Examining emotional labor in covid-19 through the lens of self-efficacy

The ongoing COVID-19 pandemic has dealt a significant blow to the restaurant industry, with many restaurants suspending operations or closing altogether. This study was aimed to investigate the effects of emotional labor on employees’ well-being and the mediating role of self-efficacy in the context of chain restaurants. Data were collected in 2020 through an online survey in China, and results revealed that emotional labor had a significant impact on well-being that was measured in life and job satisfaction. Self-efficacy not only had a significant positive impact on employees’ job-and life-related well-being but also played a fully mediating role between deep acting and life satisfaction, with a partial mediating role between deep acting and job satisfaction. Job-related well-being also played a fully mediating role between deep acting and life satisfaction, with a partial mediating role between deep acting and job satisfaction. It is important for restaurant employees to develop deep acting skills and improve self-efficacy and job satisfaction Restaurant managers must establish a healthy working environment by providing better job support and creating a more relaxed working atmosphere

How the leading Chinese real estate brokerage transformed into a digital platform business

Purpose In this article, we study how a Chinese real estate broker - Lianjia successfully transformed itself into Beike - China’s leading digital platform for housing transactions and services. We explain the motivation behind this platform transformation, how it turned out, and what are the lessons learned for other companies contemplating a platform transformation. Beike’s lessons are significant as they not only can help the companies achieve growth via platform transformation but also create social value by contributing to higher service quality in traditional service industries. Design/methodology/approach We draw upon comprehensive archival research into Beike, and our many years of ongoing research on platform strategy and business growth strategy. Findings This article provides important lessons for companies in traditional service industries on how to expand growth via digital platforms. We summarize four key lessons learned: 1) data is central to success in platform transformation; 2) industry knowledge and experience play an important role; 3) the right platform governance is critical in value creation; 4) harness the double powers of platform and digital transformation. Research limitations/implications More research on digital platforms and platform transformation in traditional service industries is needed to delve into the underlying factors and delineate the boundary conditions for specific details in this strategy and implementation. Practical implications This article is useful to business executives, academics, management consultants, and entrepreneurs interested in learning more about how to use digital platforms to achieve business growth and create economic and social value. In particular, Beike’s case offers inspiration and valuable lessons to companies in traditional service industries and helps them consider the factors that are important in the process of platform transformation. Social Implications This article on Beike provides an innovative solution to business leaders in traditional service industries grappling with a lack of professional standards and trust to use digital platforms to elevate service quality and create social value. Originality/value This article is unique and add value because Beike is a pioneer of using the digital platform to achieve growth and transform traditional service industries. Our study shows that platform transformation not only can help a company in a traditional industry achieve impressive growth but at the same time can create enormous social value by elevating the service quality of the whole industry

Inhibitor prediction by machine learning approaches

Master'sMASTER OF SCIENC

Recover From Failure: Examining the Impact of Service Recovery Stages on Relationship Marketing Strategies

Purpose: Given the digital transformation of service businesses by providing online food services and the influence of online reviews on consumers’ purchasing decisions, this study examines how service recovery attributes in different stages influence relationship marketing strategies, i.e., relationship quality and customer loyalty after service failure. This study is built upon a revised service recovery cycle model by accounting for three stages and their corresponding attributes; whereon a conceptual stage model of service recovery is proposed. This conceptual stage model incorporates stages of service recovery, their respective attributes, and how they influence relationship marketing strategies. Design/methodology/approach: An online marketing company was employed for data collection in 2019, which resulted in 301 valid responses. A Structural Equation Model (SEM) was conducted with all the data to test the relationships between the constructs. The individual measurement model was tested using the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). A structural model was estimated using AMOS to test all the hypotheses. Findings: The findings demonstrate that the attributes (i.e., response speed, compensation) paired with the first two stages of service recovery can significantly influence consumer loyalty in a positive state. The findings also manifest the intermediary role that relationship quality has played in the association of service recovery and consumer loyalty, which implies that the food delivery businesses could attain a more comprehended relationship quality with consumers through active and timely compensatory service recovery consumer loyalty to the food businesses. Originality/value: This study examines how these different stages of the service recovery cycle influence the decision-making of relationship marketing strategies (i.e., relationship quality, customer loyalty) on the prerequisite of service failure. This study aspires to expand the service recovery research by objectifying a conceptual stage model of service recovery, incorporating stages’ recovery attributes and how these recovery attributes reciprocally influence relationship quality and customer loyalty

Quantitative systems-level determinants of drug targets

Background: Modern drug discovery tends to understand disease processes at the molecular level and then determine optimal molecular targets for drug intervention. Inferences have made from all available drug targets, such as how many drug targets there are, or how many novel drug targets could be potentially found in the human genome, to what functional families these proteins belong, and what structural properties make them bind to small molecules tightly and specifically. But all these are very intuitive and qualitative. The key question of which gene or protein in a disease process could be a successful drug target remains unanswered. Results: We analyzed specific systems-level properties of human genes and proteins targeted by 919 FDA-approved drugs 1 and identified a number of quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected in a molecular interaction network, but are far from being the most highly connected; they have higher betweenness values, lower entropies of tissue expression, and lower ratios of non-synonymous to synonymous single-nucleotide polymorphisms (see Figure 1). We also tested the performance of different classification algorithms (see Figure 2). Furthermore, we have identified human tissues significantly over- or under-targeted relative to the full spectrum of genes active in each tissue. Figure 1 (A) Connectivity distribution for the entire molecular network (A) Connectivity distribution for the entire molecular network; (B) Targets of the successful drugs are significantly more connected than an average gene in the network, but are not the most highly connected genes in the network; (C) Drug targets tend to have higher than average betweenness values; (D) The successful drug targets are not statistically different from the rest of the genes in terms of their clustering coefficients; (E-F): Analysis of the ratio (Cratio) of the number of non-synonymous to synonymous single-nucleotide polymorphisms (SNPs): (E) Successful drug targets have significantly smaller Cratio than human genes on average. (F) The value of Cratio tends to correlate negatively with the gene's connectivity in the network. Figure 2 Receiver operating characteristic (ROC) curves for the four classification algorithms that we used in this study Receiver operating characteristic (ROC) curves for the four classification algorithms that we used in this study. All four methods that we tested performed significantly better than baseline (ROC score of 0.5, corresponding to a random-guess method). The logistic regression performed best. We also built a machine-learning model to demonstrate the usefulness of these quantitative descriptors for predicting drug targets. With increasing availability of experimental data, we foresee that screening the whole human genome for potential novel drug targets could be feasible in near future. Conclusion: We found that genes associated with successful FDA-approved drugs have a number of properties at the network, sequence, and tissue-expression levels that significantly distinguish them from other human genes. Although the drug-target-selection guidelines that we suggest cannot replace expensive experiments, they can help pharmaceutical researchers narrow the prospective set of drug targets at the earliest stage of a drug development project. Specifically, when the pharmaceutical company must decide which target to pursue among pathologic pathways that are not fully understood, connectivity, betweenness, Cratio, and entropy might be useful quantitative estimates of each prospective target's expected success rate

Benchmarking Ontologies: Bigger or Better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.</p

A Determination of Potential α-Glucosidase Inhibitors from Azuki Beans (Vigna angularis)

A 70% ethanol extract from azuki beans (Vigna angularis) was extracted further with CH2Cl2, EtOAc and n-BuOH to afford four fractions: CH2Cl2-soluble, EtOAc-soluble, n-BuOH-soluble and residual extract fractions. The EtOAc-soluble fractions showed the highest α-glucosidase inhibitory activity. Two pure flavonoid compounds, vitexin and isovitexin, were isolated (using the enzyme assay-guide fractionation method) from the EtOAc-soluble fractions. We further evaluated the interaction between the flavonoid compounds and α-glucosidase by fluorescence spectroscopy. Vitexin and isovitexin showed high inhibitory activities, with IC50 values of 0.4 mg·mL−1 and 4.8 mg·mL−1, respectively. This is the first study of the active compositions of azuki beans against α-glucosidase

Increased expression of MMP9 is correlated with poor prognosis of nasopharyngeal carcinoma

<p>Abstract</p> <p>Introduction</p> <p>The aim of the present study was to analyze the expression of matrix metalloproteinase 9 (<it>MMP9</it>) in nasopharyngeal carcinoma (NPC) and its correlation with clinicopathologic features, including the survival of patients with NPC.</p> <p>Methods</p> <p>Using real-time PCR, we detected the mRNA expression of <it>MMP9 </it>in normal nasopharyngeal tissues and nasopharyngeal carcinoma (NPC) tissues. Using immunohistochemistry analysis, we analyzed <it>MMP9 </it>protein expression in clinicopathologically characterized 164 NPC cases (116 male and 48 female) with age ranging from 17 to 80 years (median = 48.4 years) and 32 normal nasopharyngeal tissues. Cases with greater than or equal to 6 and less than 6 of the score value of cytoplasmic <it>MMP9 </it>immunostaining were regarded as high expression and low expression, respectively. The relationship between the expression levels of <it>MMP9 </it>and clinical features was analyzed.</p> <p>Results</p> <p>The expression level of <it>MMP9 </it>mRNA was markedly greater in NPC tissues than that in the nasopharyngeal tissues. Immunohistochemical analysis revealed that the protein expression of <it>MMP9 </it>detected in NPC tissues was higher than that in the nasopharyngeal tissues (<it>P </it>= 0.004). In addition, high levels of <it>MMP9 </it>protein were positively correlated with the status of lymph node metastasis (N classification) (<it>P </it>= 0.002) and clinical stage (<it>P </it>< 0.001) of NPC patients. Patients with higher <it>MMP9 </it>expression had a significantly shorter overall survival time than did patients with low <it>MMP9 </it>expression. Multivariate analysis suggested that the level of <it>MMP9 </it>expression was an independent prognostic indicator (<it>P </it>= 0.008) for the survival of patients with NPC.</p> <p>Conclusion</p> <p>High level of <it>MMP9 </it>expression is a potential unfavorable prognostic factor for patients with NPC.</p

Inferring the patient’s age from implicit age clues in health forum posts

Broader patient-reported experiences in oncology are largely unknown due to the lack of available information from traditional data sources. Online health community data provide an exploratory way to uncover these experiences at a large scale. Analyzing these data can guide further studies towards understanding patients’ needs and experiences. However, analysis of online health data is inherently difficult due to the unstructured nature of these data and the variety of ways information can be expressed over text. Specifically, subscribers may not disclose critical information such as the age of the patient in their posts. In fact, the number of health forum posts that explicitly mention the age of the patient is significantly lower than the number of posts that do not include this information in the Reddit r/Cancer health forum under consideration in the present paper. Health-focused studies often need to consider or control for age as a confounder, hence the importance of having sufficient age data. This paper presents a methodology that can help classify health forum posts according to four age groups (0–17, 18–39, 40–64 and 65 + years) even when the posts do not contain explicit mention of the age of the patient. First, the subset of the posts that include explicit mention of the age of the patient is identified. Second, the explicit age clues are removed from these posts and used to train the proposed age classifier. The resulting classifier is able to infer the age of the patient using only implicit age clues with an average true positive rate (TPR) of 71%. This TPR is comparable to the average TPR of 69% obtained from human annotations for the same set of posts