dataesr/scientific-paragraphs-categorization huggingface dataset:
Results In total, 103 M. tuberculosis isolates were collected and analyzed; patients providing samples were 61. 2% males and 38. 8% females, with a mean age of 45. 3 (range 3-93) and 51. 05 years old (range 22-93), respectively. Although all patients were current residents in Santiago, Chile, 11. 6% were foreign-born migrants with main country of origin being Peru (66. 6%) and Bolivia (25%). Spoligotyping of the 103 isolates revealed a total of 56 different patterns: 16 patterns (15. 5%) corresponded to orphan strains in the SITVIT2 database, not yet reported, and 40 patterns (containing 87 strains) corresponded to shared-types or SITs in the SITVIT2 database. Among these 40 shared-types, 2 isolates from the present study in Chile matched an orphan strain recorded in the database from USA, therefore a new shared-type, SIT4013, was created.
8%). The high presence of SIT37/T3 (6. 8% of strains) was noteworthy as this SIT is usually more commonly found in Eastern Africa, and migration from Africa is highly rare in Chile. With respect to 12-loci MIRU clustering, the most frequently clustered isolate was 12-MIT585 (n = 6), that in SITVIT2 database corresponds to spoligotypes SIT37/T3 (n = 5) and SIT281/T1 (n = 1). Furthermore, in the SITVIT2 database, 12-MIT585 was present in 6 isolates only: in Spain from two South American patients (from Ecuador and Peru, respectively); and in USA (n = 1), Caribbean (n = 2), and Belgium (n = 1). Minimum spanning trees (MSTs) illustrating evolutionary relationships between spoligotypes and 12-MIRU-VNTRs were constructed for all strains included in the present study (n = 103). Fig. 1A  shows a MST based on spoligotypes in which three major groups belonging to the modern group PGG2/3 (LAM, T, H, X, S) were evident as highly predominant (representing 88.
Finally, it is important to mention that both genotyping methods used in present study are based on analysis of a limited number of loci, and it has been described that VNTR loci as well as particular spoligotyping patterns can exhibit variable discriminatory power in different mycobacterial lineages. Rapid markers evolution with a tendency to converge can lead to significant amounts of homoplasy, and hence the limitations of these tools in defining deep phylogenetic groups in M. tuberculosis complex or other bacteria  [50]. This can explain discordant findings for T and H lineages classification between both SITVIT2 and MIRU-VNTRplus databases (S2 Table ). Future investigations by use of more discriminatory tools such as whole genome sequencing (WGS) might be helpful to identify true phylogenetic relationships and also obtain a more precise evaluation of TB transmission.
Among orphan isolates, we noted similar spoligotyping patterns (with absence of spacer 6) among orphans Or08, Or11, and Or12 within our study (S1 Table ). The ancestral genotypes found among orphan patterns in our study are most probably suggestive of ancestral lineages undergoing extinction after an initial distant contact with patient(s) infected with Africanum lineage or the African continent. This finding may suggest a probable ancient link with Africanum lineage and the African continent. Interestingly thanks to the "SpolSimilaritySearch tool" under development in SITVIT2 database, we detected that a similar spoligotype was found once in Italy: ■■■■■□■□□□□□■■■■■■■■□□□□■■■■■■■■■■■■□□□■■■■; octal code 764077607777071; AFRI_2. However, one cannot exclude the possibility of a recent introduction of such strains in Chile.
A supplemental figure (S2 Fig. ) has been added to support the finding of high presence of SIT37/T3 in Chile. This figure shows geographical maps based on SITVIT2 interrogation prior to entry of present dataset to summarize percentages of SIT37 (A) and strains belonging to T3 sub-lineage (B), by country. Note that this distribution should not be confounded with data in Table  3 , which shows proportions of a given spoligotype in the global database. For example, we have 7 strains for SIT37/T3 representing 6. 8% of our study sample. However when checked in the database, these 7 strains represent 1. 34% of all SIT37/T3 strains present in SITVIT2. Finally, if we look for all identical strains in the database, we see that 19. 19% were reported from AFRI-E, 11. 9% from EURO-N, 11. 52% from EURO-W, etc.
Description of clusters containing 3 or more isolates in this study, and their worldwide distribution in the SITVIT2 database. Worldwide distribution is reported for regions with more than 3% of a given SITs as compared to their total number in the SITVIT2 database. The definition of macro-geographical regions and sub-regions (http://unstats. un. org/unsd/methods/m49/m49regin. htm) is according to the United Nations;.
75%, and clustered isolates to 60/103 or 58. 25%). Note that SIT followed by an asterisk indicates "newly created" SIT due to 2 or more strains belonging to an identical new pattern within this study or after a match with an orphan in the database; SIT designation followed by number of strains: 4013* this study n = 2, USA n = 1. ** Lineage designations according to SITVIT2 using revised SpolDB4 rules; "Unknown" designates patterns with signatures that do not belong to any of the major lineages described in the database. *** * A total of 39/40 SITs containing 85 isolates matched a preexisting shared-type in the database, whereas 1/40 SIT (n = 2 isolates) was newly created. A doi:10. 1371/journal. pone. 0118007. t001.
It reflects its geographical localization as well as due to the similarity of specific TB genotypes circulating in Russia (a majority of Beijing genotypes) with those prevalent in Central, Eastern and South- Eastern Asia. ** The 3 letter country codes are according to http://en. wikipedia. org/wiki/ISO_3166-1_alpha-3; countrywide distribution is only shown for SITs with 3% of a given SITs as compared to their total number in the SITVIT2 database. Note that FXX code designates Metropolitan France. doi:10. 1371/journal. pone. 0118007. t003.
Genome alignment and data pre-processing The Enredo/Pecan/Ortheus genome alignment of the five species human, chimpanzee, gorilla, orangutan and macaque from Scally et al. [21]  was used as input. In order to remove badly sequenced and / or ambiguously alignment regions, we filtered the input 5-species alignments using the MafFilter program  [35]. We sequentially applied several filters to remove regions with low sequence quality score and high density of gaps. Details on the filters used can be found in the supplementary material of Scally et al. [21].
Distribution of ILS For the autosomal distribution of ILS, we fitted a skewed normal distribution (R package 'sn'  [38] ) using the fitdistr function from the MASS package for R. For the X chromosome ILS distribution, we fitted a mixture of gamma and Gaussian distributions. The mixed distribution follows a normal density with probability p, and a gamma density with probability 1-p. In addition to p, the mixed distribution has four parameters: the mean and standard deviation of the Gaussian component, and the shape and rate of the gamma component. The L-BFGS-B optimization method was used to account for parameter constraints. Resulting parameter estimates are 0. 209 for the mean of the Gaussian component, 0. 066 for the standard deviation of the Gaussian component, 4. 139 for the alpha parameter (shape) of the gamma component, 83.
Characterization of low-ILS regions In order to characterize the patterns of ILS at a finer scale, we computed ILS in 100 kb windows sliding by 20 kb along the posterior decoding of the alignment. To exhibit regions devoid of ILS, we selected contiguous windows with no more than 10% of ILS each. Eight of these regions were greater than 1 Mb in size, and their resulting amount of ILS is less than 5% on average (Table  1 ). The coordinates of these regions were then translated according to the human hg19 genome sequence. These data are available as a GFF file for visualization in the UCSC genome browser at http://bioweb. me/HCGILSsupp/.
Reaction network Metabolic data was extracted from MetaCyc public database version 19. 0  [20]. MetaCyc contains a large collection of curated metabolic pathways from all domains of life. In addition, metabolites, reactions, enzymes and genes are also listed. Metabolic pathways described in MetaCyc are generally short (4. 3 reactions on average) and have been experimentally elucidated in at least one organism. A metabolic network was reconstructed using MetaCyc reactions as nodes. We linked two reactions by a directed edge when the product of one reaction is the substrate of the other one. However, to avoid the high connectivity problems that are common when building such metabolic networks, we limited shared compounds to "main compounds", i. e. metabolites deemed biologically relevant to both reactions in at least one metabolic pathway.
Reaction molecular signatures Reaction Molecular Signatures (RMS) were computed for all MetaCyc reactions, belonging or not to a metabolic pathway, as described in  [19]. These signatures encode changes in atoms and bonds where the reaction is taking place. First, structures of all molecules involved in a reaction were downloaded from MetaCyc website in MDL Molfile format. Using ChemAxon MolConvert software  [21] , all molecules were standardized by adding implicit hydrogen atoms and applying aromatization when needed. Stereo signature molecular descriptors  [22]  were then computed for heights 1 and 2 with the MolSig software (http://molsig. sourceforge. net). These molecular signatures are encoded using SMILES-like strings  [23]  and the height parameter corresponds to a distance for the inclusion of neighbour atoms and bonds up from a given atom.
Then, a hierarchical clustering was build on these distances using the Ward algorithm  [25]  and the tree was cut at a cophenetic distance threshold of 90. To deal with reaction directionality, RMS having strictly opposite signatures were merged in a single entry. Higher values of the height parameter were not used because they lead to too precise signatures with many describing only one reaction. The RMS classification of reactions is available in Additional file 1 and the source code for the RMS computation was deposited in GitHub (https://github. com/mSorok/createRMS. git). The RMS method has been chosen in this work as it guarantees that all reactions described by the same signature perform the same chemical transformation, making manual post-process unnecessary.
RMS networks The reaction network was reduced in a directed network of chemical transformations represented by RMS. As shown in Fig. 1 , reactions signed by the same RMS are grouped in a single node. Two RMS are connected by a directed edge in the RMS network if there is at least one edge in the original reaction network linking reactions signed by the corresponding source and target RMS. For computational complexity reasons and the lack of explicit representation of repeated reactions in pathway databases, edges are not created if source and target RMS are identical (i. e. self-loops are avoided). This transformation was made for the two RMS heights and we obtained two networks called RMS-H1 and RMS-H2 networks. Furthermore, this graph reduction, which aggregates reaction nodes and edges, allowed us to define Markov chains transition probabilities of order 1 between connected RMS.
Indeed, Muto et al. obtained similar results but with a higher number of detected conserved paths (between 338 and 928 for the same path lengths). Although our results are not directly comparable to those of Muto et al. by the usage of different primary data sources (i. e. MetaCyc and KEGG, respectively), the RMS paths detected by our method can be directly considered as conserved modules whereas the paths obtained by Muto et al. need a manual examination to obtain conserved modules from them. In fact, they adopted a looser definition of chemical conservation without taking into account side compounds and using fingerprint similarities to group reactions without the constraint that the reactions perform the same chemical transformation. Only 34 reaction modules were finally confirmed by the authors  [15].
RMS path scoring and learning To go further, our method proposes an evaluation of chemical module conservation in the metabolism using three scores corresponding to different biological points of view. Indeed, scoreRea reflects the diversity of reactions performing the same chemical transformations on different substrates, scoreProt represents the conservation of enzymes performing these chemical transformations across the tree of life and scorePageRank shows the topological importance of the module in the network by highlighting chemical hubs. These scores were computed for all paths and analyzed more precisely for paths of length 2 in the RMS-H2 network (Table  5 ). It should be noticed that the scoreProt cannot be computed for about 20 % of paths as they contain at least one RMS without any known protein catalyzing the corresponding reactions, i.
e. 30 % of the RMS-H2 correspond to orphan enzyme activities. As depicted in Fig. 5 , paths from known metabolic pathways present statistically significant higher values for the three scores than in all possible paths computed from the RMS network (p-value < 2e -16 using Tukey's HSD tests). Similar results were obtained for RMS-H1 network (see Additional file 4). These results confirm that the defined scores are useful to capture biologically relevant paths in the RMS network and should allow us to discover new metabolic modules. Furthermore, we found only a weak correlation between scoreRea and scorePageRank (Spearmans' correlation coefficient of 0. 66) and no correlation between other pairs of scores. Therefore, the proposed scores can be considered as rather independent and then used conjointly to explore the RMS network.
Conclusions We present here a novel metabolic network representation where nodes are chemical transformations depicted by reaction molecular signatures. This data model is particularly useful for finding conserved chemical transformation modules in metabolic pathways as they correspond to paths in the RMS network. An important number of modules was detected and could be integrated in metabolic databases, like KEGG  [16]  or MetaCyc  [20] , to help biologists looking for similar pathways. Furthermore, new metrics (i. e. scoreRea, scoreProt and scorePageRank) were introduced to evaluate module conservation according to different biological meanings. We show that known metabolic paths present higher score values than random ones and that the scores, used conjointly, may predict module pathway types.
This line of approach was used in • a matched case-control study to identify association between DNA methylation levels and hepatocellular carcinoma in tumor-adjacent non-tumor tissues  [21] ; • the case-crossover study of prescription drugs and driving for the whole population  [22]. In these two studies, as well as in  [23] , the algorithms are based on the penalized IRLS, solved as a weighted Lasso problem via cyclical coordinate descent  [24, 25]. However, they differ in their actual implementations, a major benefit of our proposal relying in the simplification of the calculations involved in the likelihood function. The resulting gain in efficiency allows for the processing of large-scale datasets  [22]. We detail these calculations here and illustrate their interest on the pharmacoepidemiological study that originally motivated these algorithmical developments.
Algorithms The conditional likelihood in (6) derived for the conditional logistic model corresponds to the partial likelihood of the stratified, discrete-time Cox proportional hazards model. Standard survival data analysis software can be used for the analysis of a 1:M matched case-control study. Analogously, algorithms proposed for solving the Lasso for the Cox model allowing for stratification can be used. For the particular design consisting in one case and one control, we may apply a penalized unconditional logistic regression. Indeed, as showed above, the conditional likelihood function simplifies dramatically resulting in the likelihood function for the unconditional logistic regression without intercept term applied to the differences of the predictors. The L 1 penalized logistic regression problem (  7 ) is convex but not differentiable.
Publicly available implementation Several algorithms have been proposed for solving the Lasso for the Cox model  [45, 42, [46] [47] [48] [49] [50]. Among those proposing a publicly available code, only the method proposed by Goeman  [49]  allows for stratification (implementation publicly available through penalized R-package). However, as discussed in  [16] , this implementation is not applicable to large datasets. Among the efficient algorithms solving the Lasso for the logistic model, those proposed by  [42]  (consisting in a generalization of the Lars-Lasso algorithm described in  [36] ),  [49]  (based on a combination of gradient descent and Newton's methods), and  [24]  (based on a quadratic approximation followed by a cyclic coordinate descent method) have a publicly available R implementation (glmpath, penalized and glmnet packages, respectively).
The glmpath package  [42]  did not accommodate models without intercept. The penalized package  [49]  allows for several practical options. In particular, a nointercept Lasso logistic regression model can be fitted using the differences as independent variables and a constant response. However, though the Newton method has fast convergence, forming and solving the underlying Newton systems require excessive amounts of memory for large-scale problems. This package is optimized for situations with many covariates, but does not handle a large amount of observations. Finally, the glmnet package can deal efficiently with very large (sparse) matrices and has been shown to be faster than competing methods  [33, 24]. However, the logistic function of this package fails to converge when a constant response is used in the logistic model without intercept.
A summary is presented in Table  1. Parallel to our work, Sun et al. [21]  and Reid et al. [23]  proposed an IRLS-cyclic coordinate descent algorithm to resolve the Lasso for (un)conditional logistic regression. While all these works rely on IRLS-cyclic coordinate descent, the objectives and the strategies implemented to address these objectives are different. The algorithm developed in  [21]  was implemented into the pclogit R package which can be downloaded at http://www. columbia. edu/~sw2206/. This algorithm is based on the optimization of the original unsimplified likelihood function  (5)  with a penalty that encourages the grouping encoded by a given network graph. The algorithm developed in  [23]  was implemented into the clogitL1 R package which can be downloaded from CRAN. The main concern of the authors is the extension to matched sets consisting in more than one case (denote K the number of cases per stratum) and M > 1 controls, with large K and large M.
In this situation, the conditional likelihood function has a more complicated form, and the authors apply a recursive formula to compute the likelihood and its derivatives exactly. This scheme results in involving intensive computations that are not amenable to the processing of large datasets. Our R package clogitLasso is available at our institution's Web page, in the "links and downloads" menu, http://www. isped. u-bordeaux. fr/biostat. Two strategies are implemented. The first one, discussed in  [16] , is dedicated to small to moderate sample sizes. It is based on the stratified discrete-time Cox proportional hazards model and depending on the penalized package  [49]. The second one, discussed in the present paper, is amenable to the processing of large datasets (large N ). It directly targets the conditional logistic regression problem, relying on the lassoshooting package  [25]  for the application of cyclic coordinate descent.
The lassoshooting package is particularly well adapted for large-scale problems and provides a no-intercept option. For example, large sparse data matrices (resulting from rare exposures), can be stored in a sparse format as well as the diagonal matrix of weights and working response vector. The Lasso solver lassoshooting proceeds with X t WX and X t Wz (of dimension p × p and p × 1, respectively) instead of W 1/2 X and W 1/2 z (of dimension N M × p and N M × 1, respectively). Other practical options are available, for example penalized and unpenalized (always included in the model) variables can be specified. The methods, model selection criteria and capabilities of clogitLasso are detailed in  [51, 44].
Data sources and designs Information on drug prescriptions and road traffic accidents was obtained from the following anonymized population-based registries: the national health care insurance database (which covers the whole French population and includes data on reimbursed prescription drugs), police reports, and the national police database of injurious road traffic crashes. Drivers involved in an injurious crash in France, between July 2005 and May 2008, were included in the study. Traffic crash data including information about alcohol impairment and drivers' responsibility for the crash were collected. When the breath test is negative (concentration < 0. 5 g/L), the driver is recorded as not being under the influence of alcohol. Responsibility was determined by a standardized method that assigns a score to each driver on the basis of factors likely to reduce driver responsibility (such as road, vehicle and driving conditions, type of accident, difficulty of the task involved, and traffic rule obedience, including alcohol consumption).
Age was coded by using a discrete qualitative variable with seven categories: 18-24, 25-44, 45-64, 65-70, 70-75, 75-80, ≥80 and then using dummy variables. Long-term diseases were defined by an administrative status in the French health care insurance database allowing full reimbursement of health care expenses related to 30 chronic diseases. Chronic disease was coded by using a binary variable: presence or absence of any fully reimbursed chronic diseases. Of the 58,700 drivers with a negative alcohol test in the analytic database, 26,568 (45%) were considered responsible for their crash. After matching, 6,857 casecontrol pairs were highly comparable in terms of external factors, among them, 3,381 matched pairs showed different medicinal drug exposure (for at least one drug) on the day of the crash.
Case-crossover study The epidemiological question is: "What is different about the day of the crash for atfault drivers?" The purpose of the analysis is to compare exposure to medicinal drugs probabilities on the day of crash (case period) and on the day one, two, three and four months prior to the crash date (control periods) for each at-fault driver. Thus, we matched each case period to four control periods (1:4 matching). Each period was separated from the next one by one month, the maximal duration of a treatment dispensed at the pharmacy in France, to avoid any residual effect of an exposure in one period on the following one. Of the 26,568 at-fault drivers with a negative alcohol test in the analytic database, 15,367 (58%) showed different medicinal drug exposure (for at least one of the five periods; for at least one drug).