dataesr/scientific-paragraphs-categorization huggingface dataset:
Processing with AMPX AMPX is a modular code system, developed by the Reactor and Nuclear Systems Division (RNSD) at Oak Ridge National Laboratory (ORNL), for processing ENDF-6 format evaluations into multi-group (MG) and CE cross sections using different formats that suit different particle transport codes. In this paper, only how to generate CE cross section data is addressed. Such a task consists of generating the different parts of CE cross section data with the corresponding AMPX module: • Point cross section data is the representation of cross section data as pairs of points (σ,E), cross section values at incident particle energy respectively, and linearly interpolating between two points for incident energies not represented. Contributions from the resonance region are added if present.
This allowed us to analyze the evolutionary origin and dynamics of the RTK gene repertoire among jawed vertebrates. We show that the RTK gene family has been massively expanded through WGDs in vertebrates, with the 3R WGD having increased for more than onethird the number of RTK genes in teleost fish. (esharkgenome. imcb. a-star. edu. sg/; Venkatesh et al. 2014). Sequences from representative species were aligned using MAFFT with automatic search of the most appropriate algorithm  (Katoh and Standley 2013). In a first screening set, we selected proteins that aligned best over the whole protein sequence for each RTK genes. For each species and for each gene, ad hoc scripts and manual selection were used to keep the best annotated sequences in those alignments. To ascertain the number of RTK genes in the genome of the common ancestor of the vertebrates (non-duplicated), as well as to root the built phylogenies, we also used sequences from non-vertebrate deuterostomes, belonging either to the superphylum Chordata, that is, sea squirts (Ciona intestinalis and/or C.
savignyi: Urochordata/Tunicata) and amphioxus (Branchiostoma floridae: Cephalochordata), or to the superphylum Ambulacraria, that is, purple sea urchin (Strongylocentrotus purpuratus: Echinodermata) and acorn worm (Saccoglossus kowalevskii: Hemichordata)  (Satoh et al. 2014). At the end, 5,181 sequences were collected (supplementary table  S1 , Supplementary Material online). From this dataset, we added 2,466 sequence entries from the Genomicus database  (Louis et al. 2012 ; supplementary table S1, Supplementary Material online). At the time of analysis, data from myxine and lamprey were too incomplete to be included in the study.
Intron and Phase Position Analysis in RTKs and CTKs Intron gains and losses are very rare events exposed to selective pressures stronger than aa substitutions and can, therefore, be used as evolutionary informative parameters  (de Souza et al. 1998 ) as long as their positions along the region have not been too much drifted apart by shrinkage or extension of sequences. Intron occurrences and phase positions in the human RTK and CTK genes were retrieved from the Ensembl database. Sequences were aligned using clustal omega  (Sievers and Higgins 2014 ). An ad hoc program was designed to insert the phase positions in each of the aligned proteins. The Neighbor-Joining (BioNJ) method  (Lajoie et al. 2007 ) implemented in SeaView  (Gouy et al. 2010 ) was used to make a phylogeny based only on the observed intron phases inside the TK domain alignment.
Styk1 is more particularly associated with the Vegfr/Pdgfr/Ret/Fgfr/Tie subfamilies and shares with them three introns. In this group of genes, all but the Tie genes are characterized by a TK domain subdivided in two portions  (Lemmon and Schlessinger 2010). The Lmr gene shares common introns with Alk/Ros1/Insr. With their tiny extracellular domain compared with the long and distinct ones characterizing all other RTKs, Lmr, and Styk1 may not be considered as bona fide RTKs, and their phylogenetic position in the TK domain tree suggests that they are indeed divergent (fig. 1 ; the human protein kinome database, www. kinase. com/human/kinome;  Manning et al. 2002). However, they have a transmembrane domain and their kinase domain shares strong sequence similarity with other RTKs. Intronbased homologies suggest that Lmr and Styk1 derive from RTKs; the shortness of their extracellular region probably reflects secondary reductions or losses of the extracellular receptor domain.
Acknowledgments This work was supported by  NIH  Grant  1R24OD018555  and by the  Deutsche Forschungsgemeinschaft  through  GRK1048  and  TR17  and the  Deutsche Krebshilfe  through the Melanomverbund to M. S. This publication was supported by the  Open Access Publication Fund of the University of Wuerzburg. Part of this work was performed by F. B. during his 1-year study leave at the  Department of Biochemistry and Molecular Biology at Penn State University, 505 Wartik Laboratory, University Park , Pennsylvania. We particularly thank  Thibault Lorin  for a detailed reading and constructive comments of this manuscript.
These circularized transcripts have been shown to be produced co-transcriptionally by the spliceosome at the expense of canonical mRNA isoforms  [18]  in a process in which exons are atypically joined in a non-linear order through a head-to-tail "backsplice" junction formation. The exons forming these "backsplices" are often flanked by longer introns enriched in ALU repeats and the circularization is dependent on flanking complementary sequences  [14, 19, 20]. The circularization efficiency of backsplice forming exons seems to be regulated by their flanking introns through competition for RNA pairing between complementary sequences  [19]. These intronic sequences promoting circularization therefore appear to modulate linear splicing such that circRNA production and linear splicing mutually regulate each other by competing for splice sites.
The repertoire of circRNA candidates in ovarian cancer Total RNA was collected from the biopsies of the primary tumor, as well as peritoneum and lymph node metastases of three ovarian cancer patients presenting with stage IIIc papillary serous adenocarcinomas  [8]. Tumor samples contained at least 80% of tumor cells and displayed less than 20% necrosis. RNA was extracted with commercially available kits and prepared for Illumina sequencing by using Encore Complete library preparation kits (Nugen), which produce very low levels of ribosomal RNA reads and also amplifies non-polyA containing transcripts. Illumina deep sequencing yielded an average of 46. 96 million read pairs per library of 46-50 bp read length. Of these around 66-72% read pairs mapped concordantly to the linear genome, while 32-37% of the pairs concordantly mapped to a database of all possible non-linear backspliced exon junctions and were used to infer circRNA candidates.
To make the DNA sequence less random, in a second step only the downstream exon (Exon2) was flipped without randomizing the DNA sequence and again aligned to the paired end RNA-seq data to look for any head-tohead spliced (Exon1-flipExon2) junctions, we found only 33 such junctional events having zero overlap with our circRNA candidates. This convincingly indicates that mis-alignments and DNA sequence redundancies have been genuinely ruled out and do not contribute towards candidate circRNA identification process. To estimate the sensitivity and precision of our circRNA detection pipeline, we applied it to a simulated dataset of 50bp paired-end reads for ~200 randomly selected "backsplices" mixed with a background of their linear canonical transcripts generated using mason software  [26]. Figure  1B  and C evaluates the sensitivity and precision values of our circRNA detection pipeline as a function of read depth and indicates a remarkably better performance of our pipeline as against other published softwares  [27].
Diversity and relative abundance of candidate circRNA isoforms in ovarian cancer Canonical alternative splicing of primary RNA transcripts is an important biological process that allows a gene to have multiple RNA and protein isoforms with related or distinct functions. With more than 90% of the human genes undergoing alternative splicing, and a large fraction producing appreciable levels of two or more distinct populations of mRNA isoforms  [36] , we sought to determine the extent of difference between linear and candidate circRNA isoform diversity in our ovarian cancer samples. To estimate the linear transcript isoform abundances, RNA-seq data from our patient samples were aligned to the reference genome (GRCh38) in a paired end aware manner using tophat spliced read mapper  [37]. Only concordant primary alignments were then taken in a subsequent step for transcript-resolution assembly and estimates of changes in gene expression using the cufflinks pipeline  [38].
This filtering for concordant mappings on the genome ensured that read-pairs that contributed towards backsplice junction identification process are excluded for individual transcript isoform estimation and gene expression analysis. Cufflinks assembled a total of 63030 transcript isoforms from the merged samples with 13450 (21%) described as potentially novel isoforms belonging to 6272 known genes (Supplementary File S3). The high number of novel transcript isoforms reveals the highly uncharted state, complexity, and heterogeneity of the ovarian cancer transcriptome. We compared this diversity in linear transcripts with that of candidate circRNA isoforms (Figure  3A ) and found that candidate circRNA isoforms are even more prevalent (wilcoxon test; p-value 2. 324e-07) than their linear counterparts.
The gene mucin 16 (MUC16), a cell surface associated protein and an ovarian cancer tumor marker  [39] [40] [41]  has a total of 11 detected linear transcript isoforms in our data with 4 of them described as potentially novel (Supplementary File S3) but at least 100 scrambled backsplice exon junctions that were also enriched in the RNAse R treated samples of OVCAR-3 cell-line. Almost all of the high abundance circRNA isoforms from this gene show an elevated expression in ovarian tumor compared to the peritoneum or lymph node metastatic sites of the three patients indicating an important role for these differentially regulated circRNAs in ovarian cancer progression. Therefore it appears that circRNA candidates have a rich diversity and differential regulation. Consequently, they could potentially influence the expression of thousands of genes in novel ways to regulate tumor growth and disease progression.
We note that this analysis could in reality underestimate circRNA expression levels as it is hard to distinguish whether reads falling within an exon belong to a circular or linear transcript. However, in order to be more stringent with candidate circRNA expression, all such reads were taken as an evidence for linear transcript. The relative abundance of expression levels therefore gives an estimate for the usage of backsplice capable exons as linear or circRNA form. As shown in Figure  3B  we found that most of the backsplice junction forming exons are primarily expressed as linear transcripts but that quite a significant fraction (5-10%) of these have a relatively higher abundance in the circRNA form. Further, Figure  3C  shows a hierarchically clustered heatmap of the relative abundance levels of candidate circRNAs across the three patient samples and indicates a patient and tumor site specific expression pattern for candidate circRNAs.
Differential expression of circRNAs between primary ovarian tumor and metastases Gene expression products in ovarian cancer could represent candidate biomarkers and potential oncogenic factors involved in tumorogenesis and disease progression. We analyzed gene expression for both mRNAs and circRNAs and evaluated their abundances in an integrated manner to uncover crosstalk between networks of circRNAs, miRNAs, and mRNAs. Gene expression was estimated after aligning the RNA-seq data to reference genome (GRCh38) in a paired end aware manner using tophat spliced read mapper  [37]  and only keeping the concordant primary alignments. For each gene, expression was then quantified at the transcript level as the sum of paired-end fragments, and excluding any chimeric fragments using the featureCounts package  [52].
Similarly, expression for circRNA candidates was also summarized as the aggregate total number of paired-end fragments that mapped to a scrambled exon junction in orientation supporting a head-to-tail backspliced junction instead of linear one and included both junctional and supporting read pairs. Finally, using three patient samples as replicates, differential expression was estimated between primary ovarian tumor and metastatic lesions using edgeR package  [53]  from Bioconductor (http://www. bioconductor. org/). For estimating significant differences, low expression loci were filtered out and only loci that were expressed at a minimum of three read counts in atleast three of the samples were taken for differential expression analysis. This reduced the number of pre-filtered genes and circRNAs for differential expression analysis to 2166;2683 for ovary vs peritoneum and 2803;2613 for ovary vs lymph node respectively.
Further, the genes encoding differentially expressed circRNAs are enriched for cancer associated as well as cell adhesion signaling pathways. This indicates a causal connection for circRNA differential expression in cancer development and progression. We found that signaling pathways that are typically over expressed for mRNA in metastatic tumors  [57]  are downregulated for circRNAs. Proliferative signaling pathways like PI3k/AKT, JAK/STAT and signaling by Rho GTPase were activated for linear mRNA transcripts and down regulated for circular RNA in metastases. Signaling pathways that are essential for epithelial to mesenchymal transition like NFkB, TGF beta, ILK  [58, 59]  were also activated for mRNA and inhibited for circRNA expression. Similarly, proinflamatory cytokines like IL6 and IL8  [60]  and angiogenesis pathways like VEGF, ILK signaling and HGF signaling also showed upregulation for linear RNA but downregulation for circRNA expression in metastases.
Inactivation of PTEN signaling leads to carcinogenesis and we observed a downregulation in mRNA transcripts for PTEN and PPAR signaling pathways and a corresponding upregulation of circRNA forms in metastases. These results show a trend that exhibits an inverse correlation between circRNA and mRNA expression for these pathways. It thus appears that RNA circularization modulates linear splicing and circRNA and mRNA expression are tightly competing with each other for splice sites. Our study shows a better consistency of circRNA expression compared to mRNA in high-grade tumors. It is possible that heterogeneous microenvironment of the tumors, especially, that of the lymph node metastasis could contribute to some of the observed differences in our results. Nevertheless, we anticipate the robustness of the circRNA expression with larger patient cohort would help in uncovering the molecular signatures that affect microenvironment changes in metastatic tumors.
Differential expression (circRNA) For each circRNA candidate, expression was summarized as the aggregate total number of paired-end fragments that mapped to a scrambled exon junction in an orientation supporting a head-to-tail "backsplice" instead of a "linear splice". We included both reads spanning actual junctions ("junctional") and divergent paired-ends that supported ("supported") a circular structure. Finally considering samples from three patients as biological replicates statistical significance of differential expression was estimated at a positive predictive value of 98% (FDR < 0. 02) with a minimum of two fold change in expression between primary ovarian tumor and metastatic lesions, using edgeR package  [53]  from Bioconductor (http:// www. bioconductor. org). For estimating significant differences, only loci that were expressed at reasonable levels of a minimum of three read counts in atleast three of the samples were taken for differential expression analysis.
Differential expression (Genes) Gene expression was estimated after aligning the RNA-seq data to reference genome (GRCh38) in a paired end aware manner using tophat spliced read mapper  [37]  and only keeping the concordant primary alignments. These were again filtered to remove any potential PCR duplicates with samtools rmdup  [66]. For each gene, expression was then quantified at the transcript level as the sum of paired-end fragments, and excluding any chimeric fragments using the featureCounts package  [52]. Differential gene expression was estimated in a similar manner as described above for circRNAs by considering the three patients as biological replicates and identifying genes with at least two fold changes in expression at an FDR of < 0. 02 using edgeR.
We present the results of a gamified mobile device arithmetic application which allowed us to collect vast amount of data in simple arithmetic operations. Our results confirm and replicate, on a large sample, six of the main principles derived in a long tradition of investigation: size effect, tie effect, size-tie interaction effect, five-effect, RTs and error rates correlation effect, and most common error effect. Our dataset allowed us to perform a robust analysis of order effects for each individual problem, for which there is controversy both in experimental findings and in the predictions of theoretical models. For addition problems, the order effect was dominated by a max-then-min structure (i. e 7+4 is easier than 4+7). This result is predicted by models in which additions are performed as a translation starting from the first addend, with a distance given by the second addend.
Introduction Smartphones, with more than 3 billion users across the world [1], present a unique opportunity to collect massive data in cognitive science experiments. The validity of these large scale datasets has been partly verified by demonstrating that they can afford millisecond timing precision on both stimuli and responses  [2]  and replications of some classic psychological paradigms  [2, 3]. Here we investigate the capacity of this experimental approach to inquire about the cognitive mechanisms of arithmetic. Our aims are twofold, first to show that the main observations derived from a long tradition of laboratory based experimentation in arithmetic can be replicated with a rapid massive strategy of data acquisition. Second, to inquire whether big data in arithmetic may help us understand order effects in arithmetic that could not be reliably observed before in laboratory settings.
Their average RTs, error rates, and a graph showing RTs as a function of the trial number for correct answers are displayed for each type of operation. This was included as part of the app to engage participants by being able to monitor their progress. In the "Practice" section the user can choose one operation to train, regardless of the default flow of the game (Fig  1D ). alter our adherence to PLOS ONE policies on sharing data and materials. "El Gato y la Caja" is not a commercial company and does not receive any financial support. It is supported by its members. It is the name we give to a group of researchers interested in the public communication of science.
Ethic Statement All subjects were informed that their data were to be collected via internet through their mobile devices and used for research. The consent was obtained before playing the game's first level: a pop-up window was launched and participants had to accept that their data would be used for research. This consent procedure was approved by the ethic committee. All the experiments described in this paper were reviewed and approved by the ethics committee: "Comitede E ´tica del Centro de Educacion Medica e Investigaciones Clı ´nicas "Norberto Quirno" (CEMIC)" qualified by the Department of Health and Human Services (HHS, USA): IRb00001745-IORG 0001315. The raw data and the R code developed for the mixed model analysis are available for download at https://figshare. com/articles/moravec_data_rar/ 4288877.
Results We analyzed the data collected during the first two weeks after release. We used R  [13] and lme4  [14]  to perform a linear mixed effects analysis over it. 1. As an example, the expected RT for the operation 7+8 is, in ms, 2060 + 23. 7x56 (56 is the product of the operands), i. e. , 3387 ms. To perform this linear mixed effects analysis of the relationship between RTs and size, as fixed effects we entered product of the operands and their equality (tie or non-tie problems), with an interaction term to the model. As random effect, we had by-subject random intercepts and slopes. Visual inspection of residual plots did not reveal any obvious deviations from homoscedasticity or normality. P-values were obtained by likelihood ratio tests of the full model with the effect in question against the model without the effect in question.
Discussion and Conclusion Here we showed the validity of a research program in arithmetic based on large scale data sampling through mobile devices. Our results confirm, on a large sample, six of the main principles derived in a long tradition of investigation: size effect, tie effect, size-tie interaction effect, fiveeffect, RTs and error rates correlation effect, and most common error effects. Our large dataset allowed us to perform an analysis of order effects for each individual problem. This was an aspect of the model for which there were controversial findings. Current theoretical models  [8]  predict an order effect, but without clear specification of what this order should be. For addition problems, one study in Canada found that the order effect was dominated by a max-then-min structure (i.
Here we had an ideal dataset to examine a prediction of this model, namely, that problems that can be pronounced in a rhymed verse will 1) show the largest order effect and 2) the direction of the effect will be such that problems presented in the rhyme order will be performed faster. Results confirmed these two predictions for the four problems that are phrased in a rhyme. We also observed an order effect for multiplication that showed a max-then-min pattern. One possible explanation for this is that calculation is easier when performing fewer additions of the largest number (i. e. 8 x 3 is easier to compute as 8 + 8 +8 than as 3 + 3 +. + 3). In Spanish the linguistic expression for 8 x 3 is "8 por 3" which translates to 8 + 8 + 8. This expression, which is similar to "8 multiplied by 3" is the opposite expression than in English where "8 times 3" means literally 3 + 3 +.
We present observations collected in the CFHTLS-VIPERS region in the ultraviolet with the GALEX satellite (far-and near-ultraviolet channels) and in the near-infrared with the CFHT/WIRCam camera (K s band) over an area of 22 and 27 deg 2 , respectively. The depth of the photometry was optimised to measure the physical properties (e. g. , star formation rate, stellar masses) of all the galaxies in the VIPERS spectroscopic survey. The large volume explored by VIPERS will enable a unique investigation of the relationship between the galaxy properties and their environment (density field and cosmic web) at high redshift (0. 5 ≤ z ≤ 1. 2). In this paper, we present the observations, the data reductions, and the build-up of the multi-colour catalogues. The CFHTLS-T0007 (gri-χ 2 ) images are used as reference to detect and measure the K s -band photometry, while the T0007 u * -selected sources are used as priors to perform the GALEX photometry based on a dedicated software (EMphot).