dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition and filtering Natural sequence variations from functionally annotated human ORs  [31, 32]  were obtained from the Genome Aggregation Database (gnomAD, http://gnomad. broadinstitute. org/) using Python (v. 3. 7. 6) data mining scripts. Variant tables for each OR were imported to R (v. 3. 6. 2), including information of chromosome location, transcript consequence and allele frequencies in seven subcontinental populations  [19]  (Supplementary Table  S1 ). BLAST (v. 2. 10. 0) and Python scripts were used to compare the collected sequence information with UniProt database (release 2019_11, https://www. uniprot. org/). The collected data was then filtered to remove null values, duplicates, missing rsIDs and sequence conflicts with reference Swiss-Prot entries, resulting in a curated dataset of 118,057 nucleotide variants from 374 human OR genes (Supplementary Table  S2 ).
Development of an interactive database with the annotated variation data Substitution scores and topological annotation (including FC/BS and BW numbering) were transferred to the mutation data table using Python data mining scripts, completing the annotation process (Figure  1  and Supplementary Table  S5 ). A standalone application was programed with the open source RStudio (v. 1. 2. 5003) to manage and visualize this curated mutation dataset (https://github. com/lmc-uab/hORMdb). This database resource is also made available online as an interactive web server programed with the Shiny Server package (v. 1. 5. 12. 933) (http://lmc. uab. cat/hORMdb). This information and their associated substitution scores were transferred to each entry in the mutation data table, thus completing the annotation process.
RESULTS Natural variations in human ORs were mined from nucleotide sequence data of 138,632 unrelated individuals in the Genome Aggregation Database  [19] , and annotated at structural level with information of the class A GPCR family as resumed in Figure  1. This curated dataset comprises 118,057 nucleotide changes in 374 functional OR genes, which belong to 17 OR families (Figure  2 ). Overall average number of mutations per receptor was 316, with a prominent variation rate in the OR52 family (average of 343) and five members of the OR4 family (OR4A5, OR4A15, OR4A16, OR4C16 and OR4C46) with more than 500 mutations/receptor. On the other hand, lowest variation rates correspond to the OR14 family (average of 256) and few more than a dozen receptors with less than 100 mutation counts (Supplementary Table  S2 ).
This staggered mutational distribution supports a heterogeneous selective pressure in OR genes, as indicated by other studies  [33, 34]. Numbers in parenthesis correspond to the total number of receptors analyzed at each family. Gene names and family assignation correspond to the recommended terms by the HUGO Gene Nomenclature Committee (HGNC). Names of individual OR family members are in the format "ORnXm": a root name 'OR', followed by family numeral (n), subfamily letter (X), and a numeral (m) representing the particular gene within the subfamily. For example, OR2A1 is the first OR gene in the family 2, subfamily A. Most common variations types in the collected dataset correspond to missense (~64%) and synonymous substitutions (~25%), followed by frameshifts, non-coding (3′UTR and 5′ UTR), stop gained, and a reduced number of other minor mutations events (Figure  3A ).
Regarding the nature of the changes, transitions and transversions are the most likely mutational events, representing 96% of the entire dataset, whilst the remaining 4% correspond to deletions and insertions (inset on Figure  3A ). The large OR multigene family occupies vast amounts of genomic territory. As expected, the number of mutations per chromosome is linked to the genome distribution of the OR genes (Figure  3B ). Chromosome 11, which contains the largest number of receptors, displays the highest number of variants, followed by chromosome 1. For the rest of chromosomes hosting OR genes the number of variants ranges from ~7,000 to less than 100, and no data was recorded for chromosomes 4, 8, 13, 18, 20, 21 and Y. A graphical display of the unevenly chromosomal distribution of the mutations in the 17 human OR families is available in the Supplementary Figure  S6.
Allele frequencies and population distribution of the variant dataset Analysis of the frequency values from gnomAD disclose only 2,152 OR natural variants with global allele frequency above 1% in the collected dataset. By contrast, >95% corresponds to low frequency variants (59,829 of which are singletons), exposing an extraordinary interindividual variation in the human OR gene repertoire. Taking into account that differences in olfactory sensitivity could be at least partly explained by the prevalence of particular mutated ORs alleles in individuals within populations  [35] ; independent frequency ranges were analyzed on each of the seven sub-continental populations in the database (Figure  4  A-G and Supplementary Table  S1 ). This analysis shows a similar trend of frequency distribution among ethnic groups, characterized by an elevated number of mutations with allele frequencies below 0.
Mutability landscapes of amino acids changes Single amino acid variants in human ORs can alter the resulting phenotype, for example, by altering the odorant perception  [44]. Thus, we investigate the type and magnitude of the amino acid changes in missense substitutions (75,501 variants in the dataset) as a first approximation to evaluate their functional consequences at the molecular level. As displayed in Figure  7A , hydrophobic residues (Leu, Ile, Val and Ala) exhibited the highest levels of mutability, followed by Ser and Thr in agreement with their stabilization roles on the structure of TM helices  [45, 46]. Conversely, substitutions of Trp or polar/charged Gln, Glu, Lys, Asp, His and Asn (often associated with protein malfunction in TM proteins) were less frequent  [47, 48]. The evaluation of the magnitude of changes was conducted using amino acid substitution scores derived from more than one thousand class A GPCR sequences (including ORs) and thus, reflecting the compositional bias distinctive of this particular family of proteins  [30]  (Figure  7B  and Supplementary Figure  S5 ).
Development of an interactive application to explore the human OR mutation data It is expected that progress on OR genome association studies will continue to be made in the future. Thus, an interactive computational application was developed for the free access and analysis of this data by academics and industry professionals. The human Olfactory Receptor Mutation database (hORMdb, http://lmc. uab. cat/hORMdb) provides a curated and downloadable repository of natural variations in human ORs and several interactive tools for the selection, filtering and analysis of its contents (Figure  9 ). The hORMdb is structured as a data table (Figure  9A ), containing information about individual dbSNP entries, particular genes or entire OR families, including the types of nucleotide and amino acids changes, allele frequencies in several sub-continental populations  [58] , and topological location in the receptor structure.
Definition of the populations for analysis As this is a non-inferiority study, analyses will be performed according to both the intention-to-treat and per-protocol principles. ► Intention-to-treat analysis: all patients included in the trial, except those with consent withdrawal, will be taken into account in the analysis, regardless of adherence to or non-respect of the intervention. ► Per-protocol analysis: the population for the perprotocol analysis will be defined during a review of the data performed blind to the randomisation group. In the maintenance of caloric intake group, patients will be included in the per-protocol analysis if the enteral nutrition flow rate has been maintained until extubation without a decrease in flow rate higher than 50% within 6 hours before extubation and enteral nutrition has not been completely interrupted more than 30 min within 6 hours before extubation.
Description of patient characteristics at inclusion The groups resulting from randomisation will be described with the following statistics (no statistical tests will be performed): for qualitative variables, absolute numbers and percentages, and, for quantitative variables, mean and SD or median and IQR, depending on the data distribution. The characteristics of the patients at inclusion will be reported at the patient level or at the cluster level when appropriate. The variables identified will be age, sex, body mass index, SAPS II, type of admission (unscheduled surgical, scheduled surgical, medical), the comorbid condition, the number of spontaneous breathing trial failure, administration of steroids before extubation,the cough evaluation, the tracheal secretion evaluation and the risk factors of extubation failure (stroke, neuromuscular disease, central nervous system infection, severe head trauma, diaphragmatic dysfunction, depressive disorder, delirium).
Extubation visit This visit will take place at the time of extubation and gather data linked to extubation, data of the preceding day and the period immediately after extubation. Collected information: caloric supply over 24-hour periods, time from intubation to extubation and time Open access from the first successful spontaneous breathing trial to extubation, incidence of hypoglycaemia and hyperglycaemia, maintenance or removal of the gastric probe immediately after extubation, and time to oral refeeding after extubation. The following risk factors for extubation failure will be recorded: neuromuscular disease, acute cerebral vascular disease (ischaemic or haemorrhagic), neuropathy, myopathy, infection of the central nervous system, serious head trauma, diaphragm dysfunction, cognitive dysfunctions, anxiety-depression syndrome, delirium and Richmond Agitation Sedation Scale score in the 24 hours after extubation.
Duration of the study Patients are expected to be included during an 18-month period starting in April 2018. ► 2017: protocol approval from the ethics committee and trial tools development (case report form, randomisation system). ► 2018-2019: inclusion of patients. ► 2020: cleaning and closure of the database. Data analysis. ► 2021: writing of the manuscript and submission for publication in a peer-reviewed journal. The duration of participation for each patient in the protocol corresponds to their stay in intensive care, up to a maximum of 28 days. The first patient included dates from April 2018.
Data management Data management in this study will be the responsibility of a data manager from CIC INSERM 1415, CHRU Tours, France. The eCRF will be developed with Ennov Clinical software. Data will be managed according to the standard operating procedures in force at CIC INSERM 1415. The data will be input at the investigating centres and queries will be formulated by the data manager according to a Open access consistency control plan established during the design of the eCRF. A blind review of the data will be performed before the database is locked. The database will be locked according to standard operating procedures in force at CIC INSERM 1415, and the data will be extracted for statistical analyses.
In all cases, before inclusion in the study, express informed consent will be obtained from the patient, or, if this is not possible, from a proxy (legal representative or next of kin), and the process documented in the patient's medical file, according to French law. In all cases, the patient will be informed, and consent obtained as soon as possible. Subjects can oppose their participation secondarily and request to be withdrawn from the study at any time and for any reason. The investigator can stop the study intervention temporarily or definitively for any reason in the best interests of the subject. Research assistants will regularly monitor all the centres on site to check adherence to the protocol and the accuracy and completeness of the data recorded. The results will be submitted for publication in peerreviewed journals.
Funding This work was supported by a grant of the  French Ministry of Health  (Projet hospitalier de recherche clinique, appel d'offre inter-régional Ouest No. API16/A/063 ). Competing interests SE declares receiving consulting fees, unrestricted research grants and equipment research support from  Aerogen , unrestricted research grant from  Fisher & Paykel , unrestricted research grant from  Hamilton medical , and consulting fees from  La Diffusion Technique Française. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Acknowledgments We would like to acknowledge the technical staff of  RENECOFOR-ONF  for providing samples for this study as well datasets, apart from those for iodine, selenium and caesium. This work was financed by the  Région Nouvelle Aquitaine  and the  Agence Nationale de la Recherche  with funds allocated in the ' Investissements d'Avenir' framework program  under reference  ANR11 -RSNR-0002.
Ethical Statement This study was approved by the regional ethics committee (comitéconsultatif de protection des personnes dans la recherche biomedicale Tours-Reǵion Centre-Ouest 1 under registration number DC-2016-2651) and by the French regulatory agency (Agence de la Biomedecine, the national authority for organ procurement and transplantation in France, under registration number PFS16-005). Written informed consents were obtained for each patient according to the Declaration of Helsinki. All data were collected anonymously in a prospectively maintained database declared to the French Data Protection Authority. No potentially identifiable human images or data is presented in this study.
Statistical Methods Statistical analysis was performed using Excel ® for Mac Os (Microsoft ® Corporation) and Graph Pad software v7 (La Jolla, Inc. ). Statistical significance was set at 0. 05. Quantitative data are presented as percentages and absolute numbers. Qualitative data are presented as mean [±standard error of the mean (SEM)]. Regarding mouse model, nonparametric tests were used because of the small number of animals per group and in order to increase robustness. Experimental and control groups were compared with twotailed Mann-Whitney U test. When animals were their own control, two-tailed Wilcoxon signed-rank test was used. Considering human study, Shapiro-Wilk test was used to assess distribution of the variables. Friedmann and Spearman nonparametric tests were used to analyze evolution of serum concentration of the molecules of interest.
ACKNOWLEDGMENTS The authors would like to acknowledge the following:  Ms Sandrine Joffrion  from the  Biochemistry Laboratory of the University Hospital of Poitiers (Head: Prof Thierry Hauet ) for processing liver function tests; the technical team from the  Pathology Laboratory of Tours University Hospital of Trousseau  (Head:  Prof Gonzague de Pinieux ) for processing formol liver samples; the team of PREBIOS animal facility; program  FEDER/CPER "HABISAN" of Nouvelle Aquitaine  for their participation in equipment funding; the  PREBIOS platform  ( University of Poitiers ) for the facilities and expertise; and  Dr Adriana Delwail  of  Image Up platform, University of Poitiers , for flow cytometry.
For RNAseq, library preparation was performed with the TruSeq Stranded mRNA Sample Preparation Kit (Illumina, San Diego, CA, USA), and samples were single-end sequenced on a HiSeq4000 (Illumina). Raw data were preprocessed using cutadapt version 1. 10 (https://doi. org/10. 14806/ej. 17. 1. 200), and reads with a Phred quality score above 20 and covering at least 40 nt were mapped onto the mouse genome mm10 assembly using STAR  [21]. Gene expression was quantified using htseq-count  [22]  with annotations from Ensembl version 96 (http://www. ensembl. org/index. html) and union mode, and normalized with DESeq2  [23]. For the establishment of sample-to-sample distances heatmaps, hierarchical clustering was performed using the UPGMA (unweighted pair group method with arithmetic mean) algorithm.
-R. , A. -L. C. and S. D. ; writing-original draft preparation, R. S. -R. and J. B. ; supervision, B. G. , J. L. and J. B. ; project administration, J. L. and J. B. ; funding acquisition, J. L. and J. B. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the ANR-10-LABX-0030-INRT grant, a governmental fund managed by the ANR (Agence Nationale de la Recherche) within the ANR-10-IDEX-0002-02 Investissements d'Avenir frame program, and by the AFM (Association Française contre les Myopathies). Roberto Silva-Rojas was funded through a doctoral fellowship from the FRM (Fondation pour la Recherche Médicale, PLP20170939073). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the IGBMC (project numbers 2016031110589922, 2020052817261437) and the French Ministry of Research on 22 July 2016 and 13 August 2020.
B. ; investigation, R. S. -R. , A. -L. C. and S. D. ; writing-original draft preparation, R. S. -R. and J. B. ; supervision, B. G. , J. L. and J. B. ; project administration, J. L. and J. B. ; funding acquisition, J. L. and J. B. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the ANR-10-LABX-0030-INRT grant, a governmental fund managed by the ANR (Agence Nationale de la Recherche) within the ANR-10-IDEX-0002-02 Investissements d'Avenir frame program, and by the AFM (Association Française contre les Myopathies). Roberto Silva-Rojas was funded through a doctoral fellowship from the FRM (Fondation pour la Recherche Médicale, PLP20170939073). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the IGBMC (project numbers 2016031110589922, 2020052817261437) and the French Ministry of Research on 22 July 2016 and 13 August 2020.
Discussion The oxidative and antioxidant profile was investigated in three rabbit tissues: the liver, chosen as the lipid metabolism centre, the brain as an LCPn-3-sensitive tissue, and the testis as an oxidation-sensitive organ. All these tissues contain a high quantity of PUFAs; however, there is limited data on how their PUFA profile changes due to different dietary inputs. PUFAs have a high susceptibility to oxidation via free radicals due to their double bonds  [55] , which constitute attack sites for electrically charged molecules that start the oxidative chain. Because lipid oxidation is involved in many pathological processes, including cancer, neurodegeneration, and diabetes, ways to reduce lipid peroxidation have been widely studied  [25]. After the initialisation of the reaction, which is promoted by reactive oxygen metabolites (ROMs, Figure  9 ), the peroxyl radicals tend to neutralise themselves by reacting with other substances (Propagation, Figure  9 ), thereby causing a self-perpetuating chain  [56]  until a final step (Termination, Figure  9 ) with the generation of secondary products of the oxidative degradation (Fragmentation, Figure  9 ): i.
e. , aldehydes, such as MDA and 4-hydroxynonenenal (4HNE), conjugated dienes, and isoprostanoids  [57]. Furthermore, the oxidation of some molecules (prostaglandins) originated by the LCP metabolism generates other oxidised forms (i. e. , isoprostanoids). The oxidative and antioxidant profile was investigated in three rabbit tissues: the liver, chosen as the lipid metabolism centre, the brain as an LCPn-3-sensitive tissue, and the testis as an oxidation-sensitive organ. All these tissues contain a high quantity of PUFAs; however, there is limited data on how their PUFA profile changes due to different dietary inputs. PUFAs have a high susceptibility to oxidation via free radicals due to their double bonds  [55] , which constitute attack sites for electrically charged molecules that start the oxidative chain.
Determining current intakes of thiamine and potential fortification vehicles As described above, there are several pieces of information required to design any thiamine fortification scheme. An estimate of current thiamine intakes and its distribution in the target and nontarget populations is required. Likewise, an estimate of the amount of the food vehicle consumed is required along with its intake distribution. Although food balance sheets can be used, these estimates are best obtained through a (repeated) 24-h recall in a survey in a random sample of all population subgroups. When this is not possible, a survey of the target population and those at highest risk of excess, typically pregnant/lactating women and men, respectively, should be conducted. A detailed explanation of how to conduct surveys and 24-h recalls is provided in a manual by Gibson and Ferguson,  62  and the thiamine contents of African and Asian foods are available in Food Composition Databases.
63 A single recall tends to result in a wide distribution with more people at the extremes of intake, both high and low. A repeat recall in a subset of participants allows for statistical adjustment of intrasubject variation and represents a more "usual" intake distribution. This results in a smaller proportion of the population below the EAR or above the UL. 64 he same adjustments should be made for the fortification vehicle. There are a number of statistical approaches used to adjust intakes for intrasubject variation and software is freely available, for example, the Software for Intake Distribution Estimation (PC-SIDE) developed at Iowa State University. 65, 66 rtification modeling With knowledge of the intake distributions of both thiamine and the fortifiable food vehicle, it is possible to estimate the amount of thiamine to be added.
Abstract: Total daily energy expenditure ("total expenditure", MJ/d) reflects daily energy needs and is a critical variable in human health and physiology, yet it is unclear how daily expenditure changes over the life course. Here, we analyze a large, globally diverse database of total expenditure measured by the doubly labeled water method for males and females aged 8 days to 95 yr. We show that total expenditure is strongly related to fat free mass in a power-law manner and identify four distinct metabolic life stages. Fat free mass-adjusted daily expenditure accelerates rapidly in neonates (0-1yr) to ~46% above adult values at ~1 yr, declines slowly throughout childhood and adolescence  (1-20 yr)  to adult levels at ~20 yr, remains stable in adulthood (20-60 yr) even during pregnancy, and declines in older adults (60+ yr).
The decline in total expenditure beginning in the sixth decade of life corresponds with a decline in fat free mass  (9)  and "physical activity level", PAL (the ratio of total/basal expenditure), but may also reflect age-related reductions in organ metabolism. We investigated the effects of age, body composition, and sex on total expenditure and its components, using a large (n = 6,421; 64% female), geographically and economical diverse (n = 29 countries) database of doubly labeled water measurements for subjects aged eight days to 95 years  (16) , calculating total expenditure from isotopic measurements using a single, validated equation for all subjects  (17). Basal expenditure, measured via indirect calorimetry, was available for a n = 2,008 subjects, and we augmented the dataset with additional published meaures of basal expenditure in neonates and doubly labeled water-mesaured total expenditure in pregnant and post-partum women (Methods; Table  S1 ).