dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank all members of the Immunophysiology and  Cellular and Molecular Immunology laboratories  ( GIGA Institute, Liège, Belgium ) for discussions;  S. Ormenese ,  R. Stefan ,  A. Hego ,  G. Lefevre  and  C. Vanwinge  from the  GIGA In Vitro Imaging Platform ;  P. Drion ,  G. Lambert ,  L. B. Remy  and all staff members from the  GIGA Mouse facility and Transgenics Platform ;  W. Coppieters ,  L. Karim ,  M. Deckers ,  A. Mayer ,  A. Lavergne  and members from the  GIGA Genomics Platform ; and  R. Fares ,  I. Sbai  and  A. Lio  for their excellent administrative support. T. M. acknowledges support from the  F. R. S-FNRS  ( Incentive Grant for Scientific Research ), from the  FRFS-Welbio  and from the  Acteria Foundation. T. M. is supported by a  Research Project Grant  of the F.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy Single-cell RNA-seq and bulk RNA-seq data have been deposited at GEO and are publicly available under GEO accession GSE 194021. All original codes have been deposited at GitHub and are available via this link: https://github. com/BlanQwall/Lung_IM_differentiation. Any additional information required to reanalyze the data reported in this paper is available from the corresponding author upon request.
The iEBE-VISHNU model calculations with T R ENTo initial conditions and a temperature-dependent specific shear viscosity η/s(T )  [68]  are also shown. The iEBE-VISHNU model calculations are available at p T ranges below 3 GeV/c. The AMPT calculation presented here describes the data well in the 0-5% most central collisions and  Comparison with iEBE-VISHNU with T R ENTo initial conditions and η/s(T )  [55] , MUSIC with Glauber initial conditions and η/s = 0. 08  [56] , MUSIC with T R ENTo initial conditions and η/s = 0. 08, 0. 12, 0. 16  [56] , and AMPT  [57]  are shown as coloured bands. captures the deviation from unity in the highest p T bin. At higher centralities, the AMPT calculation overestimates the deviation from unity at high p T. The comparison of the MUSIC calculations with Glauber and T R ENTo initial conditions shows that A f 2 is sensitive to the fluctuations in the initial state with little to no sensitivity to the different values of specific shear viscosity.
In the 10-20% and 30-40% centrality intervals, the model captures the increasing trend with p T and, consequently, describes the data. The comparison of the measurements with the above models confirms that the FMF are driven by initial state fluctuations in non-central collisions. However, the dependence of M f 2 on η/s in the 0-5% central collisions with the MUSIC model is not well understood. The large discrepancy between the iEBE-VISHNU model and the data in central collisions suggests that the FMF could be an important observable in Bayesian analyses and could contribute further improvements of the extracted parameters used in the state-of-theart description of the initial state and the QGP transport properties. Equations (  4 ) and (  8 ) allow for the determination of the lower and upper limit for the contribution of the FAF and FMF, respectively, to the flow vector fluctuations defined in Eq.
Wheat Resistance Induction against M. graminicola and Wheat Biomasses Seventeen days after infection, the leaves were collected and freeze-dried to evaluate the protective effect against M. graminicola in response to the inoculation with PB2 or PGPR mixtures. To quantify the infection level of M. graminicola, we conducted DNA extraction and qPCR analysis following the method described by Selim et al. [2]. Briefly, DNA was extracted using the DNeasy 96 Plant Kit (Qiagen, Germantown, MA, USA) according to the manufacturer's protocol. To quantify the infection level of M. graminicola, we used specific primers and a TaqMan Minor Groove Binder probe targeting a 63-bp fragment of the M. graminicola β-tubulin-specific gene (For; GCCTTCCTACCCCACCATGT, Rev; CCTGAATCGCGCATCGTTA, Probe; FAM-TTACGCCAAGACATTC-MGB, GeneBank accession number AY547264;  [19] ).
Taxonomic identification of each OTU was performed up to the species level using the RDPClassifier and BLAST tools. These tools were applied to the non-redundant small sub-unit database from SILVA (v123) for bacterial communities and against the ITS Warcup Training Set (v2;  [27] ) for the fungal communities. The high-throughput sequencing of the 16S V3-V4 rRNA and ITS regions resulted in 10,293,301 and 3,487,769 reads, respectively.
Data were recorded at 15 min intervals over a 192-h period and were stored as OmniLog units in Biolog data analysis software (v1. 7). To calculate values for each well, blank values were subtracted from each well in the plate. To make comparisons between samples, only one absorption time point at 50 h was used, as recommended in  [29]. At this time point, the metabolic potential of the soil microbial communities in each sample was calculated by determining the average color development in the wells (AWCD). This calculation involved summing the optical density data for all wells and dividing the sum by 31 (the total number of substrates). To assess the functional richness of the microbial community in the soil, we counted the total number of wells in one replicate with an absorbance greater than 25 OmniLog units.
Data Analysis and Statistics Each experiment was biologically replicated twice, and each experimental condition within each experiment included a minimum of five replicates, except for high-throughput sequencing, where three technical replicates were used. To test the significance of PGPR inoculation and wheat genotype on plant growth and protection against M. graminicola, we performed variance analysis (ANOVA) and used Tukey's multiple range test for separating treatment means, with a significance level set at p ≤ 0. 05. From the OTU × sample abundance tables, we calculated taxonomic richness and Shannon indices to assess α-diversity. To ensure comparability of taxonomic richness across samples, we rarefied the number of bacterial and fungal OTUs using the rrarefy function from the vegan R package  [30].
Since the dataset did not meet the assumptions of normality, all diversity indices were compared between treatments using the R package agricolae  [31] , applying a non-parametric Kruskal-Wallis one-way analysis of variance, followed by a Conover-Iman post hoc test whenever significant. p-values were adjusted using the Benjamini-Hochberg FDR procedure in R with the p. adjust function. To examine differences in species composition between communities, we employed permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. We examined variations in bacterial and fungal community structures using Nonmetric Multidimensional Scaling (NMDS) based on Jaccard distances, utilizing the metaMDS function from the vegan R package. For this analysis, we selected the 50% most abundant bacterial or fungal genera with a 30% best axis fit, using the ordiselect function from the vegan R package.
For the CLPP analysis, since the dataset did not meet ANOVA assumptions, we compared means between treatment groups using the R package agricolae and a Kruskal-Wallis test (p < 0. 05), followed by pairwise Wilcoxon rank sum tests with Holm's p-adjust method for multiple comparisons. All statistical tests were performed with the R software (v4. 2. 2, The R Foundation, https://www. r-project. org/).
lowed by a Conover-Iman post hoc test whenever significant. p-values were adjusted using the Benjamini-Hochberg FDR procedure in R with the p. adjust function. To examine differences in species composition between communities, we employed permutational multivariate analysis of variance (PERMANOVA) with 999 permutations. We examined variations in bacterial and fungal community structures using Nonmetric Multidimensional Scaling (NMDS) based on Jaccard distances, utilizing the metaMDS function from the vegan R package. For this analysis, we selected the 50% most abundant bacterial or fungal genera with a 30% best axis fit, using the ordiselect function from the vegan R package. For the CLPP analysis, since the dataset did not meet ANOVA assumptions, we compared means between treatment groups using the R package agricolae and a Kruskal-Wallis test (p < 0.
Statistical analyses All statistical analyses were performed using the R software (version 4. 2. 1). The non-parametric statistical tests were used due to the non-normal distribution of the dataset (tested by Shapiro-Wilk normality test; p-values < 0. 05). Specifically, the Spearman's correlation was used to investigate potential correlations among different features (environmental parameters, fractional abundances of brGDGTs and brGMGTs, and proxies derived from these compounds), and the unpaired twosamples Wilcoxon test (also known as Mann-Whitney test or Wilcoxon rank sum test) was used for two independent group comparisons. Significance level is indicated by asterisks: *p-value < 0. 05; **p-value < 0. 01; ***p-value < 0. 001; ****pvalue < 0. 0001; ns (not significant), p-value > 0. 05.
A Principal Component Analysis (PCA) was performed on the fractional abundances of brGDGTs and brGMGTs, using the R packages factoextra and FactoMineR. The different groups of samples were highlighted by adding 95% concentration ellipses. The proportion of variance in brGDGT and brGMGT compositions that can be explained by different groups was evaluated by permutational multivariate analysis of variance using distance matrices (adonis) in the adonis2 function of the R package Vegan, using the Bray-Curtis distances and 999 permutations. A Redundancy analysis (RDA) was performed using the R package vegan to investigate the relationship between environmental parameters and brGDGT or brGMGT distributions in SPM. Angles between brGDGTs or brGMGTs and environmental factors were used to identify the potential correlations.
Right angles (90°) reflect a lack of linear correlations, whereas small or straight angles (close to 0° or 180°, respectively) imply positive or negative linear correlations. The compounds that are close to each other were assumed to be strongly linked, representing similar distribution patterns and comparable responses to the environmental conditions. To evaluate the relative importance of each explanatory variable (environmental parameters) on brGDGT or brGMGT distributions, a hierarchical partitioning method implemented in the R package rdacca. hp was used. This method calculated the individual importance (sum of the unique and total average shared effects) from all subset models, generating an unordered assessment of variable importance  (Lai et al. , 2022). Spatio-temporal variations of environmental factors and proxies derived from brGDGTs and brGMGTs were assessed after applying a locally estimated scatterplot smoothing (LOESS) method.
This method allows the identification of nonlinear data patterns and buffers the effect of aberrant data and outliers. LOESS was implemented by the geom_smooth function of the R package ggplot2. The total organic carbon (TOC) content was significantly higher in the upstream estuary (4. 64±1. 42 %, based on SPM and river channel sediments) than in downstream estuary (3. 30±1. 69 %, based on SPM and sediments), soils (3. 02±3. 49 %, based on surficial soils and mudflat sediments) and river (2. 88±1. 14 %, based on SPM) (Fig. 2 ). The total nitrogen (TN) content was higher in the upstream estuary (0. 51±0. 17 %, based on SPM and sediments) than in the river (0. 37±0. 15 %, based on SPM), downstream estuary (0. 31±0. 14 %, based on SPM and river channel sediments), and soils (0. 24±0. 17 %, based on surficial soils and mudflat sediments) (Fig.
Acknowledgments We would like to thank members of the  SARTRE  project for their assistance during the field campaigns. We appreciate the logistical support from  Robert Lafite ,  Frédéric Azémar ,  Anaëlle Bernard  and  Mahaut Sourzac. We are grateful to  Pascal Claquin  and  Léo Chasselin  for their support in water quality measurements and  Micky Tackx  for the scientific coordination of the SARTRE project. This study was funded by  GIP Seine-Aval (MOSAIC ,  PHARESEE ,  FEREE  and  SARTRE projects)  and  CNRS/INSU and OFB  ( EC2CO RUNTIME  project). Zhe-Xuan Zhang 's PhD scholarship is funded by  China Scholarship Council  (No. 202004910406 ) and ASDB from  Sorbonne Université. This work was supported by  CESAM  ( UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020 ). We would like to thank  Vincent Grossi  for helpful discussion.
Lactonase kinetic parameters Lactonase kinetic parameters was identified for four SsoPox enzymes: SsoPox W263F, SsoPox W263I, SsoPox V82I-A275G, and SsoPox V82I. The hydrolysis of lactones (C 8 -HSL, 3-OH-C 8 -HSL and 3-OH-C 10 -HSL) was performed in a lactonase buffer (2. 5 mM Bicine pH 8. 3, 150 mM NaCl, 0. 2 mM CoCl 2 , 0. 25 mM Cresol purple and 0. 5% DMSO, pH adjusted at 8. 3 with NaOH), in a range of concentrations between 0 and 2 mM. The reaction of AHL degradation was followed at 577 nm (SynergyHT, BioTek, USA). Mean values were fitted to the Michaelis-Menten equation using Graph-Pad Prism 7. 04 software to obtain the catalytic parameters.
2 Da for the MS and MS/MS signals, respectively. A protein was considered validated when at least two different peptides were detected (p-value below 0. 05), resulting in a protein identification false discovery rate below 1%, as verified with a reverse decoy database search. Spectral counts (number of MS/MS spectra assigned per protein) were used as proxy for the abundance of the proteins in each condition. Protein abundances in the two conditions and all biological replicates were compared with the PatternLab Tfold procedure  (Carvalho et al. , 2012; PMID: 22539673). Each protein sequence with a relative abundance between two conditions of Tfold above 1. 5 and p-value below 0. 05 was selected as differentially detected as previously recommended  (Gouveia et al. , 2020; PMID: 32133743). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD040565 and 10.
6019/PXD040565. Treated and untreated condition were analyzed using Partial Least Squares discriminant analysis (PLS-DA) as previously described  (Mion et al. , 2021). Proteins with a fold change ≥ 1. 5 and statistical p-value < 0. 05 were considered as significantly impacted by the lactonase and were classified using the Burkholderia Genome Database  (Winsor et al. , 2008). The violin plot was constructed using ggplot2 and vioplot packages R software (R-4. 1. 1) (Wickham, 2016; Adler et al. , 2022).
1% of formic acid, with a gradient from 30% to 95% of methanol for 2minutes, 95% of methanol for one minute, and back to the initial composition for 1 minute. Compounds were ionized in the positive mode using a Zspray electrospray ion source: capillary/ cone 1. 5 kV/20 V, source/desolvation 120°C/250°C. Ions were then monitored using a High Definition MS(E) data independent acquisition method, and the following settings were executed: travelling wave ion mobility survey, 50 m/z-1000 m/z, 0. 1 second scan time, 6 eV low energy ion transfer, and 20-40 eV high energy for collision-induced dissociation of all ions (low/high energy alternate scans). A lockmass correction was applied to adjust mass calibration within each run (Leucin Enkephalin 556. 2766 m/z). Beforehand, the Vion instrument was calibrated using a Major Mix solution (Waters, USA) to calculate collision cross section (CCS) and mass-to-charge ration (m/z).
4D peaks, corresponding to a chromatographic retention time, ion mobility drift time and parents/fragments masses, were then collected from raw data using UNIFI software (version 1. 9. 4, Waters, USA). Chemical structures were targeted with the following parameters: 2 ppm m/z tolerance on parent adducts (H + ), isotopic profile match of chlorine atoms, and 10 mDa m/z tolerance on common bactobolin structure fragments, 383. 07712, 312. 04000, 294. 02944 and respective [M+H] + C 1 4 H 2 1 C l 2 N 2 O 6 , C 1 1 H 1 6 C l 2 N O 5 a n d C 1 1 H 1 4 C l 2 N O 4 identified in MoNa-MassBank of North America (https:// mona. fiehnlab. ucdavis. edu/spectra/display/CCMSLIB00004679189).
Lactonase treatment largely modulates the B. thailandensis proteome In order to get a complete overview of changes involved by QQ with SsoPox V82I on B. thailandensis, proteomic analysis was undertaken. The complete proteome of B. thailandensis comprises 5,562 proteins (Proteome ID UP000001930). In this study, 2,495 proteins (i. e. 44. 9%) were detected and identified (Supplementary Data 1). Using a fold change of ≥ 1. 5 and statistical p-value < 0. 05 as selection criteria, 1,017 proteins (i. e. 18. 3% of the entire proteome and 40. 8% of the detected proteome) were identified as significantly impacted by SsoPox V82I. Statistical PLS-DA analysis revealed that the enzymatic treatment is the main separation criterion between the two conditions and accounts for 93% of the total variance (Figure  3A ) indicating the huge impact that QQ and QS have upon B.
thailandensis proteome. Clustering by biological function based on the Burkholderia Genome Database  (Winsor et al. , 2008)  was applied to significant proteins and 13 groups were identified (Figure  3B ). Among the significantly impacted proteins, 72% were upregulated, while only 28% were downregulated, indicating that treatment with SsoPox V82I and QQ led to wide changes in protein regulation and activated numerous pathways (Figure  3C ). Proteins involved in primary metabolism pathways including amino acid biosynthesis, fatty acid metabolism, sugar metabolism, purine metabolism and energy metabolism, were upregulated for over half of these. Several proteins associated with secondary metabolite pathways, including antimicrobial biosynthesis, were identified and were mostly downregulated, except for six proteins.
QQ treatment also impacted proteins involved in secretion, transporter and membrane-associated proteins, with 50%, 73% and 84% of upregulated proteins, respectively. Proteins involved in motility and protease were observed and were mainly upregulated (84% and 57%, respectively). Proteins involved in translation, ribosome formation, DNA repair, replication, transcription and regulation were also globally upregulated (Figures  3B, C ). Interestingly, among the proteins related to the QS systems, only the BtaR3 protein (WP_009896076. 1) was identified in the proteomic dataset and was downregulated (fold change of -2. 53) by SsoPox V82I treatment. BtaR3 is related to QS-3 and responsible for 3-OH-C 8 -HSL production. Previous studies have suggested that  QS-1 induces QS-3  (Majerczyk et al. , 2014a).
Funding This work was financially supported by  Investissements d'Avenir programme (Mediterraneé Infection   10-IAHU-03 ) of the  French Agence Nationale de la Recherche (ANR). MG is a Ph. D. student funded by the  "Emplois Jeunes Doctorants" programme of the Reǵion Provence-Alpes-Cote d'Azur (PACA, France).
All owners signed an informed consent form. Dogs and owners could leave the sessions at any time. Detailed subject data may be found in the Supplementary Material. Stimuli. Sound stimuli were created using the Adobe® Audition software. The frequency of the low-pitched tone was 120 Hz, while that of the high-pitched tone was 220 Hz, both well within the hearing range of both dogs (subjects) and humans (experimenters). Dogs' hearing range covers that of humans (64-23 000 Hz), however, exceeds that considerably in the high-frequency domain ranging approximately from 67-45 000 Hz  28. The sound intensity was set at 60 ± 10 dB. At this intensity, both used frequencies are well detectable for dogs  29. The 100 Hz difference makes the two tones clearly distinguishable. Procedure. The behavioural study was comprised of three phases: pre-training on the problem-solving task, associative conditioning, and testing.
All sessions were video recorded. Pre-training and baseline testing. Dogs were trained at home by their owner to open a parcel (standardly packed in three layers of brown paper, secured with a single 5 cm transparent tape -see Fig. 1 ) until dogs became capable to open the parcel without looking at their owner and without encouragement from the owner within three minutes. Parcel contained a plastic box baited with some pieces of the dog's daily diet. Once the training was completed, the dogs' performance in opening the parcel according to the criterion was tested by an experimenter at the Department of Ethology. The criterion was set as the dog being able to entirely strip the parcel of all three layers of brown paper independently within 180 s. This criterion testing was videotaped, coded and used as a baseline for data analysis.
After a 5-min delay, the task was repeated with the other tone. The order of the testing trials was counterbalanced across dogs. Testing trials were videotaped and coded and data was used as the basis of analyses. Data analysis. Baseline and testing trials were coded for latencies to open the parcel. Latencies were calculated in seconds from the first touch of the parcel until all wrapping paper was removed from the plastic container. We calculated the difference in opening latencies by subtracting the latency to open the box related to the low reward value (HRV) food sound from the latency to open the box related to the high reward value (LRV) food sound. We calculated the difference in latencies per participant. Positive differences in latencies indicate that the dog opened the box related to the HRV food sound faster.
In contrast, negative differences indicate that the dog opened the box related to the LRV food sound faster. A difference of zero indicated no difference in the dog's latency to open either box. We have cross-coded 75% of video recordings and calculated overall interobserver agreements by Intraclass Correlation Coefficients (IBM SPSS Statistics 28. 0. 0. 0) (average measure ICC = 0. 975 with a 95% confidence interval from 0. 958 to 0. 985 (F(59, 58) = 40. 698, p < 0. 001). Raw data is provided as Supplementary Material. Performance of dogs in baseline, HRV-food and LRV-food conditions were compared. To compare opening latencies, we have built a Cox mixed-effects model fit by maximum likelihood.
The partially rewarded training session aimed to familiarize dogs with sounds not being directly followed by reward and to minimize any potential frustration resulting from this experience in the scanner. This partially rewarded training session was in all other aspects similar to the previous behavioural training sessions. Then, we acquired the two post-training runs (in the inverse order of their pre-training runs). Data acquisition. We used a sparse sampling acquisition in a 3 T Philips Ingenia scanner with an eight-channel dStream Paediatric Torso coil. Dogs were fitted with earmuffs to provide noise protection during scanning, and to present the stimuli. Blood-oxygen-level dependent (BOLD) images of the whole brain were acquired with a gradient-echo-echo-planar imaging (EPI) sequence (40 transverse slices, 2 mm thickness, 0.