dataesr/scientific-paragraphs-categorization huggingface dataset:
MinION data evaluation We first sequenced the S288C genome by doing 11 MinION Mk1 runs with the R7. 3 chemistry. On average, a 48-hour run produced more than 200 Mb of sequence, and the best run throughput was 400 Mb. Two 2D library types with 8 kb and 20 kb mean fragmentation sizes were used. They led to nearly 360 000 reads with a cumulative length of approximately 2. 3 Gb and 63% of the nucleotides were in 2D reads, which represented a 187x and 118x genome coverage for 1D and 2D reads, respectively. Template reads had a median length of 8. 9 kb, while 2D reads had a median length of 7. 7 kb. All sequencing reads were aligned to the S288C reference genome using BWA  [21]  to assess their quality. We successfully aligned 95. 6% of the 2D reads with an average error rate of 17. 2% (Fig. 1a ).
Comparison of Nanopore-only assemblers We tested Canu  [22] , Miniasm  [23] , SMARTdenovo  [24] , and ABruijn  [25]  with different subsets of 1D, 2D, and 2D pass reads (Supplementary File 2 and Table  S1 ) and kept the most contiguous assembly for each software. With Canu, the assembly with the higher N50 was obtained with the whole set of 2D pass reads (67x coverage). The assembly was composed of 37 contigs with a cumulative length of 12 Mb and seven chromosomes were assembled in one or two contigs. After aligning the contigs to the S288C reference genome using Quast  [26] , we detected a high number of deletions (120 365), which were often localized in homopolymers (58%). As a consequence, only 454 of the 6243 genes found in the assembly were insertion/deletion (indel) free (Table  S2 ).
S288C assemblies with R9 data The R9 version of the pore was released too late for us to use it to sequence all the natural S. cerevisiae isolates. However, we did produce some data to compare the R7. 3 and R9 assemblies. Because SMARTdenovo produced the best results (higher continuity and higher gene content), we used it to assemble the R9 data generated from the S288C strain. We input four different read datasets: all 1D and 2D reads, only 2D reads, 30x of the longest 2D reads, or 30x of the longest 1D and 2D reads (Table  S4 ). This time, the 30x of the longest 1D and 2D reads dataset gave the best results. Indeed, the contiguity of the assembly increased, and the number of contigs decreased from 26 with the R7. 3 assembly to 23 with the R9 assembly. The number of Table  2 : Metrics of the S288C assemblies after polishing.
We also determined the fitness of the 21 isolates in the presence of CuSO 4 and observed a correlation between the number of CUP genes and the resistance of the strain to high concentration of CuSO 4 (Fig. S9 ). Besides copy number variants, we also focused on larger structural variants, such as translocations and inversions, because our highly contiguous assemblies allowed us to investigate these events. We aligned the polished assemblies of the 21 strains to the reference genome using NUCmer and inspected the alignments with the mummer software suite to search for structural variations. We detected 29 translocations and 4 inversions within the assemblies of 17 strains (Table  6 ). The median length of an inversion was 94 kb and their breakpoints were located mostly in intergenic regions.
Mitochondrial genome variation The ABruijn assembler allowed the construction of a single contig corresponding to the Mt genome for each isolate. To assess the quality of the assemblies, we aligned the polished S288C Mt contig to the reference sequence (GenBank: KP263414). Only four single nucleotide polymorphisms and few indels, representing 15 bp of cumulative length, were detected. For all but two natural isolates, all the Mt genes (8 protein coding genes, 2 rRNA subunits, and 24 tRNAs) were conserved and syntenous. The Mt genomes of the two remaining isolates (CNT and CFF) contained one and two repeated regions covering a total of 6. 5 and 8 kb, respectively. In the CNT, the repeated region was in the COX1 gene and affected its coding sequence. In the CFF isolate, the COX1, ATP6, and ATP8 genes would have been tandemly duplicated.
However, because we could not identify reads that clearly covered the repeated regions and then confirmed the structural variations, we excluded these two Mt genome assemblies from our dataset. The sizes of the 20 considered assemblies ranged from 73. 5 to 86. 9 kb, which is close to the size reported previously  [37]. The differences in size between the assemblies can mainly be attributed to the intron content of the COX1 and COB genes (from two to eight introns in COX1 and from two to six introns in COB). These variations lead to extensive gene length variability ranging from 5. 7 kb to 14. 9 kb for COX1 and from 3. 2 kb to 8. 6 kb for COB, while the coding sequences of these two genes were exactly the same length among the 20 isolates. Intergenic regions also accumulate many small indels, including those that affect the interspersed GC-clusters and a few large indels that sometimes correspond to variable hypothetical open reading frames (ORFs), leading to sizes that range from 51.
With the current R7. 3 release, homopolymers are prone to base deletion (representing 66% of the errors observed in homopolymers). It may be improved with a steadier passing speed through the pore or by increasing the speed of the molecule through the pore. In the same way, the basecaller algorithm could be optimized to increase the accuracy per base. ONT have recently reported several changes, including a fast mode (250 bp/second instead of 70 bp/second with R7. 3 chemistry) and new basecaller software based on neural networks. These new features are incorporated in the R9 version of MinION. We performed R9 experiments and observed a significant decrease in the error rate (with 1D and 2D reads, Fig. 1 ). Using this new release, homopolymers were more prone to base insertions (representing 63% of the errors observed in homopolymers).
MinION sequencing and reads filtering Read event data generated by MinKNOW control software (version 0. 50. 1. 15 to 0. 51. 1. 62) were base-called using the Metrichor software (version 2. 26. 1 to 2. 38. 3). The data generated (pores metrics, sequencing, and base-calling data) by MinION software were stored and organized using a Hierarchical Data Format. Three types of reads were obtained: template, complement, and two-directions (2D). The template and complement reads correspond to sequencing of the two DNA strands. Metrichor combines template and complement reads to produce a consensus (2D) sequence  [39]. FASTA reads were extracted from MinION Hierarchical Data Format files using poretools  [40]. To assess the quality of the MinION reads, we aligned reads against the S. cerevisiae S288C reference genome using the LAST aligner (version 588)  [41].
Illumina reads processing and quality filtering After the Illumina sequencing, an in-house quality control process was applied to the reads that passed the Illumina quality filters. The first step discards low-quality nucleotides (Q < 20) from both ends of the reads. Next, Illumina sequencing adapters and primer sequences were removed from the reads. Then, reads shorter than 30 nucleotides after trimming were discarded. These trimming and removal steps were achieved using in-house-designed software based on the FastX package  [42]. The last step identifies and discards read pairs that mapped to the phage phiX genome, using SOAP  [43]  and the phiX reference sequence (GenBank: NC˙001422. 1). This processing resulted in high-quality data and improvement of the subsequent analyses.
Genes and transposons detection To detect genes and transposons in the assemblies, we extracted the corresponding sequences from the reference genome. We then mapped these elements to the assemblies using the Last aligner. Only alignments that showed more than 80% identity over at least 90% of the sequence length were retained and considered as a match. We used a similar procedure to count the maximum number of genes in the Nanopore reads dataset; the only modification was that the percentage identity had to be at least 70% to account for the high error rate of the reads. To estimate the number of copies in the Illumina reads, we aligned paired-end reads to the reference genome with BWA aln and then computed the coverage using samtools mpileup algorithm  [44]  and divided the number we obtained for each region of interest by the median coverage of the corresponding chromosome.
Single-Crystal Studies Data were collected at 173(2) K on a Bruker Apex-II-CCD Diffractometer equipped with an Oxford Cryosystem liquid N2 device, using graphitemonochromated Mo-Kα (l= 0. 71073 Å) radiation. For all the structures, diffraction data were corrected for absorption. Structures were solved using SHELXS-97 and refined by full matrix least-squares on F2 using SHELXL-97. The hydrogen atoms were introduced at calculated positions and not refined (riding model). 21 They can be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/datarequest/cif. CCDC: 1538265-1538276.
Acknowledgements We thank the  University of Strasbourg , the C. N. R. S, the  International centre for Frontier Research in Chemistry (icFRC) , the  Labex CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02 , the  Institut Universitaire de France , the  Ministère de l'Enseignement Supérieur et de la Recherche  for financial support and the  Tunisian government  for a PhD fellowship to M. H. N.
The sequence is 2,332,206 bp long and has a GϩC content of 37. 5%. Annotation revealed 2,254 CDSs, 49 pseudogenes, and 67 RNA genes. Accession number(s). The draft genomes of S. gallolyticus subsp. gallolyticus NTS31301958 and NTS31307655 sequenced under this project have been deposited at DDBJ/EMBL/GenBank under the accession numbers MAMV00000000 and LXFC00000000, respectively. The versions described in this paper are MAMV01000000 and LXFC01000000.
Background: Quantitative indicators are needed in order to define priorities, plan policies and evaluate public health interventions in mental health. The aim of this study was to assess the contribution of a large and exhaustive French national administrative database to study and monitor treated depression by comparing the prevalence and characteristics of the population using significant healthcare resources for depression as identified by different estimation methods and sources and to discuss the advantages and drawbacks of these methods. Methods: This study included the French population covered by the main health insurance scheme in 2012 (Régime général, 86% of the insured French population). Data were extracted from the French health insurance claim database (SNIIRAM), which contains information on all reimbursements, including treatments and hospital stays in France.
The following distinct sources of the SNIIRAM were used to select persons with depression: diagnoses of long-term or costly conditions, data from national hospital claims and data concerning all national health insurance reimbursements for drugs. Results: In 2012, we included 58,753,200 individuals covered by the main health insurance scheme; 271,275 individuals had full coverage for depression; 179,470 individuals had been admitted to a psychiatric hospital and 66,595 individuals admitted to a general hospital with a diagnosis of depression during a 2-year timeframe and 144,670 individuals had more than three reimbursements for antidepressants during the study year (with a history of hospitalisation for depression during the past 5 years). Only 16% of individuals were selected by more than one source.
Background According to the World Health Organisation, 14% of the global burden of disease is attributed to mental health disorders  [1] , among which unipolar depression is the second leading cause of life years with disability worldwide. Depression per se is a major contributor to the global burden of disease  [2, 3]  and is associated with a high economic burden  [4, 5] , making it a major public health priority. Quantitative indicators are needed in order to define priorities, plan policies and evaluate public health interventions in mental health. Few studies have estimated the prevalence of depression in the general population in France, and they were based on medical interviews, symptom questionnaires, self-reported diagnoses, treatments, or full health insurance coverage with a diagnosis of depression in administrative databases  [6].
Furthermore, as studies conducted according to different methodologies can provide different perspectives on a particular issue, multiple sources of information may be complementary to more accurately study depression  [7]. Data about hospital healthcare use in psychiatry are now almost exhaustively available in France with the recent improved quality of the national hospital claim databases in psychiatry  [8] , in addition to other data sources from general hospitals or insurance claims. The contribution of a large and exhaustive national administrative database to study and monitor treated depression should be assessed, especially in terms of the potential contribution of combining information from different sources. Although such studies already exist in North America  [9] [10] [11] [12] [13] , their findings may not apply to the French setting with its specific healthcare system and information sources.
Data Data were extracted from the French health insurance claim database (SNIIRAM), which contains information on all reimbursements, including treatments, diagnostic and therapeutic procedures, and hospital stays in France for almost the entire population. The database is not public; analyses by CNAMTS were allowed by CNIL, the independent French administrative regulatory body whose mission is to ensure that data privacy law is applied to the collection, storage, and use of personal data  [16]. Diagnoses related to outpatient visits and results of procedures are not reported in the database  [17].
Selection of persons with depression The following distinct sources of the SNIIRAM were used to select persons with depression: Source 1: Diagnoses of long-term or costly conditions (Affections de Longue Durée, ALD). Patients with specific long-term or costly conditions may require full coverage for all their condition-related health expenditures upon request by their family doctor and after approval by a health insurance fund medical officer (médecin-conseil)  [18]. Source 2: Data from national hospital claims (Programme de Médicalisation des Systèmes d'Information, PMSI) for all inpatient and day-case admissions in public and private general and psychiatric hospitals, containing medical diagnoses defined as ICD-10 codes. In both general and psychiatric hospitals, a principal diagnosis is defined as the main reason for admission, while associated diagnoses provide information about conditions that significantly influenced care during the hospital stay  [19].
Source 3: Data concerning all national health insurance reimbursements for drugs, laboratory tests and outpatient medical procedures. Individuals receiving reimbursements for antidepressants (N06A section of the ATC classification except for oxitriptan) can be identified. However, these databases do not contain direct information about the diagnosis justifying the prescription, and these drugs are not specific for depression, as they can also be prescribed for other conditions (bipolar disorders, anxiety or chronic pain). An antidepressant prescription is typically valid 1 month. All three sources were not considered to be equally reliable for identifying patients with depression. Reliability of the sources was assessed as follows: for the purpose of identifying individuals suffering from depression, full coverage for depression as a specific long-term or costly condition (source 1) was more reliable than the hospital claims database (source 2), which was more reliable than reimbursement for antidepressants (source 3).
In the hospital claims database, associated diagnoses reported during general hospital stays were assumed to be less reliable than those reported during psychiatric hospital stays. The reasons underlying this classification of source reliability included (1) the mode of acquisition of the information (diagnoses resulting from medical interviews were regarded as more reliable than hospital diagnostic codes sometimes coded by non-medical staff, themselves regarded as a more reliable diagnostic markers than prescription drugs) and (  2 ) what was as stake when the information was coded (hospital diagnostic codes that had no consequence on costs were regarded as less reliable than codes influencing costs or giving access to benefits). These reasons are described and discussed more thoroughly in the Merits and drawbacks of the various methods section of the Discussion section of this article.
Main results Estimates of the prevalence of people using significant healthcare resources for depression in France vary according to the method used when analysing administrative databases, ranging from 0. 46 to 1. 65% from the most conservative to the broadest method, respectively, and from 0. 61 to 0. 93% when using intermediate methods. We propose an algorithm to study the population with significant healthcare use during the past 2 years that includes persons recently hospitalised for depression, or with a history of hospitalisation for depression and still taking antidepressants, or with full coverage for depression as a specific longterm or costly condition (method A, B and C). This algorithm yields a prevalence estimate of 0. 93% or 544,105 individuals in the major health insurance scheme.
Limitations and future directions As the databases used in this study only contain information on individuals with at least one reimbursement for medical care, this study was unable to detect the population with no contact with the healthcare system during the study period because of personal choices or problems of accessibility to health services. This drawback could constitute a major problem from an epidemiological or a need for care perspective. The proportion of the population without reimbursement in 2013 can be estimated to be around 5%, but varies according to gender, age and health status. It would therefore be difficult to estimate the impact of this population on the results of this study. On the other hand, insurance claim data are appropriate from the perspective of this study, as the primary objective was to select a population using significant healthcare resources related to a particular medical problem, and to assess the amount of resources allocated to this problem.
Monitoring of the population with a significant healthcare use is particularly important from a public health, healthcare management and policy planning perspective. Finally, the population covered by the major health insurance scheme (86% of the French population) may not be completely representative of the entire French population. In particular, farmers and self-employed workers are not included in this insurance scheme. The development of this algorithm is part of a larger work by CNAMTS to identify populations with various diseases in the SNIIRAM. Results from this research are analysed to produce a yearly report on spending per pathology and spending trends over time  [21] , or to study the national burden of more specific diseases  [31]. This data was also used to compare regional profiles of various pathologies in France overseas departments  [32] , and could be employed more widely at a regional level to better inform local health policies.
Conclusion Combined analysis of administrative databases is an alternative and complementary approach to medical interview-based epidemiological surveys  [9, 10] , which constitute the gold standard. Medical interview-based surveys can provide estimates that include individuals who had no contact with the healthcare system, who did not seek care for their disease, or for whom the contact cannot be identified as a marker in an administrative database. However, these studies are expensive, must be performed on small random samples and take a long time to be implemented and to provide results. Working on medical records and administrative databases has a low added cost and can rapidly yield results on the recent situation and on secular trends, as the data have already been collected for other reasons.
Acknowledgments This research was supported by  CNAMTS  and at the time of the study  Antoine Filipovic-Pierucci  was a public health medical resident of Paris hospitals ( Assistance Publique-Hôpitaux de Paris ). We thank  Catherine Quantin  for giving us early access to her report on depressive disorders in the SNIIRAM,  Hervé Maisonneave  and  Bruno Falissard  for their advice for submitting this article,  Chloé Le Cossec  and  Ayden Tadjahmady  for their comments and suggestions, and  Sébastien Rivière  for the statistics in Table 2.
µL -1 (±17, n = 43). After amplification, equimolar amounts of all PCR products were pooled and cleaned up using calibrated Ampure XP beads by an external laboratory (MR DNA, Shallowater, USA) and sequenced on a single run of Illumina platform using the 2 × 250 bp MiSeq chemistry. To check biases induced by the two different PCR protocols, we amplified 2 water DNA samples using both PCR kits and compared them after sequencing. They showed similar community structure (see S1). The nucleotide sequence data is available in the NCBI SRA database under the biosample numbers SAMN07278850-SAMN07278894. Sequence processing and phylogenetic analyses. Assembly of paired reads was performed by the sequencing platform. All subsequent steps of sequence processing were performed following the SOP of Kozich et al.
for MiSeq. 21 , https://www. mothur. org/wiki/MiSeq_SOP, 2016) using Mothur  22. After removing sequences with an irregular length (i. e. outside a range of 420-460 pb), sequences were aligned along the SILVA reference database  23  (release 123). Unaligned sequences were removed from the final alignment during this process. Chimeras were removed using UCHIME  24. Filtered sequences were then classified using the SILVA reference taxonomy and the non-bacterial reads were removed. After these steps, we obtained a total of 2,198,758 sequences from our 43 samples, with 51,133 ± 20,883 (expressed as Mean ± SD) sequences per sample. The number of sequences read for each sample is unlikely correlated with total abundance of bacteria in sample, while it could bias assessments of microbial biodiversity.
Therefore, to ensure that further diversity assessments were not biased by the uneven sequencing efficiency among samples, 10,000 sequences were sub-sampled within each sample (Supplementary Information S2). Non-parametric Chao's coverage estimator was computed in each community to assess effect of subsampling level using "Coverage" function provided in entropart R-package  25. This index averaged 0. 98 ± 0. 008 among microbial communities testifying for the accuracy of further diversity analyzes. Sequences were then clustered into OTUs with 99% sequence identity, and the dominant sequence for each OTU was selected as reference and aligned against the SILVA reference database using Mothur for subsequent phylogenetic tree reconstruction. An outgroup was defined using a set of archaeal sequences obtained from SILVA database and re-aligned against the previous alignment of reference sequences using the MAFFT v7 with -add option  26  before tree reconstruction using Fasttree  27.