dataesr/scientific-paragraphs-categorization huggingface dataset:
The expression of the indicated genes, specific representatives of differentiation into the three germ layers, was monitored by RT-qPCR and normalized with respect to the expression of actin. The data shown are representative of two independent experiments. Standard deviations are shown. Boskovic et al. , 2014). Combined ChIP-seq and ChIP-proteomics approaches and subsequent investigations unambiguously identify Atad2 as a generalist enhancer of chromatin dynamics, recruited at acetylated chromatin regions, such as actively transcribed genes, where chromatin-templated processes are in action. A detailed inspection of Yta7 ChIP-proteomics data revealed striking functional similarities between factors associated with both Atad2 and Yta7, among which the histone chaperone complex FACT was found associated with Atad2 and Yta7.
For measuring the size of EBs, 1000 ES cells were seeded in 96-well round bottom low cell binding plate (Thermo Scientific) to produce homogeneous EBs and cultured for 6 days. At the end of the experiment, EBs were harvested for analysis, and their diameter was calculated using ImageJ software. FRAP experiments were performed after expression of GFP-tagged histones in control or siAtad2 KD ESCs, as previously described  (Montellier et al. , 2013).
Samples from available published data of normal human tissues (GSE3526, GSE11350, GSE15431, GSE18809, GSE6872, GSE7434, and GSE9994) were used to identify and characterize ATAD2 expression levels. The Affymetrix expression data files (. CEL) corresponding to the respective studies were downloaded from the GEO website (http://www. ncbi. nlm. nih. gov/geo/). The data were normalized using the RMA algorithm for summarization and quantile normalization. ESCs were harvested with TRI Reagent (Sigma), and total RNA was isolated following the manufacturer's instructions. RNA was reversetranscribed with the SuperScript TM III First-Strand Synthesis SuperMix (Invitrogen). Real-time quantitative PCR analysis of cDNA was performed on MxPro 3005P (Stratagene) with the Brilliant SYBR Green QPCR Master Mix (Agilent Technologies).
Peptides were eluted from 5% to 80% buffer B (0. 1% formic acid, 90% acetonitrile, 10% H 2 O) in buffer A with 30 min gradient at a flow rate of 300 nl/min. The fractions were analysed by using an Orbitrap Elite mass spectrometer in a top 20 data-dependent mode. For full MS spectra, the scan range was m/z 350 -1300 with a resolution of 240000 at m/z 400; for MS/MS scan, ions with charge state 1-3 in each full MS spectrum were sequentially fragmented by collision-induced dissociation with normalized collision energy of 35%. The dynamic exclusion duration was set to be 20 sec, and the isolation window was 2 m/z. Analysis of MS raw data. All MS raw files were analysed by Mascot software (version 2. 4. 0) against the database Uniprot Mouse. Acetylation (protein N-term) and oxidation (M) were specified as variable modifications.
(D) Wild-type GFP-ATAD2 or GFP-ATAD2 bearing the Y1021F mutation in its bromodomain was expressed in Cos7 cells, treated or not with TSA. After photobleaching, the recovery of GFP fluorescence was monitored. Ten datasets for each experiment were individually fitted. The eight giving the best fitting statistics were used to calculate the half-life (t 1/2 ) of fluorescence recovery and mobile fractions (right table). The mean and standard deviation of the time points from the eight datasets are plotted on the left.
The reward sizes of one, two, or four drops of liquid were related to the cues. If the monkey released the bar before the go signal appeared or after the go signal disappeared, an error was registered. No explicit punishment was given for an error in either condition, but the monkey had to perform a correct trial to move on in the task. That is, the monkey had to repeat the same trial with a given reward size until the trial was completed correctly. Data acquisition and analysis. The lipping signal was monitored continuously and digitized at 1 kHz. For each trial, the latency of lipping response onset after cue onset was defined as the first of three successive windows in which the signal displayed a consistent increase in voltage of at least 2 SDs from a reference epoch of 250 ms taken right before cue onset.
Because we collected lipping data for a limited number of neurons in the OFC (n ϭ 22), we only report analyses concerning lipping index for VMPFC neurons (n ϭ 84). However, we included data from all neurons (OFC: n ϭ 93; VMPFC: n ϭ 104) in our analysis of reward level and trial number encoding. All of the neurons recorded were included in the analyses whether they showed sensitivity to the task conditions or not. For characterizing firing and lipping activity, we used data from nonrepeated correct trials only; data from incorrect trials were excluded, as were trials that followed incorrect trials, because in this last case the cue was not informative to the monkey. At each time point, electrophysiological activity was calculated as the number of spikes recorded during the preceding 500 ms.
Prestimulus activity was taken to be the spike count in the 500 ms preceding cue onset. Poststimulus activity was taken to be the spike count in the 500 ms following cue onset. Two sorts of regressions were performed across trials: (1) against the spike count for each individual neuron separately, and (2) to estimate the population activity, against the spike counts from all of the neurons (pooled as if they were recorded from a same single cell). Both spike counts and lipping data were z-scored on a per monkey basis to put the data on a scale that is relatively independent of individual differences. In the firing time-series (see Fig. 6a ), a median-split on lipping index was done separately for each reward level. Therefore, each of the two time series includes the same number of low, medium, and large reward trials.
Unless otherwise indicated, values labeled as ␤ indicate coefficients obtained for multiple linear regression models that were estimated using the robustfit function of MATLAB, which underweights large outliers. The p values of ␤ coefficients were estimated using bilateral t tests. All regressions were performed using the full dataset; bins shown on figures were made for display purposes only. All statistical analyses were performed with MATLAB Statistical Toolbox  (MATLAB R2006b, MathWorks). Neuro-behavioral model. Our model assumes that subjective value (lipping index) is encoded in VMPFC poststimulus activity, which mediates the effect of both reward level and prestimulus activity, which itself is modulated by contextual factors, such as trial number (see Fig. 2a ). The nodes in the model represent from left to right the independent variable (contextual factor) and then a succession of three dependent variables (prestimulus activity, poststimulus activity, and cue value).
To avoid dealing with confounding effects that could arise if there were lyrics, all extracts were strictly instrumental. We controlled that maximal sound volume was the same for all extracts. Music extracts were randomly assigned to the three musical blocks. Tasks. Tasks were programmed on a PC using the Cogent 2000 (Wellcome Department of Imaging Neuroscience, London) library of MATLAB functions for stimulus presentation. Subjects completed six blocks of a pleasantness rating task in the MRI scanner. They were warned from the start that some blocks would contain music, whereas others would not. They were also instructed to ignore any musical context while assigning a pleasantness rating to the painting. All trials started with the appearance of a fixation cross that stayed on screen for a long delay (drawn from a uniform distribution between 10 and 25 s), and then the painting was displayed for a fixed duration of 5 s.
5 mm interslice gap. T1-weighted structural images were also acquired, coregistered with the mean EPI, normalized to a standard T1 template, and averaged across subjects to allow group level anatomical localization. EPI data were analyzed in an event-related manner, within a GLM, using the statistical parametric mapping software SPM8 (Wellcome Trust center for NeuroImaging, London) 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 same transformation as structural images, and spatial smoothing using a Gaussian kernel with a FWHM of 8 mm. We used four GLMs to explain individual-level time series. To search for neural correlates of painting ratings, we preferably used finite impulse response (FIR) models, rather than hemodynamic response function (HRF), because they allow analyzing both prestimulus and poststimulus activity.
Statistical analysis. All statistical analyses were performed with MATLAB Statistical Toolbox (MATLAB R2006b, MathWorks). Painting and music ratings, as well as activity levels (␤ values) were z-scored at the subject level before entering into any statistical analysis. All values noted ␤ indicate coefficients obtained for multiple linear regression models that were estimated using the "robustfit" function of MATLAB which underweights large outliers. To predict choices from pleasantness ratings, we used a logistic regression model. Given the difference in value between the ratings of any two paintings A and B, the probability of choosing A over B was estimated using the softmax rule: P(A) ϭ 1/(1 ϩ exp(Ϫ(V A Ϫ V B )/␤)), with V A and V B the ratings for A and B, respectively. The temperature ␤ captures the randomness of decisionmaking.
This free parameter was adjusted to maximize the probability (or likelihood) of the actual choices under the model. Easy and hard choice conditions were pooled for the logistic regression fit presented in Figure  3c. Unless otherwise indicated, we report coefficients (␤) obtained for multiple linear regression models that were estimated using the "robustfit" function of MATLAB which underweights large outliers. All regression coefficients were tested against 0 at the group level (across subjects) using bilateral t tests, and not on the average data points shown in figures (bins shown were made for display purposes only). All statistical analyses were performed with MATLAB Statistical Toolbox (MATLAB R2006b, MathWorks). Neuro-behavioral model. The model applied to human data was based on the same mechanism as seen in monkeys (see Fig.
This work was supported by  European Research Council Starting Grant ERC-BioMotiv and National Institutes of Health Intra-Mural Research Program. It has also benefited from the  Fédération pour la Recherche Médicale. R. A. was supported by the  French Direction Generale de l'Armement and Investissement d'avenir   ANR-10-IAIHU-06. The authors declare no competing financial interests. *S. B. and M. P. contributed equally to this study.
Genotyping success and presence of null alleles A final dataset of 378 individuals genotyped for nine markers was used to explore the population genetics of I. glandulifera. Originally individuals from 10 populations, some located within the same municipality, along the species' northsouth distribution in Europe were genotyped for eleven microsatellite markers and compared with individuals from three populations from Kashmir in the species' native range (Table  1 , Fig. 1 ). The locus IGNSSR103 failed to amplify and was therefore excluded from further analysis. The locus IGNSSR106 (14 % successfully amplifying individuals) and one individual each from the populations Amiens1 and Kashmir1 with < 30 % successfully amplifying markers were also removed due to poor success rate, leaving the final dataset to be used for further analysis.
Latitudinal genetic structuring in European I. glandulifera In the STRUCTURE analysis of the full dataset ΔK suggested K = 3 (ΔK = 27413) as the number of clusters best describing the data (Additional file 4). This was also the level of clustering with the highest repeatability between runs according to CLUMPP H values (H = 0. 996, Additional file 4). At this level one cluster contained the Kashmir populations (with the exception of Stockholm already separated from the European populations at K = 2, ΔK = 8145. 4, H = 0. 985), another the more southern European populations (Amiens1, Amiens2, Ghent, Bremen, Lund1 and Lund2) and a final cluster the northern European populations (Stockholm, Trondheim1 -3) (Fig. 2a ). In the STRUCTURE analysis of only European individuals ΔK suggested that the data were best described by two clusters (ΔK = 17208), also the number of clusters with the highest repeatability between runs according to CLUMPP H values (0.
We additionally evaluated our data for genetic structuring using discriminant analysis of principal components (DAPC), which is free of the assumptions of HWE and no LD present in STRUCTURE. The number of DAPC clusters best describing the different data sets was not clear-cut for the full and European data sets (Additional file 5), but the automatic selection implemented in find. clusters suggested similar or higher numbers of clusters to those found by the STRUCTURE analysis (all data K = 2, European data K = 5, Kashmir data K = 2). As our primary aim was to evaluate how the violation of STRUCTURE assumptions affected the clustering we compared the results from the STRUCTURE analyses with the highest support to those from the DAPC analyses with the same number of clusters. The DAPC results showed a high degree of correspondence with the outcome of the STRUCTURE analyses suggesting that the effect of LD and deviation from HWE on the analyses had been minor.
Support of independent colonisations from approximate Bayesian computation but not principal component analyses Principal component analysis (PCA) of the full dataset clearly separated the Kashmir (black and grey) from the European populations (in colour) (Fig. 3a ) along the first two principal components (PCs). The wider spread of Kashmir individuals along PC1 and PC2 (Fig. 3a ) reflected the higher genetic diversity present in the Kashmir populations. Analysis of only the European individuals showed three individuals from Amiens1 to be highly divergent (data not shown). This proved to be the result of their genotypes at the A2 locus and excluding these genotypes from the analysis mostly removed the divergence of these individuals. After removal of the deviant A2 genotypes almost all the individuals of the Stockholm population clustered separately from all other European individuals, while the rest showed partial overlap with a gradual transition across a roughly geographical gradient (Fig.
Limited effects of mutation, migration and bottlenecks in European I. glandulifera Pairwise R ST values did in most cases not differ from the F ST values (Table  6 , Additional file 7) (Amiens1 vs Populations that have recently undergone a bottleneck will experience both loss in the number of alleles and observed heterozygosity. In spite of the relatively recent naturalisation and spread across Europe we found little evidence of genetic bottlenecks when analysing the data with the software BOTTLENECK. No heterozygosity excess was detected for any population in any of the three population group sets used (Wilcoxon sign-rank text, all p > 0. 05). However, two of the northernmost populations, Trondheim1 and Trondheim2, showed the shifted mode indicative of a recent bottleneck. The proportion of migrants into a population, assessed using the software BayesAss, was in most cases less than 1 %, and only a few populations showed indications of more than 10 % of the individuals being migrants from other populations (Additional file 7).
Data analysis The expected heterozygosity under HWE (h) was calculated with a purpose-written Perl script (available upon request). GenePop  [82, 86]  was used to test for deviations from HWE. FreeNA  [13]  was used to test for the presence of null alleles and for obtaining estimates of F ST  [105]  and D CH , the Cavalli-Sforza and Edwards genetic distance  [12]. Adjusting genetic diversity, F ST and D CH for the presence of null alleles did not affect the conclusions drawn and these measures are therefore not reported. D S (Nei's genetic distance  [65] ) and R ST , an equivalent of F ST using a stepwise mutation model,  [85, 91]  were calculated using Spagedi v 1. 4  [35]  and differences between R ST and F ST were tested using the permutation test implemented in the software. D CH and D S were highly correlated (r = 0.
949, Pearson's productmoment correlation, p < < 0. 001) and only the former is reported. Hierarchical AMOVA, F IS and tests for LD (using 10000 permutations) were calculated and carried out using Arlequin v 3. 5. 1. 3 [25]. For AMOVA of the full dataset continent and municipality were both used as the highest hierarchical level while the European dataset was analysed with both region and municipality as the highest hierarchical level. Analyses with municipality as the highest hierarchical level included also municipalities for which only a single population was available. Bayes-Ass v 3. 0  [107]  was used to estimate migration between populations. BOTTLENECK v 1. 2. 02  [16]  was used to detect recent population bottlenecks among the studied populations. For the BOTTLENECK analysis, a twopopulation group set (all European individuals and all Kashmir individuals, respectively), a three-population group set (northern European individuals from Stockholm and Trondheim, remaining southern European individuals and Kashmir individuals respectively) and a 13-population group set (each sampled population treated as a separate unit) were analysed under a stepwise mutation model.
Due to the low number of markers analysed heterozygosity was tested using the Wilcoxon sign-rank test. We examined geographic clustering of the genetic diversity using a combination of different approaches. STRUC-TURE v 2. 2  [26, 77]  was run with a burn-in length of 20 000 iterations followed by 50 000 iterations for estimating the parameters, with non-amplifying markers treated as missing data. Each analysis was repeated ten times for each number of clusters (K = 1 to 20) until the likelihood values for the runs no longer improved. We evaluated both the admixture and the no admixture models, but as the two models gave similar results we only report the former. The number of clusters observed in the dataset was evaluated by calculating ΔK according to Evanno et al. [24]. CLUMPP v 1. 1. 1 [42]  was used to compare the results of individual runs and to calculate similarity coefficients, H, and the average matrix of ancestry.
Acknowledgements This project was carried out within the research network FLEUR (http:// www. fleur. ugent. be)funded by the  Research Foundation -Flanders  (FWO) and was supported economically by  CBD (Center for Biodiversity Dynamics) ,  NTNU. Serina Trotzer ,  Sigrid Lindmo ,  Annika M. Felton ,  Gaëtan Duponchelle  and  Sharmila Phuyal  are thanked for help in the field.
Introduction Sequence-based domain recognition represents one of the most convenient and practical approaches to understand evolution and function of proteins. Domains are sequence fragments that can be independently stable and folded, they have a shape and a function, they occur alone or in groups and are the building blocks of all proteins. To account for the great diversity of domain contexts, several efforts were made in categorizing coding regions into protein domains and domain families. Some resources, like SCOP  (Murzin et al. , 1995)  and CATH  (Sillitoe et al. , 2013) , organize domains according to their structural classes, while others, such as Pfam  (Finn et al. , 2010)  and PROSITE  (Sigrist et al. , 2013) , are purely sequence based. These databases are the starting point of annotation pipelines that are commonly organized into three steps: (i) a probabilistic model or a set of models is built to represent each functional domain; (ii) the model library representing all domains in the database is used to scan query sequences identifying potential domains and (iii) conflicting predictions are resolved to propose a domain architecture (domain arrangement) for each protein to be annotated.
, 2009)  and dPUC (domain Prediction Using Context)  (Ochoa et al. , 2011). Both methods are based on the Pfam database and, for a given protein sequence, they detect a set of potential domains by setting a permissive Pfam threshold and by allowing overlaps. They also compute and use a list of known domain architectures from UniProtKB sequences  (Leinonen et al. , 2004). CODD starts by assigning to the query sequence nonoverlapping domains detected with a restrictive Pfam threshold. Then, it iteratively tries to add new non-overlapping domains that co-occur with the old ones according to the list of known architectures. dPUC differs in strategy from CODD. It represents potential domains as nodes of a graph, where edges connect non-overlapping domains. It weights each node with normalized Pfam scores, and it weights edge with a special context score that captures the propensity of pairwise domain combinations in the list of known architectures.
DAMA combines a number of criteria including multi-(possibly pairwise-) domain co-occurrence and domain overlapping. By taking into account known architectural solutions, DAMA identifies them within the protein sequence and integrates new domains into them whenever possible. DAMA has been evaluated over a benchmark containing protein sequences extracted from the Protein DataBank (PDB), over the genome of the poorly annotated malaria parasite Plasmodium falciparum and over two datasets collecting known sequences characterized by large domain architectures and repeated blocks of domains. Our results show that, for all datasets, DAMA outperforms existing computational methods and detects domain architectures presenting co-occurrences.
The PDB benchmark To evaluate DAMA performance, we have used the benchmark constructed for the evaluation of MDA. It contains 2523 multi-domain proteins extracted from the PDB, where each domain is classified in a CATH superfamily. The dataset is available at ftp://ftp. biochem. ucl. ac. uk/pub/gene3d_data/DomainFinder3/RC1/Benchmark/. Since this dataset is based on CATH superfamilies, we have extracted the list of known domain architectures by parsing the file CathDomainDescriptionFile containing CATH superfamilies (domains) for all proteins in PDB database. This file is available at ftp://ftp. biochem. ucl. ac. uk/pub/cath/latest_release/.
The P. falciparum protein dataset All data were extracted from PlasmoDB (http://PlasmoDB. org), the official repository of the P. falciparum proteins used as a reference database by malaria researchers  (Aurrecoechea et al. , 2009; Bahl et al. , 2003). PlasmoDB v11 contains 5542 proteins. To provide a list of potential domains for P. falciparum protein sequences, we used Pfam v27 (downloaded from http://pfam. sanger. ac. uk), containing 14 831 domains. The list of known domain architectures was extracted from Pfam-A. full, as well as the list of allowed domain overlaps. HMM profiles, required to produce the list of potential domains for P. falciparum, were downloaded directly from the Pfam web site. To identify potential domains, we run HMMER 3. 0 (hmmscan) on all protein sequences  (Eddy, 2011).
DAMA time complexity on P. falciparum protein sequences We computed the run time of DAMA, MDA, CODD and dPUC on the set of domain hits provided by HMMER 3. 0 and selected with an E value 1e-3. All experiments run time have been obtained on a one core single-user linux  Intel(R) Xeon(R) CPU E5-2650 v2 2. 60 GHz, with 64 GB of RAM. Single user mode was used to minimize the number of system activities.