dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Karin Aistleitner  for isolating Acanthamoeba sp. strain ML,  Julia Walochnik  for providing Acanthamoeba sp. strain 2HH, and the  Austrian Agency for Health and Food Safety (AGES)  together with  Allen Tsao  for isolating and providing the two environmental L. pneumophila strains. Assistance in the laboratory by  Gabriele Schwammel  was greatly appreciated. We also thank the team at the  Vienna Biocenter Core Facilities (VBCF)  for sequencing. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  660280  (C. W. ) and the  Austrian Science Fund  (project number  I1628-B22 ) in the context of the  InfectERA  project  Eugenpath. Work in the C. B. laboratory was financed by grant  ANR-10-LABX-62-IBEID  and  Infect-ERA  project  EUGENPATH  ( ANR-13-IFEC-0003-02 ).
Data availability. Acanthamoeba sp. strain ML partial 18S rRNA gene sequence was deposited at GenBank and is accessible through accession number MH675534. RNA-Seq data are available at the Gene Expression Omnibus (GEO) database and are accessible through accession number GSE125876 (https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?accϭGSE125876).
Background: Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1. 0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2. 0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances. Results: Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes.
Benchmarking The 34 methods predicted a total of 119,277 peptides (i. e. , about 3508 per method and 1590 per Ag), using the 75 protein Ags of the dataset (Additional file 1: Table  S1 ). The Se and the PPV parameters were used to evaluate the methods' performance (Fig. 1 ). The Se evaluates the correctly predicted aas compared to the epitope: this is the proportion of peptide residues present also in the epitope. The PPV evaluates the correctly predicted aas compared to the predicted aas: this is the proportion of epitope residues in the predicted peptide. A perfectly accurate prediction would give Se and PPV values of 1. The mean Se ranged from 0. 34 to 0. 49, according to the method, and the mean PPV was a little higher (between 0. 39 and 0. 57) (Additional file 2: Figure  S4 ). In the evaluation of discontinuous epitope prediction tools carried out by Ponomarenko & Bourne using the same dataset, the mean Se and PPV with the best method (ClusPro (DOT)) were 0.
Comparison with SUPERFICIAL We then compared our results with those obtained using SUPERFICIAL (Sfs), the only other available peptide design tool. This software predicted 143 discontinuous peptides from 30 Ags of the dataset, including 21 peptides from only one Ag. The proportion of peptides generated by SUPERFICIAL with Se and PPV values higher than 0. 7 was about 0. 7% (Fig. 4 ), a value similar to the one obtained with the lowest performing PEPOP methods. Moreover, SUPERFICIAL did generate peptides with Se and PPV values higher than 0. 7 only for one Ag among the 75 proteins of the dataset (Fig. 5 ). Finally, SUPERFICIAL did not predict any peptide with Se and PPV higher than 0. 7 when the aa positions were taken into account (Fig. 4 ). In conclusion, all PEPOP methods performed better than the only other available peptide design tool.
All PEPOP methods, except OPPala, were more successful than SUPERFICIAL and more efficient than or as much as the method predicting peptides by chance. Benchmarking of different computational methods must be done with precaution as the tools, datasets and metrics can be different from one analysis to the other, thus not allowing objective comparisons. Even the definition of epitope can be different. Indeed, some consider the part of the Ag recognized by one Ab as an epitope on its own, whereas others consider to be an entire epitope all the aa found to interact with any Ab  [16]. Moreover, some authors think that proteins have only one or few epitopes on the surface  [40] , whereas others see a protein as a mosaic of epitopes  [26]. Finally, because all the possible epitopes could not be discovered, only a few of the features that characterize epitopes are used when trying to discriminate between epitopic and non-epitopic aa.
Thus, the specific epitopes of a given Ag could be identified by running all PEPOP methods, synthesizing the generated peptides and testing them in microarrays. We showed that 1. 73% of the predicted peptides have a Se and a PPV above 0. 7 which represent about 28 peptides. This is a low number but only one peptide is suffisiant to localize an epitope using SPOT method for example (Duarte C et al. , A mimic of a discontinuous epitope from AaH II identified by combining wet and dry experiments: a new experimental methodology to localize discontinuous epitopes, in preparation). Conversely, the experimental validation of about 120,000 peptides (number of peptides designed by the PEPOP methods for the entire dataset) would require too much time and resources and probably would not be feasible for any future peptide design tool.
Structural data The dataset of 165 X-ray-determined epitopes was from Ponomarenko & Bourne  [25]. To avoid bias caused by the over-or under-representation of an epitope described by several 3D structures of the same Ag-Ab complex, epitope redundancy was eliminated by keeping only one crystallography of a given Ag-Ab complex. Therefore, 90 Ag-Ab complexes were rejected. The final dataset was of 75 unique Ag-Ab complexes (Additional file 1: Table  S1 ). The epitope size varied from 4 to 23 residues with only one exception (52 aa). The average size of an epitope was 16. 7 aa (median: 17 aa). Epitopes were all discontinuous and were composed of 3 to 14 segments, each containing 1 to 12 contiguous aa. An epitope contained on average 7 to 8 segments of 2. 38 aa. These data are in accordance with the literature  [54, 59, 60].
PEPOP methods Peptides that mimic the discontinuous epitopes of the dataset were designed using the different PEPOP methods (Fig. 8 ). To build a peptide, the PEPOP algorithm concatenates either segment sequences (a continuous stretch of surfaceaccessible aa) or single surface-accessible aa from the Ag 3D structure. Based on Euclidian distances, the PEPOP methods first select the neighboring segments or aa and then determine in which order assemble them to form the final linear peptide sequence supposed to mimic the discontinuous epitope. In three-dimensional space, the Euclidian distance between points a and b is: d a; b ð Þ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a 1 -b 1 ð Þ 2 þ a 2 -b 2 ð Þ 2 þ a 3 -b 3 ð Þ 2 q PEPOP 2.
Definition of cluster and patch In PEPOP, clusters are segments grouped according to their spatial distances. They are calculated using Kitsch from the PHYLIP package v3. 67  [62] , as previously described  [27]. PEPOP uses three types of patches. The 10 Å-and 15 Å-radius patches gather segments within a fixed distance, respectively 10 Å and 15 Å, from the center of gravity of a reference segment. The third patch type gathers the aa at a distance that varies from 15 to 20 Å from a reference aa: the final radius is the one in which the average number of aa between radius 15, 16, 17, 18, 19 and 20 Å is collected. As each segment is used in turn to define a patch, the number of 10 Å-and 15 Å-radius patches is equal to the number of segments (Additional file 2: Figure  S3 ). The number of varying patches is equal to the number of accessible aa because each aa is used in turn to define a varying patch.
Performance evaluation metrics The capacity of each peptide generated by a given PEPOP method to mimic the epitope described in the reference dataset for that protein was evaluated using two criteria: the sensitivity (Se) and the positive predictive value (PPV). Figure  1  gives the definition of Se and PPV and an example of their calculations without and by taking into account the aa positions. Se represents the proportion of epitope aa present in the peptide, whereas PPV is the proportion of peptide aa present in the epitope. The aa nature or the aa position in the peptide and reference epitope was then compared by not taking and by taking into account the aa positions of the protein. The aa used in linker methods (ALA, SA and SAS) were considered in the evaluation that takes into account the aa positions only after all the other aa of the peptide were compared with the epitopic aa.
Funding This work was supported by grants from the  Ministry of Research (France) ;  University Paris Diderot ,  Sorbonne Paris Cité (France) ; the  National Institute for Blood Transfusion (INTS, France) ; the  Institute for Health and Medical Research (INSERM, France) ; and a  Labex GR-Ex grant (France)  to  Alexandre de Brevern. The  Labex GR-Ex , reference  ANR-11-LABX-0051 , is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. The funding body had no role in the design and collection of the study, experiments, analyses and interpretations of data, and in writing the manuscript.
Data collection Using a standardized questionnaire, subjects were interviewed face-to-face by trained interviewers to collect information on sociodemographic characteristics, anthropometric measurements, personal and familial history of cancer, lifetime tobacco and alcohol consumption, and entire occupational history. An occupational history questionnaire was designed with the collaboration of industrial hygienists. It included general questions on occupational history. For each job held for at least 1 month, the subject was asked for the job title, duration of employment, activity sector, main and subsidiary tasks, and the description of the work environment. Specific questionnaires (covering 20 job titles, including work in the leather industry) were also designed with more technical questioning.
Although occupations and industries were self-reported, it is unlikely that differential reporting bias occurred because occupational exposure to leather dust is not a widely known risk factor for HNC. Data were collected by trained interviewers using standardized questionnaires, which limited eventual differential misclassification bias. Coding occupation and industry is difficult, and often not reproducible. Therefore, coders were well-trained and blinded to case-control status. If coding errors occurred, they were probably not differential between cases and controls. Residual confounding may not be excluded. Special attention was paid to adjustment for tobacco and alcohol consumption, and SES. Additional adjustments for body mass index, education level, exposure to asbestos and family history of HNC in first-degree relatives were made, but they did not substantially change the results.
Acknowledgements The authors thank all members of the MatGéné working group from Santé  Publique France  for providing the job-exposure matrix of leather work. Members of  ICARE Study Group: Anne-Valérie Guizard (Registre des cancers du Calvados, France );  Arlette Danzon ,  Anne-Sophie Woronoff  ( Registre des cancers du Doubs, France );  Michel Velten  ( Registre des cancers du Bas-Rhin, France );  Antoine Buemi ,  Émilie Marrer  ( Registre des cancers du Haut-Rhin, France );  Brigitte Trétarre  ( Registre des cancers de l'Hérault, France ); Marc.
Calcein and PI labeling Cells were washed once in PBS and resuspended in 1 ml of PBS containing 2 µl of calcein (LIVE/DEAD ® Viability/Cytotoxicity Kit for mammalian cells; Molecular Probes, Eugene, OR, USA) diluted 1/80 in DMSO and 5 µl of PI at 0. 5 mg/ml. The mixed sample was then incubated for 15-20 min at room temperature and protected from light. The cells were analyzed by flow cytometry using an excitation wavelength of 488 nm and measuring green fluorescence emission for calcein (530/30 nm bandpass) and red fluorescence emission for PI (610/20 nm bandpass) on a BD LSRFortessa ™ cell analyzer (BD, Le Pont de Claix, France). Data were exported and analyzed with Flowjo software for evaluation of the percentage of calcein-and PI-positive cells.
Acknowledgments We want to thank  Eva Gluenz  ( Oxford University ) for the Cas9/T7 L. major expressing strain and for the pT plasmids for CRISPR/Cas9 constructions, as well as  Patrick Bastien  from  Montpellier University , for the pTH6nGFPc expression vector, and the  CNR Leishmania (Montpellier, France)  for the L. major strain.
Gas chromatography with FID associated to RI Analyses were carried out using a Clarus 500 Perkin Elmer (Perkin Elmer, Courtaboeuf, France) system equipped with a FID and two fused-silica capillary columns (50m x 0. 22 mm, film thickness 0. 25 m), BP-1 (polydimethylsiloxane) and BP-20 (polyethylene glycol). The oven temperature was programmed from 60 °C to 220 °C at 2 °C/min and then held isothermal at 220 °C for 20 min; injector temperature: 250 °C; detector temperature: 250 °C; carrier gas: helium (0. 8 mL/min); split: 1/60; injected volume: 0. 5 mL. The relative proportions of the oil constituents were expressed as percentages obtained by peak-area normalization, without using correcting factors. Retention indices (RI) were determined relative to the retention times of a series of n-alkanes with linear interpolation (Target Compounds software from Perkin Elmer).
Nuclear magnetic resonance Essential oil and fractions nuclear magnetic resonance (NMR) spectra were recorded on a Bruker AVANCE 400 Fourier Transform spectrometer operating at 400. 132 MHz for 1 H and 100. 63 MHz for 13 C, equipped with a 5 mm probe, in CDCl 3 , with all shifts referred to internal TMS. The 1 H NMR spectra were recorded with the following parameters: pulse width (PW), 4. 3 μs; relaxation delay 1 s and acquisition time 2. 6 s for 32 K data table with a spectral width (SW) of 6000 Hz. 13 C NMR spectra of the oil samples were recorded with the following parameters: pulse width = 4 µs (flip angle 45°); acquisition time = 2. 7 s for 128K data table with a spectral width of 25 000 Hz (250 ppm); CPD mode decoupling; digital resolution = 0. 183 Hz/pt. Standard pulse sequences from Bruker library were used for two-dimensional spectra.
Acknowledgements This work was supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 690853. The computational studies work was performed using HPC resources from  GENCI-[TGCC/CINES/IDRIS] (Grant 2015-[c2016077586 ]) and the  Centre de calcul régional Romeo. We would like to address special acknowledgments to the  Mésocentre de calcul de Franche-Comté  for their important support in this work.
02-0. 07 Hz) and low frequency (LF: 0. 07-0. 20 Hz) bands, as a measure of the transmission of blood pressure fluctuations on CBFV  (Zhang et al. , 1998 (Zhang et al. , , 2009)). The mechanism of CA has the characteristics of a high-pass filter that dampens slow fluctuations of blood pressure  (Zhang et al. , 1998). The transfer function coherence  (Giller, 1990; Donnelly et al. , 2016) , gain  (Hamner et al. , 2010; Donnelly et al. , 2016) , and phase  (Immink et al. , 2004; Donnelly et al. , 2016)  can provide valuable information on dynamic CA. Indeed, reduced coherence and gain associated with an elevated phase shift reflect an enhanced CA and only one of these modifications is sufficient to determine a better CA. These signals were processed with the NOTOCORD hem software (Notocord Systems, France).
Implementation We have implemented the algorithms of this paper in ore_algebra [KJJ15, Mez16], a library for working with Ore polynomials in the SageMath (Sage) computer algebra system. Among the features of Sage and the many external libraries on which it is based, our code relies in an essential way on Arb  [Joh17] , which provides all necessary basic operations on intervals and truncated power series with interval coefficients. The implementation also makes use of PARI for complex root finding, and of ore_algebra itself for basic arithmetic with differential and recurrence operators. The operations on rational numbers and exact polynomials mainly come from GMP/MPIR, Flint and Singular. The ore_algebra package is available from https://github. com/mkauers/ore_ algebra/ under the GNU General Public License (version 2 or later).
The code TOME 2 (2019) for computing error bounds can be found in the file src/ore_algebra/analytic/ bounds. py of the source tree. We performed the experiments described below using the git revision dd88e8d5 under Sage 8. 2. Except when otherwise noted, we run the full version of the algorithm described in this paper, that is, the combination of Algorithms 6. 1, 6. 11, 7. 4, and 8. 1, including their optional steps, and using PARI's root finder at step 2 of Algorithm 6. 1. Numerical coefficients are represented by real or complex intervals almost everywhere, with a fixed precision of 53 bits. In examples involving generalized series expansions at singularities, the displayed truncation error and error bound correspond to the complete logarithmic series, including the non-analytic factors.
Having to perform many small steps is nevertheless sub-optimal, which raises the question of adapting the algorithm to handle apparent singularities in a way that allows for larger step sizes. We can also observe that overestimation becomes significant for the larger of these operators. Further increasing the tuning parameter does not seem to help. Other methods may hence be needed for the really efficient numerical solution of equations with coefficients of very large degree at comparatively low precision. It would be hard to draw any conclusion from a detailed analysis of the running time of our Python implementation. Still, anecdotal data confirms that the bounds of this paper are usable in practice as part of symbolic-numeric algorithms. For example, the simple variable-order interval Taylor method implemented in ore_algebra can compute an enclosure with radius less than 10 -60 of the value at z = 1 of the lattice Green function for d = 4 in less than a second on a modest laptop.
A closely related subject, the collection of data on ethnicity can be essential to study demographic tendencies, employment practices and opportunities, income distributions, educational levels, migration patterns and trends, family composition and structure, social support networks, health conditions of a population and optimal treatment and preventive measures  [10] [11] [12]. Data on ethnicity are collected using a wide range of different methodologies and often rely on self-reporting, which makes standardization difficult  10, 13. The collection of data on ethnicity is also sensitive and the decision to collect and disseminate information on ethnic or national groups of a population has to be based on a number of considerations and national circumstances  10, 13. Additionally, from a forensics viewpoint, the availability of a comprehensive database of the genetic distribution of populations worldwide would be highly beneficial.
The accumulated information about the mtDNA haplogroup composition of ancient and current communities transformed genetic ancestry testing from an abstruse academic quest to a popular and common practice among the public. However, companies providing direct-to-consumer (DTC) ancestry tests have been criticised  30  for supplying misleading information to their customers that can deeply affect their personal identities. One of the disadvantages cited is the limited amount of reference samples available in databases upon which inference of geographic ancestry is based. This aspect could be greatly improved by the global and up-to-date monitoring of the mitochondrial DNA composition of populations at different geographic locations. In this pilot study, we aimed to identify traces of human mitochondrial DNA in the global sewage dataset and determine the local mtDNA haplogroup composition of the sewage catchment area.
Our results also highlight the future possibility of monitoring demographic effects (such as global migration or the segregation of local communities) in the population in-time, as wastewater collection can be accomplished without the need for lengthy preparations and high cost investments and thus can be repeated as required. It should be emphasized that the data analysed for this study was collected for the purpose of studying the global distribution and abundance of antimicrobial resistance genes and not human genetics. Thus, potential future studies on human populations based on sewage should take into consideration the specific features of the sewage catchment area; among others their exact geographic location in the individual cities and countries, to make sure that a representative sampling of the local population is achieved by wastewater collection.
Identification of human reads. Short reads were aligned to the reference genome with the BWA-MEM  58  algorithm. The human mitochondrial revised Cambridge Reference Sequence  59  (NCBI ID: NC_012920. 1) was used as a reference genome and the default settings were used to conduct the alignment. To lower the risk of misinterpreting the results, and to verify that the reads mapped to the human mtDNA were indeed derived from humans, we also performed an alignment to several vertebrate species (Bos taurus, Sus scrofa, Danio rerio, Canis lupus familiaris, Gallus gallus, Ovis aries; data not shown) including the Norway rat (Rattus norvegicus) mitochondrion sequence (NCBI ID: NC_001665. 2) (see Fig. 2b ). Alignment results were validated for the presence of PCR duplicates with the samtools software tool  60  , but none were detected, thus eliminating the need for duplicate removal.
The data for the analyzed 44 samples was condensed into a matrix of shape (44, 4•16,569) using one hot encoding. This was achieved by assigning a value of 1 to the most dominant base in each genomic position for each sample, and a value of 0 to all the other bases. PCA was performed using scikit-learn 61 python package. t-distributed stochastic neighbor embedding. Using the above described one hot encoded matrix, a t-distributed Stochastic Neighbor Embedding (t-SNE)  34  pipeline was also run on the data with the scikit-learn  61  python module. As suggested by the manual, initial dimension reduction was achieved by selecting the top 50 most dominant PCA components of the originally 4•16,569-dimension space and t-SNE was performed in a subsequent step. phylogenetic tree construction. To obtain a general idea about how the samples might relate to each other, we performed a simple phylogenetical analysis.
As a first step, consensus sequences were generated for each sample with the help of bcftools and vcfutils. These are more refined than the above described method of simply choosing the most common base at each genomic position in the sense, that if multiple bases were found at a given position, it is also possible to assign the somewhat ambiguous "pyrimidine" or "purine" values to these sites. These sequences were then multiple aligned with the ClustalW algorithm implemented in Biopython  62. Phylogenetic trees were constructed with two different methods (neighbor joining  63  and maximum parsimony  64  ) using the Phylo module of Biopython. The robustness of the trees was accessed as the proportion of the 1000 phylogenetic trees created with bootstrapping that agreed with the topology of the original tree for each clade separately.