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Recent studies in the epidermis have shown that Far-UVC (200-230nm) is a promising candidate against Novel Coronavirus (SARS-Cov-2) with little DNA damage. Due to the consideration that conventional Far-UVC KrCl excilamps may emit 200-230 nm radiation (typically 222-nm peak wavelength) but with some harmful UV radiation beyond 230 to 280 nm, a novel design of Far-UVC KrCl excilamps with the filter and reflector is introduced to reduce the harmful UV radiation from 10.9% to 2.5% at the cost of 30%similar to 40% reduction in the total irradiance. In our study, the radiant characteristics and service life of the novel Far-UVC KrCl excilamps of 40 similar to 75 Watt (electrical power) with 222-nm peak wavelength were investigated. The service life was assessed under aging at the ambient temperatures (T-a) of 25 degrees C and 85 degrees C for 500 hours, respectively. The results showed that both the ambient temperature and the root mean square of current (I-rms) into the excilamps have a substantial effect on the lifetime of the KrCl excilamps. Furthermore, although no significant change of the off-nominal emission ratio existed during the lifetime test, it was observed that the high ambient temperature has a negative effect on the filtering of the harmful radiation.
ABSTRACT
Recent studies in the epidermis have shown that Far-UVC (200-230nm) is a promising candidate against Novel Coronavirus (SARS-Cov-2) with little DNA damage. Due to the consideration that conventional Far-UVC KrCl excilamps may emit 200-230 nm radiation (typically 222-nm peak wavelength) but with some harmful UV radiation beyond 230 to 280 nm, a novel design of Far-UVC KrCl excilamps with the filter and reflector is introduced to reduce the harmful UV radiation from 10.9% to 2.5% at the cost of 30%~40% reduction in the total irradiance. In our study, the radiant characteristics and service life of the novel Far-UVC KrCl excilamps of 40~75 Watt (electrical power) with 222-nm peak wavelength were investigated. The service life was assessed under aging at the ambient temperatures (Ta) of 25 and 85 for 500 hours, respectively. The results showed that both the ambient temperature and the root mean square of current (Irms) into the excilamps have a substantial effect on the lifetime of the KrCl excilamps. Furthermore, although no significant change of the off-nominal emission ratio existed during the lifetime test, it was observed that the high ambient temperature has a negative effect on the filtering of the harmful radiation. © 2023 IEEE.
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In mid-2022, profound inequities in the pace and level of coverage of COVID-19 vaccination persist, especially in the world's poorest countries. Yet despite this inequity, we find that global COVID-19 vaccine development and diffusion has been the most rapid in history, everywhere. This paper explores the historical record in the development and deployment of vaccines globally, and puts the COVID-19 vaccine rollout in that context. Although far more can and should be done to drive higher coverage in the lowest-income countries, it is worth noting the revolutionary speed of both the vaccine development and diffusion process, and the potential good news that this signals for the future of pandemic preparedness and response. This is an updated version of a paper initially issued in February 2022.
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Does working time affect workers' wellbeing? We studied this question in the context of the Emirate of Abu Dhabi, drawing on the results its Quality-of-Life Survey conducted in 2019/2020. The empirical analysis examined the effect of working hours on various elements of wellbeing. Preliminary analysis and path analysis justified the significance of eight variables: work-life balance, frequency of meeting with friends, happiness, stress, time spent with family, self-assessment of health, satisfaction with income, and difficulty in fulfilling family responsibilities. The model became significantly less efficient when including variables such as job satisfaction, job security, time spent in sport, sleeping and leisure. The implications were discussed in the light of international research literature and post-COVID workplace arrangements and flexibilities. © 2022 Department of Community Development
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Given the COVID-19 pandemic, this paper aims at providing a full-process information system to support the detection of pathogens for a large range of populations, satisfying the requirements of light weight, low cost, high concurrency, high reliability, quick response, and high security. The project includes functional modules such as sample collection, sample transfer, sample reception, laboratory testing, test result inquiry, pandemic analysis, and monitoring. The progress and efficiency of each collection point as well as the status of sample transfer, reception, and laboratory testing are all monitored in real time, in order to support the comprehensive surveillance of the pandemic situation and support the dynamic deployment of pandemic prevention resources in a timely and effective manner. Deployed on a cloud platform, this system can satisfy ultra-high concurrent data collection requirements with 20 million collections per day and a maximum of 5 million collections per hour, due to its advantages of high concurrency, elasticity, security, and manageability. This system has also been widely used in Jiangsu, Shaanxi provinces, for the prevention and control of COVID-19 pandemic. Over 100 million NAT data have been collected nationwide, providing strong informational support for scientific and reasonable formulation and execution of COVID-19 prevention plans. © 2022 IEEE.
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Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation. In COVID-19 computed tomography (CT) images of the lungs, ground glass turbidity is the most common finding that requires specialist diagnosis. Based on this situation, some researchers propose the relevant DL models which can replace professional diagnostic specialists in clinics when lacking expertise. However, although DL methods have a stunning performance in medical image processing, the limited datasets can be a challenge in developing the accuracy of diagnosis at the human level. In addition, deep learning algorithms face the challenge of classifying and segmenting medical images in three or even multiple dimensions and maintaining high accuracy rates. Consequently, with a guaranteed high level of accuracy, our model can classify the patients' CT images into three types: Normal, Pneumonia and COVID. Subsequently, two datasets are used for segmentation, one of the datasets even has only a limited amount of data (20 cases). Our system combined the classification model and the segmentation model together, a fully integrated diagnostic model was built on the basis of ResNet50 and 3D U-Net algorithm. By feeding with different datasets, the COVID image segmentation of the infected area will be carried out according to classification results. Our model achieves 94.52% accuracy in the classification of lung lesions by 3 types: COVID, Pneumonia and Normal. For 2 labels (ground truth, lung lesions) segmentation, the model gets 99.57% of accuracy, 0.2191 of train loss and 0.78 ± 0.03 of MeanDice±Std, while the 4 labels (ground truth, left lung, right lung, lung lesions) segmentation achieves 98.89% of accuracy, 0.1132 of train loss and 0.83 ± 0.13 of MeanDice±Std. For future medical use, embedding the model into the medical facilities might be an efficient way of assisting or substituting doctors with diagnoses, therefore, a broader range of the problem of variant viruses in the COVID-19 situation may also be successfully solved. © 2022 IEEE.
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Massive Open Online Courses (MOOCs) have recently gained great attention. However, the biggest challenge to the success of MOOCs is their low completion rate. During the lockdown of the COVID-19 pandemic, MOOCs were in high demand by many higher education institutions to replace their face-to-face lessons. MOOCs have great potential to grow and reinvent the way of learning in the 21st century. This study uses the Virtual Learning Environment (VLE) effectiveness model to understand how the five key factors (learner, instructor, course, technology system, and interactivity) influence student learning satisfaction from a holistic approach and determine the best predictor of student learning satisfaction in the MOOC learning environment. A set of online data based on a 5-point Likert scale was collected from 333 undergraduate students from the top five public universities in Malaysia whose students are actively using MOOCs in their learning. The Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was used to analyse the data. The empirical results revealed that all factors significantly influence student learning satisfaction positively. Learner and interactivity factors were the strongest predictors in determining student learning satisfaction in MOOCs. These findings provide an empirically justified framework for developing successful online courses such as MOOCs in higher education.
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Objective To analyze the clinical characteristics and changes of serum IgG, IgM antibodies in patients infected with the SARS-CoV-2 B.1.1.529 (Omicron) variant. Methods The clinical data of 82 patients with SARS-CoV-2 B.1.1.529 variant was analyzed retrospectively. Based on the presence of pneumonia on chest CT, the patients were divided into pneumonia group and non-pneumonia group. Serum IgG, IgM antibodies were observed at 5 time points T1 (1~<4 d), T2 (4~<8 d), T3 (8~<15 d), T4 (15~<22 d) and T5 (22~<30 d) after admission. Results Among the 82 patients infected with the SARSCoV-2 B.1.1.529 variant strain, there were 62 cases of cough, 31 cases of fever, 33 cases of throat discomfort, 5 cases of muscle soreness and 3 cases of diarrhea. The serum IgG antibody levels at 5 time points were 50.22 (142.20) AU/mL, 326.50 (220.63) AU / mL, 368.23 (76.21) AU / mL, 368.65 (79) AU / mL, and 385.26 (113.10) AU / mL, respectively. The level of serum IgG antibody in the pneumonia group was lower than that of the non-pneumonia group at T1 and T4 time points, and the differences were statistically significant (P<0.05), the positive rate of serum IgG antibody in the pneumonia group was lower than that of the non-pneumonia group at the T1 time point, and the difference was statistically significant (P<0.05) . The serum IgM antibody levels at 5 time points were 0.41 (0.81) AU/mL, 0.95 (1.62) AU/mL, 1.09 (2.42) AU/mL, 0.74 (3) AU/mL, and 0.81 (3.10) AU / mL respectively, and there was no significant difference between the two groups. Conclusion The clinical symptoms of patients infected with the SARS-CoV-2 B.1.1.529 variant strain are mild. Serum IgG antibodies increased after infection, but there are some differences between the pneumonia group and the non-pneumonia group, whether serum IgG has a protective effect needs further research;the serum IgM antibodies do not increase highly after infection, there are some differences between individuals. © 2022 Editorial Office of Chinese Journal of Schistosomiasis Control. All rights reserved.
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During the CIVID-19 pandemic, water samples were collected from 26 sampling points in 18 typical drinking water sources in Wuhan, located in the middle reaches of the Yangtze River. Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were used to measure the concentrations of 31 pharmaceuticals and personal care products (PPCPs) in the water samples. The pollution characteristics of PPCPs were analyzed and their ecological and health risks were assessed. The results showed that a total of 23 PPCPs were detected in the 26 sampling points. Among them, five types of PPCPs were detected with a detection rate of 100%, with total concentrations ranging from 102.44 ng•L -1 to 745.78 ng•L -1, and the average concentration was 206.87 ng•L -1. The highest concentrations were in salicylic acid (SA) and doxycycline (DIC), ranging from 28.24 to 534.24 ng•L -1 and 28.72 to 416.6 ng•L -1, respectively. According to the spatial distribution of PPCPs, the concentration of antibiotics in the Hanjiang River was higher than that in the Yangtze River, whereas the concentration of other types of PPCPs in the Yangtze River was higher than that in the Hanjiang River. The ecological risk assessment results showed that the toxic risk in algae was higher than those in invertebrates and fish. The risks of salicylic acid (SA), doxycycline (DIC), lincomycin (LIN), and chlortetracycline (CTE) to algae were at a high level, and the ecological risk of PPCPs in the Hanjiang River was generally higher than that in the Yangtze River. The health risk assessment results showed that the risk to adults and children by drinking water ranged from 1.14 × 10 -4 to 0.136 and from 1.04 × 10 -4 to 0.821, respectively. The health risk to children was higher than that to adults, although their levels were low. Compared with the concentrations of PPCPs in drinking water sources of Wuhan in recent years, under the CIVID-19 pandemic, the pollution status of PPCPs in the Yangtze River was at a medium level, whereas it was at a high level in the Hanjiang River. © 2022 Science Press. All rights reserved.
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A remote lab is a technology that allows participants to efficiently conduct experimental teaching where users can connect to lab equipment from anywhere without being in a specific physical location. The COVID-19 pandemic affects all areas of human activity. As a result, students did not receive face-to-face instruction, and access to the laboratory was limited or practically impos-sible, and access to laboratory facilities has been limited or nearly impossible. Especially in engineering education, students' practical abilities cannot be devel-oped comprehensively. In this paper, this paper built an online remote robotics experiment system using digital twin (DT) technology and IoT technology and adopted ADDIE (Analysis, Design, Development, Implementation, and Evalua-tion) teaching method. With these measures, students can design and debug robot programs at home, just like in the laboratory. This study sent questionnaires to 64 students, and 58 were returned. The results show that more than 80% of students believe that the remote labs for industrial robotics courses have improved the efficiency and quality of students' skills training as opposed to virtual simulation and watching videos on the computer.
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Objective: To investigate and analyze changes of T lymphocyte and other lymphocyte subsets in the peripheral blood of patients with coronavirus disease 2019 (COVID-19), with the goal of improving clinical understanding and the value of research applications. Method(s): General data of 66 confirmed COVID-19 patients admitted to the Fifth Medical Center of Beijing PLA General Hospital from January 2 to March 23, 2020 were collected in this retrospective case-control observational study, and they were divided into mild (n = 26), mid-grade (n = 19), and severe/critical disease groups (n = 21) according to disease severity. Neutrophils, lymphocytes, neutrophil/lymphocyte ratios, CD4 absolute counts, CD8 absolute counts, and CD4/CD8 expression ratios of peripheral whole blood among the three patient groups were compared. The study protocol was approved by the Ethics Committee of the Fifth Medical Center, General Hospital of Chinese PLA (approval No. 2020-69-D) on May 5, 2020. Result(s): Among the 66 COVID-19 patients examined, 38 were male and 28 were female, with an average age of 53 +/- 17 years. Among patients, 26 cases were mild, 19 cases were mid-grade, and 21 cases were severe/critical. Neutrophils, neutrophil/ lymphocyte ratios, and CD4+/CD8+ ratios of the severe/critical group were significantly higher compared with mild and mid-grade groups (P < 0.01);however, there was no obvious difference between mid-grade and mild groups (P > 0.05). Lymphocytes, CD4 absolute counts, and CD8+ absolute counts of the severe/critical group were significantly lower compared with mild and mid-grade groups (P < 0.01);however, there was no significant difference between mid-grade and mild groups (P > 0.05). Conclusion(s): Counts of lymphocytes and T lymphocytes in severe/critically ill patients were decreased, which is of great significance for the identification of severe and critical COVID-19 patients. Copyright © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.
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Based on the survey of 43 Marine ranches and 260 consumers, this paper uses the diversified and orderly Logit model to study the significant factors affecting the development of recreational fishery in Marine ranches.The study found that six factors, including consumer gender, individual economic strength, consumer demand for Marine pasture products, brand construction level of Marine pasture recreational fishery, online channel promotion level of Marine pasture recreational fishery and the impact of COVID-19 epidemic, had a significant impact on the development of Marine pasture recreational fishery.This paper divides tower into product development and brand building, marketing and daily operation three dimensions, suggest operators improve the level of recreational fishery product development and brand building, develop differentiation price, develop online channels, strengthen the whole process communication, innovation under the outbreak of daily operation mode, expand the market share, so as to enhance the competitiveness of Marine pasture recreational fishery. © 2022 SPIE.
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The applications of microneedles (MNs) are becoming popular with the promise of efficient and advanced drug delivery. MNs were developed to overcome the limitations of conventional drug delivery systems and bypass biological barriers. While most MN applications in the past decades focused on transdermal biomedical appli-cations, recent advancements in engineering and technology have enabled MNs to be used in a wide range of non-transdermal applications. Compared with the other types of MNs, polymer-based MN composites have attracted more attention for non-transdermal drug delivery because they exhibit excellent biological properties, including being nontoxic, biocompatible, and biodegradable, making them ideal biomaterials for drug delivery applications that overcome the metabolic constraints of drug delivery for macromolecular payloads across a variety of tissues and organs other than the skin. This review provides an overview of recent advancements in polymer-based MN composite carriers that aim to overcome the delivery challenges for non-transdermal drug delivery, specifically in the vascular, ocular, gastrointestinal tract, buccal transmucosal, periodontal, cardio-vascular, and vaginal tissue. Furthermore, this review will discuss future perspectives and challenges for poly-meric MN composites in non-transdermal drug delivery that must be resolved.
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The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS$$
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Detailed modeling of the airway tree from CT scan is important for 3D navigation involved in endobronchial intervention including for those patients infected with the novel coronavirus. Deep learning methods have the potential for automatic airway segmentation but require large annotated datasets for training, which is difficult for a small patient population and rare cases. Due to the unique attributes of noisy COVID-19 CTs (e.g., ground-glass opacity and consolidation), vanilla 3D Convolutional Neural Networks (CNNs) trained on clean CTs are difficult to be generalized to noisy CTs. In this work, a Collaborative Feature Disentanglement and Augmentation framework (CFDA) is proposed to harness the intrinsic topological knowledge of the airway tree from clean CTs incorporated with unique bias features extracted from the noisy CTs. Firstly, we utilize the clean CT scans and a small amount of labeled noisy CT scans to jointly acquire a bias-discriminative encoder. Feature-level augmentation is then designed to perform feature sharing and augmentation, which diversifies the training samples and increases the generalization ability. Detailed evaluation results on patient datasets demonstrated considerable improvements in the CFDA network. It has been shown that the proposed method achieves superior segmentation performance of airway in COVID-19 CTs against other state-of-the-art transfer learning methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This study aimed to explore the possibility of using Mauremys mutica oligopeptides as raw materials to develop a drug targeting the SARS-CoV-2 virus. M. mutica oligopeptides were prepared through the combined enzymatic hydrolysis of M. mutica meat using flavoenzyme and papain, and the optimal enzyme ratios, enzyme dosage, pH, and enzymatic hydrolysis time were determined using single-factor and orthogonal experiments. After separating the oligopeptide fractions via high-performance liquid chromatography-mass spectrometry (HPLC-MS), Peak Studio and Peptide Ranker were used for peptide sequence analysis and peptide activity prediction, respectively, to screen for oligopeptides with high bioactivity. Finally, the binding ability of oligopeptides with high predicted activity against the SARS-CoV-2 spike protein receptor-binding domain (RBD) was evaluated using molecular docking. The results show that the highest degree of hydrolysis (DH;42.56%) was obtained when M. mutica meat was treated with a flavoenzyme: papain ratio of 7:3 and an enzyme dosage of 7% at pH 5.5 for 4 h. According to Peak Studio analysis, 510 peptides with an average local confidence (ALC) of > 90% were discovered. Six oligopeptides with high Peptide Ranker scores (LDFFK, LDFFKAL, FRVL, AFRVL, AGGKPFQ, and SPFRVT) were screened to dock with S protein. Their docking scores ranged from -138.50 to -169.68, and the lowest docking score (-169.68) was exhibited by LDFFKAL. Therefore, a high DH of M. mutica meat could be attained after process optimization, and a high inhibitory potential for SARS-CoV-2 was observed in the resulting M. mutica oligopeptides. © 2022 South China University of Technology. All rights reserved.
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The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of infected patients and specific delineation of infection areas in computed tomography (CT) images. Although deep supervised learning methods have been established quickly, the scarcity of both image-level and pixel-level labels as well as the lack of explainable transparency still hinder the applicability of AI. Can we identify infected patients and delineate the infections with extreme minimal supervision? Semi-supervised learning has demonstrated promising performance under limited labelled data and sufficient unlabelled data. Inspired by semi-supervised learning, we propose a model-agnostic calibrated pseudo-labelling strategy and apply it under a consistency regularization framework to generate explainable identification and delineation results. We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data. Extensive experiments have shown that our model can efficiently utilize limited labelled data and provide explainable classification and segmentation results for decision-making in clinical routine.