Wastewater reuse: a solution with a future

A combination of water stress, fast-growing populations and the climate emergency means many African countries struggle to provide their people with sufficient clean water. For these countries, in addition to saving water, the solutions lie not so much in sharing a scarce resource as in turning to non-conventional alternative water resources, for example seawater or wastewater in place of raw water from rivers or aquifers. It is little surprise that we are seeing the emergence of ever more projects to give water a second life. Reusing wastewater seems to be the most effective bulwark against scarcity.Recycled wastewater is the only resource that increases in step with economic growth. It is a virtuous solution that protects nature by limiting the risks of pollution discharges into the environment. It is a circular economy model that strengthens countries’ water self-sufficiency by giving them access to a reliable resource located within their territory, and therefore protected from adventurous neighbors.Veolia has developed innovative solutions for reusing wastewater in industry and agriculture as well as the home. One of the pioneers is the Namibian capital of Windhoek, where 35% of the drinking water needs for the city and its surrounding area are met using recycled wastewater. Is this a pointer to the future

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets

Cerebral metabolism during vegetative state and after recovery to consciousness

peer reviewe

Whole body vibration in the treatment of fibromyalgia: Influence on muscle performances

peer reviewe

Translation validation of a new back pain screening questionnaire (the STarT Back Screening Tool) in French

Background: Low back pain (LBP) is a major public health problem and the identification of individuals at risk of persistent LBP poses substantial challenges to clinical management. The STarT Back questionnaire is a validated nine-item patient self-report questionnaire that classifies patients with LBP at low, medium or high-risk of poor prognosis for persistent non-specific LBP. The objective of this study was to translate and cross-culturally adapt the English version of the STarT Back questionnaire into French. Methods: The translation was performed using best practice translation guidelines. The following phases were performed: contact with the STarT Back questionnaire developers, initial translations (English into French), synthesis, back translations, expert committee review, test of the pre-final version on 44 individuals with LBP, final version. Results: The linguistic translation required minor semantic alterations. The participants interviewed indicated that all items of the questionnaire were globally clear and comprehensible. However, 6 subjects (14%) wondered if two questions were related to back pain or general health. After discussion within the expert committee and with the developer of the STarT Back tool, it was decided to modify the questionnaire and to add a reference to back pain in these two questions. Conclusions: The French version of the STarT Back questionnaire has been shown to be comprehensible and adapted to the French speaking general population. Investigations are now required to test the psychometric properties (reliability, internal and external validity, responsiveness) of this translated version of the questionnaire

HMRI-vQC : A visualization Quality Control toolbox for Multi-Parameter Mapping

peer reviewe

Attention Supports Verbal Short-Term Memory via Competition between Dorsal and Ventral Attention Networks

Interactions between the neural correlates of short-term memory (STM) and attention have been actively studied in the visual STM domain but much less in the verbal STM domain. Here we show that the same attention mechanisms that have been shown to shape the neural networks of visual STM also shape those of verbal STM. Based on previous research in visual STM, we contrasted the involvement of a dorsal attention network centered on the intraparietal sulcus supporting task-related attention and a ventral attention network centered on the temporoparietal junction supporting stimulus-related attention. We observed that, with increasing STM load, the dorsal attention network was activated while the ventral attention network was deactivated, especially during early maintenance. Importantly, activation in the ventral attention network increased in response to task-irrelevant stimuli briefly presented during the maintenance phase of the STM trials but only during low-load STM conditions, which were associated with the lowest levels of activity in the dorsal attention network during encoding and early maintenance. By demonstrating a trade-off between task-related and stimulus-related attention networks during verbal STM, this study highlights the dynamics of attentional processes involved in verbal ST