dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, for the study of hippocampal shape, IC1 or PW algorithms seem to be more adapted than IC2 or the variational template estimation. However, it is not clear whether the same conclusion would hold for more complex sets of anatomical structures, such as an exhaustive description of cortical sulci  (Auzias et al. , 2011). Besides, as mentioned above in section 5. 3, unlike with the variational template estimation, centroid computations do not directly provide transformations between the centroid and the population which must be computed afterwards to obtain momentum vectors. This requires N more matchings, which almost doubles the computation time. Even with this additional step, centroid-based shape analysis stills leads to a competitive computation time (about 26 h for the complete procedure on the large dataset of 1,000 subjects).
Here we computed for all our datasets the initial momentum vectors from the mean shape to each subject of the population to apply a kernel PCA on the mean shape. But this is not a mandatory step, for example, in  Cury et al. (2018)  the iterative centroid method have been applied to provide a mean shape used afterwards as a baseline shape for an other analysis. It is also possible, for visualization purposes, to estimate different templates within a population (controls vs. patients for example) and identify where shape differences are located between templates. In future work, this approach could be improved by using a discrete parametrisation of the LDDMM framework  (Durrleman et al. , 2011) , based on a finite set of control points. The control points number and position are independent from the shapes being deformed as they do not require to be aligned with the shapes' vertices.
ACKNOWLEDGMENTS The research leading to these results has received funding from  ANR  (project  HM-TC , grant number  ANR-09-EMER-006 , and project  KaraMetria , grant number  ANR-09-BLAN-0332 ), from the  CATI Project (Fondation Plan Alzheimer) and from the program Investissements d'avenir   ANR-10-IA IHU-06. IMAGEN was supported by the  European Union -funded  FP6  ( LSHM-CT-2007-037286 ), the  FP7  projects  IMAGEMEND  ( 602450 ) and MATRICS ( 603016 ), and the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ),  Medical Research Council  Programme Grant Developmental pathways into adolescent substance abuse (93558), the  NIHR Biomedical Research Centre Mental Health as well as the Swedish funding agency FORMAS. Further support was provided by the  Bundesministerium für Bildung und Forschung  (eMED SysAlc; AERIAL;  1EV0711 ).
Implementation, code availability, license kali is implemented in Go  [29] , tested with Go version go1. 9. 2 linux/amd64 under Arch Linux  [30]. kali is licensed under the GNU General Public License  [31]  and freely available on GitHub at https://github. com/arnaudporet/kali. The core of kali in pseudocode can be found in Appendix 3 page 28.
Strains within phylogroups Kp1, Kp2, Kp4, and Kp5 each shared two specific peaks not observed in other phylogroups. Kp3 strains shared a peak that was only observed otherwise in Kp5. Finally, Kp6 had a diagnostic peak shared only with Kp1. Kp3 and Kp6 could therefore be identified by exclusion criteria (lacking Kp5 and Kp1-specific peaks, respectively). Further, ranked Pearson correlation clustering of spectra grouped strains according to their phylogroup. The model was tested and successfully validated using different culture media. These results demonstrate the potential of MALDI-TOF MS for precise identification of K. pneumoniae complex members. Incorporation of spectra of all K. pneumoniae complex members into reference MALDI-TOF spectra databases, in which they are currently lacking, is desirable.
pneumoniae complex. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) has revolutionized routine identification of microorganisms, being a fast and costeffective technique. It now represents a first line identification method in many clinical, environmental, and food microbiology laboratories  (van Belkum et al. , 2017). In the case of the K. pneumoniae complex, MALDI-TOF MS identification remains largely unsatisfactory given the absence of well-characterized, representative members of the complex in spectral databases. Currently, only K. pneumoniae and K. variicola are included in the Bruker database 1  , and identification of even these two species is imprecise given the lack of reference spectra of other phylogroups  (Berry et al. , 2015; Long et al.
Spectra Analysis The spectra were preprocessed by applying the "smoothing" and "baseline subtraction" procedures available in FlexAnalysis software (Bruker Daltonics, Bremen, Germany), exported as peak lists with m/z values and signal intensities for each peak in text format, and imported into a dedicated BioNumerics v7. 6 (Applied Maths, Ghent, Belgium) database. Peak detection was performed in BioNumerics using a signal to noise ratio of 20. The instrumental replicates (24 spectra for each strain) were used to generate a mean spectrum for each strain using the following parameters: minimum similarity, 90%; minimum peak detection rate, 60%; constant tolerance, 1; and linear FIGURE 1 | Peak positions (m/z) for each of the Klebsiella pneumoniae complex strains. Stars denote those peaks that are useful for discrimination among phylogroups, as detailed in Table  1.
tolerance, 300 ppm. Finally, peak matching was performed to search all distinct peaks (called peak classes in BioNumerics) using as parameters: constant tolerance, 1. 9; linear tolerance, 550 ppm; maximum horizontal shift, 1; peak detection rate, 10. The discriminating value of each resulting peak was evaluated by a Mann-Whitney test  (Vranckx et al. , 2017). In order to test and validate our results an identification project was constructed in a BioNumerics database, using our spectra as reference set and a support vector machine (SVM, supervised algorithm) as classifier (cross-validation procedure). The application of SVMs classifier algorithms is very useful to discriminate between groups when the differences are minimal (DeMarco and Ford, 2013). In the cross-validation procedure, 70% of the available data (randomly selected) were used as model, whereas the 30% remaining spectra were used as test in order to assess the proportion (%) of correct predictions for each phylogroup.
External Validation Dataset Forty-nine isolates belonging to K. pneumoniae phylogroups Kp1 (n = 23), Kp2 (n = 7), Kp3 (n = 7), Kp4 (n = 9) and Kp5 (n = 3), previously characterized by WGS were used to assess the robustness of the MALDI-TOF MS method (no Kp6 isolates other than those used in the model construction were available). These isolates are part of a study collection recovered from fecal samples of healthy carriers in Madagascar (2015-2016) (under the BioProject PRJEB29143). The Kp1 test isolates included producers of ESBL or AmpC enzymes and represented a snapshot of clinically relevant multidrug resistant sublineages (2 isolates of ST17, 2 ST48, 2 ST101, 1 ST14, 1 ST20, 1 ST25, 1 ST45, 1 ST307, 1 ST375, 1 ST380). Isolates from Kp3 and Kp4 were randomly selected, and all available isolates of Kp2 and Kp5 were included.
RESULTS AND DISCUSSION Forty-six strains representing a diversity of genotypes within the six phylogroups currently known within the K. pneumoniae complex (Supplementary Table  S1 ) were analyzed by MALDI-TOF MS. Based on the MALDI Biotyper Compass database version 4. 1. 80 (Bruker Daltonics, Bremen, Germany), the 46 strains were identified either as K. pneumoniae (31 strains, all belonging to Kp1, Kp2, Kp4, and Kp6) or as K. variicola (15 strains, all strains of Kp3 and Kp5). Identification scores ranged between 2. 16-2. 56 for K. pneumoniae and 1. 89-2. 55 for K variicola. Of note, in two cases (Kp1-SB1139 and Kp6-SB6071) a replicate was reported in one measure as K. pneumoniae and in other as K. variicola. These data highlight the need to update the database in order to refine confidence in K.
pneumoniae/K. variicola identification and to enable identification of K. quasipneumoniae and novel phylogroups. Figure  1  summarizes the peak positions found in each strain. Most (about 97%) of the peaks were concentrated in the region below 10,000 m/z and almost no peak was found above this value. The similarity among spectra within the K. pneumoniae complex was always above 87% (data not shown), with peaks at 4363, 5379, 6287, 6298, 7241, and 9473 m/z being found in all the members of the complex. Importantly, 10 specific biomarkers associated with specific members of the K. pneumoniae complex were identified. These peaks were located within the range 3835-9553 m/z. Based on the current dataset, the specificity and sensitivity of their distribution among phylogroups ranged between 97-100 and 80-100%, respectively (Figure  1  and Table  1 ).
In the case of L28, one amino-acid change distinguished Kp1/Kp2/Kp4 (9007 Da) from Kp3/Kp5/Kp4 (A69S, 9022 Da), thus not being specific for any phylogroup, although we were not able to detect it in our spectra. Using the cross-validation procedure for SVM classifier, a 98. 1% rate of correct predictions (average of 10 experiments; range: 96. 9-100%) for the phylogroups was found, showing that this approach is promising for identification of K. pneumoniae complex members. Further, an independent validation dataset of 49 isolates identified at phylogroup level by whole genome sequence analysis was used. Bacteria were grown in LB or blood agar and either protein-extracted or directly analyzed. Consistent with results obtained for the isolates of the model, the MALDI Biotyper Compass database identified the isolates either as K.
pneumoniae (39 strains, all the Kp1, Kp2, and Kp4 isolates) or as K. variicola (10 strains, all Kp3 and Kp5 isolates) with identification scores ranging between 1. 82 and 2. 55 (mean range of 2. 34). In contrast, the projection of the validation dataset spectra in our model using the SVM classifier showed that all isolates were correctly identified at the phylogroup level with high confidence. This complete identification was obtained in all experimental conditions. The results of this external validation demonstrate the potential of MALDI-TOF MS as a precise K. pneumoniae phylogroup identification method. Moreover, they show that the culture media used as well as the two different sample preparation procedures do not seem to affect the identification results. The use of the direct transfer procedure from blood agar, the condition most frequently used in routine laboratory conditions, thus appears appropriate for K.
CONCLUSION This work demonstrates the existence of K. pneumoniae phylogroup-specific protein biomarkers that can be detected by MALDI-TOF MS. This finding opens the possibility for industrial, veterinary or medical microbiology laboratories, to identify isolates of the K. pneumoniae complex at the species or phylogroup level. We urge that reference spectra of the various taxa of the K. pneumoniae complex be incorporated into reference MALDI-TOF spectra databases. Improved identification of K. pneumoniae and related taxa will advance our understanding of the epidemiology, ecology, and links with pathogenesis of this increasingly important group of pathogens.
ACKNOWLEDGMENTS We thank the  Biological Resource Center of Institut Pasteur (Chantal Bizet and Estelle Muhle)  for support within the  MALDI-TOF MS instrument. We acknowledge  Jean-Marc Collard  ( Institut Pasteur of Madagascar ),  S. Wesley Long  ( Houston Methodist Hospital ), and  David A. Rasko  and  J. Kristie Johnson  ( University of Maryland School of Medicine, Baltimore, MD, United States ) for kindly providing strains analyzed in this study. Part of the results published in this manuscript are accessible online under the preprint publication at https://www. biorxiv. org/content/biorxiv/early/ 2018/06/19/350579. full. pdf,which is also cited in the text.
In total, 72 transcriptomes and 24 genomes were analyzed. One hundred thousand generations after its inception, C. bursa-pastoris does not show any sign of a transcriptomic shock. Instead, our data revealed highly concerted changes with the expression levels of the two subgenomes converging towards a median value. This was achieved by a balance between cis-and trans-regulation and a strong parental legacy that was also observed for the accumulation of deleterious mutations over the two subgenomes. While the differential accumulation of deleterious mutations between subgenomes could explain part of the differential expression between them there were also significant tissue-specific differences in subgenome dominance and convergence, indicating that adaptive changes may also have contributed to the evolution of the expression patterns of the two subgenomes.
Material and methods Samples, sequencing and data preparation. We obtained the whole genome and RNA-Seq data from flower, leaf and root tissues of (i) 16 accessions of C. bursa-pastoris coming from already characterized populations from Europe (EU), the Middle East (ME) and Eastern Asia (ASI)  (33)  and from hitherto unstudied Central Asian populations (CASI) and (ii) four accessions each of C. grandiflora and C. orientalis (Fig. 1 ). The genomic data included both published and newly sequenced genomes (Table  S1 ). For newly sequenced genomes, DNA was extracted from leaves with the Qiagen DNeasy Plant Mini Kit, libraries were prepared using the TruSeq Nano DNA kit, and 150-bp paired-end reads were sequenced on Illumina HiSeqX platform (SciLife, Stockholm, Sweden). All 72 RNA-Seq libraries (24 accessions×three tissues) were sequenced in this study.
Genotyping of DNA and RNA-Seq alignments was performed using Hap-lotypeCaller from the Genome Analysis Tool Kit (GATK) v3. 5  (39)  as described in  (35). The subgenomes of C. bursapastoris were phased with HapCUT version 0. 7  (40)  following the procedure by  (35). The quality of this phasing procedure was ascertained by comparing the phased subgenomes with the subgenome assembly obtained by  (36). The unphased expression data was generated for non-overlapping feature positions (option: -m union) using the htseq-count program from HTSeq v0. 6. 1  (41). To compare the expression between the two subgenomes of C. bursa-pastoris, homeologue-specific counting of alleles was performed using ASEReadCounter from GATK and phased according to the phased genomic data. We analyzed only the counts of SNPs that showed no strong deviation from the 0.
5 mapping ratio in DNA data defined with a statistical model developed by  (42). To make the homeologue-specific count data of C. bursapastoris comparable with parental read count data (allelic counting underestimates the expression of genes with a low number of heterozygous sites), we scaled the homeologuespecific counts using the unphased data and the allelic ratio in the phased data. Population structure. In order to assess the relationship of the newly obtained Central Asian samples with other populations, we characterized population structure through a principal component analysis (ade4 R package  (43) ) and by reconstructing a phylogenetic tree from the genomic Single Nucleotide Polymorphisms (SNPs) of the 24 accessions (neighbor-joining algorithm on absolute genetic distance, ape R package  (44) ).
Gene expression analyses. Given that the gene expression patterns in homeologue-specific and total expression can produce different results  (46) , we performed the differential gene expression analyses on both the unphased data and phased data. We first assessed the differences between populations of C. bursa-pastoris and parental species in unphased data by partitioning the analysis between tissues and populations. In a second step, the phased data was used to assess homeologue-specific expression differences between subgenomes in different tissues and populations. Finally, we analyzed the differences in expression patterns in parental diploid species and C. bursa-pastoris by classifying expression patterns into categories in both phased and unphased data. Differential gene expression analyses were carried out in edgeR  (47).
The TMM normalization for different library sizes  (47)  was used for differential gene expression analyses, while for all other analyses, we used the count per million (CPM) normalization (one was added to every gene count to bypass log-transformation of zero expression). Phased counts were normalized by the mean library size of the two subgenomes ( Cbp Co +Cbp Cg 2 ) and only genes showing no strong mapping bias were retained (see below). For both datasets (unphased or phased), only genes with at least one sample having a non-zero expression in every population/species were kept. Differences between the two subgenomes (homeologuespecific expression) were assessed with the integration of the information from both RNA and DNA data to exclude highly biased SNPs and to account for the noise in read counts due to statistical variability.
These differences correspond to the phylogenetic distances (Fig. 1A ). In principle, if the regulation of gene expression in Cbp Cg is independent of the regulation of gene expression in Cbp Co , then the overall ∆ sub , ∆ par , ∆ Cg and ∆ Co are expected to be equal. To compare these quantities, for each transcript i, we used a convergence index (C i ): Gene ontology enrichment test. For various lists of genes of interest detected in the analyses described above, gene ontology (GO) enrichment tests were performed using the topGO R package  (48). The GO term annotation was downloaded from PlantRegMap (http://plantregmap. cbi. pku. edu. cn/download. php#goannotation) and used as a reference set for topGO (i. e. , custom input). Fisher's exact-test procedure (weight algorithm) was performed to assess the enrichment (p < 0.
05) for either molecular functions (MF) or biological processes (BP). Finally, the REViGO software (49) was used to remove GO terms redundancy and to cluster remaining terms in a two-dimensional space derived by applying multidimensional scaling to a matrix of the GO terms semantic similarities. Cytoscape v3. 6. 1 was used to visualize GO terms networks  (50). C i = ∆ par -∆ x max(∆ par ,∆ x ) ,.
Difference between species and subgenomes in dele- terious mutations. To compare the number of deleterious mutations between the two subgenomes of C. bursa-pastoris, we classified mutations into tolerated and deleterious ones (DEL) using SIFT4G  (51). We used C. rubella  (35)  and Arabidopsis thaliana (TAIR10. 22) SIFT4G reference databases. This helps avoid reference bias towards C. rubella away from calling mutations to be deleterious in the C. grandiflora homeologue. We considered only the mutations that accumulated after speciation of C. bursa-pastoris and identified mutations specific to C. grandiflora, C. orientalis, the two subgenomes of C. bursa-pastoris, and Neslia paniculata that was used as an outgroup here. All estimates were relative to the total number of SIFT4G annotated sites to minimize the bias associated with variation in missing data as in  (35).
Under the second hypothesis, the mean should differ from 1/2. However, over-dispersion and bias can also occur because of missing data and sampling error, we thus used synonymous mutations (SYN) to control for this and built the correct null distribution. To do so, we developed a maximum likelihood method implemented in R (52) as follows. First, we identified a most likely probability distribution model by fitting four models to the SYN dataset, where nSY N is the sum of SY N mutations occurring on both homeologous genes and kSY N is the number of SY N mutations occurring on Cbp Cg genes. The four models are: • M1: kSY N ∼ B(nSY N, 0. 5), a binomial distribution with no bias between Cbp Cg and Cbp Co , • M2: kSY N ∼ B(nSY N, 0. 5 + b), a binomial distribution with bias, • M3: kSY N ∼ BB(nSY N, 0, ϕ), a beta-binomial distribution with no bias, • M4: kSY N ∼ BB(nSY N, b, ϕ), a beta-binomial distribution with bias.
We introduced a new set of models to test for the specific features of the distribution of deleterious mutations: kSY N ∼ BB(nSY N, bSY N, ϕSY N ), kDEL ∼ BB(nDEL, bSY N, ϕSY N ), The null model assumes that both parameters b and ϕ are the same for the SY N and DEL datasets, while the alternative models allow the DEL dataset to have different parameters from the SY N dataset: only bDEL, only ϕDEL, or both bDEL and ϕDEL were allowed to vary. We calculated the likelihood of each model, chose the best fitting model with hierarchical likelihood ratio tests (hLRTs) and estimated the parameters of the selected model. Bootstrap estimates of confidence intervals were estimated with 1000 bootstrap replicates.
Relationship between deleterious mutations and gene expression. The SIFT4G annotation of the C. rubella database was used to match the gene IDs of the mutation and expression data. For each tissue, the relationship between the bias in the number of deleterious mutations between subgenomes and the bias in homeologue expression was investigated by calculating, for gene i in accession j, the difference (d ij ) in the number of deleterious mutations (DEL) between homeologous gene pairs: d ij = DEL ij Cg -DEL ij Co. The expression ratio between the homeologues of genes with significant HSE was used as a measure of homeologue expression bias: e ij = Cbp ij Co Cbp ij Co +Cbp ij Cg Genes were further classified into four categories according to the deleterious mutations bias, d, and homeologue expression bias, e: Genes with no bias in the distribution of deleterious mutation (d = 0) or no significant HSE (F DR < 0.
g, RNA, nucleotide and GTP binding or MF related to transporter activity, Fig. S7  and Fig. S8 ) indicating concerted changes of gene expression between the two subgenomes. Deleterious mutations accumulate preferentially on the C. orientalis subgenome and are associated with the level of expression. Among the 11 million genomic sites segregating across the five genomes, about 3 million alleles were specific to the Capsella species, and 669,675 of these species-specific alleles were annotated by SIFT4G with the A. thaliana SIFT database, and 432,354 of them were annotated with the C. rubella database. The estimated proportion of deleterious mutations among species and among the four populations of C. bursapastoris were similar independently of whether A. thaliana or C. rubella was used for SIFT4G annotation (Fig.
Material and methods Samples for pollen analysis were prepared using standard procedures  (Faegri, Iversen, 1989). Pollen data were calculated as the percentage of total pollen excluding Cyperaceae, aquatic plants and fern spores. More methodological details are available in  Miras et al. , 2004; 2015 and Chassiot et al. , 2018. As the complete pollen diagrams were previously published in the above-mentioned studies, we present here synthetic pollen diagrams showing key taxa for the vegetation reconstruction. Summary curves adopt an "indicator species" approach (sensu  Behre, 1981) , summing pollen taxa relative to their ecological affinity  (Antonetti et al. , 2006)  and to their indicative value of anthropogenic impact  (Guenet, 1986)  in Auvergne. As the main objective of our palaeoenvironmental reconstruction is to track the main trends in "forested vs open landscape" and "natural vs anthropogenic landscape" through time, 9 summary curves have been plotted: • The "Riparian Vegetation" curve which represents the percentage values of Alnus.