Suffering | EPFL Graph Search

Suffering, or pain in a broad sense, may be an experience of unpleasantness or aversion, possibly associated with the perception of harm or threat of harm in an individual. Suffering is the basic element that makes up the negative valence of affective phenomena. The opposite of suffering is pleasure or happiness. Suffering is often categorized as physical or mental. It may come in all degrees of intensity, from mild to intolerable. Factors of duration and frequency of occurrence usually compound that of intensity. Attitudes toward suffering may vary widely, in the sufferer or other people, according to how much it is regarded as avoidable or unavoidable, useful or useless, deserved or undeserved. Suffering occurs in the lives of sentient beings in numerous manners, often dramatically. As a result, many fields of human activity are concerned with some aspects of suffering. These aspects may include the nature of suffering, its processes, its origin and causes, its meaning and significance, its related personal, social, and cultural behaviors, its remedies, management, and uses. The word suffering is sometimes used in the narrow sense of physical pain, but more often it refers to psychological pain, or more often yet it refers to pain in the broad sense, i.e. to any unpleasant feeling, emotion or sensation. The word pain usually refers to physical pain, but it is also a common synonym of suffering. The words pain and suffering are often used both together in different ways. For instance, they may be used as interchangeable synonyms. Or they may be used in 'contradistinction' to one another, as in "pain is physical, suffering is mental", or "pain is inevitable, suffering is optional". Or they may be used to define each other, as in "pain is physical suffering", or "suffering is severe physical or mental pain". Qualifiers, such as physical, mental, emotional, and psychological, are often used to refer to certain types of pain or suffering.

A novel soft cardiac assist device based on a dielectric elastomer augmented aorta: An in vivo study

Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Francesco Clavica, Armando Matthieu Walter, Silje Ekroll Jahren, Jonathan André Jean-Marie Chavanne, Lorenzo Ferrari

Although heart transplant is the preferred solution for patients suffering from heart failures, cardiac assist devices remain key substitute therapies. Among them, aortic augmentation using dielectric elastomer actuators (DEAs) might be an alternative technological application for the future. The electrically driven actuator does not require bulky pneumatic elements (such as conventional intra-aortic balloon pumps) and conforms tightly to the aorta thanks to the manufacturing method presented here. In this study, the proposed DEA-based device replaces a section of the aorta and acts as a counterpulsation device. The feasibility and validation of in vivo implantation of the device into the descending aorta in a porcine model, and the level of support provided to the heart are investigated. Additionally, the influence of the activation profile and delay compared to the start of systole is studied. We demonstrate that an activation of the DEA just before the start of systole (30 ms at 100 bpm) and deactivation just after the start of diastole (0–30 ms) leads to an optimal assistance of the heart with a maximum energy provided by the DEA. The end-diastolic and left ventricular pressures were lowered by up to 5% and 1%, respectively, compared to baseline. The early diastolic pressure was augmented in average by up to 2%.

2022

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Pablo Maceira Elvira

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.

2018

Domain adaptation using manifold alignment

Maxime Pierre Trolliet

Domain adaptation is a major challenge for future remote sensing applications. Both financial and temporal constraints of data acquisition lead to the developing of new techniques able to use knowledge from alternative sources. Different approaches have been developed by considering the statistical properties of images or by modifying already existing classifiers. We propose a intermediary approach of these two kinds of methods by using a manifold alignment technique constrained by similarity between two images. The two images are mapped in a high dimensional latent space which maximizes the proximity of similar elements, thus allowing classification of the images suffering from label scarcity by using the knowledge of the other image. Such a classification offers improvement compared to various used processes.

2012

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Pablo Maceira Elvira

2018