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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.

The Chinese white wax scale insect, Ericerus pela, is an important resource insect in China. The rapid response of E. pela to decreasing temperatures plays key roles in the population distribution. In this study, we analyzed the gene expression of E. pela treated with low temperature using transcriptome analyses and weighted gene coexpression network analysis (WGCNA). The results showed that the cold resistance of E. pela involved changes in the expression of many genes. The genes were mainly involved in alcohol formation activity, lipid metabolism, membrane and structure, and oxidoreductase activity. According to the WGCNA results, some pathways related to cold resistance were found in the genes in the modules, such as cytoskeleton proteins, cytoskeleton protein pathway, biosynthesis of unsaturated fatty acids, glycerophospholipid metabolism, ether lipid metabolism, and thermogenesis. Some of the hub genes were nonspecific lipid-transfer proteins, DnaJ homolog subfamily C member 13, paramyosin, tropomodulin, and tubulin beta chain. In particular, the hub genes of the tan module included the heat shock protein (hsp) 10, hsp 60, hsp 70, and hsp 90 genes. Thirty-five antifreeze protein (afp) genes were identified according to the annotation results. Three afp genes were further identified among the hub genes. Six of these genes were selected for heterogeneous protein expression. One of them was expressed successfully. The thermal hysteresis activity (THA) analyses showed that the THA was 1.73°C. These results showed that the cytoskeleton, lipid metabolism, thermogenesis, HSPs and AFPs may play important roles in the cold resistance of E. pela.

Offline Siamese networks have achieved very promising tracking performance, especially in accuracy and efficiency. However, they often fail to track an object in complex scenes due to the incapacity in online update. Traditional updaters are difficult to process the irregular variations and sampling noises of objects, so it is quite risky to adopt them to update Siamese networks. In this paper, we first present a two-stage one-shot learner, which can predict the local parameters of primary classifier with object samples from diverse stages. Then, an updatable Siamese network is proposed based on the learner (SiamTOL), which is able to complement online update by itself. Concretely, we introduce an extra inputting branch to sequentially capture the latest object features, and design a residual module to update the initial exemplar using these features. Besides, an effective multi-aspect training loss is designed for our network to avoid overfit. Extensive experimental results on several popular benchmarks including OTB100, VOT2018, VOT2019, LaSOT, UAV123 and GOT10k manifest that the proposed tracker achieves the leading performance and outperforms other state-of-the-art methods

Additional file 1. Fig S1. Risk of bias summary. Fig S2. Risk of bias graph. Assessment is presented as percentages across all included studies.

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.

A new detector, capillary coupled with optical fiber LED-induced fluorescence detector (CCOF-LED-IFD, using CCOF for short), is introduced for CE. The strategy of the present work was that the optical fiber and separation capillary were, in the parallel direction, fastened in a fixation capillary with larger inner diameter. By employing larger inner diameter, the fixation capillary allowed the large diameter of the optical fiber to be inserted into it. By transmitting an enhanced excitation light through the optical fiber, the detection sensitivity was improved. The advantages of the CCOF-CE system were validated by the detection of riboflavin, and the results were compared to those obtained by the in-capillary common optical fiber LED-induced fluorescence detector (IC-COF-LED-IFD, using COF for short). The LODs of CCOF-CE and COF-CE were 0.29 nM and 11.0 nM (S/N = 3), respectively. The intraday (n = 6) repeatability and interday (n = 6) reproducibility of migration time and corresponding peak area for both types of CE were all less than 1.10 and 3.30%, respectively. The accuracy of the proposed method was judged by employing standard addition method, and recoveries obtained were in the range of 98.0-102.4%. The results indicated that the sensitivity of the proposed system was largely improved, and that its reproducibility and accuracy were satisfactory. The proposed system was successfully applied to separate and determine riboflavin in real sample.

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.

Integral proportional navigation law which can be applied to strapdown laser semi-active seeker is a kind of typical guidance law. In order to analyze the precision with this kind of guidance law, the analysis based on adjoints was put forward. Firstly, the model of integral proportion navigation guidance is constructed and the implement of the integral proportional navigation law was given. Secondly, based on the analysis of characteristic of different kind of error sources, the influence of these error sources with adjoints was got. The result shows that precision has a close correlation with navigation constant N and dynamic behavior of autopilot. These conclusions are significant for the application of the strapdown laser semi-active seeker in engineering.

In order to improve the method of training the commander, the aerial instructor, back parachute commander, the guarantee unit and the aerial broadcaster, in this paper, we used Vega, Creator, and RTI implement the Parachute Jump Commanding And Simulation System to simulate the parachute jump commanding process. In this system, firstly, we used Creator build the 3D models that we need in the system; secondly, we adopted Vega to develop the aerial instructor federate with the models we build with Creator on MFC at Visual C++6.0 platform; thirdly, we implement the commander federate, the guarantee unit federate, the back parachute commander federate and the aerial broadcaster federate on MFC at VC++6.0; Finally, we interlink the federate that we implemented above with RTI, realize a cooperated parachute jump training system. Through lots of experiments and we compare the training system experiment result with the computes data, we use the compare result to direct the trainee. This system makes the parachute jump command training more effective, more safety, shorten training cycle, lower costs and weather protected.

In order to carries on the plan and the control to aircraft level flight, the attitude and the route has been analyzed. First obtain that the factors control the level attitude are the roll angle and the yaw angle, and infers the computation algorithm of attitude; then obtain that the factors control the level route are the route position and the radius of turn, and infers the algorithm of flight route; finally carries on the software simulation using this algorithm, according to the attitude and the route restraint, produce shortest aircraft level flight road map which through the giving route position on the software surface. According to this algorithm, it may control and plan the aircraft level flight, through to limit of the aircraft security attitude and the route position.

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.