Fast de novo antibody structure prediction with atomic accuracy

Background: In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2 (AF2), a breakthrough in the field of structural biology, provides a solution to this protein structure prediction problem by learning a deep learning model. However, the computational efficiency and undesirable prediction accuracy on antibody, especially on the complementarity-determining regions limit its applications in de novo antibody design. Method(s): To learn informative representation of antibodies, we trained a deep antibody language model (ALM) on curated sequences from observed antibody space database via a well-designed transformer model. We also developed a novel model named xTrimoABFold++ to predict antibody structure from antibody sequence only based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame aligned point loss. Result(s): xTrimoABFold++ outperforms AF2 and OmegaFold, HelixFold-Single with 30+% improvement on RMSD. Also, it is 151 times faster than AF2 and predicts antibody structure in atomic accuracy within 20 seconds. In recently released antibodies, for example, cemiplimab of PD1 (PDB: 7WVM) and cross-neutralizing antibody 6D6 of SARS-CoV-2 (PDB: 7EAN), the RMSD of xTrimoABFold++ are 0.344 and 0.389 respectively. Conclusion(s): To the best of our knowledge, xTrimoABFold++ achieved the state-of-the-art in antibody structure prediction. Its improvement on both accuracy and efficiency makes it a valuable tool for de novo antibody design, and could make further improvement in immuno-theory.

Effect of Different Factors on the Quality of Life and Sequelae of COVID-19 Patients after Discharge

Background The continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains an international public health emergency, resulting in a significant global disease burden. The long-term effects of SARS-CoV-2 infection in humans and the long-term prognosis of patients with coronavirus disease 2019 (COVID-19) after discharge remain unclear. We aimed to assess the quality of life (QoL) and sequelae in patients with COVID-19 after discharge from the hospital by conducting multiple follow-up visits to understand the long-term effects of SARS-CoV-2 on patients' health and its possible influencing factors. Methods COVID-19 patients discharged from Huoshenshan Hospital (Wuhan, China) between February 15 and April 5, 2020, were followed up at 6, 9, and 12 months after discharge. They completed questionnaires on postdischarge QoL and sequelae under the guidance of medical staff with professional training. The demographic and clinical characteristics of the COVID-19 patients were analyzed using descriptive statistics. A generalized estimating equation model was used to analyze the QoL-related factors. The χ2 test (or Fisher exact test) and multivariate logistic regression analysis were used to analyze the sequelae and influencing factors. Results A total of 175 patients participated in at least 1 follow-up visit, and 120 completed all 3 follow-up visits. Patients diagnosed with severe and critically ill COVID-19 had worse mental conditions (χ2 = 7.653, P = 0.022) than those with the nonsevere type (not severe or critical) and were more likely to feel fatigued (χ2 = 4.836, P = 0.028). Female patients had a higher risk of sleep disturbance (χ2 = 10.026, P = 0.002) and dyspnea (χ2 = 5.672, P = 0.017) and had more difficulty returning to their original work and life (χ2 = 8.922, P = 0.003) than male patients. Patients with diabetes had a worse appetite (χ2 = 4.669, P = 0.031) and were more prone to sleep disturbance (χ2 = 4.417, P = 0.036) after discharge. The proportion of patients with at least 1 sequela increased from 29.76% (50/168) at 6 months to 51.11% (69/135) at 9 months (χ2 = 14.305, P < 0.001). Compared with the nonsevere type, patients diagnosed with severe and critically ill COVID-19 had an odds ratio (OR) of 4.325 (95% confidence interval [CI], 1.215-15.401) for memory decline. Female patients had an OR of 4.632 (95% CI, 1.716-12.501) for joint or muscle pain. Patients with hypertension had an OR of 3.014 (95% CI, 1.193-7.615) for joint or muscle pain. Conclusion One year after discharge, there were still some patients with varying degrees of decline in QoL and sequelae, which occurred in all follow-up visits. Moreover, QoL and sequelae after discharge were related to sex, clinical classification of COVID-19, and underlying diseases. © Wolters Kluwer Health, Inc. All rights reserved.

Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Toxicity of the disinfectant benzalkonium chloride (C14) towards cyanobacterium Microcystis results from its impact on the photosynthetic apparatus and cell metabolism

Quaternary ammonium compounds (QACs) are commonly used in a variety of consumer and commercial products, typically as a component of disinfectants. During the COVID-19 pandemic, QACs became one of the primary agents utilized to inactivate the SARS-CoV-2 virus on surfaces. However, the ecotoxicological effects of QACs upon aquatic organisms have not been fully assessed. In this study, we examined the effects of a widely used QAC (benzalkonium chloride-C14, BAC-14) on two toxigenic Microcystis strains and one non-toxigenic freshwater Microcystis strain and carried out an analysis focused on primary, adaptive and compensatory stress responses at apical (growth and photosynthesis) and metabolic levels. This analysis revealed that the two toxic Microcystis strains were more tolerant than the non-toxic strain, with 96 hr-EC50 values of 0.70, 0.76, and 0.38 mg/L BAC-14 for toxigenic M. aeruginosa FACHB-905, toxigenic M. aeruginosa FACHB-469, and non-toxigenic M. wesenbergii FACHB-908, respectively. The photosynthetic activities of the Microcystis, assessed via Fv/Fm values, were significantly suppressed under 0.4 mg/L BAC-14. Furthermore, this analysis revealed that BAC-14 altered 14, 12, and 8 metabolic pathways in M. aeruginosa FACHB-905, M. aeruginosa FACHB-469, and M. wesenbergii FACHB-908, respectively. It is noteworthy that BAC-14 enhanced the level of extracellular microcystin production in the toxigenic Microcystis strains, although cell growth was not significantly affected. Collectively, these data show that BAC-14 disrupted the physiological and metabolic status of Microcystis cells and stimulated the production and release of microcystin, which could result in damage to aquatic systems. © 2022

The Future of Multilateralism in the Post-pandemic World

This paper applies an endogenous institutionalist framework to understand the evolution of the rules-based international trading system since the end of World War II. We argue that the initial success of the General Agreement on Trade and Tariffs and re-enforcement that led to the formation of the WTO can be explained by three major factors: the hegemonic position of the US, the belief that international trade would foster prosperity and peace, and Cold War politics. However, declining US hegemony along with a shift in global comparative advantage in labor-intensive manufacturing led to a shift from multilateral towards preferential trade agreements since the 1990s. Today, the WTO faces several new challenges, including increasing geo-political competition between the US and China, increasing digitization of commerce, and disrupted supply chains following COVID-19. A functioning WTO that facilitates global economic re-integration remains crucial to ensure a strong recovery from the pandemic and continued long-term prosperity and stability of the global economy. © 2023 Institute of World Economics and Politics, Chinese Academy of Social Sciences.

Effects of physical exercise on mental health and general self-efficacy of city residents in COVID-19

The purpose of this study was to investigate the relationship and impact of residents' self-efficacy and mental health in the context of COVID-19. A quantitative research approach is used in this study. This study investigated residents with a general self-efficacy questionnaire to explore the predictive effect of self-efficacy on mental health. Therefore, a questionnaires survey is employed in this study to collect primary data. While data collection, the cross-sectional research design was used, and data analysis was carried out using a statistical tool. There was a significant negative association between the epidemic's risk perception and the score of the SCL.90. The standardized burden of understanding the epidemic on its latent variables was more than 0.65 in the measurement model, indicating that the 90 percent confidence intervals for the three mediation paths did not include 0, confirming the mediation effect's establishment. This study reveals the relationship between self-efficacy and mental health and its mechanism of action, inspiring the maintenance of residents' mental health during the period of the new crown. It is concluded that there was a significant positive correlation between self-efficacy and understanding of the epidemic situation. © 2022 Sociedad Revista de Psicologia del Deporte. All rights reserved.

Load-balanced Task Allocation for Covid-19 Close Contact Detection in Heterogeneous MEC Networks

In this paper, we investigate the close contact detection for COVID-19 patients based on the heterogeneous mobile edge computing (MEC) framework. Collecting the spatial-temporal data of a large number of mobile users, the base stations equipped with MEC servers organize these data via the R-tree structure. The cloud center (CC) aggregates the spatial-temporal data from all MEC servers. Considering the mobility of users as well as various positions of MEC servers, the CC then partitions and assigns the close contact detection tasks to different servers for faster processing. Aiming to minimize the system latency, we propose a Deep Deterministic Policy Gradient-based task and resource allocation scheme, where the computing loads are balanced among different servers. Simulation results show that a minimum system latency is reached while maintaining the load balance among all servers. Up to 37% detection accuracy enhancement is achieved compared with an existing task allocation scheme without load balance.

Decreased Aviation Leads to Increased Ice Crystal Number and a Positive Radiative Effect in Cirrus Clouds

Travel restrictions in the wake of the COVID-19 pandemic resulted in an unprecedented decrease of 73% in global flight mileage in April-May 2020 compared to 2019. Here we examine the CALIPSO satellite observations and find a significant increase in ice crystal number concentrations (Ni) in cirrus clouds in the mid-latitudes of the Northern Hemisphere, which we attribute to an increase in homogeneous freezing when soot from aircraft emissions is reduced. A relatively small positive global average radiative effect of 21 mW m(-2) is estimated if a decrease in aircraft traffic continues, with an average of up to 64 mW m(-2) over the area where aviation is most active. We infer from this analysis that the worldwide adoption of biofuel blending in aircraft fuels that lead to smaller soot emissions could lead to a significant change in the microphysical properties of cirrus clouds but a rather small positive radiative effect.

An Investigation of Visualization Technologies for Remote Work in the Architecture, Engineering, and Construction Industry

The recent COVID-19 pandemic has impacted the way how everyday professional work is performed. Organizations have devised innovative ways to work remotely, staying home, or in secluded offices. One of the technologies that many industries are relying on is visualization for remote work. Professionals in the Architecture, Engineering, and Construction (AEC) industry are applying technology solutions to facilitate remote work, making the work site less crowded. Visualization technologies (VT)-video cameras, extended Reality, and Unmanned Aerial Vehicle's ability to monitor construction activities-have the potential to help the AEC industry in remote work. However, it is unknown if the use of visualization technologies in the AEC industry has increased since the beginning of the COVID-19 pandemic and what tools and technologies are used the most. To understand this, the authors designed and implemented an industry-wide survey to analyze the state of VT use and the barriers to its implementation. The study found out that although remote work had increased significantly since the onset of the COVID-19 pandemic, the rate at which people utilized VT for remote work had not increased at the same pace, which indicates that the AEC industry is yet to utilize the use of visualization technologies to make remote work easier and more efficient.

Patients' and therapists' experiences of CBT videoconferencing in anxiety disorders

Cognitive behavioural therapy (CBT) videoconferencing has been demonstrated to be an effective treatment for anxiety disorders and an equal alternative to face-to-face CBT. However, qualitative patient and therapist experiences of CBT videoconferencing have been less researched. Due to COVID-19, mental health services have shifted to remote therapy methods;thus, understanding patient and therapist experiences are crucial to better inform service policies and best practices. The current study focused on patient and therapist experiences of CBT videoconferencing at the Centre for Anxiety Disorders and Trauma (CADAT). Researchers used qualitative content analysis to explore patients' (n = 54) and therapists' (n = 15) responses to an online survey. Results yielded four themes: behavioural experiments work well if the problem lends itself to videoconferencing, overall practicalities but some home environment implications, privacy and technical issues, high telepresence and the negative impact on the therapeutic alliance, and COVID-19 influences attitude positively. The findings have clinical implications for CBT videoconferencing, including a need for specific training in assessment and intervention for therapists using videoconferencing. Key learning aims Readers of this paper will be able to: Describe patient and therapist qualitative experiences of CBT videoconferencing. Identify areas to consider when delivering CBT videoconferencing in anxiety disorders. Understand therapist training needs for CBT videoconferencing in anxiety disorders. Inform own service protocols and best practices for the delivery of CBT videoconferencing. Key learning aims Readers of this paper will be able to: (1) Describe patient and therapist qualitative experiences of CBT videoconferencing. (2) Identify areas to consider when delivering CBT videoconferencing in anxiety disorders. (3) Understand therapist training needs for CBT videoconferencing in anxiety disorders. (4) Inform own service protocols and best practices for the delivery of CBT videoconferencing.

Connectedness of commodity, exchange rate and categorical economic policy uncertainties — Evidence from China

This paper provides a fresh perspective to explore the network correlations among commodity, exchange rate, and categorical economic policy uncertainties (EPU) in China. We try to contribute to the literature by examining the spillover mechanism with a relatively novel connectedness network using the monthly data over the period between June 2006 and January 2021. Our results suggest that prior to the recession, China's commodity price is subject to greater spillovers from the exchange rate than recessions. The domestic commodity prices are more sensitive to monetary policy uncertainty and fiscal policy uncertainty. The occurrence of COVID-19 revises the dominance in the system from monetary policy uncertainty and fiscal policy uncertainty to trade policy uncertainty. © 2022 Elsevier Inc.

Medical Image Classification based on an Adaptive Size Deep Learning Model

With the rapid development of Artificial Intelligence (AI), deep learning has increasingly become a research hotspot in various fields, such as medical image classification. Traditional deep learning models use Bilinear Interpolation when processing classification tasks of multi-size medical image dataset, which will cause the loss of information of the image, and then affect the classification effect. In response to this problem, this work proposes a solution for an adaptive size deep learning model. First, according to the characteristics of the multi-size medical image dataset, the optimal size set module is proposed in combination with the unpooling process. Next, an adaptive deep learning model module is proposed based on the existing deep learning model. Then, the model is fused with the size fine-tuning module used to process multi-size medical images to obtain a solution of the adaptive size deep learning model. Finally, the proposed solution model is applied to the pneumonia CT medical image dataset. Through experiments, it can be seen that the model has strong robustness, and the classification effect is improved by about 4% compared with traditional algorithms.

Reintubation during COVID-19 pandemic: a simple self-made guiding device facilitates reintubation and minimizes transmission

Consensus guidelines to protect airway managers during COVID-19 were developed to encourage safe, accurate and swift performance in intubation and extubation, but reintubation was not considered. With the massive surge of patients requiring mechanical ventilation in this COVID-19 pandemic, great incidence of difficult airways may necessitate reintubation. Equipments could be used now in extubation and reintubation are either too expensive and time-consuming in decontamination, or have not gained wide acceptance. Here, we adapted an extubation device from an intubating stylet, which is provided as accessory of endotracheal tube. Such stylet could provide safe access for expediting reintubation both during and after the COVID-19 pandemic, which is inexpensive, single-use, readily available, straightforward to handle, and well-tolerated, thereby benefiting both the patients and healthcare providers.

Clinical characteristics and prediction analysis of the recovered COVID-19 patients with re-detectable positive RNA test

Background: Previous studies have unveiled the occurrence of re-detectable positive (RP) RNA test result after hospital discharge among recovered COVID-19 patients, but the clinical characteristics of RP patients (RP patients) and the potential features affecting RP RNA test outcome remain unclear. Methods: A total of 742 COVID-19 patients discharged between March 1st, 2020 and March 20th, 2020 were enrolled. All patients were followed-up for SARS-CoV-2 RNA test and RP patents were identified. The clinical characteristics between RP patients and NRP patients were compared, and the potential features affecting re-detectable RNA test outcome were further evaluated. Results: Up to April 9th, 2020, 60 recovered patients (8.09%) had been re-detected to be SARS-CoV-2 RNA positive. Among those 60 RP patients, the median RP time was 12 days from the last negative result of SARS-CoV-2 RNA test or 10 days from hospital discharge. RP patients were prone to be older, having mild/moderate conditions, unilateral lung involvement and fatigue, chills, stuffy or runny nose, with high lymphocyte count. Multivariate logistic analysis and COX regression analysis demonstrated that age, lymphocyte count, urea nitrogen, stuffy or runny nose as well as lung involvement were independently associated with RP RNA test (P<0.05). Conclusions: Older patients accompanied with stuffy or runny nose, low urea nitrogen as well as unilateral lung involvement were more likely to develop RP RNA test result after hospital discharge. Therefore, we strongly suggest using broncho-alveolar lavage fluid for RNA detection, extending quarantine time, and conducting continual follow-up medical examination for those discharged patients.

Associations between severe pulmonary function and residual CT abnormalities in rehabilitating COVID-19 patients

OBJECTIVE: Coronavirus disease 2019 (COVID-19) spread around the world in 2020. Abnormal pulmonary function and residual CT abnormalities were observed in COVID-19 patients during recovery. Appropriate rehabilitation training is around the corner. The correlation between spirometric impairment and residual CT abnormality remains largely unknown. PATIENTS AND METHODS: A cross-sectional study conducted on the pulmonary function of 101 convalescent COVID-19 patients before discharge. Multivariate analysis was used to establish a scoring system to evaluate the spirometric abnormality based on residual chest CT. RESULTS: Lung consolidation area >25% and severe-type COVID-19 were two independent risk factors for severe pulmonary dysfunction. Besides, a scoring system was established. People scoring more than 12 points have more chances (17 times) to get severe pulmonary function impairment before discharge. CONCLUSIONS: For the first time, a chest CT characteristics-based grading system was suggested to predict the pulmonary dysfunction of COVID-19 patients during convalescence in this study. This study may provide suggestions for pulmonary rehabilitation.

Correlation between dynamic changes of chest ct findings and therapeutic response of covid-19: A multicenter experience in shanghai, china

Background: The novel coronavirus disease 2019 (COVID-19) has become a global public health emergency. Computed tomography (CT) offers valuable clues to the diagnosis of COVID-19. However, little is known about the correlation between dynamic changes of CT scores and therapeutic response in the course of COVID-19. Objectives: To describe the temporal changes of CT findings and characterize the time window of disease progression on the follow-up CT scans of patients with COVID-19. Patients and Methods: In this historical cohort study performed in Shanghai, China, the follow-up chest CT images of 91 patients with COVID-19 with different therapeutic responses were reviewed in multiple centers, with an emphasis on characterizing the changing trend of CT scores for lung lesions at 13-15 days after the symptom onset and thereafter. The CT score curve patterns were categorized into type 1 (characterized by an increase to the peak level, followed by a decrease), type 2 (characterized by a steady change without an obvious peak), and type 3 (characterized by a progressive increase). Results: The CT scores of the progression group (n = 9) with a longer time to the peak were significantly higher than those of the non-progression group (n = 82) on the first day and days 13-15 (P < 0.05), except for the median CT scores before days 13-15. The CT curve type 1 and type 2 were commonly observed in the non-progression group (63.4% and 36.6%, respectively), while type 3 was more common in the progression group (88.9%). Conclusion: Most patients with COVID-19 show favorable responses to clinical treatments in Shanghai. Thirteen to fifteen days after the symptom onset can be considered as a turning point for the therapeutic response. The CT curve type 3 usually represents a poor response. The CT scores of patients with different therapeutic responses may overlap before days 13-15. The changing trend of longitudinal CT scores may contribute to the prediction of disease progression.

Legal Regulation and Reflection of Public Health Emergencies in China on the Example of Coronavirus Pandemic

At the moment, the shortcomings in the system of responding to crises in the health sector lie in the imperfection of the response order and management mechanisms The purpose of the paper is to investigate the legal regulation and reflection of public health emergencies in China, as exemplified by the case of 2019 coronavirus. The paper discusses the legal framework for the regulation of emergency situations in public health in China for the period from the end of 2019. The importance of preventing and controlling the epidemic is substantiated, and shortcomings in the work to combat the epidemic are analysed. Particular attention is paid to issues of informing the public and the rule of law in emergency situations in public health. The provisions of the legislation of the People's Republic of China in public health emergencies were also examined. The paper is of practical importance not only for China, but for the entire world in the prevention and control of coronavirus.

The devil is in the detail: A framework for macroscopic prediction via microscopic models

Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data. Yet, many existing approaches for predicting macroscopic behavior only use aggregated data, leaving a large amount of fine-grained microscopic information unused. In this paper, we propose a principled optimization framework for macroscopic prediction by fitting microscopic models based on conditional stochastic optimization. The framework leverages both macroscopic and microscopic information, and adapts to individual microscopic models involved in the aggregation. In addition, we propose efficient learning algorithms with convergence guarantees. In our experiments, we show that the proposed learning framework clearly outperforms other plug-in supervised learning approaches in real-world applications, including the prediction of daily infections of COVID-19 and medicare claims. © 2020 Neural information processing systems foundation. All rights reserved.