The Analysis of Oral and Fecal Virome Detects Multiple Novel Emerging Viruses in Snakes

Wild animals are considered reservoirs for emerging and reemerging viruses, such as the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Previous studies have reported that bats and ticks harbored variable important pathogenic viruses, some of which could cause potential diseases in humans and livestock, while viruses carried by reptiles were rarely reported. Our study first conducted snakes' virome analysis to establish effective surveillance of potential transboundary emerging diseases. Consequently, Adenoviridae, Circoviridae, Retroviridae, and Parvoviridae were identified in oral samples from Protobothrops mucrosquamatus, Elaphe dione, and Gloydius angusticeps based on sequence similarity to existing viruses. Picornaviridae and Adenoviridae were also identified in fecal samples of Protobothrops mucrosquamatus. Notably, the iflavirus and foamy virus were first reported in Protobothrops mucrosquamatus, enriching the transboundary viral diversity in snakes. Furthermore, phylogenetic analysis revealed that both the novel-identified viruses showed low genetic similarity with previously reported viruses. This study provided a basis for our understanding of microbiome diversity and the surveillance and prevention of emerging and unknown viruses in snakes.

Face Mask Classification based on Transfer Learning and MobileNet V2

Face mask image recognition can detect and monitor whether people wear the mask. Currently, the mask recognition model research mainly focuses on different mask detection systems. However, these methods have limited working datasets, do not give safety alerts, and do not work appropriately on masks. This paper aims to use the face mask recognition detection model in public places to monitor the people who do not wear the mask or the wrong mask to reduce the spread of Covid-19. The mask detection model supports transfer learning and image classification. Specifically, the collected data are first collected and then divided into two parts: with_mask and without_mask. Then authors build, implement the model, and obtain accurate mask recognition models. This paper uses and size of images datasets tested respectively. The experimental results show that the effect of the image size of was relatively better, and the training accuracy of different MobileNetV2 models is about 95%. Our analysis demonstrates that MobileNetV2 can correctly classify Covid-19. © 2022 ACM.

ICT-Facilitated Coproduction during COVID-19: Intelligence Innovation of Community Governance in Guangzhouĝ

Since the COVID-19 outbreak in 2020, ICT-based technology application platforms have played a prominent role in promoting cooperative governance of community epidemic prevention, realizing cooperative supply of public services, and promoting resident participation. Starting from the definition, background and prospect of cooperative production, the study explores how public services can effectively promote collaborative governance through ICTs, combined with the popularization of ICT platforms and applications to promote citizens' ability to access information, participate in public affairs and participate in the development of ways. The practice of community cooperative governance during the COVID-19 pandemic in Guangzhou demonstrated how the city can ensure the development of community public management and services while coordinating the prevention and control of COVID-19 based on ICT-related information systems and technology platforms. Based on the application of ICT, the ability of citizens to participate in community public governance has been improved, and the mode of public service supply has been changed, and the pressure on community governance has been reduced through scientific and technological governance tools, so as to promote the cooperative production and participation of public governance to achieve the sharing of results and responsibilities, providing a new way for public governance in the future intelligent society. © 2022 ACM.

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Transparent Aspect-Level Sentiment Analysis Based on Dependency Syntax Analysis and Its Application on COVID-19

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurantl4, Laptop, Restaurantl6, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on "government" and "lockdown" of 1,658,250 tweets about "#COVID-19" that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users' positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users' emotions over time based on the tweets and on our models.

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers). © 2021 Association for Computing Machinery.

Covid constraints on care: the impact of COVID-19 on decision-making for people at the end of life and the experiences of healthcare professionals and bereaved carers

IntroductionPeople are living longer with terminal illness, increasing the need for good palliative care. Projections indicate rising home deaths;accelerated by the COVID-19 pandemic but dying at home is reliant on informal carers.AimsTo identify the impact of the COVID-19 pandemic on hospice services from the perspectives of staff and bereaved carers, exploring decision-making for place-of-care and informal caring.MethodScoping reviews explored (1) place of end of life care, and (2) informal caring during the pandemic. Online interviews are being conducted with healthcare professionals in England (n=10) and Scotland (n=10) and bereaved carers who experienced Marie Curie services during lockdown in England (n=10) and Scotland (n=15-20). Once completed by January 2022 and thematically analysed key findings will drive a ‘knowledge exchange’ discussion with policy makers in England and Scotland.ResultsThe reviews and preliminary interview findings indicate the pandemic has put greater pressures on those accessing palliative care services. Decisions were influenced by the media;‘fear of contracting’ or ‘spreading the virus’ are evident in preferences for ‘home-based care. Social distancing, wearing of PPE and shielding restricted practical and emotional support that carers feel enable a good home death. The literature suggests that many carers adjusted to the altered methods of social connection and communication, but interview data suggests concerns about wellbeing especially where ‘grief’ was put ‘on hold’, delaying the bereavement process.ConclusionFindings will identify key considerations for policy and practice change around the future of hospice services if the move to community continues and how we develop and deliver hospice community based services to meet need.ImpactThis research will seek to inform Government policy and Marie Curie services to enable evidence based change and inform future research priorities.