Programmable Reconfiguration of Supramolecular Bottlebrush Block Copolymers: From Solution Self-Assembly to Co-Crystallization-Assistant Self-Assembly.

Achieving structural reconfiguration of supramolecular bottlebrush block copolymers toward topological engineering is of particular interest but challenging. Here, we address the creation of supramolecular architectures to discover how assembled topology influences the structured aggregates, combining hydrogen-bonded (H-bonded) bottlebrush block copolymers and electrostatic interaction induced polymer/inorganic eutectics. We first design H-bonding linear-brush block copolymer P(NBDAP-co-NBC)-b-P(NBPEO), bearing linear block P(NBDAP-co-NBC) (poly(norbornene-terminated diaminopyridine-co-norbornene-terminated hexane)) with pendant H-bonding DAP (diaminopyridine) motifs, and PEO (poly(ethylene oxide)) densely grafted P(NBPEO) brush block. Thanks to H-bonding association between DAP and thymine (Thy), incorporation of Thy-functionalized polystyrene (Thy-PS65) enables solution self-assembly and formation of H-bonded bottlebrush block copolymers, generating augmented nanospheres with increasing Thy-PS65 amount. Noteworthy that integration of inorganic cluster silicotungstic acid (STA) to P(NBC-co-NBDAP)-b-P(NBPEO), endows the formation of PEO/STA eutectic core. Therefore, co-crystallization-assistant self-assembly at the interfaces of polymeric, inorganic and supramolecular chemistry is realized, reflecting multi-stage morphology transformation from hexagonal platelets, needle-like, curved rod-like micelles, finally to end-to-end closed rings, by gradually increasing Thy-PS65 while fixing STA content. Interestingly, such solution self-assembly to co-crystallization-assistant self-assembly strategy not only endows unique nanostructure transition, also induce in-to-out reconfiguration of PS domains. These findings clearly provide unique methodology towards programmable fabrication of geometrical objects promising in smart materials.

FERONIA-mediated TIR1/AFB2 oxidation stimulates auxin signaling in Arabidopsis.

The phytohormone auxin plays a pivotal role in governing plant growth and development. Although the TRANSPORT INHIBITOR RESPONSE1/AUXIN SIGNALING F-BOX (TIR1/AFB) receptors function in both the nucleus and cytoplasm, the mechanism governing the distribution of TIR1/AFBs between these cellular compartments remains unknown. In this study, we demonstrate that auxin-mediated oxidation of TIR1/AFB2 is essential for their targeting to the nucleus. We showed that small active molecules, reactive oxygen species (ROS) and nitric oxide (NO), are indispensable for the nucleo-cytoplasmic distribution of TIR1/AFB2 in trichoblasts and root hairs. Further studies revealed that this process is regulated by the FERONIA receptor kinase-NADPH oxidase signaling pathway. Interestingly, ROS and NO initiate oxidative modifications in TIR1C140/516 and AFB2C135/511, facilitating their subsequent nuclear import. The oxidized forms of TIR1C140/516 and AFB2C135/511 play a crucial role in enhancing the function of TIR1 and AFB2 in transcriptional auxin responses. Collectively, our study reveals a novel mechanism by which auxin stimulates the transport of TIR1/AFB2 from the cytoplasm to the nucleus, orchestrated by the FERONIA-ROS signaling pathway.

A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML) algorithms, this study presents an innovative approach for classifying the quality grade of fresh tea leaves. This approach leverages an integration of image recognition and deep learning (DL) algorithm to accurately classify tea leaves' grades by identifying distinct bud and leaf combinations. The method begins by acquiring separate images of orderly scattered and randomly stacked fresh tea leaves. These images undergo data augmentation techniques, such as rotation, flipping, and contrast adjustment, to form the scattered and stacked tea leaves datasets. Subsequently, the YOLOv8x model was enhanced by Space pyramid pooling improvements (SPPCSPC) and the concentration-based attention module (CBAM). The established YOLOv8x-SPPCSPC-CBAM model is evaluated by comparing it with popular DL models, including Faster R-CNN, YOLOv5x, and YOLOv8x. The experimental findings reveal that the YOLOv8x-SPPCSPC-CBAM model delivers the most impressive results. For the scattered tea leaves, the mean average precision, precision, recall, and number of images processed per second rates of 98.2%, 95.8%, 96.7%, and 2.77, respectively, while for stacked tea leaves, they are 99.1%, 99.1%, 97.7% and 2.35, respectively. This study provides a robust framework for accurately classifying the quality grade of fresh tea leaves.

Point cloud registration method for maize plants based on conical surface fitting-ICP.

Reconstructing three-dimensional (3D) point cloud model of maize plants can provide reliable data for its growth observation and agricultural machinery research. The existing data collection systems and registration methods have low collection efficiency and poor registration accuracy. A point cloud registration method for maize plants based on conical surface fitting-iterative closest point (ICP) with automatic point cloud collection platform was proposed in this paper. Firstly, a Kinect V2 was selected to cooperate with an automatic point cloud collection platform to collect multi-angle point clouds. Then, the conical surface fitting algorithm was employed to fit the point clouds of the flowerpot wall to acquire the fitted rotation axis for coarse registration. Finally, the interval ICP registration algorithm was used for precise registration, and the Delaunay triangle meshing algorithm was chosen to triangulate the point clouds of maize plants. The maize plant at the flowering and kernel stage was selected for reconstruction experiments, the results show that: the full-angle registration takes 57.32 s, and the registration mean distance error is 1.98 mm. The measured value's relative errors between the reconstructed model and the material object of maize plant are controlled within 5%, the reconstructed model can replace maize plants for research.

Genome-wide analyses of SWEET family proteins reveal involvement in fruit development and abiotic/biotic stress responses in banana.

Sugars Will Eventually be Exported Transporters (SWEET) are a novel type of sugar transporter that plays crucial roles in multiple biological processes. From banana, for the first time, 25 SWEET genes which could be classified into four subfamilies were identified. Majority of MaSWEETs in each subfamily shared similar gene structures and conserved motifs. Comprehensive transcriptomic analysis of two banana genotypes revealed differential expression patterns of MaSWEETs in different tissues, at various stages of fruit development and ripening, and in response to abiotic and biotic stresses. More than 80% MaSWEETs were highly expressed in BaXi Jiao (BX, Musa acuminata AAA group, cv. Cavendish), in sharp contrast to Fen Jiao (FJ, M. acuminata AAB group) when pseudostem was first emerged. However, MaSWEETs in FJ showed elevated expression under cold, drought, salt, and fungal disease stresses, but not in BX. Interaction networks and co-expression assays further revealed that MaSWEET-mediated networks participate in fruit development signaling and abiotic/biotic stresses, which was strongly activated during early stage of fruit development in BX. This study provides new insights into the complex transcriptional regulation of SWEETs, as well as numerous candidate genes that promote early sugar transport to improve fruit quality and enhance stress resistance in banana.