dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-neuron ∆F/F time series had diverse amplitude scales and noise levels, as quantified by the distribution of their s. d. , σ, and of σ noise (Figure  S1B ). Therefore, in order to unbiasedly compare the dynamics across neurons, we needed to adaptively normalize the multivariate dataset to standardize the variance across the multiple time series. A satisfactory normalization was obtained by dividing each time series by the corresponding σ noise , defining a dimensionless quantity Z n,i that approximates the signal-to-noise ratio of the neuron n at the time sample i. The Z n,i → Z n,i+1 transitions between negative values should again be the product of B. Consequently, we used the lower left quadrant data (left in Figure  S1C ) to estimate the covariance matrix K of the baseline noise B and model it as a bivariate Gaussian process with 0 mean and covariance K.
However, its actual numerical value would not change qualitatively the inference procedure, since it is just an overall scaling factor. The denominator of the second term, the probability density of data transitions, can be estimated directly from the measured dataset. Finally, the numerator of the second term, the probability density of data transitions, given the baseline noise process, will be approximated by the already described noise model shown in the left plot of Figure  S1C. The density distributions of the two latter terms were smoothly estimated by means of a bivariate locally adaptive density estimation procedure, using the dataset and a run of the noise model, respectively. With all these variables we could then infer the posterior odds for B against A for any given Z n,i → Z n,i+1 transition in the data (equation 2).
Detection of neuronal assemblies Examination of calcium event histograms of neuronal population spontaneous activities (Figure  3B ) revealed periods of synchronous calcium events. To assess the significance of these events, we determined a threshold corresponding to the maximal neuronal population synchronization obtained, with 99 % confidence, on 1000 simulations of surrogate datasets with shuffled neuronal inter-activation intervals (IAI). Events of collective neuronal activation surpassing this threshold were considered as significant. Significant events were frequently observed in all experiments, therefore we designed a procedure to determine the neuronal identity during the synchronous calcium event episodes. In the present work, we defined a neuronal assembly as a group of neurons with significantly correlated activation dynamics, either spontaneously (spontaneous assemblies) or induced by stimulation (visually induced assemblies).
Dataset dimensionality reduction was obtained by only keeping PCs with eigenvalues bigger than λ max , a theoretical lower bound to the eigenvalues of informative PCs, given by the Tracy-Widom corrected Marcenko-Pastur distribution ̆  (Peyrache et al. , 2010; Tracy and Widom, 1992 ) (equation  8 ) λ max =(1+ √N /T ) 2 + N -2/ 3 (8) In practice this procedure reduced data dimensionality by ~280 fold (from ~14000, the number of imaging frames, to ~50 dimensions, the number of significant PCs). Each significant PC could be used to isolate clusters of correlated neurons  (Peyrache et al. , 2010)  (top panels in Figure  S2A ), however, since assemblies do not need to be exclusive (they may share neurons), we could relax the PCA orthogonality condition  (Lopes-dos-Santos et al. , 2011). Therefore, to delineate the assemblies, we decided to work on a space of obliquely rotated components (promax), that tended to sparsely concentrate the PC loadings along non-orthogonal rotated PCs (middle panels in Figure  S2A ).
The matrix C corresponds then to the matrix D that would be obtained in the ideal case of having identical spontaneous activities across neurons of the same cluster and uncorrelated activities for neurons belonging to different clusters. Therefore, Γ k is analogous to a correlation coefficient between D and the matrix C k that represents perfect clustering in k clusters, and its values range between -1 and 1, where 1 indicates that D completely matches C k. The Γ k curve for spontaneous activity datasets typically showed a clear and unique maximal peak, for the range of k tested (from 1 to 80). The final number of clusters chosen for the clustering procedure, k * , corresponds to the value that maximizes Γ (Figure  S2D ). To test the statistical significance of the Γ k* obtained for the spontaneous data, we controlled against a reference consisting of a collection of 1000 randomized surrogate datasets and calculated their performance in this clustering procedure, Γ null k*.
To build this surrogate set while keeping the overall statistical structure in the data, we replaced the spontaneous data in the PC space of reduced dimensionality by a set of data randomly drawn from a uniform distribution over the hypercube aligned to PCs that encloses the real data  (Tibshirani et al. , 2001). We then tested against the null hypothesis that the surrogate dataset was capable of producing a Γ null k* equal or bigger than Γ k*. All the final clusterings were highly significant (p < 10 -5 ; Figure  S2D ). Despite the great importance of k * for the clustering procedure, there was no simple trend in the relationship between k * and Γ k* (Figures  S2E  and S2F ), which indicates that the performance of the clustering procedure was not a simple consequence of choosing higher or lower values of k *.
We observed that the performance of the clustering was significantly better for the PCA-promax procedure compared to hierarchical clustering, as measured by both Γ (Figure  S2G ; 0. 22 ± 0. 12 for hierarchical clustering and 0. 41 ± 0. 06 for PCA-promax, p < 10 -5 ) and the Davies-Bouldin index (see below for definition; Figure  S2H ; 7. 78 ± 1. 75 for hierarchical clustering and 6. 66 ± 1 for PCApromax, p = 0. 018). Both methods gave significantly valid clusterings, as compared to the surrogate datasets. Moreover, significant positive correlations were ~5-fold more represented in the PCA-promax clusters than in the hierarchical clustering clusters (Figure  S2I ), which indicates that PCA-promax clusters are more correlated.
Random shuffled null models To assess the significance of the topographic and dynamic features of the neuronal assemblies, we compared them against surrogate assembly null models. For this purpose, we permuted the indexing of the entire neuronal population for each experiment. These permutations enabled us to create a set of shuffled surrogate assemblies for each experiment by using the original dataset clustering (i. e. the original non-permuted neuronal assemblies) but sampling neurons according to the permuted indexes. For each experiment, we generated a dataset of null models containing 1000 permutation rounds. This procedure randomized only the assemblies topographies, while keeping intact every other feature of a given dataset (e. g. the particular overlaps between assemblies, the number of neurons per assembly, the topographical position and the activation time series of each neuron).
Therefore, to quantify the validity of a particular assembly partitioning, we measured the Davies-Bouldin indexes at which the cumulative distribution peaked with respect to the null model. This difference would then represent the level of collective dynamics structure gained in comparison to a random partitioning. We then defined the clustering quality index as the area between the cumulative distributions of the Davies-Bouldin indexes for the dataset of interest and its respective null model. As measured by this index, clusters formed by spontaneous assemblies perform only 7 % worse than the light-spot-stimulated clusters in explaining the clustering structure of the light-spot-stimulated neuronal-population data, while for the moving-bar stimulation responses the decrease in performance was of 36 % (Figure  S4G ).
clustering validation techniques. (D) Example of the normalized Hubert's Γ as a function of the number of clusters, k, used for hierarchical clustering of the matrix D of a spontaneous activity dataset previously projected in the significant PC space (solid line). The control corresponding to the average of 1000 randomized surrogate datasets of neuronal activity is shown in dashed line. The final number of clusters chosen for the clustering procedure, k*, corresponds to the value that maximizes Γ (red circle, k*=17). (E, F) The normalized Hubert's Γ as a function of k*, for hierarchical clustering (left) and the combined PCA-promax procedure (right). Dots are different experiments, red lines show linear regressions. r, correlation coefficient; p, p-value. (G) Comparison of the clustering performances, as measured by Γ, for hierarchical clustering (HC) of spontaneous activity datasets and the PCA-promax of spontaneous activity datasets.
As a reference, we also show the result for HC of the surrogate datasets. Single experiments are shown in gray, average across experiments in red. (H) Same as G, but for the Davies-Bouldin index. (I) The PCA-promax to HC ratio of the probability of including a neuronal pair in a cluster as a function of their spontaneous activity correlation coefficient, for all the experiments pooled (red) and for a representative experiment (blue). Equal performance (ratio=1) is marked by dashed line. Notice how PCA-promax tends to include ~5-fold more positive correlations than HC.
ACKNOWLEDGMENTS We thank  M. -m. Poo ,  B. Barbour ,  Y. Fregnac ,  G. de Polavieja , and  Y. Loewenstein  for comments on the manuscript and  E. Schenidman ,  J. Boulanger-Weill , and  M. S. Murmu  for helpful discussions. This work was supported by  EraSysBio+ Zebrain ,  ERC  stg  243106 ,  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02 PSL * Research University , and Avenir grant INSERM.
Box Hepatitis A virus infection: European outbreak case definition  According to the European outbreak hepatitis A virus (HAV) infection case definition, a confirmed case is defined as: An EU/EEA resident with laboratory-confirmed HAV genotype IA and date of symptom onset (or date of testing if onset date not available) on or after 1 January 2013 and at least one of the following conditions: (i) identical sequence (i. e. 100. 0%) to the 2013 HAV genotype IA outbreak strain (GenBank accession number KF182323) based on a fragment of 460 nucleotides (nt) at the region of VP1-2a a (ii) 99. 8% similarity to this sequence (i. e. one nt difference in 460 nt) from 2,915 to 3,374 on NC_001489. (iii) identical sequence (i. e. 100. 0%) on a shorter fragment of at least 174 nt at the region of VP1-2a from 2,967 to 3,191 on NC_001489.
EU/EEA: European Union/European Economic Area; EFTA: European Free Trade Association. a For Norwegian isolates, identical sequence to GenBank number KF773842 based on a fragment of 466 nucleotides at the region VP3-VP1. b As for confirmed cases: at the time of writing this report (December 2014), outbreak cases were still being reported by at least one EU/EEA country, hence no end date for the outbreak case definition could be defined.
Similarly, following identification of the European outbreak strain in Ireland in July 2013 and in Norway and Sweden in February 2014, these three countries opted for sequencing available samples from all HA serologically confirmed cases in the previous months and for part of 2014  [10]. Also, in May 2013, Poland sequenced five isolates from cases with a history of travel to Italy. HAV sequencing was performed according to national (and also subnational for Italy) protocols. Comparison of the sequencing results focused on a genomic fragment in the region VP1-2A. In order to be categorised as the outbreak strain, a sample needed to have either (i) ≥ 99. 8% identity with the outbreak reference sequence (GenBank access number KF182323) in a 460 nt fragment at the region VP1-2A, or (ii) 100% identity with a shorter fragment of at least 174 nt in the same region.
Results From 1 January 2013 to 31 August 2014, a total of 1,589 HA cases were reported as associated with this outbreak from 13 EU/EEA countries (Figure  1 , Figure  2 ); most of the cases (n = 1,438; 90%) were reported in Italy. Germany, Ireland and Norway each reported around 30 cases and all other countries reported fewer cases, with Austria, Bulgaria and Denmark each reporting a single case associated with this outbreak. In most of the affected countries, cases were geographically distributed nationally. The outbreak strain (from Italy in May 2013) sequence GenBank number was KF182323 and the subgenotype was IA. Sequences from 361 viral isolates were found to be identical to the outbreak strain (Table ): individuals with this strain were classified as 'confirmed', whereas the remaining 1,228 cases were classified as 'probable' (Box).
We found that teriflunomide had no deleterious impact on the cellular responses to a sample of recall antigens (C albicans, Trichophyton, PPD). These findings supplement those of a previous phase 2 trial (the TERIVA study; NCT01403376) in which patients with relapsing MS receiving teriflunomide mounted effective humoral antibody responses to seasonal influenza vaccination. 12 Safety data from teriflunomide clinical studies also support this view, since general immune competence did not seem to be significantly altered in phase 3 double-blind treatment periods (as measured by mild decreases in neutrophil and lymphocyte counts that remained within normal range with no associated infection)  24  and over long-term extensions with up to 12 years of continuous exposure. 25, 26 In addition, several preclinical studies have demonstrated that teriflunomide does not exert cytotoxic effects and preferentially inhibits proliferative activity of strongly stimulated adaptive immune cells, providing further support for the selective impact of this agent on immune responses.
Similar to RuvB they have an ATPase activity but their helicase activity remains questioned. Both proteins can form oligomers like homo-hexamers, hetero-hexamers, or hetero-dodecamers (Figure  1 ). Gorynia et al. , 2011). In (C,D) the domain II region to the left of the red dashed line in (A) was truncated to facilitate crystallization. Molecular structures were drawn using the UCSF Chimera software  (Pettersen et al. , 2004). They were discussed during a First International Workshop held in Bordeaux, France, in 2012. It was a great success as it allowed scientists from various fields to meet for the first time, to discuss in depth their favorite proteins, and to share expertise and reagents. In particular, this Workshop led to a consensus in limiting the number of names used for these proteins to Pontin/RUVBL1 and Reptin/RUVBL2 in humans (or RVB1/Rvb1 and RVB2/Rvb2 in yeast)  (Rosenbaum et al.
Acknowledgments We thank  Andres Lopez-Perrote  for help in preparing the figures. This work was supported by  Fundação para a Ciência e Tecnologia (Portugal)  grants  PTDC/BBB-BEP/1724/2012  to PM and TB, and  PEst-OE/EQB/LA0004/2011  to PM;  Creative Research Initiatives Program (Research Center for Chromatin Dynamics ,  2009-0081563 ) to SB;  Ministry of Economy and Competitiveness of the Spanish Government  grants  SAF2011-22988  and  SAF2014-52301-R  to OL;  NIH  grants  R01 GM084465  and  CA166054  to AD; The  Canadian Institutes of Health   Research grant  ( MOP-93778 ) to WAH;  Equipe Labélisée Ligue Contre le Cancer and Institut National du Cancer  grant  PLBIO10-155  to JR.
An evaluation of these is presented in the Additional file 2: Supplementary Methods. Relaxing the MQ filter applied in the pipeline can result in both sensitivity and precision > 95 %, however the MQ filter is applied to reduce the risk of false positives arising from multiple mapping hits for an integrationfor instance if an integration were to occur within an interspersed repetitive element such as CR1. The parameters applied herein (MQ = 20, RC = 0. 25μX i ) result in 98 % precision and 66 % sensitivity when comparing interval detection in the RJF NGS data to EAV-HP LTR BLAT alignments to Galgal4. Fourteen intervals were present at high frequency (f ≥ 0. 9) in the chicken genomes analysed, and eight of these were present in all birds (S8a). The presence of these intervals was confirmed in the chicken reference genome (Galgal4) following alignment of the EAV-HP LTR sequence using BLAT (S7).
Furthermore, visual examination of the results of the BLAT alignment of softclipped sequences from each interval allowed the integration site breakpoints to be identified. The integration site breakpoints were found to be identical in each bird, suggesting that they likely occurred prior to domestication. A BLAT of the complete EAV-HP sequence (GenBank: KC632578; S8b) indicates some of these high frequency intervals in Galgal4 to host large remnants (>1 kb) of EAV-HP sequence, although none of the alignments are complete. Additional sequencing would be require to establish the integrity of the EAV-HP sequences in these intervals for the other birds/lines. Single  [16]  and multiple  [17] [18] [19]  domestication events have been suggested for domestic chicken, which may have involved several red junglefowl subspecies and introgression from other junglefowl species  [20, 21].
Conclusions Through the use of re-sequencing data, we have mapped EAV-HP integration sites in a number of chickens of diverse origin. For this purpose, we have presented and validated a means of detecting EAV-HP integration sites using paired-end NGS data. The sensitivity and precision of the approach was assessed using NGS data derived from the red junglefowl reference bird and comparing the results against the same genome. In addition, we have further demonstrated the reproducibility of our approach through the independent analysis of a single individual sequenced twice using independent libraries. A next step would be to validate a number of the intervals by conventional PCR, and ultimately to assess their impact on gene expression. Although we have demonstrated the method using a single endogenous retrovirus, the same general principles should be applicable for any mobile element present in a host genome for which paired-end sequence data and a reference genome are available.
NGS data Paired-end reads for a Silkie and Taiwan L2 chicken, totalling 30. 1 Gb and 38. 3 Gb, respectively, were downloaded from NCBI's Sequence Read Archive (SRA)  [33] , having been made available following a study on the genome-wide patterns of genetic variation in these two birds  [34]  [SRA:PRJNA202483]. Sequencing for these samples was performed on the Illumina GAIIx platform. Illumina GAIIx sequencing reads for a single red junglefowl, Gallus gallus, totalling 67. 6 Gb, were also downloaded from NCBI [SRA:SRX043655, SRA:SRX043656]. NGS data from eight inbred lines of White Leghorn from the Pirbright Institute (lines: 0, 6, 7, 15, N, C, P, and Wellcome line; http://www. narf. ac. uk/chickens/ lines) was provided by the Roslin Institute, having each been sequenced on the Illumina GAIIx platform from DNA pools of 10 birds per line, averaging 12 Gb per line.
Sequence read mapping Reads were first mapped to the chicken reference genome (Galgal4) using the Burrows-Wheeler Aligner (BWA)  [43, 44]  (Fig. 1a ). Sequence reads were then mapped to the LTR of the EAV-HP genome [Gen-Bank:NC_005947] (Fig. 1b ). NGS data provided by the Roslin Institute (University of Edinburgh, UK) was in the format of a SAM file following mapping to the EAV-HP genome, whilst all other samples were mapped at the University of Nottingham (UK). Specifically, in the Ethiopian samples the adapter pollutions in reads were trimmed at source (BGI) along with reads containing more than 50 % low quality bases (quality < 5) bases. Both the Ethiopian dataset, and the reads downloaded from the SRA, were aligned using BWA MEM. From the EAV-HP LTR mapping, unmapped reads were extracted using SAMtools  [45] , converted to the fastq format using the bam2FastQ tool from the BamUtil repository (http://genome.
Mitochondrial analysis Reads mapped to the mitochondrial genome were extracted from the BAM alignments for each bird using SAMtools, and consensus sequences aligned using Clus-talX v2. 1  [46]. A neighbour-joining tree following 1000 bootstrap replicates was constructed using MrBayes v3. 22  [47]  and plotted with FigTree (v1. 4; http://tree. bio. ed. ac. uk/software/figtree/).
Retroviral integration site analysis BAM files generated from reads remapped to Galgal4 were converted to BED format using the bamToBed tool from the BEDTools repository  [48]. BED features were filtered to exclude those with a mapping quality below 20. Overlapping features were merged using the mer-geBed tool from BEDTools and filtered to exclude those supported by a read count (RC) less than 25 % the mean depth of coverage for the sample (further details in Additional file 2: Supplementary Methods). The resulting list of intervals indicated putative EAV-HP integration sites for each chicken line analysed (Fig. 1c ). For each interval, paired reads that were initially mapped to Galgal4 were retrieved by name using Picard (http:// broadinstitute. github. io/picard), and remapped to the LTR of the EAV-HP genome (Fig.
A phylogeny was constructed using the as. phylo function of the APE package  [51] , from which a network was generated using the network package  [52]. The distribution of integration sites was plotted using Circos v0. 56  [53]. A correlation test for association between chromosome length and interval count was performed using Pearson's product moment correlation coefficient as implemented in the R function cor. test. The Galgal4. 78. gtf file was downloaded from Ensembl and used to reference nearest transcript for each of the intervals. This gene transfer format file includes all of the coding and non-coding transcripts annotated on Ensembl's genome browser for Galgal4. The rationale for selecting genes within 5 kb is that this is likely to cover the promoter region, as evidenced by the recent discovery of an EAV-HP integration site ~4 kb upstream of SLCO1B3 in chickens possessing the oocyan phenotype  [8, 9].
Functional annotation of genes was assessed using DAVID Bioinformatics Resources v6. 7  [54] , specifically the functional annotation clustering enrichment threshold for EASE was set to 1. 3 with stringency set to HIGH; from the resulting clusters only those with an enrichment score ≥ 1. 3 (P < 0. 05) were retained. Functional annotation analysis was also performed using g:Profiler  [55, 56] , the results of which are available in the Additional file 1: Supplementary Material (S11b), in addition to REVIGO  [57]  summaries of GO terms for biological process (S12a, SF1), cellular component (S12b, SF2) and molecular function (S12c, SF3), which for brevity have not been presented here (see Additional file 3). The fraction of chicken genome transcribed (41 %) was calculated from the sum of transcript lengths divided by genome length.
Acknowledgements This study was performed during DW PhD, which was supported by a  Biotechnology and Biological Sciences Research Council (BBSRC)  studentship to the  University of Nottingham. TTD's contribution was through a PhD studentship that is part of the " Reducing the impact of infectious disease on poultry production in Ethiopia " project, which originate from a  BBSRC, UK Department for International Development (DFID)  and  Scottish Government supported initiative  (grant numbers:  BB/H009396/1 ,  BB/H009159/1  and  BB/ H009051/1 ). RAL is a PhD student supported by a  University of Nottingham  vice chancellor's international scholarship. The sequencing of the Ethiopian chickens was supported by the  Rural Development Agency (RDA, CYC)  and the  International Livestock Research Institute (ILRI, SK).