dataesr/scientific-paragraphs-categorization huggingface dataset:
This suggested that, depending on the look geometry, different whitecap parts dominated the NP backscattering. In this study, we attempted to estimate how fast the DV was in comparison with the breaking wave phase velocity (estimated from the optical whitecap detection). The relationship between the Doppler and optical velocities can vary widely, as the examples in Figure  3  illustrate. However, regardless of the rather strong scatter between the DV and LOV, some average relationship between them can be deduced. Figure  4  shows the DV versus LOV scatterplots for each incidence angle based on the "instant" dataset (see the dataset definition above). The data points group around the x = y lines, but the scatter is huge since the "instant" dataset includes all stages of breaker evolution, e.
g. , the LOV may correspond to the remaining foam wake, which does not impact the DV. Independent of the incidence angle, the common feature of all panels in Figure  4  is that the PR was maximized at both edges of the point clouds. These high positive and negative DV values correspond to up-wave (positive) and down-wave (negative) radar observations. This indicates the presence of the NP in both the up-wave and down-wave cases. In the cross-wave direction (DV ≈ 0), the PR decreased, suggesting a weaker NP contribution. The general tendency reflecting PR values closer to 1 for smaller θ (where specular reflections dominate) held. Yet, even at moderate incidence angles, θ > 45 • , the PR was still about 1 for the highest velocity magnitudes. Less noisy pictures can be achieved using the "average" dataset (Figure  5 ).
Now, each point corresponds to a particular breaking event, focusing on conditions during maximum Q within the radar footprint. For this "average" dataset, the number of points was significantly less, but the noise scatter dropped as well. At large incidence angles, θ > 40 • , a tendency of DV "saturation", |DV| < |LOV|, was observed. This agreed with our manually derived estimates  [52] , also shown in Figure  5g. Next, the observed DV was normalized by the breaker phase velocity, c ph , derived from the optical velocity magnitude (Figure  6 ). This normalized DV/c ph showed that the DV was always less than the phase velocity, except for several presumably erroneous points where the optical velocity was possibly underestimated. In general, the data fell within the limiting ± sin θ corridor fairly well, while the azimuth distribution was cosine-like, cos φ, but scaled by the mean sin θ factor.
A statistical explanation for that may involve the presence of random errors inherent to both the DV and c ph measurements, which became relatively more important when the mean values vanished. For the DV, it may also be related to the orbital motions within the footprint that were not related to the observed whitecap. Their effect on the DV was presumably weak because a whitecap is a much "brighter" object in the NRCS. However, errors introduced by regular surface velocities may increase at small incidence angles, where the background may not be that weak in comparison with the breaker NRCS (Figure  7a ). An important feature revealed by the observations is that the PR was maximized for large speeds and mostly in the up-wave and down-wave directions. This is better seen for the "instant" (Figure  4 ) than for the "average" (Figures  5  and 7 ) dataset because the PR peak was not necessarily coincident with the Q peak (Figure  3 ).
To gain deeper insights into the connections between the breaker PR and DV, only near up-wave and down-wave samples (0 • < φ < 45 • and 135 • < φ < 180 • ) were selected from the "instant" dataset and plotted as the normalized DV/LOV versus the PR (Figure  8 ). Noticeably, the bin-averaged normalized DV systematically increased with the PR at all incidence angles. This means the stronger the NP return is, the closer the DV to the breaking wave phase velocity is. Yet, the bin-averaged DV never reached the phase velocity, with maximum values of ∼0. 5-0. 7c ph , again indicating that the "pure" NP return events (with the PR approaching 1) did not always propagate as fast as the breaking wave phase velocity (as demonstrated in Figure  3 ).
The model, fit to the observations by varying the mean front/rear face slope, mean-squared slope, and small-scale variance, suggested an asymmetric breaker shape with the front face being steeper and rougher than the rear face. This simplified model agreed with the NRCS analysis of the same dataset  [55]  and can help to explain the previously observed unusual "upwind < downwind" asymmetry in the Ka-band Doppler centroid  [31]  and NRCS  [69]. Our further efforts will focus on a self-consistent fusion of the NRCS and Doppler breaker models, which should be included in the advanced radar imaging model.
SCD management also involves acute and chronic blood transfusions, to reduce the proportion of mutated blood cells or to prevent poorly tolerated anemias  (10). Recently, voxelotor has introduced a new approach for the chronic management of SCD-related hemolytic anemia  (11, 12). Hematopoietic stem cell transplantation (HSCT) remains the only curative procedure currently available but is rarely performed due to a lack of donors and compatibility issues  (13, 14). Due to the acute clinical events and chronic complications, SCD burden is important in terms of hospitalizations, treatments, and general management. A previous study based on French claims database up to 2011 estimated an annual average hospital cost of about €6,000 per SCD patient  (5). This study aimed (i) to provide updated data on the main characteristics, management, and costs of SCD among patients aged ≥12 years in France and (ii) to compare those costs to that of the general population in order to assess SCD additional costs.
General design This was an observational, retrospective, cohort study performed among adolescent and adult patients with SCD between January 1st, 2016, and December 31st, 2018. These patients were matched to individuals from general population (referred to as "controls") over the same period. This study used secondary healthcare consumption data from the French permanent representative 1/97th sample (EGB -Echantillon Généraliste des Bénéficiaires), based on healthcare claims from the French national health data system (SNDS -Système National des Données de Santé)  (15).
Data source The French national claims database (Système National des Données de Santé -SNDS), exhaustively gathers the reimbursed healthcare resources of individuals covered by one of the compulsory healthcare plans, encompassing nearly 99% of French residents. The EGB is a 1/97th sample of ≈700,000 individuals extracted from SNDS. It is an exhaustive pseudonymized patient-level collection of claims data, representative of the French population in terms of age, gender, and geographical area. Healthcare reimbursements are recorded using specific coding systems for a diverse range of variables, including procedures, laboratory tests, medical devices, diagnoses (hospitalizations or LTD), or reimbursed drugs. Among other main data available, beneficiary information include age, gender, city of residence, date of care provision, care settings, as well as date of death.
Statistical methods Quantitative variables were described in terms of mean, standard deviation, median, first (Q1) and third (Q3) quartiles, and extreme values; qualitative variables in terms of absolute frequency and percentage by category. The description of healthcare resources used the proportion of patients with each relevant consumption and number per patient-years. Data from EGB were extrapolated to obtain estimates of patients, procedures, and costs on a national scale. Extrapolations were calculated for each year, adjusted on age and gender based on the French population census for the same year (16). SCD attributable cost was calculated by comparing the SCD cohort and the control population as proportions of patients with at least one healthcare consumption of interest and costs assessment.
Ethical considerations Before access to the data, the protocol was validated by an independent scientific committee and by the Health Data Hub as part of a simplified EGB procedure. Once approved, EGB data were analyzed on the secure SNDS portal by data managers and statisticians trained in patient data security standards. No individual data were extracted from the SNDS portal. This study was conducted in compliance with the French Data Protection Act and in accordance with applicable and with the ethical principles set out in the Declaration of Helsinki.
Reasons for such a low rate are multiple but ineffectiveness could be highlighted. It can be notably due to pharmacokinetic polymorphisms or      (20, 21). Due to the nature of the database, it was not possible to distinct cases when HU is not prescribedreflecting prescription patterns -from cases when HU is prescribed but not dispensed -reflecting adherence itself. Sensitization of patients and practitioners on the role of HU in the prevention of  Frontiers in Public Health 06 frontiersin. org SCD crises could help increasing the rate of patients treated with HU. Despite its status as the sole curative therapy for SCD in France to date, no HSCT procedure has been recorded during the 3-year follow-up period of this study, despite an excellent long-term SCD-related event-free survival. This result can be explained by the rarity of HLA-matched donors, and the relatively limited size of the patient sample included in this study  (14, 22).
Strengths and limitations Despite the granularity of data available in the EGB database, some pieces of data are not available. For instance, stays in postoperative rehabilitation centers and psychiatry centers data cannot be captured. Even with the majority of SCD management originating from hospitalizations and treatments, leading to expect this bias to   (27). Another unavailable source of data is the drugs paid over the counter (OTC). These OTC drugs are mostly painkillers or symptomatic treatments. In addition to representing a small cost, most of these drugs could be 100% reimbursed when prescribed by a general practitioner, hence detected in this study, leading to a very low impact. As SCD remains a rare disease, with non-specific clinical manifestations, the development of tailored algorithms for SCD patient identification remains a potential source of bias.
Deep mutational scanning of AF6 To systematically measure the fitness of each single-amino-acid variant of AF6, we performed DMS using a pooled growth competition. In brief, AF6 knockout (KO) cells were transduced with an AF6 variant library and passaged for several generations in media containing galactose as the major carbon source. Growth in galactose media is reliant on OxPhos  20, 21  , which requires functional CI. Thus, cells expressing functional variants of AF6 can grow while cells expressing non-functional variants cannot. Deep sequencing was then used to calculate a fitness score for each variant by comparing read counts before and after the galactose growth selection (Fig. 1A , Extended Data Fig. 1 , Table  S1 ). To set up the DMS system, we first created two-independent AF6 knockout (KO) cell lines in a HEK293T background.
The DMS experiment was performed under the native promoter, with five pre-selection biological replicates and ten post-selection biological replicates (Extended Data Fig. 2A ). We evaluated the robustness of our DMS data in several ways. First, we assessed the number of input read counts for each variant, which showed >500x sequencing depth across the five replicates (Extended Data Fig. 2B ). Second, we calculated correlation coefficients of the fitness scores, showing that all five replicates are highly correlated with one another (Extended Data Fig. 2C ). Third, we examined the distribution of fitness scores, which showed a strong bimodal distribution and demonstrated successful separation of tolerated and deleterious substitutions (Extended Data Fig. 2D ). Finally, we evaluated the fitness scores of premature termination substitutions.
No other CI subunits or CIAFs were among the remainder of significant hits (Fig. 3A , Extended Data Fig. 5 , Table  S3 ). NDUFS8 is a core subunit of the quinone binding module (Q module), one of three functional modules that comprise the CI holoenzyme. The two other modules are the NADH-oxidizing dehydrogenase module (N module) and the proton pump module (P module), which is further subdivided into the Pp-a, Pp-b, Pd-a, and Pd-b modules  30. NDUFS8 coordinates two iron-sulfur (Fe-S) clusters that form part of the Fe-S cluster chain responsible for shuttling electrons from the N module to the Q module 1. To further test the AF6-NDUFS8 interaction experimentally, we performed a yeast two-hybrid (Y2H) assay to assess the physical interaction between several Q module proteins: AF6, NDUFS8, NDUFS7, and NDUFAF3.
ncbi. nlm. nih. gov/clinvar/) with interpretations of pathogenicity (Table  1 ). As an orthogonal readout for AF6 function, we also generated stable cell lines expressing these variants in an AF6 KO background and measured NDUFS8 levels by immunoblot. The DMS fitness and immunoblot data were consistent with seven of the nine existing ClinVar annotations. One of the two "inconsistent" variants, NM_152416. 4:c. 296A>G:p. Gln99Arg, causes a splice site defect that abolishes transcript levels of AF6  17. Here, our data support that the pathogenicity of this splice site variant, which showed no evidence of functional impairment at the protein level by DMS or immunoblot, is not a consequence of the amino acid substitution. The second variant, p. Pro281Ala, which is currently annotated as "likely pathogenic," had an intermediate DMS fitness score and only mildly reduced NDUFS8 levels relative to the wild-type control, suggesting that it may be benign (Fig 5A ).
Previous and ongoing studies have nominated several variants in AF6 as possible causal variants for CI deficiency, but these variants have not been evaluated by experimental analyses (Table  2 ). One immediate use of the DMS data is to provide empirical support for the phenotypic annotation of these candidate variants. To that end, we examined the DMS fitness scores of these variants and, as additional validation, measured NDUFS8 levels by immunoblot from cell lines expressing these variants. The six candidate pathogenic variants and one variant with conflicting interpretations of pathogenicity all showed loss of NDUFS8 by immunoblot and decreased DMS fitness scores, adding the first experimental evidence of their pathogenicity (Fig  5B ). Beyond demonstrating the pathogenicity of these variants in our cell culture system, we also performed respiratory chain enzyme activity assays on fibroblast or muscle biopsy samples from patients harboring these genetic variants, confirming their CI deficiency (Table  2, S4 ).
Intensity threshold was 5E3. Dynamic exclusion was 30 s with the exclusion of isotopes. Other settings for DDA include Orbitrap resolution of 15,000 and HCD collision energy of 30%. Raw files were analyzed by SequestHT Search Engine incorporated in Proteome Discoverer v2. 5. 0. 400 software against human databases downloaded from Uniprot. Label-free quantification was enabled in the searches. The resulting data was analyzed by Perseus v1. 6. 15. 0 software  52.
DMS Data Analysis Raw sequencing reads were first aligned to a wild-type NDUFAF6 cDNA reference using Bowtie2 v. 2. 4. 4  55. Aligned reads were then merged using FLASH v1. 2. 11  56. A custom python script was then used to filter the merged reads for reads containing only a single expected codon substitution. Source code for read processing is available on GitHub (https://github. com/AYSung/af6-dms). Full-length reads for each variant were then reconstructed and fitness estimates were obtained for each variant using the DiMSum pipeline v1. 3  57. The mutational sensitivity of each position in NDUFAF6 is computed as the sum of substitutions with a fitness below -0. 8, divided by 2, and rounded down to the nearest integer.
Acknowledgments: We thank the members of the  Pagliarini and Keck labs  for their helpful discussion and critical evaluation of the manuscript. This work was supported by  NIH  awards  R35GM131795  (to D. J. P. ),  T32GM140935  (to A. Y. S. and L. H. S. ),  T32AG000213  (to A. Y. S. ), and  T32GM008505  (to L. H. S. ), as well as funds from the  BJC Investigator Program  (to D. J. P. ),  NIHR  award  PDF-2018-11-ST2-021  (to C. L. A. ), and funds from the  Wellcome Centre  for Mitochondrial Research  203105/Z/16/Z , the  Mitochondrial Disease Patient Cohort   G0800674 , the  Medical Research Council International Centre for Genomic Medicine in Neuromuscular Disease   MR/S005021/1  (to R. W. T. ). This work was partly supported by the  Practical Research Project for Rare/Intractable Diseases  from the  Japan Agency for Medical Research and Development  ( JP23ek0109625 ) and by the  EJP RD  project  GENOMIT  ( Italian Ministry of Health   ERP-2019-23671045 ,  German Federal Ministry of Education and Research   01GM1920A ).
Scope: The purpose of the study is to characterize the chemical diversity in rice bran (RB) lipidome and determines whether daily RB consumption for 4 weeks may modulate plasma lipid profiles in children. Methods and results: Untargeted and targeted lipidomics via ultra-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-MS/MS) are applied to identify bioactive RB lipids from a collection of 17 rice varieties. To determine the impact of RB (Calrose-USA variety) supplementation on plasma lipid profile, a secondary analysis of plasma lipidome is conducted on data recorded in a clinical study (NCT01911390, n = 18 moderately hypercholesterolemic children) before and after 4 weeks of dietary intervention with a control or RB supplemented (15 g day -1 ) snack.
Multivariate Analysis of Rice Bran Lipidomics The multivariate analysis of PLS-DA confirmed the differentiation of the global lipid profiles of each variety (Figure  5 ). In the Figure  5A , across all cultivars and all identified lipids, the PLS component 1 (PC1) explains 80% of the total variation in the dataset, and PC2 6%. The PLS-DA also shows the separation of the groups from the model. These results showed that the lipid profile is not specific to regions (or countries). Given the separation of RB variety within the same country, we conclude that the lipid profiles cannot be considered specific to any region, and that we should emphasize influence on genotype as previously described for whole grain rice. [39, 40] he PLS-DA analysis highlighted lipids responsible for differentiation of RB by cultivars by integrating the relative  Letters indicate significant differences between the rice bran genotypes for a given Phytoprostane or PhytoFuran (one-way ANOVA, p < 0.
001). total abundance, including presence or absence of each metabolite. Figure  5  shows the 10 discriminant lipids by VIP (variable importance in projection) (in red color) are fatty acids, glycerolipids, oxylipins, and a polar lipid. These included oleic acid (18:1n9c), linoleic acid (18:2n6c), palmitic acid (16:0), linoleoyl-linoleoyl-glycerol (18:2/18:2),  [1]  oleoyl-linoleoyl-glycerol (18:1/18:2),  [2]  oleoyl-oleoyl-glycerol (18:1/18:1),  [1]  and palmitoyllinoleoyl-glycerol (16:0/18:2),  [2]  two oxylipins: 13-HODE + 9-HODE and 9,10-epoxystearate and one polar lipid, the phospholipid glycerophosphorylcholine. [8] ocusing on the polar lipids and oxylipins, a new PLS-DA have been performed considering only this data. In Figure  5B , the PLS component 1 (PC1) accounted for 62% of the total variation in the dataset, and PC2 for 18%.
Reaction medium was refluxed for 10 min. 3 mL chlorohydric methanol was added to phenolphthalein discoloration and the mixture was refluxed again for 10 min and then cooled to ambient temperature. 6 mL hexane and 10 mL water were added, and the organic phase was recovered and analyzed by gas chromatography. [50] A Focus GC (Thermo Electron Corporation, MA, USA) was used and equipped with a split injector (ratio of 1/20), a CP-Sil 88 Varian capillary column (50 m × 0. 25 mm with 0. 2 m film thickness; Chrompack, Mid-delburg, the Netherlands), and helium 1 mL min -1 as the carrier gas was used. Fatty acids methyl esters (FAME) were analyzed by flame ionization detector and ChromCard software data system (version 2005, Thermo Fisher Scientific, MA, USA). The column temperature started from 150 °C, then reached 150-225 °C with a rise of 5 °C min -1 and was kept at 225 °C for 10 min.
Chromatographic separation of PhytoPs and PhytoFs was performed using a micro liquid chromatography coupled with a QTRAP-MS/MS 5500 (Sciex Applied Biosystems), using the analytical column HALO C18 (0. Electrospray ionization in negative mode was used as ionization source. Nitrogen (N 2 ) was used as curtain gas and the voltage was kept at -4. 5 kV. Data acquisition and processing were performed using MultiQuant 3. 0 software (Sciex Applied Biosystems). The quantification of PhytoPs and PhytoFs detected in RB was performed by constructing calibration curves of the ratio between analyte to IS area under the curve. Dosage Information/Dosage Regimen: The University of Colorado Health-North Institutional Review Board and the Colorado State University Research Integrity and Compliance Review Office approved the study protocols (#13-1263 and 13-4390, respectively).
Participants' guardians provided written informed consent and children participants provided written informed assent prior to participation in the blood collection and dietary intervention. Two study intervention groups were included for this study that represent a subset from a randomized controlled, 4-arm dietary intervention clinical trial (NCT01911390). [25] The plasma metabolome was previously published and described by Li et al. [24]  Briefly, for this secondary analysis of the plasma lipidome, the study analyzed 18 moderately hypercholesterolemic children (total cholesterol ≥ 180 mg dL -1 ), aged 8-13 years old before and after 4 weeks of dietary intervention with a control or RB supplemented snacks. The prepared snacks (low-fat, low-sodium, highfiber) contained 15 g day -1 of RB and consumed as either two banana muffins with hazelnut (114 g) or one banana, strawberry, and pineapple smoothie (247 g).
(Durham, NC, USA) and plasma was prepared using the automated MicroLab STAR system (Hamilton Company, USA). To remove protein of plasma and recover small molecules, the sample was precipitated with methanol and vigorously shaken for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation according to Borresen et al. [25]  Untargeted lipidomics of all RB extract samples were analyzed by UPLC-MS/MS and gas-chromatography mass spectrometry (GC-MS) in the positive and negative ionization mode platforms. The plasma extract was analyzed by reverse phase on the UPLC-MS/MS with positive and negative ion mode electrospray ionization and hydrophilic interaction liquid chromatography (HILIC) with negative ion mode electrospray ionization. The data were processed into Metabolon's Library Information Management System, according to Li et al.
[24]  Briefly, raw data were obtained and peak-identified by Metabolon. The identified lipids were confirmed by comparison to an internal library of over 3300 entries of purified standards or recurrent unknown entities maintained by Metabolon, based on the retention time/index, m/z, and chromatographic data. The sub-pathway classification scheme used by Metabolon was to provide overlap to other programs such as Metaboanalyst, KEGG etc. The subgroup study of nine classes of compounds included free fatty acids, non-esterified fatty acids, phospholipids, sphingolipids, acylglycerols, oxylipins, sterols, steroids, and endocannabinoids. These subpathways were selected were as a focus due to the strength of associations with dietary modulation of human lipid metabolism. Identified lipids were quantified in terms of relative ion abundance.