Intérêt de différents réactifs d'extraction chimique pour l'évaluation de la biodisponibilité des métaux en traces du sol

L’évaluation de la biodisponibilité des métaux en traces du sol intéresse deux grands domaines d’application : d’une part, le diagnostic de fertilité chimique basé sur l’établissement de seuils de carence, employé depuis plusieurs décennies dans différents pays ; d’autre part, l’estimation du risque de phytotoxicité ou de contamination de la chaîne alimentaire qu’entraîne la pollution du sol par les éléments en traces. Dans ce cas, très peu de pays sont allés jusqu’à l’élaboration de références de diagnostic. Afin de guider le choix d’une méthode d’extraction chimique pour permettre l’ébauche de telles références en France, une synthèse bibliographique a été entreprise. Elle reprend les principaux résultats obtenus depuis les vingt dernières années concernant l’évaluation de la biodisponibilité de Cd, Cu, Zn, Pb, Cr et Ni. De cette étude, il ressort que les solutions salines non tamponnées semblent les mieux adaptées à l’estimation rapide du transfert des éléments du sol aux végétaux et à la mise au point de valeurs guides permettant de statuer quant aux risques de toxicité susceptibles d’être engendrés par des sites pollués.The prediction of soil trace metal bioavailability using extractions has two direct applications: i) evaluation of soil chemical fertility and nutrient deficiency, as has been used widely in different countries for many years; and ii) risk assessment of phytotoxicity and contamination of the food chain induced by polluted soils. In this latter case, few countries have defined guide values. In order to choose one of the extraction methods proposed in the literature, and then define such references for France, a review of research concerning the chemical estimation of Cd, Cu, Zn, Ni, Cr and Pb plant uptake in the last twenty years was undertaken. In conclusion, the use of unbuffered salt solutions seems to be the most suitable way to i) estimate trace element transfert from polluted soil to plant and ii) define guide values for risk assessment

Seizure forecasting: Bifurcations in the long and winding road

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

AbstractIndividual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention

Imagined speech can be decoded from low- and cross-frequency intracranial EEG features

AbstractReconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.</jats:p

Imagined speech can be decoded from low- and cross-frequency features in perceptual space

SummaryReconstructing intended speech from neural activity using brain-computer interfaces (BCIs) holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech have met limited success, mainly because the associated neural signals are weak and variable hence difficult to decode by learning algorithms. Using three electrocorticography datasets totalizing 1444 electrodes from 13 patients who performed overt and imagined speech production tasks, and based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future BCIs, and assessed their performance to discriminate speech items in articulatory, phonetic, vocalic, and semantic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to successful imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings demonstrate that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding, and that exploring perceptual spaces offers a promising avenue for future imagined speech BCIs.</jats:p