dataesr/scientific-paragraphs-categorization huggingface dataset:
statistical analyses Statistical analysis was performed using the R computing environment, R version 3. 2. 3 (20). Heatmap figures were produced with the gplots package using the "heatmap. (21). In both arms of the study, induction and consolidation CTX were followed by maintenance treatment with LEN as single agent for 1 year, starting 1 month after the last VRD cycle (Table  1 ; see Materials and Methods). Blood samples were obtained from patients 1 month after the end of the last VRD cycle and during treatment with LEN, on a monthly basis over the 1-year treatment. We also monitored NK cell phenotype and function after the end of the last LEN cycle (on average 6 months after the last LEN cycle). To analyze the phenotype of fresh blood NK cells, we measured, by flow cytometry, their expression of activating and inhibitory receptors, activation markers, and molecules associated with NK cell maturation and cytotoxicity (Table  2 ).
longitudinal study of the impact of len on nK cell Phenotype and Function Lenalidomide is often cited as an NK cell-stimulating agent, based on in vitro data or on experiments in mouse models. However, evidence that LEN stimulates NK cell activity in patients is still lacking. To address this point, we took advantage of the protocol IFM/DFCI 2009. In both arms of the study, induction and consolidation CTX were indeed followed by maintenance treatment with LEN as single agent for 1 year, starting 1 month after the last VRD cycle (Table  1 ; Materials and Methods). We monitored NK cell phenotype and function before and during treatment with LEN, on a monthly basis over the 1-year treatment. We also monitored NK cell phenotype and function after the end of the last LEN cycle (on average 6 months after the last LEN cycle).
Identification of Eligible Studies The literature search and inclusion criteria are described in detail elsewhere  [11]. In brief, we searched 3 databases (PubMed, Embase, and Latin American & Caribbean Health Sciences Literature) to identify eligible studies in sub-Saharan Africa published from 1 January 2009 to 31 December 2015. Studies where patients who started cART for their own health (adults or children fulfilling respective criteria of lifelong cART) were LTFU and then actively traced to establish their vital status were eligible. We excluded studies in tuberculosis patients and those focusing on prevention of mother-to-child transmission programs or postexposure prophylaxis. For the current study, we also excluded studies from South Africa that linked clinical databases with civil registry to identify deaths because only data on mortality but no other outcome were collected in these studies.
Missing WHO stage, CD4 cell counts, age, and time on cART were estimated through multiple imputations using a modified approach suitable for the subdistribution hazards model  [38]. We imputed 20 datasets and pooled model parameter estimates using Rubin rules  [39]. Sensitivity to imputation was assessed by comparing parameter estimates of models fitted on the imputed dataset with models fitted on the complete cases  [40]. We assessed model fit using the Akaike information criterion and proportionality of hazards by inspecting model residuals against failure time and explored interactions between model covariates. We used a random intercept logistic regression model to examine the influence of the period between loss to follow-up and ascertainment of outcomes on the probability of remaining LTFU (outcome "not found").
We compared the characteristics of published studies that provided IPD data with the studies that did not. Furthermore, we used random intercept logistic meta-regression models to compare mortality, transfer to another clinic, and stop cART estimates published in the respective articles between studies included and not included in the IPD meta-analysis. For cohorts that provided data on both patients retained in care and patients LTFU, we calculated nonparametric cumulative incidence functions for the 2 groups separately to compare their mortality after the start of cART (again accounting for the presence of competing risks). Finally, we repeated analyses excluding a large study from Lilongwe, Malawi  [25]. All analyses were carried out using R statistical software (version 3. 3. 2)  [41] , including the packages "mstate" for cumulative incidence analysis, "crrSC" for stratified subdistribution hazard modeling, and "smcfcs" for multiple imputation.
Furthermore, the active involvement of investigators contributing data may enhance data quality, facilitate the provision of more recent data, and improve interpretation of results. Indeed, several datasets provided for this analysis were updated to include more patients, and the authors of the 9 studies  [12-14, 21-25, 27, 42, 43]  all contributed to this project. Another strength of this study was the comprehensive literature search, which covered several bibliographic databases and recent major conferences  [11]. IPD meta-analyses also have disadvantages. IPD are obtained typically only from a proportion of all eligible studies. Selection bias is therefore possible if the studies contributing data are not representative of all studies. We addressed this issue by comparing the 7 published articles that were included in this analysis with the 15 that were excluded.
Silent transfers have increased with the scaling up of cART, likely due to the expansion of cART access and the availability of clinics nearer to patients' home  [11, 50]. We found that about 15% of patients who were LTFU in the clinic where they started cART transferred to another facility within 4 years. Retention in care is therefore underestimated in analyses of individual clinics or programs  [51]. Clearly, improving communication between the clinics and programs, for example, through a national cART database and unique patient identifiers  [52] , is urgently needed for an accurate assessment of overall retention in care. On the other hand, we confirm the much higher risk of death among patients LTFU compared to those retained in care, and the fact that estimates of mortality that are based on patients in care and under observation through a single facility data system will underestimate mortality at the level of the program, that is, among all patients who started therapy  [8, 9, 52].
The results are useful to predict clinical outcomes in patients LTFU, and to assess program effectiveness, with less biased estimates of retention and mortality. We acknowledge that our results may not be generalizable to many settings in sub-Saharan Africa, and we recommend that cART programs trace patients LTFU and use results to estimate program-level outcomes. Indeed, to further improve our understanding of LTFU and the outcomes of patients LTFU, we are planning tracing studies using standardized methods and data collection within the framework of the International epidemiology Databases to Evaluate AIDS (IeDEA)  [53] , including studies in South Africa where mortality of patients not found by tracing can be ascertained trough linkage with the civil registry  [52].
b At last clinic visit. All models are stratified by cohort. Parameter estimates are pooled estimates from models fitted to 20 imputed datasets. Each model is fitted for the tracing outcome of interest, accounting for the alternative outcomes as competing risks. Downloaded from https://academic. oup. com/cid/advance-article-abstract/doi/10. 1093/cid/ciy347/5034717 by London School of Hygiene & Tropical Medicine user on 14 June 2018 8 • CID 2018:XX (XX XXXX) • Chammartin et al.
Applying the International Uveitis Study Group criteria, we classified uveitis into the following subtypes according to the site of inflammation: anterior uveitis, intermediate uveitis, posterior uveitis, or panuveitis  (20, 21). We considered placoid chorioretinitis to be the presence of >1 placoid, yellowish, outer retinal lesions. Fundus fluorescein angiography, indocyanine green angiography, and fundus autofluorescence indicate a typical fluorescent pattern in cases of placoid chorioretinitis  (22, 23). Biological data included the results of treponemal and HIV serologic tests and the results of cerebrospinal fluid (CSF) analysis. The decision to perform lumbar puncture was left to the clinician's discretion. We defined neurosyphilis as positive results in nontreponemal and treponemal serologic tests, combined with neurologic and CSF abnormalities, such as high leukocyte or protein concentrations (>0.
5 g/L), and 1 CSF abnormality, such as positive VDRL or fluorescent treponemal antibody absorption test results  (24) (25) (26)  or a positive PCR test result for T. pallidum. Treatment and outcome data included the type, dose, duration, and route of antibiotic administration and corticosteroid treatment (local or systemic) as well as final VA. We tested data variable distributions for normality and, when appropriate, we performed t-tests or equivalent nonparametric tests. We used 1-way analysis of variance when >3 groups were present and defined statistical significance as p<0. 05. We analyzed the data by using R 3. 2. 2 software (https://cran. rproject. org/bin/windows/base/old/3. 2. 2).
(P), putative promoter. The predictive function of each gene is reported in Table  5. supplemented with 1 mM H 2 O 2 , 1 M NaCl, 150 g. L -1 ethanol, or acidified with HCl to pH 3. 0. Phosphate buffer was maintained in a water bath at 50 • C for heat stress (four biological replicates). Total RNA isolation, cDNA synthesis, and qPCR was made as previously described  (Licandro-Seraut et al. , 2008)  using TRI Reagent (Sigma Aldrich), DNase I (Roche), iScript TM Reverse Transcription Supermix (Bio-Rad), and SsoAdvanced TM Universal SYBR R Green Supermix (Bio-Rad). Primers were designed by using Primer3Plus  (Untergasser et al. , 2007)  (Table  S1 ). Quantitative PCR (qPCR) were performed using a CFX96 Touch TM Real-Time PCR Detection System (Bio-Rad) in triplicate, in a 20 µl-reaction mixture.
Cq (threshold value) calculation was determined by a regression model of the CFX Manager TM Software. Gene expression was calculated using 2 -CT method  (Livak and Schmittgen, 2001). In order to select appropriate reference genes, 10 housekeeping genes (fusA, ileS, lepA, leuS, mutL, pcrA, pyrG, recA, recG, and rpoB) were tested with all experimental conditions and analyzed using the CFX Manager TM Software. The genes recG and rpoB were selected as the references because they displayed the lowest M-values (0. 57) and coefficients of variation (0. 20), meaning that they have the most stable expression in the tested stress conditions.
Flow cytometry Fluorochrome-conjugated anti-PD-L1 (10 F. 9 G2), anti-CD45 (30-F11), anti-CD11b (M1/70), anti-CD8 (53-6. 7), anti-CD4 (GK1. 5), anti-CD3 (145-2C11), anti-Tim3 (RMT3-23) and anti-TNFa (MP6-XT22) were from Biolegend. Anti-PD1 (J43) was from eBioscience. CD107 a (1D4B) and anti-IFNg (XMG1. 2) was from BD Bioscience. For selection of tumor cells, after Percoll performed to isolate tumor-infiltrating lymphocytes, cells from the upper phase were collected. First, tumor cells were gated based on FSC/SSC parameters using in vitro tumor cell line as reference. Then CD45-7AAD-viable tumor cells were selected. All events were acquired by a BD LSR-II cytometer equipped with BD FACSDiva software (BD Biosciences) and data were analyzed using FlowJo software (Tree Star, Ashland, Oregon).
The libraries were sequenced with paired-end 75-base pair 'reads'. FASTQ files were mapped with the BWA software package (mm10 National Center for Biotechnology Information assembly of the Mus musculus genome) for Illumina. Analysis was performed with the splice junction mapper TopHat for Illumina. The files generated were processed with Cufflinks software to obtain annotated expressed genes in each studied subtype. Heatmaps of selected genes were generated using R software (http://www. R-project. org).
Funding This work was supported by grants from the  Fondation de France  to LA and TRV, the Association pour la recherche sur le cancer to LA, FC and FG, the  FEDER  and  Conseil R egional de Bourgogne , the  French National Research Agency  (grant number  ANR-13-JSV3-0001  to LA, the  ARSEP  to LA and MD, the  Ligue R egionale contre le cancer Comit e Grand-Est  to LA, the canc eropôle grand-est to LA,  ERC starting grant  [grant number:  677251  to LA] and  Centre National de la Recherche Scientifique (CNRS)  and the  European Regional Development Fund (ERDF)  to BR.
Statistical analysis Results are presented as mean values±Standard Error of Mean (SEM) from at least 3 independent experiments unless otherwise indicated. Differences between groups were examined using the unpaired two-tailed Student's t-test, unpaired two-tailed Student's t-test with Welch's correction or two-way ANOVA with Bonferroni post-test using GraphPad Prism software version 6. 00 for Windows (GraphPad software) (La Jolla, CA, USA). For all tests, P<0. 05 (*), P<0. 01 (**) or P<0. 001 (***) were considered statistically significant. Event-free survival was analyzed using a Receiver Operating Characteristics (ROC) method. A cut-off value was then chosen which would assume a minimum error rate and a limited number of misclassifications within the cohort.
The miR-497∼195 cluster is down-regulated in human NPM-ALK + primary lymphoma samples and cell lines To determine the profile of methylated miRNA genes driven by NPM-ALK oncogene in ALCL cells, we performed a high throughput analysis of miRNA expression (small RNA sequencing) from NPM-ALK + KARPAS-299 cells following decitabine treatment or transfection with a siRNA targeting ALK mRNA (si-ALK). A set of 13 miRNAs (miR-9-5p, miR-1287-5p, miR-15a-5p, miR-22-3p, miR-26a-5p, miR-29c-3p, miR-194-5p, miR-195-5p, miR-199a-3p, miR-199a-5p, miR-223-3p, miR-3613-5p and miR-497-5p) was found to be up-regulated >1. 5-fold in treated cells in both conditions (si-ALK and decitabine) compared either to negative control siRNA (si-CTL) or drug vehicle alone (DMSO) (Figure  1A ). Amongst these 13 miRNAs, using microarray data previously published of miRNAexpression of NPM-ALK + (n=52) and NPM-ALK -(n=5) ALCL patients,  21  we observed that miR-195 and miR-497 were down-regulated only in NPM-ALK + ALCL patients compared to normal reactive lymph node (RLNs, n=3) (Online Supplementary Table  S2 ).
Effect of miR-497 on the expression of cell cycle regulatory genes in NPM-ALK + lymphoma cells To determine the mechanism(s) by which miR-497 regulates tumor growth and progression of NPM-ALK + cancer cells, we searched for mRNA targets of miR-497. To capture miR-497 associated mRNAs, KARPAS-299 cells were transfected with biotinylated human miR-497. We isolated and sequenced RNAs that co-purified with biotin-miR-497 mimic to map miR-497-mRNA interaction. As a negative control, we performed the same experiment with a biotin-negative control mimic (biotin-miR-CTL). A large set of mRNAs is regulated by miR-497 (enrichment ratio >1. 5). In this data set, 77 validated candidate targets of miR-497 were collected from the miRTarBase (http://mirtarbase. mbc. nctu. edu. tw/, Release 7. 0) (Figure  5A ).
In addition, using the miRSystem database (http://mirsystem. cgm. ntu. edu. tw/), we identified 16 miR-497 mRNA targets, experimentally validated and associated with cell cycle regulation (KEGG pathway database) (Figure  5A ). We identified 5 mRNA gene targets that were common to both databases: CCNE1, CDC25A, CDK6, CCND3 and E2F3 (Figure  5A ). Next, we used gene expression array data (Daugrois et al. , 2018, submitted publication) to identify the veracity of these 5 targets captured by biotin-miR-497. Despite the presence of a mixed population of neoplastic and normal cells, CCNE1, CDC25A, CDK6 and E2F3 mRNAs were found to be sig-nificantly over-expressed in NPM-ALK + ALCL patients (n=55) compared to reactive lymph node controls (RLN) (Figure  5B ). As previously reported by Thompson et al.
,  CCND3 was not over-expressed in NPM-ALK + ALCL patients. 30 Finally, to validate the specificity of the biotin pulldown and applicability to other cell lines, biotinylated forms of miR-497 or negative miRNA-control were used to co-purify CCNE1, CCDN3, CDC25A, CDK6 and E2F3 mRNAs in three KARPAS-299, SU-DHL1 and COST cell lines. Enrichment of target mRNAs by streptavidin-mediated purification (enrichment ratio >2) was measured by qPCR and normalized to miR-CTL enrichment. In the biotin-miR-497 pulldown of COST, SU-DHL1 and KARPAS-299 cells, CDC25A, CCNE1 and E2F3 transcripts but not CCDN3 were captured 24 h after transfection (Figure  5C ). Moreover, biotin pulldown enriches CDK6 mRNA in COST and SU-DHL1 cells but not in KARPAS-299 cells (Figure  5C ). This result was in accordance with data published by Nagen et al.
Hence, CDK6 silencing alone is sufficient to recapitulate the phenotype observed upon miR-497 ectopic expression in SU-DHL1 cells. These results are similar to those published by Nagen et al. 31  showing an addiction to CDK6 expression for proper cell cycle progression in this cell line. In contrast, even though siRNA directed against CDK6 led to a significant accumulation of COST and KARPAS-299 cells in G0/G1 phase (Online Supplementary Figure  S6B  and E ), it is not sufficient to fully recapitulate miR-497-induced blockage of cell cycle. The same observation was made after E2F3 and CCNE1 mRNA downregulation (Online Supplementary Figure  S6A  and D , and C and F, respectively). Finally, we observed that CDC25A knockdown did not modify the cell cycle profiles of COST and KARPAS-299 cells (data not shown).
We also tested the effect of each siRNA on cellular apoptosis in COST and KARPAS-299 cells. Compared to the control condition (si-CTL), CDK6 and CCNE1 silencing induced apoptosis in COST cells   but, as expected, not in KARPAS-299 cells (Online Supplementary Figure  S7 ). In addition, the silencing of E2F3 mRNA did not affect caspase activity in both NPM-ALK + COST and KARPAS-299 cells (Online Supplementary Figure  S7 ). Moreover, regardless of the siRNA transfected, cell viability following RNAi delivery was never significantly modified in NPM-ALK + cells (data not shown). Thus, individual silencing of three miR-497-targets, CDK6, CCNE1 and E2F3, can reproduce (only to a lesser extent) the miR-497-induced phenotype (Figure  4A  and B ). Consequently, in order to improve the effects observed in COST and KARPAS-299 cells, we decided to pool siRNAs targeting CCNE1, CDK6 and E2F3.
Based on our findings that CDK6, CCNE1 and E2F3 are important for efficient proliferation of NPM-ALK + ALCL cells, we hypothesized that the highest level of expression of these factors could be the most favorable for tumor development and possibly affect chemotherapy effectiveness. To test this hypothesis, we used qPCR to measure the expression of CDK6, CCNE1 and E2F3 in a cohort of NPM-ALK + primary ALCL samples (n=55) obtained from chemotherapy-naive patients at diagnosis. Compared to normal reactive lymph nodes (RLNs) (n=19) and despite the presence of a mixed population of neoplastic and normal cells, we observed a significant CDK6, CCNE1 and E2F3 gene overexpression in NPM-ALK + samples (Online Supplementary Figure  S12A ). In order to evaluate the clinical significance of CCNE1, CDK6 and E2F3 overexpression, first using linear regression, we derived a model based on microarray data on expression levels of CCNE1, CDK6, E2F3 genes in NPM-ALK + samples (n=55).
NPM-ALK + ALCL patients with a low expression of CCNE1, CDK6, E2F3 had a poor prognosis (P<0. 046) (Online Supplementary Figure  S12C ). Moreover, this trend can be observed for high (n=14) and low (n=22) expression in NPM-ALK + pediatric ALCL (Online Supplementary Figure  S12D ). In addition, for the same pediatric cases, we noted that CCNE1, CDK6, E2F3 gene-based score was increased in relapsing patients (n=23 vs. without relapse n=13) (Online Supplementary Figure  S12E ). As all patients were treated with chemotherapy following diagnosis, our data suggest that CDK4/6 inhibitors could benefit ALK + patients harboring resistant tumors.
Funding This work was supported by grants from the  "Ligue contre le Cancer", Fondation ARC pour la Recherche sur le Cancer" (FM) , and  INCa  (projet PAIR Lymphomes) (LL). CHA and AC were supported by a fellowship from the  "Labex TOUCAN / Laboratoire d'excellence Toulouse Cancer ". For their technical assistance, the authors thank  M. Tosolini  ( Pôle Technologique du CRCT -Plateau Bioinformatique INSERM-UMR1037 );  F. Capilla  and  C. Salon  at the histology facility and the staff of the zootechnie Langlade,  INSERM/UPS-US006/CREFRE  (Toulouse, France). C. Hoareau-Aveilla  et al.
The initiative consisted of three phases: preparatory (prior to the meeting; approximately 3 months), during which participants performed a systematic review of the 260 top cited (over 29,000 citations in aggregate) 2003 to 2012 preclinical publications (using ISI Web of Knowledge database; query: ''sepsis model''; 374 individual experiments analyzed) and identified the key modeling topics to be discussed, discussion during which the participants spent two days at the Wiggers-Bernard Conference examining preclinical sepsis models and ultimately voted to reach consensus on the proposed points (either at the ''recommendation'' or ''consideration'' strength), and post-meeting refinement of the accepted points and finalization of the arguments to be included in the final publications (using a modified Delphi method; approximately three months).
Cytokine/Chemokine analysis MIMIC 1 culture supernatants were analyzed using a Milliplex 1 human 16-plex multi-cytokine detection system (Millipore), per the manufacturer's protocol. The kit includes VEGF, IFN-α2, IFNγ, IL-1β, IL-1Ra, IL-6, IL-8, IL-10, IL-12, IL-12, IP-10, MCP-1, MIP-1α, MIP-1β, RANTES, and TNF-α. Analyte concentrations were calculated based on relevant standard curves using the Bio-Plex manager software.
TLR reporter cell line assay THP1-XBlue™-CD14 (InvivoGen) cell analysis was performed following the manufacturer's protocol. The cells were authenticated and confirmed to be mycoplasma free by the manufacturer. Briefly, the cells were treated with all insulin glargine compounds at a dose of 5 U/ml (30 nM) for 18-24 hours. Thereafter, the cells were incubated with QUANTI-Blue™ and SEAP levels were measured at an absorbance of 622 nm on a Bio-Tek Synergy HT multiwell reader using KC4 software (Bio-Tek Instruments, Winooski, VT, USA).
Flow cytometry MIMIC 1 PTE-derived cells were washed with PBS and stained with Live-Dead Aqua (Invitro-Gen, Carlsbad, CA, USA) for 20 minutes on ice. After washing and IgG-Fc blocking (mouse IgG1; Sigma-Aldrich), the cells were incubated with a cocktail of fluorochrome-labeled mAbs specific for non-myeloid lineage cells and the immune markers CD3, CD19, CD14, HLA-DR, CD86, and CD25 (BD Biosciences, San Jose, CA, USA). Thereafter, the cells were washed with buffered media and acquired on a BD Fortessa flow cytometer equipped with BD FACS Diva software (BD Biosciences). Data analysis was performed using FlowJo software (Tree Star, Ashland, OR, USA).
Statistical analyses Statistical analyses and graphs were prepared using GraphPad InStat version 5. 00 (GraphPad Software, San Diego, CA, USA) and SAS version 9. 4 (SAS, Cary, NC, USA). Data were presented using mean and SEM, paired t-test, and ANOVA depending on the number of variables analysed. For the randomized complete block analysis, IL-6 and IL-8 data from 12 donors were divided and the assignment of treatments was within a donor, giving a randomized complete block design that was blocked on donor variability. This is similar to the study design typically used in pharmacokinetic studies of drugs  [20, 21]. Thereafter, analysis of variance was performed and Tukey's honestly significant differences (HSD) were employed to determine statistical significance. To make the data more nearly normally distributed, logarithms of the responses were used to perform these analyses.