Electroencephalographic detection of synesthesia

In this paper the research on a person declaring synesthetic abilities will be presented.According to the current state of knowledge synesthesia activates additional cortical fields in the brainwhich can be found in the EEG. The research was conducted using an EGI-EEG system (ElectricalGeodesic Inc., Eugene, Oregon, USA) with the GeoSource software. GeoSource is a tool that implementsthe algorithms LAURA, LORETA and sLORETA. Using these algorithms for EEG analysis wecan determine where in the brain the source of activity is. The authors will try to answer the questionwhether the use of these tools can prove the occurrence of synesthesia

J-PET Framework: Software platform for PET tomography data reconstruction and analysis

J-PET Framework is an open-source software platform for data analysis, written in C++ and based on the ROOT package. It provides a common environment for implementation of reconstruction, calibration and filtering procedures, as well as for user-level analyses of Positron Emission Tomography data. The library contains a set of building blocks that can be combined by users with even little programming experience, into chains of processing tasks through a convenient, simple and well-documented API. The generic input-output interface allows processing the data from various sources: low-level data from the tomography acquisition system or from diagnostic setups such as digital oscilloscopes, as well as high-level tomography structures e.g. sinograms or a list of lines-of-response. Moreover, the environment can be interfaced with Monte Carlo simulation packages such as GEANT and GATE, which are commonly used in the medical scientific community.Comment: 14 pages, 5 figure

Low fasting glucose is associated with enhanced thrombin generation and unfavorable fibrin clot properties in type 2 diabetic patients with high cardiovascular risk

Objective To investigate the effect of low blood glucose on thrombin generation and fibrin clot properties in type 2 diabetes (T2DM). Methods In 165 patients with T2DM and high cardiovascular risk, we measured ex vivo plasma fibrin clot permeation [Ks], turbidity and efficiency of fibrinolysis including clot lysis time [t50%], together with thrombin generation and platelet activation markers in relation to fasting blood glucose. Results As compared to patients in medium (4.5-6.0 mmol/l, n = 52) and higher (>6.0 mmol/l, n = 75) glucose group, subjects with low glycemia (<4.5 mmol/l, n = 38) had lower Ks by 11% (p < 0.001) and 8% (p = 0.01), respectively, prolonged t50% by 10% (p < 0.001) and 7% (p = 0.016), respectively, and higher peak thrombin generation by 21% and 16%, respectively (p < 0.001 for both). There were no significant differences in Ks and t50% between patients in medium and higher glucose group. In the whole group, a J-shape relationship was observed between glycemia and the following factors: peak thrombin generation, Ks and t50%. Only in patients with HbA1c < 6.0% (42 mmol/mol) (n = 26) fasting glucose positively correlated with Ks (r = 0.53, P = 0.006) and inversely with t50% (r = −0.46, P = 0.02). By multiple regression analysis, after adjustment for age, fibrinogen, HbA1c, insulin treatment and T2DM duration, fasting glycemia was the independent predictor of Ks (F = 6.6, df = 2, P = 0.002), t50% (F = 8.0, df = 2, P < 0.001) and peak thrombin generation (F = 13.5, df = 2, P < 0.0001). Conclusions In T2DM patients fasting glycemia <4.5 mmol/l is associated with enhanced thrombin formation and formation of denser fibrin clots displaying lower lysability, especially when strict glycemia control was achieved (HbA1c<6.0%)

Hybrid implementation of the fastICA algorithm for high-density EEG using the capabilities of the Intel architecture and CUDA programming

High-density electroencephalographic (EEG) systems are utilized in the study of the human brain and its underlying behaviors. However, working with EEG data requires a well-cleaned signal, which is often achieved through the use of independent component analysis (ICA) methods. The calculation time for these types of algorithms is the longer the more data we have. This article presents a hybrid implementation of the fastICA algorithm that uses parallel programming techniques (libraries and extensions of the Intel processors and CUDA programming), which results in a significant acceleration of execution time on selected architectures