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Drip fertigation (DF) is a widely used technology to increase grain yield with water and fertilizer conservation. However, the mechanism of high grain yield (GY) under DF is still unclear. Here, a four-year field experiment assessed the impacts of four treatments (i.e., conventional irrigation and nitrogen application, CK; drip irrigation with conventional nitrogen fertilization, DI; split-nitrogen fertigation with conventional irrigation, SF; and drip fertigation, DF) on maize phenology, leaf photosynthetic rates, grain filling processes, plant biomass, and GY. The results showed that DF significantly increased maize GY by affecting phenology, grain filling traits, aboveground biomass (BIO) accumulation, and translocation. Specifically, DF significantly increased leaf chlorophyll content, which enhanced leaf photosynthetic rates, and together with an increase of leaf area index, promoted BIO accumulation. As a result, the BIO at the silking stage of DF increased by 29.5%, transported biomass increased by 109.2% (1.2 t ha−1), and the accumulation of BIO after silking increased by 23.1% (1.7 t ha−1) compared with CK. Meanwhile, DF prolonged grain filling days, significantly increased the grain weight of 100 kernels, and promoted GY increase. Compared with CK, the four-year averaged GY and BIO increased by 34.3% and 26.8% under DF; a 29.7%, 46.1%, and 24.2% GY increase and a 30.7%, 39.5%, and 29.9% BIO increase were contributed by irrigation, nitrogen, and coupling effects of irrigation and nitrogen, respectively. These results reveal the high yield mechanism of drip-fertigated maize, and are of important significance for promoting the application of drip fertigation. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial physiological indicator of crop growth. This paper introduces a new spectral index to overcome angle effects in estimating the LAI of crops. This study quantitatively analyzes the relationship between LAI and multi-angle hyperspectral reflectance from the canopy of winter oilseed rape (Brassica napus L.) at various growth stages, nitrogen application levels and coverage methods. The angular stability of 16 traditional vegetation indices (VIs) for monitoring the LAI was tested under nine view zenith angles (VZAs). These multi-angle VIs were input into machine learning models including support vector machine (SVM), eXtreme gradient boosting (XGBoost), and Random Forest (RF) to determine the optimal monitoring strategy. The results indicated that the back-scattering direction outperformed the vertical and forward-scattering direction in terms of monitoring the LAI. In the solar principal plane (SPP), EVI-1 and REP showed angle stability and high accuracy in monitoring the LAI. Nevertheless, this relationship was influenced by experimental conditions and growth stages. Compared with traditional VIs, the observation perspective insensitivity vegetation index (OPIVI) had the highest correlation with the LAI (r = 0.77–0.85). The linear regression model based on single-angle OPIVI was most accurate at −15° (R2 = 0.71). The LAI monitoring achieved using a multi-angle OPIVI-RF model had the higher accuracy, with an R2 of 0.77 and with a root mean square error (RMSE) of 0.38 cm2·cm−2. This study provides valuable insights for selecting VIs that overcome the angle effect in future drone and satellite applications. [ABSTRACT FROM AUTHOR]

The widespread application of electrochemical hydrogen production faces significant challenges, primarily attributed to the high overpotential of the oxygen evolution reaction (OER) in conventional water electrolysis. To address this issue, an effective strategy involves substituting OER with the value‐added oxidation of biomass feedstock, reducing the energy requirements for electrochemical hydrogen production while simultaneously upgrading the biomass. Herein, we introduce an electrocatalytic approach for the value‐added oxidation of isobutanol, a high energy density bio‐fuel, coupled with hydrogen production. This approach offers a sustainable route to produce the valuable fine chemical isobutyric acid under mild condition. The electrodeposited Ni(OH)2 electrocatalyst exhibits exceptional electrocatalytic activity and durability for the electro‐oxidation of isobutanol, achieving an impressive faradaic efficiency of up to 92.4 % for isobutyric acid at 1.45 V vs. RHE. Mechanistic insights reveal that side reactions predominantly stem from the oxidative C−C cleavage of isobutyraldehyde intermediate, forming by‐products including formic acid and acetone. Furthermore, we demonstrate the electro‐oxidation of isobutanol coupled with hydrogen production in a two‐electrode undivided cell, notably reducing the electrolysis voltage by approximately 180 mV at 40 mA cm−2. Overall, this work represents a significant step towards improving the cost‐effectiveness of hydrogen production and advancing the conversion of bio‐fuels. [ABSTRACT FROM AUTHOR]

Background: The BRAFV600E mutation is a valuable indicator for thyroid cancer diagnosis. This study aimed to develop a deep convolutional neural network (DCNN) model based on ultrasound images to predict the BRAFV600E mutation status of thyroid nodules. Methods: The ultrasound images were obtained from four hospitals between January 2017 and January 2022. We trained and validated the DCNN model based on the primary set from center 1 (979 images, 528 patients). The DCNN network consists of Conv block, Downsample block, Gaussian error linear unit, Global Average Polling, and Full Connected. The predictive performance of this model was evaluated by using areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in four independent test sets from center 1 to center 4 (531 images, 282 patients). Heatmaps were used to visualize the most predictive regions of each image. Specimens obtained through fine‐needle aspiration or surgery were used to detect the BRAFV600E mutation. Results: The DCNN model achieved encouraging predictive performance by fivefold cross‐validation (AUC 0.95) in the primary set. This performance was further confirmed in the independent internal test set (AUC 0.93) and three independent external test sets (AUC 0.84–0.88). The deep learning score revealed significant differences between BRAFV600E‐mutant and BRAFV600E‐wild‐type groups (all test sets p <.001). The heatmaps visualized the most predictive region located inside or alongside the thyroid nodules. Conclusion: A DCNN model with encouraging predictive performance was developed based on ultrasound images to predict the BRAFV600E mutation status of thyroid nodules. [ABSTRACT FROM AUTHOR]

The biomechanical characteristics of porcine corneal stroma along the depth of cornea in different anatomical orientations were experimentally investigated and quantitatively analyzed. The porcine corneal stroma tested here was cut mechanically into the three layers along the thickness of the cornea for the first time. Based on the convenient uniaxial tensile extensometry, the Young’s moduli of each stromal layer in different anatomical orientations were proved to be equal to each other. By virtue of testing the mechanical behaviors of the different layers, the Young’s moduli of the stromal layers were found to decrease gradually from the anterior to posterior corneal surface. Thus, the stroma was proved to be mechanically transversely isotropic. Furthermore, based on the analysis of the experimental data, the change of stromal Young’s modulus along the depth of the cornea was determined to be linear and equal to - 2.79 ± 0.967 MPa/mm from the anterior to posterior corneal surface. Finally, the linear gradient characteristic of the stroma was appropriately explained by virtue of stromal microstructures. [ABSTRACT FROM AUTHOR]