dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The research leading to these results has received funding from the program " Investissements d'avenir "  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche-10 - IA Agence Institut Hospitalo-Universitaire-6 ),  ANR-11-IDEX-004  ( Agence Nationale de la Recherche - 11-Initiative d'Excellence-004 , project LearnPETMR number  SU-16-R-EMR-16 ), from the  European Union   H2020 program  (project  EuroPOND , grant number  666992 , project  HBP SGA1  grant number  720270 ), from the  ICM Big Brain Theory Program  (project  DYNAMO ), from the  European Research Council  (to  Dr Durrleman  project  LEASP , grant number  678304 ), from the joint  NSF/NIH/ANR program  " Collaborative Research in Computational Neuroscience"  (project  HIPLAY7 , grant number  ANR-16-NEUC-0001-01 ) and from the  "Contrat d'Interface Local" program  (to  Dr Colliot ) from  Assistance Publique-Hôpitaux de Paris  ( AP-HP ).
N. B. receives funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. PCOFUND-GA-2013-609102 , through the  PRESTIGE programme  coordinated by  Campus France. Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc. ; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.
Mass spectrometry reveals an exceedingly complex spider-venom peptidome Mass spectrometry (MS) analyses revealed that the venom peptidome of Hadronyche infensa is more complex than that reported for any other terrestrial venomous animal. We used two complementary mass spectrometry platforms to examine the distribution of peptide masses in pooled venom from female H. infensa. Analysis of crude venom using matrix-assisted laser desorption/ionization tandem time-offlight (MALDI-TOF/TOF) or Orbitrap MS resulted in 2053 and 1649 distinguishable peptide masses, respectively (Fig. 1A-D ). Only 651 masses were detected in both MS data sets, leading to a total of 3051 unique venom peptides (Fig. 1E ). This is considerably more than the ~1000 peptides reported to be present in venom of the Australian funnel-web spider Hadronyche versuta  6  , although this difference is likely a reflection of recent improvements in MS sensitivity rather than an indication of major differences in venom complexity between these closely related species.
Transcriptomics uncovers the biochemical diversity of the H. infensa venome Consistent with the proteomic analysis of crude venom, sequencing of a venom-gland transcriptome from H. infensa revealed one of the most biochemically diverse venoms described to date, with at least 33 toxin superfamilies (Fig. 2 ). In light of their toxic function, each superfamily of toxins was named as suggested previously  30  after gods or deities of death, destruction, or the underworld. Expressed sequence tags were sequenced using the 454 platform and assembled using MIRA software v3. 2  31. This produced a total of 26980 contigs and 7194 singlets, with an average contig length of 496 bp (maximum length 3159 bp, N50 674 bp). Array-based pyrosequencing (454) was used in preference to short-read sequencing to avoid ambiguous bioinformatic re-construction of highly similar toxin isoforms: the average read length was long enough to encompass entire toxin precursors, thereby avoiding sequence ambiguities.
After assembly, all contigs and singlets were submitted to Blast2GO 32 to acquire BLAST hits and functional annotations. Subsequently, sequences were grouped into three main categories: (1) proteins and other enzymes; (2) toxins and toxin-like peptides; and (3) sequences with no hits in ArachnoServer, a manually curated database of spider toxins  33  , or NCBI. 61% of all contigs/singlets were proteins/enzymes, 14% were toxins/toxin-like peptides, and 25% had no database hits (Supplementary Fig. 1S ). BLAST hits included other spider toxins, as well as peptides and proteins found in other venomous arthropods with recently annotated genomes, including the tick Ixodes scapularis, the honeybee Apis mellifera, the jumping ant Harpegnathos saltator, and the parasitoid wasp Nasonia vitripennis. To maximise the discovery of novel DRPs, an in-house algorithm was written to allow discovery of potential open reading frames (ORFs).
Both annotated and novel peptides were then combined into a single file that was used to analyse the processing signals in all precursors. First, for each toxin group, signal peptide cleavage sites were predicted using SignalP v4. 0 34 while propeptide cleavage sites were predicted based on a sequence logo analysis  35  of all known spider-toxin precursors documented on ArachnoServer (Supplementary Fig. 2S ). After determination of signal and propeptide regions, toxins were classified into superfamilies (SF) based on their signal-peptide sequence, total number of cysteine residues, and their known or predicted cysteine framework, as shown in Figs  2  and 3. Transcript abundances for each superfamily were estimated using RSEM v1. 2. 31  36. Briefly, trimmed reads were mapped back to the assembled transcriptome.
We grouped all the identifiers of contigs containing sequences that encode toxins/proteins from each superfamily; the list of identifiers was saved on a text file and used to search the RSEM output. Transcripts Per Million (TPM), were used to estimate the transcript abundance for each superfamily and the total number of transcripts obtained are summarized in Table  1S  and Figure  4. Differences in transcript abundances vary from low TPM counts for superfamilies 11, 21 and 28 to the most abundantly expressed peptide in the venom gland, SF4, which interestingly had not been previously characterised as a major component of the venom of H. infensa. Using this approach, we determined that the venom gland of H. infensa expresses at least 26 families of DRPs, three families of enzymes (hyaluronidase, lipase, and phospholipase A 2 (PLA 2 )), one family of cysteine-rich secretory proteins (CRiSPs), and three families of secreted proteins (Figs  2  and 3 ).
Actively regenerating spider venom glands are enriched in toxin processing machinery We depleted the venoms glands of spiders by electrical stimulation three days before sacrificing them to obtain venom glands for transcriptomic analysis. These venom glands were therefore actively engaged in venom regeneration at the time of sampling. Thus, as expected, the venom-gland transcriptome was highly enriched in transcripts encoding proteins involved in translation, processing, folding and posttranslational modification of venom toxins. Non-toxin contigs/singlets were classified into EuKaryotic Orthologous Groups (KOGs) and classified according to gene ontology (GO). Blast2GO was used to generate summaries for the most common GO types: 'biological', 'cellular' and 'molecular' processes (Supplementary Figure  3S ).
Phylogenetic analyses suggests a single early recruitment of ICK toxins in H. infensa In order to investigate whether the diverse and abundant ICK toxins in spider venom have a common origin, we examined their phylogenetic relationship with homologous sequences from non-venom gland spider tissues (i. e. , hypodermal tissue from Cupiennius saliei and leg tissue from Liphistius malayanus and Neoscona arabesca) and tissue from a non-venomous uropygid arachnid (Mastigoproctus giganteus) (Fig. 6 ). BLAST searches of all identified spider venom ICK variants against these non-venom datasets returned only hits to venom peptides with classical ICK folds, suggesting the elaborate ICK structures observed for several of the venom peptide superfamilies found in H. infensa represent extreme cases of structural diversification following functional recruitment as toxins.
A loss of intramolecular disulfide bonds may seem counterintuitive during the evolution of venom DRPs for which stability is paramount. However, intriguingly, the stabilising loop-4 disulfide bridge was present in all homologues identified in the outgroup datasets (data not shown). Moreover, disulfide loss appears to have been crucial during evolution of DDH toxins  11  from ICK precursors in scorpions  23  , suggesting that this scenario may not be as unlikely as previously postulated  11, 50. Regardless of whether the ancestral ICK spider toxin contained the loop-4 disulfide, our data indicate that gain of disulfide bonds underlies the myriad of structural variations in ICK spider toxins. This not only highlights the importance of disulfide bonds in stabilising the structure of spider toxins but further illustrates the extraordinary versatility of the ICK fold.
Venom profiling The venom profile of the Australian funnel-web spider H. infensa was obtained using a combination of reversed-phase (RP) HPLC and MS techniques  6. Offline LC-MALDI-TOF/TOF was utilized in order to maximize the amount of mass information extracted. A conservative analysis was used to manually curate MS datasets. Analysis criteria included: (a) only spectra with well-defined peaks were considered as "real"; (b) a signal-to-noise ratio of 15 was set before any masses were recorded; (c) masses were excluded if they met the following criteria relative to another recorded mass: +16 Da and +32 Da (oxidized), -18 Da (dehydrated), -17 Da (deamidated), +22 Da (sodium adduct); (d) masses <1000 Da, which were presumed to correspond to small organic compounds such as polyamines or matrix clusters, rather than peptides, were excluded from the analysis.
Duplicated masses were eliminated from adjacent fractions and a final list of sorted masses was generated. Potential dimers and doubly charged species were also eliminated (± 3-5 Da). 3D contour plots, or 'venom landscapes', were constructed from LC-MALDI-TOF/TOF MS data as described previously  6. For proteomic analysis of venom composition, 5 mg of milked venom pooled from three spider specimens was fractionated on a Shimadzu Prominence HPLC (Kyoto, Japan) using a Vydac C 18 RP-HPLC column (300 Å, 5 µm, 4. 6 mm × 250 mm). Venom was eluted across a gradient of 5-80% solvent B (90% acetonitrile [ACN], 0. 043% trifluoroacetic acid [TFA]) in solvent A (0. 05% TFA) over 120 min. Twelve fractions were manually collected (one every 10 min) and absorbance was monitored at 214 nm and 280 nm. Fractions were lyophilized, reconstituted in ultrapure water, and 1/10 removed for proteomic analyses.
SFF) was processed using Cangs software, and low quality sequences discarded (Phred score cut-off of 25)  53. De novo assembly was performed using MIRA software v3. 2  31  using the following parameters: -GE:not=4 --project=Hinfensa --job=denovo,est,accurate,454 454_SETTINGS -CL:qc=no -AS:mrpc=1 -AL:mrs=99,egp=1. The assembled dataset was visualized using Geneious software (www. geneious. com). Consensus sequences from contigs and singlets were submitted to Blast2GO (www. blast2go. com) to acquire BLAST and functional annotations  32. In parallel to the functional annotation analysis, an in-house algorithm (Toxin|seek) was written to allow prediction of potential toxin ORFs that may represent novel DRPs. Once annotated and predicted, lists were merged, redundancies were removed, and signal sequences determined using SignalP  34.
Structure determination The structure of toxins in superfamily SF6, SF22 SF23 and SF26 was determined using heteronuclear NMR spectroscopy. Each sample contained 300 µL of 13 C/ 15 N-labelled peptide (100-300 µM) in 20 mM 2-(N-morpholino)-ethanesulfonic acid (MES) buffer, 0. 02% NaN 3 , 5% D 2 O, pH 6. NMR data were acquired at 25 °C on an Avance II+ 900 MHz spectrometer (Bruker BioSpin, Germany) equipped with a cryogenically cooled triple resonance probe. Resonance assignments were obtained from the following spectra: 2D 1 H-15 N HSQC, 2D 1 H-  13  C HSQC, 3D HNCACB, 3D CBCA(CO)NH, 3D HNCO, 3D HBHA(CO)NH and 4D HCC(CO)NH-TOCSY  48  spectra. All 3D and 4D spectra were acquired using nonuniform sampling and reconstructed using the maximum entropy algorithm in the Rowland NMR Toolkit  55. 13 C-aliphatic,  13  C-aromatic and  15  N-edited NOESY-HSQC spectra were acquired using uniform sampling for the extraction of interproton distance restraints.
Resonance assignments and integration of NOESY peaks were achieved using SPARKY  56  , followed by automatic peak list assignment, extraction of distance restraints, and structure calculation using the torsion angle dynamics package CYANA 3. 0 57. Dihedral-angle restraints derived from TALOS chemical shift analysis  58  were also integrated in the calculation, with the restraint range set to twice the estimated standard deviation. CYANA was used to calculate 200 structures from random starting conformations then the best 20 structures were selected based on their stereochemical quality as judged using MolProbity  59  (Supplementary Table  S2 ).
Homology modelling Superfamilies that were identified to have a structural counterpart in the Protein Data Bank (see Supplementary Table  S3 ) were modelled using the automodel parameters in Modeller v9. 12  60. A total of 100-500 models were generated for each structure. The lowest-energy models were selected and visualized using PyMOL v. 2. 0 (Pymol Molecular Graphics system, Schödinger, LLC).
Phylogenetic analyses We used Bayesian inference of phylogeny and maximum likelihood reconstruction to examine the molecular evolution of spider-venom ICK toxins. In order to search for non-venom outgroups, we examined datasets from one species of whip scorpion (Mastigoproctus giganteus (Uropygi); SRR1145698), which is non-venomous and regarded as the sister group to spiders  61  , and non-venom gland transcriptomes from three species of taxonomically diverse spiders: hypodermal tissue from Cupiennius saliei (SRR880446); leg tissue from Liphistius malayanus (SRR1145736); and leg tissue from Neoscona arabesca (SRR1145741). Transcriptomes were downloaded and converted to fastq files using fastq-dump v2. 5x included in the SRA Toolkit (https://trace. ncbi. nlm. nih. gov/Traces/sra/sra. cgi?view=software), trimmed using trimmomatic  62  with window quality 30, window size 4, minimum read length 60 bp, then assembled with Trinity 63 using default settings.
The resulting assemblies were combined and all predicted coding DNA sequences (CDSs) extracted using the "Get open reading frames (ORFs) or coding sequences (CDSs)" tool in Galaxy  64. We then used BLAST+  65, 66  to search all members of each ICK superfamily against the resulting assemblies, as well as UniProtKB and the NCBI non-redundant (nr) and transcriptome shotgun assembled (TSA) databases, with an expected cut-off value of e-3. The resulting hits were filtered based on the presence of a predicted signal peptide using SignalP  34  , and aligned with MAFFT by using L-INS-i 67. The alignment was then adjusted manually in CLC Main Workbench v7. 6. 1 (CLC-Bio, Denmark) to correct misaligned structurally equivalent cysteine residues before realigning the inter-cysteine loops using the MAFFT regional re-alignment script (http://mafft.
cbrc. jp/alignment/software/regionalrealignment. html). The resulting alignment was used for Bayesian inference of phylogeny by MrBayes v3. 2. 2  68  ; we performed two simultaneous runs for 10,000,000 generations using lset rates=gamma with prset aamo-delpr = mixed command, which enables the program to optimize between nine different amino acid substitution matrices implemented in MrBayes. The log-likelihood score of each saved tree was plotted against the number of generations to establish the point at which the log likelihood scores reached their asymptote, and the posterior probabilities for clades was established by constructing a majority-rule consensus tree for all trees generated after completed burn-in phase. We also used IQ-Tree v1. 3. 6  69  for phylogenetic reconstruction by maximum likelihood, using the TESTNEW command to let IQ-Tree determine the best fitting gene model according to a Bayesian information criterion analysis.
Node support was tested by ultrafast bootstrap approximation (UFBoot) 70 using 10,000 iterations. Trees were rooted using Mastigoproctus giganteus as an outgroup, and visualised and exported using Archaeopteryx v0. 99 (http://www. phylosoft. org/archaeopteryx) and FigTree v1. 3. 1 (http://tree. bio. ed. ac. uk/software/figtree/). ERA298588 (HACE01000001-HACE0100120). Atomic coordinates of peptide structures solved in this study were deposited in the Protein Data Bank under accession codes 2N6N, 2N6R, 6BA3 and 2N8K, while corresponding NMR chemical shifts were deposited in BioMagResBank under accession numbers BMRB27774, BMRB25778, BMRB25853 and BMRB30352. Figure Legends.
Acknowledgments We acknowledge financial support from the  Australian Research Council  ( Discovery Grant   DP130103813  to G. F. K. and  DECRA Fellowship   DE160101142  to E. A. B. U. ) and the  Australian National Health & Medical Research Council  ( Principal Research Fellowship  to G. F. K. ). We thank  David Wilson  for collecting H. infensa specimens, the  Australian Genome Research Facility  for bioinformatic assistance,  Philip Lawrence  for assistance with mass matching of MS data, and  Julie Klint ,  Niraj Bende  and  Jessie Er  for help with recombinant peptide production. We thank  Michael Nuhn  and  Lien Li  from the  Bioinformatics Resource Australia-EMBL  for help with data submission to the  European Nucleotide Archive.
} , temperature with  T ∇ 4 T, and tracers with  q ∇ 4 q. The damping coefficients for divergence ( div ), vorticity ( vor ), level thickness ( p ), temperature ( T ), and tracers  q are resolution dependent and provided in Appendix A. 3. These coefficients were determined empirically for stability and may have to be increased for specific applications such as data assimilation cycling where additional damping to remove short wavelengths due to imbalances may be necessary. Note that the viscosity coefficient for pressure,  p , and tracers,  q , should be the same; otherwise, the model is no longer free-stream preserving (i. e. , a constant mixing ratio is preserved, also referred to as mass-wind consistency in the literature). The dispersion properties of CAM-SE with hyperviscosity are similar to A-grid models.
Simulation Results As part of the CESM simpler model effort started by  Polvani et al. (2017) , the moist baroclinic wave setup has been implemented rigorously in the CESM in the sense that the configuration easily runs from CESM without code configurations using the FKESSLER compset. For instructions on how to run the moist baroclinic wave with Kessler microphysics see  Lauritzen and Goldhaber (2017). Since the test case configuration has been implemented in the full CESM, the dynamical core interacts with the physics module as in full climate model simulations. Hence, the global energy fixer is invoked  (Williamson et al. , 2015)  and for the dynamical cores using a (moist) pressure vertical coordinate there is an adjustment of specific humidity to conserve water after the moist physics updates (see section 3.
To understand how the design choices adopted by CAM-SE influence the aqua-planet solutions compared to its predecessor, CAM-HOMME, we ran a pair of simulations using the CAM6 physics package for 4. 5 years at 1 ∘ horizontal resolution (N e = 30 and N p = 4) and 32 levels in the vertical (model top is at approximately 3. 6 Pa). Figure  6  shows the total kinetic energy spectrum at the 200-hPa level in the two simulations. Compared to CAM-HOMME, the slope of the kinetic energy spectrum in CAM-SE is shallower for wavenumbers larger than 30, bringing the solutions closer to the empirically  (Nastrom & Gage, 1985)  and theoretically  (Charney, 1971 ) determined slope of -3 at synoptic scales. The increased kinetic energy at smaller scales is due to reducing the explicit diffusion coefficients used in CAM-SE compared to CAM-HOMME rather than the reformulation of the dynamical core in dry-mass vertical coordinates or a more comprehensive treatment of moist energy.
In the equatorial regions, additional heating likely facilitates buoyancy, which increases resolved vertical motion, leading to an increase in precipitation. One of the motivations for moving to a dry-mass vertical coordinate is to make the coupling between the physics and dynamics more consistent. While the developments presented in this work conclude a significant portion of the effort required to achieve consistent coupling, at present, the CAM physics packages have not yet been made consistent with a dry-mass vertical coordinate. The speculate that some of the sensitivity to c p shown in Figure  7a  is related to the definitions of internal energy between CAM-SE and the physics packages. An inconsistent energy definition between the dynamical core and the physics package can result in inconsistent forcing terms being passed back and forth.
It is clear from Figure  11  that the execution time for the dynamical core for the optimized version (CAM-SE) is significantly less than the original codebase (CAM-HOMME) for all core counts. The reduction in execution time for the optimized versus the original code varies from approximately 20% at small core counts to slightly more than 50% at larger core counts. The percentage reduction in execution time for the optimized versus original code is readily apparent by looking at the relative computational cost curves indicated by the dotted lines in Figure  11. A value greater than 1 indicates that it is more expensive to run a particular configuration than the original code on five nodes, while a value less than 1 indicates that it is cheaper to run a particular configuration. This approach allows for the comparison of both the impact of the optimizations have on a particular node code as well as the impact of optimizations to code scalability.
A set is made of three keys (which value may be equal). From that set, session keys are computed each time a new channel is established. The card manages a sequence counter related to a given keyset. Its initial value is 0 and incremented after each successful session (established with that keyset). Once the sequence counter has reached its maximum value, the card must not start anymore a session with the corresponding keyset. Depending on the security level negotiated during the key exchange 7  , the commands sent by the server and the responses sent by the card are encrypted and protected with a MAC tag. Regarding the commands, the lowest security level is data integrity. Moreover data encryption solely is not allowed. Data encryption is done with 3DES in CBC mode with a null IV [ISO17].
Once into the Android smartphone, the Trojan may escalate privileges to gain root access  [Kiv] , or inject a malicious code into system libraries, which will then be executed with system rights  [Unu]. To that point, the Trojan is able to get access to the memory space of the legitimate application. Hence, it can read the genuine SCP02 commands (carrying the symmetric key), and modify it. The crafted command is then sent to the UICC, completing the process initiated by the legitimate application. Let us assume that encrypting the applet with SCP02 yields k blocks C 0 ,. , C k-1 (possibly transmitted to the card through several successive STORE DATA commands), and that a 16-byte symmetric key lies in the blocks C i , C i+1 , i + 1 ≤ k -1. The number of SCP02 sessions a card can establish is bounded (to 2 15 ) as per specification.