First principles investigation of scandium-based borohydride NaSc(BH4)(4)

International audienceThe periodic crystal structure of NaSc(BH4)4 has been investigated by first principles density functional theory calculations. The enthalpy of formation and cohesive energy of NaSc(BH4)4 are calculated to be −72.69 kJ/mol H2 and 550.76 kJ/mol H, respectively. Total and partial density of states analysis and Mulliken atomic populations of NaSc(BH4)4, as well as total and difference charge-density analysis indicate that there is a strong hybridization between H s and B s, p electrons in BH4 complex. Sc shows a mixed chemical bonding in the [Sc(BH4)4]− complex, partly ionic and partly covalent. Na makes a bonding with other neighboring atoms by ionic bond

Early mapping of winter wheat in Henan province of China using time series of Sentinel-2 data

Accurate mapping of winter wheat in its early stages is crucial for crop growth monitoring and crop yield forecasting. However, early mapping of winter wheat using remotely sensed data is challenging because remote sensing observations can only be used for a part of the growth period. In this study, a framework was proposed for early season mapping of winter wheat using spectral and temporal information of Sentinel-2 images. First, time series of temporal and spectral features were derived using Whittaker smoothing. Subsequently, sensitivities of different parameters (i.e. input features, time interval, and length of time-series data) to early mapping were analyzed. Finally, early maps of winter wheat were generated based on optimal parameters. Results show that the earliest identifiable timing was delayed as the time interval of the time series increased. Winter wheat can be mapped in the early overwintering period (5 months before harvest) with an overall accuracy of 0.91, which is comparable to that of post-season mapping (0.94). In addition, the misclassification in early mapping was caused by uneven sample spatial patterns, natural conditions, and planting management; however, most errors can be gradually amended during the green-up and jointing periods, and the overall accuracy remained stable after the jointing stage. This study demonstrates that it is feasible to implement large-scale early mapping of winter wheat using satellite observations. The proposed approach potentially provides a reference for early mapping of other crop types in agricultural regions worldwide.</p

Modeling the Corn Residue Coverage after Harvesting and before Sowing in Northeast China by Random Forest and Soil Texture Zoning

Crop residue cover is vital for reducing soil erosion and improving soil fertility, which is an important way of conserving tillage to protect the black soil in Northeast China. How much the crop residue covers on cropland is of significance for black soil protection. Landsat-8 and Sentinel-2 images were used to estimate corn residue coverage (CRC) in Northeast China in this study. The estimation model of CRC was established for improving CRC estimation accuracy by the optimal combination of spectral indices and textural features, based on soil texture zoning, using the random forest regression method. Our results revealed that (1) the optimization C5 of spectral indices and textural features improves the CRC estimation accuracy after harvesting and before sowing with determination coefficients (R2) of 0.78 and 0.73, respectively; (2) the random forest improves the CRC estimation accuracy after harvesting and before sowing with R2 of 0.81 and 0.77, respectively; (3) considering the spatial heterogeneity of the soil background and the usage of soil texture zoning models increase the accuracy of CRC estimation after harvesting and before sowing with R2 of 0.84 and 0.81, respectively. In general, the CRC estimation accuracy after harvesting was better than that before sowing. The results revealed that the corn residue coverage in most of the study area was 0.3 to 0.6 and was mainly distributed in the Songnen Plain. By the estimated corn residue coverage results, the implementation of conservation tillage practices is identified, which is vital for protecting the black soil in Northeast China

Performance of GEDI data combined with Sentinel-2 images for automatic labelling of wall-to-wall corn mapping

Corn is the dominant crop planted in Northeast China, and its accurate and timely mapping is important for food security and agricultural management in China. However, the absence of enough labels is challenging for corn accurate mapping in a regional area using machine learning methods or deep learning methods. In this study, an efficient way of automatic labelling and mapping of corn planted areas by combining Global Ecosystem Dynamics Investigation (GEDI) data and Sentinel-2 images is proposed. We explore the height and vertical structure differences between corn and other crops derived from GEDI features and generate labels automatically by referencing crop type products and transferring models from historical years. The trained learning networks of automatic labelling from GEDI points and the trained decision trees of the Random Forest (RF) classifier can be transferred to corn mapping in arbitrary target years. The Sentinel-2 features are combined to perform wall-to-wall corn mapping using a random forest algorithm and GEDI-based labels. This approach is used to map corn planted areas in Northeast China from 2019 to 2022, and the classification results are validated using independent labels collected in field campaigns in 2023, published maps, and official statistics. Our classification results reveal that our proposed method achieves high accuracy and robustness with an average overall accuracy of 0.91 validated using testing labels from spatial-type stratified sampling. The correlation coefficient (R2) between our classified result with the official statistical data and published classification results reach 0.96 and 0.98, respectively. These results demonstrate the potential of GEDI data for automatic label collection for vegetation with height difference and provide a new approach for efficient crop mapping on a large-scale

Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images

Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to identify corn lodging accurately and efficiently. Three groups of image features and five machine-learning approaches are used for classifying non-lodged, moderately lodged, and severely lodged areas. Our results reveal that (1) the combination of spectral bands, optimized vegetation indexes, and texture features classify corn lodging with an overall accuracy of 93.81% and a Kappa coefficient of 0.91. (2) The random forest is an efficient, robust, and easy classifier to identify corn lodging with the F1-score of 0.95, 0.92, and 0.95 for non-lodged, moderately lodged, and severely lodged areas, respectively. (3) The GF-1 PMS image has great potential for identifying corn lodging on a regional scale

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

Abstract Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013–2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients

Rannasangpei Is a Therapeutic Agent in the Treatment of Vascular Dementia

Rannasangpei (RSNP) is used as a therapeutic agent in the treatment of cardiovascular diseases, neurological disorders, and neurodegeneration in China; however, its potential use in the treatment of vascular dementia (VD) was unclear. In this study, our aim was to examine the neuroprotective effect of RSNP in a VD rat model, which was induced by permanent bilateral common carotid artery occlusion (2VO). Four-week administration with two doses of RSNP was investigated in our study. Severe cognitive deficit in the VD model, which was confirmed in Morris water maze (MWM) test, was significantly restored by the administration of RSNP. ELISA revealed that the treatments with both doses of RSNP could reinstate the cholinergic activity in the VD animals by elevating the production of choline acetyltransferase (ChAT) and reducing the acetylcholinesterase (AChE); the treatment of RSNP could also reboot the level of superoxide dismutase (SOD) and decrease malondialdehyde (MDA). Moreover, Western blot and quantitative PCR (Q-PCR) results indicated that the RSNP could suppress the apoptosis in the hippocampus of the VD animals by increasing the expression ratio of B-cell lymphoma-2 (Bcl-2) to Bcl-2-associated X protein (Bax). These results suggested that RSNP might be a therapeutic agent in the treatment of vascular dementia in the future