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Deregulation of androgen receptor (AR) splice variants has been implicated to play a role in prostate cancer development and progression. To understand their functions in prostate, we established a transgenic mouse model (AR3Tg) with targeted expression of the constitutively active and androgen-independent AR splice variant AR3 (a.k.a. AR-V7) in prostate epithelium. We found that overexpression of AR3 modulates expression of a number of tumor-promoting autocrine/paracrine growth factors (including Tgfβ2 and Igf1) and expands prostatic progenitor cell population, leading to development of prostatic intraepithelial neoplasia. In addition, we showed that some epithelial-mesenchymal transition-associated genes are up-regulated in AR3Tg prostates, suggesting that AR3 may antagonize AR activity and halt the differentiation process driven by AR and androgen. This notion is supported by our observations that the number of Ck5(+)/Ck8(+) intermediate cells is increased in AR3Tg prostates after castration, and expression of AR3 transgene in these intermediate cells compromises prostate epithelium regeneration upon androgen replacement. Our results demonstrate that AR3 is a driver of prostate cancer, at least in part, through modulating multiple tumor-promoting autocrine/paracrine factors.

The detriments of intrinsic silanol groups in mesoporous silica to the photoluminescence (PL) of lead halide perovskite nanocrystals (LHP NCs) confined in the template have never been determined and clearly elucidated. Here, we disclose that silanol‐induced Cs+ and Br− deficiencies prompt the generation of traps and CsPb2Br5 impurities. The temperature‐dependent PL spectra verify the higher energetic barrier of trap states in CsPbBr3 NCs confined in silanol‐rich mesoporous silica. Femtosecond transient absorption spectra reveal the trapped state mediates a broadband photoinduced absorption and long‐lived decay pathway of CsPbBr3 NCs in silanol‐rich templates. A remarkable improvement (up to 160‐fold) in PL quantum yields is realized by simple silanol elimination. This work demonstrates the detrimental effects of silanol sites on the PL properties of LHP NCs impregnated in mesoporous silica and provides a new perspective for the ligand‐free synthesis of high‐quality LHP NCs in mesoporous templates by facile impregnation for practical applications. [ABSTRACT FROM AUTHOR]

In response to the global challenge of climate change and the shift away from fossil fuels, the accurate prediction of wind power generation is crucial for optimizing grid operations and managing energy storage. This study introduces a novel approach by integrating the proportional–integral–derivative (PID) control theory into wind power forecasting, employing a three-dimensional gated neural (TGN) unit designed to enhance error feedback mechanisms. The proposed empirical mode decomposition (EMD)–convolutional neural network (CNN)–three-dimensional gated neural network (TGNN) framework starts with the pre-processing of wind data using EMD, followed by feature extraction via a CNN, and time series forecasting using the TGN unit. This setup leverages proportional, integral, and differential control within its architecture to improve adaptability and response to dynamic wind patterns. The experimental results show significant improvements in forecasting accuracy; the EMD–CNN–TGNN model outperforms both traditional models like autoregressive integrated moving average (ARIMA) and support vector regression (SVR), and similar neural network approaches, such as EMD–CNN–GRU and EMD–CNN–LSTM, across several metrics including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination ( R 2 ). These advancements substantiate the model's effectiveness in enhancing the precision of wind power predictions, offering substantial implications for future renewable energy management and storage solutions. [ABSTRACT FROM AUTHOR]