dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional Annotation Functional annotations were made following Ferreira de  Carvalho et al. (2013)  and using the Pfam software to detect annotated protein domains from alignments to protein families databases and using a profile Hidden Markov Model (HMM;  Finn et al. 2014). All the contigs were analyzed using BLASTn and tBLASTx algorithms (e-value threshold of 10 À5 ;  Altschul et al. 1997 ) against a home-built CDS database including Oryza sativa, Setaria italica, Brachypodium distachyon, Sorghum bicolor (www. phytozome. net) and Zea mays (concatenation of two databases downloaded on www. phytozome. net and www. plantgdb. org websites; last accessed the September 1, 2016). To obtain the homologybased functional annotation Best BLAST Hits (BBH) were selected. The Gene Ontology (GO) was analyzed using the BLAST2Go software  (Conesa et al.
2005; Go ¨tz et al. 2008). GO annotations were performed using tBLASTx (e-value threshold of 10 À5 ) on the different assembled contigs against the Arabidopsis thaliana database (TAIR website, www. arabidopsis. org; e-value hit filter of 10 À6 and a cutoff of 55 which corresponding to the maximum FIG. 1. -Strategy developed for assemblies and haplotype detection. First, Roche-454 and Illumina reads were assembled separately using Newbler and Trinity, respectively. The contigs obtained were co-assembled using Newbler. The length of contigs was enhanced and redundant contigs were removed using custom scripts. Reads Mapping were done with Bowtie 2 and the different contigs were annotated using three complementary methods: tBLASTx, BLAST2Go and Pfam. Polymorphisms and haplotypes were detected and constructed using mapping data.
Reference Transcriptomes and Detection of Duplicated Copies in Hexaploid and Allododecaploid Spartina Species GBE Genome Biol. Evol. 8(9):3030-3044. doi:10. 1093/gbe/evw209 Advance Access publication August 18, 2016 similarity). Pfam 27. 0 database was used to enrich the protein domain annotations (six reading frames tested by contig; PfamB option); Pfam results were filtered by significant hits and e-value 10 À3  (Finn et al. 2014). Estimation of the number of exons and unigenes (transcripts from the same locus) in each Spartina species contigs was performed using BLASTn (!70% of identity, !60 pb of overlap) against the rice genome (GFF files downloaded from www. phytozome. net).
SNP Detection and Haplotype Assembly Using Illumina Data For each species, the Illumina reads data set was mapped on the previously built reference contigs using Bowtie 2, v2. 0  (Langmead and Salzberg 2012). The parameters used were "score-min: G, 52, 8" for the natural logarithmic function f(x)= 52 + 8Âln(x), where x correspond to the read length. Using these parameters, all reads (with a length of 80-120 bp) presenting at least 87. 06-90. 30% of identity were mapped to the reference contig. The output file ". SAM" created by Bowtie 2 during the mapping step was converted to a ". PILEUP" format using the Samtools software suite  (Li et al. 2009 ). We detected for each contig the different SNPs or Single Nucleotide Polymorphisms (minimum read depth = 30; SNP detection threshold = 2, corresponding to nucleotides that are not present more than 2/100 times per position), using custom python script.
sativa) and Pooideae (Brachypodium distachyon). The Kimura two parameters plus Gamma (K2 + G) model was selected for this analysis. Bootstrap analyses used 1,000 replicates for the data set. Visual checking of alignments and SNPs were done using the Tablet software  (Milne et al. 2009)  for ". ACE" and ". SAM" alignment files and with the Jalview software  (Waterhouse et al. 2009)  for ". FASTA" files.
The approach described here allows the detection of putative homeologs from sets of short reads and can be applied for future differential gene expression or genomics experiments to study the fate of duplicated genes in the allododecaploid S. anglica Spartina Transcriptomics Before the NGS revolution, Spartina transcriptomic resources were restricted to few EST sequences available on NCBI databases  (Baisakh et al. 2008; Chelaifa, Maheet al. 2010). Whole genome expression experiments were designed using heterologous rice microarrays to demonstrate differential expression in similar growing conditions of S. maritima and S. alterniflora  (Chelaifa, Maheet al. 2010 ) and the relative effects of hybridization and genome duplication on nonadditive expression in S. x neyrautii, S. x townsendii and S.
anglica. We assembled independently the Roche-454 (with Newbler) and Illumina read data sets (with Trinity) before co-aligning them (with Newbler and custom scripts to enhance assemblies by self-BLAST). The Newbler software is commonly used for Roche-454 data  (Margulies et al. 2005 ) and showed positive results on similar data sets  (Ferreira de Carvalho et al. 2013). The choice of the Trinity software is based on the results of several studies  (Clarke et al. 2013; Liu et al. 2013)  and comparative tests on our data set. The hybrid assembly strategy for Roche-454 and Illumina contigs showed good results in several studies  (Sirota-Madi et al. 2010; Barthelson et al. 2011; Jiang et al. 2011 ) using assemblers such as Mira or Abyss. The length of Roche-454 contigs obtained in the first step of our assembly process motivated the choice of the Newbler assembler.
The large number of Illumina contigs obtained by the Trinity software (76,010-121,733 contigs) is explained by the presence of several similar copies (identity ! 90%) and were automatically re-assembled with Newbler in the co-assembly step. The parameters used for the different assemblies (90%) are consistent with the literature  (Franchini et al. 2011; Ferreira de Carvalho et al. 2013; Liu et al. 2013)  and fitted in order to get consensus sequences of all putative homeologs for each species. The functional annotations were made using a method similar to that used by Ferreira de Carvalho and collaborators (tBLASTx, BLAST2Go approach; Ferreira de Carvalho et al. 2013) and using the Pfam software used in annotation pipeline such as TRAPID  (Van Bel et al. 2013). The number of annotated contigs represents 36.
Acknowledgments This work is being developed in the frame of the  International Associated Laboratory "Ecological Genomics of Polyploidy " supported by  CNRS  ( INEE, UMR CNRS 6553 Ecobio), University of Rennes 1, Iowa State University (Ames ) and the  Partner University Funds  (to M. A. , A. S. , and J. B. ). Sequencing was supported by  Genoscope funds  ( GENOSPART  Project). We would like to thank the members of  POLY-BNF  group: A. M. Chevre,  D. Lavenier ,  P. Peterlongo  and  C. Lemaitre  for interaction about Illumina assemblies. The analyses benefited from the  Molecular Ecology  ( UMR CNRS 6553  Ecobio) and  Genouest (Bioinformatics)  facilities. J. B. benefited from a PhD scholarship from the  University of Rennes 1 (France). We thank two anonymous reviewers for helpful comments on the article.
Material and methods Experimental procedures and tracking of larvae behaviour were the same as in  [11]. Third-instar Drosophila melanogaster larvae in the foraging stage were washed in 15%-sucrose solution and transferred to a flat-lid arena coated with a 3%-agarose slab. Animals were tracked at 7 frames s 21 , 90 mm pixel 21 for 5 min. Tracking was interrupted if the animal touched the plate boundaries. Custom-made tracking scripts  [12]  extracted the location of the centroid, head and tail from postural sequences. We used three groups of larvae exposed to different experimental conditions: overshoot, approach and dispersal  [11, 13]. Odour gradients were created by suspending a 10 ml droplet of ethyl butyrate from the arena top, out of animals' reach. For the main analyses, the x, y position samples of the centroid were low-pass filtered (second-order, zero-phase-lag Butterworth filter).
Objective: The aim of this report is to provide insight into real-world healthcare resource use (HCRU) during the critical management of patients surviving acute coronary syndromes (ACS), using data from EPICOR (long-tErm follow-up of antithrombotic management Patterns In acute CORonary syndrome patients) (NCT01171404). Methods: EPICOR was a prospective, multinational, observational study that enrolled 10 568 ACS survivors from 555 hospitals in 20 countries in Europe and Latin America, between September 2010 and March 2011. HCRU was evaluated in patients with ST-segment elevation myocardial infarction (STEMI) or non-STsegment elevation ACS (NSTE-ACS), with or without a history of cardiovascular disease (CVD). Multivariable analysis was performed to determine factors that affected resource use.
] [9]  Evidence-based management is often suboptimal,  9 10  and the risk of experiencing a new cardiovascular (CV) event remains high. 11 12 Reasons for variability in healthcare resource use (HCRU) in patients with ACS may be related to differences in availability of catheterisation laboratories and/or cardiac surgery facilities, or patientrelated factors, such as insurance coverage,  13  age  14  and sex. 15 Opportunities therefore exist to improve the management of patients with STEMI and NSTE-ACS. 16 17 rehospital resource use and costs associated with ACS are poorly documented in the literature. The aim of the present analysis is to better understand (from the perspective of healthcare payers) the variation in prehospital and in-hospital resource use after STEMI and NSTE-ACS, using acute phase data from the EPICOR (long-tErm follow-up of antithrombotic management Patterns In acute CORonary syndrome patients) study (ClinicalTrials.
Acute phase resource use analysis The specific aim of the present study is to evaluate, from the healthcare payer perspective, HCRU during the acute ACS phase ( prehospital and in-hospital data). This was a pre-specified secondary objective of EPICOR. Data collection during this phase was prospectively carried out by the investigator at each site, using electronic case report forms. The resource analysis is given below. Prehospital resources (from symptom onset to hospital admission): ECG, CV medication received (fibrinolytics, antiplatelet agents and anticoagulants) and the duration of ambulance transfer between hospitals for those patients who were transferred. Transport to the initial hospital was not recorded. In-hospital resources (including all those in-patients transferred between hospitals): laboratory tests and investigations (white cell count, creatinine, blood glucose, haemoglobin, haematocrit, cardiac markers (CK-MB and troponins)); diagnostic procedures (ECG, echocardiography, treadmill, stress echocardiography, nuclear imaging, MRI, angiographic CT scan); therapeutic procedures (resuscitation, mechanical ventilation, intra-aortic balloon pumping, temporary or permanent pacemaker, implantable cardiac defibrillator, cardiac resynchronisation therapy); interventions (any cardiac catheterisation, percutaneous coronary intervention (PCI)/stents, coronary artery bypass graft (CABG) surgery or other cardiac surgery); number (%) of patients receiving CV medication (fibrinolytics, antiplatelet agents and anticoagulants); and length of hospital stay (LOS) (overall and by hospital transfer status).
by  Liz Anfield ,  Prime Medica Ltd , and funded by  AstraZeneca. Statistical analysis was performed by  Richard Cairns ,  Worldwide Clinical Trials , the study, analysis of the study data, and opinions, conclusions and interpretation of the data. Funding The EPICOR study was funded by  AstraZeneca. Competing interests LA has received consulting and lecture fees from  AstraZeneca. ND has received consulting or speaking fees from  AstraZeneca ,  BMS ,  Boehringer-Ingelheim ,  GSK ,  MSD-Schering Plough ,  Novartis ,  Pierre Fabre ,  Pfizer ,  Roche ,  Sanofi-Aventis, Servier ,  Takeda  and  The Medicines Company. FVdW has received consulting fees and research grants from  Boehringer Ingelheim  and  Merck , and consulting fees from  Roche ,  Sanofi-Aventis ,  AstraZeneca  and  The Medicines Company.
5 lL of H 2 O 1. 5 lL of QIAGEN Multiplex Master Mix (QIAGEN, Hilden, Germany), 0. 5 lL of primer mix and 1. 5 lL of DNA. The PCR programme, performed in an Eppendorf Mastercycler EP gradient (Eppendorf, Montesson, France), consisted of initial denaturation at 95°C for 15 min and 35 cycles of 30 s at 94°C 1 min at 55°C, and 45 s at 72°C, ending with a 30 s extension at 60°C. PCR products (1-1. 5 lL diluted 1:14) were mixed with 10 lL of formamide and 0. 14 lL of internal lane size standard (GeneScan 600 LIZ; Thermo Fisher Scientific, Waltham, MA, USA) and analysed in an ABI 3130 capillary sequencer according to the manufacturer's instructions (Applied Biosystems, Thermo Fisher Scientific). DNA fragments were automatically sized with GeneMapper TM v4. 0 software (Applied Biosystems, Thermo Fisher Scientific).
Genetic structure analysis Genetic diversity (number of alleles, mean number of alleles per locus and global expected heterozygosity) was assessed with GENETIX v4. 05. 2  (Belkhir et al. 2004 ). Multilocus genotypes were identified with GENODIVE software, using a stepwise mutation model  (Meirmans and Van Tienderen 2004). Population differentiation (F ST ) was estimated for pathogen origin ('RES' versus 'SUS'), and for vine region of origin (five regions; Figure  S1 ). The 95% confidence interval of the F ST was generated with GENETIX, by bootstrap analysis of the original data with 1000 replicates. We investigated genetic structure, by performing principal component analysis (PCA) with the adegenet package in R  (Jombart 2008 ). Multivariate analysis is particularly suitable for plant-pathogen species that are partially clonal, because this method is hypothesis-free, and therefore does not require Hardy-Weinberg equilibrium, for example  (Dutech et al.
2011). This method quantifies sporulation as pixels contrasting with the leaf disc in the background. Sporulation is expressed as the proportion of the leaf disc area that is sporulating. We fitted a logistic model to these experimental data (Fig. 2 ) with PROC NLIN in SAS Studio (SAS University Edition; version 3. 3; SAS Institute Inc. , Cary, NC, USA) as follows: Y ixr ¼ 1 1 þ ðT 50 =xÞ slope þ e ixr where the observed variables are x (time, from 0 to 5 dpi) and Y irx (the relative sporulating leaf disc area which is the proportion of the leaf area displaying sporulation of isolate i repetition r on day x divided by the proportion of the leaf area displaying sporulation of isolate i repetition r on day 5). Y ird ranged from 0 to 1 and was independent of spore production 5 dpi.
Quantitative trait analysis We investigated whether quantitative pathogenicity traits differed between inoculated hosts (phenotypic plasticity) and pathogen origins (genetic adaptation), by performing a mixed general model analysis for each quantitative trait with PROC GLIMMIX in SAS software (SAS University Edition). We used the following model: Y ijk = l + H i + P j + H i P j + I(P) k + ɛ ijk where H is the inoculated host i (fixed factor), P is the pathogen origin j (fixed factor), HP their interaction (fixed factor) and I(P) is the isolate k nested in the pathogen origin (random factor). The error term of this model, ɛ, therefore corresponds to within-isolate variation (i. e. differences between replicates). We compared quantitative traits between pathogen origins ('RES' versus 'SUS') on each inoculated host, by determining post hoc least squares means of fixed effects.
For all models, we plotted studentized marginal and conditional residuals, to check that residuals are independent and identically, and normally distributed. For three of the five traits measured (spore production, latency period and the slope at T 50 ), log transformation of the data was required to satisfy these requirements. Finally, we checked for spatial autocorrelation in model residuals using Moran's I parametric test implemented in the PROC VARIO-GRAM in SAS Software. Spatial autocorrelation was not detected in any of the models (see Table  3 ). Overall trait correlations were explored with Pearson correlation statistics (PROC CORR), by the application of Fisher's Z transformation to data averaged for each interaction (isolate 9 inoculated host).
Acknowledgement We thank our colleagues from  UMR SAVE  for technical assistance during this study. We thank the following people for collecting P. viticola isolates or for helping with sample collection:  O. Viret ,  S. Werner ,  J-L. Spring  and  M. Jermini  (Agroscope);  S. Wiedemann-Merdinoglu  (INRA); and  H-H. Kassemeyer  ( Staatliches Weinbauinstitut Freiburg ). Finally, we gratefully acknowledge  Cyril Firmat  ( INRA ) for insightful suggestions that improved the last version of the manuscript. This study was supported by the  Conseil R egional d'Aquitaine ('Evoh e' , No. 20121206002 ) and by  FP7 -European Commission ('Innovine' ,  FP7/2012-2013: FP7-311775 ). This study was carried out in the framework of the  LabEx COTE  ( ANR-10-LABX-45 ).
Exogenous mRNAs can be expressed in oocytes that are transparent allowing high resolution imaging by light microscopy [3f]. Contributions: Patiria has contributed significantly to the large knowledge base on the molecular and cellular events of meiosis  (Kishimoto and Kanatani, 1976; Kishimoto, 1999; Lenart et al. , 2005). Tools: A draft genome assembly and transcriptome data are available for P. miniata (http://www. ncbi. nlm. nih. gov/genome/ ?term=txid46514[Organism:noexp]), though assembly was complicated by the unexpectedly high heterozygosity  [4, 5]. The available sequence information allows the use of all standard molecular biology tools for the study of the cell biology of meiotic divisions. Drawbacks: the generation time is very long (years) rendering classical genetics and establishment of transgenic lines impractical.
ACKNOWLEDGMENTS This work was supported by the  European Commission   FP7 Research Infrastructure Project , the  European Marine Biological Resource Centre  (ref. no. 262280 ), the  European Marine Biological Resource Centre-France  infrastructure project to the contributors from France, and  European Molecular Biology Laboratory  core funding. We thank  Alex McDougall  and  Philippe Dru  ( Sorbonne Universités ,  UPMC Université Paris 06 ,  CNRS ,  UMR 7009 ,  Laboratoire de Biologie du Développement de Villefranche-sur-Mer, Observatoire Océanologique de Villefranche-sur-Mer, Villefranche-sur-Mer, France ),  Mark Cock  ( Station Biologique de Roscoff, Roscoff, France ), and  Olivier Thomas  ( Institut de Chimie de Nice UMR 7272, Universitè Nice Sophia Antipolis, Nice, France ) for useful comments.
The centre of rotation was set 500 pixels to the right of the images to increase the final horizontal field of view in the final tomograms (so called half-acquisition protocol). Given vertical size of the incident beam (~3. 6 mm), 36 scans were needed to cover the full length of the skull, with a vertical displacement of 1. 8 mm between two consecutive scans (50% of overlap to compensate for the vertical intensity profile of the X-ray beam). Tomographic reconstruction was performed using filtered-back projection from the PyHST2 software with Paganin approach for single distance phase retrieval  [14, 15]. The generated stacks of 32bits EDF files were converted to 16 bit stacks of TIF (using min and max 32 bits crop values from the 3D histogram provided by PyHST2). For the final vertical concatenations of the series of scans, we took advantage of the important overlap between consecutive scans and performed a weighted average of similar slices, giving more weight to a slice when it was closer to the centre of the stack than to the border (as the intensity of the X-ray beam is greater at the centre).
Acknowledgments We would like to thank the  Museum of Comparative Zoology in Harvard , for the loan of the specimen. The  Field Museum of Chicago , the  Museum für Naturkunde of Berlin  and the  Musèum national d'Histoire Naturelle of Paris  should be thanked for allowing access to specimens. We are grateful to  Jessica Cundiff ,  Mark Renczkowski ,  Bill Simpson ,  Ken Angielczyk ,  Marten Schöle ,  Ronan Allain  and  Jocelyn Falconnet  for their time in organising visits to collections. The scan was carried out at the BM05 beamline of the  European Synchrotron Radiation Facility , and  Tina Aschenbach  segmented the scan. Diane Scott ,  Carola Radke  and  Elke Siebert  carried out photography and image processing, and Schöle prepared the specimen. We would further like to thank  Maren Jansen ,  Christian Kammerer  and  Roger Benson  for helpful comments and discussion.
We review and formalize eight semi-supervised learning algorithms and introduce a new method that combine supervised and unsupervised learning in order to use both labeled and unlabeled data. The main idea of this method is to produce new features derived from a first step of data clustering. These features are then used to enrich the description of the input data leading to a better use of the data distribution. The efficiency of all the methods is compared on various artificial, UCI datasets, and on the classification of a very high resolution remote sensing image. The experiments reveal that our method shows good results, especially when the number of labeled sample is very limited. It also confirms that combining labeled and unlabeled data is very useful in pattern recognition.
Introduction The number of available labeled samples is very important in supervised classification. If too few examples are given to a supervised learning algorithm, the induced predictive model will generally have poor performance. Unfortunately, in many real-world applications, labeled samples are difficult to obtain due to the cost of manual labeling. Moreover, in many applications, the user only gives few examples and the system has to find similar objects in a database (e. g. content-base image retrieval, on-line web-page recommendation, etc. ). In these cases, only few labeled samples are available although many unlabeled data are present (i. e. all the other instances in the database). In web-page recommendation for example, the user labels only interesting pages. It is not possible to ask him to produce other samples as he might not know other pages interesting him.
With such data, more details can be observed in the land cover, i. e. the number of classes of interest is increased. More features and more classes call for more samples, which are generally expensive and time consuming to acquire. The same observation can be made with the object-based analysis of Very High Spatial Resolution (VHSR) images. With these images, a first step of segmentation is generally performed to build regions (i. e. homogeneous set of pixels). These regions are then characterized by multiple features (e. g. spectral, spatial or contextual). Thus, the dataset is often composed of objects described by a high number of characteristics, but with very few samples. Despite the fact that labeled samples are rare and insufficient compared to the data space dimension, unlabeled samples are generally available in important quantities.
It is sometimes used to reduce the data before the classification step. Moreover, as unsupervised learning can only use data distribution, the classes must respect, to some extends, this distribution. If two classes form a highly homogeneous cluster in the feature space, one can not expect a simple clustering to separate them. Even if our approach can weaken this condition, it must be kept in mind. In this article, we first present some related work and motivate our method in Section 2. The contribution of this paper is also to draw an overview of the classical semi-supervised approaches in a common formalization, and to present our new way of dealing with datasets having very few samples (Section 3). They are also compared through many experiments on a large set of artificial datasets and classical datasets from the UCI repository (Section 4).