Reducing variability in along-tract analysis with diffusion profile realignment

Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g. disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the HCP, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Pairwise Student's t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows.Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before peer-review which was mistakenly included v2 : peer-reviewed version v1: preprint as submitted to journal NeuroImag

Les aménagements gagnants d'une CLAAC : ce qu'en disent les étudiants

Comprend des références bibliographiques

Les conditions d'efficacité des classes d'apprentissage actif

PA-2013-012La présente recherche a été subventionnée par le ministère de l’Éducation et de l’Enseignement supérieur dans le cadre du Programme d’aide à la recherche sur l’enseignement et l’apprentissage (PAREA).Comprend des références bibliographiques

Les habitudes technologiques au cégep : résultats d'une enquête effectuée auprès de 30 724 étudiants

Texte en anglais.Bibliographie : pages 55-56

Validation of an electrogoniometry system as a measure of knee kinematics during activities of daily living

Purpose: The increasing use of electrogoniometry (ELG) in clinical research requires the validation of different instrumentation. The purpose of this investigation was to examine the concurrent validity of an ELG system during activities of daily living. Methods: Ten asymptomatic participants gave informed consent to participate. A Biometrics SG150 electrogoniometer was directly compared to a 12 camera three dimensional motion analysis system during walking, stair ascent, stair descent, sit to stand, and stand to sit activities for the measurement of the right knee angle. Analysis of validity was undertaken by linear regression. Standard error of estimate (SEE), standardised SEE (SSEE), and Pearson’s correlation coefficient r were computed for paired trials between systems for each functional activity. Results: The 95% confidence interval of SEE was reasonable between systems across walking (LCI = 2.43 °; UCI = 2.91 °), stair ascent (LCI = 2.09 °; UCI = 2.42 °), stair descent (LCI = 1.79 °; UCI = 2.10 °), sit to stand (LCI = 1.22 °; UCI = 1.41 °), and stand to sit (LCI = 1.17 °; UCI = 1.34 °). Pearson’s correlation coefficient r across walking (LCI = 0.983; UCI = 0.990), stair ascent (LCI = 0.995; UCI = 0.997), stair descent (LCI = 0.995; UCI = 0.997), sit to stand (LCI = 0.998; UCI = 0.999), and stand to sit (LCI = 0.996; UCI = 0.997) was indicative of a strong linear relationship between systems. Conclusion: ELG is a valid method of measuring the knee angle during activities representative of daily living. The range is within that suggested to be acceptable for the clinical evaluation of patients with musculoskeletal conditions

Finding a Place for Interrogatory Theory: A Critique of Chimakonam’s Patterns of Social Deconstruction, Reconstruction and the Conversational Order in African Philosophy

No Abstrac

Characterization of health and environmental risks of pesticide use in market-gardening in the rural city of Tori-Bossito in Benin, West Africa

A study on the use of pesticides in market-gardening production was carried out on 108 market-gardeners in the rural city of Tori-Bossito in Southern Benin. The objective of the study was to characterize the potential risks of pesticides usage by farmers and the impacts on their health and on the environment. Two risk indexes were calculated for each pesticide: an environmental risk index (ERI) and a health risk index (HRI). First stage larva of the mosquito Aedes ae-gypti were used as bio-indicator for detecting insecticide residue in vegetable before their harvesting on the farms. The highest ERI were obtained for carbofuran, chlorpyriphos ethyl and endosulfan. Pesticide residues were found in 42% of the samples of leaves of eggplant, cucumber, amaranth and solanum. Vegetables growers used pesticides that may be highly hazardous and which were not registered in most cases. These situations could have unexpected consequences including the exposure of consumers to health hazards. (Résumé d'auteur

Why do models overestimate surface ozone in the Southeast United States

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx  ≡  NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°  ×  0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer