dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene sequences considered The analysis shown in Figs 1A, 2 and 3B is done considering all genes. For the backtracking analysis shown in  Fig 4,  all genes were considered; however, only such dst1Δ pause sites, for which there exists a corresponding pause site in WT dataset 0-15 nt upstream, were considered. For the rest of the analysis, in order to decrease the influence of false negatives in calculating the predictive power (a pause site cannot be found unless the region is transcribed), only the data for highly expressed genes were used, as determined by dividing the total NET-seq reads for the gene by the gene length; we took the genes with a higher than average density of reads, meaning we perform the analysis on ~25% of the entire dataset. As no selection on genes enriched for nucleosomes was performed, to ensure a fair comparison with nucleosome model, we computed the AUC for the nucleosomes on the reduced and on the total datasets and observed little difference; if all genes are considered, the AUC of the nucleosome model for dst1Δ is 0.
54, and the AUC of the TEC energy model is 0. 63 for the WT dataset and 0. 60 for the dst1Δ dataset. For reasons of technical ease, all the calculations of determining the average pause profile and determining the AUC are calculated on a concatenated set of sequences comprised of all the considered genes. Although the points at which these concatenations occur would not occur in the actual yeast genome, these edge effects constitute a very small fraction of the data.
For RNA:DNA, it matters which nucleotides are in RNA and which in DNA-the coding strand was considered to be in RNA and the template strand in DNA form. The RNA:DNA basepairing energy dataset  [26]  was originally determined in conditions of 1 M NaCl, 10 mM Na 2 HPO 4 , 1 mM Na 2 EDTA, pH 7. 0. The DNA:DNA basepairing energy dataset  [25]  was calculated unifying previous reports from multiple different conditions and reported to be in good agreement with DNA:DNA basepairing dataset measured by Sugimoto et al  [40] , which was measured in the same conditions as the RNA:DNA dataset.
The growing amount of seismic data available from space missions (SOHO, CoRoT, Kepler, SDO,. ) but also from ground-based facilities (GONG, BiSON, ground-based large programmes. ), stellar modelling and numerical simulations, creates new scientific perspectives such as characterizing stellar populations in our Galaxy or planetary systems by providing model-independent global properties of stars such as mass, radius, and surface gravity within several percent accuracy, as well as constraints on the age. These applications address a broad scientific community beyond the solar and stellar one and require combining indices elaborated with data from different databases (e. g. seismic archives and ground-based spectroscopic surveys). It is thus a basic requirement to develop a simple and efficient access to these various data resources and dedicated tools.
In the framework of the European project SpaceInn (FP7), several data sources have been developed or upgraded. The Seismic Plus Portal has been developed, where synthetic descriptions of the most relevant existing data sources can be found, as well as tools allowing to localize existing data for given objects or period and helping the data query. This project has been developed within the Virtual Observatory (VO) framework. In this paper, we give a review of the various facilities and tools developed within this programme.
Introduction With about 150,000 stars observed with CoRoT over periods going up to five months and about 200,000 stars observed with Kepler over periods up to 4 years, stellar seismology has known an unprecedented acceleration over the past decade. It has become common to use seismic data for hundreds or thousands of stars, and this information is fruitfully coupled with spectroscopic or astrometric informations available from various large surveys. This is true for studies addressing stellar structure and its evolution, but also for studies addressing galactic population formation and migration or for studies questioning planet formation. The stellar light-curves have also revealed signatures of other phenomena than just pulsation, like granulation, flares or activity, and the reference to the Sun is more than ever necessary to understand and interpret these phenomena which can be studied in great detail in the present wealth of solar data.
• Spectroscopy/individual spectra: seismic studies often rely on stellar modeling which benefits from additional constraints brought by spectroscopic analysis (e. g. for [Fe/H] and T eff estimates). On the other hand, it has become common, for red giant stars at least, to improve spectroscopic detailed analysis by using so-called seismic log g estimates as additional constraints. It is thus important to bridge the gap between these types of data in the context of seismology space missions and spectroscopic large programs. • others: The use and application of seismic data is growing and evolving quickly. The list of data sources and data products considered at the Seismic Portal has to remain open to changes in the observational landscape and in current scientific issues. The various data are distributed under these categories in a synthetic table (see figure  2 ).
Localizing and inspecting data available for a list of stars or a time period Two modes of query exist at the moment. The first one, for a given list of stars, returns a localization table stressing which kind of data are available and where (see figure  3 ). This information is available for each star of the list and quick-look tools allow a first inspection of the data in order to determine to which extent they are appropriate for the anticipated application. A second mode of query exists for solar data. For a given time period, it returns a table stressing which kind of time series exists and where. It also provides information about occurence of solar events (essentially coronal mass ejections (CME) and flares so far) over the same period. The intention here is to help establishing connexion between solar events and their signatures in a time series, thus helping the interpretation of such signatures in the light-curve of other stars.
Data sources developed or upgraded within the SpaceInn WPactivities Among the data sources referenced at the Seismic Plus Portal, several have been developed or upgraded within the SpaceInn activities. They are accessible via the Seismic Plus Portal but also via their dedicated web interface. In addition to the spectra, SISMA offers mean line profiles (computed with the LSD software  [3] ), radial velocities, Vsini, activity indices, differential rotation indicators, binarity and emmision flags. It also features the physical parameters of each target (T eff , log g, [Fe/H]) and, in the case of CoRoT targets, the relevant satellite light curves (see  [5] ).
The KASOC and SpaceInn The Kepler Asteroseismic Science Operations Center (http://kasoc. phys. au. dk/) provides asteroseismological data from the NASA Kepler mission to astronomers who are members of the Kepler Asteroseismic Science Consortium (KASC). In the framework of the SpaceInn project, efforts have been made to enhance reliability and efficiency of the access to Kepler data. A VO interface has been developed allowing for external access to KASOC database via the Virtual Observatory protocol. Various type of complementary information have been included in the database, like ground-based follow-up data and stellar models related to published studies of specific objects (figure  5 ). In addition, a solution for long-term preservation of the data has been studied in collaboration with the Royal Library of Denmark.
The German Data Center for SDO and SpaceInn The German Data Center for SDO (GDC-SDO) hosts the European depository for HMI data. The data processed and made available from the Joint Science Operations Center (JSOC) headquartered at Stanford University USA are immediatly transfered to GDC (several TBs per day). This constitutes the largest source of data available today for helioseismology (online storage capacity over 1 PB). The data products are line-of-sight Dopplergrams, lineof-sight magnetograms, continuum intensities, subset of Milne-Eddington inversions. They are available at various cadences. Within the SpaceInn Project, a dedicated GDC-SDO Help Desk has been developed for SpaceInn (http://www2. mps. mpg. de/projects/seismo/GDC-SDO/ spaceinn_access. htm). It contains information for setting up NetDRMS and receiving relevant data and users support.
The Stellar Seismic Indices database The Stellar Seismic Indices (SSI) database (http://ssi. lesia. obspm. fr/ see figure  6 ) has been developed within the SpaceInn programme (see also  [7] ). It contains stellar seismic indices of solar-like oscillating stars as well as characteristic parameters of stellar granulation. These seismic indices obey characteristic scaling relations that depend directly on the radius, mass and effective temperature of the star. From the knowledge of these quantities it is possible to estimate the mass and radius of a star, and subsequently the surface gravity of the star (log g). The indices populating the current database were extracted from about 13,000 Kepler and 5,000 CoRoT red-giant stars using an automatic analysis pipeline  [2].
The SISMA spectral time series database Thousands of high-resolution spectra were taken with the spectrograph HARPS mounted at the 3. 6m ESO telescope at La Silla, Chile ([4]) during the ESO Large Programmes LP 182. D-0356 and LP 185. D-0056 as groundbased counterpart of the CoRoT ([1]) photometric measurements ([6]). The SISMA database (http://sisma. brera. inaf. it/ see figure 4) gathers 7103 fully reduced and normalized HARPS spectra, obtained for 261 stars, among which are 71 CoRoT bright targets observed simultaneously with the satellite. These spectroscopic time series allow to better exploit the photometric data and characterize the variability classes of CoRoT targets.
The MARK-I database This historical archive (1976 till 2012) has been developed with the solar observations gathered at the Observatorio del Teide (IAC) with the pioneer Mark-I resonantscattering spectrophotometer (http://svo2. cab. inta-csic. es/ vocats/marki/). Observations extend from 1976 to 2012 with only summer campaigns from 1976 to 1983.
3. 1 1 The SISMA spectral time series database Thousands of high-resolution spectra were taken with the spectrograph HARPS mounted at the 3. 6m ESO telescope at La Silla, Chile ([4]) during the ESO Large Programmes LP 182. D-0356 and LP 185. D-0056 as groundbased counterpart of the CoRoT ([1]) photometric measurements ([6]). The SISMA database (http://sisma. brera. inaf. it/ see figure 4) gathers 7103 fully reduced and normalized HARPS spectra, obtained for 261 stars, among which are 71 CoRoT bright targets observed simultaneously with the satellite. These spectroscopic time series allow to better exploit the photometric data and characterize the variability classes of CoRoT targets.
3. 3 3 The MARK-I database This historical archive (1976 till 2012) has been developed with the solar observations gathered at the Observatorio del Teide (IAC) with the pioneer Mark-I resonantscattering spectrophotometer (http://svo2. cab. inta-csic. es/ vocats/marki/). Observations extend from 1976 to 2012 with only summer campaigns from 1976 to 1983.
Here, we use data from the European Study on Cardiovascular Risk Prevention and Management in Usual Daily Practice (EURIKA, ClinicalTrials. gov identifier: NCT00882336), a cross-sectional observational study including data on patients from  12  European countries with at least one traditional CVD risk factor but no history of cardiovascular events  [11, 12] , to assess the prevalence and treatment of two markers of atherogenic dyslipidemia: elevated TG levels and low HDL-C levels. We have previously assessed the prevalence of elevated levels of CRP in the EURIKA population  [9]. Among patients without T2DM not receiving a statin, approximately half had CRP levels of at least 2 mg/l. The impact of CRP and markers of atherogenic dyslipidemia on CVD risk is often underestimated in clinical practice, with a lack of evidence for the prevalence and treatment of the latter.
Study design and participants EURIKA was a cross-sectional study carried out in 12 European countries (Austria, Belgium, France, Germany, Greece, Norway, Russia, Spain, Sweden, Switzerland, Turkey, and the UK)  [11]. Included patients were aged 50 years or older and had at least one risk factor for CVD but no history of cardiovascular events. Data collection started in May 2009 and ended in January 2010, with a 3-month data-collection period for each country. The study protocol was approved by the appropriate clinical research ethics committees in each participating country, and all patients provided signed informed consent. The methods for the study have been reported in detail elsewhere  [12]. Briefly, the study sample was selected in a two-stage process that involved the random selection of both physicians and patients  [12, 15].
In the first stage, primary care physicians (PCPs) and specialists involved in CVD prevention (including cardiologists, endocrinologists, and internal medicine specialists) were randomly selected for invitation to participate using the OneKey database (Cegedim Dendrite, Boulogne-Billancourt, France)  [16]. In total, 809 physicians (approximately 60 per country) agreed to participate in EURIKA, 64% of whom were PCPs  [15]. In the second stage, participating physicians sequentially invited patients who met the selection criteria (aged ≥50 years, who were free from CVD but with at least one major cardiovascular risk factor [dyslipidemia, hypertension, current smoker, T2DM, or obesity]). Hypertension was defined as having a systolic blood pressure (SBP) of at least 140 mmHg, a diastolic blood pressure (DBP) of at least 90 mmHg, or receiving treatment with antihypertensive medications.
Our analysis reveals that within the EURIKA population, small proportions of patients with elevated TG levels are consuming more units of alcohol than the recommended weekly limit. Furthermore, almost 20% of the overall population reported undertaking no physical exercise. In patients with TG levels above 500 mg/dl, pharmacological intervention in the form of fibrates, niacins, or omega-3 fish oils should be considered  [23]. The EURIKA study has the strength of allowing analysis of data from a large sample of patients from Countries of origin with an OR that was not significant have been omitted. a Per year. b Relative to male participants. c Relative to not having T2DM. d BMI ≥ 30 kg/m 2 , relative to not being obese. e Per mmol/l. f Per mg/l. g Relative to never smoking. h Relative to non-use.
Previous cohort studies investigating the predictive power of HDL-C levels in CVD risk have measured HDL-C using precipitation methods. In EURIKA, HDL-C levels were measured using an enzymatic method; it has been demonstrated that enzymatic methods result in higher recorded HDL-C levels than precipitation methods  [31]. Therefore, it is possible that we have underestimated the proportion of the cohort with low HDL-C levels. The participation rate among invited physicians was also low; however, potential patient selection bias is likely to have Fig. 5  Association between markers of atherogenic dyslipidemia and CRP. (a) CRP levels of < 1 mg/L, 1-< 3 mg/L or ≥ 3 mg/L; (b) CRP levels < 2 mg/L or ≥ 2 mg/L. Data were missing for 76 patients in the overall population, and for 1 patient in each of the dyslipidemia groups.
Funding The EURIKA study was funded by AstraZeneca. The study was run by an independent academic steering committee, which worked under rules agreed a priori that allowed intellectual input of the funder (i. e. the funder contributed ideas to the study design, data analysis, and preparation of the manuscript) while granting the executive and decision making role to the committee. The authors had full access to all data and had final responsibility for the contents of the manuscript and the decision to submit it for publication.
This will further increase the robustness of uncertainty ranges for individual SSPs and estimates of differences between SSPs. The set of results comprises quantitative estimates for population, economic growth, energy system parameters, land use, emissions, and concentrations. All the data are publicly available through the interactive SSP webdatabase at https://secure. iiasa. ac. at/web-apps/ene/SspDb. The current set of SSP scenarios consists of a set of baselines, which provides a description of future developments in absence of new climate policies beyond those in place today, as well as mitigation scenarios which explore the implications of climate change mitigation policies. The baseline SSP scenarios should be considered as reference cases for mitigation, climate impacts and adaptation analyses.
Following serial imaging and 3D reconstruction, the cytoplasm of the infected cells was found to display large areas covered by CMs with VPs located at the periphery of these structures and also accumulating in the perinuclear region (Figure  3A ; Movie S2). Of note, the 3D reconstruction revealed cytoskeleton filaments surrounding most of the virus-induced VPs, while the large CM aggregates were located outside this region. This distribution nicely corroborated our immunofluorescence data described above and further implies that the perinuclear region containing both dsRNA and NS3 corresponds to the area where the majority of VPs accumulate. Next, we focused our attention on the structure of ZIKV RFs and performed transmission electron microscopy (TEM) to achieve the resolution needed to analyze the ultrastructural details.
Coverslips were mounted on microscopy slides using Fluoromount G (SouthernBiotech). Fluorescence images were acquired with a Nikon Ti Eclipse microscope and analyzed with the NIS-Element AR software package. For 3D reconstruction, 130-nm optical sections were acquired with a Nikon TE2000-E confocal microscope equipped with an Ultraview ERS spinning disc (PerkinElmer). Images were deconvoluted with Autoquant X3 software (Media Cybernetics) and 3D reconstruction was performed with Imaris 8 (Bitplane).
For electron tomography, 250-nm-thick sections were prepared. Counterstained and gold-labeled grids were placed in a high-tilt holder and digital images were recoded as single-axis tilt series over a À53 to 53 tilt range (increment, 1 ) on an FEI Tecnai TF20 (Figure  4 ) or dual-axis tilt series over a À60 to 60 tilt range on an FEI Tecnai TF30 microscope (Figure  6 ). Tomograms were reconstructed using IMOD software  (Kremer et al. , 1996)  (http://bio3d. colorado. edu/imod).
ACKNOWLEDGMENTS We gratefully acknowledge the expert support provided by the  Electron Microscopy Core Facility at Heidelberg University , the Flow Cytometry and  FACS Core Facility of the Center for Molecular Biology of Heidelberg University (ZMBH) , and the  Electron Microscopy Core Facility  at the  European Molecular Biology Laboratory (EMBL) Heidelberg. We thank the  Advanced Light Microscopy Facility (ALMF)  at the  European Molecular Biology Laboratory  (EMBL) and  Leica Microsystems  for support. We are grateful to  Dr. Vibor Laketa  and the  Infectious Diseases Imaging Platform (IDIP)  at the  Department of Infectious Diseases, University Hospital Heidelberg , for the excellent microscopy support. We are grateful to  Hilmar Bading , in whose laboratory the hNPCs were generated, to  Dr.
Gualtiero Alvisi  for constructing the YFP-Sec61b-encoding plasmid, and to the  European Virus Archive (EVAg)  for provision of ZIKV strains. We are very grateful to  Philipp Klein  for support in cell sorting and live-cell imaging. We thank  Thais Moraes  for virus stock production. This work was supported by grants from the  Deutsche Forschungsgemeinschaft  ( SFB 1129  to  R. B  and A. R). L. C. -C. holds a  Discovery Grant  from the  Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2016-05584 ). P. P. holds a fellowship by the  Excellence Cluster Cell Networks at Heidelberg University. C. J. N is funded by a  European Molecular Biology Organization (EMBO)   Long-Term Fellowship  ( ALTF 466-2016 ).
To put recent climate change in perspective, it is necessary to extend the instrumental climate records with proxy data from paleoclimate archives. Arctic climate variability for the last 2 millennia has been investigated using statistical and signal analyses from three regionally averaged records from the North Atlantic, Siberia and Alaska based on many types of proxy data archived in the Arctic 2k database v1. 1. 1. In the North Atlantic and Alaska, the major climatic trend is characterized by long-term cooling interrupted by recent warming that started at the beginning of the 19th century. This cooling is visible in the Siberian region at two sites, warming at the others. The cooling of the Little Ice Age (LIA) was identified from the individual series, but it is characterized by wide-range spatial and temporal expression of climate variability, in contrary to the Medieval Climate Anomaly.
Paleoclimate data The records used in this study were compiled by the Arctic 2k working group of the Past Global Changes (PAGES) research program. This working group released a database comprising 56 proxy records for the Arctic area  (version 1. 1. 1; McKay and Kaufman, 2014). The database contains all available records that meet data quality criteria concerning location (from north of 60 • N), time coverage (extending back to at least AD 1500), mean resolution (better than 50 years) and dating control (at least one age control point every 500 years) (Fig. 1a ). See Table  S1  in the Supplement for more information about each site (cf. also  McKay and Kaufman, 2014). Proxy records are from different archive types. Most are continental archives with very reliable chronologies (16 ice cores, 13 tree rings, 19 lake sediment cores and 1 speleothem).
Six records are from marine archives and one is a historic record (months of ice cover). Among the 56 records, 35 have an annual resolution (Fig. 1b ). Hence, the high temporal resolution of the Arctic 2k database series offers the possibility of studying the high-frequency climate variability of the last 2 millennia, assuming that the proxy record climate variability and the archiving process do not induce a bias in the multi-annual to centennial frequencies analyzed. The database has been built from paleoclimate proxy series with a demonstrated relationship to temperature variability. All the proxy data used have been published in peerreviewed journals and the sensitivity of each proxy record to temperature was evidenced either statistically (e. g. , correlation with instrumental temperature data) or mechanistically with the description of the processes through which the proxy has shown its sensitivity to temperature change  (McKay and Kaufman, 2014).
Our review of the original publications presenting the data used to develop the Arctic 2k database led us to raise some concerns about the actual temperature controls on proxy. In some case, the correlation between proxy measurements and instrumental temperatures is significant but weak, with a correlation coefficient lower than 0. 5 (e. g. ,  Bird et al. , 2009; D'Arrigo et al. , 2005 , 2009 , Spielhagen et al. , 2011; Wiles et al. , 2014). Such weak relationships suggest that the variability recorded by the proxies is not exclusively linked to the mean annual temperature but probably also relates to other parameters, climatic or not. In some cases, the authors clearly state that the relationship is not strong enough for reconstructing high-resolution variations  (D'Arrigo et al. , 2005).