dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonetheless, this structure was further confirmed by performing a new MST analysis of isolates labeled as sublineages T1 to T8 (Fig  3 ). Results were very congruent between both approaches except for few isolates belonging to sublineages T8 and T3 in the STRUCTURE analysis, but appearing separated in the MST analysis. These imperfectly structured isolates in T8 and T3 sublineages corresponded to lowest ancestry coefficient in the TESS analysis and classified as being in intermediate position (S3 and S4 Figs, S1 Table ), and should be further explored based on WGS. Since T group strains are considered part of the larger Euro-American L4 (which also comprises numerous LAM, H, X and S sublineages), we performed further MST analyses using 24-loci MIRU-VNTRs combined with spoligotyping data in order to perceive evolutionary relationships of an international collection of T group strains (n = 607) prelabeled as T1 to T8 based on STRUCTURE analysis, and LAM, H, X and S isolates (n = 416) from SITVIT2 database (Fig  4 ).
Using this approach, we intended to confirm if the sublineages T1 to T8 really constituted independent groups when compared to other L4 strains. The resulting MST (Fig  4 ) globally showed that with the exception of T8 isolates which seems to split into several sublineages, and T2 and T7 which clustered with few H and LAM isolates-revealing probable misclassification of these T strains due to homoplasy or artefacts; all other sublineages described were pretty well-structured. We further compared 24-loci MIRU-VNTR profiles of this T sublineages with profiles available in the MIRU-VNTRplus database  [39, 40] , confirming that these T1 to T8 were not related to other L4 isolates available in the database (S5 Fig). ): sublineages T4 and T7 presented higher allelic richness than T1, T3 and T8 (p-value < 0.
Conclusions This study explored for the first time 24-loci MIRU-VNTR based population structure of the so-called T group M. tuberculosis isolates from several countries around the world, and fetched new evidence about their phylogenetic structure into eight putative sublineages by phylogenetic and Bayesian analyses. Our results showed that 5 out of 8 sublineages seemed phylogeographically well-defined. This genetic now needs to be further validated by applying other genotyping approaches cumulating robustness of different methodss. We plan to start with an initial screening on a worldwide dataset using identical VNTR markers, followed by high throughput approaches avoiding homoplasy events like SNPs calling based on WGS, or Core Genome MLST (cgMLST). These studies should allow relevant worldwide exploration of evolutionary history of sublineages studied herein.
Acknowledgments This work was supported by the  European Regional Development Fund and European Social Fund (ERDF-ESF)  under the scheme " Programme Operationnel FEDER-FSE Guadeloupe Conseil Regional 2014-2020 " (Grant number pending). Yann Reynaud was awarded a Calmette and Yersin postdoctoral fellowship by the  Institut Pasteur International Network. Chao Zheng  was financially supported by  China Scholarship Council  (CSC, File NO. 201506240129) under a co-training program for the fulfillment of his PhD research program between  College of Life Sciences, Sichuan University, Chengdu, China, and Tuberculosis and Mycobacteria Unit, Institut Pasteur de la Guadeloupe. Help of David Couvin  for the construction of the SIT-VIT2 database is gratefully acknowledged. We are grateful to  Dr. Ling Cheng  ( Department of Respiratory Medicine, Affiliated Hospital of Zunyi Medical College, Zunyi, Guizhou, China ),  Dr.
In WT cells, β-lactamase activity increased over time reflecting the increase in antigen export to the cytosol. However, as displayed in Fig. 2g , β-lactamase activity was significantly delayed in 3d/3d DCs. We concluded that UNC93B1 is important for phagosomal maturation and plays an active role in regulating endo/lysosomal pH, leading to reduced proteolytic activity and antigen export to the cytosol. Expression of STIM1 is downregulated in 3d/3d spleen cells. To investigate if UNC93B1 itself or/and via the association with other protein(s) contributes to MHCI antigen cross-presentation, we analyzed the expression of genes involved in antigen crosspresentation in spleen cells from WT and 3d mice using the publicly available microarray database (GSE41496). We found that among highly expressed antigen-presentation-related genes (coding for cathepsin D, TAPL, V-ATPase subunits), the calcium sensor Stim1 gene was one of the most highly expressed in WT spleen (Fig.
HeLa cells were cultured at 37 °C in 5% CO 2 in MEM (10% FBS, 2 mM glutamine, 50 μg/ml penicillin/ streptomycin). Cells were seeded on 25-mm diameter glass coverslips and transfected at 70-80% confluence with 5 μl Lipofectamine 2000 (Invitrogen) by adding 2 μg of plasmid per coverslip and imaged 24 h after transfection. The B3Z hybridoma cell line was kindly provided by N. Shastri (University of California, USA). Cells were maintained at 37 °C in 5% CO 2 atmosphere in RPMI 1640 (10% FBS, 2 mM glutamine, 50 μg/ml penicillin/streptomycin, 50 μM of β-mercaptoethanol, 25 mM HEPES, 1× non-essential amino acids, 1 mM of sodium pyruvate). Microarrays. Data were processed from GSE41496 Gene Expression Omnibus project performed on Agilent-028005 SurePrint G3 Mouse GE 8×60K Microarrays. The original experiments intended to describe the whole transcriptome from spleens of plasmodium Chabaudi infected vs.
control mock-infected mice sufficient or deficient for various TLR-related molecules. Among these data, we analyzed control spleens (from UNC93B1 deficient (data sets: GSM1018264, GSM1018265, GSM1018266, GSM1018267) and sufficient (GSM1018240, GSM1018241, GSM1018242, GSM1018243) groups of mice). The description of the set of data can be found here: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc = GSE41496. Background noise was computed using a custom algorithm within R as follows. Assuming that a maximum of 80% of genes are expressed on any given microarray, we tagged the 20% of probes with the lowest intensity for each microarray as background. A threshold was fixed at two standard deviations over the mean of the background. All probes for which normalized intensities were lower than the computed threshold were designated as background for each array.
Martin Lochner, University of Bern, 10 μM) was applied concurrently with OVAb and images taken after 30 min. TIRF microscopy. Fibroblasts transfected with STIM1-WT or STIM1-D76A GFPtagged and UNC93B1-WT or UNC93B1-3d cherry-tagged plasmids were left to adhere on glass slides, stimulated with TG and fixed before visualized by TIRF. TIRF time-lapse videos were recorded for 5 min (exposure time, 200 ms). During the observation, the cells were kept at 37 °C. Fluorescence data were acquired with an Eclipse Ti-E TIRF imaging system (Nikon). Images were acquired with a Roper Scientific QuantEM 512 SC camera (Nikon) and NIS-Elements Advanced Research software (version 3. 1). Image sets were processed with ImageJ. Duolink, immunoprecipitation, and western blotting. Duolink (OLINK) was performed according to the manufacturer's instructions and as previously described  17.
Briefly, DCs were grown on coverslips and then fixed in 4% paraformaldehyde for 10 min before permeabilization in PBS/0. 05% saponin/0. 2% BSA for 10 min. Cells were then blocked in 3% BSA/PBS and primary antibodies were incubated (homemade rabbit anti-UNC93B1 polyclonal antiserum targeting aa 515-598 and affinity purified by Moravia and anti-GFP from Roche for STIM1-WT or STIM-D76A transduced cells). After washing the cells, PLA probes were added, followed by hybridization, ligation, and amplification for 90 min at 37 °C. Nucleus (DAPI labeling) and UNC93B1-STIM1 interactions (red) were visualized after incubation with the detection solution. Slides were analyzed by confocal microscopy. Quantification was performed by using ImageJ software. For western blotting, 5-50 μg of proteins were submitted to separation on a 4-12% SDS Nu-PAGE Bis-Tris gels (Invitrogen).
FRET measurements. STIM1 oligomerization was followed in HeLa cells in the presence of either Cherry-tagged WT or 3d-mutated UNC93B1 by measuring the increase in the apparent FRET efficiency of YFP-tagged and CFP-tagged STIM1 upon stimulation with 1 μM TG in modified Ringer's buffer  29. FRET measurements were performed using the E app E-FRET method of Zal and Gascoigne  63  , where: FRET efficiency ¼ I DA À aI AA À dI DD ð Þ =ðI DA À aI AA þ G À d ð Þ I DD Þ: ð3Þ Briefly, time-lapse images were acquired every 12 s in three channels using the following excitation/emission (in nm Images were acquired for 2 min before TG addition and for 10 min after, and single-cell fluorescence measurements extracted with ImageJ. Traces were normalized to the average FRET efficiency of the last minute before TG addition, smoothed using a 2nd order 4-neighbor smoothing, and fitted to a Boltzman sigmoidal curve using GraphPad Prism software.
When applicable, 0. 2 nM ConcA was added just prior to beads. Image analysis was performed using ImageJ software (NIH) on maximum projections of at least 5 × 15 μm z-stacks per condition. The P-L fusion index is computed by obtaining the fluorescence signal for the FRET (488/590) and Alexa-488 bead (488/ 530) channels using semi-automated segmentation based on a single visually determined threshold for the bead channel. FRET ratios (FRET/bead channel) minus the basal (external bead) FRET ratio were normalized to the total average cellular Alexa-594-HA fluorescence obtained from sum projections of the Alexa-594 (550/590) channel and multiplied by 10,000 to obtain the final P-L fusion index. ROS measurements. Intracellular ROS were measured on cells on cells seeded at 50,000 cells/well in black/clear bottom plates.
OxyBurst/Alexa568-OVA zymosan were added to cells at 20:1 in complete medium, were washed in modified Ringer's after 30 min to remove uningested zymosan, and imaged using alternate 555/590, 488/530 nm Ex/Em and brightfield illumination 30 and/or 90 min after addition. Where applicable cells were preincubated for 10 min with 10 μM DPI prior to stimulus addition. Cell borders were delineated using the brightfield image and phagosomes were defined by the Alexa-568 fluorescence within cell borders, using ImageJ software (NIH). The ratio of phagosomal OxyBurst to phagosomal Alexa-568 fluorescence minus the basal (external zymosan) ratio was calculated as an index of intracellular/phagosomal ROS. Measurement of antigen export to the cytosol. Measurement of antigen export to the cytosol was performed as previously described  32.
Acknowledgements This study was supported by grants from  ANR  ( ANR 2010 MIDI 008 01  to B. M. ,  ANR-14-CE11-0014  to P. V. E), from  FRM  (grant number  DEQ20130326539  to P. V. E. ), and the  Swiss National Foundation  (grant number  31003A-149566  to N. D. ); the  ARC  and  La Ligue Ph. D. fellowships to S. M. , a  ARC Foundation  grant to  G. D. J. ( PJA 20141201668 ), and a  Young Investigator Subsidy from The Sir Jules Thorn Overseas Charitable Trust  to P. N. The authors are grateful to  B. Ryffel  ( Orléans, France ),  C. Picard  ( IMAGINE, France ), and  P. Cresswell  ( Yale, USA ) for providing the 3d/3d mice, the fibroblast cell line lacking STIM1 from the patient  V1 , and the cDNAs coding for FcγRIIA-EGFP constructs, respectively. Also, we are grateful to  J. Mintern  ( Melbourn, Australia ) for designing the first antigen cross-presentation experiment in vivo.
Rhyme data The rhyme data used for the experiment follow the rhyme assignments for the Book of Odes provided in  Baxter (1992)  which were digitized and converted into a machinereadable format in  List (2017). The data are available online as interactive application, the Shījīng Rhyme Browser (http://digling. org/shijing/), where all rhyme decisions can be interactively searched and inspected in the reconstruction systems by  Baxter and Sagart (2014)  and  Pan 潘悟云 (2000). The former is available for download; the latter was taken from the Thesaurus Linguae Sericae  (Harbsmeier and Jiang 2009). The dataset lists all potential rhyme words in the Book of Odes, which were determined by taking the final character in each line of each stanza across the 305 poems of the Book of Odes. This list of potential rhyme words is contrasted with the actual rhyme words as assigned in  Baxter (1992).
The interactive application visualizes rhyme annotations by coloring words which are marked as rhyming in the same color, as shown in Table  2  for the poem number 60. In  List (2017) , the rhyme data are used to construct a rhyme network of all rhyming words in the Book of Odes. In this network, rhyme words (represented by Chinese characters) are represented as nodes, and links between the nodes are drawn whenever two rhyme words actually rhyme in the Book of Odes. The whole network comprises 1845 nodes and 5266 links between the nodes. The number of recurring links between two nodes is counted and weighted, using specific weighting principles, like (a) counting formulaic (recurring lines in the collection) only once, and (b) by taking the size of the group in which two words rhyme into account when establishing the weights (in order to avoid that large groups of rhyming words are scored more often than smaller ones).
As network weighting itself is not of primary importance for the approach presented in this paper, we refer the readers to  List (2017)  where the rhyme network construction process is described in detail. All data underlying the study are accessible online at https://zenodo. org/badge/latestdoi/43676744, and we used this data to create the rhyme network for our study. Figure  1  illustrates the structure of the rhyme network by showing a small part of the full graph, corresponding to the codas reconstructed as *-ar, *-an, and *-aj in the reconstruction of  Baxter and Sagart (2014).
1  Example for a small part of the rhyme network based on the data in  List (2017) , for rhyme words reconstructed with coda *-ar (black nodes), *-aj (gray nodes), and *-an (white nodes) in the reconstruction system of  Baxter and Sagart (2014). Nodes correspond to rhyme words, and edges indicate whether the nodes they connect rhyme together in the Book of Odes. Edge weights represent the frequency of rhymeword co-occurrences, and node weights represent the general frequency by which the words occur in rhyme position in the Book of Odes project (Shanghai Normal University 上海师范大学 2016, http://www. eastling. org/oc/ oldage. aspx) offers Old Chinese reconstructions for various authors, including the systems proposed in  Karlgren (1957) ,  Li (1971) ,  Wang 王力 (1980) , Zheng  Zhang 郑张尚 芳 (2003) , and the most recent proposals according to the system of  Pan 潘悟云 (2000).
Apart from a few incorrect characters in the source by  Wang 王力 (1980) , which we manually corrected, our comparison did not reveal any errors. In addition to the five reconstruction systems, Eastling also offers readings attributed to William Baxter, but since we could not identify these readings with any known published sources of Baxter corresponding to these readings, we did not use them in our analysis. The Tower of Babel project  (Starostin 2008 , http://starling. rinet. ru/) further offers an exhaustive database of character readings following the Old Chinese reconstruction system by  Starostin (1989) , which was compiled by Sergei Starostin himself from 1991 on and was expanded in the years thereafter. While the original publication by  Starostin (1989)  lists readings for all rhyme words in the Book of Odes, the online version only offers 1358 character readings for the 1845 characters in our base list, with 487 readings missing.
The Old Chinese reconstruction by  Schuessler (2007)  was collected from a recently published digital version of the book. Unfortunately, only 1224 readings for the 1845 rhyming characters in the Book of Odes could be found, leaving us with 621 missing character readings. In order to compare the different rhyme systems for vowel purity, the main vowels for all available character readings for the 1845 rhyme words in the rhyme networks were extracted and added as meta-data to each rhyme in the network. The different vowel systems proposed in the different reconstruction systems are shown in Table  3. Although each of our 8 systems has much more than 1200 readings (see column 3 in Table  3 ), the intersection between all systems is surprisingly low, and if we only retain those readings reflected in all samples, a sample of 875 nodes remains.
As a result, we cannot simply add the missing character readings in our comparative dataset without running the danger of incorrectly representing a given reconstruction system. For our comparison, we are left with what we have, and we need to address the problems resulting from gaps in the data. But since we provide all data as an Additional file with this study, we hope that collaborative efforts of the scholarly community may eventually close the gaps in the future. When comparing across datasets, it is important that we compare samples of the data containing exactly the same nodes, as in smaller or larger samples the basic characteristics, as, for example, the number of edges, may differ, thus giving the reconstruction systems we want to compare different starting chances. The difference is further confirmed by the data on network density that is the fraction of the number of edges divided by the number of potential edges in a network.
The number of potential edges in a network is the number of edges in a network in which all nodes are connected with each other and can be calculated with the help of the formula n 2 À n ð Þ=2, where n is the number of nodes in the network c. Network density for the different subgraphs is reported in Table  3. As can be seen from the scores, the subgraphs of the different reconstruction systems slightly differ in density depending on the coverage of the data sample, with the smaller datasets showing a higher density.
We can illustrate this notion of purity by creating a fictive dataset of six rhyme words which we label 1, 2,…, 6, and of which 1, 2, and 3 share the same vowel, and 4, 5, and 6 share a vowel, which is different from the vowel of 1, 2, and 3. In Table  4 , we display two matrices which contrast different fictive types of rhyme co-occurrence for our six words. If two words rhyme, this is indicated by a cross in the cell of the matrix. Impure rhymes in which two vowels of different quality rhyme with each other are further marked by shading the cell in gray. From the two different matrices, we can easily see that the first one (matrix A) would intuitively reflect a higher degree of vowel purity than the second one (matrix B), simply because the number of impure rhymes is much lower in matrix A.
The same information can be also displayed in a network, in which our words 1, 2,…, 6 are modeled as nodes, and the information, whether they rhyme with each other in the sources (matrices A and B) are displayed by drawing an edge between the nodes. This is illustrated in Fig. 2 , and we can see that the network visualization makes it even easier to see the difference between the intuitively rather pure rhyme network in A and the rather impure rhyme network in B. But our intuitive assessment may easily betray us if the data becomes more complex. For this reason, we need a way to measure to which degree a given network structure (the rhyme co-occurrences in the Book of Odes) is in conflict with a given external division of the nodes (the vowels, as annotated in the reconstruction systems of different scholars).
Based on this matrix, the assortativity coefficient can then be calculated with help of the formula: Table  4  Rather high and rather low degree of vowel purity in a fictive set of six rhyming words Tables A and B show six fictive rhyming words, how they rhyme in a set of poems, with a cross in the cell indicating that the words have been shown to rhyme together in at least one poem. Assuming that words 1, 2, and 3 have the same vowels, which is different from the vowels of 4, 5, and 6 (which also share the same vowel), we can find occurrences of impure rhymes whenever one word from the set of 1, 2, and 3 rhymes with one word from the set 4, 5, 6 (indicated by shading the cell in gray). Here, our matrix A reflects a rather "pure" dataset, with only one transition in 3 and 4, while matrix B reflects an impure dataset with as many as four transitions r ¼ TraceðmÞ-∥m 2 ∥ 1-∥m 2 ∥ ; ð1Þ where m is the attribute mixing matrix, Trace is the sum of the diagonal from top left to bottom right, and ||m|| is the sum of all cells in the matrix (see  Newman 2003 for details).
We need to make sure that the distribution we obtain for a given reconstruction system is not due to chance. More concretely, what is interesting for us, is not only whether the distribution of vowels across a rhyme network is due to chance alone, but also to compare across different reconstruction systems, which system is most unlikely to have arisen by chance. Comparability can be achieved by comparing the results obtained for a given reconstruction system with the results of a random distribution obtained for the same dataset. The random distribution can be created by shuffling the node labels (the vowels for each Chinese character in our case). In order to normalize the data, one then compares to which degree the original result differs from the results obtained for the randomized distribution, that is, one compares to how unlikely it is that a given system could have been produced by chance.
Results We computed the assortativity coefficients for the original and the randomized data based on the Book of Odes network for all eight reconstruction systems. The randomized distribution was obtained by shuffling the nodes in each network 1000 times and storing the assortativity coefficient for each run. Thanks to the NetworkX software package  (Hagberg 2009) , all computations could be carried out in Python, and all source codes to replicate the analyses reported here are given in the Additional file 1: supplementary material. In all cases, our primary question was to which degree the division of the rhyme words in the network according to their reconstructed vowels would reflect the "natural" division of the networks into rhyme classes as represented in the annotated network of rhymes in the Book of Odes.
01. As can be seen from the table, all studies on the subsets confirm the tendency we also saw in the full sample from Table  6 , and especially the ranks are remarkably stable (the only exception being the analyses by Schuessler and Karlgren in the lower ranks). What one can also see is that the size of the networks has a direct impact on the sigma scores, which is easy to understand keeping in mind that if we select only a small number of nodes the evidence for rhyme co-occurrences will drastically shrink. Table  6  Results of the analysis for the complete dataset (including all characters reflected in all reconstruction systems), a total of 875 nodes Table  7  Results of the re-sampling test on randomized subsets of the data with varying numbers of characters, and the resulting rankings for all datasets for the respective analysis.
3  Comparing the rhyme patterns across different reconstruction systems. The figure shows three subsets of 100 characters each as they occur in the rhyme data of the Book of Odes; both include missing characters and the respective vowel readings. While a definite structural similarity can be detected, we also find remarkable differences. In the figure, each cell corresponds to one reading for a given character in the row. Since the characters are too small to be readable, we offer a high-resolution version of this figure in the Additional file 1: supplementary material rhyming. If vowel quality was independent of rhyme decisions, we would expect to find assortativity coefficients to be close to zero, as we found in the random distributions. What this means more concretely is shown in Fig. 4 , where we show the full rhyme network in which nodes have been colored according to the system of  Baxter and Sagart (2014).