dataesr/scientific-paragraphs-categorization huggingface dataset:
Alexa fluor-conjugated secondary antibodies were purchased from Life Technologies. Nuclear counterstain HOECHST 33258 (Invitrogen, 1:2,000) was added before sections were washed in PBS and mounted using Mowiol as a mounting medium. Optical images were taken either using a fluorescence microscope (Axioplan2, ApoTome system, Zeiss) or a laser confocal scanning microscope (LSM510 or LSM780, Zeiss, Germany) using same settings between the conditions. Image analysis was performed using FIJI software  (Schindelin et al. , 2012). All experiments and quantifications were performed blindly to experimental groups. For Figures  1E, F , immunofluorescence were performed on the same day with the same antibody dilution. Regions of interest were drawn over the whole glomerular layer and the mean intensity of GFP or TH compared between control and UNO animals (4 control and 3 UNO animals).
Statistical analyses All measurements are presented as mean ± standard error of the mean. In box plot representation, center red line represents the median; box limits represents upper and lower quartiles and whiskers, outliers. Statistical comparisons were performed using Matlab. In Figure  1F , we used a Wilcoxon rank sum test. In Figure  1K  we used 2-way ANOVA on data ranks with UNO and cell-type as fixed effects followed by Bonferroni post-hoc tests. In Figure  2E , we used 2-way ANOVA on data ranks with repeated measures (t1-t2) and with UNO and cell-type as fixed effects followed by Wilcoxon rank sum test corrected for multiple comparison. In Figure  2F , we used ANOVA on data ranks with repeated measures (t1-t2-t3) followed by Wilcoxon signed rank test corrected for multiple comparison.
Acknowledgments We thank  Stephane Bugeon  for his help with the statistic analysis. We also thank the local  PiCSL-FBI core facility [Institut de Biologie du Développement de Marseille (IBDM), Aix-Marseille University ] supported by the  French National Research Agency  through the  "Investments for the Future" program  ( France-BioImaging ,  ANR-10-INBS-04 ) as well as the IBDM animal facilities.
Funding This work was supported by the  Fédération pour la Recherche sur le Cerveau , the  Agence National pour la Recherche  Grant  ANR-17-CE16-0025-02 ,  ANR-21-CE16-0034-01 ,  Equ 201903007806 , and the  Fondation pour la Recherche Médicale  [Program Equipe FRM 2018,  EQU201903007806  (to HC)]. ML was supported by the  Turing Center for Living Systems (CENTURI).
Participants The EDEN mother-child study is a prospective cohort designed to assess pre-and postnatal factors that influence the health and development of children  (Heude et al. , 2016). The study was approved by the ethics research committee of Kremlin-Bicêtre Hospital (ID 0270 of 12 December 2012) and by Data Protection Authority (CNIL, ID 902267 of 12 December 2012), and is in accordance with the Declaration of Helsinki  (World Medical Association, 2008). Both parents gave their written informed consents. The cohort included 2,002 mother-child pairs, who were recruited between 2003 and 2006 in two university hospital in Nancy and Poitiers (France). The inclusion criteria stipulated that the mothers were before 24 weeks of amenorrhea. Exclusion criteria were mothers younger than 18 years, who had multiple pregnancies, had a history of diabetes, were illiterate and expressed an intention to move out of the region within the next 3 years.
The children wore light underwear during these measurements to minimize any additional weight. Measurement of BMI (Body Mass Index) and BMI curve indicators. BMI was calculated at each age by dividing weight in kilograms by the square of height in meters. On average, children had 10 BMI measurements between birth and 13 years. By collecting BMI measurements at multiple points throughout childhood, we were able to track changes in body mass composition over time and identify three developmental points: the infancy BMI peak, the BMI curve inflection, and the adiposity rebound. To measure these points, we used data from specific time periods. We used data from day 3 and 24 months to measure the infancy BMI peak, data from 6 to 36 months to measure the BMI curve inflection, and data from 18 months and the maximum age available to measure the adiposity rebound.
Structural equation models Our study employed structural equation modeling (SEM) to conduct multivariate analyses on our three samples. These SEMs were carried out using R (https://www. rproject. org/) with R Studio using the R package lavaan  (Rosseel, 2012). These SEMs involved a measurement model that relates observed indicators to a smaller set of unobservable latent variables, and a structural model that estimates the strength of the link between latent variables and single indicators by specifying paths between them. The detailed specification of the measurement and structural models of each SEM used in our analysis are provided in supplementary materials section 'Supplementary Methods. Detailed specification of the structural equation models'). We specified three sets of SEMs: Models 1a and 1b test Hypothesis 1 on Sample 1, Models 2a and 2b test Hypothesis 2 on Sample 2, and Models 3a and 3b test Hypothesis 3 on Sample 3.
Missing Data Two approaches were used to explore assumptions related to missing data. Firstly, we conducted Little's test  (Little, 1988)  to examine whether the data conformed to the 'Missing Completely At Random' (MCAR) assumption, which postulates that the absence of data is independent of any observed and unobserved variable). The test results rejected the null hypothesis of MCAR (χ 2 (1463) = 2361. 6, p <. 001). Secondly, we investigated patterns of missingness by conducting pairwise comparisons. For continuous data, we used Kruskal-Wallis tests, while for discrete data, we used Chi squared tests. These comparisons examined the relationship between missingness and explanatory variables for the six indicators of language proficiency at 5-6 years. After controlling for the false discovery rate (FDR) using the Benjamini-Hochberg approach  (Benjamini & Hochberg, 1995) , the comparisons revealed a systematic relationship between missingness and two out of nine indicators of perinatal adversity.
To handle missing data, we employed a statistical technique known as full information maximum likelihood estimation (FIML). Previous research has indicated that FIML performs relatively well in several situations, even in scenarios where data may be MNAR. It demonstrates reduced bias and sampling variability when compared to ad hoc methods  (Muthén et al. , 1987; Enders & Bandalos, 2001; Baraldi & Enders, 2010). It is important to note that FIML cannot be applied to SEMs with exogenous variables that are non-random, resulting in the automatic deletion of missing values. In our case, perinatal adversity is one such non-random covariate subject to this deletion procedure. Models 1, 2 and 3 were fitted on dataset consisting of 1498 children (757 girls, 741 boys), 1334 children (674 girls, 660 boys), and 1259 children (647 girls, 612 boys), respectively.
We suppose here that early life environmental factors might increase the energy demand of the brain through direct or indirect stimulation of language functions  (Farah et al. , 2008; Tooley et al. , 2021). Subsequently, the high energy demands of a proficient language functioning at 5-6 years of age is made possible at the cost of a delayed investment in body growth. Crucially, our dataset allows for building 5 environmental constructs: Parents socio-economic status (SES) is determined by the combined scores of 3 items, which include the educational level of both the mother and the father, as well as the household density and yearly household income collected at the child's 24 th months of life. A higher sum of z-scores indicates a higher socio-economic status for the parents, i. e. a higher level of education and a more affluent life.
Availability of data and materials Data availability statement. Simulated data used in this report are publicly available from the Open Science Framework (https://osf. io/8x4pm/). Any researcher interested in exploring EDEN data should contact directly the director of the EDEN mother-child cohort [barbara. heude@inserm. fr] to complete a dedicated project form for evaluation by the EDEN steering committee. Code availability statement. All the codes needed to reproduce the results in the paper are available on the Open Science Framework (https://osf. io/8x4pm/).
Samples and methods The samples analyzed include ERM-BB124 (Pig muscle), ERM-BB186 (Pig kidney), ERM-BB184 (Bovine muscle), ERM-BB185 (Bovine liver), ERM-CE196 (Bovine blood), ERM-389 (Bovine urine), ERC -CE464 (Tuna fish), BCR668 (Mytilus mussel tissue), ERM-DB001 (Human hair), and NIST SRM 1515 (Apple leave). The United States Geological Survey (USGS) GSP-2 (Granodiorite, Silver Plume, Colorado, USA) and seawater from North Atlantic Ocean NASS-7 were analyzed during the same analytical session to compare to previous work for quality control measures, as it has been analyzed by Sapphire CC-MC-ICP-MS for multiple times and can provide a first-order sense of data precision  [ 29 , 31 ]. Thoroughly homogenizing the reference materials and employing a sample size greater than 30 mg for dissolution should guarantee the homogeneity of our digestion process.
Calcium isotopic measurements with sapphire CC-MC-ICP-MS The measurements on the Nu-Sapphire installed at IPGP follow the recommendation of Dai et al. ,  [29]. The acid molarity of the sample and the standard was kept identical. The intensities of the mass 41 ( 41 K + ) and 43. 5 ( 87 Sr ++ ) were monitored on the Faraday cups L5 and H5. The K/Ca ratio was < 1% and the Sr/Ca ratio was always < 0. 01% to safely avoid such effect as demonstrated in Dai et al. ,  [29]. A 100 ppb (μg kg -1 ) NIST SRM915b Ca standard solution and the different samples were introduced into the mass spectrometer using an Apex Omega desolvating system fitted with a 100 μL min -1 PFA nebulizer (MicroFlow nebuliser, Elemental Scientific, Omaha, NE, USA) and with standard Ni dry cones ( "dry plasma ")  [29]. Data was collected in static mode, with 40 Ca + measured in a Faraday cup (L6) connected to a pre-amplifier fitted with a 10 10 Ohm resistor and the 42 Ca, 43 Ca and 44 Ca ion beams were measured using Faraday cups at L1, H3 and H6 with 10 11 Ohm resistors.
02 ‰ and 0. 12 𝜀 -unit. It is thus essential to analyze the samples with an intensity match within 1% of the bracketing standard. Meanwhile, a simple correction was performed based on the method previously described  [ 29 , 38 ]. In this study, all the data have been acquired using SRM 915b as the standard because the original SRM 915a standard is not commercially available anymore. However, most of the Ca isotope data in literatures was reported against SRM 915a. To facilitate comparison, the 𝛿-values in this study have been normalized to SRM 915a by using 𝛿 44/40 Ca. The difference in 𝛿 44/40 Ca of the SRM 915b compared to SRM 915a is 0. 72 ‰  [ 39 , 40 ]. Validation of the overall methodology was evaluated through the repeated measurement of the isotopic composition of seawater and GSP-2.
Acknowledgments We deeply thank the two anonymous reviewers for their very insightful comments which made us to reconsider several sections of the paper. F. M. thanks the  DIM ACAV  + ( Domaine d'Interet Majeur, Astrophysique et Condition d'Apparition de la Vie ) of the region  Ile de France  for providing part of the funding for the Nu Sapphire. F. M. acknowledges financial support from the  Horizon ERC DAI  (#  101059236. Parts of this work were supported by  IPGP multidisciplinary program  PARI. W. D. thanks the financial support of the  Chinese Scholarship Council (CSC , No. 202006410008  ).
F. M. thanks the DIM ACAV + (Domaine d'Interet Majeur, Astrophysique et Condition d'Apparition de la Vie) of the region Ile-de France for providing part of the funding for the Nu Sapphire. F. M. acknowledges financial support of the UnivEarthS Labex program at Sorbonne Paris Cité (#ANR-10-LABX-0023 and #ANR-11-IDEX-0005-02). Parts of this work were supported by IPGP multidisciplinary program PARI.
Data collection process, items and study risk of bias assessment Data that was collected from the studies include publication details (title, author, year and country, journal), study design (aims of study, method of data collection, response rate, recruitment methods, eligibility criteria, duration of study), details of study patients (number, diagnostic group, sex, BMI, age, Acute Physiology and Chronic Health Evaluation (APACHE) II/IV or Sequential Organ Failure Assessment (SOFA) score), intervention measures (type of feeding, duration of intervention, protein delivery (g/kg), energy delivery (kcal/kg)) and data for all clinically relevant outcomes. Risk of bias assessment was undertaken using the Revised Cochrane's Risk of Bias tool for RCT's, ROBINS-I tool for non-randomized trials and Risk of Bias for Nutrition Observational studies tool for observational studies [15e17].
Changes in muscle mass Data on changes in muscle mass was reported by 6 studies  [26, 31, 32, 36, 37, 47] , measured as quadriceps muscle cross-sectional area with computed tomography (CT) scan  [26] , quadriceps muscle layer tissue with sonography  [31] , mid upper arm circumference  [32] , femoral muscle cross sectional area with CT scan  [36, 37]  and diaphragm volume with CT scan  [47]. Changes in muscle mass were measured between baseline (day 0e2 of admission) and 5 weeks after admission. Analysis showed a difference between groups in favour of high protein (SMD 0. 8, 95% CI 0. 4 to 1. 3; I 2 ¼ 71%, p¼<0. 01 for heterogeneity; Supplementary file 4-Q). The forest plot for changes in muscle mass is presented in Fig. 3.
The microsomes were resuspended in 100 µL of 150 mM Tris-HCl buffer pH 8, and the cytosol and microsome fractions were stored at -80 °C. The protein levels of all fractions were quantified by the Lowry method by using bovine serum albumin as a standard. Proteomic data analysis. Raw data collected during nano LC-MS/MS analyses were processed and converted into an *. mgf peak list format with Proteome Discoverer 1. 4 (Thermo Fisher Scientific). MS/MS data were analyzed using the search engine Mascot (version 2. 4. 0, Matrix Science, London, UK) installed on a local server. Searches were performed with a tolerance on mass measurement of 0. 2 Da for precursor and 0. 2 Da for fragment ions against a composite target-decoy database (20,506 × 2 total entries) built with a human Swissprot database (taxonomy 9606, January 2019, 20,388 entries) fused with the sequences of recombinant trypsin and a list of classical contaminants (118 entries).
Cysteine carbamidomethylation, methionine oxidation, protein N-terminal acetylation, and cysteine propionamidation were searched as variable modifications. Up to one missed trypsin cleavage was allowed. The identification results were imported into Proline software (http:// proli ne. profi prote omics. fr) for validation  69. Peptide spectrum matches taller than nine residues and ion scores > 10 were retained. The false discovery rate was then optimized to be below 1% at the protein level using the Mascot Modified Mudpit score. Spectral counting analyses were performed with Proline 2. 0.
After five washes in TBST, the membrane was incubated for 1 h at room temperature with agitation in TBST with goat anti-mouse HRP secondary antibody (P0447, Agilent, Santa Clara, USA, 1:12,500) for both ALDH types and with the goat anti-rabbit HRP secondary antibody (P0448, Agilent, Santa Clara, USA, 1:12,500) for AKR1B10. After five washes, the membrane was revealed by soaking for one minute in a mixture of 1. 5 mL Luminol/enhancer solution and 1. 5 mL Peroxide Reagent solution from the ECL clarity western substrate Bio-RadTM pack. The membrane was then placed in a ChemiDocTM acquisition system, and images were acquired by luminescence every 6 s for 10 min and analyzed using Image LabTM 4. 0. 1 Software (Bio-Rad). A full image for each gel is shown in Supplemental Fig. 3.
Images were acquired with a DS-Ri2 digital camera using the software NIS-Elements Basic Research (all from Nikon, Tokyo, Japan). Protein production and purification. The DNA sequences encoding human ALDH1A1 (UniProt code P00352), ALDH3A1 (UniProt code P30838), and AKR1B10 (UniProt code O60218) were optimized for expression in E. coli, and the sequence GC rate was modified to approximately 50%. They were subcloned into the pET24b, pET22b, and pET26b vectors between the NdeI and SacI restriction sites. A sequence encoding 6 histidines was added at the N-terminal extremity of ALDH3A1 and AKR1B10 for purification. ) gal dcm (DE3) pLysE(Cam R ), were used to express both ALDHs and AKR1B10. The transformed cells were grown at 37 °C in LB medium (containing 100 μg mL -1 ampicillin) and induced by the addition of isopropyl β-d-1-thiogalactopyranoside (IPTG) when the cell culture reached the selected OD measured at 600 nm.
Enzymatic assays. Enzymatic activity was determined on a UV-1800 spectrophotometer (Shimadzu, Japan) by measuring the absorbance at 340 nm, which corresponds to the NAD(P)H absorbance wavelength. Enzymatic reactions were performed in a 1 mL quartz cuvette filled with 1 mL of a mixture containing 100 mM KPi buffer pH 7. 0, a saturating concentration of cofactor, which was 1 mM NAD for both ALDH and 200 µM NADPH for AKR1B10, an odorous molecule diluted in methanol, 500 nM ALDH1A1 or 70 nM ALDH3A1 or 250 nM AKR1B10, and water to volume. Each experiment was repeated two times. The initial velocities for an increasing range of odorant concentrations were measured, and the Michaelis-Menten curve was plotted using SigmaPlot software according to the equation vi = (Vmax × [S])/(K M + [S]), where vi is the initial rate in µM min -1 , Vmax is the maximum initial rate in µM min -1 , [S] is the substrate concentration in mol L -1 , and K M is the Michaelis constant in µM.
Diffraction experiments were performed on the SOLEIL synchrotron beamline PROXIMA1. Crystals of ALDH3A1-octanal diffracted to 1. 80 Å. The datasets were indexed and integrated with XDS  70  and scaled with pointless  71. The structure was solved by molecular replacement using the coordinates of the unbound form of ALDH3A1 (PDB code 3SZA). The 3D structure was manually adjusted with COOT  72  and refined with PHENIX  73. Inspection of the electron density maps around the active site region allowed for the identification and building of ligands. Restraint files for ligand refinement were generated with the GRADE webserver (http:// grade. globa lphas ing. org). The structure.
UniProt accession Number of identified peptides (number of spectra for donors 1, 2, and 3, respectively) Retinal dehydrogenase 1 (ALDH1A1) P00352 59 (299, 212, 73) Aldehyde dehydrogenase, dimeric NADP-preferring (ALDH3A1) P30838 52 (248, 207, 30) Alcohol dehydrogenase class 4 mu/sigma chain (ADH7) P40394 35 (100, 77, 14) Alcohol dehydrogenase 1C (ADH1C or ADH3) P00326 29 (99, 74, 12) Peroxiredoxin-5, mitochondrial P30044 16 (55, 53, 14) Alcohol dehydrogenase [NADP( +)] (AKR1A1) P14550 21 (44, 48, 4) Carbonyl reductase [NADPH] 1 P16152 18 (50, 41, 4) Protein/nucleic acid deglycase DJ-1 Q99497 23 (49, 36, 6) 4-Trimethylaminobutyraldehyde dehydrogenase (ALDH9A1) P49189 22 (42, 36, 6) Thioredoxin reductase 1, cytoplasmic Q16881 21 (33, 28, 2) Prostaglandin reductase 1 Q14914 14 (35, 23, 4) Alcohol dehydrogenase class-3 (ADH5) P11766 16 (26, 20, 2) Aldo-keto reductase family 1 member B10 (AKR1B10) O60218 13 (26, 22, 0) Glutathione reductase, mitochondrial P00390 18 (15, 29, 4) Alpha-aminoadipic semialdehyde dehydrogenase (ALDH7A1) P49419 15 (24, 20, 0) Aldo-keto reductase family 1 member C2 (AKR1C2) P52895 12 (23, 19, 0) Sorbitol dehydrogenase Q00796 10 (15, 21, 1) Aldo-keto reductase family 1 member C3(AKR1C3) P42330 9 (17, 16, 0) Biliverdin reductase A P53004 11 (13, 15, 1) Alcohol dehydrogenase 1B (ADH1B) P00325 16 (17, 10, 2) Thioredoxin domain-containing protein 17 Q9BRA2 5 (14, 8, 5) Aldehyde dehydrogenase, mitochondrial (ALDH2) P05091 16 (11, 15, 0) Aldose reductase (AKR1B1) P15121 8 (15, 10, 1) Sepiapterin reductase P35270 11 (15, 11, 0) Cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) O75891 3 (13, 11, 1) Quinone oxidoreductase Q08257 8 (10, 10, 0) Isocitrate dehydrogenase [NADP], mitochondrial P48735 10 (5, 14, 0) Alcohol dehydrogenase 1A (ADH1A) P07327 16 (9, 7, 1) Dihydropteridine reductase P09417 7 (8, 7, 0) Aldehyde dehydrogenase family 1 member A3 (ALDH1A3) P47895 10 (5, 9, 0) Carbonyl reductase [NADPH] 3 O75828 8 (5, 5, 0) 25-Hydroxycholesterol 7-alpha-hydroxylase O75881 4 (3, 7, 0) Aflatoxin B1 aldehyde reductase member 2 (AKR7A2) O43488 6 (3, 7, 0) Flavin reductase (NADPH) P30043 3 (4, 5, 0) Alcohol dehydrogenase 6 (ADH6) P28332 4 (4, 4, 0) NADH-cytochrome b5 reductase 2 Q6BCY4 5 (5, 3, 0) Protein AMBP P02760 3 (1, 4, 0) Apoptosis-inducing Factor 2 Q9BRQ8 1 (2, 2, 0).
, 2020. The XAS data of the experimental samples and Se standards were collected in fluorescence and transmission mode, respectively. Selenium K-edge X-ray absorption spectra were measured at the MARS beamline (SOLEIL synchrotron facility in Paris, France), which is a bending magnet beamline for Multi Analyses on Radioactive Samples. The experimental setup and technical parameters behind the measurements were same as those followed in  Ruiz-Fresneda et al. (2020) ; they are detailed in the Supplementary Material 1. 1.
A set of biochemical pathways implicating S-doped molecules such as glutathione (GSH), glutathione reductase (GR), glutathione peroxidase (GP), or thioredoxin reductase (TrxR) in formation of Se 0 through a series of redox reactions have been widely reported before  (Xu and Barton, 2013; Eswayah et al. , 2019). Interestingly, organic Se molecules such as selenocysteine amino acids are known to be essential in the catalytic activity of TrxR or GP forming a redox active disulfide bond in the active site  (Hawkes and Alkan, 2010). Both enzymes GR and TrxR could be involved in reduction to Se 0 in S. bentonitica since the genes encoding them are present in its genome (accession number: MKCZ00000000). The formation of Se nanoparticles having diverse morphologies is widely described  (Presentato et al.
6A-C ); specifically, three different dspacings of 0. 29, 0. 37, and 0. 48 nm could correspond to crystal planes of monoclinic-Se (m-Se) (Pinel-Cabello et al. , 2021) (Fig. 6B ). The space lattices 0. 29 and 0. 37 nm are characteristic as well in trigonal Se (t-Se) according to the American Mineralogist Crystal Structure Database (http://rruff. geo. arizona. edu) [accessed December 2021]. Yet the detection of the 0. 48 nm d-spacing confirmed the monoclinic structure of the nanorods. Similar results were obtained recently for Se nanorods produced by S. bentonitica contacted with Se IV  (Pinel-Cabello et al. , 2021). HRTEM analyses, along with the FFT from a nanorod selected area, agree with the SAED pattern, revealing the presence of 0. 29-0. 3 and 0. 38 nm lattice spacings (Fig. 6C-F ). As mentioned above, these spacings could correspond to different crystal planes of both m-Se and t-Se.
The SurSaUD system collects daily data from 4 sources: emergency departments (EDs; OSCOUR ED network)  [1] , emergency general practitioners (SOS Médecins network), crude mortality (civil status data), and electronic death certification including causes of death  [2]. Since its inception, the OSCOUR network has recorded >130 million ED visits. Data are collected by the direct extraction of information from patients' electronic health records (EHRs) in a common format for the entire territory and transmitted to Santé Publique France via the OSCOUR network. Owing to the coding of the main diagnosis (International Classification of Diseases  [ICD]  10th Revision codes) and progressive improvement of data quality  [3] , the network can establish real-time monitoring of public health events such as epidemics of influenza, gastroenteritis, or bronchiolitis  [4] [5] [6] [7].