Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the reso-lution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution (RFAFN). Specifically, we design a contextual feature extraction block (CFEB) that can extract CT image features more efficiently and accurately than ordinary residual blocks. In addition, we propose a feature-weighted cascading strategy (FWCS) based on attentional feature fusion blocks (AFFB) to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information. Finally, we suggest a global hierarchical feature fusion strategy (GHFFS), which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels. Numerous experiments show that our method performs better than most of the state-of-the-art (SOTA) methods on the COVID-19 chest CT dataset. In detail, the peak signal-to-noise ratio (PSNR) is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at x3 SR compared to the suboptimal method, but the number of parameters and multi-adds are reduced by 22K and 0.43G, respectively. Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

Coronavirus murine hepatitis virus strain 3 induces intestinal injury via caspase 3 dependent apoptosis

Background: Diarrhea was typical symptoms of the coronavirus disease 2019 (COVID-19). However, the underlying mechanism had not been fully understood. Aim(s): The study aimed to explore the mechanism of intestinal injury during COVID-19 in a coronavirus murine hepatitis virus strain 3 (MHV-3) induced acute mouse model. Method(s): MHV-3 induced acute infection Balb/cJ mice model was established. Intestine samples were collected at indicated time points as 0 h, 24 h, 48 h and 60 h post infection. The mRNA and protein expression of IL1b, TNFalpha, IL6, caspase 3 and cleaved caspase 3 were examined by real-time quantitative PCR (qPCR) and western blot respectively. The intestine injury and apoptosis were measured by HE staining and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL). Moreover, Z-DEVD-FMK (caspase 3 inhibitor) pre-treated MHV-3 infection mice model were established, in which the apoptosis of intestine was evaluated as well. Meanwhile, the murine intestinal cell MODE-K was infected by MHV-3 in vitro for evaluation of virus induced apoptosis. Result(s): Post MHV-3 infection, the histopathology of intestine tissue showed extraordinary injury with time dependence, as well as high level of TUNEL positivity. The mRNA levels of inflammatory cytokine IL1b, TNFalpha and IL6 were significantly increased. The protein expressions of caspase 3 and cleaved caspase 3 in the intestine was found significantly elevated from 24 to 48 h post MHV-3 infection. Z-DEVD-FMK pretreatment inhibited caspase 3 and cleaved caspase 3 expression and decreased TUNEL positivity. Meanwhile, alleviated gut injury and inhibited TNFalpha expression were observed. In vitro treated by MHV-3, intestinal cell line MODE-K showed nine-fold increase of apoptosis by comparison with saline treated ones. The expressions of apoptosis crucial protein caspase3 and cleaved caspase3 significantly elevated, as well as TNFalpha. Conclusion(s): Coronavirus murine hepatitis virus strain 3 induces intestinal injury via caspase 3 dependent apoptosis, which might shed light on the treatment of intestinal complications in COVID-19.

Exploring Online Teaching Modes of Practice Lessons in the Epidemic Environment

The COVID-19 pandemic has changed the way that universities teach and how students learn. Operating system is the basic course of computer, software engineering, big data technology and other majors in colleges and universities, and occupies a very important position in the cultivation of computer categories. In the process of online teaching of the Linux application part of the operating system course, the teaching team explored the online teaching mode of the practical course and summarized the experience of the online teaching of the practical course. © 2023 IEEE.

[Estimation of COVID-19 incidence in Shanghai under optimized epidemic prevention and control strategies].

Objective: To quantitatively estimate the incidence of COVID-19 in different backgrounds, including vaccination coverage, non-pharmacological interventions (NPIs) measures, home quarantine willingness and international arrivals, and the demands of healthcare resource in Shanghai in the context of optimized epidemic prevention and control strategies. Methods: Based on the natural history of 2019-nCoV, local vaccination coverage and NPI performance, an age-structured Susceptible-Exposed-Infections-Removed (SEIR) epidemic dynamic model was established for the estimation of the incidence of COVID-19 and demand of hospital beds in Shanghai by using the data on December 1, 2022 as the basis. Results: Based on current vaccination coverage, it is estimated that 180 184 COVID-19 cases would need treatment in hospitals in Shanghai within 100 days. When the booster vaccination coverage reaches an ideal level, the number of the cases needing hospitalization would decrease by 73.20%. School closure or school closure plus workplace closure could reduce the peak demand of regular beds by 24.04% or 37.73%, respectively, compared with the situation without NPI. Increased willingness of home quarantine could reduce the number of daily new cases and delay incidence peak of COVID-19. The number of international arrivals has little impact on the development of the epidemic. Conclusions: According to the epidemiological characteristics of COVID-19 and the actual situation of vaccination in Shanghai, the incidence of COVID-19 and health resource demand might be reduced by increasing vaccination coverage and early implementation of NPI.

Classification and coding for Coronavirus disease 2019 by international classification ofdiseases: Updates and interpretation.

In the context of coronavirus disease 2019 (COVID-19), the leverage of specific codes of international classification of diseases(ICD) will substantially help standardize the process of data collection, classification and statistics of COVID-19-related conditions, thus facilitating the rapid research and development of diagnosis and treatment, dynamic monitoring of epidemic trend, as well as effectiveness evaluation of preventive and therapeutic measures taken in the fight against COVID-19. This review summarized and interpreted the latest ICD-10 and ICD-11 classification standards of COVID-19 related conditions and aimed to provide reference to improve and enrich the localized application of ICD coding standards for COVID-19 related conditions in China.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.

Dynamic Analysis of the Effects of Aging on China's Sustainable Economic Growth

China's aging population has deeply affected the sustainable development of the Chinese economy. Based on the provincial panel data of China's population and economic indicators from 2000 to 2020, this paper develops a panel vector autoregressive model to analyze the effect of China's aging population on economic growth under the paths of household consumption and national savings, respectively. The results show that an aging population inhibits household consumption and promotes national saving, which has both direct and indirect effects on economic growth. In particular, an aging population is not conducive to sustainable economic development in the context of China's slow population growth over the past three years and the contraction of the global economy due to the COVID-19 pandemic. In accordance with the empirical results, this paper puts forward corresponding policy recommendations, as follows: improve the pension security system;develop the silver-hair industry;expand domestic demand in China;encourage fertility;and increase human capital investment to provide an impetus for sustainable economic development.

An Intelligent Classification Diagnosis Based on Blood Oxygen Saturation Signals for Medical Data Security Including COVID-19 in Industry 5.0

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is gradually valued due to its high prevalence, high risk, and high mortality. Alternative to the polysomnography (PSG) diagnosis, the proposed method assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement efficiently and accurately. This article uses the blood oxygen saturation (SpO(2)) signal count of the number of apnea or hypoventilation events during the sleep of the subject, calculating the apnea-hypopnea index (AHI) and the subject's disease level. SpO(2) signals are used to extract 35-D features based on the time domain, including approximate entropy, central tendency measure, and Lempel-Ziv complexity to accelerate the diagnosis process in supply chains. The feature selection process is reduced from 35 to 7 dimensions that benefits to the implementation in the practical supply chains in Industry 5.0 by extracting the extracted features. This article applies Pearson correlation coefficient selection, based on minimum redundancy-maximum correlation algorithm selection, and a wrapper based on the backward search algorithm. The accuracy rate is 86.92%, and the specificity is 90.7% under the selected random forest classifier. A random forest classifier was used to calculate the AHI index, and a linear regression analysis was performed with the AHI index obtained from the PSG. The result reaches a 92% accuracy rate in assessing the prevalence of OSAHS, satisfying the industrial deployment.

Promoting Green Buildings and Low-Carbon Design Strategies of Green B&B Rooms for Sustainable Tourism after COVID-19

COVID-19 opened a window of opportunity to change the green development of the hospitality industry. For many years, Chinese tourists have been the world's largest source of outbound tourists. Therefore, this study attempted to improve built-environment strategies for green rooms at B&Bs using the empirical statistics of Chinese tourists after the end of COVID-19 control measures and different green B&B standards, combining IPA (importance-performance analysis). For the lack of a green built-environment study from a tourism perspective, this study can be used mainly for improving the green satisfaction of urban B&Bs as it attempted to fill the gaps in research on green B&B rooms. This study will significantly help improve the quality of green rooms for the B&B industry in the future, and it also provides an improved green B&B room sample for other countries and regions. Moreover, it is an optimistic attempt at hospitality and tourism recovery. © 2023 by the authors.

Preparation of two kinds of membranes with reverse wettability from waste masks for continuous oil/water separation

Resourceful use of waste masks (WMs) has become an important challenge for humanity with the spread of the coronavirus disease (COVID-19). In this work, WMs were treated by disinfection treatment and then modified via the in situ chemical deposition of SiO2 followed by the grafting of dopamine (DA) and octadecyltrichlorosilane (OTS). By controlling the amount of DA or OTS added, WM-SiO2/DA (superhydrophilic) and WM-SiO2/OTS (hydrophobic) membranes were fabricated with reverse wettability with water contact angles of 0° and 147.5°, respectively. The WM-SiO2/DA and WM-SiO2/OTS membranes possessed attractive permeability toward water (6793 L m-2 h-1) and CCl4 (13 867 L m-2 h-1), together with separation efficiencies over 98.0% under gravity. Besides, the WM-SiO2/DA and WM-SiO2/OTS membranes were used in a T-shaped device for the analysis of continuous oil/water separation processes. The results showed that oil/water mixtures could be separated continuously regardless of the density of oil/water by virtue of the attractive permeability and separation efficiency. Furthermore, the membranes also demonstrated favorable stability and reusability under harsh operating conditions. Consequently, this work provides an effective and promising way to upcycle waste masks, especially in the field of oil/water separation. © 2023 The Royal Society of Chemistry.

CampusX: An IoT-Powered Real-Time Monitoring System for University Campuses

There is an ever-urgent need for accessing real-time crowdedness and airflow information for indoor study spaces in universities, for example, to control COVID-19 transmission risk. Even before the pandemic, many students spent valuable time finding suitable study areas with proper lighting, low noise, and ample seating. This paper presents a pilot system, CampusX, which aims to provide students with useful real-time information about study spaces on campus. Our system collects and analyzes environmental data before presenting them to students as useful information. This helps them to select the most suitable study spaces. The main components of this system include a sensor platform, data collection and processing pipelines, networking, and an interactive web-application. © 2023 IEEE.

Clinical Classification and Diagnosis of COVID-19

The latency of coronavirus disease 2019 (COVID-19) is usually 1–14 days. Most patients show symptoms within 3–7 days based on the report from the current epidemiological survey. In the early stage of the disease, fever, dry cough, and fatigue are the main manifestations. Some patients may also experience additional symptoms such as nasal congestion, runny nose, pharyngeal pain, myalgia, and diarrhea. However, there are a significant number of individuals carrying SARS-CoV-2 who may not exhibit these symptoms. It is particularly important to identify these asymptomatic patients (asymptomatic infections) due to their potential close contact with healthy populations and possible transmission of SARS-CoV-2. COVID-19 patients in the severe condition often have dyspnea and/or hypoxemia one week after the disease onset. Severe patients can rapidly develop acute respiratory distress syndrome (ARDS), septic shock, metabolic acidosis, coagulation dysfunction, and multiple organ failure (MOF). It is worth noting that severe and critical patients may only present moderate and low fever, or even no obvious fever. The symptoms of children are relatively mild. Some children and newborns may have atypical symptoms, such as vomiting, diarrhea, and other abnormal digestive tract conditions, or only show poor energy and shortness of breath. Mild es may only show low fever, mild fatigue, and other symptoms, such as mild weakness, without pneumonia. Overall, most patients have a good prognosis, while a few patients were in the critical condition. The prognosis of the elderly and those with chronic basic diseases is poor. The clinical progress of COVID-19 in maternal women is similar to that of the same age group. © Henan Science and Technology Press 2020.

Imaging Features of COVID-19 in Children

The incidence rate of SARS-CoV-2 (the virus name of COVID-19) infection for children is significantly lower than that for adults with clear characteristic of clustering in terms of the incidence rate of such disease. A definite history of aggregation in infected families can be found for most children who are suffering from such disease [1–5]. Comparing with the clinical characteristics of adult patients, the clinical symptoms of the confirmed child es are relatively mild with fast recovery, shorter detoxification time, and favorable prognosis. Most child patients only show the symptoms of upper respiratory tract infection and are also self-limiting, while for some children and newborn babies, their symptoms may not typical, including emesis, diarrhea, etc., the symptoms of digestive tract, or merely weak in spirit and tachypnea [6]. An epidemiologic study of 2135 es of children (less than 18 years old) infected with COVID-19 has found that more than 90% of the child patients are either patients with no symptom or patients with mild to average symptoms, wherein the asymptomatic es account for about 13% [5]. Though the incidence rate of severe or critical e is low for children, the pediatricians still need to attach importance to and have close monitoring of it, especially for child patients with certain underlying medical conditions, efforts should be made to ensure early identification and timely medical treatment. © Henan Science and Technology Press 2020.

When and How to Use Covariate Adjustment to Improve Precision in Randomized Trials

Background In May 2021, the US Food and Drug Administration (FDA) released a revised draft guidance for industry on ''Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products.'' This guidance discusses adjustment for covariates in the statistical analysis of randomized clinical trials in drug development programs. It specifically focuses on the use of prognostic baseline factors to improve precision for estimating treatment effects. The impact depends on the specifics of the trial, but typical sample size reductions range from 5-25% (at no cost). Despite regulators such as the FDA and the European Medicines Agency recommending covariate adjustment, it remains highly underutilized leading to inefficient trials in many disease areas. This is especially true for binary, ordinal, and time-to-event outcomes, which are quite common in COVID-19 trials and are, moreover, prevalent as primary outcomes in many disease areas (e.g. Alzheimer's disease and stroke). Research and guidance on this topic could therefore not be more timely. In response to the FDA draft guidance on covariate adjustment, this session invites experts who represent a variety of viewpoints, coming from academia and Pharmaceutical industry. The aim of this session is to provide insight into the state-of-the-art methods at a high level and from a practical perspective. We moreover want to discuss the main obstacles that lead to the underutilization of covariate adjustment, all of which we aim to surmount in this session. Finally, we want to discuss the connections of the different talks to the FDA draft guidance and provide options for better practice. Talk by Min Zhang ''Covariate adjustment for randomized clinical trials when covariates are subject to missingness.'' One practical issue that may have limited the use of covariate adjustment is that covariates are often subject to missingness. Existing statistical methodologies often ignore this issue and assume covariates are completely observed. We discuss conditions under which robust covariate adjustment can be achieved when the missingness of covariates is present. We study various methods for handling missing data and compare their performances in terms of robustness and efficiency through comprehensive simulation studies. Recommendations on strategies for handling missing covariates to achieve robust covariate adjustment are provided. Talk by Mark van der Laan on ''Targeted Learning of causal effects in randomized Trials with continuous time-to-event outcomes.'' Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in the presence of high-dimensional nuisance parameters. Generally, TMLE consists of a twostep procedure that combines data-adaptive nuisance parameter estimation with semi-parametric efficiency and rigorous statistical inference obtained via a targeted update step. In this talk, we demonstrate the practical applicability of TMLE for standard survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We demonstrate TMLE updates that simultaneously target point-treatment-specific survival curves and treatmentcause- specific subdistributions in the competing risk setting, across treatment and time points. We consider the case that we only observe baseline covariates as well as the case that we also track time-dependent covariates that potentially inform censoring/drop-out. This results in estimates that are not only fully efficient, but also respect the natural monotonicity of survival functions and cause-specific subdistributions. It moreover makes sure that the sum of subdistributions and survival equals 1. We propose a super-learner for the causespecific conditional hazards that incorporate many possible Cox models as well as a variety of highly adaptive Lasso estimators. Asymptotic theoretical guarantees are given and finite-sample robust performance is demonstrated with simulations. We illustrate the usage of the considered methods for a ovo Nordisk Leader study as well as for publicly available data from a trial on adjuvant chemotherapy for colon cancer. Talk by Kelly Van Lancker on ''Combining Covariate Adjustment with Information Monitoring and Group Sequential Designs to Improve Randomized Trial Efficiency'' In this talk, we focus on the knowledge gap in statistical methodology that leads to the underutilization of covariate adjustment. A first obstacle is the uncertainty of its efficiency gain and corresponding sample size reduction at the design stage;an incorrect projection of a covariate's prognostic value risks an over- or underpowered future trial. A second open problem is the incompatibility of many covariate-adjusted estimators with the commonly used group sequential, information-based designs (GSDs). To overcome these challenges, we suggest combining covariate adjustment with information monitoring and continuing the trial until the required information level is surpassed. Since adjusted estimators typically have smaller variance than standard estimators, the information accrues faster leading to faster trials. Building on this, we propose a new statistical method that orthogonalizes estimators in order to (1) have the independent increments property needed to apply GSDs and (2) simultaneously improve (or leave unchanged) the variance at each analysis. Such a method is needed in order to fully leverage prognostic baseline variables to speed up clinical trials without sacrificing validity or power. We prove that this method has properties such as the independent increments, consistency, asymptotic normality, and correct type I error and power, and evaluate its performance in simulations and data analyses. Discussion by Frank Bretz This talk will discuss connections between the three previous presentations in the session and recommendations in the May 2021 FDA revised draft guidance for industry document on ''Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products.'' It will moreover touch on the broad impact of covariate adjustment for the pharmaceutical industry and provide advice on better practice.

Differentiating COVID-19 CT Manifestations from Other Types of Pneumonia

In the early days of COVID-19 pandemic in China, the RT-PCR test reported only 59–71% positive es among those tested [1, 2]. However, among the patients admitted to the hospitals, there were some patients with typical imaging features of viral pneumonia, but negative test results for RT-PCR tests, even after being tested for several times. The accuracy of nucleic acid RT-PCR test depends on the time of infection, samples and sampling method, quality of the reagent, and different interpretation standards. Thus, RT-PCR tests are often conducted repeatedly if patients have the typical imaging features of pneumonia. The CT manifestations of COVID-19 are mainly that of interstitial pneumonia. The distribution, shape, density, and bronchial and vascular manifestations of lesions are typical, but not specific to COVID-19. Therefore, it is necessary to make differential diagnosis to distinguish COVID-19 from other lung infections with similar CT manifestations, such as pneumonia caused by influenza A (H1N1), avian influenza (H7N9), influenza B, adenovirus, cytomegalovirus, and others (Table 8.1). The application of thoracic CT to COVID-19 diagnosis and imaging assessment of pulmonary infection and damage can add value to clinical management of patients with COVID-19. © Henan Science and Technology Press 2020.

Overview of the COVID-19

The novel coronavirus that caused pandemic in 2020 started from a number of cases of unexplained pneumonia in the city of Wuhan, Hubei Province, China, in December of 2019. Unlike the other human coronavirus previously reported, this new strain of coronavirus is much more contagious and rapidly spread in the city of Wuhan and subsequently various regions of China from the epicenter of Wuhan. Within several weeks, many countries of Asia, Europe, North America, and Oceania reported the confirmed cases with the worldwide total number quickly rising to over 5.35 million and more than 343,000 deaths on May 24, 2020. The World Health Organization (WHO) initially named this novel coronavirus as "2019 Novel Coronavirus” (2019-nCoV) on January 12, 2020. National Health Commission of China named the pneumonia caused by 2019-nCoV as Novel Coronavirus Pneumonia (NCP) on January 20, 2020. In the meantime, National Health Commission of China issued an announcement to include this disease in the category B infectious diseases as stipulated in the Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases, but to manage this disease according to Class A infectious diseases. On February 11, 2020, Tedros Adhanom Ghebreyesus, director general of WHO, announced the revised name of the pneumonia caused by novel coronavirus as "Coronavirus Disease 2019” (COVID-19). At the same time, the International Committee for Virus Classification named the novel coronavirus "Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2). Given the high incidence of SARS-CoV-2 infection and the rapid increase of the cases globally, WHO officially declared on March 11, 2020 in Geneva that the COVID-19 caused by SARS-CoV-2 has the characteristics of a global pandemic. At present, the origin of SARS-CoV-2 is still under investigation. © Henan Science and Technology Press 2020.

Mediating role of psychological resilience between social support and negative emotions in nursing interns in the post-COVID-19 era: A cross-sectional survey

Background: The increasing burden of mental health problems continues in the post-COVID-19 era, and nursing interns were particularly likely to experience negative emotions during the pandemic. Both psychological resilience and social support affect negative emotion, but the relationship among the three has not been explored in nursing interns in the postpandemic era. Objectives: To explore the current prevalence of negative emotions among nursing interns and the role of psychological resilience in mediating the relationship between social support and negative emotions in the postpandemic era. Methods: A cross-sectional survey of 788 nursing interns was conducted. The instruments included Psychological Resilience Scale, Social Support Scale, Beck Anxiety Scale and Beck Depression Scale. Structural equation modeling was applied to analyze the mediating role of psychological resilience. Results: The prevalence of anxiety disorder among nursing interns was 24.7%, while that of depression was 10.5%. Pearson correlation analysis showed that both social support and psychological resilience negatively correlated with negative emotions, while psychological resilience positively correlated with social support. Psychological resilience showed a partial mediating effect (53.9%) between social support and negative emotion, with an effect value of −0.1456. Conclusion: Psychological resilience and social support protect nursing students from negative emotions, and psychological resilience partially mediates the relationship between social support and negative emotion in the postpandemic era. © 2023 Wiley Periodicals LLC.

High-precision estimation of hourly PM2.5 concentration based on a grid scale of satellite-derived products

Continuous and accurate surface pollutant data can provide data support for health effect analysis. Based on the hourly AOD data of the Himawari-8 satellite as the basic data set, this study collected auxiliary parameters including meteorological reanalysis data and geospatial data to estimate the surface PM2.5 hourly concentration. The random forest (RF) and CatBoost models with superior performance were integrated by linear fitting. The experimental results showed that the sample-CV R2 and RMSE of the integrating model were 0.929 and 9.846 μg/m3;time-CV R2 = 0.903, RMSE = 11.521 μg/m3;station-CV R2 = 0.894, RMSE = 12.05 μg/m3, which had the best validation accuracy among all the comparison models and were also better than the estimation results of many previous studies. The spatial and temporal analysis results of PM2.5 showed that the surface PM2.5 concentration was generally high in winter and spring, and low in summer and autumn during the study period. During COVID-2019, PM2.5 concentration on the surface of China showed a significant decreasing trend. The model with the estimation method used in this study can produce reliable surface PM2.5 data products. © 2023 Turkish National Committee for Air Pollution Research and Control