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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.

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]

Adhesin-mediated bacterial interspecies interactions are important elements in oral biofilm formation. They often occur on a species-specific level, which could determine health or disease association of a biofilm community. Among the key players involved in these processes are the ubiquitous fusobacteria that have been recognized for their ability to interact with numerous different binding partners. Fusobacterial interactions with Streptococcus mutans, an important oral cariogenic pathogen, have previously been described but most studies focused on binding to non-mutans streptococci and specific cognate adhesin pairs remain to be identified. Here, we demonstrated differential binding of oral fusobacteria to S. mutans. Screening of existing mutant derivatives indicated SpaP as the major S. mutans adhesin specific for binding to Fusobacterium nucleatum ssp. polymorphum but none of the other oral fusobacteria tested. We inactivated RadD, a known adhesin of F. nucleatum ssp. nucleatum for interaction with a number of gram-positive species, in F. nucleatum ssp. polymorphum and used a Lactococcus lactis heterologous SpaP expression system to demonstrate SpaP interaction with RadD of F. nucleatum ssp. polymorphum. This is a novel function for SpaP, which has mainly been characterized as an adhesin for binding to host proteins including salivary glycoproteins. In conclusion, we describe an additional role for SpaP as adhesin in interspecies adherence with RadD-SpaP as the interacting adhesin pair for binding between S. mutans and F. nucleatum ssp. polymorphum. Furthermore, S. mutans attachment to oral fusobacteria appears to involve species- and subspecies-dependent adhesin interactions.

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]

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]

Streptococcus mutans is considered the principal cariogenic bacterium for dental caries. Despite the recognition of their importance for cariogenesis, the possible coordination among S. mutans' main virulence factors, including glucan production, acidogenicity and aciduricity, has been less well studied. In the present study, using S. mutans strains with surface-displayed pH-sensitive pHluorin, we revealed sucrose availability- and Gtf functionality-dependent proton accumulation on S. mutans surface. Consistent with this, using a pH-sensitive dye, we demonstrated that both in vivo cell-produced and in vitro enzymatically synthesized insoluble glucans displayed proton-concentrating ability. Global transcriptomics revealed proton accumulation triggers the up-regulation of genes encoding functions involved in acid tolerance response in a glucan-dependent manner. Our data suggested that this proton enrichment around S. mutans could pre-condition the bacterium for acid-stress. Consistent with this hypothesis, we found S. mutans strains defective in glucan production were more acid sensitive. Our study revealed for the first time that insoluble glucans is likely an essential factor linking acidogenicity with aciduricity. The coordination of these key virulence factors could provide new insights on how S. mutans may have become a major cariogenic pathogen.

One major challenge to studying human microbiome and its associated diseases is the lack of effective tools to achieve targeted modulation of individual species and study its ecological function within multispecies communities. Here, we show that C16G2, a specifically targeted antimicrobial peptide, was able to selectively kill cariogenic pathogen Streptococcus mutans with high efficacy within a human saliva-derived in vitro oral multispecies community. Importantly, a significant shift in the overall microbial structure of the C16G2-treated community was revealed after a 24-h recovery period: several bacterial species with metabolic dependency or physical interactions with S. mutans suffered drastic reduction in their abundance, whereas S. mutans' natural competitors, including health-associated Streptococci, became dominant. This study demonstrates the use of targeted antimicrobials to modulate the microbiome structure allowing insights into the key community role of specific bacterial species and also indicates the therapeutic potential of C16G2 to achieve a healthy oral microbiome.

It is well established that host-associated microbial communities can interfere with the colonization and establishment of microbes of foreign origins, a phenomenon often referred to as bacterial interference or colonization resistance. However, due to the complexity of the indigenous microbiota, it has been extremely difficult to elucidate the community colonization resistance mechanisms and identify the bacterial species involved. In a recent study, we have established an in vitro mice oral microbial community (O-mix) and demonstrated its colonization resistance against an Escherichia coli strain of mice gut origin. In this study, we further analyzed the community structure of the O-mix by using a dilution/regrowth approach and identified the bacterial species involved in colonization resistance against E. coli. Our results revealed that, within the O-mix there were three different types of bacterial species forming unique social structure. They act as 'Sensor', 'Mediator' and 'Killer', respectively, and have coordinated roles in initiating the antagonistic action and preventing the integration of E. coli. The functional role of each identified bacterial species was further confirmed by E. coli-specific responsiveness of the synthetic communities composed of different combination of the identified players. The study reveals for the first time the sophisticated structural and functional organization of a colonization resistance pathway within a microbial community. Furthermore, our results emphasize the importance of 'Facilitation' or positive interactions in the development of community-level functions, such as colonization resistance.

Abstract Background Our knowledge of microbial diversity in the human oral cavity has vastly expanded during the last two decades of research. However, much of what is known about the behavior of oral species to date derives from pure culture approaches and the studies combining several cultivated species, which likely does not fully reflect their function in complex microbial communities. It has been shown in studies with a limited number of cultivated species that early oral biofilm development occurs in a successional manner and that continuous low pH can lead to an enrichment of aciduric species. Observations that in vitro grown plaque biofilm microcosms can maintain similar pH profiles in response to carbohydrate addition as plaque in vivo suggests a complex microbial community can be established in the laboratory. In light of this, our primary goal was to develop a robust in vitro biofilm-model system from a pooled saliva inoculum in order to study the stability, reproducibility, and development of the oral microbiome, and its dynamic response to environmental changes from the community to the molecular level. Results Comparative metagenomic analyses confirmed a high similarity of metabolic potential in biofilms to recently available oral metagenomes from healthy subjects as part of the Human Microbiome Project. A time-series metagenomic analysis of the taxonomic community composition in biofilms revealed that the proportions of major species at 3 hours of growth are maintained during 48 hours of biofilm development. By employing deep pyrosequencing of the 16S rRNA gene to investigate this biofilm model with regards to bacterial taxonomic diversity, we show a high reproducibility of the taxonomic carriage and proportions between: 1) individual biofilm samples; 2) biofilm batches grown at different dates; 3) DNA extraction techniques and 4) research laboratories. Conclusions Our study demonstrates that we now have the capability to grow stable oral microbial in vitro biofilms containing more than one hundred operational taxonomic units (OTU) which represent 60-80% of the original inoculum OTU richness. Previously uncultivated Human Oral Taxa (HOT) were identified in the biofilms and contributed to approximately one-third of the totally captured 16S rRNA gene diversity. To our knowledge, this represents the highest oral bacterial diversity reported for an in vitro model system so far. This robust model will help investigate currently uncultivated species and the known virulence properties for many oral pathogens not solely restricted to pure culture systems, but within multi-species biofilms.