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Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation severely hinders the optimization of coconut breeding. To address this issue, we propose a new model based on the improved architecture of Deeplab V3+. We replace the original ASPP(Atrous Spatial Pyramid Pooling) structure with a dense atrous spatial pyramid pooling module and introduce CBAM(Convolutional Block Attention Module). This approach resolves the issue of information loss due to sparse sampling and effectively captures global features. Additionally, we embed a RRM(residual refinement module) after the output level of the decoder to optimize boundary information between organs. Multiple model comparisons and ablation experiments are conducted, demonstrating that the improved segmentation algorithm achieves higher accuracy when dealing with diverse coconut organ CT(Computed Tomography) images. Our work provides a new solution for accurately segmenting internal coconut organs, which facilitates scientific decision-making for coconut researchers at different stages of growth.

Abstract Background As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established. Results The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called “CCID”, which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit. Conclusion The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.

In this article, we study the Cauchy problem of the chemotaxis-Navier–Stokes system with the consumption and production of chemosignals with a logistic source. The parameters χ≠0, ξ≠0, λ>0 and μ>0. The system is a model that involves double chemosignals; one is an attractant consumed by the cells themselves, and the other is an attractant or a repellent produced by the cells themselves. We prove the global-in-time existence and uniqueness of the weak solution to the system for a large class of initial data on the whole space R2.

Novel components in the noncanonical Hippo pathway that mediate the growth, metastasis, and drug resistance of breast cancer (BC) cells need to be identified. Here, we showed that expression of SAM and SH3 domain-containing protein 1 (SASH1) is negatively correlated with expression of mitogenactivated protein kinase kinase kinase kinase 4 (MAP4K4) in a subpopulation of patients with luminal-subtype BC. Downregulated SASH1 and upregulated MAP4K4 synergistically regulated the proliferation, migration, and invasion of luminalsubtype BC cells. The expression of LATS2, SASH1, and YAP1 and the phosphorylation of YAP1 were negatively regulated by MAP4K4, and LATS2 then phosphorylated SASH1 to form a novel MAP4K4-LATS2-SASH1-YAP1 cascade. Dephosphorylation of Yes1 associated transcriptional regulator (YAP1), YAP1/TAZ nuclear translocation, and downstream transcriptional regulation of YAP1 were promoted by the combined effects of ectopic MAP4K4 expression and SASH1 silencing. Targeted inhibition of MAP4K4 blocked proliferation, cell migration, and ER signaling both in vitro and in vivo. Our findings reveal a novel MAP4K4-LATS2-SASH1-YAP1 phosphorylation cascade, a noncanonical Hippo pathway that mediates ER signaling, tumorigenesis, and metastasis in breast cancer. Targeted intervention with this noncanonical Hippo pathway may constitute a novel alternative therapeutic approach for endocrine-resistant BC. [ABSTRACT FROM AUTHOR]