Forward Modeling of Atmospheric Carbon Dioxide in GEOS-5: Uncertainties Related to Surface Fluxes and Sub-Grid Transport

Forward GEOS-5 AGCM simulations of CO2, with transport constrained by analyzed meteorology for 2009-2010, are examined. The CO2 distributions are evaluated using AIRS upper tropospheric CO2 and ACOS-GOSAT total column CO2 observations. Different combinations of surface C02 fluxes are used to generate ensembles of runs that span some uncertainty in surface emissions and uptake. The fluxes are specified in GEOS-5 from different inventories (fossil and biofuel), different data-constrained estimates of land biological emissions, and different data-constrained ocean-biology estimates. One set of fluxes is based on the established "Transcom" database and others are constructed using contemporary satellite observations to constrain land and ocean process models. Likewise, different approximations to sub-grid transport are employed, to construct an ensemble of CO2 distributions related to transport variability. This work is part of NASA's "Carbon Monitoring System Flux Pilot Project,

Androgen Regulation of 5α-Reductase Isoenzymes in Prostate Cancer: Implications for Prostate Cancer Prevention

The enzyme 5α-reductase, which converts testosterone to dihydrotestosterone (DHT), performs key functions in the androgen receptor (AR) signaling pathway. The three isoenzymes of 5α-reductase identified to date are encoded by different genes: SRD5A1, SRD5A2, and SRD5A3. In this study, we investigated mechanisms underlying androgen regulation of 5α-reductase isoenzyme expression in human prostate cells. We found that androgen regulates the mRNA level of 5α-reductase isoenzymes in a cell type–specific manner, that such regulation occurs at the transcriptional level, and that AR is necessary for this regulation. In addition, our results suggest that AR is recruited to a negative androgen response element (nARE) on the promoter of SRD5A3 in vivo and directly binds to the nARE in vitro. The different expression levels of 5α-reductase isoenzymes may confer response or resistance to 5α-reductase inhibitors and thus may have importance in prostate cancer prevention

Performance analytical modelling of mobile edge computing for mobile vehicular applications: a worst-case perspective

Quantitative performance analysis plays a pivotal role in theoretically investigating the performance of Vehicular Edge Computing (VEC) systems. Although considerable research efforts have been devoted to VEC performance analysis, all of the existing analytical models were designed to derive the average system performance, paying insufficient attention to the worst-case performance analysis, which hinders the practical deployment of VEC systems to support mission-critical vehicular applications, such as collision avoidance. To bridge this gap, we develop an original performance analytical model by virtue of Stochastic Network Calculus (SNC) to investigate the worst-case end-to-end performance of VEC systems. Specifically, to capture the bursty feature of task generation, an innovative bivariate Markov Chain is firstly established and rigorously analysed to derive the stochastic task envelope. Then, an effective service curve is created to investigate the severe resource competition among vehicular applications. Driven by the stochastic task envelope and effective service curve, a closed-form end-to-end analytical model is derived to obtain the latency bound for VEC systems. Extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model under different system configurations. Furthermore, we exploit the proposed analytical model as a cost-effective tool to investigate the resource allocation strategies in VEC systems