISO-1 decreased serum starvation-induced autophagy of HuH-7 cells. Taken together, these data suggest that cell autophagy was induced by MIF under stress conditions such as inflammation and starvation through ROS generation

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Sustaining the Well-Being of Wearable Technology Users: Leveraging SEM-Based IPMA and VIKOR Analyses to Gain Deeper Insights

Wearable technology is a self-contained computer system that can record muscular activity data. Wearable technologies are rapidly evolving that have the potential to enhance the well-being of healthier lives. However, wearable technologies are finding slow adoption rates relative to mainstream technologies such as smartphones. Consequently, both designers and manufacturers are increasingly interested in key decision factors that influence the acceptance of these technologies. As discussions relating to wearable technologies are often approached from different perspectives, a general framework featuring not only a synthesis of general acceptance issues but also with consideration of contingent factors would be a useful research undertaking. Furthermore, wearable technology acceptance studies are insufficient to supplement practical implementation and promotion issues. In this regard, methods for further analysis of results from structural equation modeling (SEM), such as importance-performance map analysis (IPMA) and VIKOR for multi-criteria optimization and compromise solution, can be used to derive greater insights. The primary research findings are extensively discussed, and practical promotion strategies for wearable technologies for health care are suggested

Sustaining the Benefits of Social Media on Users&rsquo; Health Beliefs Regarding COVID-19 Prevention

During the COVID-19 pandemic, social media has facilitated the efficient and effective dissemination of healthcare information and helped governments keep in touch with their citizens. Research has indicated that social media can exert negative and positive influences on users&rsquo; mental health. One negative effect is social media fatigue caused by information overload. However, under the current pandemic, comprehensive research has yet to be executed on the effect exerted by social media on users&rsquo; health beliefs and subjective well-being (SWB). Consequently, we conducted our research to probe the influence of social media on users&rsquo; perceptions of COVID-19 prevention. This study established a research model based on 340 valid responses to an online questionnaire survey from Taiwan. SmartPLS 3.0 was used to verify the developed measurement and structural models. We found social media users&rsquo; incidental and focused knowledge gain positively related to their social media intensity. In addition, social media intensity positively correlated with health beliefs and SWB. Accordingly, we can determine that proper social media use can enhance health beliefs. Based on our derived findings, we propose a set of practical recommendations to leverage social media effectively and sustainably during, and after, the COVID-19 pandemic

Sustainability of the Benefits of Social Media on Socializing and Learning: An Empirical Case of Facebook

Social network sites (SNSs) provide new avenues for self-expression and connectivity, and they have considerable potential to strengthen social capital and psychological well-being. SNSs have consequently become deeply rooted in people’s daily lives. During the COVID-19 pandemic, e-learning has become a dominant learning modality to maintain social distancing. Because of the excellent connectivity provided by Internet platforms, SNSs can be leveraged as collaborative learning tools to enhance learning performance. However, conflicts may emerge when extending the socializing function to learning; thus, this topic merits in-depth investigation. One potential reason for the conflicts is the various types of overload caused by the system features, information, communication, and social aspects that users experience, leading to negative emotional responses, such as social network fatigue. Although SNS overloads have been extensively studied, most of these studies were conducted from the perspective of SNSs as platforms for socializing, and the overloads were treated as linear and independent. We apply multi-criteria decision-making tools to bridge the research gaps. Specifically, we recruited 15 active Facebook learning community members as an expert panel under the saturation principle. After extensive pairwise comparisons between the primary constructs and further matrix calculations, our significant research findings include antecedents to social network fatigue and their causal effects, representing a valuable complement to conventional structural equation modeling–approaches. We also discuss the theoretical and practical implications of the study