dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, given the selected drivers in the optimal GAM, the spatial variability of skipjack Hg concentrations is more likely to result from 1) variable seawater MeHg concentrations at the base of the food web among oceanic areas and/or 2) variable amounts of surface ocean inorganic Hg loading from the atmosphere, as discussed in the two following sections. Natural Marine Biogeochemical Drivers of the Spatial Variability of Skipjack Mercury Concentrations. A key aspect when investigating Hg variability in marine biota is its relationship with seawater MeHg concentrations, which may vary as a function of biogeochemical processes. In the Pacific Ocean, observed MeHg data in seawater are scarce and are available at limited and low spatial resolution in comparison with our skipjack Hg dataset (Fig.
Materials and Methods Mercury Concentrations and Ecological Tracers in Skipjack Tuna. A total of 661 samples of skipjack tuna with Hg concentration data were compiled from published and unpublished studies (Fig. 1A  and SI Appendix, Table  S1 ). Samples were collected onboard commercial (longline, purse seine, troll, and pole-andline) and recreational fishing boats from 122°E to 73°W and 24°S to 45°N, and metadata (i. e. , dates, position, and gear type) were provided for each sample. Fork length (FL) was measured and ranged from 29 to 90 cm (58 ± 12 cm; mean ± SD). For each fish, a white muscle tissue sample was collected and stored frozen prior to analyses. Data were classified by oceanic areas: NWPO, CNPO, NEPO, EPO, SWPO, and WCPO (Fig. 1 ). Sampling spanned from 1997 to 2018, with no temporal sampling bias between oceanic areas (SI Appendix, Fig.
Depending on the laboratory, a combination of certified and/or in-house reference materials were used to determine that uncertainty was below 0. 2& (SI Appendix, Table  S1 ). For samples with elevated lipid content (C:N > 3. 5, n = 51), δ 13 C values were corrected using a mass balance equation with parameters derived from Atlantic bluefin tuna muscle  (52). Measured Seawater Methylmercury Concentrations in the Pacific Ocean. Seawater MeHg data from the Pacific Ocean were compiled from the literature from both filtered and unfiltered samples  (12, 34, 38, (53) (54) (55) (56) (57). In particular, we used the maximum total Hg and MeHg concentrations observed in the water column (picomolar) and the depth of maximum MeHg concentrations (meters). When MeHg concentrations were not measured directly from the water samples, we used the sum of monomethylmercury and dimethylmercury.
Anthropogenic and Natural Environmental Model Estimates Driving Mercury Bioaccumulation. The global chemical transport model GEOS-Chem, version v11-02 was used to simulate atmospheric Hg 0 concentrations. The model couples a three-dimensional atmosphere, a two-dimensional (2D) subsurface-slab ocean, and a 2D terrestrial reservoir at a 6 × 7. 5°horizontal resolution. Additional details on model design are given by Angot et al. (58)  and Travnikov et al. (59). The simulation was performed using an anthropogenic emissions inventory developed for the year 2015 in the context of the latest Global Mercury Assessment  (60). The simulation was driven with assimilated meteorological data from the NASA Goddard Earth Observing System (MERRA-2). In order to account for interannual variability, the simulation was performed with meteorological data for the years 2013 through 2015 following a 1-y spin-up.
Annually averaged Hg 0 concentrations were extracted from grids for which tuna samples were collected. In order to account for the spatial migration of skipjack tuna, Hg 0 concentrations were averaged over adjacent grid boxes. Note that adjacent grid boxes with a fraction of ocean <50% were not taken into account in the average calculation. Model outputs were evaluated against available globally distributed observations (SI Appendix, Fig. S2 ). As seawater MeHg data were not available at a large spatial scale and high resolution in the Pacific Ocean, we used a suite of physical and biogeochemical variables known to influence MeHg production in seawater  (4, 57, 61, 62)  in addition to Hg environmental data. Variables included sea surface temperature (SST, °C), depth of net heterotrophy (meters), depth of oxycline (meters), depth of the mixed layer (MLD, meters), total chlorophyll (Chl-a, milligrams Á meters À3 ) averaged over the upper 100 m, and vertically integrated net primary production (NPP, milligrams C Á meters À2 Á days À1 ).
Spatial interpolation was applied at a 0. 25°resolution, with a buffer of 5°around each tuna sample to avoid excessive interpolation where no data were available. GAMs were also used to test the relative importance of potential predictors on spatial variations of Hg concentrations. The response variable, log(observed Hg), was assumed to follow a Gaussian distribution. Explanatory variables tested included surface (SST, Chl-a, and NPP) and deep (MLD, depth of net heterotrophy, depth of oxycline, and [O 2 ] in subsurface waters) oceanographic variables. Fish length, baseline outputs (δ 15 N POM and δ 13 C DIC ), and atmospheric Hg 0 estimates were also tested. To account for ecological processes, we also included tuna δ 13 C values and baseline-corrected tuna δ 15 N values (i. e. , tuna bulk δ 15 N values À δ 15 N POM values) as a proxy for tuna trophic position.
Finally, for the best-fit GAM, assumptions of the residuals trend and autocorrelation were examined graphically with diagnostic plots. The percent DE of the model was compared to assess predictive capacity. All statistical analyses were performed with R 3. 6. 1  (71) , and GAMs were fitted using the mgcv package  (72). Data Availability. All study data are available at https://doi. org/10. 23708/XUVKJK.
ACKNOWLEDGMENTS. We are grateful to our sampling providers, including the many fishery observer programs and fishermen. In particular, we thank the  Western and Central Pacific Fisheries Commission Tuna Tissue Bank , the  Pacific Marine Specimen Bank  managed by the  Pacific Community (SPC) , and the  Environmental Specimen Bank in Ehime University , as well as  Robert Olson  and  Felipe Galvan-Magaña. We thank  Laure Laffont  from  G eosciences Environnement Toulouse (GET)  and  Jean-Louis Duprey  and  St ephanie Berne  from  Laboratoire des Moyens Analytiques (LAMA ) for Hg analyses, as well as the  Union College Stable Isotope Laboratory team ,  Sarah Katz , and  Madelyn Miller. Shelagh Zegers  provided valuable analytical assistance at  Stony Brook University. We thank  Laura Tremblay-Boyer  from  Dragonfly Data Science  for her contribution in designing smooth-contour maps in R.
H. A. acknowledges  Noelle E. Selin  and the use of the Svante cluster provided by the  Massachusetts Institute of Technology 's  Joint Program  on the  Science and Policy of Global Change. We are grateful to two anonymous referees for providing thoughtful comments on the manuscript. This study was funded by the  Pacific Fund   VACOPA  Project (spatial VAriations of COntaminants levels in PAcific ocean trophic webs) and  ANR-17-CE34-0010  MERTOX (unravelling the origin of methylMERcury TOXin in marine ecosystems,  2017-2021 ) from the  French Agence Nationale de la Recherche. The  US NSF funded Union College 's isotope ratio mass spectrometer and peripherals (NSF-Major Research Instrumentation No. 1229258 ). This work was supported by the  Interdisciplinary Graduate School  for the  Blue Planet  ( ANR-17-EURE-0015 ) and cofunded by a grant from the  French government  under the program " Investissements d'Avenir.
"P. B. was also supported by a grant from the  Regional Council of Brittany  ( SAD program ,  Strat egie d'Attractivit e Durable). H. A. received financial support from the  Swiss National Science Foundation  (Grant No. 200021_188478 ). 6 of 8 j PNAS M edieu et al. https://doi. org/10. 1073/pnas. 2113032119 Evidence that Pacific tuna mercury levels are driven by marine methylmercury production and anthropogenic inputs.
Data acquisition Timing: 2-3 days Data acquisition can be performed using any hardware and software combination as long as the data has the input structure that we outline here. However, as a guide we highlight the requirements of the system using our acquisition system as an example for the reader. 1. A video-tracking set-up with a resolution of 17 pixels/mm is used to monitor exploratory behavior of animals. The acquisition is carried out at 700 Hz. A high frame rate is essential for analysis of short timescale kinematics. A lower frame rate and spatial resolution (as we are only using the positional information) is sufficient for the long timescale kinematic analysis. 2. We use a Mikrotron MC1362 camera (Mikrotron-GmbH, Germany) and a Schneider apo-Xenoplan 2. 0/35 objective (Jos. Schneider Optische Werke GmbH, Germany).
To show this, using this set-up, we record several freely swimming Danionella cerebrum (DC) and Danio rerio (ZF) which show a near continuous and an intermittent swimming pattern, respectively. Animal tracking and tail segmentation are performed online using a C# (Microsoft, USA) program published by Marques et al. 2  The tracking code is available with their publication here, Mendeley Data: https://data. mendeley. com/datasets/r9vn7x287r/1. 5. The following method is used by the tracking algorithm 2 : a background is calculated and subtracted from acquired images. The subtracted image is smoothed using a boxcar filter and an empirically selected threshold is used to separate the fish from the background. The fish silhouette is selected using flood fill starting at the point of maximum intensity.
The center of mass of this object defines the larva's body position. The maximum pixel value on a 0. 7 mm diameter circle around this position is used to find the direction of the tail. To refine this measurement, the center of mass is then calculated on an arc centered along this direction. Similarly, several segments are identified by finding the center of mass of the pixel values along a 120-degree arc from the previous segment. To avoid contamination with background noise, pixels below a manually selected threshold level are removed. Please check the above link to access the tracking software. 2 6. The aforementioned program provides two important parameters for our analysis: the body position coordinates (X, Y) of the fish and the angle of each tail segment. The number and length of segments for the tail can be selected using the tracking program.
This depends on the size of the animals used and the length of their tail. In the present study, we keep it at 10 tail segments of 0. 3 mm for both the species as their size is very comparable. CRITICAL: as the raw images are not stored on the disk, before performing the experiments, it is important to make sure that the tail segmentation is correct. For this, some trial data can be acquired where the raw images are also stored along with the fish and tail data. This can be achieved at a lower frame rate without any considerable frame drop. The raw images can then be used to test the reliability of the data extracted online by employing manual segmentation using an image processing tool such as FIJI. In case of errors in the automated segmentation, the user should try to rectify this by 1) making sure that the resolution of the system is 17px/mm or more as described above and 2) improve the contrast between the animal and the background by changing the illumination or exposure time of the camera.
Timing: 20 min This section makes use of the high frequency acquisition data to calculate short timescale (milliseconds) swimming kinematics such as swimming speed, tail beat frequency and tail angle per half tail beat. This provides information about the swim pattern utilized by the larva to achieve the generated thrust. The MATLAB Signal Processing Toolbox 8. 6 and Statistics and Machine Learning Toolbox 12 are used in this section. There are a few external functions that we use and provide with this script (Data S1; licenses included wherever applicable). We describe some of the critical or uncommon external functions here: y= round_one(a). > headyaw_diff(kk) = 2* Note: In additional to the external functions that we provide with this publication and describe here, we describe other critical MATLAB functions that are used within the main script when we encounter them.
This will make the adaption of this script to any other programing language easier for readers who are more comfortable with another programing language. 1. Smooth the X and Y coordinates using the Savitzky Golay digital filter. Note: In MATLAB, sgolayfilt function is used to carry this out. a. Use a 2 nd order polynomial fit on a window of 30 ms. b. Calculate the displacement and velocity using the filtered X and Y coordinates of the fish centroid. Note: sgolayfilt is a MATLAB function which applies a Savitzky-Golay smoothing filter of nth order polynomial and frame length L to the data in vector x. Y=sgolayfilt(x,n,L); 2. Measure the tail curvature to identify the swim events. 2  a. Select the number of tail segments incorporated in the analysis based on the reliability of tracking available from raw pixel intensities of the segments.
Note: The cut-offs should work well across various swim types and species if the data is not very noisy. 5. Now, detect the half tail beats in the identified swim events. a. Define a half tail beat as a peak-to-peak half cycle and use this to calculate the short timescale swimming kinematics (Figure  2 ). b. To identify half tail beats, calculate the absolute value of the tail angle signal and then identify the peaks. In MATLAB, the peaks are identified using the findpeaks function. Note: findpeaks is a MATLAB function that identifies local maxima (peaks) in the input vector (data). It also returns the indices (locs) of the identified peaks. [peaks, loc] = findpeaks(data). 6. Using this information about peaks, calculate the kinematic parameters like swim duration, distance, mean and maximum speed, maximum tail angle and half beat frequency on the individual half tail beats.
Potential solution One can correct for this issue before data acquisition by using optical or behavioral methods such as use of a curved arena or use of positive/ negative phototactic ring at the edge to force animal to spend more time in the center. If one is unable to correct for the issue before data collection, they will have to take care of this during the data processing. As mentioned in the protocol, we do this in our analysis using two different approaches. In short time scale analysis, we use the pixel value information to discard the poor tracking events from the dataset [see step 8 in before you begin]. In the long timescale analysis, we select trajectories in a region of interest in the arena which excludes the borders of the arena where poor illumination leads tracking errors [see step 9 in stepby-step method details].
ACKNOWLEDGMENTS G. R. was supported by a  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  666003 , a  Fondation pour le Recherche Medicale  (FRM) 4th year doctoral fellowship, and a  Sorbonne University Postdoctoral Fellowship. G. D. was supported by  H2020 European Research Council  ( 71598 ). M. B. O. was supported by  Volkswagen Stiftung  ( A126151 ) and  European Research Council  ( NEUROFISH 773012 ). The work in the laboratory of F. D. B. was supported by  ANR-18-CE16-0017-01  ''iReelAx'',  ANR-21-CE16-0037-02  '' Loco-Mat '',  UNADEV  in partnership with  ITMO NNP/AVIESAN (National Alliance for Life Sciences and Health) , the  Fondation Simone and Cino del Duca  the  Programme Investissements d'Avenir IHU FOReSIGHT  ( ANR-18-IAHU-01 ).
6, Statistics and Machine Learning Toolbox 12, and Statistics and Machine Learning Toolbox 12. 1) C# (Microsoft, USA) Microsoft, USA N/A Code for animal tracking and tail segmentation Marques et al. 2 Mendeley Data: https://data. mendeley. com/ datasets/r9vn7x287r/1 Other IR-sensitive Camera (MC1362) Mikrotron-GmbH, Germany N/A Schneider apo-Xenoplan 2. 0/35 objective Jos. Schneider Optische N/A Werke GmbH, Germany 850 nm infrared bandpass filter (BP850-35. 5) Midwest Optical Systems, Inc. N/A IR-LED array Custom made IR-LED arrays or N/A from local retailer (IR-LED arrays used for night vision cameras). Deposited data Data and code Generated for this analysis Mendeley Data: https://doi. org/10. 17632/ pzwb8dj2hm. 1.
Some of these assays amplify not only B. ostreae but also other members of the family Haplosporiidae. Nevertheless, some recently developed real-time PCRs allow rapid and specific detection of B. ostreae  (Robert et al. , 2009; Ramilo et al. , 2013). Nucleotide data available in public databases for parasites of the genus Bonamia are scarce and mostly concern Bonamia exitiosa (59%) and Bonamia sp. (30%). B. ostreae represents only 7% of sequences currently available (Figure  1 ). These B. ostreae sequences are partial small subunit ribosomal RNA gene sequences (13%); internal transcribed spacer 1-5. 8S-internal transcribed spacer 2 sequences (72%); and partial actin gene sequences (13%) (Figure  1 ). In addition, a few amino acid sequences have been characterized, including two actin genes  (Loṕez-Flores et al.
Quality Control Verification and Read Classification Raw sequencing reads were quality checked (Figure  2 ) with FastQC version 0. 11. 5 (https://github. com/s-andrews/FastQC). The reads were then processed with Rcorrector version 1. 0. 4 with standard parameters to correct random sequencing errors in Illumina RNA-seq reads. Rcorrector uses a k-mer-based method and a De Bruijn graph to compactly represent all trusted k-mers in the input reads and correct them  (Song and Florea, 2015). Corrected reads were then trimmed with Trim-Galore version 0. 6. 4 with the following parameters: -paired -phred33 -length 36 -q 5 -stringency 1 -e 0. 1 -cores 4 (https://github. com/ FelixKrueger/TrimGalore). After the last QC verification with FastQC, the quality-filtered (hereafter, good-quality) RNA-Seq reads were organized into two subsets: messenger RNA (mRNA) sequences and ribosomal RNA (rRNA) sequences.
To this end, all good-quality RNA reads were searched against the SILVA rRNA database version 138. 1  (Quast et al. , 2013)  with Usearch version 11. 0. 667 with parameters: -usearch_local -db -id 0. 8 -strand both -top_hit_only  (Edgar, 2010). All mRNA reads (i. e. , reads without a Usearch hit against the SILVA database) were de novo assembled with a first run of the De novo RNA-Seq Assembly Pipeline (DRAP)-Trinity version 1. 92 with standard parameters  (Cabau et al. , 2017). The new de novo-assembled contigs were then searched for potential remaining rRNA transcripts using Infernal version 1. 1. 3 and the following parameters: -Z -cut_ga -rfam -nohmmonl -fmt 2  (Nawrocki and Eddy, 2013 ) by identifying covariance model (CM) against the Rfam database  (Kalvari et al. , 2021). After clustering and manual curation in Geneious Prime version 2020.
rRNA Reads Assembly and Analysis The rRNA reads identified were de novo assembled with Transabyss version 2. 0. 1 with the following parameters: transabyss -k 25/35/45/55/65; transabyss-merge -mink 25maxk 65 -SS  (Robertson et al. , 2010). The new de novo rRNA contigs were then clustered and size filtered (only contig >200 bp were kept for subsequent analysis) in Geneious. High-quality nonredundant rRNA contigs were annotated with Infernal by identifying rRNA CM against the Rfam database. Following rRNA prediction, small subunit (SSU) and large subunit (LSU) rRNA were extracted, curated, and clustered in Geneious. Abundance estimations were then assessed with RSEM via the Trinity script align_and_estimate_abundance. pl with the following parameters: -est_method RSEM version -aln_method bowtie2 (https://github.
com/trinityrnaseq/ trinityrnaseq/blob/master/util/align_and_estimate_abundance. pl). The top ten most expressed SSU and LSU rRNAs were then subjected to BLASTn  (Altschul et al. , 1990)  search against the nt database (accessed in February 2021) followed by a taxonomic assignment with the web-based tool SILVA ATC (https://www. arb-silva. de/aligner) (accessed February 2021)  (Pruesse et al. , 2012).
Identification and Characterization of B. ostreae rRNA Operon The longest and most abundant de novo-assembled rRNA transcripts by the Transabyss assembler were subjected to a BLASTn search against nt. Top BLASTn hit allowed us to identify the position of the previously sequenced B. ostreae rRNA operon fragment on our new putative full-length B. ostreae rRNA operon transcript. In order to characterize the diversity of the B. ostreae population in our metatranscriptomic sample, we then performed a variant calling by realigning the rRNA reads to the B. ostreae rRNA operon contig with BWA-MEM2 version 2. 1 with standard parameter  (Li and Durbin, 2010) , followed by a variant calling with Freebayes version 1. 3. 2 with the following parameters: -F 0. 01 -C 1 -pooled-continuous -report-monomorphic (https:// github.
com/freebayes/freebayes). The sequence of this B. ostreae FIGURE 2 | Flowchart of the bioinformatic pipeline. First, QC reads were performed, followed by classification reads as "ribosomal" rRNA reads or "coding" mRNA reads. Finally, each read collections has been assembled and annotated separately with dedicated tools. rRNA operon has been deposited at NCBI under accession number MZ305451.
mRNA Read Assembly and Analysis First, host (Ostrea edulis) mitochondrion reads were identified by mapping mRNA reads against the O. edulis mitochondrion genome (accession: JF274008. 1) with Bowtie2 version 2. 4. 1 with the following parameters: -very-sensitive-local  (Langmead and Salzberg, 2012). Second, the remaining mRNA reads were de novo assembled with a second run of DRAP-Trinity with the same parameters as previously described. Read-support evaluation of the assembled transcriptome was accessed by realigning mRNA reads against the new de novo-assembled transcripts with Bowtie2 version 2. 4. 1 with the following parameter: -k 20. Transcriptome completeness was assessed by submitting transcripts to BUSCO evaluation version 5. 0. 0 with the following parameters: -m transcriptome on one of the following odb10 databases: Alveolata, Archaea, Bacteria, Eukaryota, Fungi, Metazoa, Mollusca, Stramenopiles, and Viridiplantae  (Seppey et al.
, 2019). Transcript abundance was then estimated with RSEM via the Trinity script align_and_estimate_abundance. pl. Transcriptome assembly annotation was performed with Trinotate version 3. 1. 1 (https://github. com/Trinotate/Trinotate. github. io/wiki). First, DRAP contigs were scanned with Transdecoder version 5. 5. 0 with standard parameters (https://github. com/TransDecoder/ TransDecoder/wiki) for ORF prediction. Functional annotation was then performed by a series of public database interrogations with (i) BLASTx (transcripts) (parameters: -max_target_seqs 1 -evalue 1e-3) and BLASTp (CDS peptide) (parameters: -max_target_seqs 1 -evalue 1e-3) against the Swiss-Prot database (accessed in February 2021), with (ii) hmmscan version 3. 3 with standard parameters  (Mistry et al. , 2013)  against the Pfam-A database (accessed in February 2021), with (iii) TmHMM version 2.
0c with standard parameters  (Krogh et al. , 2001) , with (iv) SignalP version 5. 0b with standard parameters  (Nielsen et al. , 2019) , with (v) eggNOG-mapper version 2 with standard parameters  (Huerta-Cepas et al. , 2017)  against the eggnog v5. 0 database (accessed in February 2021)  (Huerta-Cepas et al. , 2019) , and with (vi) InterProScan implemented in ORSON (prOteome and tRanScriptome functiOnal aNnotation) (https://gitlab. ifremer. fr/bioinfo/orson).