dataesr/scientific-paragraphs-categorization huggingface dataset:
The geochemical variability of the Arequipa's batholith's magmas 7. 2. 1 Observations and questions The geochemical signatures recorded by the igneous rocks of the Arequipa's section of the Coastal Batholith of Southern Peru show a broad diversity, which mimics what has been already observed in several other batholith sections (e. g. Sierra Nevada and Chilean batholiths; DePaolo 1981; Herve et al. 2007; Parada et al. 2007; DeCelles et al. 2009). The Jurassic, Cretaceous and Maastrichtian-Paleocene rocks of the Arequipa batholith section have the Nd and Sr isotopic compositions expected for subduction related igneous rocks (Figure 9). The data spread from the mantellic to the crustal quadrant, i. e. from the almost Solid Earth Discuss. , https://doi. org/10. 5194/se-2019-43 Manuscript under review for journal Solid Earth Discussion started: 27 March 2019 c Author(s) 2019.
7. 2 2 The geochemical variability of the Arequipa's batholith's magmas 7. 2. 1 Observations and questions The geochemical signatures recorded by the igneous rocks of the Arequipa's section of the Coastal Batholith of Southern Peru show a broad diversity, which mimics what has been already observed in several other batholith sections (e. g. Sierra Nevada and Chilean batholiths; DePaolo 1981; Herve et al. 2007; Parada et al. 2007; DeCelles et al. 2009). The Jurassic, Cretaceous and Maastrichtian-Paleocene rocks of the Arequipa batholith section have the Nd and Sr isotopic compositions expected for subduction related igneous rocks (Figure 9). The data spread from the mantellic to the crustal quadrant, i. e. from the almost Solid Earth Discuss. , https://doi. org/10. 5194/se-2019-43 Manuscript under review for journal Solid Earth Discussion started: 27 March 2019 c Author(s) 2019.
The IMGT ® databases and tools, built on the IMGT-ONTOLOGY axioms and concepts, bridge the gap between genes, sequences and three-dimensional (3D) structures  [3]. The data accuracy and consistency are based on the IMGT Scientific chart rules generated from the axioms and concepts: IMGT ® standardized keywords (IDENTIFICATION axiom, concepts of identification), IMGT ® gene and allele nomenclature (CLASSIFICATION axiom, concepts of classification), IMGT ® standardized labels (DESCRIPTION axiom, concepts of description), IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION axiom, concepts of numerotation)  [3]. The antigen receptor IG and TR variable domains form a huge repertoire of 2. 10 12 different specificities per individual. Owing to the particularities of their synthesis that involve DNA rearrangements, there was a need for a systematic and coherent numbering of the amino acids and codons, whatever the molecule, configuration or chain type.
The IMGT unique numbering was therefore a breakthrough in immunogenetics and immunoinformatics when it was defined for the first time in 1997 for the variable (V) domain  [4] [5] [6]. The IMGT unique numbering bridges the gap between amino acid and codon sequences of any V and C and their two-dimensional (2D) and three-dimensional (3D) structures and has been fundamental in the creation of the IMGT Collier de Perles graphical representation  [4] [5] [6]. Both concepts have allowed the standardization of the description of mutations, amino acid changes, polymorphisms, and contact analysis in the IMGT ® databases, tools and Web resources (http://www. imgt. org) [3]. The IMGT unique numbering was created by taking into account the high conservation of the structure of the V domain and by integrating the knowledge acquired by the analysis of multiple sources: alignment of more than 5000 sequences, literature data on the framework (FR) and complementarity determining regions (CDR), structural data from X-ray diffraction studies and characterization of the CDR hypervariable loops  [4] [5] [6].
Conclusions IMGT ® , created in 1989 with the official recognition of IG and TR genes, is at the origin of immunoinformatics  [3]. The concepts of classification (nomenclature and IG and TR gene and allele names, CLASSIFICATION axiom) were soon followed by the concepts of identification (standardized IMGT keywords, IDENTIFICATION axiom) and the concepts of description (standardized IMGT labels, DESCRIPTION axiom) which led to the implementation of IMGT/LIGM-DB, the first IMGT sequence database demonstrated online at the 9th International Congress of Immunology (ICI), San Francisco (USA), in July 1995. It took two more years to conceive the concepts of numerotation, IMGT unique numbering and IMGT Collier de Perles (NUMEROTATION axiom) which bridge sequences and structures of V and C domain (at the amino acid and codon levels)  [3].
Interestingly, the first IMGT Collier de Perles, created manually in December 2007, not only identified conflicts between the SEQRES and ATOM lines of the PDB file but also the absence of a serine at position 93, demonstrating that indeed sequence and structure were bridged using the IMGT unique numbering (http://www. imgt. org/IMGTrepertoire/2D-3Dstruct/2D-representations/mouse/IG/E5. 2Fv/ighV-D-J_E5_2Fv. html). The IMGT ® databases, tools and web resources have been built to manage immunogenetics knowledge and immunoinformatics, based on the IMGT Scientific chart rules generated from the IMGT-ONTOLOGY axioms and concepts  [3]. Nowadays, IMGT ® provides standardized and integrated databases, tools and web resources for IG and TR, from gene to structure and function  [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43].
Availability and Citation: Authors who use IMGT ® databases and tools are encouraged to cite this article and to quote the IMGT ® Home page, http://www. imgt. org. Online access to IMGT ® databases and tools are freely available for academics and under licences and contracts for companies. Funding: IMGT ® was funded in part by the BIOMED1 (BIOCT930038), Biotechnology BIOTECH2 (BIO4CT960037), fifth PCRDT Quality of Life and Management of Living Resources (QLG2-2000-01287), and sixth PCRDT Information Science and Technology (ImmunoGrid, FP6 IST-028069) programmes of the European Union (EU). IMGT ® received financial support from the GIS IBiSA, BioCampus Montpellier, the Région Occitanie (Grand Plateau Technique pour la Recherche (GPTR)), the Agence Nationale de la recherche (ANR) and the Labex MabImprove (ANR-10-LABX-53-01).
Experimental database Each criterion proposed in literature is more or less adapted to a given loading type (high mean stress, bi-axial tension, …). In this study only fully reversed loadings (no mean stress) are considered. Experimental results collected by Weber  [11]  are used : fatigue limit in tension-torsion fatigue tests are given for many different metallic materials. The stress tensor()in fully reversed tension torsion can be expressed as follow 𝜎(𝑡) = [ 𝜎 𝑎 sin (𝜔𝑡) 𝜏 𝑎 sin (𝜔𝑡 + 𝜑) 0 𝜏 𝑎 sin (𝜔𝑡 + 𝜑) 0 0 0 0 0 ] (3) with   the normal stress amplitude and   the shear stress amplitude. A 0° phase difference  stands for a proportional loading meaning the stress tensor can be represented by a time dependent scalar f(t), while a non-zero phase difference implies a nonproportionality in which case no scalar function f(t) exists.
Validation database The experimental database used for validation of the corrected criteria is based on results presented by Anes et al. throughout their work in different publications  [8;13;14]. NP tension-torsion fatigue tests on 42CrMo4 samples under a wide range of amplitudes are performed in their work. The number of different loading types as well as the complete information on the 42CrMo4 material are the reasons for the choice of Anes et al. database. Seven loading types are explored (Fig. 4 ). It is worth to notice that only Case1 to Case3 are unicyclic signals in the sense of the Wang-Brown counting method.
Conclusions In this study a new invariant based NP indicator is defined and integrated in a full high cycle fatigue lifetime prediction chain based on Sines criterion. Using a complete experimental database this indicator can be used to propose a corrected Sines criterion. Based on another experimental database for multiple NP signals and using the Wang-Brown cycle breakdown method the accuracy of the corrected criterion is demonstrated. The results (Fig. 5 ) show considerable improvements over the original uncorrected approach, both for simple unicyclic signals as well as more complex signals. It should be noted that the Wang-Brown method alters the NP degree of a multiaxial signal as the weighted average of I NP for each cycle is not equal to the value of I NP for the global signal. To achieve an even better correction of the criterion a more sound experimental database could be established.
ANALYSIS To establish whether there are molecular markers present in tumors genomes that can be used for subclassfication, we utilized publicly available whole genome expression profiles from 3 datasets: 48 primary pT1 bladder cancers published by Kim et al. [10] ; 460 non-muscle-invasive bladder cancers published by Hedegaard et al  [7] ; and 412 muscle-invasive bladder cancers published by The Cancer Genome Atlas  [11]. These data sets were used to investigate the interplay between immune response, cellular proliferation, and PI3K signaling, and their association with altered likelihood of tumor recurrence in bladder cancer. For each of these biological mechanisms (immune response, proliferation, PI3K signaling) Singular Value Decomposition was used to generate metagenes from the combined expression profiles of genes known to be involved in these processes (e.
We study an ensemble of six multi-year global Bayesian CO2 atmospheric inversions that vary in terms of assimilated observations (either column retrievals from one of two satellites or surface air sample measurements) and transport model. The time series of inferred annual fluxes are first compared with each other at various spatial scales. We then objectively evaluate the small inversion ensemble based on a large dataset of accurate aircraft measurements in the free troposphere over the globe, that are independent from all assimilated data. The measured variables are connected with the inferred fluxes through 5 mass-conserving transport in the global atmosphere and are part of the inversion results. Large-scale annual fluxes estimated from the bias-corrected land retrievals of the second Orbiting Carbon Observatory (OCO-2) differ from the prior fluxes much, but are similar to the fluxes estimated from the surface network within the uncertainty of these surface-based estimates.
The OCO-2-and surface-based inversions have similar performance when projected in the space of the aircraft data, but relative strengths and weaknesses of the two flux estimates vary within the Northern and Tropical parts of the continents. The 10 verification data also suggests that the more complex and more recent transport model does not improve the inversion skill. In contrast, the inversion using bias-corrected retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or, to a larger extent, a non-Bayesian inversion that simply adjusts a recent bottom-up flux estimate with the annual growth rate diagnosed from marine surface measurements, estimate much different fluxes and fit the aircraft data less. Our study highlights a way to rate global atmospheric inversions. It suggests that some satellite retrievals can now provide inversion results that are, despite 15 their uncertainty, comparable in credibility to traditional inversions using the accurate but sparse surface network and that are therefore complementary for studies of the global carbon budget.
Introduction Carbon dioxide (CO2) is increasingly monitored in the global atmosphere due to its important role in climate change. For example, NOAA's GlobalView Plus Observation Package (ObsPack, Cooperative Global Atmospheric Data Integration 20 Project, 2018) archives high-quality measurements made at the surface or from aircraft by various institutes. Despite occasional budget difficulties  (Houweling et al. , 2012) , the number of collected data points has exponentially increased over the years, with, in reference to 1980, six times more measurements in 2000 and 100 times more measurements in 2017. In addition, the ground-based Total Carbon Column Observation Network of column retrievals (TCCON,  Wunch et al. , 2011)  is less than 15 years old but already operates about 30 sites over the globe.
The assimilated data are either surface measurements in ObsPack and related databases, retrievals from the Greenhouse Gases Observing Satellite (GOSAT) or the second Orbiting Carbon Observatory (OCO-2). The transport models are two versions of the atmospheric general circulation model of the Laboratoire de Météorologie Dynamique (LMDz,  Hourdin et al. , 2013)  nudged towards analysed meteorological variables. The Bayesian inversion system from the Copernicus Atmosphere 50 Monitoring service (CAMS, https://atmosphere. copernicus. eu/, Chevallier et al. , 2005)  is used in all six inversions. We use a "poor man's inversion"  (Chevallier et al. , 2009)  based on recent bottom-up fluxes and on the global annual atmospheric growth rate estimated from the average of marine surface measurements  (Conway et al.
, 2014)  to define a baseline for the skill of each Bayesian inversion result. Our use of airborne measurements in the free troposphere as verification data is motivated by their frequent, WMO-traceable calibration, their independence from all data assimilated here (including the measurements in the boundary layer) and their spatial distribution that samples all oceans and continents. Arguably they are the only CO2 dataset that possesses all of three 5 qualities. In the following, data and models are described in Section 2, while Section 3 presents the various results. They are discussed in Section 4. Section 5 concludes the study.
, 2011)  or ERA-5 (https://www. ecmwf. int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5, access 31 January 2019) with a relaxation time of 3 hours. On-line and Off-line models are consistently run at the same spatial resolution in order to avoid any challenging interpolation of the air mass fluxes for the subgrid processes: here 39 eta-pressure layers between the surface and around 80 km above sea level, and 96×96 grid-points, i. e. a horizontal resolution of 1. 89 o ×3. 75 o in 20 longitude. This configuration discretizes the 2-7 km above sea level region of the atmosphere, that will be a major focus in the following, into 6 to 10 layers, depending on local orography. We use two physical formulations of LMDz, called 5A (in code identification number 1649) and 6A (in code identification number 3353), as described by  Remaud et al.
Over a full year, the total 1-sigma uncertainty (resulting from assigned error variances that vary in space and time, and from assigned temporal and spatial error correlations) for these prior land fluxes amounts to about 3. 0 GtC•a -1. The error statistics for the open ocean correspond to a global air-sea flux uncertainty about 0. 5 GtC•a -1. The assimilation window is either 19 years for the surface measurements (from January 2000 until October 2018), eight years for the GOSAT retrievals (from January 2009 until December 2016) or four years for the OCO-2 retrievals (from September 45 2014 until July 2018). Assimilated surface air sample measurements have been selected from four large ongoing databases of atmospheric CO2 measurements: (i) NOAA's ObsPack  (Cooperative Global Atmospheric Data Integration Project, 2018, and CarbonTracker Team, 2018) , (ii) the World Data Centre for Greenhouse Gases archive (WDCGG, https://gaw.
kishou. go. jp/), (iii) the Réseau Atmosphérique de Mesure des Composés à Effet de Serre database (RAMCES, http://www. lsce. ipsl. fr/), and (iv) the Integrated  5 Carbon Observation System-Atmospheric Thematic Center (ICOS-ATC, https://icos-atc. lsce. ipsl. fr/). The list of selected sites is given by  Chevallier (2018). Each dataset provides at least five years of measurements. The error variances assigned to these measurements in the inversion system correspond to transport modelling uncertainty (analytical measurement uncertainty of in situ CO2 data is a negligible component) and are computed as the variance of the high frequency variability of the deseasonalized and de-trended CO2 time series of the daily-mean measurements at each site. These variances are then inflated in 10 order to give the same weight to each measurement day at a given location.
We avoid data below 2 km 50 because (i) local anthropogenic emissions affect many aircraft measurements there, and (ii) some of the aircraft flew in the vicinity of measurement sites that have been used in the surface-based inversions. We avoid data above 7 km because the measurement variations (and the flux regional signal) are much reduced there. A few outliers for which the difference between model and observation is larger than 40 ppm are rejected. We define two periods for the following statistical computations. They are based on the availability of the satellite retrievals and of the aircraft data in the databases used here: a "GOSAT period" from July 2009 until September 2016 and an "OCO-2 5 period" from December 2014 until December 2017. Note that they overlap and that there is a minimum of three months between the temporal bounds of the verification data and the temporal bounds of the assimilated data in order to account for inversion spin-up and spin-down.
Principle We build an ensemble of six Bayesian inversions using the inversion system of Section 2. 2, the two transport model versions of Section 2. 1, and the three observation datasets of Section 2. 3. The assimilation periods differ (Section 2. 2), but the prior 35 fluxes and the prior error model are the same. For each inversion, the posterior model simulation statistically fits its own assimilated data well within their 1-sigma uncertainty. Note that the surface-based inversion with LMDz5A is exactly the CO2 inversion product 18r1 of CAMS that was released in November 2018 (http://atmosphere. copernicus. eu/). In the figures, we will refer to the surface-based inversions by the generic name "SURF" for simplicity. We first present the carbon budget estimates. We choose to look at fluxes at the annual scale only, knowing that over land, the 40 inferred interannual variability is completely driven by the assimilated observations (because prior natural fluxes over land are zero on annual average for the Bayesian inversions, see Section 2.
The Tropical ocean outgassing region reduces with OCO-2 and expands to the south with GOSAT. Figure  7  presents the statistics of model-minus-measurement differences per measurement program for the GOSAT period. Note that the data number varies by several orders of magnitude among the programs: there are a few hundreds of samples for most of the 37 programs, but a few thousands for CALNEX2010, KORUS-AQ, ORCAS, SGP and ATom, a few 10,000s for ACT, DC3, DISCOVER-AQ, GSFC, HIPPO, SEAC4RS, and SONGNEX2015, and 900,000 for CONTRAIL. Obviously, many measurements may fit into a single time-space block of the global transport model. We will only discuss bias differences 5 larger than 0. 15 ppm (i. e. above the calibration uncertainty of the aircraft data, see Section 2. 4) and that are statistically significant at the 0.
05 level, as reported on the figure. The computation of the significance level is made with an unpaired ttest when comparing inversion results that assimilated different data (we assume that changing the assimilated data makes the inversion results independent), and with a paired t-test when comparing inversion results that assimilated the same data (we assume that inversion results in which only the transport model varies are dependent). In practice, changing the independency 10 assumption only affects the detail of the significance-level results, but not the overall picture. Comparing solid and dotted lines, we see no benefit of LMDz6A vs. LMDz5A, since version 6A increases the absolute bias of SURF for eight programs (three in Brazil -RBA-B, ALF, and TAB -, CALNEX2010, DISCOVER-AQ, ACT, THD and LEF) and improves it for four of them (the fourth Brazilian site -SAN -, SEAC4RS, KORUS-AQ, ETL).
If we combine all measurements together, the root-mean-square difference for the OCO-2-based and the surface-based inversions varies only between 1. 51 and 1. 56 ppm. The standard deviations are comparable between the OCO-2-based inversions and the surface-based inversions. LMDz6A improves the SURF biases for 30 KORUS-AQ and degrades them at three other ones  (INPE, LEF and ETL). This lack of improvement also appears for OCO-2 (degradation at INPE, KORUS-AQ, and ABOVE). The statistics for four programs (ORCAS, KORUS-AQ, ACT and SONGNEX2015) are directly comparable between the two periods because the corresponding data are fully in both of them: in all four, OCO-2 performs better than GOSAT. Figure  9  reformulates the bias statistics of Figure  8  on a map of the differences between the absolute biases of inversions OCO-35 2 and SURF.
The overall MSE is very similar between both (1. 5 2 ppm 2 ), but the relative performance still varies in space and time (Figure  5  8 and Figure  9 ) and we hope to extract some further insight into the relative merits of each dataset. In practice, we compute δC1+δC2 using the LMDz model linearized around the inversion prior simulation in order to respect the underlying hypothesis. However, some inconsistencies for the initial state of CO2 could not be completely removed between δC1 and δC2 due to the different starting date of each inversion. The map of the sum of all contributions of the flux changes δfi (from the surface-based inversion to the OCO-2-based inversion) at a given pixel to the change in MSE (δMSE) is presented in Figure  10. Positive 10 values occur when the OCO-2-based fluxes increase the MSE relative to the surface-based fluxes.
35 about the latitudinal distribution of the natural carbon fluxes (Le  Quéré et al. , 2018)  or about the carbon budget of relatively well-documented regions like Europe  (Reuter et al. , 2017). We have defined here quality measures for global inversion systems in order to evaluate the current skill of global inversions, through the example of the CAMS inversion system. By focussing on a specific inversion system, we have avoided the problem of heterogeneity of TransCom-type ensembles, that gather systems with various degrees of sophistication (resolution of the transport model, size of the control vector), but we still varied 40 the assimilated data (surface or satellite) and the transport model in order to generate a small inversion ensemble. In practice, quality measures for a data assimilation system must rely on unbiased and independent data  (Talagrand, 2015).
This can be done with atmospheric observations like those listed in the Introduction: surface measurements, aircraft measurements, TCCON retrievals, AirCore measurements or satellite retrievals. We remove TCCON and the satellite data from the list on the criterion of unbiasedness (Figure  9  suggests that we are interested here in signals that are smaller than the TCCON trueness), the surface data on the criterion of independence (the surface data in ObsPack-type databases that are well simulated by the transport models are usually assimilated), and the AirCore data because of limited time and space coverage so far. This leaves aircraft data as an obvious choice to define objective measures of quality of the 5 inversion systems, when they are not assimilated. They have served this role in the past to some extent, starting from  Peylin et al.