dataesr/scientific-paragraphs-categorization huggingface dataset:
ABL translocation, the Kaplan-Meier survival curves compare survival probabilities between the patients according to the presence or absence of BCR. ABL translocation (lower left panel) or between the two prognosis groups (P3 versus P1&2) within the patients whose ALL is negative (upper right panel) or positive (lower right panel) for BCR. ABL translocation. C. and D. In the 750 ALL patients from the "MILE" American study (GSE13159), the known aggressive molecular subtypes of adult ALLs co-segregate with the "P3" ALL. C. The pie chart shows the distribution of P1&2 patients (60%) and P3 patients (40%). D. The histograms show the relative proportions of P1&2 and P3 patients for each molecular subtype of ALL. The molecular subtypes are ordered from the less to the most aggressive known forms (left to right).
Data accessibility Data have been deposited into the ProteomeXchange Consortium  [2]  via the PRIDE partner repository with the dataset identifier PXD001450. Value of the data [describe in 3-5 bulleted points why this data is of value to the scientific community] Proteomic analysis of 216 chicken eggshell matrix proteins. Gene Ontology terms enrichments investigating potential functions of eggshell matrix proteins. Quantitative data on protein abundances according to stages of eggshell formation. Annotation on quantified eggshell matrix proteins.
Nano LC MS/MS analyses All experiments were performed on a linear ion trap Fourier Transform Mass Spectrometer (FT-MS) LTQ Orbitrap Velos (Thermo Fisher Scientific, Bremen, Germany) coupled to an Ultimate s 3000 RSLC Ultra-High Pressure Liquid Chromatographer (Dionex, Amsterdam, The Netherlands) as previously described in  [1, 4]. Raw data files were converted to MGF as previously described  [4]. The identification of proteins was established using MASCOT search engine (v 2. 3, Matrix Science, London, UK). The peptide and fragment masses obtained were matched automatically against the chordata section of nr NCBI database (2132453 sequences, downloaded on 2013/05/13). Enzyme specificity was set to trypsin with two missed cleavages using carbamidomethylcysteine, oxidation of methionine and Nterminal protein acetylation as variable modifications.
The tolerance of the ions was set to 5 ppm for parent and 0. 8 Da for fragment ion matches. Mascot results obtained from the target and decoy database were incorporated in Scaffold 3 software (version 3. 4, Proteome Software, Portland, USA). Peptide identifications were accepted if they could be established at greater than 95. 0% probability as specified by the Peptide Prophet algorithm  [5]. Peptides were considered distinct if they differed in sequence. Protein identifications were accepted if they could be established at greater than 95. 0% probability as specified by the Protein Prophet algorithm  [6]  and contained at least one sequenceunique peptide for global inventory and at least two sequence-unique peptides for quantitative analysis. A false discovery rate was calculated as o1% at the peptide or protein level.
The abundance of identified proteins was estimated by calculating the emPAI using Scaffold 4 Qþ software (version 4. 2, Proteome Software, Portland, USA). Additionally, label-free quantitative proteomic analyses based on spectral counting method, Scaffold 3 Q þ software (version 3. 4, Proteome Software, Portland, USA) was used to quantify the proteins at the four different stages of calcification. All proteins with greater than two sequence-unique peptides, identified in database with high confidence were considered for protein quantification. To eliminate quantitative ambiguity within protein groups, we ignored all the spectra matching any peptide which is shared between proteins. Thereby, quantification performed with normalized spectral counts was carried out on distinct proteins. The mass spectrometry proteomic data have been deposited into the ProteomeXchange Consortium  [2]  via the PRIDE partner repository with the dataset identifier PXD001450.
Data set analysis of avian eggshell matrix proteins A total of 261 proteins were identified using nr NCBI database. Keratins and bovine trypsin were eliminated from the list as they appeared to be contaminants or resulting from the digestion process. Protein sequences were aligned to eliminate all redundancies. Protein groups were determined using Clustal Omega multi-alignment algorithm  [7]. Sequences were blasted against nr NCBI database limited to Gallus gallus taxon using the blastp program (BLAST þsuite)  [8]. This was performed using R language (http://cran. r-project. org). A total of 216 non-redundant eggshell matrix protein sequences were identified. The resulting file (Supplementary Table  1 ) is made of EntrezGene Ids, GI numbers, protein symbols, short descriptions, information on their previous identification in the shell and in the uterine fluid, mean quantitative values and emPAI values at each time point.
Quantitative dataset of avian eggshell matrix proteins Two different methods were applied to discern the relative abundance of the proteins at the different stages of eggshell formation, firstly the emPAI was calculated for all proteins at each individual stage and secondly, GeLC-MS/MS analyses combined with label free spectral counting method were carried out to determine quantitative values at the four stages of mineralization for the 216 unique proteins. In the first approach, emPAI from the proteins was calculated amongst the proteins revealed at a particular stage of shell formation at 5, 6, 7 and 16 h p. o. The aim was to classify the relative abundance of the proteins within one individual stage and to determine those which were the most abundant ones. EmPAI values were reported for the 216 eggshell matrix proteins at each stage, a numerical value different from zero being introduced when the protein was present (Supplementary Table  1 ).
Supplementary functional annotations of avian eggshell matrix proteins Potential functions of proteins were determined according to the literature, data annotations and functional domain database and highlighted 77 proteins with potential functions related to a mineralization process  [1]. Beside these proteins, we identified 35 proteins with potential antimicrobial functions present at various stages of mineralization (Supplementary Table  3 ). These antimicrobial proteins were present a quite equally number throughout the different stages of shell formation. The most abundant protein in the shell matrix is lysozyme, which is known to hydrolyze 1-4 beta linkages between N-acetyl muramic acid and N-acetyl-D-glucosamine residues in peptidoglycans of Gram-positive bacteria  [16]. Another notable proteins is OVAX, which exhibits antimicrobial properties against Listeria monocytogenes and Salmonella enteritidis  [17].
Acknowledgments This research was funded by the  French National Research Agency ANR  ( ANR-13-BSV6-0007-01 ,  ANR-13-BSV6-0007-02  and  ANR-13-BSV6-0007-05 ). The high resolution mass spectrometer was financed ( SMHART  project,  35069 ) by the  European Regional Development Fund (ERDF) , the  Conseil Régional du Centre , the  French National Institute for Agricultural Research (INRA)  and the  French National Institute of Health and Medical Research (Inserm). ARN acknowledges funding through Grants  CGL2011-25906  ( Ministerio de Economia, Spain ). The authors are grateful to the experimental units ( UE-PEAT ) for care of birds, and to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  for providing help and/or computing and/or storage resources.
Virophages replicate within viral factories inside the Acanthamoeba cytoplasm, and decrease the infectivity and replication of their associated giant viruses. Culture isolation and metagenome analyses have suggested that they are common in our environment. By screening metagenomic databases in search of amoebal viruses, we detected virophage-related sequences among sequences generated from the same non-aerated bioreactor metagenome as recently screened by another team for virophage capsid-encoding genes. We describe here the assembled partial genome of a virophage closely related to Zamilon, which infects Acanthamoeba with mimiviruses of lineages B and C but not A. Searches for sequences related to amoebal giant viruses, other Megavirales representatives and virophages were conducted using BLAST against this bioreactor metagenome (PRJNA73603).
, 2013b). Besides, Yutin et al. recently described virophage-Polinton chimeric genomes  (Yutin et al. , 2015). By screening various available environmental metagenomic databases in search of giant viruses and virophages, we detected several robust matches with sequences from the Zamilon virophage, and, concurrently, mimiviruses and other amoebal giant viruses, among sequences from the same bioreactor metagenome as recently explored by  Yutin et al. (2015). Whereas, this team detected strong homologies to the Zamilon capsid gene in this metagenome, our first-line approach consisted in a broader search for any sequence matching with virophages, and not only sequences matching capsid proteins. This allowed us to map sequences to 15 genes from the Zamilon genome. We report here the assembly and analyses of the partial genome sequence of a novel Zamilon-like virophage (we named Zamilon 2) from this metagenome.
Metagenome Analyzed A non-aerated poplar wood chips Bioreactor metagenome was downloaded from the NCBI GenBank genome sequence database (GenBank Accession number: AGTN00000000. 1; BioSample: SAMN02954264; Bioproject: PRJNA73603) (Van der  Lelie et al. , 2012). This terrestrial environmental biome metagenome had been generated from poplar woody material in New York State, USA (Van der  Lelie et al. , 2012). Metagenomic sequences had been obtained from non-sterile yellow poplar sawdust taken from the inside of a pile, then placed in a plastic bucket, humidified with MgSO 4 solution and incubated at 30 • C in a microaerobic/anaerobic atmosphere for 12 months; sequencing had been performed on DNA isolated from biomass and liquid from the anaerobic zone. The bioreactor DNA had been submitted to 454 Roche and Sanger sequencing, and 748,672 contig sequences had been generated by whole genome shotgun sequencing (WGS) WGS from this bioreactor sample.
All these analyses were described in the study by  Van der Lelie et al. (2012). 1 ). Most of these sequences were available from the NCBI GenBank sequence databases (http://www. ncbi. nlm. nih. gov/ genbank/). In addition, other mimivirus and marseillevirus sequences, not publicly available and corresponding to unpublished genomes whose sequence was obtained through high-throughput sequencing at our research unit, were used for the analyses. Sputnik 2 and 3 virophage gene contents were excluded from the analysis because they are nearly identical to that of the original Sputnik strain. We also searched for metagenome sequences related to the amoebal host for giant viruses and virophages by using amino acid sequences from Acanthamoeba castellanii downloaded from GenBank.
Search Strategy for Virus and Amoeba-related Metagenome Sequences Sequences related to Megavirales representatives, virophages and A. castellanii were searched for using BLASTx (with an e ≤ 10 -3 ) in the non-aerated bioreactor metagenome WGS dataset  (Altschul et al. , 1997). The sequences showing significant BLASTx hits to amoebal virus genes and those of their host were sorted and used for further analyses. Thus, these sequences were then compared to the NCBI GenBank non-redundant protein sequence database (nr) and sequences related to Megavirales representatives only available from the URMITE sequence database using BLASTx and BLASTn with default parameters, in order to eliminate false positive matches with other organisms.
De novo Metagenomic Assembly and Prediction of Virophage-encoded Genes Contig sequences corresponding to virophages were assembled after mapping onto the genomes of virophages for which best BLASTx hits had been obtained, and visualized using the CLC Genomics version 6. 0. 1 software (http://www. clcbio. com/) with standard options. A consensus genome sequence was generated using the CLC Genomics software. Guanine and Cytosine (G+C) content of this genome was determined using the CAIcal server (http://genomes. urv. es/CAIcal/) (Puigbo et al. , 2008). Finally, open reading frame (ORF) prediction for this virophage-like genome was performed using GeneMarkS software  (Besemer and Borodovsky, 2005) , and the annotation was performed by comparative genomic analyses using BLASTx and BLASTn tools  (Altschul et al.
Phylogenetic Analysis Alignment of conserved genes was performed using Muscle software  (Edgar, 2004). Phylogenetic trees were reconstructed using MEGA6 software using the maximum-likelihood method and the most appropriate substitution model  (Tamura et al. , 2011). -C ). These best matches were mainly scattered along the giant virus genomes. They included DNA dependent RNA polymerase subunits 1 (from P. sibericum) and 2 (from mimiviruses); a DNA polymerase delta catalytic subunit and a VV D5-like helicase (from P. sibericum); a GMC oxidoreductase (from Pandoravirus salinus); a putative metaldependent phosphohydrolase, a transcription termination factor and a putative WbbJ acetyltransferase (from mimiviruses); a cysteine desulfurase and a putative DNA repair protein (from faustoviruses); and 4 ankyrin repeat-containing proteins (from mimiviruses and P.
, 2004). FIGURE 4 | Phylogenetic reconstruction based on Zamilon 2 gp06 gene product and its homologs (major capsid protein). Maximum likelihood-based phylogenetic analysis was performed using MEGA 6. 0 software. Bootstrap values >50% are labeled on the tree. ALM, Ace Lake Mavirus; MCP, major capsid protein; YSLV, Yellowstone Lake Virophage; OLV, Organic Lake virophage. FIGURE 5 | Phylogenetic reconstruction based on Zamilon 2 gp18 gene product and its homologs (DNA packaging ATPase). Maximum likelihood-based phylogenetic analysis was performed using MEGA 6. 0 software. Bootstrap values >50% are labeled on the tree. ALM, Ace Lake Mavirus; YSLV, Yellowstone Lake Virophage; OLV, Organic Lake virophage. FIGURE 6 | Phylogenetic reconstruction based on Zamilon 2 gp19 gene product and its homologs.
Maximum likelihood-based phylogenetic analysis was performed using MEGA 6. 0 software. Bootstrap values >50% are labeled on the tree. ALM, Ace Lake Mavirus; YSLV, Yellowstone Lake Virophage; OLV, Organic Lake virophage. FIGURE 7 | Phylogenetic reconstruction based on Zamilon 2 gp12 gene product and its homologs. Maximum likelihood-based phylogenetic analysis was performed using MEGA 6. 0 software. Bootstrap values >50% are labeled on the tree. ALM, Ace Lake Mavirus; YSLV, Yellowstone Lake Virophage; OLV, Organic Lake virophage. diseased humans  (Mokili et al. , 2012). Moreover, some partial and even full-length viral genomes could be assembled from metagenomic sequences  (Smits et al. , 2014). Regarding virophages, metagenomic datasets allowed these viruses to be identified and located in various environments, and full-length and partial genomes could be reconstructed  (Yau et al.
, 2011; Zhou et al. , 2013 Zhou et al. , , 2015;; Zablocki et al. , 2014; Yutin et al. , 2015). Previous findings and this work suggest that virophages are widespread in our biosphere. Zhou et al. described, through the analysis of metagenomic datasets, that sequences related to these viruses were detected in metagenomes generated from various environmental samples consisting of freshwater or marine water samples or soil samples collected worldwide in geographical areas with various climates  (Zhou et al. , 2013). Yutin et al. also found virophage-related sequences in metagenomes generated from activated sludge, freshwater sediment, marine water, and wastewater  (Yutin et al. , 2015). Hence, virophage distribution appears consistent with that of mimiviruses and Acanthamoeba spp, which are both known to be widespread in our biosphere  (Thomas and Greub, 2010; Colson and Raoult, 2012; Pagnier et al.
Main features of currently discovered virophages. Dataset of Amino Acid Sequences from Megavirales Members, Virophages, and Acanthamoeba castellanii used as Queries Query sequences used to search for related sequences among the WGS dataset from the metagenome were amino acid sequences from the members of the proposed order Megavirales, including poxviruses, phycodnaviruses, asfarviruses, ascoviruses, iridoviruses, mimiviruses, marseilleviruses, Pandoravirus salinus and P. dulcis, Pithovirus sibericum, and from virophages (Table.
Diversity Estimation of Megavirales Member-and Virophage-related Sequences Detected in the Bioreactor Metagenome The community comprised of megavirus-and virophage-related entities present in a poplar biomass non-aerated bioreactor over the course of a year (Van der Lelie et al. , 2012) was assessed through investigating metagenome sequences. Of the 748,672 contig sequences generated by WGS from this bioreactor sample (Van der Lelie et al. , 2012), 32,156 (4. 2%) showed significant similarities to amino acid sequences from Megavirales representatives and virophages. Subsequently, 72 contig sequences (0. 01% from the metagenome WGS dataset) were found to have a sequence from megaviruses or virophages as best hit (Figures 1A,B; Supplementary Table S1). These sequences ranged in size from 206 to 773 bp [mean ± standard.
d. N. d. N. d. N. d. N. d. Mont1 (mimivirus) N. d. N. d. N. d. Geographical location Cooling tower water, Paris, France Coastal waters, Texas, USA Organic Lake, Antarctica Lens liquid, Marseille, France Soil, France Dutch coastal waters, southern North Sea Yellowstone Lake, USA Yellowstone Lake, USA Yellowstone Lake, USA Yellowstone Lake, USA Ace Lake, Antarctica Tunisia Yellowstone Lake, USA Yellowstone Lake, USA Yellowstone Lake, USA Year of description 2008 2010 2011 2012 2013 2013 2013 2013 2013 2013 2013 2014 2014 2014 2014 GenBank accession no. EU606015 NC_015230 HQ704801 NC_023846 NC_023847 NC_021333 KC556924 KC556925 KC556926 KC556922 KC556923 NC_022990 KM502589 KM502590 KM502591 Virophage Sputnik Mavirus OLV Sputnik 2 Sputnik 3 Phaeocystis globosa virophage YSLV1 YSLV2 YSLV3 YSLV4 ALM Zamilon YSLV5 YSLV6 YSLV7 ALM, Ace Lake Mavirus; bp, pase pairs; N.
Diversity Estimation of Megavirales Member-and Virophage-related Sequences Detected in the Bioreactor Metagenome The community comprised of megavirus-and virophage-related entities present in a poplar biomass non-aerated bioreactor over the course of a year (Van der Lelie et al. , 2012) was assessed through investigating metagenome sequences. Of the 748,672 contig sequences generated by WGS from this bioreactor sample (Van der Lelie et al. , 2012), 32,156 (4. 2%) showed significant similarities to amino acid sequences from Megavirales representatives and virophages. Subsequently, 72 contig sequences (0. 01% from the metagenome WGS dataset) were found to have a sequence from megaviruses or virophages as best hit (Figures 1A,B; Supplementary Table S1). These sequences ranged in size from 206 to 773 bp [mean ± standard deviation (SD), 398 ± 113 bp].
In literature, we can indicate programs which helped the experts analyse the protocols. We can indicate the AVISPA tool  [13, 14]  or ProVerif  [15]  application, which verifies security properties for cryptographic protocols. From the Quality of Protection analysis point of view, AVISPA and ProVerif have two limitations. The first one refers to the types of the function which can be modelled. One can model only cryptographic primitives and cryptographic algorithms. The full QoP analysis must refer to all security factors which affect the overall system security. The second limitation is that these languages do not provide the structure for evaluation of the security factors performance. In the literature one can indicate the tool for QoP analysis which are modelled in the UMLsec  [16]. This tool can be used for automated analysis of simple models but when we would like to analyse the scenarios when thousands of hosts take part in the protocol, then the analysis is too complex and cannot be done properly.
The UMLsec is used for model-driven security as the part of model-driven development. The major contribution of this study is introduction of Automated Quality of Protection Analysis framework which performs the automatic evaluation of QoP-ML models created in the Quality of Protection Modelling Language  [9, 10]. It allows to analyse complex systems which may consist of thousands of hosts representing a wide area network which are actors in the cryptographic protocol or a complex IT system. One can balance security against performance. The full description of the AQoPA will be presented in the next sections. The AQoPA framework can be downloaded from the web page of the Quality of Protection Modelling Language Project  [11]. 2 Automated Quality of Protection Analysis framework (AQoPA) In this section we present the architecture of AQoPA and data flow of model during the analysis process.
Case Study In this section we present the analysis of complex system performed by means of the AQoPA framework. This case study presents the features of AQoPA as the tool used to analyse the wireless sensor networks in terms of security and efficiency. We analyse the wireless sensor network used for structural health monitoring deployed on roller-coaster. Its main aim is to collect acoustic emission data during the ride which is transmitted to the gateway and analysed for cracks. This method has been studied in  [18]  and is still widely used as inspection method  [19]. Using wireless sensor network gives a possibility to react to detected failures in real time. In the case study we have used the Rougarou roller-coaster from Cedar Point theme park [17]. It is interesting example because the roller-coaster was opened in 1996 and named Mantis while now it is being rebuilt to be a floor-less coaster.
Therefore the main structure remains and is going to be operated by new coaster. The Rougarou is about 1200 meters length, 44 meters high and the speed goes up to 97 kmph. In Fig. 1ffig :rollercoaster we present an example of sensor placement on 270 degrees turn. Sensing nodes are placed every 3 meters on the track while the sink is mounted to the car and collects data from sensors during ride. During the analysis we would like to evaluate how the quality of protection of exchanging data influence the system efficiency and finally the lifetime of system. C A R Fig. 2. WSN deployed on Rougarou roller-coaster.
Protocols We propose three protocols to collect acoustic emission data presented in Fig. 3, 4  and 5. When data is collected the algorithms for fatigue cracks detection are used and report any suspicious results in real time. Protocols present communication between sink, which collects data, and sensor, which senses data. The wireless sensor network consist of many sensors mounted on the track and one sink mounted to the car. The technique of sink mobility is used to data from sensors  [20]. The first protocol is the simple one where no security is guaranteed. The second and the third protocols are modified version of protocol presented in verified in  [23]  which is used to authenticate new nodes in a sensor network. In the description of protocols we use the following notation: S -Sensor, Sink -Sink, R -empty request for SHM data, D -acoustic emission data sampled by sensors, n I -nonce generated by I, sk(I) -private key of I, pk(I) -public key of I, {M } K -encryption of M with symmetric or asymmetric key K.