dataesr/scientific-paragraphs-categorization huggingface dataset:
These data were then used to predict the dietary characteristics of the IMI DIRECT study participants. Physical activity assessment Objective measures of physical activity were derived from triaxial accelerometers (ActiGraph GT3X+/GT3X+w/GT3X+bt; ActiGraph Co. , Pensacola, FL, USA) as described previously  [7]. Raw data files (. gt3x) were converted to comma separated value (. csv) format storing rawest possible accelerations for each axis at a resolution of 30 Hz using ActiLife 6 (version 6. 11. 5; ActiGraph Co. ). All inferred measures of physical activity were calculated using PAMPRO (version uploaded 21 Oct 2015; MRC Epidemiology unit, Cambridge, UK), custom open source software available under public license (https://github. com/Thomite/pampro). Data from each axis of acceleration was auto-calibrated to local gravity.
The methods used to infer these measures have been validated and described in detail elsewhere  [21]. DNA extraction and genotyping DNA extraction was carried out using Maxwell 16 Blood DNA purification kits and a Maxwell 16 semi-automated nucleic acid purification system (Promega, Southampton, UK). Genotyping was conducted using the Illumina HumanCore array (HCE24 v1. 0) and genotypes were called using Illumina's GenCall algorithm. Samples were excluded for any of the following reasons: call rate <97%; low or excess mean heterozygosity; sex discordance and monozygosity. Genotyping quality control was then performed to provide high-quality genotype data for downstream analyses using the following criteria: call rate <99%; deviation from Hardy-Weinberg equilibrium (exact p < 0. 001); variants not mapped to human genome build GRCh37 and variants with duplicate chromosome positions (a total of 30,318 markers were excluded).
A total of 3032 samples and 517,958 markers across the two studies passed quality control procedures. We took autosomal variants with MAF >1% that passed quality control and constructed axes of genetic variation using principal components analysis implemented in the GCTA (version 1. 24. 4, downloaded from https://cnsgenomics. com/software/gcta/#Download) software to identify ethnic outliers defined as non-European ancestry using the 1000 Genomes samples as reference. We identified six individuals as ethnic outliers. Additional measures (not presented here) Biomarker discovery analyses using these data also employ additional measures (including 'omic' measures), which are outside the scope of this cohort description. Additional measures that are not described here include transcriptomics (RNA sequencing from fasting whole blood), microbiomics (DNA isolation and deep sequencing in faecal samples), proteomics (targeted array in fasting plasma) and metabolomics (targeted and untargeted assays in fasting plasma).
GAD/islet antigen-2 assessments from fasting serum samples were also undertaken. Data from the Recent Physical Activity Questionnaire (RPAQ) and sleep diaries were also collected in subcohorts. Statistical power of study A detailed section on sample size and power for the study is available in the previously published rationale and design paper  [7]. Briefly, statistical power will vary depending on a number of factors specific to the analysis to be carried out, such as biomarker effect sizes, variance/frequency of outcome and biomarker, statistical modelling methods employed, number of tests (multiple testing adjustment) and of course sample size, available for the relevant variables included in the model. The dataset will therefore be well powered for some analyses while it may be underpowered for other analyses and will thus be covered in detail for the specific scenarios in subsequent analyses.
All statistics were computed using R software version 3. 4. 0 [22]. The IMI DIRECT data release version used for the analyses in this article was direct_03-29-2019. Glycaemic trait modelling Glycaemic traits were derived from the 75 g fsOGTT (sampling at 0, 15, 30, 45, 60, 90, 120 min) and MMTT (sampling at 0, 30, 60, 90, 120 min) for cohort 1 and cohort 2, respectively. Analyses used a mathematical model that describes the relationship between insulin secretion and glucose concentration  [23, 24]. The model expresses insulin secretion as the sum of two components, the first of which represents the dependence of insulin secretion on absolute glucose concentration at any time during the fsOGTT/ MMTT, through a dose-response function. Characteristic parameters of the dose-response relationship are the mean slope over the observed glucose range, denoted as glucose sensitivity.
Rate sensitivity reflects early insulin release  [25]. The model parameters were estimated from glucose and Cpeptide concentrations by regularised least-squares, as previously described  [23]. Regularisation involves the choice of smoothing factors, which were selected to obtain glucose and C-peptide model residuals with SDs close to expected measurement error (~1% for glucose and ~4% for C-peptide). Insulin secretion rates were calculated from the model every 5 min. The integral of insulin secretion during the fsOGTT was denoted as total insulin output. The validity of the fsOGTT and MMTT for the assessment of insulin sensitivity has been shown in the original Data are mean (SD) except for sex, which is n%, and reflect the data available at the time of publication Values are untransformed and unadjusted publications presenting the indices  [26] [27] [28].
Discussion Here, we report the characteristics of the IMI DIRECT cohorts at baseline, at 18 months follow-up, and at 48 or 36 months follow-up (for cohorts 1 and 2 respectively) for glycaemic deterioration and consider these results in the context of the implemented protocols and the plans outlined in the design and rationale paper published previously  [7]. The descriptive statistics, pairwise correlations and genetic substructures presented in this article are not intended for aetiological inference; instead, the purpose is to provide context and details for subsequent IMI DIRECT papers, as well as to inform scientists outside the Consortium who might in the future consider using the IMI DIRECT data in their research. Major advances in technologies and methods over the past decade make high-resolution quantification of disease phenotypes and processes possible in large sample collections.
Applying modern assays to historical biosamples is particularly useful when studying processes that take decades to unfold. However, biosamples often degrade during long-term storage and many older studies did not deploy the advanced phenotyping methods available today. Recognising these limitations, we designed and initiated two state-of-the-art prospective cohort studies as part of the IMI DIRECT Consortium. Designed for biomarker discovery in glycaemic deterioration and diabetes progression, the IMI DIRECT cohorts include conventional and cutting-edge phenotyping techniques and technologies that are repeated on multiple occasions during a follow-up period of up to 48 months (currently ongoing). We note that the subsequent biomarker discovery analyses using these cohorts will combine the clinical phenotypic data described in this paper with omic measures such as transcriptomics (RNA sequencing from fasting whole blood), microbiomics (DNA isolation and deep sequencing in faecal samples), proteomics (targeted array in fasting plasma) and metabolomics (targeted and untargeted assays in fasting plasma).
The recruitment strategies for the two IMI DIRECT cohorts differed in that cohort 1 focused on recruiting participants from an existing large sample frame (N = 24,682) derived from established prospective cohort studies, whereas cohort  2 used clinical registries to identify eligible participants. The strategy for recruiting participants from existing prospective studies for cohort 1 facilitated access to data that were used to predict risk of rapid glycaemic deterioration. However, despite the relatively large sampling frame, it was necessary to enrol lower-risk participants in order to achieve the target sample size; in doing so, we recognised that this would likely reduce the overall rate of glycaemic deterioration in the cohort, although the generalisability of the study's findings will be greater.
In cohort 2, we fell slightly short of the target sample size of 1000 participants (N = 789 with complete and highquality data eventually enrolled). This reflects the difficulties in engaging some general practices, which was necessary to access diabetes registries in some regions. We stratified cohorts 1 and 2 by broad glucose-control category (overtly normoglycaemic or impaired glycaemic regulation in any ADA category) or treatment category (lifestyle only vs lifestyle plus metformin), respectively, to reflect the basic stages of progression at baseline for descriptive purposes. The two IMI DIRECT cohorts are not identical. However, they share many methodology parallels that permit complimentary analyses to be performed, such as determining whether biomarkers for glycaemic deterioration are conditional on disease state.
With this in mind, the IMI DIRECT cohorts are being followed further with recordlinkage through 2026. It should also be noted that the IMI DIRECT cohorts are predominantly of European ancestry; therefore results from subsequent analyses on these cohorts will need to be replicated in other cohorts of relevant ancestry before generalising findings to other ethnicities. Finally, we note that the results presented here reflect the data available at publication and as long-term follow-up progresses additional data will accrue.
Conclusion The study described here is being used to unravel the heterogeneous nature of glycaemic deterioration in individuals at risk of diabetes and in those with diabetes, and to discover biomarkers that might prove useful for patient stratification and therapeutic optimisation. As more prospective data are accrued, the IMI DIRECT cohorts will grow in value. In the long term, the IMI DIRECT Consortium intends to make these data available to other researchers through a managed-access repository. holds a Wellcome Trust Investigator award (grant reference 102820/Z/ 13/Z). Contributions to this work by SBru. were co-financed by the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001).
Funding This work was supported by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. 115317  (DIRECT), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies in kind contribution. RWK was funded by a  STAR  Award Novo Nordisk co-financed PhD fellowship. The work undertaken by PWF was supported in part by programme grants from the  ERC-2015-CoG_NASCENT_681742  and the  Swedish Research Council ; strategic funding for  Lund University Diabetes Centre , where some of the work described herein was performed, was provided by the  Swedish Research Council, Strategic Research Area Exodiab , ( Dnr 2009-1039 ), the  Swedish Foundation for Strategic Research  ( IRC15-0067 ), the  Swedish Research Council , Linnaeus grant (Dnr  349-2006-237 ).
Data analysis Quantitative data analysis conducted in SPSS v. 24 included non-parametric tests (Chisquare and Mann-Whitney) to compare proportions or rankings of population subgroups by setting, considering a p-value of 0. 05. Qualitative data analysis focused on constant comparative analysis to understand the enablers for effective referral pathways. Each coordinator was sent an interview guide to discuss all aspects of the process with pharmacists from their respective countries. Interviews were conducted by SA with the coordinators, advising the purpose, using an adapted interview guide from the AF-study  6  (supplement 1). These interviews were audio-recorded, transcribed verbatim and analysed manually line-by-line and coded by LN for emergent themes using Charmaz's iteration of constant comparative analysis.
Missing data may result from difficulty understanding the English data collection form, an aspect to be addressed in the future creating a multilingual app. The main drawback seems to be the low proportion of confirmed diagnosis, which led us to highlight the detection rate identified by the mobile single-lead ECG device and a need for investing in efficient referral pathways in future work to ensure patients identified with AF receive anticoagulation where appropriate. The true detection rate is much lower, but we believe it results mainly from a culture of interprofessional collaboration that needs boosting in many countries. It is worth stressing that results are not generalizable elsewhere, as health system's functioning and professional culture seems to play a heavy influence on communication needed for such events.
Cutaneous malignant melanoma is an aggressive cancer of melanocytes with a strong propensity to metastasize. We posited that melanoma cells acquire metastatic capability by adopting an embryonic-like phenotype, and that a lineage approach would uncover novel metastatic melanoma biology. We used a genetically engineered mouse model to generate a rich melanoblast transcriptome dataset, identified melanoblast-specific genes whose expression contributed to metastatic competence, and derived a 43-gene signature that predicted patient survival. We identified a melanoblast gene, KDELR3, whose loss impaired experimental metastasis. In contrast, KDELR1 deficiency enhanced metastasis, providing the first example of different disease etiologies within the KDELR-family of retrograde transporters. We show that KDELR3 regulates the metastasis suppressor, KAI1, and report an interaction with the E3 ubiquitin-protein ligase gp78, a regulator of KAI1 degradation.
467 melanoblast-specific genes were identified from our analyses, which we hypothesize to be putative melanoma metastasis enhancer genes (MetDev genes; Fig. 1c ). To test the relevance of our melanoblast gene cohort in melanoma metastasis we interrogated this gene list in melanoma patient data. To ask if our 467-gene MetDev cohort was enriched in genes that contributed to poor progression of patients, we used a Cox proportional hazards model to associate their expression with overall survival in a training dataset of human patient samples derived from melanoma metastases (stage III and stage IV; GSE19234)  28. We discerned a 43-gene survival risk predictor (Fig. 1c , black/red arrows; Fig. 1d , black text, Kdelr3) that could accurately predict patient outcome in a separate testing dataset of late stage (stage III and stage IV) metastatic melanoma patient samples derived from metastases (GSE8401; Fig.
1e )  29. These data show that not only is our MetDev cohort enriched for metastatic progression genes, but it can also predict survival in multiple independent patient datasets. Notably, gene expression levels in samples derived from early stage (stage I and stage II) primary melanoma lesions did not predict patient outcome, suggesting that MetDev genes play a key role in late-stage disease specifically (GSE8401; Fig. 1f )  29. To allow functional validation of our MetDev candidates in both soft agar colony forming assays and in experimental metastasis models we elected to prioritize the list of MetDev gene candidates. To do this in an unbiased fashion we applied criteria based solely on melanoblast expression data, selecting for genes with no detectable gene expression in P7 postnatal pups.
Differential expression was validated using a separate microarray expression dataset derived from our iDct-GFP model (E17. 5 vs P2 and P7; q-value < 0. 1)  21. Further criteria using differences in fold-increase expression in melanoblasts vs. melanocytes and the greatest expression at embryonic stages allowed us to select 20 genes likely to be the most functionally relevant. Of these 20 we noted that 7 genes (Kdelr3, P4ha2, Gulp1, Dab2, Lum, Aspn, Mfap5) were associated with Extracellular Matrix (ECM) or trafficking. For functional analyses, we chose 4 of these 7 genes (Kdelr3, P4ha2, Gulp1, Dab2) with no established role in cutaneous melanoma metastasis (Fig. 1c , green/red arrows; Fig. 1d , red text). siRNA knockdown of our four candidate genes in B16 mouse melanoma cells inhibited both growth in soft agar colony formation assays and formation of lung metastases in experimental metastasis assays compared to non-targeting controls (Table  1 ).
Our work demonstrates that the MetDev dataset is enriched in genes that have a functional role in melanoma metastasis. We identify four new melanoma metastasis genes and highlight ECM and trafficking as important pathways common to both melanoblast development and melanoma metastasis. We further observed significant co-expression of three of the four functionally validated genes (Kdelr3, P4ha2 and Dab2) throughout four distinct mouse models of melanoma (See Methods and Supplementary Table  1 ), corroborated in a melanoma patient cohort (TCGA; Supplementary Table  2 ). Notably, expression of Kdelr3 and P4ha2 was highly correlated throughout all datasets (Supplementary Fig. 3a-b ), raising the possibility that some metastasis-associated MetDev genes may be co-regulated and serve a more coordinated role in metastasis.
We show that KDELR3 is localized to both the cisand trans-Golgi compartments in metastatic melanoma cells (Supplementary Fig. 3c ) and validate expression of KDELR3 in mouse melanoblasts (Fig. 2a ). Moreover, within the KDELR family only KDELR3 demonstrated a melanoblast-specific expression pattern and showed consistent upregulation in melanoma cell lines (Fig. 2b ; Supplementary Fig. 3d-e ). These data raise the possibility that KDELR3 plays a role in melanoma progression which is distinct from other KDELRs, despite their presumed redundancy. Analysis of human patient datasets and tumor histology microarrays confirmed an upregulation of KDELR3 expression in malignant melanoma vs. benign nevi (Fig. 2c-e ). We sought to functionally validate a role for KDELR3 in melanoma progression. We used human and mouse melanoma cells to demonstrate that small-interfering RNA (siRNA) and short-hairpin RNA (shRNA) knockdown of KDELR3 significantly reduced, and KDELR3 overexpression enhanced, anchorage-independent growth (Fig.
6c ). To test this, we asked if KDELR3 knockdown sensitizes metastatic melanoma cells to ER stress-induced death. We treated cells with tunicamycin, and measured cell death through flow cytometry using Live/Dead cell stain. We observed that siRNA-mediated knockdown of KDELR3 expression resulted in a ~5-fold increase in metastatic melanoma cell death over controls (8. 3%, siKDELR3; 1. 6%, siControl; Fig. 4d ). These data suggest that KDELR3 promotes cell survival in metastatic melanoma cells, which likely influences metastatic potential. KDELR3-knockdown cells have an enhanced sensitivity to ER stress induction with tunicamycin (>13-fold difference in cell death: 28. 4%, siKDELR3; 2. 1%, siControl; Fig. 4d ). These data indicate that the ability of KDELR3 to relieve ER stress is crucial for adaptation and survival in metastatic melanoma and may be instrumental to the metastatic phenotype.
KDELR3 correlates with late-stage metastasis and poor prognosis in melanoma patients To assess how KDELR3 contributes to melanoma progression in patients, we utilized multiple melanoma patient databases, The Cancer Genome Atlas  30, 42  (TCGA) and Gene Expression Omnibus (GEO; GSE8401 29 , GSE19234  28  ). We found increased expression of the KDELR3-001 transcript, but interestingly not the alternate transcript, KDELR3-002, in late-stage (stage III and IV) metastatic melanoma patients compared to early-stage (stage I and II) melanoma patients (Fig. 6a ), consistent with a role for KDELR3 in melanoma progression. Metastatic melanoma patients with KDELR3 copy number amplifications demonstrated reduced survival relative to patients without such alterations (Supplementary Fig. 7b ). We next assessed melanoma patient survival using KDELR3 expression as a prognostic marker (GEO  28, 29  ).
Discussion Here we propose that metastatic cancer cells exploit innate pathways that are hardwired within their cellular lineage to ensure proper development. These pathways, quieted in the differentiated cell, can be reactivated under pathologic conditions. The genetic/epigenetic reactivation of pathways that allow embryonic melanocytes to migrate, invade and colonize would represent an efficient strategy for melanoma cells to successfully metastasize. Here we employed a GEM model to identify, at the transcriptome level, novel genes that are required during melanocyte development and find that these are enriched in genes that are specific for progression of late-stage disease. We functionally validated 4 out of 4 genes tested, demonstrating the value of our dataset and supporting our hypothesis.
RNA Isolation and RNA sequencing Cells were lysed in 10-fold TRIzol reagent (w/v), phases were separated by addition of 0. 2X volume of chloroform, the aqueous phase was combined with an equal volume of 70% ethanol and applied to a RNeasy Micro column (Qiagen) and processed as per the manufacturer's instructions. Paired-end sequencing libraries were prepared using 1 μg of purified RNA following the mRNA-Seq Sample Prep Kit according to the manufacturer's instructions (Illumina). RNA-Seq libraries were sequenced on two lanes each of an Illumina GAIIx Genome Analyser to a minimum depth of 49 million reads. Sequence reads were aligned to the mm9 genome using the TopHat software (https://ccb. jhu. edu/software/tophat/index. shtml). Quantified Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were generated using the Cufflinks software (http://coletrapnell-lab.
Analysis of MetDev genes in patient survival Based on the RNASeq data for the samples E15, E17, P1 and P7, we used DESeq2 to find differentially expressed genes comparing E15, E17 vs. P1, P7. We selected 467 up regulated genes with q-value < 0. 1 (based on glm model) and log2FoldChange > 1. 5. We then used the GEO dataset GSE19234 to perform survival analysis using Cox proportional hazards model for each gene. We to selected 43 genes that were correlated patient overall survival with the patient survival with pvalue < 0. 1 and HR >1. The Figure  1c-d  showed the heatmaps of the gene expression (using zscore) for the 467 genes and 43 genes respectively. The sum of the total expression of the 43 genes forms the expression signature for prognosis prediction and the signature was tested on the new dataset GSE8401.
Analysis of The Cancer Genome Atlas (TCGA) skin cutaneous melanoma expression All patient samples were collected between 0-14 days after disease classification (101 patients). Processed level 3 RNA-seq by Expectation-Maximization (RSEM) values  67  were imported for melanoma patients from The Cancer Genome Atlas collection (TCGA-SKCM). Bioconductor edgeR (ttps://bioconductor. org/packages/release/bioc/html/edgeR. html) and limma (https://bioconductor. org/packages/release/bioc/html/limma. html) R packages were used for further processing and differential expression analysis. Transcripts with CPM (counts per million) greater than 1 in at least fifty percent of the samples were retained and processed with trimmed mean of M-values (TMM) and voom normalization methods  68. The empirical Bayes moderated t-statistic test  69  was applied to test the null hypothesis both for no difference in KDELR3 expression, or for KDELR1 expression level between early and late stage melanoma patients.