Performance of vertical up-flow-constructed wetlands integrating with microbial fuel cell (VFCW-MFC) treating ammonium in domestic wastewater.

Vertical up-flow-constructed wetlands integrating with microbial fuel cell (VFCW-MFC) were evaluated for NH4+-N removal and bioelectricity recovery. The experiments were carried out in lab-scale VFCW-MFC microcosms treating synthetic domestic wastewater under different operational conditions of pH, hydraulic retention time, and mass loading rate. Effects of wild ornamental grass (Cenchrus setaceus) on treatment performance and voltage output were investigated simultaneously. Experiments demonstrated that the neutral pH of influents favoured NH4+-N removal and power generation. Extended retention time improved treatment capacity and power output but likely depended on the substrate availability. COD removal and power output increased, while NH4+-N removal decreased with increasing mass loading rates. At the loading rate of 88.31 mg COD/L.day, planted VFCW-MFCs exhibited better NH4+-N treatment performance (36.9%) and higher voltage output (132%-143%) than unplanted systems. Plants did not affect the COD removal efficiency of VFCW-MFCs (>95%). Power density was in the range of 1.26-1.59 mW/m2 in planted microcosms with a maximum CE of 13.6%. The anode layer accounted for a major proportion of NH4+-N removal in VFCW-MFCs. This study implies that NH4+-N in domestic wastewaters with relatively high COD:N ratios can be treated effectively in up-flow CW-MFCs via anaerobic processes, including anammox and heterotrophic denitrifying processes. The mass loading rate could be a critical parameter to balance different microbial processes, thus, coincidently determining the potential of power recovery from wastewaters.

Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population.

BACKGROUND: Personalized warfarin dosing is influenced by various factors including genetic and non-genetic factors. Multiple linear regression (LR) is known as a conventional method to develop predictive models. Recently, machine learning approaches have been extensively implemented for warfarin dosing due to the hypothesis of non-linear association between covariates and stable warfarin dose. OBJECTIVE: To extend the multiple linear regression algorithm for personalized warfarin dosing in a Korean population and compare with a machine learning--based algorithm. METHOD: From this cohort study, we collected information on 650 patients taking warfarin who achieved steady state including demographic information, indications, comorbidities, comedications, habits, and genetic factors. The dataset was randomly split into training set (90%) and test set (10%). The LR and machine learning (gradient boosting machine [GBM]) models were developed on the training set and were evaluated on the test set. RESULT: LR and GBM models were comparable in terms of accuracy of ideal dose (75.38% and 73.85%), correlation (0.77 and 0.73), mean absolute error (0.58 mg/day and 0.64 mg/day), and root mean square error (0.82 mg/day and 0.9 mg/day), respectively. VKORC1 genotype, CYP2C9 genotype, age, and weight were the highest contributors and could obtain 80% of maximum performance in both models. CONCLUSION: This study shows that our LR and GMB models are satisfactory to predict warfarin dose in our dataset. Both models showed similar performance and feature contribution characteristics. LR may be the appropriate model due to its simplicity and interpretability.