dataesr/scientific-paragraphs-categorization huggingface dataset:
General spatial coverage The specimens of Promachocrinus kerguelensis gathered in this dataset were collected from most of the strategic regions in the Southern Ocean (triangles in Figure  2 ): the Antarctic continental shelf (East Weddell Sea, Davis Sea, Dumont d'Urville Sea, Ross Sea, Amundsen Sea, West Antarctic Peninsula), the Scotia Arc islands (South Shetland, South Orkney and South Sandwich) and the Sub-Antarctic islands (South Georgia, Kerguelen and Heard). Specimens were sampled at depths ranging from 65 m to 1162 m. This covers most of the known distribution area of this species (black circles in Figure  2 ), but only a portion of the bathymetric range for this species, which extends from 10 m to 2100 m  (Speel and Deardorn 1983). Coordinates 76°49'58. 8"S and 47°12'14. 4"S Latitude; 107°24'28.
The specimens were curated by each institution once back from the field and digitized in their own databases before the specimens were gathered by the authors in the purpose of the molecular study. Metadata associated with each specimen were extracted from the cruise reports. The molecular data (barcoding) were generated following the protocols described in  Ivanova et al. (2006) ,  Eléaume et al. (2011)  and  Hemery et al. (2012). Quality control description: The initial geo-referencing was done by means of the vessel onboard GPS systems. Samples identification was supervised and checked by Marc Eléaume, crinoid taxonomist at the Muséum national d'Histoire naturelle, Paris, following  Clark and Clark (1967)  taxonomic description of the species, and matched to the World Register of Marine Species (WoRMS).
Datasets Dataset description: This dataset has been generated for a molecular study of the Antarctic comatulid species Promachocrinus kerguelensis, improving the geographic coverage of the previous study by  Wilson et al. (2007). All the specimens are identified by several types of numbers that are linked together: Sample ID (characteristic of each individual), BOLD ID, GenBank ID and SeqID (all three characteristic of each sequence in different databases), Field Number (when available) and Museum ID. In some cases, the two last identifiers are shared by several individuals identifiable from each other by their own Sample ID. The dataset also includes the name of the institution storing the specimens, the complete taxonomy, names of identifiers and collectors, and information on the sampling itself: cruise names, vessel names, sampling gears, dates, regions, sectors, exact sites (when available), station numbers, latitudes and longitudes in decimal degrees, and depths in meters.
Object name: Darwin Core Archive Circumpolar dataset of sequenced specimens of Promachocrinus kerguelensis (Echinodermata, Crinoidea) Character encoding: UTF-8 Format name: Darwin Core Archive format Format version: 1. 0 Distribution: http://ipt. biodiversity. aq/archive. do?r=proke Publication date of data: 2012-03-01 Language: English Metadata language: English Date of metadata creation: 2012-04-25 Hierarchy level: Dataset.
Acknowledgements The authors thank  Sarah Henkel  and the two anonymous reviewers who helped to improve this manuscript, and all the contributors who furnished the samples:  Anderson O, Barnes DKA ,  Bohn JM ,  Bowden DA ,  Brey T, Constable A, Dahms HU, Duhamel G, Griffiths HJ, Hautecoeur M, Hibberd T, Linse K, Lockhart SJ, Lodde E, Mills S, Mooi R, Rouse GW, Sands CJ, Schiaparelli S, Schnabel K, Wilson NG.
Microarray data were analysed using R (R Development Core Team, 2010) and Bioconductor tools  (Gentleman et al. , 2004). After quality control with the ArrayQualityMetrics package  (Kauffmann et al. , 2009) , expression intensities were normalized with RMA function with the oligo package  (Carvalho and Irizarry, 2010). Genes differentially expressed between high and low glucose concentration in the culture media were identified using the moderate t-test in the LIMMA package  (Smyth, 2005). Genes with a >2-fold up-or down-regulation and an adjusted P-value <0. 05 after  Benjamini and Hochberg (1995)  false discovery rate correction were considered significantly differentially expressed between the two treatments. Mapman software  (Thimm et al. , 2004)  was used to visualize gene expression data in the context of their corresponding metabolic pathways.
Significant enrichments of functional categories of the MapMan ontology in the significantly differentially expressed genes were tested by using Mefisto Version 0. 23beta (http://www. usadellab. org) with a Bonferroni correction for multiple tests. All the data have been submitted in MIAME-compliant form within the ArrayExpress database (http://www. ebi. ac. uk/arrayexpress/; accession no. E-MTAB-1634).
We conclude that the electron-impact excitation of CN should be significant as soon as the electron fraction xe = n(e)/n(H2) exceeds ∼ 10 -5 and that these collisions will strongly favour transitions with ∆N = ∆j = ∆F = 1, in contrast to H2(j = 0) collisions which favour ∆N = ∆j = ∆F = 2. The present data will be made available in the BASECOL database  (Dubernet et al. 2013 ).
Consequently, new strategies must be used in order to design Mycobacterium genus targets with high levels of specificity and sensitivity that will be useful for studying mycobacteria in their habitat. As new mycobacterial sequences are added into genetic databases, our knowledge of mycobacterial genomes is increasing and this may help to design new primers and probes that will be both specific and sensitive. Since the whole sequencing of the first mycobacterial genome in 1998  [22]  by Sanger sequencing method (M. tuberculosis H37Rv), the number of mycobacterial sequences has considerably increased due to advances in sequencing capacity and the appearance of high throughput sequencing techniques  [23]. Today, GenBank database provides access to whole genomes of seven other strains of the MTC (M.
tuberculosis and M. bovis species), two strains of M. leprae, and eleven species and subspecies of pathogenic (P) and non-pathogenic (NP) NTM: M. abscessus (P), M. avium (P), M. avium subsp. paratuberculosis (P), M. gilvum (NP), M. marinum (P), M. smegmatis (NP), Mycobacterium sp. JLS (NP), Mycobacterium sp. KMS (NP), Mycobacterium sp. MCS (NP), M. ulcerans (P), M. vanbaalenii (NP),  [24] [25] [26]. Moreover, three whole genomes of other NTM species were sequenced and are currently assembled (M. intracellulare, M. kansasii, M. parascrofulaceum). This increasing number of completely sequenced mycobacterial genomes led to the development of the MycoHit software, which permits gene-and protein-level comparisons across mycobacteria species,  [27]. This software was originally developed to detect horizontal gene transfers and mutations among whole mycobacterial genomes  [27].
However, MycoHit should also be useful for developing new primers and probes for mycobacteria detection and quantification in environmental and clinical samples. In this paper, we used this tool for screening sensitive and specific targets of Mycobacterium spp. We compared in silico proteins of whole mycobacterial genomes with those of non-mycobacterial genomes using the MycoHit software, in order to find conserved sequences among mycobacteria that will not be shared with non-mycobacterial species. Based on the screening results a primer pair and a probe targeting the atpE gene were designed and tested by realtime PCR. This novel target proved to be totally specific and sensitive. It also offers the advantage of targeting a gene present as a single copy in the genome. Thus this new realtime PCR method appears promising for water quality survey, and should be useful for studying the ecology of mycobacteria in aquatic, terrestrial and urban environments.
These results also showed that our strategy of target design based on MycoHit software (Figure  1 ) gave very useful results for designing highly specific primers and might be applied to other microorganism clusters. In vitro validation of the real-time PCR targeting the atpE gene showed a very high specificity and sensitivity, as well as reproducible quantification of different mycobacteria species. The new real-time method was tested on a realistic number of mycobacterial species including several slow and rapid growing NTM, although not all the described mycobacterial species were tested. In addition, application of this real-time PCR method to environmental samples showed that Mycobacterium was detected in tap water samples. The discrepancy between the cultural and molecular techniques was previously described for other pathogens, and the lower level of prevalence obtained by the PCR methods was probably due to our concentration and extraction procedures.
Conclusions In conclusion, although our strategy did not take into account non-coding regions, such as insertion sequences, repetitive units, non-functional RNA, and structural ribosomal RNAs, the comparison of whole bacterial genomes for design of specific primers is a promising approach not only for mycobacteria but also for other cultured bacterial or archaeal groups for which whole sequenced genomes are accumulating in databases. Metagenomic libraries from environmental samples which are increasingly performed in microbial ecology studies  [46]  could also provide useful data for the design of specific targets toward uncultured Bacteria and Archaea. Using these databases, MycoHit, or other new software, may then be used to design new primers for real-time PCR detection or quantification, for in situ hybridization and other molecular tools.
In silico comparison strategy In order to detect M. tuberculosis genes, presenting homologue genes in other mycobacterial genomes, and not presenting homologue genes in non-mycobacteria genomes, we used the MycoHit software version 14. 17 (Zipped copy of the files and instructions for this application are available in the Behr Research Lab, https://www. mcgill. ca/molepi/) and performed an alignment search with Stand Alone tblastn algorithm as previously described  [27]. Stand Alone tblastn algorithm has been chosen because coding sequences are known to be more conserved in mycobacterial genomes than non-coding sequences, as intergenic regions, insertion sequences, or phage sequences  [30]. Genome of M. tuberculosis H37Rv has been used as a reference of the Mycobacterium genus, because it is the most historically described mycobacterial genome  [22].
Based on the 3989 predicted proteins from M. tuberculosis H37Rv, corresponding to the query sequences used in order to search for matches in the genomic DNA of other organisms (Figure  1 ), a matrix of 107703 scores (3989 protein sequences blasted against 12 non-mycobacterial genomes and 15 mycobacterial genomes) was obtained. As previously described  [27]  and according to NCBI procedures  [48] , expected value was set at e -10. Following sequence comparisons, the MycoHit software allowed to sort scores according to similarity requests which were performed on the one hand toward mycobacterial genomes, and on the other hand toward non-mycobacterial genomes (Figure  1 ). A protein list of the reference target, which can be downloaded from NCBI web site (http:// www. ncbi. nlm. nih. gov), allowed identification of the conserved mycobacterial proteins presenting no homology in non-mycobacterial genomes (Figure  1 ).
Mycobacterial genome database In order to perform comparisons of pathogenic (P) and non-pathogenic (NP) mycobacterial genomes with M. tuberculosis H37Rv genome using MycoHit software, sequences were obtained at NCBI web site (http:// www. ncbi. nlm. nih. gov) using the accession numbers: M. abscessus ATCC 19977 (CU458896. 1) (P), M. avium 104 (CP000479. 1) (P), M. avium subsp. paratuberculosis K10 (AE016958. 1) (P), M. bovis subsp. bovis AF2122/97 (BX248333. 1) (P), M. gilvum PYR-GCK (CP000656. 1) (NP), M. marinum M (CP000854. 1) (P), M. smegmatis MC2 155 (CP000480. 1) (NP), Mycobacterium sp. JLS (CP000580. 1) (NP), Mycobacterium sp. KMS (CP000518. 1) (NP), Mycobacterium sp. MCS (CP000384. 1) (NP), M. tuberculosis CDC1551 (AE000516. 2) (P), M. tuberculosis H37Ra (CP000611. 1) (NP), M. tuberculosis H37Rv (AL123456.
2) (P), M. tuberculosis KZN 1435 (CP001658. 1) (P), M. ulcerans Agy99 (CP000325. 1) (P), and M. vanbaalenii PYR-1 (CP000511. 1) (P). In order to avoid data lost during genome comparisons performed by MycoHit software, we have chosen to ignore some mycobacterial genomes. Since the number of coding proteins is much lower compared to other mycobacterial species, M. leprae Br4923 (FM211192. 1) (P), and M. leprae TN (AL450380. 1) (P) were ignored in the analysis (e. g. 1604 coding proteins in M. leprae Br4923 or 1605 coding proteins in M. leprae TN, against 6716 coding proteins in M. smegmatis MC2 155)  [22, [24] [25] [26] 35]. Genomes of M. bovis BCG Pasteur 1173P2 (AM408590. 1) (NP) and M. bovis BCG Tokyo 172 (AP010918. 1) (NP) were also not taken into account, because these vicinal genomes present mutations  [49].
Primer pair and probe design In order to check the homology of the selected mycobacterial sequences, the protein and DNA sequences of these selected proteins were aligned using the ClustalW multiple alignment of the BioEdit software 7. 0. 9. 0 with 1000 bootstraps  [50]. Primer pair and probe was designed from the best fitted gene sequences (after protein screening and selection) by visual analysis and using the Beacon Designer software version 7. 90 (Premier Biosoft International, Palo Alto, Calif. ).
The collection contained reference and environmental strains of mycobacteria, as well as, strains of the closely related CNM group, and other non-actinobacteria strains isolated from the environment  [17]. Mycobacteria collection included MTC (n = 2) and leprae species (n = 1), as well as species of slow growing NTM (n = 13), and rapid growing NTM (n = 15). TaqMan® real-time PCR were performed in duplicate using an ABI7500 real-time PCR system (Applied Biosystems), a Lifetech 7500 software version 2. 0. 6 (Applied Biosystems) and TaqMan fast virus 1-STEP Master Mix with 6-carboxy-X-rhodamine (ROX) (Applied Biosystems). The TaqMan® probes were labeled (Eurogentec) with the fluorescent dyes 6-carboxyfluorescein (5′ end) and Black Hole Quencher (3′ end). All reactions were performed in a 25 μl reaction mixture volume (2.
Acknowledgements We thank  Pr. Jacques Printems  from the laboratory of analysis and applied mathematics ( CNRS UMR 8050 ) in Paris Est University for access to his computer (MacPro3. 1,Quad-Core Intel Xeon) in order to perform tblastn algorithms, which run between 1 and 80 hours for each genome comparison according to the similarity levels with the reference genome. We also thank members of the  R&D Biology lab from Eau de Paris ,  Claire Therial  from  LEESU , as well as,  Michael Reed  and  Lynn Dery Capes  from the  Research Institute of the McGill University Health Centre.
1 ). There was no association of RIN numbers with tumor origin (peripheral versus central, p = 0. 40). Size distribution of in vitro transcription products (cRNAs) was homogeneous across samples and Illumina array summary plot including hybridization, labelling, background and housekeeping genes controls showed satisfactory overall performance of the arrays (data not shown). The number of genes detected on the Illumina array was independent of time to cryopreservation (p = 0. 80), RIN (p = 0. 095) and central versus peripheral origin (p = 0. 68), but was 3. 7% higher for LC than HCC (p = 0. 005). All samples but one passed the quality criterion P95/P05. 10. The exception, LC6_15C, was borderline with P95/P05 = 9. 51 (Table  1 ). Moreover, Figure  1  shows the distribution of the fluorescent signals across the arrays and the between array variance is not excessively large compared to that within each array.
Restricted Gene Expression Analysis to Ischemic Genes and HCC-specific Genes While the expression of several genes have been reported to be consistently deregulated at the early stage after surgery, the timecourse analysis of individuals genes performed with our sample series did not reveal any of those genes. Therefore, we analyzed independently 21 genes in our dataset (JNK3, JUNB, AP1B1, AP1S1, AP1M1, AP1S1, CA-IX, HHR6B, PRSS25, FOS, HIF1A, HO-1, JUND, JUN, KRT19, KRT20, CEA, KLF6, MDM4, FBLN2, FGRF4) that have been reported as deregulated at early stage after surgery  [14, 19, 20]. Average rates of expression change for those 21 genes showed no evidence for a gene expression deregulation over time delay to tumor freezing (Table  2 ). In addition, taking advantage of the publicly available Liverome database, which provides a large collection of well-curated HCC gene expression signatures (http://liverome.
kobic. re. kr), we selected the 34 most biologically relevant HCC genes, reported as deregulated in more than 4 studies recorded in the Liverome database  [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]. We then conducted analysis of the rates of expression changes to the 34 HCC-specific genes in our HCC dataset in order to examine whether some reported biologically significant HCC genes could be deregulated under warm ischemia conditions associated with delay to tumor freezing. Overall rates of expression changes for those 34 HCC genes showed no evidence for a gene expression deregulation over time delay to HCC tumor freezing (Table  2 ). The Neyman's test revealed that the rates of gene expression changes for the 21 selected ischemic genes and 34 HCC specific genes tended to the extremes of the distribution among the full panel of genes.
e. , up-regulated in the Liverome database and in our dataset or vice versa), 12 (35%) of which showed a deregulation of more than 10%/hr over delay to cryopreservation and one of those 12 genes (MT1F) has also been identified as significantly down-regulated under warm ischemia conditions in our HCC dataset. Altogether, we cannot exclude that the deregulation of those 12 genes is not only associated with the hepatocarcinogenesis per se but also with ischemic stress that could differ within tumor sample series of studies reported in the Liverome database.
This observation is also true for most of human tissue types as summarized in Ma et al. [14]. Using microarray analysis of RNA samples obtained from mouse embryonic stem cells, Sharova et al. have evaluated the rate of mRNA decay for 19,977 non-redundant genes. They found that the median estimated half-life was 7. 1 h and that only about 60 genes, including PRDM1, MYC, GADD45G, FOXA2, HES5 and TRIB1, had mRNA with half-lives less than 1 h  [51]. However, it cannot be excluded that microarray-based analysis is not appropriate to detect very labile mRNA, thus selecting against their representation in whole gene expression datasets. Even though RNA degradation within the first hour following surgical resection and before snap-freezing is limited, the process of collection and, specifically the lag time between resection and cryopreservation represent a complex form of metabolic and micro-structural stresses underlying a condition often defined as ''warm ischemia''.
Among the 6 down- regulated genes, 4 encoded metallothioneins (MT1E, MT1F, MT1G and MT1H). Down-regulation of metallothioneins has been previously reported in HCC  [57, 58, 59 ] and has been proposed to be associated with defective response to oxidative stress  [60]. Specific HCC genes reported in the literature  [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43-]  showed highly variable gene expression in our HCC dataset. This raises the possibility that ischemia stress associated with tumor removal may act as potential confounding factor to relevant biological HCC signature. This argument is further supported by the fact that of the 34 genes reported deregulated at least in 4 studies of the Liverome database, we identified one gene MT1F as significantly deregulated under warm ischemia following HCC surgical resection.
Whole Genome Expression Profiling of Frozen Tissues Genome-wide gene expression profiling analysis was performed on Illumina HumanHT-12 v3 Expression BeadChips, providing a coverage of more than 24,000 annotated genes  (48, 783  probes corresponding to 1 to 3 probes per gene) including well characterized genes and splice variants. Candidate probe sequences included on the HumanHT-12 v3 Expression BeadChip derive from the National Center for Biotechnology Information Reference Sequence (NCBI) RefSeq (Build 36. 2, Rel 22) and the UniGene (Build 199) databases. Using the Illumina TotalPrep RNA Amplification Kit (AmbionH), 500 ng of extracted RNAs were converted to cDNAs and subsequent biotin labeled singlestranded cRNAs. The distribution of homogeneous in vitro transcription products (cRNAs) was checked with the Agilent 2100 bioanalyzer instrument and the RNA 6000 Nano kit.
750 ng of biotin labeled cRNAs of the 48 samples were hybridized overnight to 4 HumanHT-12 Expression BeadChips. Subsequent steps included washing, streptavadin-Cy3 staining and scanning of the arrays on an Illumina BeadArray Reader. Fluorescence emission by Cy3 was quantitatively detected for downstream analysis. The Illumina Genome Studio V2010. 2 was used to obtain the signal values (AVG-Signal), with no normalization and no background subtraction. Data quality controls were performed using internal controls present on the HumanHT-12 beadchip and were visualized as a control summary plot. The microarray experiments are MIAME (Minimum Information About a Microarray Experiment) compliant and have been deposited at the NCBI Gene Expression Omnibus (GEO) database (http:// www. ncbi. nlm. nih.
2 developed by Dr Richard Simon and BRB-ArrayTools Development Team. Data were log-transformed and quantile normalized without background subtraction as described above, but with the exclusion of any probe showing excess dispersion (defined by more than 80% of individual probe values differing from the median by more than 1. 5-fold). The BRB-ArrayTools time course analysis model fits the same quadratic model as used over-all, with null hypothesis that both linear and quadratic terms were zero. Genes for which this hypothesis was rejected were identified. The tests were performed at a false discovery rate (FDR) threshold of 0. 05  [18]. 5. 4 Exploratory Cluster analysis. Unsupervised hierarchical clustering of samples was performed using both Genome Studio V2010. 2 and BRB-ArrayTools software v4.
2. 5. 5. Analysis restricted to pre-specified ischemic genes and HCC-specific genes. Twenty-one genes that have been previously reported as deregulated at an early stage after surgery were specifically selected for further analysis in the experimental dataset  [14, 19, 20]. This includes JNK3, JUNB, AP1B1, AP1S1, AP1M1, AP1S1, CA-IX, HHR6B, PRSS25, FOS, HIF1A, HO-1, JUND, JUN, KRT19, KRT20, CEA, KLF6, MDM4, FBLN2, FGRF4. Overall rates of expression changes were analyzed as described in the overall changes in expression paragraph, but since these probes may potentially be distinguished by either increasing or decreasing expression with time, a further two-stage analysis was carried out. A quadratic polynomial time effect was fitted separately for each probe. From each of these, the estimated coefficients were compared with the distribution of the values from the entire set of approximately 48,800 probes, in order to observe if they tended to the extremes of this distribution.