dataesr/scientific-paragraphs-categorization huggingface dataset:
Stage 2 of jwst was skipped, as it only changes the rates from analogue-to-digital units (ADU) per second to physical units and conducts flat fielding. This does not affect the relative measurements of the light curve (due to the high pointing precision) and allows for comparison with detector-level effects. For the photometric extraction, we used a source radius of 79 pixels and a background annulus of 79 pixels to 100 pixels. We performed a two-dimensional Gaussian fit to determine the centre of the aperture. LW spectroscopy. We performed three independent LW data reductions, using the Eureka!, Atmospheric Transmission Spectroscopy Analysis Code (HANSOLO) and tshirt pipelines. The reference files in the Calibration Reference Data System at the time of our analysis included a linear solution for wavelength as a function of x coordinate (the dispersion direction), but this is not strictly accurate at the blue end.
For all methods, we use commissioning programme 1076 to derive a third-degree polynomial wavelength solution that uses the Pfund and Bracket hydrogen series in the planetary nebula IRAS 05248-7007. The residuals in this solution are ≲0. 1 nm and the stellar absorption lines in WASP-39 agree with the solution to within 1 nm. The difference between the corrected wavelengths and the original wavelength solution is almost zero at the red end of the spectrum, but increases to about 50 nm at the blue end. Eureka! LW reduction. We investigated several variations of the Eureka! LW data reduction to minimize the median absolute deviation (MAD) of the final extracted light curves, with different settings for cosmic-ray jump detection, identifying the spectral trace, the aperture size for spectral extraction, the region for background subtraction and limits for outlier rejection.
Data analysis and fitting We used both Eureka! and tshirt to fit the SW light curves. In both cases, the light curves were fit with models that included both the transit and the systematic noise. However, to investigate the effect of different systematic models on the resulting spectra, each fit used a slightly different noise model. Extended Data Table  1  summarizes the systematics models that were used in each SW fit. For the LW fits, we summed the data into 15 nm bins (about 15 pixels). We experimented with bins as small as 10 nm, but found that reducing the bin size below 15 nm led to poor constraints on the limb darkening and added additional scatter to the resulting spectrum. Extended Data Fig. 4  shows that the noise is primarily Gaussian out to long timescales of order the length of ingress/egress.
In addition, we created a white-light curve by summing the extracted spectra over the entire 2. 420-4. 025 µm wavelength region. We experimented with different wavelength cut-offs but chose to extract spectra in this wavelength region because the low instrument throughput affected the quality of the extracted light curves beyond this region. Extended Data Fig. 5  shows all reduced transmission spectra with one bin added on the blue end and two added on the red end, as well as the relative throughput at the wavelengths of these bins. This figure shows the large error bars derived from data near the edges of the NIRCam/F322W2 bandpass. Therefore, we recommend that future works limit extracted spectra to the wavelength region between 2. 420 µm and 4. 025 µm. We fit the LW light curves using four independent pipelines: chromatic-fitting, Eureka!, HANSOLO and tshirt.
chromatic-fitting is an open-source (https://github. com/catrionamurray/chromatic_fitting/) Python tool to perform light-curve fitting, built on the data visualizer chromatic (Z. K. Berta-Thompson, manuscript in preparation; https://github. com/zkbt/chromatic/). For this work, chromatic-fitting light-curve fitting was applied to a Eureka! data reduction. As with the SW fits, we fit the LW light curves with models that include different noise parameterizations. Extended Data Table  2  summarizes the systematics models that were used in each LW fit. For all fits, the parameters were estimated with a Markov chain Monte Carlo fit, using either the emcee Python package  60  (for fits performed with Eureka!), the pymc3 Python package  61  (implemented through the Exoplanet code  62, 63  , for fits performed with chromatic-fitting or tshirt) or the CONAN Python package  57, 58  (for fits performed with HANSOLO).
Therefore, the results presented here use the quadratic limb-darkening law, in its classical form or reparameterized by ref. 69  , with one or both coefficients as free parameters. We confirmed that these parameterizations produce transmission spectra that are consistent both with each other and with the spectra resulting from using more complex limb-darkening parameterizations, such as a four-parameter law with either fixed or free parameters  70. We therefore recommend that future transmission spectrum analyses with NIRCam use similar methods. Limb-darkening conclusions from the full Transiting Exoplanet Community ERS programme will be discussed further by N. Espinoza et al. (manuscript in preparation). The final fitted light curves are shown in Extended Data Fig. 6  and the final transmission spectra are shown in Fig.
2. Both the SW and LW datasets are also available in our Reproducible Research Compendium on Zenodo at https://doi. org/10. 5281/zenodo. 7101283. The median difference between each transmission spectrum and the Eureka! spectrum is 0. 87σ (using the maximum error at each point), which demonstrates a remarkable level of agreement. In addition, the residuals showed no evidence for time-correlated noise, as shown in Extended Data Fig. 5. For ease of interpretation, we compared our atmospheric models with only one transmission spectrum. We selected the Eureka! spectrum, as it was on average nearest the median spectrum (the median transit depth at each bin).
The PHOENIX model grid. We also used a grid of atmosphere models from the PHOENIX radiative-convective equilibrium code to fit the data  [74] [75] [76]. Similar to the PICASO 3. 0 grid, parameters including the daynight energy redistribution factors, interior temperature (200 K and 400 K), bulk atmospheric metallicity (0. 1×, 1×, 10× and 100× solar) and C/O ratio (136 grid points from 0. 3 to 1) were varied. Aerosol properties were parameterized through a haze factor (0 and 10× multi-gas Rayleigh scattering) and a grey-cloud-deck pressure level (0. 3 mbar, 3 mbar and 10 mbar). Models with molecular abundances quenched at 1 bar to simulate vertical mixing were also calculated. The grid also included rainout to account for species sequestered as condensates in the deep atmosphere. Opacities are described by refs.
76, 92 and taken from ref. 88. The ATMO model grid. Similar to the model grids described above, we compared the data to a grid of models from the ATMO radiativeconvective-thermochemical equilibrium code  [71] [72] [73] 93. The ATMO grid used similar atmospheric and aerosol parameterizations to those used in the PHOENIX grid and also included rainout that accounts for species condensed in the deep atmosphere. Also included are day-night energy redistribution factors (0. 25, 0. 5, 0. 75 and 1; with 1 as full redistribution), atmospheric metallicity (0. 1×, 1×, 10× and 100× solar), interior temperature (100 K, 200 K, 300 K and 400 K), C/O ratio (0. 35, 0. 55, 0. 7, 0. 75, 1. 0 and 1. 5), cloud scattering factor (0, 0. 5×, 1×, 5×, 10×, 30× and 50× H 2 Rayleigh scattering at 350 nm between 1 mbar and 50 mbar pressure levels) and a haze scattering factor (1× and 10× multi-gas Rayleigh scattering).
Opacities for H 2 O, CO 2 and CH 4 are taken from refs. [83] [84] [85] [86]  and for H 2 S from ref. 88. Grid fits to JWST/NIRCam data. We applied each of our three grids-ATMO, PHOENIX and PICASO 3. 0-to fitting the NIRCam F322W2 spectrum (2. 4-4. 0 µm). In doing so, we found that the models strongly favoured a solar-or super-solar-metallicity atmosphere (1-100× solar), a substellar C/O ratio (≤0. 35) and substantial contribution from clouds, which are parameterized differently by each model grid (see each grid description above). We show the best fits from each model grid in Extended Data Fig. 7. This interpretation is in agreement with the results using JWST's NIRSpec/PRISM instrument from 3. 0-5. 0 µm (ref. 7 ), improving on the wider spread from previous HST-only  [13] [14] [15] 17, 18, 94  or HST and ground-based optical interpretations  16.
As with the other two grids, strongly cloudy models (cloud factor of ≥5) were preferred to clear models ( χ ν 2 of 1. 1 versus 1. 2). HST + NIRCam. In Extended Data Fig. 8 , we show the comparison between the spectra of HST/WFC3 (G141 and G102, covering 0. 8-1. 65 µm) and JWST/NIRCam (F210M + F322W2, 2. 0-4. 0 µm). We chose to show only WFC3 observations from HST, as these are of higher precision than observations from the Space Telescope Imaging Spectrograph or ground-based data  13. In addition, as HST/WFC3 has the most archival exoplanet data of any instrument on HST, the future JWST exoplanet programme will primarily rely on this HST instrument for inter-telescope comparisons or extending the wavelength coverage of JWST data. For example, the addition of optical and shorter wavelength near-infrared data can help break metallicity degeneracies by better constraining the presence and extent of clouds  13  (for example, ref.
8 µm prevents NIRCam from making a significant CO 2 detection. Given our upper limit on CH 4 abundance, we also performed the same Gaussian residual fitting for CH 4 and find a weak or non-detection at approximately 2σ. Both WASP-39b NIRSpec datasets  7, 41, 42  observed evidence for a molecular feature near 4. 0 µm, which is currently best explained by sulfur dioxide. The reddest data points (>4. 025 µm) from NIRCam also show an increase that is consistent with this feature seen in the NIRSpec data. However, as shown in Extended Data Fig. 5 , these NIRCam data points have very large error bars because the detector throughput drops off dramatically past 4. 0 µm. Future investigations to thoroughly explore the physicochemical likelihood of sulfur dioxide in the atmosphere of WASP-39b must rely on wavelengths that can fully capture the complete absorption feature, which is beyond the reach of high-fidelity NIRCam/ F322W2 measurements.
Acknowledgements This work is based in part on observations made with the  NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes (MAST)  at the  Space Telescope Science Institute (STScI) , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with programme # 1366. Support for this programme was provided by  NASA  through a grant from  STScI. This work is based in part on data collected under the  NGTS  project at the  European Southern Observatory's La Silla Paranal Observatory. The NGTS facility is operated by a consortium of institutes with support from the  UK Science and Technology Facilities Council (STFC)  under projects  ST/M001962/1 ,  ST/ S002642/1  and  ST/W003163/1.
Data availability The data used in this paper are associated with JWST programme ERS 1366 (observation 2) and are available from the Mikulski Archive for Space Telescopes (https://mast. stsci. edu). We used calibration data from programme 1076. All the data and models presented in this publication can be found at https://doi. org/10. 5281/zenodo. 7101283. Source data are provided with this paper.
There were 1832 subjects with valid brain MRI data available from the MRi-Share project, and 1227 subjects with SD-OCT data. Of these, 960 individuals had data on both brain and retinal imaging. We excluded 75 participants with missing data for covariates (7. 4% for axial length and 1. 3% for height and/or weight), and 28 subjects with ophthalmological conditions that could affect retinal nerve thickness layers. Finally, the study sample included 857 participants with available brain MRI data and valid OCT data (857 for cortical morphometry and 854 for diffusion parameters) (Figure  1 ). Compared with non-included eligible participants from the MRi-Share project (103 subjects from i-Share Bordeaux with brain MRI and ophthalmic examination), included participants did not differ according to age and sex; however, they were more often past or current smokers (26.
| MRI acquisition From November 2015 to November 2017, brain MRI scanning was carried out at the Bordeaux University on the same Siemens Prisma 3-Tesla with a 64-channels head coil (gradients: 80 mT/m-200 T/m/ s). Exclusion criteria were age over 35 years, any contraindications for MRI, or pregnancy. 3D T1-weighted and 3D FLAIR, together with 100-direction 2D diffusion-weighted imaging (DWI) multiband multishell acquisition were performed. This structural acquisition protocol follows in footsteps that of the UK Biobank brain imaging study  (Alfaro-Almagro et al. , 2018)  regarding modalities and scanning parameters. Quality control for the brain imaging datasets was performed immediately after the acquisition. Extended description of the MRi-share protocol has been detailed previously  (Tsuchida, Laurent, Crivello, Petit, Joliot, et al.
| MRI processing Brain images were processed with the Automated Brain Anatomy for Cohort Imaging platform (ABACI, IDDN. FR. 001. 410013. 000. S. P. 2016. 000. 31235) (Tsuchida, Laurent, Crivello, Petit, Joliot, et al. , 2021). The ABACI structural pipeline processed T1 and FLAIR images for multi-channel volume-and surface-based morphometry. Global image derived phenotypes included total intracranial volume (TIV), total grey matter (GM) and white matter volumes (WM) and mean cortical thickness (CT), all estimated with Freesurfer 6. 0 software. The global GM volume includes cortical volume derived from surface-based analysis and subcortical structures derived from voxel-based one. The global WM volume is defined as the volume inside the white surface minus anything that is not WM. Cerebral microstructure was examined through DWI data, used to fit (1) diffusion tensor imaging (DTI) modelling and (2) microstructural model fitting with neurite orientation dispersion and density imaging (NODDI).
, 2012). To obtain these global diffusion measurements, all imaging data were processed using a standardized pipeline included in ABACI (see preprocessing details in  Tsuchida, Laurent, Crivello, Petit, Joliot, et al. (2021). Preprocessing of DWI data and DTI fitting was performed using tools from the FMRIB software library (FSL) and the dipy package in Python (0. 12. 0, https://dipy. org) (Garyfallidis et al. , 2014) , and the AMICO (accelerated microstructure imaging via convex optimization) tool  (Daducci et al. , 2015)  was used for NODDI fitting. A modified script of the PSMD (v0. 95, first release, http://www. psmd-marker. com: Baykara et al. , 2016)  was used to obtain skeletonized DTI and NODDI metrics, based on the FSL tract-based spatial statistics (TBSS) procedure. The mean FA, MD, NDI, and ODI values were computed from the WM skeleton in the standard MNI space, masked with a skeleton mask provided with the PSMD script.
The PSMD was calculated as the difference between the 95th and 5th percentiles of the skeletonized MD values. Regional GM volume and CT measurements were based on the 34 regions determined by the Desikan-Killiany atlas segmentation  (Desikan et al. , 2006). We also analyzed more specifically volumes and CT in three visual areas defined according to the Brodmann's classification: primary visual cortex (V1-BA17), secondary visual cortex (V2-BA18) and associative visual cortex (V5/MT-BA19)  (Amunts et al. , 2000). Volumes and CT were estimated with FreeSurfer v6. 0. Regional diffusion measures were obtained from the intersection between the 27 ROIs based on the JHU atlas (6 bilateral and 21 left/right WM ROIs)  (Mori et al. , 2008)  and the FSL-TBSS skeleton output. As detailed in the publication presenting the acquisition and analysis of the MRi-Share database  (Tsuchida, Laurent, Crivello, Petit, Joliot, et al.
Participants and recruitment We obtained 805 responses from 'red wall' constituencies in Wales and the North and Midlands of England between 28 February and 9 March 2022 via prolific. co, a crowdsourcing platform for psychological and social research. The period was towards the end of COVID-19 restrictions but in the first week of Russia's invasion of Ukraine. Participants defined their gender as 412 female, 377 male and 14 non-binary or self-described another way. The mean age was 39. 27  (SD 13. 21 ). The 'red wall' constituencies were identified by the first part of postcodes, which means that a small number of participants may have lived just outside of relevant constituencies. A full list of the constituencies is available at https://doi. org/10. 17605/OSF. IO/7GRA2. No quotas were applied during recruitment, but weights were used during analysis (see 'Data analysis' section).
Data analysis Data were analysed in R (R Core Development  Team, 2018). Raw data and R scripts are freely available at https://osf. io/2n37u/. Relative to voting in these constituencies at the 2019 General Election, our sample overrepresented people who voted as compared with not voting, overrepresented Labour voters and underrepresented Conservative voters (Supplemental Table  S1 ). In the statistical analyses that follow, we have therefore applied post-stratification weights that make our sample representative of the constituencies with respect to 2019 voting behaviour. Results are very similar using unweighted responses. Table  1  and figures other than Figure  2  show unweighted data. Our main outcome variable was overall support for UBI. Although there were significant effects of which narrative was presented on this outcome, these effects did not appear to interact with demographic, socioeconomic or attitudinal factors.
NuRD increases intermediate-range enhancer-promoter contacts To understand whether the alteration of enhancer dynamics in NuRD affects genome architecture and enhancer-promoter interactions, we next carried out in-nucleus Hi-C experiments. We obtained high-quality contact maps for both wild-type and Mbd3-knockout ES cells after combining our wild-type data with previously published  52  (and consistent) datasets (Extended Data Fig. 8a ). As previously observed  6, [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] 53  , the Hi-C contact maps showed that the genome is segregated into: (1) A and B compartments (regions containing a higher or lower density of genes, respectively); (  2  chromatin interactions; and (3) loops mediated, for example, via CTCF/cohesin binding, where specific genomic regions contact each other more frequently.
Methods In-nucleus chromosome conformation capture Data acquisition. In-nucleus Hi-C 17 was carried out on E14 wild-type and 7g9 Mbd3-knockout (see Kaji et al. 31  for construction and characterization) ES cells; 50 bp paired-end sequencing was carried out on a HiSeq4000 instrument. Our own Hi-C analysis of Mbd3-knockout ES cells cultured in 2i consisted of three experiments: SLX-18035, SLX-7676 and SLX-19611 (which was sequenced twice). Alongside these four datasets, we also collected wild-type 2i data (SLX-7672) and compared this with recently published Hi-C data for ES cells cultured in 2i conditions (https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-6591/) (Extended Data Fig. 8a ). We used four replicates of this published data (ERR239137, ERR239139, ERR239141 and ERR239143; Supplementary Table  1 ).
Preprocessing. For each experimental condition and replicate, FASTQ files were first processed and aligned against the mouse GRCm38. p6 reference genome using the nuc_processing package (https://github. com/tjs23/nuc_processing). The number of unique contacts observed varied between replicates and conditions but, overall, a high number of reads were obtained in both conditions. The resulting raw contacts in NCC format were converted into hic and cooler format contact matrices using the juicer Pre (https://github. com/aidenlab/juicer/wiki/Pre) and HiCExplorer hicConvertFormat tools for downstream analysis (https://hicexplorer. readthedocs. io/en/latest/content/tools/hicCo-nvertFormat. html)  [69] [70] [71]. To assess the correlation between replicates within and between the two experimental conditions, we made use of the HiCExplorer hicPlotdistvscounts tool.
Clustering of replicates based on contact distance distributions identified clusters for the two experimental conditions showing good agreement between replicates. We therefore merged the obtained contacts resulting in 705,063,441 and 327,180,884 contacts for the wild-type and Mbd3-knockout conditions, respectively. Owing to the difference in read coverage, we downsampled the merged wild-type dataset to have the same number of contacts as our merged Mbd3-knockout dataset. Finally, we performed Knight-Ruiz normalization of the merged datasets to ensure balanced matrices.
Compartment analysis. To identify A/B chromatin compartments within our merged datasets, we made use of the recently developed CscoreTool (https://github. com/scoutzxb/CscoreTool)  72. This tool assigns each genomic window i a score ∈ [-1,1] with 1 assigning a probability 1 that window i is in the A compartment while -1 assigns a probability 1 that window i is in the B compartment. Importantly, unlike eigenvector analysis, c-scores of different samples can be compared directly since they represent probabilities. Binary compartments can also be assigned based on the sign of the c-score. TAD analysis. TAD calling was performed using Lavaburst, a recently published tool  57  that uses an insulation-style metric called the TAD separation score to identify the degree of separation between the up and downstream region at each Hi-C matrix bin.
Local minima of the TAD separation score are considered as putative TAD boundaries and assigned q-values for calculation of a false discovery rate. For this analysis, we used parameters gamma = 10 and beta = 50,000. For both datasets, we identified TADs using a binning resolution of 25 kb. Enhancer-promoter links. We wanted to investigate whether transcriptional misregulation in Mbd3-knockout ES cells was significantly associated with a disruption of cis-regulatory interactions. To identify putative promoter-regulatory element (RE) interactions, we made use of a recent study  66  that profiled RE-promoter interactions via thousands of separate CRISPR deletions. In particular, Fulco et al. 66  found that a relatively simple activity-by-contact (ABC) model could be used to identify, with reasonable precision and recall, functional RE-promoter interactions.
(6) The union of the nonpromoter peaks and promoter regions was then treated as a master list of putative REs that were each assigned a condition-specific score based on the mean H3K27ac peak strength across that RE. In particular, REs could be assigned within a single condition as being either 'intergenic' or 'promoter' associated. Since REs were defined per condition, wild-type-unique, knockout-unique and common overlapping peaks were assigned unique IDs for downstream analysis. The relevant code to perform this analysis and subsequent calculation of ABC scores can be found at https://github. com/dhall1995/ Acitivity-By-Contact_Enhancer-Promoter_Link_Prediction. Using calculated ABC scores, wild-type and knockout links were assigned unique link IDs based on the promoter and enhancer pair in question as well as the promoter genomic position in the case of a gene with multiple possible promoter regions.