Tennessee Valley Authority

ABSTRACTPlants play a key role in mediating soil response to global change, and breeding or engineering crops to increase soil organic carbon (SOC) storage is a potential route to land‐based carbon dioxide removal in agricultural systems. However, due to limited observational datasets plus shifting paradigms of SOC stabilization, it is unclear which plant traits are most important for enhancing different types of soil organic matter. Existing long‐term common gardens of genetically diverse plant populations may provide an opportunity to evaluate biological controls on SOC, separate from environmental or management variability. Here we report on soil and root chemical data collected for 24 genotypes within a 13‐year‐old common garden in northwestern Oregon planted with a large natural variant population of Populus trichocarpa. Fractionating surface soil (0–15 cm) revealed substantial variation in stocks of mineral‐associated organic matter (MAOM; 18–67 t C/ha) and particulate organic matter (POM; 2–22 t C/ha). Tree genotype explained 24% and 26% of the MAOM and POM stock variability, respectively, after controlling for background variability. We found minimal association between SOC concentration and either aboveground tree productivity or root biomass recalcitrance (C/N ratios and lignin content). In contrast, root elemental content appeared influential for MAOM‐C concentration, which showed a strong positive association with root aluminum (Al) and a strong negative association with root boron (B) and magnesium (Mg). Furthermore, root concentrations of these elements were highly heritable (57%–78%) and not simply a reflection of background variation in soil elemental concentrations. We estimate that surface SOC stocks under these 24 genotypes have diverged at rates of up to 1.2–4.3 t C/ha/year. These results suggest that long‐term genetic diversity trials have value for elucidating biological controls on soil organic matter dynamics, and that traits associated with root elemental content may be a useful target for enhancing biosequestration.

Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. We show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.

BackgroundMedications targeting the glucagon-like peptide-1 (GLP-1) pathway are an important therapeutic class currently used for the treatment of Type 2 diabetes (T2D). However, there is not enough known about which subgroups of patients would receive the most benefit from these medications.ObjectiveThe goal of this study was to develop a predictive model for patient responsiveness to medications, here collectively called GLP-1 M, that include GLP-1 receptor agonists and dipeptidyl peptidase-4 (DPP4) inhibitors (that normally degrade endogenously-produced GLP-1). Such a model could guide clinicians to consider certain patient characteristics when prescribing second line medications for T2D.MethodsWe analyzed de-identified electronic health records of 7856 subjects with T2D treated with GLP-1 M drugs at Vanderbilt University Medical Center from 2003–2019. Using common clinical features (including commonly ordered lab tests, demographic information, other T2D medications, and diabetes-associated complications), we compared four different models: logistic regression, LightGBM, artificial neural network (ANN), and support vector classifier (SVC).ResultsOur analysis revealed that the traditional logistic regression model outperforms the other machine learning models, with an area under the Receiver Operating Characteristic curve (auROC) of 0.77.Our model showed that higher pre-treatment HbA1C is a dominant feature for predicting better response to GLP-1 M, while features such as use of thiazolidinediones or sulfonylureas is correlated with poorer response to GLP-1 M, as assessed by lowering of hemoglobin A1C (HbA1C), a standard marker of glycated hemoglobin used for assessing glycemic control in individuals with diabetes. Among female subjects under 40 taking GLP-1 M, the simultaneous use of non-steroidal anti-inflammatory drugs (NSAIDs) was associated with a greater reduction in HbA1C (0.82 ± 1.72% vs 0.28 ± 1.70%, p = 0.008).ConclusionThese findings indicate a thorough analysis of real-world electronic health records could reveal new information to improve treatment decisions for the treatment of T2D. The predictive model developed in this study highlights the importance of considering individual patient characteristics and medication interactions when prescribing GLP-1 M drugs.

Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 102 to 5.68 × 103) than estimates that assume coincident failures are the result of independent hydrologic events.

River water temperature forecasting is very important for river management, including power plant operation. A substantial amount of research has been performed to forecast river water temperatures using different means. However, no research has utilized nor investigated the effect of adding the spill flow feature to forecast river water temperatures. This research will utilize weather data, water flow through the dam (turbine flow and spill flow), and spatially distant water temperatures to forecast river water temperature at a specified location of interest. Two models (Gradient Boosting Regression and Random Forrest Regression) were trained and tested with and without the spill flow feature to compare results. Also, a SHAP values study was conducted on Gradient Boosting Regression and Random Forrest Regression to rank features by importance. The study showed that the spill flow feature enhances the models' accuracy in predicting river water temperatures.

Ancient, species-poor lineages persistently occur across the Tree of life. These lineages are likely to contain unrecognized species diversity masked by the low rates of morphological evolution that characterize living fossils. Halecomorphi is a lineage of ray-finned fishes that diverged from its closest relatives before 200 Ma and is represented by only one living species in eastern North America, the bowfin, Amia calva Linnaeus. Here, we use double digest restriction-site-associated DNA sequencing and morphology to illuminate recent speciation in bowfins. Our results support the delimitation of a second living species of Amia, with the timing of diversification dating to the Plio-Pleistocene. This delimitation expands the species diversity of an ancient lineage that is integral to studies of vertebrate genomics and development, yet is facing growing conservation threats driven by the caviar fishery.

SUMMARYPersistent endoplasmic reticulum (ER) stress induces islet inflammation and β cell loss. How islet inflammation contributes to β cell loss remains uncertain. We have reported previously that chronic overnutrition-induced ER stress in β cells causes Ripk3-mediated islet inflammation, macrophage recruitment, and a reduction of β cell numbers in a zebrafish model. We show here that β cell loss results from the intricate communications among β cells, macrophages, and neutrophils. Macrophage-derived Tnfa induces cxcl8a in β cells. Cxcl8a, in turn, attracts neutrophils to macrophage-contacted “hotspots” where β cell loss occurs. We also show potentiation of chemokine expression in stressed mammalian β cells by macrophage-derived TNFA. In Akita and db/db mice, there is an increase in CXCL15-positive β cells and intra-islet neutrophils. Blocking neutrophil recruitment in Akita mice preserves β cell mass and slows diabetes progression. These results reveal an important role of neutrophils in persistent ER stress-induced β cell loss.

Type 2 diabetes (T2D) affects >30 million Americans and nearly 70% of individuals with T2D will die from cardiovascular disease (CVD). Circulating levels of the inflammatory signaling lipid, prostaglandin E2 (PGE2), are elevated in the setting of obesity and T2D and are associated with decreased cardiac function. The EP3 and EP4 PGE2 receptors have opposing actions in several tissues, including the heart: overexpression of EP3 in cardiomyocytes impairs function, while EP4 overexpression improves function. Here we performed complementary studies in vitro with isolated cardiomyocytes and in vivo using db/db mice, a model of T2D, to analyze the effects of EP3 inhibition or EP4 activation on cardiac function. Using echocardiography, we found that 2 weeks of systemic treatment of db/db mice with 20 mg/kg of EP3 antagonist, beginning at 6 weeks of age, improves ejection fraction and fractional shortening (with no effect on heart rate). We further show that either EP3 blockade or EP4 activation enhances contractility and calcium cycling in isolated mouse cardiomyocytes cultured in both normal and high glucose. Thus, peak [Ca2+]I transient amplitude was increased, while time to peak [Ca2+]I and [Ca2+]I decay were decreased. These data suggest that modulation of EP3 and EP4 activity has beneficial effects on cardiomyocyte contractility and overall heart function.

Percina freemanorum, the Etowah Bridled Darter, is described as a new species endemic to the Etowah River system in Georgia, specifically in Long Swamp Creek, Amicalola Creek, and the upper portion of the Etowah River. The earliest collection records for Percina freemanorum date to 1948 and in 2007 the species was delimited as populations of Percina kusha. Our investigation into the systematics of Percina kusha is motivated by the uncertain status of populations in the Coosawattee River system and observed morphological disparity in several meristic traits between populations in the Conasauga and Etowah River systems. Our analyses of morphological divergence, nuclear genotypes, and mitochondrial DNA (mtDNA) haplotype networks confirm the distinctiveness of Percina freemanorum. Morphologically, Percina freemanorum is distinguished from Percina kusha through lower average numbers of lateral line scales (65.4 vs. 72.3); rows of transverse scales (18.0 vs. 21.4); scales around the caudal peduncle (22.1 vs. 24.9); and modally more pectoral fin rays (14 vs. 13). The two species are not reciprocally monophyletic in phylogenetic analysis of mtDNA sequences, but the two species do not share mtDNA haplotypes. Analysis of up to 158,000 double digest restriction-site associated DNA (ddRAD) sequencing loci resolve each of the two species as reciprocally monophyletic and genomic clustering analysis of single nucleotide polymorphisms identifies two genetic clusters that correspond to the morphologically delimited Percina freemanorum and Percina kusha.