Research on comprehensive recovery of liner schedule and container flow with hard time windows constraints.

COVID-19 has had a huge impact on the global container market. Many liner companies have adopted a blank sailing for some voyages to adjust capacity, and vessel schedule reliability continues to be sluggish. From the perspective of the container liner company, this paper studies the integrated recovery of liner schedule and container flow under the background of suspension of shipping service. With the goal of minimizing the total cost of the liner company, the hard time window constraints of the container flow on the suspended routes are set to construct the integrated recovery problem.The increased carbon emission cost during the restoration of the container flow is taken into account.A mixed integer nonlinear programming model is established, and the adaptive mutation particle swarm optimization (AMPSO) is used to solve the model. The results show that the total cost of the model is reduced by 10.66% compared with the total cost of the shipping schedule recovery model that did not consider the recovery of container flow.

Roles of host small RNAs in the evolution and host tropism of coronaviruses.

Human coronaviruses (CoVs) can cause respiratory infection epidemics that sometimes expand into globally relevant pandemics. All human CoVs have sister strains isolated from animal hosts and seem to have an animal origin, yet the process of host jumping is largely unknown. RNA interference (RNAi) is an ancient mechanism in many eukaryotes to defend against viral infections through the hybridization of host endogenous small RNAs (miRNAs) with target sites in invading RNAs. Here, we developed a method to identify potential RNAi-sensitive sites in the viral genome and discovered that human-adapted coronavirus strains had deleted some of their sites targeted by miRNAs in human lungs when compared to their close zoonic relatives. We further confirmed using a phylogenetic analysis that the loss of RNAi-sensitive target sites could be a major driver of the host-jumping process, and adaptive mutations that lead to the loss-of-target might be as simple as point mutation. Up-to-date genomic data of severe acute respiratory syndrome coronavirus 2 and Middle-East respiratory syndromes-CoV strains demonstrate that the stress from host miRNA milieus sustained even after their epidemics in humans. Thus, this study illustrates a new mechanism about coronavirus to explain its host-jumping process and provides a novel avenue for pathogenesis research, epidemiological modeling, and development of drugs and vaccines against coronavirus, taking into consideration these findings.

Comparison of Clinical Features and Outcomes of Medically Attended COVID-19 and Influenza Patients in a Defined Population in the 2020 Respiratory Virus Season.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), which is causing the coronavirus disease-2019 (COVID-19) pandemic, poses a global health threat. However, it is easy to confuse COVID-19 with seasonal influenza in preliminary clinical diagnosis. In this study, the differences between influenza and COVID-19 in epidemiological features, clinical manifestations, comorbidities and pathogen biology were comprehensively compared and analyzed. SARS-CoV-2 causes a higher proportion of pneumonia (90.67 vs. 17.07%) and acute respiratory distress syndrome (12.00 vs. 0%) than influenza A virus. The proportion of leukopenia for influenza patients was 31.71% compared with 12.00% for COVID-19 patients (P = 0.0096). The creatinine and creatine kinase were significantly elevated when there were COVID-19 patients. The basic reproductive number (R0) for SARS-CoV-2 is 2.38 compared with 1.28 for seasonal influenza A virus. The mutation rate of SARS-CoV-2 ranges from 1.12 × 10-3 to 6.25 × 10-3, while seasonal influenza virus has a lower evolutionary rate (0.60-2.00 × 10-6). Overall, this study compared the clinical features and outcomes of medically attended COVID-19 and influenza patients. In addition, the S477N and N439K mutations on spike may affect the affinity with receptor ACE2. This study will contribute to COVID-19 control and epidemic surveillance in the future.

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models.

BACKGROUND: A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic. RESULTS: Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus. CONCLUSIONS: These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.