Cardiomyocyte-restricted overexpression of extracellular superoxide dismutase increases nitric oxide bioavailability and reduces infarct size after ischemia/reperfusion

published_or_final_versio

Tandem E2F Binding Sites in the Promoter of the p107 Cell Cycle Regulator Control p107 Expression and Its Cellular Functions

The retinoblastoma tumor suppressor (Rb) is a potent and ubiquitously expressed cell cycle regulator, but patients with a germline Rb mutation develop a very specific tumor spectrum. This surprising observation raises the possibility that mechanisms that compensate for loss of Rb function are present or activated in many cell types. In particular, p107, a protein related to Rb, has been shown to functionally overlap for loss of Rb in several cellular contexts. To investigate the mechanisms underlying this functional redundancy between Rb and p107 in vivo, we used gene targeting in embryonic stem cells to engineer point mutations in two consensus E2F binding sites in the endogenous p107 promoter. Analysis of normal and mutant cells by gene expression and chromatin immunoprecipitation assays showed that members of the Rb and E2F families directly bound these two sites. Furthermore, we found that these two E2F sites controlled both the repression of p107 in quiescent cells and also its activation in cycling cells, as well as in Rb mutant cells. Cell cycle assays further indicated that activation of p107 transcription during S phase through the two E2F binding sites was critical for controlled cell cycle progression, uncovering a specific role for p107 to slow proliferation in mammalian cells. Direct transcriptional repression of p107 by Rb and E2F family members provides a molecular mechanism for a critical negative feedback loop during cell cycle progression and tumorigenesis. These experiments also suggest novel therapeutic strategies to increase the p107 levels in tumor cells

Adolescent Brain Development and the Risk for Alcohol and Other Drug Problems

Dynamic changes in neurochemistry, fiber architecture, and tissue composition occur in the adolescent brain. The course of these maturational processes is being charted with greater specificity, owing to advances in neuroimaging and indicate grey matter volume reductions and protracted development of white matter in regions known to support complex cognition and behavior. Though fronto-subcortical circuitry development is notable during adolescence, asynchronous maturation of prefrontal and limbic systems may render youth more vulnerable to risky behaviors such as substance use. Indeed, binge-pattern alcohol consumption and comorbid marijuana use are common among adolescents, and are associated with neural consequences. This review summarizes the unique characteristics of adolescent brain development, particularly aspects that predispose individuals to reward seeking and risky choices during this phase of life, and discusses the influence of substance use on neuromaturation. Together, findings in this arena underscore the importance of refined research and programming efforts in adolescent health and interventional needs

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget

Technical report: The design and evaluation of a basin‐scale wireless sensor network for mountain hydrology

A network of sensors for spatially representative water-balance measurements was developed and deployed across the 2000 km2 snow-dominated portion of the upper American River basin, primarily to measure changes in snowpack and soil-water storage, air temperature, and humidity. This wireless sensor network (WSN) consists of 14 sensor clusters, each with 10 measurement nodes that were strategically placed within a 1 km2 area, across different elevations, aspects, slopes, and canopy covers. Compared to existing operational sensor installations, the WSN reduces hydrologic uncertainty in at least three ways. First, redundant measurements improved estimation of lapse rates for air and dew-point temperature. Second, distributed measurements captured local variability and constrained uncertainty in air and dew-point temperature, snow accumulation, and derived hydrologic attributes important for modeling and prediction. Third, the distributed relative-humidity measurements offer a unique capability to monitor upper-basin patterns in dew-point temperature and characterize elevation gradient of water vapor-pressure deficit across steep, variable topography. Network statistics during the first year of operation demonstrated that the WSN was robust for cold, wet, and windy conditions in the basin. The electronic technology used in the WSN-reduced adverse effects, such as high current consumption, multipath signal fading, and clock drift, seen in previous remote WSNs

Optimizing spatial distribution of watershed-scale hydrologic models using Gaussian Mixture Models

Common methods for spatial distribution, such as hydrologic response units, are subjective, time-consuming, and fail to capture the full range of basin attributes. Recent advances in statistical-learning techniques allow for new approaches to this problem. We propose the use of Gaussian Mixture Models (GMMs) for spatial distribution of hydrologic models. GMMs objectively select the set of modeling locations that best represent the distribution of watershed features relevant to the hydrologic cycle. We demonstrate this method in two hydrologically distinct headwater catchments of the Sierra Nevada and show that it meets or exceeds the performance of traditionally distributed models for multiple metrics across the water balance at a fraction of the time cost. Finally, we use univariate GMMs to identify the most-important drivers of hydrologic processes in a basin. The GMM method allows for more robust, objective, and repeatable models, which are critical for advancing hydrologic research and operational decision making