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Phenylselenenylzinc chloride (PhSeZnCl) is an air-stable selenolate, easily synthesizable through oxidative insertion of elemental zinc into the Se-halogen bond of the commercially available phenylselenyl chloride. PhSeZnCl was shown to possess a marked GPx-like activity both in NMR and in vitro tests, and to effectively react with cellular thiols, and was supposed for a potential use in the chemotherapy of drug-resistant cancers. However, activity of PhSeZnCl in hepatic cells has never been tested before now. In this in vitro approach, we evaluated the cytotoxic, genotoxic, and apoptotic activities, as well as the effects on cell cycle of PhSeZnCl in two preclinical hepatic models, namely HepG2 and HepaRG cells. Results showed that cell viability of HepG2 and HepaRG cells decreased in a dose-dependent manner, with a more marked effect in HepG2 tumour cells. Moreover, treatment with 50 µg/mL PhSeZnCl caused an increase of primary DNA damage (4 h) and a statistically significant increase of HepG2 cells arrested in G2/M phase. In addition, it altered mitochondrial membrane potential and induced chromosomal DNA fragmentation (24 h). In HepaRG cells, PhSeZnCl was able to determine a cell cycle-independent induction of apoptosis. Particularly, 50 µg/mL induced mitochondrial membrane depolarization after 24 h and apoptosis after 4 h treatment. Futhermore, all PhSeZnCl concentrations tested determined a significant increase of apoptotic cells after 24 h. Apoptosis was also highlighted by the detection of active Caspase-3 by Western Blot analysis after 24 h exposure. In conclusion, this first toxicological assessment provides new insights into the biological activity of PhSeZnCl in preclinical hepatic models that will be useful in future safety assessment investigation of this compound as a potential pharmaceutical.

Floods affect many aspects of our lives, and our improved understanding of the processes driving the historical changes in this natural hazard can provide basic information to enhance our preparation and mitigation efforts. Here we analyze thousands of long-term streamgages across the contiguous United States and attribute the changes in flood extremes to precipitation and temperature. We then leverage these physical insights to assess the future changes in flooding using outputs from global climate models part of the Coupled Model Intercomparison Project Phase 6. We find that flood peaks are projected to change across the contiguous United States. This is true even when flood changes are not detected in the more recent decades, highlighting the current needs for incorporating climate change in the future infrastructure designs and management of the water resources.

African countries are highly vulnerable to floods, with several studies reporting an increase in mortality rate and exposure in recent decades. Therefore, to improve flood forecasting and associated resilience to such natural hazards, a better understanding of the dominant flood-generating mechanisms and their evolution across Africa is of paramount importance. According to a recent study, excess rains on saturated soils in western Africa, and long rains for catchments in northern and southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. While the dominance of flood-generating mechanisms was not reported to change significantly in recent decades, the magnitude of those events may have changed in response to changing rainfall patterns (wetter or drier average conditions) across Africa. Here, we first estimate how much the magnitude of events with excess rainfall on saturated soils and long rains have changed over the last 65 years, before examining the statistical relationship between these changes, globally warming temperatures, and “natural” modes of decadal climate variability (e.g., Atlantic Multidecadal Variability [AMV], Pacific Decadal Variability [PDV]). To do so, we use a non-linear extreme value modelling approach with multiple covariates, applied to multiple observational datasets (e.g., ERA5, REGEN) and large ensembles from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). This study, therefore, contributes to further the understanding of recent flood hazards in Africa, and identifies regions that will likely become more vulnerable to climate change and its decadal variability over the decades to come.

Floods affect many aspects of our lives, and our improved understanding of the processes driving the historical changes in this natural hazard can provide basic information to enhance our preparation and mitigation efforts. Here we analyze 3,885 streamgages across the conterminous United States and attribute the inter-annual variability in annual maximum discharge to precipitation and temperature. This is accomplished by first developing gamma regression models to describe the seasonal maximum discharge in terms of basin-averaged precipitation, temperature, and antecedent wetness (i.e., the basin-averaged precipitation for the season prior to the one of interest, and used as a proxy for antecedent soil moisture conditions). These seasonal models are then mixed through a Monte Carlo approach to obtain the annual maximum discharge distribution. Despite its simplicity, our results show that the developed statistical attribution approach can describe very well the inter-annual variability in annual maximum discharge across the conterminous United States.

Heavy precipitation events are strongly affected by climate change and there is a high confidence that these extremes will become more frequent and more severe in the future. Moreover, potential changes in the seasonality of these events are important in terms of planning and preparation against these events. While efforts have been focused on changes in the magnitude and seasonality of extreme precipitation events, these studies have treated these two quantities separately.In order to overcome to this limit, a different perspective is here used by modeling the seasonality and magnitude of extreme precipitation events together through circular-linear copulae. We perform analyses at the global scale and develop bivariate models for an historical dataset. The outputs provided from several global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Shared Socioeconomic Pathways (SSPs) from 1-2.6 to 5-8.5 are then used to examine the joint projected changes in the seasonality and magnitude of extreme precipitation at the global scale.

The role of climate change in exacerbating the impacts of natural hazards has been the focus of extensive interest. However, while the emphasis is generally on a single hazard (e.g., heat stress, extreme precipitation, floods, droughts), their compounding effects under climate change have been the subject of a growing number of studies. Among compound events, heat stress was recently found to be a precursor of summer flooding across the central United States. We show for the first time that heat stress can trigger floods across large areas of North and South America, southern Africa, Asia and eastern Australia. Moreover, using global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show that the compounding of heat stress and floods is projected to worsen under climate change with effects magnified as we move from the Shared Socioeconomic Pathways (SSPs) 1-2.6 to 5-8.5. Under future conditions, the compounding between heat stress and floods is projected to extend to Europe and Russia due to the increased warming. These results highlight the need towards improved preparation and mitigation measures that account for the compound nature of these two hazards, and how the compounding is expected to be exacerbated because of climate change.

Sub-Saharan Africa is affected by a high level of temporal and spatial climate variability, with large impacts on water resources, human lives, and economies, notably through hydrological extremes such as floods. Nevertheless, the key climatic factors driving interannual variability in flood frequency remain poorly documented and understood. To address this research gap, we first compile information on large-scale climate drivers that may potential affect sub-Saharan African hydroclimate (e.g., El Niño–Southern Oscillation, Atlantic Multidecadal Variability). Then, using a new 65-year long daily streamflow dataset of over 600 stations in sub-Saharan Africa, a bootstrapped stepwise regression and relative importance analysis is applied to quantify the relative contribution of different ocean basins to interannual variability in flood frequency between 1950 and 2014. Results show that interannual variations in the frequency of flood events are significantly linked to different modes of climate variability in the Pacific, Indian, and Atlantic Oceans. These modes of climate variability together explain around 60% of observed interannual variation in seasonal flood frequency. The relative influence of each ocean basin, however, differs from one region to another. The Indian and Pacific Oceans, for instance, have significant influences on interannual variations in the frequency of floods between December and May across much of southern and eastern Africa. In western Africa, the Mediterranean and Atlantic Oceans appear to have a dominant influence between September and November. In central Africa, the relative influence of different oceans basins is seasonally variable. Using the best combination of Sea-Surface Temperature predictors, we then examine projected future trends using a large ensemble of climate models from the CMIP6 experiments.

In hydrometeorological and environmental studies, it is common to seek relations between two variables (predictand and predictor), one of which (predictor) is affected by uncertainties. These errors unavoidably affect the results of the analyses by providing erroneous estimates of the parameters of the predictor-predictand model. A possible solution is represented by the SIMulation-EXtrapolation (SIMEX) methodology. This approach follows two steps: (1) perturbation of the predictor with increasing levels of uncertainties (multiples of the known error variance); and (2) finding a relation between the model's parameters and level of uncertainty, which allows their extrapolation to the error-free case. The application of the SIMEX methodology requires a priori knowledge of the mean, variance, and distribution of the measurement errors. However, in hydrologic and climatologic studies, this is not the case, and the impact of an erroneous specification of these statistical properties on the results of the analyses has received little attention. The aim of this study is to investigate the sensitivity of the SIMEX methodology to mis-specification of the error characteristics. By using a simulation-based approach, we investigate the impact of imperfect knowledge of the characteristics of the errors associated with the predictor (mean, variance, and probability distribution). Our results suggest that SIMEX is robust against mis-specification of the moments and distribution of the measurement errors, that it performs better than standard linear regression, even when these statistical properties are erroneously specified, and that, for these reasons, it could find a useful application to seasonal forecasting of hydroclimatic variables.

Purpose Breast cancer (BC) survivors are increasingly interested in learning about healthy lifestyles to reduce the risk of BC recurrence and mortality. The DianaWeb study, a community-based participatory research, offers BC patients a specific interactive website to help them in adopting and maintain correct lifestyles, in line with World Cancer Research Fund recommendations. However, to limit the spread of the COVID-19 pandemic, most countries introduced restrictions which, inevitably, caused sudden lifestyle changes. The current study aimed at evaluating changes in lifestyle in BC survivors before, during, and after the first two waves of the COVID-19 pandemic. Methods We used data of 224 BC cancer survivors enrolled in the DianaWeb study. We evaluated the adherence to physical activity (PA) guidelines, to Mediterranean diet (MD), and WCRF recommendations, at baseline, during and after the first two waves of the COVID-19 pandemic. We estimated the association between PA, MD, and WCRF adherence with sociodemographic characteristics, years from enrolment in the DianaWeb study, and type of breast cancer. Results As expected, during confinement periods, we observed a significant decrease in walking activity and in the percentage of patients performing vigorous PA. In post-quarantine, total weekly energy expenditure increased significantly. BC patients participating in the DianaWeb study for more than 3 years were more likely to be more active. There were no changes in MD and WCRF adherence. Conclusions Our results show that the proposed interactive website was useful in achieving durable lifestyle changes, that have not been undermined either during the COVID-19 pandemic. Implications for Cancer Survivors Patient education is essential for guiding BC survivors toward improving their health outcomes; therefore, an interactive website like the one proposed by the DianaWeb study may be useful to improve healthy lifestyles.

Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes. Within an idealized simulation framework, these rainfall events are used as input to an operational distributed hydrologic model to evaluate the sensitivity of the hydrologic response to different forcing grid spacings. We consider the finest scale (i.e., 250 m) as reference, and compute event peak flows and volumes across a wide range of basin sizes. We find that the use of increasingly-coarser inputs leads to changes in the distribution of event peak flows and volumes, with the strongest sensitivity at the smallest catchment sizes. Overall, we find that 4-km rainfall simulations represent a good compromise between computational costs and hydrologic performance, providing basic information for future endeavors geared towards regional downscaling.

Predicting the annual frequency of tropical storms is of interest because it can provide basic information towards improved preparation against these storms. Sea surface temperatures (SSTs) averaged over the hurricane season can predict annual tropical cyclone activity well. But predictions need to be made before the hurricane season when the predictors are not yet observed. Several climate models issue forecasts of the SSTs, which can be used instead. Such models use the forecasts of SSTs as surrogates for the true SSTs. We develop a Bayesian negative binomial regression model, which makes a distinction between the true SSTs and their forecasts, both of which are included in the model. For prediction, the true SSTs may be regarded as unobserved predictors and sampled from their posterior predictive distribution. We also have a small fraction of missing data for the SST forecasts from the climate models. Thus, we propose a model that can simultaneously handle missing predictors and variable selection uncertainty. If the main goal is prediction, an interesting question is should we include predictors in the model that are missing at the time of prediction? We attempt to answer this question and demonstrate that our model can provide gains in prediction.