dataesr/scientific-paragraphs-categorization huggingface dataset:
Future directions and availability We propose AvP to facilitate the identification and evaluation of candidate HGTs in sequenced genomes across multiple branches of the tree of life. The most common methods used so far have been based on the difference of similarity between Donor and Ingroup sequences. We also propose and implemented AHS, a new metric aiming to address contamination and erroneous taxonomic annotation. Performing phylogenetic reconstruction and alternative topology evaluation creates a framework under which more robust HGT analyses can be performed. AvP can contribute to rapidly populate a reliable dataset with phylogenetically supported HGT cases across the tree of life. This could eventually be used in machine learning approaches in the future attempt to predict HGT events from sequence feature themselves.
Furthermore, calculating the hgt_local_score can help identify contamination and HGT hot spots in the genome. In the future, we aim to incorporate a basic module of AvP to the Alieness webserver  [19] , to facilitate usage of AvP for biologists not familiar with command line software. The AvP software is available at https://github. com/GDKO/AvP. It is released under GNU General Public License v3. 0.
Acknowledgments We are grateful to the genotoul bioinformatics platform  Toulouse Occitanie  ( Bioinfo Genotoul , doi:  10. 15454/1. 5572369328961167E12 )for providing computing resources. We are also grateful to the  OPAL infrastructure from UniversiteCote d'Azur  and to the  UniversiteCote d'Azur's Center for High-Performance Computing  for providing resources and support.
This work has been supported by the  French government , through the  UCA-JEDI "Investments in the Future " project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. GDK has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills+ fellowship  (under grant number  609398 ).
Acknowledgment We thank  Marie Salon  and  Jose Bernad  for their external advice. Thanks also to  Timothy Chubb  of  TDC SERVICES , for his proofreading and expertise as an English translator. This research was funded by a grant from  CIFRE-Association Nationale de la Recherche et de la Technologie (ANRT)  awarded to  M. Parny  ( 2017/0057 ).
Bioinformatic and microbial diversity analyses. Sequences were pre-processed for quality and length using the following parameters: amplicon size between 370 and 490 bp for V3-V4 contigs and 100-530 bp for ITS2 contigs, mismatch rate was set at 0. 1 and sequences with NNN were filtered out for both data types. Sequencing data were then analyzed using the FROGS pipelines as developed by  Escudié et al. (2018)  using Galaxy web-based platform  (Afgan et al. , 2018). Parameters and steps were as described in  Penland et al. (2020). Regarding the final affiliation step, it was performed using SILVA (V138) and UNITE 8. 2 fungi databases for 16S rRNA and ITS2 data, respectively. OTUs affiliated to contaminants such as chloroplasts or mitochondrial sequences in 16S rDNA data were excluded. When sequences were multiaffiliated by FROGS, because the targeted V3-V4 or ITS2 regions were unable to discriminate species, all the resulting putative species or genus were assigned into the final OTU table.
In the end, 5,399,810 V3-V4 16S rRNA gene sequences and 4,651,870 ITS2 sequences were kept for diversity analysis. The OTU tables were rarefied to 72, 139 and 72,788 sequences for ITS data and 16S data, respectively. Based on rarefactions curves, both depth sequencing and normalization ensured good richness coverage (supplementary figure  S2 ). Diversity and composition analyses were performed using Phyloseq and vegan packages under R software  (McMurdie & Holmes, 2013). Alpha-diversity was estimated using Shannon index, while beta-diversity analyses were conducted using weighted UniFrac distances and visualized by Principal Coordinates Analysis (PCoA). PERMANOVA (999 permutations) was used to compare microbial community structures between tanks and assess the impact of the inoculation method and of the salt level on diversity.
Statistical analyses Statistical tests were performed using FactoMiner package under R software  (Lê et al. , 2008)  and XLSTAT software (Addinsoft, Paris, France). Statistical analysis of GC-MS data was performed using a twoway analysis of variance (ANOVA) considering the different conditions and the stage of fermentation as variables. Tukey's post-hoc test was then used, in order to assess the differences among inoculated and natural fermentations. Hierarchical clustering and correlation analyses of the volatiles were performed with R Hmisc package  (Harrell and Dupont, 2020 ) using Ward's minimum variance linkage and Euclidean distance method. All representations were obtained using R ggplot2 package  (Wickham, 2016).
(2002). They showed that 8% salt concentration delayed LAB appearance during black table olive fermentations. In addition, all fermentations were analyzed for safety and hygiene criteria according to European regulation n • 2073/2005 at day 365. Criteria included enterobacteria, coagulase-positive staphylococci, E. coli population counts as well as absence of foodborne pathogens (Listeria monocytogenes and Salmonella spp. ). All criteria were satisfied (data not shown), thus, addition of the yeast consortium or reduced salt content (8% brine) did not jeopardize the safety and microbial quality of the final product.
Results for each tank over time were expressed as the percentage of isolates with the same genetic profiles as the inoculated species (Table  2 ). These data were used to strengthen metagenetic analysis results. ITS2 amplicon sequencing was used to investigate differences in fungal communities over time and between experimental conditions. Beta-diversity analysis based on weighted UniFrac distance explained 96. 3% of the variance and mostly separated samples by fermentation stage (Fig. 3. A). However, neither salt rate nor inoculation significantly impacted fungal communities (PERMANOVA test P < 0. 05) except on day 1. Following brining, as expected, fungal communities strongly differed depending on the inoculation condition as shown in Fig. 3. B. At day 1, C8 and C10 tanks reflected the inoculation of C.
Such outcomes were previously observed in olive fermentation inoculated with yeasts and/or lactic acid bacteria (Bautista  Gallego et al. , 2011; Bleve et al. , 2015; Pino et al. , 2018). Esters are known to bring fruity and flowery aromas and higher abundances could enhance pleasant aromas of Nyons table olives. However, it has to be pointed out that research focusing on odor thresholds in fermented beverages showed that specific esters, related with pleasant aromas such as ethyl acetate, could lead to off-odors if overly abundant (de la  Roza et al. , 2003). The balance of aroma compounds therefore needs to be considered and sensory attributes evaluated. In this regard, preliminary sensory evaluations were carried out by the producer which included a panel of three experts. Olives with 8% and 10% salt were appreciated while olives fermented using the backslopping method were negatively perceived due to a mild but "acetic" flavor (data not shown).
LRI = Linear Retention index -calculated based of retention time of n-alcane mixture injected in the column. b Identification performed based the following criteria: RI-comparison of LRI calculated with data published in the literature and in intern; DB-comparison of mass spectral data with those of NIST 2008 library; and S-comparison of LRI with those of authentic standards injected in the same GC-MS system. c odor descriptor associated with the compounds on The goodscentscompany database (http://www. thegoodscentscompany. com).
Acknowledgements We would like to thank our olive producer partner for being part of our project, for welcoming us at their facilities to perform fermentation assays and providing the samples. We are grateful to  Dr. Anne Thierry  for her help and advice analyzing GC-MS data. We would like to thank the  Université de Bretagne Occidentale Culture Collection  for preserving the strains used as inoculum (UBOCC, Plouzané, France, www. univ-brest. fr/ubocc) and the  INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility , doi:  10. 15454/ 1. 5572390655343293E12 ) for providing computing and storage resources.
Funding This work, which was conducted in the framework of the  ProM-edFoods project  ' Promotion of local Mediterranean  fermented foods through a better knowledge and management of microbial resources', was funded through the ARIMNet2 2016 Call by the following funding agencies:  ANR (France) ,  MESRS (Algeria) ,  ELGO-DEMETER (Greece) ,  MIPAF (Italy) ,  INIA (Spain)  and  MHESR (Tunisia). ARIMNet2  ( ERA-NET ) has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement [618127].
a LRI = Linear Retention index -calculated based of retention time of n-alcane mixture injected in the column. b Identification performed based the following criteria: RI-comparison of LRI calculated with data published in the literature and in intern; DB-comparison of mass spectral data with those of NIST 2008 library; and S-comparison of LRI with those of authentic standards injected in the same GC-MS system. c odor descriptor associated with the compounds on The goodscentscompany database (http://www. thegoodscentscompany. com).
Acknowledgements The authors thank  Mrs. Sandrine Cot  and  Léa Talbot  for their technical assistance. This work was supported by grants obtained from the  Agence Nationale de la Recherche  ( RECYFUEL  [ ANR-16-CE05-0026 ] and  DynOCP  [ ANR-18-CE11-0005-03 ], by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 675006  ( SE2B ). The FM salary was financed by the  SE2B. This research was also supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  Commissariat à l'Energie Atomique  et énergies alternatives (CEA).
Results Data from 779 participants with aSPMS were analysed. Siponimod reduced risk of 3mCDP/6mCDP vs placebo (by 31%/37%, respectively; p < 0. 01); there was no significant effect on T25FW. Siponimod increased likelihood of 6-month confirmed SDMT improvement vs placebo (by 62%; p = 0. 007) and reduced risk of 6-month confirmed SDMT worsening (by 27%; p = 0. 060). Siponimod was associated with less increase in T2LV (1316. 3 vs 13. 3 mm 3 ; p < 0. 0001), and fewer T1 Gd + and N/E T2 lesions than placebo (85% and 80% reductions, respectively; p < 0. 0001). Conclusions In aSPMS, siponimod reduced risk of disability progression and was associated with benefits on cognition and MRI outcomes vs placebo. ClinicalTrials. gov.
We performed post hoc analyses of data from the subpopulation of participants in EXPAND with active SPMS (defined as presence of relapses in the 2 years before screening and/or at least one T1 gadolinium-enhancing [Gd +] lesion at baseline). The same primary and secondary endpoints reported for the overall population of EXPAND were analysed. In addition, a significantly lower risk of having a clinically meaningful (≥ 4-point) sustained decrease in the Symbol Digit Modalities Test (SDMT) score and a significantly higher likelihood of having a clinically meaningful (≥ 4-point) sustained increase in SDMT score were seen in the overall EXPAND population with siponimod vs placebo  [20]. Given the impact that cognitive impairment has on patients with SPMS, we also analysed changes in cognitive processing speed (an exploratory endpoint in EXPAND) in the subgroup of participants with active SPMS.
| Calculations and statistics All statistical and graphical analyses were carried out using R version 4. 0. 2 (R Core Team, 2022). The graphs were designed with ggplot2 package 3. 3. 1  (Wickham, 2009). Packages used for each statistical analyses are specified later. Treatment effects on plant growth were assessed by analysing differences in canopy height, plant biomass and root mass ratio by generalised linear mixed models (GLMM) in lme4 package version 1. 1-23  (Bates et al. , 2007). Differences in growth variables were modelled with soil type, gypsum affinity and clipping treatment as fixed factors, species as random factor and preclipping plant dimensions as a covariate. This covariate was the first component of a Principal Component Analysis (PCA) including canopy area, canopy height and canopy length of plants before the clipping treatment.
Including this covariate in linear models accounted for variation between individuals due to their initial size and morphology and not treatments  (Palacio et al. , 2008). PCA was performed using the rda function in the vegan package  (Oksanen et al. , 2007). We analysed intraspecific differences between treatments within each species using generalised linear models (GLM). In all models, Shapiro-Wilk and Bartlett's K-squared tests were performed to check for normality and homoscedasticity of residuals. When there was not a normal distribution of residuals, models were fitted to a negative binomial or a gamma distribution, according to the lower AIC. Significance of differences was evaluated using a Wald test with the Anova function in the car package  (Fox & Weisberg, 2019). When differences were statistically significant, multiple comparisons between the levels of each factor or interaction of factors were assessed with the glht function in the multcomp package version 1.
4-13 in R  (Hothorn et al. , 2009). Effects on plant nutrition were analysed using the elemental concentrations and pools of different organs. Elemental concentrations were mass-based concentrations of the different elements analysed, whereas elemental pools were calculated as a percentage of the total plant biomass by multiplying the mass-based concentrations of the different elements in each organ by its biomass and dividing by total plant biomass. We described elemental nutrition using one-dimension data (the concentration of each element and sulphate) and multidimensional data (the composition of concentrations, excluding sulphate data). Compositional data was a vector of all elements for each replicate, transformed to Center Log Ratio to avoid scale invariance  (Aitchison, 1982; Soriano-Disla et al.
, 2013)  with the composition package version 1. 40-5 (van den Boogaart & Tolosana-Delgado, 2008). We assessed differences among soil type, gypsum affinity and clipping treatment for both compositional and one-dimensional elemental concentration data. Differences in one-dimensional data were assessed using GLMMs. We included taxonomic family and species nested within family as random factors. Models were fitted to a Gamma distribution when there was not a normal distribution of residuals since, in most cases, data had a constant coefficient of variation and variances increased with means  (McCullagh & Nelder, 1989). Model link functions of the Gamma distribution were selected according to the lower AIC criterion. When differences were statistically significant, multiple comparisons among levels of each factor or interaction of factors were assessed.
Differences in compositional data were assessed using PERMANOVA with Euclidean distances using the adonis function in the vegan package. Similarly, intraspecific differences between soil types and clipping treatment for each species were assessed for elemental concentrations of one-dimensional data and compositional data. These models included clipping treatment and soil type as fixed factors and preclipping plant dimensions as a covariate. We performed a PCA with the compositional data of elemental concentrations to analyse the relationships among treatments and soil composition; the procedure followed was the same as described above for the PCA of pretreatment size variables.
ACKNOWLEDGMENTS We are grateful to  María Pérez-Serrano Serrano  and  José Azorín  for help with plant cultivation, to  Isabel Llorca  and  Clara Pueyo  for help with seed preparation, to  Khadijeh Bahalkeh ,  Nate Heiden  and  Laura de la Puente  for help with plant harvest, to  Josep Antoni Martín Fernández  for advice on compositional data, and to  Jesús Revilla  and  Antonio Palma  for help with setting up the greenhouse. Pablo Tejero  and  Michael Moore  provided useful comments on the results.
FUNDING INFORMATION This work was supported by  Gobierno de España [MICINN ,  CGL2015-71360-P  and  PID2019-111159GB-C31 ]; by  European Union 's  Horizon 2020  [ H2020-MSCA-RISE-777803 ]. AC and SP were funded by a  FPI fellowship [MICINN ,  BES-2016-076455 ] and a Ram on y Cajal Fellowship [MICINN, RYC-2013-14164], respectively.
Statistical Analysis We used generalized linear mixed models (GLMMs, package 'LmerTest') following a binomial distribution to analyze the diapause termination rate, the parasitism rate, the female sex ratio, the wing deformation rate, and the emergence rate. Linear Mixed Models (LMM) have been used to analyze the number of host eggs parasitized and the population trend index (I) in the experiment two as the model residuals followed a normal distribution (visual quantile-quantile plot). In experiment one, the fixed factors were the group and the treatment. In experiment two, the fixed factor was the condition (group x treatment) as the group and the treatment selected were unbalanced. The replication has been implemented as a random effect in all the models. Statistical significance of the group, the treatment, their interaction, or the condition was determined by analysis of variance (ANOVA) with an χ 2 test (package 'car').
H. halys specimens were collected in autumn 2019 by handpicking in a soybean field in the Zagreb region  (Drenčec -long 45. 837889; lat 16. 200389 ). After species identification, individuals were separated by sex. From each sex, 60 individuals were isolated for further analysis. Each individual was photographed from the dorsal and ventral sides using a digital camera and the photographs were saved in JPEG format. The forewing and hindwing pairs were then removed from each individual, mounted on microscope slides and photographed, and the photographs were saved in JPEG format. Measurements of phenotypic traits were made using the program ImageJ. On the dorsal side of the body, body length, pronotum width, and body surface area were measured from the photographs, and the surface area of the forewing and hindwing pair was measured from the wing photographs.
Photographs of the ventral side of the body were used to determine phenotypic characteristics by visual inspection. Analyzes of the data obtained were carried out in program R. The measurement results showed that males and females of the brown marmorated stink bug differed significantly in all measured parameters (P<0. 0001). Females had, on average, a greater body length of 1. 44 mm and a greater pronotum width of 0. 84 mm. In addition, the body surface area of females was on average 19. 04 mm 2 larger than that of males, and the surface areas of the forewing and hindwing pair were also larger. The obtained results were expected since sexual size dimorphism in favor of females was observed in 90% of Hemiptera species. However, according to the literature, larger wings are characteristic of individuals responsible for migratory flights, and this study proves that females of H.
Literature Search and Publication Screening We conducted a systematic map, using the science databases Web of Science, Scopus, Science Direct, and Google Scholar. Keywords used for searching suitable publications were "genotype interaction in inter/mixed cropping system" OR "cultivars interaction in inter/mixed cropping system" OR "varieties interaction in inter/mixed cropping system" OR "cereals in inter/mixed cropping system" and scientific names (genus and species name) and common names of cereals species with intercropping and mixed cropping. The slash (/) was not used in a search; here, it is used for simplified expression of search terms (i. e. , intercropping OR mixed cropping). A full list of the search terms is given in the Supplementary Table  1. In addition, secondary literature cited in selected articles were also looked up and included if relevant.
The latest search was conducted on 12 April 2021. To select the relevant articles, we used the following inclusion criteria: (i) studies from cereal/legume intercropping with both grain and forage legumes, (ii) studies evaluated at least two genotypes of at least one of the mixed species, (iii) peer-reviewed full-length articles published in English, (iv) studies reporting original research data, and (v) only field experiments, excluding greenhouse or pot experiments. No restriction was made against the type of mixture design, e. g. , with respect to plant density, such as additive, replacement (substitution), or intermediate design. The information extracted from the original research articles was categorized in a digital database and analyzed following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines  (Moher et al.
Different variables were extracted from each study (Table  1 ) in the core set of publications. Information, like intercropping design (design of the mixing system, i. e. , substitutive or additive or intermediate), country of the experiment, number of genotypes, and other related variables, was extracted from each publication. Significance (or non-significance) of "genotype" effect, "cropping system" effect (pure vs. mixed stand), and "genotype" × "cropping system" interaction effect on yield data was extracted from ANOVA tables of the articles. This was done by extracting results from the ANOVA of each article; any differences among articles regarding the structure of statistical analysis (e. g. , fixed vs. random effects) were disregarded. The mechanisms of intercropping performance were extracted from the description of results, and the full article was consulted if needed.
Some studies reported various types of mixtures, from different species of either cereals or legumes. In addition, in these cases, data were extracted from all combinations in which at least two genotypes of at least one of the partners were evaluated. The LER (Equation  1 ) of each genotype combination was extracted from the subset of articles reporting them, either directly when represented numerically, or in figures. Data from figures were digitalized using a web-based plot digitizer  (Rohatgi, 2020) , an online system used to extract data from images efficiently and accurately  (Burda et al. , 2017; Cramond et al. , 2019). LER was reported in figures only in five articles  (Rao and Willey, 1983; Odo, 1991; Watiki et al. , 1993; Kontturi et al. , 2011; Pappa et al. , 2012; Barillot et al.
, 2014). The majority of the studies reported mean LER per genotype combination across multiple environments. However, in some cases, the studies reported data individually from each environment. If the mean LER across different environments was not reported, this mean was computed for each genotype combination of the species in the intercrop from the individual environments. When a study reported only the partial land equivalent ratio (PLER), the total LER was calculated for each genotype combination of the species in intercropping by summing the PLERs: LER c+l = PLER c/l + PLER l/c (1) where LER c+l is the LER of the cereal genotype c with the legume genotype l; and PLER c/l is the partial LER of genotype c in mixture with legume genotype l (and reciprocally for PLER l/c ). This genotype combination-specific LER was used in further analysis.