RESUMO
Currently, coronavirus disease 2019 (COVID-19) remains a global pandemic, but the prevention and control of the disease in various countries have also entered the normalization stage. To achieve economic recovery and avoid a waste of resources, different regions have developed prevention and control strategies according to their social, economic, and medical conditions and culture. COVID-19 disparities under the interaction of various factors, including interventions, need to be analyzed in advance for effective and precise prevention and control. Considering the United States as the study case, we investigated statistical and spatial disparities based on the impact of the county-level social vulnerability index (SVI) on the COVID-19 infection rate. The county-level COVID-19 infection rate showed very significant heterogeneity between states, where 67% of county-level disparities in COVID-19 infection rates come from differences between states. A hierarchical linear model (HLM) was adopted to examine the moderating effects of state-level social distancing policies on the influence of the county-level SVI on COVID-19 infection rates, considering the variation in data at a unified level and the interaction of various data at different levels. Although previous studies have shown that various social distancing policies inhibit COVID-19 transmission to varying degrees, this study explored the reasons for the disparities in COVID-19 transmission under various policies. For example, we revealed that the state-level restrictions on the internal movement policy significantly attenuate the positive effect of county-level economic vulnerability indicators on COVID-19 infection rates, indirectly inhibiting COVID-19 transmission. We also found that not all regions are suitable for the strictest social distancing policies. We considered the moderating effect of multilevel covariates on the results, allowing us to identify the causes of significant group differences across regions and to tailor measures of varying intensity more easily. This study is also necessary to accomplish targeted preventative measures and to allocate resources.
RESUMO
Recently, with the large-scale outbreak of the global financial crisis and public safety incidents (such as COVID-19), high-performance computing has been widely applied to risk prediction, vaccine development, and other fields. In scenarios where high-performance computing infrastructure responds to the instantaneous explosion of computing demands, a crucial issue is to provide large-scale flexible allocation and adjustment of computing capability by rapidly constructing computing clusters. Existing large-scale computing cluster deployment solutions usually utilize source code deployment or other deployment tools. The great challenge of existing deployment methods is to reduce excessive image distribution time and refrain from configuration defects. In this article, we design an intelligent distributed registry deployment (IDRD) architecture based on the OpenStack cloud platform, which adaptively places distributed image repositories using the containerized deployment of multiple registries. We propose a server load priority algorithm to solve multiple registries placement problems in IDRD. Furthermore, we devise a clustering algorithm based on demand density that can optimize the global performance of IDRD and improve large-scale cluster load balancing capabilities, which has been implemented in the TianHe Supercomputing environment. Extensive experimental results demonstrate that IDRD can effectively reduce [Formula Omitted]-[Formula Omitted] of the distribution time of component images and significantly improve the efficiency of large-scale cluster deployment.
RESUMO
The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging human health worldwide. Loop-mediated isothermal amplification (LAMP) has been promptly applied to the detection of SARS-CoV-2 owing to its high amplification efficacy and less requirement of the thermal cycler. However, the vast majority of these LAMP-based assays depend on the non-specific detection of LAMP products, which can not discern the undesirable amplificons, likely to yield unreliable results. Herein, a sequence-specific LAMP assay was reported to detect SARS-CoV-2 using proofreading enzyme-mediated probe cleavage (named Proofman), which could realize real-time and visual detection without uncapping. This assay, introducing a proofreading enzyme and the fluorogenic probe to reverse-transcription LAMP (RT-Proofman-LAMP), can specifically detect the SARS-CoV-2 RNA with a detection limit of 100 copies. In addition to the real-time analysis, the assay is capable of endpoint visualization under a transilluminator within 50 min, providing a convenient reporting manner under the setting of point-of-care testing (POCT). In combination with different fluorophores, the one-pot multiplex assay was successfully achieved to detect multiple targets of SARS-CoV-2 and inner control simultaneously. In summary, the development of RT-Proofman-LAMP offers a versatile and highly-specific method for fast field screening and laboratory testing of SARS-CoV-2, making it a promising platform in COVID-19 diagnosis.