dataesr/scientific-paragraphs-categorization huggingface dataset:
For a single realization of the coalescent tree, results are different and depend on the realization; for example, mutations of high frequencies can be present only for highly unbalanced genealogies  (Ledda et al. , 2015). The SFS was also studied in scenarios including selection  (Fay and Wu, 2000; Kim and Stephan, 2002) , demography  (Griffiths and Tavaré, 1994; Živković and Wiehe, 2008)  or population structure  (Alcala et al. , 2016). Besides its general interest, the SFS has been used to devise goodness-offit statistical tests to estimate the relevance of the standard neutral model for an observed dataset. SFS-based neutrality tests contrast estimations of the nucleotide variability from different bins of the sample SFS  (Tajima, 1989; Fu and Li, 1993; Achaz, 2009). Ferretti et al. (2010)  showed that, once the SFS under an alternative scenario (e.
S. 2 Simulations In this section we present a numerical result as an example to check the consistency of our results. In Figure  S1  the analytical sample spectrum is compared with those obtained by coalescent simulations. We parsed the output of ms  (Hudson, 2002)  to count the number of mutations conditional on a focal mutation of given frequency. The good agreement between the spectra supports our equations. The source code (C++) for computing analytical as well as simulated spectra can be found in the package coatli developed by one of the authors and available on http://sourceforge. net/projects/coatli/.
Acknowledgments We thank  Wolfgang Stephan  for insightful discussions. GA and LF were supported by grant  ANR-12-JSV7-0007 TempoMut  from  Agence Nationale de la Recherche. GA was also supported by grant  ANR-12-BSV7-0012  Demochips, AK and TW by grants of the  German Science Foundation  ( DFG-SFB680  and  DFG-SPP 1590 ).
For instance, we found no sex effect, and no evidence that parent and child age of onset are correlated after controlling for PRNP mutation and for child's year of birth, a variable that captures some effects of ascertainment bias 32 (Table  S7 ). A common genetic variant, PRNP M129V, is known to affect the clinical and pathological presentation of many forms of prion disease  33  as well as the risk of sporadic and acquired prion disease  34. This variant has previously been reported to affect age of onset in some forms of genetic prion disease but not others  21, 32, 35, 36. We found no evidence that codon 129 affects age of onset for P102L or E200K individuals (Table  S7  and Figure  S4 ). For D178N, our data are suggestive that a 129VV genotype may predispose to earlier onset than MM or MV genotypes (Figure  S4  and Supplementary Discussion), but in the overall dataset, codon 129 failed to explain additional variance in age of onset (Supplementary Discussion).
Perhaps of greater concern is that most of our historical controls were collected retrospectivelyindividuals are only ascertained if they become sick -and may overestimate the hazard rates for individuals followed prospectively  32. To assess this possible source of bias, we compared the survival of the limited number of individuals followed prospectively in our dataset (N=24 individuals, with a cumulative 145 person-years of follow-up), conditioned on their ages at first ascertainment, to those of individuals with no prospective follow-up. We did not observe a significant difference in hazard (P=0. 59, Cox proportional hazards test) between these two groups, although this could be due to a lack of power (see Discussion).
Our study has several limitations. First, true age of onset distributions can only be obtained prospectively  54  , whereas our data are largely retrospective. We have included asymptomatic individuals with pathogenic PRNP variants where possible, but our ascertainment of them is certainly incomplete due to limited uptake of predictive testing  23. This bias may tend to make our estimates of age of onset overly pessimistic  32, 55. To the extent that true age of onset is older, or total lifetime risk lower, than our data suggest, randomized preventive trials with a clinical endpoint would require even greater numbers of individuals, and thus further increase our caution around this study design. Second, although our dataset is, to our knowledge, the largest ever reported for genetic prion disease age of onset, our statistical power to detect genetic modifiers, which might aid in age of onset prediction, is still limited.
Third, although we have attempted to select a reasonable set of assumptions for modeling clinical trials, we have by no means exhaustively sampled the set of possible trial designs and parameters. Fourth, powering a post-marketing study will require a good historical control dataset to compare to, and our dataset, which was collected mostly retrospectively, may or may not be adequate. We found no evidence that our dataset overestimates the hazards facing prospectively followed individuals, but this could be due to a lack of power in our analysis. Fifth, the ascertainment of genetic prion disease by prion surveillance centers may be biased towards rapidly progressive phenotypes, meaning that the prevalence of more slowly progressive forms might be underestimated. Our findings highlight two priorities for the prion field.
Data collection. Age of onset data were gathered from nine study centers: the UK National Prion Clinic, the German Reference Center for TSEs, the Memory and Aging Center at University of California San Francisco, the Australian National CJD Registry, the reference center for CJD at University of Bologna, the DOXIFF study at the Mario Negri Institute, the Japanese national prion surveillance network, the French national reference center for CJD, and the Spanish National Center for Epidemiology. The data include both previously reported and newly identified families and individuals. Data were collected through clinical visits, reports to prion surveillance centers, and family histories, as previously described  32, [56] [57] [58] [59] [60] [61]. Age of onset was based on the earliest date of symptoms, determined by the patient or witnesses, that subsequently developed into prion disease.
Data on the number of positive predictive genetic tests for PRNP mutations was provided by the National Prion Disease Pathology Surveillance Center for this study. Life tables and hazard curves. We tabulated, for each PRNP mutation and for each age from 1-100, the number of individuals alive at the beginning of the interval (lives; l), becoming sick or dying within the interval (deaths; d), or being censored -alive and well at last followup or dead of a different cause -within the interval (withdrawals; w). The raw hazard (q) was computed as onsets divided by the mean number of people observed over the interval: q = d/(l -w/2), and a smoothed hazard (q_smooth) was computed by passing a Gaussian filter (sd=3 years, maximum width=15 years) over the raw hazard. The proportion surviving for each interval (p) was 100% for the first year and was computed as (1-q) times the proportion surviving in the previous interval for every year thereafter.
Hazard in the placebo group was the baseline hazard specified in the text (4. 6% for Table  2 ), and hazard for the drug group was the baseline hazard times the hazard ratio. The cumulative event rate in each group was computed as C = (h/(h+w)) * (1-exp(-(h + w)*t))), where h = hazard, w = withdrawal rate, and t = years of followup. The overall cumulative event rate Ctot was the average of the cumulative event rates for the two groups, weighted by proportion treated (in this case, 50/50). The number of randomized individuals required for d events to be observed was calculated as d /Ctot. To account for ignoring the first g years of data, we reasoned that the cumulative rate of events usable in the final dataset would be Cusable = (h/(h+w)) * (  1  Source code and data availability. Raw data cannot be made available due to identifiability concerns, but life tables have been included in supplement (Supplementary Life Tables).
Estimation of number of individuals available for trials It is possible to estimate the true number of high penetrance PRNP mutation carriers based on disease prevalence. Using data from recent case series, 1,176 prion disease cases harbored a PRNP variant classified here as highly penetrant, out of 10,460 sequenced cases or 16,025 total cases  6. Thus, 7 -11% of prion disease cases have a high penetrance PRNP variant. Prion disease is responsible for ~1 in 5,000 deaths  6  , suggesting that ~1 in 45,000 to 71,000 deaths are due to a high penetrance PRNP variant. The carrier rate among the living population will be somewhat lower because these variants reduce life expectancy, but it is reasonable to suppose that ~1 in 100,000 people harbors a high penetrance PRNP variant. This is in line with recent population control data, where out of 138,632 individuals in the gnomAD database as of December 2017 (http://gnomad.
These results suggest that the significant codon 129 effect on D178N age of onset is most likely driven primarily by a younger age of onset in V/V individuals compared to other diplotypes. Despite the strong statistical significance of this difference, the small number of D178N-129VV individuals means that codon 129 does not add any explanatory power for age of onset in the dataset as a whole. As noted in the main text, mutation alone explains limited variance in age of onset (adjusted R^2 = 0. 15, P = 1. 3e-33). Adding cis and trans codon 129 to this model decreases the variance explained (adjusted R^2 = 0. 14, P = 3. 6e-18). We also investigated in further detail previously reported associations. For disease duration, D178N M/M and V/V were significantly more rapid than either heterozygous diplotype, consistent with previous reports.
At the end of each simulated trial, the censored trial data on treated individuals are compared to our original dataset as historical controls (Supplementary Life Tables). To determine a P value we used a Cox proportional hazards counting model accounting for different left-truncation times  74  : for untreated individuals in the original dataset, we assumed age 0 as a start time, while for treated individuals, we assumed left truncation at the age at trial enrollment, plus one year to account for the "run-in" year. While we cannot currently rule out the possibility that our dataset is biased relative to the true hazards facing mutation carriers in real life (see main text Discussion), we sought to confirm that our simulation method is not itself biased. We reasoned that if our simulation was unbiased, then for a drug with hazard ratio equal to 1 (a completely ineffective drug), even long trials with large numbers of individuals should have power equal to alpha, by the definition that alpha is the false positive rate when the null hypothesis (no efficacy) is true.
We therefore ran 1000 iterations of a simulation with a hazard ratio of 1 and 1000 individuals followed for 20 years. We observed a significant result at P < 0. 05 in only 5. 5% of iterations, consistent with the expected 5%. In contrast to the result for randomized trials (see discussion above and Table  S6 ), we found that stratification by mutation in the analysis of historical control trial simulation did just slightly increase statistical power. For example, with N=156 individuals followed for 15 years with a hazard ratio of 0. 5, power was 90. 6% (906/1000 iterations) without stratification and 94. 1% (941/1000 iterations) with stratification. This difference from the randomized trial simulation may be a property of the Cox counting model, combined with the fact that our historical comparison dataset has N=1,000 individuals, and we considered follow-up periods of up to 15 years, meaning that the dataset was large enough for the small explanatory power of different PRNP mutations to matter.
Acknowledgments This study was performed under ethical approval from the  Partners Healthcare Institutional Research Board  ( 2014P000226/MGH ) and the  Broad Institute's Office of Research Subjects Protection  ( ORSP-2121  and  NHSR-4190 ). We thank the patients and families who contributed their data to this research. EVM is supported by the  National Institutes of Health  ( F31 AI122592 ), SV is supported by the  National Science Foundation  ( GRFP 2015214731 ), and both are supported by an anonymous organization. SH and JPB are supported by  Santé Publique France by the program  " Investissement avenir "  ANR-10-IAIHU-06. Work at the  NHS National Prion Clinic  was supported by the  UK National Institute of Health Research's Biomedical Research Centre at University College London Hospitals NHS Foundation Trust.
Japanese prion surveillance is funded by a  grant-in-aid  from the  Research Committee of Prion Disease and Slow Virus Infection , the  Ministry of Health, Labour and Welfare of Japan , and from the  Research Committee of Surveillance and Infection Control of Prion Disease , the  Ministry of Health, Labour and Welfare of Japan. SC is supported in part by an  NHMRC Practitioner Fellowship  (# APP1105784 ) and the  Australian National Creutzfeldt-Jakob Disease Registry  is funded by the  Commonwealth Department of Health. Data collection in Germany was supported by a grant number  1369-341  by  Bundesministerium für Gesundheit through Robert Koch Institute. Work in Italy was partially supported by  Fondazione Telethon  ( GGP10208 ). We thank  Mr. Javier Almazan  and  Dr. Maria Ruiz ,  CNE, Madrid , for help with data extraction at the  Spanish CJD Registry.
Images of cells in the 384-well plates were recorded in SD-Riboflavin at 24 °C using a ×60 air lens (NA 0. 9) and with an ORCA-ER charge-coupled device camera (Hamamatsu). Images were acquired in two channels: GFP (excitation filter 490/20 nm, emission filter 535/50 nm) and mCherry (excitation filter 572/35 nm, emission filter 632/60 nm). After acquisition, images were manually reviewed using the ScanR analysis program. Hits form the library were imaged a second time using spinning disk microscopy (as below). Manual microscopy. Manual microscopy was performed using VisiScope Confocal Cell Explorer system, composed of a Zeiss Yokogawa spinning disk scanning unit (CSU-W1) coupled with an inverted Olympus IX83 microscope. Images were acquired using a ×60 oil lens and captured by a connected PCO-Edge sCMOS camera, controlled by VisView software, with wavelength of 488 nm (GFP/Venus), 561 nm (mCherry), 405 nm (BFP/CFP), and 640 nm (Far-red/IFP1.
Measuring PerMit extent in various physiological conditions. Strains expressing the PerMit reporter and a peroxisomal marker (MS2661 Tom70-VC-His; Pex25-VN-Kan; Pex11-mCherry-Nat) were grown in synthetic media with either 2% glucose or 0. 2% oleate (+0. 1% Tween80) or 2% ethanol for 4 h and imaged using spinning disk microscopy (see above). The number of Pex11-mCherry and split PerMit puncta per cell were manually scored. n = 4, 100 cells per experiment. Data are presented as mean ± s. d. Peroxisomes motility. Peroxisome speed was analyzed using CFP-SKL as a marker. Time-lapse videos at length of 4 min were obtained using a spinning disk microscope (see above). Peroxisomes were segmented and their speed was analyzed using Imaris (see Image analysis). Data represent 25,000 segmented peroxisomes from four or more independent experiments.
Morphology phenotypes were assessed in at least 100 cells. Error bars represent the s. d. from three independent experiments. Image analysis. Image analysis was performed using Imaris v9. 02 image analysis software (Bitplane) and the batch analysis extension package. For the peroxisome motility assay, peroxisomes (marked by CFP-SKL) were identified using the Imaris built in spot and tracking functions. Boxplot was created using a matlab script to align a boxplot and the raw data points. The raw data points were split to equal sized bins so the jitter in the y axis is proportional to the relative size of the bin in the population. For the peroxisomes-mitochondria co-localization assays, the organelles identified using the Imaris built in spot function and overlapping identified organelles were assessed.
Acknowledgements Work in the Schuldiner lab was supported by the  European Research Council (ERC)  (Consolidator grants Peroxisystem  64660 ), an  SFB1190  from the  DFG  and a DIP (MitoBalance  P17516 ). M. S. is an incumbent of the  Dr. Gilbert Omenn  and  Martha Darling Professorial Chair in Molecular Genetics. The Cohen group was supported by the labex  DYNAMO  ( ANR-11-LABX-0011-DYNAMO ). A. H. is funded by  NIH  grants  AG043095  and  GM119694. F. R. was supported by  Marie Skodowska-Curie ITN  ( 765912 ),  Marie Skodowska-Curie Cofund  ( 713660 ),  ALW Open Program  ( ALWOP. 310 )and  ZonMW VICI  ( 016. 130. 606 )grants. We thank  Ralf Erdmann  for kindly sharing with us the CFP-SKL plasmid,  Christian Ungermann  for the MTS-BFP plasmid,  Tomer Ravid  for the mCherry-Nat plasmid,  Won-Ki Huh  for the Split Venus plasmids,  Stephen Michnick  for the far-red IFP plasmids and discussions, and  Christopher Stefan  for the Δtether strain.
Cell proliferation, cell cycle and cell death analyses For colorimetric exclusion the cells (2 × 10 5 cells/mL) were plated in multiwells in triplicate. After stimulations at different times and concentrations, cells were diluted in the ratio 1:1 in Trypan Blue (Sigma) and counted with an optical. For cell cycle analyses, cells were plated (2 × 10 5 cells/mL) and after stimulation (performed as indicated in the text) were harvested, centrifuged at 1200 rpm for 5ʹ and resuspended in 500 μL of a hypotonic solution containing 1X PBS, Sodium Citrate 0. 1%, 0. 1% NP-40, RNAase A and 50 mg/mL Propidium Iodide (PI). After 30' at room temperature (RT) in the dark, samples were acquired by FACS-Calibur (BD Bioscences, San Jose, CA, USA) using CellQuest software (BD Biosciences). The percentage in different phases of the cell cycle was determined by ModFit LT V3 software (Verity).
Caspase assays Caspase activity was detected within living cells using B-BRIDGE Kits with cell-permeable fluorescent substrates following manufacture's instructions. The fluorescent substrates for caspase-3, 7, -8, and -9 were FAM-DEVD-FMK, FAM-LETD-FMK, and FAM-LEHDFMK, respectively. Cells were washed twice in cold PBS and incubated for 1 h in ice with the corresponding substrates, as recommended by suppliers. Cells were analysed using Cell Quest software applied to a FACScalibur (BD Biosciences). Experiments were performed in triplicate and values expressed in mean ± SD.
ChIP-sequencing For ChIP-seq peak calling the BAM files were first filtered to remove the reads with mapping quality less than 15, followed by fragment size modeling (http://code. google. com/p/phantompeakqual-tools/). The peak-calling algorithm MACS2 (http://github. com/taoliu/MACS/) was used to detect the binding sites for the three studied histone marks at default q-value (5. 00e-02). H3K27ac peaks were called using the default (narrow) setting. H3K27ac tracks in mouse APL cells and human HCT116 cells were analyzed similarly. The intensity graphs were generated using the in house script makeColorprofiles. pl. Discriminating hyperacetylated regions were identified using DESEQ (http://bioconductor. org/packages/release/bioc/html/ DESeq. html). Box plots were generated using the normalized read counts (corrected for sequencing depth and region length) and DESEQ was used to calculate for statistical significant differences.
Tet2 -/-APL experiments MSCV backbone was used to express human PML-RARA fusion (a kind gift from H De Thé) and used to transduce marrow progenitors from 5FU-treated 5-week-old mice wild type or inactivated for Tet2. After engraftment in irradiated recipient and malignant development, leukemic cells were grown in vitro, in standard M3434 methylcellulose conditions. For the experiments, cells were transferred in liquid culture and treated with MC2884. After 16 h of treatment, apoptotic cells were stained with Annexin V/APC and 7-AAD (Beckton Dickinson) and analyzed on a FACS CantoII (Beckton Dickinson). FACS data were analyzed by FlowJo Software (v8. 8. 7).
ACKNOWLEDGMENTS We apologize to the authors whose work we could not cite due to reference restrictions. A special thanks to the generosity of the patients whose samples allowed the present study and discoveries. This work was supported by  BLUEPRINT  ( 282510 ), Programma VALERE: Vanvitelli per la Ricerca; the  Italian-Flag Project-EPIGEN ,  PRIN-20152TE5PK , the Italian association against cancer ( AIRC-17217 ), the  Dutch Cancer Foundation  (KWF kun  2011-4937 ) and  Netherlands organization for Scientific Research (NWO-VIDI).
Loss of set-2, cfp-1, or sin-3 causes similar effects on gene expression To ask whether SET-2/SET1 and SIN3 complexes have common roles in gene expression, we next performed RNA-sequencing (RNA-seq) on staged cfp-1, set-2 and sin-3 mutant embryos. Using DESeq2 (FDR<0. 05), we derived lists of differentially expressed genes in each mutant background, finding a similar number that were up-or down-regulated (Table  S2  and Figure   S6 ). The three datasets strongly overlap within the up-regulated and down-regulated genes, with 46-65% of differentially expressed genes from each mutant overlapping with those of at least one other mutant, and 14-22% misregulated in all three mutants, consistent with shared regulatory functions (Figure  3E ). In addition, pairwise comparisons identified separate sets of genes misregulated only in cfp-1 and set-2 mutants, or only in cfp-1 and sin-3 mutants (Figure  3E ).
Mass spectrometry-based proteomic analyses Proteins were stacked in the top of a SDS-PAGE gel (4-12% NuPAGE, Life Technologies) and stained with Coomassie blue R-250 before in-gel digestion using modified trypsin (Promega, sequencing grade) as previously described  (Casabona et al. , 2013). Resulting peptides were analyzed by online nanoLC-MS/MS (UltiMate 3000 and LTQ-Orbitrap Velos Pro, Thermo Scientific). For this, 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). MS and MS/MS data were acquired using Xcalibur (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2. 5. 1) through concomitant searches against Uniprot (Caenorhabditis elegans 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 FDR < 1% as calculated on peptide-spectrum match scores by employing the reverse database strategy, minimum peptide score of 25, and minimum of 1 specific peptide per identified protein group) before performing a compilation, grouping and comparison of the protein groups from the different samples.
Plasmids construction for Y2H Plasmids used for expression of BD and AD fusions were derived from pEG202 (Clontech; Genbank Accession Number U89960) and pJG4-5 plasmids (Clontech; Genbank Accession Number U89961), respectively  (Golemis and Brent, 1992). Constructions were generated by cloning the cDNA of the gene of interest in the XhoI restriction site of the pEG202 and pJG4-5 plasmids using the Gibson method  (Gibson, 2011). CFP-1 truncations were obtained by the same reaction using the cfp-1 cDNA sequence as template. PCR reactions were carried out using pHusion polymerase and primers listed in Table  S3. All products were verified by sequencing. pSH18-34, bearing a beta-galactosidase gene under the control of four overlapping LexA operators was used as reporter vector  (Estojak et al.