University of Ioannina

About: University of Ioannina is a education organization based out in Ioannina, Greece. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7654 authors who have published 20594 publications receiving 671560 citations. The organization is also known as: Panepistimio Ioanninon.

HEGESMA: genome search meta-analysis and heterogeneity testing

Abstract: Summary: Heterogeneity and genome search meta-analysis (HEGESMA) is a comprehensive software for performing genome scan meta-analysis, a quantitative method to identify genetic regions (bins) with consistently increased linkage score across multiple genome scans, and for testing the heterogeneity of the results of each bin across scans. The program provides as an output the average of ranks and three heterogeneity statistics, as well as corresponding significance levels. Statistical inferences are based on Monte Carlo permutation tests. The program allows both unweighted and weighted analysis, with the weights for each study as specified by the user. Furthermore, the program performs heterogeneity analyses restricted to the bins with similar average ranks. Availability: http://biomath.med.uth.gr Contact: [email protected]

Three-Dimensional Tibiofemoral Kinematics of the Anterior Cruciate Ligament-Deficient and Reconstructed Knee during Walking

Abstract: BackgroundIt is possible that gait abnormalities may play a role in the pathogenesis of meniscal or chondral injury as well as osteoarthritis of the knee in patients with anterior cruciate ligament deficiency.HypothesisThe three-dimensional kinematics of anterior cruciate ligament-deficient knees are changed even during low-stress activities, such as walking, but can be restored by reconstruction.Study DesignCase control study.MethodsUsing a three-dimensional optoelectronic gait analysis system, we examined 13 patients with anterior cruciate ligament-deficient knees, 21 patients with anterior cruciate ligament-reconstructed knees, and 10 control subjects with uninjured knees during walking.ResultsNormal patterns of knee flexion-extension, abduction-adduction, and internal-external rotation during the gait cycle were maintained by all subjects. A significant difference in tibial rotation angle during the initial swing phase was found in anterior cruciate ligament-deficient knees compared with reconstructed...

Automatic seizure detection based on time-frequency analysis and artificial neural networks

Abstract: The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.