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Timely and efficient monitoring of crop aboveground biomass (AGB) and grain yield (GY) forecasting before harvesting are critical for improving crop yields and ensuring food security in precision agriculture. The purpose of this study is to explore the potential of fusing source–sink‐level color, texture, and temperature values extracted from RGB images and thermal images based on proximal sensing technology to improve grain yield prediction. High‐quality images of wheat from flowering to maturity under different treatments of nitrogen application were collected using proximal sensing technology over a 2‐year trial. Numerous variables based on source and sink organs were extracted from the acquired subsample images, including 30 color features, 10 texture features, and two temperature values. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE) were used to screen variables. Support vector regression (SVR) and random forest (RF) were applied to establish AGB estimation models, and the GY prediction models were built by RF. The source dataset and sink dataset performed differently on AGB and GY estimation, but the combined source–sink dataset performed best for estimating both AGB and GY. Based on the source–sink dataset, the LASSO‐RF model was the best combination for predicting AGB and GY, with the coefficient of determination (R2) of 0.85 and 0.86, root mean square error (RMSE) of 1179.09 and 609.61 kg ha−1, and the ratio of performance to deviation (RPD) of 2.10 and 2.45, respectively. This study demonstrates that the multivariate eigenvalues of both source and sink organs have the potential to predict wheat yield and that the combination of machine learning models and variable selection methods can significantly affect the accuracy of yield prediction models and achieve effective monitoring of crop growth at late reproductive stages. [ABSTRACT FROM AUTHOR]

Aims: To perform an updated systematic review and meta‐analysis of postoperative delirium (POD) after transcatheter aortic valve replacement (TAVR). Methods: We conducted a systematic literature search of PubMed, Embase, and Cochrane Library databases from the time of the first human TAVR procedure in 2002 until December 24, 2021, which was supplemented by manual searches of bibliographies. Data were collected on incidence rates, risk factors, and/or associated mortality of POD after TAVR. Pooled analyses were conducted using random effects models to yield mean differences, odds ratios, hazard ratios, and risk ratios, with 95% confidence intervals. Results: A total of 70 articles (69 studies) comprising 413,389 patients were included. The study heterogeneity was substantial. The pooled mean incidence of POD after TAVR in all included studies was 9.8% (95% CI: 8.7%–11.0%), whereas that in studies using validated tools to assess for delirium at least once a day for at least 2 consecutive days after TAVR was 20.7% (95% CI: 17.8%–23.7%). According to the level of evidence and results of meta‐analysis, independent preoperative risk factors with a high level of evidence included increased age, male sex, prior stroke or transient ischemic attack, atrial fibrillation/flutter, weight loss, electrolyte abnormality, and impaired Instrumental Activities of Daily Living; intraoperative risk factors included non‐transfemoral access and general anesthesia; and acute kidney injury was a postoperative risk factor. POD after TAVR was associated with significantly increased mortality (pooled unadjusted RR: 2.20, 95% CI: 1.79–2.71; pooled adjusted RR: 1.62, 95% CI: 1.25–2.10), particularly long‐term mortality (pooled unadjusted HR: 2.84, 95% CI: 1.91–4.23; pooled adjusted HR: 1.88, 95% CI: 1.30–2.73). Conclusions: POD after TAVR is common and is associated with an increased risk of mortality. Accurate identification of risk factors for POD after TAVR and implementation of preventive measures are critical to improve prognosis. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important basis for wheat yield formation. It is useful to timely collect the AGB data to monitor wheat growth and to build high-yielding wheat groups. However, as traditional AGB data acquisition relies on destructive sampling, it is difficult to adapt to the modernization of agriculture, and the estimation accuracy of spectral data alone is low and cannot solve the problem of index saturation at later stages. In this study, an unmanned aerial vehicle (UAV) with an RGB camera and the real-time kinematic (RTK) was used to obtain imagery data and elevation data at the same time during the critical fertility period of wheat. The cumulative percentile and the mean value methods were then used to extract the wheat plant height (PH), and the color indices (CIS) and PH were combined to invert the AGB of wheat using parametric and non-parametric models. The results showed that the accuracy of the model improved with the addition of elevation data, and the model with the highest accuracy of multi-fertility period estimation was PLSR (PH + CIS), with R2, RMSE and NRMSE of 0.81, 1248.48 kg/ha and 21.77%, respectively. Compared to the parametric models, the non-parametric models incorporating PH and CIS greatly improved the prediction of AGB during critical fertility periods in wheat. The inclusion of elevation data therefore greatly improves the accuracy of AGB prediction in wheat compared to traditional spectral prediction models. The fusion of UAV-based elevation data and image information provides a new technical tool for multi-season wheat AGB monitoring. [ABSTRACT FROM AUTHOR]