(-)-Desmethylsibutramine

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process‐based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time‐consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data‐driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short‐Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5‐year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub‐basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT‐LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling.

We aimed to enhance process‐based models for watershed water‐quality modeling.The Soil and Water Assessment Tool‐Long Short‐Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP.It exhibited superior predictive performance when applied to other sub‐basins.The hybrid model will overcome the constraints of conventional modeling.It will also enable more effective and efficient modeling.

Due to the multifactorial nature of diabetic wounds, the most effective treatments require combinatorial approach. Herein we investigated whether engraftment of a bioengineered three-dimensional dermal derived matrix scaffold (DDMS) in combination with adipose-derived stem cells (ADSs), could accelerate diabetic wound healing. Diabetic animals were randomly planned into the control group, DDMS group, ADS group, and DDMS+ADS group. On days 7, 14, and 21, tissue samples were obtained for stereological, molecular, and tensiometrical assessments. We found that the wound contraction rate, the total volumes of new epidermis and dermis, the numerical densities of fibroblasts and blood vessels, collagen density, and tensiometrical parameters were meaningfully greater in the treated groups than in the control group, and these changes were more obvious in the DDMS+ADS ones (p < 0.05). Moreover, the expression of TGF-β, bFGF, and VEGF genes were considerably upregulated in treated groups compared to the control group and were greater in the DDMS+ADS group (p < 0.05). This is while expression of TNF-α and IL-1β, as well as the numerical densities of neutrophils and macrophages decreased more considerably in the DDMS+ADS group than in the other groups (p < 0.05). Overall, it was found that using both DDMS engraftment and ADS transplantation has more impact on diabetic wound healing.