dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The Midterm Review Team would like to acknowledge the assistance of the following individuals, all of whom made presentations on selected topics to the Review Team: Narendra Arora: Executive Director, The  INCLEN Trust International & CHNRI, INCLEN Executive Office , Chair of the Working Group. Mary Agocs: Senior Advisor ,  Measles  and  Rubella Initiative ,  International Services ,  American Red Cross. Hans Christiansen:  Team Lead ,  Measles, Tetanus ,  Yellow Fever ,  Meningitis Vaccines ,  UNICEF Supply Division, UNICEF. Steve Cochi: Senior Advisor, Global Immunization Division, Centers for Disease Control and Prevention. Matt Ferrari: Associate Professor of Biology, The Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University. Marta Gacic-Dobo: Manager ,  Strategic Information Team, Expanded Programme on Immunization  (EPI),  Department of Immunization, Vaccines and Biologicals, World Health Organization.
based on MLST data  [15]. Among the strains included in that study, 135 B. afzelii strains were reported to have been isolated from human samples, mostly originating from the Netherlands (n = 79). The present study proposed an approach that could characterize a large population of clinical B. afzelii strains originating from several European countries using MLST. Based on the resulting MLST data, the genetic diversity and phylogeny of 308 B. afzelii strains was analyzed. The aims were to compare strains originating from different European countries and to establish whether associations exist between the MLST profiles of B. afzelii and different clinical manifestations. Identifying the spatial distribution of specific bacterial genotypes and determining their associations with clinical symptoms of Lyme borreliosis is both epidemiologically and clinically relevant in our understanding of Lyme borreliosis.
Clinical isolates In total, 308 strains were included in this study. Initially, 63 B. afzelii clinical isolates, a single isolate per patient, were obtained in culture and characterized using MLST. Most of these strains were isolated from specimens collected from patients infected in France (n = 61). One specimen was obtained from a patient infected in Germany and another from a patient probably infected in Switzerland. To increase the statistical power and sample size and to retrieve samples representing as many European countries as possible, additional clinical B. afzelii strains, previously analyzed using MLST, were also included. Sequence data on 121 strains were downloaded from the online MLST database. MLST data on a further 124 strains published by Coipan et al. were also added to the dataset, after excluding possible duplicates  [15, 22].
PCR and sequencing The primers and conditions of PCR amplification of the eight MLST housekeeping loci (clpA, clpX, nifS, pepX, pyrG, recG, rplB and uvrA) were used as previously described  [13, 25]  with one modification: the use of the Phusion High-Fidelity PCR Master Mix (2×) (Thermo Fischer Scientific, Villebon-sur-Yvette, France) with an appropriate denaturing temperature of 98 °C (initial denaturation: 30 s; cycles: 10 s). The success of the amplification was checked by performing agarose gel electrophoresis, followed by cleaning and sequencing the PCR products in both directions (by the firm GATC, Konstanz, Germany). DNA traces were converted into high-quality finished DNA sequences using the SeqTrace software  [26]. All sequence data on the 63 strains typed in this study are available on the online MLST database  [22].
Nucleotide sequence analysis The sequences of the eight housekeeping loci were compared with those in the online MLST database to assign allele and ST numbers. Novel alleles and novel STs were submitted to the curator who allocated them consecutive numbers. The sequence data from the strains obtained from the online MLST database (n = 121) and the sequence data from the study by Coipan et al. [15]  (n = 124) were incorporated into the dataset. Multiple sequence alignment of the sequences of individual genes was conducted using the ClustalW algorithm of MEGA 7. 0  [27]. The sequences of every individual locus were trimmed to equivalent lengths, as in the Borrelia MLST database. Sequences of eight loci were concatenated, giving an in-frame sequence of 4785 bp (not including nucleotide insertions/deletions).
Phylogenetic analysis A maximum likelihood tree based on the concatenated sequences of all the STs under investigation was constructed using the MEGA 7. 0 software. The optimal evolutionary nucleotide substitution model used was determined with the corrected Akaike information criterion using the W-IQ-Tree tool available online  [29]. The general time reversible model, with gamma distributed rate variation across sites and a proportion of invariable sites, was selected for the phylogenetic analysis (GTR+G+I). The tree  was rooted with the European B. garinii 20047 strain as the outgroup, extracted from the MLST database. Support for internal nodes was estimated using the nonparametric bootstrap method with 1000 replications. Allelic profiles of STs were used to generate a global optimal eBurst (goeBURST) diagram using the PHYLOViZ software  [30].
Pathogenicity analysis In line with previously published classifications, EM was defined as localized infection, and ACA, NB, multiple EM, borrelial lymphocytoma, Lyme arthritis, fasciitis, and bacteremia were all defined as disseminated infection  [7, 10]. The single case of morphea reported was not classified because the existence of a causal relationship between this skin manifestation and Lyme disease has not yet been definitely established  [33]. In addition, several Slovenian strains extracted from the MLST database were not classified as they were reported as having originated from skin samples without any further explanatory detail. Detailed information about the number of strains belonging to each ST and CC and strains associated with each clinical manifestation is listed in Additional file 2: Database 2.
Intra-species diversity The MLST analysis of all 308 clinical B. afzelii strains included in this study yielded a total of 149 STs (0. 48 STs/ isolate) (Additional file 1: Database 1). Among these 149 STs, 103 each corresponded to a single strain, whereas 46 STs were each associated with at least two strains. The most prevalent ST was the ST 71 representing 7. 5% of the dataset (n = 23). Among the 63 strains typed in this study, 10 new alleles and 27 new STs were identified, none of which have been previously described (numbers assigned to the new STs: 695-718; 731-733). The sequence of one of these new alleles (uvrA No. 203) includes an insertion consisting of nine nucleotides corresponding to a tandem duplication. The analysis of concatenated sequences resulted in a haplotype diversity of 0.
Phylogenetic analyses Phylogenetic analyses were based on concatenated sequences of type strains belonging to each Borreliella species extracted from the MLST database. The analyses confirmed that the 63 strains typed using MLST in this study were definitely B. afzelii strains (Additional file 3: Figure  S1 ). The rooted phylogenetic tree based on concatenated sequences of all the included clinical B. afzelii strains showed short evolutionary distances between strains, and few bootstrap values were > 70% (Fig. 1 ). No major clade was detected. The clinical manifestations and countries of origin related to the strains were distributed throughout the phylogenetic tree. A goeBURST analysis provided a population snapshot of clinical B. afzelii strains, as shown in Fig. 2. At the DLV threshold, eight major CCs (consisting of three or more STs), five minor CCs (consisting of two STs), and 11 singletons were identified.
Spatial distribution of STs detected in European Lyme borreliosis patients In total, 27 of the 46 STs that occurred in more than one patient were isolated in at least two different European countries (Additional file 1: Database 1). Patients from Slovenia had a greater ST diversity (0. 88 STs/strain) than those from Germany, The Netherlands, and France (0. 47, 0. 62, and 0. 46 STs/strain, respectively). The main STs differed between the four countries most frequently involved, namely Germany (ST 71), the Netherlands (STs 1,071, 476 and 171), France (ST 347) and Slovenia (ST 557) (Fig. 3 ). The pairwise comparisons between population samples suggested moderate genetic differentiation between strains from the Netherlands and Germany (F ST = 0. 08975, P < 0. 0001), the Netherlands and Slovenia (F ST = 0.
STs associated with clinical manifestations of human Lyme borreliosis Among the 308 strains included in this study, a total of 270 strains were classified as associated with localized or disseminated infection (Table  2 , Additional file 2: Database 2). The 38 remaining strains were not classifiable since they originated from a morphea (n = 1), from unspecified cutaneous samples (n = 35) and from samples of unknown nature (n = 2). Among the 270 strains considered, 190 (70. 4%, corresponding to 41 STs) were collected from patients with a localized infection (EM), whereas 80 (29. 6%, corresponding to 24 STs) were associated with manifestations suggesting disseminated infection. Most of the B. afzelii strains isolated from patients with disseminated infections were involved in skin damage, including ACA, borrelial lymphocytoma, and multiple EM (n = 56, 70%).
Only 20 strains were isolated from cerebrospinal fluid in the context of NB. Among the 42 STs detected in at least two patients with localized or disseminated manifestations, 18 STs were present only in patients with localized infection (EM) (Table  6 , Additional file 2: Database 2). In particular, ST 467 and ST 1071 were associated with 12 and 11 strains obtained from patients with EM, respectively, and no strain was associated with disseminated infection. ST 467 and ST 1071 were significantly related to localized infection compared with other STs that were associated with at least two strains (EM: 100 vs 66. 67% [95% CI: 59. 23-73. 23%] and 100 vs 66. 90% [95% CI: No association was reported between clinical manifestations and the CCs identified at the DLV threshold. STs 71 (associated with disseminated infection), 467, and 1071 (associated with EM) all belong to the CC0 comprising 94 STs, which makes it impossible to draw any significant conclusions (Additional file 2: Database 2).
However, when the CCs were defined at the SLV threshold, CC1' , including ST 71, was found to be significantly (See figure on previous page. ) Fig. 1  Rooted maximum likelihood tree of B. afzelii based on concatenated sequences of eight MLST housekeeping genes. A total of 149 STs corresponding to 308 B. afzelii isolates grown in vitro from human samples were used in this study, including data previously published by Coipan et al. [15]  (n = 126) and data from the MLST database (n = 121)  [13, 22]. The total number of strains associated with each ST is indicated in brackets next to the ST. Type strains (T) of B. afzelii (strain VS461) and B. garinii (strain 20047) were also included in the dataset. The bootstrap values obtained on highly supported nodes after 1000 repetitions (with ≥ 70% support) are given below the branches.
Discussion This study was focused on B. afzelii, the main species involved in Lyme borreliosis in humans in Europe  [35] , with the aim to investigate the European strains of B. afzelii obtained from human samples to determine their diversity, spatial distribution, and pathogenicity using their MLST profiles. A total of 308 B. afzelii strains were included in the dataset. Sixty-three of these strains, mostly originating from France, were characterized using MLST, and sequence data of additional 245 strains originating from several European countries were accessed from the literature  [15, 22]. The 308 B. afzelii strains obtained from European human patients were categorized into 149 STs, 27 of which were described for the first time in this study. These data reported 0. 48 STs/strain (0. 88 STs/strain in Slovenia) Fig.
afzelii strains in Europe (0. 50 STs/strain); data on 124 of these strains were included in this study's dataset. In addition, the reported high levels of haplotype diversity and low levels of nucleotide diversity in our study were also supported by the results reported by Coipan (0. 984 vs 0. 976 and 0. 00202 vs 0. 00196, respectively). Although a larger number of strains was included in our study, this did not affect the validity of the genetic heterogeneity parameters previously described. In addition, Coipan et al. [15]  conducted a rarefaction analysis, which did not show the evidence of any significant difference in B. afzelii diversity between humans and ticks, measured in terms of the number of STs. The authors observed a high level of haplotype diversity and a low level of nucleotide diversity among the B.
afzelii (n = 319 ticks)  [15, 22]. ST 71 also belongs to a CC called CC1' , defined at the SLV threshold, which was significantly associated with NB compared with other CC's. Other associations between B. afzelii STs and disseminated forms of Lyme borreliosis in Europe were previously described by Coipan et al. [15]  using a smaller dataset of 135 clinical strains. They established a significant association between ST 335, ST 1054, ST 1073 and ACA (cases of ACA vs EM: 2/1, 2/0, and 2/0, respectively). These conclusions were not reinforced by our study, although our dataset was more than twice the size of that used in the previous study. We did not observe any supplementary strains corresponding to these STs, apart from the three strains belonging to ST 335 and associated with EM. However, we noticed that some B.
in humans  [43, 44]. Some genotypes of B. afzelii may have been selected during the in vitro growth of strains, which would affect the representativeness of the dataset, thus introducing a possible bias. This bias could also hide some mixed-strain infections. More studies wherein MLST is performed on DNA extracts from clinical samples, instead of strain cultures, would help to establish whether this selection of genotypes actually exists and to what extent it could impact our results.
Acknowledgements We acknowledge  Dr H. Sprong  for providing us with detailed data on B. afzelii strains from the study by Coipan et al. [15] and  Dr G. Margos  for her scientific support. We thank the French investigators of the  PRI 3977  for sending us skin biopsies from patients suspected of having Lyme borreliosis. We are also grateful to  L. Zilliox  and technicians from the  French Centre National de Reference des Borrelia  for their technical assistance.
Toxicological responses to chemical insult are largely regulated by transcriptionally activated pathways that may be independent, correlated and partially or fully overlapping. Investigating the dynamics of the interactions between stress responsive transcription factors from toxicogenomic data and defining the signature of each of them is an additional step toward a system level understanding of perturbation driven mechanisms. To this end, we investigated the segregation of the genes belonging to the three following transcriptionally regulated pathways: the AhR pathway, the Nrf2 pathway and the ATF4 pathway. Toxicogenomic datasets from three projects (carcinoGENOMICS, Predict-IV and TG-GATEs) obtained in various experimental conditions (in human and rat in vitro liver and kidney models and rat in vivo, with bolus administration and with repeated doses) were combined and consolidated where overlaps between datasets existed.
, 2013). Very little is known about species differences, tissue specificity, chemical specificity, or other subtleties in the activation of these pathways. To investigate this further, we performed a transcriptomic analysis of large and medium size toxicogenomic datasets from the EU 6th and 7th framework projects carcinoGENOMICS and Abbreviations: AhR, Aryl Hydrocarbon Receptor; ATF4, Activating Transcription Factor 4; CAC, Chemical Activation Capacity; ChIP-seq, Chromatin Immunoprecipitation followed by DNA sequencing; CYP1A1, Cytochrome P1-450 A1; FC, Fold Change; Nrf2, Nuclear Factor (Erythroid-derived 2)-Like 2 (NFE2L2); TF, Transcription Factors. Predict-IV, as well as from TG-GATEs. Within these studies we also identified some potentially useful specific activators of the pathways investigated.
Generation of Target Gene Lists For each of the three TF of interest (AhR, Nrf2, and ATF4), the following three search strategies, from the works of  (Limonciel et al. , 2015) , were applied in PubMed to retrieve TF target genes: (i) search for TF name and ChIP-sequencing or ChIP-microarray studies, (ii) search for TF name and TF-specific response element and "Electrophilic Mobility Shift Assay" or ChIP studies, and (iii) search for TF name and TF-specific DNA response element and name of a target gene known. In the first tier of this strategy, high-throughput sequencing datasets were retrieved, which provided extensive lists of genes shown to have the TF bind in their promoter region. In the second tier, lower throughput investigations were included, providing target genes that were more deeply investigated in the article with proven TF binding of the promoter region.
Construction of a Chemical-Effects Transcriptomics Database The database of chemical-induced transcriptomic changes comes from three projects: carcinoGENOMICS  (Vinken et al. , 2008) , Predict-IV  (Mueller et al. , 2015)  and TG-GATEs (Igarashi et al. , (1) Number of chemicals assayed in at least one of the three source projects. (2) Cyclosporine A is the only chemical that was used in the three projects. Cyclosporine A appears in every single experimental category and sub-category (except carcinoGENOMICS's Rat tests). (3) In carcinoGENOMICS, all 15 chemicals tested on rat cells, except one (Dimethylnitrosamine), were also tested on human cells. (4) Beside Cyclosporine A, and five of the chemicals that appear in TG-GATEs as well, all chemicals are specific to carcinoGENOMICS [2-Nitrofluorene and N-nitrosomorpholine (TG-GATEs "Human liver in vitro bolus" and "Rat liver in vivo bolus"); and Diclofenac, Nifedipine and Tolbutamide (all liver categories of TG-GATEs)].
Data Sources The carcinoGENOMICs and Predict-IV data are publicly accessible on the diXa database hosted by The European Bioinformatics Institute 1. In carcinoGENOMICS, in vitro renal cell experiments were performed using the human cell lines RPTEC/TERT1 (human, telomerase transfected) and NRK-52E (rat). The study no. is DIXA-003. Differentiated cell cultures were exposed to a single bolus of non or low cytotoxic (<IC10) concentration of chemical for 6, 24, or 72 h before lysis in TRIZOL, RNA purification and transcriptomic analysis on Affymetrix microarrays as described  (Limonciel et al. , 2012). Affymetrix Human Genome U133 Plus 2. 0 GeneChIP arrays were used for human samples and Rat Genome 230 2. 0 GeneChIP for rat samples. Normalization quality controls, including scaling factors, average intensities, present calls, background intensities, noise and raw Q-values were within acceptable limits for all chips.
Hybridization controls were identified on all chips and yielded the expected increases in intensities. All subsequent analyses were based on normalized expression values generated using the MAS5 normalization algorithm. It is noted that RMA or GCRMA normalization would have been preferred. Normalized data was imported into GeneSpring (Agilent) to identify log2 fold change values for selected genes. Within PREDICT-IV, in vitro testing of nephrotoxic and hepatotoxic compounds were performed on RPTEC/TERT1 cells (renal model), primary human hepatocytes, and rat hepatocytes  (PHH and PRH, respectively). The study no. on the diXa database is DIXA-095. Differentiated cell cultures were exposed daily to a high (≤10% cell death) or low concentration of chemical for 1, 3 or 14 days, as described  (Wilmes et al.
, 2013 (Wilmes et al. , , 2014;; Aschauer et al. , 2015; Crean et al. , 2015; Limonciel et al. , 2015). Transcriptomic analysis was carried out on Illumina R HT 12 v4 BeadChip arrays for kidney and PHH human samples, except RPTEC/TERT1 exposed to CsA (HT 12 v3 chips). PRH samples were analyzed with Illumina R RatRef-12 v1 BeadChIP arrays. Results were normalized by quantile normalization and expressed as log2 fold over timematched control. Where several probes existed for a given gene, the probe with the highest variation across the dataset was selected. The TG-GATEs datasets comprised in vivo rat data from liver and kidney tissue, as well as data from in vitro primary rat and human hepatocyte cultures, after a single administration of chemical and repeat dosing (see Table  1 ) 2. CEL files were downloaded from the Open TG-GATEs database of the Toxicogenomics Project and Toxicogenomics Informatics Project under CC Attribution-Share Alike 2.
1 Japan. Probe annotation for the primary human hepatocyte data was performed using the hthgu133pluspmhsentrezg. db package version 17. 1. 0 and probe mapping was performed with hthgu133pluspmhsentrezgcdf downloaded from NuGO 3. Probe annotation for the rat data was performed using the rat2302rnentrezg. db package version 19. 0. 0 and probe mapping was performed with the rat2302rnentrezgcdf package version 19. 0. 0 downloaded from NuGO. These mappings summarize the corresponding probes to a single probe set per gene. Probe-wise background correction (Robust Multi-Array Average expression measure), between-array normalization within each treatment group (quantile normalization) and probe set summaries (median polish algorithm) were calculated with the RMA function of the Affy package (Affy package, version 1.
38. 1) (Irizarry et al. , 2003). The normalized data were statistically analyzed for differential gene expression using a linear model with coefficients for each experimental group within a treatment group  (Wolfinger et al. , 2001). A contrast analysis was applied to compare each exposure with the corresponding vehicle control. For hypothesis testing the moderated t-statistics by empirical Bayes moderation was used followed by an implementation of the multiple testing correction of Benjamini and Hochberg  (Hochberg and Benjamini, 1990)  using the LIMMA package  (Smyth et al. , 2005). All interspecies gene conversions where done using the provided human gene symbols which were converted to human or rat gene identifiers using the online conversion tool of bioDBnet 4. Altogether, the collected data concern 804 genes from the 857 genes identified in PubMed as targets of AhR, Nrf2 and ATF4.
Data Selection The heterogeneity of the sources of information of our database widens its coverage and strengthens its capacity to represent multiple conditions. However, this richness makes the database's structure complex. To simplify the analysis without losing potentially important information, we focused on conditions providing the best background to study the three pathways individually. The effects observed following exposure to a chemical could vary greatly depending on exposure duration. Exposures lasting more than 24 h tend to cause mixed stress responses that make it difficult to delineate the activation of specific molecular pathways and the initial mechanisms of toxicity of chemicals. These conditions could be a potential source of noise for the analysis and were thus excluded.