dataesr/scientific-paragraphs-categorization huggingface dataset:
Meta analysis The meta-analysis was conducted using EasyMA version 2001 and Medcalc (Belgium) version 12. 6. 0. The measure of association between toxoplasmosis and epilepsy was a common oddsratio (OR), recommended for the meta-analysis of observational studies. Respective ORs of each included study were individually verified. Random effects models were used to determine common odd ratios. Homogeneity of the studies was examined using Cochrane Q, 95% confidence intervals (CI) were also derived, and the statistical significance was set at 5%. A scatter plot was drawn from the combined data obtained.
Results The results of the search are presented in Fig. 1. Five databases (Pubmed, ScienceDirect, Refdoc, IENT and Ingentaconnect), two journals (Médecine/Sciences, PlosOne) and Google search gave a total of 684 articles. Of these, 301 were cited more than once. Of the remaining 383 articles on toxoplasmosis and epilepsy, 372 articles were eliminated on the basis of title as either nonepidemiological or in a language other than English or French. The remaining 11 articles were read as abstracts, and one was excluded because its full text was not accessible. Furthermore, two were literature reviews  [12]  [13], one was a meta-analysis  [10]  and one was an epidemiological study that did not meet our inclusion criteria i. e. no control group  [14]. The references of the review articles and meta-analysis did not add any new articles.
Discussion We conducted a systematic review and meta-analysis of literature primarily to estimate the relationship between chronic toxoplasmosis and epilepsy. Our literature search was comprehensive as different databases and specific journals were searched, including those in the French language to avoid any publication bias. However, this search might have excluded important studies published exclusively in other languages. The last meta-analysis on this subject, conducted in 2007,  [10]  included only three studies, whereas ours identified six, table  1. One of the foremost lessons from our work is that despite the fact that T. gondii infection affects nearly one-third of the world's population and is likely to be epileptogenic, few data exist on the subject. This certainly calls for more studies.
5 mg ml À 1 ) in the presence of avidin-sulforhodamine (Av. SRho) and inspected by time-lapse confocal microscopy. As shown in Supplementary Movie 1 and Fig. 1a , this stimulation induced granule budding on the hMC surface as detected by labelling with Av. SRho. Interestingly, although part of the exocyted granules were released in the culture medium, a substantial part of granule content was retained on the hMC surface (Supplementary Movie 1). Measurement of the intensity of the Av. SRho staining using the Region Measurement function of the MetaMorph software showed that degranulation was detectable B10 min after anti-IgE addition and reached a plateau B15 min later (Fig. 1b ). A similar degranulation pattern was obtained when hMCs were triggered by a weak stimulus (0. 06 mg ml À 1 anti-IgE, Supplementary Fig.
5b ). We thus analysed FcgRIIA localization in hMC/B-cell conjugates by confocal microscopy. As shown in Fig. 5d , an enrichment of FcgRIIA was observed at the synaptic area in parallel with the localized exposure of secreted granules. FcgRIIA staining appeared to be punctate suggesting the presence of receptor clusters at the synapse. Scoring of a significant number of individual hMC/rituximab-opsonized B-cell conjugates confirmed that a large fraction of exocyted material was detected at the synaptic area (Fig. 5e ) and that FcgRIIA receptors were significantly enriched within this area (Fig. 5f ). Taken together with the above results, these data show that crosslinking of IgE or IgG FcRs by cell-bound antigens results in a polarized and sustained exposure of granule content at the mast cell/stimulatory cell contact site.
Acknowledgements We thank  Ellen Robey  and Loı ¨c Duprefor critical reading of the manuscript,  Fatima L'Faqihi  (CPTP, plateau technique cytometrie, Toulouse) and  Sophie Allart  ( CPTP, Plateau technique Imagerie cellulaire, Toulouse ) for assistance;  Christine Bezombes  for kindly providing Rituximab. This work was supported by grants from  the Laboratoire d'Excellence Toulouse Cancer-TOUCAN , the  Institut National du Cancer INCa  and the  Association pour la Recherche sur le Cancer.
We control for known bias-inducing aspects of enrichment based analyses, such as: 1) gene length, 2) gene set size, 3) presence of biologically related genes within the same linkage disequilibrium (LD) region, and, 4) genes shared among multiple gene sets. We applied this approach to publicly available SJS/TEN genome-wide genotype data and identified the ABC transporter and Proteasome pathways as potentially implicated in the genetic susceptibility of non-drug-specific SJS/TEN. We demonstrated that the innovative SNP-to-gene mapping phase of the method was essential in detecting the significant enrichment for those pathways. Analysis of an independent gene expression dataset provides supportive functional evidence for the involvement of Proteasome pathways in SJS/TEN cutaneous lesions. These results suggest that Pointer provides.
Pathway databases We used the Kyoto Encyclopedia of Genes and Genomes (KEGG, release December 2010) which contains information for a variety of metabolic pathways, and the Reactome pathway database (release 36). The two pathway collections were chosen on the basis of being manually curated, thus ensuring high quality content. Data was downloaded from the Molecular Signature Database (MSigDB, version 3 of the C2-collections; www. broad. mit. edu/gsea/msigdb/ msigdb_index. html).
Gene expression data from SJS lesions Chung et al. used the Affymetrix Human Genome U133 Plus 2. 0 GeneChip to profile five samples of blister fluid white cells from subjects with multidrug SJS/TEN and six samples of peripheral blood mononuclear cells (PBMCs) [GSE13816], and reported the 200 most differentially expressed genes (DEGs). [3]  We used DAVID  [17]  to calculate the enrichment of canonical pathways for those 200 DEGs.
SNP to gene mapping We used LD structure and eQTL information to map each genotyped SNP to all genes whose function or regulation that SNP (or one of its co-inherited variants) could potentially affect. First, the LD structure around each genotyped SNP was reconstructed. To that end, we utilized the HapMap CEU II SNP reference panel (release 22) which includes 3,572,677 common SNPs (minor allele frequency > 0. 01, NCBI  Build 36). Using the Haploview software  [18] , we computed the r 2 between every genotyped SNP and all HapMap II SNPs within a 4 MB window (a large window size was chosen to capture both proximal and distant co-inhered variants). All HapMap II SNPs having an r 2 ! 0. 5 were grouped together to define a LD cluster for the genotyped SNP. Finally, genes were assigned to every SNP in a LD cluster based on: 1) Physical distance: a gene was assigned to a SNP if the SNP was located within 1500 bp upstream or downstream of the gene's longest known transcript (gene transcript RefSeq annotation was downloaded from UCSC (hg18)  [19]  and mitochondrial genes coordinates from NCBI, RefSeq accession NC_012920.
The complex is implicated in several well-known biological processes, many of which are reported in the Reactome database. Indeed, the database contains 24 proteasome complex-related pathways. Therefore, we applied Pointer to the Reactome collection, aiming to more precisely characterize the proteasome complex processes that are relevant for SJS/TEN susceptibility. Additionally, given that Reactome   contains a different set of biological processes than the ones found in KEGG, we reasoned that Pointer might discover more enriched pathways. We note that the version of Reactome available through the MSigDB lacks pathways related to ABC transporter genes. After shadow analysis we identified 11 significantly enriched pathways with FDR < 0. 25 (Table  3 ). 10 of the 11 enriched pathways, including the top three, were proteasome complexrelated pathways.
Comparison with other GSA methods We benchmarked the performance of Pointer against 2 well-cited gene set enrichment methods: Magenta  [26]  and i-Gsea4gwas-V2  [27]. Both software tools were executed with default parameter settings and using as input the SJS/TEN GWAS data and the KEGG pathway database. The two algorithms utilize somewhat different approaches for the key GSA computational steps (SNP-to-gene mapping and testing for enrichment). Magenta maps SNPs to genes by physical distance and, similar to Pointer, corrects for LD structure. Pathway enrichment is estimated as the overrepresentation of leading edge genes relative to a permutation-based null distribution. Magenta reported no significant pathways after correcting for multiple testing. Although not overall significant, the ABC transporter and Proteasome pathways were among the top four nominally enriched associated pathways (p-value = 0.
Pointer also identified the proteasome complex and the propanoate metabolism pathways as marginally enriched in low-risk variants. For the proteasome complex, independent functional evidence further corroborated the role of the pathway in the pathogenesis of SJS/TEN. Specifically, we showed that the pathway is enriched not only in genetic variants but also in genes which are differentially expressed in blister cells from skin lesions of SJS-TEN versus PBMCs  [3]. As the proteasome complex participates in several biological processes that mediate protein degradation, we further investigated if any of them are specifically implicated in SJS/TEN. To that end we applied Pointer to the Reactome database which, to the best of our knowledge, has the most comprehensive collection of Proteasome-related pathways.
Indeed, although many studies have shown that phosphorylation of AKT at both Ser473 and Thr308 are needed for AKT full activation  [47] , we know now that phosphorylation of AKT at Ser473 is not needed for mTORC1 activation and by consequence for protein synthesis and cell growth, whereas phosphorylation of AKT at Thr308 is essential for these activities  [5]. On the other hand, phosphorylation of AKT at Ser473 is essential for cell survival and glucose uptake  [5] [6] [7] 10]. This could explain why RICTOR overexpression in Melan-a induced a higher number of colonies as a consequence of increased survival rather than an enlargement of colonies, which is a consequence of increased cell growth. In accordance with this, Melan-a RICTOR do not display increased proliferation compared to parental cells (data not shown).
The effect of RICTOR overexpression on clonogenicity may also be linked to other targets independent of mTORC2 because Melan-a RICTOR melanocytes are more resistant than parental cells to the mTORC2 inhibitor OSI-027. Further experiments are needed to identify RICTOR targets involved in this process. Our results demonstrated that RICTOR cooperates with oncogenic NRAS by increasing AKT activation and stimulating clonogenicity of melanoma cells. The CGH data showing that RICTOR is also amplified in BRAF mutated melanoma, suggest that RICTOR could also cooperate with oncogenic BRAF. However, RICTOR amplification is always associated with PTEN LOH in BRAF mutated melanoma, suggesting that RICTOR overexpression may not enhance AKT activation in the presence of PTEN in these melanoma. This effect could be due to the fact that V600E BRAF, but not wild type BRAF, has been found to inhibit mTORC2 induced phosphorylation of AKT Ser473 in the presence of PTEN  [48].
However, deletion of PTEN, activates AKT and overrides V600E BRAF induced inhibition of AKT  [48]. Therefore, if mTORC2 activity is inhibited by V600E BRAF in PTEN wild type melanoma, RICTOR overexpression will not enhance AKT activation in these cells. In agreement with this hypothesis, we found that RICTOR overexpression does not enhance clonogenicity of the SkMel5 melanoma cell lines, which is V600E BRAF mutated and PTEN wild type (data not shown). In conclusion, we showed that RICTOR is responsible for an important negative feedback in the PI3K pathway in melanocytes, restricting their proliferation. Activation of the PI3K/AKT pathway is frequent in melanoma and may enhance this negative feedback, so malignant cells will have to find a way to override these feedback, for example by overexpressing RICTOR.
Array-based comparative genomic hybridization (arrayCGH) We utilized previously obtained CGH array data from 44 cell lines and corresponding peripheral blood mononuclear cells (PBMC) to determine copy number changes at the RICTOR and PTEN loci  [51]. A 2. 6 Mb region covering 236 SNPs in RICTOR gene locus (Chr 5:37,845,789-40,490,831) and a 2. 1 Mb region covering 150 SNPs in PTEN locus (Chr 10:88,570, 230-90,707,272) were analyzed for amplifications and loss of heterozygosity (LOH) between the three groups of cell lines that contained either BRAF, NRAS mutations or did not carry mutations in either of the two genes. Pair wise copy number (CN) analysis and LOH at the RICTOR and PTEN loci was carried out using chromosome copy number analysis tool (CNAT 4. 0, Affymetrix). The copy number data was exported into Excel (see supplementary data) and SNPs that showed > 2 copies were colored red (amplifications) and the SNPs < 2 were colored green (LOH).
The odds of NNRTI-associated TDR also increased in Latin America/Caribbean (odds ratio [OR] = 1. 16; 95% CI: 1. 06-1. 25), North America (OR = 1. 19; 95% CI: 1. 12-1. 26), Europe (OR = 1. 07; 95% CI: 1. 01-1. 13), and upper-income Asian countries (OR = 1. 33; 95% CI: 1. 12-1. 55). In SSEA, there was no significant change in the odds of TDR since national ARV scale-up (OR = 0. 97; 95% CI: 0. 92-1. 02). An analysis limited to sequences with mixtures at less than 0. 5% of their nucleotide positions-a proxy for recent infection-yielded trends comparable to those obtained using the complete dataset. Four NNRTI SDRMs-K101E, K103N, Y181C, and G190A-accounted for >80% of NNRTI-associated TDR in all regions and subtypes. Sixteen nucleoside reverse transcriptase inhibitor (NRTI) SDRMs accounted for >69% of NRTI-associated TDR in all regions and subtypes.
Methods and Findings We reviewed all GenBank submissions of HIV-1 reverse transcriptase sequences with or without protease and identified 287 studies published between March 1, 2000, and December  31, 2013 , with more than 25 recently or chronically infected ARV-naïve individuals. These studies comprised 50,870 individuals from 111 countries. Each set of study sequences was analyzed for phylogenetic clustering and the presence of 93 surveillance drug-resistance mutations (SDRMs). The median overall TDR prevalence in sub-Saharan study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: JHK and MR are employees of the Walter Reed Army Institute of Research, however, the views expressed herein are those of the authors and do not represent the official views of the Departments of the Army or Defense.
MAP was supported in part from the United States Agency for International Development (USAID), however, the contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. SB is a staff member of the World Health Organization and the contents are the responsibility of the authors and do not necessarily reflect the views of the World Health Organization. JPAI is a member of the Editorial Board of PLOS Medicine. All other authors have declared that no competing interests exist. Abbreviations: ARV, antiretroviral; CPR, Calibrated Population Resistance; FSU, former Soviet Union; HIVDB, HIV Drug Resistance Database; IQR, interquartile range; LMIC, low-and middleincome country; MCMC, Markov chain Monte Carlo; NNRTI, non-nucleoside reverse transcriptase inhibitor; NRTI, nucleoside reverse transcriptase inhibitor; OR, odds ratio; PI, protease inhibitor; RT, reverse transcriptase; SDRM, surveillance drug-resistance mutation; SSA, sub-Saharan Africa; SSEA, south/southeast Asia; TAM, thymidine-analog mutation; TDR, transmitted drug resistance.
Study Inclusion Criteria We retrieved all published HIV-1 group M reverse transcriptase (RT) nucleic acid sequences, with or without protease sequences, using a tblastn search of the GenBank nucleotide sequence database v. 200 (released 2014-02-15). Retrieved sequences with the same GenBank "Author" and "Title" fields were grouped into submission sets (or studies). We then read the GenBank annotation and associated published papers to identify studies meeting the following two criteria: (i) studies that described a population of 25 ARV-naïve HIV-1-infected individuals characterized by country, year of virus sampling, and method and site of recruitment, and (ii) studies that contained sequences encompassing RT codons 40 to 240 determined by direct PCR sequencing of plasma, peripheral blood mononuclear cells, or dried blood spots.
Studies of unrepresentative populations, such as those in which individuals were selected based on knowledge of their ARV-resistance status, were excluded. Studies of children born to mothers receiving ARV therapy were also excluded. Studies meeting inclusion criteria were assigned to one of the following geo-economic regions: (i) sub-Saharan Africa (SSA), (ii) LMICs of south/southeast Asia (SSEA), (iii) Latin America and Caribbean, (iv) Europe, (v) United States, Canada, and Puerto Rico (North America), (vi) upper-income Asian countries, (vii) countries of the former Soviet Union (FSU), (viii) North Africa, and (ix) Australia. For studies conducted in countries on different continents, separate datasets for each continent were created, provided the study had more than 25 individuals per country.
Temporal Changes in Prevalence of Transmitted Drug Resistance For individual-patient-level meta-analyses, samples from different studies conducted in the same region were pooled and a generalized linear mixed model was used to assess the effects of national ARV scale-up on the presence or absence of any or NRTI-, NNRTI-, or PI-associated TDR  [29]. To account for study heterogeneity, we included study as a random effect in the model using the R package lme4  [30]. We calculated the yearly increase in the odds of TDR since ARV scale-up. We also assessed the associations of virus subtype, duration of HIV-1 infection, recruitment site, and sample type with the odds of TDR while accounting for the number of years since national ARV scale-up. The year of each country's national ARV scale-up was obtained from UN General Assembly special session country reports.
Mutation Analyses We compared the proportions of each SDRM in sequences from the seven most common HIV-1 subtypes (subtypes A, B, C, D, G, CRF01_AE, and CRF02_AG) and from individuals from SSA, SSEA, Latin America/Caribbean, and the pooled upper-income countries of North America, Europe, and Asia using Fisher's exact test. Holm's method was used to control the family-wise error rate for multiple hypothesis testing: associations with adjusted p < 0. 05 were considered statistically significant. Statistically significant associations are reported, along with their original unadjusted p-values. We used Spearman's rank correlation test to assess the correlation of the relative ranking of the proportions of each NRTI, NNRTI, and PI SDRM in SSA, SSEA, Latin America/Caribbean, and the pooled upper-income countries with the proportions of these mutations in HIV-1 sequences from ARV-experienced individuals from these regions in the Stanford University HIV Drug Resistance Database (HIVDB).
Molecular Epidemiology A neighbor-joining tree of each study's sequences was created using genetic distances computed using the HKY85 substitution model with a gamma distribution to model site rate variation. By traversing the tree, we identified sets of closely related sequences for which the median genetic distance was 0. 015. An SDRM cluster was defined as a set of three or more closely related sequences containing identical SDRMs. Trees were constructed using PAUP and traversed using R packages ape and igraph  [32]. The program BEAST (Bayesian Evolutionary Analysis by Sampling Trees) was used to identify extended lineages of sequence clusters with the same SDRMs  [33]. For each study, we calculated a sequence dissimilarity index, which we defined as the number of sequence clusters plus unclustered sequences divided by the total number of sequences.
Studies and Individuals The February 2014 GenBank tblastn search yielded 1,707 studies of HIV-1 group M RT sequences, with or without protease sequences. Of these studies, 340 described a population of 25 ARV-naïve individuals. Fifty-three of the 340 studies were excluded: 22 described an unrepresentative subset of a larger population, 15 included children in a program for prevention of mother-to-child transmission, nine included samples sequenced using a method other than direct PCR sequencing, and seven did not include sample years. Additionally, 111 individuals were excluded because their sequences did not meet sequence quality inclusion criteria. Finally, sequences from 50,870 ARV-naïve individuals in 287 studies were included in our analysis (Fig 1 ; S1 Table ). For 277 (97%) studies, annotation was obtained from an accompanying peer-reviewed publication.
For ten (3%) studies, annotation was obtained from the GenBank record and the sequence contributors. The primary goal for 221 (77%) studies was to estimate TDR prevalence. The primary goal for 62 (22%) was to characterize sequence diversity for molecular epidemiologic purposes or vaccine development. Four (1%) studies contained pre-therapy samples from patients enrolling in a clinical trial. For 238 (83%) of the studies, the sample year of each sequence was reported. For 49 (17%) of the studies, a range of sample years was reported for the study population rather than for each individual, and the median of the range was assigned to each sample. Sequences were obtained from plasma, peripheral blood mononuclear cells, and dried blood spots in 252 (88%), 29 (10%), and six (2%) studies, respectively.
Several studies have shown that the proportion of sequence positions with a nucleotide mixture increases with the duration of infection  [26] [27] [28]. In our study, we found that more than one-half of the sequences from studies of recently infected individuals did not contain a nucleotide mixture. The highest levels of nucleotide mixtures were in studies of individuals presenting to an HIV clinic whereas intermediate levels of nucleotide mixtures were detected among blood donors, antenatal clinic attendees, and VCT attendees. An analysis limited to only those sequences with less than 0. 5% mixed nucleotides-a proxy for recent infection-yielded comparable trends to those obtained using the complete dataset reinforcing the trends reported in this meta-analysis. Endemic TDR strains emanating from a single instance of ARV-selection pressure that spread among many individuals have different public health implications from TDR strains emanating from multiple independent episodes of ARV-selection pressure  [40] [41] [42].
What Did the Researchers Do and Find? The researchers identified 287 studies published between 2000 and 2013 from 111 countries that included the reverse transcriptase sequences of HIV viruses from 50,870 ARVnaïve, HIV-positive individuals. The researchers analyzed each virus sequence for the presence of 93 surveillance drug-resistance mutations (SDRMs) previously shown to be specific indicators of TDR. Meta-analysis of these data indicated that the average overall prevalence of TDR (the proportion of ARV-naïve, HIV-positive individuals infected with a virus carrying one or more SDRMs) ranged from 2. 8% in sub-Saharan Africa to 11. 5% in North America. In sub-Saharan Africa, the odds (chance) of TDR increased 1. 09-fold per year following national ARV scale-up; this increase was attributable to an increase in NRTI-and NNRTI-associated resistance.