dataesr/scientific-paragraphs-categorization huggingface dataset:
The program divMigrate searches for gene flow directionality between each pair of populations by identifying significant asymmetry based on allele frequency, and against a hypothetically defined pool of migrants to estimate genetic differentiation between each population pair and the hypothetical pool. The relative levels of migration between the two populations are then estimated based on the resulting directional genetic differentiation  124. To evaluate the significance of asymmetric migration, 1000 bootstraps were performed. Resulting migration matrices were then plotted using Gephi <https://gephi. org/> 125 to generate network graphs. These show directional gene flows between populations (located at the nodes), with the thickness of the lines showing the relative strength of gene flow.
Acknowledgements This work was funded by  CSIRO Health & Biosecurity  ( R-91044-01 ) to W. T. T. , T. W. , K. H. J. G. , S. E. , and S. D. W. T. T. , T. W. , S. E. , K. H. J. , and D. K. were also funded by  CSIRO Health & Biosecurity 'Genes of Biosecurity importance fund'  ( R-8681-1 ), and D. K. also acknowledged funding by  CSIRO  ( R-90035-14 ). R. R. was funded by  CSIRO and Hort Innovation Australia (HIA). A. P. was supported by  CSIRO ,  CRDC  and  Bayer  ( R10801-01 ). J. Z. and Y. W. were supported by the  National Key Research Development Program of China  (No. 2019YFD0300103  to Y. W. ) and the  Fundamental Research Funds for the Central Universities of China  ( KYZ201920  to Y. W. ). M. H. O. was funded by the  Agricultural Technology and Agribusiness Advisory Services Project  (Project  ID  No.
:  P109224 ) funded by the  World Bank. The work performed at INRAE was publicly funded through the  ANR  (the  French National Research Agency , Grant  ID 1702-018 , given to K. N. ) under the " Investissements d'avenir " programme with the reference  ANR-10-LABX-001-01 Labex Agro  and coordinated by  Agropolis Fondation  under the frame of I- SITE MUSE  ( ANR-16-IDEX-0006 ). It was also funded by a grant from the  department of Santé des Plantes et Environnement at Institut national de la recherche  agronomique for K. N. (adaptivesv). It was also financially supported by  EUPHRESCO  (FAW-spedcom, given to  Anne-Nathalie Volkoff ). We thank  Stella Adumo  ( NaCRRI ) with Uganda FAW sample collection, and  Carlos Blanco  for the Mexican FAW samples. Peru and  China FAW  preborder interception specimens were provided by the DAWE.
Data Collection and Preparation Cows and Management. Data used for this analysis were collected on 23 commercial dairy farms in Québec, on the test day with the DHI group Lactanet (Sainte-Anne-de-Bellevue, QC, Canada) between October 2014 and June 2015. To correctly characterize nutrient intake, only data from cows fed simple feed ingredients were used, excluding from the original data set data from cows fed commercial concentrates (except minerals), yielding data for 541 cows. Briefly, during the p. m. and subsequent a. m. milking, milk yield from each cow was recorded, and milk was sampled using online milk meters. Samples were stored at 4°C with a preservative (2-bromo-2-nitropan-1,3-diol) and sent to the Lactanet laboratory (Sainte-Anne-de-Bellevue, QC, Canada) for analyses of CP (infrared analyses, Foss MilkoScan FT 6000).
Animal BW were estimated from the thoracic circumference, applying the equation of  Yan et al. (2009). Milk-and protein-related EBV were also collected from Canadian Dairy Network at the time of collect (Canadian Network for Dairy Excellence, Sainte-Anne-de-Bellevue, QC, Canada). Information on management, such as feeding frequencies, feeding systems, and temperature and humidity index, were also collected on the test day. More information can be obtained from Fadul-  Pacheco et al. (2017)  and  Duplessis et al. (2021). Data of animals with major health issue were not collected. The days of gestation were unknown, so no requirement for gestation was included in NRC_2001, CNCPS, and NorFor models. For INRA_2018, however, a value for the last breeding time was required and obligate in the program, and thus an estimation was calculated from actual DIM.
08% and 2. 35% of MP for Lys and Met, respectively  (NRC, 2001). Whitehouse et al. (2010)  revised those recommendations to 6. 95% and 2. 38% of MP. However, those values for NRC_2001 may differ if the goal is maximum MPY or maximum milk protein content. The NorFor model indicates 2. 2%, 6. 4%, and 2. 2% of MP for His, Lys, and Met, respectively, for optimal production. With CNCPS, however, assuming the factorial AA repartition of each expenditure and using AA efficiencies from Van Amburgh et al. (  2015 ), AA requirements averaged, in our data set, 2. 30%, 6. 91%, and 2. 41% of MP (SD: 0. 02%, 0. 05%, 0. 08%) for His, Lys, and Met, respectively. Clearly, with variability of supply across models, recommendations to balance AA must be model-specific, as also demonstrated by  Whitehouse et al.
ACKNOWLEDGMENTS The authors thank  P. Nordgren  and  M. Åkerlind  from  NorFor (Aarhus, Denmark ) for their help on the Scandinavian model. We also thank the  Natural Sciences and Engineering Research Council of Canada (NSERC, Ottawa, ON, Canada) , the  Québec consortium  for industrial bioprocess research and innovation ( CRIBIQ, Québec, QC, Canada ), and  Novalait  ( Québec, QC, Canada ) for supporting the project and  Adisseo  ( Campbellville, ON, Canada ) for financing  S. Binggeli 's scholarship. The authors are grateful to the participating dairy producers in this study. We also acknowledge the help of  Mélissa Duplessis ,  Liliana Fadul-Pacheco ,  Roxane Robichaud ,  Valérie Audet , and  Isabelle Duval  ( Département des sciences animales, Université Laval, Québec, QC, Canada ) for data collection.
Activities and methodology of the project The fellow was involved in the different steps of evaluation of mycotoxin in vitro toxicity from the beginning of the project. To gather contamination data, literature searches were included to further study about the topic and to select the tested mycotoxin concentrations. In order to follow this work, the fellow was trained in cell culture methodology, data analysis and in vitro approaches. In this framework, to achieve the main goal of the project, toxicological effects of mycotoxins were assessed on different parameters to compare 2D and 3D approaches. -Cell growth and viability effect were analysed in 2D and 3D system at the same time on intestinal cell line (Caco-2), hepatocyte line (HepaRG). On cell lines, the proliferation was measured by common cytotoxicity tests based on the analysis of mitochondrial activity (MTS (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium assay) test or ATP assay for 3D models) after 48 h of exposure with mycotoxins alone or in cocktail.
We excited preferentially either APC (630 and 694 nm) or canthaxanthin (520 nm) to pinpoint: i) which species dissipates the excitation energy of APC and ii) what is the effect of the binding of NTD to CK on the energetics of canthaxanthin. The absorption spectra of the samples, and the excitation wavelengths used, are reported in Fig. 2. B. In the main manuscript we focus on the results of CK-NTD excited at 694 nm. Equivalent results were obtained for the 520 and 630 nm excitations and are shown in the SI (Fig. S8 ). The raw TA data (Fig. 3. A) agree with what is observed in fluorescence (Fig. 2. A, S1. B), namely that CK-NTD is strongly quenched: i. e. the bleach (≈675 nm) mostly decays in 112 ps in CK-NTD, and in 1. 54 ns in CK. By applying a global sequential model (Fig. 3. B) to the raw TA data in Fig.
3. A, we could satisfactorily fit the CK and CK-NTD datasets with 3 and 4 components, respectively. The evolutionassociated difference spectra (EADS) obtained from this analysis are reported in Fig. 3. C. The first EADS is sub-ps resolution (100 fs) and contains contributions of coherent artefacts. The second EADS is attributed to energy transfer and equilibration between APC chromophores. This component is faster in CK-NTD than in CK (2 ps versus 21 ps), suggesting that a fast event might occur in this time window in the quenched sample. The 1. 54 ns component represents the decay of the unquenched fraction of CK in both samples. In CK-NTD this component is minor, while the majority of the decay (≈76%) occurs in 112 ps. Such quenching yield and timescale, systematically observed with each excitation power used (Fig.
NPQ takes place in the core of the phycobilisome via excitation energy transfer to a singlet excited state of canthaxanthin. To identify the spectrum of the quencher in CK-NTD, compartmental models were applied to fit all the TA datasets acquired on CK and CK-NTD. The complete set of models and the results obtained at different excitation wavelengths and powers are reported in the Supporting Information. Below we focus on the model applied to the TA data of CK and CK-NTD excited at 694 nm (Fig. 5. A-C ). This model is based on the following assumptions: i) for the CK-NTD sample, the total population of excited species is modeled with two schemes, also defined as megacomplexes, describing the excited state dynamics of the quenched or the unquenched CK complexes (Fig. 5. A-C ). This is a heterogeneous model where the relative amount of initial excitation assigned to the quenched/unquenched CK fractions (75/25) is based on the amplitudes of the fluorescence components observed in CK-NTD via TCSPC (≈80/20, Fig.
The probe was dispersed via a prism spectrograph on a 1024pixels back-thinned FFT-CCD detector (S7030-1006, Hamamatsu). Pump and probe polarizations were set at magic angle (54. 7˚). Data were collected on a shotto-shot basis, and the pump and probe pulses were modulated at frequencies of 500 and 250 Hz, respectively, using mechanical choppers (THORLABS). The optical density of the samples was set to ≈4 cm -1 in the case of CK and NTD and ≈2 cm -1 in the case of CK-NTD. The sample was kept at room temperature in a 2-mm quartz cuvette and refreshed via a home-built shaker throughout the measurement. Sample integrity was checked by inspecting the signal stability over multiple scans. Experiments with different excitation powers were conducted on both CK and CK-NTD to obtain datasets with an increasing signal-to-noise ratio.
The ultrafast time constants (<<100 fs) and the ones slower than 1 ns were fixed in the global analyses, due to the time resolution of the experiments. In each analysis, the chirp of the supercontinuum probe was corrected for via a parametric description of the IRF. S* state of NTD (independently estimated, Fig. S4 ), we have then fixed the Sq decay rate to 60 ns -1. Power dependent study. These simple models fully describes the TA data of CK and CK-NTD obtained at the lowest power (40 W). To obtain also datasets with a higher signal-to-noise ratio, we additionally measured CK and CK-NTD at higher powers. However, at higher powers, singlet-singlet annihilation might contribute to the excited state dynamics. To understand at which power such contribution should be taken into account in the model, additional independent measurements were run on CK at 694 nm excitation, with a power of 100, 160 and 320 W.
and 520 nm excitations. In line with the model developed for the 694 nm excitation, compartmental models consisting of Unquenched and Quenched CK megacomplexes were also used for the TA datasets of the CK-NTD sample, after 630 and 520 nm excitations. The only difference is that both Unquenched and Quenched CK schemes now contain three APC660 compartments (630 and 520 nm excitations), instead of one (694 nm excitation). APC660A, APC660B, APC660C represents the top cylinder, APC660 disks in the basal cylinders, and the APC660 in the mixed APC660/APC680 disks, respectively. The equilibration rates chosen for these models were based on the ones previously validated on CK for excitations also selective towards APC660, via independent time-resolved spectroscopy experiments  37  and represent intradisk, interdisk and intercylinder equilibration.
The models used for the 630 and 520 nm excitations are presented in Fig. S8  and the datasets were fitted simultaneously: a precursor is instantaneously excited. This precursor decays in 0. 19 ps and transfers the excitation energy to the APC660 compartments. The three APC660 compartments (APC660A, APC660B, APC660C, Fig. S8 ) equilibrate amongst themselves and with APC680, and they all decay with the unquenched APC rate of 0. 65 ns -1. The SADS of the APC660 and APC680 species obtained via these models have their bleaches centered at ≈660 nm and ≈680 nm, respectively, as it should be expected and in agreement with the results for the 694 nm excitation (Fig.
2. B). The direct excitation of NTD was modeled by adding a megacomplex, called Canthaxanthin, which describes the excited state dynamics of the carotenoid (Fig. S8 ). The Canthaxanthin megacomplex is identical to the one used to model the TA data of the NTD sample after 520 excitation, but it misses the compartment assigned to the S1 ' species resolved in the case of NTD (Fig. S4 ). No precursor in the canthaxanthin megacomplex was necessary to be included in the 520 nm excitation models of CK-NTD and NTD. Spectral assumptions. Similar to the case of the 694 nm excitation (see above), the SADS of Sq is assumed to be zero above 649 nm, and the SADS of APC680 and APC660 are here assumed to be equal in the ESA region. The Sq SADS was guided by the SADS estimated with the 694 nm excitation. The SADS of APC660, APC680 and the NTD Sq have been linked between the two experiments at 630 and 520 nm excitations (Fig.
S8 ). This model resulted in an excellent fit, as can be assessed in Fig. S9. A. Compartmental model used to fit the TA data of NTD upon excitation at 475 and 520 nm. The yellow bolt indicates that S2 is the first state directly populated following each excitation. The models and rates used to fit the datasets obtained with the 475 and 520 nm excitations are identical, except for the rates of internal conversion from S2 to S * which are estimated independently and are different (as indicated in the scheme). The higher rate at 475 nm excitation agrees with the expected higher probability of S* to be populated in NTD with respect to lower excitation energies (photoselectivity  34, 46  ). All rates are in ns -1. SADS obtained from the model in (A), at the excitation wavelength indicated on the plot.
A to each CK dataset. In the modified model (Fig. S6. B) an additional megacomplex, called Unquenched CK with annihilation, is added to the Unquenched CK one, in which APC680 decays at a faster rate (25 ns -1 ). The fraction of CK belonging to the Unquenched CK with annihilation scheme at each power is reported in Fig. S6. C. This power-dependent model (Fig. S6. A) results in remarkably similar SADS amongst all the different powers used (Fig. S6. B).
Acknowledgments The authors thank  Mrs. Sandrine Cot  and  Adjélé Wilson  for their technical assistance. This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 675006  ( SE2B ) to D. K. and R. C. , by  NWO  via a  Veni grant  to N. L. and a  Middelgroot investment grant  to J. T. M. K. and by grants obtained from the  Agence Nationale de la Recherche  ( RECYFUEL  [ ANR-16-CE05-0026 ] and  DynOCP  [ ANR-18-CE11-0005-03 ] to D. K. This research was also supported by the  Centre National de la Recherche Scientifique (CNRS)  and the Commissariat `a l'Energie Atomique et ´energies alternatives (CEA).
Funding This study received financial support to KV from the  European Commission Horizon 2020  Infrastructures # 731060 Infravec2 ;  European Research Council Support for Frontier Research, Advanced  Grant # 323173   Ano-Path ;  Agence Nationale de la Recherche  # ANR-19-CE35-0004  ArboVec; to MMR from  National Institutes of Health ,  NIAID  # AI145999 ; and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID.
Determination of Phenolic Compound and Glucosinolate Contents HPLC quantification of PCs was performed as described elsewhere  [22]  on a UHPLC-DAD system (Ultimate 3000, Dionex, ThermoFisher, MA, USA) equipped with a quadratic pump, auto sampler, column furnace, and diode array detector. Data were analyzed with Chromeleon software (Version 6. 8, Thermofisher, MA, USA). Main PCs, including sinapine and sinapic acid, were identified by comparing their relative retention time to a standard. Synthetic sinapine chloride was used in the preparation of the calibration curve. The GSL content was determined following procedure described by Grosser and van Dam, with some modifications  [32]. Samples were diluted ten times prior to the desulfation step. One milliliter of sample was loaded onto a glass pipette, preconditioned with 0.
The experimental data were fitted using a second-order polynomial equation: Y q = β 0 + ∑ 2 i=1 β i X i + ∑ 2 i=1 β ii X 2 i + ∑ ∑ 2 i<j=1 β ij X i X j + ε residues q , (5) Yq is the different responses (q = 1-3); β0, βi, βij, and βii are the regression coefficients of the mean, linear, interaction, and quadratic terms, respectively; Xi and Xj are the independent variables; and ε residues q is the difference between the observed and the predicted value. Experimental design and analysis were performed using the software MODDE v. 12. 0 (Satorius, Gottingen, Germany). An analysis of variance (ANOVA) with 95% confidence level was carried out for each response to test the model significance and adequacy. The model coefficients were considered as significant when the Student's t-tests of significatively give a p-value inferior to 0.
Determination of Optimal Operating Conditions MODDE-a Nelder-Mead simplex method-based software optimizer tool-was used to find the optimal operating condition for the recovery of secondary metabolites. To determine those maximizing the recovery of secondary metabolites and protein extractability, Microsoft Excel (Version 16. 43) was used. Indeed, the Y 1 , Y 2 , and Y 3 responses vary antagonistically, and the MODDE optimizer tool was defeated. We plotted graphs in which Y 1 , Y 2 , and Y 3 values were calculated using prediction models according to the %EtOH for a fixed extraction temperature. Thus, six graphs were plotted for extraction temperatures ranging from 25 to 50 • C in five-degree steps. T e was limited to 50 • C to avoid the dissociation of certain proteins, such as cruciferin  [33].
Determination of the Optimal Conditions of Aqueous Ethanol Extraction and Validation of the Prediction Models The results suggested that, through the AE extraction, PCs and GSLs can be simultaneously and optimally recovered from defatted Carinata meal. The optimal conditions, suggested by the MODDE software based on the previously established models, are %EtOH = 47% and T e = 62 • C. Under these conditions, the contents of PCs and GSLs, predicted by the models, were determined to be 11. 74 ± 1. 26 mg/g DM and 103. 61 ± 16. 2 µmol/g DM , respectively. These values remain statistically similar (Table  S3 ) to the values observed (10. 87 ± 0. 54 mg/g DM and 98. 96 ± 4. 9 µmol/g DM for the PCs and GSLs, respectively), and thus confirmed the validity of the prediction models for Y 1 and Y 2.
QUANTIFICATION AND STATISTICAL ANALYSIS Data were analyzed and plotted using R software (R Core Team, 2019) and MatLab (The MathWorks, Inc. ) custom-made scripts. In all cases, data met the assumption of the tests used. Effects of treatments on food intake were analyzed with a one-factor ANOVA followed by a Tukey HSD post hoc test. Responsiveness scores were analyzed with a Kruskal-Wallis rank test followed by multiple pairwise Wilcoxon comparisons (Holm p-value adjustment method). For the ingestion experiment, the difference between two treatments was assumed to be statistically significant when the p-value was below 0. 05 and the Confidence Intervalssize effect statistic -(CIs) 95% did not contain 0. While a p-value informs on the risk of not refuting the null hypothesis (here, that there was no difference between groups), it does not inform on size effect (i.
e. large confidence intervals indicate low confidence in the sampling). Appetitive (PER-based) and aversive (SER-based) population responses were fitted with general linear mixed models (GLMM) using the glmer function of the lme4 package  (Bates et al. , 2018). PER/SER served as a binary-response variable (binomial family, 'logit' link), while treatments and stimuli were entered as fixed effects. Individual identity was entered as a random effect. Only factors with a minimum of one responding bee per group were considered for the analysis. ANOVA (package car) was performed on GLMMs and post hoc multiple comparisons were used when necessary (Tukey p-value adjustment method, R package emmeans). For the olfactory responsiveness experiment, the effect of treatment was analysed with an exact Fisher's test for proportions, followed by post hoc multiple comparisons (Holm p-value adjustment method, R package rstatix).
In addition, CIs 95% of Odds Ratio (OsR) of the pairwise comparisons were plotted and used as an effect size statistic. The odds ratio of an event (here PER) is the ratio between the frequency (or likelihood) of event occurrence and the frequency (or likelihood) of its non-occurrence  (Bland and Altman, 2000). CI 95% estimate the precision of the OsR (here the OsR is the estimate) (see above, Experiment 1). If a CI 95% does not include 1, the calculated odds ratio is considered statistically significant. In our case, it means that the odds of PER is significantly different between two groups. For calcium imaging data analysis, baseline signal was calculated as the mean fluorescence in the 5 seconds before stimulus onset. Such baseline activity was used to calculate baseline-subtracted and normalized stimulus-induced glomerular activity (DF/F).
ACKNOWLEDGMENTS We thank  Julie Rivie  `re for helping with the collection of part of the behavioral data. Funding: We thank the  French National Research Agency ANR  ( Project  APITASTE  ANR-18-CE37-0021 ), the  CNRS , and the  University Paul Sabatier  for funding. M. G. also thanks the  Institut Universitaire de France  for support. M. P. thanks the funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  893382.
Data and code availability All data needed to evaluate the conclusions in the paper are present in the paper and/or the supplemental information. All data have been deposited at DOI: https://doi. org/10. 6084/m9. figshare. 15134859 and are publicly available as of the date of publication. DOIs are listed in the key resources table. Original codes have been deposited at DOI: https://doi. org/10. 6084/m9. figshare. 16864408 and are publicly available as of the date of publication. DOIs are listed in the key resources table.
For this purpose, extensive searches were performed using the PubMed/Medline, Scopus, Google scholar, TRIP database, Springer link, Wiley and SciFinder databases as a search engines. The in vitro and in vivo studies included in this updated review highlighted the signaling pathways and molecular mechanisms of emodin. Because its bioavailability is low, there are limitations in clinical therapeutic use. In conclusion, for an increase in pharmacotherapeutic efficacy, future studies with carrier molecules to the target, thus opening up new therapeutic perspectives.