Population genetic characteristics of Aedes aegypti in 2019 and 2020 under the distinct circumstances of dengue outbreak and the COVID-19 pandemic in Yunnan Province, China.

Introduction: Since Aedes aegypti invaded Yunnan Province in 2002, its total population has continued to expand. Shi et al. used microsatellite and mitochondrial molecular markers to study the Ae. aegypti populations in Yunnan Province in 2015 and 2016, found that it showed high genetic diversity and genetic structure. However, there are few studies on the population genetic characteristics of Ae. aegypti in Yunnan Province under different levels of human intervention. This study mainly used two common types of molecular markers to analyze the genetic characteristics of Ae. aegypti, revealing the influence of different input, prevention and control pressures on the genetic diversity and structure of this species. Understanding the genetic characteristics of Ae. aegypti populations and clarifying the diversity, spread status, and source of invasion are essential for the prevention, control and elimination of this disease vector. Methods: We analyzed the genetic diversity and genetic structure of 22 populations sampled in Yunnan Province in 2019 and 17 populations sampled in 2020 through nine microsatellite loci and COI and ND4 fragments of mitochondrial DNA. In 2019, a total of 22 natural populations were obtained, each containing 30 samples, a total of 660 samples. In 2020, a total of 17 natural populations were obtained. Similarly, each population had 30 samples, and a total of 510 samples were obtained. Results: Analysis of Ae. aegypti populations in 2019 and 2020 based on microsatellite markers revealed 67 and 72 alleles, respectively. The average allelic richness of the populations in 2019 was 3.659, while that in 2020 was 3.965. The HWE analysis of the 22 populations sampled in 2019 revealed significant departure only in the QSH-2 population. The 17 populations sampled in 2020 were all in HWE. The average polymorphic information content (PIC) values were 0.546 and 0.545, respectively, showing high polymorphism. The average observed heterozygosity of the 2019 and 2020 populations was 0.538 and 0.514, respectively, and the expected average heterozygosity was 0.517 and 0.519, showing high genetic diversity in all mosquito populations. By analyzing the COI and ND4 fragments in the mitochondrial DNA of Ae. aegypti, the populations sampled in 2019 had a total of 10 COI haplotypes and 17 ND4 haplotypes. A total of 20 COI haplotypes were found in the populations sampled in 2020, and a total of 24 ND4 haplotypes were obtained. STRUCTURE, UPGMA and DAPC cluster analyses and a network diagram constructed based on COI and ND4 fragments showed that the populations of Ae. aegypti in Yunnan Province sampled in 2019 and 2020 could be divided into two clusters. At the beginning of 2020, due to the impact of COVID-19, the flow of goods between the port areas of Yunnan Province and neighboring countries was reduced, and the sterilization was more effective when goods enter the customs, leading to different immigration pressures on Ae. aegypti population in Yunnan Province between 2019 and 2020, the source populations of the 2019 and 2020 populations changed. Mantel test is generally used to detect the correlation between genetic distance and geographical distance, the analysis indicated that population geographic distance and genetic distance had a moderately significant correlation in 2019 and 2020 (2019: p < 0.05 R2 = 0.4807, 2020: p < 0.05 R2 = 0.4233). Conclusion: Ae. aegypti in Yunnan Province maintains a high degree of genetic diversity. Human interference is one reason for the changes in the genetic characteristics of this disease vector.

mRNA-Based Therapeutics in Cancer Treatment.

Over the past two decades, significant technological innovations have led to messenger RNA (mRNA) becoming a promising option for developing prophylactic and therapeutic vaccines, protein replacement therapies, and genome engineering. The success of the two COVID-19 mRNA vaccines has sparked new enthusiasm for other medical applications, particularly in cancer treatment. In vitro-transcribed (IVT) mRNAs are structurally designed to resemble naturally occurring mature mRNA. Delivery of IVT mRNA via delivery platforms such as lipid nanoparticles allows host cells to produce many copies of encoded proteins, which can serve as antigens to stimulate immune responses or as additional beneficial proteins for supplements. mRNA-based cancer therapeutics include mRNA cancer vaccines, mRNA encoding cytokines, chimeric antigen receptors, tumor suppressors, and other combination therapies. To better understand the current development and research status of mRNA therapies for cancer treatment, this review focused on the molecular design, delivery systems, and clinical indications of mRNA therapies in cancer.

Eco-friendly, disposable bamboo fiber dishware: Static and dynamic mechanical properties and creep behavior

The global ban on plastics and the COVID-2019 epidemic have accelerated the demand for eco-friendly disposable dishware. As an alternative to plastic, the characterization of low-cost, widely-sourced, and sustainable plant fibers for degradable, eco-friendly dishware is urgently needed. In this study, bamboo fiber dishware (BFD) and poly lactic acid (PLA) dishware were investigated by exploring how the properties of their microstructures affect their mechanical properties. Thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA) were used to characterize the thermal decomposition behavior and dynamic viscoelasticity of both dishware types. Their modulus reductions were predicted using the accelerating creep mode, according to the time-temperature equivalence principle. The results showed that (1) the eco-friendly BFD was light weight and strong. Its specific strength, specific modulus were 4.50 and 3.09 times higher than those of PLA dishware, respectively. The efficient three-dimensional structure formed by the bamboo fibers and held together by hydrogen bonding played an important role in maintaining the excellent physical and mechanical properties. (2) The BFD and PLA dishware exhibited glass transition temperatures of 63.2 °C and 54.7 °C, respectively, according to the loss modulus curves, and their activation energies were 70.76 kJ/mol and 132.53 kJ/mol, respectively. Compared to the PLA dishware, the thermal decomposition behavior and storage modulus of the BFD were showed a lower sensitivity to temperature. (3) The time-temperature principle could be applied to the long-term creep prediction of BFD and PLA dishware. It showed that BFD exhibited a greater creep resistance in protecting food.

Minority Language Revitalization and Social Media through the Lens of Covid-19 in Yunnan and Gansu, western China

The language vitality of non-dominant communities has gained increasing attention worldwide with international declarations and national legislation enacted to protect the right of non-dominant language use and development. As information and communication technology (ICT) has spread, extending ICT to ethnic or indigenous languages has lagged. Nevertheless, their use on social media and other platforms has been growing rapidly, raising the question of their role in supporting non-dominant language vitality. In 2020 in China, non-dominant languages were used on various social media platforms to communicate anti-COVID messages. Drawing on language revitalisation frameworks, the investigators explore how the global pandemic has provided minority nationalities in west China the opportunity to increase recognition of their cultures and languages. We explore how minority communities in Yunnan and Gansu provinces in western China participated in China’s anti-Covid-19 campaign through the creation and dissemination of multilingual materials of different forms, genres, and modes on ICT social media platforms. We argue that using multilingual and multimodal ICT platforms to publicise countermeasures against Covid-19 has the potential to contribute to recognition and use of languages of non-dominant ethnic communities and revitalisation of heritage languages and cultures. [ FROM AUTHOR] Copyright of Journal of Multilingual & Multicultural Development is the property of Taylor & Francis Ltd 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 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 . (Copyright applies to all s.)

An ensemble learning method based on ordinal regression for COVID-19 diagnosis from chest CT.

Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat to public health. X-ray computed tomography (CT) plays a central role in the management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming and error-prone, which could not meet the need for precise and rapid COVID-19 screening. Nowadays, deep learning (DL) has been successfully applied to CT image analysis, which assists radiologists in workflow scheduling and treatment planning for patients with COVID-19. Traditional methods use cross-entropy as the loss function with a Softmax classifier following a fully-connected layer. Most DL-based classification methods target intraclass relationships in a certain class (early, progressive, severe, or dissipative phases), ignoring the natural order of different phases of the disease progression,i.e.,from an early stage and progress to a late stage. To learn both intraclass and interclass relationships among different stages and improve the accuracy of classification, this paper proposes an ensemble learning method based on ordinal regression, which leverages the ordinal information on COVID-19 phases. The proposed method uses multi-binary, neuron stick-breaking (NSB), and soft labels (SL) techniques, and ensembles the ordinal outputs through a median selection. To evaluate our method, we collected 172 confirmed cases. In a 2-fold cross-validation experiment, the accuracy is increased by 22% compared with traditional methods when we use modified ResNet-18 as the backbone. And precision, recall, andF1-score are also improved. The experimental results show that our proposed method achieves a better classification performance than the traditional methods, which helps establish guidelines for the classification of COVID-19 chest CT images.

Optimized collusion prevention for online exams during social distancing.

Online education is important in the COVID-19 pandemic, but online exam at individual homes invites students to cheat in various ways, especially collusion. While physical proctoring is impossible during social distancing, online proctoring is costly, compromises privacy, and can lead to prevailing collusion. Here we develop an optimization-based anti-collusion approach for distanced online testing (DOT) by minimizing the collusion gain, which can be coupled with other techniques for cheating prevention. With prior knowledge of student competences, our DOT technology optimizes sequences of questions and assigns them to students in synchronized time slots, reducing the collusion gain by 2-3 orders of magnitude relative to the conventional exam in which students receive their common questions simultaneously. Our DOT theory allows control of the collusion gain to a sufficiently low level. Our recent final exam in the DOT format has been successful, as evidenced by statistical tests and a post-exam survey.

Infection, Screening, and Psychological Stress of Health-Care Workers With COVID-19 in a Nonfrontline Clinical Department.

OBJECTIVES: The aim of this study was to investigate risk factors and psychological stress of health-care workers (HCWs) with coronavirus disease 2019 (COVID-19) in a nonfrontline clinical department. METHODS: Data of 2 source patients and all HCWs with infection risk were obtained in a department in Wuhan from January to February 2020. A questionnaire was designed to evaluate psychological stress of COVID-19 on HCWs. RESULTS: The overall infection rate was 4.8% in HCWs. Ten of 25 HCWs who contacted with 2 source patients were diagnosed with confirmed COVID-19 (8/10) and suspected COVID-19 (2/10). Other 2 HCWs were transmitted by other patients or colleagues. Close care behaviors included physical examination (6/12), life nursing (4/12), ward rounds (4/12), endoscopic examination (2/12). Contacts fluctuated from 1 to 24 times and each contact was short (8.1 min ± 5.6 min). HCWs wore surgical masks (11/12), gloves (7/12), and isolation clothing (3/12) when providing medical care. Most HCWs experienced a mild course with 2 asymptomatic infections, taking 9.8 d and 20.9 d to obtain viral shedding and clinical cure, respectively. Psychological stress included worry (58.3%), anxiety (83.3%), depression (58.3%), and insomnia (58.3%). CONCLUSIONS: Close contact with COVID-19 patients and insufficient protection were key risk factors. Precaution measures and psychological support on COVID-19 is urgently required for HCWs.

Integrative analysis for COVID-19 patient outcome prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Integrative Analysis for COVID-19 Patient Outcome Prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.

Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis.

During times of public crises, governments must act swiftly to communicate crisis information effectively and efficiently to members of the public; failure to do so will inevitably lead citizens to become fearful, uncertain and anxious in the prevailing conditions. This pioneering study systematically investigates how Chinese central government agencies used social media to promote citizen engagement during the COVID-19 crisis. Using data scraped from 'Healthy China', an official Sina Weibo account of the National Health Commission of China, we examine how citizen engagement relates to a series of theoretically relevant factors, including media richness, dialogic loop, content type and emotional valence. Results show that media richness negatively predicts citizen engagement through government social media, but dialogic loop facilitates engagement. Information relating to the latest news about the crisis and the government's handling of the event positively affects citizen engagement through government social media. Importantly, all relationships were contingent upon the emotional valence of each Weibo post.