dataesr/scientific-paragraphs-categorization huggingface dataset:
The large maritime domain of AROME-WMED is able to well describe the atmospheric circulation over the Mediterranean Sea and the neighbouring mountains that constrain this circulation. As mentioned in Sect. 2. 3, the operational version of ARPEGE and AROME-France was upgraded during SOP1 on 25 September 2012 at 06:00 UTC. In order to provide a homogeneous field data set in the HyMeX database, a first reanalysis with a unique version of the forecasting system, taking into account additional data, is already available to the scientific community. The main changes are the following: more observations have been assimilated, the cut-off time for the available observations has been removed for the data selection, the assimilation of some mobile soundings which could not be used in real time (dissemination issue) has been performed and the final forecast range has been extended up to 54 h in order to enable comparison of the 2-day precipitation forecast to the 06:00-06:00 UTC 24 h observed precipitation accumulation.
However, a second reanalysis is also scheduled in order to improve the previous AROME-WMED versions. It will be produced from the current operational (2014) version of the AROME software and with additional observations from the campaign. These data will comprise several Spanish radars data provided in real time by AEMET, wind profilers, dropsonde data, reprocessed GPS ground stations, high-resolution radiosoundings, research aircraft in situ measurements and lidar observations. The orography of the model will also be revised with a more recent physiographic database. The definition of background error covariance matrix for the SOP1 period will be also ad-dressed. This new AROME-WMED data set should be available in 2015. The availability of AROME-WMED analyses and forecasts to the HyMeX scientific community will allow dedicated studies towards an improved understanding of high-precipitation events over the north-western Mediterranean Sea.
Acknowledgements. The authors would like to acknowledged the  MISTRALS/HyMeX programme  and the funding by  ANR  under contract  IODA-MED ANR-11-BS56-0005. The development of the AROME-WMED model was initiated by  Véronique Ducrocq  and  Geneviève Jaubert. The set-up and the validation of AROME-WMED configuration was due to the support and implication of many Météo-France services. The  CNRM/GMAP group  is firstly acknowledged for its support in many aspects of the AROME-WMED run. J. Stein  and GCO team are also thanked for helping with the set-up of the AROME-WMED model. The success of the AROME-WMED run in real time was enabled by  H. Lebras ,  N. Merlet ,  J. -L. Robert ,  M. Pottier  and  S. Stamatiou  with the desk operator team that supervised the AROME-WMED model 24 h a day as well as their routine work on operational models.
Assembly Combined sequence reads from different libraries (including 3-and 8-kb mate pairs) were assembled using the Newbler software version 2. 6  (Margulies et al. 2005) , and the Newbler contigs were then assembled into scaffolds using Bambus version 2. 3  (Pop et al. 2004) , and a custom transcriptome-guided scaffolding algorithm. The 10. 0 draft assembly is composed of 69,687 scaffolds with an N50 scaffold length of 54,474 kb and an N50 contig length of 2. 2 kb. The assembled coverage is 54. 6Â, and the assembly spans over 220 Mb. We removed from the assembly all contaminating sequences, trimmed vectors (X), and ambiguous bases (N). Additionally, shorter contigs ( 200 bp) were removed before public release. The draft genome sequence of P. polycephalum was deposited in the DDBJ/EMBL/GenBank database (Accession Number ATCM00000000.
Generation of a Reference Transcriptome All transcript data were assembled according to their sample origin, as previous analyses showed that genes have a wealth of introns and thus alternative splicing could play a role  (Glo ¨ckner et al. 2008). This initial analysis yielded 733,443 potential transcripts. After clustering with usearch (Edgar 2010), we obtained 31,770 clusters, each represented by the largest sequence in a cluster. The mean length of transcripts in the thus constructed reference transcriptome is 1,264 bases. These transcripts were mapped onto the genome using blat  (Kent 2002). Only 485 transcripts could not be mapped to the genome sequence, of which only 90 have a blast score of 250 or higher in a search against the reference sequence database (NCBI July 2013). Fifteen of these 90 appear to be of P.
polycephalum mitochondrial origin and a further 13 are certainly contaminants from the sequencing process, as they have identity values higher than 85% to some bacteria and mouse sequences. Thus, only a minor fraction of transcripts is not represented by the assembled genome. Manual inspection of some transcript/ genome alignments revealed that transcripts frequently span gaps in the scaffolds, which account for missing small exons. Inability to assemble genome junctions at these positions is likely caused by the low complexity of intron sequences and the small size of the missing exons. As contiguity of a sequence is required for accurate gene prediction, we relied heavily on the transcript data for annotation and analysis. The Transcriptome Reference Assembly has been deposited at DDBJ/EMBL/GenBank under the accession GDRG00000000.
Gene Content and Introns To further evaluate gene content and completeness, we generated transcriptome data sets from multiple life-cycle stages and constructed a reference transcriptome (see Materials and Methods). We next trained the gene prediction program Augustus  (Stanke et al. 2004 ) with sequences derived from mapped transcripts. Gene predictions using the splicing information from all transcripts initially yielded more than 47,000 gene models (table  1 ). However, because in the current assembly some shorter introns and exons are missing in scaffolds owing to simple repeat sequences at both ends, we used the reference transcripts together with the gene predictions to define gene loci. Transcripts and predicted genes overlapping each other or being in close vicinity on the assembly (in the range of an intron length) were fused to a common gene locus.
A total of 34,438 gene loci were defined in this way (table 1), half of which are represented by transcripts  (17, 158). The gene loci with corresponding transcripts have a mean length of 1951 bases, whereas those without transcript support have a mean length of only 579 bases. This suggests that the majority of unsupported gene loci may represent false-positive predictions, which is fairly typical of highly fragmented genome assemblies. There are scores of complex repeats in the genome, which contains a minimum of 484 integrase domains (PF00665) and 1,014 reverse transcriptase domains (PF07727 and PF00078). This greatly exceeds the number present in other Amoebozoa species such as A. castellanii and D. discoideum, which contain only 38 and 118 reverse transcriptase domains, respectively. In total, P.
These latter RNAs and the box C/D snoRNAs have no detectable sequence similarity with any sequence in the non-redundant nucleotide database. We ran tRNAscan-SE (PMID: 9023104) with default parameters on the genome, which resulted in 319 putative tRNA loci and 248 unique putative tRNA sequences (Supplementary Spreadsheet 1, third sheet). These included all but one (tRNA-Tyr1 in Supplementary Spreadsheet 1, second sheet) of the 56 new tRNAs identified from the sequencing experiment as well as all previously annotated P. polycephalum tRNAs with the exception of tRNA-Lys1, tRNA-Ser2, and tRNA-Asp1. A total of 201 putative tRNA loci with 156 unique putative tRNA sequences were novel. These included tRNAs for all 20 amino acids as well as 6 putative tRNA-SeC loci (each with a unique sequence) and four putative tRNA loci of undetermined type (each with a unique sequence).
Nitric oxide synthases are required for sporulation in P. polycephalum by acting via cGMP signaling  (Golderer et al. 2001). This link between animal-type NO synthases and cyclic nucleotide signaling, which is widely used in P. polycephalum (see Section Signaling), is noteworthy and to date is unique in unicellular eukaryotes. This suggests an ancient origin of this typically animal-type signaling enzyme and its signal transduction mechanism via cGMP. Another unique finding is that riboflavin biosynthetic enzymes are encoded as two tri-functional reading frames (supplementary fig. S10 , Supplementary Material online) rather than by separate cistrons, a feature thus far not observed for sequences from any other species represented in GenBank. P. polycephalum is much more diverse than D. discoideum in its pteridine metabolic equipment.
Consequently, this type of actin phosphorylation in P. polycephalum is possibly not unique as suggested above. One cannot exclude convergent evolution of kinases with similar substrate specificity also in other organisms, as these molecules may have completely unrelated sequences and are not easily detectable by sequence similarities. Perhaps most importantly, this example demonstrates the limitations of any whole-genome approach. In the end, only biochemistry and molecular cell biology will cross the border from "gene products of unknown function" to proteins with known activities. P. polycephalum possesses a wealth of motor domains. We searched all defined gene loci for the presence of such domains. With this initial search, we found 43 gene loci with a kinesin domain (PF00225), 23 gene loci with a dynein heavy-chain domain (PF03028), and 53 gene loci with myosin motors (PF00063).
*) Note that the number of response regulators could be inflated by inclusion of sequence fragments that contain response regulators from incompletely assembled SHKPs. See fig. 4  for phylogenies and functional domain architectures of the proteins. C) Cyclic nucleotide signaling. Abundance of nucleotidyl cyclases, cAMP or GMP binding proteins, and cyclic nucleotide phosphodiesterases. See supplementary figs S15 and S16 for phylogenies and functional domain architectures of cyclases and phosphodiesterases. D) Protein kinases. Proteins with S/T, S/T/Y, or Y protein kinase domains were retrieved from an Interproscan of all transcribed coding regions by the presence of the Interpro IPR002290, IPR008271, IPR001245, IPR020635, and/or IPR008266 domains. A total of 29 proteins contained the IPR020635, IPR008266 identifiers for Y protein kinases, but only four of those showed full consensus with validated tyrosine kinase-specific sequences (see fig.
4. -Sensor histidine kinases/phosphatases and phosphorelay receivers. (A) Sensor histidine kinases/phosphatase. The P. polycephalum genome was queried by BLAST with all 15 Dictyostelid SHKPs, plant and fungal SHKPs, and the most divergent prokaryote SHKPs, while transcribed proteins were queried for the presence of Interpro domains IPR003661 and IPR003594 for the HisKA and HATPase-c moieties of SHKPs, respectively. The retrieved sets largely overlapped, but the transcriptome (Phypo identifiers in the tree) contained both the largest number and more complete SHKPs. Sequences were first aligned with all their closest relatives in other organisms, as identified by BlastP of the GenBank non-redundant sequences. However, in a phylogeny constructed from this alignment, most of the outgroup sequences clustered together.
Their contribution therefore was reduced to the ones with greatest identity to individual clusters of P. polycephalum SHKPs. A second phylogeny was constructed from these outgroup and P. polycephalum sequences, using only segments encompassing the HisKA, HATPase-c, and receiver domains, which are common to all or most retrieved SHKPs. The tree was annotated with the functional domain architecture of the proteins. The methods used for sequence alignment, alignment editing, and Bayesian phylogenetic inference are the same as described in the legend to supplementary fig S13. Protein tags are color-coded to reflect the species of origin as shown, and Bayesian posterior probabilities of tree nodes are represented by colored dots. (B) Receivers. Putative effectors for SHKP activated signaling were identified by query of genome and transcriptome sequences with receiver/response regulator protein sequences or the Interpro identifier IPR001789 for this domain, respectively, leaving out all proteins that also contained HisKA or HATPase-c domains.
polycephalum  (Starostzik and Marwan 1995b; Lamparter and Marwan 2001). Two phytochrome genes, phyA and phyB, are expressed in P. polycephalum, partially encoded by transcripts 20261 (gene locus 28349) and 03416 (gene locus 5996), respectively. Although the sequence identity between PhyA and PhyB is only 33. 8%, the two proteins share a similar domain architecture with the phytochrome-type PAS_2, GAF, PHY domain arrangement at the N-terminus, and a C-terminal hybrid kinase-like part, composed of HisK, HATPase, and REC domains (fig. 3 ). A BLAST search of the PAS_2, GAF, PHY domain portion of the proteins against the UniProt database revealed closest similarity to Nostoc PHYA (Q9LCC2) and four other bacterial phytochromes, followed by plant phytochromes. Sequence alignment with the most similar phytochromes suggests that the chromophore-binding site may be a cysteine close to the N-terminus that is conserved between P.
Acknowledgments Funding for the sequence characterization of the P. polycephalum genome was provided by  National Institutes of Health (NIH)  grant  HG003079  to R. K. W. and  The Genome Institute at Washington University School of Medicine. P. S. received funding from the  Wellcome Trust  ( 100293/Z/12/Z ) and the  BBSRC   BB/K000799/1. I. B. was supported by the  International Max Planck Research School , Magdeburg. W. M. received funding from the  German Federal Ministry of Education and Research (BMBF)  via the  FORSYS program  FKZ  0313922. Pteridine work was funded by the  Austrian Science Fund (FWF) ,  P22406  to E. R. W. J. M. G. was funded by  National Institutes of Health (NIH)  grant  GM54663  and  National Science Foundation (NSF)  grant  MCB-1243687.
Introduction Dedukti is a logical framework for defining logics and expressing proofs in those logics  [8]. Following the LF legacy  [17] , it is based on the λ Π-calculus modulo rewriting, which extends the λ Π-calculus with rewrite rules. Cousineau and Dowek  [11]  showed that functional pure type systems (PTS), a large class of calculi that are at the basis of many proof systems, can be embedded in the λ Π-calculus modulo rewriting in a way that is complete and that preserves reductions (i. e. program evaluation). This led to propose Dedukti as a universal proof framework. In this paper, we focus on translating the proofs of HOL to Dedukti. HOL refers to a family of theorem provers built on a common logical system known as higher-order logic or simple type theory  [10]. It includes systems such as HOL Light, HOL4, and ProofPower-HOL.
Universal proof checking Using Dedukti as a logical framework serves two goals. First, in the short term, it serves as an alternative, independent proof checker, providing an additional layer of confidence over each system. The second, longer term goal, is interoperability. Proof systems are becoming increasingly important, both in the formalization of mathematics and in software engineering. However, they are usually developed separately, with very little interoperability in mind. As a result, it is currently very difficult to reuse a proof from one system in another one. Embedding these different systems in a single unified framework is the first step to bring them closer together, and opens the way for theory management systems  [18, 27]  to combine their proofs in order to construct and verify larger theories.
Implementation We implemented our translation in an automated tool called Holide. It works as an OpenTheory virtual machine that additionally keeps track of the corresponding proof terms for theorems. The program reads a HOL proof written in the OpenTheory article format (. art) and outputs a Dedukti file (. dk) containing its translation. We can run Dedukti on the generated file to verify it. All generated files are linked with a hand-written file hol. dk containing the signature Σ that we defined in Section 4. Our software is available online at https://www. rocq. inria. fr/deducteam/Holide/. HOL proofs are known to be very large  [19, 20, 24] , and we needed to implement sharing of proofs, terms, and types in order to reduce them to a manageable size. OpenTheory already provides some form of proof sharing but we found it easier to completely factorize the derivations into individual steps.
Future work The translation we have presented can be improved in several ways. The current implementation suffers from a lack of linking: if a package makes use of a type, constant, or theorem defined in another package, we do not have a reference to the original definition. This is due to a limitation of the OpenTheory article format. In OpenTheory, this problem is resolved by adding a theory management layer, which is responsible for composing and linking theories together  [18]. It would be beneficial to integrate this layer in our translation so that we can properly link the resulting files together. While we used several optimizations including term sharing in our implementation, there is still room for reducing the time and memory consumption of the translation and the size of the generated files.
This means that for heavy targets the DT0 trigger is not as efficient as for the hydrogen target. Therefore, a large part of the solid-state target data is recorded with prescaled LT1 and LT2. The Primakoff trigger (Prim1) uses the calorimeter trigger with a 60 GeV threshold. A secondary Primakoff trigger (Prim2) is based on a calorimeter trigger with a threshold of 40 GeV and a prescaling factor of two. Its purpose is to monitor the Prim1 trigger threshold. The kaon trigger (KT) makes use of the CEDAR detectors in the beam line, which are set to detect beam kaons (see Section 3. 3). Signals from both CEDARs need to be present for the trigger in order to maximise its purity. It is used as a kaon-enriched beam trigger for luminosity monitoring via K → 3 π decays and for systematic studies. Further auxiliary triggers are set up for monitoring purposes, systematic studies and alignment purposes (see Section 9.
Data Acquisition The COMPASS data acquisition system (DAQ) has been designed to cope with high trigger rates and large data flow. For data taking with hadron beams both interaction rate and particle multiplicity per interaction are higher than for a muon beam, making these requirements even more important. The DAQ is based on a pipelined architecture, which was fully implemented for the 2008/2009 data taking. It is complemented with a Detector Control System (DCS), which permanently monitors all parameters relevant for the operation of the setup. The general structure of the COMPASS DAQ and DCS systems were described in Ref. [1]. In this section their main characteristics are shortly reviewed; only the most important improvements and modifications are discussed.
General structure of the COMPASS data acquisition system For typical hadron beam intensities of up to 5 • 10 7 particles per spill, the various COMPASS triggers combine to a total trigger rate of more than 30 kHz. The overall number of electronic channels is larger than ∼ 250 000, and the generated event size has a mean value of 40 kB. Accordingly, a data rate of up to 1. 2 GB/s is acquired during the 9. 6 s long SPS spill. When averaged over a typical SPS accelerator 'supercycle" of 42 s (duty factor of 23%), these data generate a flow of about 270 MB/s. Note that as a function of the actual SPS accelerator usage, the length of the supercycle may change by several seconds. As a consequence, the instantaneous data flow of the COMPASS readout system must be able to reach peak throughput values of up to 350 MB/s.
The CATCH firmware merges the data from the mezzanine cards and transmits them to the central readout buffer computers through a S-LINK optical link  [42]. For detectors with low occupancies the data of up to 4 CATCH modules are multiplexed by an S-LINK MUX card before being transmitted to the readout computers. The S-LINK MUX card houses one S-LINK source card and is mounted on P3 connectors on the backside of a VME crate. The 9U GeSiCA modules can read up to four 12-channel GEM or Silicon SADC cards. The more recent 6U version named HotGeSiCA is also able to read SADC, MSADC, and APV data from the RICH and PixelGEM detectors. Although smaller in size, the HotGeSiCA module has eight RJ45 or optical HOTLink ports, instead of four for GeSiCA. In addition, the HotGeSiCA module can be equipped with 500 MB of memory and a HOTLink output interface for cascading HotGeSiCA modules and concentrating the data from up to 64 front-end cards.
This readout scheme is used for the Rich Wall detector (see Fig. 36 ). Similar to the CATCH modules, the GeSiCA and HotGeSiCA modules send data through a S-LINK interface to the readout buffers, while the information coming from the TCS receiver is transmitted to the front-end electronics through a HOTLink connection. The DAQ system is composed of two main types of computers, called Readout Buffers (ROB) and Event Builders (EB). All computers run Linux operating system (see Table  6 ). Each ROB is equipped with up to four spill-buffer PCI cards. The PCI cards collect the information from the corresponding concentrator modules via S-LINK optical fibres. The data are temporarily stored in a daughter SDRAM card of 512 MB or 1 GB memory during the spill, before being fully transmitted to the computer.
DAQ hardware and database architecture The COMPASS DAQ system operates with a large number of hardware (Table  6 ) and software components, which are controlled through various parameters. These parameters are produced by on-line processes, operator entries, slow-control of the detectors, or result from specific run conditions. Together with other monitored quantities, such as trigger rates, run and spill information, they are stored in several MySQL  [45]  and Oracle  [46]  databases. The front-end configuration database incorporates all information relevant to the front-end modules and processors. The logbook database collects a large number of experimental parameters and operator comments relevant for a specific data-taking period, usually defined as a run. Parameters that are likely to change more frequently, such as beam information, beam line settings, scalers and some monitoring values, are stored in a spill database.
The DATE configuration database is used for the description of the Attempt Rate (kHz)  The 130 GB of data from all MySQL databases are hosted on two physical servers, synchronised through a master-master replication. Clients connect to the database through a virtual address pointing to a third server. The third server runs a MySQL Proxy software  [47]  that monitors the communication between the client and the database. Besides the proxy, the third server also hosts a web server Apache  [48]  and a monitoring service (Nagios)  [49]. The web service provides interfaces to run logbook, database administration programs, and diagnostic tools. Nagios monitors the availability of database servers and the state of replication. A specific database table is used for the Detector Control System (DCS), e.