dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Duplicate Works These features are effective at tasks like duplicate detection and work reconciliation as they actually happen in digital libraries. Duplicate detection can be a poorly defined problem in digital library research: different editions of the same work, for example, may or may not count as duplicates, and older editions frequently bind multiple works within the same covers. 33 ill, a simple heuristic suffices to identify duplicates in the Hathi Trust library. As an example, take the set of all books by Charles Dickens in the corpus. There are 2,774 English-language books identifiable in the dataset here identifiable as written by Dickens; 1174 are identifiable based on title metadata as containing one or more of 19 distinct works by Dickens. 34  the scheme used here, any pair of books can have one of four relationships to each other: 1.
Corpus alignment Corpus alignment is a similar task to duplicate detection that can also be easily executed with SRP features. Corpus alignment is a rarer task than duplicate detection, and is more often practiced using bibliographic identifiers than full text. 35 Without information such as ISBNS, it can be quite difficult to-for instance-identify a hand-corrected Project Gutenberg edition for any given text in the Hathi Trust. Alignment enables the sharing of metadata across corpora. An examplary task, shown in the supplemental materials, is finding copies in the Hathi Trust of each of the 450 novels in the McGill txtlab's 450 novel corpus in English, French and German. Using a distance cutoff of 0. 1 cosine distance successfully matches 377 of 450 novels from the textlab to copies in Hathi with no errors (100% precision, 83.
19 is a British novel of four years later (Florence Maryat's 1892 How Like a Woman). As with the Dickens works, this performance is strong enough to reveal places that the existing metadata is incorrect. Some of these are fairly consequential. The metadata to the Txtlab collection identifies a book as Rachilde's Nono, when matching algorithms and inspection reveal the text is actually her Monsieur Venus. The Hathi collection describes a book as Adele Schopenhauer's Haus, Wald, und Feldmaerchen that in facts binds the 350-page novel Anna into the same volume as the tales. Others are minor misspellings that would foil many matching algorithms. 36.
3. Passable classification results are possible with as few as 40 dimensions, but more dimensions continually increase accuracy into thousands of dimensions. Figure  3. Classification accuracy on Underwood/Sellars dataset Although the results are equivalent, each of the methods here has useful applications in different frameworks. Top-n features produce more interpretable models (although logistic regressions coefficients themselves are prone to overinterpretation). But SRP features, conversely, may be easier to use in the early stages of a project or if the goal is not to study the classification in its own domain, but to quickly transfer it to a separate set of texts for some other purpose.
39 The classification is hierarchical; at the top level it contains approximately 225 distinct classes, ranging in prevalence within the Hathi collection from 177,000 volumes for the most common class, DS (Asian History), to just 9 for the least common, VD (Naval Seamen). For reference, ten random classes and their counts in the combined training and test sets are shown below. This classification presents a wide variety of classification challenges that make it useful as a general stand-in for text classification. The texts are multilingual; the classification itself requires extensive expertise to use properly and is not properly reduced to a flat series of buckets as done here. Recent work on reproducing library classifications has tended to include bibliographic metadata as well (occasional) full-text features; they achieve an accuracy between 50% and 75% into more bins than used here deploying bibliographic metadata created along with the classification, such as subject headings.
The historicity of classification This classifier has many practical uses, but it also suggests the way that classifiers can usefully augment our understanding of the history of library metadata itself. One possible form of interpretation rests in looking at the ways that the classifier fails. The accuracy of the classifier varies by publication in a striking way way. For the last decades of the nineteenth century, the accuracy of classifier rests above 75%; after 1922, the success rate is only around 68% (and even lower after 1985 or so). This is partly because the composition of the HathiTrust collection changes greatly in 1922, the copyright cutoff in the United States, because some major libraries (including the Harvard University libraries and the New York Public Library) have almost no contributions to Hathi after that date.
Conclusion: Research infrastructure As noted in the introduction, many of the tasks outline in this final section could be accomplished with any sort of infrastructure. The dimensionality reductions that feed into similarity and classification tasks are closely related to the pretrained embeddings created by artificial neural networks. As machine learning becomes more prevalent in the study of cultural artifacts, we are starting to see the distribution of pre-trained models become widespread. For example, the widely used Python module SpaCy uses a single GloVe embedding of words in the English language as the basis of its document similarity scores; and when Google distributed a dataset of 8 million YouTube videos, it released no actual video or images, but instead vectorized features of individual image frames using an image-based neural network.
Bounds for Dimension Reduction in LA (0 << α ≤ 2) Using Stable Random Projections, " in Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '08 (Philadelphia, PA, USA: Society for Industrial; Applied Mathematics, 2008), 10-19. 19 The choice of a hashing function is relatively unimportant; I choose SHA-1 because implementations are easily available in almost all programming languages. PUB FIPS, "180-1. Secure Hash Standard, " National Institute of Standards and Technology 17 (1995): 45. 20 Achlioptas, "Database-Friendly Random Projections. " 21 Marc Stevens et al. , "Announcing the First SHA1 Collision. Google Online Security Blog, " February 23, 2017.
Study population Study data were extracted from the database of the Western-Normandy MS network, which is part of the French MS Observatory (OFSEP)  [18]. All patients included in this MS database were diagnosed according to Poser and McDonald 2001 then 2005 criteria, between 1982 and 2011 in three French counties in Western-Normandy (Calvados, Manche, and Orne). The date of the first DMT treatment was known for these patients (n = 933) (Fig  1 ). The population of interest was limited to patients with an initial RR form of MS who did not convert to a secondary progressive form at the cut-off date (n = 733). The end of inclusion date was established to ensure the completeness of the data. The cutoff date was set as the end of February 2015. The MS registry and use of its data for clinical and epidemiological research were approved by the French National Ethics Committee Commission Nationale de l'Informatique et des Libertés (CNIL number 1011405/1) and written informed consent was obtained from all patients for collecting and analysing their data in medical research.
2) and assigned to an IRIS (Ilots Regroupés pour l'Information Statistique). IRIS is the smallest French geographic unit for which census data are available and an EDI score was calculated for each IRIS. In order to attribute a social deprivation score to the IRIS, we used the French EDI (European Deprivation Index)  [19]. In our study we used EDI as a categorical variable in order to increase comparability with others studies. EDIs were therefore distributed into quintiles calculated according to the national distribution rate.
A series of adjusted multivariate cox proportional hazard models was performed with the delay of second-line DMT initiation as a dependent variable and EDI quintiles as independent variables. The following covariates gender, age at disease onset, county of residence at diagnosis, period of diagnosis and year of disease onset were used as other independent adjustment variables. The year of disease onset was taken as a continuous adjustment variable for all multivariate Cox regression models. All statistical analyses of this study were performed using SAS version 9. 3 and STATA IC/SE 14.
The period of diagnosis and years to disease onset, used as adjustment covariates in the multivariate analysis, have a strong influence on access to second-line DMTs. These results were expected and can be explained by the gradual availability of second-line DMT between 2000 and 2011. In addition, the effect of disease onset is obviously explained by improved diagnostic investigations. Our study has some limitations. Data are collected from a population-based observatory and not from a registry. In the Western-Normandy regional observatory patient inclusion is completely dependent on the reporting neurologist. Most patients suffering from MS in Western-Normandy can be expected to be registered in the observatory. This represents a statistical recruitment bias. However, MS-diagnosed patients had necessarily consulted a neurologist and neurologists are advised to record each diagnosed patient in the observatory.
However, this study is based on high-resolution data, because a great deal of information was collected and recorded as exhaustively as possible for each patient thanks to subsequent neurological consultations or via medical records. Data are steadily updated and validated by a clinical research assistant. The Western-Normandy registry forms part of the National Multiple Sclerosis Observatory (OFSEP), which provides detailed guidelines for collecting data and assessing the quality of these data. Beside the new relapse under first-line DMT, which is the main predictive variable for the switch to second-line DMT, some other clinical variables could influence the switch time to a second-line DMT that are not considered because of missing or unreliable data. One of them, radiological disease activity which is obviously an important criterion for switching to a second-line DMT, is not, nowadays in France, done and/or collected in a standardized way in real life setting (various MRI scanning protocols depending on the different MRI machines) and, thus, could not be included in a population study.
Conclusion We have shown that, in RR-MS patients, SES may influence the access to second-line DMT after a few years of exposure to first-line DMT. This result does not appear to be explained by the MS care system in Western-Normandy where the effective involvement of the regional MS network and regular regional medical team meetings for treatment care may not be sufficient to prevent social inequalities in terms of access to treatment. On account of different geographical contexts and healthcare systems these results cannot be generalized to other parts of Europe, and of the world. Our results should also be confirmed with a more extended cohort of patients in France. We plan to extend this work to other French regional MS databases for this purpose and to investigate whether social inequalities play a role in other healthcare delays.
Association between SES and access to a second-line DMT for RRMS patients after a 5-years delay between first and second line DMT. Multivariate Cox model for studying EDI effect was performed using EDI as time varying covariate that allows taking into consideration the interaction between EDI and time. (tvc function of STATA 1 software). DMT: Disease Modifying Treatment. RRMS: Relapsing Remitting Multiple Sclerosis. HR: Hazard Ratio. CI: Confidence Interval. EDI: European Deprivation Index. https://doi. org/10. 1371/journal. pone. 0191646. t003.
Data processing Data reduction was performed using XDS  (Kabsch, 2010)  through the GreNAdeS automated pipeline at the ESRF  (Monaco et al. , 2013)  and data were reprocessed using the REFERENCE_DATASET keyword. All diffraction images have been deposited with Zenodo (https://doi. org/10. 5281/ zenodo. 1035765). Because even in these model systems there can be some variation in data quality and isomorphism between the sub-data sets, selection of only some of the subdata sets for merging was performed. This was performed using the CODGAS genetic algorithm (GA;  Zander et al. , 2016). CODGAS applies principles of biological natural selection in order to select which sub-data sets to merge, based on a target function which is composed of merging statistics [for example hI/(I)i, R meas , CC 1/2 and completeness].
Substructure determination Each pair of data sets ('before' and 'after') was then treated as a standard RIP experiment, varying the scaling (K) of the before and after data sets in SHELXC, which offers a native implementation of the RIP phasing strategy, as described in   31, 58. 31, 150. 98 58. 33, 58. 33, 151. 13 58. 42, 58. 42, 151. 06 58. 43, 58. 43, 151. 21 58. 52, 58. 52, 151. 34 58. 31, 58. 31   Nanao et al. (2005) ,  Ravelli et al. (2005)  and  Sheldrick (2010). Varying the scaling (K) and running SHELXC/D/E was performed using a Perl script. The sampling of K was from 0. 97 to 1. 01 in increments of 0. 00211. Substructure determination was performed in SHELXD using NTRY 5000, SHEL 500 2. 2 and FIND 9 for thaumatin, and NTRY 5000, SHEL 500 2. 0 and FIND 6 for insulin. The high-resolution limits were chosen based on the resolution at which hd 0 /(d 0 )i drops below 1.
Refinement and a posteriori analysis ANODE  (Thorn & Sheldrick, 2011)  was used for the determination of F o À F o model-phased RIP difference electron-density map peak heights. For both this calculation and the evaluation of phase errors, a refined atomic model was used. The refinement procedure was as follows. Molecular replacement was performed using MOLREP  (Vagin & Teplyakov, 2010)  with PDB entry 5fgt for thaumatin and PDB entry 9ins for insulin. The models were rebuilt manually in Coot and then refined using BUSTER  (Emsley et al. , 2010; Bricogne et al. , 2011). The final refinement step was performed with the PDB_REDO webserver  (Joosten et al. , 2014)  in both cases. The weighted mean phase errors (wMPE) were calculated using SHELXE with the -x option and the same refined model as was used in ANODE  (Sheldrick, 2010).
3b  and 3d ). However, comparing the third data set (After_UV in Table  2 , which occurred after UV-LED exposure and had the same data-collection parameters and dose as the previous two data sets) with the first data set (Before_UV. 1) revealed significant peaks in the RIP maps (Figs. 3a  and 3c ). The maximum and minimum peak heights were 23. 34 and À8. 99 standard deviations above the mean, respectively, with the largest differences over Cys7 and Cys20 around the Cys S atom. As in the X-ray RIP experiment, there was very little anomalous signal, with the highest peak being 6. 7 standard deviations above the mean density value. Quality of substructure determination with insulin and thaumatin data sets. The correctness of the substructure is expressed as the percentage of conserved sites in the experimental substructure compared with the reference structure (the reference model was determined by identifying peaks in a model-phased RIP difference map).
Background: Functional annotation of livestock genomes is a critical step to decipher the genotype-to-phenotype relationship underlying complex traits. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aims at profiling the landscape of transcription (RNA-seq) and chromatin accessibility and conformation (ATAC-seq and Hi-C) in four livestock species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells). Results: Standardized protocols were applied to produce transcriptome and chromatin datasets for the four species. RNA-seq assays considerably extended the available catalog of protein-coding and non-coding transcripts. Gene expression profiles were consistent with known metabolic/immune functions and revealed differentially expressed transcripts with unknown function, including new lncRNAs in syntenic regions.
The majority of ATAC-seq peaks of chromatin accessibility mapped to putative regulatory regions, with an enrichment of predicted transcription factor binding sites in differentially accessible peaks. Hi-C provided the first set of genome-wide maps of three-dimensional interactions across livestock and showed consistency with results from gene expression and chromatin accessibility in topological compartments of the genomes. Conclusions: We report the first multi-species and multi-assay genome annotation results obtained by a FAANG pilot project. The global consistency between gene expression and chromatin structure data in these four livestock species confirms previous findings in model animals. Overall, these results emphasize the value of FAANG for research on domesticated animals and strengthen the importance of future meta-analyses of the reference datasets being generated by this community on different species.
Here we report the main results of a pilot project (FR-AgENCODE) launched at the beginning of the FAANG initiative. The broad aim was to generate standardized FAANG reference datasets from four livestock species (cattle, goat, chicken and pig) through the adaptation and optimization of molecular assays and analysis pipelines. We first collected a panel of samples from more than 40 tissues from two males and two females of four species: Bos taurus (cattle, Holstein breed), Capra hircus (goat, Alpine breed), Gallus gallus (chicken, White Leghorn breed) and Sus scrofa (pig, Large White breed), generating a total of 4,115 corresponding entries registered at the EMBL-EBI BioSamples database (see Methods). For molecular characterization, three tissues were chosen to represent a "hub" organ (liver) and two broad immune cell populations (CD4+ and CD8+ T cells).
Results and discussion High-depth RNA-seq assays provide gene expression profiles in liver and immune cells from cattle, goat, chicken and pig For each animal (two males, two females) of the four species, we used RNA-seq to profile the transcriptome of liver, CD4+ and CD8+ cells (see Methods, Figure  1  and Table  S1 ). Briefly, stranded libraries were prepared from polyA+ selected RNAs longer than 200bp, and sequencing was performed on an Illumina HiSeq3000 (see Methods). Between 250M (chicken) and 515M (goat) read pairs were obtained per tissue, of which 94% (chicken) to 98% (pig) mapped on their respective genome using STAR (see Methods and Tables  S2-S4 ). As an initial quality control step, we processed the mapped reads with RSEM to estimate the expression levels of all genes and transcripts from the Ensembl reference annotation (hereafter called "reference" genes/transcripts/annotation) (Figure  S1 , Supplementary data file 1).
Few immune genes differentiate CD4+ and CD8+ cell populations In line with the results of the hierarchical clustering (Figure  3 ), most orthologous genes found to be differentially expressed in CD4+ versus CD8+ within species showed high variability of expression levels across species (not shown). This variability is likely caused by the natural heterogeneity in the relative proportions of cell subtypes among the different species, as already reported between mammals  [28, 29]. Nevertheless, 39 orthologous genes could consistently differentiate CD4+ and CD8+ in the four species, including both mammals and chicken. Among those, 10 and 29 genes showed significant overexpression in CD4+ and in CD8+ cells respectively (Table  S6 ). We searched for these genes in the baseline expression dataset of human CD4+ and CD8+ αβ T cell subsets generated by the Blueprint Epigenome Project  [30, 31]  and considered their relative enrichment in either cell subset (Table  S6 ).
Analysis of new transcripts improves and extends gene structure annotation In order to test if our data could improve the reference gene annotation for each species, we used STAR and Cufflinks to identify all transcripts present in our samples and predict their exon-intron structures. We then quantified their expression in each sample using STAR and RSEM (Methods, Figure  S1 ) and only retained the transcripts and corresponding genes expressed in at least two samples with TPM ≥ 0. 1. We identified between 58,000 and 85,000 transcripts depending on the species (Table  1 , Supplementary data file 5), hereafter called "FR-AgENCODE transcripts". To characterize these FR-AgENCODE transcripts with respect to the reference ones, we grouped them into four positional classes (see Methods): (1) known: a FR-AgENCODE transcript whose exon-intron structure is strictly identical to a reference transcript (i.
Identification, classification and comparative analysis of lncRNAs Using the FEELnc method  [34]  trained with mRNAs from the reference annotation as the coding set and shuffled mRNA sequences as the non-coding training set (Methods), we identified 22,724, 13,864, 7,502 and 12,587 putative lncRNA transcripts for cattle, goat, chicken and pig, respectively (Tables S9-10 and Supplementary data file 7), many more than compared to the reference (Ensembl and NCBI) annotations (0, 4,483, 4,643 and 875 lncRNAs respectively). The smaller number of lncRNAs found in chicken was due to the larger number of non-assembled small scaffolds in that genome, and can be extended to include the 2,718 additional unclassified lncRNAs located on them (217, 707 and 83 for the other species). Consistent with previous reports in several species including human  [35] , dog  [34] , and chicken  [36] , the predicted lncRNA genes were much less expressed than the reference protein-coding genes (Figure  S12 ).
The structural features of these predicted lncRNA transcripts were consistent between the four species: lncRNAs are spliced but with fewer exons (1. 5 vs. 10) and higher median exon length (660 vs. 130bp) compared to mRNAs (Figure  S12 ). lncRNAs are also smaller than mRNAs (1800 vs. 3600bp). Notably, the lower number of exons and consequent smaller size of lncRNAs compared to mRNAs could also be due to the weaker expression of lncR-NAs, which makes it more difficult for computational methods to properly identify their structure  [37]. In addition to the coding/non-coding classification, FEELnc can also categorize lncRNAs as intergenic or intragenic based on their genomic positions with respect to a provided set of reference genes (usually protein coding genes), and considering their transcription orientation with respect to these reference genes.
ATAC-seq peaks allow the characterization of accessible regions of chromatin in the four species We used ATAC-seq to profile the accessible chromatin of liver and of CD4+ and CD8+ immune cells in animals from the four species. Between 480M (chicken) and 950M (pig) ATAC-seq fragments were sequenced per species, and were processed by a standard pipeline: reads were trimmed, mapped to the genome (requiring a MAPQ higher than 10) and filtered for mitochondrial and PCR duplicate reads (see Methods). Most reads were filtered out by the MAPQ 10 and PCR duplicate filters (Figure  S15 ). Peaks were then called in each tissue separately (see Methods), resulting in between 26,000 (pig, liver) and 111,000 (pig, cd8) peaks per tissue (Table  S12 ). Those peaks were further merged into a unique set of peaks per species, resulting in between 75,000 (goat) and 149,000 (pig) peaks (Table  S12  and Supplementary data file 9), and covering between 1 and 5% of the genome.