dataesr/scientific-paragraphs-categorization huggingface dataset:
mycoides subsp. mycoides SC, Brucella abortus, and Brucella melitensis  [24, 37, 38]. Similarly, the antigenic properties of TktA and Ef-Tu have been reported in several mycoplasma species  [24, 25, 35, 39]. However, AtpD might have lower specificity as a diagnostic marker for M. bovis due to its 83% similarity with the amino acid sequence of AtpD in M. mycoides subsp. mycoides SC and M. mycoides subsp. capri (NCBI database). Three of the proteins we identified, P48-like lipoprotein, MilA, and PDHB, have been evaluated for serodiagnosis of M. bovis  [5, 17, 20] ; however, assays based on P48 and rPDHB have relatively low sensitivities of 56. 3% and 51%, respectively, in sera from naturally infected animals. The sensitivity and specificity of the recombinant MilA-based assay for clinical bovine serum samples have not yet been determined  [17].
Nucleotide alignment revealed that the Mbov_0579 gene is not only conserved among M. bovis strains, but is also unique to M. bovis compared to other pathogens. For example, the similarity of this gene at the nucleotide level is only 74% between M. bovis and M. agalactiae, and no orthologues were found in other pathogens. According to published data  [4] , the function of the MbovP579 lipoprotein is unknown. We performed a bioinformatics assay to further characterize this protein. It was predicted to be a secretory lipoprotein since it has a 24-amino-acid signal sequence at the N-terminal  [41, 42, 43]. More interestingly, the predicted MbovP579 template structure was ADP-ribosyltransferase CDTa (PDB ID-2WN4). The functional domains of ADPribosyltransferase have recently been identified in the community-acquired respiratory distress syndrome (CARDS) toxin of Mycoplasma pneumonia  [44].
8, 30% glycerol, 2% SDS] for 15 min. The second separation was performed on 10% gels (SDS-PAGE) at 12 o C for 3 h at 50 V followed by 12 h at 100 V using the Protean II xi multi-cell with 2-D conversion kit (Bio-Rad, USA). All experiments were run in triplicate. Proteins were stained on the first gel with 0. 15% Coomassie brilliant blue R-250 (CBB R-250-Bio-Rad, USA), scanned with a GS-800 Calibrated Densitometer (Bio-Rad), and evaluated with the PD Quest Basic 8. 0 program (Bio-Rad, USA). Proteins from other gels were subjected to an immunoblotting assay.
Matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry (MS) Antigenic proteins were removed from the gel and digested with trypsin. In brief, gels were first treated with 200-400 µL of destaining solution (100 mM NH 4 HCO 3 in 30% ACN) and lyophilized. Peptide extraction was performed three times with 60% ACN/0. 1% TFA, and proteins were freeze dried after gel digestion in 5 µL (2. 5-10 ng/µL) of trypsin at 37 o C overnight. Dry samples were reconstituted in 20 % ACN (2 µL) and loaded on a 384well Opti-TOF (123 mm × 81 mm) stainless steel plate. The samples in the plate were then overlaid with 0. 5 µL CHCA in 50% ACN and 0. 1% TFA. Mass spectrometry and MS/MS data for the recognition of proteins were generated with a MALDI-TOF appliance (4800 plus proteomics analyzer, Applied Biosystems).
The conditions were fixed using the 4000 Series Explorer software (Applied Biosystems). The MS ranges were documented in reflector mode at masses ranging from 800 to 4000 Da with a focal mass of 2000 Da. A CalMix5 standard was used to adjust the MS equipment (ABI 4700 Calibration Mixture). For a single core MS spectrum, 25 subspectra with 125 shots per subspectrum were collected using a random search pattern. For MS standardization, trypsin autolysis peaks ( [M+H]+842. 5100 and 2,211. 1046) were used as internal controls, and up to 10 of the strongest ion signals were chosen as precursors for MS/MS analysis, excluding the autolysis peaks of trypsin and matrix ion signals. In MS/MS positive ion mode, for a single core MS spectrum, 50 subspectra with 50 shots per subspectrum were taken with a random search pattern.
Collision energy was 2 kV, collision gas was air, and default calibration was set with Glu1-Fibrino-peptide B ( [M+H]+ 1,570. 6696) spotted onto the Cal 7 positions of the MALDI target. Collective peptide mass fingerprinting (PMF) and MS/ MS inquiries were accomplished by the MASCOT search engine 2. 2 (Matrix Science, Ltd. , U. S. ) in the GPS-Explorer Software 3. 6 (Applied Biosystems) from the NCBI database (Taxonomy: NCBI_Bacteria 5910423, 12/3/2010, NCBI_Mycoplasma 198866, 22/5/2014). The search criteria used in this study were trypsin specificity, one missed cleavage, carbamidomethylation of cysteines as fixed modifications, and oxidation of methionine residues as dynamic modifications. The peptide mass tolerance was set to ±100 ppm and the MS/MS fragment tolerance was set to ± 0. 4 Da.
Prediction of T-and B-cell epitopes in M. bovis antigenic proteins The amino acid sequences of antigenic M. bovis HB0801 proteins were subjected to T-and B-cell epitope prediction with tools cited in the Immune Epitope Database and Analysis Resource (IEDB-AR), a database of experimentally identified B-and T-cell epitopes (http:// tools. immuneepitope. org/mhci/), as described previously  [28]. Modeling of 3D structure templates was performed with the SWISS-MODEL Workspace (http://swissmodel. expasy. org) and ElliPro (http://tools. immuneepitope. org/ tools)  [51, 52]. Templates were acquired for M. bovis proteins by modeling their sequences in the FASTA format. To predict B-cell conformational epitopes, a modeled template of each protein was submitted to Ellipro using conditions similar to those reported previously for cattle pathogens  [28].
8 5. 1 128 100 Unknown - - (VspK) LacI family 21 Mbov_0839 transcriptional regulator gi|392051809| 37. 2 9. 1 124 100 Cytoplasmic 9. 97 K *C. I. %: Confidence Interval **COG (Cluster of orthologous group) database functional classes: (J) translation, ribosomal structure and biogenesis (C) energy production and conversion, (G) carbohydrate transport and metabolism, (E) amino acid transport and metabolism, (R) General function prediction only, (P) Inorganic ion transport and metabolism, (K) Transcription.
To achieve this, we analysed all butterworts endemic to Italy, along with several other species by means of nuclear molecular markers and literature data. The molecular marker chosen for this study was the ITS T (nrDNA), which proved to be the best tool for our purposes. Our marker choice is supported by: (1) high variability and discriminating capabilities of this molecular marker among Pinguicula taxa as already shown by Degtjareva et al. [14] , Kondo & Shimai  [15] , Shimai & Kondo  [16]  and Shimai et al. [17] ; (2) a complete GenBank nrDNA-Pinguicula database to be used for an exhaustive phylogenetic reconstruction; (3) completeness of information as a consequence of the biparental inheritance of nrDNA  [6, 26, 27, 28]. In fact, cpDNA markers may cause confusion in inferring phylogenetic relationships in potentially hybrid/introgressed/polypoid taxa (e.
Pinguicula sampling The plant tissues (i. e. small portion of leaf) analyzed were sampled from herbarium specimens. These source of material and Herbaria is summarized in Table  1. A total of 38 accessions, corresponding to 13 species, was sampled. A single accession from Italy (P. cf. christinae) exhibited peculiar morphological features and its taxonomic position has been investigated in the present study (see morphometric analyses in S1 File). Most of the sampled taxa are endemic to Italy, except for those accessions coming from Corsica (P. corsica), France (P. cf. apuana from Bendola, P. hirtiflora from Val Roya, P. reichenbachiana Schindl. also from Val Roya), Bulgaria (P. balcanica Casper) and Greece (P. hirtiflora). Sequence data of further 26 Pinguicula species (73 total accessions), many of them with a range not covering Italy, were obtained from GenBank, based on the works of Degtjareva et al.
DNA extraction, PCR amplification and sequence analyses Total DNA was extracted from approx. 3 mg of fresh leaf or 2 mg of dried leaf material using ZR Plant/Seed DNA MicroPrep (ZYMORESEARCH), according to the manufacturer's protocol, then resuspended in 20 μL sterile distilled water. The concentration was estimated by quantifying 1 μl of DNA extract using a Qubit dsDNA HS Assay Kit with the Qubit ver. 2 Fluorometer (Invitrogen, Thermo Fisher Scientific Inc. , Waltham, MA, USA). ITS T was amplified by using plant universal primers that were reported in the literature {Forward-18S(3') 5'-GGA GAA GTC GTA ACA AGG TTT CCG-3 0 , and Reverse-26S(5 0 )internal 5'-TTC GCT CGC CGT TAC TAA GGG-3'}  [28]  and selective internal primers designed on Pinguicula sp. accessions from GenBank (Pingui_18Sfor 5'-AAG GAT CAT TGT CGA DWY Y-3' and Pingui_26Srev 5'-TGR GGT CGC RRR IGT TGG CR-3').
Eight randomly selected clones from each transformation were amplified and sequenced with the pJET2. 1 forward and pJET2. 1 reverse primers (Thermo Fisher Scientific Inc. ) which matching in the flanking regions of the pJET1. 2/blunt vector polylinker. Complete sequences of both strands of each PCR product were processed using the AB DNA Sequencing Analysis ver. 5. 2 (Applied Biosystems, Life Technologies), and visually checked using Chromas Lite ver. 2. 1. 1. software (Chromas Lite version 2. 1, http://technelysium. com. au/? page_id=13). Then, sequences were aligned using ClustalW ver. 1. 4 software  [42]  as daughter processes of BioEdit ver. 7. 2. 5 software  [43]. The aligned sequences were visually inspected to correct gap distributions devoid of biological meaning. The sequences obtained in this study are available from GenBank and accession numbers are provided in Table  1.
Phylogenetic analyses One hundred and twenty accessions were analysed using two phylogenetic approaches: Bayesian inference (BI) and maximum parsimony (MP). The trees generated by these methods were checked for congruence. Outgroups were chosen after several preliminary analyses considering the literature data  [7, 14, 15]  The BI method for phylogenetic reconstruction was implemented using MrBayes ver. 3. 2. 2 software  [44, 45]  and jModelTest ver. 2. 1. 4 software to obtain the best nucleotide substitutions model  [46, 47]. Preliminary analyses were performed to obtain the optimal BI settings. Bayesian Markov Chain Monte Carlo (MCMC) algorithm was run for 15,000,000 generations discarding the first 12% generations (burninfrac = 0. 12). Four runs were performed and two heated chains were used.
Bayesian posterior probabilities (PP) were obtained from the 50% majority rule consensus of 52,800,000 trees  (13, 200 ,000 trees from each runs). The general time reversible (GTR) + proportion of invariant sites (I) + gamma distribution (G) model was used in the analyses (set nst = 6 rates = invgamma), according to the results obtained with jModelTest under the Akaike Information Criterion (AIC)  [48]. The same model was obtained using also the correction for sample size (AICc). MP reconstruction were performed with TNT ver. 1. 1 software  [49, 50]. Settings included: gaps scored as missing data; maximum storage space of 99,000 trees; a tree storage space per iteration of 100; 100 iterations branch swapping via tree bisection-reconnection. In addition, we used also the "new technology search" (which is guaranteed to find at least one shortest tree), setting 10 hits to the shortest length and an initial level of 100.
Phylogenetic reconstructions Inferred phylogenies from the BI and MP analyses of the ITS T datasets produced a very similar topology with similar statistical support (Figs  2  and 3 ). The MP analysis yielded 492 most-parsimonious trees with a consistency index (CI) of 0. 57 and retention index (RI) of 0. 89. The strict consensus tree was 1592 steps long. The bootstrap support showed high values (BS > 70%) for the 72% of the nodes (Figs  2  and 3 ). The Bayesian tree with posterior probabilities (PP) is also shown in Figs  2  and 3. An unresolved basal structure for some Pinguicula groups is detected, albeit many clades are well supported (Figs  2  and 3 ). [14]  or Kondo & Shimai  [15] , respectively (Table  2 ). See Table  1  for further details about our Pinguicula accessions. doi:10. 1371/journal.
Subclade E1a: the clade comprising most Apennine endemics A single clade unites four of the Pinguicula sect. Pinguicula species endemic to the Apennines: P. apuana, P. christinae, P. fiorii, and P. vallis-regiae. Only one GenBank accession of P. fiorii is falling outside this clade (Fig  3 ), but it must be noted that this accession was obtained from seeds  [14] , and may well represent a misidentification or confusion of materials. This clade includes also another Italian endemic species, the tetraploid P. mariae. On a slightly different branch within the subclade, there are two other octoploid species, P. dertosensis (Cañig. ) Mateo & M. B. Crespo, endemic to south-eastern Spain  [61, 62]  and P. macroceras Pall. Ex Link, endemic to northern America and Japan  [7]. The four species endemic to Apennines have very similar sequences, which are identical in some case (i.
Kigelia africana (Lam. ) Benth. {GenBank (GB): AY178638}, Antirrhinum majus L. (GB: EU677219) and some of its subspecies {i. e. A. majus subsp. linkianum (Boiss. & Reut. ) Rothm. , GB: EU677214; A. majus subsp. cirrhigerum (Ficalho) Franco, GB: EU677200; A. majus subsp. tortuosum (Vent. ) Rouy, GB: EU677242; A. majus subsp. litigiosum (Pau) Rothm. , GB: EU677216}. Representative of Pinguicula sister group were also.
ref. ) Origin (lat. /long. ) (date) Voucher (Herbarium) (DNA code) ITS T GC % GenBank no. 56 LN887934 P27 P. mariae Casper 32 [7] Italy, Tuscany, Apuan Alps, Isola Santa (44. 06, 10. 31) M. Ansaldi (PI) (Ei 13) 65. 7 LN887935 (27 April 2004) P28 P. poldinii J. Steiger & 32 [7] Italy, Friuli Venezia Giulia, San Francesco (Pordenone), L. Peruzzi, G. Gestri, B. 63. 4 LN887936 Casper on the left side of Arzino (46. 33, 12. 93) (29 April 2013) Pierini (PI) (Ei 27) P29 P. poldinii 32 [7] Italy, Friuli-Venezia Giulia, San Francesco (Pordenone), L. Peruzzi, G. Gestri, B. 63. 4 LN887937 on the right side of Arzino (46. 30, 12. 93) (29 April 2013) Pierini (PI) (Ei 28) P30 P. reichenbachiana 32 [37] France, Maritime Alps, Val Roya, Gorges de Bergue, L. Peruzzi, K. F. Caparelli 65. 6 LN887938 Schindl.
: Kigelia africana (Lam. ) Benth. {GenBank (GB): AY178638}, Antirrhinum majus L. (GB: EU677219) and some of its subspecies {i. e. A. majus subsp. linkianum (Boiss. & Reut. ) Rothm. , GB: EU677214; A. majus subsp. cirrhigerum (Ficalho) Franco, GB: EU677200; A. majus subsp. tortuosum (Vent. ) Rouy, GB: EU677242; A. majus subsp. litigiosum (Pau) Rothm. , GB: EU677216}. Representative of Pinguicula sister group were also included: Genlisea violacea St. -Hil. (GB: AB212116), Utricularia intermedia Hayne (GB: DQ225109) and U. minor L. (GB: AB212118).
1) and the L 1 -PWLS regression needs to be adjusted accordingly (Section 3. 2). The estimation of the additional trend component g is described in Section 3. 3. The second dimension in the latent distributions and the trend component add two additional smoothing parameters to the model and their choice is addressed in Section 3. 4. Bootstrap-based inference is discussed in Section 3. 5. All estimation procedures were implemented in R (R Core Team, 2016) and the code can be obtained from http://wileyonlinelibrary. com/journal/rss-datasets.
Let d be a full dimensional feature space, N trn and N tst the number of training and testing samples from each class. First, the classifiers construct a number L of F LD base learners (B l ) with l ∈ {1,. , L}. Each one performs its learning on a subspace of d sub dimension, where d sub << d. From the i th image, a feature vector, fi ∈ R d , is extracted, and then mapped, such as R d → {0, 1}, where 0 stands for cover and 1 for stego. In the learning phase, each classifier learns to map a feature vector fi, to the correct class number: F LD l : R d → {0, 1} fi →F LD l (fi). Each classifier uses the training database to compute the orthogonal vector to the hyperplane separating the two classes. For a test feature, the l th base learner reaches its decision by computing a projection and comparing it to a threshold.
Experimental setup and protocol All our features are calculated and formed in a one dimensional vector from 10000 color covers and 10000 color stego images for each payload of steganography methods. These features are ready to enter in the classifier. The classifiers were implemented using the ensemble classifier  [26]  with many FLD as a base learner. In this paper, the detection accuracy is measured by the total probability of the average of testing errors under equal priors as in Eq. 19. 5000 images from a database are randomly chosen for the training sets and 5000 for the testing sets. The ensemble classifiers apply a vote to estimate the error of detection. This process is repeated 10 times to obtain PE, the average of testing errors. PE quantify the detectability and are collected for each method and payload to evaluate the steganalysis method.
Image Dataset A raw image is a class of computer file containing untouched pixel information coming from the digital camera sensor (i. e. the pure information). These files hold a large amount of meta-information about the image generated by the camera  [27]. In our work, the color image database is very carefully built depending on the CF A idea. We collected raw images from two subsets which are the most standard, and have the highest number of images captured (i. e. the Dresden Image Database's  [28]  3500 full-resolution Nikon digital camera raw color images and the Break Our Steganographic System (BOSSbase ‡ ), with 1000 Canon digital camera raw color images). In order to obtain color images in Portable Pixel Map (P P M ) format of size 512×512, all images take the same CF A map layout, as illustrated in Fig.
6. For this process, two steps are required. The first step consists of using a demosaicking algorithm to convert raw images into demosaicked images. The second step consists of cropping five areas from one image. Fig. 5  shows sample images produced by the cropping step. First we used the demosaicking algorithm Patterned Pixel Grouping (PPG) from the dcraw software § to convert raw images into RGB images. As illustrated in Fig. 6 , the obtained images are such that the Bayer Pattern is always of the type RGBG (red channel pixel is placed at an even position). We wrote a spatial code to start the crop from the red channel position. Indeed, from one image, this code randomly selected the red channel position and ‡ BOSSbase can be accessed at http://www. agents. cz/boss/ BOSSFinal. § dcraw code is available at http://www.
5 Figure 5. 55 Figure 5. Sample images of our database built by random cropping from locations of red channel pixels (even position) in a Bayer pattern : a) Original raw image 3906×2602, b) crop 1 position x=2116, y=1928, c) crop 2 position x=902, y=1182, d) crop 3 position x=3080, y=436, e) crop 4 position x=1866, y=1778, f) crop 5 position x=650, y=1032.
Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of "Big Data", there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.
The acquisition, storage, and curation of neuroimaging and related data files has therefore become a major Big Data challenge in the context of online sharing initiatives  (Dinov et al. , 2014; Wood et al. , 2014). Several database systems have been constructed to address these issues, such as COINS  (Wood et al. , 2014) , LONI IDA  (Van Horn & Toga, 2009) , LORIS  (Das et al. , 2011) , XNAT  (Marcus et al. , 2007; Gutman et al. , 2014) , and others. However, many are under-equipped to deal with the wide array of data-sharing challenges  (Dinov et al. , 2014). This article focuses on the database management and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multisite data acquisition, both nationally and internationally. Specifically, this manuscript addresses the creation of database repositories, facilitation of sharing initiatives, and leveraging of existing software toolkits for large-scale processing.
Materials and methods The MNI ecosystem consists of two principaland independentlyfunctioningplatforms, LORIS and CBRAIN. The multi-purpose longitudinal database, LORIS, was created for data management, quality control (QC), and dissemination  (Das et al. , 2011) , while CBRAIN was implemented as a collaborative high-performance computing (HPC) platform for processing  (Sherif et al. , 2014). Together, they provide a comprehensive environment for large-scale processing, combining remote visualization tools  (Sherif et al. , 2015)  and fully automated pipelines such as CIVET (www. bic. mni. mcgill. ca/ServicesSoftware/CIVET; Kim et al. , 2014; Macdonald et al. , 2000) , NIAK  (Bellec et al. , 2012)  and other software pipelines (SPM-batch: www. fil. ion. ucl. ac. uk/spm; FSL: http://fsl. fmrib.
ox. ac. uk/fsl/fslwiki; Freesurfer http://freesurfer-software. org) (Fig. 1 ). A key feature of LORIS is joint management of heterogenous data (i. e. , behavioral, clinical, imaging, genomics), often collected within a longitudinal design. Each stage, from clinical and imaging acquisition to processing and statistical analysis, was designed to create a logical flow for data handling (Fig. 2 ).
(i) Core Built on LAMP technologies (Linux, Apache, MySQL, PHP), LORIS is an open-source platform (https://github. com/aces/Loris) designed as an extensible and modular system, organizing heterogeneous data for multisite longitudinal studies (Fig. 3 ). Behavioral and Clinical data are typically entered or uploaded directly via a web browser, with real-time summary statistics to track each stage of acquisition. A customizable behavioral battery of psychometric measures provides metrics of ongoing data collection. Imaging data are stored in both raw and processed forms. The original DICOM files are archived and images are then converted to MINC format  (Vincent et al. , 2004) , where extensive provenance capabilities track processing using MINC tools. Other file formats (e. g. , NIfTI, NRRD) are available for use with separate processing environments.
Genomic data are handled through a genomics module that enables hosting, visualization, and dissemination of summary genetic and epigenetic data. Typically, imported directly into LORIS from genetic analysis pipelines or other sharing platforms (e. g. , CBRAIN), input files are cross-linked with imaging and behavioral data. Integrated links to resources (e. g. , UCSC Genome Browser) facilitate rapid visualization of results  (Kent et al. , 2002). (ii) Quality control (QC) Visualization and QC of image files are tailored to each modality and customized per type of review (on-site, centralized, manual, or automated). Supported imaging modalities include structural MRI (T1, T2, PD), functional MRI (fMRI, PET, SPECT), diffusion, spectroscopy, and electrophysiology (MEG, EEG). Independent tools, such as DTIPrep, have been integrated as part of the QC pipeline for raw and processed diffusion imaging data  (Liu et al.