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Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.
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http://dx.doi.org/10.1016/j.envint.2015.07.021 | DOI Listing |
Exp Brain Res
September 2025
Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
Postdiction is a perceptual phenomenon where the perception of an earlier stimulus is influenced by a later one. This effect is commonly studied using the 'rabbit illusion', in which temporally regular, but spatially irregular, stimuli are perceived as equidistant. While previous research has focused on short inter-stimulus intervals (100-200 ms), the role of longer intervals, which may engage late attentional processes, remains unexplored.
View Article and Find Full Text PDFImaging Neurosci (Camb)
September 2025
CEA, Joliot, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France.
We propose a new, modular, open-source, Python-based 3D+time realistic functional magnetic resonance imaging (fMRI) data simulation software. SNAKE or imulator from eurovascular coupling to cquisition of -space data for xploration of fMRI acquisition techniques. It is the first simulator to simulate the entire chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various 3D sampling strategies of k-space data with multiple coils.
View Article and Find Full Text PDFJ Agric Food Chem
August 2025
State Key Laboratory of Bioreactor Engineering, New World Institute of Biotechnology, East China University of Science and Technology, Shanghai 200237, China.
()-2-chloro-1-(2,4-dichlorophenyl)ethanol (()-CPEO) is an important chiral precursor of the antifungal drug luriconazole. In this study, a mutant alcohol dehydrogenase, ADH from , was redesigned for the efficient synthesis of ()-CPEO by using virtual saturation mutagenesis to assess beneficial site combinations. Five poorly conserved sites in the active pocket of the enzyme were identified via multiple sequence alignment with enzymes exhibiting high activity toward acetophenone derivatives.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Engineering Techniques Training Center, Civil Aviation University of China, Tianjin 300300, China.
Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human-machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band-space-time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions.
View Article and Find Full Text PDFImaging Neurosci (Camb)
July 2024
University of Montréal, Montréal, Canada.
Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyncrasies are obscured by group models, but can be captured by purely individual models given sufficient amounts of training data. In this study, we compared several deep and shallow individual models on the temporal auto-regression of BOLD time-series recorded during a natural video-watching task.
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