A Weighted Moving Average Process for Forecasting

The object of the present study is to propose a forecasting model for a nonstationary stochastic realization. The subject model is based on modifying a given time series into a new k-time moving average time series to begin the development of the model. The study is based on the autoregressive integrated moving average process along with its analytical constrains. The analytical procedure of the proposed model is given. A stock XYZ selected from the Fortune 500 list of companies and its daily closing price constitute the time series. Both the classical and proposed forecasting models were developed and a comparison of the accuracy of their responses is given

A Weighted Moving Average Process for Forcasting

A forecasting model for a nonstationary stochastic realization is proposed based on modifying a given time series into a new k-time moving average time series. The study is based on the autoregressive integrated moving average process along with its analytical constrains. The analytical procedure of the proposed model is given. A stock XYZ selected from the Fortune 500 list of companies and its daily closing price constitute the time series. Both the classical and proposed forecasting models were developed and a comparison of the accuracy of their responses is given

Investigation of Hepatoprotective Activity of Induced Pluripotent Stem Cells in the Mouse Model of Liver Injury

To date liver transplantation is the only effective treatment for end-stage liver diseases. Considering the potential of pluripotency and differentiation into tridermal lineages, induced pluripotent stem cells (iPSCs) may serve as an alternative of cell-based therapy. Herein, we investigated the effect of iPSC transplantation on thioacetamide- (TAA-) induced acute/fulminant hepatic failure (AHF) in mice. Firstly, we demonstrated that iPSCs had the capacity to differentiate into hepatocyte-like cells (iPSC-Heps) that expressed various hepatic markers, including albumin, α-fetoprotein, and hepatocyte nuclear factor-3β, and exhibited biological functions. Intravenous transplantation of iPSCs effectively reduced the hepatic necrotic area, improved liver functions and motor activity, and rescued TAA-treated mice from lethal AHF. 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate cell labeling revealed that iPSCs potentially mobilized to the damaged liver area. Taken together, iPSCs can effectively rescue experimental AHF and represent a potentially favorable cell source of cell-based therapy

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population.

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Forecasting Models for Economic and Environmental Applications

The object of the present study is to introduce three analytical time series models for the purpose of developing more effective economic and environmental forecasting models, among others. Given a stochastic realization, stationary or nonstationary in nature, one can utilize exciting methodology to develop an autoregressive, moving average or a combination of both for short and long term forecasting. In the present study we analytically modify the stochastic realization utilizing (a) a k-th moving average, (b) a k-th weighted moving average and (c) a k-th exponential weighted moving average processes. Thus, we proceed in developing the appropriate forecasting models with the new (modified) time series using the more recent methodologies in the subject matter. Once the proposed statistical forecasting models have been developed, we proceed to modify the analytical process back into the original stochastic realization. The proposed methods have been successfully applied to real stock data from a Fortune 500 company. A similar forecasting model was developed and evaluated for the daily closing price of S&P Price Index of the New York Stock Exchange. The proposed forecasting model was developed along with the statistical model using classical and most recent methods. The effectiveness of the two models was compared using various statistical criteria. The proposed models gave better results. Atmospheric temperature and carbon dioxide, CO2, are the two variables most attributable to GLOBAL WARMING. Using the proposed methods we have developed forecasting statistical models for the continental United States, for both the atmospheric temperature and carbon dioxide. We have developed forecasting models that performed much better than the models using the classical Box-Jenkins type of methodology. Finally, we developed an effective statistical model that relates CO2 and temperature; that is, knowing the atmospheric temperature we can at the specific location estimate the carbon dioxide and vice versa

Forecasting Models for Economic and Environmental Applications

The object of the present study is to introduce three analytical time series models for the purpose of developing more effective economic and environmental forecasting models, among others. Given a stochastic realization, stationary or nonstationary in nature, one can utilize exciting methodology to develop an autoregressive, moving average or a combination of both for short and long term forecasting. In the present study we analytically modify the stochastic realization utilizing (a) a k-th moving average, (b) a k-th weighted moving average and (c) a k-th exponential weighted moving average processes. Thus, we proceed in developing the appropriate forecasting models with the new (modified) time series using the more recent methodologies in the subject matter. Once the proposed statistical forecasting models have been developed, we proceed to modify the analytical process back into the original stochastic realization. The proposed methods have been successfully applied to real stock data from a Fortune 500 company. A similar forecasting model was developed and evaluated for the daily closing price of S&P Price Index of the New York Stock Exchange. The proposed forecasting model was developed along with the statistical model using classical and most recent methods. The effectiveness of the two models was compared using various statistical criteria. The proposed models gave better results. Atmospheric temperature and carbon dioxide, CO2, are the two variables most attributable to GLOBAL WARMING. Using the proposed methods we have developed forecasting statistical models for the continental United States, for both the atmospheric temperature and carbon dioxide. We have developed forecasting models that performed much better than the models using the classical Box-Jenkins type of methodology. Finally, we developed an effective statistical model that relates CO2 and temperature; that is, knowing the atmospheric temperature we can at the specific location estimate the carbon dioxide and vice versa