dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar to the distribution pattern of anammox reaction rates and anammox bacteria abundances, the power supply from both the measured and modeled data agree with each other and are highest in the NATZ across all cores (Fig. 2E ), consistent with a higher standing stock of anammox bacteria in this geochemical transition zone. Given our current knowledge of the deep subsurface, this observation suggests in situ growth due to the local increase of power availability. However, another scenario that could, in principle, explain a potential increase of anammox cells in the NATZs is cell migration enabled by flagellar motility, because the dominant anammox bacterium in the NATZ has the full gene sets for flagellum synthesis (Fig. 4C ). To investigate whether these anammox bacteria have sufficient metabolic energy to fuel flagellar rotation, we estimated cell-specific metabolic rates from predicted bulk anammox rates divided by anammox bacteria abundances.
S. sediminis has the complete pathway for glycolysis, the nearcomplete the tricarboxylic acid (TCA) cycle (only fumarate hydratase missing) and the electron-transport chain for respiration (consisting of complexes I, II, III, IV (cytochrome cbb3 menaquinol oxidase), and V-type ATPase) (Fig. 4C  and Dataset S2), it is possible that this bacterium can degrade organic acid using nitrate as the electron acceptor, as observed in other characterized anammox bacteria  (46) (47) (48). This capacity may provide an additional and probably crucial fitness advantage, allowing it to conserve energy when local conditions are not favorable for conventional anammox. Microbial Nitrogen Cycling in the NATZ. The canonical anammox metabolism requires both NH 4 + and NO 2 -, which are probably mainly supplied by other organisms.
Detailed information on model structure, reaction rate expressions, and relevant parameters can be found in SI Appendix, Materials and Methods. Global Occurrence of NATZ in Global Marine Sediments. Geochemical profiles of porewater nitrate and ammonium indicating a NATZ in marine sediments were either retrieved from published literature or obtained from the PANGAEA database (https://www. pangaea. de/). Porewater profiles were manually checked and discarded if they 1) do not reach the nitrate-depletion depth (i. e. , approximately the NATZ) or 2) contain too few datapoints (<6). Calculation of Gibbs Free Energy and Power Supply of Anammox. The standard Gibbs free energy (ΔG r 0 ) was calculated using thermodynamic data of standard Gibbs free energy of formation of each reactant/product, corrected to near in situ pressure and temperature using the R package CHNOSZ  (53).
2. 5 (59) using default parameters. The genome bin of Ca. S. sediminis was manually refined using the gbtools (60) based on the GC content, taxonomic assignments, and differential coverages in different samples. The quality of the resulting Scalindua genome was checked using the CheckM v. 1. 0. 7 "lineage_wf" command, based on the Planctomycetes marker gene set (automatically selected by CheckM). Genes in the genome of Ca. S. sediminis were predicted using Prodigal  (61). Genome annotation was conducted using Prokka v. 1. 13 (62) , eggNOG  (63) , and BlastKoala (64) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Functional assignments of genes of interest were confirmed using BLASTp against the NCBI RefSeq database. Metabolic pathways were reconstructed using KEGG Mapper  (65).
Detailed information on the metagenome data analysis can be found in SI Appendix, Materials and Methods. Phylogenetic Analyses. All available high-quality anammox bacterial genomes were downloaded from the NCBI database and were included in the phylogenomic analysis, which was based on marker genes consisting of 14 syntenic ribosomal proteins (rpL2,  3, 4, 5, 6, 14, 16, 18, 22 and rpS3, 8, 10, 17, 19) , demonstrated to undergo limited lateral gene transfer  (66). These selected proteins, among the conservative single-copy ribosomal proteins included in Campbell et al. (67) , were identified in Anvi'o v. 5. 4 (68) using hidden Markov model (HMM) profiles. Sequences were aligned individually using MUSCLE  (69) , and alignment gaps were removed using trimAl  (70)  with the mode of "automated.
" Individual alignments of ribosomal proteins were concatenated. The maximal likelihood phylogenetic tree was reconstructed using IQ-TREE v. 1. 5. 5  (71)  with the best-fit model selected by ModelFinder (72), and 1,000 ultrafast boostrap iterations using UFBoot2  (73). A maximum-likelihood phylogenetic tree based on 16S rRNA genes was also constructed for known anammox bacteria and close relatives of the three Scalindua OTUs identified via BLASTn  (74)  in the NCBI database. Sequences were aligned using MAFFT-LINSi  (75)  and the maximum-likelihood phylogenetic tree was inferred using IQ-TREE with the procedure described above. Detailed information on the phylogenetic analysis of functional proteins, including the three subunits of hydrazine synthase (HzsA, HzsB, and HzsC), urease alpha subunit (UreC), cyanate dehydrogenase (CynS), respiratory NarG, and cd1 cytochrome nitrite reductase (NirS) are provided in SI Appendix, Materials and Methods.
Comparative Genomic Analysis of Scalindua. Genomes of Ca. S. rubra  (30) , Candidatus Scalindua brodae (76), Ca. S. japonica  (32) , Ca. S. profunda  (33) , Ca. S. AMX11 (31), and Ca. S. sediminis (recovered in this study) were included in the comparative genomic analysis using Anvi'o v. 5. 4 (68) according to the workflow described at merenlab. org/2016/11/08/pangenomics-v2/. All genomes were annotated using Prokka v. 1. 13 (62)  and BLASTp using the clusters of orthologous groups of proteins (COG)  (77)  as the reference database. Specific metabolic characteristics inferred from the annotations of genes with known homologs and identified with the pangenomic analysis are discussed in the main text. iRep Calculation. The iRep is a measure of the average genome copy number across populations of cells  (35).
Such a calculation was not performed in the other three depths because genome coverages were too low. Scaffolds <5 kb were discarded prior to reads mapping, which did not change the genome quality given by CheckM. Reads mapping files generated using Bowtie2 (79) were used to calculate iRep using the default settings without GC corrections  (28). Global Distribution of Ca. S. sediminis-like Anammox. The occurrence of Ca. S. sediminis-like anammox in natural environments was assessed using IMNGS (80) against the available short read archive (SRA) datasets in the NCBI database with the full-length 16S rRNA gene sequence as query. Reads were counted as matching reads if they 1) are longer than 200 bp and 2) show >97% nucleotide sequence identity to the query. Samples with fewer than 10 matching reads were discarded.
Only natural environments with more than 0. 01% matching reads were included. Data Availability. All sequencing data used in this study are available in the NCBI Short Reads Archive under the project no. PRJNA529480. In particular, raw metagenomic sequencing data are available in the NCBI database under the following BioSamples (SAMN11268098, SAMN11268104, SAMN11268106, and SAMN11268109). The Ca. S. sediminis genome is available under the accession no. SAMN12415826. Raw geochemical data can be found in Dataset S2. The R script of the reaction-transport model is available at GitHub (https://github. com/ruizhao087/Reaction-Transport-Model-for-marine-sediments). All other study data are included in the article and supporting information.
ACKNOWLEDGMENTS. We thank the chief scientist  Rolf Birger Pedersen  and the crew of  R/V G. O. Sars  for the sediment coring opportunity from the AMOR area;  Anita-Elin Fedøy  for the amplicon preparation;  Michael Melcher ,  Steffen Lydvo , and  Gustavo Ramirez  for the sampling collection and DNA extraction;  Jan-Kristoffer Landro  for sediment carbon and nitrogen contents measurements; and  Thomas Pollak  for metagenome library preparation. Computational resources were made possible through the  BIOMIX compute cluster (Delaware INBRE  grant  NIGMS P20GM103446 ). We are grateful to the two anonymous reviewers whose constructive comments greatly improved the quality of this manuscript. This work was funded by the  Research Council of Norway through the Centre for Excellence in Geobiology , the K.
colorado. edu/mms/sdc/public/datasets/hpca/, last access: 1 October 2020). HPCA data are made available in survey or burst modes. Data are collected in 0. 5 spin (10 s) time resolution at 64 energies, 16 azimuth angles, and 16 elevation angles for five different species (H + , He + , He 2+ , O + , and O 2+ ). In the survey-mode data used here, the data are reduced in energy and angles to 16 energies, eight azimuths, and eight elevation angles. The total energy range of the data is between ∼ 1 eV/e and 40 keV/e, with a mass resolution of M/ M ∼ 8. Finally, we calculate the ion partial density (without species separation), using measurements from a FPI-DIS, as a means of comparison to the partial densities obtained from HPCA. The FPI produces burst sky maps which consist of ion count arrays of 32 energies × 32 azimuth angles × 16 polar angles that are accumulated every 150 ms.
This energy is about 4 times higher for alphas compared to the protons, due to larger mass of He 2+. We note that the energy-time spectrograms show energy per charge E i Z -1 i , with the lowest energy included in the suprathermal population shown with the dashed line set to the values indicated in Figs. 3c  and 4c. Also, only those suprathermal ions measured by HPCA (up to 40 keV/e) are included in the densities, but we estimate the lost contribution due to higher energy ions to be small due to very small phase-space densities. During both time intervals the transition between the foreshock and the undisturbed solar wind was not due to any IMF rotational discontinuity, as can be seen from the MMS magnetic field components. We also checked the solar wind measurements propagated to the bow shock nose from the OMNI database  (King and Papitashvili, 2005)  and found no sharp IMF change during those intervals.
Code and data availability. Vlasiator (https://www. helsinki. fi/en/ researchgroups/vlasiator; Palmroth, 2020) is distributed under the GPL-2 open source license at https://github. com/fmihpc/vlasiator/, https://doi. org/10. 5281/zenodo. 3640593 (Palmroth and the Vlasiator team, 2020). Vlasiator uses a data structure developed inhouse at the University of Helsinki, and it is available at (https: //github. com/fmihpc/vlsv/, Sandroos, 2019). The Analysator software is available at (https://github. com/fmihpc/analysator/; Hannuksela and the Vlasiator team, 2020) and was used to produce the presented figures. The run described here takes several terabytes of disk space and is kept in storage maintained within the CSC -IT Center for Science. Data presented in this paper can be accessed by following the data policy on the Vlasiator website.
Acknowledgements. This paper was outlined and drafted at the  Third International Vlasiator Science Hackathon held in Helsinki, Finland , on 19-23 August 2019. The hackathon was funded by the  European Research Council  (grant no. 682068 ) under the  PRESTISSIMO  project. We acknowledge the  European Research Council  for the Starting grant (grant no. 200141-QuESpace ), with which Vlasiator was developed, and the Consolidator grant (grant no. 682068-PRESTISSIMO ), awarded to enable the further development of Vlasiator for scientific investigation. The  Finnish Centre of Excellence in Research of Sustainable Space , funded through the  Academy of Finland  (grant no. 312351 ), also supported the Vlasiator development and science. We also gratefully acknowl-edge the  Academy of Finland  (grant nos.
sci. gsfc. nasa. gov/index. html/(last access: 1 October 2020). Financial support. This research has been supported by the  European Research Council  (grant nos. 682068  and  200141 ), the  Academy of Finland ,  Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta  (grant nos. 312351 ,  322544 ,  328893 , and  309937 ),  UNAM PAPIIT-DGAPA  (grant nos. IN105218-3  and  IA101118 ),  CONACyT  (grant no. 255203 ),  NASA  (grant nos. NNG04EB99C ,  80NSSC18K1363 , and  80NSSC18K0999 ), and the  DGA project at École Polytechnique  (grant no. 2778/IMES ). Review statement. This paper was edited by  Nick Sergis  and reviewed by  David Sibeck  and  Jeffrey Broll.
Electrophysiology For all experiments, we used a controlled extracellular solution consisting of 1 mM CaCl2, 4 mM KCl and 1 mM Tris-Hcl, pH=7. 2. Microelectrodes of resistance » 50 MΩ were pulled with a single step from standard wall borosilicate capillary glass with filament (OD 1 mm, ID 0. 5 mm, Harvard Apparatus) using a micropipette puller (P-1000, Sutter Instrument). They were filled with a 1M KCl solution using a MicroFil non-metallic syringe needle (MF 34G-5, World Precision Instruments). Custom Python programs (https://github. com/romainbrette/clampy) are used to control the analogdigital acquisition board (USB-6343, National Instruments) connected to the amplifier (Axoclamp 2B, Axon Instruments) operating at a sampling frequency of 40 kHz. After cell immobilization, the microelectrode is lowered into the cell until the measured potential drops by about 20 mV.
B A C 100 µm 100 µm Glass microinjection pipettes are pulled to an estimated outer diameter of 0. 7-0. 9 µm in one step using the same pipette puller as described above. The back of the pipette is connected to a microfluidic flow controller (OB1-Mk3, Elveflow) and controlled with ESI software (Elveflow Smart interface). The baseline pressure is set to 5 mbar, such that there is no net flow through the micropipette. Paramecia are injected with a solution containing 60 µM Alexa-594 fluorophore dye and 20 mM KCl, by applying a 100 ms long pulse at a pressure of 300 mbar.
2 ). Widespread use of the Fourier slope in image analysis  12, 25, 43  is partly due to its biological relevance. Neurons active in the early stages of visual processing in vertebrates are specialized to respond to contrast at specific spatial frequencies. Notably, these specializations correspond to the spatial frequencies that occur with the highest energy in natural scenes  22, [44] [45] [46]. Stimuli that most closely match the energy distribution of spatial frequencies in natural scenes are the most efficiently processed, as they generate an efficient neurological code that stimulates a small number of highly specialized neurons  23. The Fourier slope thus has played a central role in demonstrating that the retina and visual cortex of vertebrates are adapted to the spatial statistics of natural scenes  18, 46, 47.
Each image was captured by holding our camera near the bottom of the stream and taking pictures facing multiple directions. This was repeated across each field site until the area where we found darters were photographed to the complete extent than conditions allowed, and until each habitat had at least 100 photographs. We also took images of a waterproof white balance card (DGK Color Tools WDKK Waterproof Color Chart) at each site. Every habitat class was represented by a minimum of 100 images representing a minimum of two sites. All images were collected in clear, shallow water on sunny days between 10:00 and 15:00, and when water turbidity was low. Image processing. To retain the high dynamic range and linearity of the RAW image files, we used a custom python script using the library libraw to extract image data from each Canon RAW file.
We then estimated the slope of the power spectrum using a linear regression on the bin values, using a custom Python script. To compare the slope of each individual fish to the average slope of its habitat class, we first computed the bin values for each habitat class, and then defined the deviation for an individual fish as the mean-squared error between the fish bin values and the habitat bin values. We then used these deviation values in a MCMCglmm model (below) to test the effect of sex on deviation of the individual pattern from habitat slopes. Statistical analysis. To ensure that images of species-typical habitat were representative of their class, we pooled images of each habitat class across multiple sites. We first used a one-way ANOVA to test whether the Fourier slope of habitats varied by habitat class.
We also used a one-way ANOVA to determine whether the Fourier slope of darter color patterns varied by species. This ANOVA was performed separately for males and females. To compare the Fourier slope of males versus females, we performed a two-tailed t test. To examine the relationship between the Fourier slope of habitats and that of darter patterns, we used generalized linear mixed models. We computed this model using the R package MCMCglmm  72, 73. MCMCglmm can account for phylogenetic covariance and uses Markov chain Monte Carlo (MCMC) sampling to generate posterior distributions. Our model predicted the value of the Fourier slope of each individual fish based on the slope of its habitat. We included capture site nested within species as a random effect. The phylogenetic tree of the ten studied species was inferred from a previously published molecular phylogeny (accessed via TreeBASE)  30, 74.
Acknowledgements We would like to thank  Dr. Thomas Cronin  for help with camera calibrations,  Matthew Dugas ,  Natalie Roberts , and  Rickesh Patel  for assistance with field collections, and the  Hancock Biological Station  for providing a home base for field work and photography. This work was supported by  National Science Foundation  grant  IOS-1708543  and by the  CNRS  ( PICS  project  n°08302 ).
Data availability The computed slopes for every image used can be accessed via github at https://github. com/svhulse/Fourier-Analysis. Any images used in this study are available upon request. The source data used for Figs. 3-5 are provided as a Source Data file. The phylogenetic data used to generate our models can be accessed via TreeBase at https://treebase. org/ treebase-web/search/study/trees. html?id=11548.
To reveal the repertoire of YqgP substrates, we generated a strain deficient in yqgP and re-expressed yqgP or its catalytically dead mutant S288A  (Lemberg et al, 2005)  in this background from an ectopic chromosomal locus (strains BS50 and BS51, respectively, see Table  EV2 ). We then conducted quantitative proteomic analysis of membrane fractions of both of these strains employing SILAC labelling and SDS-PAGE pre-fractionation (GeLC experiment), where we were seeking peptides belonging to proteins migrating at a lower-than-expected apparent molecular weight selectively in the YqgP-expressing strain relative to the S288A mutant expressing one. This analysis yielded the high-affinity magnesium transporter MgtE as the top candidate substrate of  YqgP (Fig 1A-C, Dataset EV1, PRIDE dataset PXD014578).
In a complementary approach, we used affinity co-immunopurification and label-free quantitative proteomics to identify proteins associating with YqgP (Fig  1D  and E ). We ectopically expressed either a functional YqgP-sfGFP fusion or catalytically dead YqgP. S288A-sfGFP fusion as the sole copy of YqgP in the cell. We solubilised the isolated membranes using the NP-40 detergent, isolated YqgP-sfGFP by anti-GFP affinity pull-down and analysed the co-isolated proteins by MS-based proteomics. Using this approach, we identified several high-confidence interactor candidates including the membrane-anchored protease FtsH, and ATPase subunits A,  D, F and G (Fig 1E, Dataset EV2, PRIDE dataset PXD014566). The only high-confidence overlap between the two proteomic datasets was the high-affinity magnesium transporter MgtE (Fig  1C  and E ), promoting it to the highest likelihood candidate substrate.
C Table of best substrate candidates of two GeLC experiments. In order to assess possible cleavage sites in combination with topology information, the QARIP software  (Ivankov et al, 2013)  was used to summarise results from Experiments 1 and 2. Abundance ratios (YqgP/YqgP. S288A) for the intracellular parts of proteins were calculated for each gel slice separately by QARIP from peptide ratios computed by MaxQuant  (Cox & Mann, 2008). Substrate candidates highlighted in red were identified in both experiments. D Schematic representation of the affinity co-purification experiment in B. subtilis to identify YqgP interactors. E Results of MS analyses of affinity co-purification experiments in wild-type B. subtilis control (strain BTM2, Table  EV2 ) and B. subtilis deficient in endogenous YqgP expressing the wild-type YqgP-sfGFP bait (strain BTM84, Table  EV2 ) or the proteolytically inactive YqgP.
Structural similarity search using Dali server  (Holm & Laakso, 2016 ) identified a number of structurally related proteins, with no conserved functional role but enriched in DNA/RNA polymerases and modifying enzymes (Table  EV4 ). In particular, the b-stranded central part of YqgP NTD adopts a fold characteristic for the type II restriction endonucleases (InterPro and Pfam codes IPR018573 and PF09491, respectively)  (Kachalova et al, 2008). Since Mn 2+ but not Zn 2+ provided reliable data in ITC titrations, we focused on Mn 2+ in further structural analyses aiming to understand the structural aspect of divalent cation-mediated activation via YqgP NTD. NMR titrations of YqgP NTD by the paramagnetic Mn 2+ induced peak intensity reductions and chemical shift perturbations in a number of residues along NTD.
N-H chemical shifts could be caused by direct interaction with the metal ion, or by secondary structural effects, while peak intensity could be also diminished by the paramagnetic effect of Mn 2+. The most strongly affected regions were amino acids 27-30, 35-37, 48-63 and 89-98 of the 177 amino acid NTD (Fig  6C  and D , full titration data are available in Dataset EV3). To identify potential residues involved in binding Mn 2+ , we next focused on amino acids typically involved in chelating divalent cations (D, E, H) from each of the regions affected in NMR titrations. We analysed the effect of alanine substitutions in D29, D37, H49, D50, D52, D60 and E90 on the ability of these mutants to elicit Mn-induced activation of YqgP in vivo  (Fig 6E). This analysis indicated that the hotspot in which mutations interfered with Mn-induced activation of YqgP was centred around D50, and D50A mutation abrogated Mn activation completely.
The following antibodies were used for immunodetection: anti-thioredoxin antibody produced in rabbit (Merck), polyclonal anti-MgtE (this study), polyclonal anti-YqgP NTD (this study) and polyclonal anti-YqgP CTD (this study) antibodies all raised in rabbit (Agro-Bio, France), and rabbit polyclonal anti-FtsH (gift from Prof. Thomas Wiegert, Zittau, Germany). Donkey anti-rabbit IgG (H + L) Cross-Adsorbed Secondary Antibody DyLight 800 conjugate (Invitrogen) was used for visualisation by near-infrared laser scanning on the Odyssey â CLx Imaging System (LI-COR â ) according to manufacturer's instructions. The resulting fluorescence intensity data for individual proteins were optionally used for quantification, as specified in each case. For occasional chemiluminescent detection (as noted in figure legends), goat anti-rabbit IgG conjugated to horseradish peroxidase (HRP) (Dako) was used as a secondary antibody, and HRP activity was detected by the Western Lightning Pro Kit (PerkinElmer).