Unraveling Pinus massoniana's defense mechanisms against Bursaphelenchus xylophilus under aseptic conditions: A transcriptomic analysis.

Pine wilt disease (PWD) is caused by pine wood nematode (PWN, Bursaphelenchus xylophilus) and significantly impacts pine forest ecosystems globally. This study focuses on Pinus massoniana, an important timber and oleoresin resource in China, and is highly susceptible to PWN. However, the defense mechanism of pine trees in response to PWN remains unclear. Addressing the complexities of PWD, influenced by diverse factors like bacteria, fungi, and environment, we established a reciprocal system between PWN and P. massoniana seedlings under aseptic conditions. Utilizing combined second and third-generation sequencing technologies, we identified 3,718 differentially expressed genes post-PWN infection. Transcript analysis highlighted the activation of defense mechanisms via stilbenes, salicylic acid and jasmonic acid pathways, terpene synthesis, and induction of pathogenesis-related proteins and resistance genes, predominantly at 72 hours post-infection. Notably, terpene synthesis pathways, particularly the mevalonate pathway, were crucial in defense, suggesting their significance in P. massoniana's response to PWN. This comprehensive transcriptome profiling offers insights into P. massoniana's intricate defense strategies against PWN under aseptic conditions laid a foundation for future functional analyses of key resistance genes.

Open-Set Recognition of Wood Species Based on Deep Learning Feature Extraction Using Leaves.

An open-set recognition scheme for tree leaves based on deep learning feature extraction is presented in this study. Deep learning algorithms are used to extract leaf features for different wood species, and the leaf set of a wood species is divided into two datasets: the leaf set of a known wood species and the leaf set of an unknown species. The deep learning network (CNN) is trained on the leaves of selected known wood species, and the features of the remaining known wood species and all unknown wood species are extracted using the trained CNN. Then, the single-class classification is performed using the weighted SVDD algorithm to recognize the leaves of known and unknown wood species. The features of leaves recognized as known wood species are fed back to the trained CNN to recognize the leaves of known wood species. The recognition results of a single-class classifier for known and unknown wood species are combined with the recognition results of a multi-class CNN to finally complete the open recognition of wood species. We tested the proposed method on the publicly available Swedish Leaf Dataset, which includes 15 wood species (5 species used as known and 10 species used as unknown). The test results showed that, with F1 scores of 0.7797 and 0.8644, mixed recognition rates of 95.15% and 93.14%, and Kappa coefficients of 0.7674 and 0.8644 under two different data distributions, the proposed method outperformed the state-of-the-art open-set recognition algorithms in all three aspects. And, the more wood species that are known, the better the recognition. This approach can extract effective features from tree leaf images for open-set recognition and achieve wood species recognition without compromising tree material.

Research on the correlation between component elements and psychological perception of the spatial form of underground commercial street corners based on virtual reality technology.

The corner space, an important area of underground commercial streets, not only converts space functions but also exerts a great impact on the space atmosphere by transforming the environmental quality of the commercial street space. Based on corner space investigations of several underground commercial streets in China, this paper constructs a realistic scene model using virtual reality technology and screens. This paper classifies the corner space elements of underground commercial streets through preliminary experiments. Based on the conclusions, different morphological models of the corner space were constructed by orthogonal experiments and virtual reality technology combined with psychology. A semantic differential was used to quantitatively evaluate and analyze the spatial experience cognition of the subjects. This enabled an analysis of the of underground commercial street corners' relationship to the component elements and the visitors' psychological perception of their spatial form.

Synthesis and Study of Shape-Memory Polymers Selectively Induced by Near-Infrared Lights via In Situ Copolymerization.

Shape-memory polymers (SMPs) selectively induced by near-infrared lights of 980 or 808 nm were synthesized via free radical copolymerization. Methyl methacrylate (MMA) monomer, ethylene glycol dimethylacrylate (EGDMA) as a cross-linker, and organic complexes of Yb(TTA)2AAPhen or Nd(TTA)2AAPhen containing a reactive ligand of acrylic acid (AA) were copolymerized in situ. The dispersion of the organic complexes in the copolymer matrix was highly improved, while the transparency of the copolymers was negligibly influenced in comparison with the pristine cross-linked PMMA. In addition, the thermal resistance of the copolymers was enhanced with the complex loading, while their glass transition temperature, cross-linking level, and mechanical properties were to some extent reduced. Yb(TTA)2AAPhen and Nd(TTA)2AAPhen provided the prepared copolymers with selective photothermal effects and shape-memory functions for 980 and 808 nm NIR lights, respectively. Finally, smart optical devices which exhibited localized transparency or diffraction evolution procedures were demonstrated based on the prepared copolymers, owing to the combination of good transparency and selective light wavelength responsivity.