dataesr/scientific-paragraphs-categorization huggingface dataset:
The scope of the software (basic or comprehensive functionality) -Basic: the WMDA Standards section 5. 0 defines the minimum acceptable functionality of software. At its simplest the system must be able to register donors and patients, search a database of HLA data for volunteer donors and/or cord blood units, and provide search reports for transplant centers. -Comprehensive: manages all the registry's functions with workflow support, modules for online communication and finance, interfaces to enable connection with other systems, a document management system, customer relationship management (CRM) function, automated production of letters, reports, and documents and the ability to monitor key performance indicators. Project team -The end users define the registry needs and as such are the cornerstone of the team; other key members of the team include: project manager, IT experts, administrators, quality management specialists, HLA experts, transplant physicians, donor recruitment staff, and finance department.
Quality requirements Independence. "Made-to-measure" solution. Costly and time-consuming. High level of expertize is needed. Often exceeds project plan and budget. NMDP/Be The Match, ZKRD, France Greffe de Moelle (FGM), Italian Bone Marrow Donor Registry (IBMDR), Australian BMDR and others have their own IT team who develop their own software solutions. Commercial system. Clear customer-vendor relationship (deadlines, guarantees, budget limit). Experience of other users of the system. The supplier guarantees implementation of changes on schedules set by the community. Limited number of vendors exists. The prices charged may be high if the registry has limited funding. Registries in Belgium, Finland, Sweden, South Africa and the UK (BBMR) use a commercial system. a Adopted from WMDA handbook with permission -Quality control: the system should control quality of data according to registry policies.
It has to be compliant with the WMDA guidelines. -Output report: a search report displaying the donors and cord blood units in HLA matching order, as a minimum. Only active donors should appear in search reports. -Management of requests (electronic, fax, or papers): typing requests, verification typing sample requests, infectious disease marker testing requests, donor reservation requests and work-up requests. The software must be able to display the status of the request. The system should support the workflow. management of requests for different scenarios. -WMDA annual report: Many registries do not systematically collect data for the WMDA annual report. There is a huge advantage to building in this functionality to generate data automatically at the start of the IT project. This will also help to perform annual statistics and research.
-Financial module: creation of invoices according to registry policy and behavior of users. Export of invoices to -Back-ups and disaster recovery planning: The registry must have a written policy on how its system is backed up. Back-ups should be performed regularly on a fixed schedule and stored off-site at some distance from the registry's location. c  The registry should have contracts with hardware suppliers to provide replacement hardware within an agreed timeframe. financial -Electricity power backup system and internet backup system should be documented. -Documentation: all registries must have documents to describe key computer systems and network infrastructures. -Data quality control: the system automatically checks donor database every night and warns users about discrepancies that breaks registry policy.
8. Utilization of artificial intelligence: artificial intelligence (AI) particularly with machine learning (ML) technology is revolutionizing the field of HCT in the selection of donors, and predicting outcomes after allo-HCT and even in GVHD  [42] [43] [44]. The use of AI for donor-recipient pair matching in unrelated donor registries has been evaluated by multiple investigators and some have proven to increase accuracy of predictions for matching as well as for outcomes  [45] [46] [47]. Although ML is perceived by some providers as a costly product, in fact, abundant data exist on predictions on how ML will reduce both costs and staffing along with improving clinical outcomes. As developing the ML modeling (especially via neural networks) for databases is done in collaboration with AI software engineers, a large number of startup companies exist that can help in developing algorithmic approaches for both active data management and for donor selection.
HLA data should always be valid according to the latest HLA nomenclature. -The software should support national names and characters. -Flexibility and availability of the company to update the software is crucial. -Generate letters and emails. -All incidents should be reported and assessed. The root cause of a critical incident should be identified and should form the basis of Corrective And Preventive Actions (CAPA). -Under GMP regulations, the registry system should be periodically evaluated to confirm the IT system remains in a valid state and is GMPcompliant.
Donor database -Donor identification: unique registry ID for the donor is the primary reference (other IDs are allowed). In 2017, WMDA implemented the GRID (Global Registration Identifier for Donors), which will facilitate standardization of donors identification globally. -HLA data: separate fields for serology and DNA typing results, typing laboratory, date of typing, primary typing data, NIMA, etc. -Demographics: name, title, gender, date of birth, ethnic group, insurance company etc. -Relationships: family or personal relations to other donors or patients, used for family reports of the patient. -Recruitment: donor center, date of recruitment, method (website, patient-draft, blood donor, indication of whether the donor is also a blood or platelet donor). -Donor status: active or no, and if not active is the withdrawal temporary or permanent? What's the reason of withdrawal (age, medical, personal)? -Contact details: permanent, temporary, or work address, email, phone numbers, social media networks, language, preferred contact method, history of communication with the donor.
The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. We thank research nurses  Anita Hanson ,  Ruth Westhead  and  Diane Van Eker , and administrators  Joe Fahy ,  Emma McDonough  and  Angela Foxcroft in Liverpool , for recruiting patients and administering the clinical database. We thank the  Centre for Genomic Research  lead by  Prof Christiane Hertz-Fowler , which is the MRC sequencing hub at the  University of Liverpool, UK. We thank research nurses  Ulrica Ramqvist  and  Elisabeth Stjernberg  and research assistants  Sofie Collin ,  Eva Prado Lopez ,  Agnes Kataja Knight ,  Agnes Wadelius , and  Martha Wadelius ,  Department of Medical Sciences, Clinical Pharmacology, Uppsala University, Uppsala, Sweden , for recruiting and interviewing cases and for administering the phenotype database.
We acknowledge  Barbro Sandin  at the  Swedish Twin Registry  for access to data for the Swedish controls. The Swedish Twin Registry is managed by  Karolinska Institutet  and receives funding through the  Swedish Research Council  under grant no. 2017-00641. Sequencing of the Swedish controls was performed by the  SNP&SEQ Technology Platform in Uppsala. The facility is part of the  National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory , and is supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments We thank research nurses  Anita Hanson ,  Ruth Westhead  and  Diane Van Eker , and administrators  Joe Fahy ,  Emma McDonough  and  Angela Foxcroft in Liverpool , for recruiting patients and administering the clinical database. We thank the  Centre for Genomic Research  lead by  Prof Christiane Hertz-Fowler , which is the MRC sequencing hub at the  University of Liverpool, UK. We thank research nurses  Ulrica Ramqvist  and  Elisabeth Stjernberg  and research assistants  Sofie Collin ,  Eva Prado Lopez ,  Agnes Kataja Knight ,  Agnes Wadelius , and  Martha Wadelius ,  Department of Medical Sciences, Clinical Pharmacology, Uppsala University, Uppsala, Sweden , for recruiting and interviewing cases and for administering the phenotype database. We acknowledge  Barbro Sandin  at the  Swedish Twin Registry  for access to data for the Swedish controls.
The full results from each meta-analysis will be available on dbGaP within the next release of Cohort for Heart and Aging Research in Genomic Epidemiology Results CHARGE Consortium summary results (accession phs000930) (http://www. chargeconsortium. com/ main/results). For the individual-level data, it is impossible to provide individual level data needed to replicate findings on publicly available databases. Most of the individual studies are prohibited from sharing individual-level data with any.
Funding support for "Building on GWAS for NHLBI- diseases: the U. S. CHARGE Consortium" was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). Sequencing was carried out at the Baylor Genome Center (U54 HG003273). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. PREDICTION-ADR has received funding form the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no. 602108. Work in Sweden was supported by the Swedish Research Council (Medicine 521-2011-2440 and 521-2014-3370); Swedish Heart and Lung Foundation (20120557, 20140291 and 20170711); Selander's foundation; Thureus' foundation; the Swedish Medical Products Agency; the Clinical Research Support (ALF) at Uppsala University.
Standard 1 H-NMR spectra were acquired using a 'noesypr1d' pulse sequence with a relaxation delay of 20 s. Water suppression was achieved by presaturation during the relaxation delay and mixing time. 1 H spectra were collected with 64 scans (and eight dummy scans), in 32k data points with a spectral width of 7500 Hz. Sample shimming was performed automatically. Spectra were processed using Topspin version 3. 2 software (Bruker Daltonik, Karlsruhe, Germany). The free induction decay were zero-filled to 64k data points, which provided sufficient data points for each resonance, and a line broadening factor of 0. 2 Hz was applied before Fourier transformation. 1 H-nuclear magnetic resonance spectra post-processing After manual correction of phase distortion and baseline on all spectra, 1 H-NMR spectra were automatically reduced to ASCII files using the AMIX Software package (Analysis of MIXture, version 3.
9. 14 Bruker Biospin, Karlsruhe, Germany). The regions containing water signal were removed from each 1 H-NMR spectrum to eliminate the baseline effects of imperfect water saturation. Spectral intensities were scaled to the total spectral intensity, and reduced to equidistant integrated regions of 0. 001 to 0. 03 ppm over the chemical shift range of 0. 7 to 9. 5 ppm. The resulting data table was analyzed by multivariate and univariate statistical analyses. 1 H-nuclear magnetic resonance spectra were assigned using spectra online databases, including HMDB (http://www. hmdb. ca) and ChenomX NMR Suite 8. 1 evaluation edition (ChenomX Inc. , Edmonton, Canada).
Multivariate analyses The data from the different weeks and females were analyzed by multivariate statistical data analysis. Using SIMCA-P + Software (version 13. 0; Umetrics, Umeå, Sweden) the data were analyzed using principal component analysis (PCA), an unsupervised method allowing the visualization of similarities or differences in the data sets (urine or saliva) without any a priori knowledge of sample classes. Yet an orthogonal partial least squares discriminant analysis (OPLS-DA) as a supervised classification was performed using SIMCA-P + Goudet, Nadal-Desbarats, Douet, Savoie, Staub, Venturi, Ferchaud, Boulot and Prunier Software (version 13. 0) on the two data sets: saliva and urine. All data were scaled to unit variance, to maximize the separation between the weeks. Orthogonal partial least squares discriminant analysis is a regression model that reflects the correlation between multivariate data (spectral bins) and dependent variables with class information (weeks), thereby minimizing any effects of non-relevant metabolite variability.
Orthogonal partial least squares discriminant analysis takes advantage of class information to attempt to maximize the separation between groups of observations. Discriminant spectral regions called variable importance in projection (VIP) were obtained from the OPLS-DA model VIP values >1 were considered as responsible for the differences between groups. The overall quality of the models was judged by cumulative R 2 defined as the proportion of variance in the data explained by the model and cumulative Q 2 , the class prediction ability of the model obtained by cross-validation default method of SIMCA-P + Software. Even still, Q 2 has no standard of comparison or critical value for inferring significance, an empirically inferred value Q 2 > 0. 40 is acceptable for a biological model  (Worley and Powers, 2013).
Our data showed that the concentration of oestrone in urine increases during pubertal development in agreement with  Camous et al. (1985). However, no variation was observed between 2 to 5 weeks before the first ovulatory cycle and hence around the 6 th month of age in our study. The increase in urinary oestrone was observed between week -2 and the day of puberty detection. From ultrasound imagery and slaughter data, it can be postulated that ovulation occurred shortly before day 0. Therefore, samples on day 0 were performed during the very late follicular phase or early luteal phase whereas those on week -1 were collected during the early follicular phase. The relatively high concentration of urinary oestrone at these times is in good agreement with the plasma oestradiol increase occurring a few days before ovulation (Van de  Wiel et al.
On top of this, uniform RV offsets are expected, corresponding to the relative motion of Venus with respect to the Sun and mostly of Venus with respect to the observer on the ro- tating Earth. The motions are well documented in the ephemeris database and must be taken into account. Beyond real RV fields, the rotation of the Sun as seen from Venus introduces a bias in RV measurements, as originally introduced by  Young (1975)  and subsequently completed by  Gaulme et al. (2018). Rays from a different part of the Sun, which show different RVs, reach the planet with (slightly) different incidence angles. Regions of the Sun that are closer to the horizon contribute less to the reflected solar spectrum than regions closer to zenith. Thus, the RV integrated over the whole solar disk is not zero at a given point of Venus.
No other observations of Venus with this instrument have been published since then. Gaulme et al. (2008)  reported a test of the long-slit MTR spectrometer of the THEMIS solar telescope to scan Venus and retrieve a complete RV map of it, which had never been done before. The system planned for scanning Venus did not work and the weather was poor. The result is a partial RV map that was modeled with a global zonal circulation pattern (151 ± 16 m s -1 ) without considering the Young effect. The analysis of these preliminary data was promising enough to justify further observations, which constitutes the base of the present work.
In all three papers, the complete RV dataset is interpreted as a horizontal zonal circulation, where two possible regimes are considered: a solid-body or a cylindrical regime. Except for the preliminary observations at CFHT by  Widemann et al. (2008) , which suffered of a lack of stability (zonal winds in between 92 and 155 m s -1 ), the results by  Machado et al. provide  very consistent values of the mean zonal wind at equator from day to day and in between 2011 and 2014; i. e. , 117. 3 ± 18. 0 and 117. 5 ± 14. 5 in  Machado et al. (2014) and 119. 6 ± 16. 5, 122. 6 ± 31. 3, 119. 6 ± 26. 0, and 118. 1 ± 19. 5 in Machado et al. (2017)  with 2σ errors. Beyond mean zonal winds,  Widemann et al. (2008)  introduced a meridional wind component as part of their measurements, while  Machado et al.
Calibration data and preprocessing steps Calibration files are fundamental to ensure high quality Doppler measurements. In particular, errors on flat fields can induce distortions of the shape of spectral lines along the spatial dimension. In other words, it can produce fake Doppler shift across the planet. A quasi-absence of flat fields was one of the main limitations of the data quality of G08, where a fixed Doppler pattern on the detector was identified and removed a posteriori from the RV map. We considerably improved our protocol during the 2008 and 2009 campaigns. On the one hand, dark fields were obtained before each scan, and a master dark field was obtained by averaging them together. Using individual dark fields or the master dark field did actually not make any significant difference on RV measurements.
Then, for each actual spectrum, we measured its S/N from the standard deviation of the data minus the model spectrum in the continuum regions, and we retrieved the velocity error by comparing the S/N to the reference simulated values, i. e. , σ v = σ v,ref 100 S/N (6) For the data taken on September 17, 2009, which include Mg, Fe, and Na spectra, the velocity error σ v,avg on an average map is written as σ v,avg = 1 3 σ 2 v,Mg + σ 2 v,Fe + σ 2 v,Na , (7) where σ v,Mg , σ v,Fe , and σ v,Na are the error maps in each spectral band.
Biases in the radial-velocity measurements Most of our data were acquired during daytime. Therefore, we can monitor the possible drift of the spectrometer thanks to the solar spectrum that is scattered by the Earth's blue sky. We note that the MTR spectrometer of the THEMIS telescope was not designed to perform high precision RV measurements -spectropolarimeteric observations of the Sun instead -and is not thermally stabilized. Drifts of hundreds of meters per second are expected. Figure  10  shows the mean RV that is measured on the sky. The variation of RV is much larger than that expected from the Earth's rotation (gray lines). Even though we do not have mea- surements of the temperature of the spectrometer, it is very likely that the variations we observe are dominated by temperature, which is typical of spectrometers.
Observation properties of Venus during the 2009 campaign at Teide Observatory. Columns 1 to 3: observation days, start, and end dates. Start and end times are in modified Julian date (MJD), i. e. , Julian date JD -2,400,000. 5 days. Columns 4 to 10: ephemerides from the IMCCE database. Ephemerides are calculated every day of both campaign at 06:00:00 UTC. The last two columns indicate the number of scans done each day and the atmospheric seeing.
Acknowledgments P. Gaulme was supported in part by the  German space agency (Deutsches Zentrum für Luft-und Raumfahrt)  under PLATO data grant  50OO1501. I. Gonçalves 's PhD was granted by  Observatoire de la Côte d'Azur  and the  JIVE in NM project (NASA EPSCoR  grant # NNX14AN67A ). THEMIS is a UPS of the CNRS funded by  INSU. The authors are grateful to the referee,  E. Lellouch , for his intensive review of the paper, which contributed to improving its quality. P. Gaulme  wishes to apologize to  P. Gava  for leaving her home on her thirtieth birthday while observing Venus at Teide observatory in 2008.
Simulating tomographic reconstruction First we explore the required parameter space (e. g. spectral resolution and S {N of the background galaxy spectra) for the science case of IGM tomography. It should be emphasized that the reconstruction of the IGM is only an intermediate step leading to the actual scientific questions such as the studies of voids or the interplay between galaxies and cosmic web. It is not straightforward to understand which quality of observational data is needed to successfully carry out an investigation of a given problem. Here we use a head-on approach: we take a galaxy catalogue extracted from the Horizon-AGN hydrodynamical simulation, and apply artificial noise to the simulated spectra. Our goal is to understand the quality of the reconstruction of the IGM, and to assess how well we can quantify the dependency of galaxy properties, such as stellar mass and star formation rate, on the distance to the closest filament.
Description of the simulation Horizon-AGN is a cosmological (100 h ´1Mpc on a side) hydrodynamical simulation 1 (see  Dubois et al. 2014  for details) that is run with the adaptive mesh refinement code RAMSES. The simulation assumes ΛCDM cosmology with cosmological parameters that are compatible with the WMAP-7 data (Komatsu et al. 1 https://www. horizon-simulation. org/ 2011). Heating from a uniform background starts at z " 10 and follows the formulation of  Haardt & Madau (1996). Star formation occurs in regions of a gas hydrogen number density above n 0 " 0. 1 H cm ´3. Metals and energy releases from stellar winds, supernovae, and AGNs are included in the simulation. A lightcone with an angular diameter of 1 degree (the choice for this area is justified in Sect. 4. 2) was extracted from the simulation.
As part of the Horizon-AGN Virtual Observatory effort (see  Laigle et al. 2019 , for details), galaxies were identified from the distribution of star particles with the halo finder Adap-taHOP  (Aubert et al. 2004). A mock spectrum was assigned to each galaxy using  Bruzual & Charlot (2003)  stellar population models, assuming that each particle behaves like a single stellar population. This computation also includes dust absorption using the gas-phase metallicity distribution as a proxy for dust distribution. Dust mass was calibrated based on the gasphase metal mass, with a dust-to-metal mass ratio of 0. 2. The redshift evolution of galaxy colours, luminosity, and mass functions of the extracted galaxies from Horizon-AGN simulation match the observational datasets well (e. g. Kaviraj et al.
Tomographic reconstruction method The 3D distribution of the Lyman-α flux contrast, which is commonly used as a proxy for the inverse of the density field, was reconstructed by interpolating between the LOS using Wiener filtering in comoving space (see  Pichon et al. 2001; Caucci et al. 2008; Lee et al. 2018). We recall below the main elements of the method and the choice of the parameters. Let D be the 1D array representing the dataset, and M is the 3D array of the field estimated from the data. Maximizing the penalized likelihood of the data given an assumed prior for the flux contrast field yields M " C δ 3d δ pC δδ `Nq ´1D , (1) where C δ 3d δ is the mixed parameter-data covariance matrix, and C δδ is the data covariance matrix. We assume that the noise is uncorrelated, therefore the noise covariance matrix can be expressed as N " n 2 I, n being set by the S/N on the spectra.
Background galaxy counts We first estimate the number of galaxies that we expect to have in a FOV at each pointing by considering real fields. We chose to consider the GOODS-South field  (Giavalisco et al. 2004 ) because it is one of the best-observed parts of the sky. For our purposes, we need photometric observations of galaxies in the field complete down to m rest,UV ă 25. 5 mag and information on the redshifts. We based our analysis on the data collected by the 3D-HST survey  (Skelton et al. 2014). The survey collected photometric data of many previous deep surveys and through an SED analysis computed photometric redshifts. In addition, for the bright part of the sample, Skelton and collaborators provide grism-based redshifts  (Momcheva et al. 2016). They compiled a final catalogue of sources with measured redshifts, where the redshifts were taken from three different ways of measurement: ground spectroscopy, grism redshifts, and photometry.