dataesr/scientific-paragraphs-categorization huggingface dataset:
In each model, we used a minute-point (n = 42,385) dataset where we coded resting, feeding, or traveling either as 1 when it was observed, or as 0 when not observed. We conducted this analysis, while accounting for temporal and spatial autocorrelation, using data from the entire territories of South and East groups. All activities were affected by the test predictors (full-null model comparisons: resting-LRT: χ 2 = 22. 36, df = 3, P < 0. 001; feeding-LRT: χ 2 = 47. 20, df = 3, P < 0. 001; traveling-LRT: χ 2 = 49. 95, df = 3, P < 0. 001). The interaction between location and elevation significantly affected resting (estimate ± SE = 0. 045 ± 0. 018, P = 0. 014, R 2 = 0. 001;  ). Resting was always more likely at high than at low elevation, the difference being more pronounced in the border area than in the central areas  (Fig 4A).
Data collection Human observers used nest-to-nest continuous focal follows on adult chimpanzees (males > 12 years old, females > 10 years old) and orphaned individuals (age range 3 to 12 years old)  [83]  to record party compositions and activities of the focal individual  [41]. We recorded spatial data every minute using the tracklog function of GPS devices (Garmin 62 or Rino). We individually time-matched the GPS tracks with the daily observation periods. During the study period, we followed South and East groups during 855 and 847 days, respectively. Since more than 1 focal follow within a given group can occur each day, we obtained, for South, 1,287 follow-days: 637 adult females (5,147 h), 601 adult males (4,919 h), 49 orphans (414 h); for East, 1,350 follow-days: 573 adult females (4,487 h), 656 adult males (5,140 h), 121 orphans (1,079 h).
The total observation time across both groups is 21,186 h. Since several focal individuals can be simultaneously followed within the same group, and therefore within the same party, we kept unique observations of unique parties, keeping observation corresponding to only 1 focal individual when several focal individuals were present within the same party. From these focal follows, we removed observations when the focal individual was out of sight, all events of hunt and intergroup encounters and we kept only adult follows. We obtained a dataset for behavioral analyses cumulating 14,057. 69 h of observation (3,745. 16 h South females, 3,906. 84 h South males, 2,270. 02 h of East females, 4,135. 67 h East males). Party sizes and adult party sizes correspond, respectively, to all independent individuals and to all adult individuals present within the visibility range of the focal individual  [41].
Location measurement We used the GPS tracks for the entire study period to calculate the kernel density estimations of the territory for both studied groups. We used the R function "kernelUD" of the package adehabitatHR (v0. 4. 14)  [85] , with a smoothing factor (h = 149) using the plug-in method, to determine utilization distributions. Each location was then assigned a kernel value (range: 1 to 99). The smaller the kernel value, the more used and central the area is. Larger kernel values represent areas closer to the border. We determined the territory overlap between the 2 communities as the area located between the extreme western location visited by East group and.
Statistical analysis Activity analyses. We analyzed how the likelihood to observe certain activities varies as function of the elevation and the location within the entire territory. Since observations taken each minute are strongly dependent, leading to a high spatial and temporal autocorrelation, we reduced autocorrelation issues by, first, selecting, for each day of observation, data points spaced by 30 min  [88]. We chose 30 min intervals as it corresponds to twice the average lengths of activity bouts (resting: East group 11. 6 min, South 15. 07 min; feeding: East 16. 46 min, South 17. 66 min; traveling: East 5. 61 min, South 7. 15 min). We obtained a reduced dataset of 42,385 minute-points. Second, to assess the extent of spatial and temporal autocorrelation, for each model, we calculated and reported the average of the correlation coefficients between the differences in residuals issued from the full models and the differences in time between sampling points (temporal correlation), and the average of the correlation coefficients between the differences in residuals issued from the full models and the metric distances between sampling points (spatial correlation).
In this "stop" model, we included as test predictors the following variables: direction of the movement before arrival at the hill (toward the border or toward the center, as a categorical predictor), number of peripheral hills previously passed by that day, number of peripheral hills passed by after that particular hill was used, adult party size of the followed group, and intercommunity distance. We included the interaction between adult party size and intercommunity distance (S1 Table ). To analyze movements from peripheral locations, we built a dataset including the stopping events at peripheral hills (n = 304 occurrences) and stopping events to rest at peripheral low locations (<180 m) (n = 321 occurrences), within the East-South overlap area. We used a GLMM with binomial error structure to model whether, after stopping at a peripheral location, subsequent movement was directed toward the rivals' location or not (advance toward rivals, coded binary 0/1, S9 Table ).
The weighting function followed a normal distribution with its standard deviation determined by maximizing the likelihood of the model with the temporal autocorrelation term included. All statistical analyses were performed in R 3. 6. 1, using the functions lmer or glmer of the R package "lme4" (v 7. 3-51. 4)  [93]. We used likelihood ratio test (R function anova with argument test set to "Chisq"  [94] ) to establish the significance of the full as compared to the null models (comprising only control predictors, random effects and slopes). To allow for a likelihood ratio test, we fitted the models using Maximum Likelihood rather than Restricted Maximum Likelihood  [94]. When an interaction term had no significant effect, we ran reduced models lacking these nonsignificant interactions. P-values for the individual effects were based on likelihood ratio tests comparing the full with respective reduced models (R function drop1  [95] ).
We checked for overdispersion and leverage issues for the binomial models. Dispersion statistics are provided under each respective model table. We checked for model stability by excluding each level of the random effect one at a time from the data and comparing the model estimates derived from these subsets of the data with those derived from the full dataset. This revealed that our models were stable. All continuous variables were z-transformed to a mean of zero and standard deviation of one. We derived confidence intervals from the models using parametric bootstraps (function bootMer of the "lme4" package). We assessed collinearity by deriving variance inflation factors (VIFs)  [96]  with the function vif of the R package "car"  [97]  applied to a standard linear model lacking the random effects.
Collinearity was not an issue in any model (reported under each respective model table). We calculated models' effect sizes (R2) with the function r. squaredGLMM from the R package "MuMin" and the function partR2 from the package "partR2. " For each model, we reported the variance explained by the fixed (marginal-R 2 ) and fixed and random (conditional-R 2 ) effects for the whole model. Marginal effect sizes for significant predictors are provided in the main text. All.
Acknowledgments We thank the  Ministère de l'Enseignement Superieur et de la Recherche Scientifique of Cote d'Ivoire , the  Ministère de Eaux et Forêts of Cote d'Ivoire , and the  Office Ivorien des Parcs  et Reserves for permissions to conduct the study. We are grateful to  Christophe Boesch , who founded the field site, the  Centre Suisse de Recherches Scientifiques en Cote d'Ivoire  and the staff members of the  Taï Chimpanzee Project  for their long-term support. The  Max Planck Society  provides core funding for the  Taï Chimpanzee  Project since  1997.
Funding: C. C. was supported by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement no. 679787 ). L. S. was supported by the  Minerva Foundation. S. L. , L. S. , C. C. and R. M. W. were supported by the  Max Planck Society. Research at the  Taï Chimpanzee  Project has been funded by the  Max Planck Society  since 1997. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
(a) z-transformed. (b) Test predictors. (c) Control predictors. (d) Natural logarithmic transformed. (e) Measured as kernel values from utilization distributions based on the track logs; kernel values increase with the distance to the territory center. (f) Circadian values. (g) Refers to males as compared to females. (h) Refers to South group as compared to East group. (i) Have no meaningful interpretation. Dataset N = 625, 2 groups (East and South), marginal effect sizes (R 2 ): 0. 184 and conditional R2: 0. 206. P-values in bold a statistically significant effect at α = 0. 05, p-values in italic a statistically significant effect at α = 0. 10. https://doi. org/10. 1371/journal. pbio. 3002350. t001.
c. c. infections induced an antimicrobial response in larvae with a dominant expression of antibacterial vs. antifungal peptide-encoding genes  [49]. The honey bee was also reported to be a vector of Erwinia amylovora  [50] , which can survive in the honey bee body for several days  [51]. In our experimental conditions, the presence of several proteins from a P. c. c. origin suggests that this bacterium is only partially controlled by the immune responses of the bumble bee, at least within the first three days of the infection. The sharp increase in the mortality rate observed after 48 h (Figure  1 ) indicates that P. c. c. is pathogenic to the bumble bee and represents a risk for its survival. The dots close to the red and green boxes represent the proteins (denoted by their UniProtKB accession number) that were found to be significantly up-regulated (FC > 2, p-value < 0.
Up-and down-regulated proteins in bumble bee hemolymph following an experimental bacterial challenge with Micrococcus luteus (A), Pectobacterium carotovorum subsp. carotovorum (B), or S. marcescens (C) versus the control experiment (non-experimentally infected bumble bees). Dots colored in red represent proteins with a fold change of >2 (up-regulation threshold), while the ones in green are for a fold change of <-2 (down-regulation). The dots close to the red and green boxes represent the proteins (denoted by their UniProtKB accession number) that were found to be significantly up-regulated (FC > 2, p-value < 0. 05) and down-regulated (FC < -2, p-value < 0. 05) in response to the infection, respectively. Gray dots represent all remaining proteins that did not pass the thresholds -2 > FC > 2 and the p-value < 0.
Sample Analysis and Data Acquisition Mass spectra were acquired in triplicate on a MALDI-TOF AutoFlex III Smartbeam instrument (Bruker Daltonics) using the FlexControl 4. 0 software (Bruker Daltonics) in an automatic positive linear mode. The instrument was set up with the following parameters: 200 Hz laser at a 50% global attenuation offset, an accelerating voltage of 1. 3 kV in source, 9. 25 kV lens voltage, 1. 906 kV linear detector voltage, 120 ns of pulsed ion extraction delay, and 600 Da detector gating. MALDI-MS spectra were recorded at the mass range of 600-18,000 in m/z by summing 1000 laser shots. Data were previewed using the FlexAnalysis 3. 4 software.
MALDI Data Processing MALDI data were imported into ClinProTools™ 2. 2 software (Bruker Daltonics) for reprocessing. All spectra underwent baseline correction performed with a TopHat baseline algorithm, along with smoothing according to the Savitzky-Golay algorithm (window size 2. 0 m/z in 5 cycles). The total average of the spectra was calculated based on a signalto-noise threshold of 3 for peak selection, a picking height of 80, and an application of baseline. Peak lists (maximum peak number of 100) of each spectrum were extracted for data processing and statistical analyses. Comparative analyses were carried out between the different experimental conditions depending on the intensity of the selected peaks. The software normalized the spectra before performing statistical principal component analyses (PCA).
Using PCA, we analyzed and compared the three characterized molecules specific to immunity in bumble bees, apidaecin (accession C0HKX3, average m/z of 1978), abaecin (accession D2XR04, average m/z of 4397), and defensin 1 (considering cysteine pairing), and a chymotrypsin inhibitor (AMCI, accession numbers K7WRE1, average m/z of 5938 considering cysteine pairing and an N-terminal pyroglutamic acid).
Database Searching and Protein Identification The Sequest HT search algorithm was run by Proteome Discoverer™ 2. 5 (Thermo Fisher Scientific, Bremen, Germany, GmBH) to match the acquired MS/MS spectra to a database consisting of protein sequences of the hymenopteran order (including B. terrestris), a selection of bee pathogens, and the three bacteria used in the experiment downloaded from UniProtKB, on 1 April 2022. The sequences of common protein contaminants (e. g. , human keratins) were also added to this database. The following parameters were used: trypsin digest with three maximum missed cleavages; six and 150 amino acids as the minimum and maximum peptide lengths, respectively; a tolerance of 10 ppm/0. 02 Da for precursors and fragment ions, respectively; cysteine pyridyl-ethylation was set as a fixed modification; and C-terminal protein amidation, methionine, and tryptophan oxidation were set as variable modifications.
Statistical significance was determined using an ANOVA (individual proteins) test on summed abundances with Top 3 and a max. fold change value set to 100. p-values were calculated and found significant when below 0. 05. Protein ratios of <0. 5 or >2 in the different conditions compared to the not-experimentally infected controls were considered as significantly differentially expressed (DEPs). To complete the missing protein identity information, NCBI tBlastn was used. For functional annotation of the lists of DEPs generated from the LC-ESI-MS/MS analyses, the OmicsBox bioinformatic software (v2. 1. 14, accessed on 1 September 2022) was used. To get the most complete annotation labels, analyses were performed using the four cloud-powered algorithms (Blast, InterProScan, GO Mapping, and GO slim).
Quasi-static compression test Quasi-static compression tests were carried out on different sample geometries: cubes and parallelepipeds. Raw data were collected directly from the machine acquisition setup. 15 mm edge cubes were compressed along the tangential direction up to 80 % of nominal strain to obtain the full strain-stress curve of the material. For these large strain ranges, this latest was measured by the relative displacement of the machine crosshead. The elastic modulus was calculated in the interval between 1. 5 % and 2. 3 % of strain, from the slope of the elastic region in which all samples showed a quasi-linear behaviour. When a small deformation is applied (< 3 % of strain) nominal stress σ n (MPa) is calculated as the measured force F (N) divided by the initial section area of the sample A 0 (mm 2 ): σ n = F A 0 (1) The nominal strain ε n (-) is calculated as follow: ε n = ΔL L 0 (2) With ΔL the relative displacement of the crosshead and L 0 (mm) the initial length.
Digital image Correlation (DIC) In the case of the quasi-static compression test, Digital Image Correlation (DIC) method was simultaneously used. DIC is a non-contact optical technique used to determine the full field measurements of a sample surface by measuring the displacement of pixels in digital images after deformation compared to a reference image  [33]. The images were recorded with VIC®-Snap software using a PointGrey® GrassHoper camera equipped with a telecentric objective Edmund 0. 20x (Fig. 2 ). This lens enables constant magnification regardless of the object's distance or location in the field of view. Therefore, the measurements of the dimensions of an object will be independent of its position. For cubes, 60 images were acquired in the first minute and then 20 images per minute until the end of the test.
For parallelepipeds, 60 images per minute were acquired during the test. Post-processing images for the calculation of the strain field, for both geometries, were carried out by using UFreckles V 2. 0 software  [34]. The image registration is based on the optical flow equation as defined below: f (x) = g(x + u(x) ) ( 5 ) where f is the reference image, g the deformed picture and u the searched displacement field. Using the Ufreckles software, Eq. (  5 ) is solved by using a non-linear least-squares algorithm as given in Eq. (  6 ). Er 2 = ∑ [f (x) -g(x + u(x)) ] 2 (6) The algorithm normalizes the grey level of the image minimizing the influence of lighting variation. A mesh is generated to interpolate the displacement field with four nodes quad elements  [34]. The region of interest (ROI), manually selected on the cork surface, is associated to a rectangular area used to interpolate the displacement field.
This zone does not include the boundary zone of the sample to prevent edge imperfections from corrupting the results. The DIC camera is connected to the tensile machine to record the load. The stress (MPa) as a function of strain (y axis; compression direction) is plotted. Following the data treatment with Ufreckles software, the elastic modulus is calculated from the slope of the elastic region in which samples showed a quasilinear behaviour. The Poisson's ratio has been calculated based on the DIC method during the quasi-static compression test. The pixel displacement for two directions (x, y axis) is extracted from the comparison of pattern displacement between two consecutive images. The x axis corresponds to the transverse axis (perpendicular to the compression direction), while the y axis represents the longitudinal direction in which cork sample is compressed by the crosshead as defined in Eq.
(  7 ). ν = -ε xx ε yy  (7)  Following the data treatment with Ufreckles software, the transverse strain (ε xx ) as a function of the longitudinal strain (ε yy ) is plotted. With DIC method, mean values of transverse and longitudinal strain fields are computed. Finally, a linear regression was used to obtain the Poisson's ratio mean values for each sample. The same procedure is used for the calculation of the elastic modulus. For both geometries, the elastic modulus and the Poisson's ratio for non-radial directions were calculated in the elastic region in the interval set between 0. 1 % and 1. 5 % of strain. Poisson's ration is extracted, in relation with Fig. 5 , from zone 1 (Elastic-like behaviour). In addition, for cubes, the image thresholding analysis was performed on the strain fields measured at 1 %, 5 %, 10 % and 15 % of strain.
Acknowledgments We would like to thank the regional council of  Bourgogne -Franche-Comté (BFC)  and the  French "Investissement d'Avenir" program , project  ISITE-BFC  (contrat  ANR-15-IDEX-003 ), for their financial support. We would like to thank  Trescases Company  for providing cork stoppers. The authors would like to thank the  MIFHySTO and DIVVA  facilities for the access to the equipments. The authors also thank  Dr. Laurence Dujourdy  for help in statistical analysis.
, 2020). The ability of rhizobia to form nodules can be measured during single inoculation experiments (nodulation proficiency) or in co-inoculation experiments (nodulation competitiveness). There can be significant discrepancies between these two types of assays, as the outcome of nodule occupancy following co-inoculations is unpredictable from data in single inoculation. Indeed nodulation competitiveness is a complex trait that involves a large number of bacterial genes and functions (see section 4) and that is not fully understood yet  (Younginger and Friesen, 2019; Mendoza-Suaŕez et al. , 2021). Rhizobial strains can be more competitive for nodulation if (i) they are more efficient at colonising the rhizosphere (for example by growing faster on the available nutrient sources or by producing antimicrobial compounds that inhibit the growth of sensitive competitor strains), (ii) they are faster to reach the root and induce nodulation, and/or (iii) they show an optimal compatibility with the host plant  (Handelsman et al.
Behavioural assays Behavioural experiments were performed at room temperature (20˚C). Pharyngeal pump rate on food was quantified by visual observation under a Nikon SMZ800 binocular microscope, using a timer and a handheld counter. The pumping rate was defined by the number of grinder movements per minute per worm. The pump rate was quantified for a minimum of 3 times for 1 minute each and the mean was used as pumps per minute. The body length was measured as previously described  [36]. Briefly, images of the worms were acquired through a Hamamatsu Photonics camera and visualized for recording with IC capture software (The Imaging Source©). These images were binarized and skeletonized using ImageJ software. The length of the skeleton was used to determine the body length of the nematodes.
Statistical analysis The data were collected from control paired blind trials in which the experimenter was unaware of the genotype tested in each trial. Data were analysed using GraphPad Prism 8 and are displayed as mean ± standard error of the mean (SEM) of the number of worms used for each assay. The sample size N of each experiment is specified in the corresponding figure and indicate the number of worms assessed. Statistical significance was assessed using two-way ANOVA followed by post hoc analysis with Bonferroni corrections where applicable. This post hoc test was selected among others to avoid false positives. Statistical significance for the electrophysiology data was calculated by two-tail t-test.
Discussion Here, we investigated the role of S. Typhimurium MsrP during oxidative stress. We found that the Cpx stress response activates msrP expression in response to N-ChT, an RCS that is able to oxidize Met residues. We showed that MsrP is required to cope with N-ChT, conferring a central role to this enzyme in the resistance to RCS stress in S. Typhimurium. Importantly, our work i) shows that msrP transcription in S. Typhimurium is controlled differently than in E. coli, ii) identifies msrP as a member of the S. Typhimurium Cpx regulon, and iii) shows that N-ChT triggers Cpx in an NlpE-dependent manner. Our data establish an unexpected link between N-ChT oxidative stress and an envelope stress response. During mammalian infection, pathogens experience oxidative stress, leading to the oxidation of cellular macromolecules, including proteins.
To sequence the genome of the obtained suppressor, LLS662 (cpxA*), we isolated the genomic DNA as described previously  (48). DNA fragmentation via a Covaris instrument (M220 Focused Ultrasonicator) followed by library construction using the NEBNext Ultra II DNA Library Prep Kit led to 350-bp insert size libraries. Paired-end reads (2 × 150 bp) were sequenced on an Illumina MiSeq platform. After read cleaning (Trimmomatic v0. 39), we performed variant calling using Snippy (v4. 3. 6; https://github. com/ tseemann/snippy) referring to the genome sequence GCF_000022165 (National Center for Biotechnology Information). Fluorescence Quantification with a Microplate Reader. WT cells harboring pmsrP-gfp or pcpxP-gfp grew aerobically in LB supplemented with ampicillin at 37 °C for approximately 2 h (corresponding to four cell-doubling cycles) until an OD 600 of 0.