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Clearcutting and other land-use changes are known to release terrestrial carbon and mobilize organic carbon into streamwater, significantly augmenting aquatic carbon levels in the short-term. However, little is known about the lasting impacts of forest management decisions on the riverine concentration levels of Dissolved Organic Carbon (DOC). Here we compare data from HJ Andrews Experimental Forest, a long-term ecological research (LTER) site located in the Oregon Cascades. We paired stream chemistry and discharge measurements spanning 15-30 years. Two watersheds that were 100\% clear-cut 40-50 years ago (WS01 and WS10) were compared with their unharvested and controlled counterparts (WS02 and WS09). Temporal analysis showed that, on average, DOC concentrations in the old-growth watersheds are notably higher than their harvested analogs to this day. This suggests even though clearcutting can release DOC from soil and vegetation to water, the terrestrial organic carbon stock is ultimately depleted post-clearcutting resulting in lower DOC concentrations. Concentration-discharge (CQ) analysis also revealed a sharp difference in behaviors between watersheds 1 and 2, with WS01 exhibiting a slight flushing pattern bordering on hysteresis while WS02 displayed a pronounced dilution pattern. Based on the shallow-deep hypothesis (Zhi et al. 2019; Zhi and Li, 2020) this indicates that the old-growth watershed has a pronounced groundwater DOC source, and clearcutting could have altered this source within WS01 and significantly lowered baseflow organic carbon concentrations. However, it should be noted that WS09 and WS10 displayed DOC behavior similar to that of WS01, which could also signify that the previously mentioned opposing CQ behaviors are a result of some underlying geological or lithological contribution unique to WS02. These competing hypotheses will be further tested using a watershed scale reactive transport model HBV-BioRT.", 

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.