dataesr/scientific-paragraphs-categorization huggingface dataset:
Dietary intake assessment Informal dietary questions (e. g. , "in an average week, how often do you consume meat/eggs?") from the AGP general questionnaire were available. In addition, long-term dietary intake was evaluated in a subset of 1948 participants who completed the VioScreen FFQ (version 4; VioCare) between July 2012 and June 2019. This web-based, graphical, and self-administered dietary assessment tool has been previously validated for use in general US adult populations  (22)  and utilizes the Nutrition Data System for Research (Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN) to estimate nutritional compositions from VioScreen FFQ entries. Several types of dietary information were derived from raw VioScreen entries, including: custom food items and food groups (described in Supplemental Methods and Supplemental Table  1 ), total energy intake, micro-and macronutrient intakes, and MyPyramid Equivalents Database (MPED) values  (23).
Overall diet quality was assessed using the Healthy Eating Index 2010 (HEI-2010)  (24) , which was available in the database. In addition, to ensure a high level of quality for the dietary intake data, we applied several filtering criteria to the VioScreen FFQ reports. We included reports in analyses if they met the following criteria: 1) a minimum of 25 food items consumed  (22) ; 2) a total energy intake between the 5th and 95th gender-specific percentiles from NHANES, as proposed previously  (3, 25) ; and 3) completion of the questionnaire in a window of time between 10 and 20,000 minutes (around 2 weeks).
Dietary pattern analysis The term dietary pattern is often used to describe the set of dietary behaviors that characterizes a group or subset of study participants. The same term can also refer to quantitative scores originating from a combination of several diet variables, called factors  (26). In this study, we use the term dietary patterns to describe both. We analyzed long-term diets based on VioScreen FFQ data using both a priori and a posteriori analyses. First, we defined a priori patterns based on 3 diet features: 2 individual dietary components (total dietary fibers and proteins) and HEI-2010 scores. We derived diet groups as quartiles based on total dietary fiber or total protein intake. For fibers, we analyzed the daily total dietary fiber intake (g/d) and their type (defined as the ratio g/d of soluble fiber:g/d of insoluble fiber), and created a combined indicator (dietary fiber quantity:dietary fiber type), which can simply be interpreted as fiber quantity normalized by fiber type.
For protein, we focused on the total daily intake of protein of both animal and vegetal origins, as well as their ratio (g/d of animal protein:g/d of vegetable protein). Moreover, as an a priori factor to evaluate overall diet quality, we used the HEI-2010 total score, a composite score based on 12 components that measures adherence to the 2010 US Dietary Guidelines for Americans (DGA)  (24). In addition, we applied factor analysis to identify nonredundant reduced sets of variables (i. e. , factors), that are linear combinations of the original variables and explain most of the variability in the data set. We identified factors based on MPED component values, micronutrients, and food items. In the case of MPED values and micronutrients, we first normalized variables by total energy intake and filtered those that were considered redundant (Pearson correlation above 0.
95). We then used a principal component analysis with varimax rotation [psych v2. 0. 9 R package  (27) ] and chose the number of factors by parallel analysis of the correlation matrix [nFactors v2. 4. 1 R package  (28) ]. Given the sparsity (69%) of the data, we built factors for food items using a principal coordinate analysis (PCoA) on the Jaccard distance between participants (computed from presence/absence information; Supplemental Table  1 ). We selected the number of factors by the Kaiser rule, which, in brief, retains factors with eigenvalues greater than 1 [nFactors v2. 4. 1 R package  (28) ]. Finally, we built a posteriori diet groups from food group data expressed in kcal/d (Supplemental Table  1 ). We used Dirichlet Multinomial Mixture (DMM) models [DirichletMultinomial v1. 30. 0 R package  (29) ], which apply a compositional approach for clustering (i.
e. , based on the relative kcal/d contribution; see Supplemental Methods). We defined our diet groups based on a robustness criteria (Supplemental Figure  1 ) and chose the largest number of groups that allowed a mean stability above 0. 6 in each of them [using 50 bootstrap and Jaccard similarity with the fpc v2. 2-8 R package  (30) ]. Moreover, to increase confidence in the group categorization, we defined core diet groups as participants having a probability above 0. 8 of belonging to a particular group.
(35) , were removed, and samples with fewer than 1000 reads left after filtering for blooms were excluded. We then rarefied the obtained amplicon sequence variant (ASV) table to a depth of 1000 sequences per sample in order to compute several alpha (within-subjects) and beta (between-subjects) diversity indices (Supplemental Methods). Subsequently, we performed taxonomic assignment for ASVs on nonrarefied data using the sklearn-based taxonomy classifier trained on the Greengenes reference database 13_8  (36) , and we aggregated data at the genus level for further analyses. We excluded 1580 samples flagged as outliers (Supplemental Methods). In cases where participants had submitted multiple fecal samples, we selected a unique sample based on 16S data availability, metadata completion, and the shortest time between sample collection and VioScreen FFQ completion date.
In addition, for diet groups, we verified that the observed differences were not due to dispersion heterogeneity through beta-dispersion tests. All beta-diversity analyses were performed using the vegan v2. 5-6 R package  (37). Finally, we filtered out 409 genera with a low prevalence (present in less than 10% of samples) or low relative abundance (mean below 0. 01%), and analyzed the 111 remaining genera for differential abundance. We used DESeq2 (v1. 28. 1) models with the poscounts option  (38) , including confounding variables and diet groups or factors effects. We estimated the global diet groups effects by likelihood ratio tests and the factor effects, as well as diet groups 2 by 2 comparisons, by Wald tests. Log2 fold change results are expressed as the estimate ± SE. In addition, we evaluated the robustness of our results using Songbird  (39) , an alternative statistical method often used in the microbiome field to account for data compositionality (Supplemental Methods).
General statistics Data are expressed as medians (IQRs) for quantitative variables and as n (%) for qualitative variables. We used Kruskal-Wallis and chi-squared independence tests to compare quantitative and qualitative variables, respectively, between groups with the compareGroups v4. 4. 5 R package  (40). We handled multiple testing adjustments with the Benjamini-Hochberg procedure  (41)  (see details in table legends and figure captions) and we used a 2-sided 5% alpha error threshold. All analyses were performed in R v4. 0. 3  (42)  except Songbird, which was run with Python 2. 6.
We used permutation tests (9999 permutations). There was multiple testing adjustment by Benjamini-Hochberg on the global effects obtained with the 4 indices (diet groups) or on the global effects obtained with the 5 indices * k factors (factors). Evidence [-log10(P value)] cannot be higher than 4 due to the permutation scheme. The Fk factors are from the corresponding data set as described in Supplemental Tables  4 5 6. NS: P value ≥ 0. 1; trend: 0. 05 ≤ P value < 0. 1; significance: P value < 0. 05. Abbreviations: ait, Aitchison distance; bc, Bray-Curtis dissimilarity; db-RDA, distance-based redundancy analysis; DP5, 5 dietary patterns; Fk, factor number k; HEI-2010, Healthy Eating Index 2010; IBD, inflammatory bowel disease; MPED, MyPyramid Equivalents Database; NS, not significant; rRNA, ribosomal RNA; uUni, unweighted UniFrac distance; wUni, weighted UniFrac distance.
Dietary patterns most associated with gut microbiome To identify the dietary patterns that were most associated with the gut microbiome in this cohort, we utilized both a priori and a posteriori approaches and constructed patterns in the form of diet groups or factors (Figure  1B ). We first derived a priori diet groups from individual dietary components (fibers and proteins; Supplemental Table  3 ), and then looked for a more global representation of habitual diet through combinations of food variables. To this end, we used the HEI-2010 total score, which combines both food groups and individual nutrients. A posteriori factors identified from MPED components, micronutrients, and food items led to 3 sets of 8, 15, and 8 factors explaining 54%, 75%, and 22%, respectively, of the variance in their data set (Supplemental Tables 4-6; Supplemental Figure  2 ).
Last, our cluster-based global-diet analysis on food groups data resulted in 5 diet groups, further refined into 5 core diet groups including only participants with the highest probability of belonging to their group. These core diet groups are hereon denoted as DP5 (5 dietary patterns), and were defined for 1466 Descriptions of the US adult participants who were analyzed for dietary patterns (study cohort) and who were analyzed for gut microbiome associations (microbiome cohort). Data are presented as medians [IQRs] for quantitative variables and as n (%) for qualitative variables. Missing values were excluded for the percentage calculation. Data from NHANES adults are given as a proxy for the general US population (Supplemental Methods). Participants with antibiotic intake in the last year, declared IBD, liver diseases, diabetes, or specific autoimmune diseases were excluded from the microbiome cohort to minimize confounding effects.
Characterization of the DP5 We identified 5 dietary patterns based on energy-adjusted food groups. Two of the patterns were most similar to a previously described Prudent diet  (45) , a second set of 2 patterns were most similar to Western-style diets, and the last to an Exclusion diet. An additional analysis of the data via PCoA confirmed that the Prudent patterns were similar to each other, while the 2 Western-style patterns exhibited the greatest amount of overlap in dietary intake (Supplemental Figure  4 ). In addition, the Exclusion diet pattern appears to contain the most distinct and the most variable dietary habits of the 5 eating patterns. Participants categorized in the Prudent-like dietary patterns presented lower consumption of animal products and higher consumption of fruits, whole-grain cereals, legumes, and nuts (Figure  2 ).
Evidence [-log10(P value)] cannot be higher than 4 due to the permutation scheme. The Fk factors are from the corresponding data set as described in Supplemental Tables4-6. NS: P value ≥ 0. 1; trend: 0. 05 ≤ P value < 0. 1; significance: P value < 0. 05. Abbreviations: ait, Aitchison distance; bc, Bray-Curtis dissimilarity; db-RDA, distance-based redundancy analysis; DP5, 5 dietary patterns; Fk, factor number k; HEI-2010, Healthy Eating Index 2010; IBD, inflammatory bowel disease; MPED, MyPyramid Equivalents Database; NS, not significant; rRNA, ribosomal RNA; uUni, unweighted UniFrac distance; wUni, weighted UniFrac distance.
Data Availability The data used in this study are available publicly in Qiita (https: //qiita. ucsd. edu/) under the study ID 10317, and the associated sequences can be found under EBI accession ERP012803. Raw VioScreen data can be downloaded at ftp://ftp. microbio. me/Am ericanGut/raw-vioscreen/. The source code for defining the a posteriori diet groups with Dirichlet Multinomial Mixture models and for associating them with the 16S microbiome can be obtained at: https://github. com/danone/dp5. analysis.
These characteristics are then compared with corresponding estimates of sea surface height velocity derived from 1-Hz altimetry data sets from three missions: Jason 2 (Ku-band LRM), SARAL (Ka-band LRM) and Sentinel-3A (SAR). The data from all missions were processed with the coastal-specific X-TRACK strategy. We show that near Toulon, the model is very close to the observations in terms of current estimates, providing a very good reference for altimetry data located in this area. The Northern Current is observed 15 km to the coast on average, with a mean core velocity of 0. 44 m s -1. Its signature in sea level consists of a drop whose mean value at 6. 14°E is 6. 9 +/-2. 2 cm extending over 18 +/-4 km. These variations show a clear seasonal pattern, but high frequency signals are also present most of the time.
In this paper, we propose a different strategy based on a high resolution numerical model. Our purpose is to quantitatively analyze if a particular coastal dynamical structure can be observed with satellite altimetry. Using a high resolution model may help to overcome the issue of colocation between in situ and altimetry, but given the essential condition that the physical process studied must be correctly represented by the model. After a careful model validation step in the study region, our approach will consist in using the model to quantify the SSH signature of an identified physical process along a particular satellite track. In a second time, the3 model solution will be considered as the reference and compared with the SSH signature captured in the altimetry dataset along the considered tracks.
, 1995). In the past, the NC variability has been intensively studied with in situ observations and models: mesoscale fluctuations in  Sammari et al. (1995)  ; eddies associated in  Casella et al. (2011 ), Hu et al. (2011)  and  Schaeffer et al. (2011). Birol et al. (2010)  have highlighted the contribution of along-track satellite altimetry to study the NC variability. Since then, other studies have used such data to investigate the NC circulation as well as the recirculation and associated meanders  (Borrionne et al. , 2019; Morrow et al. , 2017; Pascual et al. , 2015). Here, we will investigate in details the NC observability issue for three altimetry missions associated to different techniques: Jason 2, with the classical Ku-band LRM nadir altimeter, SARAL which uses the Ka-band frequency in LRM mode and Sentinel-3A (Sentinel-3 hereinafter) with its Synthetic Aperture Radar mode.
2. 1. a) HF radars We took advantage of the 2 years of data, from May 2012 to September 2014, provided by the HF Wellen radar (WERA) instruments installed near Toulon as part of the MOOSE network  (Zakardjian and Quentin, 2018). It corresponds to the dataset available at the time of the study. The stations are located in Cap Sicié and Cap Bénat-Porquerolles in respectively monostatic and bistatic eight-antenna configurations (now upgraded to twelve antenas by site,   Dumas et al. , 2020). Their positions enable to monitor the NC upstream the Gulf of Lions (Fig. 2a ) and the mesoscale dynamics that occur in this region of cross-shelf exchanges and strong atmospheric forcing (Mistral, Tramontane winds). They operate at 16 MHz with a 50 kHz bandwidth, resulting in a spatial resolution of 3 km, and allow an angular resolution of 2°.
2. 1. b) Gliders In the NWMed, a number of gliders have been deployed since 2005 along different transects, measuring temperature and salinity vertical profiles. We focus on a regular line, from Nice to Calvi where 36 deployments occurred from 2009 to 2016, as part of the MOOSE network. From 2011 to 2017, there are 204 sections (ascending or descending). Data were treated according to  Carret et al. (2019)  who discarded profiles being too short or deviating too much from an average Nice-Calvi trajectory. It results in temperature and salinity data down to 500m (depth reached by all gliders), gridded with a 4 km horizontal bin size along the mean trajectory considered as a reference track. The temperature and salinity data are then filtered using a 15 km cutoff wavelength. The geostrophic velocity component perpendicular to the reference track is then derived using the thermal wind equation referenced to 500 m (see  Carret et al.
2. 1. c) Satellite altimetry Jason 2 was launched in June 2008 and was in the same orbit up to October 2016. It is based on a conventional Low Resolution Mode (LRM) altimeter operating in the Ku-band and has a 10-day repetition cycle. SARAL, launched in February 2013 moved to a drifting orbit in July 2016, providing a shorter data time series (3 years). It has a 35-day repeat observation cycle. However, with its LRM altimeter operating in the Ka-band (called AltiKa), it has a lower data noise and is expected to capture smaller spatial scales than Jason 2  (Verron et al. , 2018). Sentinel-3 was launched in February 2016. With its SAR (Synthetic Aperture Radar) altimeter, its footprint is further reduced in the along-track direction, compared to SARAL. Sentinel-3 has a 27-day repeat observation cycle.
Figures  2b, c, d  indicate the satellite tracks of each mission in the NWMed, defining the spatial coverage of the corresponding nadir altimetry observations. Note that the spatial resolution of nadir 1-Hz altimetry data is in the range 5-8 km along the track (Table  1 ) but that the inter-track distance varies from 230 km for Jason 2 to 76 km for Sentinel-3 and 58 km for SARAL. For each mission, the tracks used in this study are indicated in bold in Fig. 2b, c, d. They correspond to the tracks closest to HF radars data (see below for explanation): the Sentinel-3 track 472 and the SARAL track 302 pass over the HF radars region with a different angle, whereas the Jason 2 track 222 is located a bit further to the east, at about 60 km. Table  1  summarizes the characteristics of each altimetry dataset.
4 ). For the sake of clarity, we chose to represent for SARAL only the tracks closest to in situ observations in order to not overload the figure. We compute the time-average and the standard deviation of surface current perpendicular to the tracks derived from all observations and from the simulation collocated in space and time with the observations (Fig. 4 ). These statistics are computed over the common period of data availability (if we consider only SARAL and Jason-2 in terms of altimetry missions): from March 2013 to October 2014. Both a regional view of the NW Mediterranean Sea and a zoomed-in view of the northern Ligurian Sea are provided. Note that the observation component of these diagnostics (top panels) have already been shown in  Carret et al. (2019)  to analyze how the NC is captured by the different types of instruments while the focus here is on the simulation quality.
Observability of the NC in altimetry data: from Jason-2 to Sentinel-3 In this section, we first analyze the general characteristics of the SSH and surface (crosssection) velocity profiles observed along the selected tracks (see section 2. 1 and Fig. 2 ) for both altimetry data and the model reference (section 4. 1). The altimetry data noise issue is then investigated in section 4. 2.
SSH and current statistics We compute the temporal mean and standard deviation of the individual SSH and We first focus on Jason-2 results. In Fig. 6a , we observe that on average, the raw altimetry SSH profile agrees fairly well with the model above 20 km from the coast; below this distance, the two curves diverge with a steeper slope for Jason 2. In this area, the SSH increase corresponding to the external edge of the NC starts at 60 km to the coast, i. e. further from the coast than for the 6. 14°E transect (located to the west). The 1-Hz altimetry SSH data stops at 8 km from the coast. SSH standard deviations from altimetry are slightly greater (between 0. 8 and 1. 6 cm) than from the model, except at the nearest point to the coast where the difference reaches 2. 2 cm. Figure  6b  shows the corresponding mean cross-track velocity profiles.
In terms of SSH profile (Fig. 6e ), Sentinel-3 appears very similar to SARAL (Fig. 6c ). SSH increases further south for the observations than for the model, leading to a slightly more offshore extended current. Compared to Jason 2 and SARAL, Sentinel-3 1-Hz data get much closer to the coast (around 1 km), and are also less noisy with SSH standard deviation quite identical to the model near the coast and slightly higher far from the coast. Figure  6f  shows that, thanks to its better coastal data coverage, Sentinel-3 captures the NC almost entirely. The current variability remains quite important along the track compared to the model (0. 19 m s -1 for altimetry against 0. 065 m s -1 for the model in average) and a huge standard deviation value characterizes the first point near the coast. From the results of Fig.
To obtain the filtered SSH, we tested different cutoff frequencies, ranging from 30 km to 50 km for SARAL and Sentinel-3 and extending to 70 km for Jason 2. Indeed, Morrow et al. (  2017 ) and  Raynal et al. (2017)  showed a greater noise level in Jason 2 which required larger cutoff frequency values. The histograms of current values are represented in Fig. 8  for Jason 2 track 222, in Fig. 9  for SARAL track 302 and in Fig. 10  for Sentinel-3 track 472 (altimetry in blue superposed on the model in pink). Note that for each mission, the model current values are sampled at altimetry temporal resolution (10, 35 and 27 days for Jason 2, SARAL and Sentinel-3 respectively) and at the model resolution to investigate the impact of undersampling data (bottom figures). Table  3  summarizes the statistics derived from the histograms: the median, the standard deviation, as well as the number of points outside typical current values in this area and considered as outliers (greater than 0.
25 m s -1 and smaller than -0. 6 m s -1. These values are considered the typical NC velocities). We first focus on Jason 2. The model reference shows a distribution which tends to be gaussian. It is centered around -0. 15 m s -1 , with a majority of negative values and is slightly asymmetric. Jason-2 raw velocity values are almost randomly distributed. When Jason-2 SSH data are filtered, and as the cutoff wavelength increases, the histogram's distributions change and get closer to the model ones. Regarding the statistics (Table  3 ), the too high standard deviation and too negative median values in the raw Jason-2 data get closer to the reference with the increase in cutoff wavelength. With a 60 km -filtering, we have the same standard deviation values in both Jason-2 and model velocities but the median value remains always significantly lower in Jason-2.
The number of outliers is also too large in raw Jason-2 data, but decreases rapidly with the filtering; it is the closest to the model reference for a 60 kmfiltering. From these results we conclude that Jason 2 currents tend to best converge towards the model reference with a filtering at 60 km. Beyond this cutoff wavelength, the smoothing erases the left and right-hand sides of the distribution (Fig. 8 ) and reduces the variability. We repeat the same analysis with SARAL (Fig. 9  and Table  3 ). Note that there are fewer satellite cycles for SARAL than for Jason 2, so less current data are available to compute statistics. As a result, the distributions obtained are more complex than for Jason-2. It is clearly observed when comparing Fig. 9e  and f  (distributions computed at the model resolution and at a 35-day resolution).