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Groundwater in coastal regions is threatened by saltwater intrusion (SWI). Beach nourishment is used in this study to manage SWI in the Biscayne aquifer, Florida, USA, using a 3D SEAWAT model nourishment considering the future sea level rise and freshwater over-pumping. The present study focused on the development and comparative evaluation of seven machine learning (ML) models, i.e., additive regression (AR), support vector machine (SVM), reduced error pruning tree (REPTree), Bagging, random subspace (RSS), random forest (RF), artificial neural network (ANN) to predict the SWI using beach nourishment. The performance of ML models was assessed using statistical indicators such as coefficient of determination (R 2 ), Nash-Sutcliffe efficiency (NSE), means absolute error (MAE), root mean square error (RMSE), and root relative squared error (RRSE) along with the graphical inspection (i.e., Radar and Taylor diagram). The findings indicate that applying SVM, Bagging, RSS, and RF models has great potential in predicting the SWI values with limited data in the study area. The RF model emerged as the best fit and closely matched observed values; it obtained R 2 (0.999), NSE (0.999), MAE (0.324), RRSE (0.209), and RMSE (0.416) during the testing process. The present study concludes that the RF model could be a valuable tool for accurate predictions of SWI and effective water management in coastal areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Real-time reservoir operation using inflow and irrigation demand forecasts can help reservoir system managers make effective water management decisions. Forecasting of inflow and irrigation demands is challenging, owing to the variability of the weather variables that affect inflows and irrigation demands. In this context, bias-corrected Global Forecasting System (GFS) forecasts are used here in a hybrid approach (reservoir module with Long Short Term Memory (LSTM)) to forecast the reservoir inflows. Concurrently, the bias-corrected GFS forecasts are used in irrigation demand module to forecast the irrigation demands. The 'Scaled Distribution Mapping' method is used to bias-correct the GFS data of 1-5 days lead. The study area is the Damodar river basin, India, consisting of five major reservoirs: Tenughat and Konar located upstream of Panchet, and Tilaya situated upstream of Maithon. With the upstream reservoir outflow forecasts, the inflows are forecasted in Panchet and Maithon reservoirs with NSE values of 0.88-0.96 and 0.78-0.88, respectively, up to a 5-day lead. The irrigation demand module with bias-corrected GFS forecasts forecasted the irrigation demands close to the irrigation demands with the observed weather data. The percentage errors in irrigation demand forecasts of the Kharif (June-October) season at 1-5 days lead are 9.45 %, -15.45 %, -20.52 %, -26.36 %, -27.31 %, respectively. On the contrary, percentage errors in irrigation demand forecasts of Rabi (November-February) and Boro (January-May) are in the range of 8.17-8.79 % and 3.48-8.06 %, respectively. With the inflows and irrigation demand forecasts, the Panchet and Maithon reservoirs satisfied the downstream demands and reduced the floods. The inflow and irrigation demand forecasts, based on the GFS forecasts, have substantial potential for real-time reservoir operation, leading to efficient water management downstream., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Real-time streamflow forecasting is essential to manage water resources effectively in a reservoir-regulated basin. However, forecasting becomes challenging without weather and upstream reservoir outflows forecasts in real-time. In this context, a novel hybrid approach is proposed in this study to forecast the streamflows and reservoir outflows in real-time. In this approach, the Explainable Machine Learning model is embedded with a conceptual reservoir module for forecasting streamflows using short-term weather forecasts. Long Short Term Memory (LSTM), a Machine Learning model, is used in this study to predict the streamflow, and the model's explainability is examined by Shapley additive explanations method (SHAP). Panchet reservoir catchment, which contains Tenughat and Konar reservoirs, is selected as a study area. The LSTM model performance is excellent in predicting the streamflows of Tenughat, Konar and Panchet catchments with NSE values of 0.93, 0.87, and 0.96, respectively. The SHAP method identified the high-impact variables as streamflows and precipitation of 1-day lag. In forecasting, bias-corrected Global Forecast System data is used with the LSTM model to forecast the streamflows in three catchments. The inflows are forecasted well up to a 3-day lead in Tenughat and Konar reservoirs with NSE values above 0.88 and 0.87, respectively. The reservoir module performance in forecasting Tenughat and Konar reservoirs' outflows with the inflow forecasts is also promising up to a 3-day lead with NSE values above 0.88 for both reservoirs. The inflows forecasting to Panchet reservoir with reservoirs' outflows as additional inputs is excellent up to 5-day lead (NSE = 0.96-0.88). However, the forecasting error increased from 77 m 3 /s to 134 m 3 /s with the lead time. This approach could provide an efficient way to reduce flood risks in the reservoir-regulated basin., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Rationale & Objective: Adults with chronic kidney disease (CKD) may be at increased risk of adverse effects from use of potentially inappropriate medications (PIMs). Our objective was to assess whether PIM exposure has an independent association with CKD progression, hospitalizations, mortality, or falls., Study Design: Retrospective observational study., Setting & Participants: Chronic Renal Insufficiency Cohort (CRIC) study; 3,929 adults with CKD enrolled 2003-2008 and followed prospectively until December 2011., Exposure: PIM exposure was defined as prescriptions for any medications to be avoided in older adults as defined by the 2015 American Geriatrics Society Beers Criteria., Outcome: Hospitalization count, death, a composite kidney disease end point of CKD progression or initiation of kidney replacement therapy (KRT), KRT, and fall events assessed 1 year after PIM exposure., Analytical Approach: Logistic regression and Poisson regression to estimate the associations of PIM exposure with each outcome., Results: The most commonly prescribed PIMs were proton pump inhibitors and α-blockers. In unadjusted models, any PIM exposure (compared to none) was associated with hospitalizations, death, and fall events. After adjustment, exposure to 1, 2, or≥3 PIMs had a graded association with a higher hospitalization rate (rate ratios of 1.09 [95% CI, 1.01-1.17], 1.18 [95% CI, 1.07-1.30], and 1.35 [95% CI, 1.19-1.53], respectively) and higher odds of mortality (odds ratios of 1.19 [95% CI, 0.91-1.54], 1.62 [95% CI, 1.21-2.17], and 1.65 [95% CI, 1.14-2.41], respectively). In a cohort subset reporting falls (n=1,109), prescriptions for≥3 PIMs were associated with an increased risk of falls (adjusted OR, 2.85 [95% CI, 1.54-5.26]). PIMs were not associated with CKD progression or KRT. Age did not modify the association between PIM count and outcomes., Limitations: Measurement bias; confounding by indication., Conclusions: Adults of any age with CKD who are prescribed PIMs have an increased risk of hospitalization, mortality, and falls with the greatest risk occurring after more than 1 PIM prescription., (Published by Elsevier Inc.)

Background: Slopes of eGFR have been associated with increased risks of death and cardiovascular events in a U-shaped fashion. Poor outcomes in individuals with rising eGFR are potentially attributable to sarcopenia, hemodilution, and other indicators of clinical deterioration., Methods: To investigate the association between eGFR slopes and risks of death or cardiovascular events, accounting for multiple confounders, we studied 2738 individuals with moderate to severe CKD participating in the multicenter Chronic Renal Insufficiency Cohort (CRIC) Study. We used linear, mixed-effects models to estimate slopes with up to four annual eGFR assessments, and Cox proportional hazards models to investigate the association between slopes and the risks of death and cardiovascular events., Results: Slopes of eGFR had a bell-shaped distribution (mean [SD], -1.5 [-2] ml/min per 1.73 m 2 per year). Declines of eGFR that were steeper than the average decline associated with progressively increasing risks of death (hazard ratio [HR], 1.23; 95% confidence interval [95% CI], 1.09 to 1.39; for a slope 1 SD below the average) and cardiovascular events (HR, 1.19; 95% CI, 1.03 to 1.38). Rises of eGFR or declines lower than the average decline were not associated with the risk of death or cardiovascular events., Conclusions: In a cohort of individuals with moderate to severe CKD, we observed steep declines of eGFR were associated with progressively increasing risks of death and cardiovascular events; however, we found no increased risks associated with eGFR improvement. These findings support the potential value of eGFR slopes in clinical assessment of adults with CKD., (Copyright © 2020 by the American Society of Nephrology.)

Landuse change significantly alters the hydrologic characteristics of the land surface within a watershed. In the present study, the impact of landuse change (2006-2016) on runoff and sediment yield has been assessed in Patiala-Ki-Rao watershed (5140 ha) located in Shivalik foot-hills, using remote sensing, geographical information system (GIS), and Water Erosion Prediction Project (WEPP) watershed model. The watershed has seven major landuse classes, namely agriculture, built-up, fallow land, forest, grass land, streams, and water bodies. The landuse change analysis indicated that the area under all the landuses decreased except built-up that increased by 372.27 ha (112.04%). Forest is the most affected landuse among all watershed landuses that shrinked by 194.90 ha followed by agriculture (64.57 ha), grass land (50.81 ha), streams (30.42 ha), fallow land (21.86 ha), and water bodies (9.72 ha). Runoff and sediment yield for the landuse of the years 2006 and 2016 were simulated by the WEPP model using two climate scenarios (2006 and 2016). The simulated runoff, sediment yield, and sediment delivery ratio increased by 18.62%, 48.04%, and 32.23% under Climate-2006 and 26.78%, 30.23%, and 16.09% under Climate-2016 due to change in landuse during a period of 10 years. This clearly indicates that landuse change in 10 years has greatly influenced the hydrology of the watershed and requires urgent land allocation policy in place for sustainable development in the area.

Introduction: There is limited information on effective disease monitoring for prompt interventions in childhood nephrotic syndrome. We examined the feasibility and effectiveness of a novel text messaging system (SMS) for disease monitoring in a multicenter, prospective study., Methods: A total of 127 patients <19 years with incident nephrotic syndrome were enrolled in the ongoing Nephrotic Syndrome Study Network between June 2015 and March 2018. Text messages soliciting home urine protein results, symptoms, and medication adherence were sent to a designated caregiver ( n  = 116) or adolescent patient ( n  = 3). Participants responded by texting. Feasibility of SMS was assessed by SMS adoption, retention, and engagement, and concordance between participant-reported results and laboratory/clinician assessments. The number of disease relapses and time-to-remission data captured by SMS were compared with data collected by conventional visits., Results: A total of 119 of 127 (94%) patients agreed to SMS monitoring. Retention rate was 94%, with a median follow-up of 360 days (interquartile range [IQR] 353-362). Overall engagement was high, with a median response rate of 87% (IQR, 68-97). Concordance between SMS-captured home urine protein results and edema status with same-day in-person study visit was excellent (kappa values 0.88 and 0.92, respectively). SMS detected a total of 108 relapse events compared with 41 events captured by scheduled visits. Median time to remission after enrollment was 22 days as captured by SMS versus 50 days as captured by scheduled visits., Conclusion: SMS was well accepted by caregivers and adolescent patients and reliably captured nephrotic syndrome disease activity between clinic visits. Additional studies are needed to explore the impact of SMS on disease outcomes.

PI3Kδ plays an important role controlling immune cell function and has therefore been identified as a potential target for the treatment of immunological disorders. This article highlights our work toward the identification of a potent, selective, and efficacious PI3Kδ inhibitor. Through careful SAR, the successful replacement of a polar pyrazole group by a simple chloro or trifluoromethyl group led to improved Caco-2 permeability, reduced Caco-2 efflux, reduced hERG PC activity, and increased selectivity profile while maintaining potency in the CD69 hWB assay. The optimization of the aryl substitution then identified a 4'-CN group that improved the human/rodent correlation in microsomal metabolic stability. Our lead molecule is very potent in PK/PD assays and highly efficacious in a mouse collagen-induced arthritis model.