Facilitated by the rapid progress of sequencing technology, comparative genomic studies in plants have unveiled recurrent whole genome duplication (i.e. polyploidization) events throughout plant evolution. The evolutionary past of plant genes should be analyzed in a background of recurrent polyploidy events in distinctive plant lineages. The Vascular Plant One Zinc-finger (VOZ) gene family encode transcription factors associated with a number of important traits including control of flowering time and photoperiodic pathways, but the evolutionary trajectory of this gene family remains uncharacterized. In this study, we deciphered the evolutionary history of the VOZ gene family by analyses of 107 VOZ genes in 46 plant genomes using integrated methods: phylogenic reconstruction, Ks-based age estimation and genomic synteny comparisons. By scrutinizing the VOZ gene family phylogeny the core eudicot γ event was well circumscribed, and relics of the precommelinid τ duplication event were detected by incorporating genes from oil palm and banana. The more recent T and ρ polyploidy events, closely coincident with the species diversification in Solanaceae and Poaceae, respectively, were also identified. Other important polyploidy events captured included the “salicoid” event in poplar and willow, the “early legume” and “soybean specific” events in soybean, as well as the recent polyploidy event in Physcomitrella patens. Although a small transcription factor gene family, the evolutionary history of VOZ genes provided an outstanding record of polyploidy events in plants. The evolutionary past of VOZ gene family demonstrated a close correlation with critical plant polyploidy events which generated species diversification and provided answer to Darwin’s “abominable mystery”. We deciphered the evolutionary history of VOZ transcription factor family in plants and ancestral polyploidy events in plants were recapitulated simultaneously. This analysis allowed for the generation of an idealized plant gene tree demonstrating distinctive retention and fractionation patterns following polyploidy events.

While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to investigate the impact of built environment factors on ride-hailing travel demand, including their nonlinear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. Population density, land use mix, and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.

Researchers have applied a series of global models to investigate the link between the built environment and ride-hailing ridership based on ride-hailing data from one specific transportation network company (TNC). However, these research designs inadequately represent real ride-hailing demand within a specific spatial range and cannot reflect spatiotemporal heterogeneity in the link. For the first time, this study collects all demand data of TNCs in Nanjing and analyzes their relationship with the built environment. The effect of taxi demand is considered. We adopt a multiscale geographically weighted regression model to account for the spatial non-stationarity and the multiscale effect of each built environment variable. The findings reveal spatiotemporal heterogeneous relationships of the built environment with ride-hailing ridership. Although the relationship between taxi and ride-hailing ridership varies across spatial locations, ride-hailing always acts as a cooperator for traditional taxis. The findings provide implications for policy making, urban planning, and travel demand management of ride-hailing.

While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to investigate the impact of built environment factors on ride-hailing travel demand, including their non-linear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. population density, land use mix and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.