dataesr/scientific-paragraphs-categorization huggingface dataset:
This shows the benefit of growing the set of annotated examples, and thus the utility of an active learning approach for this task. The best strategy to grow from a very small dataset, with classes with very few instances, seems to be by choosing instances that are very similar to those already labelled, which provides a faster improvement in the performance of the classifier. When examples are selected applying the "most uncertain" strategy are, they mostly belong to the "null" class, that is, they do not signal any of the classes relevant for the problem. Most of the sentences in licenses do not belong to any of the classes defined by the ODRL vocabulary and are classified as "null". Providing examples for the class "null" is specially harmful for the resulting model for two main reasons.
First, it grows the majority class, while small classes are kept with the same few examples, thus adding the problem of having an imbalanced dataset to the problem of having small classes with few instances. Second, the class "null" is composed by many heterogeneous classes that are not included in the ODRL vocabulary, and therefore its characterization is difficult and may be misleading. Besides this configuration of classes, which can be found in very different domains, the domain of IE in licenses and normative text in general may be specially prone to an improvement of performance by labeling most certain examples first, because licenses and legal texts in general tend to be very formulaic, repeating the same wordings with very few variations, and small differences in form may signal differences in meaning, much more than in other domains, where differences in meaning are signalled by bigger differences in wordings.
Both the dataset and the new system are available online. We have shown that, for the problem of inferring a classifier for normative text, where few labelled instances are available, active learning does provide a faster learning curve than traditional machine learning approaches, and it is thus an effective strategy to optimize human resources. It must be noted that in this specific setting, active learning is useful only if the most certain examples are selected to be hand-tagged, in contrast with the most frequent approach in active learning, called uncertainty sampling, where human annotators are given to annotate examples that the classifier is most uncertain about. This is caused by the fact that normative text is very formulaic thus tending to repetitions, but also to the fact that in this domain, slight differences in formulation tend to signal actual differences in meaning.
This repository should fully conversant in the community standard formats and CVs. Furthermore, the repository should assess the adherence of datasets to the minimum reporting requirements, and perform semantic validation to check whether CV terms are used out of context. However, accepting and storing data is only a small part of the role of a repository: its most relevant function is the continuous dissemination of information. The repository thus has to offer multiple modes of access, as different users will need different types of access (e. g. , manually versus automatically). It must also provide crossreferences to databases in associated domains. Ideally, the repository should even serve users outside the field, enabling integrative analyses across domains in the life sciences. Over time, the system could be extended to host free software tools that execute data processing workflows, perform data analysis, and allow results interpretation.
All sample showed good ductility (way over the 15% strain shown) and strain hardened in a similar way with the RD sample strain hardening marginally less than the others. Most notably, the elastic modulus of the ND specimen was lower than the other two. Textures measured on the GEM instrument were analyzed using the MAUD package  [7]  which utilizes the Rietveld refinement method to compute the orientation distribution function. Figure  3 (a)  shows the recalculated pole figures for the as received sample. Also, shown in figure  3 (b)  and (c ) are the GEM pole figure coverage (position of detectors on a standard stereographic projection of sample orientations corresponding to the pole figures in figure  3 (a) ) and the inverse pole figure (IPF) corresponding to the different rolled plate orientations respectively.
2% of the varieties with this allele were peaches). PHASE imputed 123 haplotypes using the 19 associated SNPs, five with a frequency > 5% and an average heterozygosity of 8. 15%. The most frequent was in high association with the recessive trait: 98% of the accessions with this haplotype in homozygosis had the recessive phenotype. Flat fruit shape. Flat fruit shape is caused by a dominant allele in obligated heterozygosity, mapped at the end of LG6  [25, 30]. In our panel, only 57 cultivars were flat whereas 1,003 were round. The low number of flat accessions hampered the analysis and it was not possible to identify significant association using the full dataset. This situation could be partially explained considering the relatedness of some flat individuals, for example those from the series UFO (12.
Conclusions A collection of 1,580 Occidental and Oriental peach accessions was analyzed with an SNP array covering the whole peach genome. Results showed that the levels and organization of variability are consistent with the known major events in the history of this species after domestication, and generally agree with published information obtained with SSR markers and SNPs. Given the high levels of LD conservation of the peach genome, whole-genome association analysis with a relatively small number of loci  (4, 271)  showed that SNPs significantly associated with seven major genes were always in the regions predicted by classical linkage analysis. For the genes still not cloned, our data provide sets of tightly linked markers that can be the starting point to identify candidate genes and eventually used for their cloning and molecular characterization.
Moreover, our results reveal that, for certain characters, the MAS strategy not only requires one or several markers sufficiently close to the gene of interest, but also identification of the different haplotypes for various markers in the region, selecting those that are diagnostic for the character based on the haplotypes of the set of parents used by each breeding program. rosaceae. org/) under the accession number tfGDR1013 and in the FruitBreedomics database (http://bioinformatics. tecnoparco. org/fruitbreedomics/).
Population Structure and Fst PCA analysis was performed using the "prcomp" R package, freely available at http://stat. ethz. ch/R-manual/R-patched/library/stats/html/prcomp. html, after genotype numericalization as follows: The following approach was followed for numericalization: the higher value (2) was assigned to the heterozygous state, the lower value (0) was assigned to the less frequent homozygous state, and the intermediate state (  1 ) was assigned to the most frequent homozygous state. Population structure was studied with the "prcomp" function of R and with the Structure v. 2  [23]  software. This program uses a clustering method that identifies K subgroups of individuals with distinctive allele frequencies. Individuals can be members of multiple subpopulations with a different coefficient, with the sum of all being equal to 1.
Linkage disequilibrium Given that the phases between alleles at two heterozygous loci are unknown, the linkage disequilibrium (LD) was calculated using the squared correlation based on genotypic allele counts as implemented in PLINK  [43]. Additionally, the LD was calculated using the R-package LDcorSV  [24] , able to correct the classical r 2 measure for population structure and relatedness. To measure LD, SNPs with a minor allele frequency (MAF) lower than 5% were discarded. The curve representing the observed r 2 values was computed using a polynomial fit as implemented in the "numpy" module (http://docs. scipy. org/doc/) of python v. 2. 7. 3.
Acknowledgments We acknowledge  Andrea Caprera  ( PTP, Lodi, Italy ) for his contribution to the organization and standardization of phenotypic datasets;  Zhi-jun Shen  ( Jiangsu Academy of Agricultural Sciences ),  Ke Cao  ( Zhengzhou Fruit Research Institute), Hui-juan Jia, Xian-qiao Meng (Zhejiang University ),  Ming Xie  ( Zhejiang Academy of Agricultural Sciences ) and  Jian-bao Tian  ( Shanxi Academy of Agricultural Sciences ) for collecting materials and collecting phenotypic data;  Martina Lama  ( CRPV, Cesena, Italy ) for her participation in lab work and the technical staff of the " Biologie du Fruit et Pathologie" unit of the INRA , in particular  Hélène Christmann , for her technical help in DNA extraction, and  Jean Claude Barbot ,  Hélène Christmann ,  Anthony Bernard  and  Aude Mear  for their active collaboration in the phenotyping of the  INRA-Bordeaux peach collection.
This work has been funded by the  EU seventh Framework Programme  (http://ec. europa. eu/research/fp7/index_en. cfm)through the project " FruitBreedomics: Integrated Approach for increasing breeding efficiency in fruit tree crops"  (Grant # FP7-265582 ; http://fruitbreedomics. com/); by the  Ministero delle Politiche Agricole Alimentari e Forestali -Italy.
Quadriceps and blood samples from nontreated mdx and WT control 12-week-old mice were taken as controls. In accordance with the previously published data  (41) (42) (43) , the restoration of the dystrophin expression and percentage of exon skipping in quadriceps were between 10 and 45% 2 weeks after injections, followed by a decrease at later time points (Fig. 7A  and B ). In a good agreement with the restoration of dystrophin levels, two weeks after injection the levels of the MYOM3 fragments in treated mdx mice substantially decreased (without reaching the level in the control mice) and then gradually increased over time (Fig. 7C ). In contrast to the MYOM3 fragments, CK levels did not reflect restoration of dystrophin expression. Thus, 2 weeks after injection the level of serum CK was lower in treated mdx mice compared to WT control mice (Fig.
Eluates were monitored by a LTQ Velos Orbitrap mass spectrometer (Thermo Fisher Scientific) and tandem MS (MS/MS) data were processed with Proteome Discoverer 1. 4 software (Thermo Fisher scientific) coupled to an in house Mascot search server (Matrix Science, 2. 3. 2 213 version) using SwissProt database as described previously  (22). The relative abundance of each protein identified in serum from DMD or healthy patients was estimated by label-free quantification using the Progenesis LC-MS software (Nonlinear Dynamics, 4. 0 version).
Western blot Protein samples were separated by SDS-PAGE electrophoresis (4-12% gradient, NuPAGE Novex Bis-Tris Gel 1. 0 mm, Life Technologies) and transferred onto Protran Premium membrane (nitrocellulose, GE Healthcare). Fifty micrograms (1 μl of serum) of human, mouse or dog serum protein were loaded per lane. Antibodies against MYOM3 (1:1000, Proteintech: 17692-1-AP) and the CK-M (1:500, Santa Cruz: sc-15161) were used as primary antibodies followed by incubation with the corresponding IRDye-800CW-conjugated antibodies (1:10 000, LI-COR Biosciences) according to the manufacturer's instructions. Infrared fluorescence of the secondary antibodies was read on an Odyssey Imaging System (LI-COR Biosciences). Band intensities were measured by the Odyssey application software (LI-COR Biosciences, Image Studio Lite 4.
Histology and Sgca immunostaining Cryosections (8 mm thickness) were prepared from frozen right and left gastrocnemius muscles. Transverse sections were processed for haematoxylin phloxine saffron (HPS) histological staining. Colorimetric immunodetection of Sgca was performed as previously described  (74). After digitization of immunostained biopsies (Axioscan, ZEISS) the total surface of the biopsies and the surface stained for α-sarcoglycan were quantified using the ImageJ sofware (version 1. 47 g 64-bits, Rasband, W. S. , ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej. nih. gov/ij/, 1997-2014) and a customized script (available on demand). The percentage of Sgca positive fibres for a given biopsy was calculated using the following equation: (number of Sgca positive pixels on the biopsy area/surface in μm² of the biopsy area) divided by the same ratio obtained for healthy control (number of Sgca positive pixels on the biopsy area/surface in μm² of the biopsy of the control C57BL/6J mouse) and multiplied by 100.
Acknowledgements We thank  Mike Gait  and members of his group at the  MRC Laboratory of Molecular Biology, Cambridge , for Pip6a-PMO used to treat mdx mice. We thank  Béatrice Marolleau ,  Laetitia Van Wittenberghe ,  Adeline Miranda ,  Séverine Charles ,  Mehdi Dayegue ,  Nicolas Guerchet ,  Guillaume Tanniou  (Généthon in vivo evaluation core) for their technical support;  Stéphanie Blaie  for the organizational help. Dog's serum samples were kindly provided by  Caroline Le Guiner  and  Inès Barthélémy.
Initial analysis of the available repli-seq data set from various human cell lines in UCSC Genome Browser and ENCODE consortium revealed that the replication timing of large domains remains the same across cell lines. We, therefore, compared the H3K9me3 ChIP-seq data set to the HeLa repli-seq data set (Figure  7D ). HeLa-S3 G1b and HeLa-S3 S1 are deep sequencing data sets for late G1 and Using the data set mentioned above, we examined the replication timing of the regions that showed reduction in H3K9me3 by ChIP-seq. On chromosome 19, the total H3K9me3 peaks in the control sample and the regions, which show greater than fivefold decrease in H3K9me3 upon ORCA depletion are represented as black bars above the HeLa-S3 G1b and HeLa-S3 S1 tracks. Upon ORCA depletion, most of the affected H3K9me3 peaks resided in late-replicating domains (Figure  7D  and Figure  7 -figure  supplement 1B ).
Louis, MO), Lamin (1:750), H3K9me2 (1:100; 07-212, Millipore, Billerica, MA), H3K9me3 (1:200, Millipore 07-523), HP1α (1:100, Millipore 3584). BrdU immunofluorescence: after primary and secondary antibody incubation for lamin immunofluorescence, cells were fixed with 2% formaldehyde solution in PBS. This was followed by acid denaturation of DNA using 4 N HCl for 25 min at RT. Three washes with PBS and two washes with PBS-NGS followed. This was followed by incubation of the cells with anti-BrdU antibody followed by secondary antibody incubation and mounting. Zeiss Axioimager z1 fluorescence microscope (Carl Zeiss Inc. , Jena, Germany) equipped with chroma filters (Chroma technology, Bellows Falls, CA) was used for observing the cells and statistics. Axiovision software (Zeiss) was used for digital imaging using Hamamatsu ORCA cooled CCD camera.
Single molecule pulldown SiMPull experiments were carried out in flow chambers prepared on quartz microscope slides, which were passivated with methoxy-polyethylene glycol (mPEG) doped with 1% biotin-PEG (Lysan Bio, Inc, Arab, Al)  (Jain et al. , 2011). Appropriate biotinylated antibody was immobilized on PEG passivated surfaces at approximately 20 nM concentration for 20 min after coating the flow chambers with 0. 2 mg/ml NeutrAvidin for 5 min. Antibodies were immobilized on NeutrAvidin (Thermo)-coated flow chambers either by incubating with biotinylated T7 antibody (Novagen) for 10 min. RIPA buffer lysed samples were then incubated in the chamber for 20 min and washed twice with the buffer (10 mM Tris-HCl pH 8. 0, 50 mM NaCl 0. 1 mg/ml BSA). Single-molecule data were acquired by a prism-type TIRF microscope and analyzed using scripts written in Matlab.
For ORCA-Orc1; ORCA-G9a; ORCA-Suv39H1 SiMPull analysis, lysates were made from the cells transiently transfected with T7-ORCA with YFP-Orc1, YFP-G9a, or YFP-Suv39H1, respectively. For multimeric complex assembly analysis using SiMPull, cells were transfected with T7-ORCA, YFP-Orc1, and mCherry-G9a or T7-ORCA, YFP-Suv39H1, and mCherry-G9a. For peptide pulldown experiments, biotinylated peptides were immobilized instead of antibodies. Cells lysed in RIPA buffer or nuclear extracts (depending on the experiment) were then incubated in the flow chamber for 20 min followed by wash with T300 buffer (20 mM Tris-HCl, pH 8. 0, 300 mM NaCl, 0. 1 mg/ml bovine serum albumin  [BSA] ). Single molecules were visualized by prism-type TIRF microscope and analyzed using Matlab scripts (https://github. com/vasuagg/SiMPull_Analysis).
SiMPull data analysis Single molecule data were acquired as the average number of YFP or mCherry fluorescent molecules per imaging area (5000 μm 2 ) as shown in the histograms. The error bars represent standard deviation of the mean values from 20 imaging areas. Number of fluorescence photobleaching steps was determined for each YFP-tagged molecule and accumulated to obtain the stoichiometry of the complex. Colocalization percentage between YFP and mCherry was calculated as the number of coaligned molecules of one fluorescent molecule with respect to the fluorescent molecules found in lower density on the surface. This was needed since the number of YFP and mCherry tagged proteins was not pulled down to the same extent due to their independent interaction with ORCA. Colocalization criterion was set at 2 pixels, which correspond to a diffraction limited spot (∼300 nm) for our TIRF setup.
BrdU ChIP after ORCA KD and data analysis Two rounds of ORCA KD were carried out, 24 hr apart. The cells were then arrested using aphidicolin for 12 hr followed by release into S-phase and samples were collected 0, 4, 8, and 12 hr post release for BrdU ChIP. Prior to each time point collection, cells were pulsed for 2 hr with BrdU (10 μM). Cells for each time point were then lysed with 300 μl of SDS lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris pH 8. 0). The lysates were subjected to sonication with Bioruptor Power-up (Diagenode). 100 μl sheared chromatin aliquots were then placed on 95˚C heat block for 10 min. This was followed by snap chilling the samples for 10 min. The samples were then diluted, precleared and processed further in a manner identical to the ChIP protocol described in the previous section.
Acknowledgements We thank members of the  Prasanth laboratory  for discussions and suggestions. We thank  Drs M Stallcup ,  D Levy ,  R Natarajan ,  F Fuks ,  M Dundr ,  A Hernandez ,  R Donthu ,  C Fields ,  D Spector , and  B Stillman  for providing reagents and suggestions. We would like to thank  Drs D Forsthoefel ,  S Ceman  and  D Rivier  for critical reading of the paper. This work was supported by  NSF-CMMB-IGERT  and  F31  ( CA180616 )  NIH  fellowship to SG;  ACS  ( RSG11-174-01RMC ) and  NIH  ( 1RO1GM088252 ) award to KVP and NSF career (1243372) and  NIH  ( 1RO1GM099669 ) awards to SGP. TH is an investigator with  Howard Hughes Medical Institute. Work in SA group was supported by the:  Agence Nationale de la Recherche  ( H3K9-methylome  and EPILIS grants),  Association Francaise contre les Myopathies (AFM) ;  Fondation ARC ;  GEFLUC ;  Institut National du Cancer (INCa , grant  2012-1-PLBIO ).
For model calibration, clear-cut reference data were produced at MODIS resolution (250 m). According to the magnitude of the detected breakpoints, probability classes for the presence of clear-cuts were defined, from greater than 90% to less than 3% probability of a clear-cut. One of the advantages of the probabilistic model is that it allows end users to choose an acceptable level of uncertainty depending on the application. In addition, the use of BFAST allows events to be dated, thus.
MODIS Data In this study, we used MODIS Terra vegetation index time series produced at 250 × 250 m resolution and 16-day compositing periods (MOD13Q1 collection 5 product). These images were generated using the constrained view maximum value compositing method to limit the effects of residual clouds and atmospheric effects, and to constrain the angles  [34]. Time series of 253 images acquired from February 2000 to February 2011 were obtained from the Land Processes Distributed Active Archive Center (https://lpaac. usgs. gov/). Dataset pre-processing included clipping the images to fit the study area, re-projecting the images onto the French geodesic system and correcting data errors using the adaptive Savitzky-Golay algorithm with TIMESAT Software  [35]. The Savitzky-Golay filter fits a quadratic polynomial for each date to the dates included in a moving window.
Here the processing window takes into account five synthesis before and after the processed date. MODIS NDVI values, theoretically coded from 1. 0 to +1. 0, were resampled between 0 and 255 (one byte) in order to facilitate computer processing and data storage  [36]. For our analysis, only pixels located within the boundaries of forest stands were selected, i. e. , 2851 MODIS pixels, covering about 17,800 hectares.
Clear-Cut Reference Layer In the first step, clear-cuts in the study area were digitized by interpreting aerial photographs acquired by the French National Geographic Institute (IGN) in 2003 and 2010. (IGN Website: www. geoportail. gouv. fr). In the study area, 28% of MODIS pixels within the forest stand boundaries were characterized by the presence of a clear-cut. Analysis of the digitized clear-cut areas revealed major differences in size (mean area = 2. 80 ha, standard deviation = 4. 63 ha) with a high proportion of very small patches. Consequently, only a few pixels on the MODIS grid were characterized by a large clear-cut area. Actually, only five pixels are fully covered by a clear-cut, which is not sufficient for calibrating the clear-cut detection model. In the second step, we needed to allocate the presence or the absence of clear-cutting to a pixel and thereby set up binary reference data to be used for calibration.
Calibration of the Clear-Cut Probability Model The aim was to establish breakpoint magnitude thresholds in order to dissociate significantly different populations of pixels depending on the presence or absence of clear-cut. First, the relationship between the probability of the presence of clear-cut and the magnitude of the breakpoint values was observed. Then, we used a recursive partitioning technique known as a "conditional inference tree"  [44]  to establish probability classes for the presence of clear-cut, using the magnitude values of the detected breakpoints as a continuous predictive variable. A conditional inference tree is a non-parametric method that splits a dataset into two populations based on the association between one or more continuous predictors and a target variable, in order to build a decision tree.
05; 9999 replications) to estimate the significance of the nodes. In order to limit the complexity of the model, the number of successive nodes is limited to three, maintaining a limited number of sub-populations. To implement the clear-cut probability model, we selected pixels characterized by the detection of a breakpoint between 2003 and 2010, to be consistent with the acquisition dates of the aerial photographs used to digitize the clear-cut reference data (1601 pixels out of the 2851 pixels in the study area). To calibrate the decision tree-based model, we randomly sampled 50% of the selected pixels (833 pixels). The remaining pixels were used to assess the accuracy of the model. Analyses were conducted using the ctree function from the party package in the R statistical environment.