Background/Objectives: Moso bamboo (Phyllostachys edulis), the most widely distributed bamboo species in China, is valued for both its shoots and timber. This species often faces challenges from high-temperature stress. To cope with this stress, Moso bamboo has evolved various adaptive mechanisms at the physiological and molecular levels. Although numerous studies have revealed that a large number of transcription factors (TFs) and genes play important roles in the regulatory network of plant heat stress responses, the regulatory network involved in heat responses remains incompletely understood. Methods: In this study, Moso bamboo was placed in a high-temperature environment of 42 °C for 1 h and 24 h, and transcriptome sequencing was carried out to accurately identify key molecules affected by high temperature and their related biological pathways. Results: Through a differential expression analysis, we successfully identified a series of key candidate genes and transcription factors involved in heat stress responses, including members of the ethylene response factor, HSF, WRKY, MYB, and bHLH families. Notably, in addition to traditional heat shock proteins/factors, multiple genes related to lipid metabolism, antioxidant enzymes, dehydration responses, and hormone signal transduction were found to play significant roles in heat stress responses. To further verify the changes in the expression of these genes, we used qRT-PCR technology for detection, and the results strongly supported their key roles in cellular physiological processes and heat stress responses. Conclusions: This study not only deepens our understanding of plant strategies for coping with and defending against extreme abiotic stresses but also provides valuable insights for future research on heat tolerance in Moso bamboo and other plants.

Accurate quantification of bamboo leaf morphology is essential for understanding plant morphogenesis and development. However, most bamboo leaves exhibit long lanceolate shape characteristic, posing challenges in finding suitable mathematical models for accurate shape description. Previous studies indicated that the simplified versions of Gielis equation, a nonlinear polar coordinate system derived from the superellipse equation, have shown promise in describing bamboo leaf geometries. Nevertheless, selecting an optimal nonlinear equation that precisely fits empirical bamboo leaf data remains a formidable challenge in morphological studies. This persistent limitation underscores the critical need for developing systematic evaluation methods to assess the performance of such nonlinear models. In the present study, three distinct versions of simplified Gielis equation, i.e., four-parameter version (referred to as SGE-1), three-parameter version (referred to as SGE-2), and two-parameter version (referred to as SGE-3), were used to fit the two-dimensional contours of bamboo leaves with a long lanceolate shape across two species (Indocalamus decorus with 254 leaves, and Indocalamus longiauritus with 251 leaves). The root-mean-square error (RMSE) and Akaike information criterion (AIC) were employed to assess the goodness of fit and model structural complexity, and the nonlinear behavior for each model was assessed using relative curvature measures of nonlinearity. Across both datasets, SGE-1 showcased the lowest RMSE and AIC values but exhibited the poorest close-to-linear behavior based on relative curvature measures among the three models. Conversely, SGE-3 had the best close-to-linear behavior among the three models, but it exhibited the highest RMSE and AIC values. These findings provide a methodological framework for selecting nonlinear models in plant morphometrics, particularly for lanceolate-shaped leaves, while highlighting the critical balance between descriptive accuracy and statistical robustness in biological shape analysis.