In Vivo Retinal Pigment Epithelium Imaging using Transscleral Optical Imaging in Healthy Eyes.

To image healthy retinal pigment epithelial (RPE) cells in vivo using Transscleral OPtical Imaging (TOPI) and to analyze statistics of RPE cell features as a function of age, axial length (AL), and eccentricity. Single-center, exploratory, prospective, and descriptive clinical study. Forty-nine eyes (AL: 24.03 ± 0.93 mm; range: 21.9-26.7 mm) from 29 participants aged 21 to 70 years (37.1 ± 13.3 years; 19 men, 10 women). Retinal images, including fundus photography and spectral-domain OCT, AL, and refractive error measurements were collected at baseline. For each eye, 6 high-resolution RPE images were acquired using TOPI at different locations, one of them being imaged 5 times to evaluate the repeatability of the method. Follow-up ophthalmic examination was repeated 1 to 3 weeks after TOPI to assess safety. Retinal pigment epithelial images were analyzed with a custom automated software to extract cell parameters. Statistical analysis of the selected high-contrast images included calculation of coefficient of variation (CoV) for each feature at each repetition and Spearman and Mann-Whitney tests to investigate the relationship between cell features and eye and subject characteristics. Retinal pigment epithelial cell features: density, area, center-to-center spacing, number of neighbors, circularity, elongation, solidity, and border distance CoV. Macular RPE cell features were extracted from TOPI images at an eccentricity of 1.6° to 16.3° from the fovea. For each feature, the mean CoV was < 4%. Spearman test showed correlation within RPE cell features. In the perifovea, the region in which images were selected for all participants, longer AL significantly correlated with decreased RPE cell density (R Spearman, Rs = -0.746; P < 0.0001) and increased cell area (Rs = 0.668; P < 0.0001), without morphologic changes. Aging was also significantly correlated with decreased RPE density (Rs = -0.391; P = 0.036) and increased cell area (Rs = 0.454; P = 0.013). Lower circular, less symmetric, more elongated, and larger cells were observed in those > 50 years. The TOPI technology imaged RPE cells in vivo with a repeatability of < 4% for the CoV and was used to analyze the influence of physiologic factors on RPE cell morphometry in the perifovea of healthy volunteers. Proprietary or commercial disclosure may be found after the references

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

2021 BEETL competition: advancing transfer learning for subject independence & heterogenous EEG data sets

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because regular machine learning methods cannot generalise well across human subjects and handle learning from different, heterogeneously collected data sets, thus limiting the scale of training data available. On the other hand, the many developments in transfer- and meta-learning fields would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for all the things that make biosignal data analysis a hard problem. We design two transfer learning challenges around a. clinical diagnostics and b. neurotechnology. These two challenges are designed to probe algorithmic performance with all the challenges of biosignal data, such as low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The successful 2021 BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmarks

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.</p

Étude de la sorption de sondes radicalaires nitroxydes sur des charbons

L'observation par Résonance Paramagnétique Electronique (R.P.E.) d'échantillons de charbon de Provence (P), Vouters (V), Méricourt (Me), Escarpelle (E.), la Mure (Mu) (conservés sous argon) et de charbon de Freyming (F') (conservé sur parc), mis en présence d'une solution (C = M/200) de tétraméthyl-2,2,6,6 pipéridinol-4 oxyle-1 (tanol) 1 dans le toluène conduit aux conclusions suivantes :Pour les charbons de bas rang (P, V), le radical est sorbé dans un milieu de polarité voisine de celle du méthanol (P) ou de l'éthanol (V). Son taux d'immobilisation est de 95 % pour P et de 91 % pour V. La polarité et le taux d'immobilisation diminuent lorsque le rang du charbon augmente.Pour le charbon F', équivalent à V mais exposé à l'air, le nitroxyde 1 est sorbé dans un milieu de polarité équivalente à celle de l'eau, immédiatement après imprégnation. Si l'imprégnation par le toluène a été faite pendant un mois, en présence ou en absence de la sonde radicalaire, la polarité de l'environnement diminue jusqu'à une polarité voisine de celle du méthanol, alors que dans les mêmes conditions, le taux d'immobilisation passe de 87 à 82 %. Ceci est compatible avec une oxydation et une hydratation superficielles.Pour les charbons de haut rang (Me, E, Mu), on n'observe pas de spectre correspondant à la sonde sorbée sur le charbon, mais une réactivité de la sonde vis-à-vis du charbon, mettant probablement en jeu un transfert monoélectronique

EEG Neurofeedback for anxiety disorders and post-traumatic stress disorders: a blueprint for a promising brain based therapy

International audiencePurpose of Review: This review provides an overview of current knowledge and understanding of EEG Neurofeedback for anxiety disorders and post-traumatic stress disorders. Recent Findings: The manifestations of anxiety disorders and post-traumatic stress disorders (PTSD) are associated with dysfunctions of neurophysiological stress axes and brain arousal circuits, which are important dimensions of the research domain criteria (RDoC). Even if the pathophysiology of these disorders is complex, one of its defining signatures is behavioral and physiological over-arousal. Interestingly, arousal-related brain activity can be modulated by electroencephalogram-based neurofeedback (EEG NF), a non-pharmacological and noninvasive method that involves neurocognitive training through a brain-computer interface (BCI). EEG NF is characterized by a simultaneous learning process where both patient and computer are involved in modifying neuronal activity or connectivity, thereby improving associated symptoms of anxiety and/or over-arousal. Summary: Positive effects of EEG NF have been described for both anxiety disorders and PTSD, yet due to a number of methodological issues, it remains unclear whether symptom improvement is the direct result of neurophysiological changes targeted by EEG NF. Thus, in this work we sought to bridge current knowledge on brain mechanisms of arousal with past and present EEG NF therapies for anxiety and PTSD. In a nutshell, we discuss the neurophysiological mechanisms underlying the effects of EEG NF in anxiety disorder and PTSD, the methodological strengths/weaknesses of existing EEG NF randomized-controlled trials (RCTs) for these disorders, and the neuropsychological factors that may impact NF training success

INFLUENCE DES VARIATIONS SAISONNIÈRES DE LA COMPOSITION DU LAIT DE CHÈVRE SUR LE RENDEMENT EN FROMAGE. CONSÉQUENCES PRATIQUES POUR LA SÉLECTION

International audienc

Paramagnetic copper(II) and diamagnetic copper(I) complexes with N

Electronic paramagnetic resonance of paramagnetic Cu(II) complexes and nuclear magnetic resonance of diamagnetic Cu(I) complexes can give thermodynamic, kinetic and structural informations on these complexes. This methodology is examplified on two new ligands of N3S2 (L1) and N2S2 (L2) type. EPR spectra of frozen solutions of Cu(II) complexes gave (from g// and A// parameters) structural data such as the tetrahedral deformation of the square-planar structure. 1H NMR allowed determinations of stoechiometry and the corresponding stability constants. It gave also informations on ligand exchanges around Cu(I) cation