dataesr/scientific-paragraphs-categorization huggingface dataset:
For MBP fluorescent intensities, images were acquired with a ×63 oil objective and submitted to a background subtraction measured in layer I. Then, the mean intensity values were directly measured with NIH ImageJ using rectangle selection tool to delineate different layers. Unsupervised cluster analysis of YFP + cells from the ePOA. The unbiased identification of YFP + cell clusters was assessed by applying an unsupervised agglomerative cluster analysis via multi-scale bootstrap resampling  20, 21. First, we extracted x, y, z positions of layers IV-VI YFP + /Olig2 + /CC1 -OPCs, YFP + /Olig2 + / CC1 + OLs and YFP + /Olig2 -/CC1 -interneurons in the confocal sections used for cell countings at PN10 and PN19 (3-4 slices per mouse; n = 4 mouse per age). To display the hierarchical relationship of identified YFP + cells according to their spatial proximity, we computed Manhattan intersomatic distances to produce a distance matrix and build a hierarchical dendrogram using the package hclust under the R environment  21.
It is noteworthy that Manhattan distances were used for building the dendrogram because, based in absolute distances, they are less influenced by outliers than Euclidean distances. Instead, the proximity of cells was evaluated with Euclidean distances. In the dendrogram, each identified YFP + cell is considered as a single object which repeatedly merges into higher-level clusters to its closest objects until forming a hierarchical tree. To determine whether unbiased clusters of YFP + cells were supported by data, we assessed the uncertainty of clusters at each branch of the dendrogram with the Pvclust package under the R environment  20. This package computes 10,000 bootstrap samples generated by randomly resampling our experimental data and by performing bootstrap replications of the dendrogram.
By computing both bootstrap probability values and approximately unbiased probability values for each cluster in the dendrogram, we inferred whether the distribution of cell clusters was random or not. If the probability of a given cluster is too low, the cluster does not exist, but if the probability is >95%, we considered that the cluster is strongly supported by the data  20. Finally, once objective cell clusters were detected, we determined the number of cells per cluster, the cell composition of each cluster, and the Euclidean intersomatic distances among all cells in a cluster. Statistics. All data are expressed as mean ± SEM from n pairs, cells or animals. GraphPad InStat software version 3. 06 was used for statistical comparisons. The nonparametric two-tailed Mann-Whitney U test for independent samples was used to determine statistical differences between two means.
Acknowledgements This work was supported by grants from  Fondation pour la Recherche Médicale  (FRM, «Equipe FRM DEQ20150331681»),  Agence Nationale de la Recherche  ( ANR ,  ANR-14-CE13-0023 ), Fondation pour l'aide à la recherche sur la  Sclérose en Plaques (ARSEP) ,  ANR  under the frame of  Neuron Cofund (Era-Net Neuron ; project No. R19068KK ) and a subaward agreement from the  University of Connecticut  with funds provided by Grant No. RG-1612-26501  from  National Multiple Sclerosis Society (NMSS)  (to M. C. A. ), from  IDEX-Paris Cité Sorbonne (TransGABA)  (to M. C. A. and A. P. ) and from  FRM («Equipe FRM DEQ20130326521»)  (to A. P. ). D. Ort. is recipient of an  INSPIRE PhD fellowship from Marie Skłodowska-Curie Action  ( European Union's Horizon 2020 ) and  Université de Paris  (grant agreement:  665850 ).
I conclude this section by arguing that, in a similar way, research in the Humanities is presently a mixed reality with a tendency to develop its forefront research through virtual research environments (VRE). In 2004, John Houghton, Colin Steele and Margaret Henty published one of the first analyses of digital scholarly practices. If their volume underlines the emergence of a new kind of knowledge, their editorial overview points principally to the access and dissemination needs of scholars: "research databases, related software and other analytical objects are new core tools, as the very nature of discourse shifts from hypothesis testing towards collecting processing and analyzing primary data. "8 This is a recurrent and universal point of DH: the first DH developments are almost always provoked by the needs of archiving and accessing to primary and secondary sources.
As MARK16 is supported by the Swiss National Foundation institution (SNSF), the material produced during the project itself has to be OA, normally in the golden way. In the SNSF project HumaReC, our team has already established its VRE publication model: HumaReC received an ISSN from the National Swiss Library52 and all the material produced by the team and published on the VRE is in license CC-BY 4. 0, including the code available on GitHub. 53 MARK16 will apply the same publication model to its data. The fourth criteria -to be "(iv) open and flexible with respect to the overall service offering and lifetime"54 -is also on the MARK16 agenda. In fact, since October 2017, all the projects submitted to the SNSF must present a data management plan, including the mention of a public depository for their data at the end of the project.
To create such a result, several collaborations have been established. First, members of the team will spend time in the institutions of the members of the scientific committee, in order to test the VRE with colleagues and students, and get regular feedback on the project from the users' point of view. Certain tools will be open to all interested scholars on request, such as a common Zotero bibliography online. 58 Second, several collaborations will be developed to build, for example, a small manuscript room with the interesting folios of Mark 16, collaborating with the Virtual Manuscript Room Collaborative Environment designed by Troy Griffits. 59 Regarding data visualization, we will collaborate and be in touch with Leif Isaksen's team and the Pelagios project. 60 In summary, one can say that MARK16 fits exactly with the five VRE criteria proposed by Candella, Castelli, and Pagano.
Immunoglobulins or antibodies are the main effector molecules of the B-cell lineage and are encoded by hundreds of variable (V), diversity (D), and joining (J) germline genes, which recombine to generate enormous IG diversity. Recently, high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) of recombined V-(D)-J genes has offered unprecedented insights into the dynamics of IG repertoires in health and disease. Faithful biological interpretation of AIRR-seq studies depends upon the annotation of raw AIRR-seq data, using reference germline gene databases to identify the germline genes within each rearrangement. Existing reference databases are incomplete, as shown by recent AIRR-seq studies that have inferred the existence of many previously unreported polymorphisms. Completing the documentation of genetic variation in germline gene databases is therefore of crucial importance.
Lymphocyte receptor genes and alleles are currently assigned by the Immunoglobulins, T cell Receptors and Major Histocompatibility Nomenclature Subcommittee of the International Union of Immunological Societies (IUIS) Ohlin et al. The Inference of Antibody Gene Sequences and managed in IMGT ® , the international ImMunoGeneTics information system ® (IMGT). In 2017, the IMGT Group reached agreement with a group of AIRR-seq researchers on the principles of a streamlined process for identifying and naming inferred allelic sequences, for their incorporation into IMGT ®. These researchers represented the AIRR Community, a network of over 300 researchers whose objective is to promote all aspects of immunoglobulin and T-cell receptor repertoire studies, including the standardization of experimental and computational aspects of AIRR-seq data generation and analysis.
INTRODUCTION Immunoglobulins (IG) are the main antigen receptors and effector molecules of the B cell lineage, and are expressed either as a component of the membrane-bound B cell receptor (BCR) or as secreted antibodies. They are encoded by large numbers of variable (V), diversity (D), and joining (J) genes, which recombine in developing B cells to generate rearranged V-(D)-J genes. This process, referred to as V-(D)-J rearrangement, occurs at the DNA level and leads to an IG V domain repertoire of immense diversity. The study of such repertoires has recently been revolutionized by high-throughput sequencing  (1) (2) (3) (4) , and this is termed Adaptive Immune Receptor Repertoire (AIRR) sequencing (AIRR-seq). The technical and biological interpretation of AIRR-seq data is facilitated by databases containing reference sequences of all known germline genes (Figure  1 ), but AIRR-seq studies have demonstrated that these databases are presently far from complete  (5) (6) (7) (8).
This compromises analysis of AIRR-seq data in many ways. For example, it can lead to the inaccurate determination of gene utilization frequencies, and the extent to which sequences have been affected by the process of somatic point mutation. The first complete nucleotide sequence of a human germline heavy chain variable gene was reported in 1980  (9). In 1989 at the Human Gene Mapping (HGM)  (10)  Workshop in New Haven, starting with the human T cell receptor gamma (TRG) locus genes as a paradigm, the variable, diversity and joining IG and TR genes were officially acknowledged as "genes" just like conventional genes, and under the HGM auspices, IMGT R , the international ImMunoGeneTics information system R (IMGT) was created by University of Montpellier and the Centre National de la Recherche Scientifique (CNRS)  (10).
Over the next 20 years, hundreds of thousands of expressed V-(D)-J genes were reported, and dedicated tools and databases were established to facilitate research  (10, 16, 17). It soon became possible to compile datasets of hundreds of rearranged human V-(D)-J gene sequences that could be used to analyse the process of V-(D)-J recombination  (18, 19). These analyses also demonstrated that such datasets could be used to identify previously unreported allelic variants of known germline IG genes  (20). In 2009, AIRR-seq data were reported for the first time  (21, 22). Even in the earliest AIRR-seq studies, thousands of independent V-(D)-J rearrangements could be identified from each subject investigated, and this facilitated the detection of previously unreported polymorphisms (5-8) (Figure  2 ).
New allelic variants of IGHV genes were detectable in these AIRRseq data because the crucial nucleotides that defined these alleles showed up as conspicuous patterns of shared mismatches within alignments to the known germline V gene sequences. Utilities have now been developed to streamline the identification of allelic variants, and to assign measures of ), and a small number of IGHD (yellow/brown) and IGHJ (purple) genes] are rearranged in cells of the B cell lineage. Following stimulation with antigen many sequences undergo somatic hypermutation and acquire base substitutions (marked *) that may impact subsequent data analysis. An investigated subject's B and plasma cells are collected and typically the cells' transcriptomes are sequenced (e. g. , using Illumina MiSeq technology) to generate reads that can be computationally processed.
(B) A germline IGHV gene database [here represented only by three genes (A, B, C)] will facilitate data analysis, though it is possible to infer genes and alleles without reference to a starting database. The database could be a collection of all known germline IGHV gene alleles (I), or an individualized subset of these (II) that best fits the set of sequence reads that are to be analyzed. Finally, computationally inferred novel germline IGHV gene alleles can be introduced into the individualized germline gene database (III) to even better account for the diversity observed in the experimentally generated sequence dataset. (C) Each sequence read is binned to the most appropriate germline gene/allele available in the used germline gene database. If germline gene alleles are present in the database but not in the subject's genotype, some reads will be binned to them as a consequence of base changes introduced by somatic hypermutation (or sequencing errors), resulting in a partial incorrect assignment of germline gene allele origin and consequently of the associated analysis of the mutational pattern.
Detailed annotations of part of the sequences are provided for reads binned to alleles of gene C. In this example, the investigated subject has an allele (C*03) of this gene that is not represented in the original pre-existing germline IGHV gene database. Two bases that differ between one or several alleles in the database and C*03, and thus may be misinterpreted in mutational analysis, are boxed. Unless valid inference of novel germline genes is also performed, the mutational analysis will substantially misinterpret the mutational pattern (highlighted in red letters/dots) targeting this gene. Dots indicate identity to the germline gene to which it is compared. confidence to each inference  (23) (24) (25) (26) (27). These utilities employ a variety of inference methodologies, as they have been designed for the analysis of different kinds of data.
IgDiscover, for example, is best suited to the analysis of relatively unmutated sequences  (23) , whereas TIgGER  (24)  and partis  (26)  are specifically designed to analyse data that include both unmutated and mutated sequences. To date, 58 sequences have been inferred in this way (see Table  1 ), and can be found in the Immunoglobulin Polymorphism database (IgPdb) (http://cgi. cse. unsw. edu. au/~ihmmune/IgPdb/). The identification of these previously unreported polymorphisms has remained unknown to many researchers because such variants lie outside the scope of the widely-used IMGT/V-QUEST reference directory of germline sequences  (28). This emerged as an early concern of the AIRR Community (https://www. antibodysociety. org/the-airr-community/), a grassroots organization that was founded in 2015 to address the challenges surrounding the generation, analysis and use of AIRR-seq data  (29).
In 2018, this community formally joined The Antibody Society, a non-profit trade association dedicated to the field of antibody research and immunotherapeutics. In 2017, the AIRR Community and IMGT agreed to an approach for evaluating the veracity of inferred germline-gene sequences, and for the incorporation of validated sequences into the IMGT Reference Directory. The Germline Database (GLDB) Working Group of the AIRR Community was formed to develop the necessary policies and procedures, and the Inferred Allele Review Committee (IARC) was formed to critically evaluate submitted inferences. Here we present challenges faced in inferring novel IGHV sequences from AIRR-seq data, and outline strategies for their mitigation. The process for submitting inferred sequences to the IARC is also described.
GERMLINE GENE INFERENCES: CHALLENGES, AND STRATEGIES FOR MINIMIZING ERRONEOUS INFERENCES Reports of inferred antibody sequences have not been immediately and universally accepted, in part because alternative explanations can account for observed nucleotide differences in IG genes (see Figure  2 ). Uniquely, IG genes within activated B cells undergo secondary diversification by somatic hypermutation (SHM)  (30). During an immune response, an IGHV gene with a 300 bp length will commonly accumulate 15-20 somatic point mutations  (31, 32)  and much higher levels of mutations can be observed  (33). The datasets of Sanger sequences that underpinned the first inferred IGHV sequences were very small-in some cases, just six or seven sequences  (20). This raised the possibility that these sequences were mutated versions of known alleles.
Importantly though, many of the early inferences have now been confirmed by genomic sequencing  (20, 34, 35) , lending support to the validity of the inference process. Today, the availability of large AIRR-seq datasets gives much greater confidence in the inference process, but challenges remain. These challenges have their origins in the biology of the B cell and of the antibody repertoire, as well as in technical issues affecting the preparation and sequencing of recombined V-(D)-J gene libraries. The following strategies and tests will aid in the identification of real allelic variants while minimizing the reporting of erroneous inferences. • Inferences must be made from AIRR-seq data of the highest quality. Experimental strategies to ensure such quality in library generation and sequencing of IG transcripts are now well-established  (36, 37) , and the assessment of the quality of library generation and of sequencing, using synthetic mRNA spike-ins, is a strategy that can build confidence in inferences made from a dataset  (38) (39) (40) (41).
• Correction of sequencing errors and PCR artifacts can be achieved by the use of unique molecular identifiers (UMI). UMIs are introduced during library preparation, labeling each individual transcript prior to amplification. Subsequent consensus building of reads employing identical UMIs can largely remove erroneous bases  (46). Technical or biological replicates can also be used to validate sequences and increase confidence that artifacts have been properly discarded. • Incomplete PCR amplifications create problems. An incompletely amplified product generated in one cycle may later anneal to a similar but distinct template, resulting in the amplification of a hybrid sequence (Figure  3 )  (47, 48). Such chimeric amplification products are often observed in datasets of IG transcripts  (49) , and unless appropriate filters are applied to AIRR-seq data, these chimeras can masquerade as novel alleles.
Preparing libraries with minimally detectable PCR bands helps reduce the problem of chimerism  (49, 50) , but this strategy is incompatible with some research objectives. • The detection and elimination of chimeric sequences can be a valuable step in the pre-processing of data. Manual identification of chimeric sequences involves assessment of the distribution of apparent mutations along the length of a sequence. Chimeric sequences often appear to have somatic point mutations clustered at one or the other end of the sequence, and utilities have been developed to automate the detection of sequences with such a non-random distribution of apparent mutations (51). • Very large AIRR-seq datasets are required if variants of some IGHV genes are to be identified. Reports from analysis of peripheral blood B cells show that usage frequencies of particular IGHV genes in V-(D)-J rearrangements can be as high as 20% for IGHV3-23 * 01 (  52 ), but as low as 0.
01% for rearranged genes incorporating IGHV3-13, IGHV4-28, or IGHV7-81  (5). Rarely utilized IGHV genes will only be present in convincing numbers in the very largest V-(D)-J FIGURE 2 | Inference of polymorphic IGHV gene alleles from immunoglobulin repertoire sequencing datasets. The genes that encode immunoglobulin heavy chain variable regions are generated through genomic recombination of single genes of three different types; variable (IGHV), diversity (IGHD), and joining (IGHJ). Each of these gene types are present in the genome as a set of tandem genes that are both polymorphic and polygenic and include approximately 50 IGHV genes, 27 IGHD genes and 6 IGHJ genes. At the mRNA level, splicing joins the rearranged V-(D)-J gene with the IGH constant region genes that confer the isotype to the IG.
For inference, IGH from clonally expanded B cells, which have each originated from a single progenitor cell must be reduced to a single representative sequence (for example, the clone member with the fewest mutations) to prevent over-counting. IGH carrying the same UMI can greatly increase confidence that the V-(D)-J rearrangement was in the original pool. In the absence of UMIs, read counts for unique IGH can provide some confidence. Finally, V-(D)-Js are aligned to germline reference datasets that report alleles for the population. Shared "mismatches" relative to the closest germline gene among many sequences from the same subject at position(s) that are not motifs for somatic hypermutation (SHM) in sequences expected to be unmutated (naïve B cells) or have low mutation (IgM+) can be suggestive of the putative allelic variant in the subject's genotype.
When a polymorphism is inferred at a heterozygous locus, confidence in the inference can be greatly increased if the putative allele is considered in the context of any other expressed alleles for the gene, with approximately equal expression of the two alleles, and the haplotype, which can phase the gene alleles to their respective chromosomes. datasets. Large datasets are also needed if the final nucleotides of a germline IGHV sequence are to be determined. The uncertainties surrounding the nucleotides at the 3 ′ end of the sequence are a consequence of the variability of the gene ends, produced by the processes of exonuclease removal and N nucleotide addition. Biases in these processes can result in the generation of relatively common motifs that may be mistaken for germline-encoded nucleotides  (53) (54) (55) (56).
Of special note, the last base of a germline sequence may not be the most common base in rearranged sequences. • Somatic point mutations accumulate in IG-encoding genes at a rate of about one mutation per 1,000 bp per cell division within the germinal center reaction  (57, 58). The existence of mutational hotspots (59-61) that can target specific germline IGHV genes  (62, 63)  means that it is inevitable that there will be some shared mutations in any dataset that includes mutated sequences. Some IGHV genes have positions that can be mutated in >30% of class-switched sequences  (24). Very high levels of mutation can occur at positions far removed from the regions encoding complementarity determining regions (CDR) of an IGHV sequence, and even at positions outside conventional mutational hotspots  (62, 64).
For these reasons, inferences of new germline IGHV genes using datasets of mutated sequences are more likely to be erroneous. Somatic point mutations may be mistaken for germline-encoded nucleotides, but this issue is substantially reduced if sequences are derived from less-mutated cell populations. This can be achieved by the amplification of IgM-encoding transcripts through the use of constant region-specific primers. The issue is partially addressed by the amplification of sequences from sorted B cells displaying a naïve phenotype. More highly mutated datasets can, however, still be the source of reliable inferences if appropriate analytical tools are used. Both the TIgGER and partis software suites, for instance, are designed to use patterns of apparent mutation to infer novel alleles  (24, 26).
While taking different overall approaches, they both use regressionbased statistical tests to identify polymorphisms at positions that appear to be recurrently mutated, in sequences that are otherwise relatively unmutated. • A useful test of an inferred allelic variant is to consider the sequence in the context of other alleles of the gene that may be present in the dataset. For single-copy genes, only two alleles should be present in the genotype of an individual. If an inference suggests that three alleles of a particular gene are present in an individual genotype, the inference should be further investigated. More than two named variants of some genes can be present in an AIRR-seq dataset for gene names without genomic information on the haplotype copy number, as a result of copy number variation (CNV)  (5, 6, 8, 35, 46, 65).