dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Electrophysiological and statistical analysis was performed using built-in and custom-written routines made for Igor Pro (WaveMetrics, Lake Oswego, OR, USA), MATLAB R2017b 9. 3. 0. 713579 Natick, Massachusetts: The MathWorks Inc; Origin (Pro) 2016 OriginLab Corporation, Northampton, MA, USA; Prism version 7. 00 for Windows, GraphPad Software, La Jolla California USA; and Python Software Foundation. Python Language Reference, version 3. 6, available at http://www. python. org.
Statistical analysis Normal distribution of samples was systematically assessed (Shapiro-Wilkinson normality test). Normal distributed samples were statistically compared using two-tailed Student's t test unless otherwise stated. When data distribution was not normal we used two-tailed Mann Whitney U-test. Compiled data are reported and presented as whisker box plots the upper and lower whiskers representing the 90th and 10th percentiles, respectively, and the upper and lower boxes representing the 75th and 25th percentiles, respectively, and the horizontal line representing the median or the mean ± s. e. m. , with single data points plotted. Differences were considered significant if p<0. 05 (*p<0. 05, **p<0. 01, ***p<0. 001).
Acknowledgements We thank  Vikaas Sohal ,  Nelson Rebola  and  Maria del Mar Dierssen Sotos  for critically reading this manuscript and  Michele Giugliano  for insightful discussions. We also thank the ICM technical facilities PHENO-ICMICE and iGENSEQ. This work was supported by ' Investissements d'avenir'   ANR-10-IAIHU-06 ,  BBT-MOCONET ;  Agence Nationale de la Recherche  ( ANR-12-EMMA-0010  ;  ANR-13-BSV4-0015-01 ;  ANR-16-CE16-0007-02 ;  ANR-17-CE16-0026-01 ;  ANR-18-CE16-0001-01 ),  Fondation Recherche Medicale (Equipe FRM   DEQ20150331684  and  EQU201903007860 ),  NARSAD  independent investigator grant,  E ´cole des Neurosciences de Paris Ile-de-France  and  Fondation Lejeune  (# 1790 ). All animal work was conducted at the  PHENO-ICMice facility. The  PHENO-ICMICE Core is supported by 2 'Investissements d'avenir'  ( ANR-10-IAIHU-06  and  ANR-11-INBS-0011-NeurATRIS ) and the  'Fondation pour la Recherche Medicale '.
From more recent EM studies, these trimeric assemblies were rediscovered and combined with high resolution crystallographic data  [33]. Moreover, from bacterially-expressed and purified NP of Mopeia virus (MOPV), we have recently measured the full length MOPV N protein by negative stain Transmission Electron Microscopy (TEM). This offers some new results concerning the multimerization shown in Figure  3  (preliminary data and unpublished observation). These RNP particles were observed with a diameter 14 ± 1 nm as well as symmetric circular heptamers of the same diameter (Figure  3a ), a result consistent with the original measurement of PICV RNP purified from the virus (Figure  1c  of  [52] ). Unfortunately, near atomic cryo-microscopy data on these polymers are missing to place the packaging position of both domains in the RNP.
The crystal structure study of the JUNV ExoN shows that the domain is conserved and similar to the ExoN of LCMV LASV, MOPV, TCRV  [47] [48] [49] [50]. However, the authors have failed to demonstrate the ExoN activity in vitro  [46]. These data do not suggest an inactive dsRNA clearance mechanism but, rather, a rescue mechanism of the inhibited pathway in these viruses. A possible variation in ExoN activity or its regulation among NW arenavirus NPs also needs to be considered. Further studies are needed in this particular field. (ii) Additionally to the common mechanism of dsRNA clearance by the ExoN during Arenavirus infection, NP engages several proteins of the host, aiming at the disruption of several antiviral pathways. Moreover, the targeted cellular proteins seem to be the result of specific adaptations of each virus to their host as LCMV, JUNV, LASV or TCRV look to have exclusive targets  [117] [118] [119].
Funding: This research was funded by  PANUVI  ( ANR-18-ASTR-0010-01 ), the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 ; MS was supported by  Erasmus+ for Traineeships   KA103/ Call 2019 , E Y. L. and THVN were funded by the  Fondation Infection Mediterrannée.
We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global highresolution (0. 1°) observation-based climatologies (1979-2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R 2 ) values ranged from 0. 76 to 0. 80 for the daily P occurrence indices, and from 0. 44 to 0. 84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0. 1 mm 3 h -1 threshold, P was estimated to occur 12.
3. Sun et al. 32  calculated P occurrence estimates directly from gauge observations, while Dietzsch et al. 33  used the gauge-based interpolated GPCC dataset  34  to estimate P characteristics for the entire land surface. Gauges provide accurate estimates of P at point scales and therefore have been widely used to evaluate satellite-and reanalysis-based P products (e. g. , Hirpa et al. 35  , Zambrano-Bigiarini et al. 36  , and Beck et al. 37  ). However, only 16% of the global land surface (excluding Antarctica) has a gauge located at <25-km distance  38, 39  , gauge placement is topographically biased towards low elevations  40, 41  , and there has been a significant decline in the number of active gauges over recent decades  42. Additionally, interpolation of gauge observations generally leads to systematic overestimation of low intensity events and underestimation of high intensity events  43, 44.
Here, we introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of high-resolution (0. 1°) fully global climatologies (1979-2018) of eight P occurrence indices (using different thresholds to discriminate between P and no-P) and nine peak P intensity indices (using different return periods) at daily and 3-hourly time-scales (Figs. 1b  and 2b ). The climatologies were produced using neural network ensembles trained to estimate the P indices using quality-controlled P observations from an unprecedented database consisting of 93,138 daily and 11,881 hourly gauges worldwide (Fig. 1a  and Supplement Fig. S2a ). Ten global P-, climate-, and topography-related predictors derived from all three main P data sources -satellites, reanalyses, and gauges -were used as input to the neural networks to enhance the accuracy of the estimates (Table  1 ).
1c ), while IMERG tended to overestimate the peak P intensity (Fig. 2d ). The uncertainty in the neural network-based estimates of the P indices was quantified globally and was generally higher in sparsely gauged, mountainous, and tropical regions (Fig. 4 ). Overall, these results suggest that the neural networks provide a useful high-resolution, global-scale picture of the P probability distribution. It should be noted that although the climatologies are provided as gridded maps, the estimates represent the point scale rather than the grid-box scale, due to the use of gauge observations for the training  43, 47. Since the eight PPDIST P occurrence climatologies were similar, we only present and discuss results for the >0. 5 mm d -1 daily P occurrence climatology (Fig. 1b ), although the others can be viewed by accessing the dataset.
The highest 15-year return-period P intensities (>200 mm d -1 ) over land were found in parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and over oceans in the intertropical convergence zone (Fig. 2b )  17, 48, 58, 59. Conversely, the lowest 15-year return-period P intensities (<50 mm d -1 ) were found in the desert regions of the world, at high latitudes where frontal P dominates, and over southeastern parts of the Atlantic and Pacific oceans where semi-permanent anticyclones prevail  54, 60. The PPDIST 15-year return-period daily P climatology (Fig. 2b ) exhibits reasonable visual agreement with a maximum one-day P climatology derived by Dietzsch et al. 33  (their Fig. 5d ) from the gauge-based GPCC Full Data Daily dataset  34  , as well as with a 3.
Methods Gauge observations and quality control. Our initial database of daily P gauge observations comprised 149,095 gauges worldwide and was compiled from (i) the Global Historical Climatology Network-Daily (GHCN-D) dataset (ftp. ncdc. noaa. gov/pub/data/ghcn/daily/) 61 (102,954 gauges), (ii) the Global Summary Of the Day (GSOD) dataset (https://data. noaa. gov; 24,730 gauges), (iii) the Latin American Climate Assessment & Dataset (LACA&D) dataset (http://lacad. ciifen. org/; 231 gauges), (iv) the Chile Climate Data Library (www. climatedatalibrary. cl; 716 gauges), and (v) national datasets for Brazil (11,678 gauges; www. snirh. gov. br/hidroweb/ apresentacao), Mexico (5398 gauges), Peru (255 gauges), and Iran (3133 gauges). Our initial database of hourly P gauge observations (resampled to 3-hourly) comprised 14,350 gauges from the Global Sub-Daily Rainfall (GSDR) dataset  62  produced as part of the INTElligent use of climate models for adaptatioN to non-Stationary hydrological Extremes (INTENSE) project  63.
5 mm 3 h -1. The resulting database comprised 93,138 gauges with daily data (Fig. 1a ) and 11,881 gauges with 3-hourly data (Supplement Fig. S2a ). See Supplement Fig. S1  for record lengths of the daily and 3-hourly gauge data. Precipitation occurrence and peak intensity indices. We considered eight P occurrence indices (related to the lower tail of the P distribution) and nine peak P intensity indices (related to the upper tail of the P distribution), all calculated for both the daily and 3-hourly time-scale. The eight P occurrence indices measure the percentage of time with P using thresholds of 0. 1, 0. 15, 0. 2, 0. 25, 0. 35, 0. 5, 0. 75, and 1 mm d -1 or mm 3 h -1 to discriminate between P and no-P. The P occurrence was calculated using the percentileofscore function of the scipy Python module  70.
Performance metrics. We assessed the performance of the PPDIST dataset, the ERA5 reanalysis, and the IMERG satellite product in estimating the P indices using the gauge observations as reference. Prior to the assessment, the P indices of the products and the reference were square-root transformed to reduce the skewness of the distributions. For the PPDIST dataset, we calculated the average of the ten scores calculated using the ten independent validation subsets of observations (one for each cross-validation iteration). For ERA5 and IMERG, we simply calculated the scores using all available observations. The following three performance metrics were used: 1. The coefficient of determination (R 2 ), which measures the proportion of variance accounted for  87  , ranges from 0 to 1, and has its optimum at 1.
Data Records The PPDIST dataset is available for download at figshare  92  and www. gloh2o. org/ppdist. The data are provided as a zip file containing four netCDF-4 files (www. unidata. ucar. edu/software/netcdf) with gridded observations, mean estimates, and uncertainty estimates of (i) the daily P occurrence (daily_occurrence_point_scale. nc), (ii) the daily peak P intensity (daily_intensity_point_scale. nc), (iii) the 3-hourly P occurrence (3hourly_occurrence_point_scale. nc), and (iv) the 3-hourly peak P intensity (3hourly_inten-sity_point_scale. nc). The grids have a 0. 1° spatial resolution and are referenced to the World Geodetic Reference System 1984 (WGS 84) ellipsoid. The observational grids were produced by averaging the observations if multiple observations were present in a single grid cell.
Technical Validation Figure  3  presents the performance of the PPDIST dataset, the ERA5 reanalysis, and the IMERG satellite product in estimating the daily P indices (Supplement Fig. S4  presents the performance in estimating the 3-hourly P indices). The PPDIST scores represent averages calculated from the ten independent validation subsets of observations (one for each cross-validation iteration). The PPDIST R 2 values ranged from 0. 76 to 0. 80 (mean 0. 78) for the daily P occurrence indices, 0. 85 to 0. 88 (mean 0. 86) for the 3-hourly P occurrence indices, 0. 44 to 0. 84 (mean 0. 72) for the daily peak P intensity indices, and 0. 72 to 0. 88 (mean 0. 83) for the 3-hourly peak P intensity indices (Fig. 3  and Supplement Fig. S4 ). The 3-hourly R 2 values were thus higher than the daily R 2 values, likely because the 3-hourly gauge observations tend to be of higher quality  62.
The markedly lower R 2 for the 3-day return-period daily P intensity reflects the fact that the majority (58%) of the observations were 0 mm d -1. For the ≥3-year return-period daily and 3-hourly P indices, the lower performance reflects the uncertainty associated with estimating the magnitude of heavy P events  [93] [94] [95]. The PPDIST scores were superior to the ERA5 and IMERG scores for every P index and performance metric by a substantial margin. The PPDIST B scores were close to 0 for all P indices, indicating that the estimates do not exhibit systematic biases, apart from those already present in the gauge data due to wind-induced under-catch  64, 96  and the underreporting of light P events  65. Overall, these results suggest that the PPDIST dataset provides a reliable global-scale picture of the P probability distribution.
Usage Notes The PPDIST dataset will be useful for numerous purposes, such as the evaluation of P from climate models (e. g. ,  Dai 4  and Bosilovich et al 5. ), the distributional correction of gridded P datasets (e. g. , Zhu and Luo 6 , Xie et al. 7  , and Karbalaee et al. 8  ), the design of infrastructure in poorly gauged regions (e. g. , Lumbroso et al. 9  ), and in hydrological modelling where P intensity is required (e. g. , Donohue et al. 106  and Liu et al. 107  ). However, some caveats should be kept in mind: 1. The uncertainty estimates included in the PPDIST dataset provide an indication of the reliability of the climatologies. The uncertainty is generally higher in sparsely gauged, mountainous, and tropical regions (Fig. 4 ). The uncertainty estimates may, however, be on the low end, because while we accounted for uncertainty in the mean annual P predictor, we did not account for uncertainty in the other predictors.
2. The P occurrence estimates using low thresholds to discriminate between P and no-P (≤0. 25 mm d -1 or mm 3 h -1 ) should be interpreted with caution due to the detection limits of gauges (~0. 25 mm for the US National Weather Service [NWS] 8" standard gauge  108  ) and satellite sensors (~0. 8 mm h -1 for the TRMM microwave imager [TMI]  109  and possibly higher for other sensors with coarser footprints) and the underreporting of light P events 65. 3. P observations from unshielded gauges (e. g. , the US NWS 8" standard gauge) are subject to wind-induced gauge under-catch and therefore underestimate rainfall by 5-15% and snowfall by 20-80%  64, 96. This underestimation is reflected in the PPDIST estimates. The Precipitation Bias CORrection (PBCOR) dataset  110  (www. gloh2o. org/pbcor) provides a means to ameliorate this bias to some degree.
Acknowledgements The  Water Center for Arid and Semi-Arid Zones in Latin America  and the  Caribbean (CAZALAC)  and the  Centro de Ciencia del Clima y la Resiliencia  ( CR) 2 (FONDAP 15110009 ) are thanked for sharing the Mexican and Chilean gauge data, respectively. We acknowledge the gauge data providers in the Latin American Climate Assessment and Dataset (LACA&D) project: IDEAM (Colombia),  INAMEH (Venezuela), INAMHI (Ecuador) ,  SENAMHI (Peru), SENAMHI (Bolivia) , and  DMC (Chile). We further wish to thank  Ali Alijanian  and  Piyush Jain  for providing additional gauge data. We gratefully acknowledge the developers of the predictor datasets for producing and making available their datasets. We thank the editor and two anonymous reviewers for their constructive comments which helped us to improve the manuscript.
Code availability The neural networks used to produce the PPDIST dataset were implemented using the MLPRegressor class of the scikit-learn Python module  113. The P occurrence for different thresholds was calculated using the percentileofscore function of the scipy Python module  70  , whereas the P magnitudes for different return periods were calculated using the percentile function of the numpy Python module  72, 73. The other codes are available upon request from the first author. The predictor, IMERG, and ERA5 data are available via the URLs listed in Table  1. Most of the gauge observations are available via the URLs provided in the "Gauge observations and quality control" subsection. Part of the GSDR database and some of the national databases are only available upon request.
copernicus. eu) ✓ ✓ IMERG b IMERG Late run (IMERGHHL) V06 (2000-2018) satellite-based P occurrence (% of time) and peak P intensity (mm d -1 or mm 3 h -1 ) indices 0. 1°H uffman et al. 45,46 (https://pmm. nasa. gov/data-access/downloads/gpm) ✓ ✓ PPDIST dly c Daily PPDIST estimates of the P occurrence and peak P intensity indices 0. 1°Th e PPDIST dataset derived in this study (www. gloh2o. org/ppdist) ✗ ✓ a Two mean annual P climatologies were used to account the uncertainty in mean annual P estimates. b The ERA5 and IMERG predictors represent estimates of the P index subject of the estimation. c Daily PPDIST estimates of the P index subject of the estimation were used as predictor for the 3-hourly estimates.
Conotoxins have been classified into various gene superfamilies based on signal sequence conservation, in addition to classification into families based on cysteine framework and receptor targets  [11]. The number of disulfide bonds is typically 2-4, but the connectivity can vary even amongst conotoxins containing the same number of cysteine residues, as can the bioactivity. The known cone snail venom peptide sequences and their known functions have been collated in the database Conoserver  [12, 13]. Twenty-nine superfamilies and thirty different cysteine frameworks have been identified in Conoserver to date, highlighting the diversity in the sequences of conotoxins. The A-superfamily is one of the most well characterised, with the majority containing cysteine framework I (CC-C-C). This framework is primarily associated with the α-conotoxin family, members of which specifically antagonise the nicotinic acetylcholine receptors (nAChRs)  [14, 15].
NMR Spectroscopy Lyophilised peptide was dissolved in 90% H 2 O:10% D 2 O at a concentration of approximately 0. 2 mM. All NMR spectra were acquired on a Bruker 600 MHz AVANCE III NMR spectrometer (Bruker, Karlsruhe, Germany) equipped with a cryogenically cooled probe. Two-dimensional 1 H-1 H TOCSY, 1 H-1 H NOESY, 1 H-1 H DQF-COSY, and collected at 290 K were used for sequence-specific assignments and structure calculations. Thus, 1 H-15 N HSQC, and 1 H-13 C HSQC spectra were acquired for carbon and nitrogen chemical shifts, respectively. All spectra were recorded with a 1 s interscan delay using standard Bruker pulse sequences with an excitation sculpting scheme for solvent suppression. Two-dimensional spectra were collected over 4096 data points in the f2 dimension and 512 increments in the f1 dimension over a spectral width of 12 ppm.
Structure Calculations Structures were calculated with the CYANA program using an automated NOE assignment protocol  [27]. Torsion-angle restraints were predicted using TALOS-N  [28]  and hydrogen bonds predicted based on preliminary structures calculated without disulfide bond restraints. Calculations were also performed with the three possible disulfide bond connectivities to determine the most likely connectivity. A set of 100 final structures was calculated with the globular disulfide connectivity and 20 structures with the lowest target function chosen to present the final ensemble. Structures were visualized and the root-mean-square deviation (RMSD) values were assessed using MOLMOL  [29].
Electrophysiological Measurements Rat nAChR cDNAs were provided by J. Patrick, Baylor College of Medicine, Houston, TX, and subcloned into the oocyte expression vector pNKS2. The cRNA was synthesized with the SP6 mMessage mMachine kit (Ambion, Austin, TX, USA), and Xenopus laevis (Nasco International) oocytes were kindly provided by Prof. Luis Pardo, Göttingen). Oocytes were injected with 50 nL aliquots of cRNA (0. 05 mg/mL). The nAChR subunits of heteromeric receptors were mixed at the ratio of 1:1 (α3:β2) or 5:1 (α4:β2). Recordings were performed as described  [30]  in ND96 (96 mM NaCl, 2 mM KCl, 1 mM CaCl 2 , 1 mM MgCl 2 , and 5 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) at pH 7. 4). Briefly, current responses to 100 µM acetylcholine (ACh) or 100 µM nicotine (in the case of α7) were recorded at -70 mV using a Turbo Tec 05X Amplifier (NPI Electronic, Tamm, Germany) and Cell Works software.
Ca V 2. 2 responses were elicited by addition of KCl (90 mM)/CaCl 2 (5 mM) in the presence of nifedipine (10 µM); α7 nAchR responses by choline (30 µM) in the presence of PNU-120596 (10 µM); α3-containing nAChR responses by nicotine (30 µM); Ca V 1 responses by KCl (90 mM)/CaCl 2 (5 mM) in the presence of ω-conotoxin CVIF (10 µM); and Na V responses by veratridine (50 µM). Data was analysed using ScreenWorks 3. 2. 0. 14 (Molecular Devices, Sunnyvale, CA, USA) and expressed as response over baseline, with baseline defined as 10 reads prior to agonist addition.
Discussion We have determined the three-dimensional structure of an A-superfamily conotoxin containing an unusual 4/8 spacing and shown that it represents a new structural sub-family. α-Conotoxins, the major family of A-superfamily peptides, have been well studied with more than 70 structures submitted to the Protein Data Bank. In general, the fold is very similar across the family, but we show here that an additional residue in loop 2 appears to have a significant effect on the structural fold. Pl168 was synthesized without selective protection of the cysteine residues, but nonetheless a major isomer was produced, which was purified and structurally analysed using NMR spectroscopy. Structural analysis indicated that the globular disulfide bond connectivity was present in the synthetic version of Pl168.
iscussion We have determined the three-dimensional structure of an A-superfamily conotoxin containing unusual 4/8 spacing and shown that it represents a new structural sub-family. α-Conotoxins, the jor family of A-superfamily peptides, have been well studied with more than 70 structures mitted to the Protein Data Bank. In general, the fold is very similar across the family, but we show e that an additional residue in loop 2 appears to have a significant effect on the structural fold. Pl168 was synthesized without selective protection of the cysteine residues, but nonetheless a jor isomer was produced, which was purified and structurally analysed using NMR spectroscopy. uctural analysis indicated that the globular disulfide bond connectivity was present in the thetic version of Pl168. The Pl168 sequence was identified from transcriptomic data and therefore o-elution with the native material was not possible.