Valvuloplasty of fetal pulmonary atresia with intact ventricular septum and hypoplastic right heart: Mid-term follow-up results.

Objective: This study aimed to analyze and evaluate the results of mid-term follow-up after fetal pulmonary valvuloplasty (FPV) in fetuses with pulmonary atresia with intact ventricular septum (PA/IVS).

Methods: From August 31, 2018, to May 31, 2019, seven fetuses with PA/IVS and hypoplastic right heart were included in this study. All underwent echocardiography by the same specialist and were operated on by the same team.

Postoperative delirium after transcatheter aortic valve replacement: An updated systematic review and meta-analysis.

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.

Rapid Recognition of Field-Grown Wheat Spikes Based on a Superpixel Segmentation Algorithm Using Digital Images.

Wheat spike number, which could be rapidly and accurately estimated by the image processing technology, serves as the basis for crop growth monitoring and yield prediction. In this research, simple linear iterative clustering (SLIC) was performed for superpixel segmentation of the digital images of field-grown wheat. Firstly, certain characteristic color parameters were extracted and analyzed from the digital images, and the classifiers with the highest accuracy were chosen for subsequent image classification.

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band).

Assessment of F/F absorbed by wheat canopies employing in-situ hyperspectral vegetation indexes.

Chlorophyll fluorescence parameter of F/F, as an important index for evaluating crop yields and biomass, is key to guide crop management. However, the shortage of good hyperspectral data can hinder the accurate assessment of wheat F/F. In this research, the relationships between wheat canopy F/F and in-situ hyperspectral vegetation indexes were explored to develop a strategy for accurate F/F assessment.

Remotely Assessing Fraction of Photosynthetically Active Radiation () for Wheat Canopies Based on Hyperspectral Vegetation Indexes.

Fraction of photosynthetically active radiation (FPAR), as an important index for evaluating yields and biomass production, is key to providing the guidance for crop management. However, the shortage of good hyperspectral data can frequently result in the hindrance of accurate and reliable FPAR assessment, especially for wheat. In the present research, aiming at developing a strategy for accurate FPAR assessment, the relationships between wheat canopy FPAR and vegetation indexes derived from concurrent ground-measured hyperspectral data were explored.

Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat.

Hyperspectral remote sensing is a rapid non-destructive method for diagnosing nitrogen status in wheat crops. In this study, a quantitative correlation was associated with following parameters: leaf nitrogen accumulation (LNA), raw hyperspectral reflectance, first-order differential hyperspectra, and hyperspectral characteristics of wheat. In this study, integrated linear regression of LNA was obtained with raw hyperspectral reflectance (measurement wavelength = 790.