dataesr/scientific-paragraphs-categorization huggingface dataset:
Output: The miRAFinder script processes information of abstracts from the PubMed database. This script allows to find miRNA names and keywords as shown in Figure  1. The result of searching for contents of the following data: PMID (publication medicine identification number) of the article, miRNA name, disease localization (organ or tissue), keywords (methods, change fold, cellular processes, functions, animal species, types of cells, biological liquids, etc. ) and genes. Some examples are shown in Table  1  (see supplementary material). The obtained data on gene names are important as they can be mentioned in abstract as host or target genes of miRNAs. The script uses the list of genes from HGNC database for the correct identification of gene names in the abstract (for an exception of different abbreviations).
Thus, the quantity of the found genes is regulated by keyword structure of the dictionary created for each separate searching (human genes, mouse genes, rat genes, etc. ). The GeneAFinder script is similar to miRAFinder (Figure  1 ) and it allows to find specific information and gene names in the abstract of PubMed. It is possible to find the list of important keywords for general characteristic in the text of abstract using the GeneAFinder script. Some examples are shown in Table  2  (see supplementary material). The PubMed database was used in this study due to its comfortable data type format for application in the scripts. There are a possibility to use various gene dictionaries and their characteristics to retrieve a different data set.
MeDIP Microarray Analysis Two replicate microarrays were hybridized and analysed for each of the 19 samples. Quality control involved visual inspection of microarray scan images, scanning software reports and generation of MvA plots (i. e. plots of log(Cy5/Cy3) vs log(Cy5 x Cy3)) to identify microarrays with dye biases or low signal. Microarrays were normalized using background subtraction followed by quantile normalization of log2-ratios of bound (Cy5) and input (Cy3) channel intensities. Differential methylation between groups of samples was determined at the probe and promoter levels to ensure both statistical significance and biological relevance as previously described  [38]. At the probe level, a modified t-statistic was computed for each probe to identify probe intensity differences between CPA and NPA groups using the 'limma' package  [48]  of Bioconductor  [49].
Then, promoterlevel methylation differences were identified as those promoters significantly enriched with probes having positive or negative tstatistics using the Wilcoxon rank-sum test. A probe and the containing promoter were called differentially methylated if the pvalue of the probe t-statistic was at most 0. 05 (uncorrected for multiple testing), log2-fold difference between the groups was at least 0. 25, and the false discovery rates (FDR) of the promoterlevel statistic was at most 0. 05. The microarray data have been deposited in Gene Expression Omnibus (GEO) at NCBI (www. ncbi. nlm. nih. gov/geo/) under the accession number GSE53193. A Bayesian deconvolution method was used to estimate methylation levels from the microarray data  [51]  and, as expected, estimated promoter methylation levels were found to be significantly anti-correlated (p = 2.
We normalized the results by calculating the ratio of the amplified specific gene over the amplified methylated control. The methylated control used for normalization was the in vitro methylated luciferase plasmid that was added in equal amount to each sample prior to the MeDIP. The Q-MeDIP analyses of the methylated control show no difference between the groups. Whole-genomic validation of MeDIP microarray data was performed using Illumina 450K arrays. This technique was applied to validate both the male (Provencal et al. under review in PLoS One) and female samples. Pooled samples were used since only limited amount of DNA was available for many of the subjects. Three different pools of genomic DNA were made per group: men CPA (2 or 3 different subjects per pool), men NPA (4 different subjects per pool), women CPA (2 different subjects per pool) and women NPA (4 or 5 different subjects per pool).
Genomic DNA was quantified using Picogreen protocol (Quant-iT TM PicoGreenH dsDNA Products, Invitrogen) and read on SpectraMAX GeminiXS Spectrophotometer. Bisulfite conversion was performed with 500 ng of genomic DNA using the EZ-96 DNA Methylation-Gold Kit (Zymo Research) according to the manufacturer's instructions. The Illumina Methylation 450K kit was used for the microarray experiment, as described by the manufacturer's protocol, except that 8 ml of bisulfite converted material was used to initiate the amplification step. Arrays were scanned using the Illumina iScan Reader. Data analysis was performed using the Methylation module (version 1. 9. 0) of the GenomeStudio software (Illumina; version 2011. 1). Significant differences in methylation levels between CPA and control groups were obtained using the Illumina custom method with a False Discovery Rate (FDR) ,0.
05. Beta-values with detection P-value. 0. 001 were removed from the analysis since they are considered to fall below the minimum intensity and threshold. All the probes that contained a SNP within 10 bp of the CpG site were excluded from the analysis to ensure equal annealing efficiency between the samples. The raw data have been deposited in Gene Expression Omnibus (GEO) at NCBI (www. ncbi. nlm. nih. gov/geo/) under the accession number GSE53193.
For pyrosequencing analysis, 20 ml of the bisulfite-PCR products were processed according to the manufacturer's standard protocol (Qiagen). Sequencing reactions were performed on a PyroMark Q96 system using the PyroMark Gold Q96 Reagent Kit (Qiagen) according to the manufacturer's instructions. The percentage methylation at each CpG site was calculated from the raw data using PyroMark Q96-CpG Software (Qiagen).
Functional Relevance of the Differentially Methylated Promoters Associated with CPA To delineate if the list of genes differentially methylated between CPA and NPA are grouped into biological functions and pathways we used Ingenuity Pathway Analysis (IPA) software. In women, IPA identified 74 functional categories significantly enriched with the affected genes including ''Cell signaling and interaction'', ''Inflammatory response'' and ''Behavior'' (Table  S2 ) as well as specific canonical signalling pathways including CCR5, TEC kinases, G-protein Gai and IL-10 (Table  S3 ). Aggression might also affect the methylation status of a specific group of genes by targeting their upstream regulators. Using IPA we were able to identify a set of potential upstream regulators including transcription factors, growth factors and transmembrane receptors.
Validation of the MeDIP-microarray Data using Illumina Human Methylation 450K Arrays To further confirm the results obtained using MeDIP-microarrays, we generated and analyzed new methylation profiles using the Infinium Human Methylation 450 BeadChipH. The Illumina 450K approach is an alternative to the MeDIP approach appropriate for validation because it is based on bisulfite conversion rather than antibody affinity. Although it has lower coverage than the MeDIP microarray, it has higher resolution (single-CpG resolution) and reasonable coverage with at least one CpG site in 96% of known CpG islands and 99% of RefSeq gene promoters. Due to small amounts of remaining DNA, samples were pooled to produce three sample pools per aggression group (both male and female), each pool hybridized to an Illumina 450K array and then the arrays analyzed to identify methylation differences between CPA and NPA sample pools.
This framework has been implemented in the software Maple R. We present its application to the complete proof of the reference problem and also to the generalizations of the first lemma, by applying second order strategies, to multi-dimensional geometrical domains, multi-dimensional thin domains and multi-domains. The paper is organized as follows. Section 2 is devoted to all mathematical aspects. This includes all definitions and properties, the lemmas and their proof. The principles of rewrite rules and strategies are formulated in Section 3. Section 4 is devoted to the theoretical framework that allows to derive a model and its generalizations. Implementation results are described in section 6.
Implementation and Experiments The framework presented in Section 4. 4 has been implemented in Maple R. The implementation includes, the language Symbtrans of strategies already presented in  [BGL]. The derivation of the reference model presented in Section 2 has been fully implemented. It starts from an input term which is the weak formulation (24) of the physical problem, a ∂u ∂x ∂v ∂x dx = f v dx, (35) where a = Fun(a, [Ω], [ ], Known), u = Fun(u, [Ω], [Dirichlet], U nknown), v = Fun(u, [Ω], [Dirichlet], T est), Ω = Reg(Ω, [1], ∅, Γ, n Ω ), Γ = Reg(Γ, [ ], ∅, ⊥ R , ⊥ F ), Dirichlet = BC(Dirichlet, Γ, 0 ) and where the shortcuts of the operators are those of Section 4. 2. The information regarding the two-scale transformation is provided through the test functions. For instance, in the first block the proof starts with the expression Ψ = ∂u ∂x B(v(x ♯ , x 1 )(x) dx, where the test function B(v(x ♯ , x 1 )(x) is also an input, with v = Fun(a, [x ♯ , x 1 ], [Dirichlet♯], T est), x ♯ = Var(x ♯ , Ω ♯ ), x 1 = Var(x 1 , Ω 1 ), Ω ♯ = Reg(Ω ♯ , [1], ∅, Γ ♯ , n Ω ♯ ), Γ ♯ = Reg(Γ ♯ , [ ], ∅, ⊥ R , ⊥ F ), Ω 1 = Reg(Ω 1 , [1], ∅, Γ 1 , n Ω 1 ), Γ 1 = Reg(Γ 1 , [ ], ∅, ⊥ R , ⊥ F ), and Dirichlet♯ = BC(Dirichlet♯, Γ ♯ , 0).
Motivation: Large-scale methods for inferring gene trees are errorprone. Correcting gene trees for weakly supported features often results in non-binary trees, i. e. trees with polytomies, thus raising the natural question of refining such polytomies into binary trees. A feature pointing toward potential errors in gene trees are duplications that are not supported by the presence of multiple gene copies. Results: We introduce the problem of refining polytomies in a gene tree while minimizing the number of created non-apparent duplications in the resulting tree. We show that this problem can be described as a graph-theoretical optimization problem. We provide a bounded heuristic with guaranteed optimality for well-characterized instances. We apply our algorithm to a set of ray-finned fish gene trees from the Ensembl database to illustrate its ability to correct dubious duplications.
, 2013; Durand et al. , 2003; Rasmussen and Kellis, 2011; Sz€ ollosi et al. , 2013; Thomas, 2010; Wapinski et al. , 2007). A large number of gene tree databases are now available  (Datta et al. , 2009; Flicek, 2012; Huerta-Cepas et al. , 2011; Mi et al. , 2012; Schreiber et al. , 2013). But constructing accurate gene trees is still challenging; for example, a significant number of nodes in the Ensembl gene trees are labelled as 'dubious'  (Flicek, 2012). In a recent study, we have been able to show that $30% of 6241 Ensembl gene trees for the genomes of the fishes Stickleback, Medaka, Tetraodon and Zebrafish exhibit at least one gene order inconsistency and thus are likely to be erroneous  (Lafond et al. , 2013). Moreover, owing to various reasons such as insufficient differentiation between gene sequences and alignment ambiguities, it is often difficult to support a single gene tree topology with high confidence.
GENE TREE CORRECTION The polytomy refinement problem is motivated by the problem of correcting gene trees. Duplication nodes can be untrusted for many reasons, one of them being the fact that they are NADs, pointing to disagreements with the species tree that are not due to the presence of duplicated genes. Different observations tend to support the hypothesis that NAD nodes may point at erroneous parts of a gene tree  (Chauve and El-Mabrouk, 2009; Swenson et al. , 2012). For example, the Ensembl Compara gene trees  (Vilella et al. , 2009)  have all their NAD nodes labelled as 'dubious'. In  (Chauve and El-Mabrouk, 2009) , using simulated datasets based on the species tree of 12 Drosophila species given in  (Hahn et al. , 2007)  and a birth-and-death process, starting from a single ancestral gene, and with different gene gain/loss rates, it has been found that 95% of gene duplications lead to an AD vertex.
We generated a thousand random S and F for each n ! 4. We stopped at n = 14, as the brute-force algorithm is too time-costly beyond this point. Over all the explored datasets simulated as described above, Algorithm MinNADref was able to output an optimal solution, i. e. a refinement with the minimum number of NADs. Therefore, the examples on which the heuristic fails seem to be rare, and the algorithm performs well on polytomies of reasonable size. We then wanted to assess how the NAD minimization criterion differs from the rearrangement cost minimization criterion. We generated 960 random instances with forests of sizes ranging between 5 and 100 (10 instances for each 5 n 100). We a 1 c 3 c 2 b 1 d 2 d 1 x c 1 b 2 a 1 b 1 b 2 c 1 c 3 c 2 d 2 d 1 G* : s s l s r Fig. 3. A gene tree G and a species tree S, from which we obtain G Ã by pushing x by multifurcation.
Further analysis of the trimmed data revealed that the novel trimming approach implemented in QTrim ensures that the prevalence of low quality bases in the resulting trimmed data is substantially lower than PRINSEQ or any of the other approaches tested. Conclusions: QTrim is a novel, highly sensitive and accurate algorithm for the quality trimming of Roche/454 sequence reads. It is implemented both as an executable program that can be integrated with standalone sequence analysis pipelines and as a web-based application to enable individuals with little or no bioinformatics experience to quality trim their sequence data.
Implementation QTrim is executed as a standalone software package for command-line use and integration into sequencing analysis pipelines. Two versions of QTrim can be invoked using the python script. The first is a "simple" version that only outputs the trimmed sequence data. A second, "graphical" version (invoked using the -plot option) also outputs graphs plotting the quality score trends across all reads, the prevalence of read lengths and the mean quality scores both before and after trimming. Further, a web-based interface is also available (http://hiv. sanbi. ac. za/tools/qtrim) for individuals wishing to quality trim their data using the graphical version of QTrim prior to further downstream analysis using other web-based tools. QTrim takes as input a fastq file or a fasta file with its associated.
Results and discussion The performance of QTrim was evaluated on two previously generated 454 datasets with extreme differences in untrimmed quality scores. While in an ideal world sequencing using high-throughput sequencing platforms would only output high-quality sequence data, the various steps involved in sample preparation and sequencing means that, in many cases, non-optimal sequence data is output from a sequencing run. Rather than discard such data, a sensitive trimming approach will enable individuals to "rescue" any high quality present as the result of a sub-optimal sequencing run. The good quality 454 sequence data used here originates from a metagenomic project sequencing bacterial 16 s genes from seawater sponges (in prep). This data was generated using the Roche/454 FLX platform and good quality was defined as sequence reads with comparable read lengths with consistently high quality scores at called bases until close to 400 bp (Figure  2A ).
Upon comparison with all other approaches, QTrim performs equally as well as the best of these methods (PRINSEQ). The trimmed reads output by PRINSEQ are, on average, slightly longer than those output by QTrim (Figure  3 ). Upon further examination, however, this is as a result of PRINSEQ outputting a higher number of low quality bases (quality score < 20) at the 3′ end of its trimmed reads. For example, PRINSEQ output 8% as many low quality bases than QTrim in the Q20 trimming of both datasets tested here, and outputs 17% and 25% as many low quality bases in the Q30 trimming of the poor quality and good quality datasets respectively. We find that this is the case in all of the methods that use an averaging approach for quality trimming. As soon as the minimum quality score in a read satisfies the quality threshold, the read is output as trimmed without any further analysis.
Conclusion QTrim is a fast, highly sensitive and accurate algorithm that outperforms all available approaches for quality trimming of 454 sequence data. A noteworthy feature is that it enables sensitive trimming of sub-optimal sequence data thereby enabling researchers to undertake downstream analysis on lesser quality sequence data that otherwise may have been discarded. The command line python version of QTrim can be easily incorporated into sequence analysis pipelines, while the web interface enables users with little or no bioinformatics experience to undertake quality trimming of their high-throughput sequencing data.
Acknowledgements This work was supported by a student bursary to RKS from  Atlantic Philanthropies  (grant #  62302 ) and by an award to SAT from the  South African Department of Science and Technology. This work was further supported by  Science Foundation Ireland  under Grant No. 07/RFP/ EEEOBF424  awarded to GPM. Data production was further supported by the  Beaufort Marine Biodiscovery  award at  NUI Galway.
Availability and requirements Project name: QTrim Project home page: http://hiv. sanbi. ac. za/tools/qtrim Operating system(s): MacOS, Linux Programming language: python v2. 6 or v2. 7 (not v3. 0) Other requirements: Executables -Nothing, Source code -Biopython, numpy, Matplotlib License: GNU GPL v3. Any restrictions to use by non-academics: Commercial use may be restricted and such users should contact the corresponding author for further details.
The channel capacity between transmitter and relay,  (7) , is given by C ¼ B lnð1 þ ad Àc e Àag Þ; ð25Þ where B = 100/ln(2) kHz, c = 2, and a = 10 6. Using these parameters, the signal-to-noise level at distance d ¼ 250 m, without fading, is SNR dB = 12 dB. The adopted transmission rate is R = 200 Kbits/s which corresponds to the channel capacity at distance d, the transmission range without fading noise. To illustrate the results, we will consider a wireless sensor network using two different simulation softwares. On the one hand, we developed a source, using MATLAB's built-in functions, which simulate random distances between nodes, linked by transmission channels affected by fading noise. On the other hand, we developed C++ functions inside the Network Simulator (NS2). In both cases, three strategies are compared, greedy routing scheme (GRS), the maximum expectation within the transmission range (MER), and our approach.
1. Failure rate vs progress First, the one-hop relay decision using MATLAB's builtin functions is simulated in several different scenarios. Within this setup, for each relay selected, the channel capacity is calculated by using Eq. (  25 ). If the channel capacity is greater than R, the message is considered successfully received by the relay; otherwise, the message has failed. When the message has failed, no retransmission method is considered. The relationship between the number of lost messages and the number of messages sent gives the failure rate. The progress is computed as the averaged distances between transmitter and relay for each message successfully received at each hop. In the simulations, each one of the N admissible relays are uniformly distributed within the distance interval d 2 ½0; d.
2. The metrics to be evaluated are the failure rate, the average delay from packet submission to reception at the destination (end-to-end transmission), and the average number of hops to reach destination. The latter is a metric proportional to the expected progress of the message. In Table  1 , the comparative results between GRS, MER, and the optimal cost with q = 1, are presented. From the tables, the same trends as in the previous simulations obtained with the Matlab's building-functions are observed. The MER improves the GRS but our proposed method improves MER. In the working scenario, we can see that the failure rate is high. This is because it has been considered a low transmission range, d = 40 m, thereby, many destinations that are far from the sender cannot be reached. As a consequence, progress, or equivalently the   average number of hops, is also low.
Acknowledgments This work was supported by the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ,  Agencia Nacional de Promoción Científica y Técnica, Universidad Nacional del Comahue , all of  Argentine Republic  and the  Centre d'Etude et De Resherche en Informatique et Communications (CEDRIC) , of the  Conservatoire National des Arts et Metiers, France.
The SHAPE reactivity data obtained for each nucleotide from 3-6 independent experiments were then applied as pseudo-energy constraints in the structure prediction program "RNAstructure" in order to develop a refined RNA structural model of the sequences required for MMTV gRNA dimerization and packaging. Consistent with the predicted structure, most of the loops and bulges containing unpaired nucleotides in the MMTV packaging signal RNA showed SHAPE reactivity (Figure  1C ). We also observed moderate and high reactivities of nucleotides forming G-U base pairs, especially at the end of helices (G120, U320, G193, U294, G324, U356, U357, U358, U415, Figure  1C ). Some base paired nucleotides (G21, C25, G84) located on either side of a bulge or loop also showed certain amount of reactivity, as expected.
These transfection efficiencies were taken into account to normalize and calculate the RNA packaging efficiency and propagation data for each mutant relative to the wild type. To determine the RNA packaging efficiency, cDNAs were prepared from cytoplasmic and pelleted virion RNAs. Before making the cDNAs these RNAs were subjected to DNase treatment to ensure that there was no contaminating plasmid DNA, followed by PCR using virus-specific primers. Following amplification, the lack of a positive signal indicated that the DNA contamination in our RNA preparations was below the detection level (Figure  2D , panels i and ii). After having confirmed this, RNA preparations were reverse transcribed and cDNAs were prepared. Since the relative packaging efficiency data is expressed in relation to the efficiently and stably expressed viral RNAs that are exported from the nucleus to the cytoplasm, we monitored the integrity of the cytoplasmic RNA fraction by checking for the In the third panel (iii), amplification was conducted on the cDNAs obtained from cytoplasmic RNAs using primers that amplify unspliced β-actin mRNA.
001). These results indicated that although pal II deletion mutants could reduce RNA dimerization, they could not completely abrogate it. Such an observation suggested the presence of another sequence that could facilitate dimerization in addition to pal II, though not to the wild type levels. Therefore, the pal II deletion mutant (SA042) sequence was folded as dimer using the RNAstructure software, which predicted that in the absence of pal II, the MMTV RNA packaging signal could potentially dimerize via the two overlapping pals in the PBS (5' CAGCU GGCGCC 3'; the first pal is in italics and the second pal is in bold (Figure  4 ). To determine the role of PBS pal, if any, on dimerization, a mutant (SA051) was generated containing a complete deletion of the 11 nt overlapping pals within the PBS.