Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.

The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

Real-time quaking-induced conversion (RT-QuIC) assays have become common in the detection of chronic wasting disease (CWD) and are very sensitive provided the assay duration is sufficient. However, a prolonged assay duration may lead to non-specific signal amplification. The wide range of pre-defined assay durations in current RT-QuIC applications presents a need for optimization of the RT-QuIC assay duration. In this study, receiver operating characteristic (ROC) analysis was applied to optimize assay duration for detection of CWD in obex and retropharyngeal lymph node (RLN) tissue specimens. Two different fluorescence thresholds were used: a fixed threshold based on background fluorescence (Tstdev) and a max-point ratio (maximum/background fluorescence) threshold (TMPR) to determine CWD positivity. The optimal assay duration was 27 h for both obex and RLN based on Tstdev, and 27 and 28 h for obex and RLN, respectively, based on TMPR. The optimized assay durations were then evaluated for screening CWD in white-tailed deer from an affected farm. Results by RT-QuIC using optimized duration based on Tstdev and TMPR were in 100% and 92.3 % or higher agreement with those by the widely used screening assay, ELISA. In comparison, when using a 40 h assay duration, the agreement between RT-QuIC and ELISA reduced to 89.2% or higher, and the RT-QuIC results were significantly (p < 0.05) different from those using optimum durations. These findings demonstrated that the application of ROC analysis for the optimization of assay duration could improve the RT-QuIC assay for screening CWD in white-tailed deer.

Background: Glioblastoma (GBM), a malignant grade IV tumor, is the most malignant brain tumor due to its hyper-proliferative and apoptosis-evading characteristics. The signal transducer and activators of transcription (STAT) family genes, including STAT3 and STAT5A, have been indicated to play important roles in GBM progression. Increasing number of reports suggest that Garcinol, a polyisoprenylated benzophenone and major bioactive component of Garcinia indica contains potent anti-cancer activities. Material & Methods: The present study investigated the anti-GBM effects of garcinol, focusing on the STAT3/STAT5A activation, using a combination of bioinformatics, in vitro, and ex vivo assays. Results: Our bioinformatics analysis of TCGA - GBM cohort (n=173) showed that STAT3 and STAT5A are preferentially elevated in primary and recurrent GBM, compared to non-tumor brain tissues, and is significantly correlated with reduced overall survival. In support, our immunohistochemical staining of a GBM cohort (n=30) showed an estimated 5.3-fold (p<0.001) elevation in STAT3 and STAT5A protein expression in primary and recurrent GBM versus the non-tumor group. In vitro, garcinol treatment significantly suppressed the proliferative, invasive, and migratory potential of U87MG or GBM8401 cells, dose-dependently. In addition, garcinol anticancer effect significantly attenuated the GBM stem cell-like phenotypes, as reflected by diminished ability of U87MG or GBM8401 to form colonies and tumorspheres and suppressed expression of OCT4 and SOX2. Furthermore, analysis on GBM transcriptome revealed an inverse correlation between the level of STAT3/5A and hsa-miR-181d. Garcinol-mediated anti-GBM effects were associated with an increased hsa-miR-181d/STAT3 and hsa-miR-181d/5A ratio. Conclusion: We present evidence of anti-GBM efficacy of garcinol mediated by enhancing the hsa-miR-181d/STAT3 and hsa-miR-181d/5A ratios in GBM cells. Our findings suggest a potential new therapeutic agent for combating aggressive GBM.

Background: Tumor microenvironment (TME) plays a crucial role in virtually every aspect of tumorigenesis of glioblastoma multiforme (GBM). The dysfunctional TME promotes drug resistance, disease recurrence and distant metastasis. Recent evidence indicates that exosomes released by stromal cells within TME may promote oncogenic phenotypes via transferring signaling molecules such as cytokines, proteins and microRNAs. Results: In this study, clinical GBM samples were collected and analyzed. We found that GBM-associated macrophages (GAMs) secreted exosomes which were enriched with oncomiR-21. Co-culture of GAMs (and GAM derived exosomes) and GBM cell lines resulted in the increased GBM cells’ resistance against temozolomide (TMZ) by upregulating pro-survival gene, PDCD4 and stemness markers Sox2, STAT3, Nestin and miR-21-5p and increased M2 cytokines, IL-6 and TGF-β1 secreted by GBM cells, promoting the M2 polarization of GAMs. Subsequently, pacritinib treatment suppressed GBM tumorigenesis and stemness; more importantly, pacritinib-treated GBM cells showed markedly reduced ability to secret M2 cytokines and reduced miR-21 enriched exosomes secreted by GAMs. Pacritinib-mediated effects were accompanied by a reduction of oncomiR miR-21-5p, by which tumor suppressor PDCD4 was targeted. We subsequently established a patient-derived xenograft models where mice bore patient GBM and GAMs. The treatment of pacritinib, and the combination of pacritinib/TMZ appeared to significantly reduce tumorigenesis of GBM/GAM PDX mice, overcome TMZ-resistance, and M2 polarization of GAMs. Conclusion: In summation, we showed that potential of pacritinib alone or in combination with TMZ for suppressing GBM tumorigenesis via modulating STAT3/miR-21/PDCD4 signaling. Further investigations are warranted for adopting pacritinib for the treatment of TMZ-resistant GBM in the clinical settings.