Chrysanthemum (Chrysanthemum x morifolium Ramat.) cultivar Jinba is a distinctive short-day chrysanthemum that can be exploited as a model organism for studying the molecular mechanism of flowering. The commercial value of Jinba can be increased in global flower markets by developing its proper regeneration and genetic transformation system. By addressing typical problems associated with Agrobacterium-mediated transformation in chrysanthemum, that is, low transformation efficiency and high cultivar specificity, we designed an efficient, stable transformation system. Here, we identify the features that significantly affect the genetic transformation of Jinba and standardize its transformation protocol by using CmTFL1a as a transgene. The appropriate concentrations of various antibiotics (kanamycin, meropenem and carbenicillin) and growth regulators (6-BA, 2,4-D and NAA) for the genetic transformation were determined to check their effects on in vitro plant regeneration from leaf segments of Jinba; thus, the transformation protocol was standardized through Agrobacterium tumefaciens (EHA105). In addition, the presence of the transgene and its stable expression in CmTFL1a transgenic plants were confirmed by polymerase chain reaction (PCR) analysis. The CmTFL1a transgene constitutively expressed in the transgenic plants was highly expressed in shoot apices as compared to stem and leaves. Overexpression of CmTFL1a led to a delay in transition to the reproductive phase and significantly affected plant morphology. This study will help to understand the biological phenomenon of TFL1 homolog in chrysanthemum. Moreover, our findings can explore innovative possibilities for genetic engineering and breeding of other chrysanthemum cultivars.

To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal.

Cloning and Sequence Analysis of SVP and AGL24 Genes in Chrysanthemum