Effects of dark or of red, blue or white light on germination of subterranean clover seeds

Dry or imbibed seeds of the negatively photoblastic burr burying subterranean clover cv. ‘Seaton Park’ were treated with dark or with red, blue or white light to evaluate the effects of light on seed germination. Dry seeds treated with constant white light, red light or blue light during 8 days and subsequently incubated in dark had final germination and duration of germination reduced, and the distribution of germination changed from highly asymmetric to symmetric respectively. Imbibing seeds incubated under constant blue or white light had final germination strongly reduced seven days after sowing (7.3% and 50.1% of the germination under dark) with significant differences between them. After transferral to dark, true complete recovery of germination of seeds treated with white light was observed 19 days after sowing, but only partial recovery in seeds treated with blue light. Results of dry and imbibed seeds are consistent with no activity of phytochromes, as expected in negatively photoblastic seeds. Results of dry seeds are seemingly contradictory because total germination data imply the inactivity of red and blue light photoreceptors, the opposite being implied by duration and shape of germination. A tentative hypothetical solution for the contradiction is presented. Results of imbibed seeds are fully consistent with cryptochromes but not with phototropins mediation of responses to light of seed germination in ‘Seaton Park’. The ecological and adaptive significance of such responses are discussed in the framework of light attenuation in soil and the requirement and ability of subterranean clover ‘Seaton Park’ to bury seeds.

Application of Mixture Models to Survival Data

Survival models are being widely applied to the engineering field to model time-to-event data once censored data is here a common issue. Using parametric models or not, for the case of heterogeneous data, they may not always represent a good fit. The present study relays on critical pumps survival data where traditional parametric regression might be improved in order to obtain better approaches. Considering censored data and using an empiric method to split the data into two subgroups to give the possibility to fit separated models to our censored data, we’ve mixture two distinct distributions according a mixture-models approach. We have concluded that it is a good method to fit data that does not fit to a usual parametric distribution and achieve reliable parameters. A constant cumulative hazard rate policy was used as well to check optimum inspection times using the obtained model from the mixture-model, which could be a plus when comparing with the actual maintenance policies to check whether changes should be introduced or not.