dataesr/scientific-paragraphs-categorization huggingface dataset:
The specific ozone levels were accomplished by an airintake scrubbing system consisting of activated charcoal filtration unit that lowered ambient air ozone levels in the greenhouse to < 10 ppb hourly average. Ozone was then added to each CSRT chamber via a controllable micro-metering system with concentrations monitored with a TECO Model 49 O 3 analyzer and data logger/computer recording system in each chamber  [103]. The augmented O 3 was delivered in square-wave fashion for 7 day/wk. , eight hours a day (0900 h to 1559 h) for 28 days, mimicking diurnal ozone fluctuation. In treatments greater than ambient, cumulative ozone exposure ranged from 864 to 1728 ppb h for 7 h treatments, from 13,992 to 25,152 ppb h for 14 day exposures, and from 28,008 to 50,328 ppb h for 28 day exposures. The metric ppb h was calculated as (ppb × 8 h × # days).
Two additional 454 libraries were constructed from the parent tree samplesone from a pooled set of equal amounts of RNA from above ground tissue samples and one from a pooled set of below ground tissue samples. For the second O 3 stress experiment, biological replicates were independently barcoded for sequencing. Illumina TruSeq libraries were prepared for each of the replicate RNA samples, following manufacturer protocols, then sequenced on an Illumina HiSeq 2500 instrument at Pennsylvania State University. All RNA-Seq data are available in the NCBI Sequence Read Archive database under BioProject accession number PRJNA273270.
RNA-seq preprocessing, de novo assembly and quality assessment The quality of generated RNA-Seq data was inspected by FastQC software  [106]  and low-quality reads (mean Phred score < 20) were cleaned by Trimmomatic using default parameters  [107]. Only reads originating from the 454 instrument or the MiSeq instrument were included in the assembly, due to their longer read lengths. Trimmed reads were assembled by Trinity (version downloaded on 2012-10-05)  [108]. The assembly was further refined by cd-hit-est v4. 6. 1 with a sequence identity threshold of 0. 95 to collapse isoforms and reduce assembly redundancy  [109]. All transcript names begin with "Quercus_rubra_ 120313_" to indicate transcriptome origin and version. This part of the transcript name has been removed from the text for brevity.
For example, transcript "Quercus_ rubra_120313_comp102049_c0_seq1" is referred to in the text as "comp102049_c0_seq1". The quality of the transcript assembly was checked by Transrate version 1. 0. 3  [110]. Transrate was also used to compare transcripts to available oak reference genomes by read mapping through Conditional Reciprocal Best BLAST with default cut-off value of 1e-5. Candidate coding regions within assembled transcripts were predicted by Transdecoder software version 5. 1. 0 [111]. Transcriptome completeness was checked by Benchmarking Universal Single-Copy Orthologs (BUSCO) version 3 based on the plant ortholog database (embryophyta_odb9)  [112]. Reads were mapped back to the transcriptome assembly with bowtie2 v2. 2. 1 using the -sensitive parameter.
Functional annotation, pathway identification and gene expression analysis Gene ontology (GO) functional classification of the transcriptome assembly was carried out using the Blast2GO program  [113]  based on the NCBI non-redundant (nr) protein sequences by fast-BLASTX  [114]  with an Evalue cut-off of 1e-5 as well as EBML-EBI InterProScan (IPS) database. Gene ontology  [115]  terms were obtained for each putative transcript from both BLAST and IPS outputs. WEGO  [116]  was used to examine the GO terms among the annotated putative transcripts. The EC numbers were retrieved through GO-EnzymeCode Mapping feature of Blast2GO software.
Identification, annotation and enrichment analyses of differentially expressed genes For differential gene expression of ozone exposure, only data from the second ozone experiment was used for analysis; this experiment had individually barcoded replicates and high depth of reads generated by the HiSeq instrument. To obtain raw read counts for each putative transcript per library HTSeq version 0. 6. 1  [117]  was used. The raw count matrix were provided to the edgeR version 3. 6 bioconductor package  [118]  to distinguish differentially expressed genes (DEGs) between treatment and control groups. Briefly, normalization by the trimmed mean of M-values (TMM) method was calculated to adjust count reads. Normalized factors, the counts per million (CPM), were used in common, trended and tag-wise dispersion analyses by Cox-Reid profile-adjusted likelihood (CR) method.
Finally, to determine significant DEGs negative binomial general linearized model (GLM) likelihood ratio was tested based on the model (treatment*time), where treatment was ozone concentrations, and time was time-points for each treated sample. Genes were considered significantly differentially expressed based on adjusted p-value < 0. 05  [119]  and |log2 (fold change)| > 1. Consensus DEGs detected by edgeR package were visualized by Venny version 2. 1  [120]  and their results were used in further annotation and enrichment analyses. GO enrichment analysis of DEGs was carried out by agriGO v2  [121]  with the significant DEGs of each model as the foreground dataset and all putative transcripts as the background reference. The statistical parameters used for identification of overrepresented GO terms were Fisher's exact test, adjusted for multiple testing by FDR with a cut-off value at the significance level of 0.
Guided by the early findings of social scientists, practitioners have long advocated for greater contact between groups to reduce prejudice and increase social cohesion. Recent work, however, suggests that intergroup contact can undermine support for social change toward greater equality, especially among disadvantaged group members. Using a large and heterogeneous dataset (N=12,997 individuals from 69 countries), we demonstrate that intergroup contact and support for social change toward greater equality are positively associated among members of advantaged groups (ethnic majorities and cis-heterosexuals), but negatively associated among disadvantaged groups (ethnic minorities and sexual and gender minorities). Specification curve analysis revealed important variation in the size-and at times, direction-of correlations, depending on how contact and support for social change were measured.
This research provides such a test using a large and heterogeneous dataset. The relation between intergroup contact and support for social change is more nuanced than is typically recognized. Among members of advantaged groups, such as ethnic majorities and cis-heterosexuals (i. e. , heterosexuals whose gender identity corresponds to their assigned sex), contact with members of disadvantaged groups, such as ethnic minorities and LGBTIQ+ individuals (i. e. , individuals identifying as lesbian, gay, bisexual, transgender, intersexual, queer, and other sexual or gender minorities) is generally-but not invariablyassociated with greater support for intergroup equality and social change  [9] [10] [11]. Yet, in some cases, contact may improve advantaged group members' feelings toward disadvantaged groups while having little impact on their support for policies or actions designed to redress group-based inequalities  12.
In this multi-national collaboration, all researchers included the same extensive array of commonly used measures of contact and support for social change in assessment (see Table  1 ). This enabled us to estimate an overall correlation between contact and social change, as well as conditional correlations that arise from different combinations of varied measures assessing contact and social change  [16] [17] [18]. <Table 1 about here> Heeding calls for more collaborative, high-powered, transparent, and reproducible research processes  19  , we test the association between contact and support for social change using a large and heterogeneous dataset, sampling 12,997 participants from 69 countries and four populations (ethnic majorities, cis-heterosexuals, ethnic minorities, and LGBTIQ+ individuals; see Supplementary Tables 1-3 for more details).
The study followed a preregistered analysis plan stored along with the questionnaires, data, and code at: https://osf. io/m5pb6/ (see also Supplementary Table  13 ). To estimate the relation between contact and support for social change, we calculated bivariate correlations after removing the sample means from the data via residualization (which is comparable to a multilevel analysis with random intercepts). Although we expected that contact and support for social change would generally be positively related among advantaged groups (ethnic majorities and cis-heterosexuals) and negatively related among disadvantaged groups (ethnic minorities and LGBTIQ+ individuals), variations in these overall associations are of particular interest. We used specification curve analysis  23  to probe the variation in the direction and magnitude of the association between contact and social change using every combination of available measures (see Supplementary Figure  3 ).
R and the underlying Functions. R script. Note:  1  The number in parentheses indicates the number of significant results after adjusting the p-values using the Benjamini-Yekutieli procedure so that the false discovery rate is at most 5%; 2 p-values correspond to the number of shuffled datasets with as many or more significant correlations than in the original data set divided by the total number of shuffled datasets (i. e. , 1,000). The smallest possible p-value with 1,000 reshuffled samples is p < 1/1,000. Figure Legends.
Acknowledgments This project received direct financial support through the  Swiss Bilateral Program of the State Secretariat for Education, Research and Innovation (SERI)  awarded to  Johannes Ullrich ,  Robert González ,  Tabea Hässler ,  Michelle Bernadino , and  Daniel Valdenegro. The Chilean research team was supported by  Fondecyt  ( 1161371 ), the  Center for Social Conflict and Cohesion Studies  ( FONDAP 15130009 ), and the  Center for Intercultural and Indigenous Research  ( FONDAP 15110006 ) awarded to  Roberto González. The Dutch part of this research was funded by  FWO Odysseus grant  (G. O. E66. 14N) awarded to  Colette van Laar. The English part of this research was funded by a grant from the  ESRC  commissioning ( 403006662 ) awarded to  Dominic Abrams  and  Giovanni Travaglino.
Stephen Wright  was funded by a grant from the  Social Science & Humanities Research Council of Canada. Iris Žeželj was funded by the  Serbian ministry of education, science and technological  development ( 179018 ). The Polish part of this research was funded by the  Foundation for Polish Science (TEAM) , co-financed by the  EU ERDF  (" Language as a Cure" Project ) awarded to  Michal Bilewicz  and  Olga Kuzawinska. Evgeny Osin  was supported by the  HSE University Basic Research Program  and the  Russian Academic Excellence Project  ' 5-100 '. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank  Luisa Liekefett ,  Dogan Kokdemir ,  Dana Indreica ,  Ana Figueiredo ,  Neela Mühlemann , and  Yasin Koc  for their additional help with the translation and/or data collection.
Because the existing implementation of multi-grid tracer transport for the NEMO ocean model is not meant to be run at the runtime with the physical ocean component, its application is strongly limited by the I/O requirements and storage capacity. Indeed, the method proposed by  Aumont et al. (1998)  is not a priori designed for coupled ocean biogeochemical models but rather restricted to offline applications where the biogeochemical model is using output from a previous ocean circulation model integration. With this algorithm, data from the ocean circulation model should be output and stored on a local file system. The frequency of the coupling between ocean dynamics and the biogeochemical model is therefore limited by the frequency of available ocean model output. The I/O and storage capacity required for such applications is a strong limiting factor with existing climate and operational systems.
, 2006) , which leads to a resolution ranging from 1 m at the surface to 450 m at depth. The bathymetry is based on a combination of ETOPO1  (Amante and Eakins, 2009)  and GEBCO08  (Becker et al. , 2009). The initial state for the ocean is at rest, with temperature and salinity from WOA13 (World Ocean Atlas; https://www. nodc. noaa. gov/, last access: 10 June 2015). At the surface, the model is forced by ERA-Interim atmospheric reanalysis  (Dee et al. , 2011)  using the CORE bulk formulae  Large and Yeager (2009)  to compute the turbulent air-sea fluxes. A monthly runoff climatology is built and applied to all configurations, using data for coastal runoffs and major rivers from  Dai and Trenberth (2002). A split-explicit time-splitting scheme is employed to compute the surface pressure gradient with a non-linear free surface  (Levier et al.
8. 4). This is why it is not possible to use an averaging procedure where the cell resulting from the averaging of T cells is centred at an F point. The present implementation is even more limited since only a coarsening factor of 3 is allowed. This limitation is related to constraints associated with multiprocessor applications. Indeed using a larger coarsening factor would require increasing the width of Message Passing Interface (MPI) overlapping areas (halo ghost cells for data exchanges with neighbouring processors). This is illustrated in Fig. 13 , which shows that using a spatial coarsening factor of 5 would require extending the MPI overlapping area with an extra grid point in each direction. But, as for NEMO v3. 6, the width of MPI overlapping areas is limited to one grid point in each direction.
Acknowledgements. The development of the multi-grid algorithm for passive tracers was supported by  EU MyOcean  and  My-Ocean2  projects, the  EMBRACE  project (European Union's  Seventh Framework Programme for research  under grant agreement no. 282672 ), then by the  EU Copernicus Marine Environment Monitoring Service (CMEMS) , the  French ANR SOBUMS  ( ANR-16-CE01-0014 ) and the CMEMS 22-GLO- HR  project. The development and the simulation shown in this paper have been produced on the ECMWF CCA Cray supercomputer. Financial support. This research has been supported by the  Eu FP7 projects Myocean  (grant no. 218812 ),  EMBRACE  (grant no. 282672 ) and  Myocean2 (grant no. 283367 ), the  Eu CMEMS service (Copernicus European Earth Observation Programme ) and the  French ANR   SOBUMS  project (grant no.
The key_crs key is added to this list to activate the multi-grid algorithm. The exact version of the model used to produce the results used in this paper is archived on Zenodo at https://doi. org/10. 5281/zenodo. 3615356 (Bricaud, 2020) , as are input data and scripts to run the model and produce the plots for all the simulations presented in this paper. Data availability. The model outputs used in this paper are available at https://doi. org/10. 5281/zenodo. 3547421 (Bricaud, 2019). Author contributions. CB, CC, GM and CE developed the model code. CB, JLS and JC designed the experiments. CB performed the simulations. CB and JLS prepared the manuscript with contributions from ML, JD and JC.
Conversely, OD 600 measurements were performed in a Microplate Reader spectrophotometer (TECAN) at a 600 nm wavelength (bandwidth 9 nm) with 15 flashes and 200 µL of culture per well. Briefly, three yeast clones were grown overnight in a raffinose containing medium (noninducing condition), diluted in triplicate to OD 600 = 0. 1 in a galactose containing medium (inducing condition), and grown for 24 h. OD 600 was then recorded immediately after the transfer (t 0 ) or after 24 h of growth (t 24 ), in the galactose medium, and the OD 600 ratio (t 24 /t 0 ) was calculated. Data were then normalized to the mean ratio of control cultures CEN. PK IMX672 (hereafter abbreviated as CENPK).
For morphometric measurements, the cell length-to-width ratio was calculated on digitalized images using the Fiji ImageJ software. For confocal microscopy observations, 1 mL of yeast cultures grown 24 h in galactose-containing medium were fixed in 4% formaldehyde (15 min, RT). To stain nuclei, DAPI (Sigma-Aldrich, 2 µg/mL final concentration) was added during cell fixation. After fixation, cells were washed twice with sterile water and then resuspended in 1 M sorbitol. Cell suspension was dropped onto a slide (Superfrost, BDH) and covered with a coverslip  (Noree and Sirinonthanawech, 2020). Cells were observed with a Leica SP5 confocal microscope using 63X HCX PL APO (NA 1. 4) oil-immersion objectives. Laser excitation line, power intensity, and emission range were chosen accordingly to fluorophore in different samples to minimize bleed-through.
Untargeted MS Data Analysis Raw files derived from HPLC-HRMS/MS runs were analyzed with a MudPIT protocol using the Proteome Discoverer 1. 4 software (Thermo Fisher Scientific) interfaced to a SEQUEST HT search engine (Thermo Fisher Scientific). All MS/MS data were searched against the UniProt S. cerevisiae database (3AUP000002311). Enzyme specificity was set to trypsin, and a maximum of one missed cleavage was allowed. The precursor and product ion mass tolerances were set to 10 ppm and 0. 2 Da, respectively. Oxidation of methionine was selected as variable modification, while 6-plex TMT at N-termini and at lysine residues, and carbamidomethylation of cysteines were set as fixed modification. Relative quantification was performed directly by the Proteome Discoverer software and only unique peptides were considered for quantification purposes.
Based on the Percolator algorithm, proteins were considered as correctly identified if at least two unique peptides were quantified with an individual q-value < 0. 05. Proteins were then grouped according to the principle of maximum parsimony. For quantification, the reporter mass tolerance was set to 20 ppm. The software detected the reporter ions  (126. 1, 127. 1, 128. 1, 129. 1, 130. 1, 131. 1 Th) , and performed the quantification of relative peptide abundance normalizing the intensity of each reporter ion to that of CENPK 1 sample (126. 1 Th). Normalized intensity values of proteins derived from Proteome Discoverer were exported in an Excel spreadsheet, and the matrix was arranged for further analysis. The final fold-change of a given protein was calculated as the mean value of the normalized ratios (TDP 1C/CENPK) of the three replicates.
PRM acquisition parameters were as follows: HCD fragmentation with normalized collision energy (NCE) of 27, AGC target of 5 × 10 5 ions, an IT of 120 ms, an isolation window of 1. 6 Th with an offset of 0. 4 Th. Two proteotypic peptides were selected for each protein included in the PRM analysis. The selected precursor-toproduct ion transitions, together with the retention time and the instrumental settings used in the acquisition method are reported in Supplementary Table  7. To ensure that no instrumental drift occurred during PRM analysis, a pooled sample prepared by mixing together equal amounts of peptide digests was injected and analyzed at the beginning and at the end of the analytical sequence. The Skyline software (version 3. 5. 0. 9191)  (MacLean et al. , 2010)  was used to assess the relative abundance of each peptide by calculating the total peak area under the curve (AUC) of the chromatographic peaks deriving from four precursor-to-product ion transitions recorded for target peptides.
Each AUC value in the different samples was normalized to the AUC of the corresponding peptide calculated in the pooled sample. Protein abundance was then calculated by averaging the normalized peptide values calculated for the two monitored peptides. Then protein abundance values were further normalized to the GAPDH abundance calculated for each sample to obtain relative protein abundance values to be compared between WT CENPK and the different TDP-43 yeast strains (with or without NCL). The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository  (Vizcaíno et al. , 2009)  with the dataset identifier PXD022432.
Computational Biology The protein-protein interaction network and gene ontology enrichment analysis was performed using the publicly available Cytoscape 3. 7 software  (Shannon et al. , 2003)  integrated with the STRING application  (Doncheva et al. , 2019). The STRING database uses a combination of prediction approaches integrated with other information (i. e. , neighborhood, transferred neighborhood, gene fusion, co-occurrence, coexpression, experimental data, databases, text mining). The protein network was built using default parameters (minimum required interaction score = 0. 4), including all active prediction parameters.
Statistical Analysis Data were analyzed using Prism 7 (GraphPad Software) and Microsoft Excel (Microsoft Corporation) software. Data are reported as mean ± standard error of the mean (SEM). The number of biological replicates (n) reported in figure legends indicates the number of cultures/transformations, or the number of transfection experiments performed in different days, for yeast and HEK293T cells, respectively. Depending on the experiment, the statistical analysis was carried out as indicated in each figure legend. A p-value < 0. 05 was considered statistically significant ( * p < 0. 05, * * , • p < 0. 01, * * * p < 0. 001, * * * * , p < 0. 0001).
We further monitored the growth rate in liquid cultures of yeast strains carrying a different copy number of the TDP-43 construct (1C, 2C, 3C, and MC) after 24 h of galactose induction compared to the CENPK control strain. As shown in Figure  1B , while a single TDP-43 copy did not significantly affect yeast growth, the detrimental effects of TDP-43 on the growth rate increased with increasing the transgene copy number. Collectively taken, the above data indicate that TDP-43 cytotoxicity in the genetically engineered yeast strains correlated with TDP-43 copy number, thus to the extent of TDP-43 expression during the first hours after galactose induction (Supplementary Figure  2 ). Taking advantage from the GFP tag in the chimeric TDP-43 construct, we also visualized its intracellular distribution in yeast cells by fluorescence microscopy.
Selection of data Radar images recorded over a whole year were scanned visually to detect patterns of clear and obvious coordination in the movement of seabird echoes. This process was facilitated by the use of a 15 min 'trail' in the radar settings, allowing for a good visualization of seabirds traces in time. Seabird traces are very well visible under calm weather conditions, and less so when sea state is such that it introduces wave echoes in the images. Data from May, July and from October to December were either not available, or not exploitable because of bad weather conditions. For the other months, each day of exploitable data was visualised and occurrences of coordinated flights were counted. For the detailed description of these flights, only the days of best data quality were chosen in each month.
Classification of tracks 16 926 tracks were constructed from three days of data (representative of echoes encountered in the dataset). All those tracks were superimposed on radar images, visualised, and manually attributed to one of the following five categories : «seabird» (3767 tracks), «boats» (83 tracks), «sea clutter» (6513 tracks), «rain clutter» (6461 tracks), «dense rainfalls» (102 tracks). A classification procedure was trained with 22 track properties, including their duration, the proportion of missing points (gap closing), the ratio of cumulative distance over absolute distance between starting and ending points of the track, and the mean, the standard deviation and the coefficient of variation of: track ground-speed, difference of azimuth between starting and ending points, averaged echo area, averaged distance to the central saturated zone edge, and mean, median and maximal intensity of the tracked echo.