dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge all the personnel of  IIF-UMSA  for assisting with the station functioning ( M. Agramont ,  Ing. P. Miranda ), maintenance ( N. Choque ,  E. Cusi ,  T. Zabaleta ) and electric troubleshooting ( F. Avila ,  I. Rivero ) and the  IRD  ( Institut de Recherche pour le Développement) delegation in Bolivia  for assisting with logistics and customs clearance for the instruments. The instrumental deployment at Global GAW station used in this study is supported by an international consortium funded by  IRD ,  Centre National de la Recherche Scientifique (CNRS  under  SNO-CLAP program ) and  Ministère de la Recherche  (under  ACTRIS-FR activities ),  Observatoire de Sciences de l'Univers de Grenoble  (under  Labex OSUG@2020 ),  Leibniz Institute for Tropospheric Research ,  Consejo Superior de Investigaciones Científicas (CSIC) ,  Laboratoire des Sciences du Climat et de l'Environnement (LSCE)  and  University of Stockholm.
| Tree and soil sampling Across the study plots, we mapped 2139 individual trees with a diameter at breast height ≥10 cm; the trees were then identified and classified into 222 tree species using herbarium voucher specimens and based on the fully resolved and dated phylogeny generated at these sites  (Baraloto et al. , 2012). Additionally, we obtained data for 13 functional traits from the BRIDGE database  (Baraloto et al. , 2012). These traits are associated with key leaf and stem functions: tree bark thickness, sapwood density and moisture con-   S1 ). The functional traits present in the BRIDGE database were gathered from the same sites as this study  (Baraloto et al. , 2012). A single 20-m 2 quadrat was placed in the centre of each plot, in which five evenly spaced sampling points were established for soil.
| Data analyses We used linear mixed-effects models, in the lme4 r package  (Bates et al. , 2020) , to test for site and topographic location differences in tree abundance, species richness and diversity and soil nutrient concentrations, with study site and topography as fixed-effects terms in the tree models and plot as a random effect nested within site and topography in the soil nutrient models. Best models were selected according to goodness of fit, likelihood-ratio tests (LRTs) and explanatory power. Overall performance and assumptions were assessed using residual diagnostics. Effects of micro and macronutrients on tree community composition were tested using generalized linear latent variable models (GLLVMs) in the r package gllvm  (Niku et al. , 2019). GLLVM allows the analysis of multivariate community abundance data ranging from low numbers of species to high-dimensional assemblages comprising thousands of taxonomic units  (Niku et al.
The functional and phylogenetic metrics were standardized against 999 randomized independent swap null communities to control for plot differences in species richness  (Swenson, 2014). We analysed trait hyperspace as a metric for occupation of functional space at the two study sites, which was computed through Gaussian kernel density estimation using the r package hypervolume  (Blonder et al. , 2018). Calculation of volume was based on the same three most informative traits selected for the GLLVMs above, for species present at the study site or at plot level, and each volume was constructed using a threshold quantile of 0. 05; consequently, each volume includes 95% of the total probability density, based on 500 Monte Carlo samples per data point, and an estimated variable kernel bandwidth for each axis  (Blonder et al.
, 2018). Thus, we obtained the overlap and unique areas of the functional space occupied by the tree communities at site or plot level, where overlap was calculated by the intersection volume divided by the union volume. We estimated the number of species per unit of plot-level trait volume as a measure of niche packing, and hole ratios at site and plot levels (i. e. the empty functional trait space) as the ratio between the volume of the detected holes and the volume of the convex expectation, as a measure of vacant niches  (Blonder, 2016). We tested the differences in functional and phylogenetic metrics between study sites, topographic positions and nutrient gradients (N, P, K) by linear models with the lme4 package  (Bates et al. , 2020) , with an automated model selection process leveraging the dredge function from the MuMIn package  (Barton, 2020 ) that ranks best fitting models according to δAICc.
We used visreg package to visualize the conditional plots for each explanatory variable  (Breheny & Burchett, 2017). The three most informative functional traits were mapped across the tree phylogeny, we calculated their phylogenetic signal (Pagel's λ), and then, their ancestral states were calculated using the phytools package, assuming a Brownian motion model of evolution for continuous traits  (Revell, 2012). All analyses were carried out in r v. 4. 0. 0 (R Core Team, 2020).
We thank the staff of  Nouragues (USR mixte LEEISA, CNRS, Cayenne)  and  Paracou (UMR Ecofog, CIRAD ,  INRAE, Kourou)  research stations. Both facilities received support from ' Investissement d 'Avenir ' grants provided by the  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ,  ANAEE-France :  ANR-11-INBS-0001 ). We thank  Vincent Freycon  for the identification and characterization of each topographical position and  Pascal Petronelli  for the botanical determination.
Data Availability Statement The InSight event catalog https://doi. org/10. 12686/a21 and waveform data are available from the IRIS-DMC http://ds. iris. edu/ds/nodes/dmc/tools/mars-events/, NASA-PDS https://pds-geosciences. wustl. edu/missions/ insight/seis. htm and IPGP data center https://doi. org/10. 18715/SEIS. INSIGHT. XB_2016.
Chromatin state annotations from ChromHMM  46  , which classify chromatin based on an ensemble of histone marks, further confirmed the pattern of OL mutation enrichment in inactive or inaccessible genomic regions, with OL sSNVs overrepresented in heterochromatin (state 9, 24% enrichment, P = 0. 0036) and quiescent regions (state 15, 11% enrichment, P < 10 -4 ), and depleted in the transcriptionally active states 1-7 (Figure  4H ). The strongest depletion of OL sSNVs across all genomic covariates analyzed in this study was observed for active transcription start sites (ChromHMM state 1, mean 50. 4% depletion across brain tissues) and was not specific to any of the 13 Roadmap Epigenomics brain tissues (Figure  S3C ). An orthogonal dataset of active promoters in neurons, oligodendrocytes, microglia and astrocytes from flow-sorted cell populations  47  further confirmed the strong depletion of OL sSNVs in promoters (mean depletion 51%) and again there was no marked preference for the cell type from which the promoters were measured (Figure  4I ).
Our scATAC-seq data allowed further cell type-specific evaluation of cancer sSNV densities. Among all tumor types in PCAWG, GBM sSNV density was best predicted by OPCspecific scATAC-seq tracks using a regression model, with 44% of variance in GBM sSNV density explained (Figure  6B ). This provides additional evidence that OPCs are the cell of origin for GBM tumors and that scATAC-seq is a powerful approach for determining the cell-of-origin for a tumor  48. Expression levels from scRNA-seq were far less effective in explaining cancer mutation density, explaining only 6% of variance in the best case (Figure  6C ). Finally, we tested whether cancer-associated genes were more likely to be mutated in OLs compared to neurons. For each tumor type, we determined the 100 most frequently mutated genes and computed an odds ratio (OR) indicating whether mutations in OLs (OR > 1), neurons (OR < 1) or neither cell type (OR=1) were more likely to occur in the frequently mutated genes.
Data and code availability Newly generated sequencing data for MDA and PTA single-cell WGS of oligodendrocytes, single-cell ATAC-seq, and single-cell RNA-seq are available on dbGaP, accession number phs001485. v3. p1. Previously generated scWGS, matched bulk controls, and scRNA-seq data for individual 1465 are also available for download at dbGaP, accession number phs001485. v3. p1. Scripts used for analyses in this manuscript are available on Zenodo (https://doi. org/10. 5281/zenodo. 7508802).
Sorting purity assessment Three populations were sorted, DAPI, NEUN+, SOX10+/NEUN-, from one 17y individual (Supp. Fig. S1 ). 10x scRNAseq was used and 6,000-10,000 nuclei from each population were sorted into individual tubes containing RT mastermix, and immediately processed for GEM generation, barcoding, and cDNA amplification, following manufacturer instructions. Each library prep was submitted to paired-end single indexing sequencing on a lane of Illumina HiSeqX to obtain ~50,000 read pairs per nucleus. Analysis of scRNA seq data was performed with Loupe Cell Browser 4. q software provided by 10x genomics. Sorting purity is critical when preforming single-cell whole-genome (scWGS) studies, hence we evaluated purity of a larger number of cells by scRNA-seq. As a control, evaluation of 3,447 DAPI+ sorted nuclei obtained from a mix of grey and white matter (PFC) from a 17yr male identified all the celltypes anticipated for this region and with significant presence of OLs following WM expectation  55.
Each library preparation was submitted for paired-end single indexing sequencing on Illumina HiSeqX or NovaSeq6000 targeting ~50,000 read pairs per nucleus. 10x scRNA-seq data analysis scRNA-seq data were demultiplexed using bcl2fastq. snRNA-seq FASTQ files were then processed using the 10X Genomics cellranger count pipeline for gene expression to perform alignment to hg19, barcode counting, UMI counting, and generation of feature-barcode matrices. Cell Ranger filtered count matrices were used for downstream analysis using Seurat 3. 0  57. For each library, we further filtered for cells with > 200 and < 3000 genes and <5% mitochondrial genes, and genes with <10,000 UMI counts and >3 cells. RNA counts were normalized using the LogNormalize method and the 2,000 most highly variable features were identified using the vst method.
Single neuron whole genome sequencing data Sequencing data for 36 PTA-amplified single neurons and matched bulk from the 12 individuals from which OLs were harvested were downloaded from dbGaP accession phs001485. v3. p1. These BAM files were re-analyzed by SCAN2 jointly with OLs as described in Somatic mutation calling with SCAN2. For a single additional individual (5171), no PTA data were generated for either neurons or OLs; in this one case, sequencing data for MDA-amplified single neurons from the prefrontal cortex and a matched bulk were downloaded from the same dbGaP accession as the PTA data (see Table  S1 ). SCAN2 somatic SNV calls, indel calls, and genome-wide burden estimates from 15 previously published PTA-amplified single neurons from 5 additional neurotypical individuals (individuals 4976, 5451, 5572, 5666, and 5943) were downloaded from https://github.
Total mutation burden estimation and aging models SCAN2 provides estimates of the total somatic SNV and indel burden in each sample (i. e. , the estimated total number of mutations per cell adjusted for sensitivity of mutation calling). These estimates were obtained from each SCAN2 output RDA file by first loading the file in R, then running object@mutburden[["snv"]]$burden [2]. Indel burdens were recovered by replacing "snv" with "indel". To account for variability within and between individuals, mixed-effects linear models were used to estimate the aging-related rates of somatic SNV and indel accumulation. These models were fit by the R lme4 package using lmer(genome. burden ~ age*celltype + (1|individual)), where celltype is either oligo or neuron, individual is one of the 12 individual IDs for which PTA OLs were available (i.
SnpEff annotation Both VAF-based and mutation signature-rescued SCAN2 somatic mutation calls were annotated for functional impact via SnpEff version 4. 3t using the hg19 database. Reported functional impacts were taken from the first ANN field in the SnpEff annotated VCF. When computing the fraction of mutations with LOW, MODERATE, and HIGH impact (Figure  1D ), duplicate and clustered mutations were removed as described in Somatic mutation enrichment analysis.
ACKNOWLEDGMENTS We thank  J. Neil  and the  University of Maryland Brain Bank  for helping with the tissue collection. We thank the donors and their families for their invaluable donations for the advancement of science. We thank the  Boston Children's Hospital IDDRC Molecular Genetics Core Facility , supported by  NIH  award  U54HD090255  from the  National Institute of Child Health and Human Development. Funding: Supported by  R01AG070921  and  R01AG078929  from the  NIA  to CAW. CAW is an Investigator of the  Howard Hughes Medical Institute. JG was supported by a  Basic Research Fellowship  from the  American Brain Tumor Association   BRF1900016  and by the  Brain SPORE  grant  P50CA165952. S. B was supported by the  Manton Center for Orphan Disease Research at Boston Children's Hospital , and is now supported by the  Horizon2020 Research and Innovation Program Marie Skłodowska -Curie Actions (MSCA) Individual Fellowship  (grant agreement no.
Survey design & data collection This study used an online survey to trace perceptions of forest managers and experts in nine European countries (Finland, France, Germany, Italy, Norway, Portugal, Spain, Sweden, and the United Kingdom). The questionnaire was designed in English and translated by native speakers and experts in forestry into respective national languages for more convenient sampling (Supplementary A). The questionnaire was also open to respondents from other countries than the nine target countries, but no translation in other languages was provided. Before data sampling, the survey was pre-tested by four researchers and foresters who were not directly involved in the survey design. Pre-testing helped to improve the content and to adapt the length of the survey. After two rounds of pre-testing, the survey was considered adequate for data collection.
77 ). The 1. 5 Å µED dataset of 2H-Car-C5 was solved by simulated annealing (see Methods section for more details), which makes this, to the best of our knowledge, one of the first examples  [36] [37] [38]  of such a low-resolution small molecule µED structure being solved. The columnar thicknesses are increased within the 2H-Car-C5 assembly compared to the crystal structure due to four carpyridine molecules contributing to the width of a single column as opposed to three in the crystal and columns interpenetrate one another to a lesser extent. Crucially, as seen in the crystal structures, the relative orientation of carpyridine cores appears to be preserved in the assembly such that there is very limited rotational freedom within the column despite the greater translational slippage. Although ultimately no proposed structure could be derived for 2H-Car-C6, the unit cell Fig.
Electron diffraction measurements All measurements were made on an ELDICO ED-1 electron diffractometer equipped with a LaB 6 source operating at 160 kV (λ = 0. 02851 Å), producing a parallel beam of 750 nm in diameter  41. The crystals were located and aligned by STEM imaging. Diffraction data were collected with a DECTRIS QUADRO® hybrid-pixel detector in steps of 0. 5 °per frame while the crystal was continuously rotated over a range of 60-150 °. The software APEX4® by Bruker 42 was used to analyze the frames, to determine the unit cell constants and the integrated intensities. For the case of 2H-Car-C5, diffraction spots were only observed until 1. 5 Å resolution. Structure solution was attempted with the programs SHELXT 43 , SHELXD  44  (both programs employ ab initio methods), Patsee 45 , and Phaser 46 (within Phenix 47 ) with the latter two using molecular replacement (MR).
Eventually, the structure was solved with a real-space method based on the simulated annealing algorithm implemented in SIR2021, the updated version of the SIR2014 software  48. The method involves the generation of a random sequence of trial structures starting from an appropriate 3D model, which is moved until a good match is attained between the calculated and experimental data through the evaluation of an appropriate cost function. For the structure solution, data up to 2. 0 Å resolution were used and 1643 reflections were selected. The starting model was assembled from the molecule of 2H-Car-C5 derived from the crystal structure of a polymorph determined by X-ray methods. Three molecules of 2H-Car-C5 were positioned into the unit cell (Z' = 3) assuming the space group P2 1 /c. A total of 66 parameters were found by SIR2021 during the minimization process: three coordinates to describe the position of the center of mass, three describing the orientation, and 16 torsion angles to describe the conformation, for each molecule.
The MPI implementation of the simulated annealing algorithm was run 100 times under a Linux virtual machine in a parallel calculation over 40 CPUs. The number of moves per step of temperature was increased to make the location of the global minimum more robust by using the directive niter 2000 in the SIR2021 input file. The total time required for the calculation was 7. ) 2 ] 1/2 , where the summation is over the number of reflections in the experimental selected range. The best solution with the lowest cost function value was selected and successfully refined with ShelxL-2018  49  in Olex2 50 against the data at 1. 5 Å obtaining R 1 values for strong data of ~27%, however, due to the low resolution, no improvement of the model could be observed.
X-ray diffraction Crystals were obtained from toluene and MeOH and were mounted on a cryo-loop and used for a low-temperature X-ray structure determination. All measurements were made on a Rigaku Oxford Diffraction XtaLAB Synergy diffractometer  51  with a Pilatus 200 K hybrid-pixel area detector using Cu Kα radiation (λ = 1. 54184 Å) from a PhotonJet microfocus X-ray source and an Oxford Cryosystems Cryostream 800 cooler. Data reduction was performed with CrysAlisPro 51. The intensities were corrected for Lorentz and polarization effects, and a numerical absorption correction  52  was applied. The space group was uniquely determined by the systematic absences. Equivalent reflections were merged. The structure was solved by dual space methods using SHELXT-2018  43  , which revealed the positions of all non-hydrogen atoms.
Acknowledgements Imaging was performed with the support of the  Center for Microscopy and Image Analysis, University of Zurich. The platform of polymer characterization at the ICS is acknowledged for DSC measurements. We also thank  T. Fox  for assistance with NMR measurements,  T. Moehl  for the use of AFM, the  Mass Spectrometry Laboratory at the University of Zurich  for MS measurements, and  K. Gademann  for generously hosting and supporting our research group. M. R. gratefully acknowledges funding from the  Swiss National Science Foundation  grant  PZ00P2_180101  and J. F. W. is thankful for support from the  University of Zurich Forschungskredit  ( FK-21-107 ).
Data availability All data are available either in the main text, the supplementary materials, or by request of the corresponding author. The following. cif files are available on the CCDC database: 2H-Car-C2 (CCDC No. 2254105), 2H-Car-C3 (CCDC No. 2254084), 2H-Car-C4 (CCDC No. 2254085) and 2H-Car-C5 (CCDC No. 2254086).
measurements in the Arctic are difficult due to the harsh environment, remote locations, and high operating costs. All of these factors lead to a scarcity of monitoring stations and a limited representation of atmospheric vertical information; as such, utilization of all available data sources is valuable in aiding model development. The Network for Detection of Atmospheric Composition Change (NDACC) has over 70 stations around the globe, collecting high-quality atmospheric composition measurements with ground-based, remote-sensing instruments  (Kurylo and Soloman, 1990; De Mazière et al. , 2018). The network's objective is to create a long-term database for various studies such as atmospheric trends, assessing links between climate, air quality and composition, and as a resource for other atmospheric investigations such as satellite validation and model development.
The models examined are chemical transport and climate models: CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-CHEM, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1 and WRF-CHEM. The objective is to utilize the high-quality, long-term Arctic FTIR datasets to assess how well the models perform. This study builds upon the model-measurement comparisons presented in the 2021 AMAP SLCF Assessment Report using an additional Arctic dataset that was not included in the original report. The remainder of this paper is organized as follows: Sect. 2 provides a description of the datasets used, Sect. 3 describes the analysis methodology, Sect. 4 examines the results and compares them with similar studies, and Sect. 5 presents the summary and conclusions.
FTIR spectroscopy and retrievals The FTIR measurement sites included in this study are summarized in Table  1. These instruments require sunlight and a clear sight to the sun to make measurements, and so the high-latitude datasets are limited to the sunlit portion of the year at each location. To ensure high data quality and consistency between sites, NDACC has several specialized instrument and theme groups; the instruments used here are part of the Infrared Working Group (IRWG). The ten standard gases reported by sites participating in the IRWG are C2H6, CH4, CO, ClONO2, HCl, HCN, HF, HNO3, N2O, and O3, while several other gases are retrieved as research data products, including C2H2, CH3OH, H2CO, HCOOH and OCS. The FTIR measurements cycle through a series of optical filters covering different spectral regions between approximately 650 and 4500 cm -1 for the retrieval of multiple atmospheric gases.
Atmospheric trace gas profiles and columns are retrieved with the SFIT4 algorithm, https://doi. org/10. 5194/egusphere-2023-1161 Preprint. Discussion started: 3 July 2023 c Author(s) 2023. CC BY 4. 0 License. using optimal estimation to iteratively adjust an a priori profile to match a modelled spectrum to the measured spectrum within a defined convergence criterion  (Rodgers, 2000; IRWG, 2020). The a priori information for the modelled spectra is provided by 40-year-average profiles from the Whole Atmosphere Community Climate Model (WACCM)  (Marsh et al. , 2013) , with spectroscopic absorption parameters from the HITRAN 2008 line-list  (Rothman et al. , 2009)  and pressure and temperature data from the U. S. National Centers for Environmental Prediction (NCEP)  (Kalnay et al. , 1996). All sites included in this paper use SFIT4, except Kiruna, which uses a comparable program called PROFFIT  (Hase et.
, 2022). Nine of the 11 models examined use the Global Fire Emissions Database  (GFED, van der Werf et al. , 2017)  or GFED-based (CMIP6) forest fire emissions, and nine of the 11 exclusively use ECLIPSEv6b for agricultural waste burning. A summary of the models is presented in Table  3. For a full description of the models, see Appendix A of  Whaley et al. (2022)  and the references in Table  3. This procedure modifies the model output to correspond to an FTIR measurement, making the resulting partial columns equivalent for further comparison. The FTIR measurements are matched with the model measurement closest in time (±<1. 5 hours). If more than one FTIR measurement coincides with a model output (i. e. , multiple measurements are within 1. 5 hours of the same model time), the FTIR measurements are averaged.