dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS This work was supported by the  Ligue Nationale Contre le Cancer  (Equipe Labellisée 2011-2013), the  Fondation de France  and the  Association pour la Recherche Contre le Cancer. This Work was also funded by the  French government (National Research Agency, ANR)  through the  'Investments for the future' LABEX SIGNALIFE : program  reference #  ANR-11-LABX-0028-01. We acknowledge the technological expertise of the  Nice Sophia-Antipolis Functional Genomics Platform. We greatly acknowledge  Dr Jacques Landry  ( Centre of recherche cancerologie, University of Laval, Canada ) for PcDNA3-Myc-HSPB8 vector.
As a consequence, one requires sophisticated statistical approaches that can deal efficiently with parameter estimation and model selection (given experimental data). If only, these are necessary to capture the inter-individual variability of neurophysiological and behavioural responses. More generally, such schemes would embed the models into the data analysis and act as a ''mathematical microscope'' that is capable of unravelling mechanisms, which are hidden deep within experimental data  [4]. This article describes a (matlab) software toolbox that is designed to perform such model-based analyses of neuroimaging and behavioural data. Essentially, it consists of a probabilistic model inversion scheme that borrows from disciplines such as inverse problems, statistical physics and machine learning.
g. [5]  for a recent review) and bayesian models for human learning and decision making  [6] [7]. Importantly, VBA includes diagnostic analyses that can be used directly to refine such models, i. e. to account for yet unpredicted features of experimental data (cf. Volterra decompositions). We will demonstrate this below. Our ambition is twofold: (i) to disseminate models and methods that serve experimental purposes, and (ii) to provide a flexible platform, which modellers can easily contribute to. This paper is organized as follows. In the next section, we will describe the design and implementation of the toolbox. In particular, we will describe the class of generative models that the VBA toolbox can handle, expose the main aspects of its algorithmic treatment, and summarize the organization of the code.
The ensuing computational cost scales with k 2. In brief, the core of the toolbox consists of a generic VB treatment of the above class of generative models. Given experimental data y, system's input u (if any), evolution/observation mappings and priors, it recovers an approximation to both the posterior density on unknown variables and the model evidence (which is used for model comparison). Practical guidance on the software implementation can be found on this wiki page: http://code. google. com/p/mbbvb-toolbox/. In particular, we have implemented a lot of examples and demonstration scripts, in the aim of accelerating users' learning (cf. ''getting started'' section of the wiki pages: http://code. google. com/p/mbb-vb-toolbox/wiki/getting_started).
Organization of the toolbox The toolbox is organized as follows: N The root folder (/DAVB) contains a core set of (matlab) routines that implement the VB approach to stochastic nonlinear state-space models  [15]. The main inversion routine is 'VBA_NLStateSpaceModel. m', which is described in more details below. The root folder also contains group-level inference schemes (cf. 'VBA_groupBMS. m', [16] ) and posthoc model selection tools (cf. 'VBA_SavageDickey. m', [17] ), as well as routines for model simulation (cf. 'simulateNLSS. m') and results visualization (cf. 'VBA_ReDisplay. m', 'VBA_PPM. m'). Functions that perform post-hoc inversion diagnostics (such as Volterra kernel estimation, cf. 'VBA_ VolterraKernels. m') are stored in this folder. Finally, this folder gathers routines that can be used to evaluate and optimize the efficiency of the experimental design, with respect to either model comparison or parameter estimation (cf.
g. maximum number of iterations, minimum increment in free energy), delay matrix (see below), flag for continuous/categorical data (see below), etc… N posterior: a structure variable whose fields contain the sufficient statistics (typically first and second order moments) of the VB approximation to the posterior densities over the observation/ evolution/precision parameters and hidden-states time series. N out: a structure variable that recapitulates the optional argu- ments (in case defaults were used) and provides supplementary information regarding the VB model inversion (e. g. , model diagnostics: free energy, percentage of variance explained, Volterra kernels, etc…). We refer the interested reader to the header of 'VBA_NLState SpaceModel. m', as well as to example scripts described in the next section.
Alternatively, one may assume that asymmetry in the behavioural response to gains and losses may be due to different learning rates, (a? a gain ,a loss À Á ). The script 'demo_asymRW. m' simulates an experiment that aims at discriminating between these hypotheses. We consider a go/no-go choice design, whereby the (artificial) subject decides, at each trial, whether to gamble or not. On the one hand, if he gambles (y~1), he receives either a positive, negative or neutral feedback (e. g. , 1J, 21J, 0J). On the other hand, if he chooses the 'no-go' option (y~0), he receives nothing (neutral feedback). The decision to gamble or not can be thought as an economic choice, which is driven by the learned value of the 'go' option. Here, we model the likelihood P y~1 x,Q j ð Þof choosing the 'go' option using the following softmax mapping of the 'go' value x: P y~1 x,Q j ð Þ ~1 1zexp{ x{Q ð Þ ð8Þ where Q captures a potential bias toward the 'no-go' option.
DCM also includes the effect of the hemodynamic response function, which effectively performs a temporal convolution operation on neural states  [28]. An exhaustive assessment of the properties of DCM simulation and VB model inversion can be found elsewhere  [29] [30] 12]. In this section, we will focus on the issue of online design optimization. We refer the interested reader to the script 'demo_ dcmonline. m'. This demo simplifies the network identification of  [26]. In brief, photic input enters V1, which is reciprocally connected to V5. The goal of the experiment is to assess whether attention modulates the feedforward connection from V1 to V5. This is addressed using Bayesian model comparison given fMRI data (i. e. model 1: attention modulates the V1RV5 connection; model 2: no modulatory effect).
Within the session, each block consists of 16 seconds of stimulation and 16 of rest. The on-line design optimization consists in deciding, before each block, whether we modulate subjects' attention. This is done by comparing the design efficiency (i. e. -minus-the Laplace-Chernoff risk that is induced by the comparison of the two models) of two canonical block designs, i. e. photic stimulation with and without attentional modulation. Then, the fMRI response to the chosen stimulation is simulated under the true (but unknown) model. Critically, the evaluation of the design efficiency changes as the experiment unfolds, because both models are inverted given each new block dataset, yielding increasingly precise model predictions. In brief, the on-line design optimization procedure implements the experimental cycle of Figure  1 , keeping the set of alternative hypotheses unchanged.
Note that the VB treatment of stochastic state-space models is systematically initialized by identifying the deterministic variant of the dynamical system. We invite the reader interested in the comparison of stochastic and deterministic model inversions to run demos such as 'demo_Lorenz. m', 'demo_VanDerPol. m' or 'demo_doubleWell. m', which reproduce the exemplar analyses in  [15]. Figure  6  summarizes the comparison of the VB inversion of deterministic and stochastic variants of the Lorenz system (this Figure can be reproduced from the script 'demo_Lorenz. m'). One can see the profound impact of state noise, both on hidden states trajectories and on the structure of model residuals (estimated measurement noise). More precisely, model inversion under the 'no state-noise' assumption leaves a lot of unexplained variance in the data.
VBA can deal with certain forms of delays by embedding the state space x into an augmented (Markovian) state-space X, where X t ~xt ,x t{1 ,:::,x t{D ð Þ. Here, D is the maximum delay considered. The dimension of the embedded state space is now nD, which can be considerably high. The script 'demo_delays. m' provides a demonstration of the inversion of a delayed stochastic dynamical system. The demo first sets up priors and optional arguments, which include the delay matrix D. In this case, the system is a two-dimensional linear system with (delayed) feedback. Figure  7  summarizes the comparison of the ensuing VB inversions of (deterministic) delayed and non-delayed variants of the system. One can see the profound impact of delays, both on hidden states trajectories and on the structure of model residuals.
e. features), and A (resp. b) is an unknown vector (resp. scalar) that encodes the linear mapping (Q~A,b f g). Figure  10  (script 'demo_bin. m') summarizes the statistical performance of the VB approach to data classification. In brief, we have simulated categorical data under two different models, namely: H0 (no systematic link between features u and data y, i. e. A~0) and H1 (which posits an arbitrary but non-zero mapping A). Both H0 and H1 were then inverted given either the first half (to perform classical cross-validation on test-data) or the full dataset (to perform Bayesian model comparison). This procedure was repeated 256 times, in order to derive average performance measures. One can see how Bayesian model comparison replicates the generalizability measure of standard cross-validation procedures, provided the inference is based upon the whole dataset (rather than splat into two train/test halves).
More precisely, one can see that classification performance reaches statistical significance when the data are simulated under H1, but not under H0. This falls from the predictive density over 'test' data obtained after the VB inversion of the H1 model given 'train' data. Equivalently, VB model comparison of H1 and H0 correctly identifies the true model, with greater confidence when the whole dataset is used for deriving the free energy. Inverting static (hierarchical) models. Static models are simplifications of the above class of generative models, where the dimension of the hidden states and the evolution parameters tend to zero (n t ~1, n~0 and n h ~0). In this case, the generative model reduces to a nonlinear observation equation, i. e. : y~g Q ð Þze, with fixed priors on the observation parameters.
Alternatively, a simple (two-levels) hierarchical extension of this static model (whereby the priors are also learned) can easily be derived by actually removing the evolution/observation parameters, but retaining the initial conditions and the first hidden states, with, e. g. , identity evolution function, i. e. : x 1 ~x0 zg 1 y 1 ~g x 1 ð Þze 1 & ð14Þ where x 0 , x 1 , a and s are estimated. This is the typical structure for so-called ''mixed-effects'' models for analysis at the group level. Critically, estimating the ''initial conditions'' x 0 as well as the ''state noise'' precision a enables one to infer on the group mean and variance. Figure  11  (script 'demo_RFX. m') summarizes the statistical performance of the VB treatment of mixed-effect models (cf. Equation  14 ). One can see that both the estimated group mean and the Bayesian model comparison are coherent, in terms of inferring whether there is a non-zero group-mean (second level effect).
These two summary statistics typically constitute the results of random effects BMS (see, e. g. ,  [33] ). In addition, the toolbox also returns the model attributions, i. e. the posterior probability, for each subject, of being best described by each model. Figure  12  (script 'demo_bmc4glm. m') demonstrates the random-effect group BMS approach, in the context of a simple static general linear model (GLM). In brief, we simulated two groups of 32 subjects (under arbitrary subject-specific GLMs), one of which only expressing half of the (four) factors. This allows us to derive the Monte-Carlo distribution of within-subjects' data under both a 'full' and a 'reduced' model. One can see the quality of the fit for a typical simulated dataset (SNR = 0 dB). The log-model evidence of both models was derived for each data (here, at the frequentist limit, cf.
g. , observed people's choices. Finally, one could then perform a Volterra decomposition of hidden states dynamics onto a set of appropriately chosen basis functions. This diagnostic analysis allows one to identify the hidden states' impulse response to experimentally controlled inputs to the system. We refer the interested reader to Text S3 in the supplementary information for mathematical details regarding Volterra decompositions. Figure  13  (script ''demo_dynLearningRate. m'') demonstrates the above procedure, in the context of the two-armed bandit problem  [34]. In brief, an agent has to choose between two alternative actions, each of which may yield positive or negative feedbacks. In our case, we reversed the action-outcome contingency every fifty trials. First, a series of choices are simulated, under an agent model that learns both the evolving actionoutcome probabilities and their volatility  [7].
Availability and Future Directions In this paper, we have exposed the main algorithmic components of the VBA toolbox, which implements a probabilistic treatment of nonlinear models for neurobiological and behavioural data. This toolbox aims at disseminating models and methods that serve experimental purposes, and providing a flexible platform, which modellers can easily contribute to. VBA is under intense development as we speak. More precisely, the following additions to the current toolbox's version are under test: N Between-conditions and between-groups second-level Bayesian model selection. This rests on quantifying the evidence for a difference in model labels (resp. frequencies) across conditions (resp. groups). We refer the interested reader to  [35]. N Dual categorical/continuous data analysis.
N Library of observation/evolution functions of models for behavioural and neuroimaging data. These include, but are not limited to: learning rules (e. g. , bayesian belief updates with different forms of priors  [5] [6] [7] , deterministic exploration, cognitive dissonance effects  [36] , …), canonical utility functions (e. g. , delay discounting  [37] , effort devaluation  [38] , risk attitude, …), neural spiking dynamics (e. g. Hodgkin-Huxley  [39] , Fitz-Hugh-Nagumo  [40] , …), neural mesoscale networks (e. g. , Jansen-Ritt  [41] , neural fields  [42] , …), etc… Some of these extensions are already available from the current code distribution. We encourage the interested reader to look for appropriate key words in the demonstration scripts. VBA's code is under open-source GNU General Public Licence (v2), and is freely downloadable from the toolbox's internet wiki pages (http://code.
Sunflower is an important oilseed crop, as well as a model system for evolutionary studies, but its 3. 6 gigabase genome has proven difficult to assemble, in part because of the high repeat content of its genome. Here we report on the sequencing, assembly, and analyses of 96 randomly chosen BACs from sunflower to provide additional information on the repeat content of the sunflower genome, assess how repetitive elements in the sunflower genome are organized relative to genes, and compare the genomic distribution of these repeats to that found in other food crops and model species. We also examine the expression of transposable element-related transcripts in EST databases for sunflower to determine the representation of repeats in the transcriptome and to measure their transcriptional activity.
A custom Perl script was used to determine the average coverage per scaffold. Scaffolds with average coverage less than 100 were eliminated. Vector sequences including pIndigo BAC-5 were identified with BLASTN and removed  [45]. Most BAC assemblies were fragmented, most likely due to the highly repetitive nature of the sunflower genome. The assembled BACs have been submitted to GenBank (GenBank accessions: AC254865; AC254997-AC255082; AC255084-AC255092).
Identification and Annotation of the Repetitive Fraction of the Genome RECON, an open-source software package for de novo repeat identification and classification  [46] , was used to identify repeats in the 96 sunflower BACs. To increase the speed and efficiency of the program, the BLAST output was parsed to discard self-hits, as well as hits with an e-value greater than 1 × e -5. The RECON output was parsed for sequences greater than 50 bp in length that were found at least five times per family. As a complementary approach, de novo repetitive sequences were also identified using RepeatScout  [47] , and the overlap was determined by RepeatMasker version 3. 1. 9 [48]. BLASTN, BLASTX  [49] , and TransposonPSI  [50]  searches against the all-plant repeat database  [51]  were used to annotate the de novo repeats.
An e-value cut-off of 1 × e -5 was employed for these searches. Repeats were compiled into a custom repeat database and used for homology-dependent repeat search using RepeatMasker. Custom Perl scripts were used to parse the RepeatMasker results to remove/minimize any overlaps between the different repeat co-ordinates and to calculate the abundance of each repeat in our dataset. The un-annotated novel repeats were used in a cluster analysis using Blastclust  [52]  at the following settings-L = 0. 51 S = 80. Low-complexity repetitive regions and simple sequence repeats (SSRs) were also identified, and their relative abundance and density were determined. The frequencies of different SSR motifs within each di-, tri-, and tetranucleotide repeats were estimated as well.
Mathematically Derived Repeats Tallymer  [53] , a program based on enhanced suffix arrays  [54] , was used to compute the 20-mer occurrence counts and construct a frequency index of each 20-mer. These frequencies were plotted logarithmically on a genomic scale to distinguish regions of high TE content from low copy regions. Based on the 20-mer frequency distribution, BAC clones were further categorized into low, mid and high repetitive clones.
Class I LTR-Retrotransposons Class I LTR-retrotransposons were identified using LTR-finder  [55]  at default parameters. LTRs of each predicted retrotransposon were analyzed with J-dotter  [56]  and ClustalX  [57]  to define their boundaries and to eliminate the false hits. The LTR-RTs were annotated based on BLASTN and BLASTX searches against the NCBI non-redundant database at an e-value of 1 × e -5. Clusters of nested repeats were identified by TE Nest  [58]. LALIGN version 35. 04 February 20, 2010 was used to find non-overlapping local alignments  [59].
Identification and Annotation of the Genic Content of the Genome AUGUSTUS, an ab initio annotation program  [65]  was used to predict genes in both the repeat-masked and the unmasked datasets (Parameters: Alternative scripts = none, Allowed gene structures = only predict complete genes; Training set = Arabidopsis thaliana). The augustus predictions were verified by BLASTN (MegaBlast) and BLASTX (1 × e -15 ) against the NCBI Helianthus EST database and the NCBI non-redundant protein database, respectively, and also parsed to eliminate the predictions without start and stop codons.
Gene Ontology (GO) Annotation To determine the functional annotation of the predicted genes, and to look for differences between the repeat-masked and unmasked gene prediction datasets, we used both sets of sequences for GO analysis. The two sets of sequences were used as queries to the NCBI non-redundant database using BLASTX (1 × e -35 ). The BLAST output in the XML format was imported into BLAST2GO (B2G) for GO analysis by mapping each blast-based high-identity match to an associated GO annotation term  [66]. The resulting annotations were converted into the "GO-Slim" format and retrieved for the three GO categories (biological process, molecular function and cell component) with an alpha score of at least 0. 6 and an ontology depth level of 3.
Transcriptome Analysis To identify the transcribed portion of the genome and representation of repeats in the transcriptome, both the repeat-masked and the unmasked datasets were used to screen the EST clusters in all six translated frames using TBLASTX at 1 × e -35. A total of 477,922 long read (Sanger) EST sequences from four plant species both within Compositae (sunflower-31,605 sequences and lettuce-50,433 sequences  [67]  and outside Compositae (rice-247,516 sequences and Arabidopsis-148,368 sequences, NCBI Taxon ID: 4530 and 3702, respectively) were used for this analysis. To determine the differences in expression patterns of TEs between the transcriptome and the whole genome, average coverage of 256 LTR-RTs was determined (as described previously in Section 2. 2. 3) for flower and root-stem HA412 RNA-seq libraries (GenBank Accessions: SRX475914; SRX475915) and compared with their coverage in the whole genome.
To estimate, characterize and classify the repetitive content of the sunflower genome further, the following analytical approaches were employed: LTR-Finder, RepeatMasker and Tallymer. The majority (83%) of the BACs were categorized as highly repetitive (70%-100% repetitive), 15 percent as mid repetitive (40%-70% repetitive) and two percent as low repetitive (0%-40% repetitive) as shown in Figure  1. [46]  and RepeatMasker  [48]  annotations along with the frequency of 20-mers  [53]  for each clone is shown. The repeat-rich and repeat-poor regions, as shown by the 20-mer frequency plot by Tallymer and supported by the RECON and RepeatMasker annotations can be differentiated easily. Assuming a representative dataset, approximately 78% of the genome is estimated to be repetitive with a repeat density of 433 bp/repeat in the repeat-dense potentially heterochromatic and 12,279 bp/repeat in the repeat-poor (containing single or low copy sequences) potentially euchromatic regions of the genome, and an average density of 815 bp/repeat (Table  1 ).
Of the top 10 SSR motifs present in the genome in order of their abundance, eight are tri-nucleotides with TGG/CCA as the most frequent tri-nucleotide motif in the genome (Supplementary Figure  S2 ). Low complexity regions in other plants are typically comprised of A/T, AT, GA/TC, CT/AG, GC, G/C, polypurine and polypyrimidine-rich regions but in the sunflower genome, they are predominantly AT-rich (~82% of the total; Supplementary Figure  S3 ). Table  1. Repeat composition of the sunflower genome as determined by RECON  [46] , RepeatScout  [47]  and RepeatMasker  [48]. Approximately 28% of the repetitive elements identified in sunflower are novel, of which at least 18% are Class I LTR-RTs as was determined by comparing the novel repeats with the output of LTR-finder. The novel repeats, in this case are sequences that are identified as being repetitive by de novo repeat finding algorithms, but show no homology to either the TIGR all plant repeat database or to any other sequence in the NCBI non-redundant databases.
The trees are drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method  [74]  and are in the units of the number of amino acid substitutions per site. The analysis involved 75 Ty1-Copia and 110 Ty3-Gypsy sequences. All ambiguous positions were removed for each sequence pair. There were a total of 250 positions in the final dataset. Evolutionary analyses were conducted in MEGA5  [76].