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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 multi-model ensemble forecast that combined predictions from dozens of different research 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 naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week 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. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.
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We developed a mobility-informed disease-transmission model for COVID-19, inspired by collision theory in gas-phase chemistry. This simple kinetic model leads to a closed-form infectious population as a function of time and cumulative mobility. This model uses fatality data from Johns Hopkins to infer the infectious population in the past, and mobility data from Google, without social-distancing policy, geological or demographic inputs. It was found that the model appears to be valid for twenty hardest hit counties in the United States. Based on this model, the number of infected people grows (shrinks) exponentially once the relative mobility exceeds (falls below) a critical value ([~]30% for New York City and [~]60% for all other counties, relative to a median mobility from January 3 to February 6, 2020). A simple mobility cap can be used by government at different levels to control COVID-19 transmission in reopening or imposing another shutdown.
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Objective: To observe the value of texture analysis based on gray level co-occurrence matrix in differential diagnosis of glioblastoma multiform (GBM) and primary central nervous system lymphoma (PCNSL). Methods: Image data of 46 cases of GBM (GBM group) and 36 cases of PCNSL (PCNSL group) confirmed by pathology were retrospectively analyzed. MaZda software was used to manually draw ROI on the maximum level of tumor on enhanced-T1WI and ADC images, and then texture parameters including angular second moment energy (AngScmom), Entropy, Contrast, correlation (Correlat) and inverse difference moment (InvDfMom) were extracted respectively. Multivariate Logistic regression model was constructed for texture feature parameters with statistically significant differences between 2 groups, and ROC curve was used to analyze differential diagnostic efficiency of GBM and PCNSL based on texture parameters and Logistic regression model. Results: There were significant differences of AngScMom, Contrast, Correlat and Entropy on enhanced-T1WI images, also of AngScMom, Correlat and Entropy on ADC images between GBM group and PCNSL group (all P<0.01). Parameters with statistical significances between 2 groups were brought into the binary Logistic regression analysis, and the Logistic regression model was obtained. ROC curve showed that the efficiency of Entropy for identifying GBM and PCNSL was the highest both on enhanced-T1WI and ADC images, AUC was 0.81 and 0.72, the sensitivity was 78.26% and 56.52%, and specificity was 77.78% and 80.56%, respectively. AUC of Logistic regression model for identifying GBM and PCNSL was 0.92, the sensitivity and specificity was 91.30% and 83.33%, respectively. Conclusion: Texture feature based on gray level co-occurrence matrix was helpful for differential diagnosis of GBM and PCNSL.
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AIM:To investigate the specific anti-tumor effects of mature dendritic cells ( DCs) transfected with amplified mucin 1 ( MUC1) mRNA in vitro.METHODS:DCs separated and purified from the peripheral blood mononu-clear cells were induced in vitro and then identified by flow cytometry .pcDNA3.1(+)-MUC1 plasmid was constructed and was able to transcribe MUC1 mRNA in vitro.The MUC1 mRNA was transfected into DCs by electroporation .MUC1-trans-fected DCs were used to induce T cells to be cytotoxic T-lymphocytes .Quantitative real-time PCR was performed to assess MUC1 mRNA expression in transfected DCs .The proliferation of T cells was examined by MTT assay .The proportion of CD8 +cells in the T cells was determined by flow cytometry and the specific cytotoxicity was measured by LDH assay .The secretion of IFN-γwas detected by ELISA .RESULTS: The marker gene expression in the DCs transfected with MUC 1 mRNA was significantly increased compared with control group , peaking at 24 h.The transfection group showed the higher capacity to stimulate the proliferation of T cells compared with control group when the ratio of DCs to T cells was 1∶10.The proportion of CD8 +cells in transfection group was higher than that in control group .The lethal effect of special cytotoxic T-lymphocytes on target cells in transfection group was stronger than that in control group .The level of IFN-γin the cell su-pernatant of transfection group was higher than that in control group .CONCLUSION:DCs plus MUC1 mRNA by electri-cal transfection induces specific anti-tumor effects , which provides an experiment evidence of using MUC 1 as a target for immunotherapeutic strategy against non-small cell lung cancer .
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This article introduces the basic theories about atomic force microscope (AFM) and electron microscope (EM), respectively. New applications of each microscopic technology in regenerative medicine, covering both material science and life science, are discussed. The advantages or disadvantages of the kinds of microscopes in working conditions, sample preparation, resolution and the like, are discussed and compared systematically to make clear each scope of applications. This could be a useful guide for selecting the appropriate microscopic analysis in research work about regenerative medicine.
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Humanos , Microscopía de Fuerza Atómica , Métodos , Microscopía Electrónica , Métodos , Medicina Regenerativa , MétodosRESUMEN
The solvent extraction of rare earths with mixtures of di-( 2-ethylhexyl) phosphoric acid (D2EHPA,H2A2) and sec-nonylphenoxy acetic acid (CA100,H_2B_2) has been carried out. The separation abilities among rare earths were determined and compared with those with D_2EHPA alone. The mechanism of synergistic extraction of lanthanum was discussed. The methods of slope analysis and constant mole were used to examine the extraction stoichiometry. Effects of acidities,concentrations of extractants,and temperature on the extractabilities have been investigated. The results showed that the synergistic effects decrease with increasing atomic numbers of rare earths. At proper ratios of the extractants,the separation abilities of some rare earths with D2EHPA +CA100 were higher than those with D2EHPA alone,which may be applied to the separation of these rare earths. The extracted complex of lanthanum with D2EHPA + CA100 was determined as LaH5A6B2. The synergistic extraction is endothermically driven.
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Objective To study the effects of tonifying kidney-yin on activities of embryo stem cell. Methods Embryonic stem cell line CRL-1825 was choosed as model cell in present study, and tonifying kidney-yin herb serum was added to CRL-1825 cell. The influence of tonifying kidney-yin on stem cell differentiation, cell cycle, apotosis and express of key genes were observed. Results Tonifying kidney-yin can inhibit stem cell differentiation and apotosis, promote stem cell proliferation and progression of cell cycle. In addition, tonifying kidney-yin can up-regulate Wnt, Oct4 gene expression, and down-regulation P16INK4a expression. Conclusions Tonifying kidney-yin can promote stem cell proliferation and maintain the state of stem cell.
