dataesr/scientific-paragraphs-categorization huggingface dataset:
In our initial analysis, the score function that selected a story that best fitted the targeted known metabolic pathway of the glutathione biosynthesis was the enzyme-activation-first scoring scheme. For this reason, we used it also to analyze the larger dataset produced in this second analysis, obtaining 20 maximal stories presented as an anthology in Figure  6. Considering all the metabolites that were measured in the experiment as black nodes in our method allows us to have a more global view of the organism's response to cadmium exposure. This enables to explore whether the other identified paths, apart from the ones involved in the glutathione pathway, are part of this response or simply side effects of the sulfur redirection, as further discussed in the next section.
Analytic tools All the compressed networks, stories and anthologies presented in this section were computed using our algorithm called Touche´  ( Borassi et al. , 2013). For visualization and analytical purposes, we used Cytoscape  (Shannon et al. , 2003) , which is a software for network visualization, enriched with a plug-in we developed to enable loading, visualizing and inspecting the three aforementioned objects (compressed networks, stories, anthologies) inside Cytoscape. The plug-in applies the given visual properties corresponding to a metabolomics experiment (e. g. colour and diameter of the nodes, the thickness of an arc corresponding to the frequency of the arc in the stories composing the anthology) and allows a zoom-in in the dashed arcs, exhibiting the paths connecting the two nodes.
On the other hand, one may easily highlight in a whole metabolic pathway map those metabolites whose concentration were detected as having changed using the YeastCyc database. However, because the pathways are presented as disconnected, it is not possible to follow a path that traverses several pathways (see Figure in Supplementary Material S5). To demonstrate the utility of our approach, we used data from  Madalinski et al. (2008)  in which the authors monitor changes in metabolite concentration as a response of the yeast S. cerevisiae to cadmium, a toxic chemical. The aim of this study is to analyze the global response of an organism to a stress. Using only the metabolomics experiment data to choose the discriminating compounds and to rank the stories, we are able to obtain stories that correspond well to the current biological understanding of the system under study, as well as to propose new alternatives that could serve as a basis for further experimental validations.
Cysteine is included in the dashed arc linking cystathionine and L-gamma-glutamyl-cysteine that is its expected place based on the biosynthetic pathway of glutathione. Homocysteine is represented as a white node in all figures. Because it is at a crossroads between three black nodes (cystathione, methionine and O-acetylhomoserine), it could not be compressed. Interestingly, the compounds involved in the methyl cycle, which is a sulfur salvage pathway (see Fig. 1 ), were not recovered in the highest score stories found in our first analysis. The reason is that the lightest path found between methionine and cystathione in that analysis passed through the reaction catalyzed by the enzyme homocysteine S-methyltransferase (Mht1), which is assigned as reversible in the YeastCyc database  (Caspi et al.
ACKNOWLEDGMENTS The authors would like to thank  S. Klamt  and  J. J. Heijnen  for fruitful discussions. Funding:  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. (247073)10; the  French  project ( ANR MIRI BLAN08-1335497 ); and the  ANR funded LabEx ECOFECT. It was partially supported by the  Plateforme Bioinformatique de Toulouse, ANR-BBSRC Systryp , the  CIRIC-INRIA Chile line Natural Resources , the  NWO-CLS   MEMESA  project and the 'DISCO'  PRIN National Research  Project.
Beam waist xy w and w z were estimated from the two-photon excitation point spread function obtained from 0. 17 μm diameter fluorescent micro beads (Molecular Probes PS-Speck Microscope Point Source Kit (P7220)). Lateral and axial FWHM were found to be FWHM x,y = 0. 4 μm and FWHM z = 2 μm at 940 nm. w xy = 0. 48 μm and w z = 2. 4 μm were deduced from these values using w xy,z = FWHM xy,z / ln2  [2, 12]. Open source ImageJ (http://rsb. info. nih. gov/ij/) and UCSF Chimera (http://www. cgl. ucsf. edu/chimera/) softwares were used for respectively SHG image analysis and 3-D reconstruction.
A brief summary is provided for each enzyme and the corresponding metabolic reactions involved in the pathway. For each enzyme we endeavored to capture the following information where available: corresponding gene name approved by HUGO Gene Nomenclature Committee  [12] , alternative names, enzymological activities according to the Enzyme Nomenclature Committee of the IUBMB  [13] , enzyme function description, subunit structure, subcellular location and related disorders. We have included a list of UniProt IDs for the proteins captured in the model (Table  1 ) and a list of metabolite names (common and systematic) as used in the LipidMaps database  [8]  (Table  2 ). Common names are used on the map where available. The pathway that we present here is described using the Systems Biology Graphical Notation (SBGN)  [14] , a community driven consensus graphical schema for capturing the molecular details of pathway systems.
It should be possible to open these files in any software designed to comply with the SBGN-ML standard, including but not limited to VANTED and Cytoscape  [17, 18]. For the purpose of this review, we compiled and tested the files using the VANTED software tool  [17]. Here we shall outline how the files can be opened and accessed using the VANTED and CYTOSCAPE  [18]  software tools. 2. 1. Accessing supplementary SBGN-ML files using the VANTED software tool 2. 1. 1 Download the files with '. sbgn' file extension from the supplementary material. 2. 1. 2 Download and install VANTED from http://vanted. ipkgatersleben. de/. 2. 1. 3 Open VANTED and you will be greeted by a screen divided into two regions: an empty area on the left for diagrams and a column on the right containing settings under various tabs.
In the case of reflection from the bottom of the box (Z = 0) the virus undergoes detailed stochastic dynamics as described by the Langevin Dynamics algorithm. The binding kinetics may depend on when the boundary condition is applied, since the probability of binding and the bond spring forces depend on the position of the virus with respect to the receptor (Eq's. 13, 14). The boundary conditions are hence applied right after the translational position of the virus is updated to time t+dt, but before the update of the angular position of the bonds. Simulation details. The computational program was implemented using the C programming language  [44]  and the code is freely available under: http://www. neuro. nano-optics. ethz. ch/ publications/index. At time t = 0 the virus particle is placed at a distance equal to the unstressed bond length l 0 from the membrane surface in order to avoid sampling the BD regime and to speed up the simulation.
HE was the recipient of a  FEBS Long-Term Fellowship  and a Holcim Fellowship and is supported by the  NCCR Neural Plasticity and Repair. AO was supported by the  Israeli Science Foundation  grant # 291/12. Fluorescence correlation spectroscopy was performed at the  Bordeaux Imaging Center (BIC). OMS and IFS acknowledge funding from the  Swiss Initiative in Systems Biology (SystemsX. ch) under grant WingX, evaluated by the  Swiss National Science Foundation (SNF). AH was supported by the  Swiss National Research Fund , the  Swiss SystemsX. chinitiative, grant  LipidX-2008/011LipidX , the  National Institutes of Health , US, by An  ERC  advanced grant, and by  ETH Zurich. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
In order to provide a method for precise identification of insulin sensitivity from clinical Oral Glucose Tolerance Test (OGTT) observations, a relatively simple mathematical model (Simple Interdependent glucose/insulin MOdel SIMO) for the OGTT, which coherently incorporates commonly accepted physiological assumptions (incretin effect and saturating glucose-driven insulin secretion) has been developed. OGTT data from 78 patients in five different glucose tolerance groups were analyzed: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), IFG+IGT, and Type 2 Diabetes Mellitus (T2DM). A comparison with the 2011 Salinari (COntinuos GI tract MOdel, COMO) and the 2002 Dalla Man (Dalla Man MOdel, DMMO) models was made with particular attention to insulin sensitivity indices IS COMO , IS DMMO and k xgi (the insulin sensitivity index for SIMO).
Interpolating noisy observations in order to represent the expected values of a state variable is incorrect and opens the door to potentially very misleading parameter estimation results: see elsewhere  [17, 18]  a complete discussion of this issue in the present glucose-modeling context. For the purpose of the present work, computations for the Dalla Man and Salinari models were performed in Matlab and in R, both interfacing with C routines. The Salinari model was implemented in the Matlab environment and the ''interp1'' function was used (piecewise linear interpolation). Predictions for the Dalla Man Model were obtained in C++ interfacing with the R environment, and as before, a linear interpolation was used. We present here a new model (Simple Interdependent glucose/ insulin MOdel SIMO) where, on one hand, insulin secretion from beta-cells after glucose intake is represented by a mathematical formulation of the theoretical predicted insulin concentrations rather than by the noisy observations themselves, and where, on the other hand, the glucose rate of appearance is derived by absorption of glucose along the gastrointestinal tract, represented by a sequence of three compartments, as a simplification of the continuous one-dimensional process presented in Salinari et.
al. [13]. The model can be rapidly implemented in standard computational software packages (e. g. Matlab, R, Scilab, Octave, etc. ) and its computation is freely available via internet at the page http:// biomat1. iasi. cnr. it/gemini/ogtt/. In the present work we have applied this model to data from 78 patients from five different glucose tolerance groups: Normal Glucose Tolerance (NGT), IGT, IFG, IFG+IGT and T2DM, showing that model parameters, identified on each subject, reflect accurately and informatively the underlying physiological status in the different conditions examined. We also compared the new model's performance with standard empirical and model-based indices of insulin sensitivity.
Validation of the Model The predictive properties of the SIMO model were assessed by performing a visual predictive check (VPC) for each of the 5 groups. For each patient 200 simulations were performed with the model and Panels A and B of Figure  2  report the 90% prediction interval (shaded area), the 25 th , 50 th and 75 th percentile (dashed lines) as well as all the observed data from patients in the considered group. In interpreting the figures, it must be noticed that scale varies after NGT for glycemia and after IFG for insulinemia. The performance of the model in predicting glycemia and insulinemia observations and the validity of the assumptions made in relation to the error variance were evaluated by means of the diagnostic plots of weighted residuals versus time, weighted residuals versus predicted concentrations and observed concentrations versus predicted concentrations.
Conversely, when ANOVA was used to test differences among groups for the other two model-derived insulin sensitivity indices, no significance was obtained. Lack of significant differences for IS COMO and IS DMMO is in fact attributable to their high variability: both very large values (of the order of 10 21 for IS COMO and of 10 22 for IS DMMO ), and very small values (of the order of 10 210 , the inferior limit of optimization, for IS COMO and 10 26 for IS DMMO ) were obtained from data fitting. It is to be noticed that a lack in the ability to differentiate groups is present also with IS BREDA , in spite of its overall strong correlation with the other insulin sensitivity indices. Despite insulin sensitivity estimates way out of the acceptable range, however, both COMO and DMMO models seemed to perform well in terms of adapting predicted levels to observed plasma glucose concentrations.
This is in effect a classical finding for over-parameterized models, where different parameter combinations may yield essentially indistinguishable data fits, making parameter identification imprecise. The bad performance of the COMO model could moreover be attributed to the lack of data related to GLP-1 measurements, making the problem of overparameterization even worse. Figure  3  summarizes this point very well: despite an apparently very good data fit, IS COMO was estimated at 10 210 for an IFG subject and IS DMMO was estimated at 3. 26610 23 (a very large value) for a T2DM subject; for these two subjects, SIMO (Figure  5  panels A2-A3 and B2-B3) estimated insulin sensitivity at respectively 3. 24610 25 and 4. 58610 25 , well within physiologically plausible limits. It is clear that, in order to overcome overparameterization for COMO and DMMO, and hence to reduce parameter variability, more observations could be used in the fitting procedure: the standard OGTT performed in clinical practice, however, yields glycemia and insulinemia observations over only seven time points.
e. the numerical values it takes under the same experimental conditions should be concentrated around the expected theoretical value for those conditions. For example, repeated testing of the same subject should give rise to index values close to each other. 5) The index should be robust, it should not vary widely upon modest variations of the data it is computed from. This criterion is somehow connected with the previous one: if an index is robust to small variations of the data, its realizations will be concentrated around the expected value upon repeated testing, hence it will be precise. An index is also robust when its computable value is relatively insensitive to missing data. In other words, an index, which can be computed to approximately the same value, notwithstanding some missing observations in the relevant dataset, is robust.
It is to be noted that the requirements of robustness and expressivity are somehow in competition: a maximally robust index could be, in fact, a constant, completely insensitive to data variations, and for this reason totally inexpressive. 6) Ideally, the index should be accessible: it should be simple to compute or a computation system for it should be readily available, so that it is not cumbersome to use and may be employed by a wide class of users at low cost (including the cost of the data necessary for its computation). Applying these concepts to the set of indices studied in the present-work, we can see that all considered indices are sound, in the sense that they all exhibits positive correlations with one another. One exception is IS COMO, whose main shortcoming, however, does not reside in the lack of soundness but in the lack of precision, from which the lack of correlation derives: on one hand the large number of parameters to be estimated with respect to the small number of observations, and on the other hand the need to fix the dynamics of GLP-1, given the lack of observed plasma GLP-1 concentrations, are the cause of its poor performance in terms of insulin sensitivity assessment for the present data set.
The problem of over-parameterization, hence of lack of precision, is also evident with the IS DMMO index, which, while being sound, is for this reason not expressive, since it is not able to discriminate at all experimental subjects coming from different pathophysiologic conditions. Both IS COMO and IS DMMO are model-derived indices, and they are as physiologically meaningful as the models from which they derive. For these two indices, the problem of overparameterization also determines a lack of robustness: small variations in data or, even worse, an incomplete set of available observations determine wide swings in the numerical values these indices take (from 1. E-10 to 1. E-1 for IS COMO and from 1. E-6 to 4. E-2 for IS DMMO ), hence neither IS COMO nor IS DMM are robust indices. While IS DMMO maintains the property of being rather accessible, as it derives from a parameter estimation procedure on data from a standard OGTT, IS COMO is less accessible, since it requires GLP-1 measurements, which are not collected in standard clinical practice.
It is worth commenting on the IS NAIF index, as an exercise in index-building. This index seems sound, in fact it correlates positively with the other indices. However, its formulation does not include any information related to insulin: there is therefore a likely lack of meaningfulness, and the apparent expressivity (it discriminates all groups from one another) derives from the fact that worsening glucose homestasis, as defined by glycemic levels (baseline and at 2 hours), implies by definition higher plasma glucose concentrations and correspondently lower IS NAIF values. This index is obviously completely insensitive to changes in insulin concentrations, given its independence from it, and therefore its robustness and precision are completely artifactual. In fact, if an index is not meaningful, it is nonsense to consider all the other desirable characteristics as gauged from the performance of the index on a given dataset.
1B  for example). As for AT2G36240 (Fig. 1B ), we showed a single localization in mitochondria for the other five. Among the nine PPR proteins thought to be out of the organelles on the base of the first 100 amino acids, seven ORFs were successfully cloned but no cytosolic localization was confirmed: the whole proteins fused to RFP were systematically addressed to one or both organelles, as exemplified by AT1G06150 and AT1G06580 in Figure  1B. Surprisingly, six of them were localized in both organelles (AT1G06150 in Fig. 1B ). Altogether, 12 PPRs proteins were verified as being localized in both mitochondria and plastids using the full-length protein. Integrative overview of the subcellular localizations of PPR protein family. In order to provide a general overview of the localization of the whole PPR protein family, we aggregated our results concerning one-third of the family, with all available data from published studies and accessible databases (Table  2 ).
We first re-examined the localization prediction of the PPR proteins encoded in the Arabidopsis genome using six available bio-informatics prediction tools: Predotar,  32  TargetP, iPSORT,  38  Loctree,  39  Multiloc,  40  and AtSubP. 41 Despite using distinct algorithms, those tools largely provide similar results and Table  2  gives a single localization prediction aggregating the six software results following a rule emphasized in the caption of Table  2. Overall, 65% and 17% of the PPR proteins are predicted to function in mitochondria and in plastids, respectively. For 18%, the results are unclear either because a majority of the software was unable to provide an organellar prediction or because they provide overmuch diverging organellar predictions. We also added the growing data coming from the proteomics identification of organelle proteins in Arabidopsis mitochondria (SUBA3  5  ) and chloroplast (SUBA3,  5  AT_Chloro,  42  PPDB  43  ), also including localization data obtained from maize chloroplast (PPDB  43  ) and rice mitochondria,  44  according to the recent concept of orthoproteomics.
45 As published,  12  a very good level of orthology observed between the members of PPR families in A. thaliana and Oriza sativa suggests that both their function and their localization of plant proteins were TargetP v1. 01  31  and Predotar v1. 03. 32 Therefore, we used them to identify Arabidopsis genes coding for PPR proteins with ambiguous localization predictions (Table  1 ). TargetP was recently improved with the TargetP v1. 1 version of the software. Among the 458 PPR genes, Predotar predicts that 244 and 92 PPR proteins are addressed, respectively, to mitochondria and plastids, whereas 122 PPRs would not have any organelle localization (Table  2 ). TargetP v1. 1 gives similar results with 232 and 123 PPRs localized to mitochondria and plastids, respectively, and 103 PPRs without organelle localizations (Table  1 ).
Taken together, 166 PPR proteins were predicted not to be addressed to either of the two main plant organelles by at least one of the two software (Predotar v1. 03 and TargetP v1. 01). Among them, 53 PPR proteins were not predicted to be addressed in the organelles by both algorithms. We chose to experimentally investigate the subcellular localization of those 166 PPR proteins as they were good candidates to have atypical functions out of the organelles. Almost all the proteins addressed to organelles contain a targeting peptide in their N terminus extremity, which is cleaved during the transfer through the organelle membranes. 8 A mitochondrial Targeting Peptide (mTP) is typically 40-50 amino acid long,  [33] [34] [35] [36]  whereas a chloroplast Targeting Peptide (cTP) is usually up to 60 amino acid long.
The rules to propose a conclusive prediction were as follows: if four or more software give the same prediction, this prediction is proposed and noted in uppercase; if three software give the same prediction and the three others do not predict any localization, the prediction is proposed and noted in uppercase; if two software give the same prediction and the four others do not predict any localization, the prediction is proposed and noted in lowercase; if three software give the same prediction and another predict a different localization, the main prediction is proposed and noted in lowercase; in the other cases, no prediction is proposed (-). (  4 ) proteomic localizations were gathered from published studies and from organelle proteomic databases as indicated in corresponding references in the last column of the table.
If not, the conclusion is indicated in lowercase. If only predictions are available, the predicted localization is indicated with a preceding "p". (7) Data of ppR embryo defective mutants (eMB) was obtained from SeedGenes database (http://www. seedgenes. org/index. html) and manually curated mutants from published studies. (8) Molecular function based on reverse genetics approaches were obtained from literature, the localization of the molecular function is indicated in brackets. Localization data is indicated as followed. M, mitochondria; c, chloroplasts; N, nucleus; V, vacuole; ct, cytosol; pM, plasma membrane. N/ct, nucleus and cytoplasm; M/c, mitochondria and chloroplasts; lower case, "probably"; "pX", predicted in compartment X (conclusion column). proved subcellular localization in at least one of the two organelles increased significantly with our study.
Materials and Methods Bioinformatic predictions and data collection. Subcellular localization prediction of the PPR proteins were performed using TargetP server (http://www. cbs. dtu. dk/services/TargetP/) (version 1. 01 was used when we initiated this work to select the 166 PPRs and version 1. 1 was used when we built Tables  1  and 2 ), Predotar v1. 03 (http://urgi. versailles. inra. fr/predotar/predotar. html), iPSORT (http://ipsort. hgc. jp/), Loctree (https://www. rostlab. org/owiki/index. php/Loctree), Multiloc (http://abi. inf. uni-tuebingen. de/Services/MultiLoc/), and AtSubP (http:// bioinfo3. noble. org/AtSubP/?dowhat=About) software using default setting. Proteomic data was recovered from published proteomic references and subcellular proteome databases: PPDB (Plant Proteome Database http://ppdb.