dataesr/scientific-paragraphs-categorization huggingface dataset:
Starting with the GTF file downloaded from GEN-CODE (GRCm38. p5), and using an in-house perl script, we extracted Olfr CDS positions based on Olfr name. Consequently, most of Olfr pseudogenes (without known CDS) were discarded. To take into account the possibility of alternative CDSs, we defined CDS position for each gene as the maximal distance from start to stop codons of all isoforms. We then calculated the distance to upstream and downstream CDS for each Olfr. For Olfr190 and Olfr912, we retained the 2 distinct loci annotated for each of these genes. In databases, Olfr643 presents an alternative transcript overlapping with multiple Olfr genes (ENSMUST00000138055. 1); since it was undergoing non-sense mediated mRNA decay, this particular transcript was discarded from our list. As Olfr56 shows multiple transcripts in databases (some overlapping with multiple genes due to very large introns), we kept the whole region between Olfr1396 et Olfr1394, including the most probable Olfr56 CDS (CCDS24597.
1 Chr11:49134290-49,135,141) and eliminating the longer Olfr56 transcripts. We defined Olfr genomic regions as the regions including a single or consecutive Olfr CDSs from the proximal end of the closer upstream non-Olfr gene to the proximal end of the closer downstream non-Olfr gene. As Olfr161 has no upstream non-Olfr gene on chromosome 16, the upstream inter-CDS distance was arbitrarily set to 25 kb. Gene density in Olfr genomic regions was calculated as the distance from the downstream end of the non-Olfr CDS upstream of the first Olfr CDS in the Olfr genomic region to the upstream end of the non-Olfr CDS downstream of the last Olfr CDS in the Olfr genomic region divided by the number of Olfr CDSs in this Olfr genomic region. Additionally, we included in the dataset genes that were either overlapping with Olfr genes (and accepted to be isoforms of the corresponding Olfr in Ensembl) or defined as Odorant Receptor genes.
We therefore set the merge_radius at 200 nt and we paid attention to discard the 18 to 61 fused transcripts (i. e. including at least part of the 5'UTR and/or CDS of a neighboring Olfr, Additional file 2: Figure  S1E-H ) that remained, simply because of the very high gene density in Olfr genomic regions (up to 67. 1 CDSs/Mb, Additional file 1: Table  S1 ). These 3'UTR isoforms due to fused transcripts, for which attribution was ambiguous between 2 adjacent Olfr genes, were discarded (Fig. 2 , step 5). Interestingly, the optimized parameters defined above were confirmed on the subset of 3′ ends obtained after merging the 3′ ends distant of less than 100 nt (Additional file 2: Figure  S1A-D , 100-nt precision subset, grey labels) and the subset of PAS-associated 3′ ends (Additional file 2: Figure  S1A-D , PAS-associated subset, orange labels).
Transcript annotations Models GTF files generated in the previous step were converted to bed files with gtf2bed tool then scanned with Bedtools 2. 23 intersect function  [88]  to detect presumably artifactual fused transcript overlapping one Olfr and adjacent non-Olfr genes. We matched curated transcripts with the position in BED format of all the Olfr genes to retrieve the list of transcripts that are uniquely overlapping to each Olfr gene.
Annotation of 3'UTR isoforms From the GTF file GRCm38. p5, we extracted Olfr stop codon positions based on Olfr name using in-house perl script. The list of stop positions was manually curated for ambiguous multiple stops per Olfr for Olfr93 and Olfr643: stop codons of transcripts thought to undergo nonsense mediated decay were discarded. In-house perl scripts matching Olfr stop codon coordinates against 3p_exon transcripts from assembled BED and GTF files were used to identify and characterize the Olfr 3'UTR isoforms. 3'UTR isoforms of the same gene were merged when 3′ ends were distant of less than 100 nt (precision window).
Detection of polyadenylation signals At each position in a 100-nt window upstream of and downstream from annotation of 3′ UTR ends, an inhouse perl script with internal implementation or the polyADQ external program  [89]  was used to detect the AAUAAA/AUUAAA canonical PASs. Variant PASs among the 16 most common non canonical hexamers  [72]  were manually detected in a 100-nt window upstream cleavage site for individual cases of 3′ end lacking a canonical PAS.
Categorical variables were compared using Pearson's Chi-squared test or Fisher's exact test. The linear regression analysis was used to estimate the fitting expression level between the different datasets. To define different relative-abundance profiles for Olfr 3'UTR isoforms, an unsupervised approach (hierarchical cluster analysis) based on principal component analysis (PCA) was used. The PCA was performed based on the relative abundance and length of alternative isoforms. Based on the PCA results, we applied hierarchical clustering to determine automatically the different relativeabundance profiles described in the results section. All PCA was performed using FactoMineR package. A p value of < 0. 05 was used as a cutoff for statistical significance. Results are presented as the mean ± SD or medians, unless otherwise stated.
During the 48 h after HI, ADC values follow a dynamic process and tend to recover, a phenomenon called pseudonormalization  (18). In our study, we found a statistically significant increase in ADC values between P7 and P9 in all HI groups. However, the recovery was complete only in the PIC groups. Finally, neuronal death, which was evaluated at P9 by histology, confirmed our MRI results at the cellular level. Taken together, all these data demonstrate that maternal PIC supplementation leads to the following (i) a reduction in the number of pups with brain lesions; (ii) a reduction in brain lesion size when brain lesions are present in PIC groups; (iii) less severe edema inside brain lesions. The neuroprotective effects of PIC were still present at P30. While two thirds of control pups still had brain lesions, only one third of those in the PIC groups had brain damage.
Moreover, in brain-damaged pups, FA were higher in all structures in the PIC groups compared to the Ct group, reflecting a higher density of neural fibers. Therefore, maternal PIC supplementation significantly improved the spatial distribution or rearrangement of white matter fiber bundles. In behavioral terms, PIC supplementation restored motor functions. When sensorimotor functions were assessed in juvenile rats (P24) using the mNSS, the PIC groups had the best performances, reaching those of the sham group. Among the deleterious effects induced by HI, cognitive impairment due to neuronal and white matter damage is often a consequence in injured neonates  (19). HI rodent models also display memory deficits  (7, (20) (21) (22). In addition to showing anatomical improvements, our data revealed a neuroprotective role of maternal PIC supplementation on the hippocampal-dependent long-term memory of pups, which was completely restored.
MRI Analysis The extent of the lesion was determined from the signal of the apparent diffusion coefficient (ADC) maps of DWI. Only lesion sizes higher than 1% were considered. ADC maps and fractional anisotropy (FA) maps were computed using the Paravision 6. 0. 1 diffusion tensor calculation module. For each rat, a region of interest (ROI) was manually drawn on each section to encompass the injured area and determine the global brain area to quantify the lesion size (relative to the entire brain size). ROIs were manually determined in the lesion area (cortex, hippocampus, striatum) to evaluate the variations in ADC at P7 and P9. FA was determined at P30 in the ipsilateral cortex, hippocampus and striatum. 4. 8. Behavioral tests 4. 8. 1. Early reflexes  (6)  Righting reflex (P8, P10, P12) Pups were placed in a supine position and the time required to turn over on all four paws and touch the surface was recorded.
Sources of Funding This work was supported by grants (2018/01) from the Fondation de Recherche en Alcoologie (FRA) from the French State in the context of the "Investments for the future" Programme IdEx (ANR-10-IDEX) and the LabEx TRAIL (ANR-10-LABX-57) and a Franco-Swiss ANR-FNS grant (ANR-15-CE37-0012). LP received financial support from the program IdEx Bordeaux ANR-10-IDEX-03-02.
Statement of financial support: This work was supported by grants ( 2018/01 ) from the  Fondation de Recherche en Alcoologie (FRA)  from the  French State  in the context of the "Investments for the future"  Programme IdEx  ( ANR-10-IDEX ) and the  LabEx TRAIL (ANR-10-a French-Swiss ANR-FNS  grant ( ANR-15-CE37-0012 ). LP received financial support from the program  IdEx Bordeaux   ANR-10-IDEX-03-02. authors declare no conflict of interest.
Data Source A secondary data analysis was performed using data collected in a longitudinal cohort study ("MyHealth" project) aiming to collect in-depth health and child's nutritional status data for at least three years to better inform the government on progress that can be made with enhanced health monitoring. A total of six districts over three provinces were conveniently selected: Russei Kaev in Phnom Penh, AD Chitr Borie and Krong Kratie in Kratie, and AD Ou Chum, Krong Ban Lung and Bar Kaev in Ratanakiri province. At baseline a sample size of 1200 children under two years of age per province was calculated, to observe a reduction in child stunting from 32% to 26% over a 3-5-year period (with a precision of 3% and a dropout of 20%). A list of all the children under two years and pregnant women was obtained from midwives and village health volunteers for all the villages within the targeted districts.
All pregnant women and mothers or caretakers of all children in the list were invited to participate to the longitudinal study. All participants provided written informed consent at baseline before their inclusion in the cohort study. At each visit, women were invited to central points, such as someone's house, a pagoda, or other village gathering point, pre-arranged by the local village health volunteers. Enumerator teams, using electronic tablet-based questionnaire, collected information on (i) socio-economic status; (ii) health knowledge; (iii) diet, including dietary diversity of children and women and breastfeeding practices; (iv) WASH practices; (v) mother and child anthropometry and (vi) cognitive development. Baseline data were collected in April-May 2016 followed by six consequent follow-ups that will run until March 2019.
Statistical Analyses The analyses were conducted using STATA/IC Release 15. 1 (Stata Corp. , College Station, Texas, USA). Significance was defined as p < 0. 05. The database was created by merging (one to one on "ChildID" key variable) baseline, follow-up 2 and follow-up 3 datasets. Missing data were enumerated for all variables of interest before analyses. Observations were unbalanced because they were not taken at regular times. Missing values were considered as missing at random, automatically corrected for in mixed-effects models. Anthropometric measures and prevalences were calculated at each study visit. Children from 0 to 59. 9 months were classified as wasted when the WHZ was inferior to -2 standard deviations (SD) of the WHO Child Growth Standards median; whereas children from 6 to 59.
9 months were classified as wasted when the WHZ was inferior to -2 standard deviations (SD) of the WHO Child Growth Standards median and/or the MUAC was inferior to 125 mm. Children from 0 to 59. 9 months were classified as stunted when the HAZ was inferior to -2 SD. Two-sample Z-tests were used to compare proportions of wasting and stunting prevalence between consecutive study visits. Due to the repeated measures along the study of most of the variables considered, mixed-effects models were used to perform univariate and multivariate analyses. The dataset was re-shaped as long where the subject's repeated responses is in a single row and each response is in a separate column. Mixed-effects linear regression models were performed using the maximum likelihood method, whereby the unique child identifier represents the random part of the model.
Discussion To our knowledge, this study is the first to investigate the longitudinal association between water, sanitation and hygiene conditions, regrouped as child-sensitive WASH composite scores, and the nutritional status of children in Cambodia. Data were collected in a longitudinal cohort study in six districts localized in Phnom Penh and Kratie and Ratanakiri, two north-eastern provinces of Cambodia. The study, started in 2016 and ending in March 2019, aims to collect in-depth data to monitor the progress on health, nutrition, WASH practices, and cognitive development of children under five years of age and to inform the government about the progress that can be made with enhanced health monitoring. Although UNICEF did not carry out interventions in the targeted districts, we cannot exclude that other development partners did.
This study brings evidence for a positive longitudinal association in the six targeted districts of Cambodia, whereby various factors of causality were accounted for, minimizing the potential residual confounding and collinearity. The large sample size (n = 5238) increases the power of the statistical analyses, but may increase study attrition and missing data. Indeed, several mothers and children did not show up at each study visit. This could be due to migration often occurring in the country, where families move across provinces seeking for seasonal jobs. The method of analysis chosen adjusts for missing data, as assuming they are missing at random. Furthermore, tablet-based data collection automatically entering information into a database, allows for data checking along the course of the collection and for recover data when they are missing.
Quality data collection was ensured during the study. Anthropometry data (length, weigh and MUAC) were measured in duplicate reducing measurement bias. These data were also recorded on paper in order to calculate the z-scores in situ and detect and refer severely malnourished children for treatment. Paper-based data collection also allows having a backup in case of any mistake occurred in the tablet data entry. The WASH questions were formulated in an understandable manner supported by figures showing the different type of source of water (piped, dug well, pumping water, river. ) and toilet (different type of flush/pour flush toilet, pit latrine with/without slab, bucket, field. ), reducing information bias. However, although only comparable variables among the study visits were used for the analysis, the questionnaire slightly changed after baseline, potentially biasing measurements.
As shown in Supplementary Table  S1 , the answers of the WASH practices questions were phrased slight differently in baseline but the values were referable to "improved" or "not improved" classification according to the guidelines. Furthermore we acknowledge weakness on the available variables used to create water, sanitation and hygiene sub-scores. Complete water quality and quantity data including indicators for accessibility (within 30 min, on premises), availability when needed and water quality, such as microbiological and chemical contaminations, were collected for only around 800 randomly selected households. These data could not be used in the longitudinal analyses because of the small proportion compared to the whole cohort and will require further analysis. The sanitation sub-scores did not include the variable indicating whether facilities were shared with other households because collected using inconsistent classifications across time.
Mailing to GPs After the validation of the electronic questionnaire by two independent French GPs and two independent French cardiologists trained in hypertension management, the form was sent by email to all GPs registered in national or regional databases (that is, Cercles de médecins généralistes in Belgium, the Fédération des Médecins Omnipraticiens du Québec in Canada, the NHS in England, the Conseil départemental de l'Ordre des médecins of 5 areas [Côtes d'Armor, Finistère, Morbihan, Orne, Seine-Maritime] in France, and the Société Médicale de la Suisse Romande in Switzerland), between 5 Oct 2017 and 27 Mar 2018. A reminder was sent 2 months after the initial message, and the study was closed on 27 Jun 2018. The participation of GPs was voluntary and anonymous. GPs were not paid to participate in this study.
Statistical analysis Statistical analyses were performed using NCSS statistical software (version 12, 2018). Χ 2 and student t-tests were used for the comparison of categorical and quantitative variables, respectively. The results were reported using beta, standard deviation (SD), and P values. A P value <0. 05 was considered statistically significant. and n = 60, 7. 8% from England), and 77 (10. 0%) were from Canada. Overall, the mean GP age was 49. 2 (SD 13. 0) years, and 379 (49. 2%) were male. Regarding other demographic data, GPs practised equally in rural and urban places (45. 1% versus 44. 9%, respectively), the main type of practice was in a group (73. 2%), and 53. 3% participated in university activities. Canadian GPs had the same sex distribution (P = 0. 19) and the same mean age (P = 0.
Acknowledgements The authors thank the  Conseil départemental de l'Ordre des médecins of 5 areas (Côtes d'Armor, Finistère, Morbihan, Orne ,  Seine-Maritime ) in France; the  Fédération des Médecins Omnipraticiens du Québec in Canada ; the  Cercles de médecins généralistes in Belgium ; the NHS in England; the  Société Médicale de la Suisse Romande in Switzerland ; and the physicians who participated in this study.
, 1995; Bartowsky, 2009). Some tasters also mention dried sausage skin, vomit, or dirty mops. The variation in descriptors for mousy off-flavor may be related to the different compounds involved in this spoilage, their concentrations, the matrix effect, and individual detection abilities  (Snowdon et al. , 2006). From a sensory point of view, a lack of consensus has been observed in the detection, identification, and characterization of mousy offflavor in wine. Interindividual differences in sensitivity to the various key compounds directly affect the quantitative and qualitative evaluation of this mousy off-flavor. For APY, individual detection threshold measurement (olfactory sensitivity evaluation) revealed that the dilution factor between the lowest and highest concentrations exceeded one thousand (personal data obtained with 23 subjects).
, 2008; Tempere et al. , 2011). The concentration/response function fitted by a sigmoid curve (y = 1/(1 + e(-λx)) was designated a psychometric function and used to determine the group threshold. The probability was corrected with the chance factor for detection: 1/3. The software used for graphic resolution and non-linear regression by ANOVA transform was Sigma Plot 13 (2014, Systat Software, Inc. ).
CONCLUSION These sensory data complement our knowledge about the standardized sensory methods used to evaluate the mousy off-flavor in wine. This work compares orthonasal evaluation methods, to reduce the interindividual variations due to retronasal evaluation. Among the three methods tested, adjusting pH to around 5 increased the segmentation capacities of panelists. This change in wine pH also ensured a good consensus among the panelists and clear discrimination among the samples, according to the contamination level. The paper strip method resulted in high interindividual diversity and adjusting pH to around 7 rather than 5 was not as effective. It is important to note that these adjustments do not fully model normal wine tasting conditions. Indeed, although oral pH is similar to the test values (average 7), pronounced decreases in pH are only observed around 15 seconds after tasting  (Obreque-Slier et al.
Data and Sample Collection Only schoolchildren aged 5-14 years who had lived in the study area for at least one year prior to the survey were included. The number of children per school was proportionally allocated according to population size in each school. Children were randomly selected using readily available school lists and identified by unique codes. A questionnaire was administered to collect data about each child's habits and behaviors, such as swimming/bathing in open freshwater bodies, washing clothes in rivers, and fishing. In total, 1187 urine and 1089 stool samples were collected from children in plastic containers and transferred to nearest health centres for parasitological examination. Urine samples were collected between 10 a. m. and 12 a. m. [20].
The current study has several limitations, and hence, the findings should be interpreted with care. First, stool and urine samples were collected only on a single day, though duplicate Kato-Katz thick smears were examined from each stool sample to enhance diagnostic sensitivity. It is conceivable that a number of infections, particularly those of light intensity were missed, and hence, the overall prevalence of both S. haematobium and S. mansoni might be somewhat higher than reported here  [41]. Second, data about sociodemographic, socioeconomic, and environmental factors were collected through a pretested questionnaire administered to children. There might be some kind of reporting bias. Third, no specific information was collected on water, sanitation, and hygiene behavior, although these are known risk factors for schistosomiasis  [42, 43].
Acknowledgments: We thank the children, parents, school communities, health workers, and the District Medical Officers in each location. The authors would like to thank the  Institut Pasteur de Côte d'Ivoire  and the  Faculty of Pharmacy  staff for technical advice during the study. Particular thanks go to  Ms. Lisa Crump  for assistance with manuscript improvement and to  Professor Christian Schindler  for statistical support. The authors thank the other team members for assistance during data collection and our colleagues at the  Swiss Tropical and Public Health Institute (Basel, Switzerland ) for logistical support.
Funding: This work was funded by the  Unité de Formation et de Recherche Sciences Pharmaceutiques et Biologiques ,  Université Félix Houphouët-Boigny (Abidjan, Côte d'Ivoire ). Additional financial support was obtained by the  Swiss National Science Foundation for E. K. A. , J. U. , and O. B. (grant no. 31003A_170113 ). K. E. A. is a recipient of a  Swiss Government Excellence Scholarship (ESKAS , grant no. 2017-0746 ) for which he is deeply grateful.
Recently, the analysis of more than 1,800 cross-sectional surveys' datasets from 47 countries shed a new light on the magnitude of this discrepancy. It showed that only  16. 5% of children fulfilled both defining criteria (MUAC <115mm and WHZ <-3)  [9]. Both the magnitude and direction of the diagnostic discrepancies were highly variable across countries. Particularly in the Sahel and South-East Asia where most cases are located  [10] , it appears that a low WHZ contributes to a relatively high proportion of all SAM children. These children would remain undetected by a program using MUAC as only criterion for admission  [9]. While the recommendation to use MUAC <115mm and WHZ <-3 independently was reiterated by WHO in 2013  [11] , the use of absolute MUAC as the only measurement for case finding and admission to therapeutic feeding programs has been increasingly promoted and applied in recent years by public and private stakeholders involved in SAM management programs  [12] [13] [14] [15].