dataesr/scientific-paragraphs-categorization huggingface dataset:
This innovative approach was applied to a comprehensive in situ dataset, acquired at the SIRTA observatory, a suburban station located 20 km southwest of Paris. The A 3 Q method provided satisfactory results over a business-as-usual period, which ensures a robust characterization of concentration changes in the Paris region during lockdown. Yet, A 3 Q requires a long-term dataset whose length is 9 years +; otherwise results can rapidly suffer from shortfall of representativeness. Also the analogy needs to be carefully inspected, notably in terms of local meteorology. Indeed, the first synoptic layer appears to be not quite enough to capture all the specificity of the sampling site. The unprecedented drop of traffic commuting due to the stay-at-home order led to an expected decrease of primary traffic pollutants NO x (-42 %), HOA (-62 %) and BC ff (-55 %), as well as an increase of ozone concentrations due to the lesser contribution of the O 3 sink by NO.
It could also be useful for the quantitative evaluation of emergency mitigation policies settled during pollution episodes. Data availability. In situ measurements at SIRTA are available through the EBAS database (https://ebas. nilu. no, last access: 23 November 2021). Ozone data from Airparif are available at https://www. airparif-asso. fr (last access: 23 November 2021). GDAS files for back-trajectory calculation are available at https:// www. arl. noaa. gov/hysplit/hysplit/ (last access: 23 November 2021). The ZeFir procedure is available at https://sites. google. com/site/ zefirproject/ (last access: 23 November 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-17167-2021-supplement. Author contributions. JEP, OF, VG, YZ, JS, LS, FT, NB, TA and JCD contributed to the availability of in situ measurements at SIRTA.
It is not associated with a conference. Acknowledgements. The authors would like to thank Robin Aujay-Plouzeau, Roland Sarda-Estève, Dominique Baisnée and Vincent Crenn for their contribution in maintaining data acquisition at SIRTA. Christophe Boitel and Marc-Antoine Drouin are acknowledged for their support in data management. This work also greatly benefited from discussions within the COLOSSAL COST action CA16109. Financial support. This research has been supported by the European Commission, Seventh Framework Programme (ACTRIS (grant no. 262254)), the European Commission, H2020 Research Infrastructures ACTRIS projects (ACTRIS 2 (grant no. 654109)), the National Center for Scientific Research (CNRS), the French alternatives energies and Atomic Energy Commission (CEA), the French Ministry of Environment, and the DIM-R2DS program from the Ile-de-France region.
This work has also been supported by CNRS-INSU for the measurements performed at the SI-SIRTA and those within the long-term monitoring aerosol program SNO-CLAP, both of which are components of the ACTRIS French Research Instructure and whose data are hosted at the AERIS data center (https://www. aeris-data. fr/, last access: 23 November 2021). Review statement. This paper was edited by Manvendra K. Dubey and reviewed by two anonymous referees.
1d , e and Supplementary Fig. 1h, i ). NCCs were initially chosen because HSS invariably presents as a neurocristopathy  32  , and CMs were chosen since progressing cardiomyopathy has been reported in some patients  34, 35. Collectively, we established a model to study the mechanism underlying HSS in diseaserelevant cell types, while also characterizing CHD6 function in both a proliferative and a post-mitotic cellular context. The I1600M CHD6 mutation affects gene expression and development. We first assessed how this CHD6 mutation affects gene expression by generating transcriptome profiles from all genotypes. Given the importance of NCCs in neurocristopathytype syndromes, we performed gene set enrichment analysis against the KEGG pathway database using total cell RNA-seq data from two independent clones of patient-derived and geneedited mutant NCCs.
1j ). These data are in line with our transcriptome analyses and demonstrate how a single CHD6 mutation can severely impact development and gene expression. CHD6 binds autophagy-relevant genes across cell types. To infer which of the deregulated genes above were direct CHD6 targets, we applied a tailored crosslinking and ChIP protocol to iPSCs, NCCs, and CMs of all genotypes (see "Methods", Fig. 2a  and Supplementary Fig. 2a, b ). The analysis of our ChIP-seq data collection revealed two unexpected features. First, that mutCHD6, irrespective of the studied cell type, displayed consistently stronger peaks and a larger binding repertoire than wtCHD6 (Fig. 2a-c  and Supplementary Fig. 2a-d ). Although we cannot formally rule out the possibility that the antibody we used recognizes CHD6 more strongly when it carries the I1600M mutation, our observations possibly signify that mutCHD6 resides longer on chromatin.
2j ). These data again support a ubiquitous role for CHD6 in regulating autophagy genes. The CHD6 mutation deregulates autophagy and increases DNA damage burden. To assess the involvement of CHD6 in autophagy regulation, we examined some of its key regulators. First, patient-derived/mutant NCCs displayed elevated phosphorylated ribosomal S6 protein levels (Supplementary Fig. 3a ) possibly signifying intensified glycolysis via partial mTORC1 activation. On the other hand, CHD6-mutant CMs displayed decreased phospho-S6 levels, suggesting dampened mTORC1 activity (Supplementary Fig. 3b ). However, both hinted towards nuclear depletion of microtubule-associated LC3 protein in mutant compared to wt cells (Supplementary Fig. 3a, b ), which implies a tendency to activate autophagy despite the absence of pro-autophagy cues.
We recorded reduced levels of H3K27me3-marked heterochromatin (Supplementary Fig. 3l ), emergence of HP1α foci in constitutive heterochromatin (Supplementary Fig. 3m ), as well as increased β-galactosidase staining (Supplementary Fig. 3n ). Importantly, these last two features could be recapitulated following autophagy inhibition by spautin in wild-type cells (Supplementary Fig. 3m, n ). Therefore, a functional CHD6 appears to be required for efficient autophagy activation in response to DNA damage. CHD6 chromatin binding and remodeling are not affected by the HSS mutation in vitro. The HSS-relevant mutation maps to the CHDCT2 domain of CHD6 (Supplementary Fig. 1a ). This domain was initially identified in CHD4 and then in CHD3 and CHD5  46. Low stringency query of the NCBI Conserved Domain Database (max.
We generated iPSC lines stably overexpressing full-length wild-type or mutant HA-tagged CHD6 using a piggybac vector (like in ref. 45  ; Fig. 4c ). Following 24 h of induction with doxycycline, we verified that these lines expressed similar levels of wt and mutant CHD6 (Fig. 4c , bottom), prior to co-immunoprecipitating interacting partners. IP eluates were analyzed via quantitative label-free mass-spectrometry. In addition, we also generated CHD6 interactomes upon 2 h of starvation or 1 h of etoposide treatment, and monitored how mutCHD6 interactions compared to wild-type ones (Supplementary Data File 2). In steady-state iPSCs, wtCHD6 presented an interactome consisting of general and specialized transcription factors, other chromatin remodelers, cell cycle regulators, RNAbinding proteins related to RNA processing, and factors involved in rRNA biogenesis (Fig.
Methods Whole-exome sequencing (WES) and reprogramming of HSS fibroblasts. Exonic and adjacent intronic sequences were enriched from genomic DNA isolated from blood and saliva of the index patient and the parents using the NimbleGen SeqCap EZ Human Exome Library v2. 0 enrichment kit, and sequenced on a HiSeq2000 platform (Illumina). WES data analysis and filtering of mapped targeted sequences was carried out using the "Varbank" exome and genome analysis pipeline v2. 6 of the Cologne Center for Genomics (CCG, University of Cologne,  Germany); we obtained a mean coverage of 75-95 reads, with 95. 9-96. 7% of target sequences covered more than 10×. Trio-WES data were filtered for high quality (coverage of >6 reads, minimum quality score of 10), rare autosomal recessive and de novo variants (i.
e. , with minor allele frequency of <0. 5% in the 1000 Genomes database, the Exome Aggregation Consortium browser, and not annotated in all inhouse WES datasets of the CCG). Primary fibroblasts from the index patient were isolated via standard skin biopsy, and all downstream work was performed in accordance to the Helsinki Declaration protocols and reviewed and approved by the local institutional Ethics boards (University Hospital Cologne, Germany; University Medical Center Göttingen, Germany). iPSC culture and differentiation into cardiomyocytes or neural crest cells. iPSCs were grown in FTDA media  67. Once confluent, cells were dissociated using accutase at 37 °C for 10 min (Sigma-Aldrich) and 450-600,000 cells were seeded per well of a 6-well plate. Differentiation into cardiomyocytes  68  used confluent iPSCs that were dissociated into single cells using accutase.
iPSCs were seeded at low density in a 6-well plate (175,000 cells/well) and transfected 12 h later by the dropwise addition of a mixture of 200 µl OptiMEM (Invitrogen), 12 µl FuGene HD (Promega) and a total of 3 µg from all plasmids. After 24 h, GFP expression was detectable microscopically; transfection efficiency was estimated to be ≥60%. Cells were then selected in 0. 5 µg/ml puromycin for 24 h and reseeded in clonal dilution (5000-8000 cells/well of a 6-well plate). Individual clones were screened using PCR and, ultimately, insertion of the desired mutation was verified by Sanger sequencing. As control lines, two "no gRNA" iPSC clones and two transfected with a gRNA targeting the "safe harbour" AAV locus on chr19 were used interchangeably. All gRNAs/ssODNs used are listed in Supplementary Data File 4.
CytoScan genome analysis of iPSC lines. Genome integrity of all CRISPR-edited and reprogrammed iPSC lines was assessed using CytoScan HD microarray technology (ThermoFisher Scientific), which allows reliable detection of 25-50 kbp-long copy number changes genome-wide. For this analysis, intact genomic DNA was isolated using the Quick DNA Miniprep kit (Zymo Research) as per the manufacturer's instructions, and data analyzed via the ChAS suite v4. 0 against the reference genome assembly (hg19). A summary is provided in Supplementary Data File 1. Generation and analyses of total RNA-seq. Cells from different genotypes/differentiations were harvested in Trizol (Life Technologies) and total RNA was isolated and DNase-treated using the Direct-zol RNA miniprep kit (Zymo Research) as per the manufacturer's instructions.
In the case of cardiomyocytes, cells at day 10-11 of differentiation were used, while in the case of NCCs, cells were obtained by collecting migratory cells from dissected and re-plated rosettes. Barcoded cDNA libraries were generated using the TruSeq RNA library kit (Illumina) via selection on poly(dT) beads. The resulting libraries were paired-end sequenced to >50 million read pairs on a HiSeq4000 platform (Illumina). Transcript quantification was performed using Kallisto v0. 44. 0 72 , which pseudoaligns RNA-seq reads to transcripts (Ensembl annotation used: GRCh37, comprising all known protein-coding sequences from hg19). After transcript quantification, pseudocounts were further processed via Sleuth  73  , which bootstraps Kallisto output to ascertain and correct for technical variation.
Chromatin was sheared to 200-500-bp fragments on a Bioruptor Plus (Diagenode; 2× 20-26 cycles of 30 s "on" and 30 s "off" at the highest power setting), and immunoprecipitations were carried out by adding 4 μg of the appropriate antibodies (CHD6, Bethyl A301-221A; TFEB, Bethyl A303-673A) to ~30 μg of chromatin and incubating on a rotator overnight at 4 °C in the presence of protease inhibitors. Following addition of protein-A/G agarose beads and washing, DNA was purified using the ChIP DNA Clean & Concentrator kit (Zymo Research) and used in qPCR or sequencing on a HiSeq4000 platform (Illumina). qPCRs were performed with the primers listed in Supplementary Data File 4. Where ChIP-seq was performed, at least 20 million reads were obtained, also for the relevant "input" samples. Raw sequencing reads (typically 50 nt-long) were analyzed using the HiChIP pipeline  78  , and peaks were called using MACS2  79.
40 (Oil) objective was used, making sure exposure times were maintained constant across samples in each imaging session for the same immunostaining. Finally, images were analyzed using the Fiji suite  81  as follows. First, background signal levels were subtracted using the embedded function (rolling ball function of 50-px radius with a sliding paraboloid and disabled smoothing), and the DAPI channel was used to determine the area of interest where signal would be quantified from. Measured mean signal intensities were used to generate plots in R or via InstantClue  82. For bean plots, dots represent the mean of the dataset; for box plots, whiskers ends represent the top and bottom quantiles, respectively. Analysis of autophagy flux. To measure autophagy flux, iPSCs were transiently transfected with a plasmid coding for a tandemly-tagged GFP-LC3-mRFP  41.
48 h after transfection, cells were incubated in starvation medium or complete FTDA medium for 2 h before being lifted using accutase diluted by F12 media and supplemented with 10 μM of the Y27632 inhibitor to prevent apoptosis. Next, iPSCs were pelleted and resuspended in 150 μl of complete FTDA or starvation medium again supplemented with Y27632 inhibitor to undergo FACS analysis. Cell were counted in a TC20 Automated cell counter (BioRad) and 100,000 cells of each genotype and treatment were stained with Zombie-NIR viability solution (1:1000; BioLegend) as per the manufacturer's instructions to exclude dead cells from the analysis. iPSCs overexpressing GFP or RFP only were used in parallel as baseline controls, and non-transfected cells served as a negative control. Measurements were performed on a Cytek Aurora flow cytometer and data analysis was performed via the SpecroFlo software.
An MS1 survey scan was acquired from 300-1750 m/z at a resolution of 70,000. The top 10 most abundant peptides were isolated within a 1. 8 Th window and subjected to HCD fragmentation at a normalized collision energy of 27%. The AGC target was set to 5e 5 charges, allowing a maximum injection time of 110 ms. Product ions were detected at a resolution of 35,000; Precursors were dynamically excluded for 10 s. All raw data were processed with Maxquant (v1. 5. 3. 8) using default parameters. Briefly, MS2 spectra were searched against the Uniprot HUMAN. fasta (16. 06. 2017) database, including a list of common contaminants. False discovery rates on were estimated by a target-decoy approach to 1% (Protein FDR) and 1% (PSM FDR), respectively. The minimal peptide length was set to 7 amino acids and carbamidomethylation at cysteine residues was considered as a fixed modification.
Oxidation (M) and Acetyl (Protein N-term) were included as variable modifications. The match-between runs option was enabled and LFQ quantification was enabled under default settings. The full list of peptide hits and their analysis is provided in Supplementary Data File 2. Western blotting. Western blotting was performed as previously described  45. In brief, ~2 × 10 6 cells were enzymatically detached or gently scraped off cell culture dishes, and pelleted for 5 min at 600 × g. The supernatant was discarded, and the pellet resuspended in 100 µl of ice-cold RIPA lysis buffer containing 1× protease inhibitor cocktail (Roche), incubated for 30 min on ice, and centrifuged for 15 min at >15,000 × g to pellet cell debris and collect supernatant. Total protein concentrations were determined using the Pierce BCA Protein Assay Kit (Ther-moFisher Scientific), before extracts were stored at -80 °C.
Finally, 250 µg RNase A were added for 2 h at 37 °C, followed by addition of 250 µg proteinase K and incubation at 37 °C overnight. Next day, DNA was isolated via standard phenolchloroform extraction, digestion efficiency was determined after electrophoresis in 1% agarose gels, and mononucleosomal DNA was isolated from the gel. Nucleosome occupancy was assessed by qPCR using the primers listed in Supplementary Data File 4. SA-ß-gal staining, organelle detection, and cell cycle analyses. Senescenceassociated β-galactosidase assays (Cell Signaling) were performed as per the manufacturer's instructions before manually counting positive cells. Acidic organelles were visualized using Lysotracker Deep Red (ThermoFisher) as per the manufacturer's instructions. Cell cycle analysis was performed using live cell nuclear staining with RedDot1 (Biotum) as per the manufacturer's recommendations, and flow cytometry data were analyzed using FlowJo [https://www.
Data mapping to the Drosophila genome were used in ChIPSeqSpike [https://bioconductor. org/packages/release/bioc/html/ ChIPSeqSpike. html] for calculating scaling factors in order to produce RPKMnormalized scaled coverage. Statistics and reproducibility. P-values associated with Student's/Welsch t-tests were calculated using GraphPad [http://graphpad. com/], and those associated with the Wilcoxon-Mann-Whitney test using the EDISON-WMW [https://ccb-compute2. cs. uni-saarland. de/wtest/] online tool. Unless otherwise stated, only Pvalues <0. 01 were deemed as significant. Also note that for immunofluorescence analyses, representative images from one of at least two independent and converging experiments are displayed and quantified.
Acknowledgements We would like to thank all members of the  Papantonis, Kurian ,  Rada-Iglesias , and  Wollnik laboratories  for helpful discussions,  Ioanna Papadionysiou  for help with ChIP, and  Alexander Brehm  for critical reading of the manuscript. We also thank the CMMC Imaging and Cell Sorting facilities, the  CECAD Proteomics facility , the  Cologne Center for Genomics , the  High-Performance Computing cluster "CHEOPS" of the University of Cologne  for continuous access to resources, as well as  Anastassis Perrakis  and  Alexander Fish  for help with nanoDSF aggregation profiles via the iNEXT- Discovery  project  12215. We are thankful to the  Baldus and Winkels labs  for support and access to the Cytek instrument. This work was supported by  UKGM  ( Project 5/2016 ), by the  Deutsche Forschungsgemeinschaft  via  TRR81 (Project 109546710 ) and CCRC2407 (Project  360043781 ), as well as by an  Else-Kroener-Fresenius-Stiftung  " Key-Project " grant (Project  2015_A125 ).
Data availability All NGS data generated in this study are available at the NCBI Gene Expression Omnibus (GEO) via GSE135832 and GSE136057 accession numbers. Publicly available SMARCB1-knockdown RNA-seq from hESCs can also be found at GEO under accession number GSE128351, SMARCA4/B1/C1 iPSC ChIP-seq data under accession number GSE124903, CHD1/2 and CHD7 hESCs ChIP-seq were generated by the ENCODE Consortium and can be found under accession numbers GSE31477 and GSE29611, respectively, H3K4me3 ChIP-seq under accession number GSE29611, and DNase I hypersensitivity and FAIRE-seq data from hESCs under accession numbers GSE32970 and GSE35239, respectively. All proteomics data are available via the PRIDE ProteomeXchange Consortium repository  87  under the dataset identifier PXD024803. All other data are available from the corresponding authors upon reasonable request.
Peat bog delineation The lagg (stream) of a raised bog is the transition zone where runoff collects from the ombrotrophic (rain-fed) bog at its margin with the adjacent mineral soils  (Howie and Meerveld, 2011). In this study, a combination of aerial images and LiDAR DTM data were analysed in ArcGIS. This allowed identification of the lagg stream at sites Gwaun Nant Ddu and Craig y Cilau, which were subsequently digitised to create a bounding polygon. At site Mawnbwll du Mawr the hydrology is more complex and the lagg stream was not easy to define from the aerial imagery. The bog perimeter was therefore established by interpreting bog to non-bog vegetation changes in aerial images and confirmed on site. Digitised boundaries were further validated in the field and in interpretation of the base peat reflection in the GPR data to ensure that in all cases the area bounded by the polygon and subsequently used for peat thickness and volume extraction, included only peat with a minimum thickness of 0.
GPR surveying for peat thickness measurements Peat thickness data for the three locations were collected by GPR survey. Data were acquired using a 100 MHz MALÅ Rough Terrain Antenna (RTA), an 'in-line' system involving a rugged, flexible cable, within which the transmitter and receiver electronics are separated by 2 m (see Figure  2 ). A single user can tow the cable behind them as they walk along the survey transect. The advantage of the flexible cable system is that good ground contact can be maintained even on rough, vegetated surfaces  (Francke, 2012)  and the cable slides continuously through the vegetation with minimal disturbance to it or the bog surface. Furthermore, continuous, rapid and fine-scale sampling (<1m spacing, depending on walking speed) can be collected. Over 5 km of GPR profiles were collected across the three sites.
GPR survey transects following the long axis of the bogs were pre-marked with tapes and start and finish points logged in a handheld GPS (Garmin eTrex handheld unit) (Figure  3 ; Gwaun Nant Ddu and Craig y Cilau also having cross-transect surveys for validation). In a GPR survey the antennae are moved along the survey line (transect) and a series of traces (a record of the measured EM wave reflection) are collected at specific points along the line. Successive, multiple radar traces are taken at each sampling location which are automatically summed and averaged to produce one composite trace, reducing noise and improving signal coherence. The spacing between traces was 0. 5 m (manually triggered) and 16 stacks were used for each trace, providing the best compromise between data quality and acquisition speed for our purposes.
Consequently, with the exception of some short sections of transects on Gwaun Nant Ddu and Craig y Cilau, the onset of the basal reflection could be manually picked throughout the data volume, with a typical precision of ±1. 5 ns  (Gusmeroli et al. , 2012). Peat thickness was also evaluated using a manual probe at 74 locations (Mawnbwll du Mawr = 17, Gwaun Nant Ddu = 26, Craig y Cilau = 31), both to infill small gaps in the radar coverage (e. g. , as a result of signal attenuation) and to provide initial calibration of GPR velocity estimates for conversion of TwTTs to peat thicknesses. Manual probing was completed following the standard method of inserting rods until resistance is felt. Resistance is assumed to be the peat-mineral interface with the depth at which this is encountered being recorded as the peat thickness  (Parsekian et al.
2012). The location of each measurement point was recorded with a hand-held GPS. Assuming that the probe had reached the base of the peat, comparing the measured thickness to the TwTT in the GPR profiles data implied a preliminary radar velocity of 0. 0343 m/ns, later refined following comparison to core data (see next section). An independent estimate of GPR velocity using, e. g. , common midpoint survey methods  (Huisman et al. , 2003)  was not possible in this study, given the fixed offset between the transmitter and receiver in the RTA system.