dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation cohort of adjacent normal tissue and DCIS To validate the methylation changes between normal tissue and DCIS, an independent set of DCIS and adjacent normal tissues was profiled using the Illumina Infinium HumanMethylation450 array. FFPE pure DCIS (n = 40) and adjacent normal tissue (n = 15) underwent pathology review and 2 mm core punches were taken for processing as described in the Illumina Infinium FFPE Restoration solution protocol. The methylation data were preprocessed using the R package ChAMP  [56]  and 397,600 probes out of 485,577 remained after quality control. A gene region collapsed data set was also constructed for this data set as described above.
Validation cohort from The Cancer Genome Atlas To validate the prognostic signature, DNA methylation data and clinical information were downloaded from TCGA data portal  [9]. Only breast cancer patients for whom there were both overall survival data and tumor DNA methylation analysis had been performed by Illumina HumanMethylation450 were used for validation (n = 583). Probes with more than 50% missing values were removed, and further missing values were imputed using the function pamr. knnimpute (R package pamr)  [57]  with k = 10.
Acknowledgements TF was a PhD fellow at the  Faculty of Medicine, University of Oslo. This research was supported by  NFR-Kreft  grant no. 193387/H10 ,  K. G. Jebsen Center  for breast cancer research, DNK: The genetic makeup of breast cancer patients grant no. 368039-6260-33220 , HSØ: OSBRAC 39346 (OUS no. ), HSØ: The participation of Ahus on the K. G. Jebsen Center  grant no. 2639032 , and  NIH  grant  P20GM104416  (BCC).
A new free-tropospheric humidity (FTH) data record is presented. It is based on observations from the Meteosat Visible and Infrared Imager (MVIRI) onboard Meteosat-2-Meteosat-5, as well as Meteosat-7, and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-8 and Meteosat-9 at the water absorption band near 6. 3 µm. The data set is available under clear-sky and low-level cloud conditions. With the extension to SE-VIRI observations, the data record covers the period 1983-2009 with a spatial resolution of 0. 625 • × 0. 625 • and a temporal resolution of 3 h. The FTH is the mean relative humidity (RH) in a broad layer in the free troposphere. The relation between the observed brightness temperature (BT) and the FTH is well established. Previous retrievals are refined by taking into account the relative humidity Jacobians in the training process of the statistical retrieval.
The temporal coverage is extended into the SEVIRI period, the homogenization of the BT record is improved, and the full archive is reprocessed using updated regression coefficients. The FTH estimated from the Meteosat observations is compared to the FTH computed from the RH profiles of the Analyzed RadioSoundings Archive (ARSA). An average relative bias of -3. 2 % and a relative root-mean-square difference (RMSD) of 16. 8 % are observed. This relative RMSD agrees with the outcome of an analysis of the total uncertainty of the FTH product. The decadal stability of the FTH data record is 0. 5 ± 0. 45 % per decade. As exemplary applications, the interannual standard deviation, the differences on decadal scales, and the linear trend in the FTH data record and in the frequency of occurrence of FTH < 10 % (FTHp10) are analyzed per season.
Interannual standard deviation maxima and maxima in absolute decadal differences are featured in gradient areas between dry and wet regions, as well as in areas where FTH reaches minima and FTHp10 reaches maxima. An analysis of the FTH linear trends and of the associated uncertainty estimates is achieved to identify possible problems with the data record. Positive trends in FTHp10 are featured in gradient areas between wet and dry regions, in regions where the FTH is minimum, in regions where FTHp10 is maximum, and in regions where differences between FTHp10 averaged over the 2000s and 1990s are negative. However, these positive trends in FTHp10 are associated with maximum standard deviation and are thus hardly significant. This analysis and intercomparisons with other humidity data records are part of the Global Energy and Water Cycle Experiment (GEWEX) Water Vapor Assessment (G-VAP).
It is unclear how these modifications have been at play over the last 25-30 years and whether they can already be identified in the observational record of the last 25-30 years. Satellites observing the humidity in the free troposphere, and in particular geostationary platforms, appear to be very well suited to contribute to constraining the available theory by providing observations at kilometer-and hour-scale resolutions over a 30-year-long period. One of the aims of the present paper is to provide a well-qualified data record to explore such possibility at the temporal and spatial scales of the Meteosat observations. Infrared imagers and sounders whose channels are centered at the water vapor absorption band around 6. 3 µm as well as microwave sounders whose channels are centered at 183.
Both retrievals were originally designed for Meteorological Satellite (Meteosat) Visible and Infrared Imager (MVIRI) observations. Buehler and John (2005)  adapted the  Soden and Bretherton (1996)  method to intercalibrated Advanced Microwave Sounding Unit-B (AMSU-B) observations  (John et al. , 2013). The AMSU-B-based FTH data record  (Buehler et al. , 2008)  is affected by orbital drift, and it contains valid observations under allsky conditions except when intense scattering occurs, such as during precipitation events. The method described in  Soden and Bretherton (1996)  has recently been applied to three of the six channels of the Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiometrie onboard the Megha-Tropiques satellite  (Brogniez et al. , 2014)  launched in October 2011.
Brogniez et al. (2009)  assessed the quality of the MVIRIbased FTH data record for the period 1984-2005. It is a precursor of the FTH data record presented here, and it was compared to FTH values which have been computed from RH profiles of a previous version of the Analyzed RadioSoundings Archive (ARSA). The mean difference between the MVIRI FTH and the ARSA FTH over the period 1984-2005 is -1. 2 % RH, and the standard deviation of the difference is 1. 7 % RH, indicating the stability of the MVIRI archive over this period. The value of the FTH product has been demonstrated in, for example,  Brogniez et al. (2005) , who compared the FTH product against 14 climate models in the framework of the Atmospheric Model Intercomparison Project phase 2. Buehler and John (2005)  estimated the theoretical uncertainty of the AMSU-B-based FTH retrieval scheme.
They found a bias of 2 % RH for low values of FTH and 7 % RH for high values of FTH. John et al. (2011)  further found a systematic clear-sky bias of 9 % RH by sampling the allsky AMSU-B product with the HIRS clear-sky mask and by comparing the clear-sky and all-sky AMSU-B FTH products. An exemplary application of the AMSU-B FTH data record is given in  Moradi et al. (2010). They found that the spatial distribution of radiosonde types coincides with the difference between AMSU-B data and data from the Integrated Global Radiosonde Archive  (Durre et al. , 2006). The HIRS data record has been evaluated in radiance space (see  Shi and Bates, 2011)  and the correlation of the BT with various climate indices has been analyzed  (Shi et al. , 2013) , highlighting the valuable contribution of this data record to the analysis of global teleconnections.
The generation of FTH data records from Meteosat, AMSU-B and HIRS was part of a pilot project within the World Meteorological Organization's (WMO) Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) network, which aims to establish a network of facilities ensuring continuous and sustained provision of high-quality satellite products. Within new SCOPE-CM projects, the Meteosat, AMSU-B and HIRS data records will be homogenized in format, metadata and documentation as much as possible, and the observations from all available geostationary imagers will be recalibrated and intercalibrated. The impact of this Fundamental Climate Data Record (FCDR) on the Meteosat-based FTH product will also be evaluated within the SCOPE-CM projects. The Global Energy and Water Cycle Experiment (GEWEX) Data and Assessments Panel has initiated the GEWEX Water Vapor Assessment (G-VAP).
One element of G-VAP is the intercomparison of available long-term FTH data records and the analysis and the comparison of temporal changes in these data records. The present work supports the latter G-VAP activity by setting up the technical framework and by starting the analysis with the Meteosat-based FTH data record. The objectives of this paper are (1) to describe the data and the algorithms used to produce the extended long-term stable FTH data record from the Meteosat observations, (2) to characterize the quality and the stability of this observational record by comparing it with independent data, and (3) to confront the variability of FTH on various temporal and spatial scales with the linear trends and their significance. The input data and the homogenization of Meteosat observations are introduced in Sects.
2 and 3, respectively. The retrieval scheme, an overview of the technical specifications, and exemplary figures on the characteristics of the FTH data record are presented in the next sections. The theoretically expected uncertainties are discussed in Sect. 5, and the evaluation results are shown in Sect. 6. The applications presented in Sect. 7 feature an analysis of the variability of the FTH and FTHp10, of the FTHp10 differences on decadal scales, and of the linear trend in FTH and FTHp10. Finally, in Sect. 8, conclusions are drawn. A series of statistical quantities and a list of abbreviations are given in the Appendices.
Input data This section briefly describes the instruments and the radiance input data records used to retrieve the FTH. MVIRI is a three-channel imaging radiometer onboard the Meteosat-2-7 platforms, which belong to the first generation of Meteosat satellites. It continuously observed the Earth from a geostationary orbit at 0 • latitude/0 • longitude every 30 min between 1982 and 2006. The spatial sampling distance of the observations is approximately 5 km at nadir and it increases with distance from the sub-satellite point. The Spinning Enhanced Visible and Infrared Imager (SE-VIRI) performs observations at 12 channels that cover the visible and thermal infrared spectral range. SEVIRI is onboard the geostationary satellites Meteosat-8-10, which are positioned at 0 • latitude/0 • longitude in operational mode.
This training database is composed of temperature (T ) and specific humidity (q) profiles extracted from ERA-Interim restricted to clear-sky profiles. The database contains profiles from the first day (four time steps per day) of January, April, July and October for the years 2001, 2006 and 2007 in order to have a significantly large set of profiles sampling the various thermodynamical conditions encountered in the area limited to 30 • N/S covered by Meteosat. The clearsky cases are defined using the ERA-Interim cloud fraction with a strict value of 0 at all levels. In addition to the cloud screening, a quality check is performed for the RH profiles (determined with respect to the water phase only) to remove the dry and supersaturated cases where the RH reaches values below 1 % RH and above 100 % RH in the free troposphere.
Data record for evaluation ARSA version 2. 7 is an archive of global radiosonde measurements of temperature, water vapor and ozone profiles which have been quality controlled and combined with auxiliary observations. The ARSA archive has been developed and provided by the Atmospheric Radiation Analysis group at the LMD. In the first processing step, the radiosonde observations are quality controlled. For example, water vapor observations are considered only when they are available up to a minimum pressure of 350 hPa, and the Thermodynamic Initial Guess Retrieval (also developed at the LMD) climatological database is used to remove outliers. In the second step, existing radiosonde measurements are combined with other reliable data sources. This step depends on ERA-Interim data, which are also used for extrapolation into upper levels of the atmosphere.
The following selection criteria are applied for the validation: -nighttime only, -FTH > 5 % to reduce potential surface contributions, -absolute differences in BTs < 3 K (i. e. , approximately 3σ ) and simulated and observed BTs > 220 K to minimize cloud detection uncertainties of the ISCCP-DX algorithm. A pair of ARSA and Meteosat observations is considered to be collocated when the temporal distance is within 1. 5 h and the spatial distance is within 0. 625 •. Although ARSA contains measurements from radiosondes launched on ships and at small islands, the validation is dominated by observations over land. To evaluate the quality of the FTH data record, the relative and absolute bias, the corresponding RMSD, and the decadal stability are determined on a monthly basis and as spatial averages.
Acknowledgements. This work was carried out within the framework of a memorandum of understanding between  CNRS  and  EUMETSAT's CM SAF. The authors acknowledge the financial support by  CNRS  and by the EUMETSAT member states. The authors are grateful to the  ARA/ABC (t)/LMD group,  NASA , the  NOAA Climate Data Center and ECMWF  for producing and making available ARSA and ISCCP-DX data records as well the El Niño 3. 4 and QBO indices and ERA-Interim data, respectively. J. Schulz  from  EUMETSAT  is acknowledged for initiating the cooperation that led to the presented results. Finally, we acknowledge the two anonymous reviewers for their comments. Edited by:  R. Krejci.
SANS sample preparation, experimental details and data reduction The samples corresponding to each peak from the final sizeexclusion chromatography were split into two: one half was conserved in 100% H  Protein concentrations were measured using the Bio-Rad protein-assay reagent (Bio-Rad) with bovine serum albumin as a standard. Correction factors were applied to pure PhTET2 and PhTET3 samples. These factors were calculated after determining the protein concentration of pure PhTET2 and PhTET3 samples by quantitative amino-acid analysis as described in  Duraet al. (2009). For the concentration of pure hetero-oligomeric samples, a weighted correction factor was calculated taking into account the correction factors obtained for PhTET2 and PhTET3 and the number of monomers of these proteins in the heteromeric complexes.
Therefore, all of the values reported in this paper refer to real protein concentrations. We estimate the accuracy of the concentration measurements to be about 30%. SANS experiments were performed on the large dynamic range small-angle diffractometer D22 at the Institut Laue-Langevin (ILL), Grenoble, France. The incident wavelength was = 6 A ˚(Á/ = 10%) at a single detector/collimator configuration (4 m/4 m) with a centred beam. All samples and buffers were prepared the night before the experiment as described above. The final sample volumes were adjusted to 160 ml and placed in Hellma 1 mm QS quartz cells. Boron, empty cell and a pure H 2 O sample were measured as references for the subsequent data treatment. In a first experiment, we verified the match points of dPhTET2 in 100% D 2 O and hPhTET2 in 42% D 2 O (Supplementary Fig.
S9 ). The temperature of the remaining samples, including buffers and references, was increased in steps of 20 C from 20 to 80 C. Typical exposure times were 20 min for the reference samples, mixtures and buffers at each temperature step. In addition, the empty cell, H 2 O and boron were measured for 10 min at each temperature. As a result, all samples were kept at each temperature for 4 h. Transmissions were measured for 3 min at the lowest temperature (20 C). The two-dimensional data sets of all samples and buffers were reduced to one-dimensional curves using the standard ILL software  (Gosh et al. , 2006). Buffer intensities were subtracted from the respective sample intensities using PRIMUS from the ATSAS program suite  (Konarev et al. , 2003).
S7 ) were calculated with MONSA (Svergun, 1999) using two phases (dPhTET2 and hPhTET3) according to the following parameters: monomer volumes V PhTET2 = V PhTET3 = 50 000 A ˚3 (6s:6s stoichiometry, where 's' designates a single subunit, i. e. a monomer), V PhTET2 = 66 700 A ˚3, V PhTET3 = 33 300 A ˚3 (8s:4s stoichiometry); the contrasts of dPhTET2 were 5. 75, 3. 34 (2. 91) and À0. 02 Â 10 10 cm À2 in 0, 42 (49) and 100% D 2 O and the contrasts of hPhTET3 were 2. 30, À0. 12 (À0. 53) and À3. 48 Â 10 10 cm À2 in 0, 42 (49) and 100% D 2 O. Connectivity of the two phases was not imposed. For the pseudo-atomic models, PhTET2 and PhTET3 dodecameric models were generated from the crystal structures (PDB entries 1y0r and 2wzn, respectively;  Borissenko & Groll, 2005; Duraet al. , 2009). Indeed, both crystal structures present a monomer within the asymmetric unit, so generating the dodecameric models relied on only the same symmetry operators.
Intriguingly, at 70% D 2 O the minimum at 60 A ˚is more pronounced for particle 1 than particle 2, indicating that in the former the dPhTET2 and hPhTET3 dimers are distributed more symmetrically (in number and geometry). 3. 3. Pseudo-atomic models of PhTET2-PhTET3 complexes using SANS contrast-variation data Fig. 1  illustrates the workflow of our approach to determine pseudo-atomic, quaternary structures of the heterododecameric PhTET2-PhTET3 particles. As a first step, homododecameric ('12s') reference structures of dPhTET2 and hPhTET3 were measured by SANS at 42 and 100% D 2 O, respectively. PhTET2 and PhTET3 dodecameric crystal structures (PDB entries 1y0r and 2wzn, respectively) were slightly modified to match these SANS reference data at 'low resolution' (Fig. 1 , top) and were subsequently used to construct a library of heterododecameric models of variable stoichiometry and geometry (Figs.
In addition, and in complement, to the pseudo-atomic models, the multiphase ab initio program MONSA  (Svergun, 1999)  was used to calculate low-resolution envelopes of the PhTET2 and PhTET3 moieties within the heterododecameric particles of peak 1 and peak 2. A comparison of the lowresolution models with the pseudo-atomic models is shown in Supplementary Fig. S7. The ab initio shapes confirm, independently of the pseudo-atomic models, that PhTET2 and PhTET3 are organized as a symmetric architecture of two intertwined Z-shapes in particle 1 and that PhTET2 is organized as two homodimers positioned on opposite ridges in particle 2. 3. 4. The homododecameric PhTET2 complex is extremely stable in solution and is not in dynamic equilibrium with free oligomeric forms of lower molecular mass As an additional stability control of the PhTET quaternary structure and to test the presence of putative equilibria between preformed dodecameric particles with smaller oligomeric particles in solution, we measured mixtures of pre-formed homododecameric deuterated and hydrogenated PhTET2 proteins (dPhTET2 '12s' and hPhTET2 '12s') by monitoring their SANS curves as a function of temperature and exposure time.
We acknowledge financial support from the  Agence Nationale de la Recherche  (grants ' MacroTET '  BLAN-07-3204002 , ' Archelyse '  ANR-12-BSV8-0019-01  and ' HYDROSAS '  ANR-11-JSV5-003-01 ). AA was supported by a PhD scholarship from the  French Ministry for Research and Technology. MAD and MC were supported by a  French National Research Agency postdoctoral fellowship. We would like to thank the  ILL  for SANS BAG beamtime on D22 and  P. Callow  and  A. Martel  for help with the set-up of the SANS instrument. MM and MH acknowledge EPSRC support ( EP/ C015452/1 ) to  V. T. Forsyth  ( Keele University, England ) for the creation of the  Deuteration Laboratory within ILL's Life Sciences Group  and from the  EU  under contract  RII3- CT-2003-505925.
A similarity matrix was then produced using DARwin 5 software  [39]  to eliminate markers with identical patterns. The Polymorphism Information Content (PIC) was calculated for the remaining markers. The accessions to be genotyped by GBS were chosen using the maximum length subtree procedure available under DARwin5. This method, which is based on allelic combinations rather than on simple allelic richness, prunes the tree of its most redundant units. It therefore minimizes the risk of spurious associations due to the genetic structure of the studied population while limiting possible reductions of allelic diversity  [39].
Genotyping with SNP markers Genotyping was conducted at Diversity Arrays Technology Pty Ltd. (Australia) using a method of GBS that combines DArT with a next-generation sequencing technique called DArTseq? , previously described by Courtois et al. [35]. The method achieves genome complexityreduction using PstI/TaqI restriction digests followed by Illumina short-read sequencing. PstI-specific adapters tagged with 96 different barcodes to encode a plate of DNA samples were ligated to the restriction fragments. The resulting products were amplified and checked for quality. The 96 samples were then pooled and run in a single lane on an Illumina Hiseq2000 instrument. The PstI adapters included a sequencing primer so that the tags generated were always read from the PstI sites. The resulting sequences were filtered and split into their respective target datasets, and the barcode sequences were trimmed.
Population structure For population structure analyses, we used only the SNP markers. We randomly selected a sub-sample of markers that showed a rate of missing data below 2. 5% and a distance to the nearest marker of at least 100 kb. Structure software v2. 3. 4 developed by Pritchard et al. [40]  was used to analyze the organization of the panel. The parameters used were haploid data, burn-in of 200,000 steps, 200,000 iterations, admixture model with correlated frequencies, K varying from 1 to 10 and 10 runs per K value. After discarding the runs that did not converge, the data were analyzed using Structure Harvester  [41]  which incorporate the criteria developed by Evano et al. [42]  that help to determine the number of populations in a panel. To further facilitate this step, the discriminant analysis of principal components (DAPC) method developed by Jombard et al.
Results DArT marker-based population structure pattern Among the 6444 DArT markers that were tested, 619 were polymorphic in our dataset (9. 6%). Among these 619 markers, 451 had a reproducibility above 99% and a quality index above 0. 80, among which 300 had a call rate above 90%. We tested the markers for their similarity and kept only one copy of the 59 groups of identical markers. The final set was therefore composed of 241 non-redundant markers. The PIC of these markers varied between 5% and 50%, with an average of 40. 0%. The distribution of the DArT markers in the genome was reasonably uniform. The number of markers per chromosome was proportional to their relative size in bp (r = 0. 78, P = 0. 003). Large marker-uncovered zones corresponding to peri-centromeric regions were observed.
Some accessions clustered at the same position indicating a very high level of similarity. Some of these accessions had similar names (e. g. , Ba Cho Kte for both G84 and G297), while others were different (e. g. , Ble Blau Da and Ble Blau Blau for G197 and G198). The DArT data were used to select 182 nonredundant Vietnamese accessions and three reference varieties (Nipponbare, a temperate japonica; Azucena, a tropical japonica; and IR64, an indica). The number of markers derived from this first analysis was clearly insufficient for the purpose of association mapping. We therefore completed the genotyping of the 185 selected accessions using GBS. Genotyping-by-sequencing-based population structure pattern GBS yielded 25,971 markers (15,284 GBS-DArTs and 10,687 SNPs) after the data-cleaning step.