Supply Chain Risk Diffusion in Partially Mapping Double-Layer Hypernetworks.

The impact of COVID-19 is global, and uncertain information will affect product quality and worker efficiency in the complex supply chain network, thus bringing risks. Aiming at individual heterogeneity, a partial mapping double-layer hypernetwork model is constructed to study the supply chain risk diffusion under uncertain information. Here, we explore the risk diffusion dynamics, drawing on epidemiology, and establish an SPIR (Susceptible-Potential-Infected-Recovered) model to simulate the risk diffusion process. The node represents the enterprise, and hyperedge represents the cooperation among enterprises. The microscopic Markov chain approach (MMCA) is used to prove the theory. Network dynamic evolution includes two removal strategies: (i) removing aging nodes; (ii) removing key nodes. Using Matlab to simulate the model, we found that it is more conducive to market stability to eliminate outdated enterprises than to control key enterprises during risk diffusion. The risk diffusion scale is related to interlayer mapping. Increasing the upper layer mapping rate to strengthen the efforts of official media to issue authoritative information will reduce the infected enterprise number. Reducing the lower layer mapping rate will reduce the misled enterprise number, thereby weakening the efficiency of risk infection. The model is helpful for understanding the risk diffusion characteristics and the importance of online information, and it has guiding significance for supply chain management.

SARS-CoV-2 spike protein induces IL-18-mediated cardiopulmonary inflammation via reduced mitophagy.

Cardiopulmonary complications are major drivers of mortality caused by the SARS-CoV-2 virus. Interleukin-18, an inflammasome-induced cytokine, has emerged as a novel mediator of cardiopulmonary pathologies but its regulation via SARS-CoV-2 signaling remains unknown. Based on a screening panel, IL-18 was identified amongst 19 cytokines to stratify mortality and hospitalization burden in patients hospitalized with COVID-19. Supporting clinical data, administration of SARS-CoV-2 Spike 1 (S1) glycoprotein or receptor-binding domain (RBD) proteins into human angiotensin-converting enzyme 2 (hACE2) transgenic mice induced cardiac fibrosis and dysfunction associated with higher NF-κB phosphorylation (pNF-κB) and cardiopulmonary-derived IL-18 and NLRP3 expression. IL-18 inhibition via IL-18BP resulted in decreased cardiac pNF-κB and improved cardiac fibrosis and dysfunction in S1- or RBD-exposed hACE2 mice. Through in vivo and in vitro work, both S1 and RBD proteins induced NLRP3 inflammasome and IL-18 expression by inhibiting mitophagy and increasing mitochondrial reactive oxygenation species. Enhancing mitophagy prevented Spike protein-mediated IL-18 expression. Moreover, IL-18 inhibition reduced Spike protein-mediated pNF-κB and EC permeability. Overall, the link between reduced mitophagy and inflammasome activation represents a novel mechanism during COVID-19 pathogenesis and suggests IL-18 and mitophagy as potential therapeutic targets.

Posner-Schlossman syndrome relapse following inactivated COVID-19 vaccination in China.

Introduction: This retrospective study aims to present the characteristics of Posner-Schlossman syndrome (PSS) relapse following inactivated COVID-19 vaccination. Methods: From 2020 to 2022, 12 out of 106 PSS patients undergoing relapses after any dose of inactivated COVID-19 vaccines were enrolled. Medical histories, information on the vaccination and systemic adverse events were collected. Patients were treated with corticosteroids, intraocular pressure (IOP)-lowering drugs and systemic immunosuppressive agents (if needed). Daily regimen and release course were noted. Results: The recurrence rate after vaccination was 11.32% (12/106, 95% CI: 5.29%-17.35%) among 106 PSS patients we surveyed. All the 12 patients were inoculated with inactivated COVID-19 vaccines developed by Sinopharm, China. The mean time of relapse was 5.27 ± 3.72 days (range: 1-13 days, median: 4 days). Higher IOP and more keratic precipitates (KPs) were seen in the relapse following vaccination (33.55 ± 12.99 mmHg, 91.67% had KPs compared to 25.38 ± 3.80 mmHg, 33.33% had KPs in previous relapse, P = 0.009). The mean release course was 30.71 ± 34.74 days for the relapse following vaccination and 7.33 ± 6.51 days for previous relapses. The attack frequency before and after vaccination was 3.56 ± 2.07 and 9.11 ± 7.34 times per year (P = 0.044). Higher daily doses of corticosteroids, IOP-lowering drugs and ganciclovir were needed to maintain stable course, though the difference did not reach statistical significance. Discussion: More frequent relapses and harder control of IOP were found in PSS relapse following COVID-19 vaccination. Ophthalmologists need to be aware of the group vulnerability and take precautions, though the pathogenesis is still under investigation.

Posner-Schlossman syndrome relapse following inactivated COVID-19 vaccination in China

Introduction This retrospective study aims to present the characteristics of Posner-Schlossman syndrome (PSS) relapse following inactivated COVID-19 vaccination. Methods From 2020 to 2022, 12 out of 106 PSS patients undergoing relapses after any dose of inactivated COVID-19 vaccines were enrolled. Medical histories, information on the vaccination and systemic adverse events were collected. Patients were treated with corticosteroids, intraocular pressure (IOP)-lowering drugs and systemic immunosuppressive agents (if needed). Daily regimen and release course were noted. Results The recurrence rate after vaccination was 11.32% (12/106, 95% CI: 5.29%–17.35%) among 106 PSS patients we surveyed. All the 12 patients were inoculated with inactivated COVID-19 vaccines developed by Sinopharm, China. The mean time of relapse was 5.27 ± 3.72 days (range: 1–13 days, median: 4 days). Higher IOP and more keratic precipitates (KPs) were seen in the relapse following vaccination (33.55 ± 12.99 mmHg, 91.67% had KPs compared to 25.38 ± 3.80 mmHg, 33.33% had KPs in previous relapse, P = 0.009). The mean release course was 30.71 ± 34.74 days for the relapse following vaccination and 7.33 ± 6.51 days for previous relapses. The attack frequency before and after vaccination was 3.56 ± 2.07 and 9.11 ± 7.34 times per year (P = 0.044). Higher daily doses of corticosteroids, IOP-lowering drugs and ganciclovir were needed to maintain stable course, though the difference did not reach statistical significance. Discussion More frequent relapses and harder control of IOP were found in PSS relapse following COVID-19 vaccination. Ophthalmologists need to be aware of the group vulnerability and take precautions, though the pathogenesis is still under investigation.

Risk Diffusion and Control under Uncertain Information Based on Hypernetwork

During the height of the COVID-19 epidemic, production lagged and enterprises could not deliver goods on time, which will bring considerable risks to the supply chain system. Modeling risk diffusion in supply chain networks is important for prediction and control. To study the influence of uncertain information on risk diffusion in a dynamic network, this paper constructs a dynamic evolution model based on a hypernetwork to study risk diffusion and control under uncertain information. First, a dynamic evolution model is constructed to represent the network topology, which includes the addition of links, rewiring of links, entry of nodes, and the exit of outdated nodes that obey the aging principle. Then, the risk diffusion scale is discussed with the Microscopic Markovian Chain Approach (MMCA), and the risk threshold is analyzed. Finally, the consistency of Monte Carlo (MC) simulation and MMCA is verified by MATLAB, and the influence of each parameter on the risk diffusion scale and risk threshold is tested. The results show that reducing the cooperation and production during the risk period, declining the attenuation factor, enhancing the work efficiency of the official media, and increasing the probability of the exit of outdated nodes in the supply chain networks will increase the risk threshold and restrain the risk diffusion.

Evolution and analysis of urban resilience and its influencing factors: a case study of Jiangsu Province, China.

This research uses panel data of cities in Jiangsu from 2009 to 2018 to construct a resilience framework that measures the level of urban resilience. A combination of the entropy method, Theil index, Moran ' sI , and the Spatial Durbin Model (SDM) is used to explore regional resilience development differences, the spatial correlation characteristics of urban resilience, and its influencing factors. The study finds that: (1) The spatial heterogeneity of regional resilience development is significant, as the overall level of resilience presents a spatial distribution pattern of descending from southern Jiangsu to central Jiangsu and to northern Jiangsu. (2) The total Theil index shows a wave-like downward trend during the study period. The differences between southern Jiangsu, central Jiangsu, and northern Jiangsu make up the main reason for the overall difference of urban resilience in Jiangsu Province. Among the three regions, the gap in resilience development level within southern Jiangsu is the largest. (3) There is a clear positive spatial correlation between urban resilience in the province and an obvious agglomeration trend of urban resilience levels. Among all subsystems, urban ecological resilience is the weakest and needs to be further improved. (4) Lastly, among the five factors affecting urban resilience, general public fiscal expenditure/GDP, which characterizes government factors, has the largest positive impact on urban resilience, while foreign trade has a negative impact. In the following studies, the theme of urban resilience should be constantly deepened, and more extensive data monitoring should be carried out for the urban system to improve the diversity of data sources, so as to assess urban resilience more accurately. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-022-05368-x.

Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space.

The COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations.