Qiong Shen

Dr. Qiong Shen


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 September 2026 in Horticulturae
Verified
Journal Article
Open access
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YMYanxiu Miao
Yanxiu Miao
JLJunxuan Lin
Junxuan Lin
张张张骏 张
张骏 张
ZZZhihao Zeng
Zhihao Zeng
QSQiong Shen
Qiong Shen
BLBin Li
Bin Li
Accurate prediction of the net photosynthetic rate (Pn) is important for understanding crop responses to environmental conditions in protected cultivation. The Pn responds nonlinearly to the temperature, photosynthetic photon flux density (PPFD), and CO2 concentration, making accurate prediction under combined environmental conditions challenging. However, these effects make it difficult for mechanistic models to precisely predict the Pn. In this study, 1800 repeated Pn records of zucchini leaves were obtained during flowering and fruiting stages and used to construct and evaluate a GA-optimized adaptive neuro-fuzzy inference system (GA-ANFIS), ANFIS, random forest (RF), and radial basis function (RBF), as well as backpropagation (BP) models. The maximum Pn values were 43.22 and 45.21 μmol·m−2·s−1 during flowering and fruiting, respectively, at 32 and 28 °C, a PPFD of 1800 μmol·m−2·s−1, and a CO2 concentration of 1200 μmol·mol−1. On the test set, the GA-ANFIS achieved R2 values of 0.9811 and 0.9901 and RMSE values of 1.4808 and 1.1889 μmol·m−2·s−1, respectively. It also achieved the highest R2 and lowest RMSE on the additional temperature interpolation set. The test set regression slopes were 0.9876 and 1.0001, with intercepts of 0.1060 and −0.0465. Overall, the GA-ANFIS showed the best predictive performance among the five models under the evaluated conditions.

24 July 2026 in BMC Plant Biology
Verified
Journal Article
Open access
PDF file
QSQiong Shen
Qiong Shen
JLJiangtao Liu
Jiangtao Liu
HMHongyu Meng
Hongyu Meng
YLYanan Liu
Yanan Liu
JSJianxin Shi
Jianxin Shi
The escalating demand for hull-less Cucurbita moschata seeds, driven by a thorough comprehensive understanding of their functional properties and the global trade in pumpkin seeds, emphasizes the necessity to expedite the breeding of hull-less varieties to satisfy market requirements. Existing knowledge regarding hull-less seeds in pumpkins primarily stems from studies on C. pepo , where the lignin pathway is considered a significant factor; however, its significance in C. moschata remains unexplored. This study aims to investigate anatomical, compositional, and gene expression alterations in the seed coat of one hulled wild type (RHS-1) and three hull-less mutant recombinant inbred lines (RHLS-2, RHLS-3 and RHLS-4) at 20 DPA (days post anthesis) and 50 DPA in C. moschata . The anatomical changes in the seed coat of hulled and hull-less lines in C. moschata during seed coat development were comparable to those observed in C. pepo , with five distinct seed coat layers appearing in the hulled wild type but a papery thin layer (composed of the epidermis and sclerenchyma) in the hull-less mutants. However, compositional changes in pectin content in the seed coat of hulled and hull-less lines in C. moschata differed from those in C. pepo . Specifically, the seed coat of hull-less mutants exhibited higher pectin levels than that of hulled wild type. Additionally, the expression of three lignin biosynthesis genes ( CmoC4H, Cmo4CL , and CmoCCR ) was upregulated at 50 DPA, which was contrary to the patterns observed in C. pepo . In summary, our results revealed both conserved and distinct features of the seed coat development between C. moschata and C. pepo . These correlative findings provide comparative features of seed coat development in hulled and hull-less C. moschata lines and provide candidate genes for future functional studies.