dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were processed in two batches, within which the samples were randomized. All samples were normalized together following background subtraction of the negative control probes, using positive control probes and housekeeping genes selected by the GNorm method as previously described  (42). Quality control for our data involved checking the following metrics: fields of view ≥0. 75; binding density 0. 05-2. 75, linearity of positive controls (R2 ≥ 0. 9), and limit of detection for 0. 5 fM positive control ≥2 standard deviations above the mean of the negative controls. Supernatants from TruCulture tubes were thawed on ice and tested by Luminex xMAP technology for a total of 32 proteins including cytokines, chemokines and growth factors as previously described  (6). Samples were measured on the Myriad RBM Inc platform (Austen, Texas) according to CLIA guidelines (set forth by the USA Clinical and Laboratory Standards Institute).
Reverse transcription of the RNA was performed using barcoding primers containing unique molecular identifiers (UMIs), as previously described  (43). The resulting cDNA was used to amplify antibody heavy chains. Antibody sequence annotation, including VDJ assignment was performed using AbStar (https://github. com/briney/abstar). Error correction of sequencing data was done by UMI-based correction using AbCorrect (https://github. com/briney/abtools/blob/master/ docs/source/abcorrect. rst). This combination of AbStar and AbCorrect allows clustering and consensus/centroid generation on just the VDJ region of the antibody sequence in the proper orientation. For clonal lineage assignment, sequences were assigned to clonal lineages using Clonify, as previously described  (43). Clonify uses an antibody-specific distance metric, incorporating length-normalized CDR3 edit distance, V and J gene usage, and shared somatic mutations to determine the relatedness of each antibody sequence pair.
Microbiome Bacterial community composition was analyzed in rectal, skin, oral and nasal samples. The data were generated via highthroughput sequencing of bacterial V4-16S rRNA amplicons, using the Illumina MiSeq platform using a refined version of an established analysis pipeline, which has been described previously (51). To identify and remove contaminating sequences, three extraction blanks were included. These were generated by first performing three extraction blanks, then using the eluate from these as a template for PCR amplification, and then sequencing in the same run as the biospecimens. Two positive controls, consisting of cloned SUP05 DNA, were also included (number of copies = 2*10^6). OTUs present in blanks are either introduced from extraction reagents, PCR reagents, or from neighboring samples during either the extraction or the PCR amplification process.
Contaminating OTUs were identified as being present in at least 50% of blanks, with count geometric mean plus one standard deviation was greater than the samples, reflecting previously described properties of contaminants arising from extraction or PCR reagents. This approach also avoids removing common contaminating OTUs from neighboring samples either during the extraction or PCR amplification steps, as these are of high relative abundance in the samples but low relative abundance or sporadic presence in the blanks. Contaminating OTUs were removed from the dataset. Samples with total read counts under 1,000 after contaminant removal were also removed from further analysis. The analyses address analytical challenges inherent to microbiome data. First, community composition data are in the form of relative abundance of populations, which do not vary independently, and are not suited to univariate methods.
Second, the data are sparse, typically with a large proportion of populations at zero relative abundance in any particular sample. Analysis is robust to sparsity, because rare populations may be functionally important. Third, because whole microbial communities often modulate biological pathways, testing individual populations, which overlooks interactions or correlations among them, may not adequately capture microbiome effects on host responses. To relate microbiome composition to immune responses, multivariate methods were employed, including a workflow for microbiome data analysis in the mixOmics R package. To maximize the correlated information between multiple datasets and optimally identify the key microbiome features that explain and reliably classify immune phenotypes, multivariate dimension reduction discriminant analysis was used.
Liquid Chromatography Coupled with Mass Spectrometry (LC-MS/MS) Analysis Protein extraction from whole seedlings, trypsin digestion and MS analysis were carried out as described in  [21]. Raw mass spectrometric data were analyzed in the Maxquant environment (v. 1. 5. 5. 1, Max-Planck-Institute of Biochemistry, Planegg, Germany)  [37]  and Andromeda was employed for database search  [38]. The MS/MS data were matched against the TAIR10 database. For protein identification and quantification, cysteine carbamidomethylation was set up as fixed modification and oxidation of methionine as a variable modification. At least two peptides were required for protein identification and quantification. Up to two missed cleavages was allowed for protease digestion. For other characteristics, Maxquant default parameters were used.
Following the quantification step and the label free quantitative (LFQ) normalization, proteins were considered as quantifiable only if they are present in all samples. Data were log2 transformed prior analysis. For statistics, pairwise t-tests p < 0. 05 were carried out. Before statistical treatment, the normal distribution of the logarithmic transformed data was assessed. For the present/absent analysis, a protein was considered as absent in the nfu3-2 mutant if it was identified with at least 2 peptides in at least 3 replicates achieved with WT plants and not in the 4 replicates achieved with nfu3-2 plants. All raw MS data and Maxquant files generated have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD020228.
Background and aims: The term "Food Allergy" refers to a complex global health problem with a wide spectrum of severity. However, a uniform definition of severe food allergy is currently missing. This systematic review is the preliminary step towards a state-of-the-art synopsis of the current evidence relating to the severity of IgE-mediated food allergy; it will inform attempts to develop a consensus to define food allergy severity by clinicians and other stakeholders. Methods: We will undertake a systematic review, which will involve searching international biomedical databases for published studies. Studies will be independently screened against predefined eligibility criteria and critically appraised by established instruments. Data will be descriptively and, if possible and applicable, quantitatively synthesised.
Studies of non-IgE mediated food allergy Primary data from studies in patients undergoing allergen immunotherapy The search strategy has been developed on OVID MEDLINE and then adapted for the other databases (see Appendix 1). In all cases, the databases will be searched from inception to December 2019. Additional references will be located through searching the references cited by the identified studies. We will invite experts who are active in the field from a range of disciplines and regions to add to the list of included studies by identifying any additional published papers. We will include only English language publications.
Quality assessment strategy Quality assessments will be carried out on each study by 2 independent reviewers using the relevant quality assessment tools. The quality of included RCTs will be assessed using the Cochrane eight domain-based evaluation for RCTs and quasi-randomized trials (see Table  8. 5a in the Cochrane Handbook). 13 Each domain and overall score will be rated as: low risk of bias, unclear risk of bias, or high risk of bias. We will assess the quality of observational studies following the Effective Public Health Practice Project (EPHPP). 14 We will focus on the following domains to assess the quality of included studies: selection bias; study design; confounders; blinding; data collection method; withdrawals and dropouts; and final global rating. Each component-specific parameter (ie, suitability of the study design for the research question; risk of selection bias; exposure measurement; outcome assessment; and generalizability of findings) will be given a judgment: "strong"; "moderate"; and "weak".
Acknowledgements This is a project of the  World Allergy Organization (WAO) , supported by a grant from  Novartis , as well as  Abbott Laboratories  and  Food Allergy Research & Education (FARE). WAO will fund the systematic review to support the development of the international consensus on the definition of severe food allergy. The funder and supporters had no role in the development of the protocol nor will they have any role in the conduct of the systematic review or its publication.
ACKNOWLEDGMENTS. This work was supported by the  Human Frontier Science Program  ( RGP0044/2014 ), the  Medical Research Foundation (FRM) , the  Neurodis Foundation , the  French National Research Agency ,  Canadian Institutes of Health Research Foundation   FDN-143212 , and by  Laboratoire d'excellence (LabEx)   CORTEX ANR-11-LABX-0042  of  Université de Lyon. K. K. L. was supported by the  LABEX CORTEX  and the FRM. E. P. and C. A. are employed by the  Centre National de la Recherche Scientifique.
Methods Subjects. A total of 22 healthy right-handed native French speakers were recruited to participate in a training session and three fMRI sessions. Data from two subjects (S2, S13) were omitted from the analyses because they had shown poor performance across the three functional neuroimaging sessions. Two other subjects (S6, S9) did not participate in any of the scanning sessions because of claustrophobia. Consequently, the final dataset consisted of 18 subjects (10 males; mean age, 26. 22 y; SD, 3. 12). The study was carried out in accordance with the recommendations of the Code de la Santé Publique and was approved by Agence Nationale de Sécurité des Médicaments et des Produits de Santé (ANSM) and Comité de Protection des Personnes (CPP) Sud-Est III (No EudraCT: 2014-A01125-42). It also received a ClinicalTrials.
gov ID (NCT03124173). All subjects provided written informed consent in accordance with the Declaration of Helsinki. Experimental Paradigm. In the present study, subjects performed three versions of the visuo-motor conditional learning and control tasks in the scanner that corresponded to three different response effectors: manual, orofacial, and vocal (nonspeech or speech; Fig. 1 , SI Appendix, Supplementary Methods, and Movies S1-S3). In the visuo-manual condition, the subjects acquired associations between three finger presses on an MRI-compatible button box (Current Designs) and visual stimuli in the conditional learning task and performed instructed button hand presses in the control task. In the visuoorofacial condition, the subjects performed the conditional learning task and control task using three different orofacial movements (Fig.
Value of the Data • The datasets are useful to characterize the subsurface water content of headwater catchments and to evaluate the storage properties of hard-rock aquifers. • Researchers interested in measurements of water contents collected in hard-rock aquifers could be inspired by these datasets. Researchers developing models of magnetic resonance sounding (MRS) responses could also use these datasets to evaluate their model. Researchers interested in hydrological models could, as well, evaluate the capacity of their models to be conditioned by this dataset on subsurface flow patterns. • The data give an idea of the noise level at the Strengbach site, which also helps testing acquisition protocols that could provide a better signal-to-noise ratio. With regard to the design of time-lapse experiments, the data also provide an insight on the minimum duration of records required to generate MRS signal variations higher than the measured noise.
Data Description The Magnetic Resonance Sounding (MRS) datasets presented here are that used in  [1]  for the calibration of a hydrological model applied for simulating transient flow at the catchment  scale. The MRS measurements are collected at 16 stations in the Strengbach catchment ( Fig. 1  ), which is the open-sky laboratory of the Observatoire Hydro-Géochimique de l'Environnement (OHGE, http://ohge. unistra. fr ; CNRS/University of Strasbourg)  [2]. MRS is a geophysical method directly sensitive to the water content of the subsurface. Details on the theory and the grounding fundaments of the method are available in  [3]. The characteristics of each station of acquisition are summarized in Table  1. At each station, data are collected by setting an eight-shaped loop made by two squares on the ground surface ( Fig.
1  ). The size and the location of the center of the loops for the different stations are listed in Table  1. The distances along the x (W-E direction) and y (N-S direction) directions of each corner of the eight-shaped loops to their center are reported in Table  2  for each station. For each station, the data files are gathered in a. zip file that contains a data and a figure folder. A. pdf metadata file gathers all the information about each station. In the data folder, a. inp file summarizes the parameters of each acquisition corresponding to the raw data files (. 0#) that are provided together with the filtered data files (. f#), with # the file number. The files follow the SAMOVAR 11. 62 format. For an easier use of the data, a. mrm file is also provided which contains information on the kernel matrix of the MRS sounding.
Different. mrm files are provided, depending on the size of the loop. As the SAMOVAR software is not freely distributed, the MRS toolbox described in  [4]  can also be used; it allows to open and process the raw data, compute a 1D kernel, and perform inversion. Here is a brief description of the files formats: Each line of the. inp file corresponds to one of the filtered files, then the number of pulse injected is provided, followed by the pulse frequency (Hz), and finally the moment pulse sequence setup (injected current intensity multiplied by duration, A. ms). In the field, the acquisition can be divided in six steps: Between each step, the system needs for a downtime to let the electronics of the instrument to switch from the acquisition to the injection mode and vice-versa. Each. 0# file supplies the results of the stacked data and corresponds to one of the injected pulses of various moments.
Experimental Design, Materials, and Methods Acquisition protocols follow the free induction decay (FID) scheme described in  [3]. Here, only one pulse is injected after the noise measurement, then the signal is recorded. Each pulse is stacked 200 to 300 times. After a time out of 40 ms, the signals are recorded with a sampling rate of 2 ms and over a duration between 205 and 466 ms. However, for the analysis performed in  [1]  , only the first 162 ms (after the time out) of the recording were kept because the longtime measurements are much more sensitive to the ambient noises. Data are analyzed and filtered using the SAMOVAR-11. 62 software package developed at the Institut de Recherche pour le Développement (IRD, France). First a band-pass filter is applied around the Larmor frequency, and then a filter removing the 50 Hz noise is applied, except if the Larmor frequency is close to a 50 Hz harmonic.
Acknowledgments The MRS acquisition campaign was funded by  CRITEX, INSU , and REALISE projects. The authors acknowledge  Daniel Sultana  (LTHE),  Troels Thomsen  (LTHE),  Sylvain Benarioumlil  (OHGE), and  Valentin Gondy  ( OHGE ) for their help in the fieldworks. OHGE is part of the OZCAR research infrastructure ( http://www. ozcar-ri. org). The authors would also like to thank the  OZCAR network  for the scientific animation that stimulated this work and the  ANR   HYDROCRIZSTO-15-CE01-0010-02  project.
2016 (Homma et al. , 2018 (Homma et al. , 2019) )  and by the discovery of Antlia II, the lowest surface brightness galaxy currently known, using RR Lyrae member stars identified in Gaia DR2  (Torrealba et al. 2019 ). In the near future, the Legacy Survey of Space and Time (LSST) conducted from the Vera C. Rubin Observatory will be able to detect satellites over the entire southern sky down to a surface brightness of µ V ∼ 32 mag arcsec -2  (Ivezić et al. 2008; Tollerud et al. 2008; Hargis et al. 2014; Nadler et al. 2019b). Interpreting the cosmological and astrophysical implications of these discoveries requires a detailed understanding of the observational selection effects for each survey under consideration. In a companion paper  (Drlica-Wagner & Bechtol et al. 2020 , hereafter Paper I), we derive observational selection functions for DES and PS1 based on searches for simulated satellites in each dataset.
Although several empirical models have recently been used to study subsets of the MW satellite population  (Jethwa et al. 2018; Kim et al. 2018; Newton et al. 2018; Nadler et al. 2019b) , this is the first analysis that is directly based on imaging data over more than ∼ 15, 000 deg 2 ; indeed, our analysis covers 75% of the high Galactic latitude sky. Moreover, our galaxy-halo connection model allows us to marginalize over astrophysical uncertainties in our fit to the observed DES and PS1 satellite populations. We quantify the impact of the largest MW satellite, the Large Magellanic Cloud (LMC), and its associated satellites on the observed DES and PS1 satellite populations. We find that the satellites accreted with a realistic LMC analog-defined in terms of its mass, heliocentric distance, and infall time-are essential to fit the DES and PS1 luminosity functions simultaneously; this finding constitutes a remarkable confirmation of hierarchical structure formation.
Observed Satellite Populations Figure  2  shows the 68% and 95% confidence intervals for the observed DES and PS1 satellite luminosity functions given by draws from the posterior of our fiducial model, which is consistent with both datasets. We note that the DES and PS1 likelihoods individually yield consistent results. Figure  3  shows the corresponding satellite size distributions drawn from our fiducial posterior. Our model is consistent with the sizes of both the observed DES and PS1 satellites. It very slightly overpredicts the sizes of observed DES systems; however, we reiterate that our size model does not allow for size reduction due to tidal stripping or size enlargement due to tidal heating, which affect satellites with close pericentric passages to the Galactic disk (e. g. , Amorisco 2019).
3. The peak mass at which 50% of halos host galaxies is inferred to be less than 8. 5 × 10 7 M at 95% confidence. Note that this summary statistic depends on the lower limit of our prior on M 50 , since the M 50 posterior flattens near its lower limit, which is chosen based on the resolution of our simulations. Thus, we also calculate Bayes factors by drawing from the posterior in bins of M 50 to confirm that this summary statistic is robust. We find that M 50 = 8. 5 × 10 7 M (M 50 = 1. 5 × 10 8 M ) is disfavored relative to arbitrarily low values of M 50 with a Bayes factor of 50 (100). The current data are not able to place a lower 7 We provide details on our Bayes factor calculations in Appendix B. 3. Figure  6. Left panel: fraction of halos that host galaxies, inferred from our fit to the DES and PS1 satellite populations.
and NASA's Astrophysics Data System for bibliographic information. Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey.
ACKNOWLEDGMENTS This paper has gone through internal review by the DES collaboration. Our code and subhalo catalogs are available at github. com/eonadler/subhalo_satellite_connection. We thank  Ralf Kaehler  for providing the visualizations of our simulations used in Figure 1. We thank  Shea Garrison-Kimmel  and  Andrew Wetzel  for sharing data from the ELVIS on FIRE simulations. We thank  Susmita Adhikari ,  Arka Banerjee ,  Ekta Patel , and  Andrew Pontzen  for useful conversations. Finally, we are grateful to the anonymous referee for constructive feedback. This research received support from the  National Science Foundation (NSF)  under grant No. NSF   AST-1517422 , grant No. NSF   PHY17-48958  through the  Kavli Institute for Theoretical Physics program  " The Small-Scale Structure of Cold(?) Dark Matter ," and grant No.
Based in part on observations at  Cerro Tololo Inter-American Observatory, NSF's National Optical-Infrared Astronomy Laboratory , which is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under grant Nos. AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-66861 ,  FPA2015-68048 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. The IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013), including  ERC  grant agreements  240672 ,  291329 , and  306478.
g: solar Lyα g-factor (photons s -1 ) at 1 au. Q: water production rates for each image (s -1 ). δQ: internal 1σ uncertainties. before perihelion. As discussed above, comet 41P was known for outbursts over the previous almost 50 yr. On the other hand, the 2017 apparition was rather flat and uneventful, with production rates remaining close to the lower end of SWAN detectability with values around 3×10 27 s -1. Levels in 2017 were factors of 2-4 below comparable times in 2006 and factors of 10-30 below those in 2001. In the SWAN 61-comet survey  (Combi et al. 2019) , power laws of water production rate versus heliocentric distance were fitted to three different pre-or post-perihelion set of individual apparition data sets; however, most did not lend themselves to being represented by a power law.
Summary Comet 46P was observed by the SOHO SWAN H Lyα allsky camera during the  1997, 2002, 2008, and 2018  apparitions. Water production rates were determined from each of the images using our standard model analysis. We find a significant change between the 1997/2002 and 2008/2018 apparitions with a marked decrease in overall production rates throughout the apparitions as well as a large steepening of the variation of water production rate with heliocentric distance. The changes are highly reminiscent of those that occurred in comet 103P/ Hartley 2, another so-called hyperactive comet, between the 1997 and 2011 apparitions  (Combi et al. 2011a ). SWAN data (below) in s -1 and are 3. 94×10 28 ×r -6. 6 and 3. 08×10 28 r -3. 2 for pre-and post-perihelion, respectively, in 1997 and 2. 31×10 28 r -14.