dataesr/scientific-paragraphs-categorization huggingface dataset:
leaching. Further sources of uncertainty in our forest ecosystem model involve missing -but possibly large -terms of the N cycle, such as N 2 fixation, N 2 loss by denitrification, DON uptake by trees and DON leaching. Ecosystem meta-modelling was required to factor out the effects of climate, soil water retention and age on forest productivity, a necessary step before estimating a generalized response of C storage to N r deposition. Neglecting these effects would lead to a large overestimation (factor of 2) of the dC/dN effect in this European dataset and possibly also in other datasets worldwide. After factoring out the effects of climate, soil water retention and forest age in the present dataset, only part of the non-linearity was removed and there www. biogeosciences. net/17/1621/2020/ Biogeosciences, 17, 1621-1654, 2020.
42 nm per pixel. Data were acquired automatically with the SerialEM software  (51). Time-lapse videos of the germination progress of WT and ΔmglA spores were recorded using an OMAX A3590U charge-coupled device (CCD) camera and a Plan Flour 40×/0. 75 Ph2 DLL objective on a phasecontrast Nikon Eclipse 600 microscope. The length, width and geometric aspect ratios (L/W) of spores/cells were determined from differential interference contrast (DIC) images using a custom algorithm written in MAT-LAB (The MathWorks, Inc. ), which is available upon request. DIC images of spores/cells were captured using a Hamamatsu ImagEM ×2 EM-CCD camera C9100-23B (effective pixel size 160 nm) on an inverted Nikon Eclipse-Ti microscope with a 100× 1. 49 N. A. TIRF objective, which are also used for capturing fluorescence images.
ACKNOWLEDGMENTS. We thank  Autumn McManis  and  Elias Topo  for technical assistance,  Drs. Joseph Sorg  and  Ritu Shrestha  for their help in the initial determination of germination phenotypes,  Drs. Michael Van Nieuwenhze  and  Yen-Pang Hsu  for providing TADA, and  Drs. David Zusman ,  Michael Manson , and  Joseph Sorg  for critical reading of this manuscript. This work is supported by the  National Institute of Health  Grants  R01GM129000  (to B. N. ) and  R01AI087946  (to J. L. ) and by  CNRS  (to T. M. ).
Cells were incubated with antibodies targeting CD16/CD32 before staining with fluorescent-labeled antibodies targeting T-cell markers. Then, cells were permeabilized and fixed with Cytofix/Cytoperm (BD Pharmingen) and stained for intracellular cytokines. Data were acquired using a FACSCanto flow cytometer with FACSDiva software (BD Pharmingen) and analyzed with FlowJo v10 for Mac (Tree Star, Oregon, USA). In vivo BrdU labeling 5-Bromo-2'-deoxyuridine (BrdU) was first administered i. p. at 50 mg/kg immediately after tumor challenge (ie, 4 days prior to CTX and 5 days prior to Ad) and maintained over the time until tissue harvest in the drinking water at 0. 8 mg/mL. BrdU-containing water was refreshed every day. Tregs that incorporated BrdU into their DNA during the S phase of the cell cycle were stained using the APC BrdU Flow Kit (BD Pharmagen) following supplier's recommendations.
Data display mean±SD of one representative experiment. Enseignement Superieur (ABES). Protected by copyright. on September 16, 2020 at Agence Bibliographique de l http://jitc. bmj. com/ Open access transplantation achieved by ablation of CTX-sensitive populations inclusive of effector lymphocytes. 14 At lower doses, CTX selectively targets CD4 + Tregs due to their low intracellular concentrations of ATP, which in turn impairs their ability to synthesize glutathione, a key molecule in the metabolic inactivation of CTX. 15  This dose-dependent effect is recapitulated in this model where we document reduced Treg proliferation as well as decreased total Treg frequencies in the absence of overt circulating CD4 + and CD8 + lymphodepletion. Open access The B16F10 model is remarkably aggressive, leading to endpoint of untreated animals within 20 days.
The CIDOC-CRM ontology is a standard for cultural heritage data modeling. Despite its large exploitation, the ontology is primarily maintained in a semiformal notation, which makes it difficult to homogeneously exploit it in digital environments. In addition, the ontology consists of several classes and relations, whereas one sometimes wishes to reuse it but only partially. The purpose of the paper is to contribute to the use of CIDOC by strengthening its foundations. On the basis of formal ontology theories, we propose a first analysis of the ontology to enhance its conceptual structure. We also present a preliminary modularization of CIDOC aimed at enhancing both its formalization and usage.
Introduction The CIDOC Conceptual Reference Model (hereafter CIDOC) is a standard ontology (ISO 21127) for cultural heritage data modeling  [1]. CIDOC has been adopted in several research projects and it constitutes the conceptual architecture for archives, libraries, and museums, among other institutions, to organize data in information systems  [2]. Despite its large exploitation, CIDOC is only weakly axiomatized and some of its modeling choices remain opaque. Existing works like  [3]  have improved its formal treatment but they have only partially contributed to improve its conceptual framework. For instance, as we will see in the next sections, the ontology adopts a representational approach at the intersection between three-(3D) and four-dimensionalism (4D), whichapart from being controversial from a theoretical standpoint  [4]  -does not seem to bring any advantage from a modeling perspective.
The cardinality of P11 is set to (0,n) on both sides. CIDOC is however liberal to alternative interpretations. This choice is unfortunate since divergent formalizations may lead to scarcely interoperable data models. For instance, consider two alternative formalizations; the first one, call it O 1 , implements cardinalities as they are given in  [1] ; the second one, O 2 , where the cardinality of P11 is restricted to (1,n) on the side of E39 Actor so that an instance of E5 must have at least one actor as participant. While O 2 's models are O 1 's models, too, the vice-versa does not hold. In this sense, by leaving open to users the choice of how to interpret cardinalities, the CIDOC's approach runs the risk of making it hard for applications to interoperate. Figure  1  shows the most general classes of CIDOC.
Towards the Modularization of CIDOC We discuss in this section a preliminary modularization of CIDOC; we do not cover the entire input ontology and future work in this regard is required. By the end of the section, we present examples about cultural heritage data modeling showing the (potential) advantages of using CIDOC in different inter-connected modules. Before presenting the modular structure, let us recall some core ideas about ontology modularization. Following  [17]  "ontology modularization can be interpreted as decomposing potentially large and monolithic ontologies into (a set of) smaller and interlinked components (modules). " An ontology module M corresponds to "[. ] a subset of a source ontology O, M ⊂ O, either by abstraction, removal or decomposition, or module M is an ontology existing in a set of modules such that, when combined, make up a larger ontology"  [18].
In our case, the latter are simply represented through OWL data properties and their value spaces (e. g. , integers) to express numerical values. This approach weakens the expressivity of the ontology in comparison to  [7]  (e. g. , we can not say that 8kg is a value within a space for weights), but it takes the benefits of a Description Logic based formalism to model quantitative dimensions' values. In addition, end-users can introduce data properties like hasWeightInKg to characterize the intended meaning of numerical values attached to dimensions (see  [16] ). With this approach, differently from the original spirit of CIDOC, dimensions can be now characterized in terms of either quantitative or qualitative values. The taxonomy of persistent items (see Fig. 3 ) is split into 6 modules, see Table  3.
Second, as said in Sect. 2, CIDOC relies on temporal entities to represent information about persistent items such as birth dates. A similar position is adopted in ontologies like DOLCE or UFO. From a data modeling perspective, however, this approach forces users to create entities which may not be required. Our proposal is to introduce shortcuts to enhance data modeling tasks, a strategy which is adopted by CIDOC itself  [1]. For example, a new binary predicate createdAt(o,d) between a physical man-made object and its production date can be defined (in FOL) as in (Def1), where all defining predicates belong to the CIDOC's signature. Because of expressivity restrictions, definitions similar to (Def1) can not be employed in SW ontologies. One can however use OWL data properties -possibly by importing them from existing SW vocabularies -while characterizing their formal interpretations in external FOL theories.
19 Following this consideration, we have included in the modules some data and object properties to facilitate data representation. A third observation is about CIDOC's use of appellations (e. g. , names, dates) and primitive values (strings, numbers). As a formalism-independent model, the relevance of these elements can not be dismissed. When choosing a specific formalism, however, they need to be handled with care (see, e. g. ,  [3] ). In the case of OWL, it is reasonable to rely on data types and data properties to handle primitive values and appellations, respectively, rather than representing them as domain instances as it is done in existing OWL releases of CIDOC such as the Erlangen release (see above for references). In this way, one can rely on value spaces to characterize values' meanings and can enable the use of algorithmic procedures to manipulate data (e.
As a first example, let us assume that we need to represent museological data about statues. These can include data about statues' dimensions, creators, creation dates, material types, identifiers, and the museums where they are preserved. To represent these data in our framework it is sufficient to use the cidoc:artifact-module and the cidoc:actor-module. The former contains the basic modeling elements for statues, whereas the latter is required to represent the statues' creators. Hence, differently from the current release of CIDOC, end-users can now exploit the ontology by reusing only the modules that are relevant for their tasks. In addition, as said, in a data modeling scenario one may not desire the explicit representation of temporal phenomena like the production events leading to the statues and their time-regions.
Although ontologically coherent, this approach would lead to verbose data models at the expenses of computational resources. By introducing shortcuts on the line of (Def1) we can link statues to their creation dates and creators while keeping a simple data representation. As a second example, we consider the design of a domain-specific ontology based on CIDOC. OpenArchaeo is a semantic mediator for archaeological datasets currently hosted by the French infrastructure Huma-Num. 21 It interconnects multiple datasets by using an ontology dedicated to archeology  [8, 23] , which is based on CIDOC plus some of its extensions, e. g. , CRMsci  22  and CRMba,  23  among others. One of the most relevant classes is the event of (archeological) site discovery represented by S19 Encounter Event from CRMsci, which is a subclass of S4 Observation, the latter subsumed by E13 Attribute Assignment.
Conclusions In order to foster the use of ontologies for knowledge representation and data management in the area of cultural heritage, we presented in the paper a first ontological analysis and modularization of the CIDOC ontology. We focused on the latter because of its wide use in both research projects and institutions. Our contribution is twofold: first, by analysing CIDOC, the goal is to enhance and make transparent its ontological commitment. As a result, we have proposed to remove some classes from the ontology and to introduce some new modeling elements. Second, by modularizing it, the purpose is to facilitate its selective reuse, maintenance, and extension with domain-specific modules. Future work to strengthen our proposal is required. First, both the analysis and modularization have to be extended to the whole ontology, conceptual objects and relations included.
The analysis of relations requires a careful evaluation of their cardinalities to check whether these are consistent with the intended meaning of the related classes. Second, a testing benchmark is necessary to evaluate both the ontology resulting from the analysis and its modular architecture. From a usability perspective, we plan to exploit the ontology modules in research projects and to test their impact on data management practices. Finally, a stable formalization of CIDOC in a language like FOL is a desiderata to unambiguously characterize its elements. This could be based on the work presented in  [3]  possibly revised and extended by the work we presented. This formalization could be then used as a foundational basis for the computational treatment of the ontology.
Oral and written information is given during the anaesthesia consultation several days to several weeks before endoscopy. Written consent is obtained after verification of the eligibility on the day of the GIE procedure. Patients have the right to withdraw their consent and discontinue their participation at any time for any reason. Patients are expected to be included during an 18-month period starting in March 2019. ► March 2019: protocol approval from the ethics committee and trial tools development (case report form, randomisation system). ► 2019-2020: inclusion of patients. ► 2020: cleaning and closure of the database. Data analysis, writing of the manuscript and submission for publication in a peer-reviewed journal.
data collection Multiparameter monitoring data recordings (SpO 2 , heart rate, blood pressure, respiratory rate and capnography if available) are made automatically. SpO 2 is recorded every minute during intervention by the usual multiparameter monitor (Datex Ohmeda or Carescape B450 or Carescape B650, General Electric Healthcare) enabling the capture of prolonged hypoxaemia defined as an SpO 2 ≤92% for ≥60 s. Outcomes are collected on paper case report forms (CRF). Collection of data stops when the patient leaves the recovery room. The trends of vital signs, including SpO 2 recordings, are printed out and put in an envelope with the CRF. The medical history is collected from the anaesthesia consultation chart and reported on the CRF. Any protocol deviations are recorded in either the CRF or the medical records; both are checked by a clinical research assistant to ensure that all protocol deviations and adverse events are in the database.
The secondary outcomes are apnoea, prolonged hypoxaemia (SpO 2 ≤92% during ≥60 s), desaturation with a drop ≥5% in SpO 2 after preoxygenation, hypoxaemia with SpO 2 ≤90%, severe hypoxaemia defined as SpO 2 ≤85%, bradycardia (defined as heart rate <50/min) and hypoxaemia during the stay in the recovery room. Modifications of oxygenation settings, need for mask ventilation, manoeuvres to maintain upper airway patency, non-invasive ventilation, laryngeal Open access mask airway or intubation, duration of endoscopy, duration of sedation, time spent in recovery room, hospitalisation and serious adverse events are also recorded. statistical analysis R statistical software V. 3. 6. 0 and MedCalc V. 18. 2. 1 (MedCalc Software, OstendMariakerke, Belgium) will be used for data processing and analysis.
EthICs And dIssEMInAtIon Investigators will make available to the persons responsible for the follow-up, the quality control or the audit of the research, the documents and individual data strictly necessary to the control, according to the provisions of law (articles L. 1121-3 and R. 5121-13 of the French Public Health Code). A paper CRF is used and is considered as a source document. Data will be captured by two members of the study team into a secured spreadsheet application (Excel). Data will be handled according to French law. All original records will be archived at trial sites for 25 years. The clean database file will be anonymised and kept for 25 years. Only four members of the ODEPHI's team (the two first authors, the statistician and the last author) will have access to the final trial dataset.
Investigators of each institution will not have access to the final trial dataset. The study can be suspended or prematurely interrupted in case of unexpected serious adverse events, requiring the examination of the evolution of all the patients already included. Orléans Regional Hospital reserves the right to interrupt the study at any time if the inclusion objectives are not reached. The investigator can definitively or temporarily stop the patient's participation for any reason which would better serve the patient's interests and especially in case of serious adverse effects. In this case, these reasons are collected, assessed and reported. An investigator at each centre is responsible for enrolling patients in the study, ensuring adherence to the protocol and completing the CRF. Research assistants regularly monitor all the centres on site to check adherence to the protocol and the accuracy and completeness of the data recorded.
Acknowledgements The authors would like to thank the clinical staff of all trial sites ( Pôle santé Oréliance ,  Centre Hospitalier Régional d'Orléans, Centre Hospitalier de DAX, Centre Hospitalier universitaire de Tours). We thank  Elsa Tavernier, PhD  ( Inserm CIC 1415, CHRU de Tours, France ) for statistical advices, and  Carissa Fonseca, PhD , from  Fisher & Paykel Healthcare  ( New Zealand ) for providing minor translational and editing assistance.
INTRODUCTION The highly polymorphic human major histocompatibility complex (MHC), also known as the human leukocyte antigen (HLA), expresses class I and II molecules (alleles) that present antigens to the T-cell receptors as part of the adaptive immune response  (1) (2) (3) (4). The high level of gene sequence diversity  [25, 756  alleles and counting; IPD IMGT/HLA database (Release 3. 38. 0), http://hla. alleles. org/nomenclature/stats. html] within the HLA system may govern the outcome of many transplants (tolerance or rejection)  (5, 6) , infections, susceptibility to autoimmune diseases  (2, (7) (8) (9) , and allergic reactions to various drugs  (10). Moreover, the efficacy of recently developed checkpoint inhibitory therapies in immuno-oncology appear to be directly linked to the so-called "tumor mutation burden" that is the status of neo-antigens presented by the patient's HLA class I alleles  (4).
Sample Information A reference set of PBMC samples from 161 donors were selected from a larger number of high-resolution genotyped samples obtained from 2,344 donors recruited for bone marrow transplantation (BMT) as part of the Japan Marrow Donor Program (JMDP)  (48). This sample of genotyped Japanese donors represented more than 99. 2% cumulative allele frequency (138 alleles) of the known HLA alleles in Japanese population at the field-2 level of resolution (an allele resolution level of sequences that differs by a non-synonymous substitution) at six HLA loci (20 alleles for HLA-A, 37 HLA-B, 19 HLA-C, 30 HLA-DRB1, 16 HLA-DQB1, and 16 HLA-DPB1) (Table  S1 ). A total of 48 UCB samples previously registered and stored at the Tokai University Cord Blood Bank also were used in this study. The high-resolution HLA genotyping data for these UCB was unknown before this study.
Data Processing and Allele Assignment of HLA Genotypes After the sequencing runs, basic sequence information such as the read numbers and quality values were calculated with a FASTX_quality_stats program included in a FASTX-Toolkit package (ver. 0. 0. 13) for short-read data preprocessing (http:// hannonlab. cshl. edu/fastx_toolkit/). The FASTQ sequence files for each sample were used for HLA genotyping and allele assignment up to the field-3 level (an allele resolution where synonymous and/or non-synonymous DNA substitutions in the coding region define alleles). The HLA alleles for the 12 classical HLA loci, HLA-A, HLA-B, HLA-C, HLA-DRA, HLA-DRB1, HLA-DRB/DRB4/DRB5, HLA-DQB1, HLA-DQA1, HLA-DPA1, and HLA-DPB1, were assigned by nucleotide similarity searches in the IPD-IMGT-HLA database (http://hla.
Calculation and Normalization of Sequence Read Numbers (RNA Levels) Mapping of the reads and the HLA allele sequences assigned by the BLAT search as references were performed using GS Reference Mapper Ver. 3. 0 software (Roche). To precisely extract in-phase read numbers that are our measure of RNA levels, we limited the mapping regions to highly polymorphic exons: 546 bp of exons 2 and 3 in HLA-A, HLA-B, and HLA-C; 239 bp of exon 2 in HLA-DRA; 270 bp of exon 2 in HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, and HLA-DQB1; 249 bp of exon 2 in HLA-DQA1; 246 bp of exon 2 in HLA-DPA1; and 264 bp of exon 2 in HLA-DPB1. The mapping parameter was set to a 100% matched condition between the reads and the references to avoid mismapping among the HLA loci and contamination of in vitro generated PCR crossover products  (51, 52).
In order to compare differences of the RNA levels (cDNA sequence read numbers) among the HLA loci and alleles, we normalized the mapped read numbers as follows. The mapped raw read numbers of each allele at each locus were first standardized by their target sizes. To derive the "normalized read numbers" within a particular set of loci, the total sizestandardized read numbers included in the set were estimated, and the total read numbers were standardized to 1 million. Then, the normalized read numbers of each allele at each locus in the set were calculated as relative read numbers in 1 million total reads. An example of deriving the normalized read numbers at the HLA-A, HLA-B, and HLA-C loci is described in Figure  S1B. For comparison of the RNA levels expressed by the same or different HLA genes and alleles, we prepared the following four datasets: (1) normalized read numbers of the 12 HLA loci and alleles (including two HLA-DRB3/DRB4/DRB5 alleles) using 78 PBMC and 18 UCB samples, (2) normalized read numbers of the HLA class I loci and alleles using 161 PBMC and 48 UCB samples, (3) normalized read numbers of the HLA class II loci (excluding HLA-DRB3/DRB4/DRB5) and alleles using 161 PBMC and 48 UCB samples, and (4) normalized read numbers of the HLA class II loci and alleles (including two HLA-DRB3/DRB4/DRB5 alleles) using 78 PBMC and 18 UCB samples (Figure  S1C ).
Genotyping to the Field-3 Level for 12 HLA Loci Nucleotide similarity searches of the sequenced HLA alleles to the field-3 level (based on synonymous and/or non-synonymous substitution in the coding region for each designated allele) using the BLAT program  (50)   S3 ). One novel allele was further detected for HLA-DQA1 [named tentatively DQB1 * 06:02new (DDBJ Accession Number: LC499658) with a synonymous substitution in the exon 1]. In contrast, the other alleles were previously known HLA alleles that were assigned to the field-2 level at six HLA loci.
Comparison of the RNA Levels for 12 HLA Loci and Alleles in PBMC Samples Figure  1  shows a box-and-whisker diagram of the normalized read numbers (RNA levels) for 12 HLA loci using dataset 1 that includes 1,252 phased alleles identified for the PBMC samples (Table  S4A ). The ANOVA statistical difference between the expression levels of the HLA loci was highly significant  (P < 1. 0 × 10 -100 ). We also found that the 99% confidence intervals for these 12 loci do not overlap (data not shown), confirming that each 12 loci are distinct in terms of RNA expression levels. Among the 12 loci compared here, HLA-B displayed the highest average expression level. The average level of expression of HLA-B was ∼2-fold higher than that of HLA-A or HLA-C, and ∼4-5 times higher than by the class II loci, HLA-DRA, HLA-DRB1, or others (Table  1 ).