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Abstract Background Inflammation plays a crucial role in coronary atherosclerosis progression, and growing evidence has demonstrated that the fibrinogen-to-albumin ratio (FAR), as a novel inflammation biomarker, is associated with the severity of coronary artery disease (CAD). However, the long-term risk of cardiovascular events remains indistinct in patients with different level of FAR and different glycemic metabolism status. This study was to assess 5-year clinical outcomes of diabetic and non-diabetic patients who underwent percutaneous coronary intervention (PCI) with different level of FAR. Methods We consecutively enrolled 10,724 patients with CAD hospitalized for PCI and followed up for the major adverse cardiac and cerebrovascular events (MACCE) covering all-cause mortality, cardiac mortality, non-fatal myocardial infarction, non-fatal ischemic stroke, and unplanned coronary revascularization. FAR was computed using the following formula: Fibrinogen (g/L)/Albumin (g/L). According to the optimal cut-off value of FAR for MACCE prediction, patients were divided into higher level of FAR (FAR-H) and lower level of FAR (FAR-L) subgroups, and were further categorized into four groups as FAR-H with DM and non-DM, and FAR-L with DM and non-DM. Results 5298 patients (58.36 ± 10.36 years, 77.7% male) were ultimately enrolled in the present study. A total of 1099 (20.7%) MACCEs were documented during the 5-year follow-up. The optimal cut-off value of FAR was 0.0783 by the surv_cutpoint function. Compared to ones with FAR-H and DM, patients with FAR-L and non-DM, FAR-H and non-DM, FAR-L and DM had decreased risk of MACCEs [adjusted hazard ratio (HR): 0.75, 95% confidence interval (CI) 0.64–0.89, P = 0.001; HR: 0.78, 95% CI 0.66–0.93, P = 0.006; HR: 0.81, 95% CI 0.68–0.97, P = 0.019; respectively]. Notably, non-diabetic patients with lower level of FAR also had lower all-cause mortality and cardiac mortality risk than those in the FAR-H/DM group (HR: 0.41, 95% CI 0.27–0.63, P

Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients’ brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.

Summary: Few studies have characterized long-term exposure to lipoprotein(a), or Lp(a), different glucose metabolism status, and their joint role in adverse cardiovascular outcomes risk. We consecutively enrolled 10,724 coronary heart disease (CAD) patients from January to December 2013 in Fuwai Hospital. Associations of cumulative lipoprotein(a) (CumLp(a)) exposure and different glucose metabolism status with major adverse cardiac and cerebrovascular events (MACCEs) risk were evaluated using Cox regression models. Compared with participants with normal glucose regulation and lower CumLp(a), those with type 2 diabetes and higher CumLp(a) were at the highest risk (HR 1.56, 95% CI 1.25-1.94), and those with prediabetes and higher CumLp(a) and those with type 2 diabetes and lower CumLp(a) were at relatively higher risk (HR 1.41, 95% CI 1.14-1.76; HR 1.37, 95% CI 1.11-1.69; respectively). Similar findings concerning the joint association were observed in sensitivity analyses. Cumulative lipoprotein(a) exposure and different glucose metabolism status were associated with 5-year MACCEs risk and may be useful concurrently for guiding secondary prevention therapy decisions.

Lead-bismuth cooled fast reactor calculation code system named MOSASAUR has been developed to meet the simulation requirements from LBFR engineering design. An overview of MOSASAUR developments is provided in this paper, four main functional modules and their models are introduced: cross-sections generation module, flux spectrum correction module, core simulation module and sensitivity and uncertainty analysis module. Verification and validation results of numerical benchmark calculations, code-to-code comparisons with the Monte-Carlo code and critical experimental calculations shown in this paper prove the capabilities of MOSASAUR in dealing with lead-bismuth cooled fast reactor analysis problems with good performances. Numerical results demonstrate that compared with the Monte-Carlo code, the relative errors of eigenvalues are smaller than 350pcm when the calculations were carried out with the same nuclear data file. Compared with the measured values, the errors will increase due to the simulation details and the measurement accuracy.

In order to solve the problems of grain lifter in sorghum harvesting, such as ear loss and serious crop leakage loss, combined with the physical and mechanical characteristics of sorghum, the segmented and reverse spiral grain lifter for sorghum harvesting and cutting table was developed, and the design method of the main structural parameters of the grain lifter was determined. The comparative test of the working effect of the clasp showed that the working effect of the cutting table with the clasp was better than that without the clasp, which effectively reduced the harvest loss of the cutting table. By using Box–Behnken experimental design method, the influence law of forward speed, tilt angle, and rotation speed of grain lifter on the rate of ear loss and harvest loss in sorghum harvesting was investigated. The regression mathematical model and response surface of the rate of ear loss and harvest loss and analysis factors were established, and the optimal working parameters of the grain lifter were determined. The forward speed was 0.8 m/s, the tilt angle of the grain lifter was 28°, and the rotation speed of the grain lifter was 330 r/min. Under these conditions, the spike loss rate was 2.01, the leakage loss rate was 2.19, and the error with the theoretical value was less than 3%, which proved the rationality of the optimized combination parameters. In the harvest of crooked and fallen sorghum, the grain lifter can effectively reduce the loss of sorghum head drop and lodging leakage, ensure the reliability of the cutting table, and achieve low loss and efficient harvest of sorghum.

Change of supraglacial lakes (SGLs) is an important hydrological activity on the Greenland ice sheet (GrIS), and storage and drainage of SGLs occur throughout the year. However, current studies tend to split SGL changes into melt/non-melt seasons, ignoring the effect of buried lakes in the exploration of drainage, and the existing threshold-based approach to SGL extraction in a synthetic aperture radar (SAR) is influenced by the choice of the study area mask. In this study, a new method (Otsu–Canny–Otsu (OCO)), which accesses the features of SGLs on optical and SAR images objectively, is proposed for full-year SGL extraction with Google Earth Engine (GEE). The SGLs on the Petermann Glacier were monitored well by OCO throughout 2021, including buried lakes and more detailed rapid drainage events. Some SGLs’ extent varied minimally in a year (area varying by 10–25%) while some had very rapid drainage (a rapid drainage event from July 26 to 30). The SGL extraction results were influenced by factors such as the mode of polarization, the surface environment, and the depth of the lake. The OCO method can provide a more comprehensive analysis for SGL changes throughout the year.