dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of mutant lines and genotyping We used the CRISPR/Cas9-mediated genome editing system to generate our mutant lines as listed in Table  2. crRNAs were designed using the online CRISPOR program (crispor. tefor. net/)  [67]  and selected to target the sequence of interest containing a restriction site (S1 and S3 Figs, panels A and B; see also Table  2 ). crRNA and universal 67mer tracrRNA were ordered from Integrated DNA Technologies. crRNA and tracrRNA were annealed in Duplex buffer and complexed with the Cas9 protein (provided by Jean-Paul Concordet lab) prior to injections into 1 cell-stage wild-type eggs. The efficacy of the crRNA was assessed directly after injection at 48 hpf by performing genotyping on usually 8 pools of 5 injected embryos. The genotyping workflow involved (1) isolating genomic DNA by lysing embryos in proteinase K solution (10 mM Tris [pH 8], 2 mM EDTA, 0.
5%) and detected on a HyD detector from 500 to 550 nm. TagRFP-CAAX labeling was excited with a white light laser at 590 nm (8. 5%), depleted with a 775 nm depletion laser (80%), and detected on a HyD detector from 600 to 650 nm, gated from 0. 6 to 5. 37 ns. Images were then processed by deconvolution using Huygens software (SVI, the Netherlands). To assess the vertical extension of CSF-cN apical extensions in espin wild-type and mutant fish, we drew polygons outlining the apical extension and fitted an ellipse to extract the "minor" measurement using the polygon tool in Fiji. sc/  [68].
Both GCaMP and RFP signals were recorded simultaneously at 5 Hz for 770 frames, allowing for 11 probe deflections to bend the tail every 70 frames, starting at 10 frames. Each stimulation therefore lasted 14 seconds. A new version of the Matlab script used in the work by Bo ¨hm and colleagues  [14]  was generated to perform the analysis. Regions of interest were manually selected in Matlab by drawing the CSF-cN cell bodies in the region of deflection. In order to correct for artefactual and/or experimental movements, we registered all image time series based on a TagRFP reference frame built as the average over 50 frames in a quiet period after the first stimulation. Background noise, calculated in a region with no signal, was subtracted to every frame. Raw signal was extracted for both GCaMP and TagRFP channels, and, to correct for motion artefacts, the 3 frames following the stimulation were removed, and the ratio ΔR/R was calculated as (F GCaMP (t) × F 0-TagRFP ) � (F TagRFP (t) × F 0-GCaMP ) at every time point left with F 0 the average fluorescence of the 10 frames preceding the stimulation.
Statistics All statistics were run on Matlab (Mathworks, Massachusetts). In most cases, student t tests were used for hypothesis testing. For obviously skewed distributions, we used the two-sample Kolmogorov-Smirnov test. Values are mean (red line) or median (black line). When displayed, the bottom and top edges of box plots indicate the 25th and the 75th percentiles, respectively, and whiskers extend to the most extreme data points not considered as outliers (outliers are plotted individually). To test the impact of wild-type espin allele numbers on apical extension size and calcium responses, we used multiple regression. More precisely, we relied on the general linear model (GLM)  [73] : the design matrix included 3 regressors of interest (encoding the number of alleles: +/+, +/-, and -/-) and, when applicable, 1 confounding variable (day of the experiment).
Acknowledgments We thank  Dr. Celine Revenu  for the Tg(cdh2:cdh2-RFP, crybb1:eCFP)Zf518Tg line;  Professor James R. Bartles ,  Professor Christine Petit , and  Professor Xiangyun Wei  for rabbit polyclonal antibodies against Espin, Myo3b, and  Crb1 , respectively;  Dr. Jean-Paul Concordet  for Cas9 protein; and  Professor Katie Kindt  for sharing her movie showing rings of actin during neuromast hair cell development and feedbacks on the manuscript. We thank the  ICM Quant imaging facility  for extensive instrument use. We thank the  NeurImag facility  for STED microscopy. We thank  Dr. Jean Daunizeau  for advice on GLM statistical analysis. We thank  Sophie Nunes-Figueiredo ,  Natalia Maties ,  Bogdan Buzurin ,  Monica Dicu , and  Antoine Arneau  for fish care;  Dr. Yasmine Cantaut-Belarif  for useful tips for performing IHC;  Dr.
CW:  European Research Council (ERC)   Starting Grant 'Optoloco ' (grant no. 311673 ) https://erc. europa. eu/. CW:  Human Frontier Science Program (HFSP)   Research Grant  (grant no. RGP063-2018 ) http://www. hfsp. org/. CW:  New York Stem Cell Foundation (NYSCF) Neuroscience Award  (grant no. NYSCF-R-NI39 ) https://nyscf. org/. PLB,CW:  Agence Nationale pour le Recherche '' Investissements d'avenir' '  ANR-10-IAIHU-06  ( Big Brain Theory ICM Program ) and  ANR-11-INBS-0011  ( NeurATRIS: Translational Research Infrastructure for Biotherapies in Neurosciences ). http://www. agence-nationale-recherche. fr/en/. Medical Research Foundation (FRM): LD: (grant no. FDT20170437143 ) and MD: (FRM grant number  ECO20170637481. https://www. frm. org/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Affinity kinetics of IgG for α-MSH and of α-MSH/anti-α-MSH IgG IC for the MC4R Affinity kinetics of plasma-extracted IgG for α-MSH and of α-MSH/IgG IC for MC4R were analyzed by biospecific interaction assay based on surface plasmon resonance (SPR) using BIAcore 1000 instrument (GE Healthcare), with coating of α-MSH peptide on the biosensor surface according to previously published studies and a protocol  15, 23, 24. A cell line of human embryonic kidney (HEK) 293 cells stably expressing human MC4R (hMC4R) with green fluorescent protein (GFP) in its promoter region was purchased from AMS Biotechnology (Abingdon, UK). Cells were coated on the biosensor surface as described in supplementary methods. Affinity kinetic data were analyzed using the BiaEvaluation 4. 1. 1 program (GE Healthcare) and fitted with the Langmuir's 1:1 model after blank value subtraction.
Confocal microscopy MC4R-expressing HEK 293 cells (AMS Biotechnology, Abingdon, UK) were cultured in glass bottom Petri dishes (MatTek, ≈250,000 cells/dish). Purified anti-α-MSH IgG of each patient and control groups (2 mg/mL in PBS) were conjugated with DyLight® 550 using the Lightning-Link® Rapid Conjugation Kit (Innova Biosciences, Cambridge, UK) and then incubated with 200 mM of α-MSH peptide overnight at 4 °C. After incubation for 30 min at 37 °C, cells were fixed in 4% paraformaldehyde and observed on an inverted confocal laser scanning microscope TCS SP2 AOBS-DMIRE2 (Leica, Wetzlar, Germany). Pictures were acquired with ×45 and ×60 oil-immersion objectives at the optical planes where cell nuclei were the most voluminous. Data were analyzed using the Leica Confocal Software (Leica) and red DyLight® 550 spots in GFP+ cells were quantified (n = 50 for each condition).
Acknowledgements The study received financial support, including a PhD stipend for N. L. , from  Nutriset, France. J. H. and K. A. received support from  EMES IUT20-40. S. F. is supported by  TMP of Inserm, France. Authors thank  Magalie Benard  and  Ludovic Galas ,  PRIMACEN platform, Rouen University  for help with confocal microscopy;  Olivier Boyer  and  Laetitia Jean ,  Inserm  for providing Rag -/-mice; and  Ebba Brakenhielm ,  Inserm  for critical reading.
The remaining cells were maintained at 37 • C and 5% CO 2 overnight. It has been shown that the adherence step does not significantly influence overall transcriptional changes in alveolar macrophages, relative to flow-cytometrybased cell-sorting  (Shaykhiev et al. , 2009). As described in previous reports from our group  (Jeyaseelan et al. , 2005; Buenestado et al. , 2010 Buenestado et al. , , 2012;; Abrial et al. , 2015; Victoni et al. , 2017) , the adherent cells (about 2 × 10 5 cells per well, for a 24-well plate) were >95% pure macrophages, as determined by May-Grünwald-Giemsa staining and CD68 immunocytochemistry (data not shown). Cell viability exceeded 90%, as assessed in a trypan blue dye exclusion assay. Culture plates with adherent macrophages were washed with warm medium. One mL of fresh medium supplemented with 1% fetal calf serum was added per well, and the culture plates were incubated overnight at 37 • C in a 5% CO 2 humidified atmosphere.
Statistical Analysis Values in the text and figures are expressed as the mean ± standard error of the mean (SEM) unless otherwise stated from experiments with n independent donors. The quantitative data obtained from RT-qPCR experiments was expressed as the relative expression (2 -Ct ), where C t is the difference between the target gene C t and the mean C t of the reference gene. Data were evaluated by using either a one-way analysis of variance for repeated measures (followed by Dunnett's post hoc test for multiple comparisons) or a paired Student's t-test, as appropriate. The threshold for statistical significance was set to p < 0. 05. Carisoprodol 100 The most recent nomenclature was adopted (Andres-  Barquin and Conte, 2004; Sainz et al. , 2007; Dotson et al. , 2008; Meyerhof et al. , 2010; Alexander et al.
mL -1 ) for 24 h. The concentrations of the cytokines in the culture supernatant were measured using ELISAs. The data are reported as the mean ± SEM of five independent paired experiments. * indicates a significant difference relative to LPS alone ( * * p < 0. 01 and * * * p < 0. 001). rule out the possibility that smoking had altered the expression of TAS2Rs by LMs. The present work was limited by the fact that it did not investigate TAS2R expression at the protein level (using flow cytometry or Western blotting, for example). However, due the very limited availability of suitable antibodies, studies of the cell surface expression of TAS2R on human blood monocytes or monocyte-derived macrophages have been mainly restricted to TAS2R38 and TAS2R43/31  (Malki et al. , 2015; Maurer et al. , 2015; Gaida et al.
Women who did not conceive after 1 year of follow-up were invited to the district maternal care centre for a medical examination. In cases of genital infection, they received medical advice and were referred to a gynaecologist. Follow-up stopped after 2 years when women did not become pregnant. Demographic and socio-economic characteristics of both women and their households were collected once upon inclusion in the primary cohort via a structured questionnaire. Data included household size, assets, housing type, women's ages, parity (number of children, alive or dead), type of union (polygamous/monogamous), ethnic group, education, and main activities. A multiple correspondence analysis  (Sourial et al. , 2010; Traissac & Martin-Prevel, 2012)  using socioeconomic data was performed to compute a wealth index and to classify households into low, middle, and high wealth levels according to tertiles.
5 kg/m 2 ), normal (18. 5 ≤ BMI ≤ 24. 9 kg/m 2 ), or overweight or obese (BMI ≥ 25 kg/m 2 ) based on WHO classification  (WHO, 2018). Data were collected by seven enumerators (five nurses and two nutritionists) holding at least bachelor's degrees, with experience in field data collection. They were trained over 6 days on the 24-hr recall technique, the questionnaire and tools, and anthropometric measurements. The questionnaire was pretested by the enumerators during the training and was adjusted where needed. During data collection, the enumerators were supervised daily by an experienced nutritionist doubling as the principal investigator and supported by a team of experts in nutritional epidemiology. The supervisor checked the proper completion of the questionnaires daily as well as consistency of the answers.
Data from dietary recalls were entered and cross-checked by repeated entry using the Epidata entry 3. 1 software  (Lauritsen & Bruus, 2004) , whereas anthropometric, socio-economic, and demographic data were entered using ACCESS 2007. Statistical analyses were performed using Stata 13 (College Station, TX, USA). We first described the basic characteristics of the sample from the primary cohort and examined whether women who became pregnant during the project (n = 316) differed from women who did not (n = 581). We presented mean (SD) for continuous variables and frequencies (%) for categorical variables. The main analysis was restricted to women who had one assessment at preconception and at least one assessment during pregnancy (n = 234). The mean WDDS, the proportion of women who consumed different food groups, and the proportion of women reaching the MDD-W were compared over the entire follow-up using a linear mixed model (for continuous variables) or a logistic mixed model (for categorical variables) including a random intercept (the individual) to take into account repeated measurements for the same subject.
| DISCUSSION The present study used unique cohort data to describe the dietary diversity of women of reproductive age before conception and during pregnancy in two districts of Southern Benin. Our findings showed that dietary diversity scores of women before conception were low as compared with the cut-off of five food groups in these semiurban areas, more so in the subdistricts of Sô-Ava and Vekky than in the subdistrict of Akassato. Barely 41% of women reached the minimum dietary diversity for women, and their diets were primarily composed of cereals, oils, vegetables, and fish. Before pregnancy, dietary diversity of women was strongly associated with the subdistrict in which they lived, the ethnic group to which they belonged, the number of children they had, and the wealth index of their household.
ACKNOWLEDGMENTS We are sincerely grateful to all families who participated to the study, as well as to the midwives, nurses, and community-health workers who worked hard for recruiting and following participants. We also thank the whole  RECIPAL and Nutripass (IRD) team. This study was supported by the  French National Research Agency (ANR ,  ANR-13-JSV1-0004 , Grant 2013) and the  Fondation de France  ( 00074147 , Grant  2017 ). DD was funded by the  French National IRD ) and the  France embassy for PhD scholarship , and received also additional research funds from the  IRD.
CF. The secondary outcomes were to assess other growth parameters, body composition, bone mineral content, digestive tolerance and safety among groups. The study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects/patients were approved by the European regulatory authority (N EudraCT 2007-A0018-50), French regulatory authority (N DGS 2007-0175) and the local ethics committee (Comit e de Protection des Personnes Nord-Ouest IV de Lille, France CPP 07/32 in 2007). Written informed consent was obtained from both parents after an explanation of the study procedures to the parents by the study paediatrician. Written informed consent was obtained from both parents for each infant before inclusion. The study was conducted from August 2007 to December 2010.
Study visits and duration of study feedings After randomization, infants were fed their assigned study formulae exclusively until introduction of complementary feeding at 4e6 months of age. The study intervention formally ended at 6 months. Clinic visits were performed at the following time intervals after enrolment: 1e7 days of life (V 0, baseline), 2 weeks (V 1 ), 1 month (V 2 ), 2 months (V 3 ), 3 months (V 4 ), 4 months (V 5 ), 6 months (V 6 ) and 12 months (V 7 ). Primary outcomes (weight gain and stool consistency) were analysed using data collected from 0 to 4 months of age; the follow-up visits at 6 and 12 months were conducted to collect further data for the secondary outcomes. All study subjects received a follow-up formula without enrichment in sn-2 palmitate from 6 to 12 months of age.
Bone mineral content and body composition At V 5 (month 4), all infants underwent a bone mineral content and body composition measurement with dual-energy x-ray absorptiometry (DXA) (Hologic Discovery, Waltham MA, USA). Whole-body scan time was 65e90 s, and the radiation dose was <0. 3 mSv. Total body image acquisition and analysis were obtained following manufacturer's instructions, with the two X-ray energies minimizing errors due to irregular soft tissue mass and body contours and providing high resolution  [21]. After body scanning, a specialized physician interpreted each scan image using the manufacturer software to provide data on bone mineral content, and on body composition as lean and fat mass.
Statistics Sample size was calculated on the basis of an O'Brien-Fleming  [22]  design with 2 stages (interim analysis at information rate of 21%). In order to detect a clinically relevant difference in stool consistency of 0. 5 (standard deviation of 1. 2) with a power of 90% and overall type I error of 5% (two comparisons, EF1 vs. CF and EF2 vs. CF), 146 infants per feeding group were required. To compensate for an expected 20% dropout rate, enrolment of approximately 183 infants per group was planned. For the one-sided test on body weight gain, a sample size of approximately 105 infants per group was required (non-inferiority margin of À3 g/day, standard deviation of 6. 1 g/day). Statistical analyses were performed using SAS version 9. 2 (SAS Institute, Cary, N. C. , USA). The evaluation of the primary variables was performed in the intention-to-treat (ITT) data set and in the per protocol (PP) data set; the primary analysis was the ITT analysis.
Data were described by calculating mean and standard deviation (SD) values for continuous variables and frequency and percentages for categorical variables. The primary outcomes were growth as measured by weight gain per day during the first 4 months of life (from V1 to V5) and stool consistency at V5 (4 months). Weight gain during the first 4 months of life was analyzed using an analysis of covariance (ANCOVA) model adjusted for baseline weight, treatment, and sex. Weight gain in EF1 or EF2 was considered non-inferior to CF if the lower bound of the 2-sided 97. 5% CI for the difference between EF1 and CF or EF2 and CF was above the non-inferiority margin of À3 g/day. Non-inferiority of EF1 or EF2 vs. CF in terms of weight gain was also assessed using a linear mixed model incorporating more dependent variables and correcting p-values according to Bonferroni.
ANCOVA was also performed to compare stool consistency among feeding groups (p-values corrected according to Dunnett). Secondary outcomes were evaluated in the ITT population. An analysis of variance (ANOVA; adjusted for sex) was conducted to compare volume of formula intake, digestive tolerance and behaviors, and of the body composition (DXA) outcomes (lean mass, fat mass, and percent fat mass) among feeding groups. An analysis of covariance (ANCOVA; adjusted for sex and baseline weight) was conducted to compare bone mineral content (DXA) among the feeding groups. For both the ANOVA and ANCOVA, p-values were corrected according to Dunnett. Adverse events/morbidity data were analyzed using Fisher's exact tests and reported as odds ratios with 95% CIs and p-values. Deblinding of randomization occurred after statistical analyses were performed.
Similar results were observed when weight gain data were analyzed separately for boys and girls. Additional assessment of non-inferiority using a linear mixed model showed a mean difference in weight gain between EF1 and CF of 0. 89 g/day (2-sided 97. 5% CI ¼ À2. 00, 3. 77), and between EF2 and CF of 0. 44 g/day (2-sided 97. 5% CI ¼ À2. 55, 3. 44), confirming non-inferiority of EF1 and EF2 compared to CF. Results were similar in the PP population. Further comparison of anthropometric parameters at 4 months (V5) among the study groups showed that weight and head circumference did not differ significantly among groups, while length was slightly but statistically significantly higher in the two EF groups compared with the CF group (62. 4 ± 2. 0 cm for CF; 63. 0 ± 2. 5 cm for EF1 and 63. 0 ± 2.
Acknowledgments We thank the parents who participated in this study. We also thank  Annie Ple  from  CIC-1403-Inserm-CHU  for logistical assistance;  Dr Patrick Gele  from  CRB-CIC-1403-Inserm-CHU  for biological sample storage and management;  Brigitte Poras ,  Jos e Cano-Morales ,  St ephane Deschilder ,  Nathalie Duquesnoy ,  Jeanne-Marie Descamps-Ben ,  Val erie Held ,  Isabelle Chardon , and  Laurence Crinon  for DEXA acquisition. Dr Georges Lion is gratefully acknowledged for DEXA interpretation. We gratefully acknowledge the  Nestl e Biostatistics & Data Management team  for statistical analysis.
Validation study Study population. The study population included volunteers from a randomly selected sample of NutriNet-Santé study participants, involved in the Dietary Validation Study  (30). The NutriNet-Santé study (ClinicalTrials. gov identifier: NCT03335644) is an ongoing web-based cohort launched in France in 2009, which included volunteers over 18 years old. Details of aims and methods have been described elsewhere  (31). The Dietary Validation Study was conducted in 2012-2013 to investigate the validity of a web-based, self-administered dietary record tool against urinary biomarkers  (30). It included a randomly selected sample of 199 participants of the NutriNet-Santé study, stratified by sex, age (<45 years, >45 years) and educational level (primary and secondary up to some college, university graduate).
Participants provided written informed consent. Study design. Data were collected during two visits performed in a clinical centre (Hôtel Dieu Hospital, Paris), both in a fasting state. At the first visit, anthropometric and clinical measurements were taken (height, weight, blood pressure) and participants were given instructions for the 24-h urine collection. Approximately 3 weeks later, a second visit was realised with the same clinical examination. At each of the two visits, a morning fasting spot urine was collected at the clinical centre. Between the two visits, three 24-h dietary records were completed and two 24-h urine samples were collected per participant. More details of material and methods are available elsewhere  (30). 24-h urine collection. A written explanation of the 24-h urine collection process was provided to all participants with material necessary for collection.
Na and K concentrations were measured by ion-selective electrodes (Siemens Dimension Vista), and creatinine concentration was measured by alkaline picrate kinetic (Siemens Dimension Vista). The intra-assay CV for all three measures were < 3 %. Study design. The ENNS is a cross-sectional study using a multistage sampling design, already described in detail elsewhere  (32). In brief, data collection included a questionnaire survey, three 24-h dietary recalls and a health examination with clinical and biochemical markers measurements for each participant. Social and demographic characteristics and physical activity were collected at home via face-to-face interview. Clinical (height, weight, blood pressure) and biochemical markers measurements were collected at health examination centres of National Health Insurance system, or at home with a nurse (if subjects did not want to go to a health centre).
Statistical methods Covariates. BMI was calculated as weight (kg)/height 2 (m 2 ). The reference values used to define BMI classes (underweight <18•5 kg/m 2 , normal 18•5-24•9 kg/m 2 , overweight 25•0-29•9 kg/m 2 and obese ≥30•0 kg/m 2 ) were those recommended by the WHO  (33). Tobacco status was categorised as: never smoke, former smoker and current smoker; alcohol use as abstainers and consumers (even occasional) and educational level as low (up to high school), medium (some college) and high (university graduate). Characteristics of participants in the validation study were compared using t tests or χ 2 tests, as appropriate. Analyses in the application study (ENNS) were weighted taking into account individual probabilities of inclusion, seasonal period of data collection and calibration based on national census data.