dataesr/scientific-paragraphs-categorization huggingface dataset:
Databases for the genome For many years, the SubtiList database was used by most investigators as the reference database for the B. subtilis 168 sequence  (Moszer et al. , 2002). It was maintained at the Institut Pasteur until year 2009, when its support was discontinued. In parallel, a mirror with significant modifications  (Fang et al. , 2005)  was established at the HKU-Pasteur Research Centre Ltd where it was supported by a grant of the Hong Kong government's Innovation and Technology Commission (Biosupport) until 2010. Lack of support from the Institut Pasteur resulted in obsolescence and the Beijing Genome Institute in Shenzhen took over the baton until 2016 via the Microme Genochore microbial support. This resource, MicroSys, which had proposed a database available on tablets (Fig. 1 ), has since been discontinued without prior notice.
The MicroScope/MaGe platform Annotation of genome sequences must be imbedded in knowledge generated for as many sources of information as possible. M edigue and co-workers designed an annotation platform, MicroScope/MaGe, meant to make the most of the diverse annotations associated to bacterial genomes by imbedding in the same platform both sequence and annotation data, together with analytical methods designed to explore the data  (Medigue et al. , 2017; Vallenet et al. , 2017). To obtain a cutting-edge annotation of the genome of B. subtilis 168, we used the MicroScope platform to collect information from as many sources as possible, based on literature and extant databases. This new annotation is now available at the INSDC and at the MicroScope Website (https://www. genoscope. cns. fr/agc/microscope).
Since the last update in databanks (January 2013), the annotation of about 96% of the protein-coding genes (4097 among 4257 CDS) was revised. Furthermore, additional bibliographical references (2097 new publications among a total of 5754) were added and cover approximately 79% of the protein-coding genes. The annotated sequence is available with the present work as Table  S2. Tentative approaches towards a unified nomenclature A major challenge facing genome sequence databases is gene nomenclature. Indeed, the first gene names were proposed based on phenotypes [for example in Escherichia coli, related to antibiotic resistance, e. g. 'ampC' for ampicillin resistance  (Normark and Burman, 1977) , or shape, e. g. 'fts' genes, yielding filamentation when mutated  (Ricard and Hirota, 1973) ]. Subsequently, names were chosen following identification of a biological function, often an enzyme function.
In parallel, authors liked to propose fancy names to genes (this is well illustrated in the current gene nomenclature of Drosophila melanogaster). Also, genes corresponding to orthologues in different species were often named differently, depending on the inclination of the authors of the first works. Naturally, of course, many enzymes are promiscuous so that the first catalytic activity discovered in one organism could differ from that in another organism, especially when the first identifications were obtained in vitro. This is obviously problematical as browsing knowledge databases using gene names could help investigators to focus rapidly on their genes of interest. It is therefore recommended that the gene names of orthologues should be conserved throughout the tree of life. However, many gene products have more than one function, mediated by interactions with a variety of partners that often differ in different organisms.
All this means that a fully consistent nomenclature is unlikely to be reached any time soon. Nevertheless, because having consistent names for common functions each time, a gene that has been correctly annotated would help users immensely and we have tried as much as possible to give identical names to orthologues of E. coli and B. subtilis. This was previously attempted in the GenoChore databases  (Fang et al. , 2005) , where, for example, the ribosomal protein S12 gene would be named rpsL in all genomes, rather than use its original access tag (e. g. in P. putida, PP0449). To name genes, we used an extended version of Demerec's nomenclature system  (Demerec et al. , 1968). A gene name is italicized and begins with three low case letters, followed by one or more capitals. In the best systems, there is no numeral in a wild type gene name: numerals are reserved to identifying mutant genes [e.
When renaming, we kept the last capital letter of the 'y' name in the final name when this did not create duplicated names (e. g. skiX replaces yknX). We finally noticed that investigators often explore the literature and databases using a gene   , 11, 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 name. It is therefore highly inconvenient when a gene name corresponds to a common English word (e. g. hinT or thiS). We therefore tried as far as possible to avoid such common spelling when creating new names, and we recommend, for future annotations, to try and replace those unwieldy names by new ones (a general possibility is to use the extended Demerec's rule, adding a second letter after the final capitalized letter of the gene name). Because many genes have a variety of names in the literature, synonyms were included in the gene data file, which is indexed using a unique accession number [e.
Bacillus subtilis in 2017 Experimentally-rooted database curation is essentially manual, and therefore considerably time-consuming. Here, data from the literature were systematically collected by exploring PubMed, PubMed Central and SubtiWiki, and browsing the Internet with 'y' gene names as keywords as sources of information. In addition, we used a functional analysis approach of the type that is fruitful when trying to construct relevant chassis in synthetic biology [SynBio  (Harwood et al. , 2013) ]. This entails considering cells as computers making computers, with all the relevant prerequisites  (Danchin, 2009).
Strain 168 among other B. subtilis strains Sequencing genomes has become much simpler and cheaper since the date of publication of the sequence of strain 168. While it remained the only B. subtilis genome available for many years, there has been a significant effort to sequence other strains in the last decade. This resulted in more than 45 completely sequenced genomes by the end of 2016, and around one hundred highquality draft genomes are deposited in the NCBI RefSeq database (ftp://ftp. ncbi. nih. gov/genomes/refseq/bacteria/ Bacillus_subtilis/). The number of complete genomes is likely to increase further in the near future thanks to long-read sequencing technologies. The availability of genomes for many strains opens up new avenues for research. Most importantly, it opens up the possibility of using population genomics data to make inferences about the function of genes in the genome and their ecological role.
For instance, GLINTERNET was shown to perform comparably to the R package hierNet, with some advantages in computing time  [8]. GLINTER-NET performed better than boosting in terms of false discovery probability  [8]. Under simulations, the DSA algorithm seemed to be competitive with Logic Regression, which only handles binary variables  [14]. Sun et al. [10]  compared Bayesian Model Averaging, DSA, LASSO, Partial Least-Squares Regression and Supervised Principal Component Analysis under simulations with up to 20 variables. They also considered a two-step modelling strategy in which variables were screened for inclusion in the second step using Classification and Regression Trees (CART). There was no uniform dominance of one method across all examined simulation scenarios. However, to the best of our knowledge, the methods considered in our study have never been systematically compared under the characteristics of the exposome context, i.
Simulating data The exposome data were simulated based on an existing dataset with the observation of 237 exposures on 655 individuals from the INMA (INfancia y Medio Ambiente) mother-child cohort  [15]. In particular, we computed the correlation matrix of the exposures,. In such matrix, 81% of absolute pairwise correlations were lower than 0. 2 while 64% were lower than 0. 1. The median absolute value was 0. 06 and the absolute percentiles 2. 5 th , 25 th , 75 th and 97. 5 th were 0. 003, 0. 03, 0. 15 and 0. 61, respectively. 78% of exposures were correlated at absolute level higher than 0. 6 with at least one other exposure (see Additional file 1: Section A). Then, this matrix was used to simulate the exposures E using a multivariate normal distribution with mean 0, E ∼ N(0, ). ( The number of participants was set to N = 1200.
5 th and 97. 5 th as a result of fitting the model to 100 simulated datasets. b The median of the mean pairwise correlation between the true predictors was 0. 12 (percentiles 2. 5 th and 97. 5 th : (0. 05, 0. 25)) for "Mixed", and 0. 78 (percentiles 2. 5 th and 97. 5 th : (0. 72, 0. 87)) for "High". The median of the mean pairwise correlation between the true predictors and the other exposures was 0. 13 (percentiles 2. 5 th and 97. 5 th : (0. 09, 0. 16)) for "Mixed", and 0. 18 (percentiles 2. 5 th and 97. 5 th : (0. 17, 0. 19)) for "High". c In all scenarios, β 0 = β 1 = • • • = β 5 = 1 assessed (sensitivity was the proportion of statistically significant terms over repeated simulations, see details on sensitivity tuning in an enlarged version of Table  1  in Additional file 1: Section C).
Statistical methods We considered several statistical methods previously recommended in the literature to detect interactions. Specifically, we focused on methods that implemented a variable selection approach to detect statistical interactions in the form of a product of two variables in a linear model. An exception was the inclusion of Boosted Regression Trees (BRT), which does not have an explicit regression equation. Besides, we restricted the simulation to methods that were or could easily be implemented in the R  [16]  software. The R code to reproduce this study is shown in Additional file 2.
DSA algorithm The Deletion/Substitution/Addition (DSA) algorithm  [9]  is an iterative process that starts with an empty model and uses deletion (removing a variable from the model), substitution (replacing a variable in the model by another not in the model) or addition (adding a variable in the model) moves to find the final model (further details are shown in Additional file 1: Section D). The final model is selected by minimizing the residual mean squared error (RMSE) using 5-fold cross-validation. We fitted two versions of DSA, one that only searches for main effects (DSA 1 ) and another that also searches for 2-way interactions (DSA 2 ). The way the DSA software is implemented, the version that searches for 2-way interactions also searches for quadratic terms. In all cases, we set the maximum model size to 10, which was never reached in the simulations.
Sun 3-step method (Sun3step) A 3-step method similar to that suggested by Sun et al. [10]  was implemented (Sun3step). In the first step, we performed a correlation analysis to assess the collinearity within each group of exposures. In our data, there were 15 groups of exposures containing from 1 to 51 exposures each (see Additional file 1: Section A). When several exposures in the same group were highly correlated (Pearson correlation coefficient above 0. 60), only the one with the smallest p-value in the single-exposure regression model was retained. In the second step, the selected exposures entered a Classification And Regression Tree (CART), which was subsequently pruned, with the criteria of minimizing the cross-validated error. The R package rpart was used. The variables selected in the construction of the regression tree entered the third step, which consisted of applying the DSA algorithm (allowing for 2-way interactions and quadratic terms), hence providing the final model.
Least absolute shrinkage and selection operator (LASSO) LASSO is a method of estimation in linear models which penalizes large model sizes. Specifically, the method minimizes the residual sum of squares penalized by the sum of the absolute value of the regression coefficients, which tends to produce some coefficients being exactly zero and hence providing a variable selection procedure  [20]. The LASSO method is not specifically designed to find interactions, but it was used to compare its performance with GLINTERNET, an extension of the LASSO method designed to look for interactions described below. Thus, no interaction terms were allowed in the LASSO method. We used 3-fold cross-validation for the sake of comparability with GLINTERNET. The R package glmnet was used.
Measures of performance To assess the performance of each method in each scenario, we estimated, among the simulated datasets, the mean value of the measures defined below. Such measures, analogous to those used by Agier et al. [13] , are based on the comparison of the fitted models when using the assessed method and when using the model that already generated the data. Note that some measures are defined according to the terms in the model, while others are based on the variables involved in the model. For example, a model including X 1 , X 2 , X 2  1 and X 1 X 2 contains two variables and four terms. • Relative model size (RMS): ratio of the fitted model size to the true model size. The model size is the number of terms in the model, excluding the intercept. • Relative number of variables (RNV): ratio of the number of variables involved in the fitted model to the number of true predictors (i.
e. , variables involved in the true model). • Relative out-of-sample R 2 (R 2 rel ): ratio between the out-of-sample R 2 of the fitted model (numerator) and the out-of-sample R 2 of the model that includes only the terms used to generate the data (denominator), using a simulated test dataset (N = 10000). • Sensitivity (Sens): Proportion of terms in the true model correctly detected by the fitted model. • Alternative sensitivity (AltSens)  [13] : The average highest correlation between a true predictor and any variable involved in the fitted model, AltSens = 1 n A i∈A max j∈B corr(X i , X j ) , where A is the subset of true predictors, n A is the number of true predictors, and B is the subset of variables in the fitted model. If all the true predictors were detected, both Sens and AltSens would take the value 1.
The inclusion of these two techniques in our simulation setting was not computationally feasible. However, the use of either of those techniques is not computationally problematic for the analysis of a single dataset of a similar size as the ones used here, so they remain as two attractive techniques to be considered in practice. These two methods can also capture complex non-linear associations. Our simulations did not consider non-linear effects, but some of the techniques used, such as DSA, Sun3step and BRT would be able to capture them (Table  2 ). The present study considered a limited number of scenarios. In particular, we only considered linear regression models and we did not consider issues such as non-linear main effects or the effect of confounders that are not in the set of exposures of interest.
Funding The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements no 308333 -the  HELIX  project, and  308610  -the  EXPOsOMICS  project. JRG has also been supported by the  Spanish Ministry of Economy and Competitiveness  ( MTM2015-68140-R ).
, 2016). Automatic drawing is thus needed to fit current growing research activity in the field. Current trend in web development is oriented toward modular architectures allowing assembling specialized web components. In this context, we designed MetExploreViz as an easy to install open source web component that can quickly and easily be embedded in any website. Only few lines of code are required to add MetExploreViz to any web page (see installation section at http://metexplore. toulouse. inra. fr/ metexploreViz/doc/).
Metabolic network visualization features In order to make MetExploreViz a versatile solution, it is possible to import networks in JSON format. These networks can for instance be retrieved by using MetExplore web services  (Cottret et al. , 2010). Drawing is achieved using an animated version of force directed layout implemented in D3. js library upon which MetExploreViz is built. It is also possible to manually edit the representation and then save the drawing in JSON format for further reuse. An issue when drawing metabolic networks is the presence of highly connected small molecules like water or CO 2 which are not necessarily of biochemical interest for data interpretation. To facilitate the visual inspection, these side compounds can be duplicated or deleted in MetExploreViz (Fig. 1b  and c ).
Each element of a metabolic network can be involved in one or more metabolic pathways and be present in one or several cellular compartments. MetExploreViz offers an original feature to visualize this information using convex hulls (Fig. 1d ). Since the force directed algorithm may lead to overlapping hulls, we implemented an algorithm to take these sub-structures into account during the drawing. MetExploreViz purpose is the visualization of omics data on reactions and metabolites. It is thus possible to import data and map them in a discrete or continuous way using predefined or selected colors (see Fig. 1e  and f ). V C The Author 2017. Published by Oxford University Press. In order to focus on a core sub-network (see Fig. 1g ), MetExploreViz implements a sub-network extraction method computing the union of lightest paths between each pairs of mapped (or selected) nodes in the network  (Frainay and Jourdan, 2017).
Lightest paths aim at avoiding side compounds by weighting nodes according to their degree defined as the number of in and out connections. Classical image exports are available and completed with an SVG export well suited for online representations. Since MetExploreViz focuses on metabolic networks, it does not provide all the features available in generic network analysis software suites like Cytoscape or Gephi. To allow compatibility with these tools, it is possible in MetExploreViz to export networks in gml and dot formats. MetExploreViz is provided as an open source web component that only requires few lines of code to be embedded in a web site.
Discussion and application MetExploreViz and metabolic pathways oriented representations like KEGG are complementary visualization solutions. MetExploreViz is focused on integrative and flexible network import and representation, while pathway oriented tools are guiding data interpretation toward a priori defined metabolic functions. MetExploreViz is embedded in MetExplore web server  (Cottret et al. , 2010). But its component oriented architecture allows it to be used in various web servers. For instance, it is available in MetaboLights data repository for metabolomics data sets  (Kale et al. , 2016). We are also currently working on the integration of MetExploreViz in galaxy pipelines  (Giacomoni et al. , 2015). MetExploreViz provides complementary features to Escher  (King et al. , 2015)  like sub-network extraction and is not focused on network curation, making it a lightweight component.
This type of approach has been pursued in a number of recent publications and there are a growing number of software packages available online, most notably MDSINE  (Bucci et al. 2016 ; also see Eq. 1 below and Figure  2 ). A detailed comparison of these packages (by performance, accessibility, versatility, etc. ) goes beyond the scope of this review, but would be a welcome contribution to this growing body of work. Here, we briefly discuss some of the major issues and proposed solutions. The MDSINE package is a suite of methods. At each time point, they infer the mean of a probabilistic model of read counts to the abundance data, connecting means across time points using splined functions of time. The unbound growth, interaction, and disturbance parameters of a gLV model are inferred on this splined representation of the data using gradient matching and also with a regularization to overcome the undersampling.
With datasets sampled at a high enough temporal resolution, the possibility to classify their trajectories arises. Once sufficiently long time-series can be measured, comparisons of cross correlations of the abundances from microbiome communities across body sites could provide signatures of leaders and laggards in the whole body microbiome dynamics. Finally, we mention that network reconstruction competitions based on benchmark datasets can be a fruitful way to attract highperforming algorithms (e. g. ,  Orlandi et al. 2014).
(Zhou et al. 201315)ework of networks and community dynamics is naturally suited for this pursuit. It can help formulate hypotheses in mathematical terms, explain high-dimensional features of large, complex datasets, and incorporate data obtained from various techniques. Nevertheless, given that network inference and analysis methods are still under development, it is important to be mindful of what are the underlying assumptions and that they best represent our most up-to-date ecological understanding. There is a risk of over-simplifying ecological concepts, which can then lead to incorrectly inferred networks or erroneous predictions. Indeed, not all past examples of research communities embracing the conceptually attractive autonomously regulated complex network have turned out well(Eichmann 2008).
In most of previous works, proxy constraints are defined by some rough approximations (box constraints, elliptic bounds, etc) leading to a certain conservatism; or it is simply ignored inside the reduced problem formulation. Footstep limits have been encoded by hyper-plane based on a dataset of robot success and failure inside a dynamic simulator  [23]. Similar constraints can be obtained by training a training a neural network  [21]. In  [32] , similar bounds are obtained by trial and errors based on stability analysis of the whole-body system. Constraining only the CoM position is not sufficient. It is also necessary to consider the constraints related to the contact forces  [29]  which must lie inside so-called friction cones, the capacity of robot to generate such value of angular momentum, etc.
B. Kernel density estimation by CoM sampling There is in general no closed form to encode p i (c) for a particular legged robot. Nevertheless, this conditional probability can be easily approximated by extensive sampling of the CoM position expressed in the end-effector frames. Sampling N samples of the CoM position expressed in the frame M i does not raise particular difficulties. For each sample, a configuration q a of the actuated joints is randomly sampled and the CoM position is computed (expressed in placement frame) by forward kinematics. The sample is rejected if joint limits or self collision are violated. The probability distribution can be approximation from the cloud of CoM points by the kernel density estimators (KDE)  [22]. KDE are in some sense the analogues of histograms but for continuous domains: for each point of the data set, it associates one kernel centered on the point and all kernels share the same parameters.
ACKNOWLEDGMENTS We warmly thank the  Simulation and Optimization of Heidelberg University  for providing the optimal control framework MUSCOD-II. This work is supported by the  European Research Council (ERC)  through the  Actanthrope project (ERC grant  agreement  340050 ), the  RoboCom++ FLAG-ERA   JTC 2016  proposal and the  French National Research Agency  through the  Loco3D  project ( ANR  grant agreement  ANR-16-CE33-0003 ).