dataesr/scientific-paragraphs-categorization huggingface dataset:
It is only after this naive detection that the SV are annotated as possible TEs or not. This approach allows to run TrEMOLO using different TE databases and also to detect new TE families. TrEMOLO accuracy in TE identification and the correction system used for TSD detection allow predicting the insertion site within a 2-base pair window. Therefore, TrEMOLO is currently one of the most efficient tools for TE detection. Moreover, TrEMOLO allows the estimation of the insertion frequency for each detected TE copy with high accuracy (when using data with sequencing depth >50x). Importantly, TrEMOLO performance is weakly influenced by the assembly quality and sequencing depth variations (up to a certain point). Therefore, it can be used for different projects (e. g. pooled DNA sequencing, population analysis, human genetics, tumor analysis) that require the fast and precise identification of TE insertions, even at very low frequency within a sample.
Simulated datasets A previously assembled reference D. melanogaster genome, called G0-F100 (  13  For the INSIDER TE frequency estimation, the sb1 and sb2 subsamples were used together with a new sb4 subsample simulated with a depth of 1X and minimum to maximum length of 10 to 50kb to obtain IS1 (10X sb1 + 45X sb2 + 1X sb4), IS2 (10X sb1 + 40X sb2 + 1X sb4) and IS3 (10X sb1 + 30X sb2 + 1X sb4).
Genome assembly Raw ONT reads for the G73-SRE sample were QC checked using Nanoplot v1. 10. 2 (https://github. com/wdecoster/NanoPlot) for sequencing run statistics. Reads with QC <7 were removed by the sequence provider (Montpellier Genomix, Montpellier, France) before QC. For all ONT sequencing data, the genome was assembled as previously published, except that a newer version of Flye (v2. 9) was used  (13).
Datasets The D. melanogaster reference genome (dm6) and annotations (Release 32) were downloaded from FlyBase (https://flybase. org/). The LR dataset of the D. melanogaster laboratory G0-F100 and G73 lines previously published  (Mohamed et al. , 2020 ) is available at ENA (https://www. ebi. ac. uk/ena) under the accession number ERP122844.
Robustness of the frequency computation dataset Data from the G73 assembly (183x; Mohamed et al, 2020) were degraded in different datasets using FiltLong  (41)  and the following options: -t 19000000000 --min_length 6000, -t 15000000000, -t 12000000000, -t 10000000000, -t 5000000000, -t 2000000000).
TE identification: to compare the reference genome and a new assembly, TrEMOLO uses the same methodology asRaGOO (v1. 1; 22)  up to the SV identification step. Then, /github. com/bergmanlab/transposons) with BLASTN (2. 9. 0) (--outfmt 6) (43). The output file is filtered based on the size and identity percentages listed in parameters. DNA fragments that appear to be part of the same sequence are grouped. For INSERTION, Repeat_expansion, Tandem_expansion, sequences are retrieved from the query (new assembly). For DELETION, Repeat_contraction, Tandem_contraction, sequences are recovered from the reference (Fig. S1) OUTSIDER TE identification: the LRs and the genome assembled from them are aligned using minimap2 (29)  and SVs are identified using Sniffles (44) / SVIM (45). For Sniffles, the reads covering the SV are recovered from the SAM/BAM file using as anchor point the Sniffles provided position and a 30bp-window with SAMtools (46).
For SVIM, only the support reads provided by SVIM are recovered. The SV reads and sequences are compared to the TE database, and filtered as done for INSIDER TEs, but only the best support reads are kept among those provided by Sniffles/SVIM or the SAM parsing (Fig. S1). TSD detection: both TE flanking sequences are recovered to check whether there are duplicate sequences at the TE junction (see Results for more details). For an OUTSIDER TE, if TrEMOLO detects duplicated sequences farther than the initially identified TE junctions, and if they fit to the TSD, then the OUTSIDER TE position is corrected accordingly. TE frequency estimation: two different approaches are used to estimate the INSIDER and OUTSIDER TE frequencies in the samples. For INSIDER TEs, from the original BAM file only reads aligned to the putative TE positions (with a 100bp window 5' and 3') are retained using SAMtools view and the -F 2048 -L option.
cycles of 95 °C for 30 s and 60 °C for 1 min (ramp rate settings of 2°C/sec), followed by a droplet stabilization step at 98 °C for 10 min, and a final hold step at 4 °C. Droplet measurement was performed on a QX200 Droplet Reader (Bio-Rad Laboratories) and data analyzed with the QuantaSoft software (Bio-Rad Laboratories). Two different fluorescent signals were measured: i) amplification of the TE inserted in a given locus sequence (TE in ) or amplification of the same locus without TE (TE out ) in the FAM channel, and ii) amplification of the RpL32 housekeeping gene in the HEX channel. The proportion of TE in or TE out to Rpl32 was calculated as follows: TE in /Rpl32 or TE out /Rpl32. Each value was expressed as copies/µl. Frequency was calculated with the following formula: Frequency in %= (TE in /Rpl32x100) / (TE in /Rpl32 + TE out /Rpl3).
Acknowledgments: We acknowledge the ISO 9001 certified IRD itrop HPC (member of the  South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported in this paper(URLs: https://bioinfo. ird. fr/ and http://www. southgreen. fr);the Genotoul platform (https://genotoul. fr/) and https://www. france-bioinformatique. fr/ for providing calculation time on their servers.
INSIDER TE identification: to compare the reference genome and a new assembly, TrEMOLO uses the same methodology asRaGOO (v1. 1; 22)  up to the SV identification step. Then, /github. com/bergmanlab/transposons) with BLASTN (2. 9. 0) (--outfmt 6) (43). The output file is filtered based on the size and identity percentages listed in parameters. DNA fragments that appear to be part of the same sequence are grouped. For INSERTION, Repeat_expansion, Tandem_expansion, sequences are retrieved from the query (new assembly). For DELETION, Repeat_contraction, Tandem_contraction, sequences are recovered from the reference (Fig. S1) OUTSIDER TE identification: the LRs and the genome assembled from them are aligned using minimap2 (29)  and SVs are identified using Sniffles (44) / SVIM (45). For Sniffles, the reads covering the SV are recovered from the SAM/BAM file using as anchor point the Sniffles provided position and a 30bp-window with SAMtools (46).
98 Ma distal ignimbrites; 8: Mid-Late Paleozoic sedimentary/volcanic basement; 9: Jurassic volcano sedimentary cover (limestones, siliciclastics), (a) pink areas: Mid-Late Paleozoic crystalline basement (igneous and metamorphic). Generated and edited in Adobe Illustrator after  21  , available https:// www. resea rchga te. net/ publi cation/ 27208 7592_ Geolo gical_ map_ of_ the_ Elbrus_ neovo lcanic_ center_ Great er_ Cauca sus_ scale_ 11000 00_ Editi on_ of_ 2011; (b) geophysical microseismic sounding data along shown cross section across Elbrus (after  23  ) potentially showing vertical structures of an extended magmatic system. Red-blue colors indicate deviation from the regional Vs model (±8%). (c) Position of Elbrus in the Caucasus (generated via GoogleEarth software).
Several depth profiles indicated in-run age variability where model ages significantly increased with depth. To capture this age variability, all analyses were subdivided into three blocks, each block corresponding to an analysis depth of ~ 1 µm determined from measuring crater depth with a Bruker DektakXT stylus profilometer. For depth profiles where the MSWD calculated for three blocks exceeded the expected value of  51  , three separate ages are reported; for all other analyses, only one age averaging the entire ~ 3 µm deep analysis crater is computed (Fig. 3 ). Whole rock samples were studied in thin section for phenocryst assemblage and proportion of crystals, by XRF and ICPMS for their major and trace elements and by laser fluorination at the University of Oregon, all analytical data is presented in the Supplementary Tables S1-S4.
Numerical methods. Modeling methods included fine-tuning of the 2D dike and sill intrusion thermochemical model of Melnik et al. 41  , which utilizes the use of zircon growth and dissolution software published in  9. The modeling effort computes temperature-melt percentage-time histories and growth and dissolution histories of individual model zircon crystals in ~ 10 5 points across the entire magmatic system. Data is then extracted and post-processed. The goal of the modeling is to find the best conditions that match the Elbrus zircon record. The employed program  41  was adjusted to Elbrus initial and boundary conditions. The model is based on the solution of heat advection-diffusion equation accounting for latent heat release during magma crystallization, and implementing simplified T-X phase diagrams for magma and host rocks.
PDS alters transcription in cultured primary neurons Previously, we reported that in neurons, stabilizing G4s with PDS promotes changes in chromatin structure (15) and reduces transcription of several genes that contain G4-DNA motifs  (15, 38, 39). Therefore, we decided to investigate whether and how stabilizing G4-DNA with PDS would affect the whole transcriptome expression in primary cultured neurons. We hypothesized that comparing transcription in neurons treated with PDS or vehicle would reveal physiologically relevant G4-dependent molecular mechanisms. Primary mouse cortical neurons were cultured for 14 days (14 days in vitro [DIV]) until they were synaptically established. Neurons were then treated with PDS or vehicle (overnight) and analyzed by RNA-Seq. With QIAGEN RNA-Seq, we found that high-quality data were obtained for all samples (Supplemental Data 1 and 2).
Principal component analysis (PCA) of RNA-Seq results revealed that the differences between control and PDS-treated samples were well pronounced (Fig. 1A ). PDS-treated samples showed greater variability in clustering compared with control, which is commonly observed in RNA-Seq analyses. By RNA-Seq, 901 genes were differentially expressed in neurons treated with PDS out of a total of 18,745 genes with measured expression. Of those, 505 genes were downregulated and 396 genes were upregulated (Fig. 1, B-E ). The upregulated genes tended to have a higher fold change compared with downregulated genes (Fig. 1, C-E ); however, the number of genes downregulated was higher (Fig. 1, B  and C ). Since PCA of RNA-Seq data demonstrated separation of one individual sample in the PDS group, we removed the separated dataset and reran our analyses, which did not considerably affect the results (Fig.
PDS alters neuron-specific and neuron-nonspecific gene networks RNA-Seq data were analyzed in the context of pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database  (43, 44)  and gene ontologies from the Gene Ontology (GO) Consortium database  (45). The underlying pathway topologies, including genes and their directional interactions, were obtained from the KEGG database using iPathwayGuide (Advaita Bioinformatics). In our analyses, 1583 GO terms were found to be significantly enriched in PDStreated samples. Among them, neuron-specific and neuronnonspecific networks were enriched (Fig. 2, A-C ). For example, nervous system development, neuronal differentiation, neurogenesis, and learning and memory networks were enriched. Cellular senescence, cell death, re-entry into mitotic cell cycle, DDR, and negative and positive regulation of autophagosome networks were affected (Fig.
3E ). Since PCA of RNA-Seq data showed separation of one individual sample in the PDS group, we again removed the separated dataset and performed our iPathwayGuide analyses. Removing the dataset did not have a considerable effect on our findings (Fig. S2 ). In particular, the same 16 genes, including Pirh2, were upregulated and one gene was downregulated with small changes in the p and fold-change values (Fig. S2 ). Overall, the p53 signaling pathway may be affected by PDS-induced DNA damage and/or altered transcription of specific genes, triggering positive and negative feedback loops, which enhance and/or attenuate the p53 protein and integrate its functions with other signaling pathways. We then used the QFS mapper (QGRS) to computationally analyze the Pirh2 gene and its promoter in several species for the presence of putative G4-DNA motifs (Fig.
PDS effects on neuronal transcription depend on neuronal development stage While analyzing our data from 14 DIV neurons treated with PDS, we noticed differences compared with our earlier data generated with four DIV neurons (39). In synaptically developed 14 DIV neurons, the most affected pathway was the neuroactive ligand-receptor interaction pathway (KEGG: 04080) that included synaptic receptor genes (Fig. 3A ). Many of these genes were not expressed in cultured neurons soon after plating, and thus, the data generated with freshly plated neurons would be expected to be different. genes were identified out of a total of 18,745 genes with measured expression. The thresholds used to select the DE genes were 0. 6 for expression change and 0. 05 for significance. C, the volcano plot illustrates 901 DE genes.
We, therefore, tested if there was a difference in the expression of Atg7 and Brca1 in 14 DIV and four DIV neurons measured by PCR using Tbp as the internal control that we discovered earlier (39). The Tbp gene was not affected by PDS in the current RNA-Seq dataset either (Supplemental Data 1). About 14 DIV and four DIV neurons were treated with PDS, and then mRNA was extracted and analyzed (Fig. S4 ). Levels of both Atg7 and Brca1 went down in four DIV neurons treated with PDS, whereas 14 DIV neuronal cells only exhibited a nonsignificant trend (Fig. S4 ). Interestingly, PDS robustly upregulated Pirh2 Pirh2-dependent DNA damage in neurons caused by pyridostatin in both 14 DIV and four DIV neurons, indicating that the p53 pathway is altered by PDS in developed and freshly plated cells. We conclude that freshly plated developing neurons and synaptically established cells respond to PDS differently, at least in part, likely reflecting developmental differences in transcriptional activity.
Having the red fluorescent protein mApple fused to the 53BP1trunc construct makes the Apple-53BP1trunc chimera a useful DNA DSB reporter in living cells  (58). Primary neurons were cotransfected with Apple-53BP1trunc, GFP, and BFP, as control, or with Apple-53BP1trunc, GFP, and BFP-Pirh2. Neurons were imaged 24 and 36 h after transfection, and Apple-53BP1trunc 0 s distribution was analyzed in the individual neurons. We discovered that BFP-Pirh2-overexpressing neurons exhibited higher levels of DNA damage than control neurons (Fig. 7, C  and D ). These data indicate that elevated Pirh2 promotes the formation of DNA DSBs in neurons. DNA DSBs are the most dangerous of the many types of DNA damage as entire fragments of DNA can be lost if the DNA is not effectively and correctly repaired, and their accumulation eventually leads to cell death.
Fluorescence microscopy and image analysis Living or fixed cells in 24-well plates were imaged using the automated EVOS microscopy system (ThermoFisher Scientific). The microscope was set to image 5% of the center of the well area with the 20× objective and to capture the red fluorescent protein, GFP, and DAPI signal. Neurons were also imaged with the Leica DMi8 confocal with the 63× ACS APO 63×/1. 30 oil lens. Images were subsequently analyzed with the ImageJ/Fiji software. Colocalization of fluorescent signals was measured with the EzColocalization plugin for ImageJ/Fiji  (92). γH2A. X fluorescence was analyzed by the puncta index, which is the standard deviation of the intensities measured among pixels within a region of interest. Low puncta index represents diffuse localization, whereas a high puncta index represents punctate localization.
Neurons that died during the imaging interval were assigned a survival time. These events were transformed into log values and plotted in risk of death curves and analyzed for statistical significance (log-rank test). JMP software (SAS Institute) was used to analyze data and generate survival curves  (38). Pirh2-dependent DNA damage in neurons caused by pyridostatin Immunocytochemistry Cultured primary neurons from rat or mouse embryos of both sexes were grown on coverslips. Neurons were washed with PBS and fixed with 4% paraformaldehyde-PBS solution for 15 min at room temperature. Cells were rinsed and incubated with 0. 1% Triton X-100-PBS solution for 5 min at room temperature to permeabilize membranes. Cells were then rinsed with PBS and incubated with 1% bovine serum albumin-PBS solution for 1 h at room temperature.
Acknowledgments-We thank  Zheng Tan  for allowing the use the G4P protein (64) in our studies. We thank members of the  A. S. T. laboratory  and the  BRAINS laboratory  for useful discussions. We also thank  Megan Taylor  (bioinformatics scientist, genomic services, QIAGEN) and  Suresh Kumar  (bioinformatics scientist,  Advaita Bioinformatics ) for helpful discussions. Nur Compan  and  Danielle Guillory  provided administrative assistance. The Leica DMi8 confocal microscope is supported in part by the  Huffington Foundation.
The data were analyzed to answer the three broad questions listed above. The first question concerning the extent of speeding was tested by analyzing time on task, the distribution of item omissions, average RTs at the task level, and RTs at the item level, including RT distributions. The second question concerning the mechanisms by which time pressure can affect accuracy was tested by analyzing accuracy, confidence, and strategy use at the task and item level (the effect of time pressure on anxiety and motivation was not tested in this study) and by modeling accuracy conditional on RTs. The third question concerning individual differences was tested by analyzing the linear and nonlinear effects of ability, working memory and NFC on accuracy, and RTs at the task level, as well as their effects on modulation of RTs at the item level.
Data Analysis Reliability was estimated based on internal consistency using Cronbach's alpha coefficients; these coefficients were compared across conditions using the Feldt test  (Feldt 1969 ; computed using package cocron for R: Diedenhofen and Musch 2015; R Core Team 2023). The effect of time pressure on average performance was analyzed using analyses of variance (ANOVAs), followed by post hoc comparisons between the three conditions using Tukey's HSD correction. The role of individual differences at the task level was primarily tested using general additive models (GAMs; see  Wood 2017) , which are similar to linear regressions, extended to include non-linear effects of predictors. Statistical tests in GAM analyses are reported based on approximate p-values (see Wood 2017), along with effective degrees of freedom (edf : effective degrees of freedom equal to 1 reflect a linear relationship between predictor and dependent variable; values greater than 1 reflect a more complex trajectory).
Data at the item level were analyzed using general additive mixed models (GAMMs), which include random effects for each participant, allowing for data analysis at the item level (see  Gonthier and Roulin 2020; Perret and Dauvier 2018). GAM and GAMM analyses were performed using the mgcv package  (Wood 2017; version 1. 8-42)  for R (R Core Team; version 4. 2. 1). All dependent variables were modeled assuming a gaussian distribution (inference based on F-tests), except for accuracy at the item level, which used a binomial distribution (inference based on χ 2 ); log-transforming RTs did not change the pattern of results, so the data are presented without transformation to make interpretation easier. Subjectlevel random effects were modeled as random intercepts; models were fit using restricted maximum likelihood; basis dimension was adjusted so as to be sufficient for all analyses; smooths were modeled with the default classes (see also  Gonthier and Roulin 2020).
Results The data for this experiment and sample R code are available at https://osf. io/9rtxf/ (uploaded 12 June 2023). A preliminary analysis showed that sample composition was equivalent in the three conditions: there were no significant differences between conditions in terms of sex: χ 2 (2) = 2. 82, p = 0. 244; age: F(2, 287) = 1. 47, p = 0. 231, η 2 p = 0. 01; WMC: F(2, 287) = 0. 02, p = 0. 980, η 2 p = 0. 00; or NFC: F(2, 287) = 0. 20, p = 0. 822, η 2 p = 0. 00. Descriptive statistics for the APM at the task level are available in Table  1. Internal consistency was acceptable overall, especially in the Unlimited time and 20 min conditions. There was a clear pattern of decreasing reliability with a high time pressure in the 10 min condition for both accuracy and RTs, with reliability below the conventional threshold of.
Effects of Time Pressure at the Item Level For the four variables showing a significant effect of time pressure at the task level, data at the item level were analyzed with GAMMs (including the main effect of item position, as a function of time pressure). The results are displayed in Figure  3. matching and response elimination with increasing time pressure (see Figure  2 ). For constructive matching, the effect of time pressure was significant, with follow-up comparisons showing a significant difference between the Unlimited time condition and the 10 min condition. The effect of time pressure did not reach significance for response elimination or the strategy use score. Overall, these results are not compatible with participants turning from constructive matching to response elimination under time pressure: with a descriptive decrease in the reported use of both strategies, time pressure seemed to mostly induce an increase in random guessing.
Effects of Time Pressure at the Item Level For the four variables showing a significant effect of time pressure at the task level, data at the item level were analyzed with GAMMs (including the main effect of item position, as a function of time pressure). The results are displayed in Figure  3. For accuracy, confidence in one's answer, and constructive matching, there was a progressive decrease throughout the task as items became more difficult; this decrease was significant for all measures in all experimental conditions (all ps <. 001). For accuracy, although the effect of time pressure was significant on average (as detailed in the previous section), there was no significant interaction between experimental condition and item position (all ps > 0. 238). In other words, the effect of time pressure was homogeneous across all items: increasing time pressure was detrimental to accuracy, for all items to the same extent (see Figure  3 ).
Limitations and Future Directions Three major questions were not explored in the present study. First, it would be interesting for future research to provide a more detailed look into the qualitative changes of processing that can occur under time pressure. Drift diffusion modeling is rarely used for this type of task and could be an interesting option  (Frischkorn and Schubert 2018; Kang et al. 2022; Lerche et al. 2020; van der Maas et al. 2011) , although this would not be straightforward and a larger dataset than was collected here would presumably be needed. Alternatively, verbal reports may be a good option for that purpose and could yield more insight into variability in time management strategies. Second, it would be worth examining the effects of time pressure on test anxiety and test motivation, as potential mediators of the detrimental effect of time pressure on performance.