dataesr/scientific-paragraphs-categorization huggingface dataset:
After cell surface staining, samples were washed, fixed in 1. 6% paraformaldehyde (PFA) (EMS) for 10 min and, finally, incubated in 1 ml of intercalator solution at a final concentration of 125 nM. The samples were kept cold and shipped in intercalator solution to the Cytometry Research Core Facility at the University of Rochester, NY. The samples were washed and resuspended in Maxpar water before being acquired using a cytometry by time of flight (CyTOF)/mass cytometry Helios instrument (Fluidigm). Data were then debarcoded using Fluidigm debarcoder software (available online).
Bioinformatics analysis. CyTOF datasets from transplanted HIO-and humanized mouse SI-isolated immune cells were generated using mass cytometry (Fluidigm) variation at three time points, 12 weeks, 16 weeks and 20 weeks. Datasets at aforementioned time points were generated in two batches for each tissue at 12 and 16 weeks post-transplantation with a total of n = 7 samples and n = 10 samples, respectively, and in one batch for each tissue at 20 weeks post-transplantation. Combining two batches, ~1. 1 million cells were analyzed, amounting to 63,076 cells in 12-week HIO, 350,132 cells in 16-week HIO, 148,274 cells in 20-week HIO, 137,887 cells in 12-week SI, 368,893 cells in 16-week SI and 71,569 cells in 20-week SI. Raw. fcs files for each sample were read and analyzed using read. flowSet function from the flowCore R (v.
This k-nearest neighbor graph was finally partitioned into clusters based on highly interconnected colonies of cells. In the end, all the processed Seurat objects for each sample from two batches were integrated together using Seurat's reciprocal principal component analysis as the numbers of cells were large (~1. 1 million). Briefly, each dataset was projected into principal component space of other datasets to learn the anchors (cell pairs) based on the mutual nearest neighbor graph requisite. Dimension reduction plots were generated using DimPlot function and heatmaps were generated using the DoHeatmap function in Seurat. Example datasets and analysis code have been deposited to GitHub: https://github. com/praneet1988/Analyze_CyTOF_Using_Seurat.
Samples were washed twice and resuspended in FACS buffer. The samples were then recorded on an Aurora instrument (Cytek) and the data were analyzed with FlowJo software (TreeStar). Detection of cytokines by Milliplex assay. Indicated cytokines were measured in supernatants from immune cells (cell density 10 6 per ml) isolated from HIO or mouse SI stimulated for 3 d with a cocktail of anti-human CD3/anti-human CD28 antibodies (STEMCELL Technologies). The presence of cytokines in supernatants was measured using Milliplex kits (Millipore), following the manufacturer's instructions.
Signals were amplified with RTU Vectastain ABC reagent (Vectorlab) and precipitated using DAB Kit solution (Vectorlab), and finally counterstained with Mayer's hematoxylin solution (Dako). Images were captured on a Nikon Eclipse Ti and analyzed using Nikon Elements Imaging software (Nikon). Immunofluorescence staining on human fetal intestinal tissue. Immunofluorescence staining was conducted as previously described  40. Briefly, human fetal intestinal tissue (~0. 5-cm fragments) was fixed in 10% neutral buffered formalin for 24 h at room temperature, then paraffin embedded and sectioned (5-μm thickness). Paraffin sections were first deparaffinized in Histo-Clear II (National Diagnostics) and re-hydrated. Antigen retrieval was performed by steaming slides in a sodium citrate buffer for 20 min.
The three lysis washes were collected in a 15-ml conical for RNA extraction. RNA was quantified with the Qubit RNA HS Assay (Life Technologies). Then, 1. 0 μg of RNA was used in cDNA synthesis using Superscript IV Reverse Transcriptase (Life Technologies) following the manufacturer's standard protocol. Gene expression was evaluated using IDT PrimeTime qPCR Assays (IDT) following both the protocol and suggested cycling conditions for 10-μl reactions. qPCR was performed on the QuantStudio 12K Flex Real-Time PCR System (Applied Biosystems) and analyzed with the QuantStudio 12K Flex software v. 1. 2. 2 (Applied Biosystems). Primers will be provided upon request.
Acknowledgements We thank  Veterinary Services  and the  Pluripotent Stem Cell Facility at CCHMC  for their technical support. We thank the  Research Flow Cytometry Core at CCHMC  (supported in part by  National Institutes of Health (NIH)  grant no. S10OD025045 ) and  A. Sproles  for assistance with the multiplex (Luminex) cytokine assay. All flow cytometric data were acquired using equipment maintained by the  Research Flow Cytometry Core in the Division of Rheumatology at CCHMC. We thank the  Pathology Research Core at CCHMC  for histology and immunohistochemical staining. We thank  Y. Rochman  from the  Allergy Division at CCHMC  for her helpful advice for flow cytometry cytokine intracellular staining. We thank  S. Andorf  from the  Biomedical Informatics Division at CCHMC  for her helpful advice for CyTOF data analysis.
We thank  M. Cochran  and his team from the  University of Rochester  for CyTOF sample acquisition. This work was supported by the  National Institute of Diabetes and Digestive and Kidney Diseases  and the  National Institute of Allergy and Infectious Diseases of the NIH  under grant no. U01DK103117  (M. A. H. and J. M. W. ) as part of the  Intestinal Stem Cell Consortium. We also received support from the  Cincinnati Digestive Disease Center Award  (grant no. P30 DK0789392 ) (M. A. H. ). In addition, funding for this study was generously provided by philanthropic support from the  Farmer Family Foundation.
Patients The analysis of clinical responses to EBS was performed from a database established on EBS collected from 329 consecutive patients (167 females, 162 males; mean age: 24. 9 ± 13. 38 years) who underwent SEEG for pre-surgical evaluation of drugresistant epilepsy between August 2000 and September 2020 in the Epileptology UniteClinical Neurophysiology Department of the Timone University Hospital in Marseille, France. The left cerebral hemisphere was explored in 254 patients (6. 93 ± 4. 44 electrodes per patient) and the right cerebral hemisphere was explored in 269 patients (7. 14 ± 4. 52 electrodes per patient). This study was approved by the local ethics committee (the institutional review board of the Assistance Publique Hopitaux de Marseille, C3BHV2), and informed written consent was obtained from all patients.
Data collection and assessment Subjective reports and behavioral responses to cortical EBS in 329 epilepsy patients were recorded with video-SEEG. They were carefully analyzed and entered into an Excel database. A total of 10 106 EBS using 50 Hz electrical stimulation (train duration: 3À5 s) were collected. After each stimulation, the patients were systematically asked to express the sensations and symptoms that may have been evoked by that stimulation. The effects on the EEG (after-discharge) as well as the clinical effects were routinely collected and systematically documented in dedicated notebooks, and the video-EEGs of positive responses were stored. For the present study, all the reported emotional responses of positive valence (feelings of happiness, well-being, joy, pleasant sensation of any intensity) were systematically retained and verified on the video-EEG by two expert clinicians (CV and FB).
, 2010). On the other hand, only few publications reported pleasant sensations during EBS in the amygdala (see  (Guillory & Bujarski, 2014)  for review). Lanteaume et al. (2007)  reported sensations of "joy" or "happiness" during left amygdala stimulation in a minority of patients, as most EBS resulted in negative sensations. This study quantified subjective responses using a basic emotion scale (Izard scale). A more recent study found that only one out of 150 amygdala stimulation resulted in a subjective feeling of joy  (Inman et al. , 2020). Our study also shows that the anterior cingulate gyrus and the temporal pole may also contribute to pleasant sensations. These observations are consistent with some data from EBS of the temporal pole. Analyzing the semiological aspects of temporal pole stimulation,  Ostrowsky et al.
Zrn, zircon. Ti contents in the IDDP-1 zircons and temperature estimates calculated according to the Ti-in-zircon thermometer  (Ferry and Watson, 2007)  are shown. (B) U-Th isochron diagrams for in-situ analyses of zircons from Viti granophyre and IDDP-1 rhyolite sample (discovered during the first and the second SEM session). Isochron dates are derived using shaded data for whole rocks and individual zircons with dates calculated using IsoplotR  (Vermeesch, 2018). Each datum represents an in-situ analysis of a single zircon crystal. Error ellipses are 2σ. diagrams for Krafla basalt and the Viti granophyre (melt fraction versus temperature) produced using of the Rhyolite-MELTS software  (Gualda et al. , 2012 , https://melts. ofm-research. org/). The system of equations has the following form: ρ i C i ∂T ∂t ∂ ∂x k i ∂T ∂x ; C i C 0 i + ∂X i ∂T L * i ; k i k i Nu i , T ( ); Nu i Q conv Q cond ωpRa i 1/3 ; Ra ρβΔTh 3 κμ T, X ( ) ; κ k m ρC i Here ρ is the density of magma, C is the heat capacity, T is the temperature, X is the crystal content, L* is the latent heat of crystallization, k is the thermal conductivity, Nu is the Nusselt number, Ra is the Rayleigh number, ω ~0.
Thick lines correspond to the basaltic magma for scenario  (1) , for the center of the sill for scenario  (2)  and for the bottom of the sill for scenario (3). Thin lines are labeled as a distance (in m) from the contact between the injected magma and host rocks. 1D transient diffusion equation is solved inside the melt shell surrounding the growing zircon. The MatLab code is available from https://github. com/crystalworkshop/krafla_sill. For the geochemical modelling (Supplementary Table  S3 ) of residence times from the Zr profiles surrounding the IDDP-1 zircon crystals, we have applied the diffusion equation t ~X2 /D (e. g. ,  Crank, 1979) , where t is the duration (s), X is the distance (µm) and D is the Zr diffusion coefficient in m 2 /s. Residence times calculated with Zr diffusion according to  Zhang and Xu (2016)  at 900 °C gives maximal values of 4 years, suggesting stable conditions of neither growth nor dissolution of zircon prior to sampling by the IDDP-1 borehole in 2009.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the  Institut Carnot ISIFoR ( TRACENEO  project), and the  INSU-CNES  project  PLAGIOGRAN  ( 2022PLAGIOGRAN ( -2023 ) ) to AYB. INB was supported by  NSF  grant  EAR 1822977. XL acknowledges support from grant  PID2019-105625RB-C21  funded by  MCIN/AEI/10. 13039/501100011033. This study has been partially supported through the grant  EUR TESS N °ANR-18-EURE-0018  in the framework of the  Programme des Investissements d'Avenir. OEM was partially supported by the  ERC  under the  European Union   Horizon 2020 Research and Innovation Programme  (Grant agreement  787399-SEISMAZE ) and  College de France   PAUSE program  (grant # C7H-PUB23A59 ).
Placental DNA methylation assessment and quality control DNA methylation was analyzed for >850,000 CpGs with the Infinium Human Methylation EPIC Kit (Illumina, San Diego, USA), according to the manufacturer's instructions. Sample plate layout was designed with the R/Bioconductor OSAT package  (Yan, 2023; Yan et al. , 2012 ) and randomized based on child's sex, maternal tobacco smoking during pregnancy, and delivery place. Fourteen samples were duplicated between two to five times across the microarrays to assess any technical batch effects. Raw methylation data were extracted using the R/Bioconductor ChAMP package  (Morris et al. , 2014; Tian et al. , 2017 Tian et al. , , 2023)). All 432 samples (395 samples and replicates) were below the quality threshold of less than 10% of failed probes detection p-value (>0.
01, global mean of 0. 11% of failed probes) and therefore no sample was removed. Probes were further filtered as follows: detection p-value >0. 01  (38,192 probes removed) ; beadcount <3 in at least 5% of samples (2,081 probes); non-CpG probes (2,745 probes); multi-hit probes as described by  Nordlund et al. , (2013) (8,419 probes). CpG sites at a distance ≤2 bp from SNPs with minor allele frequency <0. 05 and chromosomes X and Y (29, 987 probes) were removed using the R package DMRcate  (Peters, 2023; Peters et al. , 2015). Cross-hybridized probes (31,917) identified in previous studies were then filtered out using the R package maxprobes  (Benton et al. , 2015; Chen, 2023; Chen et al. , 2013). 752,577 methylation sites remained after quality control, normalization, and filtering of outliers.
, 2021). We relied on the 337,722 CpG sites of the EPIC BeadChip overlapping with the CpGs of the Infinium HumanMethylation450 BeadChip used in the previous study (Fig. 1 ). We used the comb-p Python module  (Pedersen et al. , 2012)  that accounts for spatial correlations of nominal p-values obtained for each CpG by the use of sliding windows and Stouffer-Liptak-Kechris correction  (Kechris et al. , 2010). This algorithm returns regional p-values with Šidák correction applied to account for multiple testing  ( Šidák, 1967). As this was a candidate approach, we used a p-value threshold of 0. 05 to start a region and a maximum distance of 500 bp to initiate a DMR. Finally, we considered DMRs including at least two probes and with Šidák-corrected p-values below 0. 05 as regions of interest. A region was considered replicated if it encompassed genes previously identified as associated with triclosan.
Gene annotations We retrieved gene annotations from the Illumina annotation hg19 reference genome available in IlluminaHumanMethylationEPICanno. ilm10b4. hg19 R/Bioconductor package  (Hansen, 2017)  and from the database of the University of California, Santa Cruz (UCSC, https://ge nome. ucsc. edu). We obtained basic information on genes identified as differentially methylated in association with triclosan concentrations from the GeneCards Human Gene Database  (Stelzer et al. , 2016).
Identification of imprinted genes To determine if any identified triclosan-associated genes are imprinted, we screened the list of imprinted genes in the following databases: MetaImprint  (Wei et al. , 2014)  (https://openebench. bsc. es/t ool/metaimprint), GeneImprint  (Jirtle, 1999)  (http://www. geneim print. com), igc. otago  (Morison et al. , 2005)  (http://igc. otago. ac. nz) as well as among the imprinted loci defined by  Yuen et al. , (2010)  and  Hamada et al. , (2016). From the initial list of 304 imprinted genes, 295 were covered by the Infinium Methylation EPIC Kit used in the current study.
4 ). First, we used the Robust Partial Correlations algorithm implemented in the R/Bioconductor package EpiDISH  (Teschendorff et al. , 2017; Teschendorff and Zheng, 2023)  on the methylation data to calculate the reference-based estimates of placental cell composition. We then adjusted our models with those estimates by adding them to the regression equations as explanatory variables. We considered zero estimates, if obtained for a cell type, as below the LOD and we imputed those values with impCoda function from the R package robCompositions that was designed to handle compositional data and that relies on an iterative regression-based procedure after KNN-initialization  (Hron et al. , 2010; Templ et al. , 2011 Templ et al. , , 2021)). As the six estimated cell proportions sum up to one for each individual, in the regression models we used five cell types (all but nRBC) to avoid singularity.
Research data and code Data used in this study are confidential and can only be provided upon a reasonable request to the SEPAGES steering committee. The analyses were conducted using R v. 4. 1. 2 (R Core Team and R Foundation for Statistical Computing, 2020), RStudio v. 2021. 09. 1-372  (RStudio Team, 2020), and Python v. 3. 7. 4 (van Rossum and Drake, 2009). The code is available under the link: https://gricad-gitlab. un iv-grenoble-alpes. fr/iab-env-epi/jedynak_prenatal_2023.
Acknowledgements SEPAGES biospecimens are stored at  Grenoble University Hospital (CHU-GA)  biobank (bb-0033-00069); we would like to thank the whole CRB team led by  P. Mossuz  and  P. Lorimier , and in particular the technicians for their work of biospecimen processing and pooling:  W. Jayar  and  L. Than  as well as  G. Schummer. We thank the  SEPAGES study group :  E. Eyriey ,  A. Licinia ,  A. Vellement  ( Groupe Hospitalier Mutualiste, Grenoble ),  I. Pin ,  S. Bayat ,  P. Hoffmann ,  E. Hullo ,  C. Llerena  ( Grenoble Alpes University Hospital, La Tronche ),  X. Morin  ( Clinique des Cèdres, Echirolles ),  A. Morlot  ( Clinique Belledonne, Saint-Martin d'Hères ),  J. Lepeule ,  S. Lyon-Caen ,  C. Philippat ,  I. Pin ,  J. Quentin ,  V. Siroux ,  R. Slama  ( Grenoble Alpes University, Inserm, CNRS, IAB ).
Funding/support This work was supported by the  French National Agency for Research  ( ANR ,  MEMORI  project  N •   ANR-21-CE34-0022 ,  SYMER  project  N  •  ANR-15-IDEX-02 ) and the  French Agency for Food, Environmental and Occupational Health & Safety (ANSES ,  PNREST PENDORE 2018/1/264 ). P. Jedynak  was supported by grants from  ANR   SYMER  project  N  •  ANR-15-IDEX-02  and  ANSES  ( HyPAxE  and  PENDORE  projects  N • EST-2019/1/039  and  2018/1/264 ). L. Broséus was supported by a grant from  ANR  ( N  •  ANR-18-CE36-0005 ). Placental DNA methylation  was funded by a grant from the  Plan Cancer  ( Canc'Air project ). The  SEPAGES cohort  was supported by the  European Research Council  ( N  •  311765-E-DOHaD ), the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 -N • 308333-892 HELIX ), the  European Union 's  Horizon 2020 research and innovation programme  ( N • 874583   ATHLETE  Project,  N • 825712   OBERON Project ),  ANR  ( PAPER  project  N •   ANR-12-PDOC-0029-01 ,  SHALCOH  project  N •   ANR-14-CE21-0007 ,  N  •  ANR-15-IDEX-02  and  N  •  ANR-15- IDEX5 ,  GUMME  project  N  •  ANR-18-CE36-005 ,  ETAPE  project  ANR -EDeN project N •   ANR-19-CE36-0003-01 ),  ANSES  ( CNAP  project  N • EST-2016-121 ,  HyPAxE  project  N • EST-2019/1/039 ), the  Plan Cancer ( Canc'Air project ), the  French Cancer Research Foundation Association de Recherche sur le Cancer (ARC) , the French Endowment Fund AGIR for   chronic diseases (APMC , projects  PRENAPAR  and  LCI-FOT) , the  French Endowment Fund for Respiratory Health , the  French Fund -Fondation de France  ( CLIMATHES -00081169 ,  SEPAGES 5 -00099903 ).
We defer the exploration of complex disequilibrium models to atmospheric retrieval analyses using the full set of data provided by the Transiting Exoplanet Community ERS programme. That dataset will also constrain the presence of additional O-and C-bearing species to provide a more robust constraint on the C/O ratio than we can obtain here. However, the C/O ratio estimate we report from NIRCam is broadly consistent with the C/O ratio found from the other individual ERS WASP-39b datasets, which range from best fits that are subsolar (Near InfraRed Imager and Slitless Spectrograph (NIRISS)/Single Object Slitless Spectroscopy (SOSS)  40  ; NIRSpec/PRISM 3. 0-5. 0 µm (ref. 7 ); NIRSpec/G395 41 ) to a slightly super-solar upper limit (NIRSpec/PRISM 0. 5-5. 5 µm (ref. 42 )). If disequilibrium chemistry is not prevalent in the planet's upper atmosphere, the inferred high metallicity and low C/O ratio can be tied back to the formation of WASP-39b.
Methods As part of this article's Reproducible Research Compendium, located on Zenodo at https://doi. org/10. 5281/zenodo. 7101283, we provide saved outputs from various pipeline stages and the data used to generate relevant figures, as well as a Jupyter Notebook with step-by-step data reduction instructions replicating our chosen analysis (https://doi. org/10. 5281/zenodo. 7510106).
Photometric monitoring of host star To confirm that WASP-39 is a relatively inactive star, and that the JWST observations were not adversely affected by stellar activity, we carried out photometric monitoring with the ground-based NGTS  11. Monitoring began at the end of April 2022 and continued until late August, spanning the JWST ERS transit observations of WASP-39b in July. We used one camera on most photometric nights to take a series of 10-s images lasting on average for 2 h. The resulting monitoring light curve is plotted in Extended Data Fig. 1  (top), showing one binned point for each night. Also included is the TESS sector 51 Pre-search Data Conditioned Simple Aperture Photometry (PDCSAP) light curve of WASP-39  12  , which is binned to 2 h to be comparable to NGTS. Both light curves have been detrended against sky brightness.
JWST NIRCam observation JWST observed the 2. 8-h transit of WASP-39b over a span of 8. 2 h, providing a baseline before and after transit to measure transit depths accurately. A dichroic beam splitter allows NIRCam to simultaneously observe a target in both SW and LW channels  53, 54. The LW channel used the Grism R + F322W2 filter to observe a wavelength range of 2. 420-4. 025 µm with a spectroscopic resolving power of R ≈ 1,600 at 4 µm (Extended Data Fig. 1, top ). The SW imaging channel used the WLP8 weak lens and F210M filter (2. 0-2. 2 µm) to produce the hexagonal pattern shown in Extended Data Fig. 2 (bottom). Spreading the light prevents saturation, reduces variability owing to image motion over an imperfect flat field and allows monitoring of mirror-segment alignment. Both SW and LW channels used the SUBGRISM256 subarray mode with four output amplifiers and the SHALLOW4 readout pattern to minimize data volume.
Data reduction and calibration We conducted independent data analyses using multiple pipelines and fitting tools to ensure that we obtained the same transmission spectrum using different reduction pipelines. We also varied the fitting methods within a given data reduction pipeline. Many of the reductions presented below used intermediate data products from or made minor edits to the JWST Science Calibration Pipeline ( jwst; https://jwst-pipeline. readthedocs. io/), which we briefly summarize here. jwst is a Python software suite for processing data from all JWST instruments and observing modes, and is organized into three stages. Stage 1 takes in uncal. fits files and performs detectorlevel corrections and ramp fitting for individual exposures (that is, ramps-to-slopes conversion; these ramps are the flux increases during an exposure, not to be confused with baseline ramps over the course of the entire transit).
Stage 2 takes in slope images (ramps) from Stage 1 and performs an assignment of the world coordinate system, flat fielding and assignment of a wavelength solution. Stage 3 takes in calibrated two-dimensional images from Stage 2 and extracts a time series of one-dimensional spectra. The default pipeline settings include a flux calibration step at Stage 2. In all data reductions presented below, we skipped that step, as it introduced scatter in the extracted spectral time series. This is justified because the transit depths we compute are relative, rather than absolute, flux measurements. Below we describe the independent data reductions applied to the SW photometry and LW spectroscopy, respectively. In each case, we note where data reductions deviated from the standard jwst pipeline. SW photometry.