Search results for "Diagnostic"

Raspberry breeding and biotisation for increasing plant stress tolerance and antioxidant activity

2008

International audience; Chemical inputs used in red fruit production can be detrimental to the environment and pesticides are not always efficient in controlling plant diseases. In addition, it is suspected that conventional agricultural practices have a negative impact on antioxidant activities in red fruits. Therefore new cultural approaches are required in order to produce red fruits of high quality. This involves an integrated programme of plant breeding and development of sustainable agricultural practices. Antioxidant activity was compared in leaves and berries of six raspberry cultivars for the selection of varieties for a breeding programme. This showed that cultivars that had the h…

Impact loading history modulates hip fracture load and location : A finite element simulation study of the proximal femur in female athletes

2018

Sideways falls impose high stress on the thin superolateral cortical bone of the femoral neck, the region regarded as a fracture-prone region of the hip. Exercise training is a natural mode of mechanical loading to make bone more robust. Exercise-induced adaptation of cortical bone along the femoral neck has been previously demonstrated. However, it is unknown whether this adaption modulates hip fracture behavior. The purpose of this study was to investigate the influence of specific exercise loading history on fall-induced hip fracture behavior by estimating fracture load and location with proximal femur finite element (FE) models created from magnetic resonance images (MRI) of 111 women w…

Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals

2020

Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…

Effect of high hydrostatic pressure on extraction of B-phycoerythrin from Porphyridium cruentum: Use of confocal microscopy and image processing

2019

International audience; The aim of the study was to extract B-phycoerythrin from Porphyridium cruentum while preserving its structure. The high hydrostatic pressure treatments were chosen as extraction technology. Different methods have been used to observe the effects of the treatment: spectrophotometry and confocal laser scanning microscopy followed by image processing analysis. Image processing led to the generation of masks used for the identification of three clusters: intra, extra and intercellular. All methods showed that high hydrostatic pressure treatments between 50 and 500 MPa failed to extract B-phycoerythrin from Porphyridium cruentum cells. The fluorescence emission was negati…

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

2016

Abstract Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicabl…

On the Influence of Affect in EEG-Based Subject Identification

2021

Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user’s current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the sign…

2020

Limited data are available regarding strength and endurance training adaptations to occupational physical performance during deployment. This study assessed acute training-induced changes in neuromuscular (electromyography; EMG) and metabolic (blood lactate, BLa) responses during a high-intensity military simulation test (MST), performed in the beginning (PRE) and at the end (POST) of a six-month crisis-management operation. MST time shortened (145 ± 21 vs. 129 ± 16 s, −10 ± 7%, p < 0.001) during the operation. Normalized muscle activity increased from PRE to POST in the hamstring muscles by 87 ± 146% (116 ± 52 vs. 195 ± 139%EMGMVC, p < 0.001) and in the quadriceps by 54 ± 81% (26 ± 8…

Image-Evoked Affect and its Impact on Eeg-Based Biometrics

2019

Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…

ES1D: A Deep Network for EEG-Based Subject Identification

2017

Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…

Rendimiento de las pruebas complementarias en el estudio de pacientes con dolor abdominal crónico

2021

Resumen: Introducción: El dolor abdominal crónico (DAC) en la infancia es un motivo de consulta frecuente que afecta a la vida familiar, y en ocasiones precisa realización de pruebas complementarias. El objetivo fue realizar el análisis cualitativo, cuantitativo y económico de las pruebas que se solicitan. Pacientes y métodos: Estudio observacional, prospectivo y multicéntrico, incluyendo pacientes entre 4-15 años con DAC. Se diferenciaron 2 grupos: orgánico y funcional. Se recogieron las siguientes variables: clínicas, pruebas complementarias y su coste. Resultados: Se incluyeron 235 niños con DAC (edad media 9,7 ± 2,7 años). Un 79% resultaron trastornos funcionales y un 21% orgánicos. Cas…