A behavioral analysis of Greek strike activity

This work deals with the analysis of Greek strike activity during the period 1975-1994 based on the data collected by the National Statistical Service of Greece. The work is distinguished into two parts as follows: a. For the industry sector, b. For all the sectors. Conventional strike equations are specified and estimated using the data for all strikes and the effects of the explanatory variables are compared. The study includes several explanatory variables, which have been used by many investigators of strikes. To analyze the aforementioned data, the ARIMA procedure was also used to estimate and forecast models using the methods prescribed by Box and Jenkins (1976). The logarithmic transformation of the data has demonstrated a better behavior of the respective models, fact that it was expected since in a previous work, which was presented in the Fourth Statistical Conference of Greece in Patras 1991, the goodness of fit of the data in the lognormal distribution has been proved.peer-reviewe

The Determinants of the Shadow Economy: The Case of Greece

This paper aims at assessing the relative importance of various factors as key determinants of the size of the shadow economy in a sample of OECD countries. Using panel data for a group of 19 countries for the 2003 – 2008 period, we find that the quality of governance, the regulatory framework in the product, labor and credit markets and the tax burden both in the sense of the direct cost on entrepreneurial activity and the cost of compliance to the tax administration framework, are the most important factors affecting the part of the economic activity that takes place outside the official sector, that is the shadow or underground economy. These results are used to evaluate the potential gains Greece could obtain, in the case it could converge to the best practice or even to the average levels of the determining factors of the rest of the OECD countries

A BEHAVIORAL ANALYSIS OF GREEK STRIKE ACTIVITY.

This work deals with the analysis of Greek strike activity during the period 1975-1994 based on the data collected by the National Statistical Service of Greece. The work is distinguished into two parts as follows: a. For the industry sector, b. For all the sectors. Conventional strike equations are specified and estimated using the data for all strikes and the effects of the explanatory variables are compared. The study includes several explanatory variables, which have been used by many investigators of strikes. To analyze the aforementioned data, the ARIMA procedure was also used to estimate and forecast models using the methods prescribed by Box and Jenkins (1976). The logarithmic transformation of the data has demonstrated a better behavior of the respective models, fact that it was expected since in a previous work, which was presented in the Fourth Statistical Conference of Greece in Patras 1991, the goodness of fit of the data in the lognormal distribution has been proved.Greek strike activity, empirical models, Box and Jenkins ARIMA modeling

Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC

Quantitative comparison of motion history image variants for video-based depression assessment

Abstract Depression is the most prevalent mood disorder and a leading cause of disability worldwide. Automated video-based analyses may afford objective measures to support clinical judgments. In the present paper, categorical depression assessment is addressed by proposing a novel variant of the Motion History Image (MHI) which considers Gabor-inhibited filtered data instead of the original image. Classification results obtained with this method on the AVEC’14 dataset are compared to those derived using (a) an earlier MHI variant, the Landmark Motion History Image (LMHI), and (b) the original MHI. The different motion representations were tested in several combinations of appearance-based descriptors, as well as with the use of convolutional neural networks. The F1 score of 87.4% achieved in the proposed work outperformed previously reported approaches

Additional file 2: of Semantic biomedical resource discovery: a Natural Language Processing framework

contains the list of tools and resources that were in our repository. (PDF 964 kb

Additional file 1: of Semantic biomedical resource discovery: a Natural Language Processing framework

contains extensive results of the experiments. (PDF 803 kb

Fast FPGA prototyping for real-time image processing with very high-level synthesis

International audienc

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling with Information Technology in the In Silico Oncology Context.

This paper outlines the major components and function of the Technologically Integrated Oncosimulator developed primarily within the ACGT (Advancing Clinico Genomic Trials on Cancer) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity - discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid and visualization of the predictions. A refining scenario for the eventual coupling of the Oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the Oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe

Occupational heat stress : Multi-country observations and interventions

Background: Occupational heat exposure can provoke health problems that increase the risk of certain diseases and affect workers’ ability to maintain healthy and productive lives. This study investigates the effects of occupational heat stress on workers’ physiological strain and labor productivity, as well as examining multiple interventions to mitigate the problem. Methods: We monitored 518 full work-shifts obtained from 238 experienced and acclimatized individuals who work in key industrial sectors located in Cyprus, Greece, Qatar, and Spain. Continuous core body temperature, mean skin temperature, heart rate, and labor productivity were collected from the beginning to the end of all work-shifts. Results: In workplaces where self-pacing is not feasible or very limited, we found that occupational heat stress is associated with the heat strain experienced by workers. Strategies focusing on hydration, work-rest cycles, and ventilated clothing were able to mitigate the physiological heat strain experienced by workers. Increasing mechanization enhanced labor productivity without increasing workers’ physiological strain. Conclusions: Empowering la-borers to self-pace is the basis of heat mitigation, while tailored strategies focusing on hydration, work-rest cycles, ventilated garments, and mechanization can further reduce the physiological heat strain experienced by workers under certain conditions