dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Design, Materials and Methods The materials and methods are summarized in Fig. 2. All geospatial analyses were performed using R software  [1]  and its terra  [2]  , Rsagacmd  [3]  , and whitebox  [4]  packages. The 5 m French airborne DTM raster (RGE ALTI®, IGN) and channel network vector (BD TOPAGE®, SANDRE) used as primary data were downloaded in 2020. The vertical accuracy of the DTM ranges from 30-70 cm  [12]  , depending on the type of land use and the acquisition technology. For water bodies, the DTM considers the elevation of the water's surface on the date of airborne acquisition. This DTM has a buffer zone of 500 m beyond the national borders and partially covers the foreshore up to the edges of topographic acquisitions. The DTM raw data were converted from ASCII to GeoTiff format and merged into a virtual raster image across France.
paths and are usually parallel to rivers. The channel network was then rasterized in GeoTIFF format at 5 m spatial resolution according to the DTM grid. Given the huge size of the DTM raster grid over France (228,640 × 212,167 pixels), it was iteratively geoprocessed in individual chunks. Each chunk was buffered to consider the elevation pattern beyond its actual area. At the end of geoprocessing, all chunks were merged to produce a seamless raster. This approach managed computing resources, parallelized the geoprocessing, and prevented edge effects. TWI was derived from a smoothed DTM to eliminate non-natural linear patterns that could influence water flow, such as transport networks or ditches. For this, a 7 × 7 median filter was applied to the original DTM using the focal function of the terra package  [2].
TWI was then calculated using the SAGA GIS Topographic Wetness Index (One Step) tool chain  [ 13, 14 ]  called in R software using the Rsagacmd package  [3]. Specifically, it calculated TWI using the standard Beven and Kirkby equation  [9]  in a single command line that specified the DTM and selected flow-accumulation algorithm. The Freeman FD8 multiple-flow accumulation algorithm  [ 15 ]  was selected since it is the most appropriate for predicting soil moisture in natural environments  [ 16 ]. All other parameters were set to default values  [ 16 ]. TWI was calculated per 30 × 30 km chunk with a 5 km buffer. The value of overlapping pixels equaled the maximum of the two pixels. MTPCC was based on integral image transformation  [4]. The first step entailed calculating the maximum deviation from mean elevation at micro-(5-300 m, with a 50 m step-size), meso-(30 0-1,0 0 0 m, with a 50 m step-size), and macro-(1,0 0 0-5,0 0 0 m, with a 200 m step-size) scales using the wbt_max_elevation_deviation function of the whitebox package  [4].
The distance interval for each of the three scales was set using the wbt_max_elev_dev_signature function, which displays a graph of scale signatures of maximum deviation from mean elevation, for 92 depressional wetlands, whose area ranged from a few m ² to several thousand m ² ( Fig. 3  ). MTPCC was then generated from the three DEVmax rasters at micro-, meso-, and macro-scales using the wbt_multiscale_topographic_position_image function. MTPCC was calculated per 100 × 100 km chunk with a 20 km buffer. VDCNI was calculated using the SAGA GIS Vertical Distance to Channel Network tool  [ 13 ]  called in R software using the Rsagacmd package  [3]. For this, the DTM raster and the channel network raster were combined. The elevations of pixels located on a channel were interpolated over the raster extent to generate a channel network base level, which was then subtracted from the DTM to generate the VDCNI raster, in which each pixel expresses the elevation relative to the nearest channel.
Acknowledgments The authors thank  François Chambaud  ( water agency Rhone-Méditerranée-Corse),  Joanie Catrin ,  Sébastien Abric ,  Ghislaine Ferrere  (  French Ministry of Ecology ),  Anis Guelmami  ( Tour du Valat ),  Rachel Vanacker  (MNHN), and  Bertrand Laroche  ( INRAE ) for their productive discussions. The authors also thank  Jean Nabucet  ( CNRS ) for maintaining the IT equipment and  Alexandre Guyot  ( Hytech-imaging ) for his help with computer programming. This study was supported by the  French Ministry of Ecology  (grant no. 2103207978  ).
Fire data We analyzed global burned area (BA) for the period 1982-2018 (Fig. 1A ) from the FireCCILT11 11 , developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) program under the Fire Disturbance project (https://climate. esa. int/en/projects/fire/, last access: 3 June 2021). The product was assembled using spectral information from the AVHRR sensor (Advanced Very High-Resolution Radiometer) and Land Long Term Data Record (LTDR) version 5 dataset produced by NASA (National Aeronautics and Space Administration). The Fire-CCILT11 includes monthly global BA data estimates at 0. 05°cell resolution. Data was resampled at 0. 5°resolution based on the monthly scale to facilitate data handling and processing while enhancing pattern recognition. There are several existing global BA datasets that could have been used in this study  47.
We synthesized the association with the four analyzed time lags, grouping pixels into clusters depicting similar linkages. A hierarchical clustering approach was implemented in combination with ward. D2 acting as agglomeration criterion and Canberra Distance as a measure of dissimilarity to minimize intergroup variance. The number of clusters (i. e. climate domains) was optimized in accordance with all the criteria available in the nbClust R package  62. The observed distribution of CT-fire correlations was used to characterize the resulting climate domains, highlighting the most prominent driving patterns in terms of influence and direction of the relationship at a given time lag. To do so, we displayed the pixel-based correlation distribution at each CTD and lag, highlighting their significance level.
Acknowledgements We thank  Lorea Garcia  for her insights and useful suggestions in the interpretation of CT-fire relationships during the review process of the manuscript. This project received funding from the  Spanish Ministry of Science and Innovation , project  FIREPATHS  ( PID2020-116556RA-I00 ) (authors receiving funding: A. C. and M. R. ) and the  European Union 's  Horizon 2020 research and innovation programme MSCA-ITN-2019-Innovative Training Networks  under grant agreement No. 860787  (Pyr-oLife) (authors receiving funding: A. C. , M. T. and C. S. ), and the  European Horizon   2020 research and innovation programme  under grant agreement No. 101037419  ( FIRE-RES ) (authors receiving funding: A. C. , J. R. , C. S. and S. d. M. ).
Data availability The global burned area data from the FireCCILT11 11 , developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) program under the Fire Disturbance project is publicly available (https://climate. esa. int/en/projects/fire/, last access: 4 December 2022). The climate teleconnection indices were retrieved from the National Centers for Environmental Information and National Weather Service (NOAA; https://www. cpc. ncep. noaa. gov/, last access: 4 December 2022).
2022). We then sequenced the genomic DNA of these 4 samples by long-read Nanopore sequencing and obtained 4 sets of high-quality reads of N50 > 14 kb and depth > 25× (Fig. 1A  and Supplemental Table  1 ). We previously proved that long reads allowed the accurate reconstruction of the highly repeated subtelomere structures in C. reinhardtii  (Chaux-Jukic et al. 2021) , which is instrumental for the analysis of rearrangements potentially implicating telomeres, subtelomeres and other repeated elements. The long-read datasets were processed by 3 assemblers, allowing us to generate de novo genome assemblies of CC-4533 and each telomerase mutant (Fig. 1B  and C, Supplemental Table  2 ). We reached large chromosome-scale scaffolds similar to the most contiguous reference genome of CC-1690 (O'Donnell et al.
S2C  and D ). The assemblies of the telomerase mutants displayed a moderate number of unique SVs (n = 52 and 49 for tel-m1-1 and tel-m2-1, respectively), involving sequences of median size < 600 bp. Cold Spring Harbor Laboratory Press on June 3, 2024 -Published by genome. cshlp. org Downloaded from However, we found that the quality of read mapping to subtelomeres was lower than those corresponding to the core genome both in CC-4533 and in the telomerase mutants (Supplemental Fig. S2E ), presumably because the repeated elements found at subtelomeres present a challenge for genome assembly even using long-read sequencing data  (Filloramo et al. 2021). Consistently, the assembled genomes reached only 25, 2 and 19 telomeres in CC-4533, tel-m1-1 and tel-m2-1, respectively. We thus suspected that the assembly-level analysis would miss genome rearrangements affecting chromosome extremities.
Telomere shortening and loss in telomerase mutants We were first interested in investigating changes related to telomere sequences. To detect all telomere sequences at the level of individual reads, we developed a new method called TeloReader that scores 8-mers with respect to their level of identity to any of the canonical 8-mers (TTTTAGGG/CCCTAAAA and circular permutations) in sliding windows and uses thresholds for the average score to find the boundaries (see Methods section). TeloReader allowed us to detect all telomere sequences of at least 16 bp, whether they were at a chromosome extremity (terminal) or not (interstitial, i. e. ITS), in the read datasets. We measured the length of all terminal telomere sequences in the wild-type and the mutants reads and found shorter telomeres in the mutants (mean ± SD for CC-4533: 293 ± 126 bp, tel-m1-0: 167 ± 109 bp, tel-m1-1: 162 ± 120 bp, tel-m2-1: 181 ± 88 bp; Fig.
Contraction, erosion and expansion of Sultan arrays To analyze subtelomere structure, we looked for the presence of the Spacer element in all reads because this element is present as a single copy in 29 out of 34 subtelomeres in the wild-type strain, with sufficient sequence differences to uniquely identify the corresponding subtelomere  (Chaux-Jukic et al. 2021). Reads containing a Spacer element were thus grouped according to their Spacer and displayed as a schematic representation of all the sequence features they contain (Supplemental Dataset 1). While for all Spacers, reads from CC-4533, even after hundreds of generations of culture, were consistent with a subtelomeric structure identical or near-identical to the one we inferred from the genome assembly of  CC-1690 (Chaux-Jukic et al.
2021 ) (Supplemental Dataset 1, Supplemental Fig. 1C ), reads from the telomerase mutants revealed many differences compared to CC-4533, including a large variety of rearrangements (Table  1  and Supplemental Dataset 1 ). We first focused on subtelomeres composed, in CC-4533, of an array of Sultan elements as the only repeated element, which are the vast majority (class A and B, 27 subtelomeres out of 34). In CC-4533, at each class A/B Sultan subtelomere, the reads were consistent with a single population of subtelomeres with a fixed number of Sultans and capped by telomeric repeats, as supported by a majority of reads (Fig. 3A  and D ). Reads that did not reach the extremity of the chromosome were Cold Spring Harbor Laboratory Press on June 3, 2024 -Published by genome. cshlp. org Downloaded from due to DNA molecules physically broken during the extraction and preparation procedure and would be expected to terminate at random position in the Sultan array and within a Sultan element, which we confirmed (Fig.
2003; Heacock et al. 2004; Pardo and Marcand 2005; Capper et al. 2007; Lowden et al. 2008) , although telomere sequences themselves may be completely absent at fusion sites in telomerase-negative cells  (Blasco et al. 1997; Naito et al. 1998; Nakamura et al. 1998; Hackett et al. 2001). Despite many instances of end-to-end fusions, we found no remaining telomere sequences at fusion sites in our read datasets, suggesting that in C. reinhardtii, even very short telomeres might be sufficient to inhibit fusions. Instead, we detected many subtelomere-subtelomere fusions, the most frequent involving Sultan arrays. They can occur between different subtelomeres, as in the case of Chromosome 4 circularization, or between sister chromatid subtelomeres. Sister chromatid fusions were followed by cycles of BFB, propagating genome instability over multiple divisions and over more internal genomic regions, as for extremity 9R in tel-m1-1.
Library preparation and sequencing Sequencing libraries were prepared from SRE-treated high-molecular-weight DNA (except for tel-m1-1 which was sequenced in two runs, one without SRE treatment and one with), following Oxford Nanopore Technologies (ONT) protocols for genomic DNA without pre-amplification. Kits LSK109 and LSK110 were obtained from ONT and Companion Module NebNext from New England Biolabs (NEB). As a minor modification, we started the preparation with 3 µg of DNA. The second tel-m1-1 library was barcoded using barcode NB07 from kit NBD104 (ONT). DNA libraries were sequenced on R9. 4 or R10. 4 Nanopore flow cells in a MinION Mk1C sequencer with default parameters on the MinKNOW operating software, except for the basecalling with Guppy which was set to real-time high accuracy and for the MUX scan which was decreased to 1 hr.
Genome assembly Reads in FASTQ files and with quality >7 were processed using Porechop with default parameters (https://github. com/rrwick/Porechop), for removing adapters/barcodes and splitting artefactual chimeras. Reads were assembled using Canu  (Koren et al. 2017) , SMARTdenovo  (Liu et al. 2021)   To detect telomere sequences in individual Nanopore reads, we developed TeloReader, a Python (v3. 9. 12) script with only 3 dependencies  (pandas (v1. 4. 3), numpy (v1. 19. 5)  and matplotlib (v3. 5. 1)). TeloReader scans each read in both directions, from 5' to 3' and from 3' to 5', to search for the C-rich telomere motif and the G-rich one, respectively. In the first step of TeloReader, the DNA sequence is transformed into a series of scores corresponding to the best alignment score (using pairwise2 from the package biopython (v1.
5 (min_mean_telo). A telomere sequence is considered as terminal if it is found at < 50 bp from the extremities of the input sequence (3' extremity for a C-rich telomere and 5' extremity for a G-rich telomere). See Data access section below. Cold Spring Harbor Laboratory Press on June 3, 2024 -Published by genome. cshlp. org Downloaded from (https://github. com/lh3/minimap2). Alternatively, for detection of 5mC in subpopulations, reads were first selected using fast5_subset (https://github. com/nanoporetech/ont_fast5_api) and aligned to a consensus sequence obtained through a multiple alignment using ClustalW  (Larkin et al. 2007 ).
Bioinformatic analysis For genome-to-genome comparison, genome models were aligned against references genomes (CC-1690 and CC-4533) using minimap2  (Li 2018 ) and visualized with Circos plots  (Krzywinski et al. 2009). SV caller MUM&Co (O'Donnell and Fischer 2020) (https://github. com/SAMtoBAM/MUMandCo) was used for structural variant detection and classification. For read analysis, reads were mapped to genomes using minimap2 with parameters: -a -x map-ont -K 5M -t 3. We used Integrative Genome For read level analysis, subtelomeric elements (Spacer, Sultan, rDNA and Suber) were searched in reads using BLASTN  (Altschul et al. 1990 ) with parameters: -max_target_seqs 10000 -evalue 0. 001outfmt "6 qaccver qlen saccver slen pident length mismatch gapopen qstart qend sstart send evalue bitscore".
Multiple hits at close positions were filtered using a custom bash script: hits were sorted by decreasing order of bitscore (sort -rgk 14) and overlapping matches were eliminated. Reads containing subtelomeric elements or telomere sequences (detected by TeloReader) were further blasted against a library of repeated elements  (Craig et al. 2021 ) and against reference genome, both Cold Spring Harbor Laboratory Press on June 3, 2024 -Published by genome. cshlp. org Downloaded from of which were then filtered as above to exclude multiple matches. Annotations from all BLAST hits were then plotted on reads using R Statistical Software (R Core Team 2021). All lists of filtered BLAST hits lists were merged with 'bind_rows', reads of interest were identified (e. g. match to a given Spacer or Sultan) with 'filter' and all hits on this read subset were extracted with 'semi_join'.
Acknowledgements We thank  Olivier Vallon  for his critical reading of the manuscript,  Maria Teresa Teixeira  for reagents and plasmids, and  Oana Ilioaia  for her technical assistance. Research in GF's laboratory was supported by the  French National Research Agency  (" ANR " grants:  ANR-16-CE 12-0019  and  ANR-18-CE12-0004 ). Research in ZX's laboratory was supported by  ANR  grant " AlgaTelo " ( ANR-17-CE20-0002-01 ), by  Ville de Paris  (Programme Émergence(s)) and by the  French National Cancer Institute  (grant:  INCa_15192 ). SE was supported by the " Initiative d'Excellence" program of the French State  (' DYNAMO ',  ANR-11-LABX-0011-01 ). FC is currently supported by a  Marie Skłodowska-Curie Actions Postdoctoral Fellowship  ( 101064365 -Cocco-Next ).
Data access All sequencing data, which include raw Nanopore FAST5 files and read FASTQ files, and genome assemblies (as FASTA files) have been submitted to the European Nucleotide Archive (ENA; https://www. ebi. ac. uk/ena/browser/home) under the project accession number PRJEB59713. The code for TeloReader is available as Supplemental Code and at GitHub: https://github. com/Telomere-Genome-Stability/Telomere_2023/tree/main/TELOREADER.
Video tracking We used a custom-made software (available at https://github. com/pernafrost/ Tetrahymena) written in python and based on the opencv, traktor, and scikit-image libraries to extract trajectories and measurements of cell size and morphology directly from the videos. The software returns measurements for each 'tracked particle', where a particle generally corresponds to one individual cell. However, there isn't a perfect one-to-one correspondence between tetrahymena cells and particles, in fact, a cell moving in and out of the field of view of the microscope camera would be labelled as a different particle, and occasionally the identity of two cells swimming close to each other and overlapping in the images were swapped. Given the very large number of individual measurements both across and within experimental conditions, we ignored this small intrinsic potential for pseudo-replication in our analyses.
Fitting thermal response curves Respiration, movement speed, and population growth were analysed by fitting a slightly modified version of the Sharpe-Schoolfield model for thermal response  [17, 18]  implemented in the rTCP R package (  [19] ). The model assumes that the rate R(T ) of the process at a given temperature increases following an Arrhenius-type relation with temperature, but also accounting for the plateauing and typically fast decrease of biological rates at very high temperature (See SI for the full details of the Sharpe-Schoolfield model in its rTPC implementation). In the analyses that we report here, we focus on this Arrhenius part of the curve, which can be summarized as R(T ) = R 0 e -E k B ( 1 T -1 T 0 ) (1) and is described by two parameters: a value of activation energy E, which describes how fast the rate increases with increasing temperature, and a reference rate R 0 , which is the rate of the process at the reference temperature T 0.
Diversification and emerging foraging strategy The model is investigated numerically using MATLAB. The niche traits are discretized into 31 equally distanced values (11 values for the foraging trait). In the simulations, when the density of a resource or a consumer phenotype drops below the critical threshold ε = 10 -4 , the density is set to 0 to save computational time. The simulations start with monomorphic populations at the niche center (y = x = 0) and consumers have a purely random foraging strategy (z = 0). 1 : Parameters of the model with their reference values used for the analysis of the response to disturbances, and the range used for the 6 parameters tested by the sensitivity analysis. The last column corresponds to the PRCC values, that is the correlation between the mean foraging trait z(t) and the tested parameter.
Swift/UVOT. A 1,061-s Swift observation was obtained on 9 August 2022, starting at 23:15 UT. Although not detected in X-rays (as expected, Extended Data Fig. 5 ), we detect [HP99] 159 in all filters of the Ultraviolet and Optical Telescope (UVOT), at AB magnitudes as follows: UVW2 = 15. 29 ± 0. 04 mag, UVW1 = 15. 33 ± 0. 04 mag, U = 15. 44 ± 0. 04 mag, B = 15. 73 ± 0. 04 mag and V = 15. 93 ± 0. 05 mag, for which the error is the quadratic sum of the statistical and systematic error. When added to the (non-simultaneous) measurements on the longer-wavelength bands (Extended Data Fig. 1 ), the spectral energy distribution is still well described by a straight power law, extending from 0. 2 to 8. 0 μm, without any sign of the He donor.
We used the XMM-Newton data analysis software SAS version 20. 0. 0 to process these data. Good time intervals were identified following the method described at https://www. cosmos. esa. int/web/xmm-newton/sas-thread-epicfilterbackground. A whole field-of-view light curve for single-pixel events with 10,000 < PI < 12,000 is created and visually inspected for periods of flaring. A quiescent rate of less than 0. 46 counts s -1 is determined and a GTI file satisfying this condition is created and used to filter the observation. After this filtering and given the off-axis position (8. 7 arcmin) of [HP99] 159, its resulting vignetted exposure was about 11. 5 ks. The events used for the spectral analysis were filtered with the following expression using the SAS task evselect: ′(PATTERN == 0) && (PI in [150 : 15000]) && (FLAG == 0)′.
The SAS task especget was used to extract (source and background) events from a circular region with radius 60″ centred on the position RA (2000. 0) = 05 h 20 min 15. 4 s, dec. (2000. 0) = -65° 44′ 32″, as well as to calculate the response matrix file (RMF) and ancillary response file (ARF) for these events. The same was done with a circular region with radius 110″ centred on the position RA (2000. 0) = 05 h 20 m 15. 5 s, dec. (2000. 0) = -65° 41′ 11″, to be used as the background only, after excising two point sources in that region. To estimate the spectral parameters of the source, a Bayesian approach was implemented using 3ML (refs. 52, 53 ). The analysis was restricted to the 0. 2-2. 3-keV energy band. The background and source contribution to the detected photons were modelled and folded through the appropriate responses to calculate posterior distributions of the spectral parameters.
Both effects, if taken into account in future work with improved data, would probably result in a higher X-ray luminosity (and white dwarf radius) than that estimated above. eROSITA. [HP99] 159 = eRASSU J052015. 3-654429 was detected by eROSITA  56  in each of the survey scans. Until the end of 2021, eROSITA scanned the source during five epochs as summarized in Extended Data Table  2. The X-ray position was determined from the combined four eRASS surveys to be RA (2000. 0) = 05 h 20 min 15. 52 s, dec. (2000. 0) = -65° 44′ 28. 9″ with a 1σ statistical uncertainty of 0. 6″. The positional error is usually dominated by systematic uncertainties  57  , which amount to 5″ in pointed and 1″ in scanning observations at present. Owing to the unprecedented energy resolution (about 56 eV at 0. 28 keV), eROSITA data are particularly sensitive to temperature changes of the source.