dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study, we assessed the influence of grafting, rootstock genotype, time of season, year, and local environmental conditions and their interactions on gene expression in the grapevine cultivar Chambourcin. To do this, we sampled leaf and reproductive tissues (flowers and fruits) from ungrafted (own-rooted) Chambourcin vines as well as vines where Chambourcin was grafted to one of three different rootstocks. Samples were collected at three phenological stages (anthesis, veraison, harvest-ripe) in each of three years. Through this design, we sought to answer the following questions: 1) How do grafting and rootstock genotype influence shoot system gene expression? 2) Does the influence of grafting and rootstock genotype on shoot system gene expression vary over time? and 3) Is there an environmental component to rootstock influence on shoot system gene expression? Data presented here demonstrate that the influence of rootstock genotype on shoot system gene expression is highly dependent on tissue type and time of sampling (both year and time of season), suggesting that the impact of grafting on gene expression in the scion varies over time.
Here, we identified 2,501 shared genes that were up-regulated relative to ungrafted and 758 genes that were down-regulated relative to ungrafted. Only the up-regulated gene set contained enriched functionality, many of which were shared in veraison, including 'microtubule-based process', 'microtubule-based movement', 'movement of cell or subcellular component', and 'cytoskeleton organization' (GO:0007010). window. Precipitation and radiation density were summed (rather than averaged) to build a composite statistic. Composite statistics were built for every day for the three years of this study to test the correlations across features. Given that many environmental features were highly correlated, we opted to collapse this variation using a principal components analysis (hereafter called the environmental or ePCA), from which we extracted data for each of the nine days of sampling for subsequent analyses.
In total, 12 gPCs were influenced by the interaction of rootstock genotype and the environment. We looked to see if genes that loaded heavily (>1. 96 sd away from the average loading) on the gPCs were significantly enriched for functional roles. Of the 12 gPCs influenced by interaction of rootstock genotype and the environment, six had exactly one term enriched in either highly loading genes or lowly loading genes: "RNA modification" (GO:0009451). To gain a higher resolution insight to the broad classification, we looked to see if any protein domains (Pfam and InterPro) were similarly enriched in the gene sets. Only considering the top two ePCs, many of the domains enriched on the gPC loadings were similar. For example, the Pfam domain 'NB-ARC' (PF00931) was enriched on genes loading positively on PC226 (significant for ePC1) and PC186 (significant for ePC2).
gPC226 had five Pfam domains that were enriched in negatively loading genes, including "reverse transcriptase-like", "reverse transcriptase", "retrotransposon gag protein", or "RNase H-like domain found in reverse transcriptase", and a domain of unknown function, "transposase-like DUF 659" (PF13456, PF00078, PF03732, PF17919, and PF04937, respectively). Similar domains were also enriched in gPC241 (significant for ePC1) and gPC267 (significant for ePC2). Other gPCs significant for the interaction of rootstock and the environment were additionally enriched for domains associated with the "PPR repeat family" (PF13041) and "DYW domain" (PF07727).
Extraction and Sequencing To maximize the number of samples sequenced in this study, we opted to perform a reducedrepresentation approach to RNAseq called 3'-RNAseq, which performs well in organisms with reasonably characterized genomes  [60]. For this procedure, total RNA was extracted from each tissue using the Spectrum Plant Total RNA kit (Sigma-Aldrich, St. Louis, MO, USA) with 2% PVP40 added to the extraction buffer to sequester phenolic inhibitors. Extractions were checked for quality using a Nanodrop (Thermo Fisher Scientific, Waltham, MA, USA) and sequenced using an NextSeq500 (Illumina, San Diego, CA, USA). The resulting data set contained single-end, 86 base pair reads.
This model and variance stabilizing transformed data  [66]  were saved for future use. After the initial fit, experimental metadata (tissue, year, phenology, and rootstock) were concatenated into a single composite term in order to assess higher-level interactions. Each gene was reestimated with a model containing the concatenated metadata, irrigation, and block as fixed effects, although the effects from irrigation and block were not considered in this study. Each rootstock contrast was then analyzed within each tissue × year × phenology interaction. From these models, normalized counts (using DESeq2's implementation of the variance stabilizing transformation) were extracted for genes mapping to two broad classes of constitutively expressed house-keeping gene families: ubiquitindomain related (IPR: IPR000626) and actin domain (IPR: IPR004000) (Supplemental Figure  3 ).
Environmental Data Analysis An onsite weather station  [70]  captured hourly measurements of temperature, precipitation, wind speed and direction, relative humidity, solar radiation, radiation energy density, pressure, dew point, estimated short crop evapotranspiration, and clear sky radiation. From each of these, we built composite summaries of the 24 hours preceding sampling including minimum values, maximum values, and change in values over the window. Composite statistics built from 24 hours preceding sampling were highly correlated with composite statistics built from 24 hours before sunrise on the day sampling and smaller windows including four and six hours before sampling. Moreover, many traits within the 24-hour window were highly correlated, so we collapsed the correlation structure using PCA to understand variation in gene expression as a function of broad environmental variation.
Data Availability Sequencing data are provided on the NCBI Sequence Read Archive under the following accessions: PRJNA674915 and PRJNA915033. Two samples were found to be corrupted: 471R and 624L. Both are provided in truncated forms within PRJNA915033. All code used for the analyses of these data are provided on Github: https://github. com/PGRP1546869/mt_vernon_1719_rnaseq. Additional data including experimental metadata, environmental data, and a persistent version of record of all code are provided on FigShare: https://doi. org/10. 6084/m9. figshare. 21861480.
The two predictability conditions (unpredictable and predictable) were presented in separate blocks. Each of the predictability conditions appeared in two consecutive blocks, in an alternating manner (UU-PP-UU-PP or PP-UU-PP-UU). There were eight blocks and each block consisted of 128 trials, which resulted in 1024 trials altogether. There were 192 trials for each of the four combinations of predictability and FP in the go trials and 64 trials for each of four combinations of predictability and FP in the stop trials. The experimental task was programmed in PsychoPy software  (Peirce et al. , 2019). Prior to testing, participants completed a training session, which consisted of 30 temporally predictable trials followed by 30 temporally unpredictable trials.
| EMG-EEG recording The bipolar EMG activity of the flexor pollicis brevis was recorded from each hand using Ag/AgCl active electrodes positioned 2 cm apart on the thenar eminence. EEG data were recorded from 64 Ag/AgCl active pre-amplified electrodes (Biosemi Inc. , Amsterdam, The Netherlands) at a rate of 1024 Hz (analogue bandwidth limit: from direct current (DC) to 268 Hz, -3 dB at 1/5th of the sampling rate). EEG electrodes were placed according to the extended 10-20 convention. The horizontal and vertical eye electrooculograms (EOGs) were recorded from electrodes lateral to the external canthi and below the left eye (and FP1 electrode), respectively.
| EMG-EEG processing EMG and EEG data were analyzed using BrainVision Analyzer 2. 0 (Brain Products, Germany), MNE Python toolbox  (Gramfort et al. , 2013) , and customized Python scripts (www. python. org). A high-pass filter of 10 Hz was applied offline to the EMG signal. The onsets and offsets of EMG activity were detected using a customized Python program F I G U R E 1 Temporally cued version of the stop-signal task. A temporal cue (500 ms) predicted (temporally predictable condition) or not (temporally unpredictable condition) the time of target onset. After the cue was presented, a background display appeared for one of two intervals (foreperiods or FP): short (600 ms) or long (1400 ms). Then, the target ("×" or "+") appeared within the cue outline for 1000 ms, and participants gave a lateralized response according to the shape of the target.
The EEG data were re-referenced offline to the averaged left and right mastoids, band-pass filtered between 0. 01 and 100 Hz using a second-order infinite impulse response Butterworth digital filter (slope: 12 dB/Oct) and corrected for the ocular artifacts using the MNE Python toolbox  (Gramfort et al. , 2013; Uusitalo & Ilmoniemi, 1997). Additional artifacts were visually inspected and rejected.
| DISCUSSION In the current investigation, we examined how temporal unpredictability influences error monitoring as measured by brain potentials and muscle activity. We demonstrate that (1) temporal unpredictability increased the ERN amplitude after failed attempts to stop actions that had already been initiated, (2) this result remained significant when controlling for differences in the number of failed stops in unpredictable and predictable conditions, (3) temporal unpredictability lengthened the MT of the responding hand. Our findings show that a brain response to erroneous actions (the ERN) is potentiated when events are temporally unpredictable. By contrast, temporal unpredictability had no effect on the equivalent waveform for correctly executed actions (the CRN). Our results are therefore consistent with previous data showing that uncertain contexts are associated with enhanced performance monitoring  (Jackson et al.
We have complied with all relevant ethical regulations for animal use. Chimerism was assessed by quantifying enk gene locus DNA relative to enk gene locus plus neomycin gene in the recipient's bone marrow cells. Genomic DNA was extract from bone marrow cells depleted in red blood cells from chimeras using kit Extract-Fig. 3  Penk-deficient chimeric mice display virtually no functional T cell alteration. The relative frequency of CD4 + T lymphocyte subsets in colonic lamina propria and spleen from penk +/+ (white circles and white histogram) and penk -/-(black circles and gray histogram) chimeric mice was estimated by cytofluorometry. a Gating strategy for flow cytometry data analysis. Living CD45-expressing cells gated on TCRβ chain + CD4 + T lymphocytes were immunophenotyped as FoxP3 + regulatory T cells and FoxP3 -conventional T lymphocytes.
PCR amplicons were run in 2% agarose gel, stained with GelRed® Nucleic Acid Gel Stain (biotium, Fremont, CA, USA) for 20 min and then revealed with ChemiDoc XRS + (Bio-Rad, Hercules, CA, USA). The fluorescence intensity of the bands corresponding to the enk gene locus (penk +/+ ) and to the neomycin gene (penk -/-) was quantified by using Fiji/ ImageJ software (Supplementary Fig. 1 ). Macroscopic assessment of colon. Macroscopic colonic tissue damage was evaluated using a scale ranging from 0 to 11 as follows: erythema (absent (0), length of the area less than 1 cm (1), more than 1 cm (2)), edema (absent (0), mild (1), severe (2)), strictures (absent (0), one (1), two (2), more than two (3)), ulceration (absent (0), present (1)), mucus (present (0), absent (1)), and adhesion (absent (0), moderate (1), severe (2)).
Libraries quality was assessed using the HS NGS kit on the Fragment Analyzer (Agilent Technologies, Santa Clara, USA). Libraries quantification and sequencing were performed at the GeT-PlaGe core facility (INRAe, Toulouse, France). Libraries were quantified by qPCR using the KAPA Library Quantification Kit (Roche, Basel, Switzerland) to obtain an accurate quantification. The quality control of reads was performed with FastQC and all samples pass classical quality control. Reads were then aligned and quantified with STAR (Version 2. 7. 8a) on the Mus musculus genome GRCm39. 107 from EBI databases. Differential analyses of RNA-Seq data at the gene level were performed using the DESeq2 R package (v1. 30. 0) with the recommended workflow  63. Real-time quantitative PCR analysis. Frozen colon biopsies were crushed in 500 µL TRIzol™ Reagent (Molecular Research Center, Euromedex, Souffelweyersheim, France) in Precellys lysing kit D-tubes (Bertin Technologies, Montigny le Bretonneux, France) placed in a Precellys (6000 rpm, 30 s twice; Bertin Technologies).
Briefly, plates were conditioned with 500 µL MeOH and 500 µL H 2 O/MeOH (90:10, v/v). Samples were loaded at a flow rate of about one drop per 2 s, and, after complete loading, columns were washed with 500 µL H 2 O/MeOH (90:10, v/v). The phase was thereafter dried under aspiration and lipids were eluted with  The linear gradient was as follows: 0% solution B at 0 minute, 85% solution B at 8. 5 min, 100% solution B at 9. 5 min, 100% solution B at 10. 5 min, and 0% solution B at 12 min. The flow rate was 0. 35 mL min -1. The autosampler was set at 5 °C, and the injection volume was 5 µL. Data were acquired in multiple reaction monitoring (MRM) mode with optimized conditions. Peak detection, integration, and quantitative analysis were performed with MassHunter Quantitative analysis software (Agilent Technologies).
Cell surface antigens were then stained with optimal concentrations of fluorochrome-labeled antibodies diluted in PBS 10% FBS, 2 mM EDTA for 30 min at 4 °C (Supplementary table  3 ). Before transcription factor staining, cells were permeabilized using Foxp3/Transcription Factor Staining Buffer Set (eBioscience, Thermo Fisher scientific). In addition, for intracellular cytokine staining, cells were incubated with 50 ng m L -1 phorbol myristate acetate and 500 ng m L -1 ionomycin (Sigma-Aldrich) for 4 h. The protein transport inhibitor Brefeldin A (eBioscience) was added to the cells for the last 2 h. After washing, the cells were first incubated with blocking buffer and then with labeled anti-cytokine antibodies. Data were acquired on a Fortessa X20 and Symphony (BD Biosciences) and further analyzed using the FlowJo software (Tree Star, Ashland, OR, USA).
Doublet cells and dead cells were excluded based on scatter analysis and viability dye staining respectively. For UMAP and phenograph analysis, FCS files exported from BD FACSDiva software were imported into FlowJo software and concatenated. The FlowJo plugin UMAP (V3. 1) was run on the resulting FCS file using the default settings (Distance function = Euclidean, nearest neighbors: 15 and minimum distance: 0. 5) including the following parameters: TCRβ, CD8α, TCRγδ, CD11b, FcεRI, Ly6G, Siglec F (CD170), CD103, ST2, CD117, CD4, NK1. 1, MHC-II, CD11c. For cluster identification, the FlowJo plugin phenograph (V2. 4) was run on the resulting UMAP using the default settings (nearest neighbors K = 30). Expression levels of analyzed markers were used to associate immune cell types to identify clusters according to the literature.
Colorectal distension and electromyography recording. Three days before colorectal distension, 2 nickel-chrome electrodes (Bioflex insulated wire AS631; Cooner Wire, Chatsworth, CA, USA) were implanted into the abdominal external oblique musculature of anaesthetized mice. Electrodes exteriorized at the back of the neck were connected via a Bio Amp (ADInstruments, Inc. ) to an electromyogram acquisition system (PowerLab, ADinstruments). A 9 mm diameter balloon catheter (Fogarty catheter for arterial embolectomy, 4 F; Edwards Lifesciences, Nijmegen, Netherlands) inserted into the colon at 5 mm from the anus was progressively inflated in a stepwise of 15 mmHg. Ten-second distensions were performed at pressures of 15, 30, 45, and 60 mmHg with 5 min' rest intervals. Electromyography activity of abdominal muscles was recorded and visceromotor responses were calculated using LabChart 8 software (ADinstruments)  66.
Mucus infiltrating-bacteria were estimated by image processing on Fiji by enumerating pixels corresponding to Cyanine 5 labeled 16S RNA between the edge of the lumen to the edge of the epithelium. The mucus area was manually traced, and an arbitrary distance was assigned to each Cyanine 5 pixel from the middle of the mucus layer to the edge of the lumen (Apical) and to the epithelial cell border (Basal). The number of pixels for each distance (strata) was determined  69. Taxonomic analysis of gut microbiota. Total DNA was extracted from freshly-collected snap-frozen feces  70. The 16 S rRNA gene V3-V4 regions were targeted by the 357wf-785R primers and analyzed by MiSeq at RTLGenomics (Texas, USA). An average of 18069 sequences per sample was generated. PCA was drawn and OTU-based diversity indices calculated with the software PAST4.
Each DAPI staining was considered as bacteria  72. Data were acquired on a Fortessa X20 and further analyzed using the FlowJo software. Open field test. The open field test was performed in a circular Plexiglas box (40 cm diameter) for 10 min. Experiments were performed in a room containing no conspicuous features and illuminated by a white light (30 lux). The arena was surmounted by a video camera connected to a video recorder to measure the number of entries and the time spent in the center of the open field with an automated system (Noldus, Ethovision, The Nederland). Elevated plus maze test. Mice were placed in the central platform (10 × 10 cm) of a plus shaped track apparatus with two closed and two open arms (30 × 10 × 20 cm) elevated 50 cm above the floor and illuminated by a white light (25 lux).
05 were considered significant. All the statistical analyses were performed using GraphPad Prism 9. 0 software (GraphPad Software, San Diego, CA). The sample sizes and number of replicates are indicated in the figure legends. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material.
Acknowledgements The authors wish to thank the animal care facility, Genetoul, anexplo, US006/INSERM, Toulouse, MetaToul (Toulouse metabolomics & fluxomics facilities, www. metatoul. fr) which is part of the  French National Infrastructure for Metabolomics  and Fluxomics MetaboHUB- ANR-11-INBS-0010  and the platform Aninfimip, an EquipEx (' Equipement d'Excellence ') supported by the  French government  through the  Investments for the Future program  ( ANR-11-EQPX-0003 ). We gratefully acknowledge the technical assistance provided by the  INFINITy (INSERM  1291) personnel of both the flow cytometry ( F. E. L'Faqihi-Olive ,  V. Duplan-Eche  and  Anne-Laure Iscache ) and cellular imaging ( S. Allart  and  S. Lachambre ) core facilities connected to ' Toulouse Réseau Imagerie ' network. This work was supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Université Paul Sabatier, Toulouse III , the  Association François Aupetit  (G.
D. ) and the  French Agence Nationale de la Recherche  ( NOCICEPOP-ANR-22-CE14-0011-01 , G. D. ). X. M. O. , L. R. , N. G. , C. P. and E. W. were supported by scholarship from  « Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation  » and L. B. by The  Fondation pour la Recherche Médicale.
Data availability The data generated in this study are provided in the main text and Supplementary Information. RNA sequencing raw data are available on the following link: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE244917. Source data can be obtained on the Figshare repository with the following links: Fig. 1. https://doi. org/10. 6084/m9. figshare. 24466762. v1; Fig. 2. https://doi. org/10. 6084/m9. figshare. 24467023. v1; Fig. 3. https://doi. org/10. 6084/m9. figshare. 24467032. v1; Fig. 4. https://doi. org/10. 6084/m9. figshare. 24467275. v1; Fig. 5. https://doi. org/10. 6084/m9. figshare. 24467644. v1; Fig. 6. https://doi. org/10. 6084/m9. figshare. 24467803. v1; Fig. 7. https://doi. org/10. 6084/m9. figshare. 24468358. v1. All other data are available from the corresponding author (or other sources, as applicable) on reasonable request.
So, we propose a theory extension; 2) Instrument development: we selected four cases because three companies in the electronics sector were part of the Responsible Business Alliance and should thus be transparent about their SSCM. We added Fairphone because of its self-positioning of sustainable SC; 3) Data gathering: documents, websites, publications; 4) Data analysis: individual case analysis and cross-case analysis by the two authors. In addition, we consulted Isabel Rimanoczy, the external expert in the Sustainability Mindset, for feedback on the sub-dimensions we suggest for the sustainability mindset. This is something  Seuring (2008)  noted is rare in SSCM research that uses case-studies; and 5) Case quality: SDG examples were validated with real electronic firm cases and turned into assessment sub-dimensions, which were validated by the SM expert (construct validity); information and results come directly from open access sources of the four companies (internal validity); the findings represent an assessment model for the electronics sector and its generalization to other firms in the same sector or in other sectors should be tested (external validity); consistency is assured through the refinement of the sub-dimensions proposed in an iterative process (reliability).
Acknowledge activities throughout the SC and understand multi-level and provide solutions to relations inside and encourage more outside SCs. Doing: Revise responsible consumption operations to identify (e. g. , innovation in sustainable alternatives. product, service, delivery) Develop/use metrics for (The Guardian 2020; Zero carbon emissions and Waste Europe 2018). pollution. Improve energy efficiency in operations. 13. Climate action Plan or redesign SC Knowing: Identify real activities to reduce their and forecasted carbon contributions to climate footprints. Identify ethical change (BBC, 2015). issues and (risk of) unethical practices. Identify (risk of) unsustainable practices. Doing: Revise operations to identify sustainable alternatives. Develop/use metrics for carbon emissions and pollution.
60×10-05, F-value=19. 2 and, RpoB: p=1. 14×10-65, F-value=473). DNA concentrations were also log-transformed and analyzed using glm with a gamma distribution when ratios were not relevant for the analyzes. To analyze reproduction parameters, we used generalized linear models with the function lm, glm or generalized mixed model with glmer (R package Lme4  (Bates et al. 2015) ) according to the distribution of the data and the absence (glm) or presence (glmer) of a random effect. The random effect was added for analyses targeting the offspring of tick females to account for the lack of independence of data points (since measured individuals may be offspring of the same mother). For egg incubation time and time for molting into first nymphal stage, the level of variability between mothers was not sufficient to warrant incorporating it as a random effect.