Stage-Specific Predictive Models for Cancer Survivability

Survivability of cancer strongly depends on the stage of cancer. In most previous works, machine learning survivability prediction models for a particular cancer, were trained and evaluated together on all stages of the cancer. In this work, we trained and evaluated survivability prediction models for five major cancers, together on all stages and separately for every stage. We named these models joint and stage-specific models respectively. The obtained results for the cancers which we investigated reveal that, the best model to predict the survivability of the cancer for one specific stage is the model which is specifically built for that stage. Additionally, we saw that for every stage of cancer, the most important features to predict survivability, differed from other stages. By evaluating the models separately on different stages we found that their performance differed on different stages. We also found that evaluating the models together on all stages, as was done in past, is misleading because it overestimates performance

Monthly changes in the quantity of throughfall and water infiltration of litter in Hyrcanian forest stands

The canopy architecture of different forest trees results in different species interception, quantity and quality of litterfall. Litterfall characteristics affect thickness of organic layer, water storage potential and runoff due to different decomposition rate in habitat conditions. Furthermore, throughfall content and water infiltration of litter are different through time as a result of differences in the evapotranspiration potential, leaf area index (LAI), canopy coverage, tree diameter, stand stages and other geographic factors. This paper was aimed to examine throughfall and water infiltrated of litter changes amongst three important species of Hyrcanian forest over a year. For this purpose, individual hornbeam (Carpinus betulus L.), velvet maple (Acer velutinum Boiss.) and chestnut-leaved oak (Quercus castaneifolia C. A. Mey.) species were selected in a mixed stand in Shast-Kalateh Forest, and quantity of water passing through the canopy of individual tree and organic litter layer beneath them were investigated with 9 throughfall collectors and 18 forest floor infiltration collectors after each rainfall event over a year. The results showed that species and times are two important factors in changing water balance. The hornbeam and maple species have the most throughfall and litter water contents, respectively (hornbeam with 71.1 % of throughfall and maple with 40.5 % of water infiltration of litter). The litter water infiltration of hornbeam and velvet maple in non-growing season were higher compared to the growing season, while throughfall showed no significant difference. The results of this study are concluded to be helpful for managers to regulate the stand composition as a reaction to the increasing water crisis