dataesr/scientific-paragraphs-categorization huggingface dataset:
The observations were all conducted in the fields listed in Table  1. In §3 we present our spectroscopic results from 19 nights of Keck time in semesters 2017B-2020A, discuss the performance of the method, and investigate the status of the calibration effort and the issues still to be addressed. 2. OBSERVATIONS AND DATA REDUCTIONS The observations were carried out at the Keck Observatory located on Mauna Kea in Hawaii. Both of the two 10 m telescopes were used, as LRIS and MOSFIRE are located on Keck 1 and DEIMOS on Keck 2. The observing nights and weather conditions are summarized in Table  2. Note that approximately 6 of the 19 nights were lost to bad weather. Table  3  summarizes the observed slit masks.
Description of Observations Observations for DR3 were carried out essentially in the same manner as described in M17 and M19 for the previous data releases. As discussed in more detail in M17 and M19, we estimated the exposure times needed to obtain redshifts for each target with the three main instruments available at Keck, i. e. DEIMOS, LRIS, and MOSFIRE, and assigned targets to the instrument with the lowest expected exposure time. Here we give a brief overview of the instruments and methods used in the observing.
Multi-Object Spectrograph for Infrared Exploration (MOSFIRE)  MOSFIRE (McLean et al. 2012 ) is a near-IR spectrograph which can observe up to 46 objects at the same time over a 6. 1 × 6. 1 arcmin 2 field. Data may be obtained in one of the bandpasses at typical resolutions of ∼ 3000. We used MOSFIRE in its default configuration. For instrumental details we refer the reader to  Steidel et al. (2014). For our H-band observations we used integration times of 120 s with ABAB dithering to improve sky subtraction. Reductions were performed with the MOSFIRE Data Reduction Pipeline (DRP) made available by the instrument team 1. We chose to observe only in H-band because the density of high-priority targets for which we could expect to get a secure redshift was notably higher in this band; this is largely due to the limited wavelength range of a single bandpass in MOSFIRE multislit spectroscopy, the ∼ 1.
• = 3. 5: A quality flag of 3. 5 indicates a highconfidence redshift based on a single line, with a remote possibility of an incorrect identification. An example might be a strong, isolated emission line identified as H , where other identifications of the line are highly improbable due to the lack of associated lines or continuum breaks. This flag is typically only adopted for LRIS and MOSFIRE spectra; single line redshifts in DEIMOS spectra are usually the OII doublet which is split by the DEIMOS spectral resolution. • = 3: A quality flag of 3 indicates a high-confidence redshift with a low probability of an incorrect identifi-cation. An example might be the low signal-to-noise ratio detection of an emission line, possibly corrupted by telluric emission or absorption, identified as [O II] 3727, but where the data quality is insufficient to clearly resolve the doublet.
REDSHIFT RESULTS In this data release we targeted 1959 objects in 50 masks; 18/19/13 were observed with MOSFIRE/LRIS/DEIMOS respectively. We were able to obtain 435 redshifts with = 4 and 174 redshifts with either = 3 or = 3. 5 for the targeted objects; these numbers do not include serendipitous sources which are discussed below. Thus the success rate for all high-confidence redshifts ( ≥ 3) is 31% of targeted objects. If we restrict the results to the data obtained in good conditions (32 masks) then we obtained high-confidence redshifts for 511 out of 1346 targeted objects for a success rate of 38%. We return to a discussion of the relatively low success rate in Section 3. 4. Combined with the previous DR1 and DR2 results, C3R2 now has obtained 5131 high-confidence spectroscopic redshifts.
mean (z pz s ) 1 + z s to be -0. 0025, where the scatter and bias are calculated after removing outliers. If we consider redshifts only in the range 0. 2 < p < 2. 6, corresponding to the epoch that will be used by Euclid for its cosmological weak lensing survey, the bias is -0. 0020 (after removing outliers). If we include good quality spectroscopic redshifts ( = 3 or = 3. 5) along with the = 4 redshifts from all three C3R2 data releases, then the scatter is 0. 024, the true outlier fraction is 0. 056, and the bias is -0. 0018 (after removing outliers) over the entire redshift range. In Figure  4 , which shows the results for the COSMOS field, we see that there is an increase in the fraction of outliers when all available high-quality spectroscopic redshifts are compared with the SOM photometric redshifts.
We dis-Figure  3. Same as in Figure  2 , except here we show the performance of the SOM method on the redshifts published by the all three C3R2 data releases. The true outlier fraction for this case is 0. 041, the scatter is 0. 023, and after removing outliers the bias is -0. 0025. The dotted line in the right panel is a Gaussian centered at zero with = 0. 023. 2 , except here we show the performance of the SOM method only in the COSMOS field using all high-quality ( = 4) redshifts from C3R2 combined with all high-quality spectroscopic redshifts available from the literature. For this case, the true outlier fraction is 0. 071, the scatter is 0. 024, and after removing outliers the bias is -0. 0020. The dotted line in the right panel is a Gaussian centered at zero with = 0. 024. cuss the outliers in the DR3 sample in more detail in Section 3.
3. Discussion of the other samples from the literature in more detail is beyond the scope of this work; we plan to investigate the causes of the photometric and spectroscopic redshift outliers in the future. Once all the outliers are removed, the bias is calculated to be -0. 0020 over the entire redshift range. This improvement relative to the case of only the C3R2 redshifts is likely due to the inclusion of brighter galaxies from the literature samples. In M19 we compared C3R2 spectroscopic redshifts to photometric redshifts in the COSMOS field determined with the template fitting code Le Phare  (Arnouts et al. 1999; Ilbert et al. 2006 ) using the same photometry as used here to generate the SOM and SOM-based photometric redshifts. Considering the highest quality ( = 4) redshifts from the three merged C3R2 data releases, these Le Phare photometric red-shifts show a scatter NMAD of 0.
Assessing the Photometric Redshift Outliers As a final step in the analysis, we identified all sources in DR3 with secure redshifts (i. e. , quality flag ≥ 3) where the spectroscopic redshift was discrepant from the SOM-based redshift by at least 15% (i. e. , | s -SOM |/(1 + s ) > 0. 15). At least two co-authors independently assessed each of the 80 such cases identified. For three galaxies, this reanalysis lowered the spectroscopic quality flag to < 3. For the majority of the remaining cases (41/70; 59%), we revised the assigned spectroscopic redshift based on a reconsideration of the data and the photometric redshift. Most of these cases were one-line redshifts initially assigned quality flag = 3 observed with either MOSFIRE (with its limited spectral range) or LRIS. Typically, a target had a single faint emission line detected, initially assumed to be, for example, H.
g. , potentially a SOM cell with a bimodal photometric redshift distribution). In fact, for 2/11 of these cases, we find that the SOM cell of the galaxies is on a clear "caustic" separating low-and high-redshift galaxies with similar Euclid colors, while the others tend to be in cells with intrinsically high redshift dispersion compared with most parts of the color space. It is plausible that the 30-band COSMOS data is capable of resolving these degeneracies, hence the better Le Phare photometric redshift estimates. On the other hand, a detailed examination of the Hubble ACS imaging of these sources showed that a majority seem to be affected by blending to some degree, which may also contribute to the incorrect SOM-based photometric redshifts. Another third of the outliers are due to blends.
The distribution of the good COSMOS masks by instrument is 10/5/1 for MOS-FIRE/DEIMOS/LRIS, respectively. There were 70 objects for which a low-confidence ( = 1 or 2) redshift was determined, 162 with a failure code of -91 (too faint), 39 with a failure code of -92 (reasonable detection but no redshift), and 25 have a code of -93 or -94 meaning there were issues with the data or reductions. The success rates by instrument are fairly similar for DEIMOS (43%) and MOSFIRE (37%) for the good COSMOS masks; there was only one mask done by LRIS in good conditions in the COSMOS field. The success rate for the good LRIS observations in all three fields was lower compared to that obtained with DEIMOS and MOSFIRE. High confidence redshifts were obtained of only 61 of the 413 targeted objects in the 14 good LRIS masks; no secure redshifts were obtained for 347 objects, and the reductions were problematic for five objects.
Bottom left: The relative fractions of passive galaxies in the calibrated set as a function of magnitude and redshift, as estimated from rest-frame UVJ colors. Bottom right: The same plot but for the uncalibrated galaxies. It is clear from these figures that the currently uncalibrated galaxies are fainter and tend to be more quiescent than the calibrated galaxies. In all panels the analysis uses galaxies in the COSMOS2015 sample, and white areas represent no galaxies in a bin. 4. SUMMARY We have presented 676 new high-confidence spectroscopic redshifts obtained by the C3R2 survey. Taken together with the previous two data releases, we have now collected and published 5130 high-confidence spectroscopic redshifts (and associated publicly released spectra) covering a wide range in galaxy type and redshift.
Study design and setting A cross-sectional study was conducted on patients presented to community pharmacies distributed in different Lebanese geographic districts between January and June 2018. The pharmacies were chosen by simple randomization from the list of pharmacies provided by the Lebanese Order of Pharmacists. The questionnaire was administered face-to-face by trained researchers, who had training before the start of the data collection to ensure that the same procedure was uniformly applied on all patients. All participants who presented to the community pharmacies were screened for possible enrollment in the study and included if they had a previous physician-diagnosed AF documented on their electronic medical profile available in the community pharmacies. No attempt was made to verify the accuracy of the physician's diagnosis.
Data collection The first part of the questionnaire aimed at collecting the patient's demographic data such as age, gender, work status (employed, retired, unemployed), socioeconomic status (low, moderate, high) and education (primary, middle or high school). The second part included a detailed assessment of the patient's factors to calculate the CHA 2 DS 2 -VASc score which was assessed for the purpose of the study. The score calculation is performed on all enrolled patients through an assessment of the following criteria: patients aged ≥65 years, female gender, heart failure, prior stroke or TIA, hypertension, diabetes mellitus, vascular disease, coronary heart disease or peripheral artery disease. 18 All medical diseases were assessed by checking the detailed previous medical conditions along with the prescribed medications documented on medical profiles.
These factors were incorporated into a CHA 2 DS 2 -VASc model where each element received 1 point except those aged ≥75 years and those who had a history of stroke or TIA who received 2 points. The CHA 2 DS 2 -VASc score can range from 0 to 9. The score's interpretation is categorized into "low" if the score is zero, "low-moderate" if the score is one, and "moderate-high" if the score ≥2. 19 Some of the questions under the sociodemographic characteristics (as socioeconomic status, educational level, and social history) in the questionnaire were left unanswered either because the patient did not remember or did not understand what was asked. Sources of bias were addressed by minimizing investigators interference in the data collection, patients were recruited in a way to represent the different Lebanese geographic characteristics, and information related to medical history was retrieved from the electronic medical profiles.
Statistical analysis An independent person who was unaware of the goals of this research conducted the data coding and analysis using the version 23. 0 of the Statistical Package for Social Sciences (SPSS) software. Continuous variables were presented as mean (M) and standard deviation (SD), and the categorical variables were presented as frequency (percentage). The statistical analysis adjusted for the missing values by accounting for the valid percentage in the results output. The Student t-test was used to compare the means of the maximum two groups; for means of three and above, the ANOVA test was applied. In the case of non-homogeneity, non-parametric tests substituted the parametric tests. Variables with a p < 0. 05 in the bivariate analysis were included in the linear regression to assess its association with the dependent variable, the CHA 2 DS 2 -VASc score.
Data availability Underlying data OSF: Effects of Sociodemographic and Socioeconomic Factors on Stroke Development in Lebanese Patients with Atrial Fibrillation: a cross-sectional study. https://doi. org/10. 17605/OSF. IO/PJC6A. 52 is project contains the following underlying data. -Data_Anonymous_Results_stroke with Afib_Dr Diana Malaeb_2020. sav Extended data OSF: Effects of Sociodemographic and Socioeconomic Factors on Stroke Development in Lebanese Patients with Atrial Fibrillation: a cross-sectional study. https://doi. org/10. 17605/OSF. IO/PJC6A. 52 This project contains the following extended data. -Questionnaire_Stroke with Afib_Dr Diana Malaeb_2020. doc -Formulas used in the analysis_Stroke with Afib_Dr Diana Malaeb_2020. docx -Data key_CV score_Dr Diana Malaeb_2020. docx Data are available under the terms of the Creative Commons Attribution 4.
To test if complete loss of ZRSR2, as we saw in patient samples, was sufficient to impair pDC activation, we stimulated CAL1 cells harboring CRISPR/Cas9 knockout of ZRSR2 with TLR  ZRSR2 agonists (Fig. 5B ). Upregulation of the activation marker CD80 after stimulation with lipopolysaccharide (LPS; TLR4 agonist) or R848 (TLR7) was markedly reduced in cells with knockout of ZRSR2 (Fig. 5C ). Next, we assessed the same phenotype in primary human BPDCN and pDCs. BPDCN, across genotypes, had decreased induction of CD80 compared with pDCs after R848 stimulation (Fig. 5D ). Knockdown of ZRSR2 in otherwise normal primary pDCs also impaired the R848 response (Fig. 5E ). These data suggest that loss of ZRSR2 is one, but not necessarily the only, mechanism by which BPDCN cells are rendered hyporesponsive to TLR activation.
Flow Cytometry Cells were washed with PBS containing 2% FBS before staining and then incubated with the indicated antibodies for 30 minutes in the dark at 4°C, followed by a final wash in PBS containing 2% FBS and analyzed. In the case of murine samples, we lysed red blood cells prior to staining. Flow cytometry was performed using a BD LSR Fortessa X-20 and analyzed using FlowJo software, version 10. Antibodies for flow cytometry are in Supplementary Table  S5. Cell surface markers for HSPC populations are in Supplementary Table  S6.
CRISPR/Cas9 Editing Site Sequencing Genomic DNA was extracted from GFP + /tagRFP + mouse Lineagec-kit + bone marrow cells using TRIzol (15596018; Life Technologies). PCR was performed spanning the predicted Cas9 cut sites, using the primers as below. Illumina-compatible adapters with unique barcodes were ligated onto each sample during library construction. Libraries were pooled in equimolar concentrations for multiplexed sequencing on the Illumina MiSeq platform with 2 × 150 run parameters. Upon completion of the sequencing run, data were demultiplexed and subsequently entered into an automated de novo assembly pipeline. Sequencing and analysis were performed by the CCIB DNA Core Facility at Massachusetts General Hospital (Tet2 For, GGGGGTTGGAGCAAGTACAAA; Tet2 Rev, GCCCCTGTAGA GACGAAGC; Zrsr2 For, CACACTCCCATCTTCACTGCT; Zrsr2 Rev, CAGCTAAGCACTTACTCAATGAACT).
Whole-Exome Sequencing and Analysis Tumor and paired germline DNA samples were collected and prepared for WES using the Agilent SureSelect Human All Exon V5 (50 mol/L) capture. Sequencing was performed on the Hiseq4000 platform with a 150 base pair paired-end protocol targeted to 100× mean coverage. The sequences were generated as 90 of 100 base paired-end reads using Illumina base calling software (version 1. 7). The adapter sequences and low-quality reads were filtered from the raw sequencing data, and the "clean data" were aligned by a Burrows-Wheeler Aligner  (63)  with the reference of human genome build37 (hg19). The BAM files were validated by steps of fixing mate information of the alignment, adding read group information, and removing duplicate reads and then applied to variant calling analysis using the GATK toolkit (https://gatk.
broadinstitute. org/ hc/en-us). The reference (hg19) and germline variant sites were used to detect somatic or tumor-specific mutations and copy number alterations. The details of the methods in the GATK toolkit are described elsewhere  (64, 65). The identified BPDCN somatic mutations were analyzed with those reported for AML and skin melanoma  (66)  for the presence of previously defined global DNA mutational signatures in the COSMIC database (20) using the R package, "MutationalPatterns"  (67).
RNA Sequencing and Analysis Samples from pDCs, patients with BPDCN, PDXs, CAL1, and mouse models were collected for total RNA sequencing using the Arcturus PicoPure RNA Isolation Kit (Life Technologies). Libraries were prepared using the Ovation kit (#0340; Nugen) using 50 ng input total RNA and 20 cycles of amplification. The Illumina Hi-Seq platform was used to generate single or paired-end sequencing results. The raw FASTQ data were analyzed by the VIPER pipeline  (68) , which combines STAR (69) alignment (mapped to hg19 or mm10) and gene expression analysis such as unsupervised clustering, principal component analysis based on Cufflinks  (70) , and differential expression with DESeq2  (71). The expression data were used for pathway enrichment analysis with GSEA and gene set variation analysis  (72, 73).
Acknowledgments This work was supported by the  Sumitomo Life Welfare and Culture Foundation  (to K. Togami), the  NCI   R37 CA225191  (to A. A. Lane) and  R35 CA231958  (to D. M. Weinstock), the  Department of Defense   W81XWH-20-1-0683  (to  M. Konopleva ,  N. Pemmaraju ,  O. Abdel-Wahab , and  A. A. Lane ), the  Damon Runyon Cancer Research Foundation  (to G. K. Griffin),  Mark Foundation for Cancer Research  (to A. A. Lane),  Doris Duke Charitable Foundation  (to A. A. Lane),  Bertarelli Rare Cancers Fund  (to A. A. Lane),  Ludwig Center at Harvard  (to A. A. Lane), and the  American Society of Hematology  (to A. A. Lane). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.
Acknowledgments This study was supported by the predoctoral  FPU  contract  FPU17/03349  awarded to  A. González-Lanchas  by the  Spanish Ministry of Science, Innovation and Universities. Essential financial infrastructure was provided by the programs  RTI2018-099489-B-100  of the  Spanish Ministry of Science, Innovation and Universities  granted to  GGO (Grupo de Geociencias Oceánicas de la Universidad de Salamanca ). T. Rodrigues received funds from  Portuguese National Foundation for Science and Technology FCT  -through projects:  Warm World  ( PTDC/CTA-GEO/29897/2017 ) and  CCMAR UIDB/04326/2020. D. Oliveira  acknowledges funding from  Portuguese Foundation for Science and Technology (FCT)  through the  CCMAR Research Unit  -project  UIDB/04326/2020  and contract ( CEECIND/02208/2017 ).
Data Availability Statement All original data produced for this work is available at https://data. mendeley. com/datasets/kv85rjj4y7/3 archived at the public repository Mendeley as: González-Lanchas, Alba; Flores, José-Abel; Sierro, Francisco J. (  2021 ), "High-resolution calcareous nannofossil record between the Marine Isotope Stage (MIS) 12 to 9 from the IODP Site U1385 ", Mendeley Data, V3, doi: 10. 17632/kv85rjj4y7. 3".
Acknowledgements. Computer resources were allocated by  GENCI  (project  90569 ). We thank the two anonymous reviewers for their comments, which helped to improve the overall quality of the paper. Financial support. The research leading to these results has received funding from the  ANR-17-CE01-0010   DIP-NAWDEX  project. The SAFIRE Falcon contribution to NAWDEX received direct funding from  L'Institut Pierre-Simon Laplace (IPSL) ,  Météo-France ,  Institut National des Sciences de l'Univers (INSU,  via the  LEFE program ),  EUFAR Norwegian Mesoscale Ensemble and Atmospheric River Experiment (NEAREX)  and  ESA  (EPATAN; contract no. 4000119015/16/NL/CT/gp ). Review statement. This paper was edited by  Lukas Papritz  and reviewed by two anonymous referees.
The cumulative percentages of participants reporting AEs by severity (mild, moderate and severe) within 7 days of vaccination throughout the entire study period were also calculated. Several secondary objectives were included; however, these were not the primary focus and most of them are not presented in detail here. The outcomes for which data are not reported in depth here (i. e. weekly and cumulative percentages of AEs according to MedDRA classification by risk status/age group/overall) can be found in the GSK Study Register (GSK study identifier 207750)  [24]. The 95% confidence intervals (95% CIs) accounting for the clustering effect of centres were computed for all estimated percentages of dose 1. The clustering effect was not accounted for in the calculation of the 95% CIs for all estimated percentages of dose 2 because all participants were from the same centre.
3% and 65. 9% in Belgium, Germany and Spain, respectively). Among the 96 children who received dose 2, 99. 0% were Caucasian and 71. 9% were considered at risk for influenza-associated morbidity and mortality by their HCP (Table  1 ). Co-administration of other vaccines on the same day as dose 1 of GSK's IIV4 occurred in 4 adults (1. 3%) in Belgium (pneumococcal vaccine), 4 adults (1. 4%) in Germany (encephalitis vaccine) and 83 participants (18. 0%) in Spain (primarily hepatitis, pneumococcal and other bacterial vaccines) (Table  S1  in the supplementary material). No other vaccines were co-administered with dose 2. As the study was carried out during the COVID-19 pandemic, data were collected on any COVID-19 infection (i. e. suspected, probable or confirmed based on WHO case definitions  [23] ) participants had before or during the study period.
CONCLUSIONS This ESS study assessed AEIs/AEs experienced within 7 days post-vaccination with GSK's IIV4, to address EMA requirements relating to seasonal influenza vaccines. Data were captured in near real time across all eligible vaccination age groups. The COVID-19 pandemic did not alter the safety profile, and no safety signals that impact public health or alter the benefit-risk profile of GSK's IIV4 were identified during the study or at the end of the study period. The study supports and confirms the excellent safety profile of GSK's IIV4 in all age groups for which the vaccine is indicated. Mitra Yousefi and Ugo Nwoji also hold shares in the GSK group of companies. Pooja Jindal was consultant at Parexel International on behalf of the GSK group of companies during the conduct of the study.