The tendril is a climbing organ in cucurbits and functions in physical support and to avoid shading by neighboring vegetation.However,how cucurbits produce tendrils to obtain climbing ability is largely unknown.In thi...The tendril is a climbing organ in cucurbits and functions in physical support and to avoid shading by neighboring vegetation.However,how cucurbits produce tendrils to obtain climbing ability is largely unknown.In this study,tendril phenotypes were investigated during different developmental stages.Our results revealed that tendril growth exhibited an age-dependent pattern in cucurbits.Tendril growth was inhibited,and the tendril was formed as a short tendril[nonfunctional tendril(nonF-tendril),approximately 0.1 cm]during the seedling stage.In contrast,enhanced cell proliferation and cell expansion led to rapid elongation of the tendril during the climbing stage,and the tendril formed as a functional tendril(F-tendril,approximately 30 cm)to obtain climbing ability.RT-qPCR detection showed that age-dependent tendril growth correlated negatively with the abundance of the conserved age regulator CsmiR156.Defoliation induced CsmiR156 to inhibit CsSPLs,and F-tendril formation and climbing ability were delayed in defoliated cucumbers,which confirmed the role of CsmiR156 in regulating tendril growth in vivo.Additionally,exogenous gibberellin(GA)treatment showed that GA positively regulated tendril growth,and RT-qPCR detection showed that the GA bio-synthetic genes and metabolic genes were affected by age pathway,suggesting that the age pathway depended on GA bio-synthetic and metabolic pathway to regulate cell expansion to determine tendril growth.In summary,our work reveals that change in tendril type is an important marker of phase transition in cucumber,and tendril growth is regulated by an intrinsic developmental age signal,ensuring that the cucumber obtains climbing ability at a suitable age.展开更多
Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most sta...Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end users.Some previous studies attempted to“open the black box”and increase the interpretability of generated predictions.However,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging.To overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning.The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels.Based on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns.Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.展开更多
基金supported by the Natural Science Foundation of Zhejiang province(Grant Nos.LZ20C150001,LY21C150002)National Natural Science Foundation of China(Grant No.32202583).
文摘The tendril is a climbing organ in cucurbits and functions in physical support and to avoid shading by neighboring vegetation.However,how cucurbits produce tendrils to obtain climbing ability is largely unknown.In this study,tendril phenotypes were investigated during different developmental stages.Our results revealed that tendril growth exhibited an age-dependent pattern in cucurbits.Tendril growth was inhibited,and the tendril was formed as a short tendril[nonfunctional tendril(nonF-tendril),approximately 0.1 cm]during the seedling stage.In contrast,enhanced cell proliferation and cell expansion led to rapid elongation of the tendril during the climbing stage,and the tendril formed as a functional tendril(F-tendril,approximately 30 cm)to obtain climbing ability.RT-qPCR detection showed that age-dependent tendril growth correlated negatively with the abundance of the conserved age regulator CsmiR156.Defoliation induced CsmiR156 to inhibit CsSPLs,and F-tendril formation and climbing ability were delayed in defoliated cucumbers,which confirmed the role of CsmiR156 in regulating tendril growth in vivo.Additionally,exogenous gibberellin(GA)treatment showed that GA positively regulated tendril growth,and RT-qPCR detection showed that the GA bio-synthetic genes and metabolic genes were affected by age pathway,suggesting that the age pathway depended on GA bio-synthetic and metabolic pathway to regulate cell expansion to determine tendril growth.In summary,our work reveals that change in tendril type is an important marker of phase transition in cucumber,and tendril growth is regulated by an intrinsic developmental age signal,ensuring that the cucumber obtains climbing ability at a suitable age.
基金supported in part by a Grant in-Aid for Scientific Research B(22H03573)of the Japan Society for the Promotion of Science(JSPS)in part by the National Natural Science Foundation of China(92067109,61873119)+1 种基金in part by Shenzhen Science and Technology Program(ZDSYS20210623092007023,GJHZ20210705141808024)in part by Guangdong Key Program(2021QN02X794)。
文摘Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end users.Some previous studies attempted to“open the black box”and increase the interpretability of generated predictions.However,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging.To overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning.The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels.Based on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns.Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.
