dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics Regression of potential confounds on case-control ascertainment-The PGC cohorts are a combination of many datasets drawn from the US and Europe, and it is important to ensure that any bias in sample ascertainment does not drive spurious association to SCZ. In order to ensure the robustness of the analysis, burden and gene-set analyses included potential confounding variables as covariates in a logistic regression framework. Due to the number of tests run at breakpoint level association, we employed a step-wise logistic regression approach to allow for the inclusion of covariates in our case-control association, which we term the SCZ residual phenotype. Covariates included sex, genotyping platform, and ancestry principal components derived from SNP genotypes on the same samples in a previous study  1.
Control for population stratification is described in the supplementary material. We were unable to control for dataset or genotyping batch, as a subset of the contributing datasets are fully confounded with case/control status. Only principal components that showed a significant association to small CNV burden were used (small CNV being defined as autosomal CNV burden with CNV < 100 kb in size). Among the top 20 principal components, only the 1 st , 2 nd , 3 rd , 4 th , and 8 th principal component showed association with small CNV burden (with p < 0. 01 used as the significance cutoff). Lastly, in order to control for case-control differences in CNV ascertainment due to data quality we sought to identify data quality metrics that were confounded with case status. Affymetrix (MAPD and waviness-sd) and Illumina (LRRSD, BAFSD, GCWF) QC metrics were re-examined across studies to assess if any additional outliers were present.
Only three outliers were removed as their mean B allele (or minor allele) frequency deviated significantly from 0. 5. Many CNV metrics are auto-correlated, as they measure similar patterns of variation in the probe intensity. Thus, we focused on the primary measure of probe variance -MAPD and LRRSD. Among Affymetrix 6. 0 datasets, MAPD did not differ between in cases and controls (t=1. 14, p = 0. 25). However, among non-Affymetrix 6. 0 datasets, LRRSD showed significant differences between cases and controls (t=-35. 3, p < 2e  -16  ), with controls having a higher standardized mean LRRSD (0. 227) than cases (-0. 199). Thus, to control for any spurious associations driven by CNV calling quality, we included MAPD (for Affymetrix platforms) or LRRSD (for Illumina platforms) as covariates in downstream analysis, which we designate as our "CNV metric" covariate for each individual.
Prior to inclusion in the combined dataset, the CNV metric variable was normalized within each respective genotyping platform. To calculate the SCZ residual phenotype, we first fit a logistic regression model of covariates to affection status, and then extracted the Pearson residual values for use in a quantitative association design for downstream analyses. Residual phenotype values in cases are all above zero, and controls below zero, and are graphed against overall kb burden in Supplementary Figure  9. We removed three individuals with an SCZ residual phenotype greater than three (or negative three in controls). After the post-processing round of QC, we retained a dataset with a total of 41,321 individuals comprising 21,094 SCZ cases and 20,227 controls. CNV burden analysis-We analyzed the overall CNV burden in a variety of ways to discern which general properties of CNV are contributing to SCZ risk.
PCR droplets were read & analyzed on Bio-Rad QX100 Droplet Reader with QuantaSoft software. Synaptic and ARC-complex genes intersected by a rare loss in at least 4 case or control subjects and with genic burden Benjamini-Hochberg FDR <= 25% (red discs) were used to query GeneMANIA 36 and retrieve additional protein interaction neighbors, resulting in a network of 136 synaptic genes. Genes are depicted as disks; disk centers are colored based on rare loss frequency (Freq. SZ and Freq. CT) being prevalent in cases or controls; disk borders are colored to mark (i) gene implication in human dominant or X-linked neurological or neurodevelopmental phenotype, (ii) de novo mutation (DeN) reported by Fromer et al. 28  , split between LOF (frameshift, stop-gain, core splice site) and missense or amino acid insertion / deletion, (iii) implication in mouse neurobehavioral abnormality.
Presynaptic adhesion molecules (NRXN1, NRXN3), post-synaptic scaffolds (DLG1, DLG2, DLGAP1, SHANK1, SHANK2) and glutamatergic ionotropic receptors (GRID1, GRID2, GRIN1, GRIA4) constitute a highly connected subnetwork with more losses in cases than controls. The manhattan plot (for deletions) represents empirical P-values at each deletion breakpoint. CNV tracks display duplications (blue) and deletions (red) detected in cases and controls from the PGC SCZ dataset.
Acknowledgments Core funding for the  Psychiatric Genomics Consortium  is from the  US National Institute of Mental Health  ( U01 MH094421 ). We thank  Thomas Lehner ,  Anjene Addington  and  Geetha Senthil  (NIMH). The work of the contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donation. Details are provided in the Supplementary Notes. Membership of the  Wellcome Trust Case Control Consortium and Psychosis Endophenotype International Consortium  are provided in the Supplementary Notes.
The extent Temporal signal in pathogen genomes sampled through time. A) Plot of the probability of observing no changes between two genomes of length (N) separated by the given number of days (x) for a given rate of evolution per site per year (). We used N = 13,100 and = 0. 0037 for Influenza A/H1N1  (Smith et al. 2009) , N = 30,100 and = 0. 0011 for MERS-CoV  (Cotten et al. 2014) , N = 19,800 and = 0. 0012 for EBOV  (Park et al. 2015) , N = 13,100 and = 0. 0005 for CHIK, N = 4,641,576 and = 0. 00000032 for Salmonella Typhimurium DT104  (Mather et al. 2013) , and N = 190,000 and = 0. 0000082 for Smallpox. An online application to plot such probabilities is available at http://epidemic. bio. ed. ac. uk/node/79. B. Root-to-tip divergences as a function of sampling time based on publicly available Influenza A/H1N1, MERS-CoV and EBOV genomes as well as for an unpublished CHIK data set.
The regression plots were rescaled for each virus such that the oldest genome in each dataset was set at time = 0 and the regression line has zero divergence at time = 0. to which we expect to observe genetic changes between viral sequences sampled at different times will depend on the overall rate of substitution (and its constancy), the length of the sequences that can be obtained and the differences in time of sampling. We illustrate this for complete genome sampling from four different viral pathogens that have caused relatively recent outbreaks: pandemic influenza A (A(H1N1)pdm09), Middle East respiratory syndrome coronavirus (MERS), Chikungunya virus and Ebola virus (Fig. 1 ). For comparison, we also include a DNA virus (smallpox) and a bacterial pathogen (Salmonella Typhimurium DT104).
This relaxed random walk (RRW) appeared to be critical to accommodate a very high degree of heterogeneity that can underlie the spatial spread of pathogens, as was the case for the West Nile virus invasion in the United States  (Pybus et al. 2012). The same analysis also challenged standard inference under these models, which initially relied on sampling internal node realizations of the bivariate locations but proved to be inefficient in this case. Analogous to efficient likelihood computation for discrete traits  (Felsenstein 1981) ,  Pybus et al. (2012)  pursued an approach that integrates out internal node states in the Bayesian framework (see also  Freckleton (2012) ), which makes applications to large datasets more accessible. Although diffusion rate variation can be taken into account, a phylogeographic RRW process still relies on dispersal as a function of geographic distance (or distance in Euclidean space).
B) Visualization of antigenic drift dynamics reconstructed using Bayesian MDS in a two-dimensional map. These patterns are inferred from the 2002 to 2011 subset of the influenza A/H3N2 dataset analyzed by  Bedford et al. (2014). X and Y represent the first and second antigenic dimensions. The contours represent the 80% HPD region for the node locations (both internal and external nodes). The colors range from green to blue for the lines, points and contours reflects the age between 2002 and 2011. This figure was made using SpreadD3  (Bielejec et al. 2016). extremely large, were highly predictable from the host phylogeny  (Longdon et al. 2015).
High-state spaces still challenge likelihood calculations, but massively parallel computations offer considerable speed-up in such cases, and practical solutions are now in place both in terms of hardware (e. g. , GPU cards) and software to support phylogenetic likelihood computations on such hardware (e. g. , BEAGLE)  (Suchard and Rambaut 2009; Ayres et al. 2012; Baele and Lemey 2013).
RECENT DEVELOPMENTS AND FUTURE PERSPECTIVES In this review, we highlighted several aspects of data integration in phylodynamics with a particular focus on the connection between sequences and traits. As mentioned in the respective sections, the initial applications of relatively simple but computationally efficient Bayesian implementations of random walk models in the field of pathogen evolution and epidemiology focused on reconstructions of spatial spread. There are, however, important caveats that need to be considered for phylogeographic applications, in particular related to sampling. First, spatial coverage may be restricted to particular geographic areas which VOL. 66 limits comprehensive understanding of the spatial spread dynamics. For example, although large genetic datasets have become available to study the global seasonal dynamics of all human influenza lineages, the sampling remains limited for Africa, Central America, the Middle East, and Russia  (Bedford et al.
Although this study models the host and spatial dynamics as independent processes, it leads to host-specific summaries of spatial spread and testable differences among them. In addition to combining multiple traits, the possibility to incorporate covariates in discrete diffusion processes also prompts exploration of more detailed ecological information in pathogen evolution and epidemiology. In a spatial context, cheap and mobile global positioning systems are now widely adopted in the recording of infectious disease spread, but also the variables that are associated with geo-located disease data (e. g. , environmental, infrastructural, and socio-economic) are increasingly being characterized in great detail and distributed as publicly available datasets (see e. g. , http://www. worldpop. org.
Although this is still somewhat in its infancy in the analyses of large-scale transmission dynamics based on coalescent or birth-death models, the possibility of using genetic data and time series data in tandem has been clearly demonstrated  (Rasmussen et al. 2011) , as well as the need to incorporate environmental stochasticity to accurately capture disease dynamics in some cases  (Rasmussen et al. 2014 ). These developments are generally divorced from the sequence evolutionary process and still await their implementation in the commonly used Bayesian statistical software packages like BEAST  (Drummond et al. 2012). Although BEAST implements a wide range of tree-generative processes, including time-variable coalescent and birthdeath models, these have not yet been connected to covariate or time series data.
B) Visualization of antigenic drift dynamics reconstructed using Bayesian MDS in a two-dimensional map. These patterns are inferred from the 2002 to 2011 subset of the influenza A/H3N2 dataset analyzed by Bedford et al. (2014). X and Y represent the first and second antigenic dimensions. The contours represent the 80% HPD region for the node locations (both internal and external nodes). The colors range from green to blue for the lines, points and contours reflects the age between 2002 and 2011. This figure was made using SpreadD3 (Bielejec et al. 2016 ).
ACKNOWLEDGMENTS The research leading to these results has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement no. [278433-PREDEMICS] and  ERC  Grant agreement no. [260864] and the  National Institutes of Health  ( R01 AI107034 ,  R01 HG006139  and  LM011827 ) and the  National Science Foundation  ( IIS 1251151  and  DMS 1264153 ). The  VIROGENESIS  project receives funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. [634650]. G. B. acknowledges support from a  Research Grant  of the  Research Foundation -Flanders (FWO ;  Fonds Wetenschappelijk Onderzoek -Vlaanderen ).
Background: Studying transcription factors, which are some of the key players in gene expression, is of outstanding interest for the investigation of the evolutionary history of organisms through lineage-specific features. In this study we performed the first genome-wide TF identification and comparison between haptophytes and other algal lineages. Results: For TF identification and classification, we created a comprehensive pipeline using a combination of BLAST, HMMER and InterProScan software. The accuracy evaluation of the pipeline shows its applicability for every alga, plant and cyanobacterium, with very good PPV and sensitivity. This pipeline allowed us to identify and classified the transcription factor complement of the three haptophytes Tisochrysis lutea, Emiliania huxleyi and Pavlova sp.
This kind of study can reveal taxonomic characteristics (i. e. , the specificity and expansion of TF families) of the TF complement of different organisms. In silico analysis of FTs performed on Arabidopsis thaliana (A. thaliana) showed that 45 % of TFs are plant specific. Moreover, a plant-specific expansion of the MYB superfamily was demonstrated (190 copies in the A. thaliana genome compared with 6 and 10 in Drosophila melanogaster and Saccharomyces cerevisiae, respectively)  [6]. Another example of such lineage-specific expansion of a TF family is the retinoic acid receptors in the nematode Caenorhabditis elegans. Using the Ani-malTFDB database, 239 putative TFs belonging to this family were identified, whereas in other animals, such as Tetraodon nigroviridis, this TF family is only represented by 19 members  [10].
However, in silico comparative studies in haptophytes are limited because few data are available. Here, we conducted the first genome-wide identification and comparison of the TF complement in haptophytes using an optimized and automated pipeline. This analysis pipeline combines research for similarities with known TFs and protein domains using a large database containing plant, fungal, mammal and cyanobacterial TFs. Using our pipeline, we performed the in silico identification of the TF complement in three haptophytes (Tisochrysis lutea, Emiliania huxleyi and Pavlova sp) and two stramenopiles (the eustigmatophycea, Nannochloropsis gaditana and the diatom Phaeodactylum tricornutum), which are close organism groups  [19, 22] , as well as in the green alga Chlamydomonas reinhardtii and the red alga Porphyridium purpureum.
Evaluation of transcription factor identification accuracy Pipeline analysis is essential for whole genome TF identification. Since no universal pipeline exists, each study uses its own. However, every pipeline is based on the same tools: a single identification with BLAST searches against a plant database  [9, 15] , and/or a single protein domain search with HMMER software focused on plant DBDs  [11] [12] [13]. Several pipelines combine both methods so as to be more accurate and exhaustive  [2, 8]. Moreover, the HMMER software is used either with the Pfam database or the combination of Pfam and another database. Our pipeline also combines the same identification strategies, but with some specificities: our analysis pipeline includes more protein domain databases (the eleven databases of the InterProScan consortium) and the research is not restricted to plants, but enlarged to fungi, algae and cyanobacteria.
Identification and classification of transcription factors The TF identification and classification pipeline was calibrated with the model plant A. Thaliana (TAIR 10). Overall, the pipeline uses two strategies: (1) a similarity research with BLAST software against a self-built database of known TFs from algae, A. thaliana, Saccharomyces cerevisiae and cyanobacteria; (2) identification of TF DBDs with InterProScan and HMMER software. The compilation of software results allowed us to obtain a putative list of TFs (Fig. 1 ).
Construction of a TF database for BLAST software The TF database is composed of TFs from different organisms (the model plant A. thaliana; the green algae Bathycoccus prasinos, Chlorella sp, Coccomyxa sp, Micromonas pusilla, Micromonas sp, Ostreococcus lucimarinus, Ostreococcus sp, Ostreococcus tauri and Volvox carteri; the red algae Cyanidioschyzon merolae and Galdieria sulfuraria; the diatom Thalassiosira pseudonana and the yeast Saccharomyces cerevisiae). These sequences were retrieved from online databases (Additional file 2: Table  S2 ). Since algae originate from the engulfment of a cyanobacteria-like organism by a primitive eukaryotic heterotroph, we added all cyanobacterial TFs of the cTFbase  [8]  to the self-built database.
Identification of protein functional domains Each protein domain contained in the protein domain databases is stored as a Hidden Markov Model (HMM) and linked to a putative function. This statistical method computes a matrix based on the multiple alignments of a protein domain  [122]. For functional domain annotation of all the predicted proteomes, we employed InterProScan 5 version 5. 4-47. 0 [123] , which uses a consortium of eleven protein domain databases (PROSITE, HAMP, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, CATH-Gene3D and PANTHER). However, twelve DBDs (G2-like, BELL, HD-ZIP, HRT, NF-YB, NF-YC, SAP, STAT, Trihelix, VOZ, WOX and VARL) are not supported by the eleven databases of the consortium and were added through multiple alignments available in the TF databases PlantTFDB  [13]  and PlnTFDB  [12]  with HMMER3, v3.
Second step The results of each software analysis were filtered using different homemade PERL scripts. For InterProScan, false positives were filtered out to keep only annotated domains that had an e-value above or equal to 10 -3. Among these, only TFs DBDs were conserved. For HMMER, filtration was done on the score value. Sequences with a significant hmmscan match (according to the database thresholds) were added as TF candidates. For BLAST searches, the filtering step was applied with an identity percentage threshold of 35 % and an alignment length threshold of 100 residues. Then, the best-BLAST hit was taken for each query. Finally, the results of all software processes were combined in one file. Third step Once identified, putative TFs were classified into specific families according to their DBD(s).
We used a compilation of the "family assignment rules" described by the web databases PlantTFDB  [13] , PlnTFDB  [12]  and cTFbase  [8] , as well as previous studies  [9, 11]. A PERL script was used to automatically classify the putative TFs in families following the assignment rules. Final step Manual curation was necessary, in particular for three complex cases: (1) MYB, where the calibration stage revealed that filtration of the e-value score generated false negatives. To overcome this, MYB identification was performed using the same protocol, with the exception of the validation step of the e-value scores on the InterProScan result. Moreover, each candidate was manually inspected (BLAST) to confirm each MYB domain and classify putative TFs in each family (MYB-3R, MYB-2R and MYB-related).
(2) G2-like, due to the absence of a G2-like domain in the InterProScan database and its close similarity to the MYB-SHAQKYF domain, cross-annotation between these two domains was manually checked using HMMER. (3) TF families characterized by the repetition of a single domain; for proteins identified as belonging to the DBB and AP2/ERF families, the presence of two or more B-Box or AP2/ERF domains, respectively, was verified.
Evaluation of pipeline accuracy To estimate the accuracy and reliability of our identification method, we applied our pipeline to the predicted proteome of A. thaliana (TAIR 10) and compared the identification of eleven well-annotated families to published datasets  [13] , used as a gold standard. For the identification of cyanobacterial TFs, we applied our pipeline to Synechocystis sp. PCC 6803 (GeneBank Assembly: GCA_000009725. 1), Synechococcus sp. CC9605 (downloaded from cyanobase) and Nostoc punctiforme PCC73102 (GeneBank Assembly: GCA_000020025. 1) predicted proteomes and compared our prediction results with published data  [8]. The accuracy was evaluated by the measurement of sensitivity:.
The ensemble averaging of the displacements smoothens out of the noisy fluctuations and results in a smooth fire-line contour. This formulation can be implemented into existing wildfire models as a post-processing scheme at each time step without calling for any major changes in the original framework. The efficacy of this formulation by using a LSM based fire propagation model has already been shown  [35, 36, 38, 39, 40]. In this study, the authors proceed with their preliminary attempt  [41]  to include the proposed formalism for random processes (turbulence and fire-spotting), already discussed and implemented into an Eulerian LSM based wild-land fire simulator  [35, 36, 37, 38, 39, 40] , into a Lagrangian DEVS based wild-land fire simulation model: ForeFire (https://github. com/forefireAPI/firefront) [13].
[13]. ForeFire library is developed with C/C++/Java and Fortran bindings for a UNIX compatible environment and compiled using a SWIG (http://swig. org/) build platform. NetCDF library with legacy C++ interface is required to build ForeFire, and SCons (http://www. scons. org/) python tool is used to make the library. A python based interface is used to launch the simulations with software calls to the main code. A command line mode interpreter is also available to run simulations.
LSM based wildfire simulator LSM is one of the widely used and successful tools for tracking fronts in any dimension  [48, 9]. It is particularly suitable for problems where the speed of the evolving interface is dependent on the interface properties and the boundary conditions at the interface. Wild-land fire propagation is one of problems where LSM finds a frequent application to represent the evolving fire-fronts  [6, 49, 10]. In particular, it is the basis for the operational software WRF-SFIRE (http://github. com/jbeezley/wrf-fire/). Let Γ be a simple closed curve representing the fire-front interface in two-dimensions, and let Ω be the region bounded by the fire-front Γ. If the interface is made up of more than one closed surface, the domain Ω is not simply connected and represents more than one independently evolving bounded areas.
The LSM code makes use of a general-purpose library which aims at providing a robust and efficient tool for studying the evolution of co-dimensional fronts propagating in one-, two-and three-dimensional system. The library, written in Fortran2008/OpenMP, along with standard algorithms useful for the calculation of the front evolution by means of the classical LSM, includes Fast Marching Method algorithms. All the results presented in this paper are obtained by making use of the above-mentioned software; Matlab (www. mathworks. com/products/matlab/) and open source softwares such as SciPy  [50]  and Matplotlib  [51]  in the IPython framework  [52]  have been used for visualisation purposes.