The impact of lake and reservoir parameterization on global streamflow simulation

Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values - 0.09 and -0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning’s roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations

Pre- and post-natal melatonin administration partially regulates brain oxidative stress but does not improve cognitive or histological alterations in the Ts65Dn mouse model of Down syndrome

Melatonin administered during adulthood induces beneficial effects on cognition and neuroprotection in the Ts65Dn (TS) mouse model of Down syndrome. Here, we investigated the effects of pre- and post-natal melatonin treatment on behavioral and cognitive abnormalities and on several neuromorphological alterations (hypocellularity, neurogenesis impairment and increased oxidative stress) that appear during the early developmental stages in TS mice. Pregnant TS females were orally treated with melatonin or vehicle from the time of conception until the weaning of the offspring, and the pups continued to receive the treatment from weaning until the age of 5 months. Melatonin administered during the pre- and post-natal periods did not improve the cognitive impairment of TS mice as measured by the Morris Water maze or fear conditioning tests. Histological alterations, such as decreased proliferation (Ki67+ cells) and hippocampal hypocellularity (DAPI+ cells), which are typical in TS mice, were not prevented by melatonin. However, melatonin partially regulated brain oxidative stress by modulating the activity of the primary antioxidant enzymes (superoxide dismutase in the cortex and catalase in the cortex and hippocampus) and slightly decreasing the levels of lipid peroxidation in the hippocampus of TS mice. These results show the inability of melatonin to prevent cognitive impairment in TS mice when it is administered at pre- and post-natal stages. Additionally, our findings suggest that to induce pro-cognitive effects in TS mice during the early stages of development, in addition to attenuating oxidative stress, therapies should aim to improve other altered processes, such as hippocampal neurogenesis and/or hypocellularity.This work was supported by the Jérôme Lejeune Foundation, the Spanish Ministry of Economy and Competitiveness (PSI2016-76194-R) and by a grant from CNPq/Brazil (proc. 2606/14-13)

EFFICACY AND SAFETY OF BOCEPREVIR-BASED THERAPY IN HCVG1 TREATMENT-EXPERIENCED PATIENTS WITH ADVANCED FIBROSIS/CIRRHOSIS: THE ITALIAN AND SPANISH NPP EARLY ACCESS PROGRAM

Background and Aims: To maximize cost/efficay of boceprevirbased triple therapy (BOC) in patients with HCV-related advanced fibrosis/cirrhosis. Methods: ITT SVR12, safety and futility rules value were evaluated in the multicenter national Italian and Spanish early access Name- Patient-Program which includes treatment-experienced patients with HCVG1-related advanced fibrosis/cirrhosis (Metavir F3/4) treated with BOC in both countries. Results: 402 patients (mean age 55 years; range 22–75), 316 (78.6%) G1b, 255 (63.4%) F4, 60 (30.9%) with oesophageal varices, 137 (34.1%) relapsers, 95 (23.6%) partial and 168 (41.8%) null responders were enrolled. Platelets count <100,000 and albumin levels <3.5 g/dl were present in 49 (12.2%) and 22 (6.3%) patients, respectively. 369 (91.8%) received at least 1 dose of BOC. Overall ITT SVR12 rates and according to prior response to P/R, fibrosis stage and TW8 HCV-RNA value to P/R/BOC are reported in the table. At multivariate analysis, the strongest predictors of SVR12 were TW8 HCV-RNA undetectability (RR, 30.8; 95% CI, 8.7–108.7) and HCV-RNA detectable but <1000 IU/mL (RR, 9.1; 95% CI, 2.6–31.8) compared to those with HCV-RNA ≥1000 IU/mL. Two patients (0.5%) died from multi-organ failure, 13 (3.2%) developed hepatic decompensation, 41 (10.2%) had severe anemia (<8.5 g/dl) and 31 (7.7%) required at least one blood transfusion. Conclusions: In treatment-experienced patients with advanced fibrosis/cirrhosis, SVR12 attained by BOC was satisfactory. Mortality, life-threatening adverse events and severe anemia rates were similar to those reported in other real-practice studies. A TW8 futility rule enables a safely discontinuation of BOC in patients who are extremely unlikely to achieve SVR, thus optimizing the effectiveness of treatment in this difficult-to-cure population

Bcl11b sets pro-T cell fate by site-specific cofactor recruitment and by repressing Id2 and Zbtb16

Multipotent progenitor cells confirm their T cell–lineage identity in the CD4^–CD8^– double-negative (DN) pro-T cell DN2 stages, when expression of the essential transcription factor Bcl11b begins. In vivo and in vitro stage-specific deletions globally identified Bcl11b-controlled target genes in pro-T cells. Proteomics analysis revealed that Bcl11b associated with multiple cofactors and that its direct action was needed to recruit those cofactors to selective target sites. Regions near functionally regulated target genes showed enrichment for those sites of Bcl11b-dependent recruitment of cofactors, and deletion of individual cofactors relieved the repression of many genes normally repressed by Bcl11b. Runx1 collaborated with Bcl11b most frequently for both activation and repression. In parallel, Bcl11b indirectly regulated a subset of target genes by a gene network circuit via the transcription inhibitor Id2 (encoded by Id2) and transcription factor PLZF (encoded by Zbtb16); Id2 and Zbtb16 were directly repressed by Bcl11b, and Id2 and PLZF controlled distinct alternative programs. Thus, our study defines the molecular basis of direct and indirect Bcl11b actions that promote T cell identity and block alternative potentials

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Improving Global Flood Forecasting using Satellite Detected Flood Extent

Flooding is a natural global phenomenon but in many cases is exacerbated by human activity. Although flooding generally affects humans in a negative way, bringing death, suffering, and economic impacts, it also has potentially beneficial effects. Early flood warning and forecasting systems, as well as the development of real-time monitoring systems, are recognised measures to reduce the number of flood victims and to support flood disaster responses. Importantly, they may buy time to take appropriate mitigation measures to reduce flood peaks, thus reducing the associated negative impacts. One of the main constraints for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is an even larger issue for real-time flood monitoring and forecasting. This lack of data could potentially be overcome if satellite-retrieved surface water changes signal or streamflow estimates were sufficiently accurate to serve as a surrogate for ground-based measurements. In the first part of the study, the potentials and constraints of river streamflow estimates based on the remote sensing signal of the Global Flood Detection System are evaluated. We used the merged product from the Global Flood Detection System (GFDS) that uses both AMSR-E (Advance Microwave Scanning Radiometer – Earth Observing System) and TRMM (Tropical Rainfall Measuring Mission) to derive surface water extent during the study period. The influence of the local physiographic factors which might influence the retrieval of the satellite signal was also studied. Validation is done based on ground-based streamflow observations. In the second part of the study, it was tested if the GFDS derived streamflow proxy improved the model calibration of the distributed rainfall-runoff routing model LISFLOOD, used by the Global Flood Awareness System (GloFAS). Finally, the GFDS surface water extent data were assimilated into the large-scale hydrological model LISFLOOD using an Ensemble Kalman filter (EnKF). It was evaluated if flood forecasting skill would improve, as well as the timing of the flood peak, as compared to baseline initial conditions (without data assimilation). Furthermore, two additional studies looked at the use of globally near real-time available products for flood forecasting, monitoring, and assessment to support decision makers and humanitarian organisations such as Red Cross Red Crescent and the World Food Programme

Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals

Humanitarian organizations have a crucial role in response and relief efforts after floods. The effectiveness of disaster response is contingent on accurate and timely information regarding the location, timing and impacts of the event. Here we show how two near-real-time data sources, satellite observations of water coverage and flood-related social media activity from Twitter, can be used to support rapid disaster response, using case-studies in the Philippines and Pakistan. For these countries we analyze information from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity analysis. The results demonstrate that these sources of near-real-time information can be used to gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods. In terms of location, we produce daily impact maps based on both satellite information and social media, which can dynamically and rapidly outline the affected area during a disaster. In terms of timing, the results show that GFDS and/or Twitter signals flagging ongoing or upcoming flooding are regularly available one to several days before the event was reported to humanitarian organizations. In terms of event understanding, we show that both GFDS and social media can be used to detect and understand unexpected or controversial flood events, for example due to the sudden opening of hydropower dams or the breaching of flood protection. The performance of the GFDS and Twitter data for early detection and location mapping is mixed, depending on specific hydrological circumstances (GFDS) and social media penetration (Twitter). Further research is needed to improve the interpretation of the GFDS signal in different situations, and to improve the pre-processing of social media data for operational use

Integrating remotely sensed surface water extent into continental scale hydrology

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° x 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE’, NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow