dataesr/scientific-paragraphs-categorization huggingface dataset:
Biological processes The most studied biological processes are morphology, calcification and growth, representing 33, 30 and 26 % of the papers from which data were archived (Fig. 4 ). Physiology, photosynthesis, mortality, reproduction and respiration are also well represented (13 to 23 %), followed by primary production, performance, dissolution and nitrogen fixation. Other processes such as community composition, abundance, adaption, nutrient uptake, toxicity and bleaching are represented in 26 % of the papers from which data were archived (see detail description of keywords in the user instructions of the OA-ICC bibliographic database, www. tinyurl. com/ oaicc-biblio). All processes, except calcification and primary production, are better represented today than they were before 2010, indicating that the initial imbalance (of considerably more studies on calcification and primary production than on other processes) has improved.
Recommendations As a new scientific field such as ocean acidification develops, the amount of scientific data grows considerably and the need for data archiving greatly increases to ensure that data are easily accessible for analysis and reuse. Many journals, such as the Proceedings of the Royal Society B -Biological Sciences, PLoS ONE and Nature, require or encourage authors to archive data in public data repositories. Several data journals were launched to help this process. For example, the present journal was launched by the European Geosciences Union in 2008 and Scientific Data of the Nature publishing group in 2014. Many funding agencies also have requirements on data archiving. The European Research Council (ERC) mandate funded scientists to deposit primary data in relevant databases as soon as possible after data collection.
Furthermore, some data have previously been published in PANGAEA by data curators of other projects (e. g. the project "Biological Impacts of Ocean Acid-ification", BIOACID), or in other databases (e. g. the Biological and Chemical Oceanography Data Management Office, British Oceanographic Data Centre, Australian Antarctic Data Centre), which induced duplicates when they were archived again for the OA-ICC data compilation with recalculated carbonate chemistry data. To detect duplication, we recommend that the community creates unique identification for data sets on the biological response to ocean acidification. And it is hoped that all projects and databases can define best practices for archiving ocean acidification data and cooperate to avoid duplication. To this end and to meet the need for easier and more effective access to ocean acidification data, data managers of various institutions are planning to create a joint portal ("one-stop shop") for access to global ocean acidification data by linking different data repositories  (Garcia et al.
Acknowledgements. We are grateful to all the authors for the provision of data and to the staff of PANGAEA, especially  Stefanie Schumacher , for the considerable help and support it has provided to the data compilation since 2007. The pioneering work of  Anne-Marin Nisumaa , data manager of the  EPOCA  project, is greatly appreciated. This is a contribution of the  Ocean Acidification International Coordination Centre (OA-ICC ), a project funded through the  Peaceful Uses Initiative  of the IAEA. The IAEA is grateful for the support provided to its Environment Laboratories by the  Government of the Principality of Monaco. The support of the  State Key Laboratory of Marine Environmental Science (Xiamen University)  is also gratefully acknowledged. We thank  S. Pesant  as well as an anonymous referee for their useful comments.
Data availability Access to data presented in this paper is at doi:10. 1594/PANGAEA. 149999. Access to additional data resources: (1) the growing collection of data sets: http://www. pangaea. de/search?q= Project:OA-ICC and (2) the Ocean Acidification (OA-ICC) bibliography: https://www. mendeley. com/groups/4333941/ ocean-acidification-oa-icc/papers/. The Supplement related to this article is available online at doi:10. 5194/essd-8-79-2016-supplement.
Methods Patients. Peripheral blood and bone marrow samples were collected on ethylenediaminetetraacetic acid from 245 patients with a CMML diagnosis according to the World Health Organisation criteria  1. When indicated, several peripheral blood samples were collected sequentially from a given patient (Supplementary Fig. 1 ). We initially performed WES in 49, WGS in 17 and validation of recurrent mutations by deep sequencing in 180 cases. Serial WES were performed in 17 patients, including 6 untreated and 11 treated with either decitabine (N ¼ 6; EudraCT 2008-000470-21 GFM trial; NCT01098084; https://www. clinicaltrials. gov/)  18 or AZA (N ¼ 5; following the European Medicines Agency approval; EMEA/H/C/000978). Responses were classified according to the International Working Group 2006 criteria  31.
Patients with stable disease without haematological improvement remained treated until progression  17. When indicated, sequential RNA-Seq and DNA methylation analysis  46  were performed. In treated patients, samples were collected immediately before the following drug cycle. All the procedures were approved by the institutional board of Gustave Roussy and the ethical committee Ile de France 1, and written informed consent was obtained from each patient. Data collected from French and Japanese patients were analysed homogeneously. Patient characteristics are in Supplementary Table  1 , the flow chart of analyses in Supplementary Fig. 1. Cell sorting. Bone marrow (N ¼ 9) or peripheral blood (N ¼ 7) mononucleated cells were separated on Fycoll-Hypaque. Peripheral blood CD14 þ monocytes were sorted with magnetic beads and the AutoMacs system (Miltenyi Biotech, Bergish Gladbach, Germany)  45.
DNA fragments were end-repaired, extended with an 'A' base on the 3 0 -end, ligated with paired-end adaptors and amplified (six cycles) using a Bravo automated platform (Agilent technologies). Exome-containing adaptor-ligated libraries were hybridized for 24 h with biotinylated oligo RNA baits, and enriched with streptavidin-conjugated magnetic beads using SureSelect (Agilent technologies, Les Ulis, France). The final libraries were indexed, pooled and paired-ends (2 Â 100 bp) sequenced on Illumina HiSeq 2000 (San Diego, CA). In nine cases, WES was performed in Japan following a previously described protocol  55. The mean coverage in the targeted regions was 112 Â (Supplementary Table  2 ). Two individual cases have been reported in our previous studies  4, 8. Sequencing data are deposited at the European Genome-Phenome Archive (EGA), hosted by the European Bioinformatics Institute (EBI), under the accession number EGAS00001001264.
Raw reads were filtered with Trimommatic 0. 30 (ref. 57) to remove adaptors, truncate any read whose average quality on a sliding window (six bases) was r20, remove the start and the end of a read if r20 and any read with an average quality r20 or a length o36. Statistics on alignment and coverage are given in Supplementary Table  2  and detailed analysis of each studied variant in Supplementary Table  3. Targeted resequencing was analysed similarly to WES except the suppression of PCR duplicates. We added the following public databases: ESP 6500, dbSNP 138, COSMIC 68 (Catalogue Of Somatic Mutations In Cancer) and ClinVar (20140303). Prediction of driver genes. We applied DrGaP (driver genes and pathways)  58  to synonymous and nonsynonymous somatic variants (889 in 694 genes) to measure the probability of each variant to occur by chance.
WGS was performed on five lanes for each leukaemia sample, and two lanes for the CD3 þ germline on the Illumina HiSeq X platform. The goal was to achieve 125 and 60 Â depth, respectively. Sequencing data was aligned to b37d5 reference genome with BWA MEM, and duplicates were marked, and multiple lanes merged using novosort. Somatic SNV and INDEL variant calling was performed using Strelka for tumour normal pairs. Somatic copy number variants, loss of heterozygosity regions, ploidy and purity were determined using Sequenza. Freebayes with minimum VAF ¼ 0. 01 was used to generate variants from individual samples, and to assess the number of clones. Variants were annotated using Variant Effect Predictor. Phylosub was used to reconstruct the evolutionary lineage of samples, using either high, or mediumand high-impact variants (loss of function vs missense, respectively).
Two separate samples were multiplexed into each lane. Quality of reads was evaluated using FastQC. RNA-Seq analysis. Sequences were filtered with Trimommatic and alignment was performed with Tophat2 version 2. 0. 9 (ref. 62) and Bowtie2 version 2. 1. 0 (ref. 63). The filtered reads were aligned to a reference transcriptome (downloaded from UCSC website on 20 December 2013). The remaining reads were split and segments were aligned on the reference genome, as described  62. In average, 88. 95% of reads were aligned (Supplementary Table  9 ) and counted with HTSeq (v0. 5. 4p5) (ref. 64) using the following parameters: --mode ¼ intersection-nonempty --minaqual ¼ 20 --stranded ¼ no. Differential expression analysis was performed using DESeq2 package version 1. 6. 3  (ref. 65) with R statistical software version 3.
50 bp reads were aligned against a bisulfite-converted human genome (hg19) using Bowtie and Bismark 67. Downstream analysis was performed using R version 3. 0. 3, Bioconductor 2. 13 and the MethylSig 0. 1. 3 package. Only genomic regions with coverage between 10 and 500 Â were used for the downstream analysis (Supplementary Data 5). DMR were identified by first summarizing the methylation status of genomic regions into 25-bp tiles and then identifying regions with absolute methylation difference Z25% and FDR o10%. DMRs were annotated to the RefSeq genes using the following criteria: (i) DMRs overlapping with a gene were annotated to that gene, (ii) intergenic DMRs were annotated to all neighbouring genes within a 50-kb window, and (iii) if no gene was detected within a 50-kb window, then the DMR was annotated to the nearest TSS.
We removed the variants located in low complexity regions, immunoglobulin loci (as reported in http://www. genecards. org/ for TCRA, TCRB, TCRG, IGH, IGL and IGK) and genes in which false positives have been frequently detected by new-generation sequencing61. By removing low complexity regions, as defined in the masked genome chromOut. tar. gz generated by repeatMasker (http://hgdownload. soe. ucsc. edu/goldenPath/hg19/bigZips/), we removed 45% of the genome, thereby eliminating 74, 69 and 71% of the SNVs detected by using SomaticSniper, VarScan2 and Strelka, respectively, and 87 and 77% of the indels identified by VarScan2 and Strelka, respectively. In subsequent analyses, the SNVs and indels identified by combining these stringent algorithms were used. Sequencing data are deposited at the EGA hosted by the EBI under the accession number EGAS00001001264.
Acknowledgements This programme was supported by grants from  Ligue Nationale Contre le Cancer  (equipe labellisee),  Institut National du Cancer (INCa PLBIO, SIRIC SOCRATE) ,  Institut National du Cancer and Direction Generale de l'Offre de Soins  ( PHRC-K 2011-182 ),  Agence Nationale de la Recherche (Molecular Medicine in Oncology; Paris Alliance Cancer Research Institute: France Genomique National  programs funded by 'Investissements d'avenir'). J. M. was supported by the  Fondation pour la Recherche Medicale  ( FDT20140931007 ).
Supplementary information on taxonomy, nomenclature, distribution within Greece, total range, life form and ecological traits of vascular plants known to occur in Greece is presented and the revised data are quantitatively analysed. Floristic discrepancies between Vascular plants of Greece: An annotated checklist (Dimopoulos & al. 2013) and relevant influential datasets (Flora europaea, Med-Checklist, Euro+Med PlantBase, etc. ) are explained and clarified. An additional quantity of synonyms and misapplied names used in previous Greek floristic literature is presented.
Introduction Vascular plants of Greece: An annotated checklist  (Dimopoulos & al. 2013 ) constitutes the first comprehensive inventory of the flora of Greece more than a century after Eugen von Halácsy, a Hungarian-born physician of Vienna, had finalized the most recent complete Flora of the country  (Halácsy 1900 (Halácsy -1904 (Halácsy , with supplements in 1908 (Halácsy and 1912)). The Checklist of 2013, eagerly awaited by the scientific and general public, met with a kind reception of professional and amateur botanists, as well as national and regional politicians and administrators responsible for nature conservation activities in Greece. The quite active use of the book, which started immediately after publication, revealed a certain number of alleged and factual discrepancies in content compared to contemporary influential datasets  (Tutin & al.
Overall taxonomic diversity of the vascular flora of Greece The vascular flora of Greece comprises 5758 species and 1970 subspecies (native and naturalized), representing 6620 taxa, belonging to 1073 genera and 185 families (Table  1 ). The full dataset has a total of 7739 records, comprising species and subspecies plus ten sections of Taraxacum and one aggregate (Portulaca oleracea aggr. ). The only species for which the status of "native but extinct" was confirmed are Staphylea pinnata (NE) and Stratiotes aloides (NC).
→ Leontodon hispidus L. subsp. hispidus Ophrys herae subsp. janrenzii (M. Hirth) M. Hirth → Ophrys sphegodes subsp. mammosa (Desf. ) E. Nelson Pedersen Cullen & Heywood Sagina melitensis Duthie → Sagina apetala Ard. Sium nodiflorum L. → Helosciadium nodiflorum (L. ) W. D. J. Koch A Ranunculus bullatus L. subsp. cytheraeus (Halácsy) Vierh. x. x x EM H ferent recent basic floras, checklists and databases, have Asyneuma canescens subsp. cordifolium (Bornm. ) Damboldt ► Lathyrus vernus (L. ) Bernh. ► G. Martens ► Acer monspessulanum subsp. athoum (Bornm. & Sint. ) F. K. Mey. → Acer monspessulanum L. subsp. monspessulanum Apargia aspera Waldst. & Kit. → Leontodon biscutellifolius DC. Halácsy 1894 Cystopteris fragilis subsp. dickieana (R. Sim) Hyl. → Cystopteris dickieana R. Sim Ficaria chrysocephala (P.
A non-exhaustive list, representative of the various methods tested, is  McFadden et al. (1985) ,  Abdelwahab and Sargious (1992) ,  Holguin-Veras (2002) , or  Pourhabdollahi (2013). For a more complete review and discussion, see  Combes (2014). For the same reason, freight transport models have not incorporated inventory theoretic concepts, with the exception of  De Jong and Ben-Akiva (2007). To our knowledge, only one database makes it possible to overcome this obstacle: the ECHO database, a French shipper survey of 2004-2005 which describes individual shipments including the logistical context within which they took place  (Guilbault, 2008). One of the distinctive features of the ECHO survey is that it observes the shipper-receiver relationship, in particular the total commodity flow rate from the shipper to the receiver.
With this database it was possible to validate empirically the EOQ model  (Combes, 2012). It was also possible to estimate a mode choice model based on the EOQ model, with the shipper-receiver commodity flow rate as an explanatory variable of mode choice (Lloret-Batlle and  Combes, 2013) , and to confirm its importance in mode choice. This paper focuses on another question that can be addressed with inventory theory and an adequate freight survey. This question concerns the relationship between network structure and freight rates. In other words, why do transport costs have a certain shape for direct door-to-door transport, and another for hub-and-spokes networks? Classic freight transport models usually ignore transshipments, or when they don't, they only consider multimodal transshipments.
The model was estimated with the ECHO database. It considered the main modes of transport chains, based on a predefined hierarchy (e. g. when road and rail are used, the main mode is rail, etc. ) In this paper, we discuss theoretically and econometrically the number of transshipments taking place during the transport of a shipment. A theoretical model gives some elements of explanation regarding the structure of freight rates for direct door-to-door transport vs hub-and-spoke networks, and the demand segments they are adapted to. Then, the econometric model of freight mode choice of Lloret-Batlle and Combes is extended to incorporate the number of transshipments within the transport chain and it is estimated against the ECHO database. The paper proceeds as follows: Section 2 reviews the basic elements of inventory theory on which the rest of the paper is based; Section 3 presents the data; Section 4 presents the model specification and the estimation procedure and Section 5 discusses the results.
Shipment data The ECHO database describes 10,462 shipments sent by about 3,000 shippers. Some commodity types are excluded from the database (such as mining, agriculture, waste, etc. ) The shipments observed in the ECHO survey are either sent from or received in France; the other end of the transport operation can be anywhere within or outside France. All transport modes are represented. For road transport, private carrier and common carrier transport are distinguished. Shipments sent by non-road land modes (i. e. railway, combined transport and inland waterway) were oversampled in order to obtain enough observations. The transport mode is defined as the main mode of a transport operation, according to a predefined hierarchy: for example, the main mode of a shipment transported by road and air is air.
More details are available in  Guilbault (2008). In the frame of this study, we only pay attention to the main transport mode, except when the main mode is common carrier, in which case the number of transshipments is also taken into account. Transport mode summary statistics are given by Table  1. We note here the favorable circumstance that a high number of shipments is available in the database, which are moved over more than one transport leg. For each shipment, the shipping and the receiving firms are also observed, as well as the relationship between these two firms, and the shipment itself. In each of these categories, many variables related to economic, logistic or transport related aspects are available, up to about 700 distinct variables. The variables used in this study are the commodity density value (k€/t), the commodity flow rate between the shipper and the receiver (kt/y), the main mode of the transport operation (including the number of legs if the main mode is road), a few variables related to the way the shipment should be handled, a few variables related to the transport infrastructures available to the shipper (e.
g. rail sidings), and the number of transshipments in the transport operation. Strictly speaking, the rate Q of the commodity flow between the shipper and the receiver is not available in the ECHO database. The closest available variable is the total commodity flow between the shipper and the receiver, denoted by Qtot, without distinction of commodity types. This variable is available for 81. 5% of the observations. It is expected that, given the fact that shippers and receivers are disaggregated at the level of the premises of firms both for the origin and the destination, Q and Qtot should most often coincide. The commodity value of time a is not directly available either. Fortunately, there is also a good candidate to replace it. Indeed, for 64. 5 % of shipments, the market value in euro (tax excluded) is indicated.
; they were tested without success). It should be noted that freight rates are available in the ECHO survey. However, using freight rates for estimation purposes involves the reconstitution of freight rates of all the transport alternatives for each shipment, with a distinction of their shipment size dependent and shipment size independent components; a difficult task. Also, a transport operation also incurs nonmonetary costs to shippers; those need to be accounted for in a mode choice model. Third, part of the transport cost is sometimes borne by the receiver. Fourth, the structure of freight rates is complex, and depends not only on the mode and distance, but also on many other variables which are not captured by the ECHO database. For these reasons, a simpler approach is preferred, where freight rates are not taken into account.
Qualitative analysis of transport modes in the ECHO survey Before proceeding to the statistical analyses, let us discuss qualitatively the mode choices observed in the ECHO database. Figure  2  illustrates the transport modes observed in the ECHO database, in relationship with ln(adens), ln(Qtot), and the crow-fly distance between the origin and the destination (each of the eight plots corresponds to a given distance range). In each plot, the strong correlation between distance and total flow is visible as a diagonal shape of the clouds in the boxes. But this is not the main purpose of our exposition. Clear patterns are apparent for the choice of mode. First, distance plays a major role: sea and air transport, of course, are only used on long distances (starting from 1000 km); whereas common Combes and Tavasszy Inventory theory, mode choice and network structure in freight transport carrier, railway, inland waterway and combined transport compete on intermediate distances (above 50 km and below 1000 km).
Purpose: Real-world data on the use of rivaroxaban in the perioperative period in patients undergoing major orthopedic surgery are limited. Subsets of data from the Phase IV, noninterventional XAMOS study were analyzed to explore the potential influence of timing of the first thrombo prophylactic dose, type of anesthesia, and concomitant mechanical prophylaxis on clinical outcomes in patients undergoing major orthopedic surgery in routine clinical practice. Patients and methods: In XAMOS, 8,778 patients received rivaroxaban (10 mg once daily) and 8,635 received standard-of-care (SOC) pharmacological prophylaxis (safety population). Crude incidences of symptomatic thromboembolic and treatment-emergent bleeding events were analyzed according to timing of the first postoperative thromboprophylactic dose, use of general or neuraxial anesthesia, and use of mechanical prophylaxis with pharmacological thromboprophylaxis.
10 Guidelines also recommend the use of mechanical methods to prevent VTE, particularly in patients at a high risk of bleeding. 1 However, the potential benefit of using mechanical thromboprophylaxis (such as compression stockings) in combination with pharmacological thromboprophylaxis is yet to be confirmed, because clear clinical evidence is lacking. 1  As with the other DOACs approved for clinical use, real-life data on the use of rivaroxaban in the perioperative period in patients undergoing major orthopedic surgery are limited -such as data regarding the timing of the first dose of pharmacological thromboprophylaxis, the use of neuraxial anesthesia, and the concomitant use of mechanical prophylaxis in daily practice. The aim of the subanalyses of the XAMOS study was to describe these parameters and explore their potential influence on clinical outcomes (focusing on symptomatic thromboembolic and bleeding events) in patients undergoing major orthopedic surgery of the lower limbs in routine clinical practice.