dataesr/scientific-paragraphs-categorization huggingface dataset:
3  (Hocking et al. , 2015). In our study, we use the simulated IASI-NG data obtained in Andrey-Andrés et al. (  2018 ) by adding a random Gaussian noise to the simulated perfect measurements, the noise corresponding to the material KBr which was finally selected (red curve in Figure  6a  below). Atmospheric profiles of temperature, humidity and some chemical compounds used to create the simulated dataset are thus provided. They are used as the 'true' state in our study. Background profiles for our 1D-Var retrievals are created from this true state to which a random Gaussian noise is added. This noise comes from the B matrix used in this work, which is the one provided in the NWP SAF package containing the 1D-Var software  (Smith 2016). Section 5 gives more details about this matrix. For the purpose of this project, dealing with more than 5 million simulations would have been too expensive in terms of computational resources.
RESULTS OF THE DESROZIERS DIAGNOSTICS In order to implement the Desroziers method for the evaluation of a full R matrix, observed, background and retrieved values are required (Subsection 3. 2). While the first two can be obtained from the simulated database, to get the retrievals we had to carry out 1D-Var experiments. To initialise the 1D-Var, B and R matrices are needed (Subsection 3. 1). Again, the B matrix exploited for this work is the one provided in the NWP SAF package containing the 1D-Var software  (Smith 2016). This B matrix consists of one block containing temperature (in K) error covariance information on 54 atmospheric levels, one for the 29 bottom levels of humidity (represented as ln q) error covariances, one for surface temperature, one for surface humidity and finally one with information on skin temperature.
Optimal selection The selection method described in Subsection 3. 3 was applied separately to each of the 77 representative profiles selected for the case-study. Actually, the more channels are chosen, the larger the R matrix to be inverted during the selection process to compute the DFS is, with a significant increase in the computational time as a result. Therefore, we decided to restrict the selection to 500 channels for each profile. This can be seen as a threshold considering that 500 is the current number of IASI channels distributed to the operational centres through the global telecommunication system (GTS). Indeed, if the principal component scores are also to be used for the IASI-NG data distribution, the number of disseminated channels will probably remain unchanged. For this reason, 500 is also the number of channels that we would like to provide to the community at the end of this study.
CONCLUSIONS AND PERSPECTIVES An IASI-NG channel selection was performed with the aim of serving as a starting point for future assimilations in the NWP context. The work was carried out on a database of atmospheric temperature and humidity profiles simulated with the specific purpose of serving as a basis for the preparation of this new instrument. The method used to perform the selection is the iterative procedure by  Rodgers (1996)  for clear conditions, using DFS as a figure of merit for the implementation. This method has been applied on 77 representative profiles over the globe for various seasons. Different groups of channels have been chosen at the same time, but the selection proposed in this paper consists of the 500 most frequently selected channels, or rather the channels chosen in 37.
Using this APSP spectrum for HER2-negative breast cancer germlines, we found that the HER2 signalling pathway was up-regulated compared to that in HER2-positive germlines, which was validated by RNA sequencing (RNA-Seq) and phosphoproteome analysis. Next, we calculated the germline APSP risk score and found it was able to distinguish between HER2-negative and HER2-positive breast cancers (AUC=0. 79). This result was validated using the TCGA breast cancer cohort (AUC=0. 70). Furthermore, subjects with high APSP risk scores demonstrated enhanced immune suppression, as determined by single-cell mass cytometry (CyTOF) analysis. Moreover, the germline APSP risk score could be used to evaluate the relative risk of HER2-negative breast cancer for each individual. These results show that DAGM provides relatively accurate assessment of HER2-negative breast cancer risk based on genome-wide comprehensive data of rare coding mutations and gene expression, which can facilitate high-risk subject screening, and also sheds new light on the potential pathogenic disease mechanisms.
Whole-exome sequencing and variant calling Peripheral blood mononuclear cell (PBMC) samples were collected from breast cancer patients. DNA was extracted using QIAamp DNA mini kit (Qiagen) according to the blood and body fluid protocol in the user manual. Paired-end multiplex sequencing of case samples was performed on an Illumina HiSeq X Ten sequencing platform to a median depth of 150-250X. Samples from the control cohort were sequenced on an Illumina HiSeq 2000 system to a median depth >60X. Paired-end raw sequence reads were mapped to the human reference genome (UCSC hg19) using the Burrows-Wheeler Aligner  [14]  with default settings. Variant calling was carried out using the Genome Analysis Toolkit (GATK) with the HaplotypeCaller module  [15]  according to GATK Best Practices. Briefly, the aligned BAM files were first marked for duplicate reads by Picard.
Transcriptomics A total of 3 µg of RNA per sample was used as the input material for RNA sample preparation. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer's instructions. Index codes were added to attribute the sequences to each sample. Sequencing was performed on an Illumina HiSeq platform. Raw data (raw reads) in FASTQ format were processed through our in-house Perl scripts to clean the data (clean reads), which removed reads containing adapters, reads containing poly-N, and low quality reads. All downstream analyses were based on high-quality clean data. Paired-end clean reads were aligned to the reference genome using Hisat2 v2. 2. 1  [17]. FeatureCounts v2. 0. 1  [18]  was used to count the numbers of reads mapped to each gene.
Phosphoproteome analysis Samples were minced individually in liquid nitrogen. The enrichment was carried out using PHOS-Select iron affinity gel (Sigma, P9740) following the manufacturer's instructions. Shotgun proteomics analyses were performed using an EASY-nLC TM 1200 UHPLC system (Thermo Fisher) coupled with an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher) operating in a data-dependent acquisition (DDA) mode. The resulting spectra from each fraction were searched separately against the UniProt database  [20]. For protein identification, proteins. with at least one unique peptide were identified at an FDR less than 1. 0% at the peptide and protein level, respectively.
CyTOF Antibodies were either purchased pre-conjugated (Fluidigm, DVS Sciences) or purchased and conjugated in-house using MaxPar X8 Polymer Kits (Fluidigm) according to the manufacturer's instructions (Suppl. Table  1 ). Scans were acquired on a Helios 2. 0 (Fluidigm) at an event rate of 300 events/s. After normalizing and randomizing values to near zero using the Helios software, FCS files were then generated for the analysis. Mass cytometry data was de-barcoded using a doublet filtering scheme with mass-tagged barcodes, and then manually gated to retain live, singlet, and valid immune cells. Data generated from different batches were normalized through the bead normalization method. All cell events in each individual sample were pooled and included in the analysis.
The Damage Assessment of Genomic Mutations (DAGM) framework Similar to the eQTL method that uses gene expression as an independent trait to determine its linkage site in the genome, we can correlate the gene expression in cell lines to 'traits' and associate them with a variety of different genes with rare coding mutations (RCMs). However, rather than examining how all genes with rare mutations change the gene expression traits, DAGM screens out genes defined as global driver genes, whose mutations deterministically cause almost the same alterations in the global gene expression across different cellular or tissue contexts. For example, the well-known driver gene TP53 drives similar altered gene expressions in tumour cells from different origins. We are continuously collecting RCM data and gene expression traits from cancer cell lines or primary cells to improve the database and to provide.
Classification Binary classifiers were built using the support vector machine (SVM) and logistic regression implemented in the caret R package. All data were randomly split into the training dataset (60% or 75%) and the testing dataset (40% or 25%), while preserving the overall class distribution of the data. The optimal parameters were selected by five-fold cross validation. The criteria of the area under the receiver operating characteristics curve (AUC) was used to measure the performance of the classifiers.
Statistical analysis The pheatmap R package was used to plot the clustered heatmaps using Ward's criteria  [21]. The ggpubr R package was used to draw the scatter plots, boxplots, bar plots, histograms, and linear regression lines with 95% confidence interval. The plotROC R package was used to draw ROC. Pearson correlation coefficients (PCC) and P-values were also labelled on the plots. The difference between two groups was analysed by Student's t test. A P-value < 0. 05 was considered statistically significant. All statistical analyses and plots were conducted using in-house scripts developed in Python, Perl, and R.
To validate this framework, we used another dataset from Cancer Cell Line Encyclopaedia (CCLE)  [22]  to build a 'driving force' matrix. The GDFs calculated from the two different datasets were significantly correlated (coefficient of determination: R2 = 0. 99, P-value < 1. 0×10-16, Suppl. Fig. 1a ), suggesting the first step of DAGM is robust across different datasets. Next, we selected the top 10 genes with highest GDF in the DAGM and searched public databases of cancer driver genes. We found six of these genes, KRAS, TP53, MYC, BCL2, BRAF, and RPL22, were listed in COSMIC Cancer Gene Census  [23] , whereas all 10 were listed in DriverDBv3  [24] , which confirmed the potential ability of using GDF to identify essential genes for tumourigenesis, including cancer driver genes. To test whether the APSP calculated from DAGM reflects signalling pathway activities, we applied DAGM to somatic mutations from tumour samples from 16 ERBB2-positive patients and 42 TNBC patients (Suppl.
G2 and G5 included subject with APSP risk score between one and two standard deviations lower and higher than the mean value, respectively. G3 and G4 included subject with APSP risk score within one standard deviation lower and higher than the mean value, respectively. (d) Odds ratios for HER2 negative breast cancer display exponential distribution when the subjects were stratified into six groups (G1 to G6) as in (c). The grey circles represent the sample sizes of each group. Gene mutation and gene expression data from the COSMIC Cell Lines Project Driving force (DF) Global driving force (GDF) Figure Legends.
The layers were coated on glass substrates. SEA software (Semilab company) was used to fit the spectroscopic ellipsometry measurements of tan(Ψ) and cos(Δ) and extract the optical indices n(λ) and k(λ) of the materials. The dielectric functions ϵ ¼ ϵ 1 þ i Â ϵ 2 of blend systems were fitted with the Gaussian model that is adequate for the parameterization of the optical functions of amorphous thin films in the interband region. [52] reparation and Characterization of Samples for ATEM: The pure material layers (PM6 and ITIC-4F) and the spin-coated photoactive layers of the blend system PM6:ITIC-4F were floated upon demineralized water by dissolving the poly(3,4-ethylenedioxythiophene) polystyrene sulfonate layer and collected with holey carbon grids (QUANTIFOIL). From a solar cell device with printed active layer, two lamellae with six windows were prepared using a ZEISS Crossbeam 540 (Carl Zeiss Microscopy GmbH, Germany).
Principal component analysis was applied on the normalized ESI image stacks for noise reduction using Hyperspy. [54] mage classification and segmentation were done by statistical analysis and supervised machine learning using the open source software ilastik  [55]  to classify the ESI spectra into distinct classes. By applying locally linear embedding (LLE), the dimensionality of the data was reduced. Afterward, a random forest classifier was trained on label subsets and subsequently used to classify the remaining spectra (all pixels/spectra of the image stack) yielding material distribution maps of PM6 and ITIC-4F in blend layers showing material domains and interface phases. We note that the LLE is implemented in several free-of-charge software, e. g. , in Matlab (MathWorks, USA) or in Python with the scikit-learn package.
Acknowledgements P. P. , J. -J. S. , and J. A. received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie Grant  Agreement No. 713750. W. K. , M. P. , and R. R. S. acknowledge funding by the  Ministry of Science, Research and the Arts Baden-Württemberg , through the  HEiKA materials research center FunTECH-3D  (MWK,  33-753-30-20/3/3 ). P. P. and J. A. further acknowledge support of the  Regional Council of Provence-Alpes-Côte d'Azur ,  A*MIDEX  (No. ANR-11-IDEX-0001-02 ), and the Investissements d' Avenir  project funded by the  French Government , managed by the  French National Research Agency (ANR). This work has also been carried out within the framework of a CIFRE PhD grant  2017/0529  from the  Association Nationale de la Recherche et de la Technologie (ANRT)  and the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation , awarded through the company  Dracula Technologies  (Valence, France).
The synchrotron radiation experiments were performed at BL19B2 in SPring-8 with the approval of  Japan Synchrotron Radiation Research Institute (JASRI)  (Proposal Nos. 2018B1791  and  2019B1851 ). R. R. S. also acknowledges funding by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy via the Excellence Cluster 3D Matter Made to Order ( EXC-2082/1 -390761711 ). W. K. , R. R. S. , and M. P. acknowledge the data storage service SDS@hd supported by the  Ministry of Science, Research and the Arts Baden-Württemberg  and the  German Research Foundation (DFG)  through grant  INST 35/1314-1 FUGG. A. G. and H. B. thank  University Jaume  for the financial support ( UJI-B2020-49 ).
The results of the grouping exercise retained five broad categories of waterbirds as herbivores (largely feeding on vegetation matter), insectivores (mainly feeding on insects and other aquatic invertebrates such as crustacea and annelids), piscivores (predominantly feeding on fish and amphibians), semi-omnivores (consuming a variety of invertebrates and also plant matter), and omnivores (when diet consisted of items from herbivores, insectivores and piscivores). Raptors were excluded from the dataset as they frequently forage in non-wetland areas  [20]. The gap statistic method  [23]  was used to determine the optimal number of clusters that best describe the life history data (clutch size, incubation period, fledging time, body mass and generation length) using the NbClust package  [24].
This optimal number was then used in carrying out the k-means partition cluster analysis to create reproduction-based clusters that described the data. Thus, each species was assigned to its resulting cluster. Correlations tests between all the life history trait variables were conducted with the aim of dropping those that are highly correlated (correlation coefficient r > 0. 8  [25] ). An "index of diet generalism" (i. e. , a score of how a species could be considered a generalist or specialist) was derived by taking log 10 (NTD/median of NTD for all species in the dataset). This index of diet generalism ranged from positive values (generalist species) to negative (specialist ones). Chi-squared tests were used to investigate the relationships between waterbird allocated clusters and the global trends.
Results The gathered waterbird dataset consisted of resident species (85), Afrotropical migrants  (36)  and Palaearctic migrants (42) (n = 163, Appendix A). Although most of these species (134) are considered to at least be of concern by the IUCN, 7 are vulnerable, 15 near threatened and 7 are endangered. Clutch size was negatively related to generation length (r = -0. 24, p = 0. 002) but there were no correlations between (1) clutch size and incubation period (r = 0. 12, p = 0. 139) or (2) clutch size and body mass (r = 0. 01, p = 0. 908). The rest of the relationships across the life history traits were significantly correlated, with r values ranging from 0. 33 to 0. 63 (p < 0. 001 in all cases). A large proportion of waterbirds in the dataset (48%) rely on small diet items (class 1). Insectivores formed the largest guild with 32% of all species.
Flow cytometric analysis Flow cytometry was performed using the BD FACS Canto II and analysed with FlowJo software (Tree Star, Ashland, OR). Dead cells were excluded through Fixable Viability Dye eFluor staining (eBioscience). Nonspecific antibody binding was blocked with an anti-CD16/32 (Mouse Fcy block clone 2. 4G2). The cells were incu bated for 30 min in 100 pl PBS with conjugated anti bodies (BD Biosciences, France): anti CD45 (V500 conjugated; clone 30-F11), CD11b (BV421 conjugated; clone M1/70), anti CD3 (FITC conjugated; clone 145-2C11), anti CD45R/B220 (PE-Cy7 conjugated; clone RA3-6B2), anti Ly6C (FITC conjugated; clone AL-21), anti CD31 (PE conjugated; clone MEC13. 3), anti CD34 (Alexa Fluor 647 conjugated; clone RAM34), anti CD146 (FITC conjugated; clone ME-9F1), anti CD29 (PE conju gated; clone HM B1.
Real-time PCR analysis Total RNA was extracted from the ileum and spleen cells with the RNeasy Mini kit (Qiagen), and cDNA was prepared with the SuperScript RT Reagent Kit (Applied Biosystems). Real-time PCR was performed on an ABI Prism 7900 Sequence Detection System. All Taqman primers and probes came from Life Technologies (France) (Supplemental table). The data were analysed using the 2_AACt method, with normalisation to the Ct of the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) housekeeping gene.
Marine particulate organic carbon stable isotope ratios (δ 13 C POC ) provide insights into understanding carbon cycling through the atmosphere, ocean and biosphere. They have for example been used to trace the input of anthropogenic carbon in the marine ecosystem due to the distinct isotopically light signature of anthropogenic emissions. However, δ 13 C POC is also significantly altered during photosynthesis by phytoplankton, which complicates its interpretation. For such purposes, robust spatio-temporal coverage of δ 13 C POC observations is essential. We collected all such available data sets and merged and homogenized them to provide the largest available marine δ 13 C POC data set (https://doi. org/10. 1594/PANGAEA. 929931; Verwega et al. , 2021). The data set consists of 4732 data points covering all major ocean basins beginning in the 1960s.
Only about 60 % of anthropogenic CO 2 emissions have been compensated for by natural sinks, including the dissolution of inorganic carbon in the ocean. This means the atmosphere has already been enriched with anthropogenic carbon by about 880 Gt CO 2 since 1750  (IPCC, 2014) , which is driving the increase in global temperature levels. The ocean serves as an important buffer as it absorbs a significant amount of an-Published by Copernicus Publications. M. -T. Verwega et al. :  Description of a global marine particulate organic carbon-13 isotope data set thropogenic carbon, with the ocean interior being the largest readily exchangeable reservoir of carbon in the Earth system. Marine phytoplankton convert dissolved inorganic carbon (e. g. , aqueous CO 2 ) into organic carbon via photosynthesis in the euphotic surface layer.
Data sets of marine δ 13 C POC improve our understanding of marine carbon cycling by providing another independent constraint. Recent model approaches support long-term past climate projections  (Tjiputra et al. , 2020)  and assess estimations of the Suess effect  (Liu et al. , 2021). To date, numerous individual δ 13 C POC data sets exist, while the number of accessible, merged data sets is lacking. Existing merged data sets contain data from several sources but have often been focused on a specific region or process (e. g. ,  Goericke, 1994; Tuerena et al. , 2019). Individual data sets are usually collected during a specific cruise or time series station and are often neglected since they contain relatively few data. Such data sets can easily be accessed on data platforms such as PANGAEA and, when combined, they can represent an important and significant source of data.
In this study, we provide a novel merged seawater δ 13 C POC data product  (Verwega et al. , 2021)  that -to our knowledge -contains the most expansive spatio-temporal coverage to date. It contains all available δ 13 C POC seawater data from PANGAEA and the merged data sets by  Goericke (1994) ,  Tuerena et al. (2019)  and  Young et al. (2013) , as well as unpublished data from different cruises by Anne Lorrain. No data were excluded, even if sampled at extreme locations (e. g. , trenches, hydrothermal vents). The metadata comprise information about the sampling location, time, depth and method as well as the original source, which makes original raw data values, methods, and further technical description easily accessible. Provided data files are Network Common Data Form (NetCDF) files interpolated onto two different global grids and a csv file that includes the data and their anomalies with respect to their overall mean together with all corresponding available meta-information.
Data acquisition The data set includes 4732 entries for δ 13 C POC from 185 different sources and ranges from the 1960s to the 2010s. In addition to many data sets from the data platform PANGAEA, we included unpublished data provided by Anne Lorrain and the data products from  Tuerena et al. (2019) ,  Goericke (1994)  and  Young et al. (2013). The adjustments that we conducted are described in the following.
With this extension, we could increase the data set to 626 data points for δ 13 C POC. We collected most data from the PANGAEA data platform, an open-access online library archiving and providing geo-referenced Earth system data, hosted and monitored by the Alfred-Wegener-Institut (2020) -Helmholtz Centre for Polar and Marine Research (AWI) -and the Center for Marine Environmental Sciences, University of Bremen (MARUM). With the data made available therein, we could further extend the data set by an additional ∼ 3500 measurements of δ 13 C POC. Most δ 13 C POC data from PANGAEA are associated with samples collected during the Joint Global Ocean Flux Study (JGOFS, 2020), with more than 2000 δ 13 C POC data points. Additionally, 529 samples are contributions by the Antarctic Environment and Southern Ocean Process Study (AESOPS, 2020), 342 are by the Archive of Ocean Data (EurOBIS Data Management Team, 2020) and 279 are by the SFB313 research project  (Thiede et al.