dataesr/scientific-paragraphs-categorization huggingface dataset:
Of the 875 participants who provided blood spots, 472 of the samples were judged to be suitable for DNA extraction (blood spots judged not to be suitable were primarily the first community health centre where recruitment took place, whose membership was predominantly white). Of those, 48 yielded less than 40ng of DNA, the lowest input level investigated in Study 1, so we removed them from further analysis. Although 40ng showed higher variability in study 1, most probes passed quality thresholds so we included samples with inputs as low as 40ng in study 2 to maximize study sample size whilst being mindful that the samples with lower DNA input might be of reduced quality. The amount of DNA extracted was assessed using Invitrogen Quant-iT™ PicoGreen™ (Thermo Fisher Scientific). After removing a further 96 participants from the sample set due to poor quality DNA extraction (as determined by high numbers of undetected probes on the EPIC BeadChip), there were 328 participants with DNA methylation data for analysis.
DNA methylation data generation For both studies, extracted DNA was bisulphite converted with the EZ DNA Methylation-Lightning™ Kit (Zymo Research) according to the manufacturer's instructions. The eluant from the bisulphite-converted DNA was then applied to the Illumina Infinium MethylationEPIC Beadchip to measure DNA methylation, according to the manufacturer's protocol. The EPIC BeadChips were scanned using Illumina iScan, with an initial quality review conducted with GenomeStudio. Sample QC and normalization were conducted using the pipeline implemented in the meffil R package, which has previously been described in detail  [8]. Blood cell composition was estimated for MBMS using a deconvolution algorithm  [9]  implemented in meffil, based on the 'blood gse35069 complete' cell type reference.
Study 1: assessing reliability of DNAm measurement with low input DNA Scripts to conduct all analyses can be found at https:// github. com/shwatkins/Low_input_DNA. Using the single pooled sample of DNA described above, we used two methods to assess the reliability of DNAm measurements at different input DNA levels. Firstly, we assessed how well the measurements at the lower input levels (200ng and 40ng) replicate the measurements obtained with 400ng input DNA. To do this we calculated the mean methylation at each DNAm site across the four technical replicates at each input level. We then partitioned DNAm sites into bands based on their methylation level measured at 400ng (used as the reference level) in increments of 5%. Within each partition we calculated the standard deviation of the DNA methylation levels across all sites in the partition and visualized this variation using boxplots at 40ng and 200ng.
Stronger replication of the 400ng measurements would correspond to smaller variation within each partition. Secondly, we assessed the noise in DNAm measurement within each of the three DNA input levels using their four replicates. At each DNAm site, we took the mean of replicates 1 and 2, and used these means to partition the dataset into bands of 5% methylation as we did for the first analysis. Within each partition we then calculated the mean of replicates 3 and 4 at each DNAm site. We visualized the variation within each partition using boxplots of the mean of replicates 3 and 4 for all sites within the partition. Levene's test (leveneTest in the R package car) was used to determine whether lower DNA input was associated with greater variance within each partition. Greater measurement noise would correspond to greater variance.
Study 2: assessing the impact of low input DNA in a cohort study We then assessed how low DNA input affects the quality of Illumina Infinium MethylationEPIC Beadchip data using data from our cohort study, MBMS. We conducted two sets of analyses: we calculated a variety of QC-related metrics, and evaluated the effect of input DNA level on robust associations that have been reported in the DNAm literature. We utilized two standard QC metrics to represent data quality: proportion of probes with low signal, and median methylated signal across all probes on the BeadChip. Low signal was assessed using detection p-values, which indicates confidence that the signal from a probe is detectable above background noise. We used a detection p-value threshold of 0. 01 to distinguish between detection success and failure.
We then evaluated the association between MAD and DNA input level using plots and by calculating Spearman correlation. We tested whether variance in DNAm is associated with DNA input level at each site on the BeadChip using a procedure detailed elsewhere  [10]. We firstly use the function rq (a least absolute deviation regression) from the R package quantreg to test the association between methylation at each cpg site and DNA input level, including batch, cell counts, age, gender, smoking, and BMI as covariates in the model. From this model we take the absolute values of the residuals, and then test for an association between those residuals and DNA input level using linear regression (lm in R). We extracted coefficients and p-values from the model and applied a Bonferroni-corrected threshold of 5.
Furthermore, whereas correlation between chronological age and age estimated from DNA methylation was reasonable for the age range in MBMS (SD 7. 9) in the 293 participants who did not have a mismatch (Horvath clock R = 0. 63, Hannum clock R = 0. 69), correlation among the 35 with a mismatch was very low (Horvath clock R = -0. 01, Hannum clock R = 0. 18). We included these 35 samples in our assessment of data quality using QC analyses as they displayed no evidence of low quality, and there was no relationship between predicted sex/gender mismatch and DNA concentration (p = 0. 72, Wilcoxon rank sum test); but they were removed from the power analysis, leaving 293 individuals in the power analysis.
Study 2 results: assessing the impact of low input DNA in a cohort study DNA for the MBMS cohort was extracted from dried blood spots and resulted in a range of DNA quantities for the 472 participants for whom DNA was extracted (mean 173ng, range 0ng to 1186. 8ng). As we excluded participants with less than 40ng DNA, and those with poor quality DNA extraction, DNA quantities were higher for the 328 participants who were included in the analyses included in this paper (mean 220. 7ng, range 40. 6ng to 1186. 8ng). We assessed the impact of DNA input level on the quality of the MBMS DNAm data in three ways: the proportion of probes failing detection p-value, the median strength of methylated signal, and the mean absolute deviation of samples with lower than recommended DNA (200ng) in comparison to a gold standard based on measurements from the samples with at least 200ng DNA.
Discussion and conclusions This study demonstrates that although as little as 40ng can be sufficient to produce Illumina Infinium MethylationEPIC Beadchip DNAm data that pass standard QC checks, data quality and reliability diminish as DNA input decreases; and increased numbers of samples with low DNA may fail standard QC thresholds. However, this reduction in data quality may reduce power to detect EWAS effects. We hope this demonstration can empower studies to conduct DNAm investigations where it might have previously been assumed that samples were too limited to provide sufficient DNA; but due to the increase in both noise and variance that we have demonstrated, we would recommend caution and use of sensitivity analyses when working with less than 200ng DNA on the Illumina Infinium MethylationEPIC Beadchip.
We have provided the full summary statistics from this variance EWAS in Supplementary table 3 so that researchers can utilize these results with p-value or effect thresholds appropriate to their data and research question. Finally, we show that power to detect EWAS effects in a large community-based sample is reduced for a range of effect sizes for lower DNA input levels. Strengths of our study include complementary analyses of both control and human cohort DNA  samples; and both the large number and social diversity of individuals in the cohort analysis (n = 328). Social diversity is very important for study generalizability because DNAm is affected by our social environment. The main limitation is that the impact of DNA input level may well be different for differing sample types and provenances, as DNA quality is affected by storage and extraction methods.
Acknowledgments We are extremely grateful to all the MBMS participants who took part in this study, the staff at the four participating community health centers, and the researchers who conducted participant recruitment and interviews. We thank  Elmer Freeman , Director of the  Center for Community Health Education Research and Services, Inc  ( Northeastern University, Boston ) and  Brent Coull ,  Professor of Biostatistics and Environmental Health (Harvard TH Chan School of Public Health ) for their involvement with the original MBMS study and continued engagement with this project.
Funding This work was funded by the  National Institutes of Health (NIH)/National Institute on Minority Health and Health Disparities (NIMHD)  ( 5R01MD014304 ). The prior MBMS study was funded by the  National Institutes of Health/ National Institute on Aging  ( 1 R01 AG027122 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. GDS, CR, KT, MS and SHW work within the  MRC Integrative Epidemiology Unit  at the  University of Bristol , which is supported by the  Medical Research Council  ( MC_UU_00011/1 , 3 & 5).
Therefore, nephrectomy followed by active surveillance for relapse, remains the predominant standard of care globally. Treatment with ICIs, either as a dual combination or in combination with TKIs have revolutionised the management of patients with advanced RCC. The combination of ipilimumab, a monoclonal antibody against human cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) with nivolumab, a monoclonal antibody against programmed cell death protein-1(PD-1) is now a first line treatment for patients with intermediate or poor risk advanced RCC, classified according to the International Metastatic Renal-Cell Carcinoma Database Consortium (IMDC) model  [12] ). This followed findings from CHECKMATE-214  [13] , a phase III study showing an 18 month OS of 75% (95% CI; 70-78) for the combination ICI group versus 60% (95% CI; 55-65) for sunitinib  [14].
2 months in patients receiving durvalumab compared with placebo  [20]. Durvalumab in combination with tremelimumab has shown benefit to OS and PFS in the third-line treatment of patients with metastatic NSCLC, (in those with PD-L1 tumour cells ≥25%) and is being evaluated in the advanced setting in patients with various tumour types (NCT03298451), (NCT03994393) and (NCT02516241). Therefore, the RAMPART trial, led by the MRC CTU at University College London (UCL) and working in partnership with AstraZeneca, is investigating the activity of durvalumab alone and in combination with tremelimumab after nephrectomy for patients with locally advanced RCC. There is also potential for adding additional research arms as the trial progresses.
Sample size RAMPART is powered for both the DFS and OS outcomes. The sample size calculations and design characteristics for RAMPART were obtained using nstage (version 3. 0. 1, 10-Sep-2014). Specifically, the nstage program was used to obtain the 'ideal' target number of control arm events needed at each stage for each comparison and an approximate idea for the timing of the stages. Artpep (version 1. 0. 4 PR 05-Jul-2013) was then used to project a more realistic analysis timeline using accrual and timeto-event patterns based on the SORCE trial (ISRCTN: ISRCTN38934710, EUDRACT: 2006-006079-19; NCT00492258). ART (version 1. 1. 0, 10-Dec-2013) was used to determine the absolute differences in DFS and OS at relevant time points. All calculations were performed in Stata 14. 1. Using control arm data from the SORCE trial  [9]  we anticipate a 3year DFS rate of 65% for the control arm of RAMPART.
Statistical plan All efficacy analyses will be performed in the intention-to-treat population. The treatment effect for each of the initial two treatment comparisons and each of the two co-primary outcomes will be assessed using a stratified Cox model, stratifying for the factors used in randomisation. Data will be presented graphically using Kaplan-Meier plots. The Chi-squared test or Mann-Whitney test will be implemented for categorical data comparisons, including toxicity, as appropriate. Subgroup analyses will be conducted to assess consistency of treatment effect across potential or expected prognostic factors, including subgroup analyses by PD-L1 baseline expression status. Full details of all planned analyses are documented in the RAMPART Statistical Analysis Plan.
Regulatory and ethical considerations The trial will be conducted in compliance with the approved protocol of the Declaration of Helsinki 2008, the principles of Good Clinical Practice (GCP), Commission Directive 2005/28/EC with implementation in national legislation in the UK by Statutory Instrument 2004/1031 and subsequent amendments, the EU General Data Protection Regulation (GDPR), and the National Health Service (NHS) Research Governance Framework for Health and Social Care (RGF). International centres will comply with the principles of GCP as laid down by the ICH topic E6 R2 (Note for Guidance on GCP) and applicable national regulations. Scientific advice on the clinical trial protocol has been obtained from both the European Medicines Agency (EMA) and FDA. Sufficient elements are in place to enable compliance with ICH GCP (both retrospectively and prospectively) if it is decided at a later stage that trial data are to be submitted to regulatory authorities as part of a licensing application.
Trial oversight RAMPART is sponsored by UCL. The MRC CTU at UCL has overall responsibility for the study working closely with the Chief Investigator, all members of the TMG and all collaborators. The Trial Management Team (TMT) meet on a weekly basis to discuss all aspects of trial conduct, including for example site set-up, participant accrual and safety management. The TMT report to the TMG. The TMG is responsible for the running of the trial and meets at least six times a year. An Independent Data Monitoring Committee (IDMC) meet approximately annually to review safety, compliance with treatment and efficacy data (at pre-planned interim analyses). They are the only group who see the confidential, accumulating efficacy data for the trial. The IDMC advise the TSC. The TSC provides overall supervision of the trial and provides advice and recommendations through its independent Chair.
Immunofluorescence microscopy, live-imaging, and FRAP Cells were fixed for 10 min with 4% PFA in PBS, permeabilized for 10 min (0. 2% Triton X-100), and blocked for 10 min in 4% BSA in PBS. Cells were then incubated for 1h at RT with primary antibodies. For specific staining of MT1-MMP and beta integrin 1 and 3 at the plasma membrane, cells were incubated with primary antibody without any previous permeabilization. Secondary antibodies were then added for 1h at RT. Coverslips were permanently mounted in Mowiol (EMD Millipore) containing DAPI. Imaging of fixed cells was performed on a semi-confocal microscope (Zeiss AxioImager Z1, AxioCam MRm (N/B) camera, AxioVision software) equipped with Plan-Apochromat (63x, NA 1,4 Oil, Ph3, WD 190) objective. The images were then processed with Fiji software.
For quantitative confocal imaging, all images were acquired with the same optical path and setup detection and quantified without any ImageJ processing. For live imaging, LifeAct-TagRFP-expressing cells were seeded overnight at subconfluent densities in serum-coated Lab-Tek coverglass. During imaging, cells were placed on a heated 37°C stage (ZEISS), with a CO 2 -independent medium (18045-088, Life Technologies) and imaged with an Axiovert 200M microscope (ZEISS) equipped with a Coolsnap HQ2 camera (Photometrics) and a Plan Neo-Fluor (40×, NA 1. 2) objective. Images were sampled with MetaMorph software (Molecular Devices). Time-lapse videos were then processed with ImageJ software. TIRF microscopy was performed with an Axiovert 200M microscope equipped with a Coolsnap HQ2 camera (Photometrics), Plan Apochromat (100×, NA 1.
4, oil) objectives, and a TIRF 1 slider (ZEISS). Images were acquired with MetaMorph software and processed with Fiji software. To quantify invadosome speed, surfaces delimited by invadosome rings were threshold based on the fluorescence intensity of F-actin content (fluorescent LifeAct). Then, the center of mass of this surface was defined by ImageJ and the process was repeated on our live imaging data in order to measure the time necessary to observe the displacement of at least the equivalent of two equivalent surfaces (defining a full cycle of assembly/disassembly of a ring).
FACS analysis Cells were quickly wash with cold PBS and scrapped before being fixed for 10 min with 4% PFA in PBS. After a 10 min block in 4% BSA in PBS, cells were then incubated for 1h at RT with primary antibodies without any previous permeabilization. Secondary antibodies were then added for 1h at RT. Cells were then post-fixed for 10 min with 4% PFA in PBS in order to stabilize the indicated surface staining. Fluorescence of cells were analyzed with ACCURI-C6 (BD bioscience) in order to quantify the median of fluorescence for each conditions through the software C-flow (BD bioscience). In average, 8000cells were analyzed per condition in 3-6 different experiments.
ECM degradation assays ECM degradation assay was performed as described in Sharma, 2013. Briefly, 12mm coverslips in 24 well plates were coated with 50μg/ml Poly L lysine 20min at RT, than with 0. 25% Glutaraldehyde 15min at 4°C, and finally with mix of 0. 2% unlabeled gelatin dissolved in PBS and Alexa Fluor 647-gelatin in 4:1 ratio during 10min at RT and washed with PBs, medium with serum and 70% ethanol. The cells were seeded on these gelatin coated coverslips for 6 or 24h, fixed, and observed with an semi-confocal microscope (Zeiss AxioImager Z1, AxioCam MRm (N/B) camera, AxioVision software) equipped with  NA1, 4 Oil, Ph3, WD 190objective). The quantification of cells degradation ability was established by calculating the % of total degradation area per field of view after setting a low-intensity threshold on gelatin-captured images divided by number of cells with Fiji software.
Mass spectrometry-based proteomic analyses and network analysis Proteins eluted form co-IP using Laemmli buffer were separated by SDS-PAGE (NuPAGE 4-12%, Invitrogen) before being stained with Coomassie blue R-250 (Bio-Rad Laboratories). Proteins were then in-gel digested using modified trypsin (Promega, sequencing grade) as previously described  (Salvetti et al. , 2016). Resulting peptides were analyzed by online nanoliquid chromatography coupled to tandem MS (UltiMate 3000 and LTQ-Orbitrap Velos Pro, Thermo Scientific). Peptides were sampled on a 300 µm x 5 mm PepMap C18 precolumn and separated on a 75 µm x 250 mm C18 column (PepMap, Thermo Scientific) using a 120min gradient. MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2.
6) through concomitant searches against Uniprot (Mus musculus taxonomy), classical contaminants database (homemade) and the corresponding reversed databases. The Proline software (http://proline. profiproteomics. fr) was used to filter the results: conservation of rank 1 peptides, peptide identification false discovery rate < 1% as calculated on peptide scores by employing the reverse database strategy, and minimum of 1 specific peptide per identified protein group. Proline was then used to perform a compilation, grouping and spectral counting-based comparison of the protein groups identified in the different samples. Proteins from the contaminant database and keratins were discarded from the final list of identified proteins. Only proteins identified with at least 2 specific spectral counts only in Trpv4 co-IP or enriched at least 3 times in Trpv4 co-IP compared to negative control (KO) co-IP were considered as potential Trpv4 binding partners.
We reconstructed the protein-protein interaction network by gathering BIOGRID, INTACT and MINT protein-protein interaction data from Homo sapiens with psiquic retrieval (07122017) and cytoscape environment  (Shannon et al. , 2003). To acknowledge protein functions, gene ontologies were gathered for ROS signaling in Quickgo plateform (https://www. ebi. ac. uk/QuickGO/; GO IDs: GO:0006979 and GO:1901700 and their respective child terms), as well as from the literature. On the other hand,due to their large number of referring terms, proteins involved in calcium signaling were rather collected from Uniprot database, selecting human proteins according to "calcium" keyword, as well as from thorough literature survey.
Statistical analysis Quantitative data are presented as means ± SEM and statistically analyzed using two-sided unpaired t test (PRISM software). Based on the numerous measurements for each condition, data distributions were assumed to be normal but this was not formally tested each time. Differences with a probability level P < 0. 05 were considered significant. All graphs include standard deviation or SEM error bars. Supplemental material contains additional controls, localization, representation of time series and characterization of used TRPV4 mutants. Representative images of YFP-TRPV4 specifically accumulated at the periphery of invadosomes with its closest family member, TRPV1-RFP, in MYF cells expressing an invadosome marker (LifeAct fused to RFP or GFP). Analysis of the fluorescence intensity profile over the white lines indicating the invadosome thickness confirmed the specific accumulation of TRPV4 at the invadosome periphery.
Quantification of the average intensity of surface expression per cell confirmed that TRPV4 activation specifically decreased the activation of 1-integrins (SE; N=3; 40 cells per condition; unpaired t-test). c. Representative images of MYF cells treated with control or TRPV4 siRNA stained for active 1-integrin (9EG7-Alexa 488) and F-actin (phalloidin-Alexa 543). Quantification of the average intensity of surface expression per cell confirmed that TRPV4 silencing specifically increased active 1-integrin staining (SE; N=3; 40 cells per condition; unpaired t-test). Scale bars: 7 m (a) and 15 m (b, c). a. Silver staining analysis of TRPV4 partners copurified with TgRFP-TRPV4 WT expressed in MYF-TRPV4 -/-cells using anti-TgRFP antibody (* corresponds to the TgRFP-TRPV4 WT band). Direct protein-protein interaction network were used to show that the majority of these potential TRPV4-partners were known to interact between each other in public database.
The proteins not-known to interact between each other in database were left on the right side on the network and directly connected to TRPV4. GO and literature analysis highlighted proteins exhibiting an association with ROS signaling (green), Ca 2+ signaling (dark frame) or both (green and (dark frame). b. The specific coimmunoprecipitation of ASK1-Flag and TgRFP-TRPV4 WT overexpressed in HEK 293 cells showed the specific interaction between these two proteins, which confirmed the mass spectrometry data. c. Representative images of MYF cells stably expressing LifeAct-RFP transfected with the indicated potential TRPV4 partners identified by mass spectrometry. As ASK1 binds tgRFP-TRPV4 WT , only MYO1C-YFP accumulated at the invadosome periphery on the contrary with the control GFP-Git1. d. Representative images of MYF cells treated with control or the indicated siRNAs, and stained with phalloidin-Alexa 488 and DAPI.