dataesr/scientific-paragraphs-categorization huggingface dataset:
The protocol was carried out using the automated ABI Prism 3730xl DNA sequencer in combination with the Big Dye Terminator Cycle Sequencing Ready Reaction kit 3. 1 (Applied Biosystems). PCR conditions and primers sequences are available on request. Samples from the FREX project have been sequenced using the Agilent V5 + UTR exome capture kit and genotyped on Illumina Core Exome SNP-chip. Full details of data processing, variant calling, filtering process and variant annotation in ExAC have been previously described  34. GnomAD was quality controlled and analyzed using the Hail open source framework (https://github. com/hail-is/hail). This data set can be accessed via the gnomAD Browser (http://gnomad. broadinstitute. org/). Conservation of amino acids across species. CD36 protein sequences were obtained from the UniProtKb database (https://www.
uniprot. org/help/uniprotkb). The Basic Local Alignment Search Tool (BLAST) was used to search for protein sequences with the human CD36 amino acid sequence (472 amino acids) as the input  35. Of the 250 hits found in the database, removal of redundant entries left 131 species. The sequences were imported in JalView for visualization  36. MUSCLE alignment was used with default settings for multiple sequence alignment with the human CD36 protein sequence set as the reference  37. Conservation scores for each column were calculated based on a method described by Livingstone et al. 26. Conservation is measured as a numerical index reflecting the conservation of physico-chemical properties in the alignment. This score is measured as a numerical value ranging from 1 to 11. A score of 11 indicates no amino acid substitutions and perfect conservation amongst the 131 species.
The next highest score of 10 indicates those columns with amino acid variation that preserves the physico-chemical properties  26. Scores below 10 indicate the absence of conservation of the amino acid across species. in silico analysis. The p. Pro191Leu and p. Ala252Val mutations are listed as rs143150225 and rs147624636 in the National Center for Biotechnology Information database (NCBI) dbSNP 147. The CD36 amino acid sequence was obtained from the UniProtKB database in FASTA format. The functional consequences of the CD36 p. Pro-191Leu and p. Ala252Val mutations was predicted using the default setting of the in silico analysis tools SIFT  38  , PolyPhen2  39  , FATHMM  40  , MutPred2 41 , DANN  42  , and REVEL  43. In silico prediction tools use neural network (SIFT, MutPred2), deep neural network (DANN), naïve Bayes classifier (PolyPhen2), hidden Markov models with support vector machine classifier (FATHMM), and random forests (REVEL) machine learning methods.
Finally, the cells were washed and bound ligand was measured by analysis of BODIPY-fluorescence using a Synergy HT plate reader (Bio-Tek). Binding data for each biological replicate was normalised internally versus a blank for each transfection (0 μg/ml acLDL), then averaged and compared to the untransfected control to show the level of background binding. Data points were best-fitted in Graphpad Prism v4. 0 using Langmuir adsorption isotherm equation for one-site binding: = * + B Bmax X/Kd X Where B is bound ligand (relative fluorescence units), Bmax is maximal binding (relative fluorescence units), [X] is concentration of ligand (μg/ml), Kd is the concentration of ligand (μg/ml) giving half-maximal binding and is a measure of the affinity of the receptor-ligand interaction.
Statistical analysis. In vitro data was expressed as mean values. Differences between groups were compared with the use of an unpaired Student's t-test using the Graphpad prism v7 software. Yates' chi-square tests were used to compare the allele frequencies of the p. Pro191Leu and p. Ala252Val mutations between groups. All reported P-values are two-sided. P-values of less than 0. 05 were considered significant.
Acknowledgements We are indebted to all participants of this study. We would like to thank the Genome Aggregation Database (gnomAD) and the groups that provided exome and genome variant data to this resource. A full list of contributing groups can be found at http://gnomad. broadinstitute. org/about. We would like to thank the  French Exome (FREX)  project and the groups that provided exome variant data to this resource. We thank the "Agence.
Gels were minimally stained with Coomassie Brilliant Blue (Thermo Fisher Scientific). The lane was subsequently cut into 4 pieces and subjected to in-solution digestion with 100 ng of trypsin overnight, extracted twice with 100% acetonitrile, and dried in a Savant Speed-Vac (Thermo Fisher Scientific). Peptides were then dissolved in 5% methanol and loaded onto a BioBasic C18 column (Thermo Fisher Scientific). Thermo-Finnigan LC/LC-ESI-LTQ (Thermo Fisher Scientific) was run in a data-dependent mode in which each sample was eluted in a 35-min, 0-80% acetonitrile gradient, and each full mass scan was followed by 15 tandem mass spectrometry scans of most abundant ions. Spectral data were then searched against target decoy C. elegans refSeq database [National Center for Biotechnology Information (NCBI), released September 2016, containing 28,105 entries] in Proteome Discoverer 1.
4 interface (Thermo Fisher Scientific) with Mascot algorithm (Mascot 2. 4; Matrix Science, London, United Kingdom). Assigned peptides were filtered with a 1% false discovery rate and subject to manual verifications. Variable modification of oxidation of methionine and protein N-terminal acetylation was allowed. The precursor mass tolerance was confined within 4 Da with a fragment mass tolerance of 0. 5 Da and a maximum of 2 missed cleavages allowed. The mass spectrometry data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the Mass Spectrometry Interactive Virtual Environment repository (MassIVE; http://massive. ucsd. edu; MSV MSV000081868) with the dataset identifier PXD008559.
To quantify phagocytosis, the number of internalized RBC per cell was counted in 50 cells randomly chosen on the coverslips (phagosomes are identified by combination of phase contrast and fluorescent images) corresponding to the phagocytic index. To quantify association, the number of external + internal RBC per cell was counted in 50 cells randomly chosen on the coverslips corresponding to the association index. The indexes obtained were divided by the index obtained for control cells and expressed as a percentage of control cells. Quantification of F-actin or other proteins' recruitment in the phagocytic cups was performed as previously described by Braun et al. (25). Briefly, quantification was performed on ImageJ 64-bit software (NIH) on a selected region in 1 section of a 16-bit stack.
We found that DYN-1/Dynamin and NDK-1/NDPK co-immunoprecipitated in worms transgenic for NDK-1::GFP using a monoclonal anti-GFP antibody and probed with a highly specific polyclonal anti-DYN-1 antibody (21) (Fig. 1A ). Furthermore, we performed an IP-mass spectrometry experiment in wild-type, mixed-stage worm extracts using the above-mentioned anti-DYN-1 antibody. A partial list of the proteins identified in the DYN-1 complex is presented in Table  1. The full mass spectrometry data are deposited in the ProteomeXchange Consortium. Dynamin was first identified as a microtubule-interacting protein  (26). The presence of DYN-1, the original bait, and 3 tubulins (tubulins 1, 2, and 4), which are C. elegans microtubule components, validated that our experiments were properly conducted. In the same assay, we have also identified NDK-1 as a specific member of the DYN-1complex but not of the control complex.
ACKNOWLEDGMENTS This work was supported by the  Hungarian Scientific Research Fund (OTKA)  Grants  K115587  (to K. T. -V. ),  K112112  (to T. I. O. ), and  K124244  (to Z. S. ); the  GINOP-2. 3. 2-15-2016-00006Project  (cofinanced by the  European Union  and the  European Regional Development Fund)  (to Z. S. ); and the  ÚNKP-17-3 New National Excellence Program  of the  Ministry of Human Capacities  grant (to Z. F. ). This work was completed in the  ELTE Institutional Excellence Program  ( 1783-3/2018/FEKUTSRAT ) supported by the  Hungarian Ministry of Human Capacities. Work in the F. N. laboratory was supported by  Centre National de la Recherche Scientifique (CNRS) ,  INSERM ,  Université Paris Descartes , and the  Agence Nationale de la Recherche  ( ANR 16-CE13-0007-01 ). The work of the Z.
The East African Indian (EAI) and Central Asian (CAS) lineages of Mycobacterium tuberculosis complex (MTBC) mainly infect tuberculosis (TB) patients in the eastern hemisphere which contains many of the 22 high TB burden countries including China and India. We investigated if phylogeographical, epidemiological and demographical characteristics for these 2 lineages differed in SITVIT2 database. Genotyping results and associated data (age, sex, HIV serology, drug resistance) on EAI and CAS lineages (n = 10,974 strains) were extracted. Phylogenetic and Bayesian, and other statistical analyses were used to compare isolates. The male/female sex ratio was 907/433 (2. 09) for the EAI group vs. 881/544 (1. 62) for CAS (p-value<0. 002). The proportion of younger patients aged 0-20 yrs. with CAS lineage was significantly higher than for EAI lineage (18.
The changing face of TB epidemiology due to increased migration to escape wars, conflicts and poverty (as well as travel for leisure), should be ideally addressed using a multidisciplinary approach to address this worldwide scourge. Knowing that Mycobacterium tuberculosis and humans have coevolved for thousands of years  [2] , such an objective can only be achieved using modern tools of molecular epidemiology in conjunction with international databases and web tools able to extract patient demographic and clinical data from longitudinal studies, allowing to identify and highlight the various ways of ongoing TB transmission and a better understanding of the disease. Despite known limitations  [3] , classical genotyping methods such as CRISPR-based spoligotyping or MIRU-VNTRs are still helpful to confidently identify TB strains.
Other methods such as IS6110-RFLP, Large Sequence Polymorphisms (LSP), single nucleotide polymorphisms (SNPs), and more recent Whole Genome Sequencing (WGS) are also important tools for identification of circulating MTBC clones  [4] [5] [6] [7] [8] [9]. Establishment of databases and web tools such as MIRU-VNTRplus  [10] , SITVIT WEB  [11]  or SITVIT2  [12] , fill the needful among tools already available for epidemiological surveillance of TB. Using phylogenetic and Bayesian population structure analyses  [13] [14] [15] [16] , recent studies could reveal geographical specificities of several MTBC lineages/sublineages such as Latin American and Mediterranean (LAM), T and Beijing; helping to understand prevailing TB epidemiology as well as to decipher the history and characteristics of disease propagation.
In the present study, based on the recently released SITVIT2 database  [12] , we describe the overall distribution and epidemiological features of spoligotyping-based East-African-Indian (EAI-also known as Lineage 1 or the LSP-based Indo-Oceanic lineage), and Central Asian (CAS-also known as LSP-based Lineage 3) lineages, which are predominantly found in Eastern Africa, South-East Asia, Indian subcontinent, and Western Asia  [17]. Noting confusion in the literature on the use of terms EAI and CAS per LSP-based nomenclature  [7] , the readers are reminded that we hereby use these terms as spoligotyping-based signatures defined in the SITVIT2 database  [12]. EAI and CAS lineages have been subdivided into several sublineages, and recent studies have highlighted potential existence of several geo-specific sublineages  [18] [19].
Database comparison and worldwide geographical distribution Genotyping data were collected from the SITVIT2 proprietary database of the Institut Pasteur de la Guadeloupe  [12]  which is an updated version of the previously released SITVITWEB  [11]  database. Spoligotyping data on EAI and CAS lineage strains (n = 10,974 strains corresponding to 96 countries of patient origin) were extracted. We also retrieved associated MIR-U-VNTR genotyping data available for 1650 isolates. Two formats of MIRU-VNTRs were analyzed: (i) the 12-loci format corresponded to MIRU loci in the order:  MIRU 2, 4, 10, 16, 20, 23, 24, 26, 27, 31, 39 and 40;  (ii) the 15-loci format corresponded to MIRU loci in the order: MIRU 4, 10, 16, 26, 31, and 40, ETRA, ETRC, QUB-11b, QUB-26, QUB-4156, Mtub04, Mtub21, Mtub30, and Mtub39.
In this database, Spoligotype International Type (SIT) and a 12 or 15-MIRU International Type (12MIT or 15MIT) designate patterns shared by two or more isolates, whereas "orphan" designates patterns reported for a single isolate. Most of the analysis made use of 12-loci MIRU-VNTRs since data was more widely available. Because of the lack of 24-loci MIRU-VNTR information in this study, (around 100 isolates available in only two or three countries), 24-loci MIRU-VNTR analyses were excluded from the analyses. Further analyses using this marker should be performed in the future. Worldwide distribution analyses were based on country (2 letter country codes according to http://en. wikipedia. org/wiki/ISO_3166-1_alpha-2), and macro-geographical (United Nations subregions, list available at: http://unstats.
Phylogenetical, Bayesian and statistical analyses MLVA Compare software version 1. 03 (Ridom and Genoscreen; http://www. ridom. de/) and BioNumerics software version 6. 6 (Applied Maths, Sint-Martens-Latem, Belgium; http://www. applied-maths. com/bionumerics) were used to draw minimum spanning trees (MSTs) based on spoligotypes and MIRU-VNTRs patterns, in order to visualize evolutionary relationships between clinical isolates. SpolTools software (available through http://spoltools. emi. unsw. edu. au/) was used to draw Spoligoforest trees based on the Fruchterman-Reingold algorithm  [21] [22]. Contrary to the MSTs, Spoligoforest are directed and not necessarily connected graphs, which permit to highlight the evolutionary relationships between ascendant and descendant spoligotype patterns. Bayesian population analyses was performed on 12-loci MIRU-VNTR markers using STRUCTURE version 2.
3 software  [23]  using admixture model and computation of posterior estimates for the parameters of interest based on Markov chain Monte Carlo (MCMC) algorithm. This analysis was run in 10 parallel MCMC for K populations ranging from 3 to 10 for EAI dataset and 1 to 6 for CAS dataset, with a burn-in of 100000 iterations and a run length of 10 6 iterations. The most probable number of K of population was estimated by the delta K calculated by the Evanno method  [24]  in the program STRUCTURE HAR-VESTER]  [25]. Q-matrix medians were then calculated from 10 replicates for K = 6 for EAI dataset and K = 3 for CAS dataset, by using the Greedy algorithm implemented in CLUMPP 1. 1. 2 software  [26]. Finally, MST analyses were then run using BioNumerics software on both EAI and CAS dataset with sublineages identified by STRUCTURE using a cutoff of 0.
Global distribution maps Most cases of EAI and CAS lineages were concentrated in the Eastern hemisphere. EAI lineage strains were more common in East African, South and South-Eastern Asian countries, whereas CAS lineage strains were predominantly found in South and Western Asian countries, as well as in North-and East-African countries. Dissemination of EAI and CAS lineages in North America and Europe may have potentially occurred of late due to recent migration waves. Geographical maps were constructed to provide with an overview on the worldwide distribution of these two lineages in line with the data entered in the SITVIT2 database (Figs  1  and 2 ). Regarding the distribution of EAI lineage stains, one can notice that: i. In countries boarding Indian and Pacific oceans, the highest proportion of EAI was found in the Philippines (81.
and this spoligotyping pattern was found exclusively in Trinidad and Tobago. The MST based on spoligotyping data (Fig 5 ) showed a significant split between genotypes belonging to CAS and EAI lineages. Spoligotypes of EAI group were mostly located on the top of the tree, whereas spoligotypes belonging to CAS were located on the bottom of the tree. Some main spoligotypes (identifiable by their SIT) were visible as central nodes forming starshaped complexes (or sub-networks) with other related patterns. These patterns were often those representing main patterns such as SIT26/CAS1-Delhi and SIT19/EAI2-Manila. Among the rare spoligotyping patterns belonging to EAI lineage, yet appearing close to CAS group spoligotypes, one could mention SIT2963/EAI5 (found exclusively in Sudan). This spoligotype was classified as belonging to EAI lineage but was very similar to SIT25 which belongs to CAS1-Delhi, and was tentatively reclassified as CAS1-Sudan.
Indeed, SIT2963 differs from SIT25 simply on the basis of the presence of spacer 33; it is noteworthy that the presence of spacer 33 is a criterion to distinguish EAI from CAS. Nonetheless, 12-loci MIRU-typing showed that SIT2963 was mainly associated with 12-MIT271 (226425153533) and  12-MIT1159 (226415153533) , observations that underlined its potential link to the CAS lineage. Another rare pattern SIT1400/EAI5, identifiable by the absence of spacers 4 to 24 (located on the bottom left in the MST shown on Fig 5 ), was exclusively found in Bangladesh. Thus, spoligotyping-based MST analysis (Fig  5 ) allowed to identify potential patterns which may have been misclassified as EAI (probably because of typing error) or patterns which may be the result of mixed infection. Note that further analyses will be needed to clarify such cases, in order to correct potentially misclassified patterns in SITVIT2 database.
Drug resistance Drug susceptibility testing (DST) information revealed that the proportion of multidrug resistant (MDR) and extensively drug resistant (XDR) TB strains among CAS group (30. 63% and 1. 03%, respectively) was significantly higher than in the EAI group (12. 14% and 0. 29%, respectively; p-value<0. 0001). However, the proportion of MTBC strains presenting any resistance was relatively higher among EAI group (17. 77%) as compared to CAS group (11. 20%). Furthermore, the proportion of pansusceptible strains was higher among EAI lineage (69. 80%) as compared to CAS lineage (57. 14%) (Table  1 ). Time series data representing the evolution of drug resistance for both CAS and EAI lineages during 1990-2011, showed that MDR, XDR, and any drug resistance proportions were more and more visible from 2004 onward.
HIV serology The proportion of HIV+ patients was 20. 34% among the EAI group vs. 3. 46% in the CAS group (p-value<0. 0001; Odds Ratio 0. 14, 95%CI [0. 07-0. 27]). This observation was also corroborated by data on worldwide distribution of HIV: EAI lineage strains were mostly associated with HIV positive patients potentially because of the fact that this lineage was mostly localized in geographic areas where HIV epidemic is more prevalent (such as Sub-Saharan Africa and Southeast Asia), whereas HIV serology was less prevalent in Central Asia and Middle East (https://www. avert. org/global-hiv-and-aids-statistics).
Data obtained during routine surveillance of verified MTBC cases reported in the Netherlands between 1993 and 2011  [41] , suggested that on average, cluster's propensity to propagate (CPP) values from Euro-American lineage strains were higher than Beijing and EAI strains. Furthermore, this study  [41]  suggested that CPP values from strains belonging CAS lineage were on average higher than EAI strains. The fact that EAI lineage strains were detected in Mexico suggested that this lineage may have been imported from Asia through the Bering Strait  [42] , and disseminated among Native Americans. More recent patterns of people displacement may also have played important roles in the spread of EAI lineage strains in South America and the Caribbean. One may name the displacement of Hmong people (https://en.
constituted a loosely-knit group split into several branches in the MST analysis, the other 5 sublineages described were relatively well structured (Figs3 and 4). When comparing this classification with spoligotyping, great convergences were outlined for 3 sublineages: SL1 corresponded mainly to EAI2-Manila (121/143) and in a lesser measure to EAI2-nonthaburi (15/ 143), SL3 to EAI1-SOM (81/119) and EAI5 (24/119), and finally SL6 corresponded to EAI3-IND (96/127) and EAI1-SOM (20/127); Congruence between Bayesian analysis and spoligotyping was less obvious for other sublineages: SL4 corresponded to EAI5 (59/94) but also to EAI4-VNM (17/94) and EAI1-SOM (14/94), SL5 was represented by EAI5 (42/98), EAI6-BGD1 (28/98), EAI3-IND (14/98) and EAI8-MDG (11/98). Performing same analyses on CAS dataset led to subdivision into 2 sublineages (Fig 4): SL1 possibly corresponds to CAS1-Dehli (75/138) and CAS1-Kili (36/138), while SL2 undoubtedly corresponded to CAS1-Dehli (237/288).
The instruments set Five instruments are placed on a single equatorial mount: c 1 : Hα coronagraph, 20 cm, λ 656. 3 nm, ∆λ 250 pm; c 2 : He I coronagraph, 20 cm, λ 1. 083 µm, ∆λ 250 pm; on the same c 2 : Fe XIII channel λ 1. 0747µm, ∆λ 250 pm; l 1 : Hα telescope, 15 cm, λ 656. 3 nm, ∆λ 50 pm; l 2 : Ca II telescope, 9 cm, λ 393. 4 nm, ∆λ 250 pm; and a Fresnel diffractive telescope that feeds the autoguider: 6. 2 cm, λ 632. 8 nm, ∆λ 1 nm. The four solar telescopes with their acquisition software were conceived and built in 2006 by J. -C. Noëns, L. Dettwiller, D. Romeuf and subcontractors:  (Dettwiller & Noens (2008) ). Since then, they have been providing images and films on a regular basis to the CLIMSO data base http://climso. irap. omp. eu. Among several upgrades, solar telescope l 1 now has high quality optics and a new Hα filter.
We have improved the baffling and especially the correction of filters leakage in coronagraph c 2. This led in 2015 to the 10 6 dynamic range allowing for images of the solar corona. Here we express here the "dynamic range" of a coronagraph as the ratio between the brightness in the image of the photosphere and the residual light in the surrounding field, at the final focal plane. This could also be called "rejection factor". In good weather conditions, a set of four images is taken per minute, and one of the solar corona in the Fe XIII line at 1074. 7 nm per hour. All images are uploaded to the database each night, along with several films made from them. During daytime, a few images are selected for real-time uploading to the data base.
To reduce the processing time we do not use FFTs but sparse matrix algorithms. The computed distance between the center of the reference circle and the center of the solar disk is used to determine a command sent to the equatorial mount. The source code in C++ and a detailed documentation of the autoguider are available on github : https://github. com/maelvalais/climso-auto. The guiding precision is better than 1 arc-second in good seeing conditions. Of course, due to the slow sampling rate of 1 s and the inertia of the equatorial mount, most of the tip-tilt agitation due to atmospheric seeing is not corrected. This limitation in performances is significant in bad seeing or high wind conditions. We plan to improve the algorithm that computes the commands to the equatorial mount based on the history of solar positions found; at present it is just a proportional response.
Conclusion We hope that our calibrated images contribute to the development of solar physics. We do not claim a high photometric precision immediately, but our calibrated images should be useful. This calibration uses the solar spectrum, itself based on the solar constant, hence it can be done for any image, even if obtained long ago, as long as it contains the solar disk (or a known fraction of it) with known wavelength and bandpass. To improve precision, we plan to reject spots, filaments and eruptive regions when computing the solar disk average of each image. This CLIMSO data base was started in 2007 and we hope to carry on for several solar cycles, and to complement other ground-based or space surveys. You are welcome to use it!.