dataesr/scientific-paragraphs-categorization huggingface dataset:
Both cell lines were free of Mycoplasma species and were maintained for no longer than 10 weeks in culture after recovery from frozen stocks. Mycoplasma levels were checked periodically using the MycoAlert ® Mycoplasma Detection Kit (Lonza Inc. , Allendale, NJ). The authenticity of cell lines was assessed by the ATCC carrying out short tandem repeat (STR) analysis (Verified STR Profiling Service, ATCC ® 135-XV). Additionally, we compared A2780 to the original STR profile collected by the European Collection of Cell Culture (Catalogue number 93112519). Doxycycline-inducible knockdown of PTBP1 regulated by small hairpin RNA (shRNA; Datasets II and III). In order to analyze the effect of PTBP1 depletion, two consecutive viral transductions were performed in both MDA-MB231 and A2780 cell lines.
Cells were plated on 24-well plate (10-20 × 10 4 cells/well), maintained in culture for 16 hours, and then medium containing LV-tTR/KRAB-Red lentiviral particles was added. Following 16 h of incubation, cells were transduced a second time by LVTHM/PTBshRNA or LV-THM/LUCshRNA lentiviral particles. Clones expressing both red and green fluorescent protein (dsRED and GFP respectively) were selected and expanded. Following 16 h of incubation, cells were washed and split in two subcultures, one without doxycycline (PTBP1/-DOX; Control in Figure  1 ) and the other with Doxycycline (DOX) at a final concentration of 1 µg/ml (PTBP1/+DOX; PTBP1-KD in Figure  1 ). Doxycycline was prepared according to the manufacturer's recommendations (Sigma-Aldrich, St. Louis, MO). Five days later, cells were analyzed by fluorescence microscopy, and PTBP1 gene expression was assessed using PCR and Western Blotting (data not shown).
The cells that were transduced by LV-LUCshRNA express PTBP1 regardless of the presence of DOX (LUCshRNA/+DOX). Constructs and lentivirus preparation were performed as previously described  [9]. Microarray Analysis (Dataset I and II). For each of the cell lines, MDA-MB231 and A2780, total RNAs were extracted from four biological replicates of PTBP1depleted cells, PTBP1-KD (4 × PTBP1/+DOX) and eight biological replicates control cells (4 × PTBP1/-DOX and 4 × LUCshRNA/+DOX) by Direct-zol RNA kit (Zymo Research, Irvine, CA) (Figure  1 ). All paired samples consist of PTBP1-depleted cells (PTBP1KD) and matched control cells. Qualities of RNA were assessed based on the RNA quality indicator (RQI ≥ 8) using Experion Automated Electrophoresis System (Bio-Rad, Hercules, CA). Gene expression microarray measurements were performed using the GeneChip PrimeView Human Gene Expression Array that contains 49,395 probes and measures 36,000 transcripts and variants per sample.
Labeling and hybridization were performed following Affymetrix protocols. The raw data were normalized according to the Robust Multiple-array Average (RMA) technique  [10] , using Affymetrix Power Tools (APT)  [11]. The complete set of raw and normalized data is available for download on the GEO database (GSE52493; Table  1 ). Gene Ontology annotations of Biological Processes (GO-BP)  [12, 13]. We aggregated genes into pathwaylevel mechanisms using Gene Ontology Biological Process, GO-BP. Hierarchical GO terms were retrieved using the org. Hs. eg. db package  [14]  (Homo Sapiens) and the org. Mm. eg. db package  [15]  (Mus Musculus) of Bioconductor  [16] , available for R statistical software  [17]. We used the org. Hs. egGO2ALLEGS database (downloaded on 03/15/2013), which contains a list of genes annotated to each GO term (geneset) along with all of its child nodes according to the hierarchical ontology structure.
The genesets were filtered so that only those sized between 15 and 500 were kept in the studies. These GO annotations were used for three types of GO prioritization analyses: GSEA, DEG Enrichment and N-of-1-pathways analysis (described below in Methods). Kyoto Encyclopedia of Genes and Genomes (KEGG)  [18, 19]. We aggregated genes into pathway-level mechanisms using Kyoto Encyclopedia of Genes and Genomes, KEGG. KEGG pathways were retrieved using the org. Hs. eg. db package  [14]  (Homo Sapiens) and the org. Mm. eg. db package  [15]  (Mus Musculus) of Bioconductor  [16] , available for R statistical software  [17]. We used the org. Hs. egPATH database (downloaded on 03/15/2013), which contains a list of genes annotated to each KEGG pathway (geneset). The genesets were filtered so that only those sized between 15 and 500 were kept in the studies.
These KEGG annotations were used for three types of KEGG prioritization analyses: GSEA, DEG Enrichment and N-of-1-pathways analysis. N-of-1-pathways method applied to in vitro / in vivo experiments  [6] (Figures  2, 3, 4, 5 ). MECHANISMS PRIORITIZED WITHIN ONE PAIR OF SAMPLES: The N-of-1pathways method was performed on the three datasets (Table  1 , Datasets I, II, III) independently for each paired sample (PTBP1-KD and control, Figure  1 ). The first set of the proposed method consists of a nonparametric paired Wilcoxon test (Wilcoxon signed-rank test) performed within each sample on the paired gene expression profiles restricted to a given mechanism. Wilcoxon statistics, W + and W -, provide direction on deregulated genesets as overall "up-regulated" or ''downregulated'' respectively. Both FDR and Bonferroni (Bonf.
Adjusted p-values are then transformed into Z-scores for further within-and cross-samples analyses. The N-of-1-pathways software is available in R and Java at http://Lussierlab. org/ publications/N-of-1-pathways Gene Sets Enrichment Analysis (GSEA). Gene set enrichment analysis was conducted on breast and ovarian cancer datasets only (Table  1 , Datasets II, III). In the case of the neuronal dataset, GSEA was not performed as it is underpowered with a single pair of samples (Table  1 , Dataset I). The GSEA v2. 0. 10 software  [20]  was used with the default parameters except for the permutation parameter selection, which was set to "geneset" instead of "phenotype". Geneset permutation was chosen to achieve   To evaluate the GO-BP and KEGG associated terms of deregulated mechanisms yielded by the N-of-1-pathways method in both breast and ovarian cancer internal studies, we compared these mechanisms to those found by DEG Enrichment when GSEA is chosen as the 'Proxy' Gold Standard (Proxy GS, Methods).
Of note, the up-regulated and downregulated genes were enriched independently. DE genes were directly available for neuronal RNA-Seq study, but only based on fold change cutoff (Table  1 , Dataset I). We called "FET Enrichment" the enrichment of those deregulated genes to avoid any mixed up with the standard DEG Enrichment. The breast and ovarian cancer DE genes (Table  1 , Datasets II, III) were calculated in the following way: (i) genes whose average expression differs by at least 2-fold between Control (8 samples) and PTBP1-KD samples (4 samples) were selected for analysis, (ii) then a t-test was applied between the two groups, and p-values were adjusted with Benjamini and Hochberg method (False Discovery Rate; FDR). Only DE genes with FDR ≤ 5% were retained. Information Theoretic Similarity (ITS) (only applicable for GO-BP mechanisms; Figures  3  and 4 ).
In order to further stratify mechanisms in those that are unique to a pair of samples or common to multiple samples, Information-Theory Similarity (ITS) is utilized to formally assess similarity cross sample pairs versus uniqueness to a pair. When applied on samples from an individual patient, this method allows determining mechanism unique to a patient versus those common to many, a step forward in personal therapy from transcriptome data. We calculated the similarity between GOBP terms using Jiang's information theoretic similarity  [21]  that ranges from 0 (no similarity) to 1 (exact match). Within-Study Proxy Gold Standard (Figure  3 ). Mechanisms are statistically prioritized in breast and ovarian cancer datasets by the three above described methods: N-of-1-pathways, GSEA and DEG-Enrichment.
The accuracy of the N-of-1pathways method was compared to one of the conventional methods (eg. DEG Enrichment) while the other serves as a Proxy Gold Standard (GSEA). Cross-Studies derived Gold Standards (Figure  4 ). Significant deregulated mechanisms in PTBP1 depleted neuronal cell lines unveiled by N-of-1-pathways and DEG Enrichment methods (Table  1 , Dataset I) were used as Proxy Gold Standard. For the DEG Enrichment method, the list of DEG was directly provided by the authors and further enriched. These two lists of mechanisms serve as derived Gold Standards to compare their robustness across studies, methods, and underpinning biology (PTBP1 depleted cells; mouse versus human, neuronal versus cancer cell lines; breast versus ovarian cancer cell lines. ) Precision-Recall curves (Figures  3, 4 ).
Using the R statistical software, we computed two types of Precision-Recall curves: (i) within-study (Figure  3 ) and (ii) cross-studies (Figure  4 ) of the mechanisms predicted by the N-of-1-pathways (Cross-samples; see above), GSEA and DEG Enrichment. WITHIN-STUDY: Precisionrecall curves of the "internal validation" compare breast and ovarian cancer GO-BP and KEGG associated mechanisms unveiled by the N-of-1-pathways with those predicted by DEG Enrichment and GSEA that were used alternatively as "Proxy Gold Standard" (Proxy GS) (Figure  3 ). CROSS-STUDIES: Breast and ovarian cancer GO-BP and KEGG associated mechanisms uncovered by the N-of-1-pathways, GSEA and DEG Enrichment were compared to those found in the RNA-Seq neuronal dataset by the Nof-1-pathways and DEG-Enrichment (considered as GS) (Figure  4 ).
Overview of the datasets and performed studies Figure  1  provides an overview of the experimental design. We evaluated the robustness of the N-of-1-pathways method in three transcriptome profile datasets: a single paired sample (RNA-Seq from mouse neuronal cell line, Dataset I) and two small sets of paired samples (mRNA expression microarray from human breast and ovarian cancer cell lines, Dataset II and III). Each dataset consists of a number n of PTBP1-depleted samples and matched controls. To uncover PTBP1-KD associated mechanisms, we first performed within-study analyses (N-of-1-pathways, FET enrichment, GSEA, DEG enrichment) independently for each of the datasets I, II and III (Methods, Figures  2, 3 ; Table  2 ; GSEA and DEG was not applicable to Dataset I). We then quantitatively and qualitatively compared the three analytical methods across the three studies to reveal concordant and tissue/cell specific deregulated mechanisms associated to PTBP1-KD (Figures  4, 5 ; Table  3 ).
Within-study (Dataset I). Concordance of PTBP1-KD associated mechanisms unveiled by N-of-1-pathways and FET Enrichment in neuronal cell line We first performed an independent within-study analysis from published RNA-Seq transcriptome profile of PTBP1-depleted neuronal cell lines (Dataset I; Table  2 ; Figure  2 ). To apply geneset-level enrichment analysis, conventional methods such as GSEA and DEG enrichment require three samples to reach statistical significance. The present dataset I consists of a single sample of PTBP1-KD and a single paired Control (n = 2) and therefore GSEA and DEG enrichment are not the methods of choice for such underpowered experiments. We applied our proposed method, N-of-1-pathways, which is designed for these experiments while only a Fisher's Exact Test (FET) Enrichment analysis could be performed on genes at a certain fold change level, as differentially expressed genes cannot be calculated with a Table  2  GO-BP overlap and similarity between N-of-1-pathways and FET Enrichment derived from RNA-Seq transcriptome profile of PTBP1-depleted neuronal cell line.
Thereafter, we evaluated the biological relevance of such GO-BP associated mechanism overlaps between N-of-1-pathways and FET Enrichment (Figure  2  leftmost panel; Table  2 ). Six GO-BP were found overlapping and ten others were found related based on ITS semantic similarity computed score (ITS ≥ 0. 7, Methods). All together, the 16 mechanisms belong to three GO-BP classes: Cell Cycle/DNA Replication, DNA repair and neuronal transmission. Indeed, N-of-1-pathways and FET enrichment recapitulate the tissue-specific deregulated mechanisms of synaptic transmission and synaptic vesicle exocytosis that were previously confirmed in biologic assays by the study from which the RNA-Seq dataset I was generated  [26]. The authors showed that the depletion of PTBP1 affects alternative splicing and triggers transcriptome changes on a large scale, which increased the propensity of the neuronal CAD cells to undergo neuron-like differentiation.
However, unlike N-of-1-pathways, FET enrichment did not uncover alternative splicing related mechanisms. Within-study, datasets II and III: concordance of PTBP1-KD associated mechanisms unveiled by N-of-1-pathways, DEG Enrichment and GSEA in Breast and Ovarian cancer cell lines We performed a within-study analysis independently for two different human cancer cell lines (breast and ovarian cancer; Table  1 , Dataset II, III). Each study consists of four biological replicates of PTBP1-KD and eight matched control (not depleted PTBP1) samples. We applied GSEA, DEG Enrichment and N-of-1-pathways methods independently on each dataset I and II and compared their accuracies (Figure  3 ). The results show that three out of the six N-of-1-pathways method predictions were found comparable or better to those of DEG enrichment when GSEA is chosen as a Gold Standard (GS) (Figure  3  Panels A, B and F).
Compared to DEG Enrichment, the proposed N-of-1-pathways offers the same level of results with the advantage of being applicable to a single paired sample. Additional File 1-Supp. Figure  S1  shows the results taking DEG Enrichment as the Proxy GS. However, DEG Enrichment did not provide enough statistically significant deregulated pathways to perform an accurate comparison. Cross-studies: concordance of PTBP1-KD associated mechanisms unveiled by N-of-1-pathways, DEG Enrichment and GSEA across all three datasets Using either N-of-1-pathways or DEG Enrichment as the two alternate Gold Standards (performed in the neuronal cell lines), N-of-1-pathways surpasses well-known methodologies in five out of six predictions conducted in ovarian and breast cancer cell lines (Figure  4 ). Specifically, mechanisms discovered by N-of-1-pathways method applied in the RNA-Seq neuronal cell line dataset are highly concordant to those found in breast cancer and ovarian cancer (Figure  4 , Panels A and D).
Taken together, the precision and recall curves of each method in breast and ovarian cell lines (vertical columns) and the overall accuracies of GSEA (Figure  4  To qualitatively assess the biologic relevance of mechanism overlap, we curated the associated and unrelated GO-BP deregulated mechanisms across the three datasets found by the N-of-1-pathways method. As shown in Table  3 , N-of-1-pathways discovered the most common and highly related mechanisms previously reported  [8, 9, 26]  as associated to molecular and cellular phenotypes that are triggered by PTBP1 depletion such as GO:0000398, mRNA splicing via spliceosome and GO:00010564, regulation of cell cycle process. We also studied the dissimilar mechanisms between datasets; Additional File 2-Supp. Tables S1-S3 displays sets of GO-BP classes underlying tissue-specific mechanisms affected by PTBP1 depletion.
While GSEA provided good overlap (OR≥19) between breast and cancer cell line datasets, it failed to provide an overlap between these studies and the neurological dataset, as it cannot be applied to a single paired sample studies (eg. dataset I). In contrast, N-of-1-pathways can be applied robustly in each case scenario achieving overall the best performance with high OR ≥ 13 at significant p-values (p ≤ 1 × 10 -15 ) far surpassing those of GSEA and of DEG Enrichment in every combination of dataset. Taken together, N-of-1-pathways is able to provides both PTBP1 common and tissue-specific deregulated mechanisms independently of the sample size do datasets.
Discussion Future studies. In the context of paired samples, the validation studies results are so favorable for N-of-1pathways that we are planning large-scale studies (synthetic and real datasets) systematically comparing N-of-1-pathways to multiple conventional geneset enrichment methods. Further, we are investigating the scalability of N-of-1-pathways in genome-wide measurements other than the transcriptome (e. g. methylation) to reveal mechanisms of resistance to therapy. Limitations. At the biological level, the large extent of shared mechanisms between RNA-Seq (Dataset I) and mRNA expression microarrays (Datasets I and II) attests the sheer ability of N-of-1-pathways to be applied across platforms. However, unlike the neuronal RNA-Seq dataset, the two newly generated datasets submitted to GEO were conducted using microarrays without exon-specificity measures, preventing the identification of alternative transcripts.
Figure3WITHIN-STUDY concordance of PTBP1-KD associated mechanisms found by N-of-1-pathways compared to those found by GSEA and DEG Enrichment, applied to breast and ovarian cancer gene expression microarray profile (Datasets II-III). To evaluate the GO-BP and KEGG associated terms of deregulated mechanisms yielded by the N-of-1-pathways method in both breast and ovarian cancer internal studies, we compared these mechanisms to those found by DEG Enrichment when GSEA is chosen as the 'Proxy' Gold Standard (Proxy GS, Methods). We then generated precision-recall curves based on the exact GO overlap (Without GO-ITS, panels A, D), related GO terms by Information Theory Similarity overlap (With GO-ITS, panels B, E; GO-ITS ≥ 0. 7; Methods), and the exact KEGG overlap (panels C, F).
Figure5CROSS-STUDIES accuracy of mechanism identification methods using their default parameters. Strong overlap performance of N-of-1-pathways method. We compared the three different mechanism identification methods (N-of-1-pathways) across the three different studies (neuronal, breast and ovarian cancer cell lines). The computed overlaps of PTBP1-KD associated mechanisms are represented by Venn Diagrams. Odds Ratio (OR) and p-values (p) are plotted below the Venn Diagrams to represent the statistical significance of the overlap (Methods). The symbol "X" marked in the GSEA results represents not computed analysis, as this method cannot be applied to the single paired sample form the neuronal cell line dataset I.
, Panels B and E) and DEG Enrichment (Figure 4, Panels C and F) are lower than those of N-of-1-pathways (Figure 4, Panels A and D), regardless of the GS used (GS from neuronal study). Of note, N-of-1-pathways performs better than GSEA and DEG Enrichment with only one exception (Figure 4, Panel C). Moreover, the concordance of DEG Enrichment fails in breast cancer cell lines (Figure 4, Panel C) as well as in ovarian cancer cell lines (Figure 4, Panel F). Therefore, both consistency and robustness of the mechanisms unveiled by DEG Enrichment across datasets are questionable. In summary, these results highlight the advantage of applying the N-of-1-pathways for a single paired sample analysis compared to conventional methods.
Data Collection We have obtained permission for collecting wild apple leaves from the Conservatoire des Sites Alsaciens, which is the authority responsible of the natural reserves of Erstein and Rhinau, manager of the Erstein natural reserve and the Rhinau natural reserves. The other locations did not require specific permission because forests are not protected but we have collected the leaves with the help and consent of local foresters from the Office National des Forets, manager of these forests. These field studies did not involve endangered or protected species. We collected data for 255 trees growing at six different sites in the forests of the Rhine Valley: the Hardt (N = 80), Marckolsheim (N = 47), Rhinau (N = 3), Daubensand (N = 3), Erstein (N = 80) and Illkirch (N = 42) forests.
e. , with membership coefficients. 0. 9 to the M. domestica genepool)  [22]  and the entire M. sylvestris dataset (N = 255). We retained only individuals assigned with a membership coefficient of more than 0. 7 to the M. sylvestris genepool for further genetic analyses (N = 246). Analyses of the genetic diversity of wild apples. After removing the individuals identified as potential hybrids, we used GENEPOP 4. 0  [38]  to calculate heterozygosities (expected (H E ) and observed (H O )), Weir & Cockerham F-statistics, deviation from Hardy-Weinberg equilibrium and genotypic linkage disequilibrium. Allelic richness and private allele frequencies were calculated with ADZE software, for a sample size of 4 (262 chromosomes)  [39]. In parallel, we performed frequency-based assignment (Paetkau test) with GenAlEx 6.
Ecological Analysis Size variation between populations. The significance of differences in DBH between the floodplain forests (N = 175 trees) and the Wu ¨rmian terrace (N = 80) was assessed with Student's ttests. Collection of ecological data from the erstein site. Ecological data were collected at a finer scale, in the natural reserve of Erstein (180 ha). Almost all the individuals present in this forest (227 individuals) were studied. We investigated possible associations between size, health status, morphological forms and the presence of woody vines, by collecting the following data: DBH, health status (a tree was considered as (i) in good health if upright and devoid of fungi, mistletoe, or/and dead axes; (2) in bad health when leant and reiterated axes. These trees were attacked by fungi or mistletoe and often harbour holes and dead axes), morphological plasticity, presence of Hedera helix and/or Clematis vitalba, and forest architecture above the tree (gap vs overtopping canopy).
Acknowledgments We thank  Anne-Charlotte Marsollier  for assistance with the genotyping of microsatellite markers, the  Region Ile de France (PICRI)  and the  Plateforme de Genotypage GENTYANE INRA UMR 1095 Genetique Diversiteet Ecophysiologie des Ce  ´reales for the genotyping data. We also thank  Patrick Gassmann  for the dendrochronological work in the Erstein forest, and  Roland Carbiener  for valuable and informative discussions,  Jean-Louis Le Rigolleur  and  Jean-Claude Isenman  for collecting leaves in the Rhine forests,  Louis-Felix Bersier  for statistical investigation of the aggregation indices and  Tatiana Giraud  for helpful comments on the draft. The authors also thank the '' Conservatoire des Sites Alsaciens '' association and the  Office National des Forets (ONF) , which manages the other forests, for their authorisation of leaf collection.
METHODS The working group included experts in clinical transplantation and apheresis. A list of important questions relevant to PBPC mobilization and collection was generated. A comprehensive and critical review of relevant published literature was then performed to address those questions. We screened for publications in the PubMed database by including the search terms "stem cell mobilization," "growth factor stem cell mobilization," "plerixafor stem cell mobilization," "chemotherapy stem cell mobilization," "pediatric stem cell mobilization," "mobilization algorithm," and other search terms pertinent to the questions being addressed. Relevant references in the publications were also identified and reviewed. Both retrospective and prospective studies were included. Meeting abstracts, data from nonepeer-reviewed journals, review articles, and studies with incomplete data were excluded.