Parental exposure to the herbicide diuron results in oxidative DNA damage to germinal cells of the Pacific oyster Crassostrea gigas

Chemical pollution by pesticides has been identified as a possible contributing factor to the massive mortality outbreaks observed in Crassostrea gigas for several years. A previous study demonstrated the vertical transmission of DNA damage by subjecting oyster genitors to the herbicide diuron at environmental concentrations during gametogenesis. This trans-generational effect occurs through damage to genitor-exposed gametes, as measured by the comet-assay. The presence of DNA damage in gametes could be linked to the formation of DNA damage in other germ cells. In order to explore this question, the levels and cell distribution of the oxidized base lesion 8-oxodGuo were studied in the gonads of exposed genitors. High-performance liquid chromatography coupled with UV and electrochemical detection analysis showed an increase in 8-oxodGuo levels in both male and female gonads after exposure to diuron. Immunohistochemistry analysis showed the presence of 8-oxodGuo at all stages of male germ cells, from early to mature stages. Conversely, the oxidized base was only present in early germ cell stages in female gonads. These results indicate that male and female genitors underwent oxidative stress following exposure to diuron, resulting in DNA oxidation in both early germ cells and gametes, such as spermatozoa, which could explain the transmission of diuron-induced DNA damage to offspring. Furthermore, immunostaining of early germ cells seems indicates that damages caused by exposure to diuron on germ line not only affect the current sexual cycle but also could affect future gametogenesis.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).