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The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. The probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.

SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature, and Penman–Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method, which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus has paved the way for improving knowledge on specific past meteorological events or for improving knowledge on climate variability, since the end of the 19th century. This dataset has also been designed as a forcing dataset for long-term hydrological applications and studies of the hydrological consequences of climate variability over France. SCOPE Climate is freely available for any non-commercial use and can be downloaded as NetCDF files from https://doi.org/10.5281/zenodo.1299760 for precipitation, https://doi.org/10.5281/zenodo.1299712 for temperature, and https://doi.org/10.5281/zenodo.1251843 for reference evapotranspiration.

Exposure to ambient air pollution has significant adverse health effects; however, whether air pollution is associated with urological cancer is largely unknown. We conduct a systematic review and meta-analysis with epidemiological studies, showing that a 5 μg/m 3 increase in PM 2.5 exposure is associated with a 6%, 7%, and 9%, increased risk of overall urological, bladder, and kidney cancer, respectively; and a 10 μg/m 3 increase in NO 2 is linked to a 3%, 4%, and 4% higher risk of overall urological, bladder, and prostate cancer, respectively. Were these associations to reflect causal relationships, lowering PM 2.5 levels to 5.8 μg/m 3 could reduce the age-standardized rate of urological cancer by 1.5 ~ 27/100,000 across the 15 countries with the highest PM 2.5 level from the top 30 countries with the highest urological cancer burden. Implementing global health policies that can improve air quality could potentially reduce the risk of urologic cancer and alleviate its burden. Whether air pollufion is associated with urological cancer is largely unknown. In this study, the authors reveal correlafions between air pollufion and urological cancer risk: an increase of 5 μg/m 3 in PM 2.5 and 10 μg/m 3 in NO 2 would raise risks by 6-9% and 3-4%, respecfively; while lowering PM 2.5 to 5.8 μg/m 3 may reduce urological cancer burden.

VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations. We also develop and share a new software tool called DECNEO to identify them. Single-cell gene expression data are used to identify a set of co-expressed endothelial cell receptors, conserved among species (mice and humans) and enriched, within a network, of connections to up-regulated genes. This set includes several receptors previously shown to play a role in angiogenesis. Multiple statistical tests from large datasets, including an independent validation set, support the reproducibility, evolutionary conservation and role in angiogenesis of these naturally occurring combinations of receptors. We also show tissue-specific combinations and, in the case of choroid endothelial cells, consistency with both well-established and recent experimental findings, presented in a separate paper. The results and methods presented here advance the understanding of signaling to endothelial cells. The methods are generally applicable to the decoding of intercellular combinations of signals.

Background Individuals with both Alzheimer’s Disease (AD) and cerebrospinal fluid (CSF) dynamics disorders, such as idiopathic normal-pressure hydrocephalus (iNPH), exhibit reduced CSF Aβ42 levels, complicating the interpretation of AD biomarkers. However, the influence of CSF dynamics on blood-based AD biomarkers remains unclear. This study investigated whether immunoprecipitation mass spectrometry (IP-MS)-based AD plasma biomarkers were associated with disproportionately enlarged subarachnoid space hydrocephalus (DESH) on MRI. Method This retrospective, cross-sectional study included 509 participants from the Mayo Clinic Study of Aging who underwent MRI, [¹¹C] Pittsburgh compound B (Aβ) PET imaging, and plasma assessments of phosphorylated tau 217 (p-Tau 217), Aβ40, and Aβ42. Age-adjusted logistic regression analyses were conducted to evaluate whether abnormal levels of p-Tau 217, %p-Tau 217 (ratio of phosphorylated to non-phosphorylated tau × 100%), Aβ42, or the Aβ42/40 ratio were associated with DESH, as identified by a previously validated automated algorithm. In addition, combinations of binary amyloid PET and plasma biomarkers to create 4 categories were tested for an association with DESH as a sensitivity analysis. Result Average age was 72.2 (8.8), with 54% male participants, and 30% were APOE4 carriers. Thirty-two participants (6.3%) were classified as having DESH. Abnormal (low) plasma Aβ42 (OR: 2.56 [95% CI: 1.19, 5.74]) and abnormal (high) p-tau217 (OR: 2.74 [95% CI: 1.09, 7.85]) were associated with DESH, but not the respective ratios of Aβ42/40 (OR: 1.95 [95% CI: 0.94, 4.17]) or %p-tau (OR: 1.82 [95% CI: 0.74, 4.20]). Analysis of the combined variables (amyloid PET and plasma biomarkers) demonstrated that an association with DESH was observed only in participants who were abnormal on both measures, compared with participants who were negative on both plasma biomarkers and PET. This association was attenuated when biomarker ratios were used. Conclusion The observed association between abnormal plasma biomarker levels and DESH aligns with previous CSF studies and can be mitigated by utilizing the biomarker ratios. However, further validation is needed in populations with more severe CSF flow abnormalities to better understand the role of plasma biomarkers in this population.

Seasonal shifts in phytoplankton accumulation and loss largely follow changes in mixed layer depth, but the impact of mixed layer depth on cell physiology remains unexplored. Here, we investigate the physiological state of phytoplankton populations associated with distinct bloom phases and mixing regimes in the North Atlantic. Stratification and deep mixing alter community physiology and viral production, effectively shaping accumulation rates. Communities in relatively deep, early-spring mixed layers are characterized by low levels of stress and high accumulation rates, while those in the recently shallowed mixed layers in late-spring have high levels of oxidative stress. Prolonged stratification into early autumn manifests in negative accumulation rates, along with pronounced signatures of compromised membranes, death-related protease activity, virus production, nutrient drawdown, and lipid markers indicative of nutrient stress. Positive accumulation renews during mixed layer deepening with transition into winter, concomitant with enhanced nutrient supply and lessened viral pressure. Phytoplankton are important primary producers. Here the authors investigate phytoplankton physiological changes associated with bloom phases and mixing regimes in the North Atlantic, finding that stratification and deep mixing shape accumulation rates by altering physiology and viral production.

Background The association between body mass index (BMI) and mortality among individuals with renal cell cancer (RCC) is debated, with some observational studies suggesting a lower mortality associated with higher BMI. However, methodological issues such as confounding and reverse causation may bias these findings. Using BMI-associated genetic variants can avoid these biases and generate more valid estimates. Methods In this prospective cohort study, we included 1264 RCC patients (446 deaths) from the UK Biobank. We created a BMI polygenic score (PGS) based on 336 BMI-associated genetic variants. The association between the PGS and mortality (all-cause and RCC-specific) was evaluated by logistic regression (all RCC cases) and Cox regression (906 incident cases). For comparison, the associations of measured pre-diagnostic BMI and waist-to-hip ratio (WHR) with mortality were quantified by Cox regression among incident cases. We stratified these analyses by time between anthropometric measurement and RCC diagnosis to assess the influence of reverse causation. Results We did not observe an association between the BMI PGS and all-cause mortality among RCC patients (hazard ratio (HR) per SD increase = 0.98, 95% CI: 0.88,1.10). No association was found for pre-diagnostic BMI (HR per 5 kg/m 2 increase = 0.93, 95% CI: 0.83,1.04) or WHR (HR per 0.1 increase = 0.97, 95% CI: 0.83,1.13) with mortality. In patients with anthropometrics measured within 2 years before RCC diagnosis, we observed associations of higher BMI (HR per 5 kg/m 2  = 0.76, 95% CI: 0.59,0.98) and WHR (HR = 0.67 per 0.1 increase, 95% CI: 0.45,0.98) with a lower risk of death. Similar patterns were observed for RCC-specific mortality. Conclusion We found no association between either genetic variants for high BMI or measured pre-diagnostic body adiposity and mortality among RCC patients, and our results suggested a role for reverse causation in the association of obesity with lower mortality. Future studies should be designed carefully to produce unbiased estimates that account for confounding and reverse causation.

Background Population-based cancer registry data from the Surveillance, Epidemiology, and End Results (SEER) Program at the National Cancer Institute (NCI) are mainly based on medical records and administrative information. Individual-level socioeconomic data are not routinely reported by cancer registries in the United States because they are not available in patient hospital records. The U.S. representative National Longitudinal Mortality Study (NLMS) data provide self-reported, detailed demographic and socioeconomic data from the Social and Economic Supplement to the Census Bureau's Current Population Survey (CPS). In 1999, the NCI initiated the SEER-NLMS study, linking the population-based SEER cancer registry data to NLMS data. The SEER-NLMS data provide a new unique research resource that is valuable for health disparity research on cancer burden. We describe the design, methods, and limitations of this data set. We also present findings on cancer-related health disparities according to individual-level socioeconomic status (SES) and demographic characteristics for all cancers combined and for cancers of the lung, breast, prostate, cervix, and melanoma. Methods Records of cancer patients diagnosed in 1973-2001 when residing 1 of 11 SEER registries were linked with 26 NLMS cohorts. The total number of SEER matched cancer patients that were also members of an NLMS cohort was 26,844. Of these 26,844 matched patients, 11,464 were included in the incidence analyses and 15,357 in the late-stage diagnosis analyses. Matched patients (used in the incidence analyses) and unmatched patients were compared by age group, sex, race, ethnicity, residence area, year of diagnosis, and cancer anatomic site. Cohort-based age-adjusted cancer incidence rates were computed. The impact of socioeconomic status on cancer incidence and stage of diagnosis was evaluated. Results Men and women with less than a high school education had elevated lung cancer rate ratios of 3.01 and 2.02, respectively, relative to their college educated counterparts. Those with family annual incomes less than $12,500 had incidence rates that were more than 1.7 times the lung cancer incidence rate of those with incomes $50,000 or higher. Lower income was also associated with a statistically significantly increased risk of distant-stage breast cancer among women and distant-stage prostate cancer among men. Conclusions Socioeconomic patterns in incidence varied for specific cancers, while such patterns for stage were generally consistent across cancers, with late-stage diagnoses being associated with lower SES. These findings illustrate the potential for analyzing disparities in cancer outcomes according to a variety of individual-level socioeconomic, demographic, and health care characteristics, as well as by area measures available in the linked database.

We study the private adoption and spatial diffusion of a technology that provides a local public good—PurpleAir (PA) pollution monitors, which deliver real-time, publicly accessible air quality information. From a purely informational perspective, the ideal spacing of these monitors should reflect the degree of spatial correlation in pollution. We examine whether observed adoption patterns align with an information-maximizing distribution to explore the information implications of this private provision of a public good. Using data from California between 2019 and 2021, we find that monitor adoption is highly clustered in less polluted areas, where the marginal monitor provides minimal additional public information. Moreover, monitor adoption mainly occurs in affluent, predominantly White neighborhoods, underscoring the potential environmental justice concerns associated with the private provision of this public good. Additional analyses suggest that spatial clustering in preferences that are independent of the monitors’ informational value may be driving adoption.