dataesr/scientific-paragraphs-categorization huggingface dataset:
[25, 26]  We reproduce in Figure  S1  (available as supplementary file online) the schematic diagram summarising the harmonisation process of event identification across the databases in EU-ADR. Case validation by manual review of hospitalisation records and GP records was done in a random subset of the cases. The overall positive predictive value (PPV) for identifying AMI was good, ranging from 75% (ICPC) to 95% (ICD9-CM) to 100% (ICD-10). These findings are consistent with PPV estimates for ICD9-CM and ICD-10 cited in the literature (To date there is no study Table  1. Cont. describing the PPV of ICPC codes or free text search for identifying AMI). [27]. Only the first occurrence of AMI (i. e. incident case) was considered in the analyses; patient time after an AMI was censored.
Drug Exposure Drug prescription/dispensing data were used to estimate event rates during drug exposure and were assessed according to the Anatomical Therapeutic Chemical (ATC) classification system of the World Health Organization (WHO) (http://www. whocc. no/ atc/structure_and_principles/). The duration covered by each prescription or dispensing was estimated according to legend duration (if dosing regimen is available), or otherwise based on the defined daily dose (http://www. whocc. no/ddd/definition_and_ general_considera/). Overlapping treatment episodes with the same drug were combined into a single episode of drug use that starts when the first prescription begins and stops when the last prescription ends. When a patient uses more than one drug at a time, the corresponding person-time is labelled accordingly.
We calculated the relative risk, RR LGPS and used this to rank the initial list of 'prime suspects. ' The results from the different databases were combined to generate a single risk estimate per drug as if the databases together form one large database. We did not perform any metaanalyses. A value of RR LGPS $2. 0 and a lower 95% CI of RR LGPS. 1 were used as threshold values for further processing. A more detailed description of LGPS and how the RR LGPS is calculated is given in Appendix S1 (available as supplementary file online). Alternative explanations for the identified associations: protopathic bias and confounding. Another method, LEOP-ARD (Longitudinal Evaluation of Observational Profiles of Adverse events Related to Drugs), developed in EU-ADR, attempts to single out associations that may be detected because the drug is used to treat the event, or a prodrome of the event, rather than cause it (protopathic bias).
A more comprehensive description of the EU-ADR web platform can be found in other publications. [30, 31]. Novel associations. The interest in drug safety surveillance is discovery of phenomenon describing a 'new potentially causal association, or a new aspect of a known association. '  [32]  To discriminate among potentially relevant new and already known associations, we used the abovementioned web platform to assess previous reporting of such drugs with AMI in the biomedical literature and eliminated from the list of 'prime suspects' drugs previously reported to be associated with AMI in more than one of three biomedical databases: MEDLINE (http://www. ncbi. nlm. nih. gov/pubmed); DrugBank (http://www. drugbank. ca/); or DailyMed (http://dailymed. nlm. nih. gov/dailymed/about. cfm). The Medline ADR signal filtering workflow automates literature analysis by assessing a list of publications regarding AMI.
The algorithm adopts a semantics-based approach that processes Medline annotations looking for particular MeSH terms. This workflow's output is a direct relationship between AMI and its descriptions in Medline, if present. In addition, there is a signal filtering that identifies co-occurrence of the drug and the event (in this case AMI) in Medline literature (Medline Co-occurrence) or drug databases such as DailyMed (http://dailymed. nlm. nih. gov/) or DrugBank (http://www. drugbank. ca/). The workflows use statistical and text-mining techniques to evaluate drug names, ATC codes and AMI co-occurrences in the indexed resources. Substantiation for biological plausibility. As a final assessment procedure, we retained in the 'prime suspects' list only those associations for which a possible biologic mechanism could be found.
Strengths and Limitations We took into account global health status and co-morbidities, but residual confounding cannot be ruled out. Dose-response relationships, carryover effects, and effect of concomitant use of other drugs (including drug-drug interactions) were not considered in this triage strategy. Many new molecular entities are introduced into the market every year and databases that catalog the pharmacology and toxicology of these drugs (including information on molecular targets and gene associations) need to be continually updated. Furthermore, many of these bioinformatics databases may not be publicly available and hence not easily verifiable. Automated filtering and substantiation streamlined the triage and greatly reduced manual work, but full automation is still not possible at this time.
This research has been funded by the  European Commission 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant no. 215847 -The  EU-ADR Project. The funding agency had no role in the design and conduct of the study; or in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript. Sturkenboom is running the IPCI research group that occasionally performs studies for pharmaceutical companies (including  AstraZeneca ,  Pfizer ,  Lilly ,  Boehringer ) and has been consultant to  Pfizer ,  Consumer Health ,  Novartis ,  Servier ,  Celgene , and Lundbeck on issues not related to the study. R. Herings is scientific director of the PHARMO Institute which performs studies for many pharmaceutical companies on issues not related to the study.
8 48. 1 49. 6 Yes Yes Yes Yes Yes Yes DD-MM-YY DD-MM-YY MM-YY Yes Yes MINSAN MINSAN HPK ATC ATC ATC Yes Yes Yes No No Yes Yes Yes Yes No No Yes No No Yes, as free text/codes No No Yes, as free text/codes No No Yes Yes Yes Yes, as free text/codes ICD-9CM ICD-9CM ICPC Yes Yes No No No Yes Pedianet (Italy) HSD (Italy) 160,000 children 1,500,000 2003-2007 2003-2007 General Practice General Practice pediatric database database 0-14 From 15 onwards 52. 2 47. 2 Yes Yes Yes Yes MM-YY MM-YY Yes Yes MINSAN MINSAN ATC ATC Yes Yes Yes Yes Yes Yes Yes Yes Yes, as free text/ Yes, as free codes text/codes Yes, as free text/ Yes Free codes text/codes Yes, as free text/ Yes codes Yes, as free text/ Yes, as free codes text/codes ICD-9CM ICD-9CM Yes Yes Yes Yes CHARACTERISTICS Current source population Years covered for this study Type of database Age range % Males Demographic information available Date of registration Date of transferring out Date of birth Gender Drug information available Product coding Active international principle coding system Date of prescription/dispensing Dosing regimen Quantity Indication of use Outcome information available Symptoms (Yes/No) Outpatient primary care diagnoses Outpatient specialist care diagnoses Hospital discharge diagnoses Diagnosis coding scheme Diagnostic procedures Laboratory tests.
Results To analyze gene conversion tracts in yeast, we used the highresolution recombination data published by  Mancera et al. (2008). These data were obtained by genotyping tetrads resulting from 46 meioses, in a diploid hybrid of two wild-type S. cerevisiae strains (S96 and YJM789). Several other similar data sets have been published  (Winzeler et al. 1998; Chen et al. 2008; Qi et al. 2009 ). However, the Mancera data set is currently the only one to provide exhaustive genotyping data (i. e. , almost all the sites that differ between the two strains have been genotyped) for such a large number of meioses. The median distance between two consecutive markers is 78 bp. We analyzed all recombination events associated with detectable conversion tracts (2,884 COs and 2,090 NCOs). On average, conversion tracts overlap nine SNPs.
BER leads to short patch repair and should therefore be frequently associated to complex conversion tracts. In the Mancera data set, the majority (!89%) of conversion tracts are simple, as expected given the prominent role of MMR during recombination. However, a minor contribution of BER to the repair of mismatches in heteroduplex DNA cannot be a priori excluded. Calculations show that if a fraction of SNP conversion events result from the action of BER, then such cases must be at least 10 times more frequent among complex conversion tracts compared with simple conversion tracts (for details, see supplementary text S1, Supplementary Material online). Hence, if BER is the unique cause of gBGC, it is expected that the conversion bias in favor of GC alleles should be much stronger among complex conversion tracts than among simple tracts.
Acknowledgments The authors thank  Richard Bourgon  for providing the data set and for fruitful discussions about the observed transmission biases. They thank  Bernard de Massy ,  Erika Kvikstad ,  Nicolas Lartillot , and  Gae ¨l Yvert  for their very useful comments on an early version of the manuscript. This work was supported by the  Centre National de la Recherche Scientifique.
Flow cytometry Thymic single cell suspensions were generated by using enzymatic dissociation as described for mouse fetal thymus  (Nowell et al. , 2011)  or by mincing thymic fragments through a 100-µm filter (BD). Flow cytometric analysis was performed on a LSRII Flow Cytometer (BD) and analyzed using FlowJo software (Tree Star). Antibodies used were: mouse anti-CD19 DY590 (Exbio Praha), mouse anti-BDCA2 APC (Miltenyi Biotech), rat anti-CD7 (MCA344, Serotec), mouse anti-human CD45 (clone HI30), mouse anti-human CD34 FITC (clone 581), mouse anti-CD3 Alexa700, mouse anti-CD45 PerCP-Cy5, mouse anti-HLA-DR APC-Cy7, mouse anti-CD123 PE (all BD Pharmingen). Unconjugated antibodies were detected with goat anti-mouse Alexa647 and goat anti-mouse Alexa 568 (Invitrogen).
Affymetrix microarray analysis RNA was prepared from three independent mid-week 8 thymic lobes microdissected free of any other cells or tissues (CB094, CS20; CB388, CS21/22; CB106, CS22). Note that the name of each sample includes both an identifier (e. g. CB094) and the Carnegie stage of that sample (e. g. CS20). Samples were amplified, labeled and hybridized to Affymetrix Human Genome U133Plus2 GeneChip arrays according to standard Affymetrix protocols. The microarray data were normalized independently using RMA  (Irizarry et al. , 2003)  and Micro Analysis Suite 5. 0 (MAS5)  (Liu et al. , 2002) , and the probe sets were then ranked based on the resulting expression values, with the probe set having the highest expression given rank-1. The data are deposited in the Stem Cell Database under accession number StemDB-2316 (http://www.
stemdb. org/stemdb/utils/show?id=StemDB-2316). The data presented show ranks for selected probe sets along with the corresponding MAS5 PMA flag [present (P), marginal (M) and absent (A)]. For a given probe set, the lower the rank the higher the probability of the gene being present in the sample, whereas MAS5 predicts the presence or absence of a gene using different statistics. Data were analyzed using Limma and Affymetrix Bioconductor packages and visualized using Genespring GX (Agilent).
FOXN1 expression was also seen throughout the thymic lobes at week 8 (CS20-21; Fig. 2EЈ ). The human thymus has been suggested to develop from both the third and fourth PPs  (Norris, 1938; Van Dyke, 1941). However, we did not detect FOXN1 expression in the fourth PP at any stage of development (Fig. 2E ,EЈ). This establishes that in the human, as in the mouse, the thymus arises solely from the third PP, indicating that the mechanisms of early thymus organogenesis are closely conserved between these species. Of note is that GCM2, which delineates the parathyroid domain within the pouch, is expressed in both the third and fourth PPs in human  (Liu et al. , 2010) , in keeping with the proposition that human parathyroids derive from both the third and fourth PPs. We also determined the expression pattern of P63 (TP63 -Human Gene Nomenclature Database), because in the mouse p63 (Trp63 -Mouse Genome Informatics) has a pivotal role in many stratified epithelia, including the thymus  (Candi et al.
AIRE + TECs were not present in the week 10 human thymic primordium (not shown), but were observed in medullary areas by week 13 (Fig. 4I-IЉ ) and had increased in number by week 17 (Fig. 4J-KЉ ). To investigate differentiation towards the cTEC fate, we determined the expression pattern of CD205 (LY75 -Human Gene Nomenclature Database)  (Shakib et al. , 2009)  and CDR2  (Rouse et al. , 1988). CD205 has recently been shown to mark the onset of cTEC differentiation in the mouse and is expressed as early as E12. 5 in mouse thymus development  (Shakib et al. , 2009) ; later in fetal development, it may mark a common TEPC  (Baik et al. , 2013). CDR2 is restricted to cTECs in the human adult thymus  (Rouse et al. , 1988). We were unable to detect CD205 or CDR2 expression in the early or mid-week 7 thymic primordium by immunohistochemistry (Fig.
RESEARCH ARTICLE Human thymus development Immigration of vascular progenitors into the mouse thymus is closely linked to that of neural crest-derived cells, and both of these processes are Foxn1 dependent  (Mori et al. , 2010; Nowell et al. , 2011). However, again, the timing of vascular endothelial progenitor entry into the human thymus has not been determined. Our analysis identified the presence of CD34 hi CD31 + cells in the connective tissue between the carotid artery and the thymus primordium at mid-week 7 (Fig. 6A -CЉ); occasional cells at this stage were found within the epithelial component of the primordium (Fig. 6D-DЉ ). By late week 7 to mid-week 8, CD34 hi cells were found within the thymic lobes (Fig. 6E-FЉ ) and all co-expressed both CD31 (PECAM1 -Human Gene Nomenclature Database) and VE cadherin (CDH5 -Human Gene Nomenclature Database), identifying them as endothelial progenitors (Fig.
6G-GЉ ). Of note is that the CD34 hi cells present within the thymic primordium at late week 7 to early week 8 did not express CD45 (PTPRC -Human Gene Nomenclature Database) (Fig. 6E-EЉ ). Blood vessels per se were present by mid-week 9 (note long blood vessel-like structures at mid-week 9) (Fig. 6H-IЉ ). Some variation in timing of colonization with endothelial progenitors was observed: in the thymus primordium from a CS19 fetus (mid to late week 7; 48-51 days old) and a CS20 fetus (late week 7 to early week 8), several CD34 hi cells were present within the epithelial compartment, and were localized to one discrete region (Fig. 6D-EЉ ). However, in a CS21 fetus (mid to late week 8; 53-54 days old) CD34 hi cells were present in the mesenchymal capsule but were not detected within the epithelial component of the thymus (Fig.
7H -IЉ). However, intrathymic CD34 hi CD7 hi cells were not detected until mid-week 9, when they were observed closely associated with the blood vessels (Fig. 7K-U ). Most intra-thymic CD7 + cells at early week 8 were CD34 int , consistent with the presence of CD34 int cells in the fetal liver (FL) (Fig. 7P, Q ). A minor population of CD34 lo/neg CD7 + cells were also present at week 8, which might represent the CD34 neg CD7 + CD56 + natural killer (NK) cell population previously described in the FL  (Phillips et al. , 1992; Bárcena et al. , 1993). However, the week 8 hematopoietic population contained T-cell precursors, as culture of week 8 thymic lobes microdissected free of any other tissues resulted in a robust expansion of CD45 + thymocytes (supplementary material Fig. S1 ). Furthermore, expression of c-Kit (KIT -Human Gene Nomenclature Database), IL7R, pre Tα (PTCRA -Human Gene Nomenclature Database), RAG1, CD3δ (CD3D -Human Gene Nomenclature Database), and TCR β and γ (TRB and TRG -Human Gene Nomenclature Database) constant region mRNA was detected in microarray analysis of whole mid-week 8 fetal human thymic lobes (supplementary material Table  S2 ).
However, the thymus contains Tlineage progenitors at least as early as week 8, as evidenced by the robust development of thymocytes in fetal thymic organ cultures established at this developmental stage and the intrathymic expression of genes associated with early human thymocyte development. Thus, we favor the idea that the thymus-seeding population changes once the vasculature has been established as the primary route of entry, and comprises CD45 + CD34 hi CD7 + cells only after this point. CD45 + CD34 lo/-CD7 + cells have been described previously in the FL; these are NK cells, and also express CD56 (NCAM1 -Human Gene Nomenclature Database)  (Phillips et al. , 1992; Bárcena et al. , 1993). Thus, the minor CD45 + CD34 lo/-CD7 + population present at week 8 might also be NK cells. The CD45 + CD34 int CD7 + population present in the pre-vascular thymus is, however, a novel intrathymic hematopoietic population that is probably T lineage committed based on its expression of CD7.
Acknowledgements We thank  Anne Sanderson ,  Isobel Morton ,  Joan Creiger  and the staff of the  Bruntsfield Suite of the Royal Infirmary of Edinburgh  and the staff of the  CASA clinic in Leiden  for patient recruitment for provision of samples for these studies;  Simon Monard  for flow cytometry; and  K. O'Neill  ( University of Edinburgh ) and  Sten Eirik Jacobsen  ( University of Oxford ) for critical reading of the manuscript.
Funding This work was funded by  Leukaemia and Lymphoma Research  [C. C. B. , A. M. F. ]; the  Medical Research Council, UK  [C. C. B. , R. A. A. ]; the  Wellcome Trust  [C. C. B. , L. X. M. , A. M. F. ]; the  EU   FP7  integrated project EuroSystem [C. C. B. , S. R. T. ]; and the  Netherlands Organisation for Scientific Research [Zon-MW Vidi  grant # 91710377  to T. C. ]. Deposited in PMC for immediate release.
Surdyk (2002b) , by using a similar indicator (i. e. the normalised difference between T B at vertical and horizontal polarisation) showed that it was influenced by both the internal stratification of the snowpack and the air-snow interface. However, since the temporal variations of the former factor are slow in Antarctica  (Arthern et al. , 2006) , the indicator is useful to monitor the variations of the air-snow interface properties. Therefore, polarimetric combinations of passive microwave data are sensitive to density variations of the snow near the surface and hence to the presence of hoar. Section 2 presents the datasets used in this study, and explains the method applied to detect hoar crystals from photography and microwave data. Section 3 presents the results on the presence of hoar crystals and analyses the conditions of their development and disappearance.
Passive microwave satellite observations Passive microwave observations were acquired at 18. 7 and 36. 5 GHz by the Advanced Microwave Scanning Radiometer -Earth observing system (AMSR-E) in dual-polarisation mode, vertically and horizontally, from 19 June 2002 to 3 October 2011 when the radiometer stopped operating. Dailyaveraged brightness temperatures in the pixel including the Concordia station were extracted from the AMSR-E/Aqua daily L3 25 km brightness temperature, sea ice concentration, and snow depth polar grids dataset provided by the National Snow and Ice Data Center of the United States. The pixel size is 25 km × 25 km but the effective spatial resolution is slightly different. According to the AMSR-E webpage http://nsidc. org/data/docs/daac/, the total sensor bias error is around 0.
6 K. The dataset contains, for every day, the mean of the daily-averaged ascending orbits and dailyaveraged descending orbits. At Dome C, owing to the nearpolar orbit of the Aqua satellite, there are typically seven over passes per day. A detailed analysis of the spatial and temporal variability of passive microwave data close to Dome C was given by  Long and Drinkwater (2000) ;  Macelloni et al. (2007) ;  Brucker et al. (2011). These studies included the effects of viewing azimuth angle and acquisition time on the satellite observations. Their conclusions are (1) the differences between T B of the pixel containing the area of in situ measurements (close to the Concordia base) and T B of the pixels around are weak; (2) the temporal evolutions of T B of the pixel containing the area of in situ measurements and T B of the pixels around are similar; (3) the T B variations induced by the variations of viewing azimuth angle and time, are independent of the season and frequency.
Acknowledgements. This work was supported by the  Programme National de Télédétection Spatiale  and the  Agence Nationale de la Recherche  ( MONISNOW ANR-11-JS56-005-01 ). We thank the  French polar institute (Institut Paul-Émile Victor)  for logistical support through CALVA and TASTE-IDEA programs. We thank the following institutes for providing data: the  European Centre for Medium-range Weather Forecasts  ( ERA Interim ), Antarctic.
A threshold based on uniform distance was defined for any kind of data distribution. This distance could be considered as a limit threshold to preserve the clustering results. We validate this approach in Matlab by showing that the number of non zero of the affinity matrix decreases with still some good results in terms of spectral clustering and even some gains in the time spent to compute the affinity matrix. These results are confirmed when we use sparsification with our parallel spectral clustering solver. We can show that we are able to reduce significantly the size of the affinity matrix without loosing the quality of the segmentation solution. With sparse structures to store the matrix, we will also gain a lot of memory. However, we may have to adapt our eigenvalues solver and use for example ARPACK library  [LEHOUCQ, 1998].
where I is the microarray image, 1C and 2 C two distinct clusters and P a larger cluster which in- cludes both 1 C and 2 C. We experiment this strate- gy whose principle is represented in Fig. 1 on several microarray images of the Saccharomyces cerevisiae database from the Stanford Microarray database (http://smd. stanford. edu/index. shtml) like the one in Fig. 2.
However, the long juvenile period (four to five years) in sweet cherry represents a drawback for quick and efficient breeding. Reducing the time needed to breed temperate fruit trees will be important to develop new cultivars that are able to face the challenges associated with temperate fruit production in a changing global environment. Marker-assisted breeding approaches based on genetic maps have the potential to save time and resources needed to select new sweet cherry cultivars. The recent release of the peach genome v1. 0 by the International Peach Genome Initiative  [13] ;  [14] , (GDR database: http://www. rosaceae. org/peach/genome) and the high synteny among Prunus species  [15]  will facilitate the characterization of genes responsible for agronomically important traits within this family.
Genetic Marker Analysis SNPs. Both sweet cherry mapping populations and their respective parents were genotyped using the RosBREED cherry 6K SNP array v1 that contains 5,696 SNPs of which 76% and 24% of the SNPs were chosen to target the sweet and sour cherry genomes, respectively  [23]. SNPs were obtained from re-sequencing of a detection panel containing 16 sweet and eight sour cherry accessions. The accessions were founders, intermediate ancestors or important parents used in U. S. breeding programs  [23]. SNPs were detected using SoapSNP  [28]  (SoapSNP: http://soap. genomics. org. cn/soapsnp. html) and validated by GoldenGateH assay  [29]. Genotype differences were recorded in the iSCAN platform and SNP genotypes were determined using Genome Studio Genotyping Module (Version 1. 8. 4, Illumina,  [30] ;  [31] ;  [32] ) as described in Peace et al.