dataesr/scientific-paragraphs-categorization huggingface dataset:
012 ∘ steps for NP300 and through 2 ∘ , with 0. 005 ∘ steps for NP650. The counting time per point was 0. 5 s. The detector was positioned at an out-of-plane angle of 39. 26 ∘. Phase retrieval algorithm. The reconstructed Bragg electron density and phase were obtained using the PyNX package  49. Phase retrieval was carried out on the raw diffracted intensity data. The initial support, which is the constraint in direct space, was estimated from the auto-correlation of the diffraction intensity. A series of 1400 Relaxed Averaged Alternating Reflections (RAAR  50  ) plus 300 Error-Reduction (ER  51, 52  ) steps, including shrink wrap algorithm every 20 iterations 53 , were used. A support was built from the best reconstructed object. Afterwards, the support was fixed during the first 20 iterations and then let free, while reapplying a series of 600 Hybrid Input Output (HIO  54  ) followed by 900 Relaxed Averaged Alternating Reflections plus 300 Error-Reduction steps, the shrink wrap algorithm was applied every 20 iterations.
The phasing process included a partial coherence algorithm to account for the partially incoherent incoming wave front  55. To ensure the best reconstruction possible, we kept only the best 50 reconstructions (with lowest free Log-Likelihood 56 ) from 300 with random phase start and performed the decomposition into modes  56. The reconstruction was then corrected for refraction using the bcdi package  57. After removing the phase ramp and phase offset (calculated at the centre of mass of the support), the data was finally interpolated onto an orthogonal grid for ease of visualisation. Molecular statics. In order to model accurately the experimental NPs, several material simulation tools were used. The simulated NPs were generated using the atomistic simulation code MERLIN (Rodney, D. Merlin in a nutshell.
u 111 and the corresponding strain field ϵ 111 = ∂u 111 ∂x 111 could then be directly compared to the measured experimental strain field as shown in Supplementary Fig. S25. Facet analysis. We have used the Facet Analyser plugin of the ParaView software, which is an automated 3D facet recognition algorithm that allows to detect the voxels at the surface of the particles and to index their facets by tuning parameters like sample size, angle uncertainty, splat radius or minimum relative facet size  43. The algorithm is based on the analysis of the probability distributions of the orientations of triangle normals of mesh representations of the particles. From the output list (voxels and associated global orientation) of the facet recognition algorithm, we have developed a Python script to determine the hkl Miller indices of each facet as well as their average lattice displacement, strain and associated standard deviation.
DFT calculations. DFT calculations were performed using the Vienna Ab Initio Simulation package (VASP), within the projector-augmented wave method (PAW), for the description of the interaction between the valence electrons and the ionic core. The GGA-PBE functional was used, with DFT-D3 corrections. We consider atomic valences to be 2s2 2p4 (O) and 5d9 6s1 (Pt). Total energies were minimised until the energy differences were less than 1 × 10 -6 eV between two electronic cycles. Atomic structures were relaxed until the Hellmann-Feynman forces were as low as 0. 02 eV/Å. Calculations were performed using a 450 eV cutoff energy and Γcentred 14 × 14 × 14 and 14 × 14 × 1 Monkhorst-Pack grids for bulk and (100)/ (111) surface calculations, respectively. Those parameters were chosen to achieve a precision for the total energy lower than 0.
Acknowledgements The authors are grateful to PETRA synchrotron for allocating beamtime. The measurement was performed at the P10 beamline of the  PETRA III synchrotron at DESY , a member of the  Helmholtz Association (HGF). We thank P10 beamline staff for excellent support during the experiment. M. -I. R. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 818823 ). The research leading to this result has been supported by the  project CALIPSOplus  under the Grant Agreement 730872 from the  EU   Framework Programme for Research and Innovation HORIZON 2020. M. -I. R and E. R. also wish to thank the support by a grant from the  Ministry of Science and Technology, Israel  and from the  Centre National de la Recherche Scientifique (CNRS), France.
Code availability The phasing algorithm PyNX 30 is available at http://ftp. esrf. fr/pub/scisoft/PyNX/. The scripts used for BCDI data post-processing, PRTF calculations and stereographic projections belong to the bcdi package  57  (https://doi. org/10. 5281/zenodo. 3257616), that can be downloaded from PyPI (https://pypi. org/project/bcdi/) or GitHub (https://github. com/carnisj/bcdi).
We used the checkRestrictionSites. py custom script to check the presence of the MseI enzyme at the beginning of reads R1 and discarded all the paired reads with incorrect restriction sites. We also checked for undigested and chimeric sequences and removed all reads containing intact MseI and EcoRI restriction sites. After quality check, the processed reads were mapped to a set of ddRAD single copy orthologue loci (R2SCO-  for hawksbills and loggerheads from  Driller et al. (2020). Reads were mapped using bowtie2 version 2. 3. 4. 1  (Langmead & Salzberg, 2012)  with default parameters. The mapped reads were then processed using the reference-based pipeline in stacks 2. 2  (Catchen et al. , 2013). We used Stacks' populations module to obtain loci present in at least one population and in a minimum of 70% of individuals.
Discriminant analysis of principal components (DAPC)  (Jombart et al. , 2010)  was performed in the r (R Core Team, 2017) package adegenet  (Jombart & Ahmed, 2011). The number of retained principal components (PC) was optimized with xValDapc method. In all cases a low number of PC (5-10) allowed for very good sample assignment and no overfitting. Two or three discriminant axis (DA) were retained depending on their eigenvalues. To estimate pairwise relatedness estimates among all individuals, we used vcFtools version 1. 9  (Danecek et al. , 2011)  with the command-relatedness  (Yang et al. , 2010). We used the NewHybrids  (Anderson & Thompson, 2002)  function gi. nhybrids() implemented in the r package dartr  (Gruber et al. , 2018)  to assign each individual to a "hybrid category" (pure species, F1, F2 or backcross).
We extracted SNPs that were fixed in loggerheads and hawksbills (F ST = 1) and had a minimum coverage of 15×. A data set with loggerheads, hawksbills, and their hybrids was used to calculate hybrid category probability. Two hundred loci were selected by the program, either randomly or using average polymorphism information content (PIC) values based on the 200 most informative loci. To verify potential influences of setting prior populations, we ran NewHybrids without assigning pure individuals as priors, and also using pure individuals previously identified as nonhybrids by Structure and DAPC. We used the D-statistics to calculate conflicting patterns between ancestral ("A" alleles) and derived ("B" alleles) to distinguish introgression from incomplete lineage sorting (ILS). The D statistic is based on a tree model of four populations, defined as (((P1,P2),P3),O), where P1, P2, and P3 are ingroups and O is the outgroup.
Excesses in the "ABBA" or "BABA" patterns produce deviation from D = 0, supporting introgression between P2 and P3 over the null hypothesis of ILS. We used the package D-suite  (Malinsky et al. , 2021)  and the command Dtrios. To identify potential origin of hybrids and admixture between sites, samples were divided as population of origin (parental species) based on genetic structure found in previous studies  (Shamblin et al. , 2014; Vilaça et al. , 2013) , or as hybrids. Loggerheads were divided in two nesting areas (Bahia and Espírito Santo) and one feeding (Rio Grande Elevation). Hawksbills were divided in two nesting areas (Bahia and Rio Grande do Norte) and one feeding (Rocas Atoll). Olive ridleys were considered a single population  (Vilaça et al. , 2022). Green turtles were set as outgroup.
We gathered data from  Marcovaldi et al. (2010 Marcovaldi et al. ( , 2012) )  and matched hybrid generation assignment (F1, backcross) from our Structure and NewHybrids assignments or from previous studies  (Soares et al. , 2021; Vilaça et al. , 2012). For all analysed samples, we had genetic information to confirm their hybrid/pure status, even though not all were sequenced with genome-wide markers. Therefore, we obtained ddRAD data for three hybrids and one hawksbill. For four samples (3 hawksbills, 1 hybrid) genetic information was retrieved from  Vilaça et al. (2012)  for multiple nuclear loci. All loggerheads (n = 7) were previously analysed  (Soares et al. , 2021)  and data for one mitochondrial and one nuclear locus was available. All individuals had their transmitters deployed at Praia do Forte (Bahia).
To assemble the mitogenomes we used the software geneious 8. 1. 3. All sequences were imported into the software and mapped to two references (a hawksbill and a loggerhead) with haplotypes typical of the Atlantic Ocean (GenBank accession numbers JX454986 and JX454984). The mapping was done using the Geneious mapper with Custom sensitivity (minimum overlap identity: 98%, maximum gap size: 50, index word length: 12, allow gaps of maximum: 15%, minimum mapping quality: 30, maximum ambiguity: 4). Annotations were transferred from the references to the newly assembled mitogenomes. The mitogenomes were aligned with MUSCLE  (Edgar, 2004)  and gaps and polymorphisms were manually checked.
To confirm the "leakage" pattern, 27 samples were sequenced in two different runs from independent PCRs and libraries. We subsampled 30,000 reads (expected mean coverage = 1000×) and mapped them to mitogenomes of Atlantic hawksbills (GenBank accession no. : JX454986) and loggerheads (JX454984) using stringent mapping parameters so only sequences of its respective species were mapped to each reference. The mapping was done using the Geneious mapper with Custom sensitivity (minimum overlap identity: 98%, maximum gap size: 50, index word length: 12, allow gaps of maximum: 15%, minimum mapping quality: 30, maximum ambiguity: 4). To be considered as an indication of paternal leakage, an individual needed to show sequences with a homogeneous coverage of the entire amplified fragment and a minimum average coverage of three for both species' mitochondrial DNA.
| Migratory patterns We retrieved data for 15 samples with telemetry data. Our map Among the individuals with telemetry data, we obtained ddRAD data for one hawksbill and three hybrids. The remaining had only nuclear and mtDNA information. Our genomic data confirmed that three hybrids from  Marcovaldi et al. (2012)  were all F1 (Figure  5 ). The hybrid with nuclear genetic information, but no ddRAD data, exhibited a migratory pattern of loggerheads (migrating to northern areas of Pará coast). Furthermore, the individuals assigned as pure did not show indication of introgression in any molecular marker.
| Mitogenomes Of the 37 samples from three Brazilian rookeries and feeding sites, we obtained mitogenomes with less than 5% missing data for 15 loggerheads and six hawksbills. All hybrids had a loggerhead mitogenome. The remaining genomes failed to amplify at least one fragment or had low coverage regions where variants could not be called. which is related to frequent haplotypes present in the Brazilian population (Cc-A4). We also found one polymorphism (A/G) in the ND6 gene that was fixed between the two loggerhead haplotypes common in the Atlantic Ocean (Cc-A4. 1 and Cc-A4. 2).
Vilaça wrote the manuscript with input from all authors. ACK N OWLED G EM ENTS This research was supported by an Alexander von Humboldt research fellowship awarded to STV and a CNRS grant "Incubator for interdisciplinary research projects in French Guyana" awarded to DC. Open Access funding enabled and organized by Projekt DEAL. The publication of this article was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project number 491292795. We thank the BeGenDiv team for help with laboratory and bioinformatic analysis, especially Susan Mbedi and Kirsten Richter for laboratory support. Samples were collected/transported under SISBIO permit 37499-2. Tissue samples were exported from Brazil under CITES permits 13BR011173/ DF and 14BR015253/DF and imported into Germany under CITES permits E-013161/13 and E-03346/14, respectively.
DATA AVA I L A B I L I T Y S TAT E M E N T All data has been made available on NCBI Sequence Read Archive (SRA) database under BioProject PRJNA857276. All scripts used in the ddRAD analysis are available at https://github. com/mazzo ni-izw/mazzo ni-begen div/. Other relevant data (e. g. , VCF files, reference fastas, input files) are available on Zenodo DOI: 10. 5281/ zenodo. 7038996. The original gel image (Figure  6 ) is available under request from the corresponding author.
Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. The complex composition of organic molecules in atmospheric particulates is poorly constrained, but has important implications for understanding pollutant sources, climate-aerosol interactions, and health risks of air pollution exposure. Here, comprehensive nontarget highresolution mass spectrometry was combined with in silico structural prediction to achieve greater molecular-level insight for fine particulate samples (n = 40) collected at a remote receptor site in the Maldives during January to April 2018. Spectral database matching identified 0. 5% of 60,030 molecular features observed, while a conservative computational workflow enabled structural annotation of 17% of organic structures among the remaining molecular dark matter.
Results and discussion Comprehensive nontarget analysis of PM 2. 5. After quality control and field-blank correction, the combined analyses of 40 PM 2. 5 samples (120 extracts) revealed 60,030 molecular features (Supplementary Data 2-Dataset). Each feature is defined by a retention time (Rt) in the chromatographic dimension for GC and LC, and for GC analyses a mass spectrum dimension corresponding to mass-to-charge ratio (m/z) with a base-peak ion and deconvoluted MS 1 spectrum (EI and NCI), and for LC analyses by a precursor MS 1 (full-scan) and corresponding deconvoluted data-independent (DIA) MS 2 spectrum. The number of features detected in water-soluble, polar, and nonpolar organic compound extracts (WSOC, POC, and NPOC, respectively) across the four modes of instrumental analysis (Fig.
1b, c , d GC-EI, GC-NCI, LC-ESI + , LC-ESI-) indicated that a great proportion of molecules were unique to each dataset (Fig. 1e, f ), and thus that a battery of approaches was important for achieving comprehensive analysis of PM 2. 5 (see criteria in Supplementary methods-Estimation of unique features). The greatest molecular complexity was found in the WSOC and POC extracts (98% of all features). This was not unexpected because hydrophilic compounds represent the largest fraction (up to 50-100%) of aerosol organic compounds  11. Moreover, the remote MCOH sampling location allows substantial atmospheric oxidation of transported pollution to occur prior to collection  11. Altogether, the large number of samples, and the battery of extracts and analytical modes employed here resulted in a greater view of molecular complexity than reported in previous analyses of atmospheric PM  18, 22, 35.
Annotations of putative anthropogenic substances were selected for confirmation by comparing orthogonal evidence under identical analytical conditions (Rt, MS 1 and MS 2 ) to reference standards (Supplementary methods). Across GC and LC datasets, 89 compounds were ultimately confirmed with highest confidence (Level 1; 53 compounds with Rt shift < 0. 2 min or RI < 30) or as closely related isomers (36 compounds with Rt shift < 0. 4 min or RI < 250) (Supplementary data 4-'Identifications', and Supplementary data 6-'Spectral matches'). For the NPOC extracts, these consisted mostly of n-alkanes and PACs detected by GC-EI-HRMS (Fig. 3 ), including four oxy-PACs (e. g. 4H-cyclopenta[def]phenanthren-4-one (C 15 H 8 O), 9-anthracenecarboxaldehyde (C 15 H 10 O), and 7Hbenz[de]anthracene-7-one (C 17 H 10 O)), six sulfur-containing PACs (i.
2e , and Fig. S11 ). Simazine-2-hydroxy (C 7 H 13 N 5 O) (Fig. 3 ) was detected at highest levels in two samples from the Indian Ocean, suggesting local use of simazine in the Maldives. Conversely, atrazine-2-hydroxy (C 8 H 15 N 5 O) was detected at higher levels (up to 10-fold) in samples from subcontinental regions (Fig. 3 , Fig. S11  and S16-S17), and we are not aware of any previous reports of this substance in ambient air. Characterization of PM 2. 5 molecular dark matter. Only a minor proportion (0. 5%) of all molecular features in polluted PM 2. 5 were identified or putatively annotated by MS 2 spectral database matching (see criteria in Methods). Higher but still low annotation rates (up to 1-2%) have been reported for environmental water analysis  38, 39  and metabolomics  24  despite larger and specialized databases.
The vast majority of molecules in polluted air could not be matched to known compounds, not only because spectral databases mostly cover biogenic compounds (e. g. , anthropogenic substances account for approximately 15% of records in both NIST20 and MassBankEU), but also because the major sources of air pollution include the incomplete combustion of complex fuels and heterogeneous biomass that are prone to rapid transformations in the atmosphere (oxidation, photolysis, hydrolysis) to yield molecular byproducts and secondary organic aerosols  40. Hence, we combined cheminformatics and computational strategies that leverage data-rich MS 1 and MS 2 information of unknown features, and structural information of annotated molecules, to characterize the remaining unknown features in PM 2. 5 through in silico predictions of structure and physicochemical properties  38, 39.
For all LC-HRMS features, we first calculated molecular formulae by combining two independent approaches, MFAssignR 41 and SIRIUS  42. Consensus between the two methods resulted in >19,000 features being assigned a molecular formula (i. e. , 33% of the LC-HRMS datasets, Level 4 identification)  37  (Fig. 4a ). Next, features were clustered by molecular networks constructed in GNPS (Global Natural Products Social Molecular Networking)  25, 26  , whereby neighboring molecular features (nodes) are linked by pairwise MS 2 spectral similarity (edges) representing an inferred structural analogy (Fig. 4b, c ). We then performed a high-throughput structural elucidation of all molecules in the networks with the MS 2 -guided in silico Network Annotation Propagation (NAP) GNPS workflow  31. This workflow leverages the molecular network topology to re-rank the in silico predicted candidates based on joint similarity within a molecular family cluster (e.
ESI-is optimal for weak organic acids, while ESI+ reveals nitrogenous bases and a range of polar neutral molecules such as alcohols, aldehydes, and ketones. These analyses combined allowed the detection of anthropogenic pollutants representing a wide variety of chemical classes, some potentially originating from atmospheric oxidation of incomplete combustion sources, others synthesized for commercial or industrial use, e. g. , plasticizers, generic biocides, insecticides, herbicides, and their transformation products. (variable importance for the projection) features accounted for 10% of the dataset (6088), 35% (2156) of which had been successfully assigned a molecular formula, and 17% (1049) of which had been assigned a structure consistent with the formula (Supplementary data 4-Identifications).
The dominant nitrogenous organic molecules in polluted air are likely of relevance to health and climate but must be confirmed through further studies using approaches such as those described here. As anticipated at this remote receptor site, only a small fraction of molecular structures could be confidently annotated by matching to spectral databases (Level 2) or identified with authentic standards (Level 1). We demonstrated that in silico predictions based on the underlying information-rich MS 2 spectra can be exploited for the high-throughput structural characterization of thousands of substances in atmospheric samples. High-throughput molecular structure prediction remains an imperfect tool, however, predictions herein were aided by the molecular network topology and validated by strong and statistically significant correlations between the structures' predicted physicochemical properties (i.
Sample preparation. PM 2. 5 samples were cut and extracted by three different protocols (Fig. 1a  and Supplementary methods). An accelerated solvent extraction (ASE-350, Thermo Scientific Dionex ASE) was used with hexanes and toluene for nonpolar organic compounds (NPOCs), and with methanol and toluene for polar organic compounds (POCs), and extracts concentrated under nitrogen gas. Water-soluble organic compounds (WSOCs) were extracted by sonication in 40 mL HPLC grade water, followed by centrifugation (see Supplementary methods)  11. Multiple isotopelabeled internal standards (Supplementary data 1 -Standards) were spiked to all PM 2. 5 samples, field blanks, and urban dust reference samples (NIST SRM 1649b) prior to extraction. Sample preparation was performed in a positive pressure clean laboratory.
All normalized feature areas were blank-subtracted by the average area of the corresponding feature detected in the field blanks (negative values were set to zero). Finally, the feature areas were normalized to the air volume accounted by the portion of PM 2. 5 -filter extracted each sample. Molecular formula assignments. The R Package 'MFAssignR' 41 and the software SIRIUS (v. 4. 5)  42  were used for molecular formula assignment (mass accuracy < 5 ppm). MFAssignR applies element heuristics on the MS 1 -level, then subtracts nonoxygen heteroatoms to solve for low-mass moieties (CHO), and finally assigns formula extensions via nested loops of homologous series  41. SIRIUS similarly generates molecular formulae for the MS 1 and then leverages MS 2 fragmentation decision trees (i. e. shared neutral losses) to rank the candidates  42.
nist. gov/) on the NIST20 and our in-house Orbitrap-HRMS library of environmental contaminants, and candidates were considered only for spectral match factors ≥ 700. Molecular networks and in silico structural elucidation. Feature-based molecular networks  25, 26  were built in GNPS (ver. 28. 2) and visualized using Cytoscape v. 3. 8. 2. For LC-HRMS (WSOC and POC) datasets, parameters were: MS 1 and MS 2 tolerances of 0. 02 Da, minimum spectra similarity cosines of ≥ 0. 65, and a minimum of four shared spectral peaks. For in silico structural prediction with the GNPS/NAP workflow  31  , the following parameters were used: 10 first-neighbors, 5 ppm accuracy, cosine score ≥ 0. 65, 10 maximum candidates from structural databases (GNPS, HMDB, SUPNAT, CHEBI), and Consensus + Fusion ranking algorithm.
e. lightscattering) component of the complex refractive index was computed in Python using the model developed by Bouteloup & Mathieu  34. Back-trajectories and satellite measurements. Ten-day back-trajectories were calculated every six hr using the HYSPLIT model (version 4) of the National Oceanic and Atmospheric Administration (NOAA), at 0:00, 06:00, 12:00, and 18:00 h GMT for 10 d into the past and 100 m height at MCOH (6. 80°N, 73. 20°E) (Fig. S8  and Supplementary data 3 -Back-trajectories). A model was selected with four mean back-trajectories using the clustering algorithm in HYSPLIT. Tropospheric NO 2 concentrations were averaged over the period of the campaign and prior 10 days of the first back-trajectory in 0. 25°resolution using the Giovanni web application (https://giovanni. gsfc.