Low birthweight and preterm birth in young people with special educational needs: a magnetic resonance imaging analysis

<p>Abstract</p> <p>Background</p> <p>Although neuroanatomical and cognitive sequelae of low birthweight and preterm birth have been investigated, little is understood as to the likely prevalence of a history of low birthweight or preterm birth, or neuroanatomical correlates of such a history, within the special educational needs population. Our aim was to address these issues in a sample of young people receiving additional learning support.</p> <p>Methods</p> <p>One hundred and thirty-seven participants aged 13–22 years, receiving additional learning support, were recruited via their schools or colleges and underwent structural magnetic resonance imaging (MRI). Obstetric records, available in 98 cases, included birthweight and gestational data in 90 and 95 cases, respectively. Both qualitative and quantitative voxel-based analyses of MRI data were conducted.</p> <p>Results</p> <p>A history of low birthweight and preterm birth was present in 13.3% and 13.7% of cases, respectively. Low birthweight and preterm birth were associated with specific qualitative anomalies, including enlargement of subarachnoid cisterns and thinning of the corpus callosum. Low birthweight was associated with reduced grey matter density (GMD) in the superior temporal gyrus (STG) bilaterally, left inferior temporal gyrus and left insula. Prematurity of birth was associated with reduced GMD in the STG bilaterally, right inferior frontal gyrus and left cerebellar hemisphere. Comparison of subjects with no history of low birthweight or preterm birth with a previously defined control sample of cognitively unimpaired adolescents (<it>n </it>= 72) demonstrated significantly greater scores for several anomalies, including thinning of the corpus callosum, loss of white matter and abnormalities of shape of the lateral ventricles.</p> <p>Conclusion</p> <p>Although a two-fold increased prevalence of a history of low birthweight and preterm birth exists within the special educational needs population, other aetiological factors must be considered for the overwhelming majority of cases. Neuroanatomical findings within this sample include qualitative anomalies of brain structure and grey matter deficits within temporal lobe structures and the cerebellum that persist into adolescence. These findings suggest a neurodevelopmental mechanism for the cognitive difficulties associated with these obstetric risk factors.</p

Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra

Column-averaged volume mixing ratios of carbon dioxide and methane retrieved from the Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed observation (GOSAT SWIR XCO2 and XCH4) were compared with the reference data ob- 5 tained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) participating in the Total Carbon Column Observing Network (TCCON). Through calibrations of g-b FTSs with airborne in-situ measurements, the uncertainty of XCO2 and XCH4 associated with the g-b FTS was determined to be 0.8 ppm (0.2%) and 4 ppb (0.2%), respectively. The GOSAT products are validated with 10 these calibrated g-b FTS data. Preliminary results are as follows: The GOSAT SWIR XCO2 and XCH4 (Version 01.xx) are biased low by 8.85±4.75 ppm (2.3±1.2%) and 20.4±18.9 ppb (1.2±1.1%), respectively. The precision of the GOSAT SWIR XCO2 and XCH4 is considered to be about 1%. The latitudinal distributions of zonal means of the GOSAT SWIR XCO2 and XCH4 show similar features to those of the g-b FTS data

Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra

Column-averaged volume mixing ratios of carbon dioxide and methane retrieved from the Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed observation (GOSAT SWIR XCO2 and XCH4 ) were compared with the reference calibrated data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) participating in the Total Carbon Column Observing Network (TCCON). Preliminary results are as follows: the GOSAT SWIR XCO2 and XCH4 (Version 01.xx) are biased low by 8.85 ±4.75 ppm (2.3±1.2 %) and 20.4±18.9 ppb (1.2±1.1 %), respectively. The standard deviation of the GOSAT SWIR XCO2 and XCH4 is about 1% after correcting the negative biases of XCO2 and XCH4 by 8.85 ppm and 20.4 ppb, respectively. The latitudinal distributions of zonal means of the GOSAT SWIR XCO2 and XCH4 show similar features to those of the g-b FTS data except for the negative biases in the GOSAT data

Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data

We use 2009-2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° x 5° and up to 50 km x 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a−1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2-42.7 Tg a−1, as compared to 24.9-27.0 Tg a−1 in the EDGAR and EPA bottom-up inventories, and 30.0-44.5 Tg a−1 in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern-central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29-44 % of US anthropogenic methane emissions to livestock, 22-31 % to oil/gas, 20 % to landfills/wastewater, and 11-15 % to coal. Wetlands contribute an additional 9.0-10.1 Tg a−1

Study of the footprints of short-term variation in XCO₂ observed by TCCON sites using NIES and FLEXPART atmospheric transport models

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers (FTS) that record near-infrared (NIR) spectra of the Sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fraction (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle) Lagrangian Particle Dispersion Model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future, and possible TCCON sites. We propose a footprint-based method for the colocation of satellite and TCCON XCO2 observations, and estimate the performance of the method using the NIES model and five GOSAT XCO2 product datasets. Comparison of the proposed approach with a standard geographic method shows higher number of colocation points and average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and La Réunion sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasizes that the colocation is sensitive to local meteorological conditions and flux distributions

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis : a pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/)

A decade of GOSAT Proxy satellite CH4_{4} observations

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4/XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of -0.84 ppb

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO₂) and methane (CH₄), denoted XCO₂ and XCH₄, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO₂) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO₂ or XCH₄, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO₂ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH₄ products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO₂ and XCH₄ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020)