dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and Limitations (Tool and Approach) Pictures helped the respondents to estimate their intake. Only 20 picture series were taken due to limited access to campus as a result of the COVID-19 lockdowns. Only small groups of participants were allowed to take part in the evaluation. However, overall, 1889 of the 2981 items (63%) in the database were linked to portion pictures (20 French portion pictures and 40 original myfood24 series). The aerial portion pictures were marginally more helpful to the French population for their estimation of consumption (on a smartphone). The current database is not big enough to represent the diversity of the French food market. However, the database gives a good overview of food intake in France and features more items than the official food composition tables, and more branded items are currently being added to the database.
Recommendations This is an exciting step forward for French dietary assessment, providing the first online food diary system. However, the database's size should be increased. The database used in this work does not represent French cuisine's diversity. Many commonly consumed items are missing. The French eat or drink a majority of branded items, yet they currently represent only 7. 5% of the database tested in this paper. Adding such elements would be a considerable asset. Since this project, over 10,000 branded items and 60 recipes have since been added to the first version of myfood24 France. Adding more food picture series would be a good addition to the database. The 20 French portion picture series that have been taken help reflect the French dietary habits. So far, 28% (60 out of 211) of the portion groups used in myfood24 France are available with photos.
Conclusions We have developed the first version and database for myfood24 France, a 24HDR method, which is compatible with other versions for international studies. In the version tested here, there were more than 3000 French foods in the database, and portion sizes were adapted to French dietary habits. Different approaches to the presentation of food portion pictures were tested. Overall, aerial photographs allowed a slightly better perception of portion sizes; however, the differences with angled photos were not big enough to be statistically significant (except for one item). The pictures were validated on small samples against EFSA criteria. The tool's usability was evaluated and resulted in a "good" system usability. The current version still needs further validation (including with more diverse groups) and improvement, for example, with the search function, and more pictures would be needed.
This topic was addressed in the framework of the HYD2M program. 2 WCI were studied using in-situ and High-Frequency (hereinafter referred as HF) radar measurements as well as by the use of numerical models. Cross-comparisons were carried out. The one-year of in-situ data have revealed that wave effects were absent during only 6% of the time from June 2017 to July 2018 (i. e. significant wave height (Hs) less than 0. 5 m and wind speed lower than 4 m s -1 during 22 days over 365 days) as reported in  Furgerot et al. (2020). This shows that most of the time it is necessary to consider wave effects in studies. Strong vertical shears due to waves (Stokes drift and wave-breaking turbulence) have been measured in the upper-half of the water column. In the same time, wave effects change both bottom friction and turbulence.
The velocity data were 10 min averaged to compare with numerical simulations and reach 3. 5 m s -1 during flood time period. The flow direction is oriented towards north-east and south-west for flood and ebb, respectively. At this place and time, tidal range varies from 7 to 8 m with a mean depth of 35 m. Data from Met-Office wave buoy (62103), available on EMODnet platform (http://www. emodnet. eu), were also used for comparison with the wave model results. Wind measurements at the Goury semaphore have been tested against the numerical wind forcings from Météo-France.
Numerical configuration and experiments The horizontal computational grids of MARS and WWIII are similar with an horizontal resolution of 600 m and it covers the spatial domain going from 47 • 53 ′ 60. 0 ′′  / 6 • 03 ′ 32. 4 ′′ W to 50 • 27 ′ 0. 0 ′′  / 0 • 43 ′ 12. 0 ′′ W (Fig. 2 ). Even if simulations for Alderney Race at finer resolutions (120 m) compared with in-situ data  (Bennis et al. , 2020)  highlighted very good agreements, we selected a low spatial resolution in order to: (1) be able to compare numerical results with HF radar data at 750-m of radial resolution in the future and, (2) reduce the computing time. Moreover, it is interesting to know if the model is accurate and relevant for a such spatial resolution. MARS simulations are threedimensional with 25 sigma levels evenly distributed over the vertical.
Tide effects Wind effects Wave effects Wave-current coupling Run A X X - no Run B X - - no Run C X X X yes, TW Run D X X X yes, OW The tidal forcing of MARS is based on the Shom CST France atlas that includes 114 tidal components  (Leroy and Simon, 2003). MARS is forced by the wave forcing terms computed by WWIII (Eq. (  1 ) and more details in  Bennis et al. , 2011). WWIII uses 32 frequencies from 0. 04 Hz to 0. 7678 Hz and 24 directions leading to a directional step of 15 •. WWIII is constrained at its open boundaries by bi-dimensional (frequency-direction) wave energy spectra from the HOMERE and Ifremer databases  (Boudiere et al. , 2013). According to the coupling mode, WWIII is forced by surface current and water level computed by MARS. Wind at 10-m height above the sea from the Meteo-France re-analysis (ALADIN atmospheric model) is used to force MARS and WWIII at the surface.
10. Numerical results for Run A, B and C, are compared to data. Three time periods (TP1, TP2 and TP3) are particularly studied (Fig. 12 ). They have been chosen for their different wind conditions (wind speed is greater than 15 m s -1 for TP1 and TP3 and smaller than 15 m s -1 for TP2). Flow velocity ranges from 0. 15 m s -1 to 3. 1 m s -1 in the dataset. A tidal asymmetry is observed, as reported in the bibliography, with a more intense flood flow (about 3%) compared to the ebb flow. However, the study of the velocity profiles of the pure tidal case (Fig. 10b ) and of the case with waves (Fig. 10c ) highlights that wave effects affect this asymmetry by reducing the ebb flow velocity. This reduction is clearly visible in the dataset during the TP1 and TP3 time periods, where the wind velocity at 10-m height is greater than 15 m s -1 (Fig.
Conclusions The impact of a winter storm during spring tide on the hydrodynamic and tidal stream energy in AR was studied for the first time here. The excellent scores obtained for the validation of the numerical model with in-situ data have revealed the good capacity of the coupled numerical model, MARS-WWIII, for simulating the hydrodynamic for this time period. However, data are limited in geographic and temporal scope and it is possible that lower scores are obtained at other locations. In the future, comparisons with spatialized data (e. g. HF radar data) will be carried out for completing the validation of the model. Our results have shown that ocean waves during the Eleanor's storm have significantly affected the flow although they occurred during spring tide. With such tidal conditions, one may have though that the wave effects on the flow would have been negligible.
Acknowledgments Authors are supported by the  HYD2M  project ( ANR-10-IEED-0006-07 ) funded by the  program called ''Investissements d'avenir' ' for the building of  France Energies Marines. Authors are grateful to  F. Ardhuin  for providing the WAVEWATCH-III forcing fields. Results acquired with the Ifremer MARS software. Simulations have benefited of the computing facilities of CRIANN.
Preparation of Immune Cells for Fluorescence Activated Cell Sorting After perfusion with PBS, the mice heart was excised and digested in HBSS medium containing 2. 5 mg/mL collagenase D (Roche, Meylan, France) for 30 min at 37 • C, with stirring. Erythrocytes were lysed by using red blood cell lysis buffer (MiltenyiBiotec, Paris, France). As shown in Supplementary Materials Figure  S1 , Cardiac immune cells were isolated by centrifugation, enriched by immunoselection using an anti-CD45 antibody coupled to magnetic beads (MiltenyiBiotec, Paris, France). Samples were blocked with Fc block (Ebioscience, Paris, France) prior to labeling with antibodies. Cytometry data were acquired on a BD FACSAria II cell sorter. After gating on CD11b + cells, doublets were excluded and live (PI exclusion) CD14 + /CD64 + macrophages were sorted directly into RLT lysis buffer (Qiagen, Les Ulis, France) or water and kept at -80 • C until RNAseq or lipidomic analysis, respectively, as shown in Supplementary Materials Figure  S1  and as previously reported  [5].
RNA Sequencing and Statistical Analysis Total RNA from FACS sorted CD64 + /CD14 + cells was isolated using the Nucleospin RNA XS kit (Macherey Nagel, Hoerdt, France), according to the manufacturer instructions. cDNA libraries were generated using total RNA with SMART-Seq v4 Ultra Low Input RNA Kit (TAKARA) and constructed according to manufacturer protocols as previously reported  [9]. Paired end sequencing (2 × 750 bp) was performed by Nextseq 500 machine using High Output kit (150 cycles). Raw sequencing data was quality-controlled with the FastQC program. Trimmomatic was used to remove adapter sequences, trim low-quality reads, and discard reads shorter than 40 bp. Reads were aligned to the mouse reference genome (build mm10) with the TopHat2 tool. Mapping results were quality-checked using RNA-SeQC.
Aligned reads were counted using the FeatureCounts and Express software, at the gene-level and transcript-level, respectively. Normalization and differential statistical analysis were performed with the GLM EdgeR package. RNA-Seq data has been made publicly available through the NCBI Gene Expression Omnibus (GEO), GEO accession number GSE157035, as previously reported  [5]. Gene Analyses were performed using Ingenuity Pathway Analysis (IPA) (version 448560M) (Qiagen, les Ulis, France) and Metascape software (developped by Zhou et al. [10] ).
LC/MS Analysis Lipids were quantified by LC-ESI/MS/MS using a Prominence UFLC (Shimadzu, Tokyo, Japan) and QTrap 4000 mass spectrometer (AB Sciex, Framingham, MA, USA) equipped with a turbo spray ion source (450 • C) combined with an LC20AD HPLC system, a SIL-20AC autosampler (Shimadzu, Kyoto, Japan) and the Analyst 1. 5 data acquisition system (AB Sciex, Framingham, MA, USA). Quantification of phospholipids, sphingolipids and neutral lipids was performed in positive-ion mode. Sample (4 µL) was injected to a Kinetex HILIC 2. 6 µm 2. 1 × 150 mm column (Phenomenex, CA, USA). Mobile phases consisted of water and acetonitrile containing ammonium acetate and acetic acid. Lipid species were detected using scheduled multiple reaction monitoring (sMRM). N 2 was used as nebulization and collision gas.
Statistics Analysis Echocardiographic, hypertrophic remodeling and lipidomic data analysis was performed with GraphPad Prism 8 (GraphPad software Inc. , San Diego, CA, USA), using Kruskal-Wallis followed by Dunn's post-hoc test. Quantitative data are reported as means ± SEM. For RNAseq data, normalization and differential statistical analysis were performed with the glm edgeR package. Additional comparison between ECH and HF groups was performed with GraphPad Prism 8, using Mann-Whitney test.
Statistics Analysis Echocardiographic, hypertrophic remodeling and lipidomic data analysis was performed with GraphPad Prism 8 (GraphPad software Inc, San Diego, CA, USA), using Kruskal-Wallis followed by Dunn's post-hoc test. Quantitative data are reported as means ± SEM. For RNAseq data, normalization and differential statistical analysis were performed with the glm edgeR package. Additional comparison between ECH and HF groups was performed with GraphPad Prism 8, using Mann-Whitney test.
The present study aimed at performing an in-depth compar- Mice (n = 4/group) were characterized according to the following criteria: cardiac hypertrophy (ECH and HF groups) based on the HW/TL parameter, normal (ECH group) or altered (HF group) cardiac function based on the FS parameter and presence (HF group) of dilation based on the LVd parameter (Figure  1B ). Transcriptomic data analyzed in this study were obtained from the RNA sequencing of cardiac CD64 + macrophages (gating strategy described in Supplementary Materials Figure  S1 ) isolated from Ct, ECH, and HF mice, as previously published  [5]. This initial study by Flamant et al. , focused on ECH macrophages and the role of the CX3CL1/CX3CR1 axis in the development of ECH. The present study aimed at performing an in-depth comparison between Ct, ECH, and HF global transcriptomic profiles to highlight ECH or HF macrophages signatures versus genes whose expression was progressively impacted by Iso treatment (RNAseq data publicly available through the NCBI Gene Expression Omnibus (GEO), GEO accession number GSE157035).
Using a threshold of twofold change and false discovery rate (FDR) < 0. 05, RNAseq identified 413, 152, or 94 genes selectively upregulated and 158, 46, or 143 genes selectively downregulated, in Ct, ECH, or HF macrophages, respectively (Figure  2A ). ison between Ct, ECH, and HF global transcriptomic profiles to highlight ECH or HF macrophages signatures versus genes whose expression was progressively impacted by Iso treatment (RNAseq data publicly available through the NCBI Gene Expression Omnibus (GEO), GEO accession number GSE157035). Using a threshold of twofold change and false discovery rate (FDR) < 0. 05, RNAseq identified 413, 152, or 94 genes selectively upregulated and 158, 46, or 143 genes selectively downregulated, in Ct, ECH, or HF macrophages, respectively (Figure  2A ). Figure  2B  shows the heat map illustrating selective ECH and HF regulated top genes (as compared to Ct and HF or Ct and ECH counterparts, respectively) and Figure  2C  the associated IPA analysis of canonical pathways.
Acknowledgments: The authors would like to thank  Bocar Kane  and  Maria Mihoc  for their assistance in animal care and surgical procedures,  Bénédicte Hoareau-Coudert  and  Catherine Blanc  from the  CYPS cytometry platform (La Pitié Salpétrière );  Magalie Fradet  and  Florence Deknuydt  from the  CYTO-ICAN cytometry platform (La Pitié Salpétrière );  Justine Guegan  and  Yannick Marie  from the genotyping and sequencing platform at the  ICM institute (La Pitié Salpétrière );  Romain Daveau ,  Beata Gyorgy , and  Yvan Moszer  from the ICONICS platform at the  ICM institute (La Pitié Salpétrière );  Marie Lhomme ,  Maryam Darabi , and  Maharajah Ponnaiah  from the  ICANalytics Lipidomic platform (La Pitié Salpétrière ).
1B ). We archived the full text and metadata of the HIPPYs in a Zotero web folder (Supplementary Dataset S1); a public version of the folder (that we named 'HIPPY collection') containing only the publication metadata (for copyright compliance) is accessible at the web address below. The 170 HIPPYs represent only 0. 2% of all the research articles and reviews published in the field of molecular plant immunity between 2000 and 2019, but they account for 4. 22% of the citations in this field over the same period (Fig. 1C ). HIPPYs published in 2019 received on average 29 ± 6 citations, while HIPPYs published between 2000 and 2010 received on average 1,044 citations (ranging from 217 to 5,730) (Fig. 1D ). On average, HIPPYs receive 64 citations per year (i. e. , annual citation rate of 64); reviews showing, on average, a higher cita-tion rate than research articles (81 vs.
C Step 1. To identify highly influential publications in plant immunity (HIPPYs) using Web of Science searches, and archive HIPPYs & metadata using Zotero (aka HIPPY Corpus). Key words: plant immunity; plant defense; plant pathogen Article types: research article; review article Publication period:  2000 -2004; 2005 -2008; 2009 -2012; 2013 -2015; 2016 -2017; 2018; 2019 HIPPY collection:  https://www. zotero. org/groups/4370831/hippy_collection/library Step 2. To answer key questions about HIPPYs, to better understand HIPPY science (i. e. discoveries, concepts, model objects) and HIPPY research community (journals, institutions, labs). Question 1. Who publish HIPPYs (journals, institutions, countries)? Fig. 1. Overview of the 170 highly influential publications in plant immunity (HIPPYs). A, Overview of the two-step analytical pipeline used in this study.
In Step 1, we identified 170 HIPPYs by using advanced searches on the Web of Science portal using various keywords, article type, and publication period parameters (details can be found in Supplementary Methods S1, section A). We then collected and archived the PDF version and metadata of the 170 HIPPYs into a Zotero folder (available from the HIPPY collection website) according to the search output. Then, in step 2, we analyzed the HIPPY collection by answering key questions, with the aim to better understand both the scientific knowledge brought to readers by HIPPYs (i. e. , the science) and the structure of the research community behind HIPPYs (i. e. , the people, labs, and institutions 21 countries (Supplementary Dataset S1, columns I to N; Supplementary Methods S1, section B). Interestingly, a small number of those journals, institutions, and countries contributed most of the HIPPYs (Fig.
HIPPYs ADDRESS NINE MAIN RESEARCH QUESTIONS To get an analytical understanding of HIPPYs, we aimed at identifying the research questions they address. To do so, we performed iterative readings of all the manuscripts combined with keyword tagging to categorize HIPPYs into topics and subtopics (Fig. 3A ; Supplementary Dataset S1, columns O to R; Supplementary Methods S1, section C). This analysis grouped the 170 HIPPYs into nine main topics as follows: i) pathogen recognition (25 HIPPYs; how do plants sense pathogens?), ii) immune signaling (24 HIPPYs; how do plants signal pathogen recognition?), iii) phytohormonal modulation (33 HIPPYs; how do hormones modulate immune signaling?), iv) gene regulation (nine HIPPYs; what is the genetic program of defense, and how is it activated?), v) defense responses (21 HIPPYs; how do plants effectively fend off pathogens?), vi) pathogens and pathogenicity (20 HIPPYs; how do pathogens manipulate and colonize host plants?), vii) microbiota and symbionts (22 HIPPYs; how do plants and their associated microbes interact?), viii) general reviews (15 HIPPYs; how do we conceptually understand plant immunity as a whole?), and ix) translational research (one HIPPY; how to develop better plants for agriculture? [Fig.
Box 1. Highly influential publications in plant immunity (HIPPYs) and the Top 10 unanswered questions in molecular plant-microbe interactions. In 2020, Harris and colleagues established a communitysourced list of the Top 10 fundamental questions in molecular plant-microbe interactions that will likely drive research investigations in the 2020s  (Harris et al. 2020). Such questions were ranked from Q1 to Q10 based on the number of votes that they received from the attendees of the 2019 International Congress on Molecular Plant-Microbe Interactions in Glasgow, U. K. To compare this Top 10 list with the nine HIPPY topics from this study, we systematically associated the HIPPYs and the topics with at least one of the Top 10 questions to which it pertains the most (Fig. 3A ; Supplementary Dataset S1 [column Z]; Supplementary Methods S1 [section G]).
3C ). This illustrates well the research ambition within the 2020s to tackle molecular plant immunity in a broader context and to translate findings into real-world agricultural innovations to promote crop health  (Harris et al. 2020; Michelmore et al. 2017). thaliana, fungus, or symbiont [Supplementary Fig. S2 ; Supplementary Datasets S1, columns S and T, and S2; Supplementary Methods S1, section D]). This analysis allowed building a word cloud comprising the 200 most frequently used words referring to living or molecular objects in the titles and abstracts of HIPPYs (Fig. 4A ). The largest words in the cloud highlight the prominence of key groups of plant-associated organisms (e. g. , bacteria, fungi, Pseudomonas), model plants (e. g. , A. thaliana, rice), nonproteinaceous molecules (e. g.
, jasmonate, hormones, RNA), or proteins (e. g. , NPR1, FLS2). To gain a more accurate view of the molecules and organisms central to each topic, we built word clouds for each topic, except for the general review and translational research topics (Supplementary Fig. S2B ). Some words, such as pathogen, occurred in all clouds, while others showed a topic-specific enrichment (e. g. , jasmonate, SA, and JA, as well as kinases, MAPK, and ROS for the phytohormonal modulation and immune signaling topics, respectively). To complement the above-described approach, we executed an expert reading of the HIPPYs to determine the key plant and plant-associated organisms emphasized (if any) by individual HIPPYs. This analysis first revealed that nearly 75% of the HIPPYs highlight at least one organism (or one group of organisms; Supplementary Dataset S1, columns U and V).
In total, we found that 108 HIPPYs (i. e. , over 63% of the HIPPY collection) connect with at least one other HIPPY; those 108 HIPPYs form a fragmented network comprising 174 links in total (Supplementary Figs. S4A  and S5 ; Supplementary Dataset S1, columns W to Y). The most abundant type of link was coinciding publication (49 HIPPYs), followed by follow-up review (19) and follow-up studies (16). Notably, 24 HIPPYs show multiple types of links (e. g. , simultaneously coinciding papers and follow-up review). Interestingly, those highly connected papers received more citations than isolated HIPPYs (annual citation rate of 90 vs. 52 on average; the number of links positively correlating with the annual citation rate [Supplementary Fig. S6] ). We further investigated the network of connected HIPPYs for each individual topic.
CROSS-CITATION ANALYSIS REVEALS HIPPY RESEARCH COMMUNITY STRUCTURATION To evaluate the structure of the HIPPY research community, we aimed at evaluating the transfer of information between A no emphasis (  69  topics by using cross-citation patterns as a proxy. To this end, we implemented a recently described method for cross-citation analyses (Petre 2020), which calculates a citation score that reflects how often a given group of review articles cite a given group of authors (Supplementary Methods S1, section F). Here, we considered the 80 HIPPYs that are review articles and their matching 78 corresponding authors, grouped according to their topic (Supplementary Fig. S4D ; Supplementary Dataset S3). Globally, this analysis revealed high citation scores within topics (i. e. , intratopic citation, average citation score of 1.
2017). As a concluding note, we encourage readers who wish to broaden their understanding of a given research field to implement our HIP in method to their research area of interest by using Supplementary Dataset S1 as a working template  (Louet et al. 2021 ). Among other things, such studies may help academics to apprehend and compare the structure of the research communities in which they work, which would better inform their strategic decisions, both research-and career-wise. Noteworthy, we encourage readers who want to develop a deeper expertise of a given field to complement the use of the HIP in method with a further exploration of the literature (i. e. , an exploration that goes beyond the highly cited papers), in order to access a more diverse literature and to diversify the knowledge and conceptual frameworks to which they are exposed.