dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of SNPs and DNA sequence rearrangements To identify SNPs, we followed the procedure described in Chan et al  [14]. To identify DNA sequence rearrangements across the genome we relied solely on paired-end read configuration (i. e. not sequence coverage) and screened for paired-end reads that did not map in the expected configuration (i. e. head-to-head within 1 kb from each other) using custom Perl scripts to parse the alignment output. We classified these paired-end reads in the following categories: i) reads mapping head-to-head but distant by more than 1 kb, ii) reads mapping in a head-to-tail configuration and iii) reads mapping tail-to-tail (Figure  S1 ). Note that the distance of 1 kb was selected to identify putative deletions and chosen to focus on relatively large DNA sequence deletions and to minimize false positives caused by DNA fragments slightly greater than 300 bp that had not been excluded during size selection.
Acknowledgments We thank all the patients and healthcare workers involved in this study and the staff of the  Ministries of Health of Madagascar and Cambodia  for their collaboration. We also thank  Dr. Montpetit  and the sequencing group of the  McGill University and Genome Quebec Innovation Center  for their help with HiSeq 2000 sequencing. Polymorphism and raw sequence data generated for this study are freely accessible through SRA (www. ncbi. nlm. nih. gov/sra/). The DNA sequence of the PvDBP duplication is available through GenBank (# KF159580 ).
Classification and features 16S rRNA gene sequence analysis A representative genomic 16S rRNA gene sequence of A. mobile NGA T was compared using NCBI BLAST  [5, 6]  under default settings (e. g. , considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database  [7]  and the relative frequencies of taxa and keywords (reduced to their stem  [8] ) were determined, weighted by BLAST scores. Standards in Genomic Sciences The most frequently occurring genera were Anaerobaculum (81. 9%), Acetomicrobium (9. 9%), Thermovirga (4. 5%) and Dethiosulfovibrio (3. 6%) (12 hits in total). Regarding the two hits to sequences from members of the species, the average identity within HSPs was 99. 9%, whereas the average coverage by HSPs was 100.
0%. Regarding the seven hits to sequences from other members of the genus, the average identity within HSPs was 97. 1%, whereas the average coverage by HSPs was 99. 1%. Among all other species, the one yielding the highest score was Acetomicrobium flavidum (FR733692), which corresponded to an identity of 99. 8% and an HSP coverage of 100. 0%. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification. ) The highest-scoring environmental sequence was FN436106 (Greengenes name 'Molekularbiologische Charakterisierung der bakteriellen Populationsdynamik eines thermophil betriebenen Biogasfermenters zur Vergaerung nachwachsender Rohstoffe thermophilic biogas reactor fed renewable biomass clone HAW-R60-B-745d-BC'), which showed an identity of 99.
Genome project history This organism was selected for sequencing on the basis of its phylogenetic position  [29] , and is part of the Genomic Encyclopedia of Bacteria and Archaea project  [30]. The genome project is deposited in the Genomes On Line Database  [17]  and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI) using state of the art sequencing technology  [31]. A summary of the project information is shown in Table  2.
Genome sequencing and assembly The genome was sequenced using a combination of Illumina and 454 sequencing platforms. All general aspects of library construction and sequencing can be found at the JGI website  [33]. Pyrosequencing reads were assembled using the Newbler assembler (Roche). The initial Newbler assembly, consisting of 31 contigs in one scaffold, was converted into a phrap  [34]  assembly by making fake reads from the consensus, to collect the read pairs in the 454 paired end library. Illumina GAii sequencing data (2,649. 5 Mb) was assembled with Velvet  [35]  and the consensus sequences were shredded into 1. 5 kb overlapped fake reads and assembled together with the 454 data. The 454 draft assembly was based on 113. 8 Mb 454 draft data and all of the 454 paired end data. Newbler parameters are -consed -a 50 -l 350 -g -m -ml 20.
Genome annotation Genes were identified using Prodigal  [38]  as part of the DOE-JGI genome annotation pipeline  [39] , followed by a round of manual curation using the JGI GenePRIMP pipeline  [40]. The predicted CDSs were translated and used to search the National Center for Biotechnology Information (NCBI) nonredundant database, UniProt, TIGR-Fam, Pfam, PRIAM, KEGG, COG, and InterPro databases. Additional gene prediction analysis and functional annotation was performed within the Integrated Microbial Genomes -Expert Review (IMG-ER) platform  [41].
Acknowledgements We would like to gratefully acknowledge the help of  Maren Schröder  for growing A. mobile cultures,  Evelyne-Marie Brambilla  for DNA extraction and quality control, and  Cathrin Spröer  and  Rüdiger Pukall  for helpful comments (all at DSMZ). This work was performed under the auspices of the  US Department of Energy Office of Science, Biological and Environmental Research Program , and by the  University of Cali-fornia ,  Lawrence Berkeley National Laboratory  under contract No. DE-AC02-05CH11231 ,  Lawrence Livermore National Laboratory  under Contract No. DE-AC52-07NA27344 , and  Los Alamos National Laboratory  under contract No. DE-AC02-06NA25396 ,  UT-Battelle  and  Oak Ridge National Laboratory  under contract  DE-AC05-00OR22725.
A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.
To take into account the dependence in the spatial space, Markov random field (MRF) models have been widely used for solving the image segmentation problem  [35] [36] [37]. However, one of the drawbacks of the MRF-based methods is that the computational cost is quite high. In this paper, we present a novel Bayesian segmentation method for vessel segmentation. The proposed method takes the spatial information into account. We found that maximizing log-likelihood function is equivalent to energy function minimization. The parameters of the model can be estimated via energy minimization. In order to detect the boundaries of the blood vessels, the modified level set approach is used for solving the energy function minimization problem. The method was evaluated on two publicly available databases, the DRIVE database  [38]  and the STARE database  [39].
Experiments In this section, we have evaluated and compared the proposed method for the segmentation of retinal images. The retinal images are obtained from two publicly available databases the DRIVE database  [38]  and the STARE database  [39]. In the experiments, we generally choose the parameters as follows:  = 1. 0 and the time step Δ = 0. 1. After many experiments on a small number of example images, we have found that, when  = 0. 001 × 255 2 , the performance is very good. In all the following experiments, the values of the parameters are same. The images of the DRIVE database are with 565 × 584 pixels and 8 bits per color channel. The database includes binary images with the results of manual segmentation, which have been used as ground truth to evaluate the performance of the vessel segmentation methods.
The retinal images of the STARE database are digitized to 700 × 605 pixels, 8 bits per RGB channel. The STARE database contains 20 images for blood vessel segmentation: 10 normal images and 10 abnormal images. Binary images with manual segmentations are also available for each image of this database. Evaluation of the developed method is done on the DRIVE and STARE databases. Experimental results are compared to those obtained using other vessel segmentation methods. To facilitate the comparison with other retinal vessel segmentation methods, the segmentation accuracy has been selected as performance measure. The segmentation accuracy has been defined by the ratio of the total number of correctly classified pixels by the number of pixels in the field of view (FOV). It contains values in the range [0, 1], with values closer to 1 indicating a good result.
Other important measures are sensitivity and specificity. In this paper, the sensitivity is estimated by the percentage of pixels correctly classified as vessel pixels. The specificity stands for the fraction of pixels erroneously classified as vessel pixels. The ground truth for evaluating the performance measures was a manual segmentation result which is provided together with each database image. A majority of the pixels are often easy to be classified by the previous methods; however, some of the pixels, such as those on the boundary of a vessel, those for small vessels, and those for vessels near pathology, are difficult to be classified. The proposed method provides a novel way to account for spatial dependence between image pixels. Thus, it can reduce the sensitivity of the segmented results and guarantee connectedness of the vessel structure.
In the first experiment, the proposed method is done on retinal vessel images of DRIVE database. Image in Figure  1(a)  shows the original retinal vessel images. The ground truth of the original retinal image is presented in Figure  1(b). The segmentation result obtained by using the proposed method is illustrated in Figure  1 (c). It can observed that the proposed method obtains good results. In order to test the accuracy and determine the efficiency of the proposed method, we do experiment on another retinal vessel image of the DRIVE database and compare the result with those obtained by other methods. Figure  2   [2] , Mendonca and Campilho (green intensity) method  [18] , and the proposed method, respectively. For visual inspection of the results, the proposed method produces a very good segmentation result.
To facilitate the comparison of our results to those presented by other authors in their original papers, the results have been calculated for the images of the DRIVE database. The value results of the proposed method are shown in Table  1. The other vessel segmentation methods which are reported in their published papers are also presented in Table  1. The performance measures of the proposed method in Table  1  are the average values for all the images of DRIVE database. We can view that the proposed method can capture more correctly classified vessel pixels and less erroneously classified vessel pixels than the other methods. The average accuracy of the proposed method is better than the other techniques. In order to further test the accuracy and determine the efficiency of the proposed method, the proposed method has been tested on the images of the STARE database.
Figures  3(a  can be seen that the proposed method can obtain very good results. To compare our results to those reported in other published papers, we give the performance measures in Table  2. The average accuracy, sensitivity, and specificity are used to measure the performance. We can note that the proposed method performs better than the other techniques. The execution time of the proposed method depends on many parameters. For the images of the STARE database, one iteration time of the proposed method is less than 10 seconds. Convergence of the proposed algorithm may be achieved in less than 10 iterations.
Conclusion The accurate extraction of the retinal blood vessel can provide much help for diagnosis of cardiovascular and ophthalmologic diseases. Even though many techniques and algorithms have been developed, there is still room for improvement in blood vessel segmentation methodologies. In this paper, we have presented a novel Bayesian model for vessel segmentation. To overcome the drawback that the spatial information is not taken into account, the proposed Computational and Mathematical Methods in Medicine model exploits the spatial information. To the boundaries of the blood vessels, the modified level set approach is employed for minimizing the proposed energy function. The proposed method has been tested on real retinal image databases and the experimental results have been compared with those of other methods.
Since the developed method takes the spatial information into account, it can result in very good performance in the detection of narrow and low contrast vessels and guarantee connectedness of the vessel structure. The comparison demonstrates that the proposed method outperforms other methods. In future work, we will compare the proposed method to other algorithms. Most of the techniques in the literature and the proposed method are evaluated on a limited range of databases (DRIVE and STARE). We will do more experiments on larger database and compare to more techniques in future.
FUNDING  Agence nationale de la recherche  [ ANR-10-BLAN-1013 1 -' DynRec ' to G. C. , A. R. C. and M. T. ];  Region Ile-de-France  in the framework of  DIM Centre de compe´tences en nanosciences en Ile-de-France ('RecombinaTarget')  (to G. C. );  Swiss National Science Foundation  [ 31003A-138367  to A. S. ]. Funding for open access charge:  Agence nationale de la recherche (France).
Experimental data L. lactis subsp. lactis IL1403 was grown in batch cultures in a modified chemically defined medium in exponential conditions (growth rate of 0. 88 h 21 ) and isoleucine starvation conditions (growth rate of 0. 05 h 21 )  [12]. Translatome experiments were performed to determine genome-wide ribosomal density and ribosome occupancy (fraction of mRNA molecules engaged in translation) in normal and stress conditions (Figure  S1 )  [11, 13]. Briefly, after translation arrest and cell disruption, size fractionation of mRNA-ribosome complexes on sucrose gradient was processed. In the elution fractions corresponding to different polysome sizes, total RNA was extracted and hybridized to microarrays. For each microarray series, normalization steps including intra-series and inter-series normalization, correction of intensity values to the total RNA quantity and their centering reduction were performed to determine the number of bound ribosomes on each mRNA molecules.
Acknowledgments The research leading to these results has received funding from the  Spanish Ministry of Science and Innovation (Ministerio de Ciencia e Innovacion)  under the project  GRE3NLINK-MAC (TEC2011-29006-C03-02 ), the  Catalan Government  ( 2009SGR0891 ), and the research projects:  CO2GREEN  ( TEC2010-20823 ),  GREENET  ( PITN-GA-2010-264759 ), and  GREEN-T (CP8-006 ).
Bioinformatics Analysis of LDH Promoters We extracted LDHA and LDHB promoter sequences from nucleotides 22000 to 21 starting from the transcription starting site (TSS) according to the NCBI accession NM_00566 and NM_002300. We scanned the promoters with the Matrix-Scan software (http://rsat. ulb. ac. be/rsat/) using a position-weight matrix defining ERRa binding sites as described elsewhere  [17]. The transcription-factor binding site representations were considered statistically significant at 5% risk after simultaneous comparison with a set of 100 human promoters.
Results are the mean values6SD of three experiments for the cell lines and mean values of thyroid tumors relative to control thyroid tissues. & p,0. 05 for FTC-133 versus XTC. UC1 or RO82W-1, % p,0. 05 for XTC. UC1 versus RO82W-1, m p,0. 05 for control thyroid versus follicular tumors or oncocytic tumors; D p,0. 05 for follicular tumors versus oncocytic tumors. doi:10. 1371/journal. pone. 0058683. g001 Real-time quantification was performed in a 96-well plate using the IQ SYBR Green SuperMix and Chromo4 (Biorad). Data were normalized to b-globin as described elsewhere  [6].
Microarray Analysis cDNA from RO82W-1 cells were hybridized in duplicate on human 4644,000 expression chips (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's recommendations. Data are available in the GEO database (GSE 37017). The Expression Analysis Systematic Explorer (EASE) and Gene Set analysis were used to determine the statistically over-represented and differentially expressed genes. Gene ontology enrichments in gene lists were searched for by means of the GOMiner. The most abundant gene ontology terms, representing at least 5% of the genes in the lists, with p values lower than 0. 05, were considered for interpretation.
Sequence fastq files for Litomosoides sigmodontis have been submitted to the European Read Archive with accession ERP001496. Assemblies of wLs and wDi have been deposited in ENA with accessions PRJEB4155 and PRJEB4154 respectively. The ortholog clustering data and alignment files used in the analyses have been deposited with DataDryad with accession doi:10. 5061/dryad. 4nt8m.
Joins in the assembly that had low coverage were validated using polymerase chain reaction (PCR). The assembly (wLs. 2. 0) is available through http://lito mosoides. nematod. es (last accessed September 5, 2013). The genomes of 11 Wolbachia strains and 4 outgroups were retrieved from the databases (see fig. 1 ). For wDi from the nematode Dirofilaria immitis, we reassembled the genome  (Godel et al. 2012 ) using improved informatic routines, extracting additional read data from the raw genome sequence for D. immitis. The new assembly is improved (in that it has many fewer contigs). The contiguity of this new assembly (wDi. 2. 2) was verified by directed PCR and is available from http://dirofilaria. nematod. es (last accessed September 5, 2013). Ortholog detection was performed using OrthoMCL 1.
4 with default settings  (Chen et al. 2006). All sequences of each putative orthologous cluster were automatically annotated (using BLASTP with an E-value cutoff of 10 À5 ) against the Clusters of Orthologous Groups (COG) database  (Tatusov et al. 2000 ). An orthologous cluster was selected for subsequent analyses if all sequences of the cluster were coherently annotated by comparison to the COG database. Orthologous clusters containing members from all 16 genomes and lacking within-genome duplicates were selected, and amino acid sequences aligned using Muscle  (Edgar 2004 ) with default settings. Nucleotide alignments were obtained by retrotranslation of these amino acid alignments. The Pairwise Homoplasy Index (PHI) and MaxChi were calculated for each nucleotide alignment with PhiPack  (Bruen et al.
2. 8  (Stamatakis et al. 2008). Bayesian phylogenetic analyses were carried out on unpartitioned concatenates with MrBayes 3. 2  (Ronquist et al. 2012 ) on the web-based Bioportal  (Kumar et al. 2009). Bayesian Markov chain Monte Carlo analyses were implemented in two parallel analyses, each composed of one cold and five incrementally heated chains that were run for 10 million generations. Trees were sampled every 1,000 generations and burn-in fraction was calculated according to lnL stationary analyses. Gene presence-absence information for wUni (Wolbachia endosymbiont of Muscidifurax uniraptor) was removed from all orthologous clusters before performing gene presence-absence analysis, because the wUni genome is not yet complete  (Klasson et al. 2009 ). The ortholog presenceabsence matrix was derived from the ortholog cluster data and used to calculate the Bray-Curtis dissimilarity matrix.
Results Litomosoides sigmodontis is an onchocercid filarial parasite of cotton rats  (Hoffmann et al. 2000). The L. sigmodontis Wolbachia, wLs, was assembled from data generated as part of the ongoing L. sigmodontis genome project (Koutsovoulos G, Kumar S, Babayan SA, Blaxter M, unpublished data; see http:// litomosoides. nematod. es, last accessed September 5, 2013). The wLs genome assembly was generated by identifying contigs in initial genome assemblies that contained Wolbachia genes, extracting the raw data that mapped to these contigs and performing independent assembly. The genome was refined through cycles of additional read identification and assembly and validation of some joins by PCR. The raw data have been submitted to INSDC databases under project accession ERP001496. We retrieved whole genome-derived gene data for 11 additional Wolbachia strains and four outgroup species from ENA (see Materials and Methods).
The genes used in phylogenomic analyses were selected from sets of orthologs, identified as reciprocal best BLAST hits and validated through comparison with the COG database. A subset of orthologs present in all taxa was identified. Ortholog sets showing evidence of paralog duplication, evidence of recombination between genomes, or evidence of nucleotide substitution saturation were removed. We identified 1,677 ortholog clusters, 1,519 of which were coherently annotated. The sixteen bacterial genomes shared 390 of the 1,519 gene clusters, 341 of which presented no evidence of duplication. Of these 341 clusters, 126 showed no evidence of recombination, and, of these, 90 showed no evidence of nucleotide substitution saturation. These 90 clusters were retained and used for phylogenomic analysis (see supplementary table S1, Supplementary Material online, for a complete list of these 90 genes).
We concur with their proposals to eliminate rigorously from consideration loci with abberations in phylogenetic signature, assessed independently of the derivation of the phylogeny. We note that our phylogenetic question is less problematic than their model, with fewer taxa overall and some unquestioned groupings (such as the monophyly of clades A, B, C, and D). Thus, we reduced our original set of more than 400 putative single copy orthologs to a core set of 90 genes with validated behavior. Only 3 of the 21 genes of the Bordenstein set  (Bordenstein et al. 2009 ) passed the stringent assessment for inclusion in our database. ML and Bayesian analyses of nucleotide and amino acid alignments yielded congruent topologies with high statistical support. Importantly, the branch lengths observed between the two genera in the outgroup (Anaplasma and Ehrlichia) was comparable to the branch length observed between the outgroup clade + Ehrlichia) and the Wolbachia clade, suggesting the absence of LBA effects on the phylogenies.
Acknowledgments This work was supported by postgraduate fellowships awarded to S. K. and G. K. from the  School of Biological Sciences , the  University of Edinburgh , and to G. S. from the  Wellcome Trust. Genome sequencing was supported by an award from the  EU   FP7 programme (EU Specific International Cooperation Action  [SICA] reference 242131 Enhanced Protective Immunity Against Filariasis) awarded to  Prof. David Taylor. R. C. was supported by an  European Research Council   Starting Grant  ( FP7/2007-2013 , grant  260729 EndoSexDet ).