dataesr/scientific-paragraphs-categorization huggingface dataset:
The occupancy profiles for PTH-Set1 and Myc-Set1 at selected genes were overall similar and both datasets were highly correlated (Supplementary Figure  S3A, B  and C ). Set1 occupancy was maximum beyond the H3K4me3 peak and upstream of the H3K4me2 peak and was slightly 3′-shifted relative to RNAPII occupancy (Supplementary Figure  S3D ), in agreement with previous results indicating that Set1 and its subunits are recruited at the 5′ region of active genes transcribed by RNAPII  [17]. Set1 protein amount was controlled in the different mutant strains by western blot (Figure  3a ). We reproducibly observed a reduction of the PTH-Set1 YF/AA amount by about 1. 3-1. 5-fold in all experiments  [21] , whereas deleting dRRM increased the stability of Set1. PTH-Set1 was functional, as it supported wild-type levels of H3K4me3 when expressed in a set1Δ background.
In contrast, H3K4me3 was globally abolished when the dRRM and N-SET functions were compromised (Figure  3b ). To evaluate the importance of the Set1 domains characterized in vitro, we next assessed the occupancy of PTH-Set1 and mutant forms by ChIP-seq experiments. Surprisingly, deleting dRRM or mutating it in PTH-Set1 YF/AA did not affect the distribution of the individual ChIP signals calculated per each mRNA-coding gene (Supplementary Figure  S4  for PTH-Set1 YF/AA , and data not shown). To analyze the chromatin distribution of Set1 and its mutant derivatives in more details, we generated normalized occupancy profiles over large genes, which allows a better spatial resolution of recruitment regions. As shown in Figure  3c , the aggregate signal for Set1 YF/AA and Set1 ΔdRRM shows reduced occupancy in the 5′ region of genes relative to wild-type (WT) Set1, which is compensated by an average increase in the 3′-end of genes to generate the observed unchanged overall signal on a per gene basis.
For assessing to what extent Set1 binds the RNA during transcription, we sought correlations between the Set1 CRAC signal and the levels of RNAPII occupancy as determined by RNAPII CRAC (Figure  4a ) for all mRNA-coding genes. We also compared the Set1 CRAC signal with Set1 recruitment to chromatin as determined by ChIP-Seq (Figure  4b ). We reasoned that if binding to the RNA were co-transcriptional, theses datasets should be highly correlated. Consistent with this notion, binding of Set1 to the RNA correlated remarkably well both with RNAPII CRAC (Figure  4a , r 2 = 0. 68; P = 3E -22 ) and recruitment of Set1 to chromatin as measured by ChIP (Figure  4b , r 2 = 0. 37; P = 9E -10 ). The distribution of Set1 CRAC/ChIP ratios was clearly not symmetric as could have been expected for a homogeneous population with random variability (Figure  4c ).
technology (San Diego, CA, USA). Primers are indicated in Supplementary Table  S2. Samples were demultiplexed using the pyBarcodeFilter script from the pyCRAC utility suite. Subsequently, the 3′ adaptor is clipped with Cutadapt and the resulting insert is quality trimmed from the 3′-end using Trimmomatic rolling mean clipping (window size = 5, minimum quality = 25). At this stage, the pyCRAC script pyFastqDuplicateRemover is used to collapse PCR duplicates and ensure each insert is represented only once. Each unique insert in our library is associated with a sixnucleotides random tag within the 5′ adaptor. The resulting sequences are reverse complemented with Fastx_reverse_complement (part of the fastx toolkit  [51] ), and mapped to the R64 genome (sgd) with bowtie2 (-N 1 -f). Read counts were normalized relative to reads derived from an S.
ChIP-seq, data processing and ChIP-qPCR ChIP of Myc-Set1 and PTH-Set1 were performed as previously described  [52]  with 9E10 (anti-MYC, Santa Cruz Biotechnology, Dallas, TX, USA) and anti-Set1 monoclonal antibodies (P Nagy, University of Toronto, Toronto, Canada). Libraries were prepared from fragmented DNA using the Chipseq MicroPlex Library Preparation Kit v2 samples preparation (Diagenode, Seraing, Belgium) according to the manufacturer's instructions. In all, 2 ng from IP samples were used as the starting material. Each library was barcoded using MicroPlex Single Index (Diagenode): iPCRtagT5, T6, T7 and T8 and amplified for 10 and 6 cycles for IP and input samples, respectively. Each library was quantified on Qubit with Qubit dsDNA HS Assay Kit (Life Technologies, Carlsbad, CA, USA) and then, size distribution was examined on the Bioanalyser with High Sensitivity DNA chip (Agilent, Santa Clara, CA, USA), to ensure that the samples have the proper size, no adaptor contamination and to estimate sample molarity.
Each library was diluted to 4 nM and then pulled together at equimolar ratio. Libraries were denatured according to the manufacturer's instruction and sequenced on a mid-output flow cell (130 M clusters) using the NextSeq 500/550 High Output v2 150 cycles kit (Illumina), in paired-end 75/7 nt mode, according to the manufacturer's instructions. In all, 148 million (M) paired-end reads were generated (34-39 M per sample) with 93% 4 = Q30. ChIP-Seq data quality was assessed using FastQC. FasQC: a quality control tool for high-throughput sequence data. Available online at: http://www. bioinformatics. babraham. ac. uk/pro jects/fastqc. Sequencing reads (FastQ format) were mapped to the Saccharomyces cerevisiae genome (sacCer3) using BFAST alignment tool with default parameters  [53]  (PMID 19907642) to obtain a Binary Alignment Mapped (BAM) file.
The sorted BAM files were used to determine average profiles of ChIP-Seq read density using ngs. plot software  [54] , (PMID 24735413) around the transcription start site. Read counts were normalized to the total number of million uniquely mapped reads or to read count per million of mapped reads (RPM). The RPM values allow samples to be compared regardless of differences in sequencing depth. To generate BedGraphs for visualization on genome browsers, ChIP-Seq BAM files were processed using HOMER package. The tag directory for each sample was then created using the makeTagDirectory tool and the corresponding BedGraph was generated using makeUCSCfile tool with default options. Only uniquely mappable reads (non-secondary alignment) were considered to create BedGraphs with a normalization to 10 million mapped reads for each sample.
Data access ChIP-seq data sets (PTH-Set1, PTH-Set1 YF/AA , PTH-Set1 ΔdRRM, PTH-Set1 ΔNSET, PTH-Set1 ΔRRM ΔNSET, PTHvector, Input PTH-Set1 and input PTH-Set1 YF/AA ), as well as CRAC sequences generated during this work are deposited to the NCBI Gene Expression Omnibus (GEO; http://www. ncbi. nlm. nih. gov/geo/) under the accession number GSE104486 (GSE104484 for chiPseq datasets and GSE104485 for CRAC datasets).
Acknowledgements We deeply thank  Peter Nagy  and  Bichtien Rouse  for providing the Set1 mAb and  Markus Bohnsack  and  Khaterine Sloan  for pRS415-nHTP. We thank  Luis Soares  and  Steve Buratowski  for Set1 constructs,  Tom Muir  for ubiquiytlated H2B, and  Nir Friedman  and  Alon Appleboim  for communicating H3K4 Exo-ChIP profiles. We thank  Julie Drogat ,  Ghislain Bidaud  and  Nicolas Fernandez  for their involvement in the ChIP-seq procedures and  Lorant Székvögyi  for discussions. We also thank  Judith Recht  for critical reading of the manuscript. Work in VG laboratory is supported by the  'Ligue contre le Cancer' (Equipe Labéllisée). Bioinformatic support and computing sources at CRCM were provided by the  CIBI and DISC platforms (CRCM Integrative Bioinformatics  and Datacentre IT and Scientific Computing).
High-throughput sequencing was performed at the  TGML Platform , supported by grants from  Inserm ,  GIS IBiSA ,  Aix Marseille Université , and  ANR-10-INBS-0009-10. AE is supported by a fellowship from the  Canceropole PACA. Work in the DL laboratory was supported by the  CNRS , the  ANR ('TermCut ' grant) and the  FRM. TC and DC are supported by  French MRT  fellowships. Work in the JK laboratory was supported by grants from the  National Research Foundation of Korea  ( 2012M3A9C6049938 ,  2015K1A3A1A21000296  and  2015R1A1A1A05001593 ). Collaboration between VG and JK laboratories is supported by the  PHC STAR exchange programme  ( 34325UF ).
Risk communication processes embedded in local hazard knowledge (mainly from elderly people and flood experiences from neighbours and friends) and to a lesser extent also directly from the government through official training and information initiatives were the main reasons that respondents were aware of living in a dangerous area. Additionally, the recursive partitioning analysis  (Breiman, 1984)  for the East Attica dataset showed that only the variable "income" has a significant impact on individual risk awareness; in fact, people with a higher income are more likely aware of the flood risk. Analysing the correlation between age and perception of the hydro-geological environment was found to be non-significant (τ = 0. 063 and p = 0. 355 for Evros and τ = -0. 019 and p = 0. 766 for East Attica).
This allows us to extend the current socio-hydrological concepts as well as to support practitioners in the development of enhanced flood risk management strategies at local level. Data availability. The survey data used in this study are confidential and cannot be made publicly available. Interested parties may wish to contact the corresponding author for further information. The runoff data from the Rafina catchment (Fig. 1 ) are available from the Hydrological Observatory of Athens (http://hoa. ntua. gr/ timeseries/d/897, Hydrological Observatory of Athens, 2012) and the runoff data from the Evros River are available from the Regional Authority of Eastern Macedonia and Thrace (2008) of Eastern Macedonia and Thrace.
Acknowledgements. This work received funding from the  Austrian Science Fund (FWF) :  P27400  as well as from  Austrian Climate and Energy Fund  project  SHARED  (project number  KR16AC0K13268 ). The authors highly appreciate the suggestions of  Murugesu Sivapalan ,  Giuliano Di Baldassarre , and an anonymous referee on an earlier draft of this paper. Edited by: Fuqiang Tian Reviewed by:  Giuliano Di Baldassarre ,  Murugesu Sivapalan , and one anonymous referee.
Methods Study population. We used data from the Whitehall II cohort study, established in 1985/1988 on 10,308 British civil servants (67% men) aged 35-55 years  24. Informed consent was obtained from all participants. The study was approved by the ethics committee of University College London (reference number 85/0938) and conducted in accordance with the 2008 Helsinki Declaration. The study design consists of a clinical examination every 3 to 6 years since study inception. Accelerometer measurement was added to the study at the 2012/2013 wave of data collection for participants seen at the central London clinic and for those living in the South-Eastern regions of England who had their clinical assessment at home. For logistical reasons (charging and configuration of the devices), participants seen at home who resided in other parts of UK could not be offered the accelerometer measurement.
Whitehall II data, protocols, and other metadata are available to the scientific community. Please refer to the Whitehall II data sharing policy at http://www. ucl. ac. uk/whitehallII/data-sharing. Physical Activity. Physical activity was assessed in 2012/2013 using questionnaire and accelerometer. Questionnaire. Physical activity was assessed using a modified version of the validated Minnesota leisure-time physical activity questionnaire  25, 26  , including 20-items on frequency and duration of participation in different physical activities (e. g. , walking, cycling, sports). Participants were requested to take into account activity patterns over the past four weeks, at work and leisure. For each activity, including the open-ended items, we assigned a metabolic equivalent (MET) value by using the latest version of the compendium of activity energy costs  27.
81 m. s -2 ; 1 mg = 0. 00981 m. s -2 )). Calibration error was estimated based on static periods in the data and corrected if necessary (calibration correction range = 0. 8 to 10. 0 mg, mean correction = 2. 5 mg)  32. Next, the Euclidean Norm Minus One (ENMO), was used to quantify the acceleration related to the movement registered and expressed in milligravity (mg)  33. Negative values were rounded to zero and ENMO values averaged over 5 second epochs. As part of the accelerometer measurement, sleep periods were detected using a previously validated algorithm  34. Data from the first waking to waking on the penultimate day were extracted, corresponding to 7 full days. For the present analysis, only data over the day (periods between waking and sleep onset) were used. Participants were included in the analysis if they had valid data, defined as daily wear time ≥ 2/3 of waking hours, for at least 2 weekdays and 2 week-end days.
Time spent in MVPA bouts lasting 1 minute or more was calculated as the sum of short and long bouts. The daily accelerometer-assessed time in MVPA over a week was calculated as the mean of measures over 7 days. For participants with less than 7 valid days of data, the following formula was used to standardize measurement to one week for all participants: [(5 × mean daily weekday MVPA time + 2 × mean daily week-end MVPA time)]/7. Accelerometer data were processed in R using the GGIR package (cran. r-project. org/web/packages/GGIR/index. html). Assessment of successful ageing. We used data from the study baseline (1985/1988) to 2012/2013 to ascertain successful ageing. As in previous studies, we used a comprehensive definition of successful ageing that included better function (free of disability, good mental health and better cognitive, motor and respiratory function) and absence of major chronic disease (coronary artery disease, stroke, diabetes, cancer and neurodegenerative disease)  13, 38.
Chronic diseases were assessed from 1985/1988 to 2012/2013. Coronary heart disease, including myocardial infarction and definite angina, and stroke events were identified using linkage to national hospital records. Diabetes mellitus was defined as fasting blood glucose level of 7. 0 mmol/L or more, HbA1c ≥ 6. 5% (48 mmol/mol) based on blood sample assessed at the clinical examination, self-reported doctor-diagnosed diabetes, or the use of medications for diabetes. Cases of cancer were determined using the National Health Service's cancer register. Neurodegenerative diseases included Parkinson's disease and dementia and were assessed using linkage to national hospital records and mental health service dataset. Functioning outcomes were assessed at the 2012/2013 wave of data collection. Disability was assessed based on participants' responses to 6 questions on perceived difficulties in basic activities of daily living lasting for at least 3 months (dressing, walking, bathing/showering, eating, getting in or out of bed, using the toilet), with difficulties in one or more activities taken to signal disability.
Discussion This study based on 3,749 adults aged 60 to 83 years presents three key findings. First, accelerometer data show that successful agers spend on average 8 minutes more per day in MVPA than their unhealthy counterparts; both longer daily duration and higher intensity of MVPA bouts were associated with greater probability of successful ageing. Second, associations between MVPA and successful ageing were stronger with physical activity assessed by accelerometer. Third, length of a bout of MVPA, short (1-9. 59 min) or long (≥ 10 min), did not modify the association of MVPA with successful ageing. Our data show the prevalence of successful ageing to be higher with longer duration of MVPA, with a weakening of the effect at daily durations longer than 20 or 30 minutes. Similar findings, albeit with questionnaire-assessed physical activity and a range of health outcomes, have been reported  [40] [41] [42].
Strengths of this study include its large study sample, the use of both questionnaire-and accelerometer-assessed physical activity, and high compliance for accelerometer wear among participants. Cross-sectional design is a limitation, precluding inferences about temporality, such as whether physical activity preceded successful ageing or was preceded by successful ageing. However, sensitivity analyses excluding participants with disability and those who died in 2 years after data collection suggest that our findings cannot be entirely explained by reduced physical activity in already sick participants. Furthermore, we have previously used longitudinal data, using questionnaire-assessed MVPA, to show an association of physical activity with successful ageing  13. Future longitudinal studies using accelerometers are needed to confirm the findings in the present study.
Acknowledgements This research is supported by the  US National Institutes of Health  ( R01AG013196 ;  R01AG034454 ;  R01HL036310 ), the  UK Medical Research Council  ( K013351 ), the  British Heart Foundation  ( PG/29605 ), and the  Economic and Social Research Council  ( ES/J023299 ). The funding organizations had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Surnames unambiguously indicate that a person is of Kanak or European background. 18 The order of the response of each type of applicant was randomly changed daily throughout the data collection period. The test consisted of sending short and interrogative emails in response to ads published with a view to requesting a housing unit visit. The following messages were sent: Dear Sir or Madam: Your ad fits the requirement that I am seeking. I would like to arrange a visit to see the apartment. What documents do you require? Thank you, First Name and Surname (our translation) These messages were deliberately short, being a first contact. Questions were asked in order to elicit a reaction from the recipient. The surname indicated the ethnicity of the applicant. A specific database was created, listing for each offer the date of publication; the 17 The internet is not the only channel through which housing offers are advertised.
Cross effect (European reference applicant with stable employment) Kanak whose work situation was not disclosed -16. 3*** -6. 24 -20. 5 -12. 0 Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates *** Significant at the 1%, ** 5%, * 10% threshold The p-value is 0. 1023 Source: DALTON database.
2*** 5. 9 10. 8 53. 5 72. 8 European non civil servant 57. 4*** 5. 9 9. 7 47. 6 67. 1 Kanak civil servant 64. 7*** 5. 8 11. 2 55. 1 74. 2 Kanak non civil servant 48. 4*** 6. 1 7. 9 38. 4 58. 5 Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates *** Significant at the threshold of 1%, ** of 5%, * of 10% Remark: The % of Kanaks per neighborhood is drawn from Pestana et al. (2015) using 2009 ISEE census data Source: DALTON database.
background signal effect (European reference applicant) All 0. 0 0. 00 -10. 6 10. 6 Employment stability signal 7. 0 0. 83 -6. 8 20. 7 No stability signal -6. 9 -1. 01 -18. 0 4. 3 Cross effect (European reference applicant with stable employment) Kanak whose work situation was not disclosed -13. 7* -1. 69 -27. 0 -0. 4 Student statistics and confidence intervals calculated using the bootstrap method based on 10,000 replicates *** Significant at the threshold of 1%, ** of 5%, * of 10% Remark: The % of Kanaks per neighborhood is drawn from Pestana et al. (2015) using 2009 ISEE census data Source: DALTON database.
Several factors that are lacking include: the lack of standardized pre-analytical and isolation procedures; lack of gold standard for processing, characterization and purity; an unknown influence of comorbidities, co-medication and other confounding factors; lack of disease specificity; lack of tissue-specific markers (Table  2 ). As with any biomarker study, pilot observations from small cohorts should be validated in larger patient datasets, and the reproducibility of isolation procedures, including markers of EVs and normal range levels in the healthy population should be reported. Moreover, evidence should be provided of the additional value of EVs over current gold-standard biomarkers.
Therapeutic potential of extracellular vesicles for the ischaemic heart EVs purified from defined cell types have been suggested as novel therapeutic options for various cardiac diseases including ischaemic heart disease, myocardial infarction, and heart failure, as well as for pathogen vaccination, immune-modulatory and regenerative therapies and drug delivery. However, the first clinical steps have been made using exosomes as an anti-tumour therapy: an effective way to destroy non-small cell lung cancer (NSCLC) is to activate tumour-specific cytotoxic T cells (CTL), and for this autologous dendritic cell (DC)-derived exosomes (DEX) CVR-2017-608 13 loaded with tumour antigens have been tested not only in phase I 157 but also in a phase II Trial (ClinicalTrials. gov Identifier: NCT01159288).
Although this did not induce detectable effector T cell responses, a positive effect on natural killer (NK) cells could be observed in some patients. 158  This first exosome Phase I trial highlighted the feasibility of large scale exosomes production and the safety of exosome administration. 159  Several other Phase I studies have been initiated, exploring the use of EVs / exosomes as a therapy, including the effect of MSC-derived exosomes on β-cell mass in Type 1 diabetes mellitus (T1DM) Patients (ClinicalTrials. gov Identifier: NCT02138331). Little is known about whether EVs derived from animals might be feasible for human therapy. However, EV fractions derived from human umbilical cord MSCs and administered to different healthy and diseased animal species (including rats with myocardial infarction) were found to be well tolerated.
Most important is the validation of pilot observation from small cohorts in larger dataset of patients, thereby improving the reproducibility of isolation procedures, and to provide evidence of additional value of EVs over current golden standard biomarkers. To strengthen the process of identification of EVs or EV-components as biomarkers, experiments may also be designed to identify the tissue source of the biomarker, combined with the functional validation of the identified molecule (protein and/or RNA) in the target tissues. Therapy: The translation of EVs into clinical therapies will require categorization of EV-based therapeutics in compliance with existing regulatory frameworks. Since EVs will probably be considered as biological medicinal products more specifically advanced therapy medicinal products (ATMPs), new rules explicitly regulating EV-based therapies are probably not needed.
Acknowledgments JS is supported by Horizon2020 10. Exosome density is typically 1. 06 -1. 20 g/ml. Microvesicle density is not well defined but they have been found between ~1. 03 -1. 08 g/ml  [10] [11] [12]. (D, E) Tuneable resistance pulse sensing (TRP) determines size distribution by the change in resistance as the particle crosses a small pore in a membrane (which is selected according to the size range examined). Figure legends.
ERC-2016-COG EVICARE (725229), the Project SMARTCARE-II of the BioMedicalMaterials institute, co-funded by the ZonMw-TAS program (#116002016), the Dutch Ministry of Economic Affairs, Agriculture and Innovation and the Netherlands CardioVascular Research Initiative (CVON): the Dutch Heart Foundation, Dutch Federations of University Medical Centers, the Netherlands Organization for Health Research and Development, and the Royal Netherlands Academy of Sciences. SD is supported by Project grant 169040 from the British Heart Foundation. PF and RS were supported by the European Cooperation in Science and Technology (COST EU-ROS). PF holds grants from the Hungarian National Research, Development, and Innovation Office (OTKA K 109737, OTKA ANN 107803, NVKP 16-1-2016-0017, and VEKOP-2. 3. 2-16-2016-00002).