dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  French government  (" Investissements d'Avenir "  ANR-10-LABX-54   MEMOLIFE ,  ANR-11-IDEX-0001-02 PSL Research University ,  ANR-11-LABX-0011-01 ), the  European Research Council  ( ERC Advanced Grant  # 693021  to PP), the  French Academy of Sciences  ( Prix Lamonica  to PP; fellowship to MT), the  Chinese Scholars Council  ( CSC fellowship  to MT) and  Sorbonne Université  (fellowship to LP). We thank  Shujia Zhu  ( Chinese Academy of Sciences, Institute of Neuroscience, Shanghai ) for plasmids of strep-tagged CTD-deleted GluN2 constructs (Western blots on HEK cells) and  Laetitia Mony  ( IBENS, Paris ) for comments on the paper.
Abstract. How convection couples to mesoscale vertical motion and what determines these motions is poorly understood. This study diagnoses profiles of area-averaged mesoscale divergence from measurements of horizontal winds collected by an extensive upper-air sounding network of a recent campaign over the western tropical North Atlantic, the Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC 4 A) campaign. Observed area-averaged divergence amplitudes scale approximately inversely with areaequivalent radius. This functional dependence is also confirmed in reanalysis data and a global, freely evolving simulation run at 2. 5 km horizontal resolution. Based on the numerical data it is demonstrated that the energy spectra of inertia gravity waves can explain the scaling of divergence amplitudes with area.
Observational data To discuss potential links between vertical profiles of mesoscale divergence and convection, we show images from the Geostationary Operational Environmental Satellite-16 (GOES-16), which is centered on 75. 2 • W. Infrared (10. 3 µm, Band 13) images of the GOES-16 Advanced Baseline Imager (ABI;  Schmit et al. , 2017)  are provided by NASA's Worldview application (https://worldview. earthdata. nasa. gov, last access: 24 November 2020). Information on the cloud bottom and top levels derived from the GOES-16 ABI (NOAA, 2018) is distributed by the Langley SATCORPS group of NASA. Diagnostics of mesoscale divergence and gravity wave characteristics are based on measurements of surfacelaunched radiosondes, and dropsondes released from aircraft, taken during the EUREC 4 A campaign in January and February 2020.
The experimental design of EUREC 4 A involved 85 dropsonde circles from aircraft flights and regular releases of surface-launched radiosondes from five platforms. A combined data set with 895 dropsonde soundings from the German Aerospace Center's HALO aircraft and 322 dropsonde soundings from NOAA's WP-3D will be made available as part of the Joint dropsonde-Observations of the Atmosphere in tropical North atlaNtic large-scale Environments (JOANNE) data set described elsewhere. We use the qualitycontrolled Level 2 data with a vertical resolution of 10 m. HALO's circle typically consisted of 12 sondes and is centered at ∼ 13. 3 • N, -57. 7 • E, with a diameter of 200 km, as shown in Fig. 1b. The WP-3D circles, of which there are only 14, were mostly flown further east at varying locations within 11-16 • N, 59-50 • W.
The initial altitude interval [h 0 s , h 0 e ] is [0, 18] km. The degree of polarization d is next computed for the initial window and all possible combinations of shorter windows contained in it. The window defined by [t s , t e ], [h s , h e ], where t 0 s ≤ t s < t e ≤ t 0 e and h 0 s ≤ h s < h e ≤ h 0 e , with the greatest total number of data points and a polarization d ≥ 0. 8 is saved for subsequent analysis. If no window inside [t 0 s , t 0 e ], [h 0 s , h 0 e ] with d ≥ 0. 8 can be found, then the peak is discarded. Otherwise and are computed using the Stokes parameters. The angle of the major axis of the ellipse counterclockwise from the eastward direction is the direction of wave propagation, but with an ambiguity of 180 •. To resolve this ambiguity, we also compute this angle as in Evan and Alexander (2008): T +90 = tan -1 u T +90 v T +90 , (1) where T +90 is the value of the temperature perturbation after shifting the phase by +90 • using a Hilbert transform.
Normal-mode decomposition of numerical data We derive global wave spectra as well as vertical profiles of regionally averaged divergence and vorticity from the ERA5 reanalysis and a numerical simulation. For both data sets we analyze 3-hourly horizontal winds between 20 January-29 February 2020, matching the EUREC 4 A period. The hodograph analysis is not applied to the numerical data, because we found the vertical resolution to be too coarse. ERA5 is produced and made publicly available by the European Centre for Medium Range Weather Forecasts (ECMWF). The original data with a horizontal resolution of ∼ 30 km are stored on 137 hybrid sigma/pressure levels from the surface up to an altitude of 80 km (0. 01 hPa). The numerical simulation run at 2. 5 km horizontal resolution is performed with the ICON model  (Zängl et al.
The data at each time step are then subjected to a normalmode function decomposition, using the freely available software package MODES, a detailed description of which is given in  Žagar et al. (2015). The modal decomposition projects the three-dimensional fields of geopotential height and horizontal wind onto an orthogonal set of basis functions. These functions are solutions to the Rossby and inertia-gravity wave (IG) dispersion relationships, respectively  (Kasahara, 1976). Since the former are vorticitydominated, we label them as ROT. The orthogonality of the basis functions allows filtering out specific wave modes, including the option of a subsequent inversion back to physical space. This allows us to derive the local wind fields inside the EUREC 4 A domain, as well as corresponding divergence and vorticity fields, associated with specific modes.
In both data sets the total energy follows a k -1 power law at synoptic scales that transitions to a steeper slope at k ≈ 8. For k > 8 the ROT spectrum in ERA5 scales as k -3 , and the IG spectrum follows on average a k -5/3 slope but with substantial deviations from a constant slope. Towards the largest k there is a further steepening in ERA5, which was also reported for ERA-Interim and points to a lack of energy at small scales in the reanalysis  (Žagar et al. , 2015). The spectral behavior in ICON is similar to ERA5, but the slopes for different regimes are more sharply defined. The IG curve follows a k -1/2 slope for k < 8 and then k -5/3 up to k ≈ 100. At k > 100, there is a slight steepening in ICON as well, such that the slope between k ≈ 100-150 is k -2. The smallest scale shown in Fig.
Acknowledgements. Claudia Christine Stephan was supported by the  Minerva Fast Track Programme of the Max Planck Society. She would like to thank  David Raymond  for his help with the 3D-Var software, which is openly available for download at http: //kestrel. nmt. edu/~raymond/software/candis/candis. html(last access: 21 October 2019). She is also grateful to  Nedjeljka Žagar  for her advice on using the MODES software.
Code availability. MODES software can be obtained at https: //modes. cen. uni-hamburg. de/download (last access: 25 October 2020). ERA5 is produced and made publicly available by the European Centre for Medium Range Weather Forecasts (ECMWF). Data availability. The vertically gridded (Level 2) radiosounding data described by Stephan et al. (2021)  in NetCDF format are available to the public at https://doi. org/10. 25326/62 (Stephan et al. ,  2020b). GOES-16 Advanced Baseline Imager data are provided by NASA's Worldview application (https://worldview. earthdata. nasa. gov, last access: 24 November 2020). Data from the numerical simulation are stored by the Excellence in Science of Weather and Climate in Europe (ESiWACE) project at the German Climate Computing Center (DKRZ).
Indeed, no signal was detected when a sense probe against snRNA:7SK:94F was added (data not shown). To test whether this novel snRNA shares functional properties with the 7SK RNA, we looked for physical interactions with the components of P-TEFb complexes  [28]. We first performed immuno-precipitation assays with protein extracts from adult flies to explore interactions between the snRNA:7SK:94F RNA and dmHEXIM, dmLARP7 or dmCyclin T proteins. As expected, neither dmHEXIM nor dmLARP7 protein was detected in the bound fraction without antibody, whereas dmHEXIM antibodies efficiently pulled-down both dmHEXIM and dmLARP7 (Fig. 5a ). Also, dmCyclin T antibodies pulled-down dmHEXIM and dmLARP7, showing that they form a complex in vivo. Importantly, the snRNA:7SK:94F RNA copurifies with dmHEXIM, dmLARP7 and dmCyclin T, and is completely depleted from the unbound fractions (Fig.
Materials and methods Identification of a 7SK-like analogue in D. melanogaster Given that ncRNA 7SK sequences are poorly conserved, we used the HMMER package (www. hmmer. org) to look for signatures of 7SK sequences. To this end, a structural alignment built from the 7SK sequence of humans, mice, chicken, xenopus, fugu, tetraodon, lamprey, myxin and amphioxus was used to set up a nucleotidic multiple alignment, which was used to derive a profile HMM model with hmmbuild. The model was used to scan the Drosophila genome (FB2018_06 release)  [31]  with hmmsearch. Alignments were local for both the genome and the model.
Processing of NGS datasets Sequence reads were mapped on Dmel v6 genome available from UCSC website by using the bowtie short reads aligner  [63]  with stringent mapping parameters (-m 1 -n 1 -l 49 -p 20). Reads were clipped down to 50 bp, as this does not limit mapping efficiency and specificity but prevents issues with spliced alignments. Genome wide density profiles were computed with genomeCoverageBed from the BEDTOOLS package  [64]  and converted to bigwig with the wigToBigWig software, from the Jim Kent suite available at http://hgdownload. soe. ucsc. edu/ admin/jksrc. zip. Visualization of reads density and the genomic environment is carried out with the JBROWSE genome browser  [65]. RNA-Seq datasets (SRA reference PRJNA644503; https://www. ncbi. nlm. nih. gov/bioproject/ PRJNA644503) from wild type pupae are from  [66]  and PRO-seq data (GEO reference GSE131160; https://www.
Secondary structure The secondary structure of hs7SK snRNA, snRNA:7SK: 94F, and dm7SK snRNA was computed with the mfold software, a predictive RNA-folding program  [68]. The structure of hs7SK snRNA served as a control, since it has been extensively studied by different methods  [12, 24]. Using mfold, the hs7SK snRNA is folded into a structure similar to the one determined by Wassarman and Steitz  [12].
Acknowledgements We wish to thank  Dario Coen  for helpful discussions. We also wish to thank the  Bloomington Drosophila Stock Center  for fly strains,  Marvin Wickens  for providing the three-hybrid vectors and yeast strains, and  Béatrice Denis  for taking care of the strain flies used in this study. Finally, we wish to thank  Michael Stokes  for improving the English-writing of the manuscript.
fNART -National Adult Reading Test (Nelson & O'Connell, 1978 ). The fNART is a reliable and valid method for assessing premorbid intelligence in French-speaking populations  (Mackinnon & Mulligan, 2005). Demographic data (gender, education and age) were also collected. Participants also completed other questionnaires, but that were not included in the present study. They will be analyzed as part of another study. These included the Mini Cambridge-Exeter Repetitive Thought Scale  (Douilliez et al. , 2014) , the Cognitive Bias Questionnaire for Psychosis  (Peters et al. , 2013) , and the Beck Depression Inventory  (Beck et al. , 1996). All these questionnaires were completed at the end of the experimental manipulation.
Procedure Participants completed all the measures individually. After obtaining informed consent, the experimenter administrated the Mini International Neuropsychiatric Interview for DSM-5 to assess mental health disorders. Individuals meeting the inclusion criteria were invited to join the research protocol and were assigned to either the rumination or distraction induction condition. Next, participants undertook the grandiosity induction, completed the VAS and the PANAS measures and then engaged in the ruminative or distraction inductions. They then completed the VAS and PANAS measures again. Before starting the second part of the experiment, participants completed the socio-demographic data and the fNART. The procedure was repeated so that participants could complete the alternative induction.
Statistical Analyses Data were analysed using the R Studio  (RStudio Team, 2015). Alpha was set at p < 0. 05. Mean, standard deviation and frequency of socio-demographic and clinical variables were calculated. Only a few of the variables of interest in the present study were normally distributed (i. e. , PANAS). Nevertheless, for most of those variables that did not follow a normal distribution, their level of skewness and kurtosis were within the acceptable limits of ±2  (Trochim & Donnelly, 2006; Gravetter & Wallnau, 2014). Still, square root transformations were applied to reduce skewness and kurtoses. When transformations led to reductions in skewness and kurtoses, parametric analyses were employed. Analyses of variance were conducted with the package 'ez' (Lawrence, 2016) and for the pairwise comparisons using paired t test with Bonferroni correction, we used the package 'stats'  (Bolar, 2019).
These groups associate preferentially in space and time with specific other groups, without presenting signs of territoriality  [32]. We used data from high-resolution solar-powered GPS tags fitted to 57 individuals, distributed across each of the distinct groups living in our population over 22 months. We partitioned these data into 11 two-month-long study periods, with each period having distinct seasonal conditions that are typical of our study area  [33]. During our study, we captured two extreme seasonal conditions, extreme drought and very wet seasons. From these data, we calculated home range size (Question 1), daily travel distance (Question 2), daily range fidelity (Question 3) and seasonal range fidelity (Question 4). We then tested how these patterns impacted the population-level footprint (Question 5).
If rainfall remains low for a couple of months, conditions become very dry (e. g. trees lose their leaves, there is no green vegetation). To make our data comparable across our study, we partitioned the 22 months dataset into 11 replicated two-month-long study periods. This ensured that we had similar amounts of data for each seasonal period (thus not biasing some seasons as, for example, having more data would result in larger home ranges). We a priori defined the start and end of each two-months span to correspond with distinct seasonal changes in seasonal conditions, such as the onset of rain, based on our observations of the habitat and weather in the field (see Fig. 1 ). We then used data from a weather station at the Mpala Research Centre  [37]  and Normalized Difference Vegetation Index (NDVI) from satellite imagery  [36]  to classify each of our two-month study periods into one of three seasonal categories: wet, intermediate or dry (see Supplementary Table  1  and Supplementary Figure  1  for details).
We then ran the fastgreedy community algorithm, from the R package igraph  [38] , on each of the proximity networks, to identify which clusters of connected birds formed part of the same group. The fastgreedy community algorithm detects communities in networks (sets of nodes that are more interconnected to each other than to other sets of nodes) by directly optimizing a modularity score  [39]. In previous work  [32]  we demonstrated a strong match between GPS-based proximity networks and networks collected from census observations of co-occurrences among colour-banded individuals (Mantel correlation test between two-month census-and GPS-derived networks: r > 0. 81, for more detailed results see Data S1D in Papageorgiou et al. 2019  [32] ). However, because during some seasons we could not observe some groups, we opted to use GPS-based networks for the current study.
Because group memberships should be auto-correlated across subsequent periods, we next identified the carryover of group membership from one season to the next. For this, we used the MajorTrack python library  [40] , which identifies the links between communities across time periods. Because of the long-term stability of groups in our system, we used a long history (8 seasons) over which to estimate carry-over of group membership identity, although the results were not sensitive to this choice. The algorithm identified 27 distinct group identities in our population. Because sometimes groups occasionally aggregated into one larger group or split into multiple smaller groups from one period to another, not all groups spanned the entire study period. Further, because on some occasions all the GPS tags in one group were removed and replaced with tags on new group members, we manually connected some otherwise disconnected communities.
GPS data pre-processing In the current study, we used data points collected on the tenth second of every fifth minute. Given that the GPS tags mostly operate on collecting ten positions starting in the first few seconds after every 5 min, using the tenth fix ensured we allowed the tag time to switch on and improve its accuracy as it detects more satellites, while also ensuring we collected data at precisely the same time for each bird. When tags collected highresolution (1 Hz) data, we also subset to keep only the fix 10 s after every fifth minute. Our sampling therefore allowed the data points from all GPS-tagged birds to be synchronised in time.
Movement analysis We used the GPS data from each individual in each study period to quantify key aspects of group movement characteristics. Specifically, we calculated (1) the core home range (50% auto-correlated kernel density estimate), (2) the daily travel distance, (3) the day-to-day site fidelity and (4) season-to-season range fidelity. Finally, we (5) quantified how the footprint of our population changed across seasons. For (1) and (  2 ) we used data from all individuals of all groups. However, for computational limitations, we selected one individual per group for questions  (3) (4) (5). To estimate the core home range size (1), we fitted a continuous time movement model (ctmm) to each individual's GPS data for each study period. These models follow a continuous-time stochastic process from which the maximum likelihood Gaussian home range area can be extracted after the best fitted model is selected based on AIC, these areas are termed as AKDEs (auto-correlated kernel density estimations).
Because ctmms model the actual movement, they better predict home ranges than classical approaches, such as minimum convex hulls, that are typically prone to being highly influenced by just a few data points  [30, 41]. We used the maximum likelihood home ranges to determine the area in which each bird was located 50% of the time. This procedure was done using the "ctmm" R package  [42, 43]. We selected a 50% home range to avoid overestimating the home range sizes when groups move across larger areas (see Supplementary Figure  2 ) but we also provide the analysis of the 95% AKDE polygons in the Supplementary Results. To calculate the daily travel distance (2) for each individual in each study period, we applied the function "speed" from the ctmm R package to the five-minute data. This function calculates the mean distance covered per unit time, which generates a mean daily travel distance across the study period when setting the unit of time to 1 day.
For day-to-day site fidelity (3), which reveals the extent to which animals explored new areas, we first fitted a ctmm to the daily five-minute data from one representative for each group. We then calculated the overlap of the daily auto-correlated kernel density estimation for daily calculated 50% AKDE home ranges using the "overlap" function of the "ctmm" R package  [44]. Finally we calculated the mean of the values representing the overlap across each consecutive set of 3 days to generate a measure of fidelity in each study period for each group. To calculate the season-to-season overlap (4), we used the "overlap" function of the "ctmm" R package to estimate the 50% AKDE for each individual representing each group across each pairs of study periods. To estimate the total area used (  5 ) by our study population in each different season, we pooled hourly GPS   [36]  as a measure of the greenness of the grass throughout each study period data of each study group in each season.
Statistical analysis We fitted linear mixed models (LMMs)  [46]  in R version 3. 5. 1  [47]  to test if season type (wet, intermediate, dry) predicts home range size, daily travel distance, day-today site fidelity and seasonal range fidelity. The fixed effects of the models for (  1 ), (  2 ) and (  3 ) were the season type and the number of days for which we had tracking data available (range: 41-61 days per study period, we scaled this variable to be considered in the model). To account for repeated observations of the same groups across study periods, we fitted group identifier as random effect. For questions (1-3) the reference level of season type was to dry seasons and for question (4) the reference level was the overlap between two dry seasons. We finally used the lmerTest package to run Type III analysis of variance on the calculated LMM, extracting P-values for F-tests and t-statistics following the Satterthwaite's approximation method  [48].
Results In total, our dataset comprised of 161,258,429 GPS points. These data revealed that the 50% core AKDE home-range size was around 85% smaller in wet and intermediate seasons than in dry seasons (Fig. 2A , Model summary presented in Supplementary Table  2 , F 3 , 318. 6 = 41. 916, p < 0. 001). We found similar patterns for the 95% AKDEs (Supplementary Table  3 ). Correspondingly, we found seasonal effects on daily distance travelled (F 3,314. 59 = 42. 021, p < 0. 001), with groups having around 23% shorter daily travel distances in intermediate and wet seasons compared to dry seasons (Fig. 2B , Model summary presented in Supplementary Table  4 ). We also found a significant effect of season on day-to-day site fidelity (F 3,120. 75 = 4. 231, p = 0. 007), with greater day-today fidelity in dry seasons than during when the birds were breeding (seasons 1 and 10), although there were no significant differences between dry seasons and wet or intermediate seasons during which breeding did not   5 ).
Acknowledgements We thank the  Mpala Research Centre, Ol Jogi Wildlife Conservancy , and the  Ornithological Section of the National Museums of Kenya  for supporting this research work. We are grateful to  Wismer Cherono ,  John Ewoi ,  Alex Baiywa ,  Elkana Korir ,  James Klarevas-Irby  and  Deborah Molitor  for assisting in data collection and to  Julian Evens  for sharing code on using the MajorTrack library. We also thank one anonymous reviewer, the editor,  Dr. Dan Sankey ,  Dr. Kamran Safi  and his lab members and all the members of the  Farine lab  for constructive feedback on the manuscript.
Availability of data and materials All data used in this study are stored on Movebank under the study name Avulturinum_Farine: https://www. movebank. org/cms/webapp?gwt_ fragment=page=studies,path=study475851705. The code to run the analysis and to replicate the results of the models, together with the input data of the models, is available on: https://github. com/DanPapageorgiou/ranging_seasonality.