dataesr/scientific-paragraphs-categorization huggingface dataset:
This can therefore be an indication to the quality of the image matching and feature detection process in PS for this data set, or the presence of tie points with few rays at very small angles. Based on this information, a filtering of the automatic tie points for the St-Pierre data set could be performed in order to increase the quality of the bundle adjustment. Indeed, by performing this filtering in DBAT, the high correlations disappeared. Another example of metrics which can be derived from DBAT includes the standard deviations for the external orientation parameters. This may be useful in some cases to help users in sorting images which may worsen the results of the bundle adjustment. In the case of the data sets tested, these metrics are shown in histogram form in Figure  4. In the case of S4, a slight increase in rotational standard deviation can be seen for the images numbered around 100 and 120.
The paper has also shown how DBAT was used as a quality control tool for PS. Correlations and exterior orientation standard deviations are only two among other metrics which may be interesting to PS users in order to assess their photogrammetric project. Based on this extra information, that was otherwise minimal in PS, the user may take decisions to perform modifications on the project as it fits the requirements of the project. The use of black-box like software solutions presents a big advantage to many users as they eliminate processing parameters to the bare necessities. This fact, together with developments in photogrammetric and computer vision algorithms, has largely supported the growth of more commercial and user-friendly software. However, in some cases where precision and robustness is required, a black-box solution may not be enough.
A roadmap to build, contextualize and analyze protein-GAG interaction networks We have designed a roadmap to build, contextualize, and analyze extracellular protein-protein and protein-GAG interaction networks (Fig. 1 ) including the following steps: (1) identification of new interactions by surface plasmon resonance (SPR) and SPR imaging (SPRi) using ECM protein and GAG arrays we have developed  (Faye et al. , 2009b; Fatoux-Ardore et al. , 2014; Salza et al. , 2014) , (2) collection of further interaction data by manual curation of the literature, (3) storage of interaction data in MatrixDB, the interaction database we have developed (http://matrixdb. ibcp. fr/, Chautard et al. , 2009 Chautard et al. , , 2011;; Launay et al. , 2015; cf. below) , (4) querying MatrixDB and other interaction databases to build comprehensive interaction networks of a molecule (GAG or protein), (5) contextualization of the network by adding kinetics and affinity of interactions calculated by SPR, quantitative proteomic data when available, Gene Ontology terms (http://geneontology.
org/, Gene Ontology Consortium, 2015), annotations from the Reactome pathway database (http://www. reactome. org/, Croft et al. , 2014)  and the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www. genome. jp/kegg/, Kanehisa et al. , 2015) , and expression data from UniGene (http://www. ncbi. nlm. nih. gov/unigene, Pontius et al. , 2003). The integration of the above data into the networks allows the building in MatrixDB of networks specific of a tissue, a biological process, a molecular function, a pathway or a disease. The networks are visualized using the iNavigator of MatrixDB  (Launay et al. , 2015)  and/or Cytoscape, ''an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data'' (http://www.
Diamonds: multimeric proteins (e. g. , collagens, laminins), Crosses: protein fragments (e. g. , endostatin, a fragment of collagen XVIII), Squares: proteins. The protein-GAG network has been built with the iNavigator of the MatrixDB database. (KEGG: Kyoto Encyclopedia of Genes and Genomes, SPR: surface Plasmon Resonance). ical Entities of Biological Interest) ontology and called BiNChE has been recently developed for small molecules including glycosaminoglycan constitutive disaccharides (http://www. ebi. ac. uk/chebi/tools/binche/, Moreno et al. , 2015). It has been integrated into the Gene Ontology resource  (Hill et al. , 2013 ) and we will use BiNChE for future analyses of protein-GAG interactomes.
Manual curation of the literature: storage of protein-GAG interaction data in MatrixDB, a database focused on ECM interactions To collect protein-GAG interaction data from the literature we have built a molecular interaction database called MatrixDB (http://matrixdb. ibcp. fr/, Chautard et al. , 2009 Chautard et al. , , 2011;; Launay et al. , 2015)  focused on interactions involving at least one extracellular matrix protein and/or one glycosaminoglycan. MatrixDB was the first interaction database and is currently the only one to systematically curate protein-GAG interaction data. MatrixDB belongs to the International Molecular Exchange consortium (IMEx, http://www. imexconsortium. org/) and follows the IMEx curation rules  (Orchard et al. , 2012). MatrixDB performs curation of the papers published in Matrix Biology and of other papers related to ECM assembly, ECM maturation, angiogenesis and infectious diseases through the curation platform developed by the IntAct database and currently used by eleven interaction databases (http://www.
ebi. ac. uk/intact/, Orchard et al. , 2014). Protein-protein and protein-GAG interaction data curated by MatrixDB are freely available on our website, where they can be downloaded under a tabular format and/or as interaction networks built with the iNavigator we developed  (Launay et al. , 2015). Furthermore MatrixDB has implemented a Proteomics Standard Initiative Common Query InterfaCe (PSICQUIC) for computational access to molecular interaction data resources  (Aranda et al. , 2011). The query of a single database having implemented PSICQUIC returns the interaction data stored in other interaction databases having also implemented the PSICQUIC service. One of the first issues to address for the curation of protein-GAG interactions was to select an identifier for glycosaminoglycans. In agreement with the IMEx curation rules we have selected the identifiers of the Chemical Entities of Biological Interest for glycosaminoglycans (ChEBI, http://www.
An octasaccharide of heparin is sufficient to promote the binding of the ectodomain of ␣5␤1 integrin  (Faye et al. , 2009a) , whereas a decasaccharide and an hexadecasaccharide of heparan sulfate are required to bind a fragment of collagen V  (Ricard-Blum et al. , 2006)  and collagen XVIII  (Ricard-Blum et al. , 2004) , respectively. We have used the AutoDock program and molecular dynamic simulations to localize the binding site(s) of heparin on the 3D structure of endostatin  (Ricard-Blum et al. , 2004)  and of the ectodomain of the ␣v␤3 integrin  (Ballut et al. , 2013 ) by molecular modeling. We have identified two further arginine residues of endostatin (Arg 47 and Arg 66 ) contributing to its interaction with heparin  (Ricard-Blum et al. , 2004)  and we have shown that heparan sulfate could bind to different sites on RGD-dependent and RGD-independent integrins  (Ballut et al.
, 2013). The chemical groups and features of GAGs contributing to their binding to proteins or pathogens (e. g. , sulfate groups, size, and oligosaccharide sequences) are currently stored in MatrixDB as free text, which restricts searches within the database and data exchanges with other interaction databases. A disaccharide code, based on a 4-character descriptor (uronic acid, presence and location of sulfate groups, hexosamine and the N-substituent, pattern of sulfation on the hexosamine) has been proposed for GAGs by  Lawrence et al. (2008)  but it has not been widely adopted by the GAG community. We will use in the future a unifying sequence format for carbohydrates  (GlycoCT, Herget et al. , 2008)  and/or the Web3 unique representation of carbohydrate structures (WURCS,  Tanaka et al.
, 2014) , a linear notation which can be used as a Uniform Resource Identifier, to represent heparin/heparan sulfate features responsible for protein or pathogen binding. These data are of crucial importance because the pattern of GAG post-synthetic modifications (sulfation, acetylation, epimerization) and their conformation modulate their molecular recognition, their interaction repertoire and hence their biological functions. Conformational changes occurring in proteins upon their binding to heparin/heparan sulfate may be demonstrated by circular dichroism (CD). The comparison of CD spectra of a fragment of collagen V in absence and in presence of heparin or heparan sulfate has shown that no significant structural change is induced in this protein fragment upon GAG binding  (Ricard-Blum et al.
∼2 ∼73 Chomel et al. (  2009 )  Weiss et al. , 2010). Proteins are then injected at several concentrations over captured GAGs. Each sensor chip has four flow cells, the first one being a control flow cell used to evaluate the non-specific binding of injected proteins to the sensor chip surface. Kinetics and affinity are calculated using predetermined interaction models available in the Biaevaluation software (GE Healthcare). The association rate reflects the molecular recognition of both partners, whereas the dissociation rate reflects the stability of the complex resulting from the interaction of both partners. The reciprocal of the dissociation rate (kd) is an evaluation of the complex lifetime and consequently of the duration of the biological effects triggered by the interaction. It allows the comparison of the lifetime of complexes formed via different molecular mechanisms, reported in Table  1  as the interaction model fitting the experimental interaction data.
Relationship between kinetics, affinity molecular and biological functions We have built a dataset of 125 protein-heparin/heparan sulfate interactions for which affinity and kinetic parameters are available in order to determine if there is a relationship between the molecular functions of heparin-binding proteins, their localization, the biological processes they are involved in, and the affinity or kinetics of their interactions with heparin/heparan sulfate. The dataset was comprised of data stored in MatrixDB database and of data manually curated from the literature  (Peysselon and Ricard-Blum, 2014). Lipoproteins, growth factors and cytokines bind to heparin with a higher affinity than proteins playing a role in ECM assembly and organization. Enzymes interact with heparin with a higher affinity than the other proteins and form more stable complexes with heparin than the other proteins.
, 2012). iv) The role of membrane lipids in the organization and distribution of syndecans, glypicans and part-time membrane or membrane-associated proteoglycans within the plasma membrane. Another concern is to cross-reference MatrixDB with other glycan databases to improve exchanges of protein-GAG interaction data, not only with the IMEx interaction databases, which is already done, but also with other glycan databases such as Glyco3D, a portal for structural glycosciences providing the 3D features of monosaccharides, oligosaccharides, polysaccharides, and GAG-binding proteins (http://glyco3d. cermav. cnrs. fr, Pérez et al. , 2015) , and SugarBind, which provides information on carbohydrate sequences to which pathogens specifically adhere (http://sugarbind. expasy. org/, Shakhsheer et al.
The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters' features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain.
INTRODUCTION Brain function relies on the interaction of large neuronal populations. Anomalies of these complex neuronal circuits are associated with diverse brain disorders  1. Therefore, to understand brain function both in health and disease, it is necessary to explore the activity dynamics of neuronal networks. Recent advances in optical methods and optogenetics provide unprecedented possibilities for functional imaging of large neuronal populations  [2] [3] [4] [5]  , and even whole brains  6, 7  , with high spatial (e. g. , single-neuron) resolution. These imaging datasets can be analyzed to reveal the responses of spatially distributed neuronal populations to sensory, motor, or task variables  5, [8] [9] [10] [11]. Nevertheless, the study of large neuronal circuits imposes new challenges to the processing and analysis of the complex, high-dimensional datasets typically acquired.
For instance, a neuronal assembly could perform a computation at a given time, but at a different time point, a subgroup of this assembly could be part of a different assembly with a different functional role. Hence, neurons could belong to multiple assemblies (i. e. , non-exclusive assemblies) and play diverse roles in different brain processes. Indeed, it is in neuronal assemblies' flexible and distributed nature wherein lies one of the major difficulties in identifying them  4. The ability to simultaneously record activity in progressively larger neuronal populations is thus paramount in these efforts. We recently developed a computational framework that applies dimensionality reduction and clustering techniques for the analysis of calcium imaging datasets, which outperforms other traditional algorithms in the effective detection of neuronal assemblies embedded within large neuronal networks  9.
Remarkably, this method is able to detect non-exclusive assemblies that are engaged and disengaged on a moment-to-moment basis, compatible with the distributed and dynamic nature of brain processing  19. Here, we present a detailed tutorial for the utilization of this framework, implemented in an integrated computational toolbox. The toolbox consists of a complete data processing pipeline designed for the comprehensive analysis of fluorescence imaging data. It includes modules for video pre-processing, morphological image segmentation into regions of interest (ROIs) corresponding to single neurons, extraction of fluorescence signals, analysis of ROI responses to stimulus and/or behavioral variables, detection of assemblies of ROIs, exploratory analysis of network dynamics and the automatic generation of surrogate shuffled datasets to act as controls for statistical purposes.
It thus accomplishes a complete computational workflow from raw imaging data to interpretable results on neuronal population dynamics. Typically, the full protocol can be completed on a workstation computer in 1-2 hours, depending on the size of the dataset. Analytical demands vary greatly depending on the scientific questions and the nature of the datasets. Therefore, the pipeline is organized in flexible sub-modules that can be bypassed, replaced or used in a stand-alone manner, by allowing the user to import and integrate data and/or results from other preferred methods at different critical points of the pipeline.
The methods implemented in this protocol have been extensively tested with in vivo imaging data from zebrafish larvae  7, 9, 20  and mice (see Fig. 2 ) expressing genetically encoded calcium indicators (GCaMPs; see Figs. 2b-d  and 4-9 ) or injected calcium indicator dyes (OGB 1-AM; see Fig. 2a ). The analyzed imaging data was obtained through two-photon laser scanning fluorescence microscopy  9, 10  (see Figs. 2a-c , 4 and 6-9), and single-photon light-sheet fluorescence microscopy (see Figs. 2d  and 5 ). For all modules, we provide the source code implemented in MATLAB functions and scripts, which the user can adapt and modify, if needed. Given the large scope of the pipeline, in this section we limit discussion to an initial conceptual overview of each module, but we encourage users to gain a better appre-ciation of the toolbox through hands-on protocol experience with the provided case study (see Results).
Drift correction Longitudinal imaging experiments are prone to present in-plane x-y drifts of the imaged optical section. This drift has to be compensated for to obtain stable imaging recordings. This compensation is carried out by the first sub-module (step 1). The series of image stacks is processed with the effective and widely used Template Matching image registration plugin of ImageJ. Nevertheless, we often observed that this plugin could fail at registering imaging videos that show episodes of large fluorescence variation associated with global neuronal activations. For situations like this, we included an additional program that solves this issue (see Troubleshooting in step 1).
ROI segmentation The following sub-module uses digital image processing techniques to perform morphology-based image segmentation, in order to separate the ROIs that correspond to individual cells from the signal of other cells and the surrounding neuropil (steps 10-19). As expected, the success of this module depends on the image's spatial resolution, the anatomy of the imaged region, and the cellular labeling by the fluorescent reporter. Here, we demonstrate that it can successfully process remarkably different datasets (see below and Figs. 2  and 6 ). Nevertheless, if the user is not satisfied with the results obtained, the automatic ROI segmentation can be bypassed, since the module's GUI can also be used to manually draw all the ROIs, to automatically implement a custom hexagonal grid of ROIs (see Supplementary Fig.
Both ii and iii are standard and widely used clustering methods  30, 31  , and i was introduced and described in detail in Romano et al. 2015. Here we briefly describe these, emphasizing the more novel PCA-promax approach. PCA-promax implements a fully automated method that searches for ROIs whose activations are correlated on average along the entire experiment, but it is also capable of defining significant ROI clusters episodically activated in synchrony. Since a given ROI could belong to several functional groups, the algorithm is tailored to define non-exclusive ROI assemblies (i. e. , it allows for potential overlap between the detected clusters). The procedure relies on previously proposed techniques  18, 32. Briefly, it consists of two processing steps. First, it z-scores the activity of each ROI and reduces the dimensionality of the complete z-scored dataset of ROI activities through principal component analysis (PCA).
The initial z-scoring homogenizes the variance across ROIs, allowing PCA to reveal the global structure of ROI activity covariance. To define the assemblies, it then uses a second algorithm to partition this space of reduced dimensionality, by means of non-orthogonal factor rotation, promax  33. This latter step extends the simpler PCA-clustering method  32  to non-exclusive assemblies. Dataset dimensionality reduction is obtained by only keeping principal components (PCs) with eigenvalues greater than λ max , a theoretical lower bound to the eigenvalues of informative PCs given by the Marčenko-Pastur distribution  32, 34  (equation 1) where N and T are the number of ROIs and imaging frames, respectively. In practice, for long-term imaging data (30 min videos sampled at ~5 Hz), this procedure typically reduces data dimensionality by ~100 fold.
). In all panels, assemblies are displayed with the same color code, according to the similarity of their activity dynamics (the more temporally correlated, the more similar the color; except for the temporally correlated assembly pair shown in c, which was colored differently to facilitate visualization). (h) Same as g, but for assemblies found using k-means, color-coded according to the similarity of their activity dynamics. To allow for a comparison between g and h, the dimensionality of the dataset was reduced through PCA before clustering (otherwise, clustering did not converge). A correlation metric and a complete linkage were used. Note how this clustering reveals assemblies whose spatial organization is roughly consistent with those shown in g, but exposes a much less biologically relevant fine structure.
6 (i. e. , clusters with highly similar neuronal compositions; in practice a 0. 6 value identified assembly pairs with exceptional overlap). As a final constraint, only significantly correlated and synchronous clusters are kept (p < 0. 05, compared to surrogate control datasets (see Random surrogate assemblies in Box 1). The assemblies obtained are then automatically displayed. Briefly, k-means partitions data in a fixed number of clusters k, defined by the user. It involves randomly selecting k initial centroids and assigning each point (e. g. , each neuron) to their closest centroids, thus forming k preliminary clusters. The centroids are then updated according to the points in the clusters, and this process continues until the points stop changing their clusters (i. e. , convergence of the centroids).
Typically, the clusters defined by k-means are highly independent and uncorrelated (i. e. , they present low inter-cluster correlations). On the other hand, (agglomerative) hierarchical clustering groups data by creating a cluster tree or dendrogram. The tree represents a multilevel hierarchy, where clusters at one level are joined as clusters at the next level. First, the similarity between every pair of variables (e. g. , neuronal fluorescence traces) is calculated. Then, these similarities are used to determine the proximity of variables to each other. Variables are successively paired into binary clusters, and newly formed clusters are grouped into larger clusters in a bottom-up manner, until a hierarchical tree is formed. Finally, clustering is performed by determining where to cut the hierarchical tree, and assigning all the objects below each cut to a single cluster.
For both k-means and hierarchical clustering, the user can choose to cluster either the original z-scored dataset or the dimensionality-reduced dataset obtained through PCA according to Equation 1. In contrast to PCA-promax, for both k-means and hierarchical clustering, a number of important parameters have to be set. The user must choose between using a euclidean or a pair-wise correlation metric to calculate distances between variables. For hierarchical clustering only, the user must choose if the distance between two clusters is defined by the shortest or longest distance between two points in each cluster (single and complete linkage clustering, respectively). Being classic methods, the particularities of clustering with these metrics and linkages has been extensively reviewed  30, 31.
These distances and the clustering tree are then used to arrange assemblies according to the optimal leaf order that maximizes the similarity between adjacent leaves by flipping tree branches. Colors are then automatically assigned to represent the leaf order. In this way, assemblies with similar dynamics will be similarly colored. This is an unsupervised procedure that does not need user input, and thus it is suitable for automatic analysis of large datasets. An example of this kind of ordering procedure is shown in Fig. 5  for whole-brain light-sheet imaging. For the second spatial-based analysis procedure, the user indicates an anatomical axis in the imaged plane, and the assemblies will be ordered according to the position of their centroids along the chosen axis. The user can choose the left-to-right or bottom-to-top axis, or draw an arbitrary curve.