dataesr/scientific-paragraphs-categorization huggingface dataset:
Potential role of horizontal gene transfer in development of pathogenicity Horizontal gene transfer events from bacteria and archaea appear to have contributed to some of the novel biosynthetic pathways found in the oomycetes  [47]. By utilizing codon usage and protein domain classification (see Methods), at least 100 genes could be identified with a potential phylogenetic origin outside the super-kingdom of Chromalveolates by using the program Alien_hunter  [55]  and by interrogating Pfam search results. Among these, we further identified around 40 genes belonging to 5 families that could potentially be associated with pathogenesis (Table  3 ). Many of these genes appear to have been acquired from bacterial species, in particular from Proteobacteria. Some potential acquisitions may have occurred relatively recently as they only occur in Saprolegnia and seem to have nucleotide compositions predictive of foreign acquisition (Alien_hunter score.
16 to 21 cycles of PCR were used to amplify sequencing libraries. Libraries were purified using 1-3 rounds of AMPure beads (Beckman Coulter Genomics) following manufacturer's recommendations. Libraries were sequenced with 76 base pairedend reads using an Illumina Genome Analyzer II following manufacturer's recommendations (Illumina) generating a total of 124 million reads. Saprolegnia parasitica CBS 223. 65 de novo genome assembly Sanger Fosmid paired-end reads were quality trimmed using the ARACHNE 'Assemblez' module (http://www. broadinstitute. org/ crd/wiki/index. php/Assemblez). Read headers were amended to contain template and pairing information. Sanger, 454 fragment and 3 kb jumping reads were assembled using 454's Newbler assembler version 04292009 (454 Life Sciences/Roche). The resulting assembly was further processed using the ARACHNE 'HybridAssemble' module (http://www.
broadinstitute. org/crd/ wiki/index. php/HybridAssemble) using the 454 assembly, Sanger and 454 read data as input with option 'RecycleBadContigs' turned off allowing for extra copies of repeat sequences to be assembled. The Arachne 'AddReadsAsContigs' module was run to allow assembly of additional repetitive sequence. The assembly was screened for known sequencing vector sequences using BLAST and contigs with hits to known sequence vectors were removed. The assembly was further post-processed by removing contigs and scaffolds less than 200 bp and 2 kb in length respectively.
SNP calling Illumina shotgun sequence data were used to identify polymorphisms in two S. parasitica strains (CBS 223. 65 and VI-02736). Illumina paired-end fragment reads from strains CBS 223. 65 and VI-02736 were independently aligned to the S. parasitica CBS 223. 65 reference assembly using the BWA aligner  [75]  using default settings. Read alignments were sorted by scaffold and position along the reference assembly. SNP calling was performed using the GATK Unified Genotyper module  [76]. Variant Call Format (VCF) files containing SNP calls were filters for low quality using parameters: AB. 0. 75 && DP. 40.
Genome annotation Gene finding used both evidence-based (including EST, RNA-Seq and homology data) and ab initio methods. Gene-finding algorithms FGenesH, GeneID and GeneMark were trained for S. parasitica using existing gene and EST datasets. Then a statistical sampling of gene calls as well as genes of interest were manually curated, and the results were used to validate gene calls and finetune the gene caller. RNA-Seq data was incorporated into gene structure annotations using PASA  [77]  as described in Rhind et al. [78]. Subsequently, the annotated total gene set was subjected to Pfam domain analysis, OrthoMCL clustering analysis and KEGG metabolic pathway analysis.
Expression analysis with RNA-Seq Illumina RNA-Seq data was processed as follows. Sequencing adaptors were identified and removed from reads by exact match to adaptor sequences. Reads were aligned to S. parasitica gene transcripts using Bowtie (allowing up to 2 mismatches per read, and up to 20 alignments per read). Transcript levels were calculated as FPKM (fragments per kilobase cDNA per million fragments mapped). The program EdgeR  [79]  was used to identify differentially expressed transcripts. Transcripts with significantly different levels (p, = 0. 001 and over 4-fold difference) were identified, and p-values were adjusted for multiple testing by using the Benjamini Hochberg  [80]  correction.
Horizontal gene transfer analysis Two different approaches were used to screen S. parasitica genes for candidate HGT origins. In the first approach, the genome sequence was screened with the program Alien_hunter  [55]. The program utilizes an interpolated variable order motif method to determine horizontally transferred events, purely based on compositional difference between a region and the whole genome framework. Because the methodology is independent of any existing datasets, we used it to examine the S. parasitica genome. Genomic regions were identified as alien when the Alien_hunter score was above 50. Out of 1442 S. parasitica supercontigs, 206 supercontigs had distinct regions marked as alien after running Alien_hunter. Subsequently, the 1616 gene models that lay within the candidate alien regions were extracted and compared with other oomycete genomes (P.
sojae, P. ramorum, P. infestans, H. arabidopsidis and Py. ultimum). In the second approach, the entire proteome of each oomycete was scanned for homology to Pfam-A protein families using the hmmscan algorithm from Hmmer 3. 0 applied to the Hidden Markov Model dataset (Pfam-A. hmm v. 24). A cut-off e-value threshold of 1e-3 was applied. From the Pfam domain analysis we obtained 307 sequences that had distinct domains not found in any of the Phytophthora species, and 31 of the candidates derived from the Pfam analysis overlapped with the results from Alien_hunter. We then blast-searched all 1616 genes from Alien_hunter and the 307 genes from the domain analysis against the NCBI non-redundant database (nr) to obtain the primary functions. Phylogenetic analysis using neighbor joining was then performed on the final set of genes.
Phylogeny reconstruction and domain combination analysis Gene models were analyzed using TMHMM and ClustalX. For phylogeny reconstruction, sequences were aligned using ClustalX and aligned sequences were subjected to phylogenetic analysis (NJ) using PAUP. The predicted proteome of S. parasitica was compared to that of six pathogenic oomycetes together with sixty-four other eukaryotic species covering all major groups of the eukaryotic tree of life, as previously used by Seidl et al. [48]. We used hmmer-3 [http://hmmer. org] and a local Pfam-A database to predict 1,798,601 domains in 862,909 proteins of which 19,896 domains in 10,887 proteins are found in S. parasitica. The architectures of multi-domain proteins were analyzed from the N-to the Cterminus, which identified 18,512 domain combinations consisting of two contiguous domains.
Data deposition Saprolegnia parasitica CBS 223. 65: Sanger sequence data were submitted to the NCBI Trace Archive (http://www. ncbi. nlm. nih. gov/Traces/trace. cgi) and can be retrieved using query: CEN-TER_NAME = ''BI'' and CENTER_PROJECT = ''G1848''. 454 and Illumina sequence data were submitted to the NCBI Short Read Archive (SRA) (http://www. ncbi. nlm. nih. gov/sra) and can be retrieved using the following accession numbers: 454 fragment reads (SRX007896, SRX007901, SRX007898, SRX007895, SRX005344, SRX007971, SRX007958, SRX005346); 454 3 kb jumping reads (SRX007902, SRX007899, SRX007937, SRX007574, SRX007903); Illumina fragment reads (SRX022535); Illumina RNA-Seq reads (BioProject 164643: mycelium SRX155934, sporulation mycelium SRX155933, cysts SRX155932, germinating cysts SRX155938, infected fish cell-line t = 0 SRX155937, infected fish cell-line t = 8 SRX155936 and infected fish cell-line t = 24 SRX155935.
BioProject 167986: infected fish tissue SRX155944, uninfected fish tissue SRX155942, infected salmon eggs SRX155943, SRX155940 and uninfected salmon eggs SRX155941). Draft genome assembly sequence was submitted to GenBank (BioProject ID 36583, accession ADCG00000000). Saprolegnia parasitica VI-02736 : Illumina sequence data were submitted to the SRA (BioProject 164645, accession SRX155939). SNP calls for strains CBS 223. 65 and VI-02736 can be downloaded from the Broad Institute Saprolegnia parasitica genome database website (http://www. broadinstitute. org/annotation/ genome/Saprolegnia_parasitica/MultiDownloads. html).
The phylogenetic tree was constructed by using the neighbor joining method with 5000 replicates for bootstrap analysis. (PDF) Pies are scaled to the total number of kinases in each species. (C) Kinase genes that are induced in the germinating cyst stage in S. parasitica compared to mycelia. Transcripts elevated more than four-fold relative to vegetative stages are considered to be induced. (PDF) Figure  S5  (A) Phylogram of PLCYc domains of S. parasitica PLC1 and PLCs from various organisms. For phylogenetic analysis, the PLCYc domains were determined by Smart (http://smart. embl-heidelberg. de), alignments were made and regions containing gaps were eliminated resulting in a total of 88 positions in the final dataset. The optimal tree was inferred using the Neighbor-Joining method with 5000 replicates and constructed using MEGA version 4.
PLC sequences were derived from NCBI (*), JGI databases (http://genome. jgi-psf. org/,#), the Sanger Institute (http://www. genedb. org,@), http:// bioinformatics. psb. ugent. be,$). Arabidopsis thaliana AtPLC1 (Q39032*), AtPLC2 (Q39033*), AtPLC3 (Q56W08*), AtPLC4.
FigureS5(A) Phylogram of PLCYc domains of S. parasitica PLC1 and PLCs from various organisms. For phylogenetic analysis, the PLCYc domains were determined by Smart (http://smart. embl-heidelberg. de), alignments were made and regions containing gaps were eliminated resulting in a total of 88 positions in the final dataset. The optimal tree was inferred using the Neighbor-Joining method with 5000 replicates and constructed using MEGA version 4. PLC sequences were derived from NCBI (*), JGI databases (http://genome. jgi-psf. org/,#), the Sanger Institute (http://www. genedb. org,@), http:// bioinformatics. psb. ugent. be,$). Arabidopsis thaliana AtPLC1 (Q39032*), AtPLC2 (Q39033*), AtPLC3 (Q56W08*), AtPLC4 (Q944C1*), AtPLC5 (Q944C2*), AtPLC6 (UPI000034EE4D*), AtPLC7 (Q9LY51*), AtPLC8 (Q9STZ3*), AtPLC9 (Q6NMA7*); Aureococcus anophagefferens (Auran; 18506#); Ciona intestinalis (Cioin; XP_002129990*); Cryptosporidium parvum (Crypa; Q5CR08*); Danio rerio (Danre; XP_689964*); Ectocarpus siliculosus (Ectsi; Esi0000_0131$); Emiliania huxleyi (209393#); Fragilariopsis cylindrus (Fracy;186252#); Homo sapiens (as described by [9]*); Naegleria gruberi (Naegr; 1225#); Paralichthys olivaceus (Parol; ACA05829*); Paramecium tetraurelia, (PLC1, see [10]*); Phaeodactylum tricornutum (Phatr; 42683#), Plasmodium falciparum (Plasmo; PF10_0132@);.
Acknowledgments We thank members of the 2010 Saprolegnia Annotation Jamboree for annotations, and  Shiv Kale  for useful discussions. We thank the  Broad Genomics Platform  for their contribution and support. We thank  James Bochicchio  for project management and  Jonathan Goldberg ,  Sharvari Gujja  and  Lucia Alvarado-Balderrama  for genome annotation and data release.
This work was supported by the  Agriculture and Food Research Institute of the National Institute of Food and Agriculture , grant  2008-35600-04646  (to BMT); by the  Ministerio de Ciencia e Innovacion, Spain  ( CGL2009-10032 ) (JD-U); and by the  BBSRC ,  NERC ,  The Royal Society , the  University of Aberdeen , and the  European Union  ( FP7) for a Marie Curie Initial Training Network award ''SAPRO'' (PvW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
sojae, Pram = P. ramorum, Hpa = Hyaloperonospora arabidopsidis, Pult = Pythium ultimum, Aph = Aphanomyces euteiches. b The presence of the protein families were searched in the Aphanomycete EST database by BLASTP. A positive hit (E value,1e-8 sequence similarity. 30%) is indicated by +. c Lectin and lectin-like families. doi:10. 1371/journal. pgen. 1003272. t002.
METHODS We searched the database at the Memory and Alzheimer Institute of the Pitié-Salpêtrière Hospital for patients for whom a diagnosis of bvFTD had been established according to clinical criteria. From this series, we selected those patients with a "CSF AD biomarker profile". A "CSF AD biomarker profile" was defined as a P-Tau/ Ab 42 ratio higher than 0. 21, as this distinguishes AD from bvFTD with a high sensitivity (91. 2%) and specificity (92. 6%). 11 All selected patients in this "frontal AD group" fulfilled the revised Lund-Manchester consensus criteria for bvFTD  [13] [14] [15]  including: [1] a corroborated history of initial progressive decline in social interpersonal conduct and behavioral symptoms such as emotional blunting, apathy, reduced empathy, disinhibition, stereotypic behaviors, alterations in food preference and poor self-care; [2] the presence of dysexecutive difficulties at the neuropsychological exam; [3] anatomical magnetic resonance imaging (MRI) and/or single Photon Emission Computed Tomography (SPECT) disclosing frontal atrophy and/or blood hypoperfusion.
We did not include subjects who presented with the following: [1] clinical or neuroimaging evidence of focal lesions; [2] severe depression; [3] early impairment of praxis and spatial skills; [4] patients with language dis-orders characteristic either of progressive non-fluent aphasia or semantic aphasia; [5] severe cortical or subcortical vascular lesions, and [6] inflammatory, infectious or vascular diseases that could account for cognitive/behavioral impairment. Clinical and neuropsychological data from patients with frontal AD were compared with three groups of subjects selected from the database of the Memory and Alzheimer Institute of the Pitié-Salpêtrière Hospital: [i] patients with typical amnestic AD (n=18), with CSF AD biological profile (P-Tau/Ab 42 ratio higher than 0. 21); [ii] patients with bvFTD (n=18) that fulfilled the last revised diagnostic criteria for bvFTD  15  and who had normal CSF biomarker profile (P-Tau/Ab 42 ratio lower than 0.
Statistical behavioral analysis. All statistical analyses were performed with the STATISTICA 5. 5A software (© Stat-Soft, Tulsa, Oklahoma, USA). Descriptive statistics were used to characterize each group. The Mann-Whitney test was employed to compare differences in distributions between the "frontal AD" group and each of the other three groups (healthy controls, typical AD and bvFTD groups). Measurement of CSF biomarkers. CSF samples were collected by lumbar puncture (LP) and analyzed for total Tau, Tau phosphorylated at threonine 181 (P-Tau) and Ab 42 with a double-sandwich enzyme-linked immunosorbent assay (ELISA) method (Innogenetics, Gent, Belgium) at the Metabolic Biochemistry Department of the Pitié-Salpêtrière Hospital, as previously described. 11 or all patients, the biological and clinical data were generated during a routine clinical work-up and were retrospectively extracted for this study.
While pathological data may be important for establishing diagnosis for patients, no autopsies were available in our cohort. It should be noted, however, that clinical diagnosis was established using accepted consensus criteria; all patients were extensively evaluated with clinical, biological, neuropsychological and neuroimaging exams at a center with expertise in the field of dementias. Furthermore, we selected patients with strict biological inclusion criteria based on CSF biomarker results, such as reduced Ab 42 level, high P-Tau and abnormal P-Tau/Ab 42 ratio, which have been demonstrated to be highly correlated with Alzheimer pathology at post-mortem exam. 47 t would also be of value to compare the clinical and neuroimaging features across patients with frontal AD and bvFTD. Further studies with a greater number of patients are needed to investigate whether clinical, neuroimaging and neuropsychological parameters differ during disease progression of frontal AD and bvFTD.
6 AE 0. 02 MPa and the two DIC operating studied parameters were ranged as: (1) 6 AE 2 to 40 AE 2 s for the treatment time per cycle t, and (2) 1e11 cycles for the number of cycles C. This resulted in 11 experimental trials, run at random. Statistical treatment of results was executed using the analysis design procedure of Statgraphics Plus software for  Windows (1994, version 4. 1, Levallois-Perret, France). Variance (ANOVA) was performed to determine significant differences between independent variables (P 0. 05) and Pareto charts were introduced as well as the general trends, response surface, empirical model coefficients, and R 2 were determined. The dependent variables had concerned process performances (in terms of efficiency) as well as the final extracted attributes: the response was expressed with a second-order polynomial empirical model of independent variables: Y ¼ b 0 þ X n i ¼ 1 b i x i þ X n i ¼ 1 b ii x 2 i þ X n i ¼ 1 b ij x i x j þ 3 where Y was the response, b 0 , b i , b ii and b ij stand for the regression coefficients, x i stand for the independent variables, 3 stands for the random error, and i and j for the indices of the factors.
Most components were tentatively identified by comparison of their GC retention indices (RI) calculated with the help of a series of linear alkanes C 6 eC 26 on apolar and polar columns (HP5MSÔ and CarbowaxÔ). RIs were determined with reference to a homologous series of C 5 eC 28 n-alkanes and with those of authentic standards available in the authors' laboratory. Identification was confirmed when possible by comparison of their mass spectral fragmentation patterns with those stored in the MS database (National Institute of Standards and Technology and Wiley libraries) and with mass spectra literature data  (Adams, 2001). Component relative concentrations were obtained directly from GC peak areas.
High performance liquid chromatography (HPLC) analysis HPLC analyses were performed using a Waters (Milford, MA) HPLC system consisting of a Waters 600E pump, a Waters 717 autosampler, a Waters 2996 photodiode array detector. HPLC pumps, autosampler, column temperature, and diode array system were monitored and controlled by using Waters Empower 2 Chromatography Data software program. The wavelengths used for the quantification of the phenol (rosmarinic acid) and of phenolic diterpenes (carnosol and carnosic acid) with the diode detector were 329 nm and 284 nm, respectively. The chromatographic separation was carried out on a Purospher Star RP-18 end-capped column (250 mm Â 4 mm I. D. ; 5 mm particle size from VWR), with an RP18 guard column (4 mm Â 4 mm I. D. ; 5 mm particle size also from VWR).
Optimization of instant controlled pressure drop deodorization Essential oil yields obtained for different trials of the experimental design protocol are shown in Table  1. These results were analyzed statistically through Statgraphics software in order to obtain the optimal DIC parameters. A Pareto chart of standardized effects (Fig. 3 ) was calculated in order to show significant effects of all variables (linear, quadratic and interactions between variables). The vertical line represents the limit between the significant and insignificant effects regarding the response. The length of each parameter characterizes the absolute importance of the estimated effects. It is interesting to note that Pareto chart enabled us to provide the data to determine the relevance of each parameter (number of cycles C and treatment time per cycle t).
The mathematical relationship obtained with the statistical software Statgraphics was a polynomial equation representing the quantitative effect of process variables and their interactions on the measured response. The values of the coefficients of C and t were related to the effect of these variables on the response i. e. essential oil yields. A positive value represented an effect that favours the operation, while a negative value indicated an antagonistic effect. The second-order polynomial equation of the response surface obtained is as follows: Yield EO ð%Þ ¼ 0:2879C þ 0:0391t À 0:00897C 2 À 0:001875Ct À 0:00056t 2 À 0:873 With R 2 ¼ 96. 74%. Taking into account that only the number of cycle C had a significant influence one can assume that: Yield EO ð%Þ ¼ 0:1372C À 0:1013 With R 2 ¼ 90.
g. , Ref. [5] ) and represents the aim of this Letter. This analysis is done via the excitation by EPs of a class of modes naturally existing in tokamaks: the geodesic acoustic modes (GAMs)  [6] , which are the oscillatory component of large scale E Â B zonal flows. The EP-driven GAMs are called energetic geodesic acoustic modes (EGAMs). These modes have been predicted theoretically  [7, 8] , observed experimentally  [9, 10] , and reported very recently numerically in the absence of turbulence  [11]  in gyrokinetic simulations with the 5D GYSELA code  [12]. The motivation of the present Letter relies upon fluid simulations where the turbulence level was controlled by GAMs in the transitional regime between core and edge  [13]. In addition, experimental evidence of the role of GAMs in the edge-turbulence suppression has been reported for the first time during the analysis of the L-H transition in the ASDEX Upgrade tokamak  [14].
Statistical analysis of tumor growth The Mann-Whitney test was used for the comparison between the two groups in the experiment with subcutaneous injection of pFasL. The Kaplan-Meier analysis was used to establish the survival curves without cancer, and comparison between the two groups was made using the log-rank test. Analyses were performed with Statview Software (Abacus Concepts, Berkeley, CA). For all experiments, a p#0. 05 was considered significant.
Flotation Experiments Proteins (0. 75 mM) and liposomes (0. 75 mM) were incubated in HKM buffer at room temperature for 20 min in a total volume of 150 ml. The suspension was adjusted to 30% sucrose by adding and mixing 100 ml of a 75% w/v sucrose solution in HKM buffer. The resulting high-sucrose suspension was overlaid with 200 ml HKM containing 25% w/v sucrose and 50 ml HKM containing no sucrose. The sample was centrifuged at 55000 r. p. m. (240,000 g) in a Beckman swinging rotor (TLS 55) for 1 h. The bottom (250 ml), middle (150 ml) and top (50 ml) fractions were manually collected from the bottom using a Hamilton syringe and analysed by SDS-PAGE before and after staining with Sypro orange (Molecular Probes). Quantification was performed using the ImageJ software. For each condition (GDP or GTP) the sum of the band signal recovered in the bottom and top fractions was taken as 100% (total protein mass).
Arfaptin2 Recruitment to Golgi-mix Lipid Tubes and GUVs Lipid tubes were pulled from GUVs using the motor domain of kinesin1 coupled with the bacterial biotin carrier protein as described previously  [19]. Briefly, after tube pulling, 1 to 3 mM Arf1 (bound to GDP or GTPcS) and Arfaptin2-Alexa 488 were incubated with the liposome/tube networks. Confocal fluorescence micrographs were obtained using a Zeiss Meta 510 confocal microscope. Analyses were performed using the ImageJ software.
B. Protein quantification from the Syproorange stained gels shown in A. The black bars correspond to Arfaptin2 and the white/black striped bars to Arf1. C. FRET assay. Arfaptin2-Alexa 488 (0. 5 mM) and myr-Arf1-H6 were mixed with Golgi-mix liposomes of various sizes containing 1 mol% TRITC-DHPE. At the indicated times, GTP (0. 1 mM), EDTA (2 mM), MgCl2 (2 mM) and ArfGAP1 (10 nM) were added. Alexa 488 fluorescence was followed in real time. Arfaptin2 was recruited to the membranes after Arf1 activation (GTP). Arfaptin2 recruitment increased as a function of membrane curvature. doi:10. 1371/journal. pone. 0062963. g001 membrane deformation was observed on GUVs incubated with Arf1:GTPcS and Arfaptin2-Alexa 488 at concentrations below 1 mM (data not shown). However, tubular protrusions emerging from the vesicles could be seen at higher concentration (3 mM) in the presence of Arf1:GTP (Figure  3 ).