dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Based on a comprehensive dataset covering major producing areas and a large number of crops and pathogens, our results , not yet conducted, experiment. In each case, three distributions are reported, corresponding to the first (Q 1 ), second (median) (Q 2 ) and third (Q 3 ) quartiles of the observed numbers of sprays for DSS (3,  4 and 6) and Cal (4, 7, and 10) strategies in the 80 experiments. Each probability distribution is summarised by its median, 95% probability interval and probability of the differences being positive. CrI and PI mean credibility and predictive intervals, respectively. P(>0) indicates the probabilities of the differences being positive. All credibility probability intervals include zero, thus revealing that the use of lower numbers of sprays resulting from the DSS strategies does not lead to a substantial increase in disease incidence.
e. , incidence or severity) of a DSS-driven fungicide spray schedule with that of a routine calendar program and an untreated control  21  , as was the case in all the experiments included in our meta-analysis. The fungicides and modes of action included in our metaanalysis represented all FRAC  19  categories in relation to the risk of developing resistance. The database included mainly programs involving non-systemic fungicides (n = 42) and combinations of non-systemic and systemic products (n = 31). Only n = 7 experiments included programs with systemic fungicides alone. These fungicides generally act against single biochemical targets and are thus considered of medium or high risk for the development of resistance  19. The application of fungicides with more than one mode of action, either in mixtures or in alternation, is recommended for resistance management.
Our database covered practically the full range of disease incidence values (from 1. 5-100%), representing different disease pressure scenarios. Studies reporting disease severity or derived metrics, such as the area under the disease progress curve (AUDPC), were not included (n = 28) (Supplementary Fig. S10 ). While disease incidence is the number of diseased plant organs in relation to the total number evaluated, disease severity is the proportion or the actual host area affected. Disease severity is typically evaluated using standard area diagrams, disease scales or ordinal rating scales  22. However, severity measurements are not standardised. Depending on the study, the intervals, ranges and ratings used differ greatly among diseases and even for the same disease. Moreover, they seldom represent equal gradations of the underlying continuous disease severity scale.
Methods Data collection. A comprehensive dataset was constructed to synthesise and quantify the effects of DSS-based fungicide programs as compared to calendarbased programs in terms of the number of spray applications and disease incidence. Relevant studies were identified in August 2019 by searching i) Web of Science (WoS) and ii) Fungicide and Nematicide Tests (F&N Tests and Plant Disease Management Reports) by the American Phytopathological Society. Multiple search strings based on different combinations of keywords were used for the literature search (Supplementary Table  S4 ). Additionally, our search also included relevant studies found in the reference lists of the selected studies. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses  29  ) diagram (Supplementary Fig.
S10 ) was included to present the details of the selection procedure. To be included in the database, the published experiments had to satisfy the following criteria: (i) at least one untreated control (Unt), one calendar-based strategy (Cal) and a DSS-based strategy (DSS) were tested; (ii) disease incidence (i. e. , the proportion of diseased organs); (iii) sample size (i. e. , the total number of organs evaluated) and (iv) number of fungicide spray applications were provided for the untreated control and for each fungicide strategy. The final database included data on 80 independent experiments reported in 22 published articles that evaluated 80 untreated controls, 99 calendar-based strategies and 149 DSS-based strategies. The number of independent experiments in each publication varied from 1 to 11 and the number of plots within an experiment ranged from 3 to 7 (Supplementary Table  S1 ).
A unique identifier was assigned to each experiment and the following data were also extracted: location (country, state, locality), year, crop, pathogen, disease, fungicide, plant organ evaluated, experimental design, number of replicates and other relevant characteristics (Supplementary Data 1). However, as described below, only country, crop, pathogen and fungicide type were used as moderator variables in the meta-analysis. Data preparation. Four moderator variables at the experiment level were defined based on the data provided by (i) location, (ii) crop, (iii) pathogen and, (iv) fungicide (Supplementary Table  S3 ). Based on experiment locations, three continents were distinguished: Europe, North America and South America. Two crop types were considered, namely woody and non-woody crops.
Model parameters were approximated by means of Hamiltonian Monte Carlo (HMC) simulation methods using the programming language Stan 35 via its R interface, RStan (version 2. 19. 2)  36  and the R package brms (version 2. 13. 0)  37, 38. All inference processes were set under weakly independent prior scenarios considering the default prior specifications of the brms package. For each model, the HMC algorithm was run with four Markov chains each including 10,000 iterations after a warm-up of 5000 iterations to ensure a proper convergence  35. Model evaluation and model selection. Model evaluation, that is, the checking of the adequacy and performance of all meta-analytic models (Supplementary Tables  S5  and S7 ) was carried out considering posterior predictive checks. We evaluated whether the meta-analytic models proposed were able to generate a database that resembles the observed incidence.
Thus, we compared observed disease incidence vs. the predicted disease incidence by plotting them around the regression line 1:1. (Supplementary Fig. S6  for MI models and Supplementary Fig. S8  for MIS models). Model selection between the baseline models (MI 0 and MIS 0 ) and the corresponding models including moderator variables was accomplished by means of K-fold cross-validation procedures (K-fold-CV)  39. K-fold-CV evaluates the predictive performance of the model. The principle is to re-fit each model to a subset of the original database. This approach was implemented with K = 5. With this setting, all the meta-analytic models were re-fitted 5 times randomly leaving out 20% of the experimental observations of the original data at each iteration. The K-fold-CV difference was computed by the combination of the kfold() and the loo_compare() functions of the R packages brms and loo  40  , respectively.
The fitted models were used to compute effect sizes with two different approaches: i) by including only the fixed-parameter estimates in order to compute the expected disease incidence across the experiments included in the dataset (i. e. , expected effect size values)  30, 41  , and ii) by including both the fixedparameter estimates and the random effect estimates in order to predict the disease incidence for a new experiment, not yet conducted (i. e. , the predicted effect size values)  42, 43. Meta-analysis of disease incidence under different control strategies. Considering the baseline MI model (MI 0 ), the expected disease incidences (in probability scale) associated with the three fungicide strategies were computed according to Eq. (  3 ), as follows: μ Unt ¼ exp fβ 0 g 1 þ exp fβ 0 g ; μ Cal ¼ exp fβ 0 þ β cal g 1 þ exp fβ 0 þ β cal g ; μ DSS ¼ exp fβ 0 þ β dss g 1 þ exp fβ 0 þ β dss g : ð7Þ The values of DID and DIR defined above were computed from the posterior distributions of the fixed parameters and summarised by their medians, 95% credible intervals (95% CrI) and probabilities of being positive (P(DID>0)) or of being higher than 1 (P(DIR>1)).
Gas phase jet: unforced In order to characterize the gas phase of the two-phase jet, the PDI data was conditioned for particle diameters roughly d = 1µm in diameter. We calculate a Stokes number based on the exit conditions: St d = τ p d g /U g (5) where τ p = ρ p d 2 /(18ρ g ν) is the particle response time. We find that these particles have Stokes numbers in the range St d = 0. 02 for the range of Reynolds numbers considered here. The length (velocity) scale of the jet increases (decreases) with x leading to a time scale that increase as x 2. Therefore the Stokes number of these small particles decreases quickly with down stream position. We assume this particles to be adequate tracers of the This claim is backed up a posteriori by comparisons presented below of the well known phenomenology of a self-similar turbulent round jet.
Activity and behavioral observations Two observers recorded activities, behaviors and stereotypies (Table  2 ) at D0, D8 and D30 during a 6 hours' observation period. Both observers looked at the pheromone and placebo young bulls. They used instantaneous sampling of individual young bulls in each group using 5 min scan sampling. Data were recorded 48 times day-1 each, during 6 onehour-observation-periods from 9am to noon and 2pm to 5pm.
Statistical analysis Most calculations and statistical analyses were performed in the open-source environment R version 3. 5. 1. (R Development Core Team, Vienna, Austria). The characteristics of young bulls assigned to the two treatment groups were compared to assess homogeneity: Student's t-test was used for continuous variables after checking normality using Shapiro-Wilk's test. Then, a mixed effects logistic regression model using the glm function from the package lmertest was used to characterize the association between the exposure to pheromone and BRD diagnosis (diseased or healthy). Individual bull was the experimental unit. Fattening operation and pen were considered as random factors. An interaction between the date of clinical examination and BRD diagnosis was included in the statistical model.
Proportions and count were used to describe activity and behavioral observations. Because they displayed a skewed distribution, permutation tests with 1000 replications were used to calculate P-values. The H0 hypothesis is that an activity or behavioral observation has a significant effect. A mixed linear model is used with the initial data set to test this effect and the Chi-square statistic is calculated for each activity or behavioral observation. Then 1000 replications with at each replication a random permutation of group (pheromone or control) for young bulls, while keeping the same proportion of pheromone groups and control groups, a random permutation of date of clinical examination (D0, D8, D30) and a random permutation of number of young bulls per pen while keeping the same number of young bulls receiving pheromone than in the initial data set.
For each activity or behavioral observation, 1000 values of Chi-square statistic are then obtained. The 95% percentile of repartition of this 1000 values is calculated and compared with the value obtained with the initial data set: if superior then H0 is rejected, the activity and behavioral observation has a significant effect. A mixed linear regression model using the lme function from the nlme package was used to characterize the association between the exposure to pheromone and ADG for the entire fattening period. Individual bull was the experimental unit. A "pen within fattening operation" nested random effect was added to the model. Then, relative expressions of transcripts were compared using Wilcoxon signed rank test since data were paired and non-normally distributed. All the comparisons were carried out using GraphPad Prism (GraphPad Software version 7.
Clinical signs Clinical data were obtained for 237 young bulls (137 in the pheromone group and 100 in the control group) at D8 and 230 young bulls (139 in the pheromone group and 91 in the control group) at D30. At D8, 15% (36/237) of the young bulls were affected by BRD. The diagnosis was established when the animal displayed (i) nasal or ocular dis-charge or cough and (ii) increase in respiratory rate or depression. The morbidity rate was 19% (26/137) in the pheromone group and 1% (10/100) in the control group. At D30, 8% (19/230) of the young bulls were sick with a morbidity rate of 4% (6/139) in the pheromone group and 14% (13/91) in the control group. Results of the logistic regression model (Table  4 ) shows that interaction between exposure to pheromone and day of clinical exam was significantly (P=0.
Author Contributions: S. A. , B. M. , M. G. , L. M. , A. A. , and L. B. L. conceived and designed the experiments; methodology, C. H. and J. B. ; software, B. M. , M. G. , L. M. and L. B. L. ; validation, S. A. , F. R. , F. M. , B. M. , M. G. , L. M. and L. B. L. ; formal analysis, S. A. , F. R. , F. M. , B. M. , M. G. , A. A. , L. M. , J. Z. , and L. B. L. ; investigation, C. H. and J. B. ; resources, S. A. and F. M. ; writing-original draft preparation, S. A. and F. M. ; writing-review and editing, S. A. , F. R. , J. Z. and F. M. ; supervision, S. A. and F. M. ; project administration, S. A. and F. M. ; funding acquisition, S. A. and F. M. All authors have read and agreed the published version of the manuscript. Funding: This research was supported by the research program "For and on Regional Development" PSDR 4 Grand Ouest funded by INRAE and the Régions Bretagne, Normandie, Nouvelle Aquitaine and Pays de Loire as part of the SANT'Innov project and the Plan ÉcoAntibio du Ministère de l'Agriculture et de l'Alimentation (France).
Multivariate and Statistical Analysis Multivariate and statistical analyses were carried using R based  [68]  dedicated packages and personal scripts, except for the hierarchical clustering. Data normality was checked by visual inspection of quantile-quantile plots. Principal component analysis (PCA) was carried out with the R package FactoMineR  [69]  and the two first components showing the maximum of variability were used for graphical representations (variable plots with correlation circle and individual plots). Partial least squared discriminant analysis (PLS-DA) was performed with the mixOmics R package  [70]. For hierarchical clustering, data were first visualized with a heat map after a normalization step and a mean-centered reduction step for each amino acid. Accordingly, the color scale in the heat map was expressed as the number of SDs (standard deviations).
Hierarchical clustering based on Pearson correlation coefficients was achieved with the free Multiple Experiment Viewer software (MeV 4. 9. 0). The effect of genotype, [CO 2 ] and their interaction were tested by a two-way ANOVA (p-value < 0. 05). For amino acid analysis, the percentage of variance explained by the statistical model was calculated from the sum of squares (including residuals) and normality of the residues was checked with quantile-quantile plots. Tukey's HSD tests were used for post-hoc multiple pairwise comparisons of mean groups and Student tests for the comparison of two mean groups (p-value < 0. 05). All results are expressed as the mean ± standard deviation (SD) of three independent biological replicates.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3390/metabo11080501/s1, Figure  S1 : Partial Least Square-Discriminant Analysis of rosette leaf amino acid contents of Arabidopsis Col-0 and gox1 plants exposed to short-term low CO 2 treatments (180 and 100 ppm for 4 h), Figure  S2 : Partial Least Square-Discriminant Analysis of rosette leaf amino acid contents of Arabidopsis Col-0, gox1, gox2-1 and gox2-2 plants exposed to long-term low CO 2 treatment (200 ppm for 1, 4 or 8 days), Figure  S3 : Contribution of Gly and Ser to the variation of total amino acids after either short or long-term exposure to low CO 2 treatment, Table  S1 : Complete dataset of free amino acid contents of Arabidopsis Col-0 and gox1 rosette leaves exposed to short-term low CO 2 treatments (180 and 100 ppm for 4 h), Table  S2 : Complete dataset of free amino acid contents of Arabidopsis Col-0, go1x, gox2-1 and gox2-2 plants exposed to long-term low CO 2 treatments (200 ppm for 1, 4 or 8 days), Table  S3 : Free amino acid contents of rosette leaves from Arabidopsis Col-0 and gox1 mutant exposed to short-term low CO 2 treatments (180 and 100 ppm for 4 h) expressed as percentages of total amino acids, Table  S4 : Free amino acid contents of rosette leaves from Arabidopsis Col-0, gox1 and gox2 mutants exposed to long-term low CO 2 treatment (200 ppm) for 1, 4 or 8 days expressed as percentages of total amino acids.
Author Contributions: Conceptualization, Y. D. , M. J. and M. H. ; methodology, Y. D. and C. M. ; software, Y. D. ; validation, Y. D. and C. M. ; formal analysis, Y. D. ; investigation, Y. D. , M. J. and M. H. ; data curation, Y. D. and C. M. ; writing-original draft preparation, Y. D. ; writing-review and editing, Y. D. , M. J. and M. H. ; visualization, Y. D. and M. H. ; supervision, M. J. and M. H. ; project administration, M. H. ; funding acquisition, Y. D. and M. H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by French National Research Agency (ANR), grant number "ANR-10-LABX0040-SPS" and "ANR-11-IDEX-0003-02". Y. D. was supported by a PhD grant from the French Ministry of Higher Education and Research. Institutional Review Board Statement: Not applicable.
On May 8th, the patient was discharged from the ICU, still receiving nasal oxygen therapy (2 L/min). SARS-CoV-2 RT-PCR was still positive on a naso-pharyngeal swab (18. 3 Ct, Alinity m). On May 17th, the patient was discharged home, weaned from oxygen and presenting no general symptoms except persisting asthenia. On June 3rd and June 10th, the patient remained persistently positive for SARS-CoV-2 RT-PCR on naso-pharyngeal swabs (22. 67 and 28. 59 Ct respectively, Alinity m). On June 17th, the viral load was lower (33. 75 Ct, Alinity m) and finally on June 23rd, SARS-CoV-2 RT-PCR was negative (Alinity m) (Fig. 1 ). Sanger sequencing of the S gene was performed on three samples and analysed on SeqScape 4 software, aligned on reference sequence 20A. EU2. It showed a typical B1. 1. 7 variant on the first sample (April 13th); double populations: K417K/N, E484E/K and Q493Q/R on the 2nd sample (28th April), and E484K and K417N on the 3rd sample (May 8th) (Fig.
Harvesting and Data Collection At tillering, panicle initiation, heading and grain physical maturity stage, 10 plants from each experiment unit were taken randomly to examine the plant height (cm), stem (including tillers) and panicle numbers (at maturity), leaf area index (LAI), crop growth rate (CGR), chlorophyll contents (Chl a & Chl b), and carotenoid contents at these stages and maximum values of these parameters were defined as the highest value across different stages. Chlorophyll and carotenoid contents were assessed spectrophotometrically as described by Peizhou  [30]. Plant height was recorded from the soil surface to the panicle tip, while number of panicle (m -2 ) was determined by direct count. Evaluation of spikelets panicle -1 was performed by randomly selecting 10 panicles from each plot.
Although captive breeding programs are valuable for conservation, they have been shown to be associated with genetic changes, such as adaptation to captivity or inbreeding. In addition, reproductive performance is strongly age-dependent in most animal species. These mechanisms that potentially impact reproduction have often been studied separately, while their interactions have rarely been addressed. In this study, using a large dataset of nine male and female reproductive parameters measured for 12,295 captive houbara bustards (Chlamydotis undulata undulata) over 24 years, we investigated the relative and interactive effects of age, inbreeding and number of generations in captivity on reproduction. We clearly identified (1) senescence patterns in all parameters studied; (2) negative effects of inbreeding on sperm characteristics, display behavior, egg weight, egg volume and hatching probability; and (3) changes in phenotypic values for seven parameters according to number of generations in captivity.
Within this framework, we were particularly interested in (1) quantifying the relative effects of age, inbreeding and number of generations in captivity on reproductive performance and (2) assessing their potential interactions. During this study, a set of reproductive parameters, which was different for each sex, was measured in 7242 females and 5053 males, with an individual number of generations in captivity ranging from 0 (founders) to 5. 1. Data were measured between 1997 and 2019. For males, we used the mass motility index, the number of sperm per ejaculate and the number of displaying days. The number of displaying days corresponded to the number of days where courtship display behavior was recorded for a male. This parameter was used because it is a crucial behavior in a lekking species with strong sexual selection and female mate choice, such as the houbara  43, 44.
Age, inbreeding and number of generations in captivity. Pedigree-based individual inbreeding coefficient F, which is the probability that two homologous genes of an individual are identical by descent  8  , was computed using the package optiSel 2. 0. 2 58 in R 3. 6. 1  59. Founders of the captive population were considered neither inbred nor related, and their F was set to zero  32. The number of generations in captivity was computed as the average number of its parents plus one  60. The number of generations in captivity of founders was set to zero. Individual age, in years, was computed for each record. Descriptive statistics of age, inbreeding and number of generations in captivity are provided in Table  1.
Statistics. All statistics and figures were carried out in R 3. 6. 1  59. Reproductive parameters were analyzed through generalized linear mixed-effects models using the package glmmTMB 0. 2. 3  61. The mass motility index, egg weight, egg elongation, egg volume and hatching weight were analyzed using a Gaussian distribution function (identity link function), while the number of sperm and the number of displaying days were analyzed using a negative binomial type I function (log link function) since they were count over dispersed data. Hatching probability was analyzed using a binomial function (logit link function). Finally, the number of eggs laid was analyzed using a zero-truncated negative binomial distribution function considering the large amount of 0 (7112 on 39,693 records). First, this model considered the variable to be zero versus nonzero and a binomial model was applied (logit link function).
This method was applied using the drop1 function of the package stats 3. 6. 1  59. Note that the three main explanatory variables (age, inbreeding and number of generations in captivity) were retained in the final models, even if non-significant. Subsequently, the models were validated by checking the homogeneity and normality of the residuals and multicollinearity of the remaining variables using the package performance 0. 3. 0  62. If the effect of a variable was significant, its effect on the reproductive parameters was predicted using the package ggeffects 0. 13  63  or function predict of the package stats 3. 6. 1  59  for interaction predictions. Graphs presenting predictions were only produced for the significant effects of age, inbreeding and number of generations in captivity. Figures were created using the package ggplot2 3.