dataesr/scientific-paragraphs-categorization huggingface dataset:
It has been suggested that ENCODE overrepresented tissues such as blood and immune cell types create a prediction bias for related diseases  (49). To evaluate this bias, we split associated SNPs by GWAS traits that belonged to five different experimental factor ontology (EFO) categories (Supplementary Figure  S4 ). We computed AUC values of trait-dependent associated SNPs against random SNPs. We found that traits related to immune system diseases and hematological measurements showed higher median AUC values than other diseases such as nervous system diseases (Supplementary Figure  S4 ). This observation agreed with a systematic bias towards overrepresented cell types in ENCODE that merits special attention in particular when using phenotype-dependent priorization methods such as PINES  (28).
Typical markers of intronic regions such H3K36me3, H3K79me2 and H4K20me1 showed higher correlation in intronic regions (Figure  3A ). On the other hand, the correlation of H3K4me1 and H3K27me3 with the TAGOOS score was more significant in intergenic regions (Figure  3A ). These molecular markers were consistent with known molecular markers of functional intronic and intergenic regulatory regions. eQTLs were clearly found in many biological samples for SNPs with high TAGOOS scores (Figure  3B, C ). Other markers such as DNAse-seq and H3K4me1 were correlated statistically but not visually (Figure  3A-C ). Next we carried out the same analysis using random SNPs from the DBSNP databases. We randomly sampled 10 5 common SNPs from the dbSNP database and annotated them with the TAGOOS scores and annotations.
We found that more significant eQTLs showed higher TAGOOS score median (Figure  4B, E ). Altogether, these results showed that there was a positive correlation between the TAGOOS score and the eQTL significance. Transcription factors (TFs) are very important components of the gene regulatory machinery in non-coding functional regions  (9, 57). Therefore, we hypothesized that SNPs with high TAGOOS are more often bound by TFs. To test this hypothesis, we took unseen GWAS Catalog SNPs and split them in two groups according to the TAGOOS score significance. Then we annotated both groups of SNPs with transcription factor binding sites (TFBSs) from the ReMap database  (30, 31)  and plotted the percentage of SNPs annotated for each TF (Figure  4E , F). We found that higher percentage of TAGOOS-significant SNPs are annotated with TFs in intronic and intergenic regions (P Wilcoxpaired = 2.
2 × 10 -16 ) (Figure  4C, F ). More TFBSs in TAGOOS-significant SNPs could be due to enriched DNA motifs for TFBSs. To test this hypothesis, we took the GWAS Catalog SNPs and a library of 127 non- redundant position frequency matrices based on the Hocomoco TF database  (33, 34). Then we scanned sequences of 100 bp around the SNPs with either the reference or most frequent alternative alleles and plotted the motif densities in 10 bp bins. We found local higher densities of motifs in the middle of the sequences for the reference and lower density for the alternative alleles (Figure  4G, H ). We also found that the TAGOOS-significant SNPs had higher motif density in agreement with our hypothesis (Figure  4G, H ). This analysis showed that SNPs with higher TAGOOS scores were richer in DNA motifs of TFBSs.
We annotated the LD block around the rs227727 region with the negative decimal logarithm of the TAGOOS Pvalue and common SNPs from the DBSNP database (Figure  5A , B; Supplementary Figure  S8 ; Supplementary Table  S7 ). The rs227727 showed one of the most significant TAGOOS values in the region (Figure  5B ). Furthermore, the two +87 kb and +105 kb NOGGIN regulatory elements showed uninterrumpted significant TAGOOS scores consistent with their known regulatory activities (Figure  5A , B; Supplementary Figure  S8 ; Table  S7 ). Based on these results, we conclude that the TAGOOS score was able to recover known regulatory regions and functional SNPs. Then we looked for predicted functional regions and SNPs. A zoom of the TAGOOS score in these two regions showed that the predicted functional region extends beyond the tested constructs (Supplementary Figure  S8 ; Table  S7 ).
DISCUSSION Here we develop a supervised learning approach that predicts functional scores genome-wide based on SNPs associated to complex phenotypes and regulatory annotations that predict functional loci. Compared to previous tools, the TAGOOS method lies between so-called GWAS priorization and regulatory variants priorization tools (Supplementary Tables  S1  and S2 ). The TAGOOS method takes GWAS signals, LD blocks and gene regulatory features and outputs functional scores genome-wide for regulatory variants. Previous prioritization strategies for GWAS signals that take into account LD blocks such as GWAS3D, GREGOR or GenoWAP were not evaluated genome-wide  (4, 14, 15). On the other hand, most predictors of regulatory variants such as GWAVA are trained with highly penetrant SNPs such as those from the ClinVar databases  (17, 38).
There are three main sources of annotation data to predict gene regulation potential: (i) open chromatin and chromatin modifications such as DHSs and H3K27ac, (ii) eQTLs and (iii) transcription factor binding and motifs  (7, 8, 30, 31, 56). Unlike previous approaches, we use all three types of regulatory annotations. Therefore even though eQTLs are strong predictive features of the TAGOOS model PAGE 12 OF 14 (Figure  3A-C ), the TAGOOS score can still prioritize SNPs without eQTL annotation such as the SNP rs10140101 based on chromatin and transcription factor annotations. We also did not select particular cell types. The advantage was that the TAGOOS score was able to prioritize associated SNPs from different disease types (Supplementary Figure  S4 ). Nevertheless, the performance seems to be better in diseases and traits with an overrepresentation of tissues in the public databases such as blood and immune cell types (Supplementary Figure  S4 ).
ACKNOWLEDGEMENTS We are grateful to  J. Castro  ( NCMM, Oslo, Norway ) for technical help and  D. Puthier  ( TAGC, Marseille, France ),  S. Spicuglia  ( TAGC, Marseille, France ),  J. van Helden  ( TAGC, Marseille, France ),  L. Perrin  ( TAGC, Marseille, France ) and  B. Ballester  ( TAGC, Marseille, France ) for helpful discussions.
Insulin treatment in mice does not induce glucose uptake in BMAT To test the metabolic functions of BMAT in vivo we used 18 F-FDG PET/CT in mice to determine if, like WAT, BMAT is insulin-responsive. As expected, insulin decreased blood glucose (Fig. 2A ) and increased 18 F -FDG uptake in the heart, iWAT and gWAT (Fig. 2B, C  and F). To assess 18 F-FDG uptake separately within bone and BMAT, we first applied thresholding to the PET/CT data to separate bone from BM based on their different tissue densities (data not shown). This revealed that insulin significantly increased 18 F -FDG uptake in femoral bone, whereas humoral bone uptake decreased; uptake in proximal or distal tibial bone was unaffected (Fig. 2F ). To assess BMAT-specific 18 F-FDG uptake we took advantage of the regional differences in BMAT content around the mouse skeleton.
CT-based identification of BMAT in humans We next tested if these distinct metabolic properties extend to BMAT in humans. First, we established a method to identify BMAT from clinical PET/CT scans. To determine Hounsfield Units (HU) for BMAT, we identified BMAT-rich and BMAT-deficient BM regions by magnetic resonance imaging (MRI). This revealed that sternal BM is BMAT-enriched while vertebral BM is BMAT-deficient (Fig. 4A ); WAT was also analysed as an adipose-rich control region. We then co-registered the MRI data with paired CT scans of the same subjects (Fig. 4A ). This revealed a distinct HU distribution for BMAT-rich sternal BM, intermediate between WAT and red marrow (RM) of BMAT-deficient vertebrae (Fig. 4B ). Using these distinct HU distributions, we generated a receiver operating characteristic (ROC) curve to identify optimal diagnostic HU thresholds to distinguish BMAT from RM (Fig.
S4A ). This revealed that BMAT-rich BM has HU <115, whereas RM is mostly within 115-300 HU (Fig. 4B ); bone was defined as >300 HU. To test the validity of these thresholds we applied them to clinical CT data to determine BMAT volume as % BM volume. We found that BMAT predominated in the arms, legs and sternum but was markedly lower in the clavicle, ribs and vertebrae (Fig. 4C , Fig. S4B ). Moreover, %BMAT showed age-associated increases in the axial skeleton but not in long bones (Fig. S4B ). These data are consistent with previous studies showing that BMAT predominates in the long bones by early adulthood but continues to accumulate in axial bones beyond 60 years of age  (Baum et al. , 2018; Kricun, 1985; Schraml et al. , 2015). Together, this supports the validity of our CT thresholds for BMAT identification in humans.
Human BMAT is functionally distinct to BAT and is a major site of basal glucose uptake We then applied these thresholds to human co-registered PET/CT data to assess 18 F-FDG uptake in BMAT, RM and bone. To test if BMAT is BAT-like we first compared BMAT 18 F-FDG uptake between three groups: subjects with no detectable supraclavicular BAT at room temperature (No BAT), subjects with active BAT at room temperature (Active BAT), and cold-exposed subjects (16 ºC for 2 h; Cold). PET/CT confirmed BAT 18 F-FDG uptake in the latter two groups but not in the No BAT group (Fig. 4D-E , Fig. S4C ). Cold exposure did not alter 18 F-FDG uptake in scWAT but was associated with increased uptake in sternal and clavicular bone tissue; however, these were the only skeletal sites at which 18 F-FDG uptake significantly differed between the No BAT, Active BAT and Cold subjects (Fig.
DECLARATION OF INTERESTS E. J. R. v. B. has received research support from Siemens Healthineers and is the owner of QCTIS Ltd. (F) 18 F-FDG uptake in the indicated tissues was determined from PET/CT scans. Data are presented as mean ± SEM of 5-6 mice (A,C,F) or 5-7 mice (E). Significant differences between control and insulin-treated samples are indicated by * (P <0. 05), ** (P <0. 01) or *** (P <0. 001). In (E), groups do not significantly differ if they share the same letter. Representative PET/CT images of control, acute and chronic cold mice show increased 18 F-FDG uptake in BAT but not tibiae; some 18 F-FDG is evident in skeletal muscle of each group.
As a QC measure, the distribution of probe intensities and the 5' to 3' degradation profiles were checked to be consistent across samples. The core's statistician used RMA, from the Affy package of Bioconductor, to fit log2 expression values to the data  (Irizarry et al. , 2003). Weighted, paired, linear models were then fit and contrast computed using the limma package  (Smyth, 2004). Weighting was done using a gene-by-gene algorithm designed to down-weight chips that were deemed less reproducible  (Ritchie et al. , 2006). Probe-sets with a variance over all samples less than 0. 05 were filtered out. Of the remainder, probesets with a log2-fold change of 2 or greater and an adjusted p-value of 0. 05 or less were retained. P-values were adjusted for multiple comparisons using the Benjamini and Hochberg false discovery rate approach  (Benjamini and Hochberg, 1995).
Affy, affyPLM, and limma packages of Bioconductor, implemented in the R-statistical environment were used to analyze the data, including PCA analysis  (Irizarry et al. , 2003). Pathways enriched in BMAT (combined dBMAT and ruBMAT) or WAT (combined iWAT and gWAT) of rabbits, or in isolated adipocytes from femoral BM and gluteofemoral scWAT (humans), were identified using Gene Set Enrichment Analysis (GSEA) software and the Molecular Signature Database (MSigDB)  (Subramanian et al. , 2005). For rabbits, pBMAT was not included in these analyses because it contained a high proportion of red marrow and therefore represented a less pure BMAT sample  (Cawthorn et al. , 2016). To ensure maximum compatibility with this software, rabbit gene identifiers were first converted to their corresponding human homologues using the BetterBunny algorithm  (Craig et al.
Mouse PET/CT analysis PET/CT scan images were reconstructed and data was analyzed using PMOD version 3. 806  (PMOD, Zurich, Switzerland). Standardised uptake values (SUV) were calculated for regions of interest, namely BAT, iWAT, gWAT; heart; bone tissue (without BM) from tibiae, femurs, and humeri; and the BM cavities within these bones. To distinguish bone tissue from BM, a calibration curve was generated using HU obtained from the acquisition of a CT tissue equivalent material (TEM) phantom (CIRS, model 091) and mouse CT scans. The TEM phantom consists of 2-4 mm hydroxyapatite rods representing mass densities of 1. 08 to 1. 57 g/mL. The TEM-reconstructed CT image data was exported for analysis into PMOD and, for the extraction of TEM HU values, a VOI template was generated and placed on each rod (0.
Determination of attenuation density for BMAT in humans HU of subcutaneous fat, yellow marrow and red marrow were determined using Analyze 12. 0 software (AnalyzeDirect, Overland Park, KS, USA) based on data from 33 patients who had undergone paired CT and MRI scans (Table  1 ). The MRI sequence was an axial HASTE (Half Acquisition Single Shot Turbo Spin Echo) with a TE (echo time) of 50 ms, TR (repetition time) of 1000 ms, and slice thickness 8mm. BM fat corresponds to higher signal intensity compared to surrounding bone and muscle tissues, in HASTE MR techniques. CT scanning was performed as described previously  (Williams et al. , 2017). Using Analyze 12. 0 software, the MR and CT scans were co-registered and volumes of interest were manually drawn around the sternum, vertebrae and subcutaneous adipose tissue.
HU were extracted on a per voxel basis, and data underwent post-processing using Matlab to measure the total number of voxels across all patient HU (Fig. 4B ), prior to ROC analysis (MedCalc). ROC analysis was then conducted on per voxel HU to determine threshold values with the greatest sensitivity and specificity to detect bone, yellow marrow and red marrow. Thresholds of above 300 HU were defined as bone regions, -200 to 115 HU as yellow marrow and 115 to 300 as red marrow.
A 0. 5 mm aluminum filter and two-frame averaging were used to optimize the scan. After scanning, the data were reconstructed using NRecon v1. 6. 9. 4 software (Bruker, Kontich, Belgium). The reconstruction thresholding window was optimized to encapsulate the target image. Volumetric analysis was performed using CT Analyzer v1. 13. 5. 1 (Bruker microCT, Kontich, Belgium).
Statistical analysis Microarray data were analyzed as described above. All other data were analyzed for normal distribution using the Shapiro-Wilk normality test. Normally distributed data were analyzed by ANOVA or t-tests, as appropriate. Where data were not normally distributed, nonparametric analyses were used. When appropriate, P values were adjusted for multiple comparisons. Data are presented as histograms or box and whisker plots. For the latter, boxes indicate the 25th and 75th percentiles; whiskers display the range; and horizontal lines in each box represent the median. All statistical analyses were performed using Prism software (GraphPad, USA). A P-value <0. 05 was considered statistically significant.
ACKNOWLEDGEMENTS This work was supported by grants from the  Medical Research Council  ( MR/M021394/1  to W. P. C. ;  MR/K010271/1  to R. H. S. ), the  National Institutes of Health  ( R01 DK62876  and  R24 DK092759  to O. A. M. ;  K99-DE024178  to E. L. S. ; and  P30 DK089503  to the  Michigan Nutrition Obesity Research Center) , the  Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund  (to W. P. C. and K. J. S. ), and the  British Heart Foundation  (4-year BHF PhD Studentship to R. J. S. , B. J. T. , M. C. S. and B. M. N; BHF  CoRE Bioinformatics Grant  to W. P. C. ;  BHF   CoRE grant  to A. J. D. ). W. P. C. is further supported by a  Chancellor's Fellowship  from the  University of Edinburgh. A. A. S. T was funded by the  British Heart Foundation  ( RG/16/10/32375 ).
E. J. R. v. B is supported by  SINAPSE  (the  Scottish Imaging Network). We are grateful to the  British Heart Foundation  for providing funding towards establishment of the  Edinburgh Preclinical PET/CT laboratory  ( RE/13/3/30183 ), and to  NHS Research Scotland (NRS)  for financial support of the  Edinburgh Clinical Research Facility. R. H. S and M. C. W. are supported by  The Chief Scientist Office of the Scottish Government  ( SCAT/17/02  to R. H. S. ;  PCL/17/04  to M. C. W. ). J. P. M. A is supported by  BHF   Clinical Research Training Fellowship  no. FS/17/51/33096. We are grateful to  John Henderson  ( BVS, University of Edinburgh ) for support with mouse husbandry;  Anish K. Amin  ( Department of Orthopaedic Surgery, Royal Infirmary Edinburgh ),  Beena Polouse  and  Frank Morrow  ( Edinburgh Clinical Research Facility ,) for help with human studies;  Tashfeen Walton  and  Christophe Lucatelli  ( Edinburgh Imaging, University of Edinburgh ) for radiotracer production;  Robert K.
For TaqMan Array Human MicroRNA, 6 μL of the Megaplex ™ RT product was mixed with 2× TaqMan Universal PCR Master Mix (Applied Biosystems) and 444 μL of nuclease-free water. For each sample, 100 μL of the PCR reaction mix was loaded into each port of the TaqMan Array Cards (Human MicroRNA A + B Set v3. 0; Applied Biosystems). The TaqMan Array was carried out on the 7900HT Fast Real-Time PCR System (Applied Biosystems) according to the manufacturers' protocol. The real-time expression data were analysed using the 7900 SDS RQ Manager software. The resultant C q values were exported. GeNorm was used to select the most stable reference gene, MammU6 (gene stability measure M: 0. 019 and 0. 012, respectively). All C q values were normalized to MammU6, using the ΔΔC t method. Cut-off number of cycles was set as 35.
The melt curves were examined using the LightCycler 480 software (Roche). PCR efficiency was determined using LinRegPCR software (Supporting Information, Table  S1 ). The resultant C q values were exported and normalized to loading control (UniSp6; Exiqon), using the ΔΔC t method. Cut-off number of cycles was set as 40. The thresholds used to determine significance of differentially expressed miRNAs were set as P < 0. 05 and a ΔΔC t ≥ 1 cycle. The primers used are listed in Supporting Information, Table  S2.
Network analysis Cytoscape 3. 6. 1, a widely adopted network visualization and analysis tool, was used to build a miRNA-gene-pathway network. 34 The differentially expressed miRNAs were imported into Cytoscape. Using the CyTargetLinker app in Cytoscape,  35  experimentally validated miRNA-gene interactions from miRTarBase 7. 0  36  were added creating a miRNAtarget interaction network. The targeted genes were reviewed for expression in the skeletal muscle using the Human Protein Atlas. 37 In addition, an overrepresentation analysis was performed in PathVisio 3. 3. 0 38 with the miRNAtarget genes using the human-curated analysis collection of WikiPathways  39  and the human gene identifier mapping database (Ensembl 89). 40 The pathways were then ranked based on a standardized difference score (Z score).
Identification of differentially expressed microRNAs in non-small cell lung cancer cachexia using TaqMan Array Human MicroRNA Muscle biopsies from eight NSCLC patients with cachexia and eight gender-matched and age-matched healthy controls were subjected to TaqMan Array to identify miRNAs associated with lung cancer cachexia. Clinical and demographic data of the patients and controls are detailed in Supporting Information, Table  S4. A total of 754 miRNAs were profiled from vastus lateralis muscle biopsies. Twenty-eight miRNAs were found to be differentially expressed in cachectic NSCLC patients compared with healthy controls (Figure  1  and Supporting Information, Table  S5 ). A total of 5 miRNAs were up-regulated [fold change (FC) ≥ 2 and a P < 0. 05]. Remarkably, four of them (miR-450a-5p, miR-450b-5p, miR-424-5p, and miR-424-3p) belong to the same miRNA cluster.
The data obtained from the miRNA screening and in silico network analysis suggest a potential role for the differentially expressed miRNAs in the pathology of lung cancer cachexia. However, the experimental set-up applied in the screening approach did not allow us to discriminate between lung cancer and lung cancer cachexia-specific miRNAs. For that reason, a selection of top-ranked miRNAs was measured in the entire study population consisting of healthy controls and NSCLC patients with and without cachexia. Consistent with the results of the TaqMan Array, miR-424-5p, miR424-3p, and miR-450a-5p were significantly increased, and miR-451a and miR-144-5p were significantly decreased in NSCLC patients with cachexia compared with healthy controls. Except for miR-424-3p, the three up-regulated miRNAs showed a non-significant but intermediate expression in NSCLC patients without cachexia.
To work with presence and absence data, we scored the originally distinguished primary and secondary habitat as presence and unsuitable habitat as absence. Within our study area, a total of 67 amphibians, 95 squamates, 161 mammals, and 414 birds were present. We used the dated species level phylogenetic trees inferred by Roquet et al. 44  and available on Dryad  45. Therein, the phylogenetic trees for amphibians and squamates were built from gene sequences downloaded from Genbank for the European species included in ref. 42  , which contain 102 amphibians and 236 squamates. The phylogenetic trees for amphibians were based on 9 mitochondrial (12 S, 16 S, COI, cytb, ND1, ND2, ND4, tRNA-Leu, andtRNA-Val) and 2 nuclear (RAG-1, rho) regions and were all available for at least 30% of the species.
To assess whether the found patterns are related to rather recent or rather deep lineages, we rescaled the phylogenetic trees based on the δ-transformation 53 using the Geiger package in R  54  , similar to Chalmandrier et al. 29. This transformation changes the distances of branches: δ > 1 disproportionately increase the length of recent branches, while δ < 1 disproportionately increase the length of deep branches. We chose δ-value of 0. 1, 0. 3, 1. 0 ( = no transformation), 3 and 10. An alternative method of this analysis step would be to truncate the phylogeny at certain phylogenetic depths, as developed  55  and implemented  56, 57  in recent studies. For reasons of comparison, we also implemented this method. We pruned the phylogenies at five specific time periods (depths) and collapsed all descendant leaves of each of the branches encountered by the pruning (see Supplementary Figure  5 ).
For temperature, we selected for each taxon group a variable tested for highest explanatory power for phylo-ß, which gave us: mean temperature of the driest quarter (amphibians), maximum temperature of the warmest month (squamates), and minimum temperature of the coldest month (birds and mammals). We downloaded these variables from Worldclim 60 at a 1 km resolution and up-scaled them to a 15 km resolution. In order to perform a stratified random sampling of the distribution data across Europe for analyzing diversity (alpha) and turnover (beta) measures, we stratified both environmental raster layers (temperature and moisture index) into 9 equally sized classes and combined them to one environmental stratification layer consisting of 81 possible strata (see section on robustness test to evaluate the effect of the number of strata).