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• Drought-induced xylem embolism is a primary cause of plant mortality. Although ~70% of cycads are threatened by extinction and extant cycads diversified during a period of increasing aridification, the vulnerability of cycads to embolism spread has been overlooked. • We quantified the vulnerability to drought-induced embolism, pressure-volume curves, in situ water potentials, and a suite of xylem anatomical traits of leaf pinnae and rachises for 20 cycad species. We tested whether anatomical traits were linked to hydraulic safety in cycads. • Compared to other major vascular plant clades, cycads exhibited similar embolism resistance to angiosperms and pteridophytes but were more vulnerable to embolism than non-cycad gymnosperms. All 20 cycads had both tracheids and vessels, the proportions of which were unrelated to embolism resistance. Only vessel pit membrane fraction was positively correlated to embolism resistance, contrary to angiosperms. Water potential at turgor loss was significantly correlated to embolism resistance among cycads. • Our results show that cycads exhibit low resistance to xylem embolism and that xylem anatomical traits–particularly vessels–may influence embolism resistance together with tracheids. This study highlights the importance of understanding the mechanisms of drought resistance in evolutionarily unique and threatened lineages like the cycads. 

Abstract Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.