dataesr/scientific-paragraphs-categorization huggingface dataset:
NMR relaxation enabled the characterization of the effects of drought on water distribution between leaf tissue types Following this first set of measurements comparing MWD and SWD, a second trial focused on plant response under SWD, where adjustments in water supply levels appeared to induce the most contrasted changes compared to optimized irrigation. Under Control conditions, NMR measurements were performed to characterize the water distribution in the leaf tissues according to leaf ranks. Under SWD conditions, it was possible to reproduce data previously reported in the literature on the effects of drought on crop yield and plant water relationships. For instance, a decrease in watering corresponded to a tuber yield loss of 60 to 70% compared with irrigated conditions  [29]. Likewise, a decrease in soil water potential to below -2 MPa was associated with a decrease in leaf water potential and an increase in leaf water deficit values in accordance with those observed previously under similar conditions  [30].
Plant sampling High throughput and MRI phenotyping were performed without plant sampling or destruction. All leaves of those plants used for high throughput phenotyping were collected on the last day of measurement (53 DASE) and were frozen in liquid nitrogen for further analysis (phytohormone analysis and gene expression). For the physiological and NMR measurements, different plants were harvested and used on each measurement date. For all plants, at the stress application date (25 DASE), the youngest leaf (of over 2 cm in size) that had emerged from the apical stem was tagged with a plastic wire and was allocated to rank 0 (LR 0). When the leaf in the third rank above LR 0 emerged, it was also tagged with a wire and referred to as leaf rank 3 (LR + 3). The data presented in the paper were obtained from leaves sampled from LR 0 and LR + 3 for the NMR measurements and from LR 0 for the water relation measurements.
The qRT-PCR reactions were obtained in technical triplicates using independent cDNA reactions for each biological replicate and 300 nM of gene-specific primer pairs (Additional file 1: Table  S2 ). The thermal cycler protocol included preincubation at 98 °C for 3 min, first followed by 40 cycles of amplification, each consisting of denaturation for 15 s at 98 °C, then by annealing for 30 s at 60 °C and then by elongation at 72 °C for 15 s with a final 5-min extension at 72 °C. Additionally, a melting curve analysis was performed at the end of each assay to confirm the absence of multiple products or primer dimers. The expression of all candidate genes was normalized against four potato reference genes, namely, StRPL2, StEF1α, StActin and StGAPDH. Specific primers for all candidate genes were designed using Primer3 software and are listed in Additional file 1: Table  S1.
The measurement time for T 2 (including spectrometer adjustments and the T 1 measurement) was about 10 min per sample. The CPMG signal was fitted using Scilab software in accordance with the maximum entropy method (MEM) [Mariette F, Data Handl Sci Technol 1996], which provides a continuous distribution of relaxation time components with no assumption concerning their number. In this representation, the peaks of the distribution are centered on the corresponding most probable T 2 values, while the peak areas correspond to the intensity of the T 2 components. For each leaf rank, when the number of T 2 vacuolar components was not identical across all repetitions (one or two, depending the repetition), NMR data were averaged only in homogenous groups of repetitions, with the same number of vacuolar components.
These proportions were computed for each of the 37 UGS using a land-cover map (urban planning agency of Rennes, AUDIAR) and using ArcMap 10. 2 software. The PCA was created using as dependent variables these proportions. We selected UGS to obtain the broadest possible range of land-cover compositions provided by the PCA, therefore distributed over the entire CPA plane. The goal was to select UGS that were different in terms of land-cover composition to capture a wide panel of perceptions. We also take into account the socio-demographic diversity using the 12 neighborhoods of Rennes. A total of 13 UGS were thus sampled by selecting at least 1 from each neighborhood. For the last 10 years, Rennes has been experiencing a high growth rate, with a population increase of 1. 29% per year. This city is currently the most populated city of Brittany.
We considered land-cover composition as the relative proportions of 5 main land-cover types (woodland, shrub, herbaceous area, water area, and mineral surface). These proportions were computed for each of the 37 UGS using a land-cover map (urban planning agency of Rennes, AUDIAR) and using ArcMap 10. 2 software. The PCA was created using as dependent variables these proportions. We selected UGS to obtain the broadest possible range of land-cover compositions provided by the PCA, therefore distributed over the entire CPA plane. The goal was to select UGS that were different in terms of land-cover composition to capture a wide panel of perceptions. We also take into account the socio-demographic diversity using the 12 neighborhoods of Rennes. A total of 13 UGS were thus sampled by selecting at least 1 from each neighborhood.
On-Site Questionnaires To gather data on visitor's perception of landscape heterogeneity, we conducted a total of 390 questionnaires (i. e. , 30 in each of the 13 UGS). To interview as many different visitors as possible, we conducted questionnaires from June to September 2017, at different hours (i. e. , weekends and weekdays). We randomly chose visitors at different locations within each public UGS. We only selected visitors who had already been in the UGS at few times before, to ensure that they had a relatively good overview of its entire landscape. Indeed, building a cognitive map of a landscape takes time and repeated exposure  [15]. We used two complementary quantitative and qualitative questions. We conducted preliminary questionnaires on a small number of visitors  (20)  to test the vocabulary and the meaning of our questions.
The 390 answers to the open-ended question were transcribed word for word (i. e. , without altering their wording) in 3 different corpuses by distinguishing those given by visitors who scored their perception "1" and "2" (i. e. , visitors who perceived the landscape as being not heterogeneous), "4" and "5" (i. e. , visitors who perceived the landscape as being heterogeneous), and "3" (i. e. , visitors who perceived the landscape as being simultaneously heterogeneous and not heterogeneous). The translation of the words was the object of a very rigorous work where cross-validations were made. We also analyzed the representativeness of our sample by collecting data on visitors' gender, age (3 classes: 18 to 34, 35 to 54, or 55 and more), and frequency of site visit (frequent = daily to once a week, or not frequent = 2-3 times a month to a few times per year).
We represented these associations in three graphs associated to word-clouds in order to provide a visual map of how the words composing each of the three word-clouds were connected together. We also displayed all the answers in which each of these words occurred. We conducted the textual data analyses and the calculation of the co-occurrence index using the software IraMuTeQ 0. 7 alpha 2. This software considered the words occurring at least three times in each corpus. It automatically excluded the words defined as "tools" (pronouns, additional adverbs, and adjectives), and considered each word by using its root as a reference (i. e. , the verbs were brought back to the infinitive, and the nouns to the singular form). It should be noted here that the translation of the words from French to English might have altered their original meaning.
Visitor Perception Three word-clouds were obtained (Figure  2a-c ). They were composed of the words that occurred the most frequently in the corpus of answers given by visitors who scored their perception "1-2", "3", and "4-5", respectively. We identified 23 words that were common across the three word-clouds (Table  1 ). word-clouds were associated together. We represented these associations in three graphs associated to word-clouds in order to provide a visual map of how the words composing each of the three word-clouds were connected together. We also displayed all the answers in which each of these words occurred. We conducted the textual data analyses and the calculation of the co-occurrence index using the software IraMuTeQ 0. 7 alpha 2. This software considered the words occurring at least three times in each corpus.
Acknowledgments Most of the ideas developed here originate from conversations with many colleagues, editors and authors. Even if these categories are largely overlapping, there are still a lot of people to whom we apologize for mentioning here only four names:  Denise Pons ,  Patrick Herendeen ;  Elisabeth Wheeler  and  Gea Zijlstra. Anne Laure Decombeix  and  Brigitte Meyer-Berthaud  are warmly thanked for the edition of this special volume dedicated to someone we particularly appreciate. The advice of two anonymous reviewers is much appreciated.
, 2017). The superiority of WGS data analysis for molecular epidemiological analysis was also apparent in the cases of EHEC O157  (Yokoyama et al. , 2018)  and S. Agona  (Yokoyama et al. , 2019). Therefore, the aim of this study was to clarify whether the temporal increase in S. Chester infections in the Kanto region of Japan was associated with imported seafood consumption from Morocco. First, we performed molecular epidemiological and population genetic analyses using WGS data to elucidate the relationship between S. Chester isolates from the Kanto region and those from Europe. Second, we investigated whether a correlation exists between the genetic and geographical distances of the Japanese isolates using a molecular epidemiological analysis to confirm that the Kanto region's temporal increase occurred via contaminated food.
SNP Detection From WGS Data The short sequence reads from 58 European S. Chester isolates (ST1954) that were collected from humans, chickens and food in the multinational European outbreaks were obtained in a previous study  (Fonteneau et al. , 2017). These reads are deposited in the National Center for Biotechnology Information Short Read Archive (BioProject PRJNA248792). The short sequence reads from the European isolates were kindly provided by the Institute Pasteur (France) to the National Institute of Infectious Diseases (NIID), Japan. As for the Japanese isolates, the short sequence reads from the 47 S. Chester isolates from the Kanto region of Japan were obtained by the NIID. DNA was extracted from the Japanese isolates and purified using the Isoplant II DNA extraction kit (Nippon Gene Inc.
, Tokyo, Japan). The genomic DNA libraries were prepared using the Nextera XT DNA sample prep kit (Illumina). The pooled libraries were subjected to multiplexed paired-end sequencing (300 mer × 2) using MiSeq (Illumina, San Diego, CA, United States). The adapter sequences were trimmed from the short reads, and low quality bases with Phred scores less than 15 were eliminated using the Skewer program  (Jiang et al. , 2014)  to obtain sequences of at least 50-mers in length. In the NIID, the short reads from the Japanese and European isolates were mapped using the BWA-mem program  (Li and Durbin, 2010)  against the S. Chester ATCC 11997 strain's complete genome sequence (4,660,922 bp, GenBank accession number CP019178. 1) as a reference (Supplementary Table  2 ). After excluding prophage, repeat, indel, biallelic, and recombination regions from the complete genome sequence, 91.
Molecular Epidemiology To perform multilocus sequence typing (MLST) on the Japanese isolates, the assembled sequence contigs from the A5-MiSeq pipeline  (Coil et al. , 2015)  were analyzed using the web tools available from the Center for Genomic Epidemiology website 4   (Larsen et al. , 2012). The MLST classified the Salmonella isolates into sequence types (STs) based on the analysis of seven housekeeping genes (i. e. , aroC, dnaN, hemD, hisD, purE, sucA, and thrA)  (Achtman et al. , 2012). The chromosomal positions of these housekeeping genes in the S. Chester ATCC 11997 strain (Accession number CP019178. 1) were 1,427,763-1,428,848 for aroC, 1,406-2,506 for dnaN, 4,565,282-4,566,022 for hemD, 1,788,720-1,790,024 for hisD, 1,788,720-1,790,024 for purE, and 3,104,010-3,106,100 for sucA, and 3,872,960-3,873,009 for thrA.
The European isolates were classified as belonging to ST1954 based on the MLST analysis in our previous study  (Fonteneau et al. , 2017). Cluster analysis was conducted using MEGA 7 software  (Kumar et al. , 2016). The 347 SNPs from the Japanese and European isolates were imported into the software, and a dendrogram was reconstructed by the maximumlikelihood method with the Tamura-Nei model and with 1,000 bootstraps performed for the statistics. The other conditions for reconstructing the dendrogram were set by the default settings. Bayesian cluster analysis using the 347 SNPs from the Japanese and European isolates was conducted using STRUCTURE Version 2. 3. 4 software  (Pritchard et al. , 2000). The setting options were as follows: length of burn-in period was 50,000, the Markov Chain Monte Carlo number was 100,000, the ancestry model was the "admixture model, " and the frequency model was "allele frequency correlated.
Population Genetic Analysis The difference between the Japanese and European isolates was evaluated using PT , which is an analogue of Fst  (Peakall and Smouse, 2012). Genetic Analysis in Excel (GenAlEx), version 6. 51b2 software, an add-on package from Microsoft Excel, was used to calculate a PT value with 999 permutations. PT was calculated as the proportion of the variance among populations from: PT = V AP V AP + V WP Where V AP was the variance among the populations and V WP was the variance within a population. If a PT value differed significantly from zero, the Japanese and European isolates would be different populations. Linkage disequilibrium in the Japanese and European isolates was evaluated using the standardized index of association (I S A )  (Haubold and Hudson, 2000). LIAN Ver.
Comparison of Genetic Traits Between the Japanese and European Isolates A root-to-tip analysis was performed using Path-O-Gen software to evaluate whether changes occurred in the diversity of the Japanese and European isolates as time proceeds  (Rambaut et al. , 2016). In the root-to-tip analysis, the X-axis shows the isolation date for each isolate, and the Y-axis shows the genetic distance to each isolate from the root on the dendrogram. If the isolates originating from the same clone are assumed to have a uniform mutation rate, they would show a proportionate relationship between the X-and Y-axes. The frequencies of the SNPs causing non-synonymous (P N ) and synonymous (P S ) substitutions in the amino acids were compared to evaluate the selection pressure on each of the Japanese and European isolates as follows  (Bierne and Eyre-Walker, 2004).
, 2020)  was investigated. The chromosomal positions of these genes in the S. Chester strain ATCC 11997 (Accession number CP019178. 1) were 956,162-958,729 for mutS, 4,121,661-4,123,517 for mutL, 3,712,806-3,713,201 for mutT, and 4,551,149-4,553,311 for uvrD. When at least one SNP was located in these genes, we examined whether the SNP caused either non-synonymous or synonymous substitutions.
Chester have been rare among all the reported NTS infections in Japan  (Katoh et al. , 2015). If the emerging S. Chester clone had been present in the Japanese environment, similar emergences of the S. Chester isolates investigated in this study would have occurred. There was only one large S. Chester outbreak in 1998. However, the isolates from 1998 were classified as belonging to ST343 using MLST. In the present study, the Japanese strains all belonged to ST1954. Therefore, the genetic distance between our Japanese isolates and the isolates from 1998 was too large to evaluate their genetic relationships. Even if the S. Chester isolates isolated before 2013 were to be collected from all the local public health institutes throughout Japan and the WGS data were compared between our Japanese isolates and the pre-2013 collected isolates, it is unlikely that the comparison would provide new insights into the findings of this study.
ACKNOWLEDGMENTS We thank the staff at the  Kaiso Health and Welfare Center, Itako Public Health Center , and all the public health centers that participated in the epidemic prevention of S. Chester infections in the Kanto Region. We are grateful to  Yoshihiro Aoki ,  Yudai Watanabe , and  Katsuhiko Kitazawa  for providing S. Chester isolates from patients in  Asahi General Hospital. We thank  T. Doi ,  T. Yamada , and  J. Kobayashi  of the  National Institute of Infectious Diseases  for their technical support.
FUNDING This work was supported by grants from the  Japanese Society for the Promotion of Science (KAKENHI)  (Grant Numbers:  JP19K10590  and  JP20K18920 ),  Health   and Labour Sciences Research Grant  (Research on Food Safety) from the  Ministry of Health, Labour and Welfare, Japan  (Grant Number:  JPMH20KA1004 ), and the  Research Program on Emerging and Re-emerging Infectious Diseases  from the  Japan Agency for Medical Research and Development (AMED)  (Grant Numbers:  JP20fk0108120  and  JP19fk0108103 ).
, 2012; Mariette et al. , 2016; Dey et al. , 2018). Initia surveys of the composition of Algerian P. infestans populations collected from tomato and potato fields have been undertaken in recent years. These studies showed that the clonal lineage EU_13_A2 was present in Algerian P. infestans populations along with two other clonal lineages,  EU_2_A1 and EU_23_A1, between 2007 and 2014 (Corbière et al. , 2015; Rekad et al. , 2017). However, these data remain insufficient, because they do not cover all potato production areas in Algeria and more recent epidemics. Thus, in this study, a larger number of isolates from the main potato producing areas of the three main regions (west, east, central) were collected and genotypically characterized with 17plex SSR markers set, and also characterized for mating type.
Data analysis Mating type frequency across regions was calculated and the proportions of each type calculated according to each potato growing season (main and late-seasons). SSR genotypes were grouped according to their geographical location. Parameters were studied such as: total number of alleles (Ar), effective number of alleles (Ae) at each locus and at each population and the number of private alleles (Pa). Genetic diversity, such as expected heterozygosity in the dataset (Ht) and that calculated from expected heterozygosity (Hs) at each locus and in each population, were estimated using Genodive2. 0 (Meirmans & VanTienderen, 2004). A kinship covariance matrix constructed in Genodive2. 0 was loaded into Primer 7. A twoway analysis of variance (permanova+), (factor1 = regions; factors2 = MLLs), was conducted with 10000 unrestricted permutations of raw data and type III sums of squares.
Genetic structure The genetic structure for the whole data set revealed that individuals were distributed into three main groups, matching the three major lineages described above (Fig. 4 ). One group contained all individuals belonging to the A1 mating type. EU_13_A2 MLL divided into two groups. No introgression was found except for only one individual from EU_23_A1 MLL that showed a mixed assignment to group 3 (Fig. 5 ) and might be a recombinant. The EU_13_A2 is the most prevalent lineage, representing 70% of all isolates collected from 2010 to 2016, and was present in all Algerian potato production regions. The EU_2_A1 clonal lineage, representing 16% of the P. infestans population collected from 2010 to 2015, was found mainly in the center of Algeria (Algiers, AinDefla and Tizi Ouzou).
Accepted Article Figure  4 : Principal component analysis results for the whole dataset (N=206) grouped according to MLL on the two factorial that explain 81. 195 % of the total variance. Blue dotted (EU_13_A2), red dotted (EU_2_A1) and green dotted (EU_23_A1). EU_23_A1 and EU_2_A1 were negatively correlated to axis 1 while EU_13_A2 was positively correlated with it.
Accepted Article Table6: Results of AMOVA tests for the whole dataset (N=206) divided according to geography and MLL Source Percentage of Nested in Sum of Squared Deviation d. f. Mean Squares of variance Variation (%) Within Population 1662. 39 210 7. 916 98. 2 Region Among Population 21. 39 2 10. 70 1. 8 Within Population 953. 5 203 4. 697 40. 9 MLL Among Population 593. 6 2 296. 8 59. 1* *statistical significance with p< 0.
In a European context using Brazilian SBM associated with LUC, Le Cour Grandmaison et al. (  61 ) calculated, from recent performance trials, that a 10 g/kg dietary CP reduction reduced climate change impact of a ton of feed by 101 kg CO 2 eq. It corresponds to an 8% decrease of climate change impact and, for broilers, to a reduction of 226 kg CO 2 eq per ton of live weight  (62). This is associated with a reduction of SBM inclusion of 39 kg/t in fattening pigs and 35 kg/t in broilers for each point of dietary CP reduction (Figure  4 ). Beyond the context of production, quantitative benefits of low-CP strategies for the climate change impact of diets relies heavily on the value taken for LUC impact of SBM. Those values vary greatly between LCA databases, even for equivalent products. Some approaches have also taken into consideration indirect LUC, i.
This does not exist in broilers, and the effect of reducing CP on N emissions has only been quantified in a few trials. Most publications study NH 3 emissions, but more work is needed for other types of emissions, especially nitrous oxide. Moreover, studies on reducing CP diets focus on emissions at the farm, and few data is available on the effects at the field after spreading. Agronomic valorization of the organic fertilizer produced should also be taken into account to evaluate nutritional strategies. Reducing dietary CP will be more interesting in a territory producing excess organic N fertilizer compared with a territory where it is in demand. LCA studies on low-CP diets were first performed at the feed level  (86). This measures the effects of the strategy on the impact of feed production.
The context of production influences greatly the results of all those studies as LUC impact has the highest weight  (60). To have comparable data, a standardized method should be used, and LCA values of crops and feed ingredients (especially feed-grade AA and SBM) should be harmonized between databases to avoid cherry-picking of values. The PEFCR method for evaluation of feed provides guidelines and should be used more broadly. In European and South American contexts, a low-CP diet supplemented with amino acids is shown to reduce the environmental impacts of feed production, but the magnitude of this impact is low. In contexts in which SBM associated with recent LUC is not used, low-CP strategies do not intend to reduce environmental impacts of feed production and have a low potential of doing so.
For example, the average Brazilian SBM climate change impact triples between EcoAlim (ADEME, 2016) and GFLI (Global Feed LCA institute, 2019) or Agri-footprint 5. 0 (Blonk Consultants, 2019) databases. Da Silva et al. (57) also frequently uses in LCAs reports of even lower climate change impacts with a low contribution of LUC (under 30%). Climate change impact values of Brazilian soybeans are presented in Table 2. Efforts have been made to propose a standardized method to calculate LUC (63), and those guidelines should be broadly used to improve the consistency and comparability of studies.