dataesr/scientific-paragraphs-categorization huggingface dataset:
This is a clear signal of radial mixing of disc stars, and has been noted in a number of studies (e. g. Casagrande et al. 2011; Frankel et al. 2018 Frankel et al. , 2019 Frankel et al. , 2020;; Miglio et al. 2020; Sharma et al. 2020). Similarly, at lower mass, stars have a wider distribution in z max , a clear example of dynamical heating of the disc (e. g. Aumer et al. 2016; Ting & Rix 2018; Mackereth et al. 2019). The measurement of precise ages for nearby stellar samples provides an ideal dataset for testing models of radial mixing and dynamical heating processes. Further, Figure  13   This gradient in R mean across the range in [Fe/H] is to be expected given that the Milky Way disc has a shallow, yet significant, metallicity gradient (e. g. Cheng et al. 2012; Bergemann et al. 2014; Anders et al.
SUMMARY & CONCLUSIONS We have demonstrated the strong potential for Galactic archaeology and stellar astrophysics and asteroseismic quality of year-long photometry from the TESS-CVZ. Studying a sample of 15, 405 giants in-and outside the CVZ, we demonstrate that currently achieved seismic yields are comparable or better to those estimated using a model of detection probability in TESS and produce precise stellar ages, masses and radii when combined with spectroscopic constraints. Power spectra derived from the time series data provide clear detections of oscillation modes in stars in a large range of evolutionary states, providing great potential for new constraints on stellar evolution models. To summarise our findings: • Systematics: A number of systematics are evident in the light curves that we generate based on the TESS FFI, which appear worsened in the 1 year data.
By comparing the uncertainties on mass and age when Gaia luminosities are used as constraints or not, we show that without refining the current seismic parameter measurmements, an external luminosity constraint is required to achieve adequate precision (i. e. less than 10% in mass and 30% in age) on these parameters. • Seismology across the Hertzsprung-Russell diagram: The TESS-CVZ data provides good power spectral resolution even for relatively low ν max stars. We show that stars on the AGB bump are clearly detected in our sample, and the detection of mixed modes and, tentatively, rotational splitting is possible in RC stars with 12 sectors of data. Importantly, target selection effects are more simply accounted for in the TESS-FFI samples than in, for example, Kepler. We demonstrate that our data compare well with Kepler data analysed in similar ways by showing • Galactic archaeology in the Milky Way disc: The TESS-SCVZ sample provides a good sampling of velocity space in the solar vicinity.
Trends between stellar mass (and therefore age) and the mean radii and maximum vertical excursions of stellar orbits are clearly visible. By integrating the orbits of stars in our sample, we demonstrate that the data from the CVZ, although limited in spatial extent, 'dynamically' samples a large section of the Milky Way disc with 5 R 10 kpc. The age precision afforded by TESS, in combination with high resolution spectroscopic data from APOGEE, is enough to distinguish clear differences in age between the high and low [Mg/Fe] disc stars and will be of great utility in properly reconstructing the star formation history of the disc. In particular, this paper has demonstrated the great importance of spectroscopic follow up for giants in the TESS-CVZs. We predict that seismic samples of the order of a few 10 4 giants could be attainable in the CVZs, but age and mass estimates will only be readily available -and sufficiently precise -for stars which also have spectroscopic information.
For each star with missing log(g), we use rejection sampling to take samples of this posterior within the range of parameters represented in the full data set. We verify that this procedure gives a reliable constraint on log(g) by making an inference of the parameter for the set of stars with TESS-HERMES log(g) constraints, and find that the SkyMapper prior-based log(g) constraint is within 3σ of the TESS-HERMES measurement for all 1186 stars, and within 1σ for ∼ 93% of the sample. This suggests that this inference is reliable for the rest of the dataset, and the resulting uncertainty on the log(g) constraint is realistic and will hence be propagated into our inference of luminosity, mass, age and radii consistently. Figure  A1  shows the comparison between our inferred log(g) and that from TESS-HERMES.
2013). The resulting fit and posterior samples compared to the data are shown in Figure  B1. The data are shown as a two-dimensional histogram of the samples from the posteriors on ∆ and K S , to give a sense of the large uncertainties on ∆. Despite these large uncertainties, an offset of the mean ∆ from 0 is clear. We show offsets of 30 and 50 µas as dotted and dashed lines respectively, as a guide to how our derived offset compares with those from the literature. Our best fit model is consistent with being flat as a function of K S , with a zero-point offset of ∼ 30 µas, such that ∆ = (-5 ± 6)(K S -8. ) + (30 ± 3) µas. This offset is somewhat smaller than many derived using currently available asteroseismic samples (e. g. Hall et al. 2019; Khan et al. 2019; Zinn et al. 2019 ). This may be a genuine population effect, due to the fact that this sample is somewhat closer and thus brighter than the Kepler sample.
In the catalogue, these numbers are found under CHI2_gof_PARAM_BHM and CHI2_gof_APO_PARAM_BHM for the results using SkyMapper and APOGEE, respectively. Table  D1. The data model of the catalogue of TESS-SCVZ targets which we release in this work. We compile seismic, photometric, spectroscopic and kinematic properties from Gaia (Gaia Collaboration et al. 2018), 2MASS  (Skrutskie et al. 2006) , TESS-HERMES  (Sharma et al. 2018) , SkyMapper  (Onken et al. 2019)  and APOGEE  (Majewski et al. 2017). Orbital parameters are computed using the method presented in  Mackereth & Bovy (2018) , implemented in galpy  (Bovy 2015). All relevant columns are accompanied by an associated uncertainty, defined either as the standard deviation or the 0. 16 and 0. 84 quantiles. Uncertainties are found in accompanying columns labeled with the suffix 'err'.
ACKNOWLEDGEMENTS JTM and AM acknowledge support from the  ERC   Consolidator Grant funding scheme  (project  ASTEROCHRONOMETRY , G. A. n. 772293). JTM acknowledges support from the  Banting Postdoctoral Fellowship  programme administered by the  Government of Canada , and a  CITA/Dunlap Institute  fellowship. The Dunlap Institute is funded through an endowment established by the  David Dunlap family  and the  University of Toronto. S. M. acknowledges support from the  Spanish Ministry  with the Ramon y Cajal fellowship number  RYC-2015-17697. RAG acknowledges the support from the  PLATO CNES  grant. LC acknowledges support from the  Australian Research Council  grants  FT160100402. TC acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No.
A second meeting was held in Nioro du Rip to develop two RAPs, a Sustainable Development pathway (SDP) and Fossil Fuel Development (FFD) pathway. The second session was centred on discussion of identifying RAP elements and the direction and magnitude in which each one of them will change under each RAP. Participants included experts in agriculture, livestock, horticulture, extension specialists, farm leaders, NGO representatives, and elected officials. During the meeting, we developed the SDP RAP and the FFD RAP based on the AgMIP protocols (see Appendix 1 in this Volume). A stakeholder engagement meeting was also organized to share results for Nioro. The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) platform provided support through information and invitation to its members.
Crops Crop yields and crop management information on millet, maize, and peanut were collected from the World Bank household survey data  (WLD, 2008) , which served as input data for the crop models. A total of 225 households were covered: 219 cultivated peanut, 221 cultivated millet, and 98 cultivated maize. Data collected include observed yields and crop management (sowing date, time, and amount of fertilizer/manure applied). Data on cultivar information were obtained from literature  (MacCarthy et al. , 2009 (MacCarthy et al. , , 2010;; Akponikpè et al. , 2010; Naab et al. , 2004; Dzotsi et al. , 2003). Weather data used were those described in the climate section. Simulation of crop yields was done using two of the most commonly used crop models in the sub region; DSSAT  (Hoogenboom et al.
Millet Simulated millet yields in Nioro were significantly enhanced under both the management intervention (increased fertilizer amount, number of split application by one, and plant population) and the intervention with the genetic adaptation package (shortening juvenile stage by 20% and extending reproductive stage by the same magnitude) (Fig. 12b ). Under the management intervention, millet yields of 896 and 1107 kg ha -1 compared to baseline yields of 585 and 445 kg ha -1 were simulated using DSSAT and APSIM models, respectively. With the genetically improved cultivar, average yields increased to 1345 and 1384 kg ha -1 for DSSAT and APSIM, respectively. The use of interventions reduced grain yield variability among farms. Yield variabilities of 37% and 13% were simulated with the management intervention and between 39% and 15% were simulated with genetic adaptation compared to between 50% and 57% simulated under current management practices.
Percent change in PCI on aggregate was 20% with Intervention Package 1 and 35% with Intervention Package 2. Finally, in terms of percent change in poverty, the first intervention package generated a 12% drop in poverty and the second intervention yields a decrease of 23%. Overall, the TOA-MD estimations on the impact of the intervention packages based on APSIM and DSSAT simulations in the current climate led to the following conclusions: • Intervention Package 1, which comprised increased fertilizer and improved crop management including appropriate plant population density and split fertilizer applications, yielded higher returns, resulting in a simulated higher level of adoption. • Adding an improved variety to the package brings additional gain in yield and economic return. However, the largest proportion of additional gains came from changes in agronomic management.
Peanut Average simulated SDP yields with climate change and without the use of the adaptation package ranged from 699-1030 kg ha -1 for DSSAT and from 857-937 kg ha -1 for APSIM across GCMs. Likewise, average simulated FFD pathway yields with climate change ranged from 455-1046 kg ha -1 for DSSAT and from 826-980 kg ha -1 for APSIM. The use of adaptation packages resulted in SDP yields ranging from 758-1077 kg ha -1 using DSSAT and from 863-942 kg ha -1 using APSIM. Likewise, average simulated FFD yields ranged from 474-1102 kg ha -1 using DSSAT and from 829-984 kg ha -1 using APSIM. These represent increases of 3-9% (DSSAT) and 1% (APSIM) under SDP, and 4-6% (DSSAT) and 0. 2-0. 6% (APSIM) under FFD pathway (Table  5 ). Simulated benefits of the adaptation packages were always higher with DSSAT than with APSIM, irrespective of the crop and agriculture development pathway.
Economic analysis In this section, we report on the four outcome variables (adoption rate of adaptation packages, change in net farm returns, change in PCI, and change in poverty) (see Table  6 ). Adoption rate. There were between 47% and 63% adopters of the adaptation package across all climate scenarios, crop models, RAPs, and prices. In both price scenarios, adoption rates were higher for the SDP. DSSAT consistently displayed higher adoption rates across RAPs and prices mainly due to the higher sensitivity of DSSAT to the climate change adaptation packages. There were more adopters under low prices when we control for RAPs and crop models. This means that more farmers tend to adopt the adaptation package when they produce under unfavorable price conditions. Changes in net farm returns and PCI.
The integration over the velocity, for some values of x F = M X /T F (T F is the freeze-out temperature) is performed through two dedicated integrators, one calling Python libraries and the other with Mathematica. Moreover, analytical formulae when the test (cross-section) function is of the form 1/((s -M 2 h ) 2 + (Γ h M h ) 2 ), (s -M 2 h )/((s -M 2 h ) 2 + (Γ h M h ) 2 ), 1  5  with the convolution over the velocity distribution v 2 e (-x F v 2 ) exist when the integration is carried over the range v = 0, ∞. The analytical formulae are then compared with the numerical evaluation of the dedicated integrators. Only then do we first feed micrOMEGAs these test functions to conduct sanity checks and then feed in the exact one-loop cross-sections resulting from our code, SloopS. Of course micrOMEGAs solves also the Boltzmann equation and returns the effective x F which we use for the integrators to conduct our checks.
Acknowledgments We thank  Alexander Pukhov  for several helpful discussions. HS is supported by the  National Natural Science Foundation of China  (Grant No. 12075043 , No. 11675033 ). He thanks the  CPTGA and LAPTh  for support during his visit to France in 2019 when this work was initiated. SB is grateful for the support received from  IPPP, Durham, UK , where most of this work was performed. SB also acknowledges the support received from LAPTh, during his visit, when this work was started. NC is financially supported by  IISc (Indian Institute of Science, Bangalore, India)  through the C. V. Raman postdoctoral fellowship. NC also thanks the support received from  DST, India , under grant number  IFA19-PH237  ( INSPIRE Faculty Award ).
Statistical analysis Statistical analyses were performed using the R software. First, homoscedasticity and normality were tested, and log-transformation was performed if needed. The effects of the treatments on the measured parameters were then compared by analysis of variance (ANOVA) with 3 factors: 'soil' (Luvisol, Anthrosol), 'level of compaction' (compacted, loose), 'earthworm' (anecic, endogeic, mixed, no earthworm). As the soil effect was systematically significant apart for branching rate (Table  2 ), two-way anova were then carried out separately for each soil to evaluate the mean effects of the factors 'earthworm' and 'level of compaction'. The R package multcomp  (Hothorn et al. , 2008)  was used for pair-wise comparisons based on the Tukey test for each soil separately.
Finite Element Model Definition Three finite element models were developed based on the Navarro's theoretical model (N) and two real patient-specific corneal geometries that were measured by a Sirius tomographer (CSO, Italy). The first was a healthy cornea (G0), whereas the second one was affected by keratoconus in an incipient phase (G1) according to the Amsler-Krumeich criteria  [32]  (provided from Vissum Hospital, Miguel Hernandez University and Keratoconus IBERIA database, Alicante, Spain). This study was approved by the research ethics committee of the Technical University of Cartagena (CEI21_001). The corneal model was extended to the limbus zone, where it was attached to sclera tissue. An almost 40° cut angle was used according to the average measured values  [38]. To implement the finite element model, the ANSYS software (Swanson Analysis System Inc.
Displacement Method This method was performed in an iterative process implemented in Python 3. 8 (Python Software Foundation). Ansys files (Swanson Analysis System Inc. , Pensilvania, USA) were read and updated following the iteration method found in Figure  2. The initial node coordinates were based on the geometrical values obtained with the topographer. From this geometry, the iterative process was based on the estimation of a free-stress geometry, which was submitted to IOP. The deformed geometry obtained is compared with the corneal topography and if the maximum difference between points is lower than a previously defined tolerance, the proposed geometry is considered the free stress geometry; elsewhere, the iterative process continued until the tolerance criteria was fulfilled or a maximum number of iterations was reached (Figure  2 ).
Prestress Method This method was performed in an iterative process implemented in Python 3. 8 (Python Software Foundation). Ansys files (Swanson Analysis System Inc. , USA) were read and updated following the iteration method shown in Figure  3. The objective of this method is to calculate a stress distribution in the cornea that equilibrates the applied IOP. From the model based on the geometry obtained with the topographer, IOP was applied, and the deformation gradient of the previous case was applied up to the displacement level was lower than a previously established tolerance. If the displacement level evaluation was higher than tolerance, the process continued until the tolerance criteria was fulfilled or a maximum number of iterations was reached (Figure  3 ). The prestress method allows the stress distribution in the measurement phase to be computed.
Potential confounders and variables which were statistically signi cant in the univariate analysis were included in the models. Risks were expressed as odds ratio (OR) along with its 95% con dence interval (95% CI). Results were considered statistically signi cant when p< 0. 05. SPSS (IBM SPSS Statistics for Windows, version 27. 0) and GraphPad Prism (GraphPad Software, Inc; v8. 00. ) statistical software was used for statistical analysis.
91; 95% CI 0. 85-0. 98, p<0. 013). The odd of being fremanezumab responder would decrease by 9% for each year of age, suggesting that its effectiveness could be higher in younger patients. This assumption, however, must be considered with caution, because the population studied is small and mostly represented by CM patients. Similarly, the apparent clinical advantage of the monthly dosing regimen over the quarterly one cannot be established because most patients (83%) had preferred a monthly 225 mg fremanezumab dose. The results of the present study should be interpreted with caution due to some limitations. The patient sample is small, due to limited pre-reimbursement access to fremanezumab in our Country. In addition, patients reported migraine data using paper-pencil diaries, a less reliable tool compared to modern electronic diaries.
Acknowledgements FRIEND Study Group Collaborators:  Fabio Bombardieri 9  ,  Nicoletta Brunelli 10  ,  Bruno Colombo 5  ,  Carmelina Maria Costa 10  ,  Lara Di Clemente  7 ,  Luigi d'Onofrio 14  ,  Luisa Fo 1  ,  Giulia Fiorentini 2  ,  Fabio Frediani 6  ,  Domenica Le Pera  1,15 ,  Giuseppe Magro  9 , Alessandro Mechelli 9 ,  Nicola Biagio Mercuri 4  ,  Roberta Messina 5  ,  Daniele Spitaleri 3  ,  Carlo Tomino 15  ,  Giovanna Viticchi   16.
Bioinformatic Analysis Prokaryotic 16S rRNA gene and fungal ITS paired-end reads were merged with a maximum of 10% mismatches in the overlap region using Vsearch  [45]. Denoising procedures consisted of discarding reads with no expected length (i. e. , expected size between 250 and 580 bp for 16S rRNA genes and between 200 and 600 bp for fungal ITS) and the ones containing ambiguous bases (N). After dereplication, a clusterization tool that runs with SWARM  [46]  using a local clustering threshold, and not a global clustering threshold as in other software, was employed. In the present work, the aggregation distance equaled 3. Chimeras were then removed using Vsearch  [45] , and low abundance sequences were filtered at 0. 005% (i. e. , keeping OTUs with at least 0. 005% of all sequences)  [47]  to discard singletons from the datasets.
Taxonomic affiliation was performed with both RDP classifier  [48]  and Blastn+  [49]  against the SILVA database v. 132  [50]  for the prokaryotic 16S rRNA genes and against the UNITE database v. 8. 0 [51] for the fungal ITS. This procedure was automated in the FROGS pipeline  [52]. All OTUs retrieved in the control condition sample were discarded from the datasets. Then, a normalization procedure was applied to compare samples. The prokaryotic dataset was randomly resampled down to 7541 sequences and the fungal dataset down to 32,983 sequences.
Statistical Analyses For each dataset (prokaryotes and fungi), we tested the relative importance of different sources of variability (soil, treatment, and presence of lettuce) on the composition of microbial communities using nonparametric permutation-based multivariate analysis of variance (PERMANOVA, function adonis in R package vegan  [53] ) with abundancebased Bray-Curtis dissimilarity matrices. A stress value was calculated to measure the difference between the ranks on the ordination configuration and the ranks in the original similarity matrix for each repetition  [54]. An acceptable stress value should be below 0. 1. We also created an ordination of the samples using an NMDS approach with Bray-Curtis dissimilarity matrices (function metaMDS in R package vegan). Distribution of the most abundant prokaryotic orders and fungal genera (>0.
04% of total 16S rRNA gene and ITS sequences, respectively) in each condition was evaluated using a heatmap coupled to hierarchical clustering. Common OTUs and specific ones were determined through the construction of Venn diagrams (http://bioinformatics. psb. ugent. be/webtools/Venn/) (accessed on 3 December 2020). To explain the prokaryotic and fungal community structure for different conditions in the present work, a canonical correspondence analysis (CCA) was performed linking community composition (inferred from prokaryotic phyla and fungal classes representing >1% of sequences in their respective dataset) and a combination of 9 environmental variables (sand, silt, clay, organic carbon, organic matter, total nitrogen, C/N ratio, calcium, and cation exchange capacity (CEC)). This analysis was performed with the vegan package (http://cran.
Functional Inference The functional potential of the prokaryotic community was predicted with R package Tax4Fun based on taxonomy and abundancy of 16S rRNA OTUs  (Aßhauer et al. , 2015 ) using a probabilistic model to assign multiple reference genomes in the SILVA database (v. 123). For the subsequent transformation of SILVA-based genera to prokaryotic KEGG organisms, the latest available association matrix based on SILVA release 123 was used according to the programmer's instructions. Taxonomic abundances were transformed to metabolic capabilities using the precomputed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway reference profiles according to the MoP approach  [55]. A linear discriminant analysis (LDA) effect size (LEfSe) was used  [56]  to identify the obtained categories that statistically differed according to the soil (Manziat versus Serail) considering lettuce planted with or without procyanidins as well as the obtained categories that varied according to procyanidin treatment.
Soil Type: Predominant Factor Affecting Microbiome Richness and Diversity Differences in soil proprieties were found between both unplanted soils with more organic matter and organic carbon in Manziat soil compared to Serail soil. After lettuce growth, organic and carbon matter was enhanced in Serail soil, while it remain unchanged in Manziat soil. Procyanidin amendment had no impact on soil proprieties for Manziat soil but enhanced organic matter concentration in unplanted soil. To assess the influence of procyanidin amendment on soil microbiota, 16S and ITS metabarcoding was used to reveal the microbial community's patterns. Rarefaction curves indicated that the sequencing effort was sufficient to describe fungal diversity, while in the prokaryotic dataset, some samples did not appear to have reached an asymptote (Figure  S1 ).
7 and 54. 1% of the variance explained for prokaryotes and fungal OTUs abundance, respectively, and 60. 9 and 79. 0% of the variance explained for prokaryote and fungal richness, respectively (Table  2 , Table  S1 ). This was confirmed using a nonmetric dimensional scaling analysis (NMDS) at the OTU level for prokaryote and fungal abundance datasets (Figure  2 ).