Abstract
To investigate the potential prognostic value of Serum cystatin C (sCys C) in patients with COVID-19 and determine the association of sCys C with severe COVID-19 illness. We performed a ...retrospective review of medical records of 162 (61.7 ± 13.5 years) patients with COVID-19. We assessed the predictive accuracy of sCys C for COVID-19 severity by the receiver operating characteristic (ROC) curve analysis. The participants were divided into two groups based on the sCys C cut-off value. We evaluated the association between high sCys C level and the development of severe COVID-19 disease, using a COX proportional hazards regression model. The area under the ROC curve was 0.708 (95% CI 0.594–0.822), the cut-off value was 1.245 (mg/L), and the sensitivity and specificity was 79.1% and 60.7%, respectively. A multivariable Cox analysis showed that a higher level of sCys C (adjusted HR 2.78 95% CI 1.25–6.18,
p
= 0.012) was significantly associated with an increased risk of developing a severe COVID-19 illness. Patients with a higher sCys C level have an increased risk of severe COVID-19 disease. Our findings suggest that early assessing sCys C could help to identify potential severe COVID-19 patients.
Energy production and consumption is the primary source of global carbon emissions. In order to achieve the goal of Intended Nationally Determined Contributions (INDCs) in the Paris Agreement, ...countries around the world have set off an upsurge of energy transition. The EU has ambitious INDCs. The Energy crisis has made it accelerate the energy transition, thus promoting the development of its circular economy. However, the EU focuses on carbon leakage during this process. To address this issue and maintain the competitiveness of EU industries during the transition, the EU proposed the Carbon Border Adjustment Mechanism (CBAM). The EU proposed the background and development process of the CBAM, and organized the core content of the tripartite plan and final bill of the European Commission, European Parliament, and European Council. What's more, the framework system of the CBAM is clarified, and further analysis is conducted on the future development of CBAM of the EU based on various factors. In response to the new development of the CBAM, it is suggested that developing countries should adhere to the principle of common but differentiated responsibilities on the international front and attempt to build and improve a carbon market system to partially offset carbon tariffs. Finally, we should strengthen the research on the response plan of the EU CBAM and establish an effective communication mechanism with the EU, in order to better cope with the impact of the CBAM on the trade of developing countries in the future.
Abstract
Grazing by domestic herbivores is the most widespread land use on the planet, and also a major global change driver in grasslands. Yet, experimental evidence on the long-term impacts of ...livestock grazing on biodiversity and function is largely lacking. Here, we report results from a network of 10 experimental sites from paired grazed and ungrazed grasslands across an aridity gradient, including some of the largest remaining native grasslands on the planet. We show that aridity partly explains the responses of biodiversity and multifunctionality to long-term livestock grazing. Grazing greatly reduced biodiversity and multifunctionality in steppes with higher aridity, while had no effects in steppes with relatively lower aridity. Moreover, we found that long-term grazing further changed the capacity of above- and below-ground biodiversity to explain multifunctionality. Thus, while plant diversity was positively correlated with multifunctionality across grasslands with excluded livestock, soil biodiversity was positively correlated with multifunctionality across grazed grasslands. Together, our cross-site experiment reveals that the impacts of long-term grazing on biodiversity and function depend on aridity levels, with the more arid sites experiencing more negative impacts on biodiversity and ecosystem multifunctionality. We also highlight the fundamental importance of conserving soil biodiversity for protecting multifunctionality in widespread grazed grasslands.
A three-dimensional variational (3DVAR) data assimilation (DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry ...(MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO
2
, NO
2
, CO, and O
3
as well as particulate matter (PM
2.5
, PM
10
) observational data can be assimilated simultaneously. Two one-month parallel simulation experiments were conducted, one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre (CNEMC) and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts. Results show that, in the first place, use of the DA system can provide a more accurate model initial field. The root-mean-square error of PM
2.5
, PM
10
, SO
2
, NO
2
, CO, and O
3
mass concentrations in analysis field fell by 29.27 μg m
−3
(53.5%), 34.5 μg m
−3
(50.9%), 30.36 μg m
−3
(64.2%), 8.91 μg m
−3
(39.5%), 0.46 mg m
−3
(47.4%), and 15.11 μg m
−3
(51.0%), respectively, compared to a background field without assimilation. At the same time, mean fraction error was reduced by 42.6%, 53.1%, 45.2%, 43.1%, 69.9%, and 48.8%, respectively, while the correlation coefficient increased by 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively. Secondly, the results of this analysis reveal variable benefits from assimilation on different pollutants. DA significantly improves PM
2.5
, PM
10
, and CO forecasts leading to positive effects that last more than 48 h. The positive effects of DA on SO
2
and O
3
forecasts last up to 8 h but that remains relatively poor for NO
2
forecasts. Thirdly, the influence of assimilation varies in different areas. It is possible that the positive effects of DA on PM
2.5
and PM
10
forecasts can last more than 48 h across most regions of China. Indeed, DA significantly improves SO
2
forecasts within 48 h over north China, and much longer CO assimilation benefits (48 h) are found in most regions apart from north and east China and across the Sichuan Basin. DA is able to improve O
3
forecasts within 48 h across China with the exception of southwest and northwest regions and the O
3
DA benefits in southern China are more evident, while from a spatial distribution perspective, NO
2
DA benefits remain relatively poor.
Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate ...and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.
Three kinds of iron nanoparticles (FeNPs) were prepared via green route based on pomegranate (PG), green tea (GT), and mulberry (ML) extracts under ambient conditions. The obtained materials were ...characterized by scanning electron microscopy (SEM), transmission electronic microscopy (TEM), X-ray energy-dispersive spectrometer (EDS), X-ray diffraction (XRD), fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) techniques. The experimental results show that FeNPs were in the form of amorphous iron (II, III)-polyphenol complex with different dispersity and morphologies. GT-Fe has the smallest size range of 25–35 nm, PG-Fe has a moderate size-distribution of 30–40 nm, while ML-Fe formed a tuberous net-type with a sheeting structure. PG-Fe displays the highest removal efficiency of 90.2% in 20 min towards cationic dye of malachite green (16.6% by ML-Fe and 69.3% by GT-Fe), which is attributed to its highest polyphenol content, lowest zeta potential, as well as the most Fe
2+
on the surface of FeNPs. The removal mechanism was mainly induced by electrostatic adsorption based on pH and zeta potential tests.
Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted ...intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance.
Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models.
A total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including renal system score, central nervous system score and cardio vascular system score were used to establish prediction models. The accuracy of the LR, GNB, SVM were 0.851, 0.844 and 0.862, respectively, which were better than linear regression analysis (0.123) and LASSO (0.130). The AUROCs of LR, GNB and SVM were 0.76, 0.76 and 0.67, respectively. K-fold cross validation showed that the average AUROCs of LR, GNB and SVM were 0.757 ± 0.005, 0.762 ± 0.006, 0.630 ± 0.013, respectively. For the probability threshold of 5-50%, LY and GNB models both showed positive net benefits.
The two machine learning-based models (LR and GNB models) based on SOFA components can be used to predict in-hospital mortality of septic patients admitted to ICU.
Identifying the biological subclasses of septic shock might provide specific targeted therapies for the treatment and prognosis of septic shock. It might be possible to find biological markers for ...the early prediction of septic shock prognosis. The data were obtained from the Gene Expression Omnibus databases (GEO) in NCBI. GO enrichment and KEGG pathway analyses were performed to investigate the functional annotation of up- and downregulated DEGs. ROC curves were drawn, and their areas under the curves (AUCs) were determined to evaluate the predictive value of the key genes. 117 DEGs were obtained, including 36 up- and 81 downregulated DEGs. The AUC for the MME gene was 0.879, as a key gene with the most obvious upregulation in septic shock. The AUC for the THBS1 gene was 0.889, as a key downregulated gene with the most obvious downregulation in septic shock. The upregulation of MME via the renin-angiotensin system pathway and the downregulation of THBS1 through the PI3K-Akt signaling pathway might have implications for the early prediction of prognosis of septic shock in patients with pneumopathies.
Observing system experiments are presented to characterise impacts of surface and vertical profile measurements on aerosol analysis and forecast skill. A three-dimensional (3D) variational data ...assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the 3D profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, and surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time are assimilated. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.
Background:
Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine ...learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.
Methods:
High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.
Results:
Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.
Conclusion:
The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.
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