dataesr/scientific-paragraphs-categorization huggingface dataset:
To date for ABR data, a mixed model approach has been employed in IMPC, using sex and genotype as fixed parameters and date-of-procedure as a random term (in order to allow for any batch effects). The genotype parameter is tested for its significant effect on the model, and this p-value is returned for the genotype effect within the data. However, mixed models cannot be fitted when the target values are all the same, which occasionally happens in ABR data (e. g. , when every mutant animal is profoundly deaf at all frequencies). Thus we adopted a different method to undertake a more robust analysis of the ABR data set. The PhenStat package includes a 'reference range plus' function  13. Within this function, a reference range is created by ordering all of the baseline data points and creating a 98% reference range.
This is achieved by taking the 1% percentile and 99% percentile data values, between which 98% of all values lie. The reference range is a non-parametric technique that makes no assumptions about the data distribution. The number of observations within and without this range is counted for the baseline and the mutant data values. A two-way contingency table is thus created. A Fisher's exact test is then used to compare the baseline values against the mutant value, producing a p-value that reflects whether the two sets of counts are from the same data set. For our analysis we employed all the available IMPC ABR mutant data, and C57BL/6N wild-type data. For each genotype, the mutant and matched wild-type data were used to create reference ranges, build contingency tables, and calculate p-values.
Using this approach, the available ABR data from 3006 lines yielded 328 candidate hearing loss genes (Supplementary Data 1). The genotype p-values provide an initial indicator of possible biological significance. As such, the ABR phenotyping data for each of these 328 lines underwent expert-led manual curation to assess phenodeviance and identify a robust data set of candidate hearing loss genes with a low false discovery rate. Manual curation. The ABR threshold data for each of the 328 individual mutant strains were examined to ascertain the spread of data points in relation to their respective centre wild-type mean. Strains showing discordant thresholds within sex were removed from the list. In addition, strains that exhibited elevated thresholds at only one middle-range frequency (12, 18 or 24 kHz) were also removed from the list.
The stringent critical p-value and manual curation parameters were selected to allow production of a robust gene list with few false positives. However, we recognise that our analyses will likely generate some false negatives. To test this, we examined the set of 261 excluded genes across all relevant gene function databases for evidence of an association with hearing loss. PubMed, OMIM and MGI searches demonstrated that only nine had evidence for an auditory phenotype in either human or mouse (Supplementary Table  2 ). Six of these genes, however, showed association with hearing loss in human patients only and studies of mouse mutants have failed to uncover an auditory phenotype. This may reflect a difference in the role of these genes between mouse and human, or alternatively, the impact of genetic background on penetrance.
Candidate hearing loss gene classification. Dependent on their auditory profile, we assigned each of the 67 candidate genes to four broad classes of hearing loss using the following criteria: only the higher frequencies affected-high-frequency hearing loss (13 genes); only the lower frequencies affected-Low-frequency Frequency (kHz) hearing loss (10 genes); if three or more frequency thresholds are ≥45 dB above the centre median-severe hearing loss (25 genes); and, if <3 frequency thresholds are ≥45 dB above the centre median-mild hearing loss (19 genes) (Fig. 2 ; Table  1 ; Methods section). PubMed, OMIM and MGI searches indicated that 15 of the 67 genes were known hearing loss-associated genes. However, despite these database searches 52 genes had not been associated with an auditory phenotype in either mouse or human.
The majority of IMPC novel candidate genes (41/52) are unconnected to the central network. Thin grey edges show interactions with a combined confidence score of ≥0. 4, summated from evidence types: 'curated databases'; 'experimentally determined'; and, 'automated text mining'. Bold red edges show 'known' interactions with a combined confidence score of ≥0. 4, summated from 'curated databases' and 'experimentally determined' only substantially to the total number of hearing loss loci  20. Finally, the data from a parallel screen of around 1000 genes reports similar findings to IMPC  27. In summary, the IMPC programme has identified a significant number of novel candidate hearing loss genes, amplifying our understanding of the auditory genetic landscape and providing the bases for new insights into auditory mechanisms, as well as a fund of new candidate genes for the many unidentified loci in the human population.
Methods Ethical approval. IMPC Centres breeding mice and collecting phenotyping data were guided by their own ethical review panels and licensing and accrediting bodies, reflecting the national legislation under which they operate. Details of their ethical review bodies and licenses are provided in Table  2. All efforts were made to minimise suffering by considerate housing and husbandry. All phenotyping procedures were examined for potential refinements that were disseminated throughout the Consortium. Animal welfare was assessed routinely for all mice involved. Mouse generation. In the majority of cases, targeted ES cell clones were obtained from the European Conditional Mouse Mutagenesis Program (EUCOMM) and Knockout Mouse Project (KOMP) resource  12, 28  and injected into BALB/cAnN or C57BL/6J blastocysts for chimera generation.
These included allele-specific assays undertaken at the level of the ES cell and of the resulting mouse. The CRISPR-engineered alleles were validated by PCR amplification of the targeted locus and Sanger sequencing to confirm introduction of the required editing event. All QC data, including for CRISPR-engineered alleles and the guides employed, and Sanger sequencing of the engineered allele, are centrally deposited in the iMits (International Micro-injection tracking system) database. This centrally held IMPC database tracks allele generation and enables all mouse clinics to assess data of any generated strain. These data are freely available on request. In addition, when mice or germplasms are distributed from an IMPC repository, the full allele-specific genotyping details are communicated.
Housing and Husbandry. Housing and husbandry data are captured for each IMPC Centre as described in Karp et al. 31  and is available from the IMPC portal (http://www. mousephenotype. org/about-impc/arrive-guidelines). In addition, pertinent metadata parameters for each IMPC centre are also available from the portal including, cage-type, caging density, bedding and enrichment details, feed constituents, lighting regimes, temperature and humidity of animal holding rooms, and full strain nomenclature. Auditory threshold phenotyping data collection. We have used data collected from the IMPC high-throughput phenotyping pipeline, which is based on a pipeline design where a mouse is characterised by a series of standardised and validated set of tests underpinned by standard operating procedures (SOPs).
The IMPReSS (International Mouse Phenotyping Resource of Standardised Screens) database (https://www. mousephenotype. org/impress), defines the experimental design, detailed procedural information, the data that is to be collected, age of the mice, significant metadata parameters and data QC for IMPC pipeline tests. Unlike most experiments, we cannot randomly allocate animals to experiment groups, rather we rely on Mendelian inheritance to provide the randomisation method. However, at different institutes a variety of approaches are taken to minimise bias such as order effects including: alternate animal order; cage casual randomisation; and casual randomisation within a cage. There were no consistent approaches to blinding for ABR data collection and annotation across the institutes within IMPC.
A click-evoked ABR is optional, and some centres, but not all, also acquired click data, but we have not included this in our analysis. The ABR test is conducted open field with hearing thresholds determined for one ear. Importantly, each centre records significant metadata parameters including: equipment manufacturer; equipment model; recording environment; anaesthetic agent; and anaesthetic dose. In addition, the Combined SHIRPA and Dysmorphology test [IMPC_CSD_003], carried out at 9 weeks, includes a click box test that provides a crude assessment of hearing  9. The click box, which emits a high-frequency (~20 kHz)   4 ). The number of genes associated with any given term were counted and split into known and novel groups. Manual inspection of the enriched gene ontology terms and the gene count highlighted examples, where IMPC novel candidate genes were depleted at the lower ontology level.
Here we show three examples of enriched gene ontology terms mapped onto their respective directed acyclic graphs (DAGs), alongside the number of known and novel candidate genes associated with enriched terms. For instance, the first example shows 34 known genes and 10 IMPC novel genes associated with the level 1 GO term 'Cell Projection'. From the 34 genes, 24 are annotated with 'Stereocilium' at level 3 while no novel candidate genes are annotated at this level tone stimulus at 90 dB SPL, was held 30 cm above the mouse. When the click box is activated, a hearing mouse will elicit a 'Preyer' reflex (the pinnae flick backwards). A lack or reduction of Preyer reflex indicates possible hearing impairment. ABR experimental design. At each IMPC Centre, ABR phenotyping data from each mutant strain, and age-matched wild-type mice of equivalent genetic backgrounds, were collected at 14 weeks of age.
31  and is available from the IMPC portal (http://www. mousephenotype. org/about-impc/arrive-guidelines). As a high-throughput project, the target sample size of four animals per knockout strain is relatively low. The IMPC determined to set the lowest sample size that would consume the least amount of resources while achieving the goal of detecting auditory threshold abnormalities. At times, viability issues or the difficulty in administering the ABR test might further limit the number of animals. As such, when data are visualised on the IMPC portal, the number of animals phenotyped is listed. Data quality control. The data generated in each IMPC centre are captured centrally by the Data Coordination Centre (DCC), where a team of data wranglers perform QC. The QC process involves data wranglers checking all data both manually and with automated methods.
QC issues are raised through a QC web interface, where IMPC centres can respond to confirm it is an issue or alternatively that the data are correct. Each QC issue is then tracked with the corresponding data points until it has been corrected by the data contributing centre. Once the data have passed QC, it is released to the Core Data Archive (CDA) at the European Bioinformatics Institute, through a regular release schedule. The data set analysed here consists largely of data from the IMPC data release 5. 0 with a smaller set of pre-QC data from the DCC. For the purpose of this article, the ABR data in this smaller set were QC'd and manually curated. Wild-type data sets. Wild-type data sets were assembled for a trait by selecting wild-type mice that were generated by the same IMPC Centre.
The wild-type mice are analysed with the same genetic background, sex, pipeline and metadata parameters (e. g. , instrument). Statistical analysis. ABR data from IMPC release v5. 0 along with additional pre-QC data deposited up until 6 September 2016 was used for the detection of putative pheno-deviants. In total, data from 3006 mutant strains were available for analysis. Each knockout strain was examined in three ways: only males, only females and both sexes combined. A minimum of 20 wild-type data points and 3 mutant data points was required for the analysis to proceed. The parameters assigned for annotation in the ABR procedure, stored in IMPReSS (http://www. mousephenotype. org/impress/) were analysed by comparing the knockout strain with the appropriate wild-type control data. The default statistical analysis employed for the IMPC annotation pipeline is a mixed model approach, or a Mann-Whitney inference test may be employed as an alternative.
However, for analysis of ABR data neither approach is suitable when there is lack of variation within the data set (e. g. , if all mutant mice are profoundly deaf at all the frequencies tested) or due to the step nature of the data, respectively. Therefore for this analysis, we used an enhanced version of PhenStat version 2. 6. 0 13 that includes a reference range plus approach, which allows identification of hearing impairment even when there are few data points or a lack of variation within the data set. The 'testDataset' function within range plus approach was used with a 98% reference range, produced from the wildtypes for each sex. Per knockout line, range plus approach classifies phenotype values as normal or low (below the 1% critical value) or high (above the 99% critical value) for each sex.
A Fisher's exact test is used to compare the proportions of classification, independent of sex, between wild-type and knockout (low/normal vs. high and high/normal vs. low) and the lowest p-value returned. This approach was used due to the small sample size of the experiment. A critical p-value (p ≤ 0. 01) was used to generate a list of putative auditory pheno-deviant knockout lines. This list was used for further manual curation by domain experts. Assessing the functional enrichment of hearing loss genes. A single data set consisting of 103 known human hereditary hearing loss genes (DFNA, DFNB and DFNX) and 52 IMPC novel hearing loss genes was used for functional assessment. We used g:Profiler (version r1665_e85_eg32) to find enrichment within the gene ontology, categories include; molecular function, cellular component and biological process  33.
Acknowledgements Research reported in this publication was supported by:  Agence Nationale de la Recherche  ( ANR-10-IDEX-0002-02 ,  ANR-10-INBS-07 PHENOMIN to Y. H. ) ;  Centre Européen de Recherche en Biologie et en Médecine  (Y. H. );  European Commission  (EUMODIC contract no. LSHG-CT-2006-037918  to G. P. T. -V, W. W. , M. H. d. A. , K. L. S. , K. P. S. and S. D. M. B. );  French National Institute of Health and Medical Research-INSERM  (Y. H. );  Government of Canada through Genome Canada  and  Ontario Genomics  ( OGI-051  to C. M. and S. D. M. B. );  Medical Research Council  ( 53650  to S. W. and S. D. M. B. ) (G0300212 and  MC_qA137918  to K. P. S. ) (MC_U142684175 and  MC_U142684172  to S. D. M. B. );  National Centre for Scientific Research-CNRS  (Y. H. );  National Institutes of Health  ( U42OD011185  to S.
Data availability. Data from this study are available from IMPC at www. mousephenotype. org. An API in the form of RESTful interfaces is available for accessing post-QC data from the Core Data Archive (http://www. mousephenotype. org/ data/documentation/data-access), and pre-QC data are also available for manual download (via Phenoview) or on request (http://www. mousephenotype. org/contact-us).
Physicochemical properties of the fruit The physical and chemical analyses of the fruit were conducted monthly from June to November in 2010, corresponding to 9 to 14 months after anthesis, and the maturation curve was determined from the results. Fruits were collected for analysis in the outer portion of the trees between 1. 0 and 2. 0 m above ground level and throughout the perimeter of the plant; resulting in a total of 5 samples containing 10 fruits each. The fruits were sent to the Laboratory Quality and Post-Harvest Centro APTA Citros Sylvio Moreira/IAC, Brazil. The following determinations were measured: • Soluble solids (SS) were determined by a direct reading on a B & S refractometer, RFM model 330. The data were corrected by temperature and acidity of the juice. • Titratable acidity (TA) was obtained by titrating 25 mL juice with a 0.
Short food frequency questionnaire The food list of the SFFQ was defined on the basis of existing national food questionnaires  [21] [22] [23] [29] [30] [31] [32]  and data from the second national study of individual food intakes of French adults (INCA 2)  [33]. The SFFQ was self-administered, completed at home and then returned by mail. The questionnaire asked participants to report their usual dietary intake over the past year and was divided into two parts. In total, 49 items were used to obtain the nutritional data (see Additional file 2). The first part was composed of 40 food group items. It quantified consumption by frequency (never or less than once a month, x times a day, x times a week or x times a month) and portion sizes per food group item. Photos previously validated  [34]  were directly integrated into the questionnaire to help participants estimate the consumed quantities of 21 food items (see Additional file 3).
The two remaining questions enquired about extra-salt added when cooking and the consumption of processed foods. All the information collected was used to calculate daily intakes of each food group. Frequencies were converted into numbers of servings per day and multiplied by the declared portion size. An ad hoc composition table was developed using data from the INCA2 French representative population survey  [33]  to estimate the percentage of contribution of each food included in a food group item. Nutritional data were then obtained using the French food composition database established by the French Data Centre on Food Quality (Ciqual, last updated in 2013)  [35]. Besides nutritional information, the SFFQ elicited information on sex, birth date, and anthropometric data. It also questioned the participants about dietitian visits and eventual changes of food habits during the past year due to particular situations (diet, pregnancy, move, surgery, depression,…).
24-h recalls The reference method used to compare results from the SFFQ consisted of six 24-h recalls carried out each 2 months, during the year between the first and the second administration of the SFFQ. Study participants were asked to recall all foods and beverages consumed on the previous day (due to logistics, data for Saturdays were collected on Mondays). Participants were not informed in advance of the day of the recall. All weekdays and all seasons were covered by the recalls in order to account for intra-individual variation. Phone interviews were carried out by a trained dietitian who entered the food data into the Nutrilog Software (v2. 70d). These data were instantaneously converted into nutrient intakes by the software, using the Ciqual food composition database  [35]. A validated photo album of 42 foods  [32]  was previously sent to the participants' homes in order to help them quantify the amount of consumed food during the phone interview.
Validity To study relative validity, data evaluated by the second SFFQ were compared with the mean of the six 24-h recalls, since both methods covered the same period of time. A list of concordance was established between food group items from the SFFQ and food items provided by 24-h recalls. Regarding food groups, unadjusted Spearman's correlation coefficients were calculated. Regarding nutrient intakes, unadjusted and energy-adjusted Pearson correlation coefficients were calculated. Energy-adjusted coefficients, corrected for attenuation due to withinperson variation in the reference method (de-attenuated coefficients)  [36, 37] , were also produced. Energy adjustment was performed using the residual method  [37]. To improve the normal distribution, nutrient intakes were logarithmically transformed before analysis.
Reproducibility To evaluate reproducibility, data obtained from the first and second SFFQ were compared. Regarding food groups, unadjusted Spearman's correlation coefficients and intraclass correlation coefficients (ICC) were estimated. Regarding nutrient intakes, unadjusted and energy-adjusted Pearson correlation coefficients as well as ICC were calculated. Nutrient intakes were logarithmically transformed before analysis, to improve the normal distribution. The level of agreement in ranking subjects between the two SFFQs in terms of food group and nutrient intakes was examined through cross-classification into quintiles. All statistical analyses were performed on SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). A P-value < 0. 05 was considered statistically significant.
55) (see additional file 4). We did not observe any major differences between the two methods. Under or overestimation are not necessarily problematic in epidemiological studies if ranking of people according to their dietary intake is valid  [37]. Our work also has several strengths. In comparison to other validity and reproducibility studies, we worked on data from a large sample  [42]  and managed to maintain high response levels all over the one-year interval despite working with an aging population, with CKD. Several CKD cohorts exist on an international level  [61]. However few of them assess diet despite the impact of diet in CKD management. We developed a rapid tool to assess energy intake in CKD patients. It showed acceptable validity and reproducibility to rank people according to their food group and nutrient intakes, including nutrients of interest for CKD patients.
Acknowledgments The authors are grateful to all participants for providing the data used in this validity and reproducibility study. We thank  Anne Forhan  for her advices. CKD-REIN study investigators autorship:  Bénédicte Stengel  ( PI-Inserm, Villejuif, France ),  Christian Jacquelinet  ( Agence de Biomédecine, Saint-Denis, France ),  Bruce Robinson  ( Arbor Research Collaborative for Health, Ann Arbor, USA ),  Ziad A. Massy  ( CHU A. Paré, Boulogne, France ),  Christian Combe  ( CHU de Bordeaux, Bordeaux, France ),  Denis Fouque  et  Maurice Laville  ( CHU Lyon-Sud ),  Luc Frimat  ( CHU de Nancy, Nancy, France ),  Carole Ayav  ( CHU de Nancy, Nancy, France ) et  Serge Briançon  ( CHU de Nancy, Nancy, France), Christophe Pascal (IFROSS, Université de Lyon ),  Yves-Edouard Herpe  ( Biobanque de Picardie, Picardie, France ),  François Deleuze  ( CEA-CNG, Evry, France ),  Joost Schanstra  ( Inserm Toulouse, Toulouse, France ),  Pascal Morel  ( EFS, Bourgogne, France ).
Funding The CKD-REIN cohort is funded by the  Agence Nationale de la Recherche  ( ANR-10-COHO-01-05 ) through the  2010 'Cohortes-Investissements d'Avenir' programme  and by the 2010 national  Programme Hospitalier de Recherche Clinique. CKD-REIN  is also supported through partnership agreements with  Amgen, Baxter ,  Fresenius Medical Care ,  GlaxoSmithKline ,  Lilly ,  Merck Sharp and Dohme-Chibret  ( MSD France ), and  Otsuka. This present work was supported by a grant from the  French Research Institute in Public Health (IReSP)  and the French multi-organism thematic Institutes in Public Health (ITMO Santé Publique). AA is supported by a doctoral grant from the  French National Cancer Institute. SW is funded by the  F-CRIN INI-CRCT network. GF is supported by the  French Research Agency (ANR, Agence Nationale de la Recherche)  via an  "Investissement d'Avenir" grant (investment for the future grant ,  ANR-10-COHO-0006 ) that supports the E4N study.