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Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Global efforts are focused on discussing effective measures for minimizing the impact of COVID-19 on global community. It is clear that the ongoing pandemic of this virus caused an immense threat to public health and economic development. Mathematical models with real data simulations are powerful tools that can identify key factors of pandemic and improve control or mitigation strategies. Compared with integer-order and left-hand side fractional models, two-side fractional models can better capture the state of pandemic spreading. In this paper, two-side fractional models are first proposed to qualitative and quantitative analysis of the COVID-19 pandemic. A basic framework are given for the prediction and analysis of infectious diseases by these types of models. By means of asymptotic stability analysis of disease-free and endemic equilibrium points, basic reproduction number R0 can be obtained, which is helpful for estimating the severity of an outbreak qualitatively. Sensitivity analysis of R0 is performed to identify and rank key epidemiological parameters. Based on the real data of the United States, numerical tests reveal that the model with both left-hand side fractional derivative and right-hand side fractional integral terms has a better forecast ability for the epidemic trend in the next ten days. Our extensive computational results also quantitatively reveal that non-pharmaceutical interventions, such as isolation, stay at home, strict control of social distancing, and rapid testing can play an important role in preventing the pandemic of the disease. Thus, the two-side fractional models are proposed in this paper can successfully capture the change rule of COVID-19, which provide a strong tool for understanding and analyzing the trend of the outbreak.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Background: Non–small cell lung cancer (NSCLC) is one of the cancers with the highest mortality and morbidity in the world. Circular RNAs (circRNAs) are newly identified players in carcinogenesis and development of various cancers. This study is aimed at exploring the functional effects and mechanism of circ_0028826 in the development of NSCLC. Methods: Real‐time quantitative PCR (RT‐qPCR) was used to detect the expression levels of circ_0028826, IDH2 mRNA, and miR‐758‐3p. IDH2, Bcl2, Bax, and E‐cadherin protein levels were detected using a western blot. Cell Counting Kit‐8 (CCK‐8), 5‐ethynyl‐2′‐deoxyuridine (EdU), flow cytometry, wound healing, and transwell assays were used to assess the capacities of proliferation, apoptosis, migration, and invasion. Interaction between miR‐758‐3p and circ_0028826 or IDH2 was validated using a dual‐luciferase reporter assay. The role of circ_0028826 in vivo was checked based on a xenograft tumor model. Results: Circ_0028826 was elevated in NSCLC, and its absence inhibited NSCLC cell proliferation, migration, invasion, and induced apoptosis. In terms of mechanism, circ_0028826 increased IDH2 expression by targeting miR‐758‐3p. In addition, circ_0028826 knockdown also regulated IDH2 by targeting miR‐758‐3p to inhibit tumor growth in vivo. Conclusion: Circ_0028826 promoted the development of NSCLC via regulation of the miR‐758‐3p/IDH2 axis, providing a new strategy for NSCLC treatment. [ABSTRACT FROM AUTHOR]

Myocardial ischemia–reperfusion injury (MIRI) is a common clinic scenario that occurs in the context of reperfusion therapy for acute myocardial infarction. It has been shown that cocaine and amphetamine‐regulated transcript (CART) can ameliorate cerebral ischemia–reperfusion (I/R) injury, but the effect of CART on MIRI has not been studied yet. Here, we revealed that CART protected the heart during I/R process by inhibiting apoptosis and excessive autophagy, indicating that CART would be a potential drug candidate for the treatment of MIRI. Further analysis showed that CART upregulated the activation of phospho‐AKT, leading to downregulation of lactate dehydrogenase (LDH) release, apoptosis, oxidative stress and excessive autophagy after I/R, which was inhibited by PI3K inhibitor, LY294002. Collectively, CART attenuated MIRI through inhibition of cardiomyocytes apoptosis and excessive autophagy, and the protective effect was dependent on PI3K/AKT signalling pathway. [ABSTRACT FROM AUTHOR]

Using a sample of 5,383 investment events of venture capital firms (VCs) investing in Chinese companies between 1997 and 2017, we investigate how levels of human capital of chief executive officers (CEOs) of portfolio companies affect tendencies of foreign and domestic VCs to replace CEOs. We find that domestic and foreign VCs are similar in that they are both less likely to replace CEOs of their portfolio companies if CEOs have higher levels of human capital. Furthermore, we find that domestic and foreign VCs exhibit significant differences in CEO replacement when not considering CEO human capital or when CEOs have lower levels of human capital. Results are consistent with our hypotheses that foreign VCs rely on CEO human capital as a stronger quality signal than domestic VCs, because they have more difficulty monitoring CEOs' post-investment performance. On the contrary, the geographical and cultural proximity of domestic VCs enables them to obtain information other than CEO human capital, such as CEOs' social skills, potential, entrepreneurial passion, and charisma, to make replacement decisions. The study advances our understanding of the behavioral differences between domestic and foreign VCs in the post-investment management process. [ABSTRACT FROM AUTHOR]

To investigate the cell-cell interactions of intergeneric bacterial species, the study detected the survival of Enterococcus faecalis (Ef) under monospecies or coaggregation state with Fusobacterium nucleatum subsp. polymorphum (Fnp) in environmental stress. Ef and Fnp infected the human macrophages with different forms (Ef and Fnp monospecies, Ef-Fnp coaggregates, Ef + Fnp cocultures) for exploring the immunoregulatory effects and the relevant molecular mechanisms. Meanwhile, the transcriptomic profiles of coaggregated Ef and Fnp were analyzed. Ef was shown to coaggregate with Fnp strongly in CAB within 90 min by forming multiplexes clumps. Coaggregation with Fnp reinforced Ef resistance against unfavorable conditions including alkaline, hypertonic, nutrient-starvation, and antibiotic challenges. Compared with monospecies and coculture species, the coaggregation of Ef and Fnp significantly facilitates both species to invade dTHP-1 cells and aid Ef to survive within the cells. Compared with coculture species, dual-species interaction of Ef and Fnp significantly decreased the levels of pro-inflammatory cytokines IL-6, TNF-α, and chemokines MCP-1 secreted by dTHP-1 cells and lessened the phosphorylation of p38, JNK, and p65 signaling pathways. The transcriptome sequencing results showed that 111 genes were differentially expressed or Ef-Fnp coaggregated species compared to Ef monospecies; 651 genes were differentially expressed for Fnp when coaggregation with Ef. The analysis of KEGG pathway showed that Ef differentially expressed genes (DEGs) were enriched in quorum sensing and arginine biosynthesis pathway; Fnp DEGs were differentially concentrated in lipopolysaccharide (LPS) biosynthesis, biofilm formation, and lysine degradation pathway compared to monospecies. Key points: • Coaggregated with Fnp aids Ef's survival in environmental stress, especially in root canals after endodontic treatment. • The coaggregation of Ef and Fnp may weaken the pro-inflammatory response and facilitate Ef to evade killed by macrophages. • The coaggregation between Ef and Fnp altered interspecies transcriptional profiles. [ABSTRACT FROM AUTHOR]

BACKGROUND: The exploration of a robust solid acid catalyst for the efficient hydrolysis of cellulose is a research hotspot due to the need for clean production and sustainable development. KIT‐6 silica offers abundant three‐dimensionally interconnected mesopores, which are expected to be superior to mesoporous structures with one‐ or two‐dimensional channels due to faster diffusion of reactants and products during reaction. The catalytic conversion of microcrystalline cellulose is examined in detail over a functionalized KIT‐6 catalyst prepared by the method of Zr addition and subsequent sulfation. RESULTS: In terms of cellulose conversion and product yield, the activity of the catalysts followed the order of sulfated Zr‐KIT‐6 > Zr‐KIT‐6 > SO42−‐KIT‐6 > KIT‐6. Over the sulfated Zr‐KIT‐6 catalyst with a Si/Zr molar ratio of 7:1, a maximum cellulose conversion of 95.0% was achieved with glucose yield of 23.4%, 5‐hydroxymethylfufural yield of 4.9% and levulinic acid yield of 11.7% under the optimized reaction conditions: reaction temperature of 200 °C, reaction time of 2 h, catalyst amount of 0.3 g, cellulose‐to‐water mass ratio of 1:1. CONCLUSION: The sulfated Zr‐KIT‐6 catalyst possessed the strongest and richest Brønsted acid sites, which readily provided protons for the cleavage of β‐1,4 glycosidic bonds in cellulose, thus conferring superior catalytic activity in cellulose hydrolysis. A plausible pathway at the initial stage of cellulose hydrolysis over the catalyst was proposed. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]