dataesr/scientific-paragraphs-categorization huggingface dataset:
e. myocardial infarction, placement of coronary artery bypass graft or angioplasty, carotid endarterectomy, stroke), and end-stage renal disease. Death from all causes except for AIDS related conditions was counted as non-AIDS defining. Diagnoses of AIDS are recorded using the clinical 1993 CDC definition  [13]  and causes of deaths are categorized using the Coding Cause of Death in HIV (CoDe) classification  [14]. Members of the coordinating office visit all centers at least once a year to ensure correct patient selection and to verify that data are accurate. In patients with a clinical event, all entered data are checked against case files and 10% of case files are checked in all other patients.
A software based on directed acyclic graphs  [15]  was employed to identify the appropriate model to correctly estimate the direct effect of ID condition on the risk of the outcomes. To be consistent with previous analysis of the data of the cohort, current CD4 count was fitted on a log2 scale as this was shown to improve the goodness of fit of the model. Models also included time from initiation of new regimen to viral suppression, current or previous smoking, history of impaired renal function at baseline (defined as an estimated Cockroft-Gault glomerular filtration rate ,60 mg/dL/1. 73 m 2 ), anaemia (defined as haemoglobin levels ,14 mg/dl in males and ,12 mg/dl in females), diabetes (defined as having previously started any antidiabetic treatment or diagnosis of insulin dependent diabetes), and hypertension (defined as a systolic/diastolic blood pressure.
Primary support is provided by the  European Commission   BIOMED 1  ( CT94-1637 ),  BIOMED 2  ( CT97-2713 ), the 5th Framework ( QLK2-2000-00773 ), the 6th Framework ( LSHP-CT-2006-018632 ), and the 7th Framework (FP7/2007-2013, EuroCoord no. 260694 ) programmes. Current support also includes unrestricted grants by  Gilead ,  Pfizer , and  Merck and Co. The participation of centres from Switzerland was supported by The  Swiss National Science Foundation  (Grant  108787 ). AZ received a grant from  MSD SHARP & DOHME GmbH  for the performance of this research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Primary support is provided by the  European Commission   BIOMED 1  ( CT94-1637 ),  BIOMED 2  ( CT97-2713 ), the 5th Framework ( QLK2-2000-00773 ), the 6th Framework ( LSHP-CT-2006-018632 ), and the 7th Framework (FP7/2007-2013, EuroCoord no.
Marker BC7 was amplified singly at 59°C. To determine the size of the microsatellites, the PCR products were diluted and multiplexed prior to scanning with the help of a Megabase sequencer (Amersham Pharmacia). The multiplexing consisted of mixing the PCR products of several markers (either BC1, BC2, BC5 and BC9 or BC3, BC4, BC6, BC7 and BC10) in the same well. In each well, ET-400 labelled with Rox dye (Amersham Pharmacia) was used as a size marker. GENETIC PROFILER software (Amersham Biosciences) was then used for the microsatellite size analysis. Complete microsatellite size profiles (referred to as haplotypes hereafter) were obtained for 44 lettuce strains and 42 tomato strains.
Genetic diversity of tomato and lettuce strains Unbiased gene diversity (Hnb) and allelic richness were computed separately for the tomato and lettuce strains with GENETIX software  (Nei, 1978). The software FSTAT v. 2. 9. 3  (Goudet, 1995)  was used to compute allelic richness per locus corrected for minimum sample size of 16 isolates. Several other diversity indices (all taking values between 0 and 1; 1 indicating the maximum level of diversity) were also computed with GENCLONE v. 1. 0 software  (Arnaud-Haond & Belkhir, 2007)  as follows. The Simpson diversity index (D*) was used to measure the probability that two randomly selected individuals in a population have different genotypes. The evenness index, whose value tends to 1 when genotypes have a similar abundance (equitable distribution of clones) was also used  (Arnaud-Haond et al.
Genetic structure To test the hypothesis of host specialization in B. cinerea, the discriminant analysis of principal components (DAPC) was used, implemented in the R package ADEGENET  (Jombart, 2008). In this analysis, the number of clusters was fixed to two, with one cluster including all strains from tomato and the other one including all strains from lettuce. The optimization procedure implemented in ADEGENET was used to select the optimal number of principal components to be retained in the analysis  (Jombart et al. , 2010). In a second step, the DAPC was also performed without any a priori hypothesis on the genetic structure. In this case, the optimal number of clusters in the data set was firstly identified, using the k mean clustering approach (function 'find. clusters' in ADEGENET).
The best clustering solution was determined, using Bayesian information criterion (BIC). The distribution of tomato and lettuce strains among clusters and their probability of assignation were analysed. A strain was considered as accurately assigned to a cluster when its membership probability was >0Á8. The level of genetic differentiation between the identified clusters was assessed by computing Weir & Cockerham's F ST values with the software ARLEQUIN v. 3. 5  (Excoffier et al. , 2005). Finally, to better understand the relationships between the different strains, a neighbour-joining (NJ) tree was computed with the program POPULATIONS (v. 1. 2. 32, provided by Olivier Langella, UMR de g en etique v eg etale, Gif-sur-Yvette, France). The NJ tree was based on the distances of  Cavalli-Sforza & Edwards (1967) , computed from the microsatellite loci.
The length of resulting stem lesions was monitored daily from the third to the seventh day after inoculation and these data were used to compute the area under the disease progression curves (AUDPC). Based on prior work, strain BC1 was used as a reference for aggressiveness  (Decognet et al. , 2009). For each strain, an index of aggressiveness (IA) was calculated relative to BC1 as IA = 100 9 (AUDPC strain /AUDPC BC1 ). The test was conducted twice independently.
Evaluation of aggressiveness on lettuce leaves The aggressiveness of the strains was also compared on detached lettuce leaves (cv. Mantilia). Leaves were taken from 8-week-old lettuce plants and placed in clear polystyrene boxes over watersaturated filter paper in order to keep high humidity. Each leaf was inoculated in its centre with a mycelial plug (diameter 5 mm) excised from a 3-day-old colony on PDA. Care was taken to position the mycelium in direct contact with the plant tissue. The inoculated leaves were incubated in a growth chamber at 21°C with a light intensity of 162 lmol m À2 s À1 and a photoperiod of 10 h. The leaves were photographed at 48, 72 and 96 h after inoculation and the surface of lesions was measured with image analysis software ASSESS v. 2. 0 (APS Press). The AUDPC and IA were calculated as described above.
Statistical analysis Statistical analyses were performed with STATVIEW (v. 5; SAS Institute). Nonparametric Mann-Whitney tests were used to determine significant differences between gene diversity, allelic richness and aggressiveness of tomato and lettuce strains. Correlation between aggressiveness on tomato and on lettuce was estimated using the nonparametric Spearman's rank correlation coefficient (Rho). Statistical inferences were made at the 5% level of significance, unless indicated otherwise.
Genetic structure A first DAPC was performed by fixing the number of clusters (K = 2) to test the hypothesis that the genetic structure of B. cinerea strains corresponded to their host of origin. The scatter plot of the analysis showed large overlap between tomato and lettuce strains (Fig. 2a ). Moreover, a low percentage of strains were clearly assigned to a given cluster (data not shown): only 51% of lettuce and 25% of tomato strains had a membership probability above 0Á8. This result is supported by the distribution of the strains in the NJ tree (Fig. 3 ). Although lettuce strains were more abundant than tomato strains in certain branches, all strains were widely intermixed in the tree regardless of their host of origin. A second DAPC was performed to investigate the genetic structure of lettuce and tomato strains without any a priori hypothesis about the factors shaping the structure.
The analysis was done on the set of data corrected for clonality. Support for the nodes was assessed by bootstrapping loci 1500 times. Shaded boxes identify tomato strains. Circles identify haplotypes common to lettuce strains and tomato strains. Plant Pathology (2015) 64, 119-127 lettuce. The significant difference observed when strains were inoculated on tomato could be attributed, as hypothesized above, to a progressive selection on tomato of the strains with the best fitness on that plant. This hypothesis could be tested in further experiments by combining genotypic and phenotypic characterization of sequentially sampled strains of B. cinerea during a growing season. In conclusion, the results showed an ability of all examined B. cinerea strains to attack both tomato and lettuce and point to a lack of clear host specialization.
Figure3Neighbour-joining tree (based on the distance of Cavalli-Sforza & Edwards (1967) ) showing the genetic relationships among 31 strains of Botrytis cinerea collected from lettuce and 16 strains collected from tomato. The analysis was done on the set of data corrected for clonality. Support for the nodes was assessed by bootstrapping loci 1500 times. Shaded boxes identify tomato strains. Circles identify haplotypes common to lettuce strains and tomato strains.
Acknowledgements This study was supported by a grant of the  French National Research Agency  ( Sysbiotel  project). The authors thank  Dr Karine Berthier  for her precious help with genetic structure analyses. The authors also thank the interns who participated in strain sampling and phenotypic characterization ( A. Pinault ,  M. Bordier ,  A. Del Vitto ) and the plant production teams of the  Experimental Unit of INRA Alenya-Roussillon and of the Plant Pathology Research Unit of INRA Avignon.
Diversity curves The temporal evolution of two variables is analysed here: the taxonomic diversity at the specific and the generic levels. Both are simple counts of the parvipelvian taxonomic richness for each time interval (the stage level), from the Hettangian (Early Jurassic) to the Turonian (Late Cretaceous). The dataset compiled is available in Text S7. Stages characterize periods of Earth's history with supposed rather constant climate, ocean dynamics, etc. , but sometimes greatly differ in duration. Stage duration influences the number of specimens and thus the biodiversity. Rarefaction methods (e. g. [31] ) cannot be employed here because numerous stages of Cretaceous record a very small number of specimens and should therefore be omitted from the analysis using this method. We divided the largest stages (Aptian and Albian) into their usual substages (lower and upper Aptian; lower, middle, and upper Albian), based on ammonite stratigraphy  [32] [33] [34] [35] [36] [37] [38] , rather than using temporal bins.
Because both the Upper Greensand Formation and Cambridge Greensand Member can occur together and all specimens were collected without precise stratigraphic data, it is possible that some specimens were listed as belonging to the wrong 'greensand' deposit in the collection database. Text S2 lists all ichthyosaur specimens from the Upper Greensand Formation. Cambridge Greensand member, UK. The Cambridge Greensand Member is a glauconitic and phosphatic sandstone forming the basal part of the Lower Chalk Formation in the Bedfordshire 'Straits' area/Transitional Zone (i. e. central England, East Anglia Massif)  [47, 51, 52]. Hopson et al. [52]  revised the stratigraphy of the English Upper Cretaceous. The 'Lower Chalk' of previous authors is called the Grey Chalk Subgroup, containing two formations in the central England zone: the West Melbury Marly Chalk Formation at the base, overlapped by the Zig Zag Chalk Formation.
Acknowledgments We warmly thank  S. Chapman  and  P. Barrett  at the NHMUK,  M. Riley  and the staff of the CAMSM,  J. Liston  and  J. Keith Ingham  at the GLAHM,  M. Evans  at the LEICT,  Brice Etienne  and  Thierry Beving  at the RGHP, and  A. Dreze  at the RBINS for access to specimens, logistical help and valuable discussions. This manuscript also benefited from careful and insightful reviews from  A. Farke ,  J. Pardo Pe  ´rez and one anonymous reviewer. V. F. was an Aspirant of the  Fonds de la Recherche Scientifique-FNRS  when this research was conducted.
Recruitment Three cases of ICEP were identified from the national, internet-linked database (created in 2008) for pediatric interstitial lung disease (ILD) of the National Reference Center for Rare Lung Diseases (Respirare®, http://www. respirare. fr) [2]. Data were available for 217 cases of ILD, with "eosinophilic lung diseases" being one of the lessfrequent etiologies (5/217)  [2]. As acute diseases are not included in the registry, cases of IAEP were retrieved by mailing to all members of the National Reference Center and to all members of the "Groupe Francophone de Réanimation et d'Urgences Pédiatriques". This mailing process also permitted the identification of two supplementary cases of ICEP not yet registered and four other cases of eosinophilic lung diseases.
Peripheral blood eosinophilia is rarely found in UIP and DIP (around 10% of cases)  [27]. ICEP can evolve, moreover, spontaneously into lung fibrosis with a DIP-like reaction  [28]. All of these data indicate that there is still a place for lung biopsy in the diagnosis of ICEP when the initial setting and evolution are not usual. The modality of these biopsies also needs to be clarified, as case 3, with an unusual evolution, was initially described as having a standard histopahologic finding of ICEP on transbronchial biopsies. In cases not responding to initial oral corticosteroid treatment, diagnostic biopsies should be performed after a period of weaning off treatment, to contribute to the differential diagnosis between ICEP and idiopathic interstitial pneumonia. However, the optimal length and the feasibility of this weaning period need to be established.
Residue-residue contact prediction The aim of the original DCA publication  [8]  was the identification of inter-protein residue-residue contacts in protein complexes, more precisely in the SK/RR complex in bacterial signal transduction. More recently, global methods for inferring direct co-evolution attacked the problem prediction of intra-domain contacts for large protein domain families  [9] [10] [11] [12] [13] [14] [15] [16] 26]. Thanks to the development of more efficient approximation techniques triggered by the wide availability of single-domain data on databases like Pfam  [40] , one can now easily undertake co-evolutionary analysis of a large number of protein families on normal desktop computer. To give a comparison, whereas the message-passing algorithm in  [8]  was limited to alignments with up to about 70 columns at a time (typically requiring some ad-hoc pre-processing of larger alignments to select the 70 potentially most interesting columns), the subsequent approaches easily handle MSA of proteins with up to ten times this number of columns.
In this context, our multivariate Gaussian DCA is particularly efficient: parameter estimation can be done explicitly in one step, and the computation of the relevant coupling measures such as the direct information (DI) and the log-likelihood also uses explicit analytical formulae. The analytical tractability of Gaussian probability distributions results in a major advantage in algorithmic complexity, and therefore in real running time. In the included implementation of the algorithm the largest alignment analyzed (PF00078, L = 214 residues, M = 126258 sequences) the DI is obtained in about 20 minutes, whereas a more typical alignment (e. g. PF00089, L = 219, M = 15894) is analyzed in less than a minute on a normal @2270 MHz Intel R Core i5 M430 CPU on a Linux desktop. With respect to the computational complexity of the algorithm, the sequence reweighting step is O M 2 L (since it requires a computation of sequence similarity for all sequence pairs in the MSA), while the model's parameters estimate is O L 3 (since it requires to invert a covariance matrix whose size is proportional to L).
Here, we will show that this gain in running time has no detectable cost in terms of predictive power. To this aim, we first studied the prediction of intra-domain contacts (see Fig. 1 ). From the Pfam database  [40] , a set of 50 families was selected for which the number of representative sequences is high enough to allow for a meaningful statistical analysis (average length L = 173. 48 residues, average number of sequences per alignment M = 32660. 2), cf. the Methods section. For each family, 4 measures were determined: DI in mean-field approximation, DI and Frobenius norm (FN) in the Gaussian model, Average-product-corrected mutual information (MI) as described in  [41]. As mentioned above, the FN in the Gaussian model is the same as that computed in the mean-field approximation of the discrete DCA model.
0. 2. As a second test we ran on the same data-set a direct comparison between our method's best score, PSICOV  [12]  and plmDCA  [15]. Fig. 2  shows that our method's performance is comparable to that of PSICOV (and even marginally better after the first 50 inferred couplings), and that the two methods are slightly better for the first 10 predicted contacts (with a 100% accuracy on the first contact). At ten predicted contacts, the true positive average is about 95% for all three methods. From ten predicted pairs on, both our method and PSICOV perform slightly worse than plmDCA: at 100 predicted contacts, the true positive rate is about 72% for PSICOV, 77% for the Gaussian model and 80% for plmDCA. A sample of running times for the three methods and different problem sizes, reported in Table  1 , shows that our code can be at least an order of magnitude faster then PSICOV, and two orders of magnitude faster then plmDCA.
These results suggest that our method is a good candidate for large scale problems of inference of protein contacts. Visual inspection of the predicted contacts does not reveal any significant bias with respect to the residue position, nor with respect to the sencondary or tertiary structures of the proteins. As an example, in Fig. 3  we show the first 40 predicted contacts (39 out of which are true positives) for the protein familiy PF00069 (Protein kinase domain) using the Gaussian DCA methods with the FN score: the pictures seem to indicate a sparse, fair sampling across the set of all true contacts. Finally, we have used the SK/RR data set containing 8,998 cognate SK/RR pairs, cf. Methods, to predict inter-protein residue-residue contacts. Results can be compared with those presented in  [18] , where the original message-passing DCA was applied to the same data-set, and 9 true contact prediction were reported before the first false positive appeared.
In Fig. 4 , results are shown for mean-field and Gaussian DCA, using the DI score: both methods improve substantially over the message-passing scheme (20 true positive predictions at specificity equal to one), but are highly comparable (with a little but not significant advantage of the Gaussian scheme). Again, we find that the improved efficiency and analytical tractability of Gaussian DCA comes at no cost for the predictive power. True positive rate plotted against number of predicted pairs. Data for plmDCA  [15]  (green) and PSICOV version 1. 11  [12]  (red) was obtained using the code provided by the authors with standard parameters as found in the distributed code, except that PSICOV was run with the -o flag to override the check against insufficient effective number of sequences. The true positive rate is an arithmetic mean over 50 Pfam families (see Table  2  for the list); thin lines represent standard deviations.
Predicting interactions between proteins in bacterial signal transduction A typical bacterium uses, on average, about 20 two-component signal transduction systems to sense external signals, and to trigger a specific response. In bacteria living in complex environments, the number of different TCS may even reach 200. While the signals and consequently the mechanisms of signal detection vary strongly from one TCS to another, the internal phosphotransfer mechanism from the SK to the RR, which activates the RR, is widely conserved across bacteria: A majority of the kinase domains of SK belong to the protein domain family HisKA (PF00512), all RR to family Response reg (PF00072)  [40] , cf. the Methods section. Despite their closely related functionality, the interactions in the different pathways have to be highly specific, to induce the correct specific answer for each recognized external signal.
Discussion In this work we have derived a multivariate Gaussian approach to co-evolutionary analysis, whereby we cast the problem of the inference of contacts in MSAs, as well as candidate interacting partners within two MSAs of interacting proteins, into a simple Bayesian formalism, under the hypothesis of normal inverse Wishart distribution of the Gaussian parameters. The major advantage of this method is the very simple structure of the resulting probability distribution, which allows to derive analytical expressions for many relevant quantities (e. g. likelihoods and posterior probabilities). As a result, the computations performed with this model can be very efficient, as demonstrated by the code accompanying this paper. Furthermore, our tests indicate that the prediction accuracy of residue contacts using the Gaussian model is comparable or superior to that achieved using the mean-field Potts model of  [10] , or by using the PSICOV method of  [12]  with default settings; accuracy in pairing interaction partners is comparable to that achieved in  [18].
Data Input data is given as multiple sequence alignments of protein domains. For the first question (inference of residue-residue contacts in protein domains), we directly use MSAs downloaded from the Pfam database version 27. 0  [40, 46] , which are generated by aligning successively sequences to profile hidden Markov models (HMMs)  [47]  generated from curated seed alignments. We have selected 50 domain families, which were chosen according to the following criteria: (i) each family contains at least 2,000 sequences, to provide sufficient statistics for statistical inference; (ii) each family has at least one member sequence with an experimentally resolved high-resolution crystal structure available from the Protein Data Bank (PDB)  [48] , for assessing a posteriori the predictive quality of the purely sequence-based inference.
The average sequence length of these 50 MSAs is L 173 residues, the longest sequences are those of family PF00012 whose profile HMM contains 602 residues. The list of included protein domains, together with their PDB structure, is provided in Table  2   Table  2. 50 Pfam families used in the benchmarks, together with their associated PDB entries Following  [12] , we discarded the sequences in which the fraction of gaps was larger then 0. 9. However, in  [12] , an additional pre-processing stage was applied, in which a target sequence is chosen as the one for which prediction of contacts is desired, and all residue positions in the alignment (i. e. columns in the alignment matrix X) where the target sequence alignment has gaps are removed. We did not find this Figure  7. Illustration of the encoding of a sequence from FASTA format to its intermediate numeric representation (matrix A) to its final binarized representation (matrix X).