In rubber tree, bark is one of important agricultural and biological organs. However, the molecular mechanism involved in the bark formation and development in rubber tree remains largely unknown, which is at least partially due to lack of bark transcriptomic and genomic information. Therefore, it is necessary to carried out high-throughput transcriptome sequencing of rubber tree bark to generate enormous transcript sequences for the functional characterization and molecular marker development.

Tapping panel dryness (TPD) is one of the most serious threats to natural rubber production. Although a great deal of effort has been made to study TPD in rubber tree, the molecular mechanisms underlying TPD remain poorly understood. Identification and systematical analyses of the genes associated with TPD are the prerequisites for elucidating the molecular mechanisms involved in TPD. The present study is undertaken to generate information about the genes related to TPD in rubber tree.

Breeding rubber tree seedling with growth heterosis is vital for natural rubber production. It is the prerequisites for effectively utilizing growth heterosis to elucidate its molecular mechanisms, but the molecular mechanisms remain poorly understood in rubber tree. To elucidate seedling growth heterosis, we conducted comparative transcriptomic analyses between the two hybrids and their parents. By identifying and comparing differently expressed genes (DEGs), we found that the hybrids (BT 3410 and WC 11) show significantly differential expression profiles from their parents (PR 107 and RRIM 600). In BT 3410-parent triad, 1092 (49.95%) and 1094 (50.05%) DEGs indicated clear underdominance or overdominance, respectively. Whereas in WC 11-parent triad, most DEGs (78.2%, 721) showed low- or high-parent dominance; 160 (17.35%) exhibited expression patterns that are not statistically distinguishable from additivity, and 8 (0.87%) and 33 (3.58%) DEGs exhibited underdominance and overdominance, respectively. Furthermore, some biological processes are differentially regulated between two hybrids. Interestingly, the pathway in response to stimulus is significantly downregulated and metabolic pathways are more highly regulated in BT 3410. Taken together, the genotypes, transcriptomes and biological pathways (especially, carbohydrate metabolism) are highly divergent between two hybrids, which may be associated with growth heterosis and weakness. Analyzing gene action models in hybrid-parent triads, we propose that overdominance may play important roles on growth heterosis, whereas dominance on hybrid weakness in rubber tree seedlings. These findings bring new insights into our understanding of growth heterosis of rubber tree seedling.

Recently, neural implicit function-based representation has attracted more and more attention, and has been widely used to represent surfaces using differentiable neural networks. However, surface reconstruction from point clouds or multi-view images using existing neural geometry representations still suffer from slow computation and poor accuracy. To alleviate these issues, we propose a multi-scale hash encoding-based neural geometry representation which effectively and efficiently represents the surface as a signed distance field. Our novel neural network structure carefully combines low-frequency Fourier position encoding with multi-scale hash encoding. The initialization of the geometry network and geometry features of the rendering module are accordingly redesigned. Our experiments demonstrate that the proposed representation is at least 10 times faster for reconstructing point clouds with millions of points. It also significantly improves speed and accuracy of multi-view reconstruction. Our code and models are available at https://github.com/Dengzhi-USTC/Neural-Geometry-Reconstruction.