dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, the cost penalty to pay (irrespectively if the solution is mono or multiprocess) is a replica of the flow manager data structure to avoid locking. Given a fixed memory budget, it is important to finely tune this structure, as the memory has to be partitioned among multiple independent structures, which are not shared among threadshence they require no synchronization, or they have no locking issues which reduce the system performance. At the same time, as each structure receives only a portion of the traffic, it may work efficiently with a reduced footprint. From a more general viewpoint, it would be interesting to optimize flow management operation (as this would possibly lead to extend performance well beyond the 14. 2Mpps limit of  [31] ). Optimization includes not only sizing, but also critical design choices, such as the type of structure (e.
g. , intrusion detection may block subsequent packets of a flow regardless of whether they are received in-order). As such, flow reordering, as for the scope of this work, is left to the spare CPU cores of Fig. 1  and is not performed in what follows. We release this emulator as an open source software at [2]: with two thousands lines of C code, it embeds both the flow feeding interfaces, the main hash table look-up and the collision managers. About the latter, while the one based on lists pre-allocates directly in the hash table one empty flow element per table line, the one based on the STL RBtree library creates an hash table of tree roots  [21]. Clearly this has an impact when chaining is low, as the first flow inside a list is simply copied inside the corresponding empty flow element, while the analogous in the empty tree root requires an initial tree adjustment.
If otherwise we simulate a time horizon by adding up the delay required to transmit every single packet over a 10Gbps channel (in that case we would have to store each packet length together with the corresponding tuple), we alter the performance evaluation as the flow matching is in real time. For this reason, we opt for using the system clock: while this does not accurately account for performance of the real system, it represents a conservative lower bound of the actual performance (due to higher average occupancy of the data structure with respect to the expected one in real time). All emulation results are gathered over a 4-cores 3. 60 GHz Intel Xeon ES1620 CPU board, equipped with 4x8 GB DIM DDR3 1600Mz RAM memory modules, running Ubuntu 12. 04LTS with 64bit Linux kernel version 3. 5.
As empirical settings of the emulation environment may severely affect the emulation outcomes, we disabled any "smart" feature that dynamically change the rate at which the CPU is working, by (i) disabling hyper-threading features at the BIOS level (ii) fixing CPU frequency governors to performance settings, so that CPUs always run at full 3. 60 GHz rate (unlike in default on-demand configuration that dynamically tunes the CPU frequency settings). Similarly, we notice that an aggressive caching policy of the Linux kernel severely affects the performance, depending on the data structure being used: e. g. , while in case of lists the whole structure is proactively allocated, in case of trees allocations happen on demand, and system caching may impact pages where the memory is allocated. We therefore sync and drop caches (/proc/sys/vm/drop_caches) prior to run any emulation to remove bias due to kernel-level memory management policies.
Related Work Monitoring at the flow level requires matching each packet to the correct flow bin. In software-based solutions such as Bro  [25] , Snort  [29] , Tstat  [30] ,  CoralReef [5]  or YAF  [20]  this is usually accomplished by using hash-based structures over the flow 5tuple. In  [23]  the authors point out that most time-consuming operations in systems such as Bro and Snort, are related to tracking the connections -which precisely motivates this work. However, to the best of our knowledge, the performance of flow matching code in complex monitoring and intrusion detection systems is rarely evaluated in a systematic fashion. In particular, only limited works report on the performance of the data structure and of the hashing functions being used when implementing such operations  [24].
Comparing the performance of flow management modules of heterogeneous systems (e. g. , Bro, Snort, Tstat and YAF) is hard since the full set of operations performed beyond flow-management are different, and so are the traces used as input to the evaluation. As such, extrapolating such data from overall measurements  [17, 20, 23, 30, 33]  can be misleading: to prevent this risk, we rely on publicly available dataset and software. Explicit performance for systems using dedicated hardware is reported instead in  [7, 8, 15, 26, 32] , that we summarize in Tab. 2. In [7], using a dual Xeon box hosting a dedicate Endace DAG card, authors match flows at a rate up to 6 Mpps. In  [26]  an Intel IXP2850 Network Processor is shown matching 10 million concurrent flows at 10 Gbps at full packet rate. Switching to off-the-shelf setup, an application note from Intel [8] reports flow matching of trains of 64 bytes packets at 17 Mpps out of 24 Mpps received over 16× 1 Gbps interfaces, where each NIC is tied to a different core of an Intel multicore CPU system (unfortunately the study does not report the number of concurrent flows).
-Hash functions play a minor role, at least when structures are properly sized, with computational complexity eroding the advantage of hashes with better entropy properties. Clearly, this work is by no means complete: expansions can include a larger spectrum of hash functions (e. g. , CRC32, One-at-a-Time, FNV [9], Hsieh [6] among others, especially aiming at lower computational complexity) or data structures (e. g. , double hash, cuckoo hashing  [34] , denser hashes as in DPDK [10]). Additionally, low-level system aspects such as memory management, including translation lookaside buffer, memory fragmentation and alignment, and NUMA allocation (the latter considered by us in  [31] ), likely play an important role and deserve attention. A second direction is to replicate this study over a wider dataset including traces from different network segments, in an attempt to find consistently good setting that can be recommended for different environments.
Sequencing, assembly and annotation of the apicoplast genome Genomic DNA used to complete the sequence of the R1 apicoplast genome was previously described  [11, 12]. The apicoplast genome sequence was obtained by primer walking using long PCR reactions. PCR amplification was performed using total genomic DNA and primers designed following sequencing of apicoplast genome contigs obtained after the first genome assembly  [11]. Sequencing was performed on both strands and from at least two PCR products using specific primer pairs (Table  S1  in File S1). The complete sequence of the apicoplast genome was deposited in the European Nucleotide Archive with Accession Number LK028575. Genome annotation was performed using Artemis (https://www. sanger. ac. uk/resources/software/artemis/)  [17]  and BLAST (http://blast.
ncbi. nlm. nih. gov/Blast. cgi). Prediction of tRNA genes was performed using tRNAscanSE 1. 21 (http://lowelab. ucsc. edu/tRNAscan-SE/) with the following parameters: search mode: ''default'', and source: ''Mito/Chloroplast''  [18]. Genetic maps were obtained using CGView (http:// stothard. afns. ualberta. ca/cgview_server) [19]  and GenomeVx (http://wolfe. ucd. ie/GenomeVx) [19]. Genome comparisons were performed using Mauve (http://gel. ahabs. wisc. edu/mauve) [20]  and edited using ACT (https://www. sanger. ac. uk/resources/ software/act)  [21]. Manual editing to correct the above automated analyses was completed on a gene-by-gene basis as needed. The following entries were used for comparative analyses: AAXT01000007 (B. Bovis), HM222968 (Chromera), fusion of X95275 (IRA) and X95276 (IRB) (P.
falciparum), AAGK01000009 (T. parva), U87145 (T. gondii). Phylogenetic analyses were performed using Phylogeny. fr with default options  [22]. TMpred (http://www. ch. embnet. org/software/ TMPRED_form. html) and Pfam (http://pfam. sanger. ac. uk/) were used for transmembrane domain and motif predictions  [23, 24].
Noteworthy, whereas the chloroplast genome of Chromera expresses an rff gene, no rff genes were found in the apicoplast genomes of all apicomplexan parasites sequenced to date. Annotation of the apicoplast genome of B. microti revealed 5 hypothetical coding sequences (hypA-E). The encoded proteins do not share significant homology with any protein in available databases and do not contain any recognizable functional domains. Examination of the apicoplast genomes of other apicomplexan parasites shows the presence of unknown but dissimilar proteins in the same genomic regions. Whether these CDSs are expressed or are an artifact of annotation remains to be determined.
This work was partly supported by the  National Institutes of Health  [ AI097218  to CBM], and the  Bill and Melinda Gates Foundation  (Awards  OPP1069779  and  OPP1086229  to  CBM) , the  ANR   program Investissement d'avenir  ( ANR-11-BINF-0002  to EC) and the  French ministry of research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Challenges Argumentation mining of this type of discourse will be challenging. A challenge that is shared with BioNLP text mining in general is dealing with the extensive use of biological, chemical, and clinical terminology in the BioNLP domain. A number of challenges specific to argumentation mining are discussed next. To specify an argument it is necessary to identify the premises (or data and warrant), conclusion, and argumentation scheme. However, as illustrated in the previous examples, arguments with implicit components (enthymemes) are common, e. g. , where a conclusion is implicit or used as an implicit premise of another argument. A related challenge is to supply domain knowledge for reconstructing implicit warrants in this genre. Another related challenge is the need to make use of discourse context to supply missing information, e.
Results and discussion Crystal packing analysis revealed that two symmetryrelated molecules in the P4 1 22 crystal formed an intermolecular b-sheet resulting in an interface area of 585 A ˚2. The same interface was also observed for chains B and C in the P4 3 2 1 2 crystal form. However, a single population with a sedimentation coefficient of 1. 85 S corresponding to a monomer with a hydrodynamic radius of 25 A ˚was clearly assessed using analytical ultracentrifugation (AUC; figure  2a ). A similar hydrodynamic radius value was also derived from the retention volume of the BR 187 -385 monomer using size exclusion chromatography (SEC; data not shown). Finally, small angle X-ray scattering (SAXS) analysis of BR 187 -385 revealed a monomer in solution with a molecular weight estimated from the forward scattering I(0) to 18 + 2 kDa and from the Porod volume to 23 + 2 kDa (expected at 22 kDa; figure  2b ; electronic supplementary material, table  S1 ).
Although they shared only 5-17% sequence identity with BR 187 -385 , they all superimposed to BR 187 -385 with r. m. s. d. values stretching from 3. 0 to 4. 7 A ˚. In particular, both crystal structures of the CnaA-subdomains of the SRRPs Fap1 and GspB superimposed to BR 187 -385 with r. m. s. d. values of 3. 0 and 3. 2 A ˚, despite rsob. royalsocietypublishing. org Open Biol. 4: 130090 Table  1. X-ray data collection and refinement statistics.
Data collection and determination of the crystal structure of BR 187 -385 X-ray diffraction data from crystals of native and Se-Metsubstituted BR 187 -385 , both collected at beam line ID29 at the synchrotron radiation facility at the ESRF (Grenoble, France), were processed using the XDS program package  [36]  (table  1 ). The SAD dataset of a P4 3 2 1 2 Se-Met-substituted BR 187 -385 crystal diffracting to 2. 25 A ˚was used to determine the crystal structure of BR 187 -385 , based on the SAS protocol from AUTO-RICKSHAW  [37]. Almost complete models for three BR 187 -385 molecules were obtained that were complemented through automatic rebuilding in BUCCANEER  [38]. COOT was used for all subsequent model building  [39]. An MR search was performed using PHASER  [40] , and a single BR 187 -385 molecule was located in the asymmetric unit of the native crystal.
Initial rigid body and restrained refinement rounds were performed in CCP4 REFMAC  [38]  followed by model refinement using PHENIX  [41]  with individual rsob. royalsocietypublishing. org Open Biol. 4: 130090 isotropic ADP factors and TLS refinement of the entire chain. A single Ramachandran plot outlier was found in the final model corresponding to residue T271, located in a b-turn motif with weak electron density. Finally, the side-chain atoms O g1 and C g2 of residue T378 were not built as a result of poor electron density. The structural model was used to further refine the model corresponding to the anomalous dataset, using a simulated annealing protocol with subsequent LBFGS minimization with individual isotropic ADP factors, whole-chain TLS group refinement and NCS Cartesian restraints.
Structural analysis of BR 187 -385 The hydrophobicity of BR 187 -385 was assessed using the program package VASCo 1. 0. 2 [42]. PDB2PQR 1. 7. 1  [43]  and APBS 1. 3 in PYMOL  [44]  were used to calculate the electrostatic surface potentials. All figures were created using PYMOL version 1. 3. 0 [45]. Further programs used for structural analysis are listed in the electronic supplementary material.
Analytical ultracentrifugation analysis and sedimentation velocity experiments Sedimentation velocity experiments were carried out on an analytical ultracentrifuge XLI (Beckman Coulter, Palo Alto, CA) with a rotor speed of 50 000 rpm, at 208C, using a rotor Anti-50, and double-sector cells of optical path length 12 or 3 mm equipped with sapphire windows. Acquisitions were made using absorbance at 280 nm. Two samples of BR 187 -385 in 20 mM sodium citrate, 250 mM NaCl, 2. 5% glycerol pH 5. 5 were investigated. Solvent density of 1. 017 g ml 21 and viscosity of 1. 11 mPa s were measured at 208C on density-meter DMA 5000 and viscosity-meter AMVn (Anton Paar), and the partial specific volume was estimated to 0. 721 ml g 21 with the program SEDNTERP. The analysis was carried out in terms of distribution of sedimentation coefficients, c(s), and non-interacting species, with SEDFIT software, version 14.
The molecular mass (MM) of BR 187 -385 was evaluated by comparison of the forward scattering with that from a reference solution of bovine serum albumin (MM ¼ 66 kDa). The excluded volume of the hydrated protein was computed with the program AUTOPOROD  [49]. For globular proteins, the hydrated volumes in A ˚3 are about 1. 6 times the MMs in Dalton. To assess the flexibility of BR 187 -385 , the EOM  [50]  was used. See the electronic supplementary materials for details about data collection and analysis. 4. 8. Pull-down assay KRT10-FL was immobilized to 30 ml Ni-NTA magnetic agarose beads (Qiagen, Germany) in 10 mM potassium phosphate, pH 7. 2, 250 mM NaCl, 0. 05% Triton X-100 (magnetic PBS buffer). Beads not loaded with KRT10-FL protein were used as negative control. Beads were washed using 10 mM potassium phosphate, pH 7.
Acknowledgements. We gratefully acknowledge excellent technical assist- ance by A ˚ke Engstro ¨m, fruitful discussions with  Tatyana Sandalova ,  Frederico Fogilari  and  Peter Mellroth , as well as access to synchrotron beamline ID29 at  ESRF (Grenoble, France). This work was facilitated by the  Protein Science Facility at Karolinska Institutet/SciLifeLab  (http://psf. ki. se). rsob. royalsocietypublishing. org  Open Biol. 4: 130090.
Funding statement. This work was supported by grants from the  Swedish Research Council  (A. A. , B. H. -N. , S. N. ), the  Knut and Alice Wallenberg Foundation  (A. A. , B. H. -N. , S. N. ), the  Wenner Gren Foundation  (T. S. ), the  European Community 's  Seventh Framework Programme  (FP7/ 2007-2013) under BioStruct-X (grant agreement no. 283570 ), and used the platforms of the  Grenoble Instruct Centre (ISBG ;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
Background: Eukaryotic cellular machineries are intricately regulated by several molecular mechanisms involving transcriptional control, post-translational control and post-translational modifications of proteins (PTMs). Reversible protein phosphorylation/dephosphorylation process, which involves kinases as well as phosphatases, represents an important regulatory mechanism for diverse pathways and systems in all organisms including human malaria parasite, Plasmodium falciparum. Earlier analysis on P. falciparum protein-phosphatome revealed presence of 34 phosphatases in Plasmodium genome. Recently, we re-analysed P. falciparum phosphatome aimed at identifying parasite specific phosphatases. Results: Plasmodium database (PlasmoDB 9. 2) search, combined with PFAM and CDD searches, revealed 67 candidate phosphatases in P.
In contrast to a large number of kinase catalytic subunits, phosphatases catalytic subunits are much lower in number. Phosphatases are in general less discriminating than the most of the kinases in substrate selectivity but have very high catalytic efficiency making it highly toxic for the cells in active form  [27]. A number of accessory proteins called phosphatase regulatory subunits controls the specificity and regulation of most phosphatases  [22] , bringing about an element of specificity in its actions. In the present study, we re-examined the Plasmodium phosphatome using PlasmoDB database  [28]  and conserved domain database (CDD) analysis. The results showed identification of sixty seven putative phosphatase sequences in the Plasmodium genome, which is significantly higher than previously reported  [22].
Number and percentage of phosphatases belonging to each group varies among different organisms (Additional file 2). For example, the maximum number of H. sapiens phosphatases belongs to phosphotyrosine phosphatase family, whereas majority of the A. thaliana phosphatases belong to PP2Cc and MPP family. Based on the classification adapted for human phosphatases and considering the other functional phosphatase domain, we grouped Plasmodium phosphatases into 13 super families using CDD. Schematic diagram of phosphatases in each superfamily is shown in Figure  2  (Additional file 3). Conserved domain alignment of P. falciparum phosphatases are shown in Additional files 4 and 5. Table  1  shows the name, accession number and classes of Plasmodium phosphatases analysed in the present study. Identity and the number of phosphatases observed in each family remained proportionally similar in the CDD or PFAM domain classifications (Figure  3 ).
STRING database analysis shows that PP1 interacts with Pfn conserved proteins essential for the invasive blood stages of the parasite. Blisnick et al. have shown that PP1 plays an important role in the release of infectious merozoites  [37]. Experimental evidences show that PfLRR1  [38]  and PP1 regulators (PfI2, pfI3) inhibit PP1 catalytic activity  [39, 40]. PP1 is able to functionally complement a glc7 (PP1) mutation in S. cerevisiae  [41]. Inhibition activity using PP1 specific inhibitors I-1 and I-2 shows that PP1 is required for the erythrocyte reinvasion but might be dispensable for schizont maturation. PP1 depletion analysis using siRNA, showed inhibition of cell cycle progression in all erythrocytic stages  [42]. It is also constitutively expressed  [41, 42]  as shown by mass spectroscopic studies  [43].
BSU1, an A. thaliana PPKL is a nucleus localized protein studied in detail and appears to be involved in regulating signals from the brassinosteroid plant hormones  [45]. The limited distribution of PPKLs (these proteins have been found only in plants and apicomplexans) is reminiscent with that of other gene families and is in line with the proposed photosynthetic ancestry of apicomplexa  [46, 22]. Recent publications on PPKL have shown that it plays an important role in ookinetes morphology, motility and invasion  [18, 47]. PF3D7_1018200 also contain MPP_BSU1 catalytic domain but lacks Kelch like motif in the N-terminal region. STRING database analysis indicates that this phosphatase may interact with the various vacuolar proteins. It is also predicted to interact with cysteine proteinase falcipain, which plays important role in haemoglobin degradation, an essential process for parasite survival.
PP2A, PP4, and PP6: PF3D7_0314400 was assigned to PP2A group of protein phosphatases  [22, 48]. PP2A (Protein phosphatase 2A) is a critical regulator of many cellular activities, and comprises about 1% of total parasite cellular proteins. PP2A, together with protein phosphatase 1 (PP1), accounts for more than 90% of all serine/ threonine phosphatase activities in most cells and tissues. Interacting partner prediction analysis indicates that it interacts with a wide range of proteins and enzymes like phosphotyrosyl phosphatase activator, Hsp70/Hsp90 organizing protein (HOP), tRNA m5C-methyltransferase, mRNA-decapping enzyme 2 (DCP2), and actin-like protein. Vandomme et al. reported that phosphotyrosyl phosphatase activator binds and activates PfPP2A  [49]. STRING database analysis identifies interacting partners similar as of PPKL phosphatase; this similarity might be because of the high similarity of PPKL catalytic subunit with PP1 catalytic domain.
Subsequent analysis demonstrated the primary PF3D7_1423300 translation product to be much larger than the PP catalytic domain, with two EF-hand motifs that must be occupied by Ca 2+ for the enzyme to become fully active  [57]. The small size originally predicted for PfPPJ was due to a spurious stop codon in the original cDNA  [56] , but a fragment corresponding to this size is apparently produced by post-translational processing, detected by Western blotting. Despite the presence of calmodulin binding motif in N-terminal domain, recombinant PfPP7 expressed in E. coli is insensitive to calmodulin, but is stimulated by the presence of motifs in C-terminal domain; and its activity is regulated by auto-inhibition by Ca 2+ binding  [57]. STRING database analysis shows its co-expression with cAMP dependent protein kinase, rhoptry protein, merozoites surface protein and others showing it may play a vital role in parasitic growth and its invasion process.
Shelphs: PF3D7_1206000 and PF3D7_1469200 belongs to Shewanella-like phosphatases  [36]. The characteristic feature of these phosphatases is the absence of SAPNY motif and the presence of another motif, (I/L/V) D(S/T) G, which may alter their substrate preference from Ser/Thr to broad or even Tyr specific  [36]. PF3D7_1469200 is involved in microneme development in Plasmodium ookinetes development and parasite transmission  [20]. PF3D7_1206000 (SHLP1) and PF3D7_1469200 (SHLP2) are characterized by N-terminal signal sequences. PF3D7_1469200 possess an additional apicoplast targeted motif  [58] [59] [60]. PF3D7_1206000 has been likely to be involved in invasion of merozoites stage  [19, 61, 62]. STRING database analysis reveals that PF3D7_1469200 interacts with myosin d, actin-depolymerising factor, calcium-dependent protein kinase 4 and Erythrocyte membrane protein.