Income inequality and inflation in the EU

The main aim of this research is to analyze the relationship between income inequality and inflation in 13 European countries for the period 2000 to 2009 using panel data methodology. The GINI coefficient has been used to measure the income inequality while the inflation rate, the growth rate, the employment level and the openness of the economies have been used as independent variables. The results support the hypothesis that inflation has a positive significant effect on income inequality.peer-reviewe

Modeling volatility in the stock markets using GARCH models : European emerging economies and Turkey

This paper examines the use of GARCH-type models for modeling volatility of stock markets returns for four European emerging countries and Turkey. We use daily data from Bulgaria (SOFIX), Czech Republic (PX), Poland (WIG), Hungary (BUX) and Turkey (XU100) which are considered as emerging markets in finance. We find that GARCH, GJR-GARCH and EGARCH effects are apparent for returns of PX and BUX, WIG and XU whereas for SOFIX there is no significant GARCH effect. For both markets, we conclude that volatility shocks are quite persistent and the impact of old news on volatility is significant. Future research should examine the performance of multivariate time series models while using daily returns of international emerging markets.peer-reviewe

High insecticidal activity of Leclercia adecarboxylata isolated from Leptinotarsa decemlineata (Col.: Chrysomelidae)

Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say), is an important pest on solanaceous crops worldwide. CPB has developed resistance to insecticides used for its control. In this study, in order to find a more effective and safer biological control agent against L. decemlineata, we studied the bacterial flora of CPB, and tested them for insecticidal effects on it. The highest insecticidal effect determined on L. decemlineata within 5 days was 100% and this effect was exhibited by Ld1 isolate. According to the morphological, physiological and biochemical tests, and 16S rRNA sequencehomologies, Ld1 was identified as Leclercia adecarboxylata. This is the first time that this bacterium has been isolated from any insect pests. Our results indicate that Lecl. adecarboxylata may be valuable as a biological control agent for L. decemlineata

Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network

Plain radiography is widely used to detect mechanical loosening of total hip replacement (THR) implants. Currently, radiographs are assessed manually by medical professionals, which may be prone to poor inter and intra observer reliability and low accuracy. Furthermore, manual detection of mechanical loosening of THR implants requires experienced clinicians who might not always be readily available, potentially resulting in delayed diagnosis. In this study, we present a novel, fully automatic and interpretable approach to detect mechanical loosening of THR implants from plain radiographs using deep convolutional neural network (CNN). We trained a CNN on 40 patients anteroposterior hip x rays using five fold cross validation and compared its performance with a high volume board certified orthopaedic surgeon (AFC). To increase the confidence in the machine outcome, we also implemented saliency maps to visualize where the CNN looked at to make a diagnosis. CNN outperformed the orthopaedic surgeon in diagnosing mechanical loosening of THR implants achieving significantly higher sensitively (0.94) than the orthopaedic surgeon (0.53) with the same specificity (0.96). The saliency maps showed that the CNN looked at clinically relevant features to make a diagnosis. Such CNNs can be used for automatic radiologic assessment of mechanical loosening of THR implants to supplement the practitioners decision making process, increasing their diagnostic accuracy, and freeing them to engage in more patient centric care

Intimate relationship between the genes of two transcriptional coactivators, ADA2a and PIMT, of Drosophila

PIMT, a transcriptional coactivator which interacts with and enhances nuclear receptor coactivator PRIP function, was identified recently in mammalian cells and suggested to function as a link between two major multiprotein complexes anchored by CBP/p300 and PBP. Here we describe that the gene of the Drosophila homologue of PIMT, designated as Dtl, is closely associated and has an overlapping promoter with a gene encoding another transcriptional coactivator, ADA2a, which in turn participates in GCN5 HAT-containing complexes. Ada2a also produces an RNA polII subunit, RPB4, via alternative splicing; consequently, an overlapping regulatory region serves for the production of three proteins, each involved in transcription. By studying expression of reporter gene fusions in tissue culture cells and transgenic animals we have demonstrated that the regulatory regions of Ada2a/Rpb4 and Dtl overlap and the Dtl promoter is partly within the Ada2a/Rpb4 coding region. The shared regulatory region contains a DRE element, binding site of DREF, the protein factor involved in the regulation of a number of genes which play a role in DNA replication and cell proliferation. Despite the perfectly symmetrical DRE, DREF seems to have a more decisive role in Ada2a/Rpb4 transcription than in the transcription of Dtl

Comparison of Forecasting Volatility in the Czech Republic Stock Market

The aim of this paper is to examine different GARCH models with three different distributions in order to compare their forecasting power in terms of volatility existing in the returns of the Czech Stock Market and more specific in the PX index, for the period 08.01.2001-20.07.2012. We have employed GARCH, GJR-GARCH and EGARCH models against normal, student-t and generalized error distributions. Then, we have forecasted stock market volatility for the Czech Republic by its returns using the same models, GARCH, GJR-GARCH and EGARCH comparing their forecasting performance. The results show that return volatility can be characterized by significant persistence and asymmetric effects. We have estimated the corresponding variances for all models for the full sample period using static forecasts. After comparing the forecasting performance of all nine models it was found that the EGARCH model has the best forecasting performance compared to others