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ObjectiveTo quickly and accurately assess the situation of crop lodging disasters, it is necessary to promptly obtain information such as the location and area of the lodging occurrences. Currently, there are no corresponding technical standards for identifying crop lodging based on UAV remote sensing, which is not conducive to standardizing the process of obtaining UAV data and proposing solutions to problems. This study aims to explore the impact of different spatial resolution remote sensing images and feature optimization methods on the accuracy of identifying wheat lodging areas.MethodsDigital orthophoto images (DOM) and digital surface models (DSM) were collected by UAVs with high-resolution sensors at different flight altitudes after wheat lodging. The spatial resolutions of these image data were 1.05, 2.09, and 3.26 cm. A full feature set was constructed by extracting 5 spectral features, 2 height features, 5 vegetation indices, and 40 texture features from the pre-processed data. Then three feature selection methods, ReliefF algorithm, RF-RFE algorithm, and Boruta-Shap algorithm, were used to construct an optimized subset of features at different flight altitudes to select the best feature selection method. The ReliefF algorithm retains features with weights greater than 0.2 by setting a threshold of 0.2; the RF-RFE algorithm quantitatively evaluated the importance of each feature and introduces variables in descending order of importance to determine classification accuracy; the Boruta-Shap algorithm performed feature subset screening on the full feature set and labels a feature as green when its importance score was higher than that of the shaded feature, defining it as an important variable for model construction. Based on the above-mentioned feature subset, an object-oriented classification model on remote sensing images was conducted using eCognition9.0 software. Firstly, after several experiments, the feature parameters for multi-scale segmentation in the object-oriented classification were determined, namely a segmentation scale of 1, a shape factor of 0.1, and a tightness of 0.5. Three object-oriented supervised classification algorithms, support vector machine (SVM), random forest (RF), and K nearest neighbor (KNN), were selected to construct wheat lodging classification models. The Overall classification accuracy and Kappa coefficient were used to evaluate the accuracy of wheat lodging identification. By constructing a wheat lodging classification model, the appropriate classification strategy was clarified and a technical path for lodging classification was established. This technical path can be used for wheat lodging monitoring, providing a scientific basis for agricultural production and improving agricultural production efficiency.Results and DiscussionsThe results showed that increasing the altitude of the UAV to 90 m significantly improved flight efficiency of wheat lodging areas. In comparison to flying at 30 m for the same monitoring range, data acquisition time was reduced to approximately 1/6th, and the number of photos needed decreased from 62 to 6. In terms of classification accuracy, the overall classification effect of SVM is better than that of RF and KNN. Additionally, when the image spatial resolution varied from 1.05 to 3.26 cm, the full feature set and all three optimized feature subsets had the highest classification accuracy at a resolution of 1.05 cm, which was better than at resolutions of 2.09 and 3.26 cm. As the image spatial resolution decreased, the overall classification effect gradually deteriorated and the positioning accuracy decreased, resulting in poor spatial consistency of the classification results. Further research has found that the Boruta-Shap feature selection method can reduce data dimensionality and improve computational speed while maintaining high classification accuracy. Among the three tested spatial resolution conditions (1.05, 2.09, and 3.26 cm), the combination of SVM and Boruta-Shap algorithms demonstrated the highest overall classification accuracy. Specifically, the accuracy rates were 95.6%, 94.6%, and 93.9% for the respective spatial resolutions. These results highlighted the superior performance of this combination in accurately classifying the data and adapt to changes in spatial resolution. When the image resolution was 3.26 cm, the overall classification accuracy decreased by 1.81% and 0.75% compared to 1.05 and 2.09 cm; when the image resolution was 2.09 cm, the overall classification accuracy decreased by 1.06% compared to 1.05 cm, showing a relatively small difference in classification accuracy under different flight altitudes. The overall classification accuracy at an altitude of 90 m reached 95.6%, with Kappa coefficient of 0.914, meeting the requirements for classification accuracy.ConclusionsThe study shows that the object-oriented SVM classifier and the Boruta-Shap feature optimization algorithm have strong application extension advantages in identifying lodging areas in remote sensing images at multiple flight altitudes. These methods can achieve high-precision crop lodging area identification and reduce the influence of image spatial resolution on model stability. This helps to increase flight altitude, expand the monitoring range, improve UAV operation efficiency, and reduce flight costs. In practical applications, it is possible to strike a balance between classification accuracy and efficiency based on specific requirements and the actual scenario, thus providing guidance and support for the development of strategies for acquiring crop lodging information and evaluating wheat disasters.

Standardization is the universal language of the world, and standardization of traditional Chinese medicine (TCM) is essential for its communication in China and globally. However, the principles and methods of TCM acupuncture standardization have been unclear and inadequate in the early stages. Based on an investigative approach to understanding the current status, identifying problems, and finding solutions, our team has established basic principles of TCM acupuncture that embody Chinese wisdom, evaluated the international strategic environment systematically, proposed the principle of “importance of harmony and exercise of impartiality”, and established basic working principles. A series of methods for TCM acupuncture standard development and evaluation have been constructed, including general standards for the revision of TCM acupuncture standards, the first TCM acupuncture clinical research management specification, a shared full chain technology platform, a data center, and an evaluation research base for TCM acupuncture clinical research. Evaluation criteria for ancient literature and expert experience, a recommendation method for the “three main and three auxiliaries” TCM guideline for prevention were established, and quantifiable assessment methods of TCM standard applicability were proposed. These findings provide methodological guidance for TCM acupuncture standardization.

The accurate instance segmentation of individual crop plants is crucial for achieving a high-throughput phenotypic analysis of seedlings and smart field management in agriculture. Current crop monitoring techniques employing remote sensing predominantly focus on population analysis, thereby lacking precise estimations for individual plants. This study concentrates on maize, a critical staple crop, and leverages multispectral remote sensing data sourced from unmanned aerial vehicles (UAVs). A large-scale SAM image segmentation model is employed to efficiently annotate maize plant instances, thereby constructing a dataset for maize seedling instance segmentation. The study evaluates the experimental accuracy of six instance segmentation algorithms: Mask R-CNN, Cascade Mask R-CNN, PointRend, YOLOv5, Mask Scoring R-CNN, and YOLOv8, employing various combinations of multispectral bands for a comparative analysis. The experimental findings indicate that the YOLOv8 model exhibits exceptional segmentation accuracy, notably in the NRG band, with bbox_mAP50 and segm_mAP50 accuracies reaching 95.2% and 94%, respectively, surpassing other models. Furthermore, YOLOv8 demonstrates robust performance in generalization experiments, indicating its adaptability across diverse environments and conditions. Additionally, this study simulates and analyzes the impact of different resolutions on the model's segmentation accuracy. The findings reveal that the YOLOv8 model sustains high segmentation accuracy even at reduced resolutions (1.333 cm/px), meeting the phenotypic analysis and field management criteria. [ABSTRACT FROM AUTHOR]

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections are a great threat to public health worldwide. Ceftazidime-avibactam (CZA) is an effective β-lactam/β-lactamase inhibitors against CRKP. However, reports of resistance to CZA, mainly caused by Klebsiella pneumoniae carbapenemase (KPC) variants, have increased in recent years. In this study, we aimed to describe the resistance characteristics of KPC-12, a novel KPC variant identified from a CZA resistant K. pneumoniae. Methods: The K. pneumoniae YFKP-97 collected from a patient with respiratory tract infection was performed whole-genome sequencing (WGS) on the Illumina NovaSeq 6000 platform. Genomic characteristics were analyzed using bioinformatics methods. Antimicrobial susceptibility testing was conducted by the broth microdilution method. Induction of resistant strain was carried out in vitro as previously described. The G. mellonella killing assay was used to evaluate the pathogenicity of strains, and the conjugation experiment was performed to evaluate plasmid transfer ability. Results: Strain YFKP-97 was a multidrug-resistant clinical ST11-KL47 K. pneumoniae confers high-level resistance to CZA (16/4 μg/mL). WGS revealed that a KPC variant, KPC-12, was carried by the IncFII (pHN7A8) plasmids (pYFKP-97_a and pYFKP-97_b) and showed significantly decreased activity against carbapenems. In addition, there was a dose-dependent effect of blaKPC-12 on its activity against ceftazidime. In vitro inducible resistance assay results demonstrated that the KPC-12 variant was more likely to confer resistance to CZA than the KPC-2 and KPC-3 variants. Discussion: Our study revealed that patients who was not treated with CZA are also possible to be infected with CZA-resistant strains harbored a novel KPC variant. Given that the transformant carrying blaKPC-12 was more likely to exhibit a CZA-resistance phenotype. Therefore, it is important to accurately identify the KPC variants as early as possible. [ABSTRACT FROM AUTHOR]

An organic-inorganic hybrid polyoxometalate (POM) based on bi-nuclear copper complexes and Dawson-type phosphotungstate (POT) [CuL(H2O)][CuL]2[P2W18O62]·3H2O (abbreviated as CPW) (L = 2-(1H-pyrazol-3-yl)pyridine) was synthesized and structurally characterized by IR spectrum, and single-crystal X-ray diffraction. Structural analysis indicated that CPW is nanoscale molecule and extends into a two-dimensional layer, in which three bi-nuclear copper complexes are substituted at different sites of Dawson-type POT and joined adjacent POT fragments together. Notably, CPW can efficiently catalyze the production of reactive oxygen species. [ABSTRACT FROM AUTHOR]

The widespread prevalence and dissemination of antibiotic-resistant bacteria, coupled with the diminishing supply of new antibiotics, emphasize the pressing necessity for the exploration of innovative antibacterial agents. Previously, we detailed the impact of the small-molecule compound CY-158-11 on S. aureus biofilm. By hindering adhesion and PIA-mediated biofilm formation, subinhibitory concentrations of CY-158-11 exhibit antibiofilm activity toward S. aureus . Here, we sought to elucidate the antibacterial activity and mode of action of this compound. Upon CY-158-11 treatment in culture, the inhibition of bacterial growth, coupled with MBC to MIC of >4, indicated that CY-158-11 exerted a bacteriostatic effect. Particularly, CY-158-11 showed strong antibacterial activity against a wide variety of S. aureus , including multidrug-resistant bacteria. We found that CY-158-11 promoted the permeability of cell membrane and propidium iodide absorption as well as caused the dissipation of membrane potential. The effect of CY-158-11 on the mammalian cytoplasmic membrane was measured using hemolytic and cytotoxicity assays, and the skin irritation and systemic toxicity of the drug were measured by injecting the compound into the skin and tail vein of mice. Moreover, CY-158-11 exhibited considerable efficacy in a subcutaneous abscess mouse model of S. aureus infection. In conclusion, CY-158-11 possesses antibacterial properties, including inhibition of bacterial growth, damage to cell membranes, and treatment of skin abscesses, which can be a promising therapeutic option for combating S. aureus ., Importance: The combination of the rising incidence of antibiotic resistance and the shrinking antibiotic pipeline has raised concern about the postantibiotic era. New antibacterial agents and targets are required to combat S. aureus -associated infections. In this study, we identified a maleimide-diselenide hybrid compound CY-158-11 exhibiting antibacterial activity against S. aureus in vitro and in vivo at relatively low concentrations. Furthermore, the investigation of its mode of action revealed that CY-158-11 can selectively perturb the cytoplasmic membrane of bacteria without harming mammalian cells or mouse organs. Thus, CY-158-11 is a compelling novel drug for development as a new therapy for S. aureus infections.

Here, we elucidate the interaction between IAA and melatonin (MT) in response to chilling in cucumber. The results showed that chilling stress induced the increase of endogenous MT and IAA, and the application of MT promoted the synthesis of IAA, while IAA could not affect endogenous MT content under chilling stress. Moreover, MT and IAA application both remarkably increased the chilling tolerance of cucumber seedlings in terms of lower contents of MDA and ROS, higher mRNA abundance of cold response genes, net photosynthetic rate (P n ), maximum regeneration rate of ribulose-1,5-diphosphate (J max ), Rubisco maximum carboxylation efficiency (V cmax ), the activities and gene expression of RCA and Rubisco, as well as the content of active P700 (I/I 0 ) and photosynthetic electron transport, compared with the plants in H 2 O treatment. Further analysis revealed that the inhibition of IAA transportation significantly reduced the chilling tolerance induced by MT, whereas the inhibition of endogenous MT did not affect the chilling tolerance induced by IAA. Meanwhile, we found that overexpression of the MT biosynthesis gene CsASMT increased the chilling tolerance, which was blocked by inhibition of endogenous IAA, and the silence of IAA biosynthesis gene CsYUCCA10 decreased the chilling tolerance of cucumber, which could not be alleviated by MT. These data implied IAA acted as a downstream signal to participate in the MT-induced chilling tolerance of cucumber seedlings. The study has implications for the production of greenhouse cucumber in winter seasons., (© 2024 Scandinavian Plant Physiology Society.)