dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with imaging dataset suggests that psbO is a robust marker gene for estimating relative cell abundance of phytoplankton from metagenomes To assess the accuracy of the psbO gene for determining phytoplankton cell relative abundances, we carried out comparative analyses with imaging datasets. For the 0. 2-3 µm size fraction, we compared relative abundances based on 16S and psbO counts with those inferred from flow cytometry (Figure  2C ). Both genes were found to correlate well with flow cytometry. Although the correlations for eukaryotic picophytoplankton were strong (Spearman's rho=0. 64-0. 71, p-value<<<0. 001), the relationships were not linear and picoeukaryotes appeared at much higher relative abundances in metagenomes than in flow cytometry. This is consistent with the fact that flow cytometry can count cells of up to 10-20 µm diameter and was performed on seawater aliquots pre-filtered through a 200-μm mesh (see Methods), whereas DNA isolation of picoplankton was carried out on seawater volumes mainly filtered through 3-μm pore sizes.
Trans-domain comparison reveals unexpected abundance of picocyanobacteria in large size fractions To further examine the distribution of both prokaryotic and eukaryotic phytoplankton across the whole size spectrum, we continued the analysis of the mapped metagenomic reads against our catalogue of psbO sequences (Figure  S1 ). We observed a high abundance of cyanobacteria in the large size fractions in relation to the eukaryotic phytoplankton (Figure  6A ). The nitrogen-fixers Trichodesmium and Richelia/Calothrix were found principally in the 20-180 and 180-2000 μm size fractions (Figure  6A ), which is expected as the former forms filaments and colonies while the second group are symbionts of certain diatoms (Figures  7B-D ). These genera were recently quantified in the high-throughput confocal microscopy dataset from the 20-180 µm size fraction  (Pierella Karlusich et al.
We examined these possibilities by looking at the Tara Oceans confocal microscopy dataset, and found many microscopy images evidencing colony formation and symbiosis in the 20-180 μm size fraction (Figure  6E-F ). This is in agreement with the mapping of the Tara Oceans metagenomes against a recently sequenced single cell genome of a Synechococcus living as a dinoflagellate symbiont  (Nakayama et al. , 2019). In addition, there are reports of picocyanobacterial symbionts among isolates of planktonic foraminifers, radiolarians, tintinnids, and dinoflagellates  (Bird et al. , 2017; Foster, Collier, & Carpenter, 2006; Kim, Choi, & Park, 2021; Yuasa, Horiguchi, Mayama, Matsuoka, & Takahashi, 2012)  and picocyanobacterial colonies were observed in a regional study based on optical methods  (Masquelier & Vaulot, 2008)  and in lab cultures (W.
While our work demonstrated that psbO reflects the relative cell abundance of phytoplankton, some previous studies suggested that rRNA genes reflect the relative biovolume of the corresponding taxa. However, there is still no clear consensus for rRNA genes as proxies of biovolume. Here, we did not observe major differences between rRNA gene or psbO when correlated against optical-based biovolumes. In addition to correcting for the abovementioned biases, we revealed unexpected ecological features missed by 18S metabarcoding. For example, our trans-domain comparison detected picocyanobacteria in high numbers in large size fractions, which was supported by the observation of numerous images of picocyanobacterial aggregates and endosymbionts in the Tara Oceans imaging dataset. Moreover, when the analysis of metagenomes includes housekeeping marker genes (in addition to photosynthetic genes), we observed small dinoflagellates (0.
In the short term, a barcode approach using psbO primers is a promising cheap alternative, although it will be subject to PCR biases and affected by the presence of introns. The use of functional genes as taxonomic markers for phytoplankton has been restricted to some surveys (using plastid-encoded genes)  (Farrant et al. , 2016; Man-Aharonovich et al. , 2010a; Paul et al. , 2000; Zeidner, Preston, Delong, Massana, Post, Scanlan, & Beja, 2003). This is not the case for other functional groups, such as nitrogen-fixers, which are studied by targeting a gene encoding a subunit of the nitrogenase enzymatic complex  (Zehr & Paerl, 1998)  and for which extensive reference sequence databases are now available (https://www. jzehrlab. com; (Heller, Tripp, Turk-Kubo, & Zehr, 2014) ). To facilitate the incorporation of psbO into future molecular-based surveys, we have generated a database of >18,000 annotated psbO sequences (https://www.
Acknowledgments We would like to thank all colleagues from the  Tara Oceans consortium  as well as the  Tara Ocean Foundation  for their inspirational vision. We also acknowledge  Quentin Carradec  for his help with genes encoding ribosomal proteins. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (Diatomic ; grant agreement No. 835067 ). Additional funding is acknowledged from the  FFEM -French Facility for Global Environment (Fonds Français pour l'Environnement Mondial) , and the  French Government "Investissements d'Avenir" Programmes MEMO LIFE  (Grant  ANR-10-LABX-54 ),  Université de Recherche Paris Sciences et Lettres (PSL)  (Grant  ANR-1253 11-IDEX-0001-02 ),  France Genomique  ( ANR-10-INBS-09 ), and  OCEANOMICS  (Grant  ANR-11-BTBR-0008 ).
0433°, 16. 7672°; GenBank acc. no. JF920077). The MT-1 genotype was identified by sequencing the mitochondrial cytochrome c oxidase subunit I gene fragment (COI)  (Skoracka et al. 2013). All experimental animals were reared on bread wheat plants, Triticum aestivum L. , variety 'Muszelka', grown in pots from commercially available seeds. Infested plants were kept in cages consisting of metal frames wrapped in fine nylon mesh (micron size 44) to avoid contamination, under laboratory conditions (22-24 °C, ca. 45% RH, L12:D12).
Acknowledgements The authors give special thanks to  Jacek Radwan  from the  Evolutionary Biology Group, Adam Mickiewicz University , for lending a growing chamber,  Jarosław Raubic  from the  Population Ecology Lab, AMU , for his help in conducting experiments, and the company DANKO  Hodowla Roślin  for providing the Triticum aestivum seeds. The study was supported by the  National Science Centre, Poland (NSC) ,  Research Grant  No. 2016/21/B/NZ8/00786. AL scholarships was funded by  European Social Funds   POWR. 03. 02. 00-00-I006/17 , NSC  (PhD Scholarship No. 2019/32/T/NZ8/00151 ) and  Adam Mickiewicz University Foundation  (Awarded in  2019/2020 ).
Funding The study was supported by the National Science Centre, Poland (NSC); Research Grant No. 2016/21/B/NZ8/00786. AL scholarships was funded by European Social Funds POWR. 03. 02. 00-00-I006/17, NSC (PhD Scholarship No. 2019/32/T/NZ8/00151) and Adam Mickiewicz University Foundation (Awarded in 2019/2020).
Objectives: Existing information on Arctic marine food web structure is fragmented. Integrating data across research programs is an important strategy for building a baseline understanding of food web structure and function in many Arctic regions. Naturally-occurring stable isotope ratios of nitrogen (δ 15 N) and carbon (δ 13 C) measured directly in the tissues of organisms are a commonly-employed method for estimating food web structure. The objective of the current dataset was to synthesize disparate δ 15 N, and secondarily δ 13 C, data in the Canadian Beaufort continental shelf region relevant to trophic and ecological studies at the local and pan-Arctic scales.
Data description: The dataset presented here contains nitrogen and carbon stable isotope ratios (δ 15 N, δ 13 C) measured in marine organisms from the Canadian Beaufort continental shelf region between 1983 and 2013, gathered from 27 published and unpublished sources with associated sampling metadata. A total of 1077 entries were collected, summarizing 8859 individual organisms/samples representing 333 taxa across the Arctic food web, from top marine mammal predators to primary producers.
Objective Severe and rapid climate-driven changes in temperature, sea-ice cover, nutrient delivery, and physical mixing dynamics have led to significant changes in species distributions, food webs, and ecosystem function across the Arctic  [1, 2]. Existing information on Arctic marine food web structure is fragmented, such that integrating data across research programs is an important strategy for building a baseline understanding of food web structure and function in many Arctic regions. Trophic level is a primary tenet of food web ecology that describes the vertical structure of food sources supporting top predators. Trophic level can be estimated from stable isotope ratios of nitrogen (δ 15 N) measured directly in consumer tissues because the heavy 15 N isotope typically exhibits stepwise enrichment between predator and prey  [3, 4].
The dataset presented here contains nitrogen stable isotope ratios (δ 15 N) measured in marine organisms from the Canadian Beaufort continental shelf region between 1983 and 2013, gathered from published and unpublished sources. Taxa are included from across all trophic levels, from top marine mammal predators to primary producers. The dataset was compiled for the purpose of cross-validating trophic level estimates calculated from a mass-balance food web model  [5]  with those calculated from observed δ 15 N values, to evaluate how well the model may capture ecosystem structure  [6, 7]. However, δ 13 C data were also retained in the dataset where available because δ 15 N and δ 13 C are commonly used together in ecosystem and food web studies. The dataset may be of broad use for trophic and ecological investigations in Arctic marine ecosystems, either locally or incorporated into pan-Arctic studies.
Data description The dataset generated during the current study is available in the Polar Data Catalogue repository (CCIN Ref #13207), along with a detailed description of data collection methods and references for source publications (Table  1 ): https:// doi. org/ 10. 21963/ 13207  [8]. Readers should use version 2 of the available files (see Table  1 ). A standardized search of primary literature was conducted to compile δ 15 N values measured in marine taxa captured on or near the Canadian Beaufort Sea continental shelf, including marine mammals, fish, benthic invertebrates, zooplankton, and primary producers, as well as organic matter and detritus. The Web of Science electronic database and Google Scholar were searched in 2018 using the phrases: 'Beaufort Sea OR Western Arctic AND δ 15 N OR stable nitrogen'.
Publications were discarded if they did not include raw or averaged δ 15 N data, did not report data for organisms captured on the Beaufort Sea Shelf and/or adjacent regions, did not cover the period from 2013 and earlier, or contained data already reported another publication. To supplement primary literature sources, expert knowledge identified grey literature, dissertations, and unpublished sources containing δ 15 N data generated by field programs led by Fisheries and Oceans Canada, and in collaboration with the University of Waterloo (Waterloo, Ontario, Canada) and University of Manitoba (Winnipeg, Manitoba, Canada). If matching δ 13 C data were available from the resultant publications, they were added to the dataset. Note, however, that primary literature search terms did not include those specific to carbon stable isotopes, and therefore publications containing only δ 13 C data for the region were not included.
A total of 27 sources were retained, from which data were summarized as mean δ 15 N and δ 13 C by taxon, year, and/or sample site. The resulting dataset comprised 1077 entries, summarizing 8859 individual organisms/samples across 333 taxa, sediment, and detritus. All elemental stable isotope ratios ( 15 N: 14 N, 13 C: 12 C) were expressed in standard δ notation as parts per thousand (‰) relative to the international standard atmospheric N 2 for nitrogen and Pee Dee Belemnite (or Vienne Pee Dee Belemnite) for carbon. Detailed methodologies for stable isotope analyses are outlined in the respective publications cited for each entry in the database. Raw data were summarized as presented numerically in the source publications; we did not perform any standardizations (e. g. , isotopic baseline standardizations, mathematic lipid adjustments, etc.
,) and did not estimate data from plots. The following metadata were summarized for each entry whenever available: sample size, error (standard deviation, standard error, and 95% confidence limits), trophic level estimated by the source paper, tissue type analyzed, sampling location, sampling depth(s), sampling station(s), sampling year, and month/season. Information on sample pre-treatment that may influence interpretation of δ 15 N and δ 13 C data (e. g. , acidification to remove inorganic carbon, lipid removal  [13, 14] ) was additionally captured in the dataset, albeit in simplified terms. Users should consult source publications when pre-treatment is relevant to their investigations. Taxonomic names provided in the original source publications were checked for updates or changes on the World Register of Marine Species  [9].
The currently accepted scientific names, authority, and unique Aphia ID were recorded for each entry to increase taxonomic certainty for future applications. Each entry was assigned to one of the trophic groups used in the existing Ecopath with Ecosim model (31 of 36 groups represented;  [6, 10]  A subset of the data pertaining specifically to the continental shelf (< 200 m depths) was used to calculate mean trophic level for each model group, weighted by sample size, using two commonlyemployed stable isotope modelling approaches  [11, 12]. Cross-validation indicated the Ecopath with Ecosim model generally performed well when compared to δ 15 N-derived trophic level estimates, but performance was poorer for trophic groups whose diets have not yet been well-characterized  [7].
| Participants A total of 193 cognitively normal older adults (CN) from Alzheimer's Disease Neuroimaging Initiative (ADNI) projects (http://www. loni. ucla. edu/ADNI/) who completed cognitive assessment, T1-weighted magnetic resonance imaging (MRI), 18F-Florbetapir (FBP) PET, and 18F-AV-1451 (tau) PET at baseline visits were investigated. We excluded nine subjects due to technical limitations of image processing, such as a failure of coregistration between T1-MRI and PET or the failure of the partial volume correction (PVC) process in PET images. All data used in this study was acquired from ADNI-3 project between August 2016 and December 2019  (Weiner et al. , 2017). Cognitive assessment was performed using NP tests, such as Clinical Dementia Rating, Mini Mental State Examination (MMSE)  (Folstein, Folstein, & McHugh, 1975) , Geriatric Depression Scale, Rey Auditory Verbal Learning Test (RAVALT)  (Rey, 1958 ), Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog)  (Mohs et al.
, 1997a (Mohs et al. , , 1997b)) , ADNI composite score for memory (ADNI-MEM)  (Crane et al. , 2012) , and ADNI composite score for executive function (ADNI-EF)  (Gibbons et al. , 2012). We additionally investigated the FU data in twenty CN, who completed the cognitive assessment, T1-weighted MRI, FBP PET and tau PET at the 2-year FU time point, for the validation analysis in this study. Two FU data were excluded due to the failure of the PETpreprocessing. Finally, we defined three groups of CN, with 184 CN at baseline, 18 CN at 2-year FU, and FU-matched 18 CN at baseline, for the longitudinal analysis. FU-matched CN at baseline was subselected from all CN at baseline. Detailed demographic characteristics and NP test scores of all participants are described in Table  1.
| PET acquisition FBP (Aβ) PET scan was performed for 20 min in duration by 4-by-5 min frames and was acquired starting 50-70 min after injection of 10. 0 ± 1. 0 mCi F18-Florbetapir. Tau PET scan was performed for 30 min in duration by 6-by-5 min frames and was acquired starting 75-105 min after injection of 10. 0 ± 1. 0 mCi F18-AV-1451. Both FBP PET and tau PET images were acquired using one of several PET scanners, including GE, Siemens, or Phillips Medical Systems. The detailed methods for FBP PET and tau PET acquisitions are described elsewhere  (Weiner et al. , 2017). We used an averaged single FBP PET and the averaged single tau PET from ADNI database, which were generated by averaging all frames of FBP PET or tau PET image set. All PET images were reconstructed with 1. 02 × 1. 02 mm pixel size and 2.
| PET image processing All FBP PET and tau PET at both baseline and 2-year FU were processed by FMRIB Software Library (FSL; http://fsl. fmrib. ox. ac. uk) and FreeSurfer PetSurfer procedure (FreeSurfer version 6. 0. 0; http:// surfer. nmr. mgh. harvard. edu/fswikiPetSurfer/) to perform coregistration, calculation of SUVR, and to perform PVC  (Greve et al. , 2016). Each individual FBP PET or tau PET image was coregistered to the corresponding native T1-weighted MRI using a rigidbody registration with mutual information cost function. All ROIs that segmented from individual MRI were inversely registered to each individual FBP PET or tau PET by using an inverse transformation of each coregistration matrix. Then, each individual FBP PET or tau PET was scaled by a mean value in the cerebellar gray reference region to calculate the SUVR  (Sepulcre et al.
| Graph theory-based PET correlation within single PET modality and between PET modalities We measured voxel-level correlations within single PET modality and different PET modalities. We created graph theory-based correlation matrices, which defined a node as a voxel in GM and an edge as a partial correlation coefficient between a pair of voxels of PET SUVR data corrected for multiple comparisons in each correlation matrix D withinPET by using a FDR set at q < 0. 05  (Benjamini & Hochberg, 1995).
| Linear association between the similarity scores and cognitive scores To better visualize whether the high similarity scores are significantly associated with the worsening of the cognitive test performances across individuals, we performed linear regression analysis between the similarity scores and cognitive test scores. All linear regression analyses were performed with SurfStat MATLAB toolbox (http://www. math. mcgill. ca/ keith/surfstat). The group difference in slopes of the linear regression lines between the FU-matched CN at baseline and CN at 2-year FU data were tested by analysis of variance (ANOVA) within MATLAB toolbox.
Moreover, we used multicenter ADNI data, which may have a potential bias in PET images caused by multiple scanner types or visit sites. We observed consistency in the cortical distribution of the mean tau-SUVRs and mean FBP-SUVRs across three scanner types in CN at baseline (Supplementary Figure  1 ). We performed one-way ANOVA to determine whether there are any statistically significant differences in the means of PET-SUVRs of GM between the three types of scanner, and we found no significant difference in mean tau-SUVR or mean FBP-SUVR between three types of scanners (Supplementary Figure  2 ). No meaningful trends of group difference between two groups in the mean PET-SUVRs was noted (Supplementary Figure  2 ). Nevertheless, future studies should apply similar network PET-based strategies within unicenter data to avoid potential scanner biases.
Our study has an additional limitation: we mainly focused on a large CN sample at baseline, and a relatively small longitudinal sample from ADNI-3 study dataset. Therefore, larger number of FU individuals should be used and tested in future studies. We examined network interaction patterns within single PET modality, such as Aβ-to-Aβ or Tau-to-Tau correlations, and between different PET modalities, such as Aβ-to-Tau or Tau-to-Aβ, at highresolution (voxel-level) in CN, using a graph theory-based analysis. We observed that the PET uptakes derived from Aβ-to-Tau interdigitations were significantly associated with ADAS-Cog in AD vulnerable brain areas, a finding confirmed by our longitudinal investigation. Therefore, our work suggests the preceding contribution of network interactions between Aβ and tau deposits to explain initial cognitive changes in CN prior to the conversion of dementia.
DATA AVAILABILITY STATEMENT Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_ List. pdf.
Other reporting guidelines addressing a specific surgical technique from other clinical specialties are much less stringent, such as the CORDES  (27)  for deep endometriosis surgery. Many reporting guidelines in surgery poorly describe the methodology used, e. g. , many do not report which databases they have searched  (20) (21) (22) (23) 26, 27). While reporting guidelines for surgical technique exist with incomplete methodological reporting, inconsistent coverage of a surgical discipline, and varying concerns and requirements for surgical technique, a systematic review or scoping review of reporting guidelines for surgical techniques is not found after searching in PubMed and Google Scholar. Due to this gap, there is no way to systematically know the reporting guidelines for surgical techniques with regards to the overall number, the distribution of disciplines, the methodological rigour with which they are developed, the focus on steps, and the extent to which they required detailed description, etc.
Methodology 10. A clear description of methodology Extract by Yes or No. If yes, whether the guideline was developed in accordance with established methodologies. Extract the methodology used. 11. A detailed description of the development team Whether the guideline has reported the team who developed the guideline including country, numbers of team members, specialty, and gender, including those unsigned as authors. 12. A detailed description of the steps to develop the guideline Extract by Yes or No. 13. Steps of developing the guideline If the guideline was developed in accordance with Equator recommended steps/Moher et al. steps (30), extract by Yes or No regarding step 1-6 and 8-11. For step 2, extract whether the guideline has reported databases they have searched; if yes, extract the names.
Genome features Two genomes with similar coding capacities. A total of 10,631 putative genes were predicted on the raw assembly, which could be splitted into two sets of similar size: 5270 genes in genome A vs. 5361 genes in genome B (Table  1 , Figure  2 ). The completeness of both A and B genomes was addressed by using the BUSCO software  29  and the Apicomplexa geneset (n=446). Genomes A and B respectively showed a completeness score of 70% (n=312) and 67. 7% (n=302) using this Apicomplexa geneset (all BUSCO's scores are shown on Figure  S4 ). Orthologues were searched between A and B. The proteins of P. cf. gigantea A and B were splitted into 5656 orthogroups including 4443 groups (88%) with at least one orthologous gene for both A and B. This percentage of common orthogroups between genomes A and B is higher than the percentage of common orthogroups observed between P.