Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study.

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

Closed-Loop Multi-Objective Optimization for Cu-Sb-S Photo-Electrocatalytic Materials' Discovery.

Copper antimony sulfides are regarded as promising catalysts for photo-electrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulfides is dependent on their various crystalline structures and atomic compositions. Here, a closed-loop workflow is built, which explores Cu-Sb-S compositional space to optimize its photo-electrocatalytic hydrogen evolution from water, by integrating a high-throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multi-objective optimization model discovers optimum experimental conditions after only nine cycles of integrated experiments-machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirms the expected correlation between the materials' properties and photocurrent. An optimum photocurrent of -186 µA cm-2 is observed on Cu-Sb-S in the ratio of 9:45:46 in the form of single-layer coating on F-doped SnO2 (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu1+ /Cu species content. The targeted intelligent search reveals a nonobvious CuSbS composition that exhibits 2.3 times greater activity than baseline results from random sampling.

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting.

Green hydrogen produced via electrochemical water splitting is a suitable candidate to replace emission-intensive fuels. However, the successful widespread adoption of green hydrogen is contingent on the development of low-cost, earth-abundant catalysts. Herein, machine learning models built on experimental data were used to optimize the precursor ratios of hydroxide-based electrocatalysts, with the objective of improving the product's electrocatalytic performance for overall water splitting. The Neural Network-based models were found to be the most effective in predicting and minimizing the overpotentials of the catalysts, reaching a minimum in two iterations. The relatively mild reaction conditions of the synthesis procedure, coupled with its scalability demonstrated herein, renders the optimized catalyst relevant for industrial implementation in the future. The optimized catalyst, characterized to be a molybdate-intercalated CoFe LDH, demonstrated overpotentials of 266 and 272 mV at 10 mA cm-2 for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.

Surface charge as activity descriptors for electrochemical CO2 reduction to multi-carbon products on organic-functionalised Cu.

Intensive research in electrochemical CO2 reduction reaction has resulted in the discovery of numerous high-performance catalysts selective to multi-carbon products, with most of these catalysts still being purely transition metal based. Herein, we present high and stable multi-carbon products selectivity of up to 76.6% across a wide potential range of 1 V on histidine-functionalised Cu. In-situ Raman and density functional theory calculations revealed alternative reaction pathways that involve direct interactions between adsorbed histidine and CO2 reduction intermediates at more cathodic potentials. Strikingly, we found that the yield of multi-carbon products is closely correlated to the surface charge on the catalyst surface, quantified by a pulsed voltammetry-based technique which proved reliable even at very cathodic potentials. We ascribe the surface charge to the population density of adsorbed species on the catalyst surface, which may be exploited as a powerful tool to explain CO2 reduction activity and as a proxy for future catalyst discovery, including organic-inorganic hybrids.

Ontogeny in the visual system of Nile tilapia.

Retinal neurogenesis in fish facilitates cellular rearrangement throughout ontogeny, potentially allowing for optimization of the visual system to shifts in habitat and behaviour. To test this possibility, we studied the developmental trajectory of the photopic visual process in the Nile tilapia. We examined ontogenetic changes in lens transmission, photoreceptor sensitivity and post-receptoral sensitivity, and used these to estimate changes in cone pigment frequency and retinal circuitry. We observed an ontogenetic decrease in ultraviolet (UV) photoreceptor sensitivity, which resulted from a reduction in the SWS1 cone pigment frequency, and was associated with a reduction in lens transmission at UV wavelengths. Additionally, post-receptoral sensitivity to both UV and long wavelengths decreased with age, probably reflecting changes in photoreceptor sensitivity and retinal circuitry. This novel remodelling of retinal circuitry occurred following maturation of the visual system but prior to reaching adulthood, and thus may facilitate optimization of the visual system to the changing sensory demands. Interestingly, the changes in post-receptoral sensitivity to long wavelengths could not be predicted by the changes observed in lens transmission, cone pigment frequency or photoreceptor sensitivity. This finding emphasizes the importance of considering knowledge of visual sensitivity and retinal processing when studying visual adaptations and attempting to relate visual function to the natural environment. This study advances our understanding of ontogeny in visual systems and demonstrates that the association between different elements of the visual process can be explored effectively by examining visual function throughout ontogeny.

Validity of the Fitbit One for Measuring Activity in Community-Dwelling Stroke Survivors.

Purpose: We determined the correlation between the Fitbit One and Actical accelerometer for quantifying the 3-day step count and activity levels in community-dwelling individuals with stroke. Method: Twelve participants with a mean age of 62.6 (SD 9.3) years wore both the Fitbit One and the Actical on the non-paretic ankle for 3 days. Regression analyses were performed to examine concurrent validity between the devices for step counts and sedentary, light, moderate, and vigorous activity. The relative error of the Fitbit One compared with the Actical in measuring step count was calculated. Results: Participants spent about 80% of their days being sedentary. The associations between the Fitbit One and the Actical were r>0.80 for step count and light-intensity activity across the 3 days of free-living activity. The overall relative error in measuring step count was 3.8%, with differences between those with walking speeds of less than 0.58 metres per second and 0.58 metres per second or more (27.4% [SD 34.2] vs. -8.0% [SD 10.7], p<0.001). Conclusions: The Fitbit One was associated with the Actical accelerometer in measuring step count and light-intensity activity during free-living activity after stroke, but had lower error in capturing step count for those with faster walking speeds. The Fitbit One may not be valid for capturing higher intensity activity after stroke.

Objectif : sur une période de trois jours, déterminer la corrélation entre le capteur d'activité Fitbit One et l'accéléromètre Actical pour quantifier le nombre de pas et le niveau d'activité de personnes ayant subi un accident vasculaire cérébral (AVC) et qui sont revenues en milieu ambulatoire. Méthodologie : au total, 12 participants, d'un âge moyen de 62,6 ans (ÉT 9,3), ont porté à la fois le Fitbit One et l'Actical sur leur cheville non parétique pendant trois jours. Les chercheurs ont effectué des analyses de régression pour examiner la validité convergente entre les appareils pour calculer le nombre de pas et l'activité sédentaire, légère, modérée et vigoureuse. Ils ont mesuré l'erreur relative du Fitbit One pour calculer le nombre de pas par rapport à celle de l'Actical. Résultats : Les participants consacraient environ 80 % de leur journée à des activités sédentaires. L'association entre le Fitbit One et l'Actical étaient de r>0,80 pour le calcul du nombre de pas et l'activité de faible intensité au cours des trois jours d'activités libres. L'erreur relative globale pour le calcul du nombre de pas s'élevait à 3,8 % et comportait des différences entre les personnes qui marchaient à une vitesse inférieure à 0,58 mètre par seconde et celles qui marchaient à au moins 0,58 mètre par seconde (27,4 [ÉT 34,2 %] par rapport à −8,0 [ÉT 10,7 %], p<0,001). Conclusions : Les chercheurs ont associé le capteur d'activité Fitbit One à l'accéléromètre Actical pour calculer le nombre de pas et l'activité d'intensité légère dans le cadre des activités libres après un AVC, mais le taux d'erreur dans le calcul du nombre de pas était plus faible chez les personnes qui marchaient plus vite. Le Fitbit One n'est peut-être pas valide pour capter les activités d'intensité plus élevée après un AVC.