STAMP alters the growth of transformed and ovarian cancer cells

<p>Abstract</p> <p>Background</p> <p>Steroid receptors play major roles in the development, differentiation, and homeostasis of normal and malignant tissue. STAMP is a novel coregulator that not only enhances the ability of p160 coactivator family members TIF2 and SRC-1 to increase gene induction by many of the classical steroid receptors but also modulates the potency (or EC₅₀) of agonists and the partial agonist activity of antisteroids. These modulatory activities of STAMP are not limited to gene induction but are also observed for receptor-mediated gene repression. However, a physiological role for STAMP remains unclear.</p> <p>Methods</p> <p>The growth rate of HEK293 cells stably transfected with STAMP plasmid and overexpressing STAMP protein is found to be decreased. We therefore asked whether different STAMP levels might also contribute to the abnormal growth rates of cancer cells. Panels of different stage human cancers were screened for altered levels of STAMP mRNA. Those cancers with the greatest apparent changes in STAMP mRNA were pursued in cultured cancer cell lines.</p> <p>Results</p> <p>Higher levels of STAMP are shown to have the physiologically relevant function of reducing the growth of HEK293 cells but, unexpectedly, in a steroid-independent manner. STAMP expression was examined in eight human cancer panels. More extensive studies of ovarian cancers suggested the presence of higher levels of STAMP mRNA. Lowering STAMP mRNA levels with siRNAs alters the proliferation of several ovarian cancer tissue culture lines in a cell line-specific manner. This cell line-specific effect of STAMP is not unique and is also seen for the conventional effects of STAMP on glucocorticoid receptor-regulated gene transactivation.</p> <p>Conclusions</p> <p>This study indicates that a physiological function of STAMP in several settings is to modify cell growth rates in a manner that can be independent of steroid hormones. Studies with eleven tissue culture cell lines of ovarian cancer revealed a cell line-dependent effect of reduced STAMP mRNA on cell growth rates. This cell-line dependency is also seen for STAMP effects on glucocorticoid receptor-mediated transactivation. These preliminary findings suggest that further studies of STAMP in ovarian cancer may yield insight into ovarian cancer proliferation and may be useful in the development of biomarker panels.</p

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.Comment: Accepted by IEEE Transactions on Intelligent Vehicle

Long-term effects of restoration on the links between above-and belowground biodiversity in degraded Horqin sandy grassland, Northern China

Long-term ecological restoration plays an important role in the sustainable development of degraded grassland ecosystem. In this study, the levels of species diversity, genetic diversity and soil microbial diversity in restored grassland were measured by vegetation survey, DNA barcoding and soil microbial high-throughput sequencing technology, so as to explore the relationship between above- and belowground biodiversity and its driving factors in Horqin sandy grassland. In this study, the results found that herb are dominated in restoration grassland types. Plant species richness (SR) from post-non-grazing restoration plot (NGR) communities was significantly higher than other restoration communities (10 ± 1.1, p = 0.004). Genetic diversity indices of dominant plant species in chloroplast DNA (cpDNA), were remarkable greater than nuclear DNA (nrDNA) in each recovering sandy grassland plots (amplitude of difference was 44.8%–70.5% in allelic richness (AR), 81.9%–128.1% in expected heterozygosity (HE)). The soil bacterial and fungal richness from natural mobile dune grassland (NM) communities was notably lower than that from recovering grassland types (1641.9 ± 100.4, p &lt; 0.001; 533 ± 16.6, p &lt; 0.001). In this study, heterogeneous levels of genetic variability among different recovering sandy grassland types were detected. Correlation analyses revealed that there were positive correlations between species diversity and genetic diversity (SR &amp; AR: r = 0.56, R2 = 0.31, p &lt; 0.001; SR &amp; HE: r = 0.33, R2 = 0.11, p = 0.045) and a negative correlation between soil microbial diversity and genetic diversity (r = -0.44, R2 = 0.19, p = 0.005). The final structural equation model explained 38% of the variance in SR, 57% in AR, 52% in soil microbial diversity (SD), 49% in aboveground biomass (AGB), 87% in soil organic carbon (SOC), 47% in soil alkali-hydrolyzable nitrogen (SAN) and 69% in soil available phosphorus (SOP). Long-term ecological restoration had significant direct positive effects on AGB, SOC, SAN, SOP, AR, SR and SD. There was a negative correlation between above- and belowground biodiversity and biological and abiotic factors. The results of this study have clarified the above- and underground biodiversity levels of sandy grassland and the relationship with driving factors under long-term ecological restoration measures, and will provide effective support for the management and sustainable development of sandy grassland