dataesr/scientific-paragraphs-categorization huggingface dataset:
Morphometric measurements were performed with iTEM v5. 2 (Olympus, Tokyo, Japan) and analyzed using Image J v1. 52a program. Live-cell imaging. After transfection, HeLa cells were plated in a cell culture dish with four individual compartments. Live-imaging for 24 h at a time lapse of 5 min was performed with a Nikon A1R microscope. Image J v1. 52a program was used for image analysis. Cell cycle analysis. The relative percentage of cells in each stage of the cell cycle was analyzed by labeling nuclei with propidium iodide (PI) followed by flow cytometry analysis with FACS Calibur II ™ and ModFit LT ™ v3. 2. 1 program.
Apoptosis analysis. Apoptosis was measured with the FITC-Annexin V apoptosis Detection Kit I (BD Biosciences, #556547) according to manufacturer's instructions. Flow cytometry was performed and data were analyzed with CellQuest software v3. 2 (BD Biosciences). Annexin V + /PI -and annexin V + /PI + cells were considered apoptotic. Growth curve analysis. After transfection, HeLa cells were trypsinized and viable cells were plated at a density of 100,000 cells per well in a six-well plate. Cell counting was carried out every 24 h for 4 days, and the medium was replaced every 2 days. Chromosome spreads. HeLa cells were treated with 0. 1 µg/ml of colcemid for 16 h. Adherent and floating cells were collected, pelleted and resuspended in 10 ml heated hypotonic solution (8 g/l Na citrate, 0. 075 M KCl, H 2 O, 1:1:1) at 37 °C for 20 min.
Library quality was assessed using a Bioanalyzer 2100 HS DNA assay and quantified by qPCR. Library sequencing was performed on 4 lanes of a Nextseq 550 (Illumina) (NextSeq 500/550 HM kit v2. 5 75 cycles) to yield 92 nt long raw sequences. Fastq files were processed using the iCount v2. 0. 0 suite (https://hub. docker. com/r/ tomazc/icount/). Briefly, data were demultiplexed based on barcode (NNNGGTTNN, NNNTTGTNN, NNNCAATNN or NNNGGCGNN) and adapters (AGATCGGAA GAGCGGTTCAGCAGGAATGCCGAGACCGATCTCGTATGCCGTCTTCTGCT TG) were processed. All reads originating from the same libraries were pooled and mapped to the hg19 genome using the version of STAR included with iCount v2. 0. 0. Unique cDNA were counted and significant peaks called using the iCount v2. 0. 0 peaks function. To determine density of iCLIP tag mapping to different regions of snoRNAs, snoReport 2.
0 60 was used to determine locations of secondary structures based on primary transcript sequence. Box C/D snoRNAs were divided into stem, 5' half of loop and 3' half of loop regions. Box H/ACA snoRNAs were divided into 3' stemloop, H box and 5' stemloop regions. Tags mapping to each region were counted and divided by the total number of tags mapping to the snoRNA. Tag mapping locations were analyzed with R packages including dplyr v1. 0. 2; ggplot v2 3. 3. 2; tidyr v1. 1. 2; rtracklayer v1. 34. 2; biomaRt v2. 30. 0 and SuperExactTest. v1. 0. 7.
RNA-seq read quality was evaluated using FastQC v0. 11. 3. Reads containing adapter sequences and/or low-quality ends were identified and trimmed using bbduk v35. 59. The processed reads were mapped to the human genome (hg19) using STAR v2. 5. 2b. PCR duplicates were removed from aligned files using Picard tools v1. 139, gene expression was quantified using featureCounts v1. 4. 6-p5 and GENCODE v. 26 annotation and differential expression analysis was performed using edgeR v3. 14. 0. Differential intron-retention analysis was performed using IRFinder v1. 2. 3 using the audic-claverie-test as a test statistic. Features with a test statistic less than 0. 01 were considered to be significantly different in terms of intron retention. Other alternative splicing events were assessed with rMATS v3.
8  62. Small non-coding RNA-seq. For small non-coding RNA-seq from HeLa cells, libraries were prepared using the Clontech SMARTer smRNA-Seq Kit for Illumina protocol. To enrich for transcripts in the snoRNA size range, size selection was performed with magnetic beads. 1 ×100-bp sequencing was performed on an Illumina NextSeq 500 instrument. Adapters, poly(A) tails, and 3' bases were removed from RNA-seq reads with Cutadapt as per Clontech recommendations. RNA-seq read quality was checked using FastQC. Transcript expression (GEN-CODE v. 26 annotation) was quantified using Salmon v0. 8. 2, which includes information from reads that map to multiple locations in the genome/transcriptome (including some reads arising from sn/snoRNA genes), and summed to gene level using tximport v1. 2. 0. Differential expression was assessed using DESeq2 v1.
4. Mapped reads were analyzed using samtools v1. 9, snakemake-minimal v5. 24. 1 and the R package RNAmodR. RiboMethSeq v1. 6. 0 and the Score C/RiboMethScore were used as a measurement for 2'-O-methylation. For the analysis of methylation levels on positions known to be methylated, data from the snoRNAdb 2016 was used  63  and updated to newer rRNA sequence coordinates based on NCBI accession NR_046235. 3 available from the 'EpiTxDb' package v1. 3. 3. Only the reads shorter than 90 nt were used for the analysis based on the length distribution of reads after adapter trimming. Only bases with sufficient read coverage for analysis were displayed. Summary information was obtained using multiqc v1. 9. Investigation of intrinsic disorder properties. Three algorithms were used: MFDp2 v2. 0, PrDOS v2.
Acknowledgements The authors thank the GIGA Cell Imaging and  Flow Cytometry  and the  GIGA Transcriptomic  core facilities for technical assistance. We also thank the  GIGA scientific illustrator ,  Adeline Deward , for designing and formatting figures and  Rümeyza Bascetin  ( RNA Molecular Biology, ULB, Belgium ) for her advice in microscopy. We also thank the Genouest bioinformatic platform at the  IGDR (Rennes, France ) and the BRIGHTcore facility at the  Université Libre de Bruxelles (ULB, Belgium)  for providing computing resources for iCLIP-Seq and RiboMethSeq experiments, respectively. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  National Fund for Scientific Research (FNRS)  under Grant No. 2. 5020. 11and by the  Walloon Region.
Demographics and summary statistics After data cleaning and aggregation of the first three months of data (Fig. 1 ), respondents in our sample (N=39,129) were between 18 to 80 years old (median=33 years; Mage=34. 22 years, SD=9. 18). All participants were fully vaccinated (at least fourteen days after one or two required doses as this was before boosters) and had not contracted COVID-19 (diagnosed or suspected). This sample included 35,572 (90. 9%) woman-only identifying and 3,557 (9. 1%) gender diverse respondents; 32,983 (84. 3%) white-only identifying and 6,146 (15. 7%) racially diverse respondents; and 31,134 (79. 6%) non-Hispanic or Latinx and 7,995 (20. 4%) Hispanic, Latinx, or other respondents (summary demographics in Table  1 ; more detailed in Table  S1 ). Respondents were vaccinated with Pfizer (N=21,620), Moderna, (N=13,001), AstraZeneca (N=751), Johnson & Johnson (N=3,469), Novavax (N=61), or other (N=204) vaccines, with 23 not reporting vaccine type.
Of the thirty-one respondents who reported this bleeding to their physician, thirteen had endometrial biopsies collected, and two had transvaginal ultrasounds. While all results were benign, endometrial biopsies are known to be painful and invasive procedures  (55, 56). Though these data have been reported in the literature for at least a decade, no patient-facing information about epidurals that we could find makes note of the risk of unexpected bleeding, which means potentially unnecessary, expensive, painful diagnostic procedures may continue today. Unexpected bleeding has other major and even life-threatening consequences. Trans men, trans masculine people, and masculine of center genderqueer people, many of whom suppress periods with a combination of LARC and masculinizing therapies, may find themselves suddenly navigating public bathrooms or workplaces while menstruating.
Recruitment and Survey information This research was designated as exempt by the University of Illinois Institutional Review Board and Washington University in St. Louis Institutional Review Board. Data were collected and managed using REDCap hosted at the University of Illinois at Urbana-Champaign  (98, 99). REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies. The survey launched on April 7, 2021, and data for these preliminary results were downloaded on June 29, 2021 (approximately 12 weeks of data collection). The survey was initially announced on Twitter to recruit people who currently or previously menstruated and had been vaccinated  (100, 101) , but it quickly propagated through multiple social media platforms.
Media coverage (TV news, public radio, online journalism, print journalism, science blogs, etc. ) of the study included links to the survey and provided wide-spread participant recruitment. Additionally, many participants learned of the survey after performing an online search to investigate their own menstrual experiences and finding social media and/or news coverage of this project. Thus, the data collected by this survey represents extensive snowball sampling via many channels. The survey included a mixture of multiple-choice and text entry questions about typical menstrual experiences (e. g. , period flow, cycle length, bleeding duration, common menstrual symptoms), menstrual experiences after each vaccine which make comparison to expected period symptoms (e. g. , heavier/lighter/same), other menstrual symptoms, time between vaccine and menstrual side-effects (multiple choice question with ranges), reproductive history (e.
Data Cleaning Data cleaning was performed on select text entries. Specifically, use of gender affirming hormones, reasons for irregular menstruation or non-menstruation, current pregnancy, IUD type, age, and post-vaccine menstrual experience (e. g. , breakthrough and menstrual flow) were coded from text responses or from existing survey questions. Common reasons for irregular menstruation or non-menstruation were categorized by combining text entry responses with checkbox options added after the survey was live (i. e. , using gender affirming hormones, using long-acting reversible contraceptives, perimenopausal status, postmenopausal status, history of hysterectomy, current or recent lactation, and other). We screened for current pregnancy by evaluating respondent text responses regarding reasons for irregular or non-menstrual status.
g. ,  Moutou et al. 2014; Díaz et al. 2016; Hébrard et al. 2016). Some of the targets presented in this work were already observed with elodie, and here we provide time baselines of up to 15 years. Such data sets are gold mines for detecting new cold Jupiters and refining our understanding of this population. In Section 2 we present the RV data sets. In Section 3 and 4 we explain our derivation of the host star and planet properties. Finally, in Section 5 we put the newly detected cold Jupiters in perspective and emphasize their contribution to our understanding of planetary systems.
The data sets: High-resolution spectra and radial velocities In this section, we present the high-resolution spectra of six stars in our sample obtained with sophie and elodie. elodie has a resolution of 42000  (Baranne et al. 1996). sophie is a cross-dispersed, stabilized échelle spectrograph on sky since 2006, dedicated to high-precision RV measurements  (Perruchot et al. 2008; Bouchy et al. 2009). Observations were taken in the fast-readout mode of the detector and in the high-resolution (λ/∆λ = 75000) configuration of the spectrograph. To evaluate the sky or moonlight pollution, one of the optical fibers was placed on the sky while the other was used for the starlight. Depending on the star, data were recorded over time spans of 2 to 15 years. Most of the spectra have a typical signal-to-noise ratio (S/N) per pixel of 50 at a wavelength of 550 nm.
Wavelength calibrations were performed at the beginning and end of each observing night, and approximately every two hours during the night. We observed the stars using elodie and sophie. However, for the analysis, we considered the data as coming from three different instruments: elodie, sophie, and sophie+. sophie+ is the result of an upgrade made in June 2011 consisting mainly in the addition of octagonal fibers to guide the light to the dispersive elements (Sect. 1 of  Bouchy et al. 2013). Three of the stars were observed with elodie, sophie, and sophie+, while the three remaining stars were only observed with sophie+. The sophie pipeline  (Bouchy et al. 2009 ) was used for extracting the spectra and cross-correlating them with a numerical stellar mask. We first considered a G2 mask and incorporated all the spectral orders to produce cross-correlation functions (CCFs).
A value of 4. 73 mag for M bol, is adopted in Eq. 1 since the  Flower (1996)  polynomials are used to determine BC v. This is because the bolometric magnitude of the Sun must be consistent with the zero point of BC v so that the apparent brightness of the Sun is reproduced (see  Torres 2010). We note that A v was obtained based on multiband photometry (Bailer-Jones 2011) since two of the stars in our sample (namely, HD 27969 and HD 211403) have low galactic latitudes and have a much lower true A v than the full extinction of the Galaxy along their line of sight, as provided by commonly used dust maps such as MWDUST3 3  code  (Bovy et al. 2016). We note that AIMS combines a Markov chain Monte Carlo (MCMC) approach and Bayesian technique to explore the model parameter space and find models that have parameters comparable to the specified sets of observables.
The planetary model Since our RV survey aims at discovering exoplanets, we first analyzed our six data sets under the planetary hypothesis. As we see in Section 4. 8, this hypothesis is indeed the one favored by the data for all data sets. We used part of the radvel. kepler. rv_drive function of the Python package radvel 4   (Fulton et al. 2018)  to model the RV signal induced by the presence of a planet. To ease the exploration of the parameter space, we used a parametrization of the model that minimizes the correlation between parameters (modified from  Eastman et al. 2013 ): P the orbital period, t ic the planet's time of inferior conjunction, e cos ω * and e sin ω * , where e is the orbital eccentricity and ω * is the stellar orbital argument of periastron, K the RV semi-amplitude, and v 0 the systemic RV.
This approach allows a blind search of the data to be performed. In all cases except for HD80869 (see Section 4. 3), it allows us to confirm the most significant peak of the GLSP as the planetary orbital period. For HD80869, the highest peak is actually an alias of the orbital period. For the likelihood functions, we used multidimensional Gaussians, including the additive jitter terms (σ inst ) as described by  Baluev (2009). To locate the maximum of the posterior pdf (map) and infer reliable error bars for the parameters, we explored the parameter space using an affine-invariant ensemble sampler for mcmc thanks to the Python package emcee 4 (see  Foreman-Mackey et al. 2013). We adapted the number of walkers to the number of free parameters in our model. As a compromise between the speed and the efficiency of the exploration, we chose to use five times the number of free parameters for the number of walkers.
HD109286 For this system, we have 45 measurements from sophie+. The GLSP of the data (see  shows five peaks with an fap lower than 0. 1 % around 0. 23, 0. 33, 0. 5, 1 and 500 days. We performed one exploration with 50,000 iterations per walker. It identified five local maxima. The difference of the logarithm of the posterior pdf values of the five lowest local maxima compared to the highest one are ∆ ln(post) > 100. After selecting the (f) HD211403: rms of the residuals = 35. 9, 37. 9 and 76. 6 m s -1 (sophie+, sophie, elodie) Fig. 1 : Phase-folded RV curves for our six planets with the best-fit models. The color and filling of the points indicate the instrument used to acquire the data: empty blue for elodie, filled orange for sophie, and empty orange for sophie+. The error bars provided with the RV data are displayed with the same opacity and color as the points.
Astrometry To investigate the CSB or HTS hypothesis even further, we inspected the Gaia Archive database 9  (Gaia Collaboration et al. 2016 , 2018)  to find possible astrometric motions for these systems. The presence of an astrometric motion could show either an inclination different from edge-on or the presence of an unseen long-period companion  (Kiefer et al. 2019; Kiefer 2019). The six sources presented in this paper were all observed by the Gaia space telescope with data published in Data Release (DR) 1 and 2. Somewhat significant excess noise (>0. 5 mas;  Kiefer et al. 2019 ) was found in the DR1 for only two systems, HD27969 ( DR1 =0. 62 mas) and HD109286 ( DR1 =0. 88 mas). Nevertheless, these excess noises hardly stand out compared to the distribution of astrometric excess noise published in the DR1 for all primary sources monitored with Gaia, of which a majority are single stars, with a median at 0.
Type) indicates the astrometric model adopted by the new reduction. For the standard five-parameter solution it is "5. " The parameter N orb represents the number of orbital periods covered by the Hipparcos observation time span. N Hip is the number of astrometric measurements with a median precision of σ λ. The last column in Table  3  shows the maximum companion mass (M 2,max ) that would be compatible with a non-detection in the Hipparcos astrometry. We do not detect significant orbital motion in the astrometry for any of these sources, but we determine upper mass limits of 0. 14, 12. 02, and 0. 6 M for the companions of HD27969, HD109286, and HD211403, respectively. For our six targets, the Gaia and Hipparcos data thus agree with the planetary origin of the signal observed. They do not provide any indication that it could instead be produced by an SB, a CSB, or an HTS.
Search for additional planetary signals Kima 10 is a Python/C++ open-source package developed for the detection of exoplanets with RV data  (Faria et al. 2018). It makes use of the diffusive nested sampling algorithm  (Brewer et al. 2011)  to infer the number of planets present in a set of RV measurements. To do this, kima calculates the evidence for a model with a given number of planets (N p ). In our work N p is set as a free parameter between 0 and 2, and we evaluate the posterior distribution for N p to determine the number of planets detected. This means that to claim the detection of N p planets, the probability of N p planets needs to be at least 150 times greater than the probability of N p-1 planets  (Kass & Raftery 1995). The analyses of the RV series of our six targets conclude that in all cases the best model is a model with only one planet.
Discussion In this paper we have presented the detection of six giant planets with minimum masses ranging from 2. 99 to 8. 29 M jup and long orbital periods ranging from 223. 7 to 3700 days (∼ 10 years). 10 https://github. com/j-faria/kima We now discuss the importance of these planets in the context of the known exoplanet population. Reaching a statistical understanding of the eccentricity distribution of the exoplanet population, and in particular its high tail, is essential for constraining planetary formation and migration models (e. g. ,  Chatterjee et al. 2008; Jurić & Tremaine 2008; Bowler et al. 2020 ). Eccentric planets are not a direct output of planet formation from core-accretion in a protoplanetary disk (see for example  Lin & Ida 1997). The interaction between the planet and the disk usually damps the eccentricity.
-The best-fit values are computed as the median of the cleaned MCMC chains. -The errors bars correspond to 68% confidence intervals. -The v sin i * values were obtained from the ccf using the sophie pipeline  (Boisse et al. 2010) , except for the fast rotator HD211403 for which the sme software was used  (Valenti & Piskunov 1996). However, we not that for this star  (Boisse et al. 2010 ) provides an estimate of 17. 9 ± 1 km s -1 in good agreement with the sme value. -The micro-turbulence velocity (vturb) is estimated as part of the spectroscopic stellar parameter derivation (Section 3. 1). We refer the reader to  Sousa (2014)  for more details. -The reference time for the RV slope model is the time of the first data point available in the data set. -• indicates that the parameter is a main or jumping parameter for the mcmc explorations performed in Section 4.
Acknowledgements. O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through  Fundação para a Ciência e a Tecnologia (FCT). This work was supported by  Fundação para a Ciência e a Tecnologia (FCT)  and  Fundo Europeu de Desenvolvimento Regional (FEDER)  via  COMPETE2020  through the research grants  UIDB/04434/2020 ,  UIDP/04434/2020 ,  PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113 ,  PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953. This work has been carried out in part within the framework of the  NCCR PlanetS  supported by the  Swiss National Science Foundation. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  Spice Dune ; grant agreement No  947634 ).
Shape Dataset For our dataset, we used Distmesh  [18]  to create a set of synthetic triangular meshes. As shown in Figure  2 , the shapes ranged from simple convex shapes to nonconvex shapes with different levels of complexity. The dataset was divided into two subsets: one subset of 8 coarse meshes composed approximately of 100 triangles  C ← 1 Nt Nt i=1 P(i, :) -P(i, :) Average displacement 13: end while and one subset of 8 fine meshes composed approximately 1000 triangles. The meshes were designed so the distances between connected vertices were approximately the same. We use the areas of each of the triangles as prescribed areas A o.
Methodology We test the effectiveness of PBD-opt by applying an initial deformation to a synthetic mesh and measuring the number iterations it takes to converge compared to PBD-lin. Convergence is achieved when the average displacement of the mesh's vertices is lower than some displacement threshold T c. We test both methods with the coarse and fine mesh datasets. For each dataset, we perform 200 random deformations per mesh (1600 deformations in total). We applied initial deformations to the meshes of 5%, 10% and 20% of the maximum inter-vertex distance D. We measure the convergence speed as the number of iterations it takes to reach 3 different displacement thresholds T c at 5%, 2. 5% and 1% of D. A run stops when a method's cost reaches the lowest threshold (T c = 1%) or after it reaches a stopping time (10,000 iterations 3  ).
Results Results can be found in figures 4 and 5. In the left column of each figure we use box plots to compare the median and variability of convergence speed of both methods according to their deformation and displacement threshold. Due to the large number of outliers obtained, especially with PBD-lin, we decided not to include them in the box plots but rather to represent them in stacked bar graphs in the right column of each figure. For the coarse database we observe that the median convergence time for PBDopt is higher compared to PBD-lin for a threshold of 5% and relatively similar or lower for 2. 5% and 1%. However, since all the box plot pairs overlap each other, we cannot conclude with 95% confidence, that the medians do differ. However, we can claim that the results of PBD-opt are more stable since they have either similar or smaller variances compared to the results of PBD-lin.