dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Filippo Capolino  ( Irvine, USA ) and  Changguo Wang  ( Harbin Institute of Technology, China ) for discussions. Y. C. is grateful for support by the  Alexander von Humboldt Foundation. This research was additionally funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany's Excellence Strategy via the Excellence Cluster "3D Matter Made to Order" (Grant  No. EXC-2082/1-390761711 ), which had also been supported by the  Carl Zeiss Foundation , through the  "Carl-Zeiss-Foundation-Focus@HEiKA ," by the  State of Baden-Württemberg , and by the  Karlsruhe Institute of Technology (KIT). The authors further acknowledge support by the  Helmholtz program  " Materials Systems Engineering " (MSE). M. K. is grateful for support by the  Carl Zeiss Foundation  (contract  ANR-17-EURE-0002 ).
To our knowledge, one only one study attempted to prevent the generation of seizures in epileptic patients using tDCS, and this approach was not effective  [4]. Here, thanks to a rare opportunity of simultaneous tDCS and intracranial EEG (iEEG) recordings, we investigated the effect of tDCS on focal epileptic seizure with direct and online video-iEEG recordings in a drug-resistant epileptic patient. With this rare dataset, our aim was to demonstrate that ictal epileptic discharges and clinical symptoms recorded during tDCS were significantly different and reduced as compared to those recorded without tDCS. MC is a female (19-year-old) suffering from refractory insuloopercular epilepsy. She gave informed consent to participate in this study (NCT03644732). Based on the presurgical evaluation, a video-iEEG recordings was planned to localize the epileptogenic zone (EZ) and the eloquent cortices around.
e. , just before the removal of the intracerebral electrodes. The fourday of video-iEEG recordings were visually analyzed by the two epileptologists blind to the tDCS experiment. Datasets of iEEG signals were sorted into ictal epileptic discharges without tDCS (IED) and ictal epileptic discharges during tDCS (IED*). Same-sized epochs (12 seconds) were defined considering the mean seizure duration. FFT analysis of iEEG signals was performed using bipolar montage. The mean amplitude and standard deviation were computed for each frequency band (D À q À a À b À g1 À g2) of the different epochs. Then, a Friedman statistical test, with posthoc pairwise Bonferroni corrections, was performed to compare all IED. The global percentage differences at the different frequency bands were calculated. In addition, the epileptogenicity of the brain structures was quantified using a semi-automatic calculation of the "epileptogenicity index"  [7]  (EI).
Brain Stimulation Thanks to this rare dataset, we provide here original evidence of both electrophysiological and clinical epilepsy symptoms reductions during a single tDCS session at low intensity and for few minutes duration. Our report is important because it offers both fundamental insights in the intra-cortical effects of tDCS on epileptic discharges and therapeutic perspectives. The most crucial feature of the epileptic seizure, i. e. , the low amplitude fast discharge, was completely cancelled and the most disabling and dangerous part of the clinical symptoms disappeared. Regarding the underlying mechanism of the tDCS-related seizure reduction, the main hypothesis would be the inhibition of the EZ i. e. , a reduction of cortical hyperexcitability and thus of the ability of cortical tissue to produce a high-energetic fast epileptic discharge.
Source data of (c-h) are provided in the Source Data file. model dX/dt = production -removal + noise, or mathematically dX/dt = G(X,t) + ffiffiffiffiffiffi 2σ p ξ, where ξ is white noise of amplitude σ. To define the production and removal terms that make up G(X,t), we used timescale separation, by assuming that at each time point the damage distribution among cells P(X,t) is a steady-state solution of the equation. The analytical solution for the steady-state is P(X,t) = e -U(X,t)/σ , where U(X,t) is a potential function defined by ∂U/∂X = -G(X,t). This is analogous to the Boltzmann distribution in statistical mechanics. Using the measured distribution of damage at different timepoints, P(X,t), we estimated U, differentiated it to provide G(X,t) and hence the production-removal terms in the model.
Blue lines and regions represent the means and 95% confidence intervals respectively from subsampling n = 6200 simulated cells. Source data (both simulated trajectories and statistics) are provided in the Source Data file. are growth arrested cells that cause inflammation, to infer a mechanism for senescent-cell accumulation  20. This mechanism, called the saturating removal (SR) model, is a stochastic differential equation with a production rate that rises linearly with age and a removal rate that saturates, so that high senescent cell levels slow their own removal. The removal terms in the SR and MP-SR models both saturate; the difference between them may stem from the type of damage: X is senescent cell abundance in the SR model, an extensive variable, whereas X is membrane integrity in the MP-SR model in units of potential, which is intensive and can enter the dynamics in terms of Boltzmann-like factors.
In the meantime, the chip was mounted and stabilized onto the objective stage of an inverted microscope (Nikon ECLIPSE Ti2, 100× oilimmersion objective, controlled with MetaMorph software) with temperature controlled at 37 °C. Phase-contrast and fluorescence (PI signal excitation, 546/12 nm; emission, 605/75 nm; CFP excitation 436/20 nm; emission 480/40 nm) images were automatically taken for up to 90 imaging positions every hour for up to 120 h. Focus was maintained by the hardware-based Perfect Focus System (PFS) from Nikon.
Acknowledgements We thank  Valery Krizhanovsky  for discussions,  Nicholas Stroustrup  for discussion and sharing data on shortening twilight,  David S. Glass  for general discussions and editing help. Warm thanks to  Chantal Lotton  for technical lab support. European Research Council (ERC)  grant agreement No  856487  (UA); Axa Foundation Longevity Chair (ABL, PC, INSERM  U1284 );  Bettencourt Schueller Foundation (FBS)  (ABL, PC, INSERM  U1284 ).
Sample Data were collected as part of an international research collaboration on gender roles (ucom2 017. wordp ress. com). Exclusion criteria, hypotheses, and analyses were preregistered (https://osf. io/7psh5/ ?view_only=a6ef2 88322 88414 0b788 04281 9d926c9; see the online supporting information for minor deviations from the preregistration). Because the question about leave intentions may be interpreted as only hypothetical in countries that do not offer leave, we preregistered excluding data from 12 countries that did not offer parental or paternity leave to fathers  (ILO, 2014). The present focus is on how gender norms influence a traditional gender division of labor and future child-rearing intentions between women and men in straight relationships. Lesbian and gay couples are more likely to engage in "degendered parenting," where personal choice, aptitude, and fairness rather than gender guide the division of labor  (Fulcher et al.
Country-Level Variables Indicators of different parental-leave policies (ILO, 2014) and gender equality (WEF, 2017) were collected from publicly available datasets. As preregistered, to maximize the degrees of freedom (by limiting the number of predictors in each model), we applied a data-driven approach to select which indicators of gender equality to include as predictors in Model 2 (see the online supporting information for more details). To address missing data, we imputed 10 datasets from a larger dataset of 63 country-level economic, political, and social indicators using Amelia II in R  (Honaker et al. , 2011;  for imputation code, see https://osf. io/9tshr/ ?view_only=becdb 1e590 a64ff ca6cc ef74f 131fea8). No multicollinearity was detected as indicated by VIF < 10 between hypothesized countrylevel variables in each model  (Kutner et al.
ACKNOWLEDGMENTS We want to thank  Jeremy Biesanz  for statistical advice. This research project was conceived following the award of a  SSHRC Insight Development Grant  to  Toni Schmader  ( 430-2018-00361 ). Additional funding included: a  SSHRC Insight Grant  awarded to  J. R. Steele  ( 435-2014-1247 ) and a  SSHRC doctoral fellowship  awarded to  C. Lapytskaia Aidy ; funding from the  Basic Research Program   at HSE University , RF, awarded to  Tatiana Ryabichenko  and  Maria Efremova ; a grant from the  Economic and Social Research Council  awarded to  Teri A. Kirby  ( ES/S00274X/1 ); funding from  State Research Agency  awarded to  Soledad de Lemus  ( PID2019-111549GB-I00/10. 13039/501100011033 ); funding from  Guangdong 13th-five Philosophy and Social Science Planning Project  ( GD20CXL06 ) +  National Natural Science Foundation of China  awarded to  XiaoXiao Zhang  ( 31600912 ); funding from the research infrastructure  HUME Lab Experimental Humanities Laboratory, Faculty of Arts ,  Masaryk University  awarded to  Eva Kundtová-Klocová ; two grants from the  Swiss National Science Foundation  awarded to  Tabea Hässler  ( P1ZHP1_184553 ) and  Léïla Eisner  ( P500PS_206546 ); funding from the  Center for Social Conflict and Cohesion Studies (ANID/ FONDAP  # 15130009 ) and the  Center for Intercultural and Indigenous Research (ANID/ Gender Gap in Leave Intentions FONDAP  # 15110006 ) awarded to  Roberto González ; a  SSHRC Postdoctoral Fellowship  ( 756-2017-0249 ) awarded to  William Hall ; funding awarded to  Denisa Fedakova  from the  Slovak Research and Development Agency  project ( APVV 20-0319 ); funding awarded to  Léïla Eisner  from the  Swiss National Science Foundation  ( P2LAP1_194987 ) and funding from  Canada Research Chairs  ( CRC 152583 ), the  Social Sciences and Humanities Research Council  ( Insight Grant   140649 ), and the  Ontario Ministry of Research and Innovation  ( Early Research Award   152655 ) awarded to  Elizabeth Page -Gould.
Scale is labeled "gender essentialist attitudes" in the dataset. 14679221, 0, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/pops. 12880 by Universidad De Granada, Wiley Online Library on [15/03/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
5B ). We verified that inhibition of TOR activity by treatment with AZD8055 led to similar effects (Fig. S8 ). Collectively, although no morphological sign of LR initiation in the pericycle could be observed upon TOR knockdown or inhibition, the auxin-induced transcriptional response was globally unchanged. The SLR/IAA14 protein is a central regulator of LR initiation that controls the auxin-dependent expression of genes in the pericycle essential for LR initiation  (Fukaki et al, 2002; Vanneste et al, 2005; Ramakrishna et al, 2019). We thus examined in detail how this set of IAA-induced genes behaved upon TOR knockdown. For this we took advantage of an existing dataset that profiles the response of the pericycle in similar conditions upon inhibition of slr-dependent auxin signalling in the pericycle  (Ramakrishna et al, 2019)  and identified 475 SLR-dependent genes responsive to auxin (Fig.
GC-MS data processing Raw GC-MS data files were first converted into an ANDI-MS universal file format for spectrum deconvolution and compound identification. Baseline correction, peak identification, retention time (RT) alignment and library matching with the reference collection of the Golm Metabolome Database (GMD, http://gmd. mpimp-golm. mpg. de/) were obtained using the TargetSearch R package from bioconductor  (Cuadros-Inostroza et al, 2009). Kovats retention indices used for library matching were calculated for deconvoluted mass spectra from measurements of an alkane mixture (Sigma-Aldrich, St. Louis, MO). The Shimadzu GCMS solutions software (v2. 72) interface was further used for manual curation of annotation of some metabolites versus authentic standards analysed under the above described analytical conditions.
Reads were mapped onto the Arabidopsis thaliana genome (TAIR10) and numbers of reads per transcripts computed using STAR (version 2. 5. 2b). All the subsequent analysis was done with R (www. r-project. org/) using the DESeq2 package  (Love et al, 2014). Differentially expressed genes were identified using a grouping variable that combines the Treatment (NPA, NPA_Est, IAA, IAA_ESt) and Genotype (col-0, UB10pro>>amiR-TOR) variables at log2 fold change >1 and false discovery rate < 0. 05. The procedure was inspired by http://master. bioconductor. org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rn aseqGene. html#time-course-experiments. The 1141 genes induced by IAA, were defined as the differentially expressed genes (NPA vs. IAA) common that were insensitive to the effect of Estradiol in col-0.
Acknowledgements We thank  A. Leibfreid  for critically reading the manuscript,  M. Burow  ( DynaMo Center, Dept. of Plant and Environmental Sciences, University of Copenhagen, Denmark ) for TOR-oe seeds,  S. Savaldi-Goldstein  ( Faculty of Biology, Technion Haifa, Israel ), for the p35S:HF-GFP-RPL18 line,  A. Bishopp  ( University of Nottingham, UK ) for the pARF19-5'UTR::mVenus line and  Lin Xu  ( Institute of Plant Physiology and Ecology, Shanghai, PRC ) for the gLBD16-GUS line and  Jazmín Reyes  for help with microscopy. The authors gratefully acknowledge the data storage service SDS@hd supported by the  Ministry of Science, Research and the Arts Baden-Württemberg (MWK) , the  COS Metabolomics Core Technology Platform  for GC/MS instrument access , the  Cluster of Excellence Cellular Networks of the University of Heidelberg (CellNetworks)  through grant  EcTOP6  " Metabolism and Development " and the  German Research Foundation (DFG)  through grant  INST 35/1314-1 FUGG  and  INST 35/1503-1 FUGG.
Analysis Methods Multi-Taper Method: We use a cross-spectral analysis, which is performed by using the multi-taper method as described by  Årthun et al. (2018) , to analyze if and where the MPI-GE can represent the sub-decadal time scales well. We perform the multi-tapering for all 100 ensemble members and each grid point to ascertain the dominant timescale (e. g. Årthun et al. , 2018; Ghil et al. , 2002). Doing so, the dominant timescale is given by the highest significant peak. The significance of spectral peaks is determined by comparison with a red noise spectrum with a 95% confidence interval around the red noise. Detrending and Bandpass-Filtering: We detrend all of our data in order to exclude the influence of global warming and other external forcings, therefore we use the Climate Data Operators (CDO) command "detrend"  (von Storch and Zwiers, 1984).
Acknowledgements. This work was funded by the  International Max Planck Research School on Earth System Modelling (IMPRS-ESM ; L. H. ), the  ClimXtreme project DecHeat  (Grant number  01LP1901F ; L. S. G. , W. A. M. ), the  European Union 's  Horizon Europe Framework Programme  under the  Marie Skłodowska-Curie grant  agreement ( 101064940 ; L. S. G. ), and the  JPI Oceans NextG-Climate Science-ROADMAP  project ( 01LP2002A ; D. M. ). We thank  Luis Kornblueh ,  Jürgen Kröger , and  Michael Botzet  for producing the MPI-GE simulations we used for our analysis,  Rohit Ghosh  for providing the MPI-GE ocean heat transport data we used for our analysis, and  Johann.
Lower QI prior to the manual correction due to disagreement among loci between replicates or inability to form a consensus at a locus can inform about potential degradation of eoDNA (Appendix S1: Supp_01 5), and manual correction would likely weaken such signal. Compared to a manually corrected dataset, only 8. 83% of usable samples (>7 consensus loci) in this study would be corrected with a majority (91. 7%) being altered at only one consensus locus leading to a theoretical maximum QI deviation of 0. 11-0. 13 in the few samples distributed over the RVs (Appendix S1: Supp_02). We also removed one locus (RHC108) in 2018 because its consistently low peak height and amplification failure indicated a laboratory error for that marker in that year (Appendix S1: Supp_03).
| Statistical analyses Following protocols proposed by  Zuur et al. (2016)  and  Harrison et al. (2018) , we developed an information theory-based frequentist multi-model inference pipeline to quantify and predict degradation of eoDNA, based on environmental factors, in seven steps. Steps 1-3 describe the iterative process of building a final global model fulfilling model assumptions (Figure  1a ). In Steps 4-7, we use this final global model to derive all subset models to achieve robust quantification of DNA-degrading factors. We then evaluate their fit, and select one or more best predictive model to inform sampling strategy (Figure  1b ). We split the data from the 25 RVs of the 10 roosts into a modelbuilding and a model-testing dataset. The model-building dataset contained 21 RVs from eight roosts where the logger was placed in the same room or floor where droppings were collected.
The test dataset contained four RVs from two roosts where the logger was placed on a different floor (Appendix S1: Supp_04). We used the generalized linear mixed model (glmm) function of the glmmTMB package  (Brooks et al. , 2017)  for all models. We standardized all continuous predictor variables in the model-building dataset by two standard deviations and centered at zero to correct for differing measuring units and remove correlation between interaction effects and their main effects  (Schielzeth, 2010)  and to allow for direct comparison of estimate strengths of continuous and binary categorical predictor within a model  (Gelman, 2008). We standardized and centered the test dataset based on the mean and the two standard deviations from above to ensure that the same unstandardized values in both datasets result in the same standardized values without informing the model building about the distribution of the test dataset.
All statistical analyses were performed in R 4. 1 (R Core Team, 2021) and R  Studio 1. 4. 1717 (RStudio Team, 2021). The complete modeling pipeline, including the raw data table, is available on Dryad as portable R-Project  (Naef et al. , 2023)  to facilitate reproducibility. The structure and comments of the main pipeline R-Script "01_eoDNAQuantificationandSimulation. R" together with the 18 documented custom functions further facilitate the application of our pipeline to other systems.
, 2017). For crossvalidation, we split the data into groups according to the 21 RVs included in the model-building dataset, reflecting our sampling strategy with distinct visits in distinct sites. Then, we excluded one RV and built a model with the remaining 20 RVs. With that model, we then predicted QI for the excluded RV and compared it to the observed QI. This was repeated 21 times until every RV was excluded once. As a measure of the predictive power under cross-validation, we used the mean of the 21 squared errors between observed mean QI and predicted mean QI per RV (MSE). Predictions were always made assuming zero random variance introduced by laboratory and sampling effects, to ensure MSE as most robust and generalizable model selection criterion considering only environmental predictors.
| Step 6: Test on new data As a final assessment of generalizability of our findings, we tested the predictive power of our best predictive model to predict QI on the test dataset, not used in any of the previous steps. Its four RVs (two new roosts, each sampled twice; Appendix S1: Supp_04) make up 16% of all 25 RVs, which roughly corresponds to the recommended 90% model building and 10% testing data  (Zuur et al. , 2016). We predicted QI for the test dataset, and calculated the MSE between the mean predicted and observed QI for each RV. In this process, we assumed no additional variance due to laboratory and sampling effects (setting random variance to zero). et al. , 2016)  to estimate the maximum number of exposure days per roost ("IdealDays") that still yielded a predicted QI of at least 0.
| eoDNA quality and its environment For model-building and test dataset combined, QI per sample ranged between 0 and 1 with an average of 0. 85. Per RV, the average sample QI varied between 0. 59 and 0. 98. For better comparison with other eoDNA degradation studies, we also provide the commonly used PCR success rate, ranging from 0 to 1 per sample with an average of 0. 9. The average PCR success rate per RV varied between 0. 65 and 1 (Appendix S1: Supp_11 2). Environmental variables differed greatly between RVs (Table  1 ; see Appendix S1: Supp_12 1 for a visual overview).
| Model evaluation All 19 top-model predictions of mean QI per RV (Figure  1b,  Step 5) came close to their mean observed values (Figure  4 ), with an AICcaveraged prediction MSE over all RVs of 0. 00125, which increased to 0. 00849 under cross-validation (Figure  4 , squares). The single best predictive model, including only "HMax," "Days," "TMax," "HottestDay," and "Sess" effects (Figure  2 , asterisks), could predict mean QI per RV with an MSE of 0. 00736 under cross-validation (Figure  4 , asterisks). When we applied this best-predictive model to the test dataset (two F I G U R E 2 Individual effect estimates (darkest shapes and black ticks) and 95% confidence intervals (CI) (lighter shapes and ticks), and their corrected CI for family-wise multiple testing (lightest shapes and ticks) of the 19 top models (ΔAICc < 6).
| Model simulations to inform sampling The median of the ideal sampling duration over all roosts (Step 7) to achieve a mean QI of 0. 95 per roost was 5. 5 days, rather than the median of 9 days in our empirical dataset. Roost-specific ideal exposure duration ranged from 2 to 8 days (Figure  4d ; Appendix S1: Supp_14). Achieving a mean QI of 0. 99 required reducing exposure duration to just 2-3 days, and was only possible in five roosts. In the other three roosts, no amount of reduction could sufficiently increase QI (Appendix S1: Supp_15). We detected an unexpectedly high proportion of droppings with zero QI (11. 6%) in RV "Thu21/2017/1," likely due to laboratory effects (Figure  4a , gap between full vs hollow circle). Therefore, we excluded this one roost visit in the calculations for ideal exposure duration for "Thu21.
| Case study In agreement with the published literature (e. g. ,  Eichmiller et al. , 2016; Murphy et al. , 2007; Vili et al. , 2013; Walker et al. , 2019) , our model identified the negative effects of high humidity, temperature, and long exposure duration on eoDNA quality. Notably, we also revealed the importance of timing and succession of such environmental conditions, captured by the positive effect of a late occurrence of the hottest day. These effects also remained stable in and order among the top models, even after correction for multiple testing, underlining the qualitative and quantitative robustness of our findings. Under cross-validation, when random effect variance was set to zero, QI could still be predicted accurately. However, the model was only able to accurately predict QI for one of the four additional RVs of the test dataset (Figure  4 , Test data, gray plus vs.
red asterisks). This reduced prediction accuracy in the test dataset was likely because the test dataset did not only introduce completely new sites but also further logger distance. The samples I G U E Single predicted marginal fixed effects on QI the 19 top models (ΔAICc < 6) and their AICc averages. The sorted effects from most negative (red) to most positive (blue) are plotted on the same y-scale. Histograms on the x-axis indicate the distribution of the unstandardized predictors with their range in bold over the 21 roost-visits, to assist interpretation of confidence intervals. were pooled across multiple floors with varying environmental conditions, but only one logger was used to model them while the model-building dataset pooling was restricted to the same room or floor. This likely introduced misrepresentation of logged environmental data for a majority of pooled eoDNA within a RV, potentially reducing prediction accuracy.