dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on our dataset, we thus selected ncRVDs that maintained a robust activity on a nucleotide while allowing discrimination of one or more other nucleotides when incorporated in a TALEN (Fig. 4a  and Supplementary Fig. 7 ). As proof of concept, we aimed at targeting a sequence (in-site) close to the mutation responsible for the sickle cell disease in the HBB gene, knowing that this locus of interest is highly similar to a portion (off-site) of the HBD gene (93% of identity on the 140 base pairs surrounding the mutation, Fig. 4b ). Using features from our TALEN scaffold (TAL DNA binding array of 15. 5 RVDs and spacer length of 15 bp), we found an optimal target that do not present within the human genome any off-site sequences with less than 5 mismatches at the exception of the HBD locus that contain 2 mismatches.
The two HBB and HBD loci presenting 94% of identity with 32/34 identical nucleotides in the targeted sequence (Fig. 4b ). The corresponding TALEN may therefore present significant processing of not only the HBB but also the HBD locus  12, 17, 18. A careful examination of (i) the identity of the mismatches between the HBB and HBD sequences and (ii) our ncRVD dataset, enabled determining ncRVDs that would permit controlled targeting, ultimately excluding the processing of the HBD locus. We thus implemented either SD and VG or NM and LP as alternative ncRVDs to replace an HD and NG RVD in the classical TALEN (Fig. 4b  and c ). To assess the discriminative potential of ncRVDs we performed targeted mutagenesis experiments at the HBB and HBD loci in a human cell line using one classical TALEN and two that contained the combination of ncRVDs (Fig.
4c ). We monitored mutagenic events (small deletions and insertions, Indels), generated by the non-homologous end joining (NHEJ) repair pathway, at the double strand break (DSB) site induced by the TALEN. We thus quantified, by deep sequencing, targeted mutagenesis frequencies on specific PCR products surrounding the HBB or HBD loci. We observed that the three TALEN induced similar levels of targeted mutagenesis at the expected HBB locus (Table  1 , Fig. 4d ). Remarkably, while the classical TALEN was unable to discriminate between the two loci, the incorporation of the two NM and LP ncRVDs allowed a dramatic decreased of off-site targeting at the HBD locus (Fig. 4d ). Although a larger in vivo data set would be desirable to precisely define the limits of ncRVD incorporation in TALEN, taken together our data indicates that implementation of such ncRVDs in TALENs is a valuable strategy to control and tune TALEN specificity.
Discussion The TALE scaffold has proven over the past few years to be one of the most robust platforms to generate molecular tools to edit, regulate and detect genes of interest  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]. However, as with all engineered DNA binding domains, depending on the experimental conditions and the targeted sequence, a certain extent of unwanted (off-site) targeting events might need to be controlled. In this work, we aimed at extending the level of TALEN targeting control by taking advantage of the vast repertoire of possibilities offered by the DNA targeting characteristics of RVDs (position 12 and 13). We and others recently demonstrated that only a small number of mismatches relative to the ''standard'' association code (NI:A, HD:C, NN:G and NG:T) are tolerated in a TALEN  12, 17, 18.
Identification of HET Domain Loci in N. crassa Isolates The majority of het genes previously characterized in N. crassa and other Sordariomycetes encode proteins containing an HET domain  (Glass and Dementhon 2006; Zhang et al. 2014). We therefore mined the N. crassa FGSC 2489 reference genome  (Galagan et al. 2003)  for this specific domain to identify all candidate het loci. BLAST searches using a consensus HET domain protein sequence identified 69 HET domainencoding loci (fig. 1 ), two of which were not identified in the latest annotated version of the reference genome. One of these two HET domain genes produces an antisense transcript to NCU10839  (Xue et al. 2014) , and the second locus was named HET069. To determine whether the distribution and number of HET domain loci were conserved among wild isolates of N.
crassa, we generated whole-genome sequence from a subset of 25 strains from a collection of 112 strains (supplementary table S1, Supplementary Material online; Sequence read archive study accession number: SRP052921) from a single Louisiana population  (Palma-Guerrero et al. 2013). The reference genome strain, FGSC 2489, is a member of this population. The predicted genome size of the 25 isolates was approximately the same as for the reference strain (~42 Mb), with the exception of one strain, P4476, whose genome was predicted to be only approximately 36 Mb. The number of genes predicted in the genome of the 25 isolates ranged from 9,615 to 9,658 (supplementary table  S1 , Supplementary Material online); the predicted number of genes in P4476 was only marginally lower (9,433 genes). In order to ascertain orthologous relationships between the sets of genes identified in the reference genome sequence and in de novo assemblies of resequenced genomes, we performed synteny analysis based on whole-genome alignments combined with comparisons of gene content using reciprocal tBLASTn and BLASTp searches.
We used the PROMER program in the MUMMER3 package  (Kurtz et al. 2004 ), which performs alignment on the six-frame amino acid translation of the DNA input sequence. The tBLASTn and BLASTp searches were used to identify homologous sequences between de novo assemblies and genes identified in the FGSC 2489 reference genome. Genes in syntenic regions and for which identity between query and subject sequences was maximal and above 70% amino acid identity were considered as orthologous. Using this approach, orthologs of 95-97% of the 9,730 genes predicted in FGSC 2489 were identified in the genomes of the wild isolates. The repertoire and location of HET domain loci were almost identical between the 25 de novo genome assemblies and the reference FGSC 2489 genome. Only three HET domain loci identified in FGSC 2489 (NCU09954, NCU17026, and NCU17077) were not present in the genomes of all resequenced Louisiana isolates (NCU09954, NCU17026, and NCU17077 were detected in only 17, 23, and 14 strains, respectively).
6 ). To assess subcellular localization of epitopetagged HET-E, we performed sucrose gradient fractionation of disrupted cells. As controls, we used a strain bearing cytoplasmic GFP and anti-GFP antibodies for cytoplasmic fractions, antibodies to ERV25, an endoplasmic reticulum (ER)localized protein  (Palma-Guerrero et al. 2014) , and antibodies to a vacuolar and plasma membrane localized protein, PMA-1  (Fajardo-Somera et al. 2013). Cytoplasmic GFP was detected in fractions 1-2 (cytoplasmic fraction), ERV25 was enriched in fractions 3-4 (endomembranes), and PMA-1 was enriched in fractions 7-10 (endomembranes and plasma membrane). In gfp-het-e1 strains, GFP-HET-E was enriched in fractions 2-6 (fig. 7B ). These data showed that HET-E is associated with the endomembranes of the cell as well as in the cytoplasm, suggesting that signaling or disruption of endomembrane function in the cell may be associated with HET-E-mediated vegetative incompatibility.
Strains and Growth Conditions We sequenced genomes of 25 N. crassa isolates randomly selected from Louisiana population whose structure is described in Palma-Guerrero et al. (2013) (supplementary table S1, Supplementary Material online) (Sequence read archive study accession number: SRP052921). Reference strains FGSC 2489, FGSC 9716 (his-3 a), FGSC 6103 (his-3 A), FGSC 11308 (Ávib-1::hph a), and FGSC 12034 (Áhete::hph A) were obtained from the Fungal Genetic Stock Center. FGSC 12034 was changed to mat a background BH22 through crossing. KD02-14 (his-3; pan-2 A) and KD02-15 (his-3; pyr-4 A) were used to cross with BH22 to obtain BH10 (his-3; pan-2; Áhet-e::hph a) and BH11 (his-3; pyr-4; Áhet-e::hph a. ). All strains were grown on Vogel's minimal medium (VMM)  (Vogel 1956 ) and crossed on Westergaard's medium  (Westergaard and Mitchell 1947)  unless otherwise noted.
Sequencing reads were processed using PRINSEQ  (Schmieder and Edwards 2011)  and custom scripts to remove polymerase chain reaction (PCR) duplicates, poor quality reads, and orphan reads. Filtered sequencing reads were cleaned using CUTADAPT (https://github. com/marcelm/cutadapt, last accessed June 22, 2015) to remove adapters and sorted by FASTQCOMBINEPAIREDEND. PY to make paired-end files (https://github. com/enormandeau/Scripts/ blob/master/fastqCombinePairedEnd. py, last accessed June 22, 2015). Sequencing reads were assembled using the short read assembler SOAPDENOVO2  (Luo et al. 2012). Gene prediction was carried out using AUGUSTUS  (Hoff and Stanke 2013)  and protein sequences from FGSC 2489 as query sequences (supplementary table  S1 , Supplementary Material online). HET domain sequences predicted in the N.
crassa reference genome (Broad Institute N. crassa database) were aligned to de novo assemblies using BLAST+ (Basic Local Alignment Search Tool) (ftp://ftp. ncbi. nlm. nih. gov/blast/executables/bla st+/). HET domain sequences were aligned using MACSE  (Ranwez et al. 2011). Multiple alignments were visualized using JALVIEW (http://www. jalview. org). Genome assemblies of N. tetrasperma, N. discreta, and P. anserina were obtained from the U. S. Department of Energy Joint Genome Institute database (http://genome. jgi-psf. org/programs/fungi/index. jsf). Sordaria macrospora genome assembly was downloaded from http://fungidb. org/fungidb/ and the A. oryzae genome assembly was obtained from Comparative Database (http://www. broadinstitute. org/annotation/genome/aspergillus_group/MultiHome. html).
For these genes, we used tBLASTn to confirm gene prediction and to identify potential homologous sequences, and subsequently used neighboring genes to establish shared synteny. If a gene fell into a region that was syntenic with other genomes, and the identity of the sequence in this region was the highest in reciprocal homology searching, the homologous sequences were considered as orthologs. To identify HET domain genes in other species, we searched Pfam database (http://pfam. xfam. org). The obtained HET domain sequences in protist and plant species were aligned to JGI Phytozome 10. 1 database (http://phytozome. jgi. doe. gov/pz/portal. html#) by BLAST to confirm the corresponding gene loci sequences. The extracted HET domain sequences were aligned and viewed by JALVIEW (http:// www. jalview.
Signatures of Balancing Selection at HET-Domain Genes HET domain genes were scanned for signatures of balancing selection by comparing them to a reference set of 8,621 genes (out of the 9,730 genes identified in the N. crassa genome), after removal of genes without SNPs (because of no coverage, or no polymorphism) and of genes with too small a sample size (N < 6). Summary statistics were calculated using the BIO++  (Dutheil et al. 2006 ) and EGGLIB python/C++ libraries (De Mita and Siol 2012): The number of segregating sites standardized by sequence length S (a measure of polymorphism), the average number of nucleotide differences between sequence pairs (a measure of nucleotide diversity), Tajima's D (which measures skews in allele frequency distributions), and the maximum number of synonymous substitutions between sequence pairs K S max (a measure of gene tree depth).
Phylogenetic Analysis and Positive Selection Analyses of het-e Haplogroups Multiple alignments for het-e sequences that excluded the highly diverse 3 0 -end of the coding sequence (CDS) as well as alignments that included these highly diverse sequences were used for phylogenetic inference. As the two methods produced the same tree topology, only alignments including the 3 0 -end of the CDS of the HET-E protein were used for analyses. Codon alignments were carried out using MACSE  (Ranwez et al. 2011). Phylogenetic trees were inferred using MrBayes 3. 2  (Ronquist et al. 2012)  with four separate chains run for 1,000,000 generations, a burn-in value of 10,000, and sampling every 100 generations. Codon-based-likelihood models implemented in the PAML package  (Yang 2007)  were used to test for positive selection by recurrent changes in amino acids of the het-e gene and the different het-e haplogroups.
Acknowledgments This work was supported by a  National Institutes of Health  Grant ( R01GM060468 ) to N. L. G. and a  Marie Curie postdoctoral fellowship  awarded to  P. G. ( FP7-PEOPLE-2010-IOF-No. 273086 ). The authors thank  JW Taylor  ( University of California, Berkeley ) for sharing unpublished data,  David Kowbel  ( UCB ) for assistance with computational analyses, and  Juliet Welch  and  Dr Jens Heller  ( UCB ) for experimental assistance.
They simultaneously record the spatially resolved spectral information in an extended continuous field. This can be tessellated a posteriori, preserving the relevant spatial information as best possible, but at the same time optimizing the quality of the signal by co-adding the spectra belonging to a given portion of the mapped area -the tile -(e. g. Monreal-Ibero et al. 2005; Sandin et al. 2008). Here, we use this principle to explore the capabilities of MUSE to obtain spatially resolved information of extragalactic DIBs. A&A proofs: manuscript no. ms We carried out our experiment in the interacting system AM 1353-272. The system consists of two components. The main galaxy (A) presents two prominent ∼40 kpc long tidal tails and was thoroughly studied by  Weilbacher et al. (2000 Weilbacher et al.
Observations and data reduction The interacting system AM 1353-272 was observed as part of commissioning run 2a  (Bacon et al. 2014 ) of the MUSE instrument at the VLT. Six 900 s exposures were taken in two positions between 2014-04-29T04:22:00 and 06:00 under conditions of a few thin clouds and an average effective airmass of 1. 04. The seeing as given by the Gaussian FWHM of the stars in the white-light image of the final cube is 0 ′′. 8. Each position was observed at position angles (P. A. ) of 0 • , 90 • , and 180 •. The instrument was set to wide-field mode (∼ 1 ′ ×1 ′ field of view) with a nominal wavelength range (∼ 4800. 9300Å). The instrument resolution at 7000Å is R ∼ 2800. The standard star GJ 754. 1A was observed on 2014-04-28T09:57:00, and the reduction made use of twilight skyflats taken on 2014-04-27.
Standard reduction steps were followed with MUSE pipeline v1. 0  (Weilbacher et al. 2014) , with bias subtraction, flat-fielding, and spectral tracing using the lamp-flat exposures, wavelength calibration, all using standard daytime calibration exposures made the morning after the observations. The standard geometry table and astrometric solution for commissioning run 2a were used to then transform the science data into pixel tables. This last step included correction for atmospheric refraction, flux calibration, and sky subtraction. The sky spectrum was measured in the blank areas of each exposure, set to the darkest 40% of the field of view, and data were corrected to barycentric velocities, resulting in corrections of -0. 6 to -0. 7 km s -1 for the time of observations. The final, combined datacube was then reconstructed from all six exposures, sampling the data at 0 ′′.
Acknowledgements. We thank the referee for the diligent reading of the manuscript as well as for providing valuable comments that helped us to clarify and improve the first submitted version of this letter. We thank the  MUSE collaboration  and  PI R. Bacon  for building this extraordinary spectrograph and the rest of the commissioning team,  G. Zins  in particular, for the tireless effort to continue improving the instrument through all runs. AM-I and RL acknowledge support from  Agence Nationale de la Recherche  through the  STILISM  project ( ANR-12-BS05-0016-02 ). PMW and SK received support through  BMBF Verbundforschung  (project  MUSE-AO , grants  05A14BAC  and  05A14MGA ).
This phenomenon, which lies at the heart of well-known semi-supervised learning techniques like self-training (or bootstrapping), has also been noted by approaches combining density estimation methods when very few examples are available, and uncertainty sampling when the training dataset has grown  [5, 17]. Other approaches have been applied to fight the problem of learning with few examples, by finding the optimal seed examples to build a training set  [4, 7]. However, these approaches are complex and difficult to implement, thus lie beyond the capacities of the regular NLP practitioner. In contrast, the approach presented here is conceptually simple and easy to implement, as it is a wrapper method over your best-know classifier. We developed an active learning tool inspired on Dualist  [12].
As in Dualist, we provide a graphical user interface for the human oracle to answer the queries of the active learning algorithm. The base machine learning algorithm is also a Multinomial Naïve Bayes, but our method for ranking instances is uncertainty/certainty sampling based on the confidence of the classifier. Features can also be labelled, using Information Gain to select them, but sequentially with respect to instances, not simultaneously as in Dualist. As an addition, our approach allows for multiclass labeling, that is, an instance can be labelled with more than one class. Our active learning framework source together with the dataset is available at https://github. com/crscardellino/nll2rdf-active-learner.
Overview of the system The architecture of the system is based on a machine learning core, with an SVM classifier that learns from examples. Examples are manually assigned to one of a predefined set of classes associated to the licenses ontology. Many vocabularies exist to model licensing information. Some examples include LiMO 3  , L4LOD 4  , ODRS  5  and the well known Creative Commons Rights Expression Language (CC REL) Ontology 6. So far the Linked Data community has mainly used the CC REL vocabulary, the standard recommended by Creative Commons, for machine-readable expression of licensing terms. However, more complex licenses information can be defined using the Open Digital Rights Language (ODRL) Ontology 7  , that allows to declare rights and permissions using the terms as defined in the Rights Data Dictionary 8.
This vocabulary, in particular, has not been specifically conceived for the Web of Data scenario, but it intends to provide flexible mechanisms to support transparent and innovative use of digital content in publishing, distribution and consumption of digital media across all sectors. ODRL allows to specify fine grained licensing terms both for data (thus satisfying the Web of Data scenario) and for all other digital media. The ODRL vocabulary defines the classes to which each text fragment needs to be translated by the system. It specifies different kinds of Policies (i. e. , Agreement, Offer, Privacy, Request, Set and Ticket). We adopt Set, a policy expression that consists in entities from the complete model. Permissions, prohibitions and duties (i. e. , the requirements specified in CC REL) are specified in terms of an action.
Licenses IE within an active learning loop The benefits of active learning, as discussed before, are a faster learning curve and an optimization of the human effort needed to train an automatic classifier. We want to assess the impact of an active learning approach in the task of License Information Extraction. We apply uncertainty sampling to assess the utility of instances and IG to assess the utility of features for a given model. We then explored the effects of ranking instances either by highest or lowest uncertainty. We implemented a system to apply active learning to the kind of annotation that we aim to develop, with functionalities similar to those of Dualist  [12]. The architecture of the system is visualized in Figure  1. The system is provided with an annotated and an unannotated dataset.
A model is learnt from the annotated dataset, applying MNB in a one-vs-all setting with separated feature selection for each classifier. Then, the model is applied to an unannotated dataset, and instances in this dataset are ranked according to the certainty of the model to label them, ranking highest those with most certainty or with most uncertainty. The highest ranking instances are presented to the oracle, who annotates them, associating each instance to one or more of the classes defined by the ODRL ontology or the class "null" if none of the available classes apply for the instance. Then the oracle is given the possibility to annotate features that she finds as clearly indicative of a given class. For each class, the list of features with highest IG with the class is provided, and the oracle selects those that she finds are indicative of the class.
If the user chooses not to annotate features, they are selected by the automated feature selection technique, that is, the system keeps for each one-vs. -all classifier only the top 50 features with highest IG with the class or only those features with more than 0. 001 IG with the class, whichever condition produces the biggest set. If the user chooses to annotate features, these are added to the pool of features selected with the default method. Finally, the system is trained again with the annotated corpus, now enhanced with the newly annotated examples and possibly newly annotated features. The system is built as a hybrid application with Perl 10  , Scala 11  and Java 12. It uses the libraries within Weka  [16] , including LibSVM  [2] , for the training and evaluation of the classifier and has a hybrid web application (that uses both Perl and Scala), to use in the local machine, created with Play! 13  , a Model-View-Controller (MVC) Web application framework for the Scala language, in order to use HTML for a graphic interface.
Dataset We evaluated the performance of different learning methods using a manually annotated dataset of licenses. The corpus consists of the original labelled set of 37 licenses, and an unlabeled set of 396 licenses. It is composed of software licenses, source code licenses, data licenses, and content licenses; they are public as well as private domain licenses. The labeled corpus has a total of 41,340 words, 2,660 of them unique. The mean of words per license is 1117. 30, with a median of 400. This corpus has a total of 162 labelled instances, with a mean of 12. 46 instances per class. The class with the most instances is permission-to-distribute, with a total of 33 instances, while there are three classes with just one instance: permission-to-read, prohibitionto-derive and requirement-to-attach-source.
The unlabeled corpus is gathered manually, and has no overlap with the annotated corpus. This corpus has a total of 482,259 words, 8,134 unique. The mean of words per license is 1217. 83, with a median of 505. 50. For the manual dataset annotation we adopted the CONLL IOB format. , The B and I tags are suffixed with the chunk type according to our annotation task, e. g. B-PERMISSION, I-PERMISSION. We first tokenized the sentences using Stanford Parser  [8] , and we then added two columns, the first one for the annotation of the relation, and the second one for the value The Stanford Parser is also used to parse the instances of the unannotated corpus. From the unannotated corpus, sentences are taken as instances to be annotated by the automated classifier or the oracle.