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Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring. [ABSTRACT FROM AUTHOR]

Timely and accurate information on crop planting structures is crucial for ensuring national food security and formulating economic policies. This study presents a method for high-precision crop classification using time-series UAV (unmanned aerial vehicle) images. Before constructing the time-series UAV images, Euclidian distance (ED) was utilized to calculate the separability of samples under various vegetation indices. Second, co-occurrence measures and the gray-level co-occurrence matrix (GLCM) were employed to derive texture characteristics, and the spectral and texture features of the crops were successfully fused. Finally, random forest (RF) and other algorithms were utilized to classify crops, and the confusion matrix was applied to assess the accuracy. The experimental results indicate the following: (1) Time-series UAV remote sensing images considerably increased the accuracy of crop classification. Compared to a single-period image, the overall accuracy and kappa coefficient increased by 26.65% and 0.3496, respectively. (2) The object-oriented classification method was better suited for the precise classification of crops. The overall accuracy and kappa coefficient increased by 3.13% and 0.0419, respectively, as compared to the pixel-based classification results. (3) RF obtained the highest overall accuracy and kappa coefficient in both pixel-based and object-oriented crop classification. RF's producer accuracy and user accuracy for cotton, spring wheat, cocozelle, and corn in the study area were both more than 92%. These results provide a reference for crop area statistics and agricultural precision management. [ABSTRACT FROM AUTHOR]

Systematic first-principles calculations were performed to investigate the adsorption and diffusion of Li on different graphene layers with B/N-doping and/or C-vacancy, so as to understand why doping heteroatoms in graphene anode could significantly improve the performance of lithium-ion batteries. We found that the formation of single or double carbon vacancies in graphene are critical for the adsorption of Li atoms. While the N-doping facilitates the formation of vacancies, it introduces over binding issue and hinders the Li diffusion. The presence of B takes the excessive electrons from Li and N and reduces the energy barrier of Li diffusion on substrates. We perceive that these clear insights are crucial for the further development of graphene based anode materials for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

While the structures of Brønsted acid sites (BAS) in zeolites are well understood, those of Lewis acid sites (LAS) remain an active area of investigation. Under hydrated conditions, the reversible formation of framework-associated octahedral aluminum has been observed in zeolites in the acidic form. However, the structure and formation mechanisms are currently unknown. In this work, combined experimental 27 Al NMR spectroscopy and computational data reveal for the first time the details of the zeolite framework-associated octahedral aluminium. The octahedral LAS site becomes kinetically allowed and thermodynamically stable under wet conditions in the presence of multiple nearby BAS sites. The critical condition for the existence of such octahedral LAS appears to be the availability of three protons: at lower proton concentration, either by increasing the Si/Al or by ion-exchange to non-acidic form, the tetrahedral BAS becomes thermodynamically more stable. This work resolves the question about the nature and reversibility of framework-associated octahedral aluminium in zeolites., (© 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Mesenchymal stromal/stem cells (MSCs) have been considered an attractive source of cytotherapy due to their promising effects on treating various diseases. Allogeneic MSCs (allo-MSCs) are extensively used in clinical trials due to their convenient preparation and credible performance. Traditionally, allo-MSCs are considered immunoprivileged with minimal immunogenicity and potent immunomodulatory capacity. However, growing evidence has suggested that allo-MSCs also induce immune response and cause rejection after transplantation, but the underlying cellular and molecular mechanisms remain to be elucidated. Here, we demonstrated that allografted MSCs upregulated MHC-II upon stimulation of IFN-γ in hepatic inflammatory environment by using mouse model of CCl 4 -induced liver injury. MHC-II upregulation enhanced the immunogenicity of allo-MSCs, leading to the activation of alloreactive T cells and rejection of allo-MSCs. However, MHC-II deficiency impaired the allogenic reactivity, thereby rescuing the loss of allo-MSCs. Mechanistically, CD4 + cytotoxic T lymphocytes (CTLs), rather than CD8 + CTLs, acted as the major effector for allo-MSC rejection. Under liver injury condition, the transplanted allo-MSCs upregulated CD80 and PD-L1, and CD8 + CTLs highly expressed CTLA-4 and PD-1, thereby inducing immune tolerance of CD8 + T cells to allo-MSCs. On the contrary, CD4 + CTLs minimally expressed CTLA-4 and PD-1; thus, they remain cytotoxic to allo-MSCs. Consequently, transplantation of MHC-II-deficient allo-MSCs substantially promoted their therapeutic effects in treating liver injury. This study revealed a novel mechanism of MSC allograft rejection mediated by CD4 + CTLs in injured liver, which provided new strategies for improving clinical performance of allo-MSCs in benefiting hepatic injury repair., Competing Interests: Conflict of Interest The authors declare that they have no conflicts of interest with the contents of this article., (copyright: © 2022 Shen et al.)

Systematic first-principles calculations were performed to investigate the adsorption and diffusion of Li on different graphene layers with B/N-doping and/or C-vacancy, so as to understand why doping heteroatoms in graphene anode could significantly improve the performance of lithium-ion batteries. We found that the formation of single or double carbon vacancies in graphene are critical for the adsorption of Li atoms. While the N-doping facilitates the formation of vacancies, it introduces over binding issue and hinders the Li diffusion. The presence of B takes the excessive electrons from Li and N and reduces the energy barrier of Li diffusion on substrates. We perceive that these clear insights are crucial for the further development of graphene based anode materials for lithium-ion batteries.