dataesr/scientific-paragraphs-categorization huggingface dataset:
Modern Human Methylomes To compare ancient and present-day methylomes, we downloaded RRBS methylation data for 101 human tissues from UCSC (see supplementary table S7, Supplementary Material online) (http://hgdownload. cse. ucsc. edu/goldenPath/ hg19/encodeDCC/wgEncodeHaibMethylRrbs/; last accessed September 1, 2016). This represented a total number of $1. 6 millions of CpG sites, for which we calculated Ms in their 1,000 flanking bases (i. e. , 2,000 bp centered around each CpG). This window size was selected to reflect the correlation of the methylation levels within neighboring regions in the human genome  (Eckhardt et al. 2006). As no hair tissue was represented in the RRBS data, we downloaded from UCSC (http://hgdownload. cse. ucsc. edu/ goldenPath/hg19/encodeDCC/wgEncodeHaibMethyl450/; last accessed September 1, 2016) methylation data estimated using the Infinium HumanMethylation450 BeadChip system (450K Illumina array;  Sandoval et al.
Acknowledgments This work was supported by the  Danish Council for Independent Research, Natural Sciences  ( 4002-00152B ); the  Danish National Research Foundation  ( DNRF94 ); the  Villum Fonden (miGENEPI) ;  and Initiative d'Excellence Chaires d'attractivite, Universitede Toulouse (OURASI). L. O. conceived the study; K. H. and L. O. designed the study; A. S. O. performed molecular experiments; E. W. and L. O. provided material and reagents; K. H. performed all analyses, with input from L. O. ; K. H. programmed the epiPALEOMIX pipeline, with input from M. S. ; L. O. wrote the text, with significant input from K. H. , and all other coauthors.
Integrons recombine gene arrays and favor the spread of antibiotic resistance. Their broader roles in bacterial adaptation remain mysterious, partly due to lack of computational tools. We made a program -IntegronFinder -to identify integrons with high accuracy and sensitivity. IntegronFinder is available as a standalone program and as a web application. It searches for attC sites using covariance models, for integron-integrases using HMM profiles, and for other features (promoters, attI site) using pattern matching. We searched for integrons, integron-integrases lacking attC sites, and clusters of attC sites lacking a neighboring integron-integrase in bacterial genomes. All these elements are especially frequent in genomes of intermediate size. They are missing in some key phyla, such as ␣-Proteobacteria, which might reflect selection against cell lineages that acquire integrons.
The analysis of metagenomics data has unraveled a vast pool of novel cassettes in microbial communities  (30). Although antibiotic-resistance integrons were found to be abundant in human-associated environments such as sewage  (31) (32) (33) , most cassettes in environmental datasets encode different functions (or genes of unknown function)  (31) (32) (33). The study of these functions, and of the adaptive impact of integrons, has been hindered by the difficulty in identifying integron cassettes. The bottleneck in these analyses is the recognition of attC sites, for which few tools were made available. The program XXR identifies attC sites in the large Vibrio integrons using pattern-matching techniques  (20). The programs ACID (6) (no longer available) and ATTACCA  (34)  (now a part of RAC, available under private login) were designed to search for class 1 to class 3 mobile integrons.
Such classical motif detection tools based on sequence conservation identify attC sites only within restricted classes of integrons. They are inadequate to identify or align distantly related attC sites because their sequences are too dissimilar. Yet, these sites have conserved structural constraints that can be used to identify highly divergent sequences. We built a program named IntegronFinder (Figure  3 , https://github. com/gem-pasteur/Integron Finder) to detect integrons and their most distinctive components: the integron-integrase with the use of HMM profiles and the attC sites with the use of a covariance model (Figure  2 ). Covariance models use stochastic context-free grammars to model the constraints imposed by sequence pairing to form secondary structures. Such models have been previously used to detect structured motifs, such as tRNAs  (35).
They provide a good balance between sensitivity, the ability to identify true elements even if very diverse in sequence, and specificity, the ability to exclude false elements  (36). They are ideally suited to model elements with high conservation of structure and poor conservation of sequence, such as attC sites. IntegronFinder also annotates known attI sites, P intI and P C , and any pre-defined type of protein coding genes in the cassettes (e. g. , antibiotic resistance genes). IntegronFinder was built to accurately identify integronintegrases and attC sites of any generic integron. Importantly, we provide the program on a webserver that is free, requires no login, and has a long track record of stability (37) (http://mobyle. pasteur. fr/cgi-bin/portal. py#forms:: integron finder). We also provide a standalone application for large-scale genomics and metagenomics projects.
Data The sequences and annotations of complete genomes were downloaded from NCBI RefSeq (last accessed in November 2013, http://ftp. ncbi. nih. gov/genomes/refseq/bacteria/). Our analysis included 2484 bacterial genomes (see Supplementary Table  S1 ). We used the classification of replicons in plasmids and chromosomes as provided in the GenBank files. Our dataset included 2626 replicons labeled as chromosomes and 2006 as plasmids. The attC sites used to build the covariance model and the accession numbers of the replicons manually curated for the presence or absence of attC sites were retrieved from INTEGRALL, the reference database of integron sequences (http://integrall. bio. ua. pt/)  (38). We used a set of 291 attC sites (Supplementary File 1) to build and test the model, and a set of 346 sequences with expert annotation of 596 attC sites to analyze the quality of the program predictions (Supplementary Tables  S2a  and S2b ).
1b1  (42). This profile was named intI Cterm (Supplementary File 2). <<<< < << <<<-< <<< -------- >>>>>>>---------->>>>>>> A B C D 5' 3' UCS R' R'' L' L'' EHB VTS R'' R' L' L'' UCS UCS VTS EHB We used 119 protein profiles of the Resfams database (core version, last accessed on January 20, 2015 v1. 1), to search for genes conferring resistance to antibiotics (http://www. dantaslab. org/resfams, (43) ). We retrieved from PFAM the generic protein profile for the tyrosine recombinases (PF00589, phage integrase, http://pfam. xfam. org/, (44) ). All the protein profiles were searched using hmmsearch from the HMMer suite version 3. 1b1. Hits were regarded as significant when their e-value was smaller than 0. 001 and their alignment covered at least 50% of the profile.
Overview of IntegronFinder: a program for the identification of attC sites, intI genes, integrons and CALIN elements The input of IntegronFinder is a sequence of DNA in FASTA format. The sequence is annotated with Prodigal v2. 6. 2 (46) using the default mode for replicons larger than 200 kb and the metagenomic mode for smaller replicons ('-p meta' in Prodigal) (Figure  3 ). In the present work, we omitted the annotation part and used the NCBI RefSeq annotations because they are curated. The annotation step is particularly useful to study newly acquired sequences or poorly annotated ones. The program searches for the two protein profiles of the integron-integrase using hmmsearch with default parameters from HMMER suite version 3. 1b1 and for the attC sites with the default mode of cmsearch from INFERNAL 1.
More precisely, if a locus contains an integronintegrase and attC sites (complete integron), the search is constrained to the strand encoding attC sites between the end of the integron-integrase and 4 kb after its most distant attC. If other attC sites are found after this one, the search is extended by 4 kb in that direction until no more new sites are found. If the element contains only attC sites (CALIN), the search is performed on the same strand on both directions. If the integron is In0, the search for attC sites is done on both strands in the 4 kb flanking the integron-integrase on each side. The program then searches for promoters and attI sites near the integron-integrase. Finally, it can annotate the integron genes' cassettes (defined in the program as the CDS found between intI and 200 bp after the last attC site, or 200 bp before the first and 200 bp after the last attC site if there is no integron-integrase) using a database of protein profiles (option '--func annot').
For example, in the present study we used the ResFams database to search for antibiotic resistance genes. One can use any hmmercompatible profile databases with the program. The program outputs tabular and GenBank files listing all the identified genetic elements associated with an integron. The program also produces a figure in pdf format representing each complete integron. For an interactive view of all the hits, one can use the GenBank file as input in specific programs such as Geneious  (48). The user can change the profiles of the integrases and the covariance model of the attC site. Thus, if novel models of attC sites were to be built in the future, e. g. , for novel types of attC sites, they could easily be plugged in IntegronFinder.
Phylogenetic analyses We have made two phylogenetic analyses. One analysis encompasses the set of all tyrosine recombinases and the other focuses on IntI. The phylogenetic tree of tyrosine recombinases (Supplementary Figure  S1 ) was built using 204 proteins, including: 21 integrases adjacent to attC sites and matching the PF00589 profile but lacking the intI Cterm domain, seven proteins identified by both profiles and representative of the diversity of IntI, and 176 known tyrosine recombinases from phages and from the literature  (12). We aligned the protein sequences with Muscle v3. 8. 31 with default options  (49). We curated the alignment with BMGE using default options  (50). The tree was then built with IQ-TREE multicore version 1. 2. 3 with the model LG+I+G4. This model was the one minimizing the Bayesian Information Criterion (BIC) among all models available ('-m TEST' option in IQ-TREE).
We made 10 000 ultra fast bootstraps to evaluate node support (Supplementary Figure  S1 , Tree S1). The phylogenetic analysis of IntI was done using the sequences from complete integrons or In0 elements (i. e. , integrases identified by both HMM profiles) (Supplementary Figure  S2 ). We added to this dataset some of the known integron-integrases of class 1, 2, 3, 4 and 5 retrieved from INTEGRALL. Given the previous phylogenetic analysis we used known XerC and XerD proteins to root the tree. Alignment and phylogenetic reconstruction were done using the same procedure; except that we built ten trees independently, and picked the one with best log-likelihood for the analysis (as recommended by the IQ-TREE authors  (51) ). The robustness of the branches was assessed using 1000 bootstraps (Supplementary Figure  S2 , Tree S2, Table  S4 ).
Pan-genomes Pan-genomes are the full complement of genes in the species. They were built by clustering homologous proteins into families for each of the species (as previously described in (  52 )). Briefly, we determined the lists of putative homologs between pairs of genomes with BLASTP (53) (default parameters) and used the e-values (<10 -4 ) to cluster them using SILIX  (54). SILIX parameters were set such that a protein was homologous to another in a given family if the aligned part had at least 80% identity and if it included more than 80% of the smallest protein. We built pan-genomes for the 12 species having at least four complete genomes available in Genbank RefSeq and encoding at least one IntI. The genomes of these species carried 40% of the complete integrons in our dataset. We did not build a pan-genome for Xanthomonas oryzae because it contained too many rearrangements and repeated elements  (55).
Pseudo-genes detection We translated the six reading frames of the region containing the CALIN elements (10 kb on each side) to detect intI pseudo-genes. We then ran hmmsearch with default options from HMMER suite v3. 1b1 to search for hits matching the profile intI Cterm and the profile PF00589 among the translated reading frames. We recovered the hits with evalues lower than 10 -3 and alignments covering more than 50% of the profiles.
Identification of integron-integrases We identified tyrosine recombinases using the protein profile PF00589 (from PFAM). To distinguish IntI from the other tyrosine recombinases, we built an additional protein profile corresponding to the IntI specific region near the patch III domain  (17)  (henceforth named intI Cterm, see Methods). We found 215 proteins matching both profiles in the complete genomes of bacteria. Only six genes matched intI Cterm but not PF00589. There were 18 808 occurrences of PF00589 not matching intI Cterm, among which only 50 co-localized with an attC site. Among the latter, 29 were in genomes that encoded IntI elsewhere in the replicon (Supplementary Figure  S5 ). The remaining 21 integrases were scattered in the phylogenetic tree of tyrosine recombinases, and only four of them were placed in an intermediate position between IntI and Xer (Supplementary Figure  S1 ).
These four sequences resembled typical phage integrases at the region of the patch III domain characteristic of IntI and they co-localized with very few attC sites (always less than three). This analysis strongly suggests that tyrosine recombinases lacking the intI Cterm domain identified near attC sites are not IntI. Most intI genes identified in bacterial genomes colocalized with attC sites (76%, Supplementary Figure  S5 ). It is difficult to assess if the remaining intI genes are true or false, since In0 elements have often been described in the literature  (47, 58). We were able to identify IntI in the integrons of class 1 to class 5, as well as in well-known chromosomal integrons (e. g. , in Vibrio super-integrons). We also identified all In0 elements in the INTEGRALL dataset mentioned above.
Integrons in bacterial genomes We built a program--IntegronFinder--to identify integrons in DNA sequences. This program searches for intI genes and attC sites, clusters them in function of their colocalization and then annotates cassettes and other accessory genetic elements (see Figure  3  and Methods). The use of this program led to the identification of 215 IntI and 4597 attC sites in complete bacterial genomes. The combination of this data resulted in a dataset of 164 complete integrons, 51 In0 and 279 CALIN elements (see Figure  1  for their description). The observed abundance of complete integrons is compatible with previous data  (7). While most genomes encoded a single integron-integrase, we found 36 genomes encoding more than one, suggesting that multiple integrons are relatively frequent (20% of genomes encoding integrons).
We found such pseudo-genes near 15 out of 279 CALIN elements. It is worth noting that out of the 15 hits, 11 pseudogenes were also matched by the PF00589 profile, which is consistent with the idea that they previously encoded IntI. Overall, our analysis showed that most CALIN (95%) are not close to recognizable intI pseudogenes. We enquired on the possibility that some CALIN might actually be part of an integron and that we have missed this association because of the small 4 kb threshold used in the definition of the clusters. To test this hypothesis, we rerun IntegronFinder with a distance threshold of 10 kb. This analysis found 252 of the previously identified 279 CALIN elements, the remaining 27 being merged with a integron or In0 element with the 10 kb threshold. This shows that in-creasing the distance threshold in the clustering procedure does not significantly change the observed abundance of CALIN.
Considering that many previous studies opposed mobile to chromosomal integrons, we tested if our results remained valid when following this dichotomy. We split our dataset into sedentary chromosomal integrons, mobile integrons and others (unclassified) (see Materials and Methods). Integrons from all three sets were found in the major clades of the IntI phylogeny (Supplementary Figure  S2 ). Around 67% of the integrons encoded in chromosomes were classed as mobile in the species with computed pan-genomes (see Materials and Methods), showing that the separation between chromosomal and mobile integrons may be misleading. Expectedly, given their longer arrays of cassettes (Figure  7 ), the sedentary chromosomal integrons showed more similar attC sites than the mobile ones (Supplementary Fig- Relationship between the number of attC sites in an integron and the mean sequence distance between attC sites within an integron.
The cost of these elements should scale with the inverse of genome size, if larger genomes have fewer constraints on the amount of incoming genetic material and if they select for more frequent horizontal transfer. Hence, the distribution of integrons might result from the combined effect of the frequency of transfer (increasing with genome size) and selection for compact transfer (decreasing with genome size). Further work will be necessary to test this hypothesis. Most integrons with taxonomic identification available in INTEGRALL are from ␥ -proteobacteria (90%)  (38). Our dataset is more diverse; we found many integrons in ␤-Proteobacteria and in other large phyla (such as Spirochaetes, Chloroflexi, Chlorobi or Planctomycetes). This shows that our method identifies integrons in clades distant from ␥ -proteobacteria.
Surprisingly, the genomes from ␣-Proteobacteria had no integrons, even if they encoded many tyrosine recombinases involved in the integration of a variety of mobile genetic elements. The complete absence of integrons, In0 and CALIN in ␣-Proteobacteria is extremely puzzling. It cannot solely be ascribed to the frequency of small genomes in certain branches of ␣-Proteobacteria, since our dataset included 99 genomes larger than 4 Mb in the clade. We also did not find complete integrons in Gram-positive bacteria. It is well known that differences in the translation machinery hinder the expression of transferred genetic information from Proteobacteria to Firmicutes (e. g. , due specificities of the protein S1 (  67 )), but these differences cannot explain the lack of integrons in ␣-Proteobacteria.
ACKNOWLEDGEMENTS J. C. is a member of the ' Ecole Doctorale Frontière du Vivant (FdV) -Programme Bettencourt '. We thank  Didier Mazel ,  Jose A. Escudero ,  Céline Loot ,  Aleksandra Nivina ,  Philippe Glaser ,  Claudine Médigue ,  Alexandra Moura  and  Julian E. Davies  for fruitful discussions and comments on the manuscript. Author contributions: J. C. EPCR designed the study. J. C. made the analysis. J. C. and B. N. wrote the software and webserver. M. T. T. J. contributed with data. J. C. EPCR. drafted the manuscript. All authors contributed to the final text of the manuscript.
AVAILABILITY The program was written in Python 2. 7. It is freely available on a webserver (http://mobyle. pasteur. fr/cgi-bin/ portal. py#forms::integron finder). The standalone program is distributed under an open-source GPLv3 license and can be downloaded from Github (https://github. com/ gem-pasteur/Integron Finder/) to be run using the command line. Supplementary materials include tables containing all integrons found at different level (elements, integrons, genomes, in Supplementary Tables S6, S7 and S8). It includes the list of the 596 attC sites with their annotated position (Supplementary Table  S2a ), and the corresponding file with observed position (Supplementary Table  S2b ). We provide the intI Cterm HMM profile (File S2) and the covariance model for the attC site (File S3).
The aim of this paper is to investigate whether the Matlab-based Damped Bundle Adjustment Toolbox (DBAT) can be used to provide independent verification of the BA computation of two popular software -PhotoModeler (PM) and PhotoScan (PS). For frame camera data sets with lens distortion, DBAT is able to reprocess and replicate subsets of PM results with high accuracy. For lens-distortion-free data sets, DBAT can furthermore provide comparative results between PM and PS. Data sets for the discussed projects are available from the authors. The use of an external verification tool such as DBAT will enable users to get an independent verification of the computations of their software. In addition, DBAT can provide computation of quality parameters such as estimated standard deviations, correlation between parameters, etc.
Or at least, it is hard to verify why two different pieces of software generate slightly (or very) different results. This may be especially true if the software in question are designed from the differing frames of mind of Photogrammetry and Computer Vision. The aim of this paper is to investigate whether the Matlab-based Damped Bundle Adjustment Toolbox (DBAT)  (Börlin and Grussenmeyer, 2013, 2014)  can be used to provide independent verification of the BA computation of two popular software -Photo-Modeler (PM) and PhotoScan (PS).
PhotoModeler and PhotoScan The software that were used in this paper were the EOS Systems PhotoModeler Scanner (PM) v2016. 0. 5 (Feb 2016) 1 and AgiSoft PhotoScan (PS) v1. 2. 3 (Jan 2016) 2. PM is a typical Photogrammetric tool in several respects. It uses the "correction" lens distortion model (4) (EOS Systems, 2016), the internal camera unit is mm, and it uses photogrammetric terminology (external orientation, control points, etc. ). In contrast, PS has an clear Computer Vision heritage: The cited camera model is the "distortion" model (  5 )  (Agisoft, 2016 , App. C), the internal camera unit is pixels, and it uses Computer Vision terminology (extrinsic camera parameters, reprojection error, etc. ). Both software have support for manual and automatic measurements. However, the typical PS project is describes the typical Structure-from-Motion work-flow with mostly an automatic process  (Tomasi and Kanade, 1992; Huang and Netravali, 1994).
CAM The CAM network consisted of a convergent 21-image closerange data set of the PM camera calibration target (figures 2a, 4ab). The camera was an Olympus C4040Z with 7. 5 mm focal length, an image size 2272-by-1704 pixels, and a sensor pixel size of 3. 2 microns. Four circular targets were used as CPs. The remaining 96 targets were used as OPs. All targets were measured automatically with the PM sub-pixel target measurement technique.
SXB The SXB network consisted of a 5-camera subset of an aerial project of the city of Strasbourg (figures 2b, 4cde). The DiMAC aerial camera was pre-calibrated with a focal length of 123. 94 cm and 6 micron pixel size. The images were distortion-free at 8858by-12996 pixels with approximately 75% overlap and 40% sidelap. The flight height was about 1800 m above ground. A project was created in each of PM and PS. The same 16 control points were measured manually in either software. Each project was extended with automatic points by either software (SmartPoints and Tie points, respectively). After removal of two-ray points, 368 SmartPoints and 1166 Tie points remained in the respective projects. A further object point was manually measured in PM.
EXPERIMENTS Six experiments were set up in PM using both data sets. A seventh experiment was set up in PS using the SXB data set. See Table  1  for details. The five first experiments used the same measurements and were setup to compare the PM and DBAT BA algorithms given the same measurements. The C1 and C2 experiments used all available CP and OP in the CAM data set. In C1, the CP were treated as fixed whereas the CPs were weighted in C2. Similarly, the S1 and S2 used all available CP (no OP) as fixed and weighted, respectively. The S3 experiment was a extension of S2 with a single OP. The final two experiments, S4 and S5, were set up to compare the PM and PS computational pipeline given the same image data as well as to compare the results with DBAT. Each PM experiment was run in two versions; (a) one where the bundle adjustment process was preceded by an orientation, and (b) one when it was not.