dataesr/scientific-paragraphs-categorization huggingface dataset:
Meteorological Measurements We decided to instrument the two stations that were the most far apart (i. e. , Nadhour Rwag and Menzel Habib) with a meteorological station. However, it was not possible to install a meteorological station at Nadhour Rwag for security reasons. Therefore, we instrumented the Sidi Toui site, some ten kilometres away from Nadhour Rwag. Wind speed and direction were measured using a two-dimensional (2D) sonic anemometer (Wind-Sonic TM Gill Instruments Ltd. ) installed at 3 m above ground level (AGL). Data acquisition was performed using a CR200X data logger (Campbell ® Scientific Instruments, Shepshed, Loughborough, UK) powered by a solar panel, with a nominal data acquisition time of 5 min for all the parameters measured with a frequency of 0. 1 Hz. Considering the high variability of both wind speed and direction over a 5 min time step, the maximum and mean values of wind speed and the mean and standard deviation of wind direction measured over this interval were recorded.
Meteorological Measurements We decided to instrument the two stations that were the most far apart (i. e. , Nadhour Rwag and Menzel Habib) with a meteorological station. However, it was not possible to install a meteorological station at Nadhour Rwag for security reasons. Therefore, we instrumented the Sidi Toui site, some ten kilometres away from Nadhour Rwag. Wind speed and direction were measured using a two-dimensional (2D) sonic anemometer (WindSonic TM Gill Instruments Ltd. ) installed at 3 m above ground level (AGL). Data acquisition was performed using a CR200X data logger (Campbell ® Scientific Instruments, Shepshed, Loughborough, UK) powered by a solar panel, with a nominal data acquisition time of 5 min for all the parameters measured with a frequency of 0. 1 Hz. Considering the high variability of both wind speed and direction over a 5 min time step, the maximum and mean values of wind speed and the mean and standard deviation of wind direction measured over this interval were recorded.
Acknowledgments: The authors would like to thank  Houcine Khatteli , Director of the  Institut des Régions Arides (IRA ), for the constant support of IRA in all research related to wind erosion. The authors also would like to thank the  Office des Terres Domaniales (OTD) of Chammakh , and the  Cellule Territoriale de Vulgarisation (CTV)  of  Menzel Habib  who allowed the installation of the measurements in Chammakh and  Menzel Habib , respectively. Wind rose (Figure 6) was made adapting the R program by  Andy Clifton.
Microbiological analyses A total of 250 colonies were randomly selected from the agar plates used for enumeration of the S. spp strains and subjected to inter-delta fingerprinting in order to verify their identity. The inability of the S. pastorianus strain to grow adequately at 37 o C was verified in all cases; therefore, the temperature-based differentiation that was employed in the present study was accurate. In all cases, alcoholic fermentation was driven by Saccharomyces yeasts while the non-Saccharomyces kept a low population along the fermentation process (data not shown). Most interesting is to assess the results got on the proportion of S. pastorianus vs S. cerevisiae or bayanus as presented in Fig. 2. Whereas the percentage of S. pastorianus differed a bit from a PCR determination on 16 clones or from the differentiation of growth at 37°C and room temperature, we can observe that the S.
In many previous Lepidoptera toxicity tests, the leaf dip method has been utilised as the basis for exposure  (Morse et al. , 1986; Santos et al. , 2011; Hill and Foster, 2000). This protocol involves dipping a section of leaf into a chemical that is then fed to the larval stage. Survival is assessed usually at a single 48-hour time point. The leaf dip method has become widely adopted for lepidopteran toxicity testing, primarily because of its ease of use. However, this method is restricted in its application as a bioassay by factors such as leaf freshness. These effects mean that this approach may not be practical for longer term monitoring. Further, the current leaf dip methods generate data only suitable for ad hoc statistics at a single time point to calculate a no observed effect concentration (NOEC), lowest observed effect concentration (LOEC), or lethal concentrations (LCₓ).
Data analysis The survival data obtained from each experiment was analysed with the openGUTS software (http://openguts. info/). We used the GUTS reduced (RED) model which combines TK and TD damage dynamics to link external concentrations to the observed effect on survival over time. The GUTS-RED model is a relatively simple derivation of the classical GUTS TK/TD modelling framework. GUTS-RED includes relatively few parameters. As such, it is an efficient model for the analysis of survival in time datasets, because it requires a relatively limited amount of timedependent data. For example, no measurement of body residues are needed and no other information on the toxicokinetics is required. Indeed, the model can be parametrised by the survival data in time alone. GUTS-RED models provide a number of TK/TD parameters, including the no effect concentration (NEC) -a time independent metric that differs from traditional time bound toxicity metrics such as the LC 50 (n.
b. which can also be calculated from the GUTS TK/TD parameters for any timepoint). While both of these types of toxicity metric are valuable from a regulatory perspective, the NEC is considered as more ecotoxicologically relevant, because it does not change over time, unlike the LC 50 , the value of which will depend on "when" the response is measured  (Ockleford et al. , 2018). The openGUTS software provides analysis with both the stochastic death (SD) and individual tolerance (IT) approach. Briefly, both forms consider that individuals have a threshold value in terms of chemical concentration, and once this value exceeded, they die  (Jager et al. , 2011). The SD approach considers that all individuals have the same internal threshold concentration for effects survival (given by the no effect concentration, NEC) and that the death process is based on a statistical concept of hazard modelling, which considers that every individual has a certain chance to die (stochastic death) that increases with greater exposure to the chemical.
In addition to GUTS-RED-SD modelling, we also used classic doseresponse analysis with conventional probit analysis for the survival data measured at the 48 h and final 96-hour time points using SPSS v. 27  (IBM, 2020). Plots were generated using R version 4. 0. 1 and the 'Ecotox' package  (Hlina et al. , 2020). Effects on weight change were analysed by calculating the body weight change (BWC) index given as: BWC= (W96h-Wt0)/Wt0; where W96h is the weigh at the end of exposure (after 96 h) and Wt0 is the initial weight at time 0 (start of exposure). Body weight change was analysed using a one-way-ANOVA to assess significance and a post hoc Tukey's test to further identify any significant differences between treatment.
Experimental set up The developed bioassay was relatively easy to set up and to monitor. Compared to other widely used methods such as leaf dip assay, the plates containing the spiked diet used for the exposure are more labour intensive to establish. This increased effort is, however, countered by the ability to easily generate survival data over time through continuous monitoring as needed as input for the GUTS-RED-SD model. Assessment of larval survival for all individuals on a single plate can be done in little more than two minutes, meaning that within an experiment, replicate plates each with 12 individuals for a typical design using a control and six test concentrations can be checked in 12 min. In addition to measuring survival, at each time point assessed, the larvae can be taken from the diet surface, damped to remove any adhered diet or frass and weighed immediately.
Background mortality Optimised test systems for assessing toxicity either through traditional concentration response modelling or using TK/TD model such as GUTS-RED-SD should, where feasible, achieve a relatively low level of control (background) mortality. Within test guidelines, control survival of > 80% is usually seen as a suitable maximum for acceptable test performance. For the developed bioassay, 4th instar larva were selected for testing at day 12-16 post hatching, because in initial trials, larvae at this stage showed lower background mortality, compared with earlier instars and also an extended time between moults. To assess the rates of control survival in the assay, the data on survival over time of individuals added to the control treatment of each of the three bioassays can be used.
3 ), supported also by the model goodness of fits (see Supplementary material). The no effect concentration (NEC, i. e. median of the threshold distribution, m w , ± 95% CI) was estimated at 7. 4 (7. 0-9. 6), 15. 6 (15. 0-15. 9) and 3. 2 (0. 003-5. 83) mg/kg for test 1, 2 and 3 respectively. The final test reported a much lower NEC value. Notably, this test also recorded the highest background mortality and did not have an intermediate concentration between 0 and 10. 35 mg/kg. As the GUTS-RED-SD model parameters are derived from the data for the full exposure concentration range over time, they can be used to derive LC₅₀ values for any point in time in the experiment. Using the calibrated GUTS-RED-SD model, the LC₅₀s at 48 h were of 13. 1, 18. 7 and 8. 1 mg/Kg respectively in the three test replicates (Table  1 ).
Materials and methods Literature citations were retrieved from online databases (principally PubMed Central R ) using Boolean search procedures. Search terms included: ALS, Lou-Gehrig disease; motor neuron(e) disease; epidemiology, clusters, cases, and studies of neurodegenerative disease; intoxication/poisoning linked to hydrazine; hydrazine-related chemicals; cycads; mushrooms/fungi; gyromitrin; pesticides; agrochemicals; organic solvents; jet airplane fumes; ALS-related regional geographic information, populations, customs, practices, military service, and war. Focus was limited to conjugal, single-affected twin, young-onset, migrant, and clustered sALS cases, and their reported and potential association with chemicals of natural and synthetic origin.
Acknowledgments We thank  Dr. Andrew Miller , Mycologist and Director of the  Herbarium/Fungarium, University of Illinois  for information on the distribution of Gyromitra gigas in southeast France. We thank  Dr. Nicola Sitta  for information on the current food use of Gyromitra spp. in Piedmont, Italy. We thank  Kim Coverdale  of the  U. K. Wildlife Trust and David Earl of Lancashire County Council  for their efforts to identify fungal genera in the  Foxhill Nature Reserve. We thank  Mr. Alden Dirks ,  University of Michigan  for sharing his unpublished data on the gyromitrin content of U. S. samples of G. esculenta (detected) and G. gigas (not detected). We also thank the  Oregon Mycological Society  for their interest and information.
We also performed conventional Hi-C on DN3 and DP cells for genome-wide but lower-resolution assessment of any chromatin topology changes (overview of datasets in this study given in S3 Table ). Capture Hi-C was additionally performed on ESCs to provide a reference point and comparison with high-resolution conventional Hi-C data  [21]. As expected, the Capture Hi-C provided improved resolution at the target regions compared to previously generated Hi-C maps for thymocytes  [46] , even though the latter dataset had approximately 4-fold greater sequencing depth (Fig  1A ). Biological replicates were highly correlated (Spearman correlation coefficient �0. 91; S1B and S1C Fig;  S4 Table ) and were pooled for improved resolution in subsequent analyses. The resultant maps were of sufficient quality to resolve chromatin interactions between promoters and putative enhancers, marked with acetylation of lysine-27 on histone H3 (H3K27ac), and between CTCF-bound sites, further confirmed by 4C-seq analysis (Figs 1B-1D and S1D).
Visual inspection of the Capture Hi-C maps showed that thymocytes seem to have better defined TADs and more heterogeneous structures than ESCs (Figs 1B and S2-S7), in line with observations that ESC chromatin is generally more open and "plastic" than differentiated cells  [47]. Observing the contact strength decay of the Capture Hi-C datasets with increasing genomic distance reinforces these trends by showing greater variability of contact strength at a given genomic separation within thymocytes (Fig  1E ). Further, the cis-decay profiles for both Capture Hi-C and, at lower resolution, the genome-wide Hi-C, show that DP chromatin is globally more compact than the other cell types, in agreement with previous observations  [48]. Overall, these results validate the quality of the Capture Hi-C datasets for subsequent in-depth comparisons across cell types.
(E) Plot of median normalized interaction score against genomic separation from the pooled Capture Hi-C (top) and Hi-C (bottom) datasets, at 5 kb and 10 kb resolution, respectively, for ESCs (green), DN3 (blue), and DP (red) cells. For the Capture Hi-C data, shading denotes the interquartile range to indicate the variability of these cis-decay distributions. Source data can be found in S1 Data. DN3, double negative; DP, double positive; ESC, embryonic stem cellAU : AbbreviationlistshavebeencompiledforthoseusedinFigs1 À 5:Pleaseverifythatallentriesarecorrectlyabb. https://doi. org/10. 1371/journal. pbio. 3002424. g001 potentially more direct links between transcription and chromatin architecture. Firstly, a strong, conserved border at the Bcl6 promoter is altered in DP cells where the gene is highly expressed: An additional weak "stripe" extending the upstream spatial domain indicates the presence of a secondary broader border, adjacent to the major sharp boundary at the promoter (see rectangle and arrow in Figs 3A  and S7 ).
Analysis of the thymocyte Hi-C datasets, restricted to sufficiently long genes to accommodate for the resolution limit, also showed an overall positive correlation between intragene contact strength and RNA polymerase occupancy, as well as other epigenetic marks of gene activity, such as H3K27ac and monomethylation of lysine-4 on histone H3 (H3K4me1) (Fig  5A ). The RNA polymerase correlation was quantitatively very similar to what was observed in ESCs (Spearman correlation coefficient of 0. 60  (35) , compared to DN3 (0. 62) and DP (0. 61) in this study), and presence of the repressive histone modification, trimethylation of lysine-27 on histone H3 (H3K27me3), weakly anticorrelated with intragene contact strength. When clustering genes into 4 groups based on transcriptional output, metagene analysis further confirms the gradual increase of intragene compaction by augmenting gene expression (Fig  5B ).
Experimental design Capture Hi-C and Hi-C experiments were performed in biological duplicates on purified mouse thymocytes or cultured ESCs before and after manipulations such as CRISPRa for specific genes or homozygous deletion of candidate CTCF binding sites. Detailed below, biological replicates were first assessed for reproducibility, before pooling the data for higherresolution analysis. TAD analyses via computation of insulation scores was performed over multiple window sizes to ensure robustness of subsequent findings. 4C-seq experiments were performed as single experiments. qRT-PCR verification of CRISPRa gene induction was performed in 2 biological replicates, once for each induction experiment.
Hi-C analysis For convenience, Hi-C data were processed using the FAN-C suite of tools  [69] , entailing read mapping to the mm10 genome assembly, mating pairs, attribution to restriction fragments with filtering of self-ligated fragments and PCR duplicates, matrix balancing, and computing insulation scores. For compatibility with Capture Hi-C plots, the data for the relevant regions were extracted using the dump tool and visualized as for the Capture Hi-C matrices.
Capture Hi-C preprocessing To ensure that the captured regions were treated independently of the noncaptured regions, the datasets were processed with custom scripts, as entailed in  [70]. The initial steps are essentially the same as FAN-C, entailing read mapping with Bowtie  [67]  to the mm10 genome assembly, mating pairs, and attribution to restriction fragments with filtering of self-ligated fragments and PCR duplicates. Custom perl scripts filter the data to include only those where both ends of a paired read correspond to regions targeted by capture oligonucleotides and to bin these submatrices to fixed genomic bins. Each captured subregion was treated separately for matrix balancing with the Knight-Ruiz method  [11] ; R code kindly shared by Aleksandra Pękowska  [71]. All plots in this study visualizing (Capture)-HiC matrices alongside ChIP-seq tracks and insulation scores were generated by custom R scripts.
For Capture Hi-C, insulation score was computed with custom R scripts on the balanced submatrices at 5 kb (for individual replicates) and 2 kb (for pooled data) resolutions, using sliding windows of  15, 25, 35, 50, and   Biological replicates were compared by Spearman correlation coefficients of normalized interaction scores (for Capture Hi-C) and insulation scores (for Hi-C and Capture Hi-C). Principal component analysis was performed on the biological replicates of Capture Hi-C insulation scores using the prcomp function of R. To derive the differential interaction maps (e. g. , Fig 3A ) the normalized scores of one matrix were subtracted from the other, and the difference was expressed as a z-score: z ¼ diff À meanðdiff Þ standard devðdiff Þ Virtual 4C plots were derived as one row from the relevant Capture Hi-C normalized interaction score submatrix, applying smoothing with a running mean over 3 bins.
CHESS  [60]  was applied to each captured region independently after first converting the normalized submatrices to FAN-C objects. The sim function of CHESS was applied on sliding 40-bin (200 kb) windows, with steps of 1 bin (5 kb). The results of the submatrices were pooled, and regions with structural differences were filtered as having a signal-to-noise threshold >0. 5 and a z-score for ssim, computed for the grouped dataset, <-1, as per recommendations in the original article  [60].
Epigenomic datasets Publicly available thymocyte and ESC RNA-seq and ChIP-seq data were obtained from the Genome Expression Omnibus (see S13 Table ). Fastq files were mapped to mm10 with Bowtie2, and then normalized BigWig tracks (counts per million reads) were generated with DeepTools  [73]. The tracks were plotted in R using the same y-axis scale when comparing across tissues.
Acknowledgments We thank  Susan Chan  for sharing reagents and  Aleksandra Pękowska  for sharing R code for Hi-C matrix balancing. Sequencing was performed by the  IGBMC GenomEast platform , a member of the  France Ge  ´nomique consortium ( ANR-10-INBS-0009 ). This study was made possible because of the IGBMC flow cytometry and molecular biology platforms. We thank  PSMN  ( Pole Scientifique de Modelisation Numerique ) of the  ENS de Lyon  for computing resources.
Work in the TS lab was supported by funds from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( Starting Grant   678624 -CHROMTOPOLOGY ), the  ATIP-Avenir program , and the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame program  Investissements d'Avenir   ANR-10-IDEX-0002-02. YBZ was additionally supported by  la Region Grand Est. Work in the DJ lab was supported by funding from  Agence Nationale de la Recherche  ( ANR-18-CE12-0006-03 ;  ANR-18-CE45-0022-01 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
FFig 2. 2 Fig 2. TADs are largely conserved, with cell type-specific differences. (A) Violin plots for distributions of insulation scores computed on pooled Capture Hi-C datasets at 5 kb resolution, using a 75-kb (15 bin) window, on ESCs (green), DN3 (blue), and DP (red) cells. The more homogeneous ESC distribution is significantly different to DN3 (p = 1. 4 × 10 -5 ) and DP (p = 0. 0041) cells, but the 2 thymocyte distributions are not significantly different to each other (p = 0. 12; Kolmogorov-Smirnov test). (B) Scatter plots of insulation scores computed on pooled Capture Hi-C datasets at 5 kb resolution, using a 25-kb (5 bin; left) or 75-kb (15 bin;.
18. 12), adapter trimming (Cutadapt 1. 15), quality control (FastQC v0. 11. 5), mapping (BWA v0. 6. 2-r126) against B. thuringiensis 407 strain. This generated between 9 M and 18 M of uniquely mapped reads per sample and counts for 6530 genes. Differentially expressed genes (DEG) were determined using the R package "DESeq2" (v1. 30. 1) to estimate p-values and log2 fold-changes. DEG with padj ≤ 10 -2 and log2FC≤-2 or log2FC ≥ 2 were used for further analyses. Gene expression data are being deposited at the public repository Gene Expression Omnibus. Using the functional re-annotation database available in the laboratory (see details in Using Subtiwiki  [25] , KEGG orthology database  [26]  and COG letters, we retrieved genes from these functional categories: oxidative stress, iron homeostasis, DNA recombination and repair, necrotrophism, sporulation, and germination (see supplemental material, table  S4 , S5, S6, for necrotrophism, sporulation and germination, respectively).
ACKNOWLEDGEMENTS The authors are indebted to  Christophe Buisson  for insect rearing. This work has benefited from the facilities and expertise of the high throughput sequencing core facility of  I2BC (Centre de Recherche de Gif  -http://www. i2bc. paris-saclay. fr/) and the authors thank  Delphine Naquin  for primary analysis. We are grateful to  Tarek Toukabri ,  Agnès Réjasse  and  Sébastien Gélis-CyFlow Space  flow cytometer was funded by the  DIM Astrea (French regional program   Ast11 0137 ). HT was the recipient of a doctoral grant from the ministry of French higher education, delivered by the  Doctoral School ABIES at AgroParisTech-Université Paris Saclay. This work was supported by the  MICA department of INRAE (French National Research Institute for Agriculture, Food and Environment).
Acknowledgements The author would like to thank  Elias Ehrlich  for helpful comments on an earlier draft, as well as two reviewers and the recommender  Cédric Gaucherel  for their constructive suggestions to improve this manuscript. Preprint version 3 of this article has been peer-reviewed and recommended by  Peer Community In Ecology  (Gaucherel, 2023, https://doi. org/10. 24072/pci. ecology. 100527 ).
The study of morphological adaptations to different ecological parameters among fossil vertebrates has been an important challenge in recent decades. In this paper, we focus on the link between morphological traits and locomotor behavior such as terrestriality, fossoriality and arboreality (including gliding). One of the most diverse groups in which various locomotor habits are represented is rodents, occupying a wide range of ecological niches. This work highlights morphological variations in skulls and humerus in extant rodents with varying locomotion, to predict this parameter in the extinct species Diamantomys luederitzi (Early Miocene, Napak, Uganda). Linear discriminant analysis and phylogenetic flexible discriminant analysis are used to analyze datasets obtained via traditional morphometry (measurements) and geometric morphometrics (landmarks).