Does inclusive leadership influence task performance of hospitality industry employees? Role of psychological empowerment and trust in leader.

Drawing on the social exchange theory, this research advances the understanding of leadership and task performance in the hospitality industry in China by exploring the impact of inclusive leadership on the task performance of subordinates working in dyadic forms. The current literature is scarce on the role of leadership in increasing the task performance of employees working in teams in dyadic forms. Multi-level sample of 410 leaders-subordinates in the hospitality industry was used to derive the research findings using PLS-SEM. The results indicated a positive influence of inclusive leadership on the task performance of subordinates. Psychological empowerment mediated this direct relationship. In addition, trust in leaders strengthened the direct link of inclusive leadership with task performance and psychological empowerment. The findings demonstrate that leaders in the hospitality industry should adopt an inclusive leadership style as it contributes to employee task performance, which improves the industry's performance.

Needs, Challenges and Countermeasures of SARS-CoV-2 Surveillance in Cold-Chain Foods and Packaging to Prevent Possible COVID-19 Resurgence: A Perspective from Advanced Detections.

The pandemic caused by SARS-CoV-2 has a huge impact on the global economy. SARS-CoV-2 could possibly and potentially be transmitted to humans through cold-chain foods and packaging (namely good-to-human), although it mainly depends on a human-to-human route. It is imperative to develop countermeasures to cope with the spread of viruses and fulfil effective surveillance of cold-chain foods and packaging. This review outlined SARS-CoV-2-related cold-chain food incidents and current methods for detecting SARS-CoV-2. Then the needs, challenges and practicable countermeasures for SARS-CoV-2 detection, specifically for cold-chain foods and packaging, were underlined. In fact, currently established detection methods for SARS-CoV-2 are mostly used for humans; thus, these may not be ideally applied to cold-chain foods directly. Therefore, it creates a need to develop novel methods and low-cost, automatic, mini-sized devices specifically for cold-chain foods and packaging. The review intended to draw people's attention to the possible spread of SARS-CoV-2 with cold-chain foods and proposed perspectives for futuristic cold-chain foods monitoring during the pandemic.

Comparison of CRISPR/Cas and Argonaute for nucleic acid tests.

Guided, programmable, and target-activated nucleases, exemplified by Cas in the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) system and Argonaute (Ago), are emerging as a new generation of nucleic acid tests (NATs). A specific approach for comparison of these two nucleases side by side in terms of similarities, differences, and complementarities is instrumental for the sensible design of novel NATs.

A SERS-signalled, CRISPR/Cas-powered bioassay for amplification-free and anti-interference detection of SARS-CoV-2 in foods and environmental samples using a single tube-in-tube vessel

The pandemic of COVID-19 creates an imperative need for sensitive and portable detection of SARS-CoV-2. We devised a SERS-read, CRISPR/Cas-powered nanobioassay, termed as OVER-SARS-CoV-2 (One-Vessel Enhanced RNA test on SARS-CoV-2), which enabled supersensitive, ultrafast, accurate and portable detection of SARS-CoV-2 in a single vessel in an amplification-free and anti-interference manner. The SERS nanoprobes were constructed by conjugating gold nanoparticles with Raman reporting molecular and single-stranded DNA (ssDNA) probes, whose aggregation-to-dispersion changes can be finely tuned by target-activated Cas12a though trans-cleavage of linker ssDNA. As such, the nucleic acid signals could be dexterously converted and amplified to SERS signals. By customizing an ingenious vessel, the steps of RNA reverse transcription, Cas12a trans-cleavage and SERS nanoprobes crosslinking can be integrated into a single and disposal vessel. It was proved that our proposed nanobioassay was able to detect SARS-CoV-2 as low as 200 copies/mL without any pre-amplification within 45 min. In addition, the proposed nanobioassay was confirmed by clinical swab samples and challenged for SARS-CoV-2 detection in simulated complex environmental and food samples. This work enriches the arsenal of CRISPR-based diagnostics (CRISPR-Dx) and provides a novel and robust platform for SARS-CoV-2 decentralized detection, which can be put into practice in the near future. Graphical abstract

Leveraging MOOCs for learners in economically disadvantaged regions

While Massive Online Open Courses (MOOCs) have seen a surge in enrollments in higher education around the world especially during the Covid-19 pandemic, it is unclear if learners from the economically disadvantaged regions (EDR) are also able to capitalize on them. Specifically, challenges related to using MOOCs in these regions have been reported in the literature. Thus, the objective of this paper is to address the pedagogical challenge by investigating approaches to leverage MOOCs for learners in EDR. Drawing from the ARCS (i.e. Attention, Relevance, Confidence and Satisfaction) model, we proposed an embedded MOOCs approach where bite-sized MOOCs segments are integrated into in-class lectures under the guidance of the instructors. The effectiveness of the embedded MOOCs approach was evaluated and compared with other instructional methods. Results from randomized experiments showed that the embedded MOOCs approach had higher evaluations in terms of attention, relevance and satisfaction than face-to-face learning approach. In addition, the embedded MOOCs approach outperformed asynchronously blended MOOCs in enhancing students' relevance perception. Regression analysis further revealed that attention, confidence, and satisfaction perceptions were positively associated with students' intention to adopt the embedded MOOCs approach in their future studies. The findings shed light on how to utilize MOOCs and re-use content in MOOCs for global benefits and enable new pedagogical developments. The findings also underscore the importance of local social support and offline interactions to support the online learning materials.

A SERS-signalled, CRISPR/Cas-powered bioassay for amplification-free and anti-interference detection of SARS-CoV-2 in foods and environmental samples using a single tube-in-tube vessel.

The pandemic of COVID-19 creates an imperative need for sensitive and portable detection of SARS-CoV-2. We devised a SERS-read, CRISPR/Cas-powered nanobioassay, termed as OVER-SARS-CoV-2 (One-Vessel Enhanced RNA test on SARS-CoV-2), which enabled supersensitive, ultrafast, accurate and portable detection of SARS-CoV-2 in a single vessel in an amplification-free and anti-interference manner. The SERS nanoprobes were constructed by conjugating gold nanoparticles with Raman reporting molecular and single-stranded DNA (ssDNA) probes, whose aggregation-to-dispersion changes can be finely tuned by target-activated Cas12a though trans-cleavage of linker ssDNA. As such, the nucleic acid signals could be dexterously converted and amplified to SERS signals. By customizing an ingenious vessel, the steps of RNA reverse transcription, Cas12a trans-cleavage and SERS nanoprobes crosslinking can be integrated into a single and disposal vessel. It was proved that our proposed nanobioassay was able to detect SARS-CoV-2 as low as 200 copies/mL without any pre-amplification within 45 min. In addition, the proposed nanobioassay was confirmed by clinical swab samples and challenged for SARS-CoV-2 detection in simulated complex environmental and food samples. This work enriches the arsenal of CRISPR-based diagnostics (CRISPR-Dx) and provides a novel and robust platform for SARS-CoV-2 decentralized detection, which can be put into practice in the near future.

Leveraging MOOCs for learners in economically disadvantaged regions.

While Massive Online Open Courses (MOOCs) have seen a surge in enrollments in higher education around the world especially during the Covid-19 pandemic, it is unclear if learners from the economically disadvantaged regions (EDR) are also able to capitalize on them. Specifically, challenges related to using MOOCs in these regions have been reported in the literature. Thus, the objective of this paper is to address the pedagogical challenge by investigating approaches to leverage MOOCs for learners in EDR. Drawing from the ARCS (i.e. Attention, Relevance, Confidence and Satisfaction) model, we proposed an embedded MOOCs approach where bite-sized MOOCs segments are integrated into in-class lectures under the guidance of the instructors. The effectiveness of the embedded MOOCs approach was evaluated and compared with other instructional methods. Results from randomized experiments showed that the embedded MOOCs approach had higher evaluations in terms of attention, relevance and satisfaction than face-to-face learning approach. In addition, the embedded MOOCs approach outperformed asynchronously blended MOOCs in enhancing students' relevance perception. Regression analysis further revealed that attention, confidence, and satisfaction perceptions were positively associated with students' intention to adopt the embedded MOOCs approach in their future studies. The findings shed light on how to utilize MOOCs and re-use content in MOOCs for global benefits and enable new pedagogical developments. The findings also underscore the importance of local social support and offline interactions to support the online learning materials.

A Smartphone-Based Detection Method of Colloidal Gold Immunochromatographic Strip

The outbreak of the new coronavirus (SARS-CoV-2) infection has become a global public health crisis. Antigen detection strips (colloidal gold) can be widely used in novel coronavirus clinical screening and can even be extended to home self-testing, which provides a practical and effective way for people to obtain health status information away from the crowd. In this paper, a colloidal gold detection system without complex devices is proposed, which is based on smartphone usage along with a mobile-phone software embedded with normalization algorithms and a special designed background paper. The basic principle of the device relies on image processing. First, the data of the green channel of the image captured by a smartphone are selected to be processed. Second, the calibration curves are established using standard black and white card, and the calibration values under different detection environments are obtained by calibration curves. Finally, to verify the validity of the proposed method, various standard solutions with different concentrations are tested. Results show that this method can eliminate the influence of different environments on the test results, the test results in different detection environments have good stability and the variation coefficients are less than 5%. It fully proves that the detection system designed in this paper can detect the result of colloidal gold immunochromatographic strip in time, conveniently and accurately in different environments.

Characterizing the BCR repertoire during lymphocyte reduction and recovery mediated by cyclophosphamide and granulocyte-macrophage colony-stimulating factor.

Leukopenia is a common manifestation of many diseases, including global outbreak SAS-CoV-2 infection. Granulocyte-macrophage colony-stimulating factor (GM -CSF) has been proved to be effective in promoting lymphocyte regeneration, but adverse immunological effects have also emerged. This study aim to investigate the effect of GM -CSF on BCR heavy chain CDR3 repertoire while promoting lymphocyte regeneration. Cyclophosphamide (CTX) and GM -CSF were used to inhibit and stimulate bone marrow hematopoiesis, respectively. High throughput sequencing was applied to detect the characteristics of BCR CDR3 repertoire in controls, CTX group and GM -CSF group. The white blood cells (WBCs) were quickly reduced (P < 0.05) with lymphocytes decreasing causing by CTX, and the WBCs and lymphocytes returned to the level of controls after GM -CSF treatment. The diversity of BCR heavy chain CDR3 repertoire was also significantly decreased in CTX group. Although there is still a big gap from the controls, the diversity was picked up after GM -CSF treatment. The expression of IGHD01-01, IGHD02-14 and IGHJ04-01 with high-frequency usage regularly and significantly changed in three groups, and many genes with low-frequency usage lost in CTX group and did not reappear in GM -CSF group. Moreover, two shared sequences and accounted for the highest proportion in GM -CSF group have been detected in animal model of chronic lymphocytic leukemia. These results revealed that GM -CSF can partially restore changes in the BCR heavy chain CDR3 repertoire while promoting lymphocyte regeneration, but it may also lead to rearrangement, proliferation and activation of abnormal B cells, which can provide a basis for further study on the adverse immunological effects and mechanism of GM -CSF treatment.

A smartphone-based visual biosensor for CRISPR-Cas powered SARS-CoV-2 diagnostics.

The pandemic of coronavirus disease 2019 (COVID-19) resulted from novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a worldwide concern. It is imperative to develop rapid, sensitive, and specific biosensing methods. Herein, we developed a CRISPR-Cas12a powered visual biosensor with a smartphone readout for ultrasensitive and selective detection of SARS-CoV-2. Simply, the SARS-CoV-2 derived nucleic acids triggered CRISPR-Cas12a based indiscriminate degradation of a single-stranded DNA that was supposed to link two gold nanoparticles, inducing the dis-aggregation of gold nanoparticles and thus generating observable color changes. This change can be readily distinguished by naked eyes as well as a smartphone with a Color Picker App. The proposed biosensor was successfully applied to detect SARS-CoV-2 gene in synthetic vectors, transcribed RNA and SARS-CoV-2 pseudoviruses. It rendered "single copy resolution" as evidenced by the 1 copy/µL limit of detection of pseudoviruses with no cross-reactivity. When the developed biosensor was challenged with SARS-CoV-2 clinical bio-samples, it provided 100% agreement (both positive and negative) with qPCR results. The sample-to-result time was roughly 90 min. Our work provides a novel and robust technology for ultrasensitive detection of SARS-CoV-2 that could be used clinically.

CRISPR-Cas based virus detection: Recent advances and perspectives.

Viral infections are one of the most intimidating threats to human beings. One convincing example is the coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2. Rapid, sensitive, specific and field-deployable identification of causal viruses is critical for disease surveillance, control and treatment. The shortcomings of current methods create an impending need for developing novel biosensing platforms. CRISPR-Cas systems, especially CRISPR-Cas12a and CRISPR-Cas13a, characterized by their sensitivity, specificity, high base resolution and programmability upon nucleic acid recognition, have been repurposed for molecular diagnostics, surging a new path forward in biosensing. They, as the core of some robust diagnostic tools, are revolutionizing the way that virus can be detected. This review focuses on recent advances in virus detection with CRISPR-Cas systems especially CRISPR-Cas12a/Cas13a. We started with a short introduction to CRISPR-Cas systems and the properties of Cas12a and Cas13a effectors, and continued with reviewing the current advances of virus detection utilizing CRISPR-Cas systems. The significance and advantages of such methods were then discussed. Finally, the challenges and perspectives were proposed. We tried to provide readers with a concise profile of emerging and fast-expanding CRISPR-Cas based biosensing technology, and highlighted its potential applications in a range of scenarios with regard to virus detection.

Evaluating the effectiveness of blended learning using the ARCS model

While the educational disruption caused by the Covid‐19 pandemic underscores the importance of blended learning in higher education, research on the effectiveness of blended learning is still inconclusive. Drawing from the motivational design model of the ARCS (i.e., attention, relevance, confidence, and satisfaction), this study attempts to fill the gap to evaluate effectiveness of blended learning from a multi‐dimensional perspective. Participants were randomly assigned into three experimental groups (i.e., face‐to‐face, pure online, and blended). A questionnaire survey was administered in each group after the trial courses. The data was analysed by using the one‐way ANOVA with post hoc tests. The results showed that blended learning outperformed pure online learning in enhancing students' attention, confidence, and satisfaction perceptions. Additionally, blended learning had a higher level of satisfaction perception than face‐to‐face learning. Follow‐up interviews were also conducted to provide an in‐depth understanding of how blended learning motivated students during the learning process. Considering that blended learning may become a new normal in higher education after the Covid‐19 pandemic, the findings of the present study provide evidences to support the effectiveness of the blended learning approach in addressing students' motivational needs. [ABSTRACT FROM AUTHOR] Copyright of Journal of Computer Assisted Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effect of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on reproductive system.

Since the emergence of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in December 2019, it has rapidly spread across many countries and it has become a crucial global health concern. Furthermore, SARS-CoV-2 infection not only effect on respiratory system, but on reproductive system of human. However, there has been not any review described the transmission paths and effects of SARS-CoV-2 infection on human reproductive system, systematically. In order to describe the transmission paths of SARS-CoV-2, effect on the male/female reproductive system of SARS-CoV-2 and some successful prevention measures. We would like to review effect of SARS-CoV-2 on reproductive system. To conclude, SARS-CoV-2 infection might damage to male reproductive system via ACE2 receptor mediating and male patients were reportedly slightly more affected than women by SARS-CoV-2 infections.

Comparing the accuracy of several network-based COVID-19 prediction algorithms.

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei.

At the moment of writing, the future evolution of the COVID-19 epidemic is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the COVID-19 epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose the Network-Inference-Based Prediction Algorithm (NIPA) to forecast the future prevalence of the COVID-19 epidemic in every city. Our results indicate that NIPA is beneficial for an accurate forecast of the epidemic outbreak.