dataesr/scientific-paragraphs-categorization huggingface dataset:
Results From the initial 61 children enrolled (n = 25 in each training group and n = 11 in the CTR group), 49 completed the study, resulting in n = 19 for MICT (12 females and 7 males), n = 19 for HIIT (11 females and 8 males), and n = 11 for CTR (6 females and 5 males). None of the drop-out subjects (n = 7) were related to the intervention itself, but were due to family, disciplinary, or school-related reasons. Bone parameter data were incomplete for n = 5 subjects. The mean patient age was 13. 0 ± 0. 8 years in the MICT group, 13. 0 ± 1. 1 years in the HIIT group, and 13. 2 ± 1. 0 years in the CTR group (p = 0. 56).
RNA-seq libraries were then prepared from the mRNA using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs, Massachusetts, USA). Libraries were quantified by a fluorimetric method and their quality assessed on a Fragment Analyzer (Agilent Technologies). Cluster generation was performed with the resulting libraries using Illumina HiSeq 3000/4000 SR Cluster Kit reagents. Libraries were sequenced on the Illumina HiSeq 4000 with HiSeq 3000/4000 SBS Kit reagents for 150 cycles. Sequencing data were demultiplexed with the bcl2fastq Conversion Software (v. 2. 20, Illumina; San Diego, California, USA).
Microscopy and GUS Staining DII-Venus microscopy and image quantification were performed as indicated in  (Kohnen et al. , 2016). In short, 4-day-old seedlings grown as described above were transferred to LRFR or kept in WL for 1h before imaging. We used an inverted Zeiss confocal microscope (LSM 710, 20X objective, 0. 8 DIC). VENUS signal was detected using an Argon laser (excitation at 514 nm and band pass emission between 520 and 560 nm). Image stacks (5-6/seedling) were acquired for every hypocotyl until the VENUS signal was lost. The pinhole was opened to collect the maximal signal intensity together with the minimal stack number (5. 42 airy units, 20. 2 μm section, 10. 08 μm interval). Images were processed with ImageJ software (http://rsb. info. nih. gov/ij). To quantify the VENUS signal, we used the SUM slices projection of 4 slices from the stack excluding the first layer with the stomata.
For the lipid droplet (LD) quantification, 4-day-old seedlings grown as described above were transferred to LRFR or kept in WL. Seedlings were treated with BODIPY ™ 493/503 (2 µg/mL in H2O) for 20 min at room temperature and imaged on inverted Zeiss confocal microscope (LSM 710, 20X objective, 0. 8 DIC) 30h after the beginning of light treatment. BODIPY ™ 493/503 signal was detected using an Argon laser (excitation at 488 nm and band pass emission between 500 to 540 nm). Image stacks (6/seedling) were acquired for every hypocotyl. The pinhole was opened to 3. 15 Airy Units (11. 4 µm section, 10. 00 µm interval). Images were processed with ImageJ software (http://rsb. info. nih. gov/ij). To quantify the signal, we selected a region of interest (ROI) on the upper half of the hypocotyls and analyzed the fluorescence intensity of particles (size=0-75 circularity=0.
Data processing Raw LC-HRMS and HR(MS/MS) data was processed using MS-Dial software  (Tsugawa et al. , 2015)  (http://prime. psc. riken. jp/Metabolomics_Software/MS-DIAL/). Relative quantification of lipids was based on EIC (Extracted Ion Chromatogram) areas for the monitored precursor ions at the MS1 level. Peak areas were normalized considering the sample amount (mg) (full lists of lipid species are given in Table  S9 ). The obtained tables (containing peak areas of detected and identified lipids by MS and MS/MS, and using MS only across all samples) were exported to "R" software http://cran. rproject. org/ where the signal intensity drift correction was done within the LOWESS/Spline normalization program  (Tsugawa et al. , 2015)  followed by noise filtering (CV (QC features) > 30%) and visual inspection of linear response.
RNA-seq initial data analysis Purity-filtered reads were adapters and quality trimmed with Cutadapt (v. 1. 8)  (Martin, 2011). Reads matching to ribosomal RNA sequences were removed with fastq_screen (v. 0. 11. 1). Remaining reads were further filtered for low complexity with reaper (v. 15-065)  (Davis et al. , 2013). More than 30 million uniquely mapped reads were obtained per library and reads were aligned against the Arabidopsis thaliana. TAIR10. 39 genome using STAR (v. 2. 5. 3a) (Dobin et al. , 2013). The number of read counts per gene locus was summarized with htseq-count (v. 0. 9. 1)  (Anders et al. , 2015)  using the Arabidopsis thaliana. TAIR10. 39 gene annotation. Quality of the RNA-seq data alignment was assessed using RSeQC (v. 2. 3. 7) (Wang et al. , 2012). Statistical analysis was performed for genes independently in R (R version 4.
0. 2). All steps described here were performed separately for the samples from hypocotyls and cotyledons (except for the initial clustering of all samples together). Genes with low counts were filtered out according to the rule of 1 count(s) per million (cpm) in at least 1 sample. The number of genes retained in the analyses based on this filtering is different for hypocotyls and cotyledons. Library sizes were scaled using TMM normalization. Subsequently, the normalized counts were transformed to cpm values and a log2 transformation was applied by means of the function cpm with the parameter setting prior. counts = 1 (edgeR)  (Robinson et al. , 2010). Differential expression was computed with the R Bioconductor package "limma"  (Ritchie et al. , 2015)  by fitting data to a linear model. The approach limma-trend was used.
Gene set enrichment analysis for Gene Ontology Gene set enrichment analysis were conducted with ShinyGO v0. 61:Gene Ontology Enrichment Analysis + more (http://bioinformatics. sdstate. edu/go/) (Ge et al. , 2020)  in Arabidopsis thaliana using a P-value cutoff (FDR) 0. 05 and 500 most significant terms to show. The networks of enriched GO categories were visualized with R software (https://www. r-project. org/) using "visNetwork" and "igraph" libraries. Two terms (nodes) were connected if they share 20% or more genes. The size of the nodes indicates fold change for each term. We highlighted selected terms for each organ and light condition that we could easily relate to growth regulation (Fig 1D  , S1C ). The highlighted terms are not necessarily the most significant ones (full lists are available in Table  S2  and as interactive versions).
Acknowledgements We thank  Martina Legris ,  Laure Allenbach Petrolati ,  Olivier Michaud ,  Mieke de Wit ,  Anupama Goyal ,  Ana Lopez Vazquez ,  Ganesh Mahadeo Nawkar  and  Maud Lagier  for providing resources, technical support and/or comments on the manuscript;  René Dreos  for advice on statistical methods;  Niko Geldner ,  Ersin Gül  ( ETH Zurich ),  Sinem Celebioven  ( University Zurich ),  Soner Yildiz  ( Icahn School of Medicine ) and  Yasin Dagdas  ( GMI, Vienna ) for suggestions and comments on the manuscript;  Christian Hardtke ,  Teva Vernoux  ( ENS Lyon ),  Richard Vierstra  ( Washington University, St Louis ),  Dany Geelen  ( University Gent ) and  Yasin Dagdas  for providing resources. We are grateful to the  CIF  (https://cif. unil. ch/)for help with microscopy, the  GTF  (https://wp.
unil. ch/gtf/)for RNA seq. experiments and the  Metabolomics Unit  (https://www. unil. ch/metabolomics/en/home. html) for the lipidomic analysis and the  Bordeaux-Metabolome  platform for sterol analysis (https://www. biomemb. cnrs. fr/en/lipidomic-plateform/). Work in the Fankhauser lab is supported by the  University of Lausanne  and the  Swiss National Science Foundation  ( 310030B_179558 ), the  Bordeaux Metabolome Facility-MetaboHUB by a grant  from  ANR  (no. ANR-11-INBS-0010 ).
The chemical composition of all three materials is given in Table  1. The full composition of the wrought material is indicated (data from the steel provider) in the first line of Table  1. Additional measurements were conducted on both wrought and SLM-ed materials (lines 2 and 3 of Table  1 ). These measurements were carried out using X-ray fluorescence (XRF) (FISCHERSCOPE X-Ray XAN-FD) for elements Ni, Cr and Cu, combustion elemental analysis (CEA) for C, S (LECO CS 444/LS), N and O (LECO TC-436), and inductively coupled plasma optical emission spectroscopy (ICP-OES) for Mn, Si and Nb. Table  1 also     For microstructural observations, Electron BackScatter Diffraction (EBSD) observations were conducted at 20 kV on specimens polished down to 1200 grit and then electropolished using 94% ethanol + 6% perchloric acid as electrolyte at 25 V for 60 s.
EBSD data were processed using the MTEX software package  [31]  working under Matlab so as to extract the phase and orientation maps. From the EBSD data obtained on martensite, the parent austenite microstructure was reconstructed using the algorithm developed by Nyyssönen et al. [32] , which can be used in the MTEX/Matlab environment. This algorithm determines whether some neighboring grains, based on their orientation, share the same prior austenitic grain orientation or not. The Kurdjumov-Sachs relationships  [33]  are used as a first estimation of the martensite/austenite orientation relationship (OR). However the actual OR is iteratively determined by the algorithm and can slightly differ from Kurdjumov-Sachs. The nature of different boundaries present in the martensitic microstructure (block boundaries, packet boundaries and former austenitic grain boundaries  [34] ) can then be extracted from the reconstruction procedure.
Neutron diffraction data was collected using the High Resolution Powder Diffractometer (HRPD) at the ISIS facility, Rutherford Appleton Laboratory (RAL), UK  [35]. Each neutron diffraction measurement was performed while continuously heating the specimen in-situ (i. e. in the neutron instrument) under high vacuum (4x10 -6 mBar) to avoid any surface oxidation. The samples used for the experiment were cylinders of 6 mm diameter and of 50 mm length. For accurate temperature control, a type C thermocouple was inserted into a hole drilled directly into the sample. Typical temperature gradients expected along the sample's 50 mm vertical axis during measurement had been ascertained prior to the neutron experiment using offline test apparatus. Above 1300 °C, the temperature was found to be homogeneous within 15 to 20 °C along said axis.
Instead, only the most important datasets were selected for refinement (about 10 patterns for each experiment, covering the whole range of possible phase constitution). It was shown that the phase fractions obtained from the complete Rietveld refinement can be approximated by the simple following equation: % = ( ) ( ) +. ( ) × (1) where ( ) and ( ) are the peak intensities (peak areas) of the BCC (110) and the FCC (111) lines respectively. Here the 1. 40 term was adjusted so as to obtain the best agreement between the BCC mass fractions obtained from Rietveld refinement and those obtained using Eq. 1 (Figure  3 ). It was found that for 26 patterns out of 29, the difference is within 6%, which is reasonable. For all the patterns that were not processed using Rietveld refinement, the BCC mass fraction was determined using Eq.
4PH steel (~5x10 15 m -2 ). Dislocations start to annihilate from 550°C / 600°C in all the materials studied, but a measurable dislocation density of ~10 13 m -2 is still observed at 950°C / 1000°C. namely Chris Goodway, Paul Mclntyre and Adam Sears, are kindly acknowledged for their work in preparing and setting-up the equipment for the neutron diffraction tests. The authors would like to acknowledge financial support from Institut CARNOT M. I. N. E. S (project #60678) and from LABEX MANUTECH-SISE (ANR-10-LABX-0075) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). Max Boudes is kindly acknowledged for his help in EDX analyses.
17 - 17 4PH steel (~5x10 15 m -2 ). Dislocations start to annihilate from 550°C / 600°C in all the materials studied, but a measurable dislocation density of ~10 13 m -2 is still observed at 950°C / 1000°C. namely Chris Goodway, Paul Mclntyre and Adam Sears, are kindly acknowledged for their work in preparing and setting-up the equipment for the neutron diffraction tests. The authors would like to acknowledge financial support from Institut CARNOT M. I. N. E. S (project #60678) and from LABEX MANUTECH-SISE (ANR-10-LABX-0075) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). Max Boudes is kindly acknowledged for his help in EDX analyses.
Electromagnetic Simulations of Antenna We decided on microstrip patch antennas for this study. This type of antenna belongs to a well-studied antenna class and has good, predictable radiation properties under front-side illumination. Detectors have been implemented using a similar approach, as detailed in Reference  [19]. The patch antenna was implemented in the top metal layer of the CMOS technological process, with dimensions of 110 × 110 µm (see insert in Figure  2a ). We applied the finite element method with an adaptive mesh using the CST Studio Suite software, in order to model the investigated antenna structures and evaluate the electromagnetic field's far-field distributions. One of the distinctive properties of patch antennas with ground planes is that the lower metal planes allow for efficient suppression of radiation propagation into the thick dielectric substrate.
Cohort 1: AECOPD group Forty-five people with an acute exacerbation of COPD (AECOPD) were assessed when medically stable and fit for discharge. Inclusion criteria for the AECOPD cohort were; ≥18 years old, a confirmed clinical diagnosis of COPD from medical records, and admission with a primary diagnosis of exacerbation of COPD (Fig. 1 ). For the AECOPD group, baseline assessments were measured during their hospital stay and a follow-up assessment was performed after a 6-week natural recovery period, where no PR was undertaken (Fig. 2 ). PR uptake was obtained from the PR services database, but the AECOPD group were not followed during their course of PR (where applicable). Cohort 2: PR group Sixty-seven people with stable COPD symptoms were assessed prior to starting pulmonary rehabilitation and inclusion criteria for the PR group were; ≥18 years old and a confirmed clinical diagnosis of COPD from medical records or the GP referral.
Statistical analyses Data were analysed using R version 1. 2. 5042 and graphs were generated using Microsoft Excel. Continuous variables distributions were tested for normality using the Shapiro-Wilk test, where p > 0. 05 assumes a Gaussian distribution. Data are reported as mean (SD) or median (inter-quartile range) and differences between groups were assessed using two sample unpaired T test or Mann Whitney U test. Frequency comparisons between groups were assessed using Fisher's test. A paired T test was used to compare baseline and follow up assessments within groups. A statistical significance was indicated as p < 0. 05.
Limitations of the study The main limitation of the study is that cognition and other health measures were not assessed following PR in the AECOPD group, as these data were not collected as part of the larger, original study. Lack of an age-matched healthy control group prevented us from exploring the broader impact of COPD on cognition. The use of a single measure of cognition did not provide a comprehensive cognitive assessment compared with some studies  [11, 23] , however, a complete neuropsychological assessment was not feasible in the present study due to logistical constraints. A more complete cognitive assessment may have provided further detail on cognitive function and other aspects of cognition, but the MoCA was used in the present study as it is a relatively concise and practical measure of cognition that can be used in the clinical environment  [33].
Acknowledgements The authors would like to thank  Nikki Gardiner ,  Samuel Briggs-Price ,  Sarah Ward ,  Jakub Zatloukal ,  Fiona Powell ,  Olivia Revitt ,  Sabrina Lohar ,  Emily Mullen ,  Claire Chebbout ,  Chrissie Mitchell-Issitt ,  Emma Vincent ,  Kim Hall ,  Zak Hawksley ,  Declan Tomlin ,  Louise Irvine ,  Amy Stenson  and , Centre for Exercise and Rehabilitation Science, National Institute for Health Research (NIHR) Leicester Biomedical Research Centre -Respiratory , for their contribution towards data collection. The authors acknowledge support from the  NIHR Leicester Biomedical Research Centre  which is a partnership between  University Hospitals of Leicester NHS Trust  and  University of Leicester  and acknowledge support from the  NIHR Collaboration for Leadership in Applied Health Research and Care East Midlands (CLAHRC EM).
002 and 0. 6 Å -1 , where q is the wave vector transfer. Samples were measured in single stopper cylindrical cells 120-QS Hellma quartz cuvettes with a 1 mm path length. Measurements were taken at 37 °C with the use of a rotating rack to prevent the sedimentation of the fibrils. Samples 5-7 were measured at the LOQ beamline located at the ISIS Neutron and Muon Source, Chilton, United Kingdom. Samples were measured in single stopper cylindrical cells 120-QS Hellma quartz cuvettes with a 1 and 2 mm path length. A fixed sample-to-detector distance (4 m) combined with a white beam and time-of-flight detection provided a q range of 0. 009-0. 25 Å -1. The raw scattering data collected at the LOQ instrument in ISIS were corrected for the efficiency and spatial linearity of the detectors, the sample transmission and the background scattering using the instrument dedicated software Mantid (https://www.
mantidproject. org/) and the standard procedure indicated in the software guide. Data were then converted into scattered intensity data I(Q). These data were then placed on an absolute scale (cm -1 ) by comparison with the scattering profile collected from a calibration standard, constituted of a solid blend of hydrogenous and perdeuterated polystyrene which has been measured with the same instrument configuration as per established procedures  (Wignall and Bates 1987). Measurements were performed at 37 °C with the aid of a rotating rack in order to prevent the sedimentation of the fibrils. Samples 8-19 were measured at NG7 SANS instrument located at NIST Center for Neutron Research, Gaithersburg, MD, United States. Measurements were performed at four sample to-detector distances (1, 4, 13, and 15.
3 m with lenses), and a neutron wavelength of 6. 0 Å (sample-to-detector distances of 1, 4, and 13 m) and 8. 1 Å (15. 3 m with lenses), to obtain a q range spanning from 0. 001 to 0. 5 Å -1. The wavelength spread is approximately 12%  (Glinka, Barker et al. , 1998). The data was reduced to the absolute scale using the Igor software by following the standard protocol at NCNR to correct the effect of the background, empty cell, detector efficiency, and the transmission of each sample  (Kline 2006). Samples 8-11 were measured in 2 mm path length demountable Ti cells with quartz windows, and samples 12-19 were measured in 1 mm path length banjo quartz cells. Measurements were performed at room temperature. The cells were mounted on a slowly rotating stage to prevent sedimentation during the experiment.
SANS Studies of αS Fibrils The current work explores, with the use of SANS, the structural organization of αS fibrils at pH 5. 5, which is close to their isoelectric point. In these conditions, the fibrils are not colloidally stable, but precipitate out of solution by aggregating into clusters that are prone to sediment  (Pogostin, Linse et al. , 2019). A total of 19 different samples, for simplicity labeled from 1 to 19, were investigated. Figure  1  shows SANS patterns, I(q), acquired from all 19 samples probed. The data are shifted with an arbitrary scale for better representation. The data on the absolute scale are shown in Supplementary Figure  S1. As can be observed from Figure  1 , the scattering patterns from the different samples are strikingly similar. They show a power law scattering, I(q) ∼ q -d , over essentially the full q-range covered by the experiments.
Targeted Gene Sequencing Results Through targeted gene sequencing of 87 genes involved in NER pathway, we identified in patient XP21 a novel homozygous missense variation in ERCC4, within exon 8, (NM_005236. 2 c. 1762G>T p. Val588Phe) (Figure  2A ). Bioinformatic prediction tools (SIFT, POLYPHEN, mutation taster. ) indicated that this mutation was pathogenic (Table  2 ). Moreover, through the prediction algorithm of human splicing finder tools, this variation was suggested to create an exonic splicing silencer (ESS) probably altering the splicing process. Regarding the patient XP134, we found that he carried a double homozygous missense variation in DDB2 gene in exon 5 (NM_000107. 2: c. 613T>C p. Cys205Arg) and c. 618C>A p. Ser206Arg (rs759622121) (Figure  2B ). Both variants were not described in any population database (Exac, GnomAD, 1000 Genomes.
Molecular Modeling of the Mutant Protein Structures Modeling XP-F Mutation The search for a template using BLAST-based approaches was not able to identify an appropriate template. Therefore, we used a fold recognition based approach for such an end. First, we predicted the repartition of the domains of XPF along the sequence. We found that the segment 374-623 matches a hit with a significant E-value of 7. 60e-98 corresponding to a helicase insert domain superfamily in accordance with the functional properties of XPF. We then proceeded to model this domain using a threading approach. pGenTHREADER was able to identify several hits with high confidence values. We selected the PDB entry, 1WP9, which corresponds to the crystal structure of Pyrococcus furiosus Hef helicase domain (PubMed: 15642269) as a template to construct the model.
DNA Extraction, Library Construction, Sequencing, and Data Analysis Genomic DNA was extracted from peripheral blood. Amplicon libraries were prepared from 1,000 ng of genomic DNA from patients. Custom design of DNA repair disorders' panel was performed using SureDesign (Agilent Technologies Inc. ), with probes that cover the exons in 87 genes and 15 bp of the surrounding intronic sequences. Library preparation for NGS was done using the Agilent's HaloPlex HS workflow as a target enrichment method. Massively parallel sequencing was performed on an Ion Torrent PGM (Thermo Fisher Scientific). The data from the sequencing runs were analyzed using the Torrent Suite v4. 0. 2 analysis pipeline, and aligned using TMAPv. 3. The output variant call format (VCF) file was then annotated using the in-house VarAft software version 2.
5, which is available online (http://varaft. eu/index. php) (Desvignes et al. , 2018). Sequence variants were compared with data in Exome Variant Server (http://evs. gs. washington. edu/EVS/), 1000 Genomes Project (http://www. 1000genomes. org/), or GnomAD (https://gnomad. broadinstitute. org/), including Exome Aggregation Consortium database (ExAC), Cambridge, MA (http://exac. broadinstitute. org). A number of online tools were used to predict the functional impact and pathogenicity of the variants such as MutationTaster (http://www. mutationtaster. org/), PolyPhen (http://genetics. bwh. harvard. edu/pph2/),.
Protein Modeling The primary structure of XPF endonuclease was deposited in UiprotKB database under the accession Q92889. The sequence consists of 916 amino acids of which only the structure corresponding to segment 834-916 was solved experimentally. To solve the structure of the region containing the mutation we first searched for conserved domains of the entire sequence. The search for the appropriate template to construct the 3D structure of XPF was performed using the pGenTHREADER program. The sequences of the best hits were then processed and aligned to the target sequence of XPF using 3D-expresso. The resulting alignment was then used to guide the model building process by MODELER (version 9. 15). Twenty conformers were generated by the comparative modeling software of which we selected the best model according to the DOPE and the GA341 scores.