dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Patient data obtained retrospectively from electronic medical files included: age, gender, smoking status, date of diagnosis of CD, Montreal classification at diagnosis, duration of follow-up in the gastroenterology department, change of phenotype during the follow-up, extraintestinal manifestations of CD (musculoskeletal and cutaneous), previous treatments of CD (5-aminosalicylic acid, systemic or topical corticosteroids, immunosuppressants, anti-TNF therapy, artificial nutrition), duration of anti-TNF therapy, surgery, number of unscheduled hospital stays (gastroenterology department or digestive surgery department) and number of outpatient visits to the gastroenterology department. If missing in electronic medical record, data were searched in patient's paper file. For each included patient, the number of admissions to the emergency department of the hospital was assessed from the beginning of the follow-up in the gastroenterology department.
For patients with at least one admission to the emergency department, additional data were recorded: disease characteristics at the admission in emergency department (phenotype of the disease, on-going treatment, change or optimization of treatment within last 3 months, surgery within last 3 months), data on admission in the emergency department (date, main reason for admission, CT-scan), biological parameters (C-reactive protein), management of patient after visit to the emergency department (hospitalization in gastroenterology department or digestive surgery department, consultation in gastroenterology department within 3 months, change or optimization of treatment within 3 months, medical surgery within 3 months).
Statistical analysis Qualitative data were analyzed with Chi-square test and quantitative data with ANOVA test. A multivariate regression analysis was performed to assess the predictive factors of finding a complication of CD using abdominopelvic CT-scan. Only variates with less than 10% of missing data were included in the univariate analysis. Variates with p < 0. 1 in univariate analysis were selected and included in the multivariate analysis. Tests were two-sided and a p-value lower than 0. 05 was considered to be statistically significant. SPSS software was used for statistical analyses (SPSS Inc. , Chicago, IL, USA).
To express y according to x in hives 'with super' with a linear relation: { if x < b 1 , y = 0 if x > b 1 , y = s(x -b 1 ) + ε (3) To express y according to x in hives 'with super' with a logarithmic relation: ⎧ ⎪ ⎨ ⎪ ⎩ if x < b 1 , y = 0 if x > b 1 , y = s ln ( x b 1 ) + ε (4) where b 1 is the bee population from which bees start to be visible in inter-frames, s is the slope of filling inter-frames by bees, and ε is the error parameter. The calculations made to obtain Eqs. (  1 ), (  2 ), (  3 ) and (  4 ) are given in Appendix B. All the statistics were computed with the software R, version 3. 2. 0 (R Core  Team, 2015). Asymptotic 95% confidence intervals of parameters of piecewise polynomial functions were estimated with the package nlstools, version 1. 0-2  (Baty et al. , 2015). As simple observations were repeated per colony (see Section 2.
GLMMs were generated with package lme4, version 1. 1-14  (Bates et al. , 2015b). To check for collinearity between fixed explanatory quantitative variables, two variables were incorporated together in GLMMs only if absolute value of correlation coefficient was less than 0. 7  (Dormann et al. , 2013). Fig. A. 2 displaying correlation coefficients values was generated with package Rarity, version 1. 3-6  (Leroy, 2016). To standardize effect sizes, fixed explanatory variables were centered and standardized  (Schielzeth, 2010). As the null hypothesis significance testing approach is discussed (e. g. Stephens et al. , 2007; Stanton-Geddes et al. , 2014; McShane et al. , 2019) , we set the p value threshold to 0. 001 in order to be more conservative  (Johnson, 2013). Effect size and 95% confidence intervals were also reported  (Nakagawa and Cuthill, 2007).
$interGframes$covered$ with$adult$bees$(IFB)$counted$ from$above$or$the$bottom$of$ the$hive$body,$plus$in$the$ super(s)$when$present Langstroth$hive Estimated$population$size$of$adult$honey$bees Hives$without$any$super$in$the$study$(October) IFB$counted$frome$above$the$hive$body$+$in$the$super(s)$when$present Dadant$hive IFB$counted$from$below$the$hive$body$+$in$the$super(s)$when$present Correlation coefficients between each quantitative explanatory variable, taken two by two for the GLMMs (Table  1 ). Figure generated with the R package Rarity  (Leroy, 2016). ) and the temporal shift between the counts of the number of inter-frames covered with adult bees (IFB) and the weighing of bee populations (c, d), given for each period, each kind of observers, and for the IFB recorded from above (a, c) and below (b, d) the hive body.
Acknowledgements We thank all the beekeepers for providing us with the colonies to carry out this study or preliminary works,  Simon Bellot ,  Raymond Bernadet ,  Philippe Dauzet ,  Cyril Folton ,  Alexandre Gorit ,  Eric Lelong ,  Sonia Martaresche ,  John Rigon ,  Jacques Sénéchal ,  Bertrand Stiers  and Paul Thirion. We are also grateful to  Cécilia Alin ,  Cécile Antoine ,  Jérémy Bellanger ,  Arthur Cambert ,  Elisa De Santis ,  Julie Dietrich ,  Alexandra Drouet ,  Louna Fronteau ,  Olivier Geist ,  Tommy Gerez ,  Coralie Guerry ,  Anne Laure Guirao ,  Zhor Lebbar ,  Pierre Le Bivic ,  Estienne Liegeois ,  Solène Marion ,  Nicolas Nalpowik ,  Cyril Scomparin ,  Julie Valognes  and  Marine Willemin  who helped with the field work. We thank  Jean-Christophe Conjeaud  of ANAMSO who, as a financial and technical partner, made this study possible.
Pre-processing of the data-de-multiplexing and alignment The sample reads de-multiplexing was done using BRB-seqTools (http://github. com/DeplanckeLab/ BRB-seqTools) as described before  (Alpern et al. , 2019). The sequencing reads were aligned to the Ensembl gene annotation of the Drosophila melanogaster BDGP6. 23 genome using STAR (version 020201)  (Dobin et al. , 2013) , and count matrices were generated with HTSeq (version 0. 9. 1)  (Love et al. , 2014).
Bioinformatics analysis of BRB-Seq data BRB-seq data quality was assessed in several ways. First, we excluded five samples from further analysis due to low numbers of aligned reads (<500K; removed were 1 Â 24 hr APF wild type, 1x Dlg5-IR 24 hr, 1x yorkie-CA 24 hr and 2 Â 32 hr APF wild type). Using raw read counts, we performed PCA analysis, calculated heatmaps and Pearson's correlation in R (Version 3. 3. 1, https://cran. r-project. org/). One additional replicate from 32 hr APF wild type was removed from further analysis as it represented a clear outlier. For the remaining samples, we performed library normalisation (RLE) as well as differential expression analysis using DESeq2  (version 1. 22. 2, Love et al. , 2014). Genes were considered differentially expressed with a log2FC ! |1| and an FDR 0.
Acknowledgements The authors are indebted to  Reinhard Fa  ¨ssler for hosting part of this work in his department, and to Ju ¨rg Mu ¨ller and  Anne-Kathrin Classen  for hosting AK-C ¸during parts of this study. We are grateful to the  IBDM and LIC (Albert-Ludwigs University of Freiburg ) imaging facilities for help with image acquisition and maintenance of the microscopes and the  Max Planck  service facilities for protein expression and antibody production. We acknowledge the  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04-01 , Investments for the future). Fly stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) and the  Vienna Drosophila Resource Center (VDRC)  were used in this study. The authors thank  Barry Thompson ,  Richard Fehon ,  Jo ¨rg Großhans ,  Georg Halder  and  Duojia Pan  for fly stocks and antibodies, as well as  Pierre Mangeol  and  Clara Sidor  for helpful discussions and critical comments for this manuscript.
2 Genomic data were obtained from the breeding company NUCLEUS. The reproducers from the G0 population, 82 boars and 676 sows, were genotyped with the Illumina Porcine Chip, Porc XT 60K. The reference map was based on the Sus scrofa 11. 1 pig genome assembly. Quality control of genotypes was performed with PLINK v1. 9 software  (Chang et al. , 2015). Only markers on autosomes were kept. Markers with more than 5% of missing genotypes were discarded. Minor allele frequency (MAF) pruning was performed to eliminate alleles with a frequency below 0. 01%. After quality control, 758 animals and 46 628 SNP were used for analysis. Genotypes were then phased with the BEAGLE v5. 1 software  (Browning et al. , 2018). Phenotypic data on the G0 population were collected by the breeding company NUCLEUS. To simplify the simulations, only one trait was considered for selection: the age of the pigs at 100 kg (A100), an indicator of growth rate.
Scenario with selection based on breeding values In the second alternative scenario, called Selection on BV, breeding values (BV) were estimated with MoBPS from marker effects estimated for the G0 population. BV were estimated based on the custom implementation of gBLUP  (Meuwissen 2001)  in MoBPS with known heritabilities. We selected reproducers with the lowest BV because the objective of selection was to improve the growth rate by minimizing A100. We computed a coancestry coefficient f P ED based on pedigree data with function pedIBD() in the optiSel R package  (Wellmann, 2019). This coefficient was based on the pedigree of real G0 animals (over 10 generations) concatenated with the simulations without errors. A mating plan was established with an optimization algorithm (in-house Fortran script) based on the minimization of f P ED sum between matings.
Scenario with genomic data to manage genetic diversity The third scenario, called Genomic strategy, is based on a strategy where genomic data are used both for selection and genetic diversity management. Two principles were combined to select reproducers. The first was to choose reproducers among animals with balanced genomic composition between founding breeds, with a particular focus on the proportion of Duroc origin. The second principle was to promote original animals in terms of alleles. An animal is original if it carries alleles that are uncommon in the population. Genomic compositions between PLW and Duroc were calculated based on the real points of recombination in each meiosis. Note that this is not possible in practice and needs to be estimated and thus represents an idealized estimate compared to a real-world scenario.
If a chromosomal fragment originated from the maternal chromosome of a G0 animal, its origin was a PLW founder. Otherwise, if a chromosomal fragment originated from a paternal chromosome of a G0 animal, its origin was a Duroc founder. We considered an animal to be balanced when the proportions of PLW and Duroc origins were between 0. 30 and 0. 70. For original animals,  Danchin-Burge et al. (2016)  proposed the computation of an originality index (ORI) to highlight individuals with uncommon alleles compared to a target population. An original animal has a positive ORI index, whereas an animal with common alleles has a negative ORI index. For each animal i, ORI index was computed with in-house Python script with the following formula: ORI i = - f i -f pop σ fpop where f i is the mean allele frequency of markers carried by individual i, f pop is the mean frequency of all individuals in the population and σf pop is the standard deviation.
Selection based on breeding values and pedigree data This scenario, called BV + pedigree data represented a strategy where genomic data are used to accurately estimate BV but not manage diversity. For the choice of reproducers, animals with a high growth rate according to BV were selected with simple constraints based on family structure: one male per sire, a minimum of one female per sire and a maximum of two full sisters. For mating, we assumed that reproducers were genotyped and we mated reproducers as described in the subsection 2. 3. 3, with minimization of f seg sum.
Selection based on breeding values and genomic data with a relaxed or stringent ORI index We simulated a scenario which included two sub-scenarios: BV + genomic data with relaxed ORI and BV + genomic data with stringent ORI. The difference between these two sub-scenarios was the constraint on the ORI index. For these two sub-scenarios, we applied a step-by-step selection to select reproducers. First, we sorted the animals based on the ORI index. In BV + genomic data with relaxed ORI, we kept animals with ORI index greater than -1. 5. In BV + genomic data with stringent ORI, we kept 25% of males and 66% of females with the highest ORI index. The following steps in reproducer selection were the same in the two sub-scenarios. We kept only animals balanced PLW and Duroc genomic origins and then selected animals with the best BV.
ROH-based inbreeding ROH was detected with PLINK software based on the same parameters defined in  Ganteil et al. (2021). The values selected to define a ROH were 30 SNP and 1,000 kb and the minimum density was set at one SNP per 100 kb. One missing SNP was allowed per sliding window. To obtain strictly homozygous ROH, no heterozygous SNP were allowed per sliding window. All the other parameters available in PLINK that are not mentioned above were default settings. We then computed a ROH-based inbreeding coefficient for each animal, F ROH with detectRUNS R package  (Biscarini et al. , 2019)  as: F ROH = L ROH L autosomes , where L ROH is the sum of the length of all the ROH detected in an animal in bp, and L autosomes is the total length of the autosomes covered by markers in bp.
BV + genomic data with relaxed ORI BV + genomic data with stringent ORI BV + pedigree data G3 G4 G5 G6 G7 G8 G9 G10 G3 G4 G5 G6 G7 G8 G9 G10 G3 G4 G5 G6 G7 G8 G9 G10.
Inbreeding The evolution of inbreeding was characterized with the simulation of selection scenarios. As mentioned above, we studied F ROH , F M and F P ED (Figure  6 ). Figure  6A  shows the results for F ROH were similar in the two scenarios with genomic data and ORI index. F ROH first increased, then decreased, and then increased again. The shape of the curve was the same whether selection started at G3 or G4, the two curves only shifted by one generation. However, at G10, in the two scenarios, the simulation that started at G3 had a higher F ROH than the simulation that 11 started at G4. Regarding the difference between the scenarios BV + genomic data with relaxed ORI and BV + genomic data with stringent ORI, in limiting inbreeding, the second scenario was more efficient. In the third selection scenario, BV + pedigree data, F ROH increased immediately after the start of selection until G10.
The increase was lower when selection started in G4 than when it started in G3. The second genomic-based inbreeding coefficient was F M (Figure  6B ). The results for this coefficient were similar to previous results for F ROH. Once again, starting selection at G4 limited the increase in inbreeding better. Last, we studied F P ED (Figure  6C ), the shapes of the curves in the scenarios with genomic data with ORI index were similar. The results of the scenario BV + pedigree were very close results for the two selection starting points. For the three coefficients, the scenario BV + genomic data with stringent ORI limited inbreeding the most. However, in the first generations of selection (G4 to G6), the scenario BV + pedigree with a start of selection at G4 showed the lowest level of inbreeding.
Evolution of breeding values The estimated genetic progress was compared in the three selection scenarios (Figure  7 ). The selection applied in the three scenarios aimed to decrease the BV of the studied trait (A100). In all scenarios and in line with the breeding objective, BV decreased over the 6 or 7 simulated generations. These results highlighted improved growth of simulated animals. In the BV + genomic data with relaxed ORI and BV + pedigree data scenarios, there was a shift of approximately one generation between starting selection at G3 and at G4 in BV evolution. This result was not observed for BV + genomic data with stringent ORI where the improvement of BV from G7 was somewhat limited when selection started at G4. As expected, in G10, the best improvement of BV was observed for BV + pedigree data, where in simulations when selection started at G3, the mean of the BV was 93.
Simulations using real data This article describes a simulation study to compare different strategies to create a synthetic line in pig breeding. Founder animals are generated based on real genomic data and the trait is generated using real phenotypic data. The G0 population is the product of two steps three-way crossbreeding and G0 animals constitute the first generation of a new synthetic line. We started all simulations using all G0 reproducers' genotypes and phenotypes available. Additional genomic data for G1 and G2 animals were also used and we were thus able to compute F ROH (Supplementary File S2). To validate our simulation approach, changes in F ROH were compared between the real population and the simulated data in the scenario Genomic strategy for the first three generations. This scenario was based on the strategy carried out on the line management.
For this reason, we proposed the Genomic strategy as an innovative way to manage genetic diversity in the first generations of a new pig line. Our scenario included genomic data for the choice of reproducers and their mating. We estimated two genomic criteria to select reproducers: the ORI index and balanced animals for one of the three founding populations. The ORI index makes it possible to enhance animals with uncommon alleles in each generation and helps maintain rare alleles in the population. In the long term, using the ORI index, we expect to obtain animals with ORI index close to 0 which means an increase in heterozygosity in animals. The second criterion based on the origin of the founders' genomes in crossbred animals limits genetic drift towards a particular founder breed. As we did not introduce different genetic levels between the founder breeds for the selected trait, no such systematic breed favoritism was expected, except by chance.
Systems that use maximization of diversity like Genomic strategy followed by selection with diversity management using genomic data like BV + genomic data with relaxed ORI, BV + genomic data with stringent ORI or a combination of pedigree and genomic like BV + pedigree data appear to be appropriate ways to limit an increase in inbreeding. The results we obtained for He in the Genomic strategy is an additional argument for the use of a diversity management strategy with genomic data during the first generations of a new line. Indeed, in this scenario, He was constant after G3. He measures the ability of a population to respond to selection in the short term. We hypothesize that starting selection at G3 or at G4 after diversity management, for exampling using the Genomic strategy would generate genetic progress.
Comparison of selection strategies Our objective was to analyze the impact of two alternative selection starting points on genetic diversity and BV evolution. In this context, we studied a breeding program with first generations of the new line managed using a Genomic strategy. We then tested a starting selection at G3 and G4 with three different selection scenarios BV + genomic data with relaxed ORI, BV + genomic data with stringent ORI and BV + pedigree data. It would have been possible to start selection at several generations, but we chose G3 and G4 in accordance with results obtained for He which showed stabilization of this indicator after G3. Moreover, in a real synthetic line project, it would be difficult to put off selection to later generations because of breeders' expectations in terms of genetic progress.
The three selection scenarios we used to compare starting selection at G3 and G4 can be divided into two groups. In the BV + genomic data with relaxed ORI and BV + genomic data with stringent ORI scenarios, we considered that all the candidates for selection had genomic data whereas in the BV + pedigree data scenario, genomic data were available only for selected reproducers. However, in all three scenarios, selection was focused on growth efficiency so minimization of A100's BV and BV were estimated from genomic data. We made this choice to ensure precise BV and the same method to estimate them in all simulations. So, in the BV + pedigree data scenario, the idea was to have genomic data to manage diversity only for reproducers mating. Regarding diversity parameters like allele frequencies and inbreeding, BV + genomic data with relaxed ORI and BV + genomic data with stringent ORI proved to be efficient to limit the loss of diversity especially with a stringent selection on the ORI index.
This phenomenon was not observed in BV + pedigree data scenario, so it was probably due to the interaction between ORI index and the start of overlapping generations. In fact, we selected original animals with the ORI index and the selection on original animals was high, but with the selection on BV, we observed an increase in inbreeding. This increase was higher in the BV + genomic data with relaxed ORI scenario than in the scenario BV + genomic data with stringent ORI scenario because we selected with relaxed ORI, animals with higher BV and maybe genetically closer, the constraint on diversity is less strong than with the stringent ORI selection. The decrease in inbreeding observed after the second generation of selection is probably due to the overlapping of generations. Indeed, overlapping generations promote the minimization of parents' coancestry and hence reduce inbreeding in offspring  (Sonesson and Meuwissen, 2001).
This variation in inbreeding described above was not observed in the scenario BV + pedigree data. Using pedigree structure to control for inbreeding was more efficient in limiting inbreeding in the first generations of selection than in the scenarios that use the ORI index. However, after several generations, the two scenarios that use the ORI index were more efficient. Although BV + pedigree data was less efficient in limiting inbreeding than BV + genomic data with relaxed ORI and BV + genomic data with stringent ORI, all three scenarios respected the recommended rates of inbreeding for small livestock populations. These rates are 0. 5-1% rise per generation for F P ED and 0. 25-0. 50% rise per generation for F M (Meuwissen and Oldenbroek, 2017). If we extrapolate these three scenarios to real conditions, scenarios with genomic data available for all candidates for selection have a significant additional cost over the scenario with genomic data available only for reproducers.
Therefore, the first three generations of a new line managed with Genomic strategy followed by BV + genomic data with relaxed ORI appears to be a good compromise between conserving diversity and achieving genetic progress. We did not analyze a scenario with OCS in this paper. This method of selection uses the average coancestry of the selected parents to manage genetic diversity. This can be implemented in different ways, such as maximizing genetic progress with a fixed rate of inbreeding or minimizing the loss of genetic diversity. OCS can be used with pedigree or genomic data, and is considered as a method of choice for simultaneous improvement of genetic progress and the preservation of diversity  (Woolliams et al. , 2015). The efficiency of OCS methods is impacted by the estimation of the relationship matrix and it appears that the appropriate genomic relationship matrix should be chosen depending on the diversity targeted  (Meuwissen, 2020; Morales-González et al.