Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X and dual Ku bands; 10.2, 13.3, and 16.7 GHz), with VV polarization obtained at a 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assumed only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased monthly SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables were iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a) based on the improved Born approximation. Results show that BASE-AM achieved an RMSE of ∼ 10 cm for snow depth and less than 30 mm for SWE, compared with the RMSE of ∼ 20 cm snow depth and ∼ 50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Kuband radar for SWE retrieval and show that the role of a precise snow microstructure prior in SWE retrieval may be substituted by an SWE prior from exterior sources

Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China

Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth’s surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms cannot capture snow thickness well. In this study, we present a new operational retrieval algorithm, hereafter referred to as the pixel-based method (0.25° × 0.25° grid-level), to provide more accurate and nearly real-time snow depth estimates. First, the reference snow depth was retrieved using a previously proposed model in which a microwave snow emission model was coupled with a machine learning (ML) approach. In this process, an effective grain size (effGS) value was optimized by utilizing the snow microwave emission model, and then the nonlinear relationship between snow depth and multiple predictive variables, e.g., effGS, longitude, elevation, and brightness temperature (Tb) gradients, was established with the ML technique to retrieve reference snow depth data. To select a robust and well-performing ML approach, we compared the performance of widely used support vector regression (SVR), artificial neural network (ANN) and random forest (RF) algorithms over China. The results show that the three ML models performed similarly in snow depth estimation, which was attributed to the inclusion of effGS in the training samples. In this study, the RF model was used to retrieve the snow depth reference dataset due to its slightly stronger robustness according to our comparison of results. Second, the pixel-based algorithm was built based on the retrieved reference snow depth dataset and satellite Tb observations (18.7 GHz and 36.5 GHz) from Advanced Microwave Scanning Radiometer 2 (AMSR2) during the 2012–2020 period. For the pixel-based algorithm, the fitting coefficients were achieved dynamically pixel by pixel, making it superior to the traditional static methods. Third, the built pixel-based algorithm was verified using ground-based observations and was compared to the AMSR2, GlobSnow-v3.0, and ERA5-land products during the 2012–2020 period. The pixel-based algorithm exhibited an overall unbiased root mean square error (unRMSE) and R2 of 5.8 cm and 0.65, respectively, outperforming GlobSnow-v3.0, with unRMSE and R2 values of 9.2 cm and 0.22, AMSR2, with unRMSE and R2 values of 18.5 cm and 0.13, and ERA5-land, with unRMSE and R2 values of 10.5 cm and 0.33, respectively. However, the pixel-based algorithm estimates were still challenged by the complex terrain, e.g., the unRMSE was up to 17.4 cm near the Tien Shan Mountains. The proposed pixel-based algorithm in this study is a simple and operational method that can retrieve accurate snow depths based solely on spaceborne PM data in comparatively flat areas

Evaluation of the electrocardiogram RV5/V6 criteria in the diagnosis of left ventricular hypertrophy in marathon runners

Abstract To assess the value of electrocardiogram (ECG) RV5/V6 criteria for diagnosing left ventricular hypertrophy (LVH) in marathons. A total of 112 marathon runners who met the requirements for “Class A1” events certified by the Chinese Athletics Association in Changzhou City were selected, and their general clinical information was collected. ECG examinations were performed using a Fukuda FX7402 Cardimax Comprehensive Electrocardiograph Automatic Analyser, whereas routine cardiac ultrasound examinations were performed using a Philips EPIQ 7C echocardiography system. Real‐time 3‐dimensional echocardiography (RT‐3DE) was performed to acquire 3‐dimensional images of the left ventricle and to calculate the left ventricular mass index (LVMI). According to the LVMI criteria of the American Society of Echocardiography for the diagnosis of LVH, the participants were divided into an LVMI normal group (n = 96) and an LVH group (n = 16). The correlation between the ECG RV5/V6 criteria and LVH in marathon runners was analysed using multiple linear regression stratified by sex and compared with the Cornell (SV3 + RaVL), modified Cornell (SD + RaVL), Sokolow–Lyon (SV1 + RV5/V6), Peguero–Lo Presti (SD + SV4), SV1, SV3, SV4, and SD criteria. In marathon runners, the ECG parameters SV3 + RaVL, SD + RaVL, SV1 + RV5/V6, SD + SV4, SV3, SD, and RV5/V6 were able to identify LVH (all p < .05). When stratified by sex, linear regression analysis revealed that a significantly higher number of ECG RV5/V6 criteria were evident in the LVH group than in the LVMI normal group (p < .05), both with no adjustment and after initial adjustment (including age and body mass index), as well as after full adjustment (including age, body mass index, interventricular septal thickness, left ventricular end‐diastolic diameter, left ventricular posterior wall thickness, and history of hypertension). Additionally, curve fitting showed that the ECG RV5/V6 values increased with increasing LVMI in marathon runners, exhibiting a nearly linear positive correlation. In conclusions, the ECG RV5/V6 criteria were correlated with LVH in marathon runners

A Snow Water Equivalent Retrieval Framework Coupling 1D Hydrology and Passive Microwave Radiative Transfer Models

The retrieval of continuous snow water equivalent (SWE) directly from passive microwave observations is hampered by ambiguity, which can potentially be mitigated by incorporating knowledge on snow hydrological processes. In this paper, we present a data assimilation (DA)-based SWE retrieval framework coupling the QCA-Mie scattering (DMRT-QMS) model (a dense medium radiative transfer (RT) microwave scattering model) and a one-dimensional column-based multiple-layer snow hydrology model. The snow hydrology model provides realistic estimates of the snowpack physical parameters required to drive the DMRT-QMS model. This paper devises a strategy to specify those internal parameters in the snow hydrology and RT models that lack observational records. The modeled snow depth is updated by assimilating brightness temperatures (Tbs) from the X, Ku, and Ka bands using an ensemble Kalman filter (EnKF). The updated snow depth is then used to predict the SWE. The proposed framework was tested using the European Space Agency’s Nordic Snow Radar Experiment (ESA NoSREx) dataset for a snow field experiment from 2009 to 2012 in Sodankylä, Finland. The achieved SWE retrieval root mean square error of 34.31 mm meets the requirements of NASA and ESA snow missions and is about 70% less than the open-loop SWE. In summary, this paper introduces a novel SWE retrieval framework that leverages the combined strengths of a snow hydrology model and a radiative transfer model. This approach ensures physically realistic retrievals of snow depth and SWE. We investigated the impact of various factors on the framework’s performance, including observation time intervals and combinations of microwave observation channels. Our results demonstrate that a one-week observation interval achieves acceptable retrieval accuracy. Furthermore, the use of multi-channel and multi-polarization Tbs is preferred for optimal SWE retrieval performance

MoNap1, a Nucleosome Assemble Protein 1, Regulates Growth, Development, and Pathogenicity in <i>Magnaporthe oryzae</i>

Nap1 is an evolutionarily conserved protein from yeast to human and is involved in diverse physiological processes, such as nucleosome assembly, histone shuttling between the nucleus and cytoplasm, transcriptional regulation, and the cell cycle regulation. In this paper, we identified nucleosome assemble protein MoNap1 in Magnaporthe oryzae and investigated its function in pathogenicity. Deletion of MoNAP1 resulted in reduced growth and conidiation, decreased appressorium formation rate, and impaired virulence. MoNap1 affects appressorium turgor and utilization of glycogen and lipid droplets. In addition, MoNap1 is involved in the regulation of cell wall, oxidation, and hyperosmotic stress. The subcellular localization experiments showed that MoNap1 is located in the cytoplasm. MoNap1 interacts with MoNbp2, MoClb3, and MoClb1 in M. oryzae. Moreover, deletion of MoNBP2 and MoCLB3 has no effects on vegetative growth, conidiation, and pathogenicity. Transcriptome analysis reveals that MoNAP1 is involved in regulating pathogenicity, the melanin biosynthetic process. Taken together, our results showed that MoNap1 plays a crucial role in growth, conidiation, and pathogenicity of M. oryzae

Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg−1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg−1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved

Predicting Leaf Nitrogen Content in Cotton with UAV RGB Images

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg&minus;1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg&minus;1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved

Preparation of Low-Cost Magnesium Oxychloride Cement Using Magnesium Residue Byproducts from the Production of Lithium Carbonate from Salt Lakes

Magnesium oxychloride cement (abbreviated as MOC) was prepared using magnesium residue obtained from Li2CO3 extraction from salt lakes as raw material instead of light magnesium oxide. The properties of magnesium residue calcined at different temperatures were researched by XRD, SEM, LSPA, and SNAA. The preparation of MOC specimens with magnesium residue at different calcination temperatures (from 500 °C to 800 °C) and magnesium chloride solutions with different Baume degrees (24 Baume and 28 Baume) were studied. Compression strength tests were conducted at different curing ages from 3 d to 28 d. The hydration products, microstructure, and porosity of the specimens were analyzed by XRD, SEM, and MIP, respectively. The experimental results showed that magnesium residue’s properties, the BET surface gradually decreased and the crystal size increased with increasing calcination temperature, resulting in a longer setting time of MOC cement. Additionally, the experiment also indicated that magnesium chloride solution with a high Baume makes the MOC cement have higher strength. The MOC specimens prepared by magnesium residue at 800 °C and magnesium chloride solution Baume 28 exhibited a compressive of 123.3 MPa at 28 d, which met the mechanical property requirement of MOC materials. At the same time, magnesium oxychloride cement can be an effective alternative to Portland cement-based materials. In addition, it can reduce environmental pollution and improve the environmental impact of the construction industry, which is of great significance for sustainable development