dataesr/scientific-paragraphs-categorization huggingface dataset:
We used a curated dataset to build a robust PPI network from the genes of interest  56  and then carried out a Power Graph Analysis (PGA)  57  to identify coherent (and simplified) PPI network structures. Power Graphs are lossless representations of graphs based on power nodes (sets of nodes brought together) and power edges (connecting two power nodes, so that all the nodes in the first power node are connected to all the nodes in the second power node). To further simplify and rationalize the Power Graph structures, here we introduce the term super-power node (SPN) to refer to a set of power nodes that form a connected graph. When applied to all genes present in the three co-expression networks, we identified high-confidence connections between 96 proteins. The PGA revealed that 71 of these proteins belong to either one of two main SPNs: SPN1 or SPN2 (Figure  3 , Supplementary Figure  6 ).
To independently corroborate the identification of these two SPNs we used a separate approach (DAPPLE)  58  , which interrogates a large database of experimentally derived proteinprotein interactions to assess the physical connections among proteins encoded by the genes of interest. In this case, the significance of the PPI derived from the input gene list is assessed empirically (Methods). This analysis replicated the identification of 23/46 edges from SPN1 and 36/41 edges from SPN2, providing further empirical support for the significance of these PPI (permutation-based p-value < 0. 001, Supplementary Figure  7 ). At present, PPIs have rarely been measured in the context of distinct cell types, tissues, or in specific disease conditions, making it challenging to model and understand context-related phenotypes  59  , but it has also been shown that heterogeneous genomic data contain functional information of protein-DNA, protein-RNA, protein-protein and metabolite-protein interactions  [60] [61] [62] [63] [64].
We therefore attempted to gain additional external validation of cell-type specificity for SPN1 and SPN2 by querying the GIANT database of tissue-specific gene networks that includes mapping to tissue and cell-lineage specific functional contexts  50. Glia-specific gene interactions within SPN1 and SPN2 were reconstructed with high confidence from prior experimental data (accessed from GIANT database 50 ) (Supplementary Figure  8 , panel a), further supporting the consistency and specificity of our findings. Genes coding proteins in the SPNs came from all three gene networks (IL1B response, Interaction and Development) and there was minimal overlap in gene network origin of these proteins (Supplementary Figure  9 ). This suggests that topologically and functionally different transcriptional networks (Figure  2 ) might converge to less redundant structures at the protein level (Figure  3 ), probably reflecting the known modular architecture of PPI networks  65  which in our case is reflected in the identification of SPN1 and SPN2.
Functional annotation of the two SPNs indicated that these are distinct in function, with SPN1 being significantly enriched for proteins involved in nervous system processes and SPN2 relating to cell signalling and transcriptional regulation (Supp. Table  9 , Figure  3 ). Tissue specificity of expression of SPN genes was queried from the GTEx database  66, 67  , indicating a pattern of expression of SPN1 genes specific to the brain, whereas SPN2 had a more general tissue distribution (Supp. Tables  10  and 11 ), in keeping with the protein-based data above. We then explored whether SPN1 and SPN2 have been involved in brain disorders, and to this aim we tested whether SPN members are enriched for genes annotated for disease association terms using two separate approaches. For this we used the Gene Disease Annotation tool (GDA)  68  , which assesses enrichment of a set of interest (e.
This analysis across SNP1-2 indicated that a member of SPN2, STAT3 (Signal transducer and activator of transcription 3), as well as other members of the Signal Transducers and Activators of Transcription (STAT) family of TFs (STAT6 and STAT1-alpha), are significantly predicted to bind the promoters of a set of 22/36 (61%) members of SPN1 (p<0. 05, Supp. Tables  15  and 16 ). Given the broad starting point of an unsupervised genome-wide network analysis, we were interested to note that the combined PPI and TF analysis revealed a possible functional relationship between two protein interaction subnetworks. The same relationship was not observed between the STATs TFs and SPN2 genes (Supp. Table  17 ). The link between the STAT family of TFs and SPN1 genes was also supported by an independent analysis using the Gene Set Enrichment Analysis (GSEA) tool in the Molecular Signatures Database (MSigDB) database (Broad Institute)  78  to assess the overlap between 615 gene sets (containing genes that share a common TFBS, defined in the TRANSFAC database  79  ) and the genes in SPN1/2.
This analysis showed a significant association of members of SPN1 (Med13, Kcnj16 and Ifit3) with STAT1 and STAT2 transcription factors (FDR = 2. 8%), further supporting a link between SPN2 and SPN1 via the STAT family of TFs. To seek additional experimental support for the predicted interaction between SPN1 members and the STATs TFs, we investigated the genomic distribution of binding sites of the family of Signal transducers and activators of transcription (STAT1, STAT3, and STAT5) in a publicly available dataset of lipopolysaccharide (LPS)-stimulated primary microglial cultures  80. Chromatin immunoprecipitation-promoter microarray data (ChIP-Chip) indicated STAT binding at the promoters of 8/35 (22%) of the genes in SPN1 with either STAT1, STAT3 or STAT5 (Supp. Table 18). On inspection of the STAT3 TF gene expression response to IL1B exposure (Figure  4 , panel A), its mRNA level was found to be significantly higher at P1 in microglia exposed to IL1B versus controls, and significantly lower at P5 (FDR = 0.
We visualised DLG4 cortical expression in adults using the Allen Brain Atlas Brain Explorer 101 which indicates widespread cortical expression in adulthood (Supplementary Figure  12 , Panel C). Previous work has demonstrated that Dlg4 mRNA is targeted for degradation via specific post-translational modifications  102  , and this may impact its protein distribution during development. To describe in more detail the regional pattern of expression in the human brain including white matter, we used human brain whole tissue gene expression data from the UK Brain Expression Consortium (UKBEC) database  103  , which stores paired gene expression and genotype data from 134 brains from normal individuals free of neurodegenerative disorders. We charted DLG4 gene expression across tissues and found that it is widely expressed in both grey and white matter in adults (Supplementary Figure  8 , panel C).
DLG4 is expressed by microglia in the developing human brain We then assessed cell-type specific expression of DLG4 protein from microglia of the developing human brain in isolated CD11B cells and in tissue sections. We performed co-localisation of IBA1/DLG4 in MACS-isolated human fetal microglia at 19 gestational weeks (GW) and 21 GW (roughly equivalent to P1 in mouse 47 ) (Methods). We observed that isolated human microglia expressed DLG4 protein together with IBA1 and that this staining appears to increase by activation of microglia to a pro-inflammatory state using lipopolysaccharide (LPS) (data not shown). Further to this, we stained human fetal brain sections through the dorsal cortex at 20, 26 and 30 GW (Methods). We observed a clear co-localisation of IBA1 and DLG4 protein (Figure  8 ) limited to cells in the proliferative zone and the absence of any other Dlg4 positive cells at 20 GW.
Effect of DLG4 common genetic variants on in-vivo white matter structure in preterm infants We then set out to examine whether common genetic variants in DLG4 might have an effect on in vivo brain features in preterm infants, focusing on white matter given the relevance of this tissue to the preterm neurobehavioural phenotype and its well established diffusion imaging correlation with outcome  30-32, 105 29  , plus its high content of microglia particularly at this stage of development  12, [106] [107] [108]. MR brain images for two comparable groups of preterm infants (n= 70 and n = 271, Supp. Table  19 ) were acquired at term-equivalent age, and white matter features (fractional anisotropy, FA) were extracted using Tract-Based Spatial Statistics (TBSS) (Methods). Preterm infant DNA from saliva was genotyped on the Illumina HumanOmniExpress-12 array (Methods), and seven single nucleotide polymorphisms (SNPs) on this array mapped to DLG4 (dbSNP database  109  , Supp.
eQTL effect of DLG4 in human brain We then queried whether the prioritised common genetic variant (rs17203281) in the DLG4 region might be regulating DLG4 mRNA expression in the human brain, and so we searched for a possible expression quantitative trait locus (eQTL) effect. To this aim we set out to analyse DLG4 mRNA expression within individual tissues in the brain, treating the expression levels of the gene as a quantitative trait, so that variations in gene expression that are highly correlated with genetic variation can be identified as expression QTLs (eQTLs). When a genetic variant (SNP) is located within (or is in close proximity) a gene that is significantly associated with the gene's mRNA variation, this defines a cis-acting eQTL  113. We returned to the UKBEC database and extracted genotypes for the DLG4 SNP rs17203281 plus DLG4 gene expression in the white matter, and carried out an eQTL analysis to link variations in gene expression levels to genotypes (Methods).
This analysis of the UKBEC data suggested that normal individuals who are homozygotes for the rs17203281 minor allele (AA) genotype have a significantly higher expression of DLG4 in white matter than those with the alternate genotypes (AG or GG)(p<0. 05), suggesting a cis-eQTL effect for rs17203281 (Supplementary Figure  14 ). To cross-validate this result we queried the GTEx Portal (www. gtexportal. org/), which provides a searchable resource of multiple different human tissues with genotyping, gene expression profiling, whole genome sequencing and RNA sequencing data  114. This revealed that DLG4 is preferentially expressed in the brain (Supplementary Figure  8 , panel B), and has multiple SNPs regulating its expression levels in cis (Supp. Table  21 ), of which one SNP (rs3826408) is in high linkage disequilibrium (i.
e. , is a proxy SNP)  115  with rs17203281 (r 2 = 0. 667, D' = 1). Therefore using separate genetic and gene expression datasets, these analyses suggest cis-acting genetic regulation of DLG4 mRNA expression in the brain, in particular, including the SNP rs17203281 which we previously and independently associated with white matter features in preterm infants. Overlaps between SPN1/2 genes and disease-associated gene modules in neuropsychiatric disease Rare genetic variants in DLG4 have previously been associated with autism, schizophrenia and epilepsy in an independent gene network study using exome sequencing data in cohorts of patients with neuropsychiatric disorders  70. This identified DLG4 within a small module of 24 genes involved with synaptic function, in which de novo and more severe missense mutations were more likely in individuals with significantly higher intellectual impairment.
DLG4 is the only member of either SPN1 or SPN2 that qualifies as a hub (under the working definition of a hub as a node with more than 10 connections  121  ), and analysis with the Composition Profiler tool to analyse fractional differences in amino acid composition  123  indicated that DLG4 is significantly enriched for disorder promoting amino acids (p < 0. 02). From a neurodevelopment point of view, DLG4 is involved in synaptic plasticity  72, 91, 98  , mediating clustering of both NMDA and potassium channels on the neuronal surface  89, 90. At the gene level, a query of the DisGeNET database  124  , which integrates human gene-disease associations through gene and disease vocabulary mapping, returned consistent association of DLG4 with neuropsychiatric diseases including schizophrenia and autism (Supp.
The disease-gene links used by the Gene Disease Association tool (GDA)  68  are assembled from the Genopedia compendium in the HuGE database of Human Genetic Epidemiology  173  and the OMIM database  174  , which are collections of data retrieved from biomedical literature and do not provide cell or tissue specific annotations. While these disease-gene associations are not tissue specific, the relevance and usefulness of proteinprotein interaction modules to tissue or cell type specific transcriptional programs has been previously shown by our group and others  64, 175, 176.
Multiplex cytokine/chemokine assay Microglia media harvested at the end of the treatment was centrifuged briefly to remove particulates (300xg for 10 minutes). Cytokine and chemokine levels in the microglial media were measured using a Bio-plex 200 with a 96-well magnetic plate assay according to the manufacturer's instructions (Biorad laboratories, Marnes la Coquette, France). Cytokine and chemokine measured included IL-1α, IL1B, IL-2, IL-6, IL-10, IL-12 (p70), IL-13, G-CSF, GM-CSF, IFNg, TNFá, CXCL1 (KC), CCL2 (MCP-1) and CCL5 (RANTES). All samples were run in duplicate and data was analysed with the Bio-Plex Manager software.
Braincloud data Data on DLG4 expression in the developing brain were accessed from the Braincloud resource, which includes 30,176 probes on 269 samples across the lifespan (fetal through the aged)  97  and plotted (Supplementary Figure  12 , panel A). Allen Brain Atlas data: developing brain transcriptome and adult brain. Developing Transcriptome data as described in the technical white paper were downloaded for DLG4  179  and plotted (Supplementary Figure  12 , panel B). Expression of DLG4 in the adult brain was visualized in the Allen Brain Atlas Brain Explorer software application (http://human. brain-map. org/static/brainexplorer), in which samples from the cerebral cortex are overlaid on an inflated white matter surfaces for each donor's brain, while samples in the subcortical regions of the brain are represented as spheres below the inflated surfaces.
Imaging data selection and quality control The T2-weighted MRI anatomical scans were reviewed in order to exclude subjects with extensive brain abnormalities, major focal destructive parenchymal lesions, multiple punctate white matter lesions or white matter cysts, since these infants represent a heterogeneous minority (1-3%) with different underlying biology and clinical features to the general preterm population  [180] [181] [182] [183]. All MR-images were assessed for the presence of image artifacts (inferior-temporal signal dropout, aliasing, field inhomogeneity, etc. ) and severe motion (for head-motion criteria see below). All exclusion criteria were designed so as not to bias the study but preserve the full spectrum of clinical heterogeneity typical of a preterm born population. Diffusion tensor imaging (DTI) analysis was performed using FMRIB's Diffusion Toolbox (FDT v2.
DLG4 expression quantitative trait loci (eQTL) analysis UKBEC genotype and expression data as described above were downloaded for DLG4 white matter samples and SNP rs17203281. We used the transcript-level expression profile provided in the UKBEC dataset, which is estimated as the Windsorized mean (similar to trimmed mean) of the exon-level probesets that are considered expressed above background noise. It is chosen because it is robust to statistical outliers that may arise from alternative splicing. Individuals were grouped according to minor allele count 0,1,2 or presence/absence of the minor allele (A) and outliers in the expression value were removed following thresholding by the boxplot. stats function in R. Significant group differences in expression values for the transcription-level were identified using a Student's t-test (Supplementary Figure  14 ).
ACKNOWLEDGEMENTS Our thanks to the children and families who participated in the study, and the nurses, doctors and scientists who supported the project. This work was supported by grants from  Inserm ,  Université Paris Diderot, Université Sorbonne-Paris-Cité ,  Investissement d'Avenir  ( ANR-11-INBS-0011 , NeurATRIS),  ERA-NET Neuron (Micromet) ,  DHU PROTECT, PremUP ,  Fondation de France ,  Fondation pour la Recherche sur le Cerveau ,  Fondation des Gueules Cassées ,  Roger de Spoelberch Foundation ,  Grace de Monaco Foundation ,  Leducq Foundation ,  Cerebral Palsy Alliance Research Foundation Australia, Wellcome Trust  ( WSCR P32674 ) and  The Swedish Research Council  ( 2015-02493 ). MRI scans of preterm infants were in part obtained in an independent programme of research funded by the  National Institute for Health Research (NIHR)   Programme Grants for Applied Research Programme  ( RP-PG-0707-10154.
) and in the future a compendium report of the programme will be published in the NIHR Journal. The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the  NHS , the  NIHR, MRIC, CCF, NETSCC , the  Programme Grants for Applied Research  programme or the Department of Health. In addition, the authors acknowledge financial support from the  Department of Health  via the  NIHR comprehensive Biomedical Research Centre  award to Guy's  & St Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust , as well as support from the  Medical Research Council (MRC)  through  Strategic Grant  to ADE and  Clinical Training Fellowship to MLK (Grant Ref :  MR/L001578/1 ),  Duke-NUS Medical School and Singapore Ministry of Health (EP).
Prolonging survival and improving QoL are the primary treatment goals of myeloma therapy, with improvements in QoL coming from reducing disease-related symptoms and avoiding side-effects of treatment. As MM remains an incurable disease, the impact of longterm anti-myeloma therapies on HRQoL is an important assessment in clinical trials  [7]  especially since patients are surviving longer  [4, 8, 9]. The randomized pivotal, phase III FIRST trial (Frontline Investigation of Revlimid and Dexamethasone vs. Standard Thalidomide; IFM 2007-01/MM-020; ClinicalTrials. gov identifier: NCT00689936; EudraCT 2007-004823-39) demonstrated in transplantineligible patients with newly diagnosed multiple myeloma (NDMM) that treatment with lenalidomide plus low-dose dexamethasone until disease progression (Rd continuous) extended both progression-free survival (PFS) and overall survival (OS) compared with a fixed 18-month course of melphalan, prednisone, and thalidomide (MPT)  [10] , a regimen that was previously considered as a standard first-line treatment option in this patient population  [11].
The FIRST trial also demonstrated that the improvement in OS did not come at the expense of worse QoL during therapy. Both Rd and MPT improved patients' HRQoL from baseline over the first 18 months of treatment, as measured using standardized questionnaires and assessed across seven HRQoL domains preselected for clinical relevance. Compared with MPT, Rd therapy led to a greater reduction in Disease Symptoms at Month 3 and significantly lower scores for side effects of treatment at all post-baseline assessments except Month 18  [12]. However, an important limitation of the FIRST trial was that HRQoL data were not collected beyond 18 months. The analysis was, therefore, unable to draw conclusions on the effects of long-term Rd treatment on patients' HRQoL beyond 18 months of therapy. The present post hoc analysis estimates the same preselected HRQoL domain values during continuous Rd therapy, by modeling QoL scores based on variables that were collected throughout Rd therapy, including beyond 18 months.
HRQoL assessments Analyses were performed on the intention to treat (ITT) population in the Rd treatment arm (data cutoff 3 March 2014). Details of the FIRST trial study design have been published previously  [10]. HRQoL assessments were conducted at baseline, at the end of treatment cycle 1 (28 day cycles of Rd treatment), at months 3, 6, 12 and 18, and at study discontinuation. Data were collected using three QoL questionnaires  [13] [14] [15] , which are among the most extensively validated in MM  [16]. The analysis focused on seven QoL domains preselected for their perceived clinical relevance following a workshop discussion with hematologists: EORTC QLQ-C30 and EORTC QLQ-MY20 scores are transformed into a range from 0 to 100. For Global QoL and Physical Functioning, a higher score indicates better HRQoL, whereas a higher score indicates greater symptom burden (and worse HRQoL) for the Fatigue, Pain, Disease Symptoms, and Side Effects of Treatment domains.
Coding of potential baseline and time-varying determinants To identify key baseline and time-varying determinants of change in HRQoL score for each preselected domain over the first 18 months, an extensive list of factors was chosen (Supplementary Table  S1 ), for which the 'predictability of change' score was examined for each domain using a linear mixed effects regression model (PROC MIXED in SAS V R ). Significant determinants of change in HRQoL domain scores over the first 18 months of the progression-free period were identified, and the final mixed-effects regression models for predicting the score change beyond 18 months for patients who remained progression-free were determined. The models used a restricted maximum likelihood estimation method. An unstructured covariance matrix was applied to obtain the random effects variance components.
The dependent variable for each model was change in score from baseline (prior to treatment initiation). First, potential factors relevant to a specific domain (Supplementary Table  S1 ) were examined univariately in models that included time (in months) and baseline domain score. Second, significant predictors (p <. 1) identified from the previous step were included in multivariate analyses that also included time and baseline domain score. By removing the non-significant predictors one at a time, the models were trimmed to keep only significant predictors (p <. 1). Relevant covariates that were not kept in the model were reintroduced and tested to confirm that the model did not inadvertently exclude significant covariates. Different combinations of covariates for each domain were tested, and the model with best goodness of fit (based on À2 Log Likelihood and Akaike Information Criteria) to the observed data was chosen for use to predict change in HRQoL beyond 18 months.
The precision was verified by duplicate analyses and the test reproducibility was measured using the coefficient of variation (CVs). The CVs should be lower than 10% for standard duplicates. If the CVs exceeded the above values, the whole procedure was repeated in order to achieve good quality of the obtained results. The minimum method detection limit was 0. 20 µg dm -3. Statistical data analysis. Data analysis was conducted using Microsoft Excel 2010 and STATISTICA 10 (StatSoft, 2011). The analysed data was collected by repeated measurements in the form of a matrix with n rows and k columns. Rows correspond to objects and columns to measurements, giving n objects and k groups of dependent measurements. The number (n) of objects is not large enough to verify whether the data inside the columns is normally distributed.