dataesr/scientific-paragraphs-categorization huggingface dataset:
The abbreviations used are: FAPP2, phosphoinositol 4-phosphate adaptor protein 2; GLTP, glycolipid transfer protein; GLTPH, GLTP homology; GLTP fold, the fold first found in human GLTP; SL, sphingolipid; GSL, glycosphingolipid; GlcCer, glucosylceramide; GalCer, galactosylceramide; LacCer, lactosylceramide; POPC, 1-palmitoyl-2-oleyl phosphatidylcholine; Per, perylenoyl; AV, anthrylvinyl; SF, sulfatide; PDB, Protein Data Bank; LTP, lipid-transfer protein; C1P, ceramide-1-phosphate; ACD11, accelerated cell death 11 protein; AU, asymmetric unit; SPR, surface plasmon resonance; SUMO, small ubiquitin-like modifier.
Viruses preparation and strain We used Zika virus (ZIKV) human isolate from Cambodia 2010 (Genbank KU955593) passaged in C6-36 cell line and harvested at 72 h post infection. Dengue virus serotype 2 ET300 (DENV2) isolated from a soldier in Australia returning from East Timor, was passaged 7 times in C6-36 cell line.
An indirect Infrared light facilitated "Night" time recording. Pictures were then converted into a time-lapse video, which was processed by a custom-developed software to monitor mosquitos' position variations across video frames. The data were then normalized at each time point with the number of females alive in the cage (see survival curves Supplementary Figure  S1B ). The experimenter manually detected the time instances of ovipositions, defined as presence of females in the container, by watching the recorded videos. The data were exported and analyzed with R statistical software  53. The algorithm also gives the two dimensions (2D) location of the females when they move during the recording to visualize space occupation linked to their activity. Finally, the eggs were collected after the end of each oviposition period and counted with Icount tool  54.
0, Focus Bioscience). Coverslips were washed three times with PBSA for 5 min. Coverslips were then incubated in species-specific secondary antibodies diluted in 5% BSA in PBSA for 1 h at room temperature, followed by two washes of 5 min with PBSA. Coverslips were then washed twice with water and counterstain nuclei with DAPI for 10 min, before a final wash in water and mounted carefully on glass slides. The slides were observed with a ×63 objective (with oil) using a Leica SP5 confocal microscope for Futsch and 3C11 signal quantification and a ZEISS LSM 800 confocal microscope for virus detection in the cultures. For the number of coverslips and images taken per treatment and condition to quantify the Futsch and 3C11 antibody signals, see Supplementary Table  S4. To quantify Futsch and 3C11 signals, raw images were extracted with LAS AF Lite software (Leica Microsystems, Germany) in single channel format for analysis.
Image analysis is done with ImageJ software  57. Images were first converted into 16-bits format with default threshold (dark background, B&W parameters). Results were normalized with the number of cells by count of DAPI particles. For DAPI counting, the option "Watershed" was applied before analysis to avoid counting merged particles. The Plugin "Analyze particles" was then used with the parameters "0. 00-1. 00" for circularity and "50-Inf" for size. Futsch and 3C11 signal quantification analysis were done with the automatically calculated value of the "Measure', and "Raw integrated density" options. The presence of the viruses in the primary neuron cultures was assessed by using the ZEN software from Zeiss microscopy, with the Z-stack capture option. An optical section area was captured every 0.
Data acquisition with microelectrode array Recording material: microelectrode array (MEA) were used for electrophysiological recording of neuronal networks (60MEA 200/30iR-Ti-gr, Multi-Channel Systems, Reutlingen, Germany). The glass devices consist of 59 TiN/SiN planar round electrodes (10-30 µm diameter; 100-200 µm center-to-center inter-electrode intervals) arranged in a square grid without corners. A single larger electrode served as reference ground electrode, replacing one recording electrode. Online cellular activity was recorded using the MEA60inv System (Multi Channel Systems, Reutlingen, Germany). Action potential was recorded at each electrode sampled at 10 kHz. MC_Rack software (Multi Channel Systems), installed on the acquisition computer, allowed files acquisition. Data analysis was performed off-line using MC_Rack software by Multi Channel Systems and NeuroSigX software developed by researchers at Institute for Intelligent Systems Research and Innovation, Deakin University, Australia.
The high pass filter was comprised of a 2nd order Butterworth filter with cutoff frequency of 200 Hz. The spike detection threshold was set at 8 times the high-pass filtered signal's standard deviation  58  (as measured by MC_Rack software, 22 µV) within a 500 ms window. Recording time points: once the dissociated neurons from mosquitoes were plated on the MEA, the cultures were let to grow and maturate for seven days. At 7 days in vitro (div), Ae. aegypti primary neuron networks were infected with ZIKV or DENV2 (used as a control) at a MOI of 0. 2. For each experimental assay, six electrophysiological recordings of spontaneous activity were made. The first was done at 7 div before infection and set as reference for further analysis. After infection, records were sampled a 2, 3 and 7 days post infection (dpi).
Microelectrode array data analysis with NeuroSigX: MC_Rack files are cut into 6 filtered files of 5 min for spontaneous activity at the different times post infection and 6 files of 2 min post gabazine stimulation at 8 dpi. Mcd files are then converted into txt files (MC_Data tool, MultiChannel System, Reutlingen, Germany) and analyzed by NeuroSigX. The NeuroSigX software uses novel spike sorting and data analysis algorithms to explore the neural spike activity and spatio-temporal behavior of the neuronal network  59. A threshold of 22 µV is employed to maintain the analytical consistency between the preliminary analysis by MC_Rack software and analysis by NeuroSigX. Electrode activity maps figures are extracted for spatial analysis and illustration.
Statistical analysis and graphics Graphs and statistical analysis are done using GraphPad Prism 5 software. All statistical tests are done using a twotailed analysis except when one-tailed mentioned. All statistical results are expressed with the p value using the following annotations: ns for P > 0. 05, *P ≤ 0. 05, **P ≤ 0. 01, and ***P ≤ 0. 001. For mosquito's behavioral activity analysis, modeling and statistical analysis were done using the R software version 3. 4. 0  53 with "ggplot2"  60  and "mgcv" 61 packages to perform generalized additive modeling for a mosquito activity time series analysis. The principle behind GAM is similar a regression one, a method which models the response variable by independent variable(s), except that instead of summing effects of individual predictors, GAM calculates a sum of smooth functions.
Acknowledgements We are thankful for the 4G4 antibody, kindly provided by  Professor Roy Hall  from  Australian Infectious Diseases Research Centre. Zika virus strains were a gift from  Prof. Robert Tesh  and  Dr. Nikos Vasilakis  ( UTMB, Texas, USA ). We acknowledge the technical assistance from the  Pathology and Pathogenesis team  and the facilities of the  Australian Microscopy & Microanalysis Research Facility linked laboratory at the CSIRO Australian Animal Health laboratories.
The camera gate width is set to 100 ns and temporal synchronization between the laser and the camera is obtained with an Oxford DG535 Digital Delay generator. The gate width has been chosen to maximize the signal to noise ratio for every operating condition assuring a reliable proportionality between LII signals and soot volume fraction  [22]. Spatial resolution of the camera is 100 pixels for 1 cm (∆x = 0. 1 mm). The calibration a. -2 0 2 x [cm] -2 0 2 x [cm]  of the LII signal was performed on the basis of recent investigations  [23, 24]  relying on the Modulated Absorption Emission technique (MAE  [25, 26] ) applied to a laminar diffusion flame  [3]. The quantitative interpretation of the data requires a value of the soot complex refractive index and this quantity was taken to be E(m, 645 nm) = 0.
ACKNOWLEDGMENT Support from  G. Legros  and  J. Bonnety  ( UPMC ) for the LII calibration through the  MAE technique  is gratefully acknowledged. This study is supported by the  Air Liquide , CentraleSupelec and CNRS Chair on oxycombustion and heat transfer for energy and environment and by the  OXYTEC  project, grant  ANR-12-CHIN-0001  of the  French Agence Nationale de la Recherche.
Avalanche forecasts Avalanche forecasts are the primary means for avalanche warning services to provide publicly available information about current and forecast snow and avalanche conditions in their territory. They may take the form of a single advisory, a Rounded to nearest 100 km 2. b Rounded to nearest 10 km 2. c Rounded to nearest 10 m. The size, as shown here and in Fig. 3a , was calculated using the R package raster  (Hijmans, 2016). The range of the maximum elevations describes the range of the highest elevation calculated using a digital elevation model with 90 × 90 m cell resolution  (Jarvis et al. , 2008; SRTM, 2017)  per warning region and forecast center (Fig. 3b ). describing the current situation, or an advisory and forecast for one or more days. Typically, avalanche forecasts contain the following information, ranked according to importance (information pyramid; EAWS, 2017b): avalanche danger level according to the EADS (Table 1); most exposed terrain -defining the terrain where the danger is particularly significant (see Sect.
Avalanche danger level data In most cases, data were provided directly from the warning services or forecast centers. Exceptions include the following: -Kärnten (KAE, Austria), where data were extracted from the annual reports ÖLWD (2012)-ÖLWD (2015); -Bayern (BAY, Germany), where data were collected from the web archive of the Bavarian warning service; -AINEVA forecast centers Friuli Venezia Giulia (FRI), Lombardia (LOM), and Veneto (VEN) in Italy, where data were provided by M. Valt/VEN (extracted from the central AINEVA database). The most relevant information concerning differences in raw data analyzed is displayed in Table  3. The danger level was generally valid for the day of publication (day + 0) in Austria, Germany, and LIV (scenario 1 in Sect. 2. 3), represented essentially a 1-day forecast (day + 1) in France and Switzerland (although the valid period started already on the afternoon of publication; scenarios 2 and 3 in Sect.
Temporal differences in danger level within the forecast period were available for all forecasts except those by BOR and LIG (Italy) and KAE (Austria). In both cases, only the highest danger level per day was available. The data extracted from the AINEVA database (forecast centers FRI, LOM, and VEN) indicated not only the danger level but also whether the danger rating increased, stayed the same, or decreased. In France spatial variations in the danger level within the same warning region (D spatial ) were sometimes indicated (e. g. , in a bulletin this could read "2 -moderate in the west, 3 -considerable in the east" of a region).
Topological neighbors We defined warning regions i and j as topological neighbors whenever they shared more than one point of their polygon boundary with each other (rook mode; Dale and Fortin, 2014; R package spdep:  Bivand et al. , 2013; Bivand and Piras, 2015). For this purpose, the shapes of the warning regions had to be slightly adjusted so that the coordinates of joint borders matched. This also reflects challenges of working across borders, with different map projections and simplified outlines of warning regions. For the particular case of the three forecast centers in Lombardia (BOR, LIV, and LOM), we defined them as neighbors if they shared either a common polygon boundary or at least partially the same territory (shapefiles in data by  Techel, 2018).
Data extracted from the AINEVA database provided the danger level valid in the morning, and whether the danger level changed during the day (increase, no change, decrease), but not which danger level was forecast following the change. To supplement this information, we utilized the distributions of the four AINEVA forecast centers which consistently provided the second danger rating (BOZ, PIE, TRE, VDA). In these forecasts, danger level changed by one level in 85 % of cases and by two levels in 15 % of cases. For the bulletins in FRI, LOM, and VEN we assumed a one-level difference for days with changing conditions, and hence a somewhat more conservative value than in the other Italian bulletins. Standardizing the length of the forecasting period during the season. The length of the main forecasting season is considered as being between 14 December and 16 April.
Danger ratings D max and D morning We created two subsets of data (D max and D morning ) to accommodate the different ways avalanche danger ratings are communicated in forecasts and stored in databases, and to ascertain that no bias was introduced by these differences. We defined D max as the highest danger rating valid during a forecast period, regardless of whether this was the only rating provided, whether this was for a first or second time step, or whether it corresponded to a difference in danger level by elevation. It is of note that D max is sometimes only valid for part of the day or part of the elevation range. In contrast, D morning refers to the maximum danger rating for the first of the two time steps, which in many cases would be considered valid for the morning. Here, it is of note that exact time when a change occurs is never provided in the published forecasts and is only categorically described within the danger description.
Summary statistics Warning-region-specific summary statistics. For each warning region, we calculated the proportion of forecasts issuing a specific danger level (i. e. , forecasts with danger level D = 4). Furthermore, for each warning region we calculated the surface area, which we refer to as the size of a warning region, using the R package raster  (Hijmans, 2016)  and the maximum elevation (ArcGIS software). The latter is based on a 90 × 90 m digital elevation model  (ESRI, 2017). Pairwise comparison of immediately neighboring warning regions. We compare the forecast danger level in two neighboring warning regions i and j by calculating the difference in the forecast danger level D for each day D = D i -D j for all days with D i ≥ 1 and D j ≥ 1, where D may refer to D max or D morning.
test; R Core Team, 2017). The resulting p value depends on the deviation of B ij from 1 and on the number of days N( D = 0). In general, bias values B ij < 0. 95 or B ij > 1. 05 were statistically significant (p < 0. 05). The distance between warning regions refers to the distance between the center points of the respective warning regions. Sensitivity and correlation. We tested whether removing subsets of the data (for instance individual years) or using D morning compared to D max influenced the rank order of the warning regions using the Spearman rank order correlation coefficient ρ. Similarly, we used ρ to explore whether the frequency at which a specific danger level was issued correlated with differences in the size ( size ) or in the maximum elevation ( elevation ) of two warning regions: i and j.
, 2013   2  and marked with dark gray polygon borders; national borders are marked with black lines. To visualize the (at least partially) overlapping forecast regions in the Italian region of Lombardia, LIV is superposed onto parts of LOM, while BOR is placed as an inset to the south of LOM. Thresholds for the color classes were defined using the Fisher-Jenks algorithm minimizing within-class variation  (Slocum et al. , 2005 ; R package classInt: Bivand, 2017). Techel and Schweizer, 2017). Similar to VDA, each of the Swiss warning regions can be linked to a higher-order spatial hierarchy (SLF, 2015, p. 41) 3. In either case, these predefined regional aggregations are not of great importance anymore in the communication of a regional danger level, due to the flexibility with which the forecaster can assign danger ratings to regions (VDA) or aggregate regions (SWI).
Dataset: four winter seasons We explored avalanche forecasts published during four winter seasons (477 forecast days). These included not only the 2011/2012 winter with extended periods of heavy snowfalls affecting particularly the regions north of the main Alpine divide (northern French Alps, large parts of Switzerland and Austria, Bavarian Alps;  Coléou, 2012; ÖLWD, 2012; Techel et al. , 2013) , but also the 2013/2014 winter, which was one of the snowiest winters on record in the southern Alps (Italy, southern parts of Switzerland;  Goetz, 2014; ÖLWD, 2014; Techel et al. , 2015a; Valt and Cianfarra, 2014). These two winters, or removing one of them during data analysis, had an effect particularly on the absolute values of the proportion of forecasts with D ≥ 4 (P v. crit ), while the overall rank order remained comparably similar, regardless of which subset was analyzed (Supplement S2).
However, we relied exclusively on forecast danger levels and cannot compare the agreement rate or bias with differences in avalanche winter regime. This is an important limitation in our study. Incorporating avalanche winter regimes in this study, and/or typical avalanche problems if these were used consistently, would clearly be beneficial for the interpretation of our findings. Such an analysis would require, besides meteorological data, a common database containing snow structure and avalanche information for the entire Alpine mountain range, as already exists for the US and Canada  (Mock and Birkeland, 2000; Haegeli and McClung, 2007; Shandro and Haegeli, 2018).
Figure7. Map showing the European Alps with the proportion of days (a) with very critical forecast conditions (P v. crit , D max ≥ 4) and (b) with a forecast danger level of 1 (P favor ; D max = 1). The color shading of the individual warning regions (white borders) corresponds to the values of P v. crit and P favor , respectively. Forecast centers are labeled according to Table2and marked with dark gray polygon borders; national borders are marked with black lines. To visualize the (at least partially) overlapping forecast regions in the Italian region of Lombardia, LIV is superposed onto parts of LOM, while BOR is placed as an inset to the south of LOM. Thresholds for the color classes were defined using the Fisher-Jenks algorithm minimizing within-class variation (Slocum et al. , 2005;  R package classInt: Bivand, 2017).
CountryForecast center D t1 D t2 D e D spatial Day + 0 Day + 1 Day + 2 Source (yes): AINEVA database provided information on whether danger level changed, but not to which danger level; D e : concept of assigning 1, 2, or 3 danger ratings (Sect. 2. 5); data source: ÖLWD -from Austrian winter reports ÖLWD (2012)-ÖLWD (2015); directly -directly from respective forecast center; website -from website of Bavarian Avalanche Warning Service; AINEVA -extracted from central AINEVA database (M. Valt (VEN)).
Acknowledgements. This study would not have been possible without the contributions of numerous avalanche forecasters and researchers who provided data and/or feedback on the manuscript (in alphabetical order):  Igor Chiambretti ,  Jean-Louis Dumas ,  Mattia Faletto ,  Thomas Feistl ,  Elisabeth Hafner ,  Fabiano Monti ,  Patrick Nairz ,  Bernhard Niedermoser ,  Andreas Pecl ,  Evelyne Pougatch ,  Jürg Schweizer ,  Luca Silvestri ,  Florian Stifter ,  Thomas Stucki ,  Arnold Studeregger ,  Lukas Rastner ,  Mauro Valt ,  Romeo Vincenzo ,  Alec van Herwijnen , and  Gernot Zenkl. We greatly appreciate the detailed and constructive comments by the three reviewers:  Karsten Müller ,  Rune Engeset , and  Karl Birkeland. The statistical analysis was performed using the open-source software R, version 3.
Data availability. The data are freely available at https://www. envidat. ch/dataset/forecast-avalanche-danger-leveleuropean-alps-2011-2015 (https://doi. org/10. 16904/envidat. 53; last access: 20 October 2018; Techel, 2018). The Supplement related to this article is available online at: https://doi. org/10. 5194/nhess-18-2697-2018supplement.
III. SIMULATIONS Figure  2  illustrates numerical results of radiationhydrodynamic simulations modeling the two-step compression process inside the described polystyrene samples using the hydrodynamic code package HELIOS-CR  49  with the SESAME 7592 equation of state (EOS) table for polystyrene. 50 Three different drive conditions are shown: "low drive" (1. 42 TW/cm 2 followed by 6. 04 TW/cm 2 ), "intermediate drive" (2. 25 TW/cm 2 followed by 6. 70 TW/cm 2 ), and "high drive" (2. 67 TW/cm 2 followed by 7. 55 TW/cm 2 ). The "intermediate drive" simulation has been tuned to reproduce the step pulse shape and the shock dynamics in the experiment (release and coalescence around 7. 6 ns after the onset of the laser drive). Both for "low drive" and "high drive," the simulated drive intensities were then linearly scaled in relation to the laser energy measurement in the experiment.
ACKNOWLEDGMENTS This work was performed at the  Matter at Extreme Conditions (MEC) instrument of LCLS , supported by the  U. S. Department of Energy Office of Science, Fusion Energy Science  under Contract No. SF00515. D. K. , A. M. S. , and R. W. F. acknowledge the support by the  U. S. Department of Energy, Office of Science, Office of Fusion Energy Sciences , and by the  National Nuclear Security Administration  under Award Nos. DE-FG52-10NA29649  and  DE-NA0001859. D. K. , N. J. H. , A. K. S. , and K. R. were supported by the  Helmholtz Association  under  VH-NG-1141. N. J. H. was supported by  Kakenhi  Grant No. 16K17846. SLAC HED was supported by  DOE Office of Science, Fusion Energy Science , under FWP  100182. S. F. was supported by  German Bundesministerium f€ ur Bildung und Forschung  Project No.
Illumina DNA sequencing and de novo assembly Extracted DNA underwent rolling circle amplification (RCA, TempliPhi kit, GE Healthcare Life Science) to concentrate/enrich the sample with circular forms and was sent to Fasteris S. A. (Geneva, Switzerland) for library preparation and sequencing using Illumina MiSeq along with Illumina HiSeq rapid run technology which resulted in paired-end reads of 250-bp mean length (Table  3  and 4 ). Paired-end reads were trimmed using the cutadapt script  (Martin, 2011)  to remove adaptors and filter for quality. The resulting reads were mapped with BWA  (Li and Durbin, 2010)  against the Theobroma cacao reference genome  (Argout et al. , 2011;  http://cocoa-genome-hub. southgreen. fr/). Unmapped reads were assembled using SPAdes v3. 6. 2  (Bankevich et al.
2012 ) with k-mers ranging from 21 to 127  (21, 33, 43, 55, 77, 99, 127) , giving the number of contigs described in Table  3  and 4. All contigs were used to perform a BLAST analysis against a locally created database containing all available CSSV sequences in order to identify contigs demonstrating clear CSSV origin (Table  3  and 4 ). This CSSV database was continuously updated with the newly obtained sequences during the analysis. The libraries were prepared with two different protocols depending on the DNA concentration of the sample after RCA: the Nano protocol was used from starting material greater than 100ng (samples HUX8 to HUX61, HUX104, HUX106 to HUX108, Tables  3  and 4 ) and the Nextera XT protocol is used from starting material less than 100ng (samples HUX68 to HUX88 and HUX105 ).
The sequencing cover of the libraries obtained by the Nano protocol was double that of the sequencing cover of libraries obtained by the Nextera XT protocol. As shown in Tables  3  and 4 , the percentage of reads mapped to T. cacao varies from 28. 6% (sample Gha52-15) to 66. 6% (sample CI632-10). The number of contigs obtained with the SPAdes assembly varies from 36986 (corresponding to the samples with the lowest number of reads) to 137411 and from 0 to 3782 of these contigs have positive results in the BLAST analysis against a CSSV database.