dataesr/scientific-paragraphs-categorization huggingface dataset:
This work, through the use of the GENSEQ platform (http://www. labexcemeb. org/fr/genomique-environnementale-2)from the  labEx CeMEB , benefited from the support of the  National Research Agency  under the " Investissements d'avenir" program  (reference  ANR-10-LABX-04-01 ). We also thank the " Bio-Environnement" technological UPVD platform  ( Région Occitanie , CPER 2007-2013 Tecnoviv, CPER 2015-2020 Bioenvironnement) for access to the sequencing platform. This study is set within the framework of the  "Laboratoires d'Excellence (LABEX) "  TULIP  ( ANR-10-LABX-41 ). We thank  Life Science Editors  for editorial assistance. references.
Samples were then ultracentrifuged using an SW40 Ti Rotor (Beckman Coulter) for 2 h at 4°C. Gradients were fractionated using an Auto-Densi Flow Pump (Labconco) and a Foxy R2 Fraction Collector (Teledyne Isco), while continuously monitoring absorbance at 254 nm using UA-6 UV detector and LoggerPro Software (Vernier). RNA was extracted from the fractions corresponding to polysomes using RNA Stat-60 (Amsbio); RNA fractions from each sample were then pooled, and cDNA was made using SuperScript III Reverse Transcriptase (Thermo Fisher). Quantitative real-time PCR (qRT-PCR) analysis was performed using SYBR Green (Thermo Fisher) and ViiA7 Real-Time PCR System (Applied Biosystems). Cytokine and Gapdh transcripts were quantified against a standard curve of known amounts. RNA sequencing-QC of total RNA was done to determine their quantity and quality.
Cells were stained intracellularly with fluorescently conjugated antibodies to CCL2 (BD clone 2H5) and IL-6 (BioLegend clone MP5-20F3), diluted 1:100 in BD Perm/Wash, for 1 h at 4°C. All flow cytometry was performed on a BD LSRII flow cytometer. FlowJo (Tree Star, Inc. ) software was used for all flow cytometric analysis.
QUANTIFICATION AND STATISTICAL ANALYSIS Experimental replicate information is indicated in the figure legends. All results are given as mean ± SD and analyzed by using statistical tools in GraphPad Prism software (La Jolla, CA). For in vivo experiments, unpaired t test (for percentage) or Mann-Whitney test (for total cell numbers) were used. For protein quantification by ELISA, unpaired t tests were used. Differences with p < 0. 05 were considered significant. RNA Seq Differential analysis-FASTQ files were aligned to the mouse reference genome (mm9) using the GSNAP alignment tool. The count data from the alignment was normalized to rpkm and then filtered to remove lowly expressed genes. The resulting expression data were then adjusted by library size and corrected with voom precision weights.
The differential expression analysis was performed using limma by defining the adequate contrasts and using the ebayes method. The resulting p values were adjusted by false discovery rate correction and the top deferentially expressed genes were ranked by fold change. All the analyses were conducted using R version 3. 5. 0. (B and C) RNA was extracted from the fractions corresponding to polysome peaks, and mRNA transcripts were analyzed by qPCR. Graphs represent the ratio of total cytokine transcripts for RIPK3-dependent (B) and -independent (C) genes to a housekeeping gene (GAPDH) being actively translated in each condition. (D) mRNA from whole necroptotic corpses, or from polysome fractions isolated from these corpses, was analyzed by RNA sequencing. Fold change relative to untreated cells or polysomes isolated from untreated cells (respectively) is depicted.
Values reaching significance (FC > 1; p < 0. 05) on x axis only are depicted in red and those significant on both axes in blue. (E) NIH 3T3 + acRIPK3 cells were induced to undergo necroptosis with dimerizer, and the amount of ATP present in cells at the indicated time points was assessed using CellTiter-Glo. (F) NIH 3T3 + acRIPK3 were treated with dimerizer, digitonin, or treated with dimerizer followed by digitonin (→). 4 h later, ATP levels were quantified using CellTiter-Glo. All data are representative of three independent experiments. NT, no treatment. Error bars indicate SD from the mean.
ACKNOWLEDGMENTS We thank  Drs. Ram Savan  and  Lomon So  for helpful discussions and technical assistance with polysome experiments,  Dr. William Kaiser  for generously providing MCMV M45DRHIM virus,  Dr. Jennifer Martinez  for reagents, and  Zora Modrusan  for assistance with RNA sequencing. This work was supported by  NIH  grants  R01AI132595  and  R01CA228098  and a  CRI Wade F. B. Thompson   CLIP award  (to A. O. ) and an  NIH NIAID R01 Diversity Supplement  (to S. L. O. ).
Experimentation on Some Real-World Networks In this section, we analyze the performance of our algorithms for approximating eccentricities and distances on a number of real-world networks. Our experimental results show that the estimates on eccentricities and distances obtained are even better than the theoretical bounds described in Corollary 2 and Theorems 2,4. We apply our algorithms to six social networks, four email communication networks, four biological networks, six internet graphs, four peer-to-peer networks, three web networks, two product-co-purchasing networks, and four infrastructure networks. Most of the networks listed are part of the Stanford Large Network Dataset Collection (snap) and the Koblenz Network Collection (konect), and are available at [1] and [2]. Characteristics of these networks, such as the number of vertices and edges, the average degree, the radius and the diameter, are given in Table  1.
The numbers listed in Table  1  are based on the largest connected component of each network, when the entire network is disconnected. We ignore the directions of the edges and remove all self-loops from each network. Additionally, in Table  1 , for each network we report the size (as the number of vertices) of its center C(G). We also analyze the diameter and the connectivity of the center of each network. The diameter of the center diam G (C(G)) is defined as the maximum distance between any two central vertices in the graph. In the last column of Table  1 , we report the Gromov hyperbolicity δ of majority of networks 2. Computing the hyperbolicity of a graph is computationally expensive; therefore, we provide the exact δ values for the smaller networks (those with |V | ≤ 30K) in our dataset (in some cases, the algorithm proposed in  [41]  was used).
Estimation of Eccentricities Following Proposition 2, for each graph in our dataset, we found a pair u, v of mutually distant vertices. In column two of Table  2 , we report on how many BF S sweeps of a graph were needed to locate u and v. Interestingly, for almost all graphs (28 out 33) only two sweeps were sufficient. For four other graphs (including road-pa network whose hyperbolicity is large) three sweeps were needed, and only for one graph (power-grid network) we needed four sweeps.
Network Type Ref. In column four of Table  2 , we report for each graph G the difference between 2rad(G) and d G (u, v). Proposition 2(c) says that the difference must be at most 2δ + 1, where δ is the thinness of geodesic triangles in G. Actually, for large number (27 out of 33) of graphs in our dataset, the difference is at most two. Five other graphs have the difference equal to 3, and only road-pa network has the difference equal to 10. We have d G (u, v) = diam(G) for 27 graphs in our dataset, including road-pa network whose geodesic triangles thinness is at least 196. For remaining six graphs d G (u, v) = diam(G) -1 holds.
Following Theorem 1, for each graph G = (V, E) in our dataset, we constructed an arbitrary BF S(c)-tree T 1 = (V, E ′ ), rooted at vertex c, and analyzed how well T 1 preserves or approximates the eccentricities of vertices in G. By Theorem 1, ecc G (v) ≤ ecc T1 (v) ≤ ecc G (v)+3δ+1 holds for every v ∈ V. In our experiments, for each graph G and the constructed for it BF S(c)-tree T 1 , we computed k max := max v∈V {ecc T1 (v)ecc G (v)} (maximum distortion) and k avg := 1 n v∈V ecc T1 (v)-ecc G (v) (average distortion). For most graphs (see Table  2 ), the value of k max is small: k max = 0 for one graph, k max = 2 for eight graphs, k max = 3 for nine graphs, k max = 4 for four graphs, k max = 5 for two graphs, and k max > 5 for nine graphs. Also, the average  distortion k avg is much smaller than k max for all graphs.
In fact, k avg < 3 in all but five graphs (gnutella-30, gnutella-31, amazon-2, power-grid, and road-pa). In graphs with high k max , close inspection reveals that only small percent of vertices achieve this maximum. For example, in graph web-stanford, k max = 28 was only achieved by 17 vertices. The distributions of the values of k(v) := ecc T1 (v) -ecc G (v) of all graphs are listed in Table  6  (see Appendix). Similar experiments were performed following Proposition 3. For each graph G in our dataset, we picked a random vertex u ∈ V and a random vertex v ∈ F (u). Then, we identified in a randomly picked (u, v)geodesic a vertex w at distance rad(G) from v. We did not consider a vertex c defined in Proposition 3(d) since, for majority of graphs in our dataset, c will be a middle vertex of a geodesic between two mutually distant vertices, and working with c we will duplicate previous experiments.
It turns out also (see columns five and six of Table  2 ) that vertex w either belongs to the center C(G) or is very close to the center. The only exception is again road-pa network where 2rad(G) -ecc G (w) = 32 and d(w, C(G)) = 21. For every graph G = (V, E) in our dataset, we constructed also an arbitrary BF S(w)-tree T 2 = (V, E ′ ), rooted at vertex w, and analyzed how well T 2 preserves or approximates the eccentricities of vertices in G. The value of k max is at most five for 23 graphs. The average distortion k avg is much smaller than k max in all graphs. The distributions of the values of k(x) for all graphs are presented in Table  7  (see Appendix). In Table  4 , we compare these two eccentricity approximating spanning trees T 1 and T 2 with each other and with a third BF S(c * )-tree T 3 which we have constructed starting from a randomly chosen central vertex c * ∈ C(G).
For each graph in the dataset, three values of k max (k T1 max , k T2 max and k T3 max ) and three values of k avg (k T1 avg , k T2 avg and k T3 avg ) are listed. We observe that the smallest k max (out of three) is achieved by tree T 3 in 28 graphs, by tree T 2 in 20 graphs and by tree T 1 in 20 graphs (in 14 graphs, the smallest k max is achieved by all three trees). The difference between the largest and the smallest k max of a graph is at most one for 26 graphs in the dataset. The largest difference is observed for road-pa network: the largest k max (98) is given by tree T 1 , the smallest k max  (46)  is given by tree T 3. Two other graphs have the difference larger than three: for sc-ppi network, the largest k max  (7)  is given by tree T 2 , the smallest k max (3) is given by tree T 1 ; for power-grid network, the largest k max  (13)  is given by tree T 1 , the smallest k max (4) is shared by remaining trees T 2 , T 3.
Estimation of Distances Following Theorem 3, we experimented also on how well our approach approximates the distances in graphs from our dataset. To analyze the quality of approximation provided by our method for a given graph G = (V, E), for every δ := 0, 1, 2,. , we computed an estimate d δ (x, y) on d G (x, y) and the error ∆ xy (δ) = d δ (x, y) -d G (x, y) for all x, y ∈ V. In Table  5 , we report ∆ max (δ) = max x,y∈V ∆ xy (δ) and ∆ avg (δ) = 1 n 2 x,y∈V ∆ xy (δ) for the smallest δ such that ∆ max (δ) ≤ δ + 1. We omitted some very large graphs in this experiment. For some other large graphs, we did only sampling; we calculated ∆ max (δ) and ∆ avg (δ) based only on a set of sampled vertices. We sampled vertices that are most distant from the root. The number of sampled vertices ranged from 10 to 100 in each network.
A total of 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data are used to quantify possible changes in the cloud vertical distribution over the tropical Atlantic. For the analysis multiple linear regression techniques are used. For the investigated time period significant linear changes were found in the domain-averaged cloud-top height (CTH) ( -178 m per decade), the high-cloud fraction (HCF) (-0. 0006 per decade), and the low-cloud amount (0. 001 per decade). The interannual variability of the time series (especially CTH and HCF) is highly influenced by the El Niño-Southern Oscillation (ENSO). Separating the time series into two phases, we quantified the linear change associated with the transition from more La Niña-like conditions to a phase with El Niño conditions (Phase 2) and vice versa (Phase 1).
, 2015; Heinze et al. , 2017). In this work 14 years of the MODIS level 2 cloud product  (Platnick et al. , 2015)  were analyzed using multiple linear regression model (MLRM) techniques in order to obtain insights into the long-term variability and possible anomalies within the CTH and cloud vertical distributions due to a warming climate. The influence of the transition from one ENSO phase to another on the tropical Atlantic cloud vertical distribution was analyzed and quantified. The passive imager Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the polar orbiting NASA satellites Terra and Aqua provides the possibility to obtain observational information about the cloud vertical distribution in high temporal (on climate scales) and spatial resolution. MODIS aboard Aqua has delivered reliably high-resolution data of cloud properties since 2002  (Platnick et al.
MODIS MODIS measures reflected shortwave and emitted longwave radiation in 36 spectral bands. MODIS is aboard the NASA Earth Observing System (EOS) satellites Aqua (13:30 LT ascending node) and Terra (10:30 LT descending node). Both satellites are in a polar sun-synchronous orbit at an altitude of 705 km. In this work solely data from MODIS aboard Aqua  (Platnick et al. , 2015) , which is part of the Afternoon Train (A-Train) constellation, are considered. With a swath of 2330 km (cross track) and a scan rate of 20. 3 rpm (cross track), global coverage is acquired almost daily. The level 2 data of collection 6  (Platnick et al. , 2015)  provide cloud optical and microphysical property data at a spatial resolution of 1 km and cloud-top property data at 5 km as well as at 1 km resolution  (Menzel and Strabala, 2015; Platnick et al.
, 2017). For the analysis only data from overpasses during the daytime (ascending node) were used. The vertically resolved cloud fractions were calculated using the 1 km cloud mask and the International Satellite Cloud Climatology Project (ISCCP) cloud classification scheme explained in Sect. 3. 1. In order to obtain daily composites from the MODIS overpasses, the segments were regridded on a regular 0. 1 • × 0. 1 • grid. For every grid cell the mean, minimum, maximum, and variance were computed. Due to the proximity to the Equator of the analyzed region for the majority of the grid cells, the pixels arise from a single overpass. To account for possible misclassification of the MODIS cloud mask (clear-sky conservative) due to cloud edges, broken clouds, smoke, dust, or sun glint, pixels that were poor retrieval candidates were excluded using the clear-sky restoral flag  (Hubanks, 2015).
DARDAR To compare the results obtained with MODIS, additional data for total cloud fraction (TCF), high-cloud fraction (HCF), middle-cloud fraction (MCF), and low-cloud fraction (LCF) computed from DARDAR data  (Delanoë and Hogan, 2010) , also using the ISCCP cloud classification scheme, were analyzed. DARDAR is a project from Laboratoire Atmosphères, Milieux, Observations Spatiales and the Cloud Group of the Department of Meteorology at the University of Reading to retrieve cloud properties by combining CloudSat radar and CALIPSO lidar measurements. Both the CloudSat and CALIPSO satellite fly within the A-Train. CloudSat lags behind Aqua by between 30 s and 2 min and CALIPSO lags behind CloudSat by no more than 15 s. The data are available for the period from June 2006 to June 2016 with gap from May 2011 to April 2012.
AMSR-E Furthermore, data from the AMSR-E instrument aboard Aqua were used to acquire information about the SST and TCWV. AMSR-E measures the brightness temperature at six different wavelengths in the microwave range between 0. 34 and 4. 35 cm. For every wavelength, the horizontal and vertical polarized radiation is measured, leading to 12 channels in total. The spatial resolution depends on the channel and varies from 5. 4 to 56 km. The radiometer has a viewing swath width of 1445 km and an incidence angle of 55 •. For the analysis the SST and the TCWV from version 2 of the monthly level 3 product (AE_MoOcn) with a spatial resolution of 0. 25 • × 0. 25 • were used  (Wentz, 2004). The AMSR-E data are available from 8 June 2002 through 4 October 2011.
ERA-Interim To gain insights into the predominant large-scale dynamics, the vertical velocity ω at 500 hPa was acquired from the ERA-Interim reanalysis  (Dee et al. , 2011). In order to roughly match the MODIS overflight time, the daily ERA-Interim analysis data at 12:00 were used. From the daily data (0. 2 • × 0. 2 • ), monthly means were computed the same way as explained in Sect. 2. 1.
The CTH in Fig. 4a  and the HCF in Fig. 4c  show a significant El Niño-related CTH decrease in P2 of -579 m per decade and of -0. 024 per decade for HCF. This indicates that observed changes in the CTH are mainly due to changes in high-cloud amount. The LCF (d) shows neither significant changes in P1 nor in P2. The changes of the TCF in P2 are visible, but statistically not significant (b). In P1 all variables (a-d) show a linear increase that is significant, except for the LCF. To evaluate the anomalies observed with the passive MODIS instrument, DARDAR data (see Sect. 2. 2) from active lidar and radar instruments were analyzed. Due to the low sampling, monthly means were computed and compared to MODIS with a 2 • × 2 • resolution. As Fig. 5  illustrates, the seasonal and interannual variability of the HCF, as well as its decrease (-0.
Summary and conclusion In this study the Moderate Resolution Imaging Spectroradiometer (MODIS) (aboard Aqua) cloud products were used to analyze the interannual variability of the cloud vertical distribution at the tropical Atlantic for a period of 14 years (2002 to 2016). Furthermore, the influence of the transition from one El Niño-Southern Oscillation (ENSO) phase to another on the tropical Atlantic cloud vertical distribution was analyzed and quantified. Based on level 2 data, daily composites on a 0. 1 • × 0. 1 • regular grid were generated. For the analysis, 0. 2 • × 0. 2 • monthly means, calculated on the basis of the daily composites, were used. The data represented large circulation patterns such as the band with high cloud tops and cloud amount in the ITCZ and its seasonal shift well.
Acknowledgements. This work was performed within the  research program High Definition Clouds and Precipitation for Advancing Climate Prediction -HD (CP) 2. We further thank  Florian Tornow  for giving valuable comments on the draft version. We also thank the two anonymous reviewers for helpful and critical comments that helped to shape and improve the paper. Financial support. This research has been supported by the  Bundesministerium für Bildung und Forschung, BMBF  (grant no. 01LK1503G ). We acknowledge support from the  Open Access Publication Initiative of Freie Universität Berlin. Review statement. This paper was edited by  Bernhard Mayer  and reviewed by three anonymous referees.
Northern blotting analysis Ten micrograms of whole-cell RNA was fractionated by electrophoresis on a 6% polyacrylamide/7 M urea denaturing gel. Electro transferred onto a nylon membrane (Amersham Hybond-N, GE Healthcare) followed by UV crosslinking (Stratalinker). Hybridizations were carried out with 5′-end 32 P-labelled-DNA oligonucleotide probes (Table  S2 ). Membranes were incubated overnight at 50°C in 5× SSPE, 5× Denhardt's, 1% SDS, 150 μg/mL yeast tRNA and washed twice in 0. 1% SSPE, 0. 1% SDS for 15 min at room temperature. Radioactive signals from the probes were revealed using a Typhoon™ Biomolecular Imager (Amersham) and intensities were quantified using Multi Gauge V3. 0 software.
Analysis of Gas5 splicing RNA splicing at Gas5 pre-mRNA were assayed by RT-PCR, as described above. DNA products were then fractionated by electrophoresis on a 2% agarose gel and stained by ethidium bromide. The identity of the RT-PCR products was further confirmed by DNA sequencing and the relative intensities of PCR products were quantified using Image J software. Primer sequences can be found in Table  S2.
Cells were harvested and fixed in ice-cold 70% ethanol, washed in PBS + 0. 5% Tween and suspended in PI/RNase staining buffer (BD Pharmingen). Data were collected on a FACSCalibur cytometer (Becton Dickinson) and analysed using FlowJo software (FlowJo, LCC). Two independent experiments were performed. Apoptosis was detected using Annexin V-FITC/propidium iodide staining according to the manufacturer's instructions (BioLegend). Cells were analysed using FacsVerse (Becton Dickinson) with acquisition of 20,000 total events (BD FacsSuite). Two independent experiments were performed. Dual luciferase reporter assay 100,000 cells were seeded into a 24-well plate for 24 h. Cells were transfected using lipofectamine 2000 (Invitrogen) with 0. 06 ng Renilla vector (internal control) and 500 ng of reporter Firefly vectors (a gift of Drs.
Statistical methods Results are expressed as mean ± standard error of the mean (SEM) except for RiboMeth-seq, cell cycle profiling, and apoptosis assay results which were presented as mean ± standard deviation (SD). Comparison of two groups was analysed by twotailed Student's unpaired t-test. Statistically significant differences between groups are indicated as *P < 0. 05, **P < 0. 01, and ***P < 0. 001. Hierarchical cluster analysis and heatmap were generated in R using the pheatmap package (https://CRAN. R-project. org/package=pheatmap). A number of different clustering methods separated robustly embryonic and adult tissues. In the figure, the result using default settings and the complete linkage method was used.
Acknowledgments We are grateful to  A. Zakaroff-Girard  and  E. Riant  ( Cytometry Core Facility ,  INSERM U1048 , part of  TRI Imaging Platform, Genotoul ) and  V. Daburon  ( CBI-LBCMCP, Toulouse ) for cytometry analysis, advice and technical assistance. We thank  Sowmya Ramachandran  for critical reading of the manuscript.
Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP.