L'Evangile du Sauveur, He 5,7 et la prière de supplication: en quête d'autres traditions sur la prière au Mont des Oliviers

L'Évangile du Sauveur 45-59 rapporte une interprétation peu connue de la prière de Jésus au Mont des Oliviers, comme prière d'intercession pour le peuple d'Israël. Cette interprètation est connue d'Origène, Jérôme et Epiphane. Cet article souhaite montrer que la prise en compte d'une telle tradition permet de reprendre sur une base nouvelle la question de la signification des prières et supplications, avec grand cri et larmes d'He 5,7. En amont de ces deux passages se tient en effet le topos d'une intense prière de supplication, efficace et faisant place aux motions. Evoquée par Justin Martyr et Origène comme d'origine hébraïque, cette prière reçoit dans le cadre du judaïsme hellénistique une tournure caractéristique, quand elle y est associée à la terminologie grecque du suppliant : elle témoigne alors d'un point de contact entre cultures juive et hellénistique. La conclusion souligne que l'Evangile du Sauveur est vecteur à la fois de la mémoire et de l'oubli culturels de la prière de supplication évoquée en He 5,7.

Embedded Monitoring Server

This study investigates, designs, and implements an inexpensive application that allows local and remote monitoring of a home. The application consists of an array of sensors for monitoring different conditions in a home environment and also for accessing the devices that might be connected to the system. Only a few sensors are initially involved in this study and information about the temperature level, forced entry detection, smoke and water leakage detection can be obtained at any time from any location with an Internet connection. The application software (coded in C language) runs on an embedded system which is basically a wireless Linksys router running on a GNU/Linux based firmware for embedded systems. Interaction between the sensors and the application software is achieved through an implemented sensor interfacing circuit. The communication with the sensor interfacing unit is done through the serial port, and accessibility of the connected sensors is achieved through a telnet client. The sensors can be accessed from local and remote locations with the sensors giving reliable information. The resulting application shows that it is possible to use the router for other applications other than what it is intended for.

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

A Theta-Band EEG Based Index for Early Diagnosis of Alzheimer’s Disease

Despite recent advances, early diagnosis of Alzheimer’s disease (AD) from electroencephalography (EEG) remains a difficult task. In this paper, we offer an added measure through which such early diagnoses can potentially be improved. One feature that has been used for discriminative classification is changes in EEG synchrony. So far, only the decrease of synchrony in the higher frequencies has been deeply analyzed. In this paper, we investigate the increase of synchrony found in narrow frequency ranges within the θ band. This particular increase of synchrony is used with the well-known decrease of synchrony in the band to enhance detectable differences between AD patients and healthy subjects. We propose a new synchrony ratio that maximizes the differences between two populations. The ratio is tested using two different data sets, one of them containing mild cognitive impairment patients and healthy subjects, and another one, containing mild AD patients and healthy subjects. The results presented in this paper show that classification rate is improved, and the statistical difference between AD patients and healthy subjects is increased using the proposed ratio.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Alternative Techniques of Neural Signal Processing in Neuroengineering

Neural signal processing is a discipline within neuroengineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuroengineering is a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders. Much current research in neuroengineering is focused on understanding the coding and processing of information in the sensory and motor systems, quantifying how this processing is altered in the pathological state, and how it can be manipulated through interactions with artificial devices including brain–computer interfaces and neuroprosthetics.

Suomalainen suurtyö Seychellien hämähäkeistä

Kirjallisuusarvostelu

Spotlight: A Blacklight Plugin for Showcasing Digital Collections

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

De infinitate extenso perperam tributa

Invokaatio: D.D.