dataesr/scientific-paragraphs-categorization huggingface dataset:
To distinguish between these two possibilities, we used sucrose gradient centrifugation to separate DNA-protein complexes (hereafter named particles) according to their size. The 70, 110, and 150 bp DNA fragments are recovered at gradually lower position on the gradient, clearly indicating that these DNA fragments are present in nucleoprotein complex of increasing size (Fig. 1d ). To elucidate the composition of these particles we performed quantitative mass spectrometry (tandem mass tag, TMT) on proteins collected from the sucrose gradient fractions containing the 150 or 70 bp DNA fragments, in four biological replicates. The 110 bp particles were excluded from the analysis as we could not isolate a fraction containing only these types of DNA fragments. Using this approach, we identified 840 proteins associated with the purified chromatin particles (Supplementary Data 1).
a Experimental setup to monitor the fate of sperm-methylated histone peaks after replication. b Overlap between peaks of H3K4me3 and H3K27me3 before and after replication of sperm chromatin in egg extract. c Boxplots of HMD for all sperm peaks and for peaks that are lost or retained after replication. d Boxplots of the size of all sperm peaks, and of peaks that are lost or retained after replication. Data in c and d are obtained from N. sperm peaks H3K4me3: 36020; N. sperm retained H3K4me3: 27300; N sperm lost H3K4me3: 8715. N. sperm peaks H3K27me3: 55854; N. sperm retained H3K27me3: 13214; N sperm lost H3K27me3: 42635. ***p value < 1e-3 (two-sample Kolmogorov-Smirnov test). e Ratio of fold enrichment of peaks retained after replication over those lost after replication at indicated genomic features.
Therefore, we would expect that if peaks of high HMD methylation on H3K27 reflected the situation in most sperm cells, they would be associated with genomic regions that are uniformly DNA hypomethylated in a human sperm population. We checked this proposition using published bisulfite sequencing data from single human sperm cells  31. This dataset allowed us to identify genomic region that harbour unmethylated DNA in all sperm. We found that high HMD peaks (HMD > 80) are indeed more likely to be associated with such unmethylated sperm DNA than low HMD peaks (0 < HMD < 80) (Fig. 7e ). The observed correlation between lack of DNA methylation in all sperm and high HMD peak of H3K27 fit with the hypothesis that high HMD peaks represent methylated histones present in most human sperm cells. In addition, the set of genes with high HMD on H3K27 in sperm is enriched for genes that have a closed TSS configuration in most cells of the human embryos undergoing ZGA, as shown by ATACseq  31  (Fig.
To visualise replication in individual nuclei, 33 μM rhodamine dUTP (Roche, 11534378910) was added at the indicated times (20, 40, 60, 90 min after incubation) for 5 min as indicated, and the reaction was stopped with 1× PBS and fixed in 4% paraformaldehyde. The reaction was spun through a 30% sucrose cushion in PBS. The sperm cells were rinsed twice in 1× PBS for 10 min and mounted in Vectashield mounting medium with DAPI (Vector laboratories, H-1200). Images were acquired with a Zeiss 510 META confocal LSM microscope (Zeiss). Counting of rhodamine positive cells and measurement of rhodamine signal intensity were performed in Image J software. Sperm chromatin immunoprecipitation (ChIP). Chromatin fractionation and ChIP were performed as described before  2, 43  with slight modifications. Eighty microlitre of Magnetic beads (M-280 Sheep anti-rabbit IgG, Invitrogen, #11204D) were used per reaction and all wash steps were carried out with a magnet.
LC-MS/MS Peptide fractions were analysed on a Fusion Lumos Orbitrap. Mass spectrometer (Thermo Scientific) was coupled with RSLC nano Ultimate 3000 system. Peptides were trapped on a 100 μm ID × 2 cm microcapillary C18 column (5 µm, 100 A) followed by 2 h elution on a 75 μm ID × 25 cm C18 RP column (3 µm, 100 A) with 5-45% acetonitrile gradient in 0. 1% formic acid at 300 nl/min flow rate. In each data collection cycle, one full MS Orbitrap scan (380-1500 m/z) was acquired at 120 K resolution, automatic gain control (AGC) setting of 3 × 10 5 and maximum injection time (MIT) of 100 ms. Subsequent MS2 scans were acquired with a top speed approach using a 3-s duration. The most abundant ions were selected for fragmentation by collision induced dissociation (CID) with a collision energy of 35%, an AGC setting of 1 × 10 4 , a quadrupole isolation window of 0.
7 Da and MIT of 35 ms and detected in ion trap. Previously analysed precursor ions were dynamically excluded for 45 s. During the MS3 analyses for TMT quantification, precursor ion selection was based on the previous MS2 scan and isolated using a 2. 0 Da m/z window. MS2-MS3 was conducted using sequential precursor selection methodology with the top 10 setting. HCD was used for the MS3, using 55% collision energy. Reporter ions were detected using the Orbitrap at 50 K resolution, an AGC setting of 5 × 10 4 and MIT of 86 ms. Quantitative mass spectrometry data processing. The collected CID tandem mass spectra were processed with the SequestHT search engine against the Xenopus laevis proteome database on the Proteome Discoverer 2. 1 software for peptide and protein identifications. The node for SequestHT included the following parameters: precursor mass tolerance 20 ppm, FRagment Mass tolerance 0.
5 Da, dynamic modifications were methionine oxidation (+15. 995 Da), aspartamine and glutamine deamination (+0. 984 Da) and Static Modifications were TMT6plex at any N-Terminus, K (+229. 163 Da) for the quantitative data. Methylthio at C (+45. 988) was included for the total proteome data. The Reporter Ion Quantifier node included a TMT 6plex (Thermo Scientific Instruments) Quantification Method, for MS3 scan events, HCD activation type, integration window tolerance 20 ppm and integration method most confident centroid. The consensus workflow included S/ N calculation for TMT intensities, and the level of confidence for peptide identifications was estimated using the Percolator node with decoy database search. Strict FDR was set at q value < 0. 01. Quantitative proteomics data were analysed by qPLEXanalyzer, an R Bioconductor package  46.
Acknowledgements J. J. is funded from a  Wellcome Trust  grant ( 101050/Z/13/Z ). M. O is funded from a  Wellcome Trust  ( 101050/Z/13/Z ),  MRC  ( MR/K011022/1 ) grant and postdoctoral fellowship from the  Japan Society for the Promotion of Science (JSPS). A. S. is funded from a  Wellcome Trust  ( 101050/Z/13/Z ) and  MRC  ( MR/K011022/1 ) grant. All members of the Gurdon group acknowledge the core support provided by the  Gurdon Institute  core grant from  Cancer Research UK  ( C6946/A14492 ) and the  Wellcome Trust  ( 092096/Z/ 10/Z ). A. O. D. is supported by a grant from the  Science Foundation Ireland Industry Fellowship  (SFI: 15/IFB/350 ). C. D. S. and V. N. R. F. are supported by  Cancer Research UK. The Fusion Lumos Orbitrap mass spectrometer was purchased with the support from a  Wellcome Trust   Multi-user Equipment Grant  (Grant # 108467/Z/15/Z ).
Data availability The data that support this study are available from the corresponding author upon reasonable request. The Xenopus laevis ChIP-seq have been deposited on GEO with accession number GSE125982. Customised codes and scripts are available on GitLab. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  47  partner repository with the dataset identifier PXD012853. Publicly available data were obtained from GEO with the following accession numbers: GSE75164, GSE76915, GSE73430 and GSE100272. The source data underlying Figs. 1a, b, d, e, 3a, b, d, e , 4c and 7b, e and Supplementary Figs. 2a, c, g, 3c, d, 4c , e and 8a are provided as a Source Data file. Source data are provided with this paper.
Study the impact of the connection of the PMSG neutral and the mid-point of the DC bus Two cases were investigated according to the PMSG neutral connection mode (Figures  (5. , 13. ) ). The entire inverter-grid is modelled by a variable resistive load. The simulations were performed using Matlab/Simulink software. Figure  14. represents the phase current of the machine for the case neutral floating, the regulators (PSSOCC) ensure a good tracking of the current references within the physical limit of the system. Indeed, when the reference current passes through zero, the (PSSOCC) maintains the control signal of the ON switch so that the measured current reaches its reference. If the neutral is connected, the zero-sequence current and multiples of 3 exist as shown by the harmonic analysis of the phase current.
The database of Centre d'information et de ressources pour les musiques actuelles (IRMA) provides information on almost all popular music artists and groups (8996 in 2008), and their intermediaries (2510 in 2008)  (Lizé et al, 2011). These populations of artists and intermediaries are composed of some well-integrated professionals, alongside a majority of semi-professionals, and some volunteers. The 2510 intermediaries include: Beyond the clear numerical superiority of managers, note that 527 (21%) intermediaries have numerous functions. This indicates the porosity of boundaries among these functions and the pluriactivity inside the music world. Other data also indicate there is a strong pluri-activity of intermediaries with jobs outside the music world  (Lizé et al, 2014). Pluri-activity therefore appears to be a necessity to try to build a decent revenue, and to invest in the career development of the group or musician.
Bike sharing systems (BSS) have been growing fast all over the world, along with the number of articles analyzing such systems. However the lack of databases at the individual level and covering several years has limited the analysis of BSS users' behavior in the long term. This article gives a first detailed description of the temporal evolution of individual customers. Using a 5-year dataset covering 120,827 distinct year-long subscribers, we show the heterogeneous individual trajectories masked by the overall system stability. Users follow two main trajectories: about half remain in the system for at most one year, showing a low median activity (47 trips); the remaining half corresponds to more active users (median activity of 91 trips in their first year) that remain continuously active for several years (mean time = 2.
Most BSS riders are indeed people who used to walk or take public transportation. Among all the above research axes, questions remain open on the commitment of BSS subscribers in the long term: Who are the long-term users, those that benefit most from the system? This question could not be answered by lack of accurate trip datasets over long periods of time, as mentioned in  [23, 26]. Some articles have tried to characterize travel behaviors using surveys, such as  [18, 27]. But the loyalty of users to BSS systems, which affects their long-term sustainability, has never been deeply investigated. This is the topic we address in detail in this article: How long do users remain active over the years? Does their activity increase, decrease or remain stable? Is it possible to predict these evolutions? These questions are addressed using a unique five years long dataset covering 120,827 distinct year-long users, among which 15,466 have stayed in the system for the whole period.
This article follows previous work  [28]  on Lyon's BSS, Vélo'v, which, using a single year dataset (2011), characterized users according to their intensity and frequency of uses at different time scales (day, week, month and year). This work found 9 classes of users, ranging from 'extreme users', that use Velo'v twice a day on average to 'sunday cyclists', who only use the system a few week-ends per year. Using a single year dataset to classify users has however two main limitations. Firstly, there is no way to distinguish between two possible interpretations for a user that appears to be very active from September to December. This could correspond either to (a) someone arriving in town in September that remains very active for the months/ years to come or (b) someone who for an unknown reason uses the system only in those months.
The second limitation arises from the impossibility to test the stability of users' characteristics over years, which would allow to interpret them as real user properties. For example, do users classified in 2011 as 'sunday cyclists' retain this characteristic over the years? Have they only used Velo'v in this way in 2011 or is this pattern a more personal-and stable-use of the system that lasts for longer periods? After presenting our dataset in next Section, we study in detail the evolutions of users' behaviors over the years and then investigate the main limitations of single year datasets  [28].
Dataset The Velo'v program started in 2005 in Lyon, France. The Velo'v network now has 340 stations, where roughly 4000 bicycles are available. The stations are in the street and can be accessed at anytime (24/7) for rental or return. More information about the history of Velo'v and the deployment of stations can be found in  [2]. The Velo'v autonomous system is deployed mostly in Lyon (* 500,000 inhabitants) and Villeurbanne (* 150,000 inhabitants) and completes a quote dense system of public transportation (including subways, tramways, trolleys and buses). Still, the system had an increasing popularity, as seen through the large increase of the number of use during the early years of deployment (as analysed in  [2] ). It has now a slightly increase of the yearly number of trips, as it will be seen in Table  1.
Joint information about users and space is not available for anonymity reasons. The new dataset used in this work records all bicycle trips from 2011/01 to 2015/12 for the Velo'v system, from now the point of view of users, as in  [28]. The data were anonymized by their provider before communicating them to us. No location data about the bicycle trajectories were given. This analysis only relies on time stamps of bicycle trajectories from anonymized users. We are not aware of any explicit consent from the users to have their data analysed for research purposes. No ethics committee or data protection agency were consulted before carrying out this research. The dataset contains more than 38 million trips made by more than 3. 8 million users. Each trip is documented with starting and ending times, duration, a user ID code and a tag describing the class of user (year-long subscriber, weekly or daily subscription, maintenance operation, etc).
We also know user age, gender and residence zip code (corresponding mainly to the 10 different residence areas in Lyon-Villeurbanne). Note that for anonymity reasons, there are is no spatial information about the trip (start/end stations for example). Data are filtered according to the process used in  [28] , keeping only holders of yearlong subscription cards (such as Tecely and Velo'v cards) and eliminating any anomalies. This leads to a subset of the original population, containing 120,827 users having done more than 27 million trips over 5 years. For each person, we count years from the first active day: For example, a user appearing in the records for the first time on March 14 th , 2011 will end the first adapted year on March 13 th , 2012. To avoid boundary artifacts for users that are active over several years, we stop recording trips at the anniversary date in 2015, even if there are recorded trips later in 2015.
Individual evolutions The overall system stationarity (slow increase of user numbers) hides a great variability at the individual level that can be uncovered only using long-term datasets at the individual level as ours. Every year, there is a strong user renewal, as the majority of users leave the system after their first year of a activity and are replaced by a greater number of new users. Fig  1  shows that every year the new users represent around 35% of the total. Then, they progressively leave the system, in a quite predictable way: They represent 26-28% of users the year after and 11-12% two years later. The only exception is the 2011 cohort, which by lack of data over the previous years, also includes users that entered the system before 2011 and may be more loyal than average.
Clearly, loyalty steeply increases with age, from 11. 7% for 13-22 years old users up to 52% for 63-72 years old users. Men are over-represented among the long-term users for all ages, but the difference is highly significant among younger users. It would be interesting to understand why there are (comparatively) so few young woman among the most loyal BSS users. The main point is that these long-term users benefit more from the BSS than the average users, as they use the system more often than them (Fig 2 ), and remain active for a longer number of years. This original result, which could only be obtained using a long-term dataset, provides further support for the view that BSS are "convenient luxuries"  [19]  that mostly benefit the already privileged, i. e. male, wealthier and more educated than the average population  [13-16, 18, 19, 26, 29].
Comparing the 5-years and 1-year classes We now use our 5-years dataset to test the limitations of classifications based on a single year dataset. We already showed that the activity level observed over a single year is likely to change over time. This relativizes the categorization of a user into a specific user profile based on a single year observation, as in  [28]. We now show that the important 1-year category of 'part-time'  users found in  [28] , which represents almost a third of all users, actually gathers users that have a regular behavior, which appears to be 'part-time' because it is observed over a too limited time window.
User classes The 9 classes correspond to different profiles of use. There are 6% of 'one-off' users, who make on average only 3 trips per year, generally the same month and then disappear from the database. Another almost 12% of users are mainly active in week-ends, either for shopping (Saturdays) or leisure (Sundays) (second line of Table  3 ). The last 6 lines of Table  3  represent users that show a regular activity over the year and differ mainly by their intensity of use, from twice a month (regular0 class, gathering 27% of users) to nearly twice a day (regular5, 1% of users). The part-time class is quite peculiar: We will show below that it can be interpreted as the class where users end up for the last year of activity. Note that since the entity is a 'person-year', these counts do not directly represent proportions of individuals, because users that stay in the system for long periods are over-represented.
Discussion and conclusion There was so far a lack of analysis of the temporal evolution of individual long-term bicycle usage, mainly due to the unavailability of appropriate datasets. So far, there were a few longitudinal analysis of bike sharing systems  [26, 30, 31] , but these could not investigate the dynamics of individual users. Using a unique dataset spanning over 5 years, we have been able to show that: • There are two main profiles: 'Leavers' represent half of the users, quit after a single year and show a low median activity (47 trips); 'Long-term' users are more active (median activity of 91 trips in their first year) and remain continuously active for several years (mean time = 2. 9 years). • Long-term users, which benefit most from the BSS, are even more privileged than average users, as male, old and city center residents are over-represented.
VXD data quality monitoring The main components and flow of data in the VXD data acquisition system are shown schematically in Fig. 2. Event builder 1 collects detector data from all subdetectors except the PXD and sends them for processing to the High-Level Trigger (HLT) cluster, which does fast reconstruction to identify interesting events. The HLT produces some data quality monitoring  (DQM) data that are then sent to the DQM server. Event Builder 2 adds PXD data to the data of other detectors and sends a subset to the ExpressReco cluster for express reconstruction (up to vertexing). The primary purpose of express reconstruction is online data quality monitoring to check that detectors, slow-and run control work correctly and produce useful data -it produces other DQM data and sends them to the DQM server.
ONSEN combines data for export to Event Builder 2. It can buffer up to 2. 5 seconds of data flow and reduce the raw data volume by a factor of 30. Strip detector signals are transferred over Junction Boards to Flash Analog-to-Digital Converter modules (FADC) for zero suppression and data formatting. Local SVD data quality monitoring and calibration generators use data from this phase of data processing. The signal is then processed on the COmmon Pipelined Platform for Electronics Readout (COPPER  [5] ) boards of the central Belle II DAQ for the HLT and Event Builder 1. Apart from that, data are also sent to the PXD ROI finding and data reduction blocks. PXD ROIs are built using both the HLT and the data concentrator (DATCON) modules that find track candidates and then ONSEN extracts PXD data from the ROIs.
DQM data production Data monitors provide a condensed overview of the essential aspects of detector performance. They allow fast discovery of detector problems and identification of their sources so that we can prevent significant data losses and achieve uniformly high data quality. More than 7500 histograms are updated every 2 minutes. Separate sets of DQM histograms are produced by HLT and by Express Reco, cf. Fig. 2. At Belle II, ExpressReco runs full basf2  [6]  reconstruction (up to vertexing) on a sampled data flow from all subdetectors, including the PXD. The sampling rate is adjusted based on the immediate trigger rate. Histogram production for data quality monitoring is the primary purpose of ExpressReco. These and HLT DQM histograms are collected by the DQM server and uploaded to the Web for Control Room or remote presentation.
Also, at the end of each run, histograms are saved to disk. DQM Control computer manages DQM data processing and communicates with the Run Control system. ExpressReco monitors confirm data quality and identify runs usable for further processing. They provide a finer footprint of the data allowing to reveal any irregularities in the data acquisition chain and detector settings. The collection of data for DQM uses the basf2 framework  [6]  running on a set of isolated computers (HLT and ExpressReco) with conservative update plans to ensure the stability of production-critical processing. Any outside connections to databases or public networks are forbidden; only local services are allowed.
Scope of DQM monitoring The VXD data quality monitoring system provides several hundred plots of various quantities. Part of them is intended for expert shifters to diagnose sources of DAQ problems. Automatic comparisons with reference data are provided together with color-coded plot canvases indicating serious deviations from reference (green plot frame meaning no problem) so that problems can be spotted quickly among a large number of plots. A more restricted set of plots is selected for monitoring by Control Room (CR) shifters so that they can spot problems quickly, even without expert knowledge about individual subdetectors. In case of problems or unclear situations, the CR shifter can inform expert detector shifters by a dedicated chat channel, e-mail, or phone. Any problems are logged, and shifters can subsequently label runs as OK or problematic.