dataesr/scientific-paragraphs-categorization huggingface dataset:
Describing cooperative interactions in the PSI-MI format Although the PSI-MI data specification does not intrinsically capture interdependencies between distinct binding events, we defined a new set of CV terms to enable the representation of cooperative interactions using the current version of the format (Tables  1  and 2 ). As this strategy avoids making structural changes to the XML schema, backward compatibility is maintained. To illustrate how such interactions can be captured in the PSI-MI format, data from several publications studying the molecular mechanisms involved in the phosphorylation of substrates, in this case Cell division control protein 6 homolog (Cdc6), by the Cyclin A-Cdk2 complex were collected, and the interdependencies between the different binding events that are involved were annotated in a PSI-MI XML file.
The new CV terms were also integrated into the web-based IntAct editorial tool (freely available at http://code. google. com/p/intact/wiki/Editor) (26) , which provides a user interface for easy and efficient curation of interaction data in the PSI-MI format, hence allowing it to be used for annotation of cooperative interactions. In addition, several tools have been developed based on the PSI-MI XML schema (http://www. psidev. info/ mif#tools), including the Java-based PSI Validator that can  be used for syntactic and semantic validation of PSI-MI XML files and is available as a web application (http://www. ebi. ac. uk/intact/validator/start. xhtml) (77). Also, style sheets are available to convert PSI-MI XML files to HTML, making them more human-readable. The recently developed switches.
ELM resource (http:// switches. elm. eu. org)  (78) , a database and analysis tool for switching mechanisms that regulate the functions of short linear motifs, and thus the interactions mediated by these interfaces, captures the context-dependency and interdependency of molecular interactions in great detail. Similar to the large interaction databases like IntAct  (26) , DIP  (27)  and MINT  (28) , switches. ELM will provide the possibility to export curated interaction data in PSI-MI format. However, switches. ELM will additionally use the newly defined CV terms to annotate interdependencies between the distinct binding events, as well as the underlying mechanisms, and as such represent a first implementation of the standard data format presented here.
We chose the PSI-MI format to capture cooperative interaction data because this commonly used data standard provides the means to unambiguously and consistently annotate experimentally validated molecular interactions and allows annotation of molecular details such as binding sites or modifications  (33). Currently, our main goal is to link interdependent interactions, each of which can be individually captured in-depth in a PSI-MI entry, and qualitatively describe their cooperative behaviour. This could act as a link between PSI-MI, describing independent interactions, and BioPAX, describing sets of interactions constituting a pathway, because these two formats were designed to be compatible. The use of SBML seemed appropriate only when the emphasis would have been on quantification of cooperative interaction models for simulation, which is beyond our scope for now and might prove difficult owing to the limited availability of quantitative cooperative interaction data and appropriate models.
Although cooperatively assembled complexes, considered as a single interactor, can be reused in other entries, the format is not intended to describe whole pathways. To this end, the more appropriate BioPAX standard is available  (80). Ideally, as cooperativity is intrinsic to molecular interactions in biology, it should be inherent to a molecular interaction standard. As standards development is a continuous process addressing the user's changing needs, this format is likely to alter and improve in the future. We already addressed these limitations by defining an extended PSI-MI XML schema that allows more efficient and structured annotation of cooperative interaction data. These changes can be incorporated when a new version of the PSI-MI XML schema is released. One important aspect of data representation standards, however, is their stability over time, because a wide variety of tools are available that are developed to be compatible with the current versions of the underlying schemas.
Making changes to a schema implies redeveloping these tools, which is time and effort consuming. Because the strategy described here does not involve any changes to the current PSI-MI XML schema, backward compatibility is maintained. Another advantage is that it provides flexibility, as new CV terms can be defined to describe additional features of cooperative interactions, for instance, based on the classification scheme for allosteric mechanisms that has been defined previously  (81)  or information captured in the AlloSteric Database (ASD)  (82). In conclusion, the new CV terms introduced here, in combination with the already available PSI-MI toolkit, provide an adequate solution to describe cooperative interactions using the current version of the PSI-MI format. It allows both experimentalists and bioinformaticians to capture, exchange and analyse cooperative interaction data.
Integration of these terms into the previously developed IntAct editorial tool facilitates curation of such complex interaction data  (26). The format can be used to import these data into existing interaction databases, but also serve as a basis for new databases that aim to explicitly capture the interdependencies between molecular interactions. It also allows export of cooperative interaction data, for instance, for analysis and visualization with the PSI-MI-compliant Cytoscape software  (37). Moreover, it might promote the development of improved or new visualization tools that address the community need for a graphical interface capable of producing a more appropriate rendition of cooperative interactions and macromolecular complexes from a PSI-MI XML file compared with the current network representations.
As a first implementation,  data from the switches. ELM resource (78) will be made available in this enhanced PSI-MI format. In addition, this strategy might be used to, at least partially, recover interaction data from the BIND database that could not be mapped to the PSI-MI format, such as interaction interdependencies, during a recent conversion that was aimed at making the BIND data more generally accessible and compatible with current software  (36). Last but not least, the availability of this format will hopefully further increase awareness of the importance of cooperative interactions for cell regulation among the scientific community and advance their comprehensive representation in bioinformatics.
ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat066, doi:10. 1093/database/bat066.
, 'CyclinA-Cdk2' in the XML/ HTML files) facilitates phosphorylation of Cdk2 by Cdk7 (Interaction B in Figure 4, 'CyclinA_Cdk2-Cdk7' in the XML/HTML files) (Figure 4. 4) and promotes binding and phosphorylation of Cdc6 (Interaction C in Figure 4,. Page 5 of 16 Database, Vol. 2013, Article ID bat066, doi:10. 1093/database/bat066 Original article. 'CyclinA_pCdk2-Cdc6' in the XML/HTML files) through distinct mechanisms (Figure 4. 1-4. 3). Subsequent phosphorylation of Cyclin A-bound Cdk2 by Cdk7 also has several positive effects on Cdc6 recruitment and phosphorylation (Figure 4. 5-4. 7).
Page 1 of 16 ( 16. ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat066, doi:10. 1093/database/bat066.
Behavioral and imaging data were acquired in all trials. In each functional run, the subjects performed 24 problems (i. e. , 6 problems of each condition). Data analysis. Performance in the four task conditions was compared using an ANOVA. In addition, performance in the four task conditions was compared between the subjects exhibiting different patterns of cingulate/paracingulate complex morphology using a factorial ANOVA (with morphology patterns and conditions as factors). Finally, in the four conditions, reaction times in NEGexplore, POSexplore, and POSexploit trials in problems solved correctly were also compared using a factorial ANOVA (with trial type and conditions as factors). ANOVAs and Fisher post hoc tests were performed using Statistica. Preprocessing and data analysis were performed with Statistical Parametric Mapping software (SPM8B; Wellcome Department of Cognitive Neurology, University of College London, London, UK; http://www.
fil. ion. ucl. ac. uk/spm) and Matlab 7. 11 (www. mathworks. com). First, the first five volumes of each run were removed to allow for T1 equilibrium effects. We applied a slice-timing correction using the time center of the volume as reference. The subject-mean functional MR images were coregistered with the corresponding structural MR images using mutual information optimization. Then, head motion correction was applied using rigid-body realignment. Realignment parameters were used as covariates during the statistical analysis to model out potential nonlinear head motion artifacts. Functional and morphological images were spatially normalized into standard MNI space using SPM's default templates. Finally, functional images were smoothed using a 6-mm fullwidth half-maximum Gaussian kernel  (Friston et al.
In conclusion, our study shows clear relationships between the local morphology of the cingulate/paracingulate complex and feedback-related activity in the human brain. Such a conclusion could only have been made on the basis of individual subject analyses that take into account individual morphological variation in the region of interest, and not on the basis of group average analyses. This conclusion has direct implications regarding brain imaging methods. We tested our dataset with one example of template ROIs (marsbar, http://marsbar. sourceforge. net/) that had been produced from one single individual  (Tzourio-Mazoyer et al. , 2002) , and the feedback-related activity site fell outside (dorsal) of the midcingulate ROI for most subjects exhibiting a pcgs, falling into the superior medial frontal ROI.
Functional neuroimaging data confirmed that hippocampal activity lends a preference to choosing simulated options (irrespective of time), whereas dorsal prefrontal cortex brain activity supports the preference for delayed options (irrespective of presentation mode). Structural neuroimaging in healthy subjects and in patients with brain atrophy, due to Alzheimer's disease (with hippocampal damage) or Fronto-Temporal Dementia (with damage to the prefrontal cortex), further demonstrated the critical implication of the hippocampus. Individuals with higher neuronal density in the hippocampus, but not in the dorsal prefrontal cortex, were more likely to choose future rewards that have to be mentally simulated. first performed the monetary and episodic intertemporal choice tasks and then were asked how many details they imagined when reading each Sim option.
Discussion In this study, we extended standard delay discounting paradigms investigating intertemporal choices between smallersooner and bigger-later monetary payoffs. First, we developed an episodic choice task using more concrete options such as food, sport, and culture items. Second, we manipulated the mode of presentation to investigate ecological choices where the immediate option is directly observable, while the delayed option requires simulating a future episode. Behavioral data showed that richness of mental simulation is a crucial factor in valuating and hence choosing delayed options during ecological intertemporal conflicts. Imaging data revealed that interindividual variability in the propensity to favor simulated options can be explained by hippocampus functional activation during both valuation and choice, which in turn can be explained by the hippocampus grey matter density.
9062. 31) were recruited via the ''Laboratoire d'Economie Experimentale de Paris'' for Experiment 1 (behavioral pilot). Twenty different participants (8 females, age 23. 6560. 87) were recruited via the ''Relais d'Information sur les Sciences de la Cognition'' website for Experiment 2 (fMRI study). One was excluded for always choosing the option displayed on the right side of the screen. Behavioral tasks. All tasks were programmed on a PC, using the Cogent 2000 (Wellcome Department of Imaging Neuroscience, London) library of Matlab functions for stimuli presentation. We implemented two types of tasks: intertemporal choice tasks and likeability rating tasks. In intertemporal choice tasks, subjects had to choose between two options: a less pleasant but immediate option versus a more pleasant but delayed (by 1 month, 1 year, or 10 years) option.
The free parameters (k, b) were individually adjusted over all conditions and domains to maximize the log likelihood of the actual choices under the model. For fMRI analyses of the episodic task, a single set of parameters was used for all subjects, such that estimations were based on a more comprehensive data set. MRI data acquisition. T2*-weighted echo planar images (EPI) were acquired with blood oxygen-level dependent (BOLD) contrast on a 3. 0 Tesla magnetic resonance scanner (Siemens Trio). We employed a tilted plane acquisition sequence designed to optimize functional sensitivity in the orbitofrontal cortex and medial temporal lobes  [49, 50] , with the following parameters: TR = 2. 0 s, 35 slices, 2 mm slice thickness, 1. 5 mm interslice gap. T1-weighted structural images were acquired (1 mm isotropic, 176 slices), co-registered with the mean EPI, segmented and normalized to a standard T1 template, and averaged across subjects to allow group-level anatomical localization.
Imaging data were preprocessed and analyzed using SPM8 (Wellcome Trust Center for NeuroImaging, London, UK) implemented in Matlab. The first five volumes of each session were discarded to allow for T1 equilibration effects. Preprocessing consisted of spatial realignment, normalization using the transformation computed for the segmentation of structural images, and spatial smoothing using a Gaussian kernel with a full-width at half-maximum (FWHM) of 8 mm. MRI data analysis. Functional images acquired during the episodic task were analyzed in an event-related manner, using three general linear models (GLMs) to explain individual-level functional scans. GLMs 1 and 3 modeled three events per trial (immediate and delayed option presentations, plus the choice period), with boxcar functions. Because there were two types of trials (control and ecological), the two GLM included six categorical regressors.
We nonetheless verified that the results hold when defining ROI based on purely anatomical criteria. Anatomical masks were based on the AAL brain parcellation  [53]  and created using MARINA software (http://www. bion. de/index. php?title = Home&lang = eng). The signal (functional contrast or GM density) was averaged over the whole ROI for each subject before statistical testing. Statistical significance was assessed either at the group level using one-sided one-sample t tests, or for intersubject correlations using one-sided t test on the studentized coefficient returned by the robust regression tool implemented in Matlab. The mediation analysis between GM density, functional contrast, and choice impulsivity was implemented using the 8. 14. 2012 Mediation Toolbox (available at http://wagerlab.
MRI data acquisition and analysis. All images were T1weighted anatomical whole-brain scans recorded in the neuroradiology department of the hospital using three different MRI scanners at 1T (Panorama Philips), 1. 5T (Sigma 1. 5T G. E Medical systems), and 3T (Sigma 3T HDX G. E Medical systems). Various sequences were used for an acquisition of 116 to 240 slices, with an interpolated thickness of the following range: [0. 488 to 1]* [0. 488 to 1]* [0. 7 to 1. 5] mm. Importantly, the repartition of the different scanners and sequences was equivalent for AD and bvFTD groups (see Table  1 ). VBM preprocessing was identical to that used in healthy subjects, with 12-mm FWHM for spatial smoothing. Groups were compared with a second-level two-sample t test on subject-specific modulated GM maps. Additional covariates of noninterest included gender, age, total intracranial volume (TIV), and two dummy regressors to account for differences in scanner and sequence.
The study was funded by a  Starting Grant  for the  European Research Council (ERC-BioMotiv) , the  Emergence Program  from the  Ville de Paris , and a  Research Grant  from the  Schlumberger Foundation. ML and MB received PhD fellowships from the  Ministere de la Recherche  and the  Direction Generale de l'Armement , respectively. This work also benefited from the  program ''Investissements d'avenir'' ( ANR-10-IAIHU-06 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Characterization of pig adipose tissue transcriptome Sequencing yielded around 236 M of 75-bp paired-end reads and approximately 84% of reads were mapped against the reference pig genome assembly Sscrofa10. 2 using Tophat software. Percentages of mapped reads observed per individual ranged from 80% to 87%, and were equally distributed in the H and L groups. These values were higher than were the percentages reported in previous porcine transcriptome studies: 71. 4%-77. 8% [22] , 61. 4%-65. 6% [27] , 66. 7%  [28]  and 54%  [21]. The Inter-sectBed tool from BEDtools was employed to calculate the proportion of mapped reads annotated in exons, introns and intergenic regions. The highest percentages of reads were mapped to exons (66%-71%), while 19%-20% fell in intergenic regions, and the lowest percentages were located in introns (10%-14%) (Table  1 ).
The proportion of reads mapped to exons was slightly higher than were the values reported by Ramayo-Caldas et al. (2012) and  Chen et al. (2012) , due likely to the better pig genome assembly (Sscrofa10. 2) and annotation version employed in our work. Nevertheless, the current gene annotation still needs to be improved in order to determine the function of approximately 20% of reads, which mapped in intergenic regions. Finally, transcripts generated from assembling the short reads by cufflinks fell mostly into annotated exons (42%-47%). The remaining reads were classified into the following categories: intron retention events (7%-12%), intergenic transcripts (17%-20%), potentially novel isoforms of genes (19%-21%), pre-mRNA molecules (3%-4%) and polymerase run-on fragments (2%-3%) (Additional file 1: Table  S2 ).
Exploring for novel coding transcripts and transposable elements in the adipose tissue transcriptome Transcript annotation performed with cufflinks showed a mean value of 10,862 total unknown intergenic transcripts (Additional file 1: Table  S2 ). This value doubles the number of intergenic transcripts detected in previous studies  [22, 28]. To determine which of these transcripts encoded a protein, Augustus software  [29]  was used and, as was expected, the total amount of predicted proteins was also higher, as compared to previous studies: 4,130 predicted proteins against 326  [22]  and 714  [28]. Our analysis showed an improvement in the sequence length and sequence coverage. Furthermore, the conservative approach used in  [22] , in which only those transcripts expressed in at least four of the five animals of each group were considered, could aid to explain the differences obtained (4,130 vs.
326). BLASTP analysis was performed to compare the putative proteins predicted by Augustus against the predicted proteins reported by Ramayo-Caldas et al. (2012) in the liver and Esteve-Codina et al. (2011) in gonads. A total of 269 new putative proteins fitted in with those previously described: 93 putative proteins were expressed in liver transcriptome (34%) and 211 were expressed in gonad transcriptome (78%). Additionally, a functional annotation was performed using BLAST2GO software. BLASTP analysis of the 4,130 predicted proteins revealed that 2,100 proteins (50. 8%) displayed significant similarity with existing protein sequences (the top hit species was Sus scrofa, 59. 1%). These proteins corresponded to: 16 novel computationally predicted proteins and 1,108 known human proteins, 361 novel and 598 known bovine proteins and 997 novel and 599 known porcine proteins.
Repetitive elements (RE) were identified in the adipose tissue transcriptome using the RepeatMasker software. The total interspersed repeats represented 36% of the intergenic transcripts (Additional file 3: Table  S4 ), a percentage higher than those observed in previous porcine transcriptome analyses: 5. 8%-7. 3% [22]  and 7. 3%  [28]. As described above, the differences obtained could be explained by the assembly used for each analysis. The description of these regions has been improved in the current assembly (Sscrofa10. 2), as demonstrated in the results observed. The major part of the RE were classified as SINEs (n = 92,007), representing 13. 96% of the transcriptome sequenced. The second group was LINEs (n = 43,428), but these RE constitute a larger part of the transcriptome than do SINEs due to the bigger size of LINEs (15.
Differential gene expression among animals with extreme phenotypes of intramuscular FA composition Biological functions over-represented in the differentially expressed genes are candidate functions to explain the variation in intramuscular FA composition among the analyzed animals. Gene-expression data from each group was compared using DESeq software, which allowed for the detection of differentially expressed genes between groups. The data were filtered, discarding those genes with a mean of less than 20 reads mapped in one of the extreme groups. Finally, a total of 13,086 genes were used to perform the differential expression analysis using a standard cut-off of: |fold difference| ≥ 1. 2, a p-value ≤ 0. 01 and a q-value ≤ 0. 1 (Figure  1 ). DESeq software identified a total of 396 differentially expressed genes (Figure  2 ), from which 247 were up-regulated in the L group and the remaining 149 genes were down-regulated in the same group (Additional file 6: Table  S5 ).
Mapping, assembling and annotation of reads In order to map all reads generated, the software TopHat v2. 0. 1  [63, 64]  was employed, using as reference version 10. 2 of the pig genome (Sscrofa10. 2) and the annotation database Ensembl Genes 67 [http://www. ensembl. org/ info/data/ftp/index. html]. Tophat was used with an expected mean inner-distance of 160-bp between paired-end reads. Quality control and reads statistics were determined with FASTQC [http://www. bioinformatics. babraham. ac. uk/projects/fastqc/]. Transcripts were assembled and quantified by Cufflinks v2. 0. 2  [64, 65]  with a minimum alignment count per locus of 20. Additionally, for counting the number of reads mapping to exons, introns and intergenic positions, the intersectBED tool from BEDtools was used  [66].