Groundwater Management Optimization and Saltwater Intrusion Mitigation under Uncertainty

Groundwater is valuable to supply fresh water to the public, industries, agriculture, etc. However, excessive pumping has caused groundwater storage degradation, water quality deterioration and saltwater intrusion problems. Reliable groundwater flow and solute transport modeling is needed for sustainable groundwater management and aquifer remediation design. However, challenges exist because of highly complex subsurface environments, computationally intensive groundwater models as well as inevitable uncertainties. The first research goal is to explore conjunctive use of feasible hydraulic control approaches for groundwater management and aquifer remediation. Water budget analysis is conducted to understand how groundwater withdrawals affect water levels. A mixed integer multi-objective optimization model is constructed to derive optimal freshwater pumping strategies and investigate how to promote the optimality through regulating pumping locations. A solute transport model for the Baton Rouge multi-aquifer system is developed to assess saltwater encroachment under current condition. Potential saltwater scavenging approach is proposed to mitigate the salinization issue in the Baton Rouge area. The second research goal aims to develop robust surrogate-assisted simulation-optimization modeling methods for saltwater intrusion mitigation. Machine learning based surrogate models (response surface regression model, artificial neural network and support vector machine) were developed to replace a complex high-fidelity solute transport model for predicting saltwater intrusion. Two different methods including Bayesian model averaging and Bayesian set pair analysis are used to construct ensemble surrogates and quantify model prediction uncertainties. Besides. different optimization models that incorporate multiple ensemble surrogates are formulated to obtain optimal saltwater scavenging strategies. Chance-constrained programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. The results show that conjunctive use of hydraulic control approaches would be effective to mitigate saltwater intrusion but needs decades. Machine learning based ensemble surrogates can build accurate models with high computing efficiency, and hence save great efforts in groundwater remediation design. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach

Evaluation of Supporting Tools for Health Coaches Providing Nutrition and Exercise Coaching to Older Adults in Singapore

A rapidly ageing population and multimorbidity are questioning the sustainability of healthcare systems in Singapore, whereby health coaches, who help older adults build and sustain a healthy lifestyle, have become increasingly popular in recent years. Health coaches however face multiple challenges due to caregiver burden. In this study, we designed and evaluated web and mobile applications (“apps”) to support health coaches with scheduling, managing reports, and providing access to resources related to nutrition and exercise for older adults. We examined the acceptance and usability of the apps with health coaches after they completed 8 weekly online health coaching sessions. We found desirable functions included consideration of ethnic diversity in the context of a multi-culture society, potential of providing just-in-time information when they face problems during coaching, and helpful materials and links for health coaches to refer to

Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process

Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate

Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process

Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate

Cell-matrix mechanics and pattern formation in inflammatory cardiovascular calcification

Calcific diseases of the cardiovascular system, such as atherosclerotic calcification and calcific aortic valve disease, are widespread and clinically significant, causing substantial morbidity and mortality. Vascular cells, like bone cells, interact with their matrix substrate through molecular signals, and through biomechanical signals, such as traction forces transmitted from cytoskeleton to matrix. The interaction of contractile vascular cells with their matrix may be one of the most important factors controlling pathological mineralisation of the artery wall and cardiac valves. In many respects, the matricrine and matrix mechanical changes in calcific vasculopathy and valvulopathy resemble those occurring in embryonic bone development and normal bone mineralisation. The matrix proteins provide a microenvironment for propagation of crystal growth and provide mechanical cues to the cells that direct differentiation. Small contractions of the cytoskeleton may tug on integrin links to sites on matrix proteins, and thereby sense the stiffness, possibly through deformation of binding proteins causing release of differentiation factors such as products of the members of the transforming growth factor-β superfamily. Inflammation and matrix characteristics are intertwined: inflammation alters the matrix such as through matrix metalloproteinases, while matrix mechanical properties affect cellular sensitivity to inflammatory cytokines. The adhesive properties of the matrix also regulate self-organisation of vascular cells into patterns through reaction-diffusion phenomena and left-right chirality. In this review, we summarise the roles of extracellular matrix proteins and biomechanics in the development of inflammatory cardiovascular calcification

Protective Role of Smad6 in Inflammation-Induced Valvular Cell Calcification.

Calcific aortic vascular and valvular disease (CAVD) is associated with hyperlipidemia, the effects of which occur through chronic inflammation. Evidence suggests that inhibitory small mothers against decapentaplegic (I-Smads; Smad6 and 7) regulate valve embryogenesis and may serve as a mitigating factor in CAVD. However, whether I-Smads regulate inflammation-induced calcific vasculopathy is not clear. Therefore, we investigated the role of I-Smads in atherosclerotic calcification. Results showed that expression of Smad6, but not Smad7, was reduced in aortic and valve tissues of hyperlipidemic compared with normolipemic mice, while expression of tumor necrosis factor alpha (TNF-α) was upregulated. To test whether the effects are in response to inflammatory cytokines, we isolated murine aortic valve leaflets and cultured valvular interstitial cells (mVIC) from the normolipemic mice. By immunochemistry, mVICs were strongly positive for vimentin, weakly positive for smooth muscle α actin, and negative for an endothelial cell marker. TNF-α upregulated alkaline phosphatase (ALP) activity and matrix mineralization in mVICs. By gene expression analysis, TNF-α significantly upregulated bone morphogenetic protein 2 (BMP-2) expression while downregulating Smad6 expression. Smad7 expression was not significantly affected. To further test the role of Smad6 on TNF-α-induced valvular cell calcification, we knocked down Smad6 expression using lentiviral transfection. In cells transfected with Smad6 shRNA, TNF-α further augmented ALP activity, expression of BMP-2, Wnt- and redox-regulated genes, and matrix mineralization compared with the control cells. These findings suggest that TNF-α induces valvular and vascular cell calcification, in part, by specifically reducing the expression of a BMP-2 signaling inhibitor, Smad6

Monomethyl fumarate prevents alloimmune rejection in mouse heart transplantation by inducing tolerogenic dendritic cells

Dendritic cells (DCs) are important targets for eliciting allograft rejection after transplantation. Previous studies have demonstrated that metabolic reprogramming of DCs can transform their immune functions and induce their differentiation into tolerogenic DCs. In this study, we aim to investigate the protective effects and mechanisms of monomethyl fumarate (MMF), a bioactive metabolite of fumaric acid esters, in a mouse model of allogeneic heart transplantation. Bone marrow-derived DCs are harvested and treated with MMF to determine the impact of MMF on the phenotype and immunosuppressive function of DCs by flow cytometry and T-cell proliferation assays. RNA sequencing and Seahorse analyses are performed for mature DCs and MMF-treated DCs (MMF-DCs) to investigate the underlying mechanism. Our results show that MMF prolongs the survival time of heart grafts and inhibits the activation of DCs in vivo. MMF-DCs exhibit a tolerogenic phenotype and function in vitro. RNA sequencing and Seahorse analyses reveal that MMF activates the Nrf2 pathway and mediates metabolic reprogramming. Additionally, MMF-DC infusion prolongs cardiac allograft survival, induces regulatory T cells, and inhibits T-cell activation. MMF prevents allograft rejection in mouse heart transplantation by inducing tolerogenic DCs

Inflammation Drives Retraction, Stiffening, and Nodule Formation via Cytoskeletal Machinery in a Three-Dimensional Culture Model of Aortic Stenosis.

In calcific aortic valve disease, the valve cusps undergo retraction, stiffening, and nodular calcification. The inflammatory cytokine, tumor necrosis factor (TNF)-α, contributes to valve disease progression; however, the mechanisms of its actions on cusp retraction and stiffening are unclear. We investigated effects of TNF-α on murine aortic valvular interstitial cells (VICs) within three-dimensional, free-floating, compliant, collagen hydrogels, simulating their natural substrate and biomechanics. TNF-α increased retraction (percentage of diameter), stiffness, and formation of macroscopic, nodular structures with calcification in the VIC-laden hydrogels. The effects of TNF-α were attenuated by blebbistatin inhibition of myosin II-mediated cytoskeletal contraction. Inhibition of actin polymerization with cytochalasin-D, but not inhibition of Rho kinase with Y27632, blocked TNF-α-induced retraction in three-dimensional VIC hydrogels, suggesting that actin stress fibers mediate TNF-α-induced effects. In the hydrogels, inhibitors of NF-κB blocked TNF-α-induced retraction, whereas simultaneous inhibition of c-Jun N-terminal kinase was required to block TNF-α-induced stiffness. TNF-α also significantly increased collagen deposition, as visualized by Masson's trichrome staining, and up-regulated mRNA expression of discoidin domain receptor tyrosine kinase 2, fibronectin, and α-smooth muscle actin. In human aortic valves, calcified cusps were stiffer and had more collagen deposition than noncalcified cusps. These findings suggest that inflammation, through stimulation of cytoskeletal contractile activity, may be responsible for valvular cusp retraction, stiffening, and formation of calcified nodules