dataesr/scientific-paragraphs-categorization huggingface dataset:
Yield and antioxidant activities of seal meat hydrolysates The yield of the seal meat hydrolysates generated using alcalase enzyme was determined based on the protein content. The enzymatic hydrolysis produced 49. 3 mg hydrolysates per 1 g of fresh harp seal meat under the conditions described in subsection 2. 1. The seal meat hydrolysates were adjusted to 5 mg/mL and subjected to free radicals scavenging assays. The hydrolysates showed DPPH free radical scavenging activity of 33. 55 ± 0. 26% and hydroxyl free radical scavenging activity of 40. 76 ± 0. 01%. The data showed that the seal meat hydrolysates had antioxidant activities, which suggest the hydrolysate has potential benefits for gut health  (Kumar Singh et al. , 2019; Million et al. , 2020).
The geometric mean of these plant reference genes was used for data normalization. Fungal gene expression was normalized with these plant reference gene expressions as already described  [9, 16]. Target gene expression in AMF-inoculated plants was quantified relatively to the mean expression of 5 non-inoculated controls (Pfaffl 2001 method  [34] ). When the target gene was not expressed in controls, Cq value of 38 was used to calculate ∆Ct. When the target gene was not expressed in inoculated plants, the relative expression was considered as null. The formula used for calculation of relative expression (R) was: R = E ∆Ct Target gene 3 E (∆Ct Vvact) × E (∆Ct Vv60SRP) × E (∆Ct VvEF1α) with E = PCR efficiency. Table  1. Oligonucleotide primers. (1) : GenBank; (2) : Genoscope Vitis.
5 mM RA was unsuccessful. The particle washing step in distilled water led to the dissolution of the solid phase obtained by co-precipitation so that almost no solid could be harvested after this step. In order to recover a few mg of material to perform XRD analysis, the washing step was skipped. The X-ray diffractogram of this raw sample did not show peaks corresponding to the hydroxyapatite structure but comparison of experimental data with the database showed that the pattern could be assigned to the calcium carbonate (CaCO3) calcite phase. HAp formation was thus totally inhibited by the presence of rosmarinic acid. Therefore, this compound was referred as CaP (for calcium phase)-RA. The identification of CaP-RA as calcite is consistent with its solubilization during washing, as calcite is known to be much more soluble in water near neutral pH than in alkaline conditions  [45].
Acknowledgments S. M. , X. S. , J. R. , and H. T. acknowledge the support of  FCT-Fundação para a Ciência e a Tecnologia (Base Fund   UIDB/00674/2020  and  Programmatic Fund   UIDP/00674/2020 ,  Portuguese Government Funds)  and  ARDITI-Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação  through the project  M1420-01-0145-FEDER-000005-CQM+ (Madeira 14-20  Program). X. S. , S. M. , and J-P. M. acknowledge support from  PRC NSFC-CNRS  2019 (21911530230 for X. S. and 199,675 for S. M. and J-P. M. ). J. P. M. also thanks the  CNRS  for financial support.
, 2011 (Makarova et al. , , 2013)). The systematic investigation of genes found in association with known defense genes has considerably expanded our knowledge of bacterial immunity  (Doron et al. , 2018; Gao et al. , 2020). Despite these notable findings, it is believed that many defense systems remain to be discovered. Beyond the description of the defensive arsenal of bacteria, the mechanisms that drive the variability, diversity, and number of defensive functions in bacterial genomes are just beginning to be explored  (Tesson et al. , 2021). Recent studies of large ecological datasets of phage-bacteria interactions in Vibrio have highlighted how mobile genetic elements (MGEs) such as the SXT integrative conjugative element (ICE) carry anti-phage functions  (LeGault et al. , 2021). MGEs seem to largely explain the differences in susceptibility of closely related strains to different phages  (Hussain et al.
Genes are colored by protein family. Genome accession numbers are provided in Table  S2A. Systems are under the control of their native promoters, with the exception of PARIS-1, which was only active when expressed from a ptet promoter (pFD237) in the presence of anhydrotetracycline (aTc, 0,5 mg/mL). Defense was measured at 37 C, with the exception of the RT-nitrilase + 1TM system, which was measured at room temperature. RT, reversetranscriptase; TIR, Toll/interleukin-1 receptor; HAD, haloacid dehydrogenase-like; DUF, domain of unknown function; TM, transmembrane helix. See also key resources table, Figure  S2 , and Tables  S2A  and S3. completion of the phage cycle, a typical feature of abortive infection systems  (Lopatina et al. , 2020). We aimed at identifying the viral trigger by isolating phages that were able to overcome this system, reasoning that mutations in the trigger component would enable phages to escape defense.
Altogether, our results show that PARIS acts as a second line of defense when a phage is able to inactivate RM systems (Figure  3F ), but that anti-restriction proteins can evolve to bypass PARIS while maintaining RM inhibition; e. g. , with the F54V mutation in Ocr. A sequence search against the NCBI Viral Database  (Hatcher et al. , 2017)  revealed that the F54 residue is conserved in 93% of Ocr homologs, suggesting that the F54V mutation might have disadvantages in some conditions. Accordingly, a F54A mutation was previously shown to abrogate Ocr dimerization that is required for full inhibition of type-I RM and BREX  (Isaev et al. , 2020; Zavilgelsky and Kotova, 2014). A hotspot in P2-like phages encodes diverse anti-phage systems P4 is a phage satellite that lacks structural genes encoding capsid and tail proteins.
Role of defense systems in P2-P4 interactions The presence of defense systems in P4-like satellites can provide a competitive advantage to a host by providing protection (A) Visualization of genomic regions from five E. coli strains containing a P2-like prophage, highlighting genetic diversity between Q and A genes, including known anti-phage defense systems. Genome accession numbers and positions are shown on the left. (B) Systematic analysis of genetic systems encoded between gpA and gpQ from P2-like phages. The pie chart shows the 30 most abundant systems classified by prevalence and shown as gene cassettes colored by protein domains (not to scale). When a system comprises accessory genes, different variants are shown, with the percentage of each occurrence on the left. A validated system providing phage defense (Figure  5A ) is highlighted with a dashed linker.
ACKNOWLEDGMENTS We thank  Erick Denamur  for providing E. coli strains and  Sylvain Brisse  for providing K. pneumoniae strains. We also thank  Laurent Debarbieux  and  Mathieu de Jode  for providing phages CLB_P2,  LF82_P8 , and  AL505_P2 , the  P2M platform (Institut Pasteur, Paris, France)  for genome sequencing, and  Alicia Calvo-Villaman  ˜an and  Rapha€ el Laurenceau  for their contribution to the PARIS acronym. This work was supported by the  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation program  (grant agreement no. 677823 ), by the  French Government 's  Investissement d'Avenir program , and by  Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases''  ( ANR-10-LABX-62-IBEID ). F. R. is supported by a doctoral scholarship from E ´cole normale superieure.
, 2020). The stations were placed in the center of each plot and included an air temperature and relative humidity sensor positioned 1. 5 m from the ground (HMP45C), four leaf wetness sensors 1. 2 m from the ground, oriented in four opposite directions (Dielectric LWS) and six T-type thermocouples (copper/constantan) placed at three different heights on two coffee plants. Each thermocouple was subdivided into four secondary thermocouples placed in contact with leaf laminas on the underside of four leaves from the same stratum  (Miller, 1971). The leaf temperature measurements were therefore an average of four leaves. Only the stations located in the reference plots with full sun exposure had a rain gauge placed above coffee trees 2 m from the ground (TE525MM, accuracy 0. 1 mm). The dataloggers recorded data every five seconds and stored average, minimum and maximum values every fifteen minutes.
Characterization of plot shade tree height and canopy openness To account for shade tree growth due to seasonal microclimatic variations as well as pruning practices, we chose to measure shade tree height and canopy openness above the coffee plants, which vary along year, rather than focusing on seedling density (Table  1 ). We measured the shade tree height with a clinometer. Canopy openness (%) was calculated by using hemispherical photographs analyzed with Gap Light Analyzer software  (Frazer et al. , 1999). The hemispherical photographs were taken using a GoPro camera placed above the coffee plants and equipped with a fish eye lens allowing the capture of images with an ultra-wide angle (Fig. 2 ). The software then estimated the percentage of canopy openness for different angles by manually classifying the pixels with a software feature that manages the contrast level.
, 2010). The linearity of the dependence is checked using the partial dependence function showed the marginal effect of each variable on the count response after averaging the effects of all the other variables  (Bhatt et al. , 2013). The five dependent variables were then fitted to a Gaussian distribution using generalized linear models (GLM) and keeping the independent variables with the greatest relative level of influence only (Table  3 ), the sum of whose influences accounted for 95 % of the dependent variable. To compare factor modality effects, we used Tukey's multiple comparison post hoc test. In order to assess the validity of the equations on an independent dataset, two plots were excluded from the entire construction of the model (BRT and GLM), the plot of the site at the altitude of 770 m, under shade provided by tall shade trees, and the plot of the banana agroforestry system at the altitude of 1140 m.
In these two plots, the predicted and observed values were then compared using the root mean square error. The extreme values excluded from the model building stage were  similarly used for evaluation purposes. All the statistical analyses were performed with R 3. 6. 1 (R Development Core Team, 2019) and with an alpha level of 0. 05. Boosted regressions trees were constructed using the gbm package version 2. 1. 5 (Greenwell et al. , 2019; Ridgeway, 2007)  and the dismo package  (Hijmans et al. , 2017). GLM were fitted using the lme4 package version 1. 1-21  (Bates et al. , 2015) , and we carried out Tukey's multiple comparison post hoc test using the multcomp package version 1. 4-12  (Hothorn et al. , 2008).
5 whose parameter values are presented in Table  4 ). HoursLW = ε 1 + ε 2 × RHSun + ε 3 × MaxTairSun + ε 4 × RainfallSun + ε 5,classAgroforSyst (5) In the agroforestry systems with tall trees, the leaf wetness duration was higher than in the full sunlight systems, with a maximum difference of up to 2 h. A comparison of the model's predictions provided root mean square errors of 2. 87 with the data used for its construction (Fig. 5A ), of 3. 73 with the extreme values excluded from model building (Fig. 5B ), and 2. 38 with the independent dataset (Fig. 5C ). The model inaccuracy was higher for non-rainy days since the RMSE was 3. 50 versus 2. 59 for rainy days.
Acknowledgements For their valuable technical work, we thank  Alejandra Barquero ,  Hugo Mendez  and  Steven Cerdas. This work was developed as part of the  "Programa Centroamericano de Gestión Integral de la Roya del Café" (PROCAGICA)  funded by the  EU  ( DCI-ALA/2015/365-17 ). We thank the  Ernesto Illy Foundation  and  CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement)  for their financial support and  P. Biggins  for reviewing the English. We should like to thank in particular the coffee growers who were involved in the project for their help, for allowing access to their plots and for their willingness to participate in the trial we set up. Special thanks to  Rogelio Villarreyna-Acuña , our dear friend and co-author, who passed away while this article was being finalized.
The objectives of this study were three-fold: (i) laboratory and growth chamber phenotyping of sunflower varieties with different MGs to test their response to a range of temperature, water potential, seedling heterotrophic growth, and resistance to mechanical obstacles; (ii) field testing of these varieties in terms of seed germination and seedling emergence dynamics, and final rates, and; (iii) quantification of seedling emergence losses, postemergence damage, and identification of the key factors affecting the establishment of sunflower in double cropping. Besides determining the potential differences among the tested varieties in terms of measured variables, we also focused to create a database that could be used in the future for simulation purposes using the crop emergence model, SIMulation of Plant Emergence (SIMPLE;  Dürr et al.
Seedbed Structure and Climate Data In general, the seedbed structure and the number, size, and, in particular, the spatial distribution of the soil aggregated in the seedbed, affect the speed and final rates of seedling emergence. A simplified method for seedbed structure characterization, based on photographic comparisons of seedbed structure, was used as described previously  (Dürr et al. , 2016). To this aim, photos of the seedbed surface were taken immediately after sowing and compared with the historical database  (Lamichhane et al. , 2021). This approximation was used to choose a seedbed structure similar to the one already described in a database associated with the SIMPLE model that describes different types of seedbeds  (Lamichhane et al. , 2021). Soil temperature and water content were recorded using climate sensors (ECH O 5TM, METER Group, Inc.
USA) as described previously  (Lamichhane et al. , 2020a). The sensors were installed immediately after sowing at three soil horizons (1 sensor/depth/block at -3 cm, -5 cm, and -10 cm, 4 sensors/depth in total). These sensors delivered hourly temperatures, measured by an onboard thermistor, along with accurate volumetric water content. Data were recorded from sowing until crop emergence. Hourly data of rainfall and air temperatures were obtained from an automatic meteorological station installed at the experimental site. The cumulative degreedays ( • Cd) were calculated from sowing (time 0) as the sum of the average daily air temperature minus the base temperature of each sunflower variety (Table  3 ).
However, future studies are needed to test the effect of irrigated vs. non-irrigated and protected vs. non-protected plots on the establishment of quality and yield of sunflower in double cropping. Among all the tested varieties, the RGT Capitoll resulted to be the most tolerant to water stress under laboratory conditions ( b = -1. 26 MPa). Therefore, this variety could have a good potential for double cropping where water stress in the seedbed is the most important limiting factor for sunflower establishment under southern European conditions. Overall, our study offers an important dataset on the genetic variability of early growth traits in sunflower, in general, and those adapted for double cropping system, in particular, which were poorly available to date in the literature. Consequently, the results of our study can make a significant contribution to improve or better parameterize both the specific sunflower crop models, such as Sunflo  (Casadebaig et al.
ACKNOWLEDGMENTS The authors thank the technicians of the Vasco research team, UMR AGIR, and those of the  Auzeville experimental unit for field crops (UE APC, Auzeville ,  INRAE ; https://doi. org/10. 15454/1. 5483266728434124E12 ), for their kind support during this study. A special thanks to  Gilles Tison  and  Clément Lemouzy ,  UE APC Auzeville , for implementing field experiments;  Damien Marchand ,  Béatrice Quinquiry ,  Pierre Perrin , and  Benoit Gleizes  for their help in the field and laboratory experiments. Thanks also to  Euralis Semences ,  RAGT2n  and  Syngenta  for providing seeds. This study was supported by the  Ministry of Agriculture  through  CASDAR IPHARD (Idéotypage et PHénotypage pour l'Adaptation des variétés de soja et de tournesol à la culture en Relai ou en Dérobé ) and by  UMT PACTOLE.
| Life-history traits We collected life-history trait values for the species from which we measured yolk TH levels from the literature. All literature sources, along with the trait values used in our analyses, are presented in the Supplementary Data. Species' body mass was collected from the amniote life-history database compiled by  Myhrvold et al. (2015). To test whether a species' developmental mode explains yolk TH variation, we categorized our species as either precocial or altricial species based on  Cramp (1977 Cramp ( -1994) )  and  Starck and Ricklefs (1998). The four semiprecocial species, which all belong to the family Laridae (gulls and terns), were pooled with precocial species and the one semi-altricial species (the kestrel, Falco tinnunculus) was pooled with altricial species.
In addition to further explore whether the variation in yolk THs may be at least partly attributed to differences in developmental duration between precocial and altricial species, we collected data on incubation duration and the age at fledging (i. e. number of days from hatching to fledging) from  Myhrvold et al. (2015)  for each species to represent the length of the prenatal and postnatal developmental period respectively. When such data were not available in  Myhrvold et al. (2015) , we consulted other literature (documented in Supplementary Data). To test whether a species' migratory status explains yolk TH variation, we categorized each species as either migratory or resident, based on  Lehikoinen et al. (2003 ), McNab (2009)  and  Pap et al. (2015). When the categorization was not consistent in these references, we determined the migratory status based on the population from which we collected eggs.
To explore associations between yolk THs and pace-of-liferelated traits, we collected the following traits: (i) Growth rate, measured as the logistic growth rate constants from  Starck and Ricklefs (1998)  for better comparability. More recent literature was also consulted. When data from multiple sources were available, we calculated mean value. All literature we consulted are documented in Supplementary Data. (iv) Maximum life span and age at sexual maturity were obtained from the database AnAge  (Tacutu et al. , 2013). We chose maximum life span rather than other measures, such as median life span because of its much higher availability  (de Magalhães et al. , 2007; Healy et al. , 2014). Maximum life span is also mostly determined by intrinsic factors and less sensitive to extrinsic cause of death, such as predation, and, therefore, is an arguably better measure for the true potential of longevity of a species  (Barja, 2013; Vágási et al.
, 2019). When consulting the literature, we assumed that the trait values are representative of the traits from wild populations. For all captive species (see above and the Supplementary Data), we used the trait values from the literature based on the specific colony from which the eggs were collected when available (e. g. the rock pigeons and zebra finches). In other cases, we assumed that the literature data are representative (e. g. for pheasants and grey partridges). Among our captive species, four are considered domesticated (domestic chicken, Japanese quail, homing pigeon and zebra finch), for which we additionally checked whether the trait values we obtained from the literature are relevant. These species may have highly different trait values compared to their wild ancestors. For example, the body mass of homing pigeons is known to be much larger than that of the wild-type rock pigeons.
The OU process builds on a BM model, with an additional parameter to describe a force of stabilizing selection (α) that pulls the trait value back towards an optimum  (Hansen & Martins, 1996). Using the r package geiger  (Pennell et al. , 2014) , the BM model was favoured, as the Pagel's λ for yolk T3 and T4 are both at bound (T3, λ = 1; T4, λ = 0. 999), consistent with BM performance  (Freckleton et al. , 2002). The assessment also suggested the pulling force α is very small (T3, α = 0. 006; T4, α = 0. 012), rendering the model essentially equivalent to a BM process  (Hansen & Martins, 1996). We therefore considered BM an appropriate assumption for our phylogenetic mixed models to control for phylogenetic non-independence.
| Model specifications We used phylogenetic mixed models  (Housworth et al. , 2004)  to test our hypotheses and chose a Bayesian approach following  Hadfield and Nakagawa (2010)  with the r package MCMCglMM  (Hadfield, 2010). Yolk T3 and T4 concentrations (pg/mg yolk) as well as total contents (ng/yolk) were ln-transformed and standardized and used as dependent variables in separated univariate models. Because yolk T3 and T4 may exhibit different patterns of variation (as found e. g. in great tits,  Hsu, Verhagen, et al. , 2019) , we analysed T3 and T4 with separate models. In all models, species body mass and captivity status (captive versus wild) were always included, along with the life-history traits of interest, as fixed factors in the respective models (see below and Table  1 ). In order to facilitate model fitting, body mass was ln-transformed and standardized and all twolevel categorical variables were dummy-coded as -0.
5 and 0. 5 (Table  S2 ). The life-history traits that are known to be associated with body mass (BMR, growth rate, maximum life span, age at sexual maturity) were first corrected for body mass and phylogenetic relatedness among species using the r package phytools  (Revell, 2009 (Revell, , 2012)). BMR data were first converted to mass-specific BMR (KJ hr -1 g -1 ) and ln-transformed and corrected for body mass and phylogeny as pointed out previously. Because data on life-history traits were not available for all species, we defined several sets of models depending on data availability, listed in Table  1. Each model and dataset were thus pruned for the specific set of species where data were available. Unlike placental animals where mothers may continuously transfer maternal hormones to the embryo throughout the gestational period, the total amounts of maternal hormones in egg yolks are determined during yolk formation and mothers cannot supplement more thereafter.
Response variables were all assumed to follow a Gaussian distribution. All models were run for 750,000 iterations with a 350 thinning interval and a burn-in of 50,000, aiming for an effective sample size of approximately 2,000 for all parameters. Following Houslay and Wilson (2017), we used an uninformative parameter-expanded prior following a Cauchy distribution (V = 1, nu = 1, alpha. mu = 0 and alpha. V = 25 2 ) for all random factors. Using an uninformative inverse-Wishart prior or improper flat prior gave qualitatively very similar results, suggesting that the results are robust against the choice of priors. aIn Model sets 2 and 3, both Japanese quail and domesticated chicken were removed from the 'dataset but only Coturnix japonica was removed from the phylogeny because Gallus gallus was still needed in the phylogeny for the data from red junglefowl.
| Model diagnostics Because results were highly similar across all possible phylogenetic trees, we examined model performance by refitting all models with a consensus tree. The consensus tree was derived using the r package phytools  (Revell, 2012)  for each phylogeny set. Model diagnostics were conducted by visual inspection on the trace plots for proper mixing and on autocorrelations and raised no concern regarding poor mixing or substantial autocorrelation. In support of our inspection, the autocorrelations for all models were <0. 05 and Gelman-Rubin diagnostics  (Gelman & Rubin, 1992 , via the r package Coda,  Plummer et al. , 2006)  were all <1. 05.