dataesr/scientific-paragraphs-categorization huggingface dataset:
Size-exclusion chromatography-multiangle light scattering (SEC-MALS). Purified Fsx1 E samples (120-150 µg) were measured using an Ettan LC highperformance liquid chromatography system with a UV-900 detector (Amersham Pharmacia Biotech; λ = 280 nm), coupled with a miniDawn Treos MALS detector (Wyatt Technology; λ = 658 nm) and an Optilab T-rEX dRI detector (Wyatt Technology; λ = 660 nm). Separation was performed at 20 °C using a Superdex 200 Increase 10/300 GL column (GE Healthcare) with a flow rate of 0. 5 mL min -1 and mobile phases consisting of 20 mM Tris-HCl pH 8. 5, 150 mM NaCl (normal salt condition) or 20 mM Tris-HCl pH 8. 5, 2. 0 M NaCl and 0. 2 M CaCl 2 (high salt condition) (Supplementary Fig. 2c ). Data processing and weight-averaged molecular mass calculations were performed using ASTRA (Wyatt Technology).
BSA (150 µg) was used as a control. Small-angle X-ray scattering (SAXS). SAXS experiments were performed at beamline BM29 of the European Synchrotron Radiation Facility (ESRF)  80  , using Fsx1 E (4. 5 mg mL -1 ) in 20 mM Na-HEPES pH 7. 8, 150 mM NaCl. Sample delivery and measurements were performed using a 1 mm-thick quartz capillary, which is part of the BM29 BioSAXS automated sample changer unit  81. Data were collected at 1 Å wavelength in 10 frames of 1 s at 20 °C, using an estimated beam size of 1 mm × 100 µm; buffer blank measurements were carried out under the same conditions, both before and after sample measurement. Data were averaged and subtracted using PRIMUS  82  from the ATSAS package  83  , which was also used to calculate the pair-distance distribution function, as well as the radius of gyration and the Porod volume.
Rhomboidal plates of Fsx1 E grew in 1-3 months from protein precipitate that appeared after overnight equilibration of the crystallization drops (Supplementary Fig. 2e ). For data collection, specimens were freed from the precipitate by micromanipulation with MicroMounts (MiTeGen) and flash frozen in liquid nitrogen. More than a hundred crystals were screened at beamlines ID23-1 of the ESRF  89  and I04 of Diamond Light Source, yielding datasets of highly variable quality. The final X-ray diffraction dataset at 2. 3 Å resolution was collected at ESRF ID23-1. Data reduction and non-crystallographic symmetry analysis. Datasets were processed in space group C2 with XDS  90  (Supplementary Table  2 ). By revealing a strong non-origin peak at chi = 120 (Supplementary Fig. 2f ), self rotation functions calculated with MOLREP  91  or POLARRFN  92  clearly indicated the presence of three-fold non-crystallographic symmetry (NCS) within the asymmetric unit of the centered monoclinic crystals.
3 Å resolution data could also be successfully applied to an initial dataset at much lower resolution (3. 5 Å, with outer shell mean I/σI 0. 6 and CC 1/2 0. 31); in this case, the Phaser LLG and TFZ values for the solution were 361 and 13. 5, respectively, and initial automatic refinement of the corresponding model yielded R 0. 44, R free 0. 48. Model building, refinement, and validation. The initial model of Fsx1 E was first automatically rebuilt using PHENIX AutoBuild 98 (1083 residues; R 0. 34, R free 0. 38) and then significantly improved with the machine learning-based sequencedocking method of ARP/wARP  99  , as implemented in CCP4  92  (1390 residues; REFMAC 100 R 0. 23). The resulting set of coordinates was subsequently subjected to alternating cycles of manual rebuilding with Coot 101 /ISOLDE  102  and refinement with phenix.
Subunit interfaces were analyzed using PDBsum  114  , PIC  115  and PDBePISA  116. Molecular charge was calculated using the YASARA2 force field of YASARA Structure 117 and electrostatic surface potential calculations were performed with PDB2PQR  118  and APBS  119  , via the APBS Tools plugin of PyMOL (Schrödinger, LLC). Mapping of amino acid conservation onto the 3D structure of Fsx1 E was carried out by analyzing a sequence alignment of archaeal homologs with ConSurf 120. Structural figures were generated with PyMOL. Structural modeling of trimeric Fsx1. Models of homotrimeric Fsx1 were generated using a local copy of AlphaFold-Multimer 121 , installed using the open source code and instructions available at https://github. com/deepmind/alphafold. Cells and reagents. Baby Hamster Kidney (BHK-21) cells (kindly obtained from Judith White, University of Virginia) were maintained in DMEM supplemented with 10% FBS (Biological Industries), 100 U/ml penicillin, 100 µg/ml streptomycin (Biological Industries), 2 mM L-glutamine (Biological Industries), 1 mM sodium pyruvate (Gibco), and 30 mM HEPES buffer, pH 7.
Loading was controlled using anti-actin C4 monoclonal (1:2000; MP Biomedicals). Integrated mobile element (IME) identification by k-mer spectra analysis and comparative genomics. Comparison between close species with presence (fsx1+) or absence (fsx1-) of archaeal fusexins to detect insertion sites was done performing sequence similarity searches in complete genomes from the closest relatives available in the PATRIC database  125  (Fig. 7a  and Supplementary Table  4 ). Coordinates of fsx1-containing IMEs present in PCGs are annotated in Supplementary Table  4. Among different methodologies that rely on DNA composition to identify horizontally transferred genomic regions  126  , k-mer spectrum analysis is a standard tool for this purpose  127, 128. Normalized k-mer spectra for DNA sequences of arbitrary length were generated by counting occurrences of all k-mers and normalizing by the total amount of words counted.
ORF inference was done by means of getorf of the EMBOSS package  131  , specifying genetic code by Table  11  (Bacteria and Archaea) and other parameters running by default. The similarity of inferred ORFs and annotated features in these mobile elements (i. e. , features in their GFF annotation file) was established by means of BLASTP reciprocal searches  74. We kept all the predicted ORFs and homologs that were at least annotated in one genome, in this way we tried to recover missanotated conserved ORFs. Initial sets of homologs were generated with get_homologs  132. Sequence identity and query coverage thresholds were set to 35% and 70%, respectively. Inparalogues were not allowed within these groups (option '-e'), and remaining parameters were run by default. HMM profiles were constructed for each homolog group.
To this aim, homologous sequences were retrieved for members of each group from the UniRef50 database  133  with jackhmmer from the HMMER package  73  running with one iteration ('-N 1' parameter). MSAs were then generated for each group and its relevant hits with MAFFT  134  running under '-auto' parameter, and HMMs were created with HMMER hmmbuild. Homolog groups were enriched by means of HMM searches with HMMER hmmsearch, using each HMM as a query against a database comprising all predicted ORFs described above. Hits showing an e-value < 1e-10 and covering at least 50% of the HMM were added to the groups. Enriched homology groups showing homology were collapsed. For this purpose, HMM-vs. -HMM comparisons were performed with HHalign from the HHsuite  135. A graph was created with the networkx Python library (https://networkx.
This was done in Python with seaborn  136  , employing the clustermap function. A subset of 11 mobile elements (red cluster in Supplementary Fig. 9 ), which included ME from PCGs and JGI12330J12834-1000008 (Supplementary Data 1, 3 and 4), was selected for synteny conservation analysis. Plots depicting synteny in gene content between homolog groups were generated employing the MCscan tool  137. HMMER and Pfam  138  were used on default parameters to assign domains and their associated arCOG  139, 140  identifiers to ORFs (Supplementary Data 2). These analyses, including collapsed clusters, can be found in Zenodo  63. Sequence and structure phylogenies. Maximum-likelihood phylogenetic trees were generated with sequences aligned with MAFFT (L-INS-i option) as input for IQ-TREE2  141  and selecting the best evolutionary model with ModelFinder  142.
Acknowledgements We thank  Sonja-Verena Albers ,  Dan Cassel ,  Alejandro Colman-Lerner ,  Uri Gophna ,  Yael Iosilevskii ,  Shahar Lavid ,  Peter Walter , as well as members of our laboratories for discussion and comments on the manuscript;  Jose Flores  for advice on searches of fusexins in metagenomes;  Olivier Gascuel  for discussions on phylogenetics;  Yoav Henis  for the GPI-BHA plasmid;  Kira Makarova  for sharing insights on mobile elements. Part of the computations for this work was performed at the  Vital-IT Center  for high-performance computing of the  SIB Swiss Institute of Bioinformatics. This work was supported by  Beca de Doctorado Consejo Nacional de Investigaciones Científicas y Técnicas ,  Argentina  (D. M. );  Comisión Sectorial de Investigación Científica  grant  CSIC I + D-2020-682 , Uruguay (H.
Code availability All relevant codes, notebooks and datasets necessary for: HHblits and HMMER searches and comparisons (Fig. 1a , Supplementary Fig. 1a ); genomic comparisons and K-mer spectra analyses (Fig. 7 ); bioinformatic workflow for IMEs, IMEs clustering, content and synteny analyses (Supplementary Figs. 8  and 9 , Fig. 8  and Supplementary Data 2 and 3); and structure-and sequence-based comparisons (Supplementary Fig. 10  and.
Data availability Crystallographic structure factors and atomic coordinates have been deposited in the Protein Data Bank under accession code 7P4L. Source Data for this paperʼs structure-function analyses (Figs. 4  and 5 , and Supplementary Fig. 1 ) are provided in the Source Data file; sequences of synthesized fsx1 genes are in Supplementary Data 4. Source data are provided with this paper. Supplementary Data 5), are provided on GitHub (https://github. com/DessimozLab/ Archaeal-Fusexins) and Zenodo (https://doi. org/10. 5281/zenodo. 6677729)  63.
Remote sensing data were used to obtain four indices associated with vegetation cover over the 2003-2012 period. More specifically, from the Moderate Resolution Imaging Spectroradiometer (MODIS) database (ORNL DAAC, 2012), we extracted monthly readings for the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), and the Fraction of Photosynthetically Active Radiation Index (FPAR) (Figure  1b ). These indices are commonly used in studies of spatiotemporal variation in vegetation at global  (Alexandridis et al. , 2020) , regional  (Pettorelli et al. , 2005) , and local (e. g. , the Galápagos Islands)  (Charney et al. , 2021)  scales, and they provide robust indicators of primary productivity and vegetation cover state  (Charney et al.
| Capture and measurement of finches Morphological data were collected for the medium ground finch (G. fortis) each year from 2003 to 2012 in the three study sites (DM, AB, EG). In all cases, the birds were captured with mist nets and then banded with uniquely numbered metal leg bands to ensure that individuals were not sampled multiple times. Each bird was inspected and classified-based on plumage, beak color, and the presence of a brood patch-as a juvenile, male, or female  (Grant, 1999). Distinguishing females from juveniles sometimes can be difficult, whereas adult males can be readily identified based on their black plumage  (Grant, 1999). Each bird was measured following  Boag and Grant (1984,  see also  Grant, 1999)  for beak length (anterior edge of nares to anterior tip of upper mandible), beak depth (at the nares), beak width (at the base of the lower mandible), mass (weight), wing chord (length of longest relaxed right primary feather), and tarsus length (between the nuchal notch at the upper end of the right tarsometatarsus and the lowest undivided scute).
Beak and tarsus measurements were made to the nearest 0. 01 mm using calipers for EG and AB birds, and dividers (compasses) for DM birds. Wing chord measurements were made to the nearest 0. 01 cm using a wing and tail ruler. Mass measurements were made to the nearest F I G U R E 1 (a) Map of the Galápagos archipelago showing the three study sites: DM for Daphne major (white star), EG for El Garrapatero (gray star), and AB for Academy Bay (black star). (b) Site-specific daily values for enhanced vegetation index (EVI; green lines) superimposed on daily rainfall (log-transformed; gray dots) from 2003 to 2012. Rainfall data were not available for El Garrapatero, and values for Daphne major are from the adjacent Baltra Island. 0. 01 g using a portable digital scale for AB and EG birds, and a spring scale for DM birds.
These results follow those seen for rainfall and vegetation indices in that the main contribution of terroir lies in generating site-specific phenotypic differences that mainly persist across year. After removing DM from ANOVA and MANOVA analyses of finch morphology, overall effect sizes for site decreased (relative to the models with DM) by 95%, year effects increased by 28%, and site-by-year effects decreased by 50% (Table  3 ). Thus, variation was now (considering only AB and EG) explained roughly equally across the year and site-by-year terms, which were both slightly greater than the site term. These changes in statistical outcomes reveal that terroir in our G. fortis dataset revolves mostly around the beak size and body size (but not shape) of DM birds relative the Santa Cruz (AB and EG) populations.
First, as previously mentioned, DM is much drier and has less vegetation than AB or EG, a difference verified by our vegetation indices. Hence, smaller body sizes (and thus smaller beak sizes) might reflect their more extreme and challenging environment. This hypothesis could be tested by analyzing phenotypic variation among additional populations in relation to average climate and vegetation measures. G. fortis exist on many islands and existing finch data  (Grant, 1999; Grant & Grant, 2008; Lack, 1947; Schluter & Grant, 1984 ) could be combined with newly available remote sensing datasets to achieve this goal. At the same time, overall local climate harshness cannot be the only reason for the distinctiveness of the DM site. For instance, the morphology of G. fortis at Borrero Bay on Santa Cruz is more similar to that at climatologically-different AB (~26 km away) and EG (~25 km away)  (Foster et al.
DATA AVA I L A B I L I T Y S TAT E M E N T The morphological data are available on Dryad for Daphne Major (https://doi. org/10. 5061/dryad. g6g3h), and on OSF for Santa Cruz island (https://doi. org/10. 17605/OSF. IO/5AFUS). Environmental data and scripts of statistical analyses are available on OSF (https://doi. org/10. 17605/OSF. IO/5AFUS).
Because of the large diversity of case 2 waters ranging from extremely absorbing to extremely scattering waters and the complexity of light transfer due to external terrestrial inputs, retrieving main biogeochemical parameters such as chlorophyll-a or suspended particulate matter concentration in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from 1 to 700 FNU and from 0. 9 to 180 mg m -3 respectively, the HYPER-MAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms. The HYPERMAQ dataset provides biogeochemical parameters (i. e. turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties (i.
e. water reflectance from above water measurements) and inherent optical properties (i. e. absorption and attenuation coefficients) from six different study areas. These study areas include large estuaries (i. e. the Rio de la Plata in Argentina, the Yangtze estuary in China, and the Gironde estuary in France), inland (i. e. the Spuikom in Belgium and Chascomùs lake in Argentina), and coastal waters (Belgium). The dataset is available from Lavigne et al.
, 2012)  and requires datasets covering the Published by Copernicus Publications. extreme variability of case 2 water conditions. Hence, additional data obtained in optically complex waters are valuable to the scientific community as they will help to better understand marine optics in such waters and to design ocean color algorithms. The present dataset  (Lavigne et al. , 2022 ; https://doi. org/10. 1594/PANGAEA. 944313) has been collected as part of the HYPERMAQ project. During this project, different types of optically complex waters with turbidity ranging from moderate to extreme (1 to 700 FNU) and Chl-a ranging from low to very high (0. 9 to 180 mg m -3 ) have been sampled in various locations around the world. Their main optical and biogeochemical parameters are shared in this dataset, including measurements of waterleaving reflectance, turbidity, non-water light absorption, and attenuation coefficients, as well as SPM, Chl-a, and other pigment composition.
For instance,  Hieronymi et al. (2017)  proposed a multi-neural networks algorithm for case 2 waters atmospheric correction, but the algorithm was mostly trained and validated with synthetic datasets. In this context, the HYPERMAQ dataset provides bio-optical data from C2S and C2SX waters by sampling a very large diversity of waters affected by additional sediments inputs (see sample sites in Fig. 1 ). Sample sites allow one to obtain a large range in SPM and turbidity (1 to 700 FNU) by sampling Belgian coastal waters which are extremely turbid locally close to the coast and less turbid further offshore. In addition, three estuaries known to be extremely turbid have been sampled (the Gironde (France), the Yangtze (China), and the Rio de la Plata (Argantina) estuaries). Since they are affected by tides, a gradient of turbidity could be sampled along the day with diverse influences of oceanic waters.
These three estuaries, located on different continents, carry suspended particles with their own mineral properties, thus enriching the database. Finally, two terres-trial lagoons were sampled. One with low to moderate concentrations in suspended sediments (Spuikom in Belgium) and one with extreme concentrations of both algae and nonalgal suspended particles (Chascomùs in Argentina). This large diversity of sampled sites should then help to improve our knowledge of case 2 moderately to extremely scattering waters. A detailed description of each site is provided below.
Data collection The dataset contains measurements of the turbidity and, if available, concomitant SPM, absorption and attenuation coefficients, and Chl-a, and reflectance measurements are also included  (Lavigne et al. , 2022;  https://doi. org/10. 1594/PANGAEA. 944313). An overview of the dataset, with the number of observations after quality control for each site and parameter, is provided in Table 2. The measurement methodology for each parameter is described below.
Zenith angles of the sea-and sky-viewing radiance sensors were set to 40 •. Prior to each measurement, the azimuth angle of the sensors was adjusted to obtain a relative azimuth angle with respect to the sun of 90 • , either left or right to get the best unobstructed view of the water and minimize structure perturbation when measuring from pontoons. Simultaneous upwelling water radiance (L u ), downwelling sky radiance (L sky ), and downwelling irradiance (E d ) were collected every 10 s for 10 min. Data were acquired using MSDA-XE software and radiometrically calibrated using the latest calibration update from annual laboratory calibration. Water reflectance (ρ w ) was calculated following ρ w (λ) = L u (λ) -ρ sky L sky (λ) E d (λ) π, (1) where ρ sky is the air-sea interface reflection coefficient which is calculated based, when available, on wind speed as in  Ruddick et al.
Turbidity Turbidity was measured with two handheld HACH 2100P/Q ISO turbidimeters from RBINS and IAFE institutions. In the HYPERMAQ dataset, turbidity data measured with the instrument from IAFE were provided by default as they cover the most of the campaigns. However, when turbidity data from IAFE instrument were not available (Belgian coastal waters, April 2018 and Spuikom April 2018), the values obtained with the instrument of the RBINS were used. Figure  2  shows the good consistency of both instruments (r 2 = 0. 99). Water samples were collected from the surface with a bucket or from subsurface with a NISKIN bottle for measurements in coastal waters. A 10 mL vial was filled and turbidity was determined in formazin nephelometric unit (FNU) with the ratio of light scattered at 90 • compared to the transmitted light at 860 nm.
Turbidity was recorded in triplicates and the median value was used. Turbidimeters were controlled with standards STABCAL stabilized formazin turbidity of 0. 1, 20, 100, and 800 FNU before and after each campaign. In water, turbidity was also measured with an OBS501 (OBS hereafter) using a CR200 data logger. Turbidity measurements are derived from back-scattering with a field-ofview ranging from 125 to 170 • and 90 • side-scattering of a signal emitted at 850 nm, and data are provided in formazin backscatter unit (FBU) and in formazin nephelometric unit (FNU), respectively. When deployed from a pier, OBS was continuously recording data at subsurface throughout the whole day and values corresponding to specific stations were https://doi. org/10. 5194/essd-14-4935-2022 Earth Syst. Sci. Data, 14, 4935-4947, 2022  extracted from the time series in a time window of 10 min centered on the timing of the radiometric measurement and water sampling.
In situ absorption, beam attenuation, and scattering coefficients The underwater absorption and attenuation meter (AC-9, WETLabs, Inc. ) used was modified to cover the visible and near-infrared (NIR; 700 to 900 nm) spectral regions. It was designed with three visible (centered at 440, 555, and 630 nm) and six NIR (centered at 715, 730, 750, 767, 820, and 870 nm) spectral channels, and a short pathlength (10 cm) appropriate for turbid coastal waters. At the sampling sites, the AC-9 sensor was either deployed within the water column using an electrical water pump (SBE, SeaBird, Inc. ) or used as a bench photometer passing the water samples right after collection through the tubes by gravimetry. The AC-9 data recorded just below the water surface were averaged over the last minute of acquisition to obtain the mean attenuation and absorption spectra for each station.
One of the main issues encountered when sampling highly turbid waters was the saturation of the measured absorption and/or attenuation coefficients, which sometimes occurred at short visible wavelengths and even in near-infrared bands in the case of extremely turbid waters. This saturation was easily detected and the corresponding spectra were systematically removed from the dataset. When possible, after AC-9 data measurements, the water sample collected was directly filtered through disc filters  (pore size 0. 2 µm, Whatman). As in  Doxaran et al. (2009b) , the tube was rinsed twice with Milli-Q water and once with the filtrate, and then filled with the filtrate. The absorption signal of the filtrate was measured, providing a CDOM in m -1 after applying corrections for temperature and salinity.
SPM and turbidity results In the HYPERMAQ dataset, SPM ranges between 1 and 474 g m -3 (Table  3 ) and turbidity measured from HACH and OBS (side-scattering measurements) ranges between 0. 9 and 771 FNU and between 0. 2 and 632 FNU respectively. A very good relationship is observed between SPM and turbidity which almost follows the 1 : 1 line for both instruments (Fig. 3 ). A linear model between both parameters gives very good coefficients of determination (R 2 = 0. 98 for HACH and R 2 = 0. 95 for OBS) and slopes (0. 92 for HACH and 0. 86 for OBS). However, we can notice that for very high turbidity (>500 FNU), turbidity values measured by HACH tend to be slightly higher than SPM values (Fig. 3a ) but not OBS turbidity values. As expected from previous results, when comparing side-scattering turbidity obtained from OBS and turbidity measured by HACH, a good relationship is retrieved (Fig.