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The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km × 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa.

The In-service Aircraft for a Global Observing System (IAGOS) is a European research infrastructure that equips the Airbus A340/330 with a system for monitoring atmospheric composition. The IAGOS instruments have three different configurations: IAGOS-Core, IAGOS – Measurement of Ozone and Water Vapor by Airbus In Service Aircraft (IAGOS-MOZAIC) and IAGOS – Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container (IAGOS-CARIBIC). Since 1994, there have been a total of 17 aircraft equipped. In this study, we perform an intercomparison of about 8000 landing and takeoff profiles to compare the O3 and CO measurements performed from these different configurations. The collocated profiles used in the study met various selection criteria. The first was a maximal 1 h time difference between an ascent or descent by two different aircraft at the same airport and the second was a selection based on the similarity of air masses based on the meteorological data acquired by the aircraft. We provide here an evaluation of the internal consistency of the O3 and CO measurements since 1994. For both O3 and CO, we find no drift in the bias amongst the different instrument units (six O3 and six CO IAGOS-MOZAIC instruments, nine IAGOS-Core Package1 and the two instruments used in the IAGOS-CARIBIC aircraft). This result gives us confidence that the entire IAGOS database can be treated as one continuous program and is therefore appropriate for studies of long-term trends.

This paper investigates in an innovative way the climatological vertical stratification of relative humidity (RH), ozone (O3) and carbon monoxide (CO) mixing ratios within the planetary boundary layer (PBL) and at the interface with the free troposphere (FT). The climatology includes all vertical profiles available at northern mid-latitudes over the period 1994–2016 in both the IAGOS (In-service Aircraft for a Global Observing System) and WOUDC (World Ozone and Ultraviolet Radiation Data Centre) databases, which represents more than 90 000 vertical profiles. For all individual profiles, apart from the specific case of surface-based temperature inversions (SBIs), the PBL height is estimated following the elevated temperature inversion (EI) method. Several features of both SBIs and EIs are analysed, including their diurnal and seasonal variations. Based on these PBL height estimates (denoted h), the novel approach introduced in this paper consists of building a so-called PBL-referenced vertical distribution of O3, CO and RH by averaging all individual profiles beforehand expressed as a function of z∕h rather than z (with z the altitude). Using this vertical coordinate system allows us to highlight the features existing at the PBL–FT interface that would have been smoothed otherwise.Results demonstrate that the frequently assumed well-mixed PBL remains an exception for both chemical species. Within the PBL, CO profiles are characterized by a mean vertical stratification (here defined as the standard deviation of the CO profile between the surface and the PBL top, normalized by the mean) of 11 %, with moderate seasonal and diurnal variations. A higher vertical stratification is observed for O3 mixing ratios (18 %), with stronger seasonal and diurnal variability (from ∼ 10 % in spring–summer midday–afternoon to ∼ 25 % in winter–fall night). This vertical stratification is distributed heterogeneously in the PBL with stronger vertical gradients observed at both the surface (due to dry deposition and titration by NO for O3 and due to surface emissions for CO) and the PBL–FT interface. These gradients vary with the season from the lowest values in summer to the highest ones in winter. In contrast to CO, the O3 vertical stratification was found to vary with the surface potential temperature following an interesting bell shape with the weakest stratification for both the lowest (typically negative) and highest temperatures, which could be due to much lower O3 dry deposition in the presence of snow.Therefore, results demonstrate that EIs act as a geophysical interface separating air masses of distinct chemical composition and/or chemical regime. This is further supported by the analysis of the correlation of O3 and CO mixing ratios between the different altitude levels in the PBL and FT (the so-called vertical autocorrelation). Results indeed highlight lower correlations apart from the PBL–FT interface and higher correlations within each of the two atmospheric compartments (PBL and FT).The mean climatological O3 and CO PBL-referenced profiles analysed in this study are freely available on the IAGOS portal for all seasons and times of day (https://doi.org/10.25326/4).

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Supplemental material is available for this article.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

In the framework of the In Service Aircraft for Global Observing System (IAGOS) program, airborne in-situ O3 and CO measurements are performed routinely using in-service aircraft, providing vertical profiles from the surface to about 10–12 km. Due to the specificity of IAGOS measurements (measurements around busy international airports), uncertainties exist on their representativeness in the lower troposphere as they may be impacted by emissions related to airport activities and/or other aircraft. In this study, we thus investigate how the IAGOS measurements in the lower troposphere compare with nearby surface stations (from the local Air Quality monitoring network (AQN)) and more distant regional surface stations (from the Global Atmospheric Watch (GAW) network). The study focuses on Frankfurt but some results at other European airports (Vienna, Paris) are also discussed. Results indicate that the IAGOS observations close to the surface do not appear to be strongly impacted by local emissions related to airport activities. In terms of mixing ratio distribution, seasonal variations and trends, the CO and O3 mixing ratios measured by IAGOS in the first few hundred metres above the surface have similar characteristics to the mixing ratios measured at surrounding urban background stations. Higher in altitude, both the difference with data from the local AQN and the consistency with the GAW regional stations are higher, which indicates a larger representativeness of the IAGOS data. Despite few quantitative differences with Frankfurt, consistent results are obtained in the two other cities Vienna and Paris. Based on 11 years of data (2002–2012), this study thus demonstrates that IAGOS observations in the lowest troposphere can be used as a complement to surface stations to study the air quality in/around the agglomeration, providing important information on the vertical distribution of pollution.

A compact (500 cm3), lightweight (500 g), near-field, single particle backscattering optical spectrometer is described that mounts flush with the skin of an aircraft and measures the concentration and optical equivalent diameter of particles from 5 to 75 μm. The backscatter cloud probe (BCP) was designed as a real-time qualitative cloud detector primarily for data quality control of trace gas instruments developed for the climate monitoring instrument packages that are being installed on commercial passenger aircraft as part of the European Union In-Service Aircraft for a Global Observing System (IAGOS) program (http://www.iagos.org/). Subsequent evaluations of the BCP measurements on a number of research aircraft, however, have revealed it capable of delivering quantitative particle data products including size distributions, liquid-water content and other information on cloud properties. We demonstrate the instrument's capability for delivering useful long-term climatological, as well as aviation performance information, across a wide range of environmental conditions. The BCP has been evaluated by comparing its measurements with those from other cloud particle spectrometers on research aircraft and several BCPs are currently flying on commercial A340/A330 Airbus passenger airliners. The design and calibration of the BCP is described in this article, along with an evaluation of measurements made on the research and commercial aircraft. Preliminary results from more than 7000 h of airborne measurements by the BCP on two Airbus A340s operating on routine global traffic routes (one Lufthansa, the other China Airlines) show that more than 340 h of cloud data have been recorded at normal cruise altitudes (> 10 km) and more than 40% of the > 1200 flights were through clouds at some point between takeoff and landing. These data are a valuable contribution to databases of cloud properties, including sub-visible cirrus, in the upper troposphere and useful for validating satellite retrievals of cloud water and effective radius; in addition, providing a broader, geographically and climatologically relevant view of cloud microphysical variability that is useful for improving parameterizations of clouds in climate models. Moreover, they are also useful for monitoring the vertical climatology of clouds over airports, especially those over megacities where pollution emissions may be impacting local and regional climate.

We present a global data set of free tropospheric ozone–CO correlations with 2° × 2.5° spatial resolution from the Ozone Monitoring Instrument (OMI) and Atmospheric Infrared Sounder (AIRS) satellite instruments for each season of 2008. OMI and AIRS have near-daily global coverage of ozone and CO respectively and observe coincident scenes with similar vertical sensitivities. The resulting ozone–CO correlations are highly statistically significant (positive or negative) in most regions of the world, and are less noisy than previous satellite-based studies that used sparser data. Comparison with ozone–CO correlations and regression slopes (dO3/dCO) from MOZAIC (Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) aircraft profiles shows good general agreement. We interpret the observed ozone–CO correlations with the GEOS (Goddard Earth Observing System)-Chem chemical transport model to infer constraints on ozone sources. Driving GEOS-Chem with different meteorological fields generally shows consistent ozone–CO correlation patterns, except in some tropical regions where the correlations are strongly sensitive to model transport error associated with deep convection. GEOS-Chem reproduces the general structure of the observed ozone–CO correlations and regression slopes, although there are some large regional discrepancies. We examine the model sensitivity of dO3/dCO to different ozone sources (combustion, biosphere, stratosphere, and lightning NOx) by correlating the ozone change from that source to CO from the standard simulation. The model reproduces the observed positive dO3/dCO in the extratropical Northern Hemisphere in spring–summer, driven by combustion sources. Stratospheric influence there is also associated with a positive dO3/dCO because of the interweaving of stratospheric downwelling with continental outflow. The well-known ozone maximum over the tropical South Atlantic is associated with negative dO3/dCO in the observations; this feature is reproduced in GEOS-Chem and supports a dominant contribution from lightning to the ozone maximum. A major model discrepancy is found over the northeastern Pacific in summer–fall where dO3/dCO is positive in the observations but negative in the model, for all ozone sources. We suggest that this reflects a model overestimate of lightning at northern midlatitudes combined with an underestimate of the East Asian CO source.

The IASI (Infrared Atmospheric Sounding Interferometer) nadir-looking thermal infrared sounder onboard MetOp-A enables the monitoring of atmospheric constituents on a global scale. This paper presents a quality assessment of IASI CO profiles retrieved by the two different retrieval algorithms SOFRID and FORLI, by an intercomparison with airborne in-situ CO profiles from the MOZAIC program for the 2008–2009 period. Lower (surface–480 hPa) and upper tropospheric partial column (480–225 hPa) comparisons as well as profile comparisons are made. The retrieval errors of the IASI products are less than 21% in the lower troposphere and less than 10% in the upper troposphere. A statistical analysis shows similar correlation coefficients for the two retrieval algorithms and smoothed MOZAIC of r ~ 0.8 and r ~ 0.7 in the lower and upper troposphere respectively. Comparison with smoothed MOZAIC data of the temporal variation of the CO profiles at the airports of Frankfurt and Windhoek demonstrates that the IASI products are able to capture the seasonal variability at these sites. At Frankfurt SOFRID (respectively FORLI) is positively biased by 10.5% (13.0%) compared to smoothed MOZAIC in the upper (lower) troposphere, and the limited sensitivity of the IASI instrument to the boundary layer when thermal contrast is low is identified. At Windhoek, the impact of the vegetation fires in Southern Africa from July to November is captured by both SOFRID and FORLI, with an overestimation of the CO background values (fire maxima) by SOFRID (FORLI) by 12.8% (10%). Profile comparisons at Frankfurt and Windhoek show that the largest discrepancies are found between the two IASI products and MOZAIC for the nighttime retrievals.

This work evaluates the IASI CO product against independent in-situ aircraft data from the MOZAIC program and the POLARCAT aircraft campaign. The validation is carried out by analysing the impact of assimilation of eight months of IASI CO columns retrieved for the period of May to December 2008 into the global chemistry transport model LMDz-INCA. A modelling system based on a sub-optimal Kalman filter was developed and a specific treatment that takes into account the representativeness of observations at the scale of the model grid is applied to the IASI CO columns and associated errors before their assimilation in the model. Comparisons of the assimilated CO profiles with in situ CO measurements indicate that the assimilation leads to a considerable improvement of the model simulations in the middle troposphere as compared with a control run with no assimilation. Model biases in the simulation of background values are reduced and improvement in the simulation of very high concentrations is observed. The improvement is due to the transport by the model of the information present in the IASI CO retrievals. Our analysis also shows the impact of assimilation of CO on the representation of transport into the Arctic region during the POLARCAT summer campaign. A considerable increase in CO mixing ratios over the Asian source region was observed when assimilation was used leading to much higher values of CO during the cross-pole transport episode. These higher values are in good agreement with data from the POLARCAT flights that sampled this plume.

Atmospheric airborne measurements of CO2 are very well suited for estimating the time-varying distribution of carbon sources and sinks at the regional scale due to the large geographical area covered over a short time. We present here an analysis of two cross-European airborne campaigns carried out on 23–26 May 2001 (CAATER-1) and 2–3 October 2002 (CAATER-2) over Western Europe. The area covered during CAATER-1 and CAATER-2 was 4° W to 14° E long; 44° N to 52° N lat and 1° E to 17° E long; 46° N to 52° N lat respectively. High precision in situ CO2, CO and Radon 222 measurements were recorded. Flask samples were collected during both campaigns to cross-validate the in situ data. During CAATER-1 and CAATER-2, the mean CO2 concentration was 370.1 ± 4.0 (1-σ standard deviation) ppm and 371.7 ± 5.0 (1-σ) ppm respectively. A HYSPLIT back-trajectories analysis shows that during CAATER 1, northwesterly winds prevailed. In the planetary boundary layer (PBL) air masses became contaminated over Benelux and Western Germany by emissions from these highly urbanized areas, reaching about 380 ppm. Air masses passing over rural areas were depleted in CO2 because of the photosynthesis activity of the vegetation, with observations as low as 355 ppm. During CAATER-2, the back-trajectory analysis showed that air masses were distributed among the 4 sectors. Air masses were enriched in CO2 and CO over anthropogenic emission spots in Germany but also in Poland, as these countries have part of the most CO2-emitting coal-based plants in Europe. Simultaneous measurements of in situ CO2 and CO combined with back-trajectories helped us to distinguish between fossil fuel emissions and other CO2 sources. The ΔCO/ΔCO2 ratios (R2 = 0.33 to 0.88, slopes = 2.42 to 10.37), calculated for anthropogenic-influenced air masses over different countries/regions matched national inventories quite well, showing that airborne measurements can help to identify the origin of fossil fuel emissions in the PBL even when distanced by several days/hundreds of kms from their sources. We have compared airborne CO2 observations to nearby ground station measurements and thereby, confirmed that measurements taken in the lower few meters of the PBL (low-level ground stations) are representative of the local scale, while those located in the free troposphere (FT) (moutain stations) are representative of atmospheric CO2 regionally on a scale of a few hundred kilometers. Stations located several 100 km away from each other differ from a few ppm in their measurements indicating the existence of a gradient within the free troposphere. Observations at stations located on top of small mountains may match the airborne data if the sampled air comes from the FT rather than coming up from the valley. Finally, the analysis of the CO2 vertical variability conducted on the 14 profiles recorded in each campaign shows a variability at least 5 to 8 times higher in the PBL (the 1-σ standard deviation associated to the CO2 mean of all profiles within the PBL is 4.0 ppm and 5.7 ppm for CAATER-1 and CAATER-2, respectively) than in the FT (within the FT, 1-σ is 0.5 ppm and 1.1 ppm for CAATER-1 and CAATER-2, respectively). The CO2 jump between the PBL and the FT equals 3.7 ppm for the first campaign and −0.3 ppm for the second campaign. A very striking zonal CO2 gradient of about 11 ppm was observed in the mid-PBL during CAATER-2, with higher concentrations in the west than in the east. This gradient may originate from differences in atmospheric mixing, ground emission rates or Autumn's earlier start in the west. More airborne campaigns are currently under analysis in the framework of the CARBOEUROPE-IP project to better assess the likelihood of these different hypotheses. In a companion paper (Xueref-Remy et al., 2011, Part 2), a comparison of vertical profiles from observations and several modeling frameworks was conducted for both campaigns.

We use the GEOS-Chem chemistry-transport model (CTM) to interpret the spatial and temporal variations of tropical tropospheric CO observed by the Microwave Limb Sounder (MLS) and the Tropospheric Emission Spectrometer (TES). In so doing, we diagnose and evaluate transport in the GEOS-4 and GEOS-5 assimilated meteorological fields that drive the model, with a particular focus on vertical mixing at the end of the dry season when convection moves over the source regions. The results indicate that over South America, deep convection in both GEOS-4 and GEOS-5 decays at too low an altitude early in the wet season, and the source of CO from isoprene in the model (MEGAN v2.1) is too large, causing a lag in the model's seasonal maximum of CO compared to MLS CO in the upper troposphere (UT). TES and MLS data reveal problems with excessive transport of CO to the eastern equatorial Pacific and lofting in the ITCZ in August and September, particularly in GEOS-4. Over southern Africa, GEOS-4 and GEOS-5 simulations match the phase of the observed CO variation from the lower troposphere (LT) to the UT fairly well, although the magnitude of the seasonal maximum is underestimated considerably due to low emissions in the model. A sensitivity run with increased emissions leads to improved agreement with observed CO in the LT and middle troposphere (MT), but the amplitude of the seasonal variation is too high in the UT in GEOS-4. Difficulty in matching CO in the LT and UT implies there may be overly vigorous vertical mixing in GEOS-4 early in the wet season. Both simulations and observations show a time lag between the peak in fire emissions (July and August) and in CO (September and October). We argue that it is caused by the prevailing subsidence in the LT until convection moves south in September, as well as the low sensitivity of TES data in the LT over the African Plateau. The MLS data suggest that too much CO has been transported from fires in northern Africa to the UT in the model during the burning season, as does MOZAIC aircraft data, perhaps as a result of the combined influence of too strong Harmattan winds in the LT and too strong vertical mixing over the Gulf of Guinea in the model.

We combine CO column measurements from the MOPITT, AIRS, SCIAMACHY, and TES satellite instruments in a full-year (May 2004–April 2005) global inversion of CO sources at 4°×5° spatial resolution and monthly temporal resolution. The inversion uses the GEOS-Chem chemical transport model (CTM) and its adjoint applied to MOPITT, AIRS, and SCIAMACHY. Observations from TES, surface sites (NOAA/GMD), and aircraft (MOZAIC) are used for evaluation of the a posteriori solution. Using GEOS-Chem as a common intercomparison platform shows global consistency between the different satellite datasets and with the in situ data. Differences can be largely explained by different averaging kernels and a priori information. The global CO emission from combustion as constrained in the inversion is 1350 Tg a−1. This is much higher than current bottom-up emission inventories. A large fraction of the correction results from a seasonal underestimate of CO sources at northern mid-latitudes in winter and suggests a larger-than-expected CO source from vehicle cold starts and residential heating. Implementing this seasonal variation of emissions solves the long-standing problem of models underestimating CO in the northern extratropics in winter-spring. A posteriori emissions also indicate a general underestimation of biomass burning in the GFED2 inventory. However, the tropical biomass burning constraints are not quantitatively consistent across the different datasets.

The African Monsoon Multidisciplinary Analyses (AMMA) fourth airborne campaign was conducted in July–August 2006 to study the chemical composition of the middle and upper troposphere in West Africa with the major objective to better understand the processing of chemical emissions by the West African Monsoon (WAM) and its associated regional-scale and vertical transports. In particular, the french airborne experiment was organized around two goals. The first was to characterize the impact of Mesoscale Convective Systems (MCSs) on the ozone budget in the upper troposphere and the evolution of the chemical composition of these convective plumes as they move westward toward the Atlantic Ocean. The second objective was to discriminate the impact of remote sources of pollution over West Africa, including transport from the middle east, Europe, Asia and from southern hemispheric fires. Observations of O3, CO, NOx, H2O and hydroperoxide above West Africa along repeated meridional transects were coupled with transport analysis based on the FLEXPART lagrangian model. The cross analysis of trace gas concentrations and transport pathways revealed 5 types of air masses: convective uplift of industrial and urban emissions, convective uplift of biogenic emissions, slow advection from Cotonou polluted plumes near the coast, meridional transport of upper tropospheric air from the subtropical barrier region, and meridional transport of Southern Hemisphere (SH) biomass burning emissions. O3/CO correlation plots and the correlation plots of H2O2 with a OH proxy revealed not only a control of the trace gas variability by transport processes but also significant photochemical reactivity in the mid- and upper troposphere. The study of four MCSs outflow showed contrasted chemical composition and air mass origins depending on the MCSs lifetime and latitudinal position. Favorables conditions for ozone production were found for MCSs with increased MCS lifetime (>1.5 days), which allowed for more H2O2 formation, and with trajectories crossing the 10° N latitude, which increased CO transport to the upper troposphere. The upper tropospheric concentrations sampled in the MCS outflow regions showed mixed origins including local vertical convective transport, and uplifting of air from the low troposphere over the middle-east related to the summer Asian low pressure system or from the southern hemispheric fires.

A statistical analysis for the comparability of water (H2O) and ozone (O3) data sets sampled during the SPURT aircraft campaigns and the MOZAIC passenger aircraft flights is presented. The Kolmogoroff-Smirnoff test reveals that the distribution functions from SPURT and MOZAIC trace gases differ from each other with a confidence of 95%. A variance analysis shows a different variability character in both trace gas data sets. While the SPURT H2O data only contain atmospheric processes variable on a diurnal or synoptical timescale, MOZAIC H2O data also reveal processes, which vary on inter-seasonal and seasonal timescales. The SPURT H2O data set does not represent the full MOZAIC H2O variance in the UT/LS for climatological investigations, whereas the variance of O3 is much better represented. SPURT H2O data are better suited in the stratosphere, where the MOZAIC RH sensor looses its sensitivity.

The AMMA (African Monsoon Multidisciplinary Analysis) program is dedicated to providing a better understanding of the West African monsoon and its influence on the physical, chemical and biological environment regionally and globally, as well as relating variability of this monsoon system to issues of health, water resources, food security and demography for West African nations. Within this framework, an intensive field campaign took place during the summer of 2006 to better document specific processes and weather systems at various key stages of this monsoon season. This campaign was embedded within a longer observation period that documented the annual cycle of surface and atmospheric conditions between 2005 and 2007. The present paper provides a large and regional scale overview of the 2006 summer monsoon season, that includes consideration of of the convective activity, mean atmospheric circulation and synoptic/intraseasonal weather systems, oceanic and land surface conditions, continental hydrology, dust concentration and ozone distribution. The 2006 African summer monsoon was a near-normal rainy season except for a large-scale rainfall excess north of 15° N. This monsoon season was also characterized by a 10-day delayed onset compared to climatology, with convection becoming developed only after 10 July. This onset delay impacted the continental hydrology, soil moisture and vegetation dynamics as well as dust emission. More details of some less-well-known atmospheric features in the African monsoon at intraseasonal and synoptic scales are provided in order to promote future research in these areas.

This study presents an analysis of both MOZAIC profiles above Frankfurt and Lagrangian dispersion model simulations for the 2003 European heat wave. The comparison of MOZAIC measurements in summer 2003 with the 11-year MOZAIC climatology reflects strong temperature anomalies (exceeding 4°C) throughout the lower troposphere. Higher positive anomalies of temperature and negative anomalies of both wind speed and relative humidity are found for the period defined here as the heat wave (2–14 August 2003), compared to the periods before (16–31 July 2003) and after (16–31 August 2003) the heat wave. In addition, Lagrangian model simulations in backward mode indicate the suppressed long-range transport in the mid- to lower troposphere and the enhanced southern origin of air masses for all tropospheric levels during the heat wave. Ozone and carbon monoxide also present strong anomalies (both ~+40 ppbv) during the heat wave, with a maximum vertical extension reaching 6 km altitude around 11 August 2003. Pollution in the planetary boundary layer (PBL) is enhanced during the day, with ozone mixing ratios two times higher than climatological values. This is due to a combination of factors, such as high temperature and radiation, stagnation of air masses and weak dry deposition, which favour the accumulation of ozone precursors and the build-up of ozone. A negligible role of a stratospheric-origin ozone tracer has been found for the lower troposphere in this study. From 29 July to 15 August 2003 forest fires burnt around 0.3×106 ha in Portugal and added to atmospheric pollution in Europe. Layers with enhanced CO and NOy mixing ratios, advected from Portugal, were crossed by the MOZAIC aircraft in the free troposphere over Frankfurt. A series of forward and backward Lagrangian model simulations have been performed to investigate the origin of anomalies during the whole heat wave. European anthropogenic emissions present the strongest contribution to the measured CO levels in the lower troposphere (near 30%). This source is followed by Portuguese forest fires which affect the lower troposphere after 6 August 2003 and even the PBL around 10 August 2003. The averaged biomass burning contribution reaches 35% during the affected period. Anthropogenic CO of North American origin only marginally influences CO levels over Europe during that period.

BACKGROUND AND PURPOSE: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages. MATERIALS AND METHODS: This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains. RESULTS: Fetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson r = 0.996, P 2 SDs from this age-specific reference mean. There were no effects of fetal sex, maternal diabetes, or maternal age on intracranial or brain volumes across gestational ages. CONCLUSIONS: Deep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard.

Spinal motor neurons (MNs) integrate sensory stimuli and brain commands to generate movements. In vertebrates, the molecular identities of the cardinal MN types such as those innervating limb versus trunk muscles are well elucidated. Yet the identities of finer subtypes within these cell populations that innervate individual muscle groups remain enigmatic. Here we investigate heterogeneity in mouse MNs using single-cell transcriptomics. Among limb-innervating MNs, we reveal a diverse neuropeptide code for delineating putative motor pool identities. Additionally, we uncover that axial MNs are subdivided into three molecularly distinct subtypes, defined by mediolaterally-biased Satb2, Nr2f2 or Bcl11b expression patterns with different axon guidance signatures. These three subtypes are present in chicken and human embryos, suggesting a conserved axial MN expression pattern across higher vertebrates. Overall, our study provides a molecular resource of spinal MN types and paves the way towards deciphering how neuronal subtypes evolved to accommodate vertebrate motor behaviors.

The European-funded MOZAIC programme (Measurements of ozone and water vapour by Airbus in-service aircraft) has been operational since 1994 aboard 5 commercial Airbus A340. It has gathered ozone and water vapour data between the ground and an altitude of 12 km from more than 20 000 long-range flights. A new infrared carbon monoxide analyser has been developed for installation on the MOZAIC equipped aircraft. Improvements in the basic characteristics of a commercial CO analysers have achieved performance suitable for routine aircraft measurements : ±5 ppbv, ±5% precision for a 30 s response time. The first year of operation on board 4 aircraft with more than 900 flights has proven the reliability and the usefulness of this CO analyser. The first scientific results are presented here, including UTLS exchange events and pollution within the boundary layer.

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During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

PurposeTo assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.Materials and methodsIn this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients.ResultsThe U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98).ConclusionFor brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

BackgroundNeurocognitive deficits in pediatric cancer survivors occur frequently; however, individual outcomes are unpredictable. We investigate clinical, genetic, and imaging predictors of neurocognition in pediatric cancer survivors, with a focus on survivors of central nervous system (CNS) tumors exposed to radiation.MethodsOne hundred eighteen patients with benign or malignant cancers (median diagnosis age: 7; 32% embryonal CNS tumors) were selected from an existing multi-institutional cohort (RadART Pro) if they had: 1) neurocognitive evaluation; 2) available DNA; 3) standard imaging. Utilizing RadART Pro, we collected clinical history, genomic sequencing, CNS imaging, and neurocognitive outcomes. We performed single nucleotide polymorphism (SNP) genotyping for candidate genes associated with neurocognition: COMT, BDNF, KIBRA, APOE, KLOTHO. Longitudinal neurocognitive testing were performed using validated computer-based CogState batteries. The imaging cohort was made of patients with available iron-sensitive (n = 28) and/or T2 FLAIR (n = 41) sequences. Cerebral microbleeds (CMB) were identified using a semi-automated algorithm. Volume of T2 FLAIR white matter lesions (WML) was measured using an automated method based on a convolutional neural network. Summary statistics were performed for patient characteristics, neurocognitive assessments, and imaging. Linear mixed effects and hierarchical models assessed patient characteristics and SNP relationship with neurocognition over time. Nested case-control analysis was performed to compare candidate gene carriers to non-carriers.ResultsCMB presence at baseline correlated with worse performance in 3 of 7 domains, including executive function. Higher baseline WML volumes correlated with worse performance in executive function and verbal learning. No candidate gene reliably predicted neurocognitive outcomes; however, APOE ϵ4 carriers trended toward worse neurocognitive function over time compared to other candidate genes and carried the highest odds of low neurocognitive performance across all domains (odds ratio 2.85, P=0.002). Hydrocephalus and seizures at diagnosis were the clinical characteristics most frequently associated with worse performance in neurocognitive domains (5 of 7 domains). Overall, executive function and verbal learning were the most frequently negatively impacted neurocognitive domains.ConclusionPresence of CMB, APOE ϵ4 carrier status, hydrocephalus, and seizures correlate with worse neurocognitive outcomes in pediatric cancer survivors, enriched with CNS tumors exposed to radiation. Ongoing research is underway to verify trends in larger cohorts.