dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank  Edwin Cuppen  ( Hubrecht Laboratory, the Netherlands ) for providing heterozygous SERT knockout breeding pairs and  Monika Verdouw ,  Johnny Chan ,  Wendy Timmermans ,  Arda Kroesen ,  Jurre van Gool ,  Saskia Oosterbroek  and  Linda Koene  for their excellent practical assistance. This study was financially supported by  Neuroscience & Cognition Utrecht , the  Netherlands.
1. The data show that heterogeneities do not affect the rheological measurements so much, and confirms that E ∼ 3 G ′ (at low frequencies), as expected for incompressible elastic materials (ν ∼ 0. 5). Rheometry measurements were carried out on a controlled stress rheometer (Malvern, Gemini 150) at low fre-quencies [0. 001 Hz-2 Hz] in the linear regime (deformation of 1%), and possible slip was avoided. Interestingly, an excellent agreement was found between these measurements and AFM microrheology experiments for all gels, as seen in Fig. 2  where matching occurs around 2 Hz for the 10% gel. Note that with an adequate AFM equipment operating at lower frequencies, all data should be obtained only with the AFM. Still it is interesting here to use rheometry and match both sets of data. The variations of the dynamic moduli (Fig.
Acknowledgments. -We thank the  ANR  for grant n o  12-BS09-020-01  (TRANSMIG), the  Nanoscience foundation  for support of the AFM platform. The LIPhy laboratory is part of the  LabeX Tec21  ( Investissements d ′ Avenir-grant  agreement-n o  ANR-11-LABX-0030 ). Special thanks go to  E. Geissler  for fruitful discussions.
Of note, the percentages of fluorescent signal-positive cells were 80, 90, and 60%, respectively (data not shown). This result indicated that the SIN lentiviral vectors represented an efficient vector for the delivery of Myr(+)Ank GAG 1D4EGFP and 2LTRZFPmCherry genes along with, high-level expression of the antiviral scaffolds in target cells. The production of VSV-G-pseudotyped lentiviral vectors carrying the gene for 2LTRZFPmCherry, Myr(+)Ank GAG- 1D4EGFP, or both anti-HIV scaffolds, was also performed in HEK293T cells cotransduced with vectors (i), (ii), or (iii), along with two additional vectors required for genome packaging and particle production (pMDLgag/polRRE and pRSV-Rev), and a third vector for VSV-G pseudotyping (pMD. 2G). 10.
In coexpressing cells, the two proteins reached separately the cellular compartments for which they were designed, with no apparent trafficking interference. Images were taken using a Nikon C2 Plus confocal microscope, at 600× magnification. Images were analyzed using the NIS-Elements AR 4. 20. 00 software. supernatants collected at D12, D21, and D30 postinfection (pi) using a p24 enzyme-linked immunosorbent assay (ELISA) (Figure  2b ). The monitoring of HIV-1-infected cells for the stability of expression of the antiviral molecular scaffolds showed that >80% of the cells were positive for EGFP or mCherry at D12 pi, and between 70 and 95% cells remain positive at D30 pi (Figure  2c ). In control SupT1 cells as expected, the supernatant fluids collected on D12 and D21 from the nontransduced cells infected with HIV-1 showed high p24 levels.
22 We have aligned the amino acid sequences of helix 1 and helix 7 of the HIV-1 subtype B prototype with newly isolated strains, deposited in the HIV sequence database from 2008 to 2013. We found that R18 in helix 1, and R132 and R143 in helix 7, are highly conserved residues in the CA sequence of all isolates (not shown). These amino acid residues are known to be crucial for the morphogenesis of the virions and their stability, and the chances that mutations of these amino acids would result in viable viruses are minimal. However, the possibility of a viral escape from the Myr(+)Ank GAG 1D4 antiviral activity should be only definitely eliminated by challenging Myr(+) Ank GAG 1D4-expressing cells with various HIV-1 CA mutants. The explanation to the persistence of p24 antigen in the extracellular medium of HIV-1-infected SupT1/Myr(+)Ank-GAG 1D4 cells more likely resided in the intrinsic properties of Myr(+)Ank GAG 1D4.
Confocal microscopy. HEK293T cells transduced with each VSV-G-pseudotype lentiviral vector at MOI 1 were seeded on cover glass slides, and left overnight for culturing. Cells were fixed with 4% formaldehyde in PBS, and permeabilized with 0. 2% Triton X-100. Then, the nucleus was counterstained with DAPI. Images were acquired using Nikon C2 Plus confocal microscope with 600× magnification, and analyzed using the NIS-Elements AR 4. 20. 00 software. Viral stocks. Replication-competent HIV-1 NL4-3 virus (X4-tropic) was produced by transient transfection of pNL4-3 plasmid into HEK293T cells. Monolayers of HEK293T cells (3. 5 × 10 6 cells per 10-cm dishes) were transfected with 5 μg of the pNL4-3 plasmid, using Lipofectamine and Plus reagents (Life Technologies, Carlsbad, CA), as previously described.
Cells were incubated with control IgG isotype antibodies to evaluate the level of nonspecific binding. Stained cells were then analyzed using the BD FACS Calibur LSR II flow cytometer, using the CELLQUEST software. HIV-1 integration assay. The number of viral genome copies integrated into the host DNA of control SupT1 and SupT1 cells stably expressing molecular scaffolds was determined by using a conventional Alu-gag qPCR assay, as described previously. 49, 50 Briefly, the DNA of HIV-1-infected control SupT1 and CGW-vector-transduced SupT1 cells was extracted (High Pure PCR template kit; Roche, Mannheim, Germany), and a first round of PCR performed using a pair of primers specific for the Alu (human) and gag (HIV-1) sequences. The primers for the first-round amplification were the following: Alu forward, 5′-GCC TCC CAA AGT GCT GGG ATT ACA G-3′, and HIV-1 gag reverse, 5′-GTT CCT GCT ATG TCA CTT CC-3′.
Acknowledgments. This work was supported by the  Thailand Research Fund  through the  Royal Golden Jubilee Ph. D. program  (grant number  PHD/0024/2552  to W. K. ), the  Cluster and Program Management Office (CPMO) , the  National Science and Technology Development Agency (NSTDA) , the  National Research Council of Thailand (NRCT) , the  Health Systems Research Institute (HSRI) , the  National Research University  project under the  Thailand's Office of the Commission on Higher Education , and  National Institutes of Health (NIH)  grant  CFAR-P30 AI036214 ,  GM083658  and  HL091219. The authors express their gratefulness to  Research Institute for Health Sciences, Chiang Mai University  for the determination of viral loads. We are very grateful to  Supattara Suwanpairoj ,  Jinyun Yuan ,  Cathy Wang , and  Deborah Noack  for their precious technical help during the completion of this study.
Acknowledgements We thank  Sabah Parks , especially  Dr Jamili Nais  and  Rimi Repin , for research support and for the loan of the herbarium material. AVDE is grateful to  Rositti Karim  and  Sukaibin Sumail  for help during the fieldwork in  Kinabalu Park , and  Anthea Phillipps  for an earlier identification of Christisonia. KMW acknowledges the support of the  National Parks Board, Singapore  and the  Singapore Botanic Gardens.
After filtering, the two platforms called an average of 76,195 total SNVs per sample, and the mean proportion of variants for which the same genotype was obtained with both techniques was 99. 92% (range, 99. 91-99. 93%). We then studied the high-quality (HQ) variants satisfying the filtering criterion but called by only one platform. On average, 2,734 variants (range, 2,344-2,915) were called by WES but not by WGS (Fig. S3A ), and 6,841 variants (5,623-7,231) were called Four types of genomic units were analyzed: exons from protein-coding genes, microRNA (miRNA) exons, small nucleolar RNA (snoRNA) exons, and large intergenic noncoding RNA (lincRNA) exons as defined in Ensembl Biomart  (19). We determined the number of these units using the R Biomart package  (20)  on the GRCh37/hg19 reference.
We first considered exons from protein-coding genes (denoted as "All") obtained from Ensembl. The intronic essential splice sites (i. e. , the two intronic bp at the intron/exon junction) were not included in our analysis of exons. Then we focused on protein-coding exons with a known CDNA coding start and CDNA coding end that were present in CCDS transcripts  (21). For the counts, we excluded one of the duplicated units of the same type, or units entirely included in other units of the same type (only the longest unit would be counted in this case). We then determined the number of the remaining units that were fully or partly covered when considering the genomic regions defined by the Agilent Sure Select Human All Exon kit 71 Mb (v4 + UTR) with the 50-bp flanking regions. by WGS but not WES (Fig.
S3A ). We used Annovar software  (25)  to annotate these HQ variants as coding variants: i. e. , variants overlapping a coding exon, this term referring to the coding part of the exon but not the UTR portion. Overall, 651 of the 2,734 WES-exclusive HQ variants and 1,113 of the 6,841 WGSexclusive HQ variants were coding variants (Fig. S3A ). Using the Integrative Genomics Viewer (IGV) tool  (26) , we noticed that most WES-exclusive HQ variants were also present on the WGS tracks with quality criteria that were above our defined thresholds. We were unable to determine why they were not called by the Unified Genotyper. We therefore used the GATK Haplotype Caller to repeat the calling of SNVs for the WES and WGS experiments. We combined the results obtained with Unified Genotyper and Haplotype Caller and limited subsequent analyses to the variants called by both callers.
Of those identified only by WGS (692, on average), 21% were fully exclusive and 79% were partly exclusive. We performed Sanger sequencing on a random selection of 170 fully and partly exclusive WES/WGS variants. Of the 44 fully exclusive WES variants successfully Sanger-sequenced, 40 (91%) were absent from the true sequence, indicating that most fully exclusive WES variants were false positives (Table  2  and Dataset S1). By contrast, 39 (75%) of the 52 Sanger-sequenced fully exclusive WGS variants were found in the sequence, with the same genotype as predicted by WGS (including 2 homozygous), and 13 (25%) were false positives (Table  2  and Dataset S1). These results are consistent with the observation that only 27. 2% of the fully exclusive WES variants were reported in the 1000 Genomes database  (27)  whereas most of the fully exclusive WGS variants (84.
7%) were present in this database, with a broad distribution of minor allele frequencies (MAFs) (Fig. S5A ). Similar results were obtained for the partly exclusive variants. Only 10 (48%) of the 21 partly exclusive WES variants (including 3 homozygous) were real whereas all (100%) of the 24 partly exclusive WGS variants (including 8 homozygous) were real. Using these findings, we estimated the overall numbers of false-positive and false-negative SNVs detected by these two techniques. WES identified a mean of 26 estimated real-coding variants per sample (including 5 homozygous) that were missed by WGS, and a mean of 79 estimated false-positive variants. WGS identified a mean of 656 estimated real-coding variants per sample (including 104 homozygous) that were missed by WES, and a mean of 36 estimated false-positive variants.
S6 B  and C ), which suggests that most of these indels called exclusively by one method could be real. We Sanger-sequenced a random selection of 164 coding indels exclusively called by WES or WGS. We found that the Sanger sequences of 32 of the 58 successfully sequenced WES-exclusive indels (55. 2%) were consistent with WES findings (Table  2  and Dataset S1). Similarly, 36 (52. 2%) of the 69 Sanger sequences obtained for WGS-exclusive indels were consistent with WGS findings (Table  2  and Dataset S1). These Sanger results indicate that, by contrast to what was found for SNVs, the estimated proportion of false positives among exclusive HQ-coding indels was equally high for both WES and WGS, at almost 50%. More indels were detected exclusively by WGS (61 + 44 = 105) than exclusively by WES (15 +17 = 32) so the number of real coding indels per sample detected by WGS and missed by WES was estimated to be higher (57 indels) than that detected by WES and missed by WGS (18 indels).
However, there are three arguments to suggest that our general findings for the WES/ WGS comparison are valid. First, it was equally difficult to analyze Sanger sequences for WES-exclusive and WGS-exclusive indels. Second, the proportion of false positives was lower for partly exclusive than for fully exclusive indels for both WES (67. 6% vs. 37. 5%) and WGS (64. 8% vs. 37. 5%) (Table  2 ), as observed for SNVs. Finally, our Sanger results are consistent with the observed similar fractions of WES-exclusive and WGSexclusive indels reported in the 1000 Genomes database (Fig. S5C ). Overall, these results indicate that the proportion of falsepositive coding indels is similar for both WES and WGS. The last step in our study was the comparison of WES and WGS for the detection of CNVs. The methods currently used to identify CNVs from WES data were already known to perform poorly for a number of technical reasons  (31) , including the fact that CNV breakpoints could often lie outside the regions targeted by the exome kit  (32).
We hypothesized that most true CNVs are common and should therefore be reported in public databases for CNVs, such as the Database of Genomic Variants (DGV)  (35). The vast majority of deletions detected by WGS (105/113, 93%) were present in DGV, suggesting that most CNVs detected by WGS are true CNVs. Ten of the 14 deletions detected by WGS and at least one WES-based method (71. 4%) were reported in DGV whereas only 5/34 (14. 7%) Conifer-exclusive and 5/22 (22. 7%) XHMM-exclusive deletions were present in DGV. For 110 of the 113 deletions detected by WGS, one  (24)  or both (86) putative breakpoints were located outside the exome-capture regions, providing a plausible explanation. Two of the three deletions with both breakpoints in the exome regions corresponded to the same 1. 1-kb deletion identified in two different patients.
1% of these genes were common to at least four samples, and 38. 4% were present in all six individuals. The percentage of exonic base pairs with more than 8× coverage for these 384 genes was, on average, 73. 2% for WES (range, 0-86. 6%) and 99. 5% for WGS (range, 63. 6-100%) (Dataset S2A). These genes with low WES coverage in all patients comprised 47 genes underlying Mendelian diseases, including three genes (IMPDH1, RDH12, NMNAT1) responsible for Leber congenital amaurosis, and two genes (IFNGR2, IL12B) responsible for Mendelian susceptibility to mycobacterial diseases (Dataset S2A). We then focused on the protein-coding exons that were fully outside the WES71+50 region (Table  1 ). We restricted our analyses to the highest-quality protein-coding exons, those present in a consensus coding sequence (CCDS) transcript  (21) , with known start and end points of the coding sequence in the cDNA.
These CCDS exons comprise a total of 46,227,845 bases that belong to translated regions, including 8,566,582 (18. 5%) lying outside the WES71+50 region. The average CADD score for all possible variants was lower among the bases of the CCDS protein-coding exons not targeted by the kit (median, 7. 362) compared with the bases targeted by the exome kit (median, 14. 87) (Fig. S5D ). However, an important proportion (41. 5%) of possible variants at bases not targeted by the kit had a CADD score of >10, suggesting that potentially deleterious variations (with high CADD scores) might be missed by WES. We also found that 5,762 CCDS exons (3. 1%), from 1,223 genes were located entirely outside the WES71+50 region. Of these genes, 140 were associated with Mendelian diseases (Dataset S2B). We conducted the same analyses with the latest Agilent all-exon kit (v5+utr; 75 Mb), taking 50-bp flanking regions into account.
These global studies should facilitate individual decisions, determining whether the better detection of SNVs, indels, and CNVs merits the additional cost of WGS, if WGS remains more expensive than WES. Finally, we carried out a detailed characterization of the variants and genes for which the two methods yielded the most strongly contrasting results, providing a useful resource for investigators trying to identify the most appropriate sequencing method for their research projects. We provide open access to all of the scripts used to perform this analysis at the software website GITHUB (https://github. com/ HGID/WES_vs_WGS). We hope that researchers will find these tools useful for analyses of data obtained by WES and WGS  (4, 7, 9, 11, 38) , two techniques that will continue to revolutionize human genetics and medicine in the foreseeable future.
Sequencing was done with 2 × 100-bp paired-end reads, and five samples per lane were pooled. WGS was performed with the TruSeq DNA prep kit. Sequencing was carried out so as to obtain 30× coverage from 2 × 100-bp paired-end reads. Analysis of High-Throughput Sequencing Data. We used the Genome Analysis Software Kit (GATK) best practice pipeline  (17)  to analyze our WES and WGS data, as detailed in SI Text. We filtered out SNVs and indels with a CD of <8 or a GQ of <20 or an MRR of <20%, as previously suggested  (24) , with an in-house script. We used the Annovar tool  (25)  to annotate the resulting highquality (HQ) variants. CNVs were detected in WES data with XHMM  (32)  and Conifer  (33) , and deletions were detected in WGS data with Genome STRiP  (34) , as detailed in SI Text. All scripts are available from https://github.
ACKNOWLEDGMENTS. We thank  Vincent Barlogis ,  Carlos Rodriguez Gallego ,  Jadranka Kelecic , and  Malgorzata Pac  for the recruitment of patients;  Fabienne Jabot-Hanin ,  Maya Chrabieh , and  Yelena Nemirovskaya  for invaluable help; and the  New York Genome Center  for conducting WES and WGS. We thank the reviewers for their critical suggestions. A. Bolze was funded by a fellowship from the  Jane Coffin Childs Memorial Fund for Medical Research. The  Laboratory of Human Genetics of Infectious Diseases  is supported by grants from the  March of Dimes  ( 1-F12-440 ), the  National Center for Research Resources , the  National Center for Advancing Translational Sciences  of the  National Institutes of Health  ( 8UL1TR000043 ), the  St. Giles Foundation , The  Rockefeller University, INSERM , and  Paris Descartes University.
Introduction Licence specification is an important part of the data publishing process on the web. Recently, a part of the Semantic Web and Linked Data community has been focusing on providing support to the expression of policies on the semantic web. The Open Digital Rights Language (ODRL) provides an ontology for representing policies in the semantic web, and it is used and extended to formally express permissions, prohibitions and duties that licences include  3. The RDF Licenses database 4  is a first notable attempt at developing a knowledge base of licences described following ODRL. However, identifying suitable licences is still not a trivial task for a data publisher. In the current version, ODRL identifies more than fifty possible actions to be used as permissions, prohibitions or obligations, and there are ontologies that extend ODRL adding even more fine grained policies (e.
The RDF Licenses database  [2]  includes the description of licences in RDF  8. We used this database as starting point for the present work. However, population and curation of such knowledge base is clearly a necessary step for licence recommendation systems. For example, the descriptions do not specify the types of assets a licence is eligible for (and we don't cover this aspect in the present paper). The enrichment of the possible terms to express policies will contribute to increase the precision and quality of the descriptions (see LiMO 9  , L4LOD 10  and ODRS  11  ). Applying natural language processing techniques, like the ones proposed in  [3] , can facilitate the process of data acquisition. Licentia  [4]  is a tool for supporting users in choosing a licence for the web of data. Similarly to our approach, it is based on the RDF licence database.
The user selects possible permissions, obligations and duties extracted from the licence descriptions, in order to specify her requirements. The system applies reasoning over the databases of licences, proposing a list of compatible ones to choose from. With this approach the user needs to perform an action for each of its requirements. Our approach restricts the number of questions through the inferences implied by the classification of licences in a hierarchy (e. g. : any "share alike" licence allows distribution) and only suggests the ones for which a solution actually exists. The approach proposed in this paper relies on an ontology of licences as a means for licence selection. Such an ontology has been created following a bottom-up approach. Bottom-up approaches for ontology design have been commonly applied in knowledge engineering  [5]  and we use here one particular method based on Formal Concept Analysis (FCA)  [6].
Building the ontology Our hypothesis is that an ontology can help on orienting the user in the complex set of existing licences and policies. The RDF Licenses database contains 139 licences expressed in RDF/ODRL. Our idea is therefore to start from the data to create the ontology. The reason for choosing a bottom-up approach to ontology construction is also that the data will include only policies that are relevant. In order to support the production of the ontology we implemented a bottomup ontology construction tool called Contento, which relies on FCA. FCA has the capability of classifying collections of objects depending on their features. The input of a FCA algorithm is a formal context -being a binary matrix having the full set of objects as rows and the full set of attributes as columns.
The ontology For the use case at hand, we used Contento to support the creation of the Licence Picker Ontology (LiPiO)  13  , starting from data in the RDF Licenses database. The data has been preprocessed in order to produce a binary matrix to be imported in Contento. The preprocessing included reasoning on SKOS-like relations between ODRL actions 14. Moreover, we reduced the number of licences from the initial 139 to 48 by removing localized versions (for instance Creative Commons CC-BY-SA 3. 0 Portugal). In this case, the licences are the objects of the matrix, while the set of attributes represent the policies, expressed as ODRL permissions, prohibitions or duties. Below is an example taken from the input CSV: http://purl. org/NET/rdflicense/cc-by4. 0,permission http://www. w3. org/ns/odrl/2/copy,1 http://purl.
As a result, the system proposes to pick one of four licences. The user notices that all of them require an attribution statement and prohibit to produce derivative works. Two of them also prohibit the use for commercial purposes, so the user decides to choose the Creative Commons CC-BY-ND 4. 0 licence. The example above shows an important property of the approach presented in the paper. As the licences are classified by the mean of their features, and the classes organized in a hierarchy, we can notably reduce the number of actions to be taken to obtain a short list of comparable licences. The user had four requirements to fullfill, more then fifty existed in the database, and she could get an easy comparable number of licences with only two steps.