Enhanced anticancer activity of a combination of docetaxel and Aneustat (OMN54) in a patient-derived, advanced prostate cancer tissue xenograft model.

The current first-line treatment for advanced metastatic prostate cancer, i.e. docetaxel-based therapy, is only marginally effective. The aim of the present study was to determine whether such therapy can be improved by combining docetaxel with Aneustat (OMN54), a multivalent botanical drug candidate shown to have anti-prostate cancer activity in preliminary in vitro experiments, which is currently undergoing a Phase-I Clinical Trial. Human metastatic, androgen-independent C4-2 prostate cancer cells and NOD-SCID mice bearing PTEN-deficient, metastatic and PSA-secreting, patient-derived subrenal capsule LTL-313H prostate cancer tissue xenografts were treated with docetaxel and Aneustat, alone and in combination. In vitro, Aneustat markedly inhibited C4-2 cell replication in a dose-dependent manner. When Aneustat was combined with docetaxel, the growth inhibitions of the drugs were essentially additive. In vivo, however, the combination of docetaxel and Aneustat enhanced anti-tumor activity synergistically and very markedly, without inducing major host toxicity. Complete growth inhibition and shrinkage of the xenografts could be obtained with the combined drugs as distinct from the drugs on their own. Analysis of the gene expression of the xenografts using microarray indicated that docetaxel + Aneustat led to expanded anticancer activity, in particular to targeting of cancer hallmarks that were not affected by the single drugs. Our findings, obtained with a highly clinically relevant prostate cancer model, suggest, for the first time, that docetaxel-based therapy of advanced human prostate cancer may be improved by combining docetaxel with Aneustat

A Grey NGM

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1,k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1,k) model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1,k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span

Self-Memory Coupling Prediction Model for Energy Consumption Prediction

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1, 1, ) selfmemory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1, 1, ) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1, 1, ) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span

Using grey Holt-Winters model to predict the air quality index for cties in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe randomness, non-stationarity and irregularity of air quality index series bring the di fficulty of air quality index forecasting. To enhance forecast accuracy, a novel model combining grey accumulated generating technique and Holt-Winters method is developed for air quality index forecasting in this paper. The grey accumulated generating technique is utilized to handle non-stationarity of random and irregular data series and Holt-Winters method is employed to deal with the seasonal e ects. To verify and validate the proposed model, two monthly air quality index series from January in 2014 to December in 2016 collected from Shijiazhuang and Handan in China are taken as the test cases. The experimental results show that the proposed model is remarkably superior to conventional Holt-Winters method for its higher forecast accuracy

Heterochromatin protein 1α mediates development and aggressiveness of neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer (PCa) arising mostly from adenocarcinoma via NE transdifferentiation following androgen deprivation therapy. Mechanisms contributing to both NEPC development and its aggressiveness remain elusive. In light of the fact that hyperchromatic nuclei are a distinguishing histopathological feature of NEPC, we utilized transcriptomic analyses of our patient-derived xenograft (PDX) models, multiple clinical cohorts, and genetically engineered mouse models to identify 36 heterochromatin-related genes that are significantly enriched in NEPC. Longitudinal analysis using our unique, first-in-field PDX model of adenocarcinoma-to-NEPC transdifferentiation revealed that, among those 36 heterochromatin-related genes, heterochromatin protein 1α (HP1α) expression increased early and steadily during NEPC development and remained elevated in the developed NEPC tumor. Its elevated expression was further confirmed in multiple PDX and clinical NEPC samples. HP1α knockdown in the NCI-H660 NEPC cell line inhibited proliferation, ablated colony formation, and induced apoptotic cell death, ultimately leading to tumor growth arrest. Its ectopic expression significantly promoted NE transdifferentiation in adenocarcinoma cells subjected to androgen deprivation treatment. Mechanistically, HP1α reduced expression of androgen receptor (AR) and RE1 silencing transcription factor (REST) and enriched the repressive trimethylated histone H3 at Lys9 (H3K9me3) mark on their respective gene promoters. These observations indicate a novel mechanism underlying NEPC development mediated by abnormally expressed heterochromatin genes, with HP1α as an early functional mediator and a potential therapeutic target for NEPC prevention and management

Understanding drivers of changing fire activities: from the perspective of causality

Understanding the drivers of environmental changes is critical for predicting and managing this rapidly evolving world. One of the challenges commonly faced by existing studies of analyzing the drivers of an environmental change, be it at a small scale or a large one, is that the drivers presented are not necessarily the true causes that have causal relations with the target of their study. In this thesis, I used state-of-the-art techniques from causal inference to understand the drivers of changing fire activities at different scales. Fire is a fundamental process of the earth system, and the widely observed changing patterns of fire behavior is an important aspect of the global changes that can have profound impacts on ecosystems and human societies. Therefore, understanding the drivers of the changing fire behavior is of vital importance. I first propose a causal framework that integrate models that can detect causality among variables, and interpretable machine learning models to select and quantify the impact of drivers on fire emissions. I tested this framework on 12 selected regions, and the results showed that the framework can effectively select the true causes as drivers of fire emissions, and also provide informative evaluations of their impacts on fire emissions. Then, I apply this causal framework to a global analysis for drivers of fire emission trend at a scale specific to biome and geological continents. Global fire-derived carbon emissions (fire emissions hereafter) are relatively stable over the period 2001-2019. This was mainly caused by the decrease in African savannas and the increase in Asian boreal forests. The main drivers for the decrease of fire emissions in African savannas are decreased vegetation caused by anthropogenic intervention, mainly through agricultural expansion. The increasing fire emissions in Asian boreal forests was mainly driven by agricultural activities and changing climates, especially drier climates in this region. For the other parts of the world, their drivers differ. In general, vegetation is the most widely observed driver which usually has a positive impact; climates is also widely observed as a fire emission driver, while the impact of the several aspects of climate, namely temperature, humidity, water availability and wind, differ among areas; and anthropogenic interventions were relatively less important because it was identified as a driver for fewer locations. I also apply scenario analysis to a theoretical grass-savanna-tree model to under- stand the role of climate change and anthropogenic interventions on the process of forest degradation where fire plays a critical role. I found that for scenarios with high level of climate change, the system displays bistability and hysteresis; while for scenarios with low level of climate change, the system responds to in- creasing anthropogenic interventions nonlinearly but does not display bistability. A tree dominated system will degrade into a grass dominated one under a high level of anthropogenic intervention, and the degradation process can be accelerated and worsened by higher level of climate change. At last, I proposed an indicator to evaluate the risk of irreversible degradation for a system with hysteresis using Bayesian inference for model parameterization. I apply this indicator to a real world case and found that the risk of irreversible degradation can indeed be high. The risk of irreversible degradation should be taking into account and measured in future management decisions due to the consequences and potential loss of irreversible degradation. This thesis investigated the drivers of fire activities with emphasis on climate change and anthropogenic intervention as potential drivers. With the causal framework and its application to understanding drivers of fire-derived carbon emissions at the global scale from 2001 to 2019, climate change towards drier and warmer climates and anthropogenic intervention through deforestation are of vital importance in driving increasing carbon emissions in forest such as Asian boreal forests and Amazon forests. And the forest-savanna transition model showed the synergetic effects between the two drivers that can make the transitioning process from forest to savannas and grasslands in a shorter time and to a more degraded state. In the end, I proposed an indicator that can be adopted to measure the risk of irreversible degradation from forests to savannas, which is a promising research for my future plan.Doctor of Philosoph

Research on energy-saving effect of technological progress based on Cobb-Douglas production function

Energy issues receive more and more attention these days. And it is considered that technological progress is an essential approach to save energy. This essay is to analyze the relation between energy intensity and technological progress by Cobb-Douglas production function in which energy, labor, capital and technological progress are taken as independent variables. It proves that the growth of output per capital and output per labor will increase energy intensity while technological progress will decrease energy intensity. Empirical research on Chinese industry is used here to indicate technological progress greatly decreases energy intensity. Because of the interferences of Asian financial crisis, there is something abnormal in the data. So in the empirical research, average weaken buffer operator (ABWO) is applied to weaken the interference of Asian financial crisis to the fixed assets, energy and value added. The results of the empirical research show that technological progress decreases energy intensity of Chinese industry an average of 6.3% every year in China.Energy intensity Technological progress Cobb-Douglas production function

Application of a novel grey self-memory coupling model to forecast the incidence rates of two notifiable diseases in China: dysentery and gonorrhea.

OBJECTIVE: In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. METHODS: The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. RESULTS: Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. CONCLUSION: The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control