dataesr/scientific-paragraphs-categorization huggingface dataset:
The InputDataAgent runs continuously querying both the TransformationDB and the Archive catalog in order to select files matching the inputdataquery. The selected files are then attached to the corresponding transformations and registered in the TransformationDB. At the same time, the TransformationAgent queries the TransformationDB to determine if there are new transformations to process and creates the corresponding tasks according to a given plugin. Tasks may be jobs (workflow tasks) submitted to the WMS through the WorkflowTaskAgent, or requests submitted to the RMS by the RequestTaskAgent. In this schema, the Transformation System is data-driven, in the sense that the production manager simply defines a transformation with an inputdataquery and the associated tasks are automatically created and executed as soon as new data are registered in the catalog.
Acknowledgments We gratefully acknowledge support from the following agencies and organisations:  Ministerio de Ciencia, Tecnología e Innovación Productiva (MinCyT) ,  Comisión Nacional de Energía Atómica (CNEA)  and  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina ;  State Committee of Science of Armenia; Ministry for Research, CNRS-INSU  and  CNRS-IN2P3 ,  Irfu-CEA ,  ANR, France ;  Max Planck Society ,  BMBF, DESY, Helmholtz Association, Germany ; MIUR, Italy;  Netherlands Research School for Astronomy (NOVA) ,  Netherlands Organization for Scientific Research (NWO) ;  Ministry of Science and Higher Education  and the  National Centre for Research and Development, Poland ; MICINN support through the  National R+D+I, CDTI  funding plans and the CPAN and MultiDark Consolider-Ingenio 2010 programme, Spain;  Swedish Research Council, Royal Swedish Academy of Sciences  financed, Sweden;  Swiss National Science Foundation (SNSF) ,  Switzerland ;  Leverhulme Trust ,  Royal Society, Science and Technologies Facilities Council, Durham University, UK ;  National Science Foundation ,  Department of Energy, Argonne National Laboratory, University of California, University of Chicago, Iowa State University, Institute for Nuclear and Particle Astrophysics (INPAC-MRPI program ),  Washington University McDonnell Center for the Space Sciences, USA.
We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge. Results: Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6 % to 11 %). On a real Escherichia coli compendium, an improvement of 11. 8 % compared to CLR and 3 % compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://www-syscom.
CLR was shown to outperform RN, ARACNE and MRNET on several datasets  [8]. While RN removes edges whose mutual information is lower than a threshold, CLR exhibits improved performance by computing a score derived from Z-statistics on mutualinformation, leading to more robust results. Model-based methods include Bayesian approaches, Gaussian graphical models  [9, 10] , or differential equations  [4, 11]. Graphical models rely on strong hypotheses on data distribution, that may yield poor performance when tested on real datasets where the number of replicates or conditions is very small in proportion to the number of genes. The performance of such inferred networks can be sensibly improved by a network deconvolution approach (  [12] , thereafter denoted by ND) that removes global transitive or indirect effects by eigen-decompositions.
3. The computation time of our method is negligible in comparison with other model-based approaches with inference improvements. 4. It can be used as a generic GRN post-processing with any input weights and supplementary information on transcription factors. The paper is organized as follows: we propose in the next section the novel variational approach for building GRNs and we detail the efficient optimization strategy used to solve the related minimization problem. BRANE Cut outcomes and performance on benchmark datasets coming from the DREAM4 challenge and the Escherichia coli compendium are provided in the 'Results and discussion' section. We finally conclude and offer perspectives.
Mathematical modeling of the structural a priori We first introduce our notations before detailing our structural models and variational formulation. Let G represent the total number of genes for which expression data is collected. Expression data is gathered in a symmetric weighted adjacency matrix W ∈ R G×G. Its (i, j) element corresponds to a statistical measure reflecting the strength of the link, or information shared, between the expression profiles of gene i ∈ {1,. , G} and gene j ∈ {1,. , G}. Our approach uses non-negative weights. A convenient choice for ω i,j is the normalized mutual information (ω i,j ∈ [0, 1]) computed between the expression profiles of genes i and j. Let G(V, E) be a fully connected, undirected and nonreflexive graph where V = {v 1 ,. v G } is a set of nodes (corresponding to genes), and E = (e i,j ) (i,j)∈V 2 |i<j is a set of edges (corresponding to plausible gene interactions).
Each edge e i,j is weighted by the value ω i,j from matrix W. The initial number of gene-to-gene edges of G, denoted by , is equal to G(G -1)/2. Inferring a GRN from G aims to construct a final graph selecting a subset of edges E * ⊂ E which reflects true gene regulatory processes. We formulate the search for this graph by computing an edge indicator vector x ∈ {0, 1} whose components x i,j are such that x i,j = 1 if e i,j ∈ E * , 0 otherwise. ( 1 ) We assume in this work that a list of putative transcription factors is available. A gene supposed to code for a transcription factor is metonymically denoted by TF. A gene not identified with this property is designated by TF. The TFs/TFs notation defines two complementary subsets of the ensemble of genes V. Subsequently, T ⊆ V denotes the set of putative TFs.
When λ TF = λ TF , no distinction is made on the type of edges. This is equivalent to using a unique threshold value, as in classical gene network thresholding. This can be interpreted as if, without further a priori, all genes were indistinguishable from putative TFs. However, different λ TF and λ TF may be beneficial. We indeed show in the Additional file 1 that for any fixed value of λ TF , smaller values for λ TF improve graph inference results. A simple linear dependence λ TF = βλ TF , with β ≥ 1 suffices to define a Fig. 1  Illustration of our first a priori on a synthetic GRN. The TF-TF edges (red edges) are less represented than the TF-TF edges (blue edges). The red nodes code for the TF genes while the green nodes code for TF genes. The ratio of the number of TF on the total number of genes is 5/18 in this example.
Results and discussion We compare the BRANE Cut approach to the top performing graph inference methods on synthetic and real data. The considered state-of-the-art methods are CLR, which outperforms ARACNE and Relevance Networks on the E. coli dataset, and GENIE3, winner of the DREAM4 multifactorial challenge  [27]  on synthetic data among a large number of competing methods, and also outperforming other approaches on the real E. coli dataset. For a fair evaluation, all networks are inferred using the same Fig. 3  Schematic view of the proposed BRANE Cut method. The initial graph (a) is transformed into an intermediate graph (b) in which a max-flow computation is performed to return an optimal edge labeling x * leading to the inferred graph (c). We choose to present the method in its full generality with unscaled weights (i.
Validation datasets The DREAM4 dataset The Dialogue for Reverse Engineering Assessments and Methods fourth (DREAM4)  [27]  multifactorial challenge provides five simulated datasets with real network topologies from the prokaryote E. coli and the eukaryote S. cerevisiae, and simulated expression data. At the time of the challenge, the competing approaches did not have access to a list of putative transcription factors, which is now available online. As this list is a requirement of our method, we benchmark the best performing network inference methods using this additional information. The networks are composed of 100 genes, with a total of 100 expression levels per gene. The evaluation of the inferred networks was performed using the gold standard provided in the DREAM4 multifactorial challenge.
The Escherichia coli dataset This dataset was first introduced in  [8]  and is composed of 4345 gene expression profiles, each profile containing 445 gene expression levels. This compendium contains steady-state and time-course expression profiles. Regu-lonDB  [28]  is the primary database on transcriptional regulation in Escherichia coli K-12 containing manually curated knowledge from original scientific publications. As in  [8] , we used the version 3. 9 to evaluate inferred networks. This database offers a set of 1211 genes for which 3216 regulatory interactions are confirmed.
The DREAM5 dataset The DREAM5 challenge (Dialogue for Reverse Engineering Assessments and Methods fifth)  [2]  provides four networks. The first one contains an in-silico dataset while the three others correspond to real datasets. For the four networks, the list of putative transcription factors is known. In this work, we used the three networks (1, 3 and 4) for which a ground truth is provided. The first network is composed of 1643 genes (195 TFs) and expression data in 805 conditions. Network 3 contains information about 4511 genes (334 TFs) in 805 conditions, while network 4 compiles 5950 genes (333 TFs) and 536 conditions. The evaluation of the inferred networks was performed using the gold standard provided in the DREAM5 challenge.
Results on the E. coli dataset We now present the results of the BRANE Cut method on the real E. coli dataset. Our approach uses the normalized weights ω i,j defined by CLR (with μ = 1000) and GENIE3 (with μ = 10). A discussion about the choice of the μ parameter is given in the Additional file 1. The parameter γ is set as in the previous section. The different Precision-Recall curves are reported in Fig. 5 , to compare BRANE Cut to GENIE3 and CLR. Fig. 4  AUPR improvements for different parts of the PR curves on the five networks of DREAM4. In order to show the differential improvement over the Precision, relative AUPR are computed for different PR curves, truncated at different range of Precision: [0,100],  [10, 100] ,. ,  [90, 100]. Here, the improvement is defined as the AUPR ratio of BC-GENIE3 and GENIE3 Fig.
5  Precision-Recall curve comparison on the E. coli dataset. CLR (dashed purple line) and GENIE3 (dashed green line) are compared to our BRANE Cut method initialized with the weights CLR (solid purple line) or GENIE3 (solid green line) Best performance is expected toward the upper right side of Precision-Recall curves, with both high recall and precision. However, GRN Precision-Recall curves traditionally exhibit low Precision values over the whole curve on real datasets due to the difficulty in inferring accurate regulation relationships among large amounts of genes. For instance with the E. coli dataset, we observe that a recall below 0. 05 corresponds to small inferred graphs, with less than 300 edges and a high precision (more than 75 %). Due to their higher predictive power and their readability, such small networks are often preferred by biologists.
Inferred network example on E. coli An example of regulatory network on the E. coli dataset obtained with BRANE Cut, initialized with GENIE3 weights, is displayed in Fig. 6. The inferred network obtains a Precision score of 85 %, with a better predictive power than the network produced by the GENIE3 method alone. The binary network for GENIE3 is obtained by selecting edges having a weight higher than 0. 707. This threshold renders a network with 85 % of Precision. In comparison to the reference, we discover 20 additional plausible regulatory interactions. Among these 20 predictions, ten were also predicted by the GENIE3 method, leading to ten predictions specific to BRANE Cut. By analyzing the predictions using STRING  [29]  and EcoCyc  [30]  databases, we observe that the predicted groups of genes were already identified as co-expressed and are known to belong to the same functional mechanism.
Influence of the proposed structural a priori We start to analyze the influence of our first a priori on the E. coli dataset using CLR weights. Hence, using the first two terms with λ TF = λ TF leads to an AUPR of 0. 0870, which constitutes a relative improvement of 10. 7 % over CLR, without co-regulation a priori. More generally, as λ TF and λ TF are interpreted as a pair of thresholds, the higher these parameters, the greater the stringency in the inferred graph. These results show that allowing a different threshold value in the neighborhood of transcription factors than for other genes does play a positive role by itself. The regulator coupling term controlled by μ brings further improvements. Indeed, the addition of the third term results in an AUPR equal to 0. 0879, corresponding to a relative improvement of 11.
Results on DREAM5 We have evaluated BRANE Cut on three DREAM5 networks  (1, 3 and 4)  for which a validation exists. BRANE Cut parameters are initialized with the proposed heuristics and results are obtained using the validation procedure previously detailed. BRANE Cut outperforms CLR and GENIE3 by 7 % and 5 % respectively on Network 1. The improvement is 2. 8 % and 2. 1 % for Network 3. For the fourth network, the maximum Precision only reaches about 35 %. The AUPR computed with every method is exceptionally low. As such, the relative AUPR differences are insignificant, within the numerical precision. The detailed AUPR are given in the Additional file 1. Regarding the results in these additional datasets, the proposed heuristics lead to improvements over state-of-the-art.
Conclusions By using structural a priori that are often available but rarely used, we managed to infer networks that recover more true interactions than previous approaches, on both synthetic and real datasets. We have expressed the graph inference as an optimization problem, and used the generic Graph cuts approach, very popular in computer vision, to compute the optimal edge labeling of our inferred graph. Comparisons are performed with simple regularization parameters based on gene set cardinality. We obtain better results than both CLR and GENIE3 in terms of Area Under the Precision-Recall curves, even with ND deconvolved networks. BRANE Cut yields state-of-the-art results, with a negligible computation time. While the GENIE3 method needs about five hours to obtain a 4345-gene network, limited to interactions involving transcription factors, we obtain a network with similar accuracy with our method in a few seconds, only using CLR weights computed in about forty minutes.
This graph inference acceleration is thus useful to explore large datasets. Some predictions specifically identified by our method indeed appear as relevant interactions. As mentioned in  [2] , community-based methods represent a promising future for gene network inference, by aggregating the predictions of existing GRNs approaches. As our method takes any weights as an input, it has the potential to improve other GRN approaches providing pairwise weights. Results provided in the Additional file 1 illustrate these remarks. Based on these assessments, a perspective consists of the integration of various weights, provided by competing GRN methods, to further improve and strengthen present results. This integration may involve multi-valued graphs or network fusion  [31].
1 % 11. 8 % 9. 1 % 9. 9 % 11. 9 % 10. 6 % BC-GENIE3 vs GENIE3 10. 7 % 9. 9 % 7. 8 % 6. 5 % 7. 0 % 8. 4 % BC-ND-CLR vs ND-CLR 6. 6 % 10. 7 % 3. 0 % 5. 5 % 3. 7 % 5. 9 % BC-ND-GENIE3 vs ND-GENIE3 4. 4 % 13. 4 % 9. 2 % 5. 4 % 3. 8 % 7. 2 % (c) Post-processing method comparison on the DREAM4 dataset. Relative gain are given for BRANE Cut using CLR (resp. GENIE3) weights compared to ND-CLR (resp. ND-GENIE3) BC-CLR vs ND-CLR 11 % 23. 2 % 5. 9 % 8. 2 % 7. 5 % 11. 2 % BC-GENIE3 vs ND-GENIE3 13. 8 % 14. 9 % 6. 2 % 4. 9 % -0. 6 % 7. 7 %.
Cut is O(mn 2 |C|), where m (respectively n) is the number of edges (respectively the number of nodes) in the flow network G f , and |C| the cost of the minimal cut. Specifically, in our case (without the dimension reduction trick presented in the 'Problem dimension reduction' section) 6ig. 6Example of network built using BRANE Cut on the E. coli dataset. Legend: black nodes: transcription factors, gray nodes: other genes. Green edges: inferred regulations also reported in the gold standard, blue edges: new inferred regulations that are also inferred by GENIE3, and pink edges: new inferred regulations.
Animal grouping is a very complex process that occurs in many species, involving many individuals under the influence of different mechanisms. To investigate this process, we have created an image processing software, called NEIGHBOUR-IN, designed to analyse individuals' coordinates belonging to up to three different groups. The software also includes statistical analysis and indexes to discriminate aggregates based on spatial localisation of individuals and their neighbours. After the description of the software, the indexes computed by the software are illustrated using both artificial patterns and case studies using the spatial distribution of woodlice. The added strengths of this software and methods are also discussed.
2010)  (2) semi-manual : sonar and echo sound  (Axelsen et al. 2001; Gerlotto et al. 2006; Gerlotto and Paramo 2003; Handegard 2007; Soria et al. 2003) , (3) automatic : microtransponders  (Jeanson 2012) , RFID tagging  (Planas-Sitja et al. 2015). In the current study we propose a data processing software called NEIGHBOUR-IN which allows spatial coordinates to be attributed to individual (object) of up to three different categories (groups), such as species, sex, age, size, moult stage, etc. We illustrate the software applications with artificial patterns and experimental study of aggregation in terrestrial isopods (Crustacea, Oniscidea). The software then calculates indexes qualifying spatial distribution, composition of groups and many other parameters. All data outputs can be used for further analysis and quantification of spatial variations.
Methods NEIGHBOUR-IN is a software designed to analyse individuals' coordinates belonging to up to three different groups. The software can be used only for picture analysis and not for film analysis. However film screenshots in the appropriate format could be made to follow the dynamics of the studied processes. Once the image of the objects (i. e. individuals) is loaded, the user identifies each individual by clicking on anterior and posterior extremities. Each object belongs to up to three different groups. The software includes statistical analysis and indexes to discriminate aggregates based on spatial location of individuals and their neighbours. At the end of the treatment the table of coordinates can be exported for further spatial analysis. Different displays are available in order to show the aggregates or the influence distance of each object (for example the area where the antennas can touch and interact with another individual in the case of insects).
Discussion Our image processing software, NEIGHBOUR-IN, provides indices with efficient discriminatory power to characterize and analyse group structure using individuals' coordinates. In the features of the software we integrate elementary statistical analysis and complementary index calculation that are important tools to describe aggregations, as well as a new index for more precise analysis. We provided examples based on random data and a case study using gregarious arthropods to highlight the accuracy of the output information. Moreover, the raw data, individual coordinates and location can be directly manipulated by the researcher for specific analysis such as simulation, modelling, and classic spatial statistics. One of the assets of NEIGHBOUR-IN is the distinction between up to three groups and the open group size, which allow for a variety of applications.
In comparison with the tools available in aggregation analysis, NEIGHBOUR-IN appears to be an accessible, light, and open solution. Our software is designed for analysis at a point-time however continuous monitoring of behavioural is not possible. The level of integration and data analysis complexity is smaller in comparison to GIS systems, which require user training and specific data templates and libraries. Other powerful softwares that track and analyse animal movement, such as Noldus ETHOVISION, analyse in real time and are not designed for snapshot analysis. Noldus ETHOVISION analyses are automated and qualitative information on aggregates could be missed. Many species present complex aggregate structures and often in three dimensions so that a fully automatic tracking is necessarily imprecise.
For example, when two individuals are superimposed, softwares like Noldus ETHOVISION lose track of the two individuals (one source of imprecision) and then randomly assign the initial characteristics to individuals when they separate, so that both intermediate and final results can lead to incoherency. Our semi-manual software allows manual localisation of individuals, increasing the precision of the spatial encoding, while keeping an automatic acquisition of results and analyses. Moreover, the user is able to identify orientation of individual (anterior and posterior extremities) with its perception area and consequently the possibility of interactions or not. Finally, this software promises to evolve with new features, and could be used to generate and export distribution and coordinates database for other purposes.
Potential fields of applications could be evolution of invasive species (animals and plants) distribution using aerial image, competition of fungus or microbial colonies, identification of harems in marine mammals grouping and so on. The main limitations of NEIGHBOUR-IN software are in the potential excessive overlap of the individuals in an aggregate, and the total number of group and individuals taken into account. However, in both case, the user himself/herself is confronted to difficulties and the task, even if it is complicated, will be easier using NEIGHBOUR-IN. In comparison to a direct analysis of the image, the advantage to generating NEIGHBOUR-IN data files is that the image is saved with the coordinates but the statistics are managed separately, which allows the researcher to re-use the same files with further statistical analysis, as well as the integration of new graphic outputs and indexes.
Using these profiles, reactions are identified as potential knockout, overexpression, or dampening targets. The identified reactions are ranked according to their suitability, providing flexibility in strain design for users. The software was tested by designing a butanol-producing Escherichia coli strain, and was compared against the popular OptKnock and RobustKnock methods. RobOKoD shows favorable design predictions, when predictions from these methods are compared to a successful butanol-producing experimentally-validated strain. Overall RobOKoD provides users with rankings of predicted beneficial genetic interventions with which to support optimized strain design.