Process flow of the hybrid GA and BP algorithm.

<p>Process flow of the hybrid GA and BP algorithm.</p

Plots showing the daily Nikkei 225 closing prices from January 23, 2007 to December 30, 2013.

<p>Plots showing the daily Nikkei 225 closing prices from January 23, 2007 to December 30, 2013.</p

Selected technical indicators and their formulas (Type 1).

<p>Selected technical indicators and their formulas (Type 1).</p

Comparison of our study with prior research reports.

<p>Comparison of our study with prior research reports.</p

Description of parameters that are used in the hybrid model.

<p>Description of parameters that are used in the hybrid model.</p

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

<div><p>In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.</p></div

Comparison of the hit ratio between the two types of input variables.

<p>Comparison of the hit ratio between the two types of input variables.</p

Selected technical indicators and their formulas (Type 2).

<p>Selected technical indicators and their formulas (Type 2).</p

The architecture of the back propagation neural network.

<p>The architecture of the back propagation neural network.</p

A Comparative Transcriptomic Study and Key Gene Targeting of Lamprey Gonadal Immune Response

The mammalian testis and ovary possess special immunocompetence, which is central to provide protection against pathogens. However, the innate immune responses to immune challenges in lamprey gonads are poorly understood. In this study, we extracted RNA from testis and ovary tissues of lampreys at 0 hour, 8 hours and 17 days after lipopolysaccharides (LPS) stimulation and performed transcriptome sequencing. While the transcriptome profiles of the two tissues were different for the most part, genes LIP, LECT2, LAL2, GRN, ITLN, and C1q were found to be the most significantly up-regulated genes in both. Quantitative Real-time PCR (qRT-PCR) analysis confirmed that these genes were upregulated after stimulation. Furthermore, immunohistochemical staining showed that these genes in lamprey gonads are expressed in high quantities and have a specific distribution. Taken together, our results suggest that these genes could play an essential role in response of the gonads to LPS induction. This research establishes a basis for investigating the immune mechanism of vertebrate gonads and presents a fresh concept for gaining insight into the evolutionary development of jawless vertebrates.</p