Rivers host a myriad of invisible chemical solutes that define the baseline quality of life-sustaining flowing waters. River chemistry reflects the response of Earth's Critical Zone, the zone from the tree top to the bottom of groundwater, to external climate forcing and human perturbations (Brantley et al., 2017). River water originates from precipitation, most of which infiltrates and flows via subsurface and river corridors (Figure 1). Along its journey, water mobilizes solutes by interacting with roots, microbes, soils, sediments, and rocks. As it eventually exits at river outlets, it carries the chemical signature of its interactions along its flow paths, and reflect the relative magnitude of biogeochemical reactions that produce solutes and export processes that transport solutes (Li et al., 2021).River chemistry is essential in regulating carbon-climate feedbacks, water quality, and aquatic ecosystem health. Solutes such as dissolved organic and inorganic carbon (DOC and DIC) and nutrients readily transform in rivers and emit greenhouse gases including CO 2 , N 2 O, and CH 4 (

Tissue engineering, after decades of exciting progress and monumental breakthroughs, has yet to make a significant impact on patient health. It has become apparent that a dearth of biomaterial scaffolds which possess the material properties of human tissue while remaining bioactive and cytocompatible, has been partly responsible for this lack of clinical translation. Herein, we propose the development of interpenetrating polymer network (IPN) hydrogels as materials that can provide cells with an adhesive extracellular matrix-like 3D microenvironment while possessing the mechanical integrity to withstand physiological forces. These hydrogels can be synthesized from biologically derived or synthetic polymers, the former polymer offering preservation of adhesion, degradability, and microstructure and the latter polymer offering tunability and superior mechanical properties. We review critical advances in the enhancement of mechanical strength, substrate-scale stiffness, electrical conductivity, and degradation in IPN hydrogels intended as bioactive scaffolds in the past 5 years. We also highlight the exciting incorporation of IPN hydrogels into state-of-the-art tissue engineering technologies, such as organ-on-a-chip and bioprinting platforms. These materials will be critical in the engineering of functional tissue for transplant, disease modeling and drug screening.

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Background: Apolipoprotein E (APOE) genotypes typically increase risk of amyloid-β deposition and onset of clinical Alzheimer’s disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord. Objective: Analysis of 31 peer-reviewed AD APOE mouse publications (n = 3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition. Methods: T-tests with Bonferroni correction (significance = p <  0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t–) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t–, t0), mouse age, and mouse gender (male = g_m; female = g_f, mixed gender = g_mi). Results: KI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g_m, KI4, t0, t+, KO, WT, g_mi, t–, g_f, KI2] = [0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023]. Conclusion: APOE3, age, and male gender was most important for predicting superior mouse cognitive performance.