dataesr/scientific-paragraphs-categorization huggingface dataset:
Erratum After the publication of this work  [1]  it was noticed that the legend of Fig. 1a  was incorrect. The correct legend should read: Flowchart of MCP-counter's development and validation. It was also noted that in Fig. 2c  the name of the last two genes was partially removed. The horizontal lines that were delineating the specificity of the displayed genes are missing. The corrected Fig. 2c  is noted below. The equal contributors symbol was missing for authors Aurélien de Reyniès and Wolf H. Fridman. This has now been corrected. In the PDF version of the article, equation 5 in the section 'Correlation profiles of TM in microenvironment and tumor datasets' is incorrect. Please see the correct equation below: π k;j ¼ f S;j f k ¼ g S;j g k ð5Þ The publisher apologises for these errors.
Preeclampsia (PE) is a pregnancy disorder defined by hypertension and proteinuria. This disease remains a major cause of maternal and fetal morbidity and mortality. Defective placentation is generally described as being at the root of the disease. The characterization of the transcriptome signature of the preeclamptic placenta has allowed to identify differentially expressed genes (DEGs). However, we still lack a detailed knowledge on how these DEGs impact the function of the placenta. The tools of network biology offer a methodology to explore complex diseases at a systems level. In this study we performed a cross-platform meta-analysis of seven publically available gene expression datasets comparing non-pathological and preeclamptic placentas. Using the rank product algorithm we identified a total of 369 DEGs consistently modified in PE.
In these networks, the components (genes/proteins/metabolites) of the system are represented as nodes and their interactions as edges  [9]. Network analysis detects the relationships between its components and enables the physical or functional relationships between them to be interrogated. This methodology is useful to dissect the complexity of a biological process, providing information relevant to understand the process, including the pathways and the prioritization of genes involved in it  [10]. Herein, we performed a cross-platform meta-analysis of several public gene expression datasets to identify DEGs that are consistently modified in PE. Then we used these DEGs to build a composite extended network integrating physical protein interactions (PPIs) as well as regulatory interactions with transcriptional factors (TFs).
Meta-analysis of microarray datasets We performed a meta-analysis of several published gene expression data in preeclampsia. We searched the Gene Expression Omnibus (GEO) repository (http://www. ncbi. nlm. nih. gov/geo), to identify microarray datasets that compared gene expression in preeclamptic versus normal placentas. The keywords: preeclampsia, placenta, microarrays and gene-expression, were used for this search. This way, we identified a total of 12 microarray studies in the GEO repository. However, to be included in our study the microarray experiments had to carried out with RNAs obtained from placental biopsies collected at delivery and at relatively comparable gestational ages (30-39 weeks), include a minimal number of samples (! 5) and be comparable in terms of the microarray technology used.
Thus finally, seven datasets were found meeting our criteria and considered eligible for our study, Table 1  [11] [12] [13] [14] [15] [16] [17]. Since these studies used different microarray platforms, we performed our meta-analysis using the rank product algorithm implemented in the online INMEX software (http://inmex. ca),  [18]. Before the datasets were analyzed, we used the processing modules (utilities) implemented in the INMEX software to annotate all probe identities from each dataset as Entrez gene IDs (for data consistency), transform the intensity values for gene expression to log 2 and perform quantile normalization. A list of 369 differentially expressed genes (DEGs) were identified based on adjusted p-value (threshold was set at adjP < 0. 05) and average fold change (FC ! 1. 3 and FC -1.
Enrichment analysis The enrichment analysis of the DEGs was performed with the WebGestalt (http://bioinfo. vanderbilt. edu/webgestalt) bioinformatics resource  [19]. WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) incorporates updated information from different public resources and provides an easy way to make sense out of gene lists. Databases interrogated include: Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways  [20] [21] [22]. The significance of the detected enrichments was calculated using the Benjamini & Hochberg multiple test adjustment. In addition, WebGestalt was also used to perform enrichment analysis of the network modules identified by community clustering.
Extended Protein-Protein Interaction networks: Construction and Analysis The DEGs identified in our meta-analysis were used as seeds to build a network of protein-protein interactions (PPIs). We constructed a network that not only consists of the seed proteins but also of their direct PPI neighbors and the interactions between these proteins (first order network). We obtained high confidence PPIs from the Biological General Repository for Interaction Datasets (BioGRID), (http://thebiogrid. org), [23]. The PPI network was constructed, visualized and analyzed using the Cytoscape 3. 2. 1 software and its complementary applications  [24]. The network was analyzed for both centrality and modularity parameters. The centrality parameters of the networks were analyzed using the Cytoscape application NetworkAnalyzer  [25].
Two topological parameters Betweenness Centrality (BC) and node degree were used to identify hub genes  [26]. In PPI networks genes or proteins are represented by nodes and the interactions between the nodes are represented as edges. BC is defined as the number of shortest paths from all vertices to all others that pass through that node. BC reflects the amount of control that the node exerts over the interaction of other nodes in the network. Degree indicates the number of edges linked to a given node. Nodes with higher degree and high BC are hub genes which correspond to the most functionally relevant elements in the network. Modular analysis was performed using the fast-greedy HE (G) algorithm of the Community Clusters Glay implemented in the Cytoscape Apps  [27]. The idea behind identifying functional modules is that proteins interacting with each other have higher probability of sharing the same function than none interacting proteins.
Regulatory Network Construction We built a regulatory network by extracting gene regulatory relationships from RegNetwork (http://www. regnetworkweb. org) an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse  [28]. This database integrates the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases including: JASPAR, TRANSFAC, TRED and USCS  [29-31] [32]. It also incorporates potential regulatory relationships based on TFs binding sites. The DEGs were used as seeds to extract high confidence transcriptional regulatory relationships from the human RegNetwork.
Identification of DEGs by meta-analysis Seven microarray datasets were extracted from the GEO database, which meet our criteria for meta-analysis (Table  1 ). Cross-platform meta-analysis of the selected studies using the rank product algorithm resulted in the identification of a total 1940 DEGs at adjP <0. 05. Significant genes having the same direction of fold change in at least 5 out of 7 datasets and with an average log 2 FC ! 0. 3 were considered for further analysis (The average log 2 FC being the average log 2 FC for every single gene for the 7 datasets). This resulted in a list of 369 DEGs, of which 212 were up-regulated in PE and 157 down-regulated genes in PE (S1 Table ). Among the consistently up-regulated genes in PE with the higher mean log 2 FC and lowest p-value we detected in descending order LEP, FSTL3, PAPPA2, INHA, FLT1, SPAG4 and BHLHE40.
Functional and pathway enrichment analysis Enrichment analysis was performed to gain insight on the biological role of the DEGs. The most over-represented Gene Ontology (GO) terms in the biological process category included: "response to steroid hormone stimulus", "response to wounding", "cell migration", "and response to lipid" and "cell proliferation". In the category of GO cellular component most enriched terms were "extracellular region", "extracellular matrix" and "plasma membrane". A complete list of GO terms is provided in S3 Table. KEGG and WIKI pathways analysis revealed that the DEGs are associated with a number of significant pathways (Table  2 ). These included: "focal adhesion", "pathways in cancer", "metabolic pathways", and "cytokine-cytokine receptor interaction". "Focal adhesion" and "regulation of actin cytoskeleton" were detected in both the KEGG and Wiki pathway databases.
Construction of a physical PPI network To interpret the functional meaning of the identified DEGs at the protein level, we constructed a physical PPI network for the proteins encoded by these genes. Physical PPI interactions were retrieved from the BioGRID database  [23]. We constructed a network that not only consists of the seed proteins but also their direct PPI neighbors (first order interaction network) and the interactions between these proteins. The proteins (nodes) and resulting interactions (edges) were imported into Cytoscape for visualization and further analysis  [24]. This resulted in a network composed 1231 proteins and 5486 interactions. The network contained 214 out of the 369 DEGs identified in the meta-analysis. For the remaining DEGs no high confidence PPIs were reported in the BioGRID database.
Module detection using connectivity patterns The PPI network was further subdivided into connectivity modules using the fast-greedy HE (G) algorithm of the Community Clusters Glay  [27]. This resulted in the identification of nine connectivity modules closely connected. The Modules were analyzed for functionality enrichment using the gene ontology (GO) database (Table  3 ). Thus, the bigger module, module1 was composed of 292 nodes and significantly enriched in the biological processes terms: positive regulation of transcription from RNA polymerase II promoter (adjP = 9. 52e-58), positive and regulation metabolic process activity (adjP = 5. 58e-51). The smaller module, module 9, contained 22 nodes and was associated with healing (adjP = 0. 0059) and coagulation processes (adjP = 0. 0059).
Construction of a transcription factor regulatory network We used RegNetwork  [28]  a database of transcriptional and post-transcriptional regulatory networks, to extract regulatory interactions among the 369 DEGs of the preeclampsia meta-signature and transcription factors (TF). Regulatory relationships experimentally validated and predicted were considered. This generated a network composed of 571 nodes and 2214 regulatory interactions. Among the nodes, 365 corresponded to DEGs identified in the meta-analysis and the remaining 206 were inferred TFs. Among the DEGs we identified 30 genes encoding TFs and regulating other DEGs. Topological analysis of the network using Cytoscape Networ-kAnalyzer shows that the node degree distribution follows a power law (R 2 = 0. 841); (S2B Fig). The TFs with the higher out degree (number of regulatory interactions), included the SP1, POU2F1, JUN, CREB1, MYC, CEBPA, TFAP2A, ARNT, NFKB1, GATA1, STAT1, TP53, ATF2 and HIF1A.
Composite interactions PE network The physical interactions PPI Network and the TFs-DEGs regulatory network were merged using Cytoscape to generate a composite network which integrates both kinds of interactions. Thus we obtained a network composed of 1542 nodes and 7718 edges. To illustrate it, Fig  1  shows a portion of the composite network displaying the transcription factors AP2A, its target among the DEGs, its physical protein interactions and its transcriptional regulators. For visualization purposes TFs were differentiated from their targets or other proteins in the network using a different symbol. Regulatory interactions were colored in green and physical interactions in mauve. The mean log 2 FC of the DEGs was incorporated in the network to visualize up or down-regulation. The complete composite network is provided as supplementary data in form of an XML file which can be visualized and explored using Cytoscape.
Discussion Herein we have performed a cross-platform meta-analysis of seven gene-expression datasets comparing non-pathological and preeclamptic placentas. Using a rank product algorithm we identified 369 DEGs consistently modified across the considered datasets. In a previous study we used a vote counting strategy which led to the identification of only 98 DEGs in preeclamptic placentas  [8]. Sixty one DEGs identified in our previous study were also identified in the present study. Vote counting is a simple method for summarizing evidence from multiple datasets; however it does not take into account the quality of the studies, the size of the samples, or the size of the effect. In the present study we used a rank product algorithm, a simple non-parametric statistical method based on ranks of fold changes  [33].
Methods combining P-values or effect sizes have the disadvantage that the results can be often dominated by outliers, a significant problem when thousands of genes are compared in the noisy environment of microarray experiments. The methods combining rank statistics can be used to alleviate this problem. Comparative studies suggest that the rank product method outperforms the other methods in terms of sensitivity and specificity, especially in a setting of small sample size and large between-study variation  [34]. In addition to our studies, two other meta-analysis on transcriptomic datasets of preeclamptic placentas have been conducted recently  [5, 6]. The study by Moslehi and coworkers was conducted on 4 datasets using a combining P-values method and identified a total of 419 DEGs. The overlap with the present study shows 139 common DEGs.
The second one published by van Uitert and coworkers, was conducted on 11 placenta gene expression datasets which were preprocessed to conduct quality analysis. A combining size effect algorithm using inverse variance random effects was applied, resulting in the identification of 388 DEGs. In this case the overlap with the current study is of 148 common DEGs. Discrepancies between the studies can be attributed to the different number of datasets and different statistics meta-analysis methodologies. Functional enrichment analysis of the DEGs identified in the present study, revealed that they are mainly related to biological functions such as Focal adhesion, metabolic pathways, ECM-interactions, cytokine-cytokine interactions, complement and coagulation cascades, etc. All these processes have been previously associated with preeclampsia  [3].
Conclusions In this study we have used publically available microarray datasets comparing gene expression in preeclamptic versus non-pathologic placentas. Other studies analyzing global gene expression in the preeclamptic placenta have been published but the datasets are not publically accessible. Thus, the number of datasets constitutes a first limitation of our study which could have reached stronger value if more datasets were included. Another limitation of the present study comes from the coverage and quality of the human interactome data. Despite the best curation efforts, the interactome remains incomplete, current coverage is estimated at 20% and biased toward much-studied proteins  [74] [75] [76]. In principle the incompleteness of the human PPI network poses limitation to any study of network properties in the context of normal physiology or disease.
0048 Endocytosis 7 201 0. 0060 Regulation of actin cytoskeleton 7 213 0. 0079 Pathways (Wiki) Genes in DataSet Genes in Pathway adj P-Value Adipogenesis 14 130 6. 93E-10 Corticotropin-releasing hormone 10 123 3. 49E-06 Selenium Pathway 8 108 6. 53E-05 Oxidative Stress 5 30 6. 70E-05 TGF-Beta Signaling Pathway 6 61 0. 0001 Prostaglandin Synthesis and Regulation 4 31 0. 0006 Senescence and Autophagy 7 120 0. 0006 Metapathway biotransformation 8 177 0. 0010 Cytochrome P450 5 65 0. 0010 doi:10. 1371/journal. pone. 0165849. t002.
This approach may represent the most practical option to improve treatment of advanced SM. Absent a trial, we propose homogenizing the alloHCT procedure for indications, conditioning, and post-transplantation follow-up observations. Furthermore, through a collaborative partnership among MC registries or databases and by capturing all patients with SM referred to a transplantation center for a consideration of alloHCT, one could possibly compare the characteristics and outcomes (eg, survival) between patients receiving alloHCT and those who do not. These efforts may heighten our understanding and improve alloHCT outcomes for patients with advanced SM as well as suggest new opportunities for further investigation.
The rearranged junctions were analysed for length, size range and junctional diversity of CDR3. Our data showed that the average length, size range and length distribution of CDR3 of rearranged VDJ H4 genes as well as the proportion of in-frame (vs. out-of-frame sequences were similar between Fanca -/-and WT mice (Table  1  and Fig. 2A ). A determination of the length of each V, D, and J segment contributing to the CDR3 region showed no considerable difference between WT and the Fanca -/-mice (Table  S2 ). Additionally, we found that the average number of N-additions, whether estimated for both the V H -D and D-J H junctions (total) or for either the V H -D or the D-J H junction alone appeared to be similar between the two groups of mice (Fig. 2B ). On the contrary, P-additions at the V H -D or D-J H junctions differ significantly between Fanca -/-and WT mice (Fig.
All V(D)J recombined products were analysed with the IgBLAST webserver (NCBI). Flow cytometry for TdT staining. Cells were stained for surface antigens using anti-B220-PacificBlue, anti-IgM-FITC, anti-CD43-APC (all from eBioscience, San Diego, CA). Then, a fixation/permeabilization procedure was performed using the Foxp3 Staining Buffer Set (eBioscience) according to the manufacturer's protocol, followed by staining with anti-TdT-PE (19-3; eBioscience). Cells were analysed with an LSR II flow cytometer with FlowJo (TreeStar Inc. , Ashland, OR, USA) software. qRT-PCR. The total RNA from sorted pro-B and pre-B cells was isolated using the RNeasy Plus Micro kit (Qiagen, Hilden, Germany) and reverse-transcribed using an Affinity Script Multi Temperature cDNA synthesis kit (Agilent Technologies, Santa Clara, CA, USA).
Acknowledgements The authors would like to thank all the colleagues of the UMR8200 for helpful discussion and  B. Bertocci  of the  Institut Necker-Enfants Malades, Paris. We also would like to thank  P. Rameau  and  Y. Lecluse  from the  Cytomery Platform of Gustave Roussy  for cell sorting. This work was supported by grants from  "La Ligue Contre le Cancer " and " INCa-DGOS-INSERM 6043.
Data are representative for at least 2 independent experiments with n≥7 per genotype. Unpaired t-test was used for statistical analysis of each maturation stage and failed to reach statistical significance for each stage (bar graphs not shown). (E) Spheroids of endothelial cells were embedded into a collagen matrix and IL-2 media (control), supernantant of IL-2 cultured Vegf-A fl/fl and Vegf-A Δ/Δ Ncr1-iCre Tg NK cells or mVEGF-A (positive control) were added and sprouting was documented after 24 hours. Average sprout number and cumulative sprouting length was quantified by ImageJ software. Tukey's posthoc test was applied for statistical analysis. (F) NK cells were depleted twice before and after s. c. injections of 10 6 v-abl + cells into Vegf-A fl/fl and Vegf-A Δ/Δ Ncr1-iCre Tg mice. After 11 days the tumor weight (n≥10 per group) was determined.
Temprano trial procedures At D0, a clinical examination including weight, height, waist size, blood pressure, and temperature was conducted, and chest radiography was prescribed. Data on socio-demographic characteristics, occupation, level of schooling, and use of tobacco and alcohol were collected using questionnaires. Blood samples were collected for analysis of blood cell count, CD4 cell count (True Count ® technique, FACScan ® , Becton-Dickinson), serum transaminases, serum creatinine, serum glucose, total and LDL cholesterol, triglyceride, and plasma HIV-1 RNA (real-time PCR, Taq Man technology ABI Prism 7000, Applied Biosystems, detectability 100 copies/mL). The first-line regimen was preferably a fixed-dose combination of tenofovir disoproxil fumarate (TDF) 300 mg and emtricitabine (FTC) 250 mg (Truvada ® , Gilead) plus efavirenz (EFV) 600 mg (Stocrin ® , MSD).
Participants In this sub-study, we included all participants who initiated early ART prior to December 31, 2010. We assessed baseline characteristics according to BMI category and analyzed factors associated with being overweight or obese in univariate and multivariate analyses. We also assessed incidence of death, morbidity and loss to follow-up during the first 24 months. Severe morbidity was defined as any new AIDS-defining diseases, non-AIDS defining severe bacterial diseases, non-AIDS defining cancers, any event leading to death, and any adverse event of grade 3 or 4 on the ANRS scale (including cardiovascular, renal and neurologic events). We estimated the change in BMI between baseline and M24 in patients who survived, attended the M24 visit, and had available data. We therefore excluded the following patients: women who became pregnant, as well as patients who died, were lost to followup, or had missing data at the M24 visit.
4c ), which suggests that absence of host kinin B1 receptor confers growth advantages to melanoma in the lung. We performed full autopsies and as expected for this model  30  , we only found metastasis in the lungs with no incidence of tumors spread into other organs. Given that host immune response is a key component during tumor growth and progression  31  , that high immune response has been correlated with better prognosis in melanomas  27  , and that our primary tumor data showed a remarkable increase in tumor expression of the anti-inflammatory cytokine IL-10 when host B1 receptor is absent (Fig. 2e ), we investigated whether the content of immune cells in metastatic lungs of WT and B1 -/-mice was altered. Using immunohistochemistry assay, we analyzed the content of CD4 + T cells as well as of the effector CD8 + T cells in the metastatic lungs of WT and B1 -/-mice.
0 and treated with 3% hydrogen peroxide to inhibit endogenous peroxidase activity. After blocking slides with fetal bovine serum, sections were incubated overnight at 4 °C with a 1:100 dilution of CD4 monoclonal antibody (Abcam) and CD8 monoclonal antibody (Santa Cruz Biotechnology). Slides were treated with biotin-conjugated secondary anti-mouse antibody (Dako) and with horseradish peroxidase-conjugated avidin (Dako). Peroxidase activity was localized for all samples with 3,3′ -diaminobenzidine, counterstained with Harrys's hematoxylin, dehydrated, cleared and mounted in mounting medium (Entellan, Merck). Image Quantification. For evaluation of the presence CD4 + and CD8 + cells, we determined the surface density by optical density in the image analysis using the Leica QWin software (Leica Microsystems Image Solutions, Cambridge, UK) in conjunction with a Leica DMR microscope (Leica, Microsystems GmbH, Wetzlar, Germany), video camera (Leica Microsystems, Heerbrugg, Switzerland) and an online computer.