dataesr/scientific-paragraphs-categorization huggingface dataset:
Since only observational data are available, a fair comparison between various ways of treatment would necessitate adjustment for all potential confounders in the treatment comparison (e. g. , by propensity score methods). These methods, however, need larger datasets than ours, which remains a real difficulty when reporting on rare diseases. Therefore, we chose to approximate this optimal analysis in our observational study by way of an adjustment of the treatment comparison for a small number of important and available variables. We considered the two groups of XLH children to be comparable at the start of burosumab, concerning the potential effect of burosumab on growth, even though the children of group 2 consisted of pre-selected individuals who had previously received rhGH due to short stature.
Study design and population, auxological and biochemical measurements Since March 1st, 2018, all children diagnosed with XLH and treated with burosumab are included in a prospective longitudinal single-center cohort at the French National Reference Center for Rare Disorders of the Calcium and Phosphate Metabolism, Filière OSCAR and Platform of expertise for rare diseases, ENDO-ERN and ERN-BOND, Bicêtre Paris-Saclay Hospital, France. The cohort study is registered with ClinicalTrials. gov (identifier NCT04419363, https:// clini caltr ials. gov/ ct2/ show/ NCT04 419363). Its main purpose was of having a prospective longitudinal cohort of children treated with burosumab, and to gather important auxological and biochemical data for future studies. At the date of study, i. e. , Dec 31st, 2021, among the 60 children already included in the cohort, 36 had completed at least 1 year of burosumab therapy at the time of the analysis and were included in this observational analysis.
Data collected for this specific study were processed in a pseudonymized format. All patients initially received conventional therapy with vitamin D analogs and phosphate supplements, in accordance with international guidelines  (2). In accordance with the French indication for burosumab, all patients were eventually switched from conventional therapy to burosumab. We then distinguished the following two groups: group 1 consisted of 23 individuals who received only burosumab, i. e. , they were "rhGH naïve" (Fig. 1 ); these patients either did not present short stature or declined the offer of rhGH in combination with conventional therapy. Group 2 consisted of 13 participants who received rhGH for short stature, initiated during conventional therapy due to severe short stature, as an attempt to improve final height.
Auxological (height, body mass index [BMI]) and biochemical data were gathered from medical charts for the key time points defined as: GH0, initiation of rhGH therapy (Group 2), GH1, 1 year after initiation of rhGH therapy (Group 2), B0, initiation of burosumab (Group 1 and 2), and B1, 1 year after burosumab initiation (Groups 1 and 2). Additionally, height was also documented 1 year before these time points (GH-1 and B-1) (Fig. 1 ). The participants did not present with any other health problems that might influence growth (e. g. , chronic systemic diseases or inflammatory intestinal disorders). All participants had their height and weight measured at the follow-up visits to our clinic, at least twice a year during conventional therapy, and every 3 months during burosumab therapy. For the measurement of height, we used a Harpenden stadiometer.
These parameters, as well as calculated BMI, were analyzed as standard deviation scores, using WHO reference data for height and BMI  [43]. Delta height (∆H) parameters were also calculated as follows: Group 1: ∆H B1-B0 for the difference in height SDS after 1 year of burosumab therapy, Group 2: ∆H GH1-GH0 for the difference in height SDS after 1 year of rhGH therapy during conventional therapy, ∆H B1-B0 for the difference in height SDS after 1 year of burosumab combined with rhGH, and ∆H B0-GH0 for the difference in height SDS between the initiation of burosumab and the start of rhGH. Intra-individual change in BMI-SDS (∆BMI) was analyzed in the same way as for ∆H. Parental height and genetic target height were documented for all patients, but not included in the analysis due to the high proportion of familial XLH cases in the study population, which might have biased the interpretation.
We had also planned to gather data on bone age at the start of burosumab (B0) and after 1 year of treatment (B1), but on the one hand, the severity of rickets on some radiographs hindered analysis of bone age, and on the other hand, we felt there were too many missing data at these time points. For these reasons, we decided to exclude this parameter. Routine biochemical tests were performed, either in our hospital at the follow-up visit or in designated external laboratories. The parameters documented were the following: levels of serum phosphate, alkaline phosphatase, calcium, creatinine, 25-hydroxyvitamin D (25-OHD), 1,25-dihydroxyvitamin D (1,25-(OH) 2 D), parathyroid hormone (PTH), urinary calcium excretion (urinary calcium/creatinine ratio), phosphate reabsorption (tubular reabsorption of phosphate (TRP), and renal tubular maximum reabsorption rate of phosphate to glomerular filtration rate (TmP/GFR]).
For patients treated with rhGH (Group 2), serum insulin-like growth factor-I (IGF-I) levels were measured to analyze the safety of rhGH therapy; for the analysis, we considered only values from the time of the visit or no more than 6 months earlier. The dose of rhGH was regularly adjusted in this group, based on growth velocity, weight and serum IGF-I levels. The target was an IGF-I in the normal range for age, gender, and pubertal stage, when compared to reference population data provided by the assay's manufacturer.
Each run lasted 20 min. The compounds present in the extracts were identified by comparing their MS spectra with those obtained after the injection of pure compounds and with an internal (INRAMASS) and commercial mass spectra database  (NIST 2008, Wiley 138). The linearity and repeatability of the procedure in an aqueous solution composed of a mixture of 5 aroma compounds at seven concentrations were measured to validate the methodology (Fig. A. 1).
As described previously  (Muñoz-González et al. , 2021) , the individual's nose space was measured through a Teflon nose piece via a helmet connected to a proton transfer reaction-mass spectrometer (PTR-MS) instrument equipped with a Time-of-Flight (ToF) analyser (PTR-ToF 8000, Ionicon Analytik, Innsbruck, Austria). Analytical conditions followed for the PTR-MS procedure can be found in  (Muñoz-González et al. , 2021). The release curves of the ions corresponding to the aroma compounds were extracted, and the area under the curve (AUC) was calculated for each of the selected ions and monitoring time, and the monitoring times corresponded to the five swallowing events performed by the individuals (t0-30 s, t30-60 s, t60-90 s, t90-120 s, t120-150 s). The release data were analysed from the breath concentration data using IGOR Pro (WaveMetrics, Inc.
Immediately afterwards and every 5 s, they were asked to rate the intensity of the aroma descriptor on the scale. The individuals were instructed to keep their lips closed during all the evaluations that lasted 90 s. They had to wait at least 2 min to evaluate the following sample. A warm-up sample was presented before starting the evaluation. Bread and water were used as mouth cleansers between tests. In each case, the individuals knew the aroma descriptor to be evaluated. The compound hexane-2,3-dione was evaluated first. From the notation at the different scales, time-intensity curves were reconstituted for each aroma descriptor. For each attribute, average time-intensity curves were determined by averaging the data across the two compounds and the three replicates at each time point. The first point (5 sec) was considered 100%, and aroma persistence was calculated with respect to this point.
Statistical analyses Retention data of the aroma compounds in the three conditions (control, cells, and MP) were analysed by static headspace analyses, and data of the effects of the oral mucosa and saliva on aroma compounds analysed by liquid phase analyses were submitted to univariate analysis of variance (ANOVA) followed by a Tukey multiple comparison test (significance for p < 0. 005). For PTR-MS and sensory analyses, the persistence of aroma compounds was evaluated by ANOVA followed by a post hoc Tukey multiple comparison test (significance for p < 0. 05). The XL-Stat (Addinsoft, Paris, France) and R (The R Foundation for Statistical Computing, Vienna, Austria) programs were used for data processing.
Instrumental measurements of in vivo aroma persistence by PTR-MS A real-time instrumental approach was used to validate our hypothesis in an in vivo context by coupling PTR-MS with the nasal cavities of 54 subjects. In vivo aroma persistence was monitored after the individuals consumed a model solution flavoured with five aroma compounds following an imposed consumption protocol. Data from the panel (n = 54) averaged considering 30-second intervals after sample swallowing, are shown in Fig. 4. Data are expressed considering the AUC values of the first 30 s after sample swallowing as 100% to allow compound comparison and calculate the percentage relative to it for the next 150 s. As expected, once the sample was swallowed, there was a progressive decrease in aroma persistence for all compounds (Fig.
Aroma intensity perceived by the subjects was monitored every 5 s for 90 s after sample consumption. Fig. 5  shows the averaged data of the panel. To allow compound comparison, data were expressed considering the data of the first monitoring time (5 s) after sample swallowing as 100% and calculating the percentage relative to it for the rest of the monitoring times. As observed in the instrumental PTR-MS experiment, once the sample was swallowed, there was a progressive decrease in aroma persistence for both compounds (Fig. 5 ), although the extent of the decrease was compound dependent. Thus, from 60 s of monitoring time after sample swallowing onwards, the persistence of the butter descriptor (elicited by hexane-2,3-dione) was significantly lower than that of the floral descriptor (elicited by linalool) (p < 0.
Acknowledgements We gratefully acknowledge the technical support offered by  Chantal Septier ,  Aurelie Prot ,  Pauline Jeltsch  and  Elvina Guinchard  and especially the graphical help offered by  Dr. M. Panettieri. C. M-G acknowledges the  AgreenSkills fellowship , which has received funding from the  EU's Seventh Framework Programme  under grant agreement N  • FP7-609398  (AgreenSkills + contract; https://www. agreenskills. eu/ Fellows-Labs/Fellows-Alumni2/Carolina-Munoz-Gonzalez ), the  French Nutrition Society  for her research award and the  AT programme  ( 2019T1/BIO13748 ). This work was also supported by the  "Agence Nationale de la Recherche" (ANR) :  ANR-14-CE20-0001 ,  ANR-14-CE20-0003 & ANR-20-CE21-0002.
Flax is an important fiber crop that is subject to lodging. In order to gain more information about the potential role of the bast fiber cell wall in the return to the vertical position, 6-week-old flax plants were subjected to a long-term (6 week) gravitropic stress by stem tilting in an experimental set-up that excluded autotropism. Stress induced significant morphometric changes (lumen surface, lumen diameter, and cell wall thickness and lumen surface/total fiber surface ratio) in pulling-and opposite-side fibers compared to control fibers. Changes in the relative amounts and spatial distribution of cell wall polymers in flax bast fibers were determined by Raman vibrational spectroscopy. Following spectra acquisition, datasets (control, pulling-and opposite sides) were analyzed by principal component analysis, PC score imaging, and Raman chemical cartography of significant chemical bonds.
Morphometric data treatment Measures were acquired with FIJI free software 1. The criteria selected were: surface of the lumen excluding the cell wall, longest lumen diameter excluding the cell wall (as fibers are usually oval), cell wall thickness measured from the middle lamella to the lumen, and surface ratio [lumen surface/fiber total surface, in%]. The surface of the fibers was measured using FIJI tool positioned all around the fiber at the middle lamella location. Measurements were made on three categories of bast fibers: (i) bast fibers from control plant sections, (ii) bast fibers located in the "pulling side" (top side) of tilted plants and (iii) bast fibers from the "opposite side" (bottom side) of tilted plants. Stems were collected from 3 control to 3 tilted plants. A total of 50 to 150 measures were performed for each criterion and were acquired from 3-5 cross sections/stem sample.
1 µm was used. The maps were recorded with spatial resolution of 1 µm in both (x and y) directions. The z direction was fixed during the map recording. For wavenumber calibration, a silicium plate was used as a reference for subsequent data pre-processing. Spectra were acquired at 515 nm, with 5 min bleaching, 100 accumulation/sec, from 300 to 1,800 cm -1 corresponding to the plant cell wall fingerprint region. All statistical analyses were performed (A) Average spectra of bast fibers from control conditions (blue), and stressed conditions: pulling side (red) and opposite side (green). Acquisition was performed in the plant cell wall fingerprint range (300: 1,800 cm -1 ). Spectra were treated for the baseline, then aligned on the cellulose band (379 cm -1 ). Main chemical bonds were assigned to cell wall polymers (Black asterisk: cellulose, Red asterisk: lignin, Green asterisk: xylan and xyloglucan).
Principal component analysis and imaging Principal component analysis (PCA) was used to analyze high-dimensional spectral data sets and generate 2D images (PC score images) as described previously  (Moncayo et al. , 2018). Briefly, the Raman spectral data can be considered as a 3-D matrix (a hyperspectral cube) that contains both spatial (X and Y dimensions) and Raman spectral (Z dimension) data for each pixel. For PCA analysis this data set is "unfolded" to generate a 2-D matrix of dimension N (Number of pixels × Number of wavelengths). PCA analysis of the unfolded matrix is then performed to generate a selected number of PCs that explain the majority of the variability. PCA was applied to the first derivative spectral data according to the Savitzky-Golay algorithm  (Savitzky and Golay, 1964; Spectra, 1985; Robert et al.
Design and participants We used data from the US National Health and Nutrition Examination Survey (NHANES). The NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention (CDC) that uses a representative sample of noninstitutionalized civilians in the United States; selected by a complex, multistage, stratified, clustered probability design. Trained staff collected information on participants by interviews and physical examinations. The interview includes background information such as sociodemographic, dietary, and health-related questions. The examination component consists of medical and physiological measurements, and laboratory tests. The National Center for Health Statistics Ethics Review Board approved all NHANES protocols, and all survey participants completed a consent form.
The detailed protocol on NHANES methodology and data collection is available on https://www. cdc. gov/nchs/ nhanes/index. htm. For this study, we included participants with data on blood metals concentrations (n = 2,068). We excluded participants with missing data on diet (n = 291). The final study population included 1,777 adults ≥60 years old with data on dietary intake who participated in the cognitive function test of the 2011-2012 and 2013-2014 cycles.
Dietary data and diet quality indices Dietary intake was collected from the two NHANES cycles using two 24-hour dietary recalls. The first 24-hour recall interview was conducted at a Mobile Examination Center using a standard set of measuring guides to assist in estimating portion sizes. The second dietary recall, which was collected by telephone, was scheduled 3 to 10 days later. We derived the ADII and the HEI-2015 from the mean daily intakes of foods/beverages, energy, and nutrients of these two nonconsecutive 24-hour dietary recalls. The Adapted Dietary Inflammatory Index (ADII) was constructed to measure the inflammatory potential of the diet. 30 riefly, the ADII index proposed by Van Woudenbergh et al  31  was used in combination with the updated dietary components inflammatory weights designed by Shivappa et al  32  instead of the weights proposed by Cavicchia et al.
33  These updated inflammatory weights, based on 3 additional years of published data (2008-2010, inclusive), resulted in a doubling of the total number of articles scored. The ADII was based on a nutritional rationale: first, the inflammatory weights of dietary components are multiplied by the standardized energy-adjusted intake,  34  which acts to reduce between-person variation; second, the intakes of all components are standardized; third, the ADII calculation did not include alcoholic beverages such as beer, wine, and liquor, total fat, and energy, to avoid overestimation of the inflammatory effects of ethanol, fat, and energy. A total of 24 of the 35 possible dietary components were used for the ADII calculation (carbohydrates, proteins, alcohol, fibers, cholesterol, saturated fatty acids, monounsaturated fatty acids, omega 3, omega 6, niacin, thiamin, riboflavin, vitamin B6, vitamin B12, iron, magnesium, zinc, vitamin A, vitamin C, vitamin D, vitamin E, folic acid, beta carotene, and caffeine).
Statistical analyses Means and SDs, and medians and interquartile ranges (IQRs) were calculated for cognitive performance tests scores and blood metals concentrations and also presented by population characteristics. All blood metals concentrations were log 10 -transformed to reduce the influence of outliers. We used quantile g-computation to evaluate the joint mixture association of the three blood metals concentrations with the cognitive performance tests scores. This method is a parametric, generalized linear model-based implementation of g-computation. Quantile g-computation is adapted from weighted quantile sum regression, but unlike weighted quantile sum, this method is not constrained to a single direction of effect, does not require splitting of the dataset, and is more robust against unmeasured confounders.
Electroolfactogram (EOG) recordings EOG recordings were performed on the OM as previously described  [8, 22]. Briefly, after sacrifice, the head was cut longitudinally, and the nasal septum was removed to expose the OM endoturbinates. A recording electrode inserted in a glass micropipette filled with a saline solution was positioned in the middle of endoturbinate III. Odorant stimulations were performed by blowing air puffs through an exchangeable Pasteur pipette enclosed in a plastic tube positioned 2 cm from the epithelial surface. A filter paper impregnated with 10 μL of an odorant solution was placed in the Pasteur pipette. Phenylethyl alcohol (PEA) and acetophenone (ACE) were used as odorants. EOG voltage signals were recorded using an Axoclamp amplifier (Axon Instruments, Molecular Devices, San Jose, USA) and digitized at a rate of 1 kHz using a Digidata 1440 (Axon Instruments).
Statistical analyses Statistical tests were performed with Prism software (GraphPad, San Diego, USA). FA, anisotropy and gene expression data were analyzed using the nonparametric Mann-Whitney U test. A p value <0. 05 was considered statistically significant. EOG recordings and sniffing behavior data were analyzed using two-way repeated-measures ANOVA followed by Bonferroni's multiple comparison test. Data obtained in the habituation and dishabituation tests were analyzed using two-way repeated-measures ANOVA followed by Fischer's LSD post hoc test.
Acknowledgements The authors acknowledge the staff of the animal facility of the  Centre des Sciences du Goût et de l'Alimentation (Dijon, France)  for animal care. The authors also thank  Pascale Winckler  and  Jean-Marie Perrier-Cornet  from the  Dimacell Plateform (Agrosup Dijon ,  INRAE ,  Univ. Bourgogne Franche-Comté, F-21000 Dijon, France)  for their help in performing the anisotropy analyses.
, 2017). A study showed that school nutrition policies, particularly those aiming to restrict sugar consumption, may have positive impacts on students' oral health, and are likely to minimize the healthcare costs linked with dental caries. An observational cross-sectional investigation was carried out to check the role of dietary sugar in the development of caries. Data were obtained from a questionnaire, 24-hour dietary recall interviews, and clinical examination of 128 subjects aged 11-12 years. Consumption of free sugars before bed and between meals was assessed through dietary assessment. A result of this study showed that a significant risk factor for dental caries may be consuming sweets before bedtime  (Goodwin et al. , 2017). Sugar intake is considered a vital risk for dental caries development, which is also likely to influence the etiology of rheumatic fever  (Thornley et al.
Study Results References Drinking water was replaced with an 8% sucrose solution in rats and within a week tachycardia and hypertension developed This animal study showed a strong link between the onset of arterial hypertension and the intake of highly refined sugar intake  (Goran et al. , 2013)  From different countries, an online survey was conducted that included 2496 participants and questions about the consumption of soft drinks, the presence or absence of specific diseases, physical exercise and medication were asked. A greater presence of obesity, gastritis constipation and mental illness among people who consume cola soft drinks more. (Martín et al. , 2018)  A cross-sectional study was carried out on 6-12 years of school-going children. Data about the consumption of sugar-containing foods and dental history were obtained.
(DiNicolantonio and O'Keefe, 2016) Only a few prospective studies have examined the link between added sugar consumption and death from cardiovascular disease. Many adults, In the US consume more added sugar than the one suggested for a healthy diet and found a significant association between added sugar intake and decreased risk of death from CVDs. (Yang et al. , 2014)  Six studies for type 2 diabetes mellitus, three for the metabolic syndrome, and one for coronary heart disease were included in a meta-analysis of ten prospective cohort studies. Evaluated direct relation between consumption of sugar containing soft drinks and weight gain with cardio-metabolic disease risk. (Bray, 2013)  A meta-analysis of observational studies was conducted, and data were obtained via articles published up to February 2013 on different websites The result showed that intake of sugar-sweetened beverages (derived from caloric sweeteners like table sugar or sucrose, high fructose corn syrup) may raise the risk of Coronary heart diseases, particularly in males and American populations.