dataesr/scientific-paragraphs-categorization huggingface dataset:
(2008)  (subfolder CellTypesDatabase/tune in above repository). This single compartment model featured a fast Na + , delayed rectifier and M-type K + , L-type Ca 2+ and leak conductances. Conductance densities of active and leak channels, reversal potentials of ions (over a restricted, physiologically plausible range), cell capacitance and voltage dependence of the Na + conductance were used as free parameters in the model tuning, for which the package Neurotune (https://github. com/ NeuralEnsemble/neurotune) was used. Two cells were selected from Allen Cell Types Database for the E and I neurons in our network model. One cell from layer 2/3 which exhibited spiny dendrites and showed positive expression for the gene Slc17a6 (vesicular glutamate transporter) was chosen for the E cell (http://celltypes.
To detect the ISN signature in the detailed cells, the somata of two morphologically complex cells were voltage clamped at À80mV and 0 mV, revealing the E and I postsynaptic currents respectively  (Figure 7D, inset). This process was repeated for 10 independent realizations of the model, to obtain a reliable estimate of the inputs. In order to generate multiple instances of a network model which had varying numbers of point neurons/detailed cells and configurable inputs, a script was created in Python (ISN. py, available in https://github. com/OpenSourceBrain/MultiscaleISN) which used libNeuroML to position the cells in 3D space, connect the populations and apply external spiking inputs. Both Figures 7C and 7D were generated from this script.
ACKNOWLEDGMENTS This work was funded by the  Wellcome Trust  ( 086699 ,  101445 ,  212941 , and  095667 ) and an  ERC  advanced grant ( 294667 ) to R. A. S. , who is in receipt of a  Wellcome Trust Principal Research Fellowship  ( 203048 ). OSB and NSG integration was funded by the  BBSRC -NSF/BIO program  ( BB/N005236/1  and  NSF  no. 1458840 ) and  NSF  no. 1458495. E. P. was supported on the  EU Marie Curie Initial Training Network CEREBNET  ( FP7-ITN-PEOPLE-2008 ;  238686 ). S. D. -B. and W. W. L. were funded by  NIBIB   U01EB017695 ,  DOH01-C32250GG-3450000 , and  NIH   R01EB022903. M. L. H. and R. A. M. were funded by  NIH  grant  DC009977  and M. L. H. by  NIH   NS11613. B. M. was funded by  FAPESP , proc. 2017/04748-0. S. C. and J. B. were partially funded by  NIH   R01MH106674  and  NIH R01EB021711.
S. J. v. A. and A. P. D. were funded by  EU   7th Framework Program  ( FP7/2007-2013)  under grant agreement no. 604102 (Human  Brain  Project, ramp up phase) and  EU Horizon 2020  grant no. 720270  (HBP,  SGA1 ). A. E. , J. B. , and R. S. were partially funded by the  Google Summer of Code program , coordinated through the INCF. We thank  Marianne Bezaire  for assistance in converting the CA1 network model and  Eilif Muller  for help converting the Blue Brain Project cells. We would like to thank the other developers who have made contributions to OSB models and supporting applications. Specific contributions can be found on individual GitHub repositories. We thank  Andreas Schaefer ,  Tommy Younts , and  Diccon Coyle  for feedback on the manuscript.
DATA AND SOFTWARE AVAILABILITY All of the code for the OSB platform and the models presented here is open source. The Ruby on Rails based frontend for OSB is at https://github. com/OpenSourceBrain/redmine (GNU General Public License v2) and the Geppetto repositories are listed at https:// github. com/openworm/org. geppetto (MIT License). The repositories for the majority of models in Figure  2A  can be found at https:// github. com/OpenSourceBrain (generally released under MIT License, with information on how to cite the models when reused in CITATION files). Direct links to individual OSB projects can be found in Table  S1  and also at http://www. opensourcebrain. org/ projects.
METHOD DETAILS OSB frontend: project and user management The main OSB web interface (at http://www. opensourcebrain. org) is based on a heavily customized version of Redmine (http://www. redmine. org). This platform, developed in Ruby on Rails (https://rubyonrails. org), allows users to create accounts, make new projects and add other users to them, create user groups, and associate a version control repository (hosted on GitHub [https://github. com], BitBucket [https://bitbucket. org/], SourceForge [https://sourceforge. net], etc. ) to each project. We have extended this framework with a new user interface, and added custom fields for projects with metadata relevant to the model, brain region, species, and simulators supported. Close integration with platforms hosting the version control repositories allows the files and change history to be deeply integrated into the OSB interface.
Issues raised on GitHub, or forks of the repository are highlighted on the OSB page. Wikis describing the installation/usage of the models can be added directly on OSB, or content from README files in the associated repositories can be retrieved and displayed. An application programming interface (API) is provided to programmatically access information on all current projects and metadata (https://github. com/OpenSourceBrain/OSB_API). OSB project repositories are automatically searched for NeuroML files (in either XML or HDF5 format, see below) and these are presented to users for visualization through the browser. OSB frontend: visualizing & simulating models Visualization of models on OSB has been enabled through our contribution to the development of Geppetto (http://www. geppetto. org;  Cantarelli et al.
More precisely, the cross-field transport of impurities is at least partly driven by turbulent structures that require demanding numerical load to be dealt with. In this contribution, we present the latest advances of the suite of fluid codes SOLEDGE-TOKAM, which has been developed in the perspective to tackle the problem of electrostatic edge plasma turbulence. Thanks to the cross-fertilization between the transport code SOLEDGE2D  [1, 2]  and the turbulence code TOKAM3X  [8] , one now solves Braginskii's equations for multi-species plasma taking into account the realistic geometry of the vessel using the penalization technique to treat Bohm boundary conditions. Neutrals transport and interaction with the plasma are taken into account by coupling with EIRENE  [5].
Bohm-Chodura boundary conditions The penalization technique used in SOLEDGE2D and described in  [4]  has been extended to 3D. The code is based on a finite volume algorithm based on a multi-domain structured grid, each sub-domain of the mesh being a slab in the (ψ, θ, φ) space, ψ denoting the radial coordinate, θ the poloidal coordinate and φ the toroidal coordinate. The grid is extended into the solid and a mask function χ distinguishes the points in the wall where = 1 and the points in the plasma where = 0. At the present stage, forcing terms at the interface where χ value changes from 0 to 1 ensure Bohm-Chodura boundary condition: v n c b n | • | | • | wall s wall , (21) where = + c T ZT m ( )/ s e ,. This boundary conditions should be  for electrons -a more accurate sheath model should be implemented in the future for improved consistency between sheath transmission coefficients and electric potential).
Acknowledgments This work was granted access to the HPC resources of CINES, under the allocation 2018-A0040510482 made by  GENCI  and to the  HPC resources of Aix-Marseille University  financed by the project  Equip@ Meso  ( ANR-10-EQPX-29-01 ). The project leading to this publication ( TOP  project) has received funding from  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a french " Investissements d'Avenir" programme. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  633053  for the project  WP17-ENR-CEA-08. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
Background Comparative analysis of DNA and amino acid sequences is of fundamental importance in biological research, particularly in molecular biology and genomics. It is the first and key step in molecular evolutionary analysis, gene function and regulatory region prediction, sequence assembly, homology searching, molecular structure prediction, gene discovery, and protein structure-function relationship analysis. Traditionally, sequence comparison was based on pairwise or multiple sequence alignment (MSA). Software tools for sequence alignment, such as BLAST  [1]  and CLUSTAL  [2] , are the most widely used bioinformatics methods. Although alignment-based approaches generally remain the references for sequence comparison, MSA-based methods do not scale with the very large data sets that are available today  [3, 4].
It can be used to comprehensively evaluate AF methods under five different sequence analysis scenarios: protein sequence classification, gene tree inference, regulatory sequence identification, genomebased phylogenetics, and HGT (Table  2 ). To evaluate the existing AF methods with these data sets, we asked the developers of 24 AF tools to run their software on our data sets or to recommend suitable input parameter values appropriate for each data set. In total, our study involved 10,202 program runs, resulting in 1,020,493,359 pairwise sequence comparisons (Table  1 ; Additional file 1: Table  S1 ). All benchmarking results are stored and can be downloaded, reproduced, and inspected with the AFproject website. Thus, any future evaluation results can be seamlessly compared to the existing ones obtained using the same reference data sets with precisely defined software parameters.
Alignment-free method catalog To evaluate the performance of currently available AF tools and create a reference data set for future comparisons, we benchmarked 24 standalone tools (Table  1 ), covering a large proportion of the currently available AF methods. Some tools offer multiple related methods to measure pairwise distances (or dissimilarity) between sequences; for instance, jD2Stat  [37]  supports three different distance measures based on the D 2 statistic: jD2Stat--d2n, jD2Stat--d2s, and jD2Stat--d2st. In this study, we included these different distance measures, resulting in a total of 74 tested tool variants (Fig. 2 ). Each of these tool variants was run with various combinations of parameter values (Additional file 1: Table  S1 ). The values yielding the best performance for a given method were selected and saved in the AFproject database; if multiple parameters produced the same best-performing results for a tool, we selected only the values that were least computationally demanding (e.
g. , the shortest word length for word-counting methods or the smallest sketch size). Full information about the benchmarking results, including all combinations of parameter values of the evaluated tools, can be downloaded from http://afproject. org/down load/  [66]. Only three tools (Alignment-Free-Kmer-Statistics (AFKS)  [34] , FFP  [39] , and mash  [11] ) are sufficiently generic to be applied to all 12 benchmarking data sets; the remaining tools can handle only subsets of our reference data sets, either because they have been designed only for a specific purpose (e. g. , to handle only certain sequence types, such as nucleotides, proteins, and unassembled or assembled genomic sequences) or-less frequently-because of some unexpected software behavior (e. g. , a program stops functioning, does not terminate in a reasonable amount of time, or produces invalid results; Additional file 1: Table  S1 ).
Protein sequence classification Recognition of structural and evolutionary relationships among amino acid sequences is central to the understanding of the function and evolution of proteins. Historically, the first comprehensive evaluation of AF methods  [8]  investigated the accuracy of the tools for protein structure classification at four hierarchical levels used in the Structural Classification of Proteins (SCOP) database  [68] , namely, family, superfamily, class, and fold (for details about SCOP levels see the "Methods" section). The original protocol tested six k-mer-based distance measures against a subset of the SCOP database, containing protein family members sharing less than 40% sequence identity  [8]. In the present study, we extend the original analysis  [8]  to test the accuracy of 56 tool variants in recognition of structural relationships of protein sequences sharing both low (< 40%) and high (≥ 40%) sequence identity (Fig.
2 ). The area under the receiver operating characteristic (ROC) curve (AUC), which indicates whether a method is able to discriminate between homologous and nonhomologous protein sequences ("Methods" section), showed the favorable performance of AFKS  [34]  software. AFKS with parameters set to the simratio  [34]  distance and a word length of k = 2 is the best-performing tool for both low-and high-sequence-identity data sets (Fig. 2 ). For the latter type of the data set, the method produces the highest AUC values across all four structural levels, with an average AUC of 0. 798 ± 0. 139 (Additional file 1: Table  S2 ). When considering the low-sequence-identity data set (Additional file 1: Table  S3 ), AFKS--simratio also has the highest average AUC of 0. 742 ± 0. 079 but lower performance at the superfamily and family levels than alfpy  [5]  (set to the Google distance and k = 1).
alfpy--google is ranked second (0. 738 ± 0. 091) and fourth (0. 778 ± 0. 142) for Fig. 1  Overview of the AFproject benchmarking service facilitating assessment and comparison of AF methods. AF method developers run their methods on a reference sequence set and submit the computed pairwise sequence distances to the service. The submitted distances are subjected to a test specific to given data sets, and the results are returned to the method developer, who can choose to make the results publicly available the low-and high-sequence-identity data sets, respectively. Notably, the top-seven-ranking positions in both the low-and high-sequence-identity data sets are occupied, though in a different order, by the same measures from AFKS and alfpy software (Fig. 2 ). In general, the tested tools achieve greater discriminatory power in recognizing structural relationships (higher average AUCs) in our high-sequence-identity data set than in the low-sequence-identity data set (Fig.
3296 ± 0. 1511; Additional file 1: Table  S4 ), which can be interpreted as 33% (± 15%) of incongruent bipartitions between the inferred and the reference tree. To put this number into perspective, the corresponding gene trees based on MSA (i. e. , neighbor-joining trees inferred using ClustalW alignments generated with default parameters) yielded a similar average accuracy (nRF = 0. 2995 ± 0. 1511). In general, the nRF distances obtained by the tested methods vary greatly across the gene families (Friedman rank sum test, p < 2. 2 × 10 -16 ; df = 10; Friedman chi-square = 463. 88) due to different complexities of the encoded protein families (e. g. , evolutionary distance between proteins, domain architecture, and structural and functional affiliations). Consequently, the tools obtain their best accuracy in phylogenetic inference of the eukaryotic protein family of sulfatase modifying factor (SUMF) proteins, which are characterized by a single protein domain and the smallest number of gene duplications; four distance measures in AFKS software generated trees (nRF = 0.
This simulation strategy has been shown to yield data that are appropriate to assess the performance accuracy of a range of AF methods an earlier study  [62]. The tools, AFKS (Markov measure, with a word length of k = 12) and mash (k = 17-24), achieved the highest general accuracy (Fig. 2 ) by obtaining the lowest average nRF (0. 05 ± 0. 05) and perfect topological agreement with the reference trees at the two lowest frequencies of simulated HGT (l = 0 and 250; Additional file 1: Table  S11 ). As expected, for most AF methods, the accuracy of phylogenetic inference declines with an increase in the extent of HGT. Nevertheless, the seven best-performing software applications-AFKS, mash, CAFE, alfpy, FFP, jD2Stat, and ALFRED-G  [45] -were capable of reconstructing the reference tree with little incongruence at almost all HGT frequency levels (nRF ≤ 0.
Protein homology The reference data sets of protein family members sharing a high (≥ 40%) and low (< 40%) sequence identity were constructed based on two sections of the SCOPe database v. 2. 07  [68] , namely, ASTRAL95 and AS-TRAL40 v. 2. 07  [86] , respectively. The SCOPe database provides a structural classification of proteins at four levels: classes (proteins with similar secondary structure composition, but different sequences and overall tertiary structures), folds (protein domains of similar topology and structure without detectable sequence similarity), superfamilies (proteins with similar structures and weak sequence similarity), and families (proteins with readily detectable sequence similarity). According to previous studies  [5, 8] , the ASTRAL data sets were subsequently trimmed to exclude sequences with unknown amino acids and families with fewer than 5 proteins and included only the four major classes (i.
e. , α, β, α/β, and α + β). To minimize the requirements for AF method submission related to performing all-versus-all sequence comparisons and uploading the output to the AFproject server, we further reduced the data sets by randomly selecting only two protein members in each family. As ASTRAL95 also contains protein family members sharing a sequence identity lower than 40%, the Needleman-Wunsch alignment was performed (using needle software in the EMBOSS package  [87] ) to select proteins with a sequence identity ≥ 40% to acquire a reference data set of proteins with high sequence identity.
The tool includes 25 k-mer-based measures (e. g. , Euclidean, Minkowski, Jaccard, and Hamming), eight information-theoretic measures (e. g. , Lempel-Ziv complexity and normalized compression distance), three graph-based measures, and two hybrid measures (e. g. , Kullback-Leibler divergence and W-metric). alfpy is also available as a web application and Python package. In this study, the results based on 14 dissimilarity measures are evaluated. ALFRED-G  [45]  uses an efficient algorithm to calculate the length of maximal k-mismatch common substrings between two sequences. Specifically, to measure the degree of dissimilarity between two nucleic acid or protein sequences, the program calculates the length of maximal word pairs-one word from each of the sequences-with up to k mismatches. andi  [24]  estimates phylogenetic distances between genomes of closely related species by identifying pairs of maximal unique word matches a certain distance from each other and on the same diagonal in the comparison matrix of two sequences.
Such word matches can be efficiently found using enhanced suffix arrays. The tool then uses these gap-free alignments to estimate the number of substitutions per position. CAFE  [36]  is a package for efficient calculation of 28 AF dissimilarity measures, including 10 conventional measures based on k-mer counts, such as Chebyshev, Euclidean, Manhattan, uncentered correlation distance, and Jensen-Shannon divergence. It also offers 15 measures based on the presence/absence of k-mers, such as Jaccard and Hamming distances. Most importantly, it provides a fast calculation of background-adjusted dissimilarity measures including CVTree, d2star, and d2shepp. CAFE allows for both assembled genome sequences and unassembled next-generation sequencing shotgun reads as inputs. However, it does not deal with amino acid sequences.
EP-sim estimates a phylogenetic distance, similar to D 2 , by summing the entropic profile scores over all positions, or similar to D Ã 2 , with the sum of normalized entropic profile scores. FFP  [35, 39]  estimates the distances among nucleotide or amino acid sequences. The tool calculates the count of each k-mer and then divides the count by the total count of all k-mers to normalize the counts into frequencies of a given sequence. This process leads to the conversion of each sequence into its feature frequency profile (FFP). The pairwise distance between two sequences is then calculated by the Jensen-Shannon divergence between their respective FFPs. FSWM  [26]  estimates the phylogenetic distance between two DNA sequences. The program first defines a fixed binary pattern P of length l representing "match positions" and "don't care positions.
" Up to 1,000,000 such multiple SpaMs with a score above some threshold are randomly sampled, and a quartet tree is calculated for each of them with RAxML  [95]. The program Quartet Max-Cut  [96]  is used to calculate a final tree of all input sequences from the obtained quartet trees. phylonium  [49]  estimates phylogenetic distances among closely related genomes. The tool selects one reference from a given set of sequences and finds matching sequence segments of all other sequences against this reference. These long and unique matching segments (anchors) are calculated using an enhanced suffix array. Two equidistant anchors constitute homologous region, in which SNPs are counted. With the analysis of SNPs, phylonium estimates the evolutionary distances between the sequences. RTD-Phylogeny  [51]  computes phylogenetic distances among nucleotide or protein sequences based on the time required for the reappearance of k-mers.
Slope-SpaM  [97]  estimates the phylogenetic distance between two DNA sequences by calculating the number N k of k-mer matches for a range of values of k. The distance between the sequences can then be accurately estimated from the slope of a certain function that depends on N k. Instead of exact word matches, the program can also use SpaMs w. r. t. a predefined binary pattern of "match positions" and "don't care positions. " spaced  [41] [42] [43]  is similar to previous methods that compare the k-mer composition of DNA or protein sequences. However, the program uses the so-called spaced words instead of k-mers. For a given binary pattern P of length l representing "match positions" and "don't care positions," a spaced word w. r. t. P is a word of length l with nucleotide or amino acid symbols at the "match positions" and "wildcard characters" at the "do not care positions.
" The advantage of using spaced words instead of exact k-mers is that the obtained results are statistically more stable. This idea has been previously proposed for database searching  [98, 99]. The original version of Spaced  [41]  used the Euclidean or Jensen-Shannon  [100]  distance to compare the spacedword composition of genomic sequences. By default, the program now uses a distance measure introduced by Morgenstern et al. [43]  that estimates the number of substitutions per sequence position. Underlying Approach  [47]  estimates phylogenetic distances between whole genomes using matching statistics of common words between two sequences. The matching statistics are derived from a small set of independent subwords with variable lengths (termed irredundant common subwords). The dissimilarity between sequences is calculated based on the length of the longest common subwords, such that each region of genomes contributes only once, thus avoiding counting shared subwords multiple times (i.
Evaluation of structural and evolutionary relationships among proteins To test the capacity of AF distance measures to recognize SCOPe relationships (i. e. , family, superfamily, fold, and class), we used a benchmarking protocol from previous studies  [5, 8]. Accordingly, the benchmarking procedure takes the distances between all sequence pairs present in the data set file. The distances between all protein pairs are subsequently sorted from minimum to maximum (i. e. , from the maximum to minimum similarity). The comparative test procedure is based on a binary classification of each protein pair, where 1 corresponds to the two proteins sharing the same group in the SCOPe database and 0 corresponds to other outcomes. The group can be defined at one of the four different levels of the database (family, superfamily, fold, and class), exploring the hierarchical organization of the proteins in that structure.