dataesr/scientific-paragraphs-categorization huggingface dataset:
Code Availability Data were processed with CrystFEL 0. 6. 3. CrystFEL 0. 6. 3 is a free open source software under the GNU Public License version 3 and can be downloaded from http://www. desy. de/~twhite/crystfel/. CASS is publicly available on GitLab (https://gitlab. gwdg. de/p. lfoucar/cass). The script used to optimize detector geometry is publicly available on GitHub (https://github. com/tbarends/ pygeom).
We recruited a total of 82 male participants via a participant pool mailing list and student job advertisement websites. Participants were between 17 and 32 years old, reported not to be regular smokers or under medical treatment, and to not have a history of endocrine illness, neurological and psychiatric disorders, or dependency to alcohol or other drugs. All participants provided written informed consent and were paid for their participation. The experimental protocol was approved by INSERM and licensed by the local research ethics committee (Comité de protection des personnes Ile de France III-Project CO7-28, N Eudract: 207-A01125-48) and carried out in accordance with the Declaration of Helsinki. It should be noted that our sample size was estimated with a power-analysis based on an effect size (r = 0.
This setting provided a credible cover story, namely that the saliva samples were collected to assess associations between face categorization and physiological indices. The cover story for the postures was that a second, unrelated project on the impact of body posture on heart rate was conducted simultaneously. Participants would adopt the posture three times for 2 min each time in between the blocks of the face categorization task. This supposedly served to acquire heart-rate data for a total of 6 min while avoiding discomfort from holding the same posture for too long, and offered breaks during the visually demanding task. At this Thereafter, the female experimenter determined the posture condition using a randomizing function and provided corresponding instructions for either the expansive (n = 42) or the constrictive (n = 40) posture.
Funding This work was supported by  Foundation ROGER DE SPOELBERCH, INSERM , the  French National Research Agency  under Grants  ANR-17-EURE-0017  and  ANR-10-LABX-0087 IEC  and by a doctoral fellowship of the  École des Neurosciences de Paris Ile-de-France  and the  Région Ile-de-France (DIM Cerveau et Pensée)  to  Hannah Metzler. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
in computing discharge. Flow routing is often corrected by filling depressions (e. g. ,  Jenson and Domingue, 1988) , carving channels through artificial sills  (Rieger, 1998)  or by using hybrid breaching-filling techniques  (Lindsay, 2016). Although not having a linear time complexity, the most recent algorithms of depression removal -e. g. , the "Priority-Flood" algorithm and its variants  (Barnes et al. , 2014a; Zhou et al. , 2016; Wei et al. , 2018)  -have been optimized so that they can be used efficiently on large datasets. To increase performance for very large datasets, further optimization efforts have been focused primarily on rather complex, parallel variants of these algorithms  (Barnes, 2016; Zhou et al. , 2017 ). Yet, in some applications flow path enforcement still remains the main bottleneck.
Kruskal's algorithm Kruskal's algorithm  (Kruskal, 1956)   on the complexity of our solution. This algorithm uses a Union-Find structure to store and merge equivalence classes of objects (see Algorithm 1). The idea here is to parse all links L ∈ L sorted by increasing elevation z pass(L) , progressively grouping each pair of basins as a larger, virtual one (equivalence class). All subsequent paths between basins within this equivalence class are discarded to prevent loops. The Union-Find data structure has three operations: MakeSet Create an equivalence class containing a single element. Union Merge two equivalence classes. Find Get the equivalence class of an object. The optimal implementation of the Union-Find structure provides a O(α(N )) complexity for these operations, where N is the number of elements in the structure (i.
(2014b). We apply the same treatment on the  Zhou et al. (2016)  variant. All variants fill the depressions by directly updating the elevation values on the grid. To ensure proper comparison with our algorithm, we thus need to run them on a temporary copy of the elevation values before computing the flow receiver for each node of the grid. Our algorithm being optimized for a sequential usage, we chose, however, not to compare it to the parallel versions of  Barnes (2016)  and  Zhou et al. (2017). Both the algorithms and the benchmarks are implemented using the C++ language. For the state-of-the-art algorithms, we reuse the implementations available in the RichDEM library v2. 2. 9  (Barnes, 2018). The benchmarks where computed on an Intel Xeon Silver 4110 CPU (2. 1 GHz, 32. 0 Go RAM). We used Microsoft Visual Studio compiler with fast optimization options.
-Alternative, MDF-compliant methods should be implemented to update the flow receivers within the depressions. Other algorithms like the Priority-Flood don't have that limitation: they act directly on elevation values and don't prevent us from applying MDF flow routing methods on the modified topography. Another limitation of this algorithm is its sequential implementation. Further work is needed to adapt it so that it could be run on modern, multi-core and/or GPU-based architectures. Still, many use cases would benefit from the current implementation. These include processing datasets of moderate size on a single computer or running batches of simulations or analysis pipelines, e. g. , in the context of sensitivity analyses or inferences on model parameters.
Assessment of short-term fluxes For most evaluations of biophysical and biogeochemical cycles, we ran ED-2. 2 for two sites in the Amazon where both eddy flux towers and forest inventories were available for a long period: the Guyaflux tower (5 • 17 N, 52 • 55 W) at Paracou, French Guiana (GYF;  Bonal et al. , 2008) , and the Tapajós National Forest site (2 • 51 S, 54 • 58 W), located in the central Amazon (TNF;  Hutyra et al. , 2008; Pyle et al. , 2008). Both data sets underwent multiple-stage quality control. We used the meteorological variables measured at the eddy covariance tower sites to drive these ED-2. 2 simulations. These variables included temperature, specific humidity, pressure, wind speed, incoming solar radiation, incoming longwave radiation, and precipitation. All meteorological variables used as inputs for ED-2.
(2016). We also compared the model's predictions of LAI with estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS, product MCD15A2H, Collection 6)  (Yan et al. , 2016). We used all cloud-free, high-quality data from MODIS-MCD15A2H that were available between August 2002 and July 2004. We selected this period to reduce temporal -and land-use-related -differences between the model simulation and MODIS-MCD15A2H, and we aggregated the average LAI to 1 • resolution to be consistent with the ED-2. 2 simulations. Finally, to evaluate the model's predictions of fire regime across tropical South America, we compared the average fire disturbance rate over predicted by ED-2. 2 over one full cycle of the meteorological drivers (last 40 years of the simulation) with the Global Fire Emissions Database version 4.
1 (GFED4. 1; Giglio et al. , 2013; Randerson et al. , 2018). Similarly to aboveground biomass and LAI, we aggregated GFED4. 1 relative burned area to 1 • ; because GFED4. 1 data have a short overlapping period with the PGMF drivers, we used all available years  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  to compute the average fire disturbance rate. To evaluate the model's ability to predict emergent properties, we used published values of biomass and mortality obtained from the RAINFOR field inventory network in the Amazon  (Phillips et al. , 2004; Baker et al. , 2004a, b)  and the results from long-term simulations near the field inventory sites  (Levine et al. , 2016). Specifically, we investigated the relationship between aboveground biomass and longterm average mortality rates, and community-averaged wood density across these sites.
S5c ). The predicted transitions between the Amazon biome and the Cerrado (savanna biome in central Brazil) and Los Llanos (grassland area in Venezuela) are driven by increased fire activity, consistent with the Global Fire Emission Database (GFED4. 1  Giglio et al. , 2013; Randerson et al. , 2018)  regional distribution of burned area (α; Fig. 9 ). In addition, ED-2. 2 correctly predicted higher burned area in the savanna region at the Brazil-Guyana-Venezuela border and low fire activity over most of the Amazon biome and in the Caatinga (low-biomass, semi-arid region in northeastern Brazil) (Fig. 9 ). In contrast, ED-2. 2 underestimated fire activity in El Beni (Bolivian savannas, α = α ED-2. 2α GFED4. 1 = -6. 8 % year -1 ) in the Colombian Los Llanos ( α = -9. 2 % year -1 ), while it overestimated fire disturbance in forest areas near the border of Brazil and Bolivia ( α = 1.
, 2004; ter Steege et al. , 2006). This has been linked to the fact that soils in the western Amazon have higher nutrient availability  (Quesada et al. , 2012) , which was not accounted for in these ED-2. 2 simulations. Moreover, the model assessment suggests that ED-2. 2 overestimates growth rates (Fig. 13 ) and AGB (Fig. 8 ), despite evidence that ED-2. 2 may be underestimating net primary productivity (NPP; Figs. 5, 11d ; Table  1 ). This result likely indicates that allocation to growth, maintenance, and reproduction may be biased. Trait and allometric databases have considerably expanded over the past decade (e. g. ,  Kattge et al. , 2011; Falster et al. , 2015; Keenan and Niinemets, 2016) , with many of them have adopted openaccess platforms. Incorporating these databases to develop and constrain allocation and maintenance costs of the functional groups in ED-2.
Finally, the excessive tree growth rates identified in nutrient-poor sites may be addressed by the representation of biogeochemical cycles in tropical forests that accounts for nitrogen and phosphorus dynamics, which was recently implemented in ED-2. 2 Medvigy et al. (  2019 ) and could significant improve the characterization of the carbon cycle. Financial support. This research has been supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico  (grant no. 200686/2005-4) , the NASA Earth and Space Science Fellowship (grant no. NNX08AU95H), the National Science Foundation, Office of International Science and Engineering (grant no. OISE-0730305), the National Science Foundation (grant no. ATM-0449793), the National Aeronautics and Space Administration (grant no. NNG06GD63G), and the Fundação de Amparo à Pesquisa do Estado de São Paulo (grant no.
Acknowledgements. The research was partially carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. We thank the two reviewers,  Miriam Johnston  and  Luciana Alves , for suggestions that improved the manuscript;  Luciana Alves ,  Bruce Daube ,  David Fitzjarrald ,  Elaine Gottlieb ,  Elizabeth Hammond-Pyle ,  Lucy Hutyra ,  Natalia Restrepo-Coupe ,  Raphael Tapajós ,  Scott Stark , and  Kenia Wiedemann  for the management and data processing; and  Valerio Avitabile ,  Alessandro Baccini , and  Sassan Saatchi  for providing remote-sensing estimates of biomass. Marcos Longo  was supported the  NASA   Postdoctoral Program , administered by  Universities Space Research Association  under contract with  NASA.
Code and data availability. The ED-2. 2 software and further developments are publicly available. The most up-to-date source code, post-processing R scripts, and an open discussion forum are available on https://github. com/EDmodel/ED2 (last access: 24 September 2019). The code described in this paper, along with a wiki-based technical manual, is stored as a release at https://github. com/mpaiao/ED2/releases/tag/rev-86 (last access: 24 September 2019) and permanently stored at https://doi. org/10. 5281/zenodo. 2579481 (Longo et al. , 2019b). Eddy covariance data from GYF and TNF sites are available at https://fluxnet. fluxdata. org (last access: 24 September 2019). Forest inventory is available upon request to the data PI: GYF -D. B. (damien. bonal@inra. fr); TNF -P. B. d. C (pcamargo@cena.
usp. br). The PGMF meteorological data set can be requested at http: //hydrology. princeton. edu/data. pgf. php (last access: 24 September 2019). The Oak Ridge National Laboratory Distributed Active Archive Center provides access to the RADAMBRASIL (https://doi. org/10. 3334/ORNLDAAC/941, de  Negreiros et al. , 2009) , IGBP (https://doi. org/10. 3334/ORNLDAAC/565, Global Soil Data Task, 2000), and the photosynthetically active radiation (https://doi. org/10. 3334/ORNLDAAC/899, Tomasella et al. ,  2008)  data sets. Remote-sensing biomass maps were obtained by contacting the corresponding authors  (Saatchi et al. , 2011;   Baccini et al. , 2012)  and from http://lucid. wur. nl (last access: 24 September 2019; Avitabile et al. , 2016). CERES EBAF-Surface and TMPA-3B43 products are available at https: //ceres.
Methods Molecular dynamics simulations. Ab initio molecular dynamics simulations based on projector-augmented wave (PAW) flavor 33 of density functional theory (DFT) were performed with the Vienna Ab Initio Simulation Package (VASP)  34  using the generalized gradient approximation (GGA) in the Perdew-Burke-Ernzerhof (PBE) form  35  to treat electron exchange and correlation. Kinetic-energy cutoffs of 550 and 800 eV were used for the plane-wave expansion of the wavefunctions and the augmentation charges, respectively. Molecular dynamics simulations were performed in the canonical ensemble (NVT) via a Nosé-Hoover thermostat  36, 37  with a time step of 1-2 fs for 10-30 ps, depending on the density. The Brillouin zone sampling was performed at the gamma point. The simulations were spin-polarized at all temperatures, pressures, and compositions.
35 wt% CO) (dark blue), pyrolite + 4CO 2 (5. 16 wt% CO 2 ) (light blue), pyrolite + 8CO (6. 48 wt% CO) (dark orange), and pyrolite + 8CO 2 (9. 82 wt% CO 2 ) (yellow). The axes are identical for each panel. The scatter in the diffusion rates for Na, Ca, and Al is due to the low abundance of those atoms in our melt (our melt contains one Na, two Ca, and three Al atoms) Code availability. The Vienna Ab Initio Simulation Package (VASP) is a proprietary software available for purchase at https://www. vasp. at/. Data processing scripts written to process output files and create figures are available upon request.
Acknowledgements The authors would like to thank  Z. Li ,  F. Soubiran , and  A. Kobsch  for valuable discussions. This research was supported in part by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant agreement no. 681818-IMPACT  to R. C. ) and by the  Deep Carbon Observatory. The authors acknowledge access to the  GENCI supercomputers (Occigen, Ada, and Curie)  through the stl2816 series of eDARI computing grants.
Staining for the vesicular glutamate transporter vGluT2 Selected sections containing the CeA were then immunolabeled for darkfield and brightfield microscopy. For darkfield fluorescent staining, the cocktail containing different combinations of primary antibodies: anti-vGluT2 (1:2000; rabbit; SySy), anti-OT (1:2000, mouse), anti-DsRed (1:1000; rabbit; Clontech), anti-GFP (1:10,000, chicken, Abcam) were used. The named antigens were detected using appropriate secondary antibodies conjugated with fluoropores of various excitation ranges (Alexa488, Alexa459, Alexa680, Thermofisher). All tissue samples, labeled with fluorescent markers were imaged using Leica SP5 CLSM, and digitized using Adobe Photoshop software. To evaluate the CeL vGluT2 expression level, we processed the tissue for triple GFP, OT and vGluT2 immunolabeling in rats of OT Constitutive and OT vGATE groups.
In total, 2000 varicosities per animal group were counted and visually examined to detect the presence of double GFP-vGluT2 signals. The final numbers were computed as a proportion (percentage). For brightfield immunostainings, secondary antibodies conjugated to biotin were used. Final visualization of labeling was carried out with standard ABC HRP Kit (Vector) using DAB as a chromogene. The bright microscopic images were captured using a Nikon Eclipse microscope E200 (Software: Nikon NIS-Elements Version 4. 30). We analyzed the number of varicosities and the mean length of OT fiber segments residing in the CeL to trace possible structural changes. Altogether, 72 coronal planes of 6 rats in the OT vGATE group (and 7 in control) were used for this type of analysis. Length of GFP-positive fibers and number of axonal varicosities were measured and counted using free hand tracing and touch-count options in the latest version of ImageJ software (NIH).
and virus-delivered genetic-based targeted therapeutics. 1 (VG); PHC PROCOP program 32975SA (DAAD and Campus France), PICS program 07882 (CNRS), and ANR-DFG grant GR 3619/701 (VG and AC); FP7 Career Integration grant 334455, University of Strasbourg Institute for Advanced Study (USIAS) fellowship 2014-17, Foundation Fyssen research grant 2015 and NARSAD Young Investigator Grant 24821 (AC). The authors thank Sadia Oumohand and Jonas Metz for their help with behavioral experiments and histology, Judith Müller for cloning Credepended rAAVs, Barbara Kurpiers and the Interdisciplinary Neurobehavioral Core Facility of Heidelberg University for behavioral experiments performed there, Diego Benusiglio for the help with statistical analysis, Inga Neumann (Regensburg), Ron Stoop (Lausanne) and Mary Lee (Bethesda) for their valuable comments on the manuscript, Thomas Splettstoesser (SciStyle; www.
ACKNOWLEDGEMENTS The authors dedicate this manuscript to the memory of  Professor Peter H. Seeburg  (1944Seeburg ( -2016)), who generously provided us with his enormous support for this twelve-year long project. This work was supported by the  Max Planck Society  ( MTH, RS, VG ),  Fritz Thyssen Stiftung (MTH) ,  Ministry of Economy and Business (MINECO)  under the framework of  ERA-NET Neuron Cofund (MTH)  and  Chica and Heinz Schaller Research Foundation (VG) ,  German Research Foundation (DFG)  grant  GR 3619/4-1  (VG),  Human Frontier Science Program   RGP0019/2015  (VG),  DFG  within the  Collaborative Research Center (SFB)   1134  (VG, RS, and HB) and 1158 (VG, SCH, RS, and HB),  SNSF-DFG  grant  GR 3619/8 -.
To test their propensity to form a G4-structure, we selected origins in different chromatin domains, transcription status and replication activity. Because each sequence needed to be individually synthesized and tested by CD and IDS, we did a selection of 7 origins. The bioinformatics prediction for a potential of G4-structure was first tested at the bioinformatical level, using the G4H algorithm (similar results were obtained with the Quadparser software), and indicated a high capacity for G4-formation for all tested sequenced (Fig. 1a  and Supplementary Table  1 ). Circular dichroism (CD) is a highly sensitive assay, which can determine the conformational state of quadruplex structures  16. Isothermal differential spectra (IDS) are obtained using a method derived from that for thermal denaturation spectra  17  ; they provide information on the nature of the folded structure.
Both assays showed that all these sequences exhibited hallmarks of quadruplex formation, as shown by the strong negative peak around 295 nm and the two positive peaks around 240 and 273 nm for IDS (Fig. 1a , left panel), and the strong positive peak around 260 nm with CD (Fig. 1a , right panel). Such data suggested a predominantly parallel quadruplex conformation for all sequences and confirmed G4 formation by these sequences. OGRE/G4 elements confer replication origin activity. We then selected a strong and reproducible origin that was present in all our five independent experimental replicates (Ori 1, Supplementary Table  1 ; Supplementary Fig. 2A  shows the raw data in our replicates). The replication origin positions were defined in a genome-wide manner using MACS2 and SICER peaks calling softwares, as previously described  4.
3e ). We used the RSAT peak-motifs program (see "Methods" section) to find a specific motif in these origin classes. This led to the de novo identification of a G-rich motif upstream the IS, similar to the OGRE/G4 element  2, 4  , in all origin classes but for the suppressed class (Fig. 4a  and Supplementary Fig. 5A ). Suppressed origins were G4-poor, and preferentially contained a GC-rich motif (Supplementary Fig. 5A ), possibility reflecting the enrichment of these origins in GCI promoters and a link with transcription (see below). In reduced origins, the OGRE/G4 element was at almost 400 bp upstream of the IS (Fig. 4b ). This could be a consequence of their localization close to a promoter. We concluded that G4 stabilization by PhenDC3 did not reveal any new motif in the replication origin repertoire, but led to the We experimentally tested G4 formation in vitro in a subset of new origins using CD and IDS, as previously described (Fig.
MACS2 peaks overlapping SICER regions were considered as actual replication initiation sites (IS). Three biological replicates of control mouse ES cells incubated with 0. 5% DMSO and two replicates of mouse ES cells incubated with 10 μM PhenDC3 were used as well as one RNase A-treated sample prior to λ exonuclease digestion (control). Only origins reproducibly present in at least two replicates in each condition were retained for further analysis. For figures representing raw data (UCSC tracks Fig. 3c , and Heatmap Fig. 3d ), the mapped reads from replicates incubated with DMSO or PhenDC3 were merged for simplicity. Differential binding analysis was performed using the DESeq2 option in the DiffBind R package (version 1. 12. 3). The resulting p values were subjected to Benjamini-Hochberg multiple testing correction to derive the false discovery rates (FDR); only sites differentially bound with a FDR ≤ 1% were considered as differential.
As a negative control for peak clustering, correlation with chromatin marks and motif discovery, the shuffle program from the Bedtools suite (v2. 25. 0 29 was used to select random genomic regions of the same number and sizes as the origin peaks. Genomic localization. Origin localized at promoters (2 kb upstream TSS) in transcribed and intergenic regions were identified using the GenomicRanges R package and the TxDb. Mmusculus. UCSC. mm10. knownGene, version 3. 0. 0, genome database. For negative controls, the IS coordinates were shuffled 1000 times while keeping the chromosomal distribution of each class and avoiding long regions lacking genomic information. G4 assignation. Putative G4 were identified using the G4-Hunter algorithm  26  and a score higher than 2. An IS was considered as G4-positive if the G4 (with a G4Hunter_score ≥2) was located ±500 bp from its center.
The TopHat software (version 2. 1. 1) was used for splice junction mapping with Bowtie2 (version 2. 2. 9) for mapping reads. Reads counting on genes was done using HTSeqcount (version 0. 6. 1p1). Data were normalized to the relative log expression implemented in edgeR (version 3. 16. 5), and the statistical analysis to identify differentially expressed genes was performed using DESeq2 (version 1. 14. 1). Differential gene expression was considered when the adjusted p value was ≤0. 05 after multi-testing correction using the Benjamini-Hochberg method. Genomic Set Enrichment Analysis (GSEA). The GSEA was performed using the R package fgsea (version 1. 2. 1) and the data obtained in the differential RNA-seq analysis. Genes were ranked from upregulated to downregulated using the adjusted p value and the sign of the fold change obtained from the DESeq2 analysis.