dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge  NASA ,  CNES , partner agencies and institutions ( UKSA ,  SSO ,  DLR ,  JPL ,  IPGP-CNRS ,  ETHZ ,  ICL ,  MPS-MPG) , and the operators of  JPL ,  SISMOC ,  MSDS ,  IRISDMC  and  PDS  for providing the SEIS data. Marsquake Service operations at ETH are supported by  ETH Research  Grant  ETH-06 17-02. The authors recognize support from the  ETH+  funding scheme ( ETH+02 19-1 : " Planet Mars "). This is  InSight Contribution  Number 195. Open access funding provided by  Eidgenossische Technische Hochschule Zurich.
Data Availability Statement The waveform data and seismicity catalog are available from InSight Mars SEIS Data Service (2019a, 2019b). The InSight seismicity catalog is from InSight Marsquake Service (2023). We used the ObsPy  (Krischer et al. , 2015) , NumPy  (Harris et al. , 2020)  and scipy  (Virtanen et al. , 2020)  packages for data processing. We benefited from the tslearn package of  Tavenard et al. (2020)  for similarity analysis, and  Elson et al. (2022)  for maps. The python scripts and seismic data used for the similarity analysis can be found in  Ceylan et al. (2023).
The two Charterhouse Warren genomes show 2 transversion mismatches out of 939,710 sites with a minimum sequence depth of 5 (a mismatch rate of 0. 0002%). Both of these are due to apparent changes in the C10091 genome, and they are located adjacent to each other at positions 2227793 and 2227794 when aligned to the CO92 (NC_003143. 1) reference genome. While BlastN searches of the NCBI nucleotide archive found that all sequences had Yersinia pseudotuberculosis and Yersinia pestis as the top match, the observed transversions were adjacent to an insertion (one sequence read further contained a deletion), and it therefore seems possible that they represent spurious alignment from a source uncharacterised in the database (Supplementary Fig. 2 , Supplementary Table  2 30  ). 29  with 1000 bootstrap replicates using the TVMe+ASC substitution model, including previously published LNBA genomes (Supplementary Table  1 ) that passed the filtering criteria (Methods) as well as the two Charterhouse Warren genomes from this study.
Radiocarbon dates made directly on the individuals were calibrated in OxCal v4. 4. 4 using IntCal20  18, 19  (Supplementary Table  3 ). D Map showing the distribution of LNBA and Neolithic Yersinia pestis strains. Points in purple show genomes newly sequenced in this study. Purple and orange points represent LNBA ymt-genomes, denoting the absence of the ymt gene and turquoise points represent LNBA ymt+ genomes (with ymt present). Points in grey show Neolithic strains of Yersinia pestis, which also lack the ymt virulence gene. Map made in R using 'maps' package and ggplot2. We identified no new deletions larger than 1000 bp, but the previously identified 'event 1', the oldest deletion in LNBA strains, which resulted in the loss of a 36 kb region and was observed in multiple previous sequenced individuals  8, 11  , is also observed in both the Charterhouse Warren and Levens Park genomes.
Metagenomic screening and authentication We analysed sequences that did not align successfully to the human genome using Kraken 2  17  and identified individuals as putatively positive for Yersinia pestis by assessing the number of observed kmers (sequence matches) to Yersinia pestis compared to Yersinia pseudotuberculosis. These libraries were subsequently aligned to the CO92 Yersinia pestis reference genome (NC_003143. 1) and the CO92 plasmids, pMT1 (NC_003134. 1), pCD1 (NC_003131. 1) and pPCP1 (NC_003132. 1) using the BWA aln  50  parameters "-l 16500 -n 0. 01 -o 2". Duplicates were removed by keeping only the first sequence out of any set of sequences with the same start position and length (https://github. com/pontussk/samremovedup). We assessed the authenticity of the final set of sequences using the following criteria:  25  the observation of postmortem damage, with the number of sequences being negatively correlated with edit distance from the reference genome, and a unimodal fragment length distribution via DamageProfiler 53.
Analysis of functional elements and virulence factors We used previously identified virulence genes  11  on the Yersinia pestis chromosome, pCD1, pMT1 and pPCP1 plasmids, and analysed the coverage of these genes with BEDTools v2. 29. 2  55  (Fig. 2A ). The ggplot2 56 package in R was then used to make a coloured heatmap of the percentage of positions in each virulence gene covered by at least one sequence. Additionally, we used BEDTools v2. 29. 2  55  genomecov to assess the missingness across the genome and identify regions that were larger than 1 kb in the two Charterhouse individuals. Some differences in coverage across the Yersinia pestis chromosome between the pre-designed Arbor RNA capture baits compared to shotgun data can be observed in direct comparisons, most likely due to preferential capture of some parts of the chromosome over others with the bait set (Supplementary Fig.
Mismatches between the two Charterhouse genomes We compared the two Charterhouse Warren genomes to identify transversion SNPs at differing base-fold coverages (Supplementary Fig. 2 , Supplementary Table  2 ) after filtering for a mapping and base quality of 30 using samtools calmd  54  and removing heterozygous sites using an in-house script (https://github. com/pontussk/mpileup_ mismatch_pathogen. py).
Phylogenetic reconstruction Previously published data from Bronze age and Neolithic plague genomes and the IP32881 strain of Yersinia pseudotuberculosis were downloaded from the European Nucleotide Archive (Supplementary Table  1 ) and processed using the same parameters as data generated in this study, described above (Bioinformatic processing) with the exception of duplicate removal, which removed duplicates with the same start position regardless of length to adjust for single-end sequencing in published data. After duplicate read removal, we used an in-house script (https:// github. com/pontussk/mpileup2consensus. py) to keep only homozygous sites with a base quality score of Q30, thus excluding any heterozygous sites and small (shorter than a sequence read length) insertions or deletions and a maximum edit distance of 90% using samtools calmd  54.
Acknowledgements We thank the  Advanced Sequencing Facility and Chris Barrington from the Bioinformatics and Biostatistics Science Technology Platform at The Francis Crick Institute  for technical support. P. Sk. was supported by the  Vallee Foundation , the  European Research Council  (grant no. 852558 ), the  European Molecular Biology Organisation , the  Wellcome Trust  ( 217223/Z/19/Z ), and  Francis Crick Institute  core funding ( FC001595 ) from  Cancer Research UK , the  UK Medical Research Council , and the  Wellcome Trust. M. H. was supported by  Marie Skłodowska Curie Actions  (grant no. 844014 ). Osteological analyses of Charterhouse Warren human skeletal assemblage were supported by the  British Academy  ( SG163375 ), with funding for radiocarbon dating supplied through  NERC 's  NEIF programme  ( NF/2018/1/3 ) to R.
Metabolomics applied to an organ or an entire organism constitutes one of the most reliable methods of chemical phenotyping for investigating the homeostatic responses to environmental stresses from multiple origins  [1] [2] [3]  in organisms like marine or freshwater bivalves  [4] [5] [6] [7]. Metabolomics greatly helps in characterizing the molecular effects of different stressors on metabolic pathways and increasing our understanding of the mechanism of impairment. Metabolomic investigations are independent of the background genomic dataset of the model organism and therefore provide data applicable to all organisms. The freshwater zebra mussel, D. polymorpha, is a filter-feeding dreissenid mussel native to the Ponto-Caspian regions. This sessile bivalve lives mostly on hard substrates and is present in a wide range of habitats, from freshwater lakes and rivers to brackish estuaries  [8].
5 ppm, with an internal calibrant of Na formate) with filtering of 5000 counts of minimal intensity and a minimal occurrence in at least 10% of all samples. All classical adducts ([M + H]+, [M + 2H]+, [M + 3H]+, [M + Na]+, [M + K]+, and [M + NH4]+) and related isotopic forms were searched and grouped together using a threshold value of 0. 8 for the co-elution coefficient factor with MetaboScape 4. 0 software (Bruker, Bremen, Germany). All data were acquired from the same single LC-MS run. Data QC and Blank samples (injected every 6 injections) were examined in order to ensure the reproducibility and robustness of the whole data series. Data quality in terms of intensity, retention time and mass drift of ions were carefully inspected and recalibration was automatically performed individually by the software on raw data of all samples using internal standards (reference Na formate solution) injected at the beginning of every sample acquisition.
Different states of charge and adducts were grouped together and the area under the peak was determined to generate a unique global data matrix containing semi-quantification results for each analyte in all analyzed sample peaks for each analyte (characterized by the respective mean mass of its neutral form and its corresponding retention time). The annotation of analytes was attempted from MS and MS/MS using the Meta-boScape 4. 0 (Bruker, Germany) with NPAtlas V1. 0 structural databases  [49]  and the Met-Gem 1. 3. 6  [50]  interrogating spectral database available on GNPS and MS-DIAL (HMDB, Massbank, EMBL, GNPS, CASMI, NIH).
Data Treatment, Statistical Analyses, and Annotation by Molecular Network The two data tables with quantification of area under the peak for the different analytes (one with all analytes and one with only the annotated analytes) were evaluated using MetaboAnalyst 5 (www. metaboanalyst. ca (accessed on 5 June 2021)) for Pareto's normalization, univariate group variance analyses (ANOVA and t-test with Benjamini FDR calculation), multivariate statistical methods (unsupervised principal component analysis, PCA) and data representation by heatmap with hierarchical clustering based on Euclidean distances (https://www. metaboanalyst. ca (accessed on 5 June 2021)). Quantifications of annotated analytes and of all analytes were also compared by gender and between tissues using PERMANOVA analyses applied on PCA in the vegan R package (vegan: community ecology package; https://cran.
r-project. org/web/packages/vegan/index. html (accessed on 20 July 2021)). Pairwise comparisons were carried out with the pairwise Adonis R package. The significance threshold was set at p < 0. 05. Analyte annotation was attempted using combined ion annotation with the MetaboScape software based on mass and isotopic pattern accuracy and with MetGem molecular networking based on the presence of ions in certain molecular clusters for which substantial annotation could be retrieved according to the MS/MS fragment occurrence. Previously uncharacterized analytes belonging to annotated metabolite clusters were considered potential analogue molecules.
1% sex-discriminated pattern (Figure  2D2 ). Interestingly, in three out of the four analyzed tissues, females showed significantly higher concentrations of analytes than males (299 vs. 245 in the gonad, 124 vs. 30 in the digestive gland, and 115 vs. 50 in the mantle). Overall, female organs exhibited higher analyte contents than males, suggesting that more intense and/or more complex metabolic activities may occur in females, potentially in relation to their higher reproductive effort resulting in a richer integration of reserves in female oocytes than in spermatozoids. The molecular networks were identified using the GNP algorithm from MetGem software with the 579 MS/MS spectra of the most concentrated analytes detected in the gonads, digestive glands, gills, and mantle of D. polymorpha males and females (Figure  3 ).
The appearance of analytes with common MS/MS fragments within the same clusters connected analytes sharing a structural similarity based on their respective fragmentation patterns (cosine score < 0. 7). Among all the analytes, MetGem aligned some in parallel with annotated metabolites from public databases based on accurate mass correspondence and sharing of MS/MS fragments. Thus, based on correspondence with both the mass and the isotopic MS/MS fragmentation pattern, we were able to annotate various analytes as genuine or potential phospholipids such as lysophosphatidylcholine (LPC) principally found in the digestive glands, gills and gonads of males and females (Figure  3 ). Analytes annotated as lysophosphatidylethanolamine (LPE) were also abundant and located mainly in the digestive gland and the gills, and to a lesser extent in the gonads; phosphatidylethanolamine (PE) was also found at relatively high concentrations.
Molluscs have been shown to share some steroidogenic and steroid metabolic pathways with vertebrates. Three steroids, progesterone, testosterone, and 17β-estradiol, have been proposed as functional hormones in gastropods and bivalves  [65, 66] , including D. polymorpha  [67, 68]. Such gender-specific differences in concentrations of endocrine-active metabolites may influence the response of D. polymorpha to contaminant exposure. However, it remains important to note that the putative annotation obtained from the present investigation cannot be considered as a genuine molecular identification, but rather provides insights into the molecular structure of these analytes as cholic-acid-related components, according to the annotations from spectral databases. Various isobaric cholic-acid-related derivatives can have the same mass and similar fragmentation patterns, and therefore cannot be differentiated in our present analysis because of the lack of specific spectral information for these molecules in public databases.
Thus, the hypothesis that these gender-specific metabolites could be steroid compounds acting as potential sexual hormones or prohormones involved in steroid metabolic pathways in D. polymorpha remains to be confirmed. Our study showed that several lipids could be discriminated between male and female organs in the sexual maturation (pre-spawning) period, and we predict a quantitative or qualitative evolution of these sexual differences along the entire spawning cycle of the zebra mussel. The lipid content in dressenids has already been shown to fluctuate annually in synchrony with the gametogenic cycle, exhibiting high concentrations of lipophilic organic compounds in fully mature individuals during late spring and summer, and a decrease in the post-spawning period  [69]. isobaric cholic-acid-related derivatives can have the same mass and similar fragmentation patterns, and therefore cannot be differentiated in our present analysis because of the lack of specific spectral information for these molecules in public databases.
Conductivity-Temperature-Depth (CTD) profiles Historical CTD-profiles from the slope region north of the Filchner Trough (roughly 72-74°S, 25-45°W) were obtained from Pangaea, BODC, Coriolis, Seanoe, World Ocean Database, and WOCE; see list below for references to the data sets included. CTD profiles collected by instrumented Weddell seals during 2011  21  over the continental shelf east and west of FT were downloaded from www. meop. net. The bottom depths at the profile locations were interpolated from the BEDMAP2 dataset  27. Where the maximum depth of the CTD profile exceeds the bottom depth from BEDMAP2, it is used as the bottom depth for that station. The dataset from the slope comprises over 2000 profiles between 1973 and 2021, of which instrumented seals collected the vast majority (1700 profiles).
Acknowledgements This study have received funding from the  Norwegian Research council work  through project  267660  (E. D. , K. D. ), from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  N°821001  (J. -B. S. , S. Ø. ) and  N°820575  (S. Ø. ). Suggestions and comments by  Prof. Anna Wåhlin ,  Prof. Marie-Noelle Houssais , and  S. Semper  were much appreciated. The numeric simulations were performed on resources provided by Sigma2-the  National Infrastructure for High Performance Computing and Data Storage in Norway , under the project  NN9608K  (K. D. ).
Data availability CTD data used in this study are available from www. pangaea. de, www. bodc. ac. uk, www. seanoe. org, www. coriolis. eu. org, www. ewoce. org, www. ncei. noaa. gov/products/world-ocean-database, and www. meop. net. CTD data from cruise ES006 (2003) are not searchable but are available from BODC on request (under accession number BAS220012). Mooring data used in this study are available from www. pangaea. de. The doi of individual observational data sets included in the study are listed below. The model output files used in this study are available in the Norstore database under accession code https://archive. norstore. no/ pages/public/datasetDetail. jsf?id=10. 11582/2017. 00003. Doi of mooring records: https://doi. org/10. 1594/PANGAEA. 792882https://doi. org/10. 1594/ PANGAEA.
792883https://doi. org/10. 1594/PANGAEA. 792884https:// doi. org/10. 1594/PANGAEA. 792885https://doi. org/10. 1594/PANGAEA. 869799https://doi. org/10. 1594/PANGAEA. 871146https://doi. org/10. 1594/PANGAEA. 869820https://doi. org/10. 1594/PANGAEA. 869799 Doi of CTD data: https://doi. org/10. 17882/54012https://doi. org/10. 1594/PANGAEA. 61240https://doi. org/10. 1594/PANGAEA. 527319https://doi. org/10. 1594/PANGAEA. 527410https://doi. org/10. 1594/PANGAEA. 527497https://doi. org/10. 1594/PANGAEA. 527593https://doi. org/10. 1594/PANGAEA. 527643https://doi. org/10. 1594/PANGAEA. 527812https://doi. org/10. 1594/PANGAEA. 734988https://doi. org/10. 1594/PANGAEA. 735189https://doi. org/10. 1594/PANGAEA. 735530https://doi. org/10. 1594/PANGAEA. 742577https://doi. org/10. 1594/PANGAEA. 742579https://doi.
org/10. 1594/PANGAEA. 742581https://doi. org/10. 1594/PANGAEA. 833299https://doi. org/10. 1594/PANGAEA. 854148https://doi. org/10. 1594/PANGAEA. 859040https://doi. org/10. 1594/PANGAEA. 897280https://doi. org/10. 1594/PANGAEA. 527645-527691* * Datasets from Pangaea in the range 527645-527691 are included in the study.
The LIBS Major-element Oxide Composition (MOC) data comprising the eight most common major elements  (Anderson et al. , 2022) , the H score  (Forni et al. , 2013) , and S and Cl scores  (Meslin et al. , this issue)  averaged for each point (not including the top 5 shots) were analyzed using principal component analysis (PCA) in OriginPro. In order to examine fine grains that would be more likely to form soil crust, coarse grains were removed based on a standard deviation of the total emissivity of >11. 5% as described above. PCA analysis using OriginPro was also performed for selected shot-to-shot data including Naltsos for comparison with the PIXL data. A special subset of soil surfaces includes soils that were disturbed by the wheel track, including targets Rougon (sol 282), Brandis (sol 302), Chandon (sol 315) and Toudon (sol 333).
Accepted Article excavation of a deep ablation crater and a loss of the laser-matter coupling with depth. Therefore, microphone data are used here to test the cohesion of the soils targets ablated with LIBS and to help discriminate between fine-and coarse-grain soils. Because laser-induced acoustic signal amplitudes are scattered by the turbulence of the Mars atmosphere, shot-to-shot amplitudes are smoothed with a running mean over 3 shots. The microphone points used in this paper are the same as the LIBS points presented in Table  1. Microphone data are available on the Planetary Data System  (Wiens et al. , 2021b).
Acknowledgements: We would like to thank the Mars2020 Science and Engineering teams for their work supporting the mission that has enabled the scientific research presented in this manuscript. We would also like to thank Editor Brad Thomson, reviewer Vincent Chevrier, and one anonymous reviewer for very helpful comments that greatly improved the manuscript. No conflicts of interest are reported for the authors. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U. S. Government.
This limits the coverage of this figure to LMST mostly between 00:00-08:00 and 20:00-24:00, with no measurements whatsoever shown from between 11:00 -18:00. All Mars 2020 MEDA data necessary to reproduce each figure shown in this manuscript are available via the Planetary Data System (PDS) Atmospheres node (Rodriguez-Manfredi & de la Torre Juarez, 2021).
Transmission Electronic Microscopy The observations of the ZnO NPs were carried out using a JEM 1000 (JEOL, Tokyo, Japan). For sample preparation, copper grids with a plain carbon film (Elmo, Cordouan Technologies, Bordeaux, France) were used. A 5 μL drop of the sample was deposited on the grid and allowed to adsorb for 1 minute; then, any excess solution was removed using a piece of filter paper. Image processing and size measurements were carried out using ImageJ 1. 4 software.
Acknowledgments The authors thank to  University of Strasbourg-Institut Charles Sadron (Strasbourg, France) ,  French PAUSE program  for emergency welcome of Ukrainian scientist  Dr. Nataliya Kutsevol  (2022-2023),  Catherine Foussat  and  Mélanie Legros  from the characterization group of the  Institut Charles Sadron  for size exclusion chromatography for polymer. Authors are grateful to brave defenders of Ukraine for the opportunity to finalize this publication.
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In particular, it is important to understand how they combine to form the complex structures, how they allow deformation to occur as an ensemble, and how each is affected by changes in chemical composition at the atomic scale of the crystal lattice. Furthermore, the recurrent nature of the few fundamental building blocks  [3]  will allow a transfer of knowledge to a large number of complex phases. Such knowledge would allow us to mine the existing, enormous crystal databases for exceptional phases that could form the basis for future high performance alloys. The topologically close-packed (TCP) phases are commonly found in superalloys  [4]  but their plasticity is lesser known due to their complex crystal structures. Laves phases are the most common intermetallic compounds with a relatively simple TCP structure formed by stacking of quadruple Laves phase layers which are composed of a kagomé layer and a triple layer.
[20]  To minimize the indentation size effect, the hardness and indentation modulus were obtained by averaging the values measured at an indentation depth greater than 350 nm. The macroscopic hardness was estimated following the model proposed by Nix and Gao  [15]  and using the indentation depth and hardness data above 200 nm. From this indentation depth, the Nix-Gao plot of hardness squared versus the inverse indentation depth yielded the expected, approximately linear relationship, as observed for MgO as a similarly hard material. [21] A minimum of 220 indentation tests were performed in each alloy. The indents were at least 50 μm from visible cracks and 20 μm from consecutive indents. After indentation tests, the indents were imaged by SEM and the indents in the vicinity of cracks and pores were excluded from the analysis.