Recent progress in microfluidic biosensors with different driving forces

Microfluidic biosensors integrating fluid control, target recognition, as well as signal transduction and output, have been widely used in the field of disease diagnosis, drug screening, food safety and environmental monitoring in the past two decades. As the central part and technical characteristics of microfluidic biosensors, the fluid control is not only associated with accuracy and convenience of the sensors, but also affects the material selection and working mode of the sensors. This review summarizes the fluid driving forces for microfluidic biosensors, including gravity, capillary force, centrifugal force, pressure, light, sound, electrical, and magnetic forces. Then, the recent advances in microfluidic biosensors for the detection of viruses, cells, nucleic acids, proteins and small molecules are discussed. Finally, we propose the current challenges and future perspectives of microfluidic biosensors. We hope this review can provide readers with a new perspective to understand the technical characteristics and application potential of microfluidic biosensors.Copyright © 2022 Elsevier B.V.

Recognition System of Multi-model Chest X-Ray Diseases Based on YOLO v4

We use three models to build and design a multi-model identification system, and the identification results between the models are verified to increase accuracy. The identified lung diseases are classified into four categories, namely Normal, COVID-19, Tuberculosis, and Viral Pneumonia cases. After the user uploads the chest X-ray image, the system displays the results of the three identification types, and the calculation time is about 5 to 10 s. The accuracy of the multi-model system is better than that of the single-model system. If the Normal cases are included, the specificity is 77.41% for the traditional single-model system and 89.81% for the multi-model system. Additionally, if Normal cases are excluded, the F1 score is 70.00% for the single-model system and 80.7% for the multi-model system. Compared with the neural network with Faster R-CNN F1-Score of 90%, Mask R-CNN F1-Score of 85% and resNet-50 F1-Score of 80% are obtained. © 2022 IEEE.

A Cultural Approach to Understanding Publics and their Information Behaviors during COVID-19: Self-Construal and Identity Salience

Integrating the situational and cross-situational approaches to understanding publics, this study examined cultural antecedents (self-construal and political identity salience) to situational perceptions (problem recognition, involvement recognition, constraint recognition), situational motivation, and key information behavior regarding the COVID-19 pandemic. Results from an online survey (N = 556) showed that political identity salience and interdependent self-construal triggered publics' situational perceptions, which in turn activated their situational motivation and information forwarding behaviors. The study contributed to public research through examining important cultural influences on value-laden and polarized issues and revealing additional nuances in communicative activeness. © 2022 Taylor & Francis Group, LLC.

Evolutionary Multiobjective Site Selection of Xi'an Medical Emergency Material Warehouse Based on Multiple Memetic Directions

Due to the lack of medical materials in some emergency public events, for example, the outbreak of COVID-19, it is urgent to establish a medical emergency material warehouse. Taking Xi'an, China, as an example, this study aims to select suitable sites of Xi'an medical emergency material warehouse. In this study, the problem of site selection models as a multiobjective optimization problem. The coverage function and comprehensive efficiency function are designed as two conflicting objectives. Then, a multiobjective evolutionary algorithm based on multiple memetic direction is proposed to optimize the two objectives concurrently. The crossover and mutation operators are designed for evolutionary multiobjective site selection. The proposed crossover operator is able to balance the global and local search abilities, and the proposed mutation operator fuses the distribution information of hospital location, service population, and the overall coverage. Experiments on real dataset verify the superiority of the proposed evolutionary multiobjective site selection method.

A Cultural Approach to Understanding Publics and their Information Behaviors during COVID-19: Self-Construal and Identity Salience

Integrating the situational and cross-situational approaches to understanding publics, this study examined cultural antecedents (self-construal and political identity salience) to situational perceptions (problem recognition, involvement recognition, constraint recognition), situational motivation, and key information behavior regarding the COVID-19 pandemic. Results from an online survey (N = 556) showed that political identity salience and interdependent self-construal triggered publics' situational perceptions, which in turn activated their situational motivation and information forwarding behaviors. The study contributed to public research through examining important cultural influences on value-laden and polarized issues and revealing additional nuances in communicative activeness.

Transmission Dynamics and Quarantine Control of COVID-19 in Cluster Community

Coronavirus Disease 2019 (COVID-19) is a zoonotic illness which has spread rapidly and widely in past two years and was identified as a global pandemic by the World Health Organization (WHO). The pandemic to date has been characterized by ongoing cluster community transmission. Quarantine intervention to prevent and control the transmission is expected to have a substantial impact on delaying the growth and mitigating the size of the epidemic. To our best knowledge, this study is among the initial efforts to analyze the interplay between transmission dynamics and quarantine intervention of the COVID-19 outbreak in a cluster community. In the chapter, we propose a novel Transmission-Quarantine epidemiological model by non-linear ordinary differential equations system. With the use of detailed epidemiologic data from the Cruise ship “Diamond Princess,” we design a Transmission-Quarantine work-flow to determine the optimal case-specific parameters and validate the proposed model by comparing the simulated curve with the real data. Firstly, we apply a general SEIR-type epidemic model to study the transmission dynamics of COVID-19 without quarantine intervention and present the analytic and simulation results for the epidemiological parameters such as the basic reproduction number, the maximal scale of infectious cases, the instant number of recovered cases, the popularity level, and the final scope of the epidemic of COVID-19. Secondly, we adopt the proposed Transmission-Quarantine interplay model to predict the varying trend of COVID-19 with quarantine intervention and compare the transmission dynamics with and without quarantine to illustrate the effectiveness of the quarantine measure, which indicates that with quarantine intervention, the number of infectious cases in 7 days decreases by about 60%, compared with the scenario of no intervention. Finally, we conduct sensitivity analysis to simulate the impacts of different parameters and different quarantine measures and identify the optimal quarantine strategy that can be used by the decision makers to achieve the maximal protection of population with the minimal interruption of economic and social development. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A smartphone-assisted high-throughput integrated color-sensing platform for the rapid detection of Campylobacter coli

Campylobacter is one of the most common foodborne pathogens worldwide. A new smartphone-assisted high-throughput integrated color-sensing platform, called the HICS platform, was developed for the rapid detection of Campylobacter coli. This platform was based on the visual loop-mediated isothermal amplification method. Using this system, as many as 64 samples could be assessed in less than an hour after enrichment. 60 meat samples were tested to compare the performance of the HICS platform and other methods. Having the consistent detection result with TaqMan qPCR (Quantitative Real-time Polymerase Chain Reaction), the HICS platform was able to reliably detect C. coli in meat samples, and its limit of detection is 550 CFU/mL and 120 copies/mu L, which was 10-fold higher than that of the PCR (Polymerase Chain Reaction) method. To conclude, considering that our platform showed robust performance and does not require any expensive equipment, it can also be reliably applied for the high-throughput detection of other pathogens.

Cook and Move for Your Life: A pilot study of an online intervention to improve diet and physical activity among breast cancer survivors

Purpose: The Cook & Move for Your Life randomized pilot study assessed the feasibility and relative efficacy of two dose levels of a remotely-delivered diet and physical activity (PA) intervention for breast cancer (BC) survivors. Methods: Women with a history of stage 0-III BC who were >60 days post-treatment, ate <5 servings per day of fruits/vegetables or engaged in <150 minutes per week of moderate to vigorous physical activity (MVPA), and had smartphone or computer access were enrolled. Participants were randomized to receive one of two doses of an online diet and PA didactic and experiential program, with outcomes measured at 6 months. The low-dose arm received a single 2-hour Zoom session delivered by a dietitian, a chef, a culinary educator, and an exercise physiologist;the high-dose arm received 12 2-hour Zoom sessions over 6 months. All participants received weekly motivational text messages, a Fitbit to self-monitor PA, and study website access. The primary objective was to evaluate overall feasibility based on accrual, adherence, and retention. Prespecified feasibility endpoints were 75% retention at 6 months and 60% of high-dose arm participants attending at least 8 of the 12 sessions. Secondary objectives were to compare high vs. low dose intervention effects on 6-month changes in fruit/vegetable servings per day (24-hour dietary recall), MVPA minutes per week (accelerometry), and blood and stool biomarkers.Results: From December 2019 to January 2021, 74 women were accrued. On average, women were 57.9 years old, 4.8 years post-diagnosis, with body mass index of 29.1 kg/m2 . Most were nonHispanic white (89.2%), 51.4% were diagnosed at stage I, and 40.5% were on endocrine therapy. Questionnaire and biospecimen data collection at 6-months were completed for 93.2% and 83.8% of the sample, respectively. In the low-dose arm (n=36), 94.4% of participants attended the single class, while in the high-dose arm (n=38) 84.2% of participants attended at least 8 of the 12 sessions live or via video archived on the website (mean 9.4 sessions). On average over the 6-month intervention period, participants responded to 71.5% of the text messages, 73.0% wore their Fitbit device ≥50% of the time, and 77.0% accessed the study website. Mean vegetable intake increased by 1 serving per day among women in the high-dose arm and decreased slightly among women in the low-dose arm (P=0.03). Changes in fruit/vegetable intake and MVPA varied little by arm. Blood and stool biomarker analyses are ongoing. Conclusion: We successfully conducted a remotely-delivered diet and PA intervention for BC survivors with high accrual, adherence, and retention during the COVID-19 pandemic. Women in the high-dose arm increased vegetable intake relative to the low-dose arm. Future research will refine and test the intervention in a larger and more diverse study population.

Predictors of objectively-measured sedentary time during the COVID-19 pandemic among breast cancer survivors participating in a diet and physical activity intervention

Purpose of the study: The purpose of this study was to investigate the predictors of objectively-measured sedentary time (ST) among breast cancer (BC) survivors who were 60 days post-treatment and were initiating participation in an intervention to improve diet and physical activity (PA) during the early phase of the COVID-19 pandemic. Methods: Cook and Move for Your Life (CMFYL) was a pilot and feasibility study of stage 0-III BC survivors testing the effects of a remotely-delivered and remotely-assessed nutrition and PA intervention. Women were ≥60 days post-treatment (current endocrine therapy allowed), consumed <5 servings of fruits/vegetables per day and/or engaged in <150 minutes/week of moderate to vigorous physical activity (MVPA). Hip-worn Actigraph GT3X accelerometers measured ST for 7 consecutive days at baseline. ST was defined as minutes/day (continuous) based on the Troiano cutpoint (<100 counts/minute), during awake (6am-11pm) wear time, and non-wear was identified using the Choi algorithm on the vector magnitude counts/minute. Multivariable linear regression models adjusting for wear time (average minutes/day) and minutes of MVPA/day were used to examine whether the following factors were predictors of ST at baseline: self-reported demographics, psychosocial factors (assessed via PROMIS Physical Function and PROMIS Anxiety forms), diet quality (Healthy Eating Index 2015 score), caloric intake (calories/day), and fruit and vegetable intake (servings/day). Results: Among the 84 women included in this analysis who had actigraphy measurements at baseline, the average ST/day was 684±79 minutes. On average, women were 58±10 years in age and most self-identified as non-Hispanic white (87%). The average time since diagnosis at time of enrollment was 4.5 years and 59% of women were receiving endocrine therapy at baseline. Adjusted models show that participants with a college degree had 24.7 (95%CI 2.0, 47.4) more minutes of ST than those with less than a college degree, and for every 1-point increase in PROMIS Physical Function scores participants had 2.5 (95%CI -4.9, -0.2) fewer minutes of ST. Conclusion: In a sample of BC survivors enrolled in a diet and PA intervention, higher level of education and poorer physical function were associated with higher ST during the early phase of the COVID-19 pandemic. These findings provide preliminary insight into factors associated with ST. Future work will investigate how these factors influence change in ST after participation in the CMFYL intervention.

MDN: Meta-transfer Learning Method for Fake News Detection

The rapid development of social media has brought convenience to people’s lives, but at the same time, it has also led to the widespread and rapid dissemination of false information among the population, which has had a bad impact on society. Therefore, effective detection of fake news is of great significance. Traditional fake news detection methods require a large amount of labeled data for model training. For emerging events (such as COVID-19), it is often hard to collect high-quality labeled data required for training models in a short period of time. To solve the above problems, this paper proposes a fake news detection method MDN (Meta Detection Network) based on meta-transfer learning. This method can extract the text and image features of tweets to improve accuracy. On this basis, a meta-training method is proposed based on the model-agnostic meta-learning algorithm, so that the model can use the knowledge of different kinds of events, and can realize rapid detection on new events. Finally, it was trained on a multi-modal real data set. The experimental results show that the detection accuracy has reached 76.7%, the accuracy rate has reached 77.8%, and the recall rate has reached 85.3%, which is at a better level among the baseline methods. © 2022, Springer Nature Singapore Pte Ltd.

Souvenirs: A Systematic Literature Review (1981-2020) and Research Agenda

With the great economic significance of the souvenir business, academic interest in the souvenir field is increasing. The purposes of this study are to examine the holistic development of souvenirs research from 1981 to 2020, identify research themes and gaps, and suggest future research directions. With the tool of VOSViewer software, bibliometric analysis and systematic quantitative literature review were conducted. The research identifies five existing themes: (1) the souvenir object itself;(2) economic significance and socio-cultural impact;(3) souvenir business and ecology;(4) souvenir shopping behavior;and (5) souvenir shopping satisfaction and its consequences. This thematic map contributes to understanding the essence of souvenirs and their relationship with other tourism system elements;it reveals the possibility of exploring tourism phenomena and addressing the challenges through the souvenir field perspective. It has practical implications for the stakeholders to address issues and struggles for development in the context of the COVID-19 pandemic.

A Human Mobility Data Driven Hybrid GNN plus RNN Based Model For Epidemic Prediction

Epidemic simulation traditionally serves as one of the important methods to forecast how an epidemic may spread among a population. However, there are two key limitations that restrict the scope of such methods. The first limitation is that the existing tools rely on different sets of static parameters (e.g., infection probability, recovering probability) for simulating an epidemic spread that may fail to capture the dynamic nature of population interactions that acts as a dominant factor in an epidemic spread scenario such as COVID-19 pandemic. To handle this challenge, we propose a machine learning based model that combines a Graph Convolutional Neural Network (GCN) and a Recurrent Neural Network (RNN). It integrates the ability of the GCN to capture spatial dependency in human interaction and the ability of the RNN to incorporate temporal effects of the virus spread. The second limitation is that these methods do not address the computation overhead problem when dealing with time-dynamic graphs. Training a GCN on a very large graph suffers from the communication overhead from different graph partitions and the computation overheads stemming from partitioning dynamic graphs. This limitation impacts the scalability of the existing systems. To solve this challenge, we partition the graph in a computationally less expensive manner by partitioning the graph using the min-cut principle. We conducted comprehensive large scale real-world human mobility data driven experiments. Our experimental result shows that the proposed machine learning based forecasting model achieves overall 84% classification accuracy with greater than 72% precision and 62% recall. Also, the proposed graph partitioning approach reduces computation time and commutation overhead by a significant margin.

Long Short-Term Memory Based Framework for Longitudinal Assessment of COVID-19 Using CT Imaging and Laboratory Data

Automatic longitudinal assessment of the disease progression of coronavirus disease 2019 (COVID-19) is invaluable to ensure timely treatment for severe or critical patients. An artificial intelligence system that combines chest computed tomography (CT) and laboratory examinations may provide a more accurate diagnosis. To explore an artificial intelligence solution to longitudinally assess the condition of COVID-19 using CT imaging and laboratory findings, from January 27, 2020, to April 3, 2020, multiple follow-up examinations of COVID-19 inpatients were retrospectively collected. CT imaging features were automatically extracted using a deep learning method and combined with laboratory tests. The progression sequences were generated with two follow-ups, each of which contained 60 imaging and 24 laboratory features. Pearson's correlation was conducted to rank the importance of each univariate feature, and multivariate logistic regression was adopted for feature selection. The selected features were used to train a 2-layer long short-term memory network (LSTM) with pulse oxygen saturation (SpO(2)) as an indicator of disease progression in three classes: alleviated, stable, and aggravated. The performance of models trained on various feature subsets was compared with five-fold cross validation.559 patients with 1734 examinations were collected, and 1450 progression sequences were generated. Of the 559 patients, 262 (46.9%) were male. The mean age of the patients was 60 +/- 14 years. The mean hospitalization duration was 31 +/- 12 days. Based on the ranking of importance, 26 features from the imaging and laboratory tests were selected, achieving the best accuracy of 0.85 for progression assessment. The comparisons demonstrated that CT features outperformed laboratory features. The best sensitivities for alleviated and aggravated obtained with CT features alone were 0.83 and 0.85, respectively, while laboratory features improved the assessment precision by about 3%. Longitudinal assessment using deep learning with combined features from CT imaging and laboratory tests better predicts the progression of COVID-19 than either of them.

Moderating Effect of a Cross-Level Social Distancing Policy on the Disparity of COVID-19 Transmission in the United States

Currently, coronavirus disease 2019 (COVID-19) remains a global pandemic, but the prevention and control of the disease in various countries have also entered the normalization stage. To achieve economic recovery and avoid a waste of resources, different regions have developed prevention and control strategies according to their social, economic, and medical conditions and culture. COVID-19 disparities under the interaction of various factors, including interventions, need to be analyzed in advance for effective and precise prevention and control. Considering the United States as the study case, we investigated statistical and spatial disparities based on the impact of the county-level social vulnerability index (SVI) on the COVID-19 infection rate. The county-level COVID-19 infection rate showed very significant heterogeneity between states, where 67% of county-level disparities in COVID-19 infection rates come from differences between states. A hierarchical linear model (HLM) was adopted to examine the moderating effects of state-level social distancing policies on the influence of the county-level SVI on COVID-19 infection rates, considering the variation in data at a unified level and the interaction of various data at different levels. Although previous studies have shown that various social distancing policies inhibit COVID-19 transmission to varying degrees, this study explored the reasons for the disparities in COVID-19 transmission under various policies. For example, we revealed that the state-level restrictions on the internal movement policy significantly attenuate the positive effect of county-level economic vulnerability indicators on COVID-19 infection rates, indirectly inhibiting COVID-19 transmission. We also found that not all regions are suitable for the strictest social distancing policies. We considered the moderating effect of multilevel covariates on the results, allowing us to identify the causes of significant group differences across regions and to tailor measures of varying intensity more easily. This study is also necessary to accomplish targeted preventative measures and to allocate resources.

COVID-19 Infection Detection Using Machine Learning

The COVID-19 pandemic is an ongoing pandemic of coronavirus disease since 2019. Millions of cases and deaths attributed to it have been confirmed in the world. So far the detection of COVID-19 heavily relies on the specialized tests (e.g., based on saliva or respiratory swabs). Some approaches use smart devices (e.g., Whoop) for coronavirus infection detection using respiratory rate. Machine learning (ML) techniques have become a promising approach for the coronavirus infection detection. Therefore, in this paper, we introduce a machine learning based COVID infection predictor. We measure the prediction accuracy of five ML models. We use Chi-square test and knowledge-based manual feature selection to select important features for prediction to reduce prediction time overhead without compromising prediction accuracy. We also study the accuracy with different input features (those that can be measured by medical devices and by smart devices) and find that removing some features has no or slight influence on the prediction accuracy. Since insufficient or unbalanced training data decreases the prediction accuracy, we further propose a Generative Adversarial Network (GAN) ML based predictor that produces synthetic data (close to real data) for ML training. Our extensive experiments show the effectiveness of our methods in improving the detection accuracy. Our study results can provide guidance on developing the coronavirus infection predictors based on different data sources and devices. We open sourced our code in GitHub. © 2021 IEEE.

"A home away from hem": exploring and assessing hotel staycation as the new normal in the Covid-19 era

Purpose Enlightened by the theoretical framework of adapted protection motivation, this study aims to explore and assess the viability and resilience of hospitality consumption in the ongoing Covid-19 era as embodied in the activity of staycation, which is gaining popularity as a rare escape from the hemming in of the pandemic. Design/methodology/approach This study collects data from staycation guests in Hong Kong, which at the time was under semi-lockdown imposing compulsory quarantine for inbound visitors. The data was analyzed through structural equation modeling (SEM). Findings It is revealed by the study results that staycation experiences in Hong Kong are underpinned by a full mediating effect between place attachment and experience quality is performed by sense of presence, together with consolidation of experience quality and psychological detachment as tenable mediators in the research model. Aside from the utilitarian and hedonic values, hospitality and tourism consumption have been engrained with profound socio-cultural implications congenial to the collective identities, recollection and contemplation of a civilized society, with the Covid-19 era and the foreseeable future expected to be no exception. Practical implications The results of this study can serve as reference regarding better planning and development of the staycation product as effective responses against the grave repercussions of the Covid-19 pandemic by hotel and hospitality practitioners and destination marketers and managers at large. In particular, the pandemic has inadvertently availed the opportunity for the destinationalization of the hotel and hospitality sector, with rich implications for industrial consolidations and coordination with destination authorities. Originality/value The holistic structural research model derived and empirically examined entails major antecedents and consequences of the experience quality of staycation guests in locked-down Hong Kong, with the incorporation of the variables of place attachment and extended conceptualization of sense of presence accounting for the efficacy factors of the staycation takers in terms of locality and recollection appraisals, respectively. This study enriches theoretical articulations on staycation as the new normal of hospitality consumption in the lingering pandemic era.

Combating COVID-19 and workplace safety among non-healthcare workers in Hong Kong, Nanjing and Wuhan, China

Introduction: We aimed to develop a new tool to measure workplace safety towards infection control and prevention of COVID-19 for non-healthcare workers in China. Methods and materials: During 07/2020 to 04/2021, 6684 non-healthcare workers were recruited from Hong Kong, Nanjing and Wuhan of China and responded a standard questionnaire of prevention measures towards infectious control. The workplace safety towards SARS-Cov-2 and COVID-19 index (WSSC index) was developed and validated using exploratory factor analysis and confirmatory factor analysis. Robustness of the index was verified by the uptake of SARS-Cov-2 testing. Results: Fourteen variables were identified in the WSSC index, with three sub-domains of workplace’s implementation of OSH prevention measures, company’s OSH management and worker’s prevention behavior and awareness. The new WSCS index obtained a good internal consistency reliability (Cronbach's alpha coefficients: 0.76-0.91), good composite reliability (composite reliability: 0.70-0.95) and satisfactory fit of the model (GFI=0.95;SRMR=0.05;RMSEA=0.07). Workers with higher scores of the WSCS index were more likely to uptake virus testing. Conclusions: This novel index is a validated tool to horizontally measure the performance of workplace safety towards SARS-Cov-2 & COVID-19 among non-healthcare workers across different industries and cities of China. Whether the tool is valid for longitudinally monitoring is under testing.