dataesr/scientific-paragraphs-categorization huggingface dataset:
Data extraction and quality assessment Four authors (MB, SP, DM, and MD) performed data extraction, working in pairs. Disagreements were resolved by discussion among authors and by adjudication of a third evaluator in case of persistent disagreement. For each included trial, information was extracted on the following items: study design, study setting, followup period, study population, the proportion of HSTC recipients, the proportion of allogeneic HSCT recipients, the number of included patients, the number of patients with neutropenia, the outcome of patients with and without neutropenia. ICU admission period was defined as the median inclusion year. Risk of bias was assessed using the Cochrane's Tool to Assess Risk of Bias in Cohort Studies (http://methods. cochrane. org/bias/sites/methods.
Statistical analysis Results were analyzed using Review Manager 5. 1 (Cochrane Collaboration, Oxford, UK). Overall mortality of included patients and mortality in included studies are reported as medians (interquartile ranges). Publication bias was assessed by visually inspecting the funnel plot and summary estimates of risk differences were calculated using the random-effects model. To enable data comparison, we transformed illness severity scores (SAPS II and APACHE III) into the equivalent APACHE II score, using a previously described methodology  [54]. Four subgroup analyses were preplanned, and they included the influence of neutropenia according to underlying malignancy (solid tumor, hematological malignancy, or both), median ICU admission periods in the included studies, the use of mechanical ventilation, and the use of G-CSF.
Treatment with sPIF also downregulated the fetal gamma and epsilon globin genes (HBG1 and HBE1, respectively, which are expressed in the yolk sac and turned down after trophoblast differentiation). Gene names, (FC) and adjusted P-value for each over-or underexpressed gene are described in Table  1. To confirm the gene chip data, we used quantitative realtime PCRs to analyze (i) transcripts of the most upregulated gene (AZU1) and (ii) transcripts of the two most downregulated genes (HBG1 and HBE1). The expression patterns are correlated with the microarray profiling data (Figure  1b ). Functional networks regulated by sPIF in treated human EVT. First, genes with altered expression in sPIF-treated EVT were analyzed using QIAGEN's Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA, USA).
All these genes are related to DNA degradation, DNA fragmentation and tumorigenic processes, and three of them are under the control of interferon gamma (IFNG). However, the FC for the IFNG gene (-1. 05) was not statistically significant in this experiment. In order to extract further biological informations, we performed a gene set enrichment analysis (GSEA) of the whole-gene expression data set and found 478 significantly enriched gene sets (false discovery rate (FDR) o0. 25). Critical keywords for the gene sets were 'cancer', 'immunity' and 'ion channel transport'. The 'cancer' gene set was downregulated, and the 'immunity' and 'ion channel transport' gene sets were upregulated (data not shown). Differentially expressed genes (DEGs) were analyzed with the Database for Annotation, Visualization, and Integrated Discovery (DAVID).
The protein-protein interaction (PPI) relationship was obtained from STRING database, and the PPI network and the functional modules were visualized using Cytoscape (Figure  3 ). The PPI network contained 424 nodes (representing the DEGs) and 1045 edges (representing the interactions between the DEGs). Next, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to link higher-order biological information to the genomic information associated with these distinct genes and sPIF treatment. The distinct functional categories for the sPIF-associated gene lists are shown in Table  2. Our analysis showed that a few enriched KEGG pathways were related to the 'immune system' and 'viral infection', such as tuberculosis, graft-versus-host disease, type I diabetes mellitus and autoimmune thyroid disease.
In confirmation of the gene expression profile data shown in Table  1 , sPIF treatment in EVT seems to affect olfactory signal transduction. Finally, the observed expression changes seem to be highly associated with 'tumorigenesis pathways', such as pathways in cancer, apoptosis, and p53 signaling pathway. Effect of sPIF on human trophoblast viability and apoptosis. To confirm sPIF's effects on apoptosis pathway in human trophoblasts, we performed a number of functional assays. We first used lactate dehydrogenase (LDH) activity and Trypan blue exclusion assays to ascertain viability in EVT and HTR-8/SVneo cells. Our results showed that sPIF (at 50 or 100 nM) did not affect LDH activity in either cell type (Figure  4a ). The Trypan blue exclusion assay confirmed that sPIF did not significantly affect the viability of HTR-8/SVneo cell (87% ± 1.
Developmental process Figure  3  A PPI network analysis. The DEGs identified in the microarray analysis were used to build a PPI network. The STRING database was used to screen interacting proteins, which were then visualized as a PPI network using Cytoscape software. The network nodes represent genes and the edges represented interactions. The size of the nodes is proportional to the number of connections established with other genes. The 'developmental process', 'olfactory receptors' and 'apoptosis' clusters are presented on a larger scale. The gene expression levels were mapped on the network. Red indicates upregulation and green downregulation this context, we performed a genome-wide assessment to better understand the effects of sPIF in the human trophoblast. We tested two different sPIF concentrations (50 and 100 nM), which correspond to the range observed in the maternal circulation  21  and where sPIF is effective at modulating trophoblast invasion.
Hybridization on the microarray was then performed with 825 ng of each linearly amplified and then Cy3-or Cy5-labeled cRNA sample, according to the manufacturer's instructions (Agilent SureHyb Chamber; 1650 ng of labeled extract; 17 h of hybridization; 40 μl per array; 65 °C). After washing in acetonitrile, slides were scanned with an Agilent G2565 C DNA microarray scanner, using the latter's default parameters. Microarray images were analyzed with Feature Extraction software (version 10. 7. 3. 1, Agilent Technologies). Again, the default settings were used. Microarray data processing and analysis. Raw data files from Feature Extraction were imported into R using LIMMA  53  and a R package from the Bioconductor project, and processed as follow: gMedianSignal and rMedianSignal data were imported, control probes were systematically removed, and flagged probes (gIsSaturated, gIsFeatpopnOL, gIsFeatNonUnifOL, rIsSaturated, rIsFeat-popnOL and rIsFeatNonUnifOL) were set to 'NA'.
Intra-array normalization was performed by 'loess' normalization, followed by quantile normalization of both the Cy3 and Cy5 channels. Next, inter-array difference was normalized by quantile normalization on M values. All probes were then summarized into single genes by taking the mean of all probes that corresponds to the same gene. The missing values were eventually replaced using the KNN algorithm. Bioinformatics analysis was completed by applying ArrayMining software (http:// www. arraymining. net/) and the Cytoscape package. The transcriptome analysis for microarrays was carried out using complementary approaches: a DAVID analysis of the most deregulated genes and a GSEA analysis of enriched pathways (FDRo0. 25). The Search Tool for the Retrieval of Interacting Genes (STRING: http://string-db.
A total of 1000 events were acquired at × 40 magnification, and annexin V-positive cells were quantified using IDEAS 5. 0 software (Amnis/EMD, Darmstadt, Germany). Bright field images were used to quantify and verify cell integrity. The apoptosis rate was determined by normalizing annexin V-positive cells against the total number of cells. Etoposide (42 μM) was used as positive control (data not shown). TUNEL assay. HTR-8/SVneo cells were cultured at a density of 2x10 5 cells per well in 12-well culture plates. After 48 h of sPIF treatment, cells were fixed in 4% paraformaldehyde for 20 min at room temperature, washed, and stained using a TUNEL apoptosis kit (In Situ Cell Death Detection Kit, Fluorescein, Roche Diagnostics GmbH, Mannheim, Germany) according to the manufacturer's instructions.
Cells were counterstained with 1 μg/ml Hoechst33258 (Sigma, St. Louis, MO, USA). Ten fields in each well were imaged using an inverted fluorescence microscope (Olympus, ScanR, Tokyo, Japan), in order to obtain a minimum of 200 cells for analysis. The apoptosis rate was determined by normalizing the number of TUNEL-positive cells against the total number of Hoechst-positive cells, using ScanR software (Olympus, ScanR). Etoposide (42 μM) was used as positive control (data not shown). Immunoblotting. HTR-8/SVneo cells (1 × 10 6 cells per well) were plated onto six-well culture plates. After 48 h of sPIF treatment, cells were lysed in extraction buffer, as previously described. 64 Twenty micrograms of total protein was resolved by SDS-PAGE (4-20% acrylamide) and transferred onto nitrocellulose membranes using a wet transfer method.
Specific immunostaining in three representative fields was quantified using the positive pixel count algorithm in Aperio Imagescope software (Leica Biosystems). Statistical analysis. Results were analyzed using Graphpad Prism software (version 5. 0, GraphPad Software, Inc. , La Jolla, CA, USA) and expressed as the mean ± S. E. M. of 4-11 separate experiments. Statistical analyses were performed using a non-parametric, paired Wilcoxon's test or a non-parametric Mann-Whitney test. The statistics for assessing the enrichment of group of genes is always based on Chi 2 contingency tests that are directly performed online by DAVID, STRING or Cytoscape tools. The threshold for statistical significance was set to Po0. 05.
Acknowledgements. This work was funded in part by a  Grant for Fertility Innovation  from  Merck Serono (Darmstadt, Germany)  and by the  Université de Versailles Saint-Quentin-en-Yvelines (Versailles, France). BioIncept (Cherry Hill, NJ, USA)  supplied the sPIF reagent. The microarray data processing and analysis was performed on  Plateforme de Génomique Fonctionnelle of Gustave Roussy Institut. We thank  Dr. Daniel Vaiman  for his help with the bioinformatics analysis. TUNEL and  Annexin V-FITC  image acquisition and analysis were performed on CYMAGES platform. Immunoperoxidase image acquisition and analysis were performed on immunohistochemistry platform, both supported by the  community of Saint-Quentinen-Yvelines  and the  University of Versailles Saint-Quentin. We thank  Caroline Pirou ,  Fabien Duval  and  Laurent Ruiz  for their technical help.
Mass spectrometry analyses The mass spectrometry proteomics data has been deposited on the ProteomeXchange Consortium (21) via the PRIDE partner repository with the dataset identifier PXD003174. Detailed mass spectrometry analysis procedures are available in the supplementary methods. Only proteins with a Mascot score higher than 150 were considered for further analysis. Protein score distributions of immunoprecipitations (IP) from the control (untagged BY4741) or proteinA-tagged strains were compared in order to distinguish specifically interacting proteins from the background (see Supplementary Figure  S1  for an example). Based on the control distribution, a threshold score was chosen to keep <10% false positives. Proteins above this threshold score in the control or with a ratio of IP to control inferior to 2 were excluded.
Microarray hybridization and analysis The microarray data and the related protocols are available at the GEO web site (www. ncbi. nlm. nih. gov/geo/) with the dataset identifiers E-MTAB-3922 and E-MTAB-3881. Briefly, 1g of RNA from each sample to be compared was reverse-transcribed and labeled with Cy3 or Cy5 dye using the indirect labeling procedure. We then hybridized labeled cDNAs with Agilent's ab 8×60K S. cerevisiae custom DNA chip (AMADID: 027945). Each hybridization was replicated using dye-swap. Arrays were read using an Agilent scanner at 2 m resolution and the signal segmentation was done using the feature extraction software (Agilent). The data was normalized without background subtraction using the global Lowess method  (22). For the analysis of mitochondrion-associated RNAs, the enrichment ratios between cellular and mitochondrial extraction were calculated.
A comparative analysis of BY4743 and tma108Δ/Δ mRNA localization was performed using LIMMA  (23, 24)  to detect significant changes (P-value<0. 05) in mRNA association to mitochondria. For the analysis of ribosome-associated mRNA, we performed microarray hybridizations using cDNA reversetranscribed from immunoprecipitation and the corresponding input sample. The LIMMA algorithm was used to determine the list of mRNAs specifically enriched in Tma108, Tma46 and Scp160 immunoprecipitations compared to Rpl16A. Only mRNAs with log 2 ratios superior to 0. 8 and an adjusted P-value of <0. 05 were kept in the final list. R software was used for category enrichment analysis (http://www. r-project. org/). GO (http://www. yeastgenome. org/download-data/curation) and Pfam (25) categories were assigned using the corresponding the databases.
Molecular modeling of Tma108 structure The tertiary structure of Tma108 was predicted using the Modeller  (27)  program interfaced with the HHpred server  (28). The protein sequence of Tma108 from S. cerevisiae (UNIPROT code: P40462, 946 residues) was queried against the pdb70 database (Protein Data Bank (29) clustered at 70% sequence identity) using HHblits  (30)  to retrieve homologous sequences with known structures. Two of the best hits were selected as templates: human aminopeptidase A (PDB code: 4KX7, chain A) and porcine aminopeptidase N (PDB code: 4FKE, chain A). They both cover the entire protein (residues 4-944) with 20% of identities. The quality of the model was evaluated with Procheck  (31). 85% of the residues were found in the most favored regions of the Ramachandran plot, indicating reasonable quality.
Multiple sequence alignments Two multiple sequence alignments were generated using MAFFT  (32)  via the EMBL-EBI webserver (33) comprised of: (i) 14 sequences of M1 aminopeptidases and the sequence of Tma108 from S. cerevisiae, (ii) 18 sequences of Tma108 proteins from saccharomycetaceae (for details on the sequences used for the alignments see Table  S4  in supplementary methods). We used Seaview to compute phylogenetic trees from the multiple sequence alignments with the pHyML program (parameters set to default). Name and accession numbers of the sequences used are available in the supplementary methods. The complete annotated alignment of Tma108 with M1 aminopeptidases is available in supplementary data 1. Tma108 orthologs used in the second alignment were obtained by reciprocal blast in the Saccharomycetaceae genome databases.
This last observation is consistent with our previous finding that only ATP2 mRNA localization is disturbed in the absence of Tma108 (Figure  1B ). Our results show that Tma108 may interact with ribosomes engaged in translation. However, this interaction would be restricted to ribosome translating specific mR-NAs, such as ATP2. Tma108 targets ribosomes that translate particular sets of mRNAs. Subsequently, we used microarray technology to identify the whole set of mRNAs which were enriched in the Tma108 IP (Figure  3A ). In order to determine the selectivity of Tma108-mRNA association, we also analyzed the mRNAs obtained from the immunoprecipitation of Rpl16a, a core component of the ribosome, and two other ribosomal partners, Scp160 and Tma46  (16, 35). In each case, the isolation of ribosomal particles was confirmed by the detection of rRNA in the immunopurified extracts (data not shown).
Two independent IP experiments were used for microarray hybridization and the replicates were highly consistent, as shown in the hierarchical clustering analysis in Figure  3B. Statistical analysis defined 174 mRNA specifically enriched in the Tma108 IP dataset compared to the Rpl16a dataset, which was used as a proxy for general translational activity  (36, 37). We also identified 369 mRNAs enriched in the Scp160 IP, a result consistent with previous publications, demonstrating that Scp160 associates with specific subsets of mRNAs  (2, 38). However, we did not detect any specific subset of mRNA associated with Tma46 as compared to Rpl16a, suggesting that Tma46 interacts with the entire ribosomal population without selectivity for any translated mRNAs. Interestingly, Tma108 and Scp160 targets show almost no overlap (only 6 mRNAs are shared in the two datasets).
Tma108-associated ribosomes preferentially target mRNAs encoding proteins bearing ATP-binding, RNA-binding or Zinc-binding domains In order to further understand Tma108 selectivity, we analyzed the representation of the diverse Gene Ontology categories (process and function) within the Tma108 mRNA target dataset. Two cellular processes were significantly over-represented amongst Tma108 targets: Glutamine metabolic process (P-value: 7. 8 × 10 -7 ) and Stress granule assembly (P-value: 5. 2 × 10 -5 ). However, the mR-NAs linked to these processes only represent 6% of the mRNA enriched in the Tma108 dataset. In contrast, 45% of the mRNAs co-immunoprecipitated with Tma108 belong to at least one of the following three "biochemical function" categories: Nucleotide binding (P-value: 2. 3 × 10 -8 ), Zinc ion binding (P-value: 1.
These domains were found in 51% of the nucleotide-binding proteins, 67% of the RNA-binding proteins and 38% of Zinc-binding proteins identified in the Tma108 dataset respectively. Interestingly, six out of the seven identified nucleotidebinding domains had ATPase activity (Figure  3E , panel 1). In several cases, we observed that all the members of the Pfam family corresponded to mRNAs targeted by Tma108 (Figure  3E ). The most striking result was obtained for the Glutamine amidotransferase domain category (GATase), whose members (Asn1, Asn2, Ade4, Gfa1, Yml096w, Dug3) had the highest enrichment values in the microarray experiments (Figure  3E , panel 4). This selectivity for the GATase domain accounts for the over-representation of proteins involved in glutamine metabolism as mentioned above.
The extent of the enrichment for each GO term is expressed as a fold enrichment over genome. Significant over-and under-enrichments are indicated (***P-value < 10 -3 , **P-value < 5 × 10 -2 ). (D) Tma108 targets are highly enriched in defined molecular functions. Analysis of enrichment of the GO terms revealed three main categories of over-represented functions: Nucleotide binding (P-value: 2. 3 × 10 -8 ), Zinc ion binding (P-value: 1. 5 × 10 -5 ) and RNA binding (P-value: 9. 7 × 10 -5 ). Over-represented functional categories included in these three GO terms are not represented (Supplementary Table  S10 ). (E) Analysis of Pfam domains over-represented in proteins encoded by Tma108 targets. Eighteen Pfam domains that showed a significant enrichment (P-value < 0. 05) in Tma108 dataset were selected (identifiers and P-value in Supplementary Table  S11 ).
Tma108 directly interacts with the proteins translated by the Tma108-associated ribosomes In order to gain insight into Tma108 interactions, we tried to identify proteins that specifically associate with Tma108 ribosomes. For this purpose, a comparative proteomic analysis of the proteins co-immunoprecipitated with Tma108, Scp160, Tma46 and Rpl16a was performed (Figure  5A ). Proteins found in the four immunoprecipitation datasets mainly corresponded to ribosomal proteins and translation factors. In addition, ribosomal biogenesis factors were exclusively detected in Rpl16a immunoprecipitation, demonstrating that the ribosomes associated with Tma108, Scp160 and Tma46 were mature translating ribosomes. Strikingly, the only proteins which were specific to the Tma108 IPs were Asn1, Asn2 and Atp2, whose mRNAs were amongst those most enriched in the Tma108 IPs (Figure  3E , panels 1 and 4).
Interestingly, according to the Pfam database  (25) , the GATase domain of Asn1 corresponds to amino acids 3-165, demonstrating that the signal responsible for the recruitment of Tma108 overlaps with this domain. Nevertheless, we were not able to isolate a common motif within the first 50 amino acids of the Tma108 targets (data not shown) thus, it would be interesting to investigate whether a specific structure adopted by the nascent chain is involved in the recruitment of Tma108.
The extent of the enrichment for each GO term is expressed as a fold enrichment over genome. Significant over-and under-enrichments are indicated (***P-value < 10 -3 , **P-value < 5 × 10 -2 ). (D) Tma108 targets are highly enriched in defined molecular functions. Analysis of enrichment of the GO terms revealed three main categories of over-represented functions: Nucleotide binding (P-value: 2. 3 × 10 -8 ), Zinc ion binding (P-value: 1. 5 × 10 -5 ) and RNA binding (P-value: 9. 7 × 10 -5 ). Over-represented functional categories included in these three GO terms are not represented (Supplementary TableS10). (E) Analysis of Pfam domains over-represented in proteins encoded by Tma108 targets. Eighteen Pfam domains that showed a significant enrichment (P-value < 0. 05) in Tma108 dataset were selected (identifiers and P-value in Supplementary TableS11).
They were classified according to their belonging to the different GO terms (function) enriched in Tma108 list (e1: Nucleotide binding, e2: RNA binding, e3:Zinc ion binding, e4: Others). The boxplots represent the distribution of mean log2(IP/Input) values obtained in microarray analyses of Tma108 immunoprecipitation for the whole cellular mRNA and for the analyzed GO categories. For each Pfam domain, the values of log 2 (IP/Input) measured for each individual gene included in the Pfam dataset are plotted (yellow diamond) overlapped on the boxplot (light gray) of the corresponding GO term (e1-3) or of the total RNA dataset (e4). Pfam domains indicated with green circles correspond to domains with ATPase activity. Dashed lines correspond to the 0. 8 threshold that was chosen to determine Tma108 targets.
ACKNOWLEDGEMENTS We are grateful to  Thierry Foulon ,  Sandrine Cadel , and  Micheline Fromont-Racine  for their useful advice and critical readings of the manuscript. We thank the  "Plateforme de protéomique structurale et fonctionnelle de l'Institut Jacques Monod"  for our mass spectrometry analyses. We thank  Stéphane Le Crom  and  Jean-Charles Cadoret  for granting us access to their microarray facilities. We also thank  Alice Lambert  and  Baptiste Saudemont  for the careful proofreading of the manuscript.
Data were normalised to the DMSO control to determine the IC 50 values. Analysis was carried out using the GraphPad software version 4. 02 (GraphPad software, San Diego, CA). Sensitivity was determined as IC 50 value <0. 5 mM as published before  [25]. Apoptosis studies were carried out using the Apo-Tox-Glo TM Triplex Assay (Promega, Madison, WI) essentially according to the manufacturer's instructions. The experimental conditions were as described above for the MTT assay except that the assay was performed in triplicates.
Louis, MO). The Cell lines with IC 50 < 0. 5 mM are scored as sensitive to HDACi (highlighted in bold numbers), cell lines with IC 50 >0. 5 mM are scored as resistant to HDACi. Normalised protein expression was scored as low (0-40%), medium (41-69%) and high (70-100%); see Figure  1  for details). CTCL, cutaneous T-cell lymphoma; GIST, gastrointestinal stromal tumour; WDLS, well differentiated liposarcoma; DDLS, dedifferentiated liposarcoma; MLS, myxoid liposarcoma; LMS, leiomyosarcoma; SS, synovial sarcoma; MPNST, malignant peripheral nerve sheath tumour; ES, Ewing sarcoma. *Protein expression was determined by western blot analyses, /: no data (sigmoidal dose curve did not fit). HR23b antibody was established and validated on protein extracts of the positive control cell line HUT78. Dilution of 1:250 showed best results.