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Regression-based methods are commonly used for riverine constituent concentration/flux estimation, which is essential for guiding water quality protection practices and environmental decision making. This paper developed a multivariate adaptive regression splines model for estimating riverine constituent concentrations (MARS-EC). The process, interpretability and flexibility of the MARS-EC modelling approach, was demonstrated for total nitrogen in the Patuxent River, a major river input to Chesapeake Bay. Model accuracy and uncertainty of the MARS-EC approach was further analysed using nitrate plus nitrite datasets from eight tributary rivers to Chesapeake Bay. Results showed that the MARS-EC approach integrated the advantages of both parametric and nonparametric regression methods, and model accuracy was demonstrated to be superior to the traditionally used ESTIMATOR model. MARS-EC is flexible and allows consideration of auxiliary variables; the variables and interactions can be selected automatically. MARS-EC does not constrain concentration-predictor curves to be constant but rather is able to identify shifts in these curves from mathematical expressions and visual graphics. The MARS-EC approach provides an effective and complementary tool along with existing approaches for estimating riverine constituent concentrations.

The inconsistency between mismatch repair (MMR) protein immunohistochemistry (IHC) and microsatellite instability PCR (MSI‐PCR) methods has been widely reported. We aim to investigate the prognosis and the effect of immunotherapy in dMMR by IHC but MSS by MSI‐PCR (dMMR&MSS) colorectal cancer (CRC) patients. A microsatellite instability (MSI) predicting model was established to help find dMMR&MSS patients. MMR and MSI states were detected by the IHC and MSI‐PCR in 1622 CRC patients (ZS6Y‐1 cohort). Logistic regression analysis was used to screen clinical features to construct an MSI‐predicting nomogram. We propose a new nomogram‐based assay to find patients with dMMR&MSS, in which the MSI‐PCR assay only detects dMMR patients with MSS predictive results. We applied the new strategy to a random cohort of 248 CRC patients (ZS6Y‐2 cohort). The consistency of MMR IHC and MSI‐PCR in the ZS6Y‐1 cohort was 95.7% (1553/1622). Both pMMR&MSS and dMMR&MSS groups experienced significantly shorter overall survival (OS) than those in dMMR by IHC and MSI‐H by MSI‐PCR (dMMR&MSI‐H) group (hazard ratio [HR] = 2.429, 95% confidence interval [CI]: 1.89–3.116, p <.01; HR = 21.96, 95% CI: 7.24–66.61, p <.01). The dMMR&MSS group experienced shorter OS than the pMMR&MSS group, but the difference did not reach significance (log rank test, p =.0686). In the immunotherapy group, the progression‐free survival of dMMR&MSS patients was significantly shorter than that of dMMR&MSI‐H patients (HR = 13.83, 95% CI: 1.508–126.8, p <.05). The ZS6Y‐MSI‐Pre nomogram (C‐index = 0.816, 95% CI: 0.792–0.841, already online) found 66% (2/3) dMMR&MSS patients in the ZS6Y‐2 cohort. There are significant differences in OS and immunotherapy effect between dMMR&MSI‐H and dMMR&MSS patients. Our prediction model provides an economical way to screen dMMR&MSS patients. [ABSTRACT FROM AUTHOR]