Emergence of coronavirus in December 2019 and its spread across the world in the following months has made it a global health concern. The uncertainty about its evolution, transmission and effect of SARS-CoV-2, has left the countries and their governments in a worrisome state. Ambiguity about the strategies that would work towards mitigating the impact of virus has prompted them to use data-driven methods. Several countries started applying big data and advanced analytics technology for management of the crisis. This study aims to understand how different nations have employed analytics to deal with COVID-19. This paper reviews various strategies employed by different governments and organizations across nations that use advanced analytics to tackle pandemic. In the current emergency of corona virus, there have been several measures that organizations have taken to mitigate its impact, thanks to the evolution of computing technology. Big data and analytical tools provide various solutions like detection of existing COVID-19 cases, prediction of future outbreak, anticipation of potential preventive and therapeutic agents, and assistance in informed decision-making. This review discusses the big data analytics and artificial intelligence approaches that policy makers, researchers, epidemiologists and private organizations have adopted. By examining the different ways and areas where data analytics has been utilized, this study provides the other nations with the progressive scheme to address the pandemic.
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http://dx.doi.org/10.1007/s42979-021-00923-y | DOI Listing |
Adv Sci (Weinh)
January 2025
Research Institute of Big Data Science and Industry, Shanxi University, Taiyuan, Shanxi, 030006, China.
The Streptococcus canis Cas9 protein (ScCas9) recognizes the NNG protospacer adjacent motif (PAM), offering a wider range of targets than that offered by the commonly used S. pyogenes Cas9 protein (SpCas9). However, both ScCas9 and its evolved Sc++ variant still exhibit low genome editing efficiency in plants, particularly at the less preferred NTG and NCG PAM targets.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women's Diseases (Yantai Yuhuangding Hospital), Yantai, Shandong 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China. Electronic address:
Purpose: To elucidate the structural-functional connectivity (SC-FC) coupling in white matter (WM) tracts in patients with major depressive disorder (MDD).
Methods: A total of 178 individuals diagnosed with MDD and 173 healthy controls (HCs) were recruited for this study. The Euclidean distance was calculated to assess SC-FC coupling.
Accid Anal Prev
January 2025
School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.
View Article and Find Full Text PDFNeural Netw
December 2024
Institute of Automation, Chinese Academy of Sciences, MAIS, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
In the rapidly evolving field of deep learning, Convolutional Neural Networks (CNNs) retain their unique strengths and applicability in processing grid-structured data such as images, despite the surge of Transformer architectures. This paper explores alternatives to the standard convolution, with the objective of augmenting its feature extraction prowess while maintaining a similar parameter count. We propose innovative solutions targeting depthwise separable convolution and standard convolution, culminating in our Multi-scale Progressive Inference Convolution (MPIC).
View Article and Find Full Text PDFJ Med Chem
January 2025
Department of Respiratory and Critical Care Medicine, Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-Related Molecular Network, Precision Medicine Key Laboratory of Sichuan Province & Precision Medicine Center, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital of Sichuan University, Chengdu 610041, China.
Radiolabeled peptides are vital for positron emission tomography (PET) imaging, yet the F-labeling peptides remain challenging due to harsh conditions and time-consuming premodification requirements. Herein, we developed a novel vinyltetrazine-mediated bioorthogonal approach for highly efficient F-radiolabeling of a native peptide under mild conditions. This approach enabled radiosynthesis of various tumor-targeting PET tracers, including targeting the neurofibromin receptor (), the integrin αβ (), and the platelet-derived growth factor receptor β (), with a radiochemical yield exceeding 90%.
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