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The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method's performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm's 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes. [ABSTRACT FROM AUTHOR]

In recent years, with the development of the nanomaterials discipline, many new pesticide drug-carrying systems—such as pesticide nano-metal particles, nano-metal oxides, and other drug-carrying materials—had been developed and applied to pesticide formulations. Although these new drug-loading systems are relatively friendly to the environment, the direct exposure of many metal nanoparticles to the environment will inevitably lead to potential effects. In response to these problems, organic nanomaterials have been rapidly developed due to their high-quality biodegradation and biocompatibility. Most of these organic nanomaterials were mainly polysaccharide materials, such as chitosan, carboxymethyl chitosan, sodium alginate, β-cyclodextrin, cellulose, starch, guar gum, etc. Some of these materials could be used to carry inorganic materials to develop a temperature- or pH-sensitive pesticide drug delivery system. Herein, the pesticide drug-carrying system developed based on polysaccharide materials, such as chitosan, was referred to as the pesticide polymer drug-carrying system based on polysaccharide materials. This kind of drug-loading system could be used to protect the pesticide molecules from harsh environments, such as pH, light, temperature, etc., and was used to develop the function of a sustained release, targeted release of pesticides in the intestine of insects, and achieve the goal of precise application, reduction, and efficiency of pesticides. In this review, the recent progress in the field of polysaccharide-based polymer drug delivery systems for pesticides has been discussed, and suggestions for future development were proposed based on the current situation. [ABSTRACT FROM AUTHOR]

Simple Summary: Smell is an important sense for insects. The olfactory sense of insects is involved in their feeding, mating, egg-laying, predation avoidance, and communication behaviors. The use of insect-specific odorants to control insect behavior is an important control method. Here, we built iORandLigandDB, a platform for the batch prediction of insect-specific odorants based on artificial intelligence technology. The 3D structure of existing ORs in insects and the docking data with relevant odorants can be retrieved from the database. The use of insect-specific odorants to control the behavior of insects has always been a hot spot in research on "green" control strategies of insects. However, it is generally time-consuming and laborious to explore insect-specific odorants with traditional reverse chemical ecology methods. Here, an insect odorant receptor (OR) and ligand database website (iORandLigandDB) was developed for the specific exploration of insect-specific odorants by using deep learning algorithms. The website provides a range of specific odorants before molecular biology experiments as well as the properties of ORs in closely related insects. At present, the existing three-dimensional structures of ORs in insects and the docking data with related odorants can be retrieved from the database and further analyzed. [ABSTRACT FROM AUTHOR]

The development of drug-resistance in the opportunistic pathogen Escherichia coli has become a global public health concern. Due to the share of similar flora between pets and their owners, the detection of pet-origin antibiotic-resistant E. coli is necessary. This study aimed to detect the prevalence of feline-origin ESBL E. coli in China and to explore the resistance elimination effect of garlic oil to cefquinome on ESBL E. coli. Cat fecal samples were collected from animal hospitals. The E. coli isolates were separated and purified by indicator media and polymerase chain reaction (PCR). ESBL genes were detected by PCR and Sanger sequencing. The MICs were determined. The synergistic effect of garlic oil and cefquinome against ESBL E. coli was investigated by checkerboard assays, time-kill and growth curves, drug-resistance curves, PI and NPN staining, and a scanning electronic microscope. A total of 80 E. coli strains were isolated from 101 fecal samples. The rate of ESBL E. coli was 52.5% (42/80). The prevailing ESBL genotypes in China were CTX-M-1, CTX-M-14, and TEM-116. In ESBL E. coli, garlic oil increased the susceptibility to cefquinome with FICIs from 0.2 to 0.7 and enhanced the killing effect of cefquinome with membrane destruction. Resistance to cefquinome decreased with treatment of garlic oil after 15 generations. Our study indicates that ESBL E. coli has been detected in cats kept as pets. The sensitivity of ESBL E. coli to cefquinome was enhanced by garlic oil, indicating that garlic oil may be a potential antibiotic enhancer. [ABSTRACT FROM AUTHOR]

Tungsten (W) is considered to be the most promising plasma-facing material in fusion reactors. During their service, severe irradiation conditions create plenty of point defects in W, which can significantly degrade their performance. In this work, we first employ the molecular static simulations to investigate the interaction between a 1/2[111] dislocation loop and a vacancy-type defect including a vacancy, di-vacancy, and vacancy cluster in W. The distributions of the binding energies of a 1/2[111] interstitial and vacancy dislocation loop to a vacancy along different directions at 0 K are obtained, which are validated by using the elasticity theory. The calculated distributions of the binding energies of a 1/2[111] interstitial dislocation loop to a di-vacancy and a vacancy cluster, showing a similar behavior to the case of a vacancy. Furthermore, we use the molecular dynamics simulation to study the effect of a vacancy cluster on the mobility of the 1/2[111] interstitial dislocation loop. The interaction is closely related to the temperature and their relative positions. A vacancy cluster can attract the 1/2[111] interstitial dislocation loop and pin it at low temperatures. At high temperatures, the 1/2[111] interstitial dislocation loop can move randomly. These results will help us to understand the essence of the interaction behaviors between the dislocation loop and a vacancy-type defect and provide necessary parameters for mesoscopic scale simulations. [ABSTRACT FROM AUTHOR]

Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, image compression, image enhancement, etc. Recently, Transformer has been a hot technique, which is widely used to tackle computer vision tasks. However, few Transformer-based methods have been proposed for low-level vision tasks. In this paper, we proposed an image denoising network structure based on Transformer, which is named DenSformer. DenSformer consists of three modules, including a preprocessing module, a local-global feature extraction module, and a reconstruction module. Specifically, the local-global feature extraction module consists of several Sformer groups, each of which has several ETransformer layers and a convolution layer, together with a residual connection. These Sformer groups are densely skip-connected to fuse the feature of different layers, and they jointly capture the local and global information from the given noisy images. We conduct our model on comprehensive experiments. In synthetic noise removal, DenSformer outperforms other state-of-the-art methods by up to 0.06–0.28 dB in gray-scale images and 0.57–1.19 dB in color images. In real noise removal, DenSformer can achieve comparable performance, while the number of parameters can be reduced by up to 40%. Experimental results prove that our DenSformer achieves improvement compared to some state-of-the-art methods, both for the synthetic noise data and real noise data, in the objective and subjective evaluations. [ABSTRACT FROM AUTHOR]