dataesr/scientific-paragraphs-categorization huggingface dataset:
C. Further thoughts As shown in previous section, the emergent riparian vegetation could have a significant impact on water extent estimated from SWOT. This is an important finding, as it will impact SWOT water storage variation estimates. However, Mamawi Lake is in a particularly wet environment, and it is unclear what percentages of lake in the world seen by SWOT will be affected by emergent riparian vegetation similarly to Mamawi lake. Hence, there is a need to identify lakes in the SWOT a priori lake database  [31]  where SWOT might show the same issue. Here, the hypothesis is that lakes that have similar emergent riparian vegetation conditions as Mamawi lake are located in wetlands. An attempt was made to compare wetland classifications from the World Wildlife Foundation's Global Lake and Wetland Database (GLWD  [32] ) with the a priori SWOT lake data base to estimate this percentage globally.
ACKNOWLEDGEMENT The study was made possible through funding provided by the  Canadian Space Agency. We extend our thanks to  Environment and Climate Change Canada, Parks Canada , and the research teams of  Laurence Smith  ( UCLA ) and  Colin Gleason  ( U. of Massachussets, Amherst ) for their immense help with fieldwork. SWOT simulations were performed by the  Centre Nationale d'Étude Spatiale (CNES). Several authors (S. B. , D. D. and D. C. ) were supported by  CNES. This work is based upon virtual observations with KaRIn, which will be carried aboard SWOT. The JPL is acknowledged for providing binaries of the SWOT simulator that was used in this study and for their help, particularly  Brent Williams , in analyzing simulator outputs.
The magnetic refinements were carried out using the FullProf Table  1. BV for GaV 4 S 8 used in the magnetic refinements for each propagation vector. BV1 BV2 BV3 k = 0 V1 (3a) x, y, z 0 0 1 V2 (9b) x, y, z 1 2 0 0 0 1 -y, x -y, z -2 -1 0 0 0 1 -x + y, -x, z -1 -1 0 0 0 1 k 0k0 = [0K0] V1 (3a) x, y, z 1 2 0 0 0 1 V2 1 (9b) x, y, z 1 2 0 0 0 1 V2 2 (9b) x, y, z 1 0 0 0 1 0 0 0 1 -x + y, y, z -1 0 0 1 1 0 0 0 1 k 00l = [00L] V1 (3a) x, y, z 0 0 1 V2 1 ,V2 2 , and V2 3 (9b) x, y, z 1 2 0 0 0 1 -y, x -y, z -2 -1 0 0 0 1 -x + y,-x, z 1 -1 0 0 0 1 k ip and k ip-c V1 (3a), V2 1 ,V2 2 , and V2 3 (9b) x, y, z 1 0 0 0 1 0 0 0 1 software suite  [5]. Ac susceptibility measurements as a function of dc applied field were performed on a single crystal of GaV 4 S 8 grown as described in reference  [4]. An ac field of 113 Hz and 0.
3 mT was applied after zero-field-cooling then application of a dc magnetic field along the cubic [111] direction at intervals of approximately of 0. 25 K and 2 mT. Parameters refined for the magnetic models of GaV 4 S 8 at 1. 5 and 9 K using the FullProf software suite  [5]. λ is the magnetic modulation length calculated from the refined propagation vector (k vector) using.
Acknowledgments This work is financially supported by the  EPSRC UK Skyrmion  Project Grant  EP/N032128/1. We are grateful for the beamtime allocated to us at the  Institut Laue-Langevin (ILL)  under the experiment code 5-31-2684. Data are available from the  ILL at DOI :  10. 5291/ILL-DATA. 5-31-2684. We would like to thank  O A Petrenko  ( University of Warwick ) for their critical reading of the manuscript and  T J Hicken  ( Durham University ) for useful discussions.
For this purpose, we cultured patient fibroblasts and performed cycloheximide chase assay to measure ACLY half-life in several lines of control and FD fibroblasts. We showed a reduction of ACLY stability in FD fibroblasts as compared to control lines (Fig. 5g-h ), a phenotype that may result from the disruption of an interaction between the Elongator and Acly, as suggested by co-immunoprecipitation experiments performed in HEK293 cells (Supplementary Fig. 5a ). Altogether, these data suggest that loss of Elongator activity reduces Acly/ACLY stability, thereby affecting the local production of acetyl-CoA required by ATAT1 to promote MT acetylation. Such regulation is likely to take place in motile vesicles (Fig. 5i ).
The mass spectrometry data were analyzed using the MaxQuant software 1. 5. 1. 2. (//www. maxquant. org) using the Andromeda search engine, searching against the mouse uniprot database with a mass tolerance of 20 ppm for the precursor masses and 20 ppm for the fragment ions. Peptide-and protein-level false discovery rates (FDRs) were filtered to 1% using the target-decoy strategy. Protein table were filtered to eliminate the identifications from the reverse database, and common contaminants and single peptide identifications. The data were quantified by label-free analysis using the same software, based on extracted ion currents (XICs) of peptides enabling quantitation from each LC/MS run for each peptide identified in any of the experiments. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD021186.
Acknowledgements We thank  Maria M. Magiera  and  Carsten Janke  for providing non acetylated  Hela Tubulin. We thank  Patrik Verstreken  for sharing Elp3KD flies,  M. Nachury  for sharing ATAT1 antibody and  X. J. Yang  for providing the Atat1 KO mice,  T. Lahusen  from  American Gene Technologies  for creating viral sh-RNA particles, the  Smoler Proteomics Center at the Technion  for proteomic analysis and  E. Even  for graphical design. All graphical figures were created with  BioRender. com. We are grateful to  Francesca Bartolini  and  Marina Mikhaylova  for their constructive feedback on the manuscript as well as to all members of the  Nguyen and Weil laboratories  for their critical reading. L. N. is Director from F. R. S-F. N. R. S. This work was supported by the F. R. S. -F. N. R.
S. (Synet;  EOS 0019118F-RG36 ), the  Fonds Leon Fredericq  (L. N. ), the  Fondation Médicale Reine Elisabeth  (L. N. ), the  Fondation Simone et Pierre Clerdent  (L. N), the  Belgian Science Policy (IAP-VII  network  P7/20 (L. N. ) ), and the  ERANET Neuron STEM-MCD  and  NeuroTalk  (L. N. ); grants from  Agence Nationale de la Recherche  ( ANR-18-CE16-0009-01  AXYON (F. S. );  ANR-15-IDEX-02  NeuroCoG (F. S. ) in the framework of the " Investissements d'avenir" program );  Fondation pour la Recherche Médicale  (FRM,  DEI20151234418 , F. S. ). A. E. 's stay at  GIGA Research Institute of the University of Liège  was funded by  EMBO Short-Term Fellowships  ( ASTF 174-2016 ), A. E. , M. S. , and M. W. 's research was supported by the  Israel Science Foundation  (grant no. 1688/16 ). The authors declare no competing financial interests.
Genome sequences An assembly search was done on ncbi. nlm. nih. gov/ assembly (8 September 2017) for 'Pseudomonas aeruginosa'. Status='latest RefSeq' and Assembly level='complete genome' were selected. This resulted in 96 available full genome assemblies. Two strains were subsequently removed from the dataset. Strain PA7 (GenBank accession GCF_000017205) was removed because it was found to be a taxonomic outlier (see also  [21] ). Possibly, mutation rates in this strain differ strongly from other strains, making branch length comparison and divergence time with other strains unreliable. Strain H47921 (GenBank accession GCF_001516345) was removed because it was found to be highly recombinant, making a correct phylogenetic placement of this strain difficult. Table  S1  provides a list of the genomic sequences included in this study.
Pan-genome analysis An all-against-all nucleotide blast was performed on the full set of genes using blastn 2. 2. 31+. To cluster genes into groups of homologues, blast results were used as input for SiLiX 1. 2. 9  [24]  with the following parameter values: overlap=0. 7 (min. % overlap to accept blast hits for building families=70 %) and identity=0. 6 (min. % identity to accept blast hits for building families=60 %). In parallel, the genome dataset was analysed with the Roary pipeline  [25]  to obtain an additional pan-genome analysis and to use its orthology assignments for splitting up large gene clusters produced by SiLiX. Our goal here was not to define the minimal core genome of P. aeruginosa, but to obtain genes for constructing a strain phylogeny and to determine patterns of codon usage for genes expected to be in equilibrium with forces shaping codon usage.
Therefore, we adopted a looser definition of core genes to maximize the number of genes included in this dataset, which is crucial to reconstruct a meaningful strain phylogeny in light of the similarity of the strains. This is expected to result in a larger number of core genes compared to other estimates  [26]. Single copy homologous gene clusters predicted by SiLiX present in at least 95 % of all strains (i. e. between 89 and 94 strains) were directly assigned as core genes. Multicopy clusters predicted by SiLiX present in at least 95 % of all strains were split up into single-copy clusters using the orthology assignments produced by Roary, and were additionally assigned as core genes. The remaining homologous gene clusters were assigned as accessory genes. Different reasons could have caused these genes to be classified as accessory genes: HGT, multiple deletions of ancestral core genes and erroneous classification of genes in a separate cluster due to, for example, gene duplication followed by sequence evolution leading to pseudogenization or sequencing and assembly errors (here termed pseudo-accessory genes).
Inference of horizontal gene transfers The overall small number of genes and low amount of sequence variation within homologous clusters of accessory genes did not allow us to construct reliable gene trees, preventing analysis of HGT based on phylogeny comparison (reviewed in  [32] ). Therefore, we used ancestral state reconstruction to infer HGT events. For each accessory gene, we constructed a phyletic pattern with gene copy number as character states. The probability of each character state was inferred for all nodes on the strain phylogeny using ancestral state reconstruction with stochastic character mapping. This was done using the Phytools R package running 1000 simulations for each character under the equal rate model. We used the probabilities of presence at the nodes of the strain phylogeny to infer HGT events.
Horizontal transfer of an accessory gene was predicted to have occurred along a branch on the strain phylogeny if the probability of presence of this gene was lower than 0. 5 on its parent node (most probable gene copy number=0) and higher than 0. 5 on its child node (most probable gene copy number >0). To verify results obtained by Phytools, we additionally inferred HGT events with ancestral reconstruction by posterior probabilities in a phylogenetic birth-and-death model using the count program  [33]. This analysis inferred the majority of transfers on exactly the same branch (76. 7 %). Despite some differences between Phytools and count, general patterns in subsequent analyses were highly similar using inferred HGTs from either program, indicating the robustness of our results. We therefore only included results obtained from HGT's inferred by Phytools in this study.
After applying the previous filtering step, we performed an ancestral state reconstruction for all the remaining accessory genes. This analysis inferred that 6. 9 % of the accessory genes were present in the common ancestor of all strains included in this dataset, indicating that these genes were classified as accessory genes because of multiple deletions throughout the evolutionary history of P. aeruginosa. Further evidence for multiple deletions of genes present in the common ancestor was provided by the large number of genes that were found at the same genomic location, but with a highly dispersed occurrence on the strain phylogeny (545 genes were present in only a small number of strains on both sides of the root at the same genomic location). As in our analysis we were only concerned with accessory genes that originated through HGT, we excluded genes that were classified as accessory genes by potential misclassification or gene deletion.
A high number of HGT events was also recorded along the branch departing from this node, leading to a sub-clade of 44 strains (87 events, 146 genes). An exceptional high number of HGT events was further found along the branch leading to the common ancestor of strains Carb01_63 and RIVM-EMC2982, two multidrugresistant clinical strains isolated from different locations in the Netherlands (99 events, 188 genes). A total of 1165 HGT events, involving 4437 genes, were found to be strain-specific. Strain DHS01, a multidrug-resistant strain isolated in France, had the highest number of strain-specific HGT events (64 events involving 131 genes), although this number could be biassed by the absence of closely related strains in our dataset. Each gene acquired through HGT was assigned a residence time, representing the evolutionary distance between the leaf on the phylogeny where a gene was found and the midpoint of the branch where this gene was acquired.
Gene amelioration does not systematically affect HGT genes We further assessed the potential importance of gene amelioration for changes in codon usage in function of residence time (i. e. are genes evolving towards the codon usage bias of the receiving genome through mutational processes?). To evaluate this hypothesis, we inferred the ancestral sequences for HGT genes that were represented by at least 20 or more extant sequences in our dataset. By applying this threshold we consequentially selected genes with the longest residence times, and thus those for which the strongest responses are expected. Codon usage bias in extant and ancestral sequences were then compared to evaluate changes due to mutational processes. This analysis showed variable responses for different genes (Fig. 3 ). Out of the 22 genes included in the analysis, six genes showed directional evolution towards the codon usage of the core genome, ten genes showed directional evolution opposite to the codon usage of the core genome, and six genes did not show any sign of directional evolution.
To analyse compensatory evolution on a gene level, we hypothesized that an expected increase in translation due to variation in tRNA gene content would be proportionally larger for recently acquired HGT genes than for other genes. In our dataset, there were 45 changes in tRNA gene content with an expected overall positive effect on translation, as determined by a mean positive ΔtAI abs of all genes affected by this change. Of these, 29 changes in tRNA gene content caused a proportionally larger ΔtAI abs for HGT genes than for genes that were already present at the parent node (Mann-Whitney U-test for each case: P<0. 05; Fig. 5 ). In only five cases was the ΔtAI abs higher for genes present at the parent node than for HGT genes (Mann-Whitney U-test for each case: P<0. 05), while in the remaining 11 cases there was no difference between the ΔtAI abs of HGT genes and genes present at the parent node (Fig.
(b) Statistical analysis In this paper, we have analysed grid point scale (2. 5 × 2. 5 km 2 ) hourly precipitation. In most of the paper, we use a coarse-graining procedure by considering two statistics from (non-overlapping) blocks of 5 × 5 grid points: pr max , the maximum value of the 25 grid points per block for each hourly interval, and pr sample , a sample value taken from the centre point of the 5 × 5 block. This procedure is the same as in Lenderink et al. [31]  and keeps the data volume relatively low and removes some of the spatial dependencies. Since we are interested in extremes we mostly use pr max , because this guarantees that we capture all of the extreme values in the limited dataset. We only used data for the six months, May to October, in order to capture the extremes in the warm season, realizing that hourly extremes also occur in late spring and early to mid-autumn  [35, 36].
The spatially and temporally pooled precipitation data was ordered, and we computed the 'pooled fraction of exceedance' (PFOE). This statistic is practically equivalent to the commonly used percentiles (PCTL)-with PFOE = 1 -PCTL/100, so e. g. the 99th percentile corresponds to a PFOE of 0. 01  [31]. We mostly analysed the change in the distribution derived from all hours, but also used the wet hour conditioned distributions to investigate the influence of changes in the frequency of rain hours on extreme statistics  [24]. For the climate change response in hourly extremes, derived from comparing the future simulation with the present-day simulation, changes are normalized by representative dew point temperature changes. These dew point temperature changes are derived from the dew point temperature data which is paired to the rainfall occurring 4 h later (as also used in the apparent scaling analysis).
We used two measures: (i) Td wet , the change in dew point temperature for wet events exceeding the threshold of 0. 1 mm and (ii) Td P99 , the change in dew point temperature for rainfall events exceeding the 99th percentile of rainfall. The latter is more representative of the dew point temperature change of extreme rain events and is therefore used as the reference with which to scale the precipitation changes. To estimate uncertainty levels, we used a bootstrapping procedure (with replacement), sampling data blocks of 6 h and full fields (the same time blocks are used for both present-day and future periods). Plotted are 5-95% uncertainty ranges estimated from 100 resamples (note this is computationally quite expensive).
2017). The existence of a UFO has been confirmed in 1H 0707 (e. g. Dauser et al. 2012; Hagino et al. 2016; Kosec et al. 2018b ) at a velocity of ∼ 0. 13 with an ionization parameter log( /erg cm s -1 ) ∼ 4. 3. Blustin & Fabian (2009)  detected Doppler-shifted emission lines and PK18 found that the velocities of the blueshifted emission increase in higher ionization species. This implies that the wind in 1H 0707 is likely stratified and is perhaps slowing down and cooling at larger distances from the SMBH. The large amount of XMM-Newton data and high spectral resolution of RGS allows us to search for any correlation between the strength of the UFO absorption lines and the source X-ray luminosity, and to study the wind stratification in different regions, by using both soft and hard X-ray absorption features.
DATA REDUCTION AND PRODUCTS In this paper, we use the full XMM-Newton dataset  (Jansen et al. 2001 ) of 1H 0707, including 16 observations ranging from 2000 to 2019, totaling 1. 4 Ms gross archival data. We employ the data from the European Photon Imaging Camera (EPIC) with two EPIC-MOS CCDs  (Turner et al. 2001 ) and an EPIC-pn CCD  (Strüder et al. 2001) , the Reflection Grating Spectrometers (RGS; den  Herder et al. 2001 ) and the Optical Monitor (OM;  Mason et al. 2001 ) in order to constrain the spectral energy distribution (SED) of the source and the absorption lines of the wind. The data are processed with the XMM-Newton Science Analysis System (SAS v19. 0. 0) and calibration files available on February 2021, following the standard SAS threads. Briefly, EPIC-pn and EPIC-MOS data are reduced with and tools respectively.
The background flare contamination are filtered with a standard filtering criterion of 0. 5 and 0. 35 counts/sec (in the 10-12 keV band) for pn and MOS separately. The source spectra are extracted from a circle region with a radius of 20 arcsec centered on the object (X-ray flux peak), and the background spectra are extracted from a nearby circular region with a radius of 60 arcsec away from the source and the copper hole. We adopt a radius of 20 arcsec for the source extraction in order to decrease the background contamination following  Chartas & Canas (2018). The RGS data are reduced with the task, for which high-background contamination is corrected by a threshold of 0. 2 counts/sec. We extract the first-order RGS spectra in a cross-dispersion region of 1 arcmin width, and the background spectra are generated by photons beyond the 98 percent of the source point spread function as default.
EPIC-RGS Continuum Modelling We use the SPEX (v3. 06. 01) package  (Kaastra et al. 1996 ) in this paper to analyze the spectra. All XMM-Newton spectra are thus transferred into the format which can be read by SPEX using 2 script. We employ the Cash-statistics  (Cash 1979 ) and estimate all parameter uncertainties at the 68% confidence level. The calibration differences between the detectors are taken into account by allowing a relative instrument normalization free to vary (with that of EPIC-pn fixed to unity). The Galactic ISM absorption is described by the hot model, using the default proto-solar abundances of  Lodders et al. (2009). The temperature is fixed at 0. 02 eV to have a quasi-neutral ISM. The Galactic column density is set at Gal H = 4 × 10 20 cm - 2   (Kalberla et al. 2005)  during the spectral fitting.
Wind Modelling In this section, we employ the photoionization code pion in the SPEX package to model the wind in 1H 0707 and to study the emission/absorption lines produced by the ionized gas. This model self-consistently calculates the transmission and emission of a slab of matter, where the ionic column densities are linked through photoionization equilibrium. The relevant parameter is the ionization parameter: = ion / H 2 , with ion the ionizing luminosity, H the hydrogen volume density and the distance from the ionizing source to the matter. Once a radiation field is provided, this model reproduces the X-ray spectrum of a photoionized plasma.
Owing to the missing far-ultraviolet (FUV) data due to the interstellar absorption, the SEDs covering this domain are interpolated (i. e. assuming a straight line to link the UVW2 and the 0. 4 keV X-ray data points) to avoid over-estimating the UV flux, but From the soft to hard energies, the SEDs deviate above 0. 01 keV and get close at high energy (≥ 7 keV), which has already been indicated by the steady UV/optical fluxes and smaller X-ray variability above 8 keV (see in Fig. A1 ). We caution that the SEDs above 10 keV are predicted by a power-law model without any constraint from XMM-Newton data. An SED, cut at 10 keV, has thus been input in the following analysis to estimate the influence of the high-energy tail and we find it does not make any significant difference for our results due to soft spectrum.
1), which corresponds to t = Δ ∼ 23 -117 g. This should be an approximate distance between the wind and the X-ray emitting source, which is consistent with the former estimate. For the upper limit, if we assume the ionized gas is able to recombine within each segment at a given flux level, the lower limit of the density could be estimated by using rec_time code in SPEX, which is H ≥ 4. 16 × 10 5 cm -3 by adopting rec = 1150 s (frame time for F1) and the heating/cooling rate of O VIII line, H = 4. 78 × 10 8 cm -3 s -1. This could also be used to estimate the maximal launch radius through = ion / H ≤ 1. 5 × 10 17 cm ≈ 5 × 10 5 g. On the other hand, if we presume the thickness of the absorbing gas is lower than or equal to its maximal distance from the source ( H = H Δ ≤ H ), the maximal launch radius is ≤ ion / H = 5.
CONCLUSIONS In this work, we perform a flux-resolved X-ray spectroscopy on all available archival XMM-Newton data of the highly accreting NLS1 1H 0707 to investigate the variability of UFO at different flux levels. We have found that the ionization parameter of the UFO is correlated and the velocity is anticorrelated with the luminosity. In the brightest state, the wind features are weak, implying the gas is highly ionized, similar to the phenomenon seen in NLS1 IRAS 13224. The emitting gas is nearly constant with a potential cooling tendency with luminosity, indicating that it originates from a large-scale wind. We propose that all of these results might be consistent with a scenario where the stronger radiation field pushes the wind launching region outwards, leading to a slower and broader wind.