19 research outputs found
Complex nature of apparently balanced chromosomal rearrangements in patients with autism spectrum disorder
Get PDFBackground:
Apparently balanced chromosomal rearrangements can be associated with an abnormal phenotype, including intellectual disability and autism spectrum disorder (ASD). Genome-wide microarrays reveal cryptic genomic imbalances, related or not to the breakpoints, in 25% to 50% of patients with an abnormal phenotype carrying a microscopically balanced chromosomal rearrangement. Here we performed microarray analysis of 18 patients with ASD carrying balanced chromosomal abnormalities to identify submicroscopic imbalances implicated in abnormal neurodevelopment.
Methods:
Eighteen patients with ASD carrying apparently balanced chromosomal abnormalities were screened using single nucleotide polymorphism (SNP) arrays. Nine rearrangements were de novo, seven inherited, and two of unknown inheritance. Genomic imbalances were confirmed by fluorescence in situ hybridization and quantitative PCR.
Results:
We detected clinically significant de novo copy number variants in four patients (22%), including three with de novo rearrangements and one with an inherited abnormality. The sizes ranged from 3.3 to 4.9 Mb; three were related to the breakpoint regions and one occurred elsewhere. We report a patient with a duplication of the Wolf-Hirschhorn syndrome critical region, contributing to the delineation of this rare genomic disorder. The patient has a chromosome 4p inverted duplication deletion, with a 0.5 Mb deletion of terminal 4p and a 4.2 Mb duplication of 4p16.2p16.3. The other cases included an apparently balanced de novo translocation t(5;18)(q12;p11.2) with a 4.2 Mb deletion at the 18p breakpoint, a subject with de novo pericentric inversion inv(11)(p14q23.2) in whom the array revealed a de novo 4.9 Mb deletion in 7q21.3q22.1, and a patient with a maternal inv(2)(q14.2q37.3) with a de novo 3.3 Mb terminal 2q deletion and a 4.2 Mb duplication at the proximal breakpoint. In addition, we identified a rare de novo deletion of unknown significance on a chromosome unrelated to the initial rearrangement, disrupting a single gene, RFX3.
Conclusions:
These findings underscore the utility of SNP arrays for investigating apparently balanced chromosomal abnormalities in subjects with ASD or related neurodevelopmental disorders in both clinical and research settings
Radiometric and Spectral Inter-comparison of IASI: IASI-A / IASI-B, IASI / AIRS, IASI / CrIS
Get PDFIASI-A has been in operation on board MetOp-A since June 2007 and is now considered as a reference sensor for the radiometric calibration at high spectral resolution. IASI-B on board MetOp-B was launched on September 17th, 2012, and finished its commissioning phase in April 2013. CNES is in charge of the performance monitoring of the two IASI instruments. One of the associated tasks is to compare the calibration of IASI-A with IASI-B, and to compare each one with other similar sensors (AIRS and CrIS). Our goal is to check the IASI data quality with an external reference and to establish the high radiometric accuracy required for climate data records. After more than one year of IASI-B data, we are now in long-term routine monitoring. Our inter-comparisons are now valid on every season and observation condition.
We first present the radiometric inter-calibration between IASI-A and IASI-B. An original method has been developed based on the observations of common regions with a 50 minutes temporal gap and in different viewing conditions due to the orbital configuration. The input dataset filtering has been tuned to minimize the geophysical biases. The sounding pixels are spatially averaged on a 300x300kmÂČ area. Radiometry is compared at full spectral resolution. We also present the radiometric inter-calibration of IASI (A and B) with Aqua/AIRS and NPP/CrIS. Our method is based on simultaneous nadir overpasses, occurring at high altitudes. The difference in spectral sampling and resolution is handled through either the use of spectrally broad pseudo-channels or the reconvolution of IASI spectra. Results for with more than one year of data for IASI-A / AIRS, IASI-B / AIRS, IASI-A / CRIS, IASI-B / CRIS and IASI-A / IASI-B show small biases of the order of 0 to 0.2K. The five sounders show very stable inter-calibration. Several diagnosis tools are presented to understand the small residuals of inter-calibration. An indirect inter-comparison IASI-B / IASI-A is also presented using double differences with AIRS and CRIS. We also present a long-term monitoring of the spectral cross-calibration, based on the cross-correlation of spectra on several spectrally broad pseudo-channels
SMOS first results over land
No full textInternational audienceThe Soil Moisture and Ocean Salinity (SMOS) mission is ESA's (European Space Agency ) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint programme between ESA CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere and hence the instrument probes the Earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. In order to prepare the data use and dissemination, the ground segment will produce level 1 and 2 data. Level 1 consists mainly of angular brightness temperatures while level 2 consists of geophysical products. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis documents (ATBD) to be used to produce the operational algorithm. The principle of the soil moisture retrieval algorithm is based on an iterative approach which aims at minimizing a cost function given by the sum of the squared weighted differences between measured and modelled brightness temperature (TB) data, for a variety of incidence angles. This is achieved by finding the best suited set of the parameters which drive the direct TB model, e.g. soil moisture (SM) and vegetation characteristics. Despite the simplicity of this principle, the main reason for the complexity of the algorithm is that SMOS "pixels" can correspond to rather large, inhomogeneous surface areas whose contribution to the radiometric signal is difficult to model. Moreover, the exact description of pixels, given by a weighting function which expresses the directional pattern of the SMOS interferometric radiometer, depends on the incidence angle. The goal is to retrieve soil moisture over fairly large and thus inhomogeneous areas. The retrieval is carried out at nodes of a fixed Earth surface grid. To achieve this purpose, after checking input data quality and ingesting auxiliary data, the retrieval process per se can be initiated. This cannot be done blindly as the direct model will be dependent upon surface characteristics. It is thus necessary to first assess what is the dominant land use of a node. For this, an average weighing function (MEAN_WEF) which takes into account the "antenna"pattern is run over the high resolution land use map to assess the dominant cover type. This is used to drive the decision tree which, step by step, selects the type of model to be used as per surface conditions. All this being said and done the retrieval procedure starts if all the conditions are satisfied, ideally to retrieve 3 parameters over the dominant class (the so-called rich retrieval). If the algorithm does not converge satisfactorily, a new trial is made with less floating parameters ("poorer retrieval") until either results are satisfactory or the algorithm is considered to fail. The retrieval algorithm also delivers whenever possible a dielectric constant parameter (using the-so called cardioid approach). Finally, once the retrieval converged, it is possible to compute the brightness temperature at a given fixed angle (42.5°) using the selected forward models applied to the set of parameters obtained at the end of the retrieval process. So the output product of the level 2 soil moisture algorithm should be node position, soil moisture, dielectric constants, computed brightness temperature at 42.5°, flags and quality indices. During the presentation we will describe in more details the algorithm and accompanying work in particular decision tree principle and characteristics, the auxiliary data used and the special and "exotic"cases. We will also be more explicit on the algorithm validation and verification through the data collected during the commissioning phase. The main hurdle being working in spite of spurious signals (RFI) on some areas of the globe
SMOS first results over land
No full textInternational audienceThe Soil Moisture and Ocean Salinity (SMOS) mission is ESA's (European Space Agency ) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint programme between ESA CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere and hence the instrument probes the Earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. In order to prepare the data use and dissemination, the ground segment will produce level 1 and 2 data. Level 1 consists mainly of angular brightness temperatures while level 2 consists of geophysical products. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis documents (ATBD) to be used to produce the operational algorithm. The principle of the soil moisture retrieval algorithm is based on an iterative approach which aims at minimizing a cost function given by the sum of the squared weighted differences between measured and modelled brightness temperature (TB) data, for a variety of incidence angles. This is achieved by finding the best suited set of the parameters which drive the direct TB model, e.g. soil moisture (SM) and vegetation characteristics. Despite the simplicity of this principle, the main reason for the complexity of the algorithm is that SMOS "pixels" can correspond to rather large, inhomogeneous surface areas whose contribution to the radiometric signal is difficult to model. Moreover, the exact description of pixels, given by a weighting function which expresses the directional pattern of the SMOS interferometric radiometer, depends on the incidence angle. The goal is to retrieve soil moisture over fairly large and thus inhomogeneous areas. The retrieval is carried out at nodes of a fixed Earth surface grid. To achieve this purpose, after checking input data quality and ingesting auxiliary data, the retrieval process per se can be initiated. This cannot be done blindly as the direct model will be dependent upon surface characteristics. It is thus necessary to first assess what is the dominant land use of a node. For this, an average weighing function (MEAN_WEF) which takes into account the "antenna"pattern is run over the high resolution land use map to assess the dominant cover type. This is used to drive the decision tree which, step by step, selects the type of model to be used as per surface conditions. All this being said and done the retrieval procedure starts if all the conditions are satisfied, ideally to retrieve 3 parameters over the dominant class (the so-called rich retrieval). If the algorithm does not converge satisfactorily, a new trial is made with less floating parameters ("poorer retrieval") until either results are satisfactory or the algorithm is considered to fail. The retrieval algorithm also delivers whenever possible a dielectric constant parameter (using the-so called cardioid approach). Finally, once the retrieval converged, it is possible to compute the brightness temperature at a given fixed angle (42.5°) using the selected forward models applied to the set of parameters obtained at the end of the retrieval process. So the output product of the level 2 soil moisture algorithm should be node position, soil moisture, dielectric constants, computed brightness temperature at 42.5°, flags and quality indices. During the presentation we will describe in more details the algorithm and accompanying work in particular decision tree principle and characteristics, the auxiliary data used and the special and "exotic"cases. We will also be more explicit on the algorithm validation and verification through the data collected during the commissioning phase. The main hurdle being working in spite of spurious signals (RFI) on some areas of the globe
Ten-year assessment of IASI radiance and temperature
No full textInternational audienceThe Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI's large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007-2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm-1 to 0.1 K at 2200 cm-1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5-10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends
