dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, we used the "protocol of reliability" proposed earlier  [31] , considering "reliable" the grid cells with one large lake or at least three small sites. Those grid cells with less than three small sites (lake or bog) or a large bog that violates assumptions of the model (i. e. , no pollen-bearing plants grow on the sedimentary basin) were considered "unreliable" (Figure  1 ). The availability of pollen records from large sites in Europe remains limited, which means that the multiple small sites approach [27] had to be implemented to obtain a larger spatial density of REVEALS estimates across Europe. Through time, the reliability of an individual grid cell may change, as not all pollen sequences cover the whole Holocene. In this dataset, 186 out of 539 grids are sufficiently reliable (Figure  1 ) because at least 50% of the time windows are based on large lakes or more than 3 small sites.
Conclusions This paper describes how a different selection of input parameters (three RPP-means datasets, RPPs. sts) affects grid-based REVEALS estimates (Gb-RVest) across the Holocene at a pan-European scale. Using major axis regression, we have shown that the choice of RPP values can result in significant differences in Gb-RVest. RPPs. sts were validated for the first time on a European scale. We had shown that REVEALS performed better when RPPs. st1 was used. This RPP set excluded entomophilous taxa but included those with mixed dispersal mechanisms. Thus, the addition of a larger number of entomophilous taxa does not significantly improve the overall result, and it is more important to obtain reliable RPP values for taxa than broaden the number used. The validation process confirmed earlier studies that have demonstrated that the REVEALS model improves the accuracy of vegetation reconstruction (RVest) significantly over the pollen proportions (raw pollen data) alone.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/land12050986/s1. File S1 Glossary of abbreviations used in the paper. O. V. D. K. , J. F. N. V. L. , E. V. , G. V. , S. V. , R. V. , H. V. S. , M. P. W. , J. W. , K. J. W. , S. W. and V. P. Z. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the TERRANOVA Project, H2020 Marie Sklodowska-Curie grant agreement no. 813904.
Acknowledgments: The work was supported by the project TERRANOVA, the European Landscape Learning Initiative, which has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 813904. The output reflects the views of the authors only, and the European Union cannot be held responsible for any use that may be made of the information contained therein. This study is also a contribution to the Past Global Change (PAGES) project and its working group LandCover6k (http://pastglobalchanges. org/ landcover6k, accessed on 24 April 2023) that in turn received support from the Swiss National Science Foundation, the Swiss Academy of Sciences, the U. S. National Science Foundation, and the Chinese Academy of Sciences. M.
In the case of multiple RPP values for one taxon in Europe, the mean was calculated to equalize within-taxon variabilities. In the synthesis, we seek to select and calculate mean values coming from boreal, temperate, and Mediterranean Europe without separating the datasets in the base of the regions. This is not straightforward to achieve because the borders of these regions shifted over the Holocene  [31]. Table  A1. Available RPPs values for the selected taxa used to calculate the RPPs-means and their SDs of the 31 and 46 taxa in the datasets (i. e. , RPPs. st1, RPPs. st2, RPPs. st3). RPPs (with their standard deviations SDs) of 26 tree pollen taxa and 23 herbs, and small shrubs in 17 study areas. The type of surface samples and the ERV submodel used to calculate the original RPPs are indicated.
The superscript numbers 1, 2, 3 indicate the values that were not included in the RPPs. st1, st2, and st3, respectively. If no superscript number, the value is included in all three datasets. Poaceae was selected as reference taxon. * RPPs from Germany  [68] , reference taxon Pinus. RPPs converted to Poaceae as reference taxon. The RPP estimates selected in this case were obtained with a vegetation dataset including only the trees that had reached their flowering age. ** RPPs from Germany  [95] ; in the original publication, the ERV analysis was performed with the Lagrangian stochastic model (LSM) for the dispersal of pollen and with Pinus as a reference taxon. In  Githumbi et al. (2022) , Martin Theuerkauf redid the analysis with the Gaussian Plume model for the dispersal of pollen  [45, 90]  and with Poaceae as a reference taxon.
1 Serge, M. A. ; Fyfe, R. ; Gaillard, M. -J. ; Klein, T. ; Lagnoux, A. ; Galop, D. ; Githumbi, E. ; Mindrescu, M. ; Nielsen, A. -B. ; Trondman, A. -K. ; Poska, A. ; Sugita, S. ; Woodbridge, J. ; TERRANOVA, contributors; Mazier, F. Spatially extensive and temporally continuous three REVEALS pollen-based vegetation reconstructions in Europe over the Holocene. 1 Laboratoire Géographie de l'Environnement, UMR 5602, CNRS, Université de Toulouse-Jean Jaurès, Toulouse, France; data. InDoRES, V1, https://doi. org/10. 48579/PRO/J5GZUO (accessed on 24 April 2023). Computer Code and Software: This study uses the original R-code available on https://github. com/ petrkunes/LRA (last version v0. 1. 0, 8 December 2020, accessed on 24 April 2023) developed for the LandClim II project (Swedish Research Council, VR; PI: M.
Measurements Since pea was in the focus in this study, specific traits were measured in this species in addition to the yield recorded for the two species. These twelve pea traits were measured/scored in pea pure stands only: early vigor, early canopy height (at two dates), canopy height at grain filling and at maturity, onset of flowering, plant biomass, stipule length, diameter, and area (derived from length and diameter), lodging at grain filling and at maturity. See Table  S1  for an overview over the data structure of the 12 traits measured. Early vigor was assessed between BBCH 25 and 36 as a score from 1 to 9 with 9 given for highly vigorous plants. The four canopy-height were measured at end of leaf development (BBCH 19), elongation (BBCH34-39), grain filling (BBCH 74-79) and maturity (BBCH 85).
Pea and barley fraction yields were measured, and moisture standardized (14%) after NIRS measurement of water content. The total yield was then computed as the sum of these two fraction yields. When a high amount of broken pea kernels was present in the barley fraction yield, a cutoff-value of > 15% broken pea kernels was set for which the barley fraction yield data was not used, only total yield data (1. 4% of all mixtures). The raw data of the four experiments, including yield and trait data, is available under https://doi. org/10. 5281/zenodo. 6480567.
e. , for an arbitrary pea genotype i, the formula was pLER pi = y p imix / y p ipure (calculation for an arbitrary barley genotype j accordingly). The LER of a mixture plot was then calculated by the sum of both partial LERs: LER ij = pLER pi + pLER bj. For PS yields formula (1) was used, omitting all effects of the absent species (including interactions). Within each species, all genotype-effects were tested against the null hypothesis H 0 of being indifferent to the mean, using a t-test with a false-discovery-rate p-value adjustment for multiple testing  (Benjamini and Hochberg, 1995). For GMA, Pr-and As-effects, the sum of genotype × experiment interactions of each genotype (ecovalence, W i ) was computed as a measure of variability of genotypic effects across experiments  (Wricke, 1962; Becker and Leon, 1988) , using the ge_stats function of the GNU R package 'metan'  (Olivoto and Lúcio, 2020)  and regarding each of the four experiments as a separate environment.
Acknowledgements The authors thank the Rindisbacher farmer family and the  Berchtold farmer  family for providing the on-farm trial locations and  Getreidezüchtung Peter Kunz  for the trial management, and post-harvest processing. We thank  Agroscope  for the sowing and the harvest of the field trials. We further thank the seed companies for the initial seed supply and the  FiBL-interns  for their support in trial management and data acquisition. We thank  Emma Forst  for the valuable input that this project profited from. The authors want to thank  Hans-Peter Piepho ,  Nathalie Moutier ,  Samuel Wuest ,  Alison Karley ,  Niels Anten ,  Wolfgang Link  and  Cyril Firmat  for valuable discussions and feedback. This work was a part of the project ReMIX "Redesigning European cropping systems based on species MIXtures," funded by the  EU 's  Horizon 2020 Research and Innovation Programme  (Grant Agreement No.
Climate change is a major challenge for the French wine industry. Climatic conditions in French vineyards have already changed and will continue to evolve impacting viticulture. This study aims to analyse the evolution of agro-and eco-climatic indices based on phenology simulation of French wine-growing regions. This evolution was analysed on a recent-past period (1962-1991 to  1992-2021)  using SAFRAN climate data and on a future projected period (1985-2014 to 2041-2070)   with two SSP trajectories (SSP2-4. 5 and SSP5-8. 5). A set of 19 CMIP6 climate models downscaled at 8 km grid resolution over France coupled with three phenological and a water balance model were used. Phenological model parameters and training system characteristics were adapted to each region to match as much as possible current practices.
In 2021, France is the second largest country in the world regarding planted vineyards areas (797,600 ha) and the second largest wine producer (more than 37 million hl; OIV statistics). The French vineyards are divided into a large number of wine-growing regions characterised by different climates, cultivars and vine training systems impacting the typicality of the wine produced. Thus French vineyards have been and will be impacted by climate change in various ways, with greater or smaller differences between wine regions. The purpose of this study is to use a large statistically downscaled CMIP6 climate database (19 models on an 8 km grid) over France covering the 1960-2100 period with two Shared Socio-Economic Pathways (SSP2 and SSP5) to (i) calculate agroclimatic indices during the growing season; (ii) simulate the evolution of grapevine phenology and grape maturity evolution using 8 cultivars; (iii) calculate the evolution of ecoclimatic indices based on simulated FIGURE 1.
French wine regions identied using the Corine Land Cover database, including a 4 km buffer. Each region belongs to a distinct zone dened by the major climatic type in the wine region: Oceanic (OCE), Semi-Continental Oceanic (SCO) and Mediterranean (MED). All SAFRAN grid centre points within the buffer zone (in red on the gure) are selected. All different grape varieties selected (see after Table  1 ) are represented on the map by the colour of the boxes for each wine region. Sébastien  Zito et al. phenology or each o the 21 wine-growing regions identied in France. The modelling approach used here integrates the fact that different varieties and different training systems are used in French wine regions. A set of robust phenological models and water balance modelling validated in the context of viticulture were used to integrate the response of phenological features of regional cropped varieties at the present French wine region scale.
Climate data Observed climate data came from the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) database produced by the French national weather service (Meteo-France). Daily minimum and maximum temperature and precipitation at an 8 km resolution grid from 1960 to 2021 were extracted over the French wine-growing regions. A total o 21 wine-growing regions were dened (Figure  1 ), based on the digitalized and geo-referenced map of the French wine-growing basins  (Simonovici, 2019) , and using an identication o the areas planted with vines provided by the Corine Land Cover (CLC) database from Copernicus land cover products (https://land. copernicus. eu/ pan-european/corine-land-cover/clc2018). SAFRAN grid cells representative of vineyard areas were selected with a 4 km buffer zone around CLC vineyard polygons (Figure  1 ).
A spatial scale possibly underestimating extremes The SAFRAN database, with an 8 km resolution, enables the selection of a set of grid cells whose spatial representativeness is intrinsically homogeneous and proportional to the wine-growing region's suraces. In addition, a signicant time depth is available (since 1959) with no missing data. However, there are some limitations to the use of the SAFRAN database. The comparison over the recent period between SAFRAN and weather station data in Burgundy and Champagne shows some bias with similar seasonal characteristics on minimum and maximum temperatures  (Zito, 2021). These biases occur particularly during spring and summer, with minimum temperature overestimated (0. 8 to 1 °C) and maximum temperatures underestimated (1 to 1. 4 °C). Other studies comparing SAFRAN to observed climate data nd similar biases in France  (Vidal et al.
, 2010). Hence, the use of SAFRAN data leads to an underestimation of thermal extremes compared to weather station records, and, particularly in a wine-growing area. Vineyards are often established on rugged topography areas, enhancing local climate diversity as previously observed in France  (Bois, 2007; Bonnefoy, 2013; Cuccia, 2013; de Rességuier et al. , 2020). Ecoclimatic indices using a threshold temperature such as FrostDays and HeatDays may potentially be underestimated in magnitude, depending on the wine region. Concerning spring frost risk, in addition to the potential overestimation of minimum temperatures, projections for viticulture in Europe throughout the 21st century  (Sgubin et al. , 2018)  show that its evolution strongly depends on the model chosen to simulate budburst. A recent study  (Gavrilescu et al.
The estimates in Equation (3) are made for the 13 agricultural products featured in the SAM by the ARDL approach, which was proposed by  Pesaran et al. (2001). These estimates are made using data from FAOSTAT, the World Development Indicators (WDI) and the Climate Change Knowledge Portal. The reasons for the choice of this method, the various tests that were carried out and the results of the estimates and their analysis are presented in Appendix 8. 2. The variations in agricultural crop yields because of climate change, calculated from these results, are presented in Table  1. The optimistic scenario corresponds to the projection of fairly small changes in climate change parameters based on available information, while the pessimistic scenario corresponds to the projection of large changes in these parameters.
Contigs were ordered as described  [55]  against B. abortus 2308 Wisconsin under accession code ERS568782 at the European Nucleotide Archive. The transposon position was inferred from BLAST comparisons and confirmed by mapping against the reference and the ordered contigs, using sequencing (Phred>30) and mapping (>50) quality filters. The reads covered 98. 81% of B. abortus 2308 Wisconsin. Visualizations were done with Artemis and comparisons with the Artemis Comparison Tool  [64]. The sequence data from B. abortus bvrS is available at the European Nucleotide Archive under accession number ERS374426.
Confirmation of non-polar effects in B abortus bvrS was achieved using cDNA as template for PCR and the following primers 5′ GTGCCCGCTTTATCGTTGATC 3′ and 5′ GATGCAGGAGTGTGCGAT-CATC 3′, designed to amplify the downstream bvrR/bvrS region. The PCR products were analyzed on agarose gels using standard procedures. DNA sequencing of the obtained amplicons was performed as described above. For relative quantification of virB5 expression by real-time PCR the different strains were grown at 37 • C with shaking in TSB, to different growth phases. RNA samples were extracted as described and retrotranscribed using Applied Biosystems high-capacity cDNA kit and random primers. Relative quantification and data analysis were carried out as described  [26]. For bvrS expression, RNA extraction and cDNA production were carried out as described for virB5 expression.
For the PCR step, the following primers were used: qbvrS_F1 (5′ CCGCCGATA-GAGGATGAGAC 3′) and qbvrS_R1 (5′ ATAGAAAAGGCGCGACAGC 3′), together with SYBR™ Green PCR Master Mix (Thermo Fisher Scientific) under the following conditions: an initial denaturation step at 95 • C for 10 min, followed by 45 cycles at 95 • C for 5 s and 60 • C for 10 s. Fluorescence signals were acquired in the green channel using a Rotor-Gene Q 5plex HRM instrument (Qiagen). Relative quantification and data analysis were carried out as described  [26]. Polymyxin B sensitivity. Sensitivity to polymyxin B was tested as described elsewhere with some modifications  [7, 11]. Strains were grown in TSB until the exponential growth phase and concentration was adjusted to 5 × 10 6 CFU/ml. For the time-dependent survival curve, triplicates of 100 μl aliquots of each B.
For 2D-electrophoresis, proteins were first focused on an electric field according to their isoelectric points using 3-10 immobiline dry strips (GE, Healthcare) and then separated in 12. 5% SDS-PAGE. Gels were silver stained using standard protocols. Protein spots were quantified by densitometry using Image J  [25]. Structural comparisons. The amino acid sequences of BvrR and ExoS (Accession numbers 068164 and P72292) were retrieved from the UniProt database (https://www. uniprot. org/) to build a structural model with SWISS-MODEL (http://swissmodel. expasy. org/) using histidine kinase (PDB accession No. 4I5S) as a template. The structural model was visualized using Discovery Studio Visualizer. Protein sequences alignment was performed using LALIGN (https://www. ebi. ac. uk/Tools/psa/lalign/).
Acknowledgments This work was supported by the  International Centre for Genetic Engineering and Biotechnology  ( CRP/COS11-02  to  ECO  and  CRP/ CRI20-02  to CGV), the  International Foundation for Science  ( B/3456-1  to CGV), the  National Council of Science and Technology  ( FORINVESFV-0004-13  to ECO), the  National Rector's Council (FEES-CONARE) , the  Vice-rectory for Research of the University of Costa Rica  (program " Redes Temáticas"  to ECO), the  National Institutes of Health  ( GM031030  to G. C. W and  GM081147  to HP. C), The  Swedish Research Council  to TF and  Wellcome Trust. Authors from the  Sanger Institute  are supported by  Wellcome Trust  [ 098051 ]. KB was founded by a  Wellcome Trust   Postdoctoral Training Fellowship for Clinicians  [ 106690/Z/14/Z ]. G. C. W. is an  American Cancer Society  Professor.
Data Our analysis employs several data sources that we can link using unique individual identifiers. Information on the court cases is from the Norwegian Courts Administration. The main data set contains information on all court cases over the period 2005-2014. We observe the start and end dates of every trial, various case characteristics, the verdict, and unique identifiers for judges, defendants, and district courts. We link this information with administrative data containing a complete record of all criminal charges, including the type of crime, when it took place, and suspected offenders. These data can additionally be linked to the prison register with information on the actual time spent in prison. We merge the court data with administrative registers provided by Statistics Norway using a rich longitudinal database that covers every resident from 1967 to 2019.
For each year, it contains individual demographic information (including sex, age, and the number of children), socioeconomic data (such as years of education, earnings, and employment), as well as geographical and firm identifiers. Finally, we link these data to a database of all reimbursement claims (KUHR) for the period 2006-2019 that are filed by publicly-funded health-care providers to the national health insurance system. 23  Using this information, we categorize a health-care visit as a separate entry identified by the date of visit. Further, an entry in this database has information on the type of health-care provider (e. g. , type of practice, speciality, municipality of practice), the patient (e. g. , identification number, month of birth, municipality of residence, sex, age), the associated health diagnosis, and the reimbursement rate and deductible paid by the patient.
Finally, this also leads to the spurious identification of the long-term effects given that no nontreated unit can serve as a reference group in the final period. We therefore implement the methodology and associated Stata package did_imputation developed by Borusyak (2021). See Borusyak et al. (2021) for further details. of individual i. 34  Under the assumptions of instrument exogeneity and monotonicity, the 2SLS estimand can be interpreted as the positive weighted average of the causal effect of incarceration among defendants that are more likely to receive an incarceration decision if assigned to a stricter judge, and vice versa. This means that, unlike the event study, the IV approach yields an estimate of the effect of incarceration on the population of compliers. To improve precision, we include, in addition to the court-by-year FEs, a rich set of background characteristics capturing defendants demography, type of crime, past work, and criminal history in the vector of control variables X 0 i.
g. , job displacement, family disruption, victimization). Such concurrent events may also affect defendants' health outcomes and thus confound the TWFE estimates of incarceration. On the contrary, to the extent that such events are not caused by incarceration, the IV estimates purge their influence on health outcomes. Note, however, that one may expect negative shocks to adversely impact incarcerated defendants' mental health, which might negatively bias the TWFE estimates. The presence of concurrent negative shocks is thus unlikely to explain the differences between our TWFE and IV estimates, as we find the latter to be larger than the former. 40  Inmates in Norwegian prisons have equal rights to public health services as the population at large (Moe, 2018). However, the health-care database (KUHR) we have access to covers primarily out-of-prison public health services, along with health services procured by the prison authorities from out-of-prison practitioners for inmates that require such special services.
Standard in-prison health services or checkups are not usually recorded in this database. To the extent that in-prison health services substitute for out-of-prison health services, we would thus expect a decline in health-care utilization measures based on the KUHR database during the period inmates are incarcerated. incapacitation. 41  Indeed, prison sentences are typically short in Norway, with a median length of 6 months, and with most inmates having a sentence of 1 year or less (see Appendix Figure  A2 ). As shown in Table  1 , we reach the same conclusion regarding persistent declines in mental health visits when we split the window of observation to be the first year and then the ensuing 4 years post-incarceration. By contrast, the temporary decline in physical health visits and no significant differences beyond the first year that we found in Figure  1c  could be attributed to incapacitation effects.