dataesr/scientific-paragraphs-categorization huggingface dataset:
To first test this possibility, we also performed 20 randomizations of the data as above for use with cWords. We found that the randomized data resulted in observed P-values that were much higher than expected (i. e. close to 1), suggesting that the P-values are not properly estimated in this particular dataset. Furthermore, little improvement was gained by using any kmer background correction as judged by PP plots (Supplementary Figure  S2 ), so we did not use this as a criterion for model selection, and relied instead on prediction performance on the T conv dataset. We saw a large drop in performance for k = 5 and k = 6, with many fewer matches to miRBase miRNAs and T conv cell highly expressed miRNAs than for k = 2, 3 and 4. This is entirely consistent with the authors' recommendation and our observation above that there is insufficient 3' UTR sequence data to train higher orders of the Markov model.
We chose to truncate the full ROC curve, which is typically constructed over all possible 6mer motifs, both because the methods did not return the same number of predictions and most importantly because we believe that focusing attention on only the top motifs is a more biologically meaningful comparison since only a few motifs are likely to be biologically relevant (i. e. only a small fraction of all possible miRNAs in the database are actually expressed in a cell  (24) ). It is important both that truncating the ROC curve does not change the ranking of the methods and that we believe our results are robust in that the ROC curves for MixMir dominate the other curves over essentially the entire range of sensitivity settings (Figure  2 ). We caution that the truncated AUC value should not be interpreted as a typical AUC with a baseline value of 0.
Validation of our computational results using experimental datasets of miRNA expression As discussed above, one issue with the above analysis is that miRNAs are generally tissue-specific  (32) , and so comparing the predicted motifs to all miRNAs in miRBase, while informative, may not be the most biologically meaningful representation of their performance. We therefore further validated our results using miRNA expression levels in CD4+ T cells determined in two independent experiments by Cobb et al. (22)  and Sommers et al. (23). These two experiments were conducted using different technologies, the latter measuring miRNA expression using the nCounter system (Nanostring Technologies). The Cobb et al. data compared miRNA expression profiles between CD4+CD25-and CD4+CD25+ T-cells. This experiment has the benefit of being performed in the same laboratory and on the same WT T conv cells from which we obtained the mRNA microarray data used in our analysis Several, but not all, of the most highly expressed miRNAs in each of the two datasets overlapped.
Notably, the let-7 family (consisting of let-7b, let-7c and let-7d), miR-30b, miR-26b, miR-142-3p and miR-15a are among the miR-NAs found to be expressed in T conv cells in both datasets. This is consistent with differences between the studies, including the particular labs, quantification technologies and the comparison between two cell types in the case of the Cobb et al. data. We considered the top 20 highly expressed miRNAs found in Cobb et al. and the top 21 miRNAs found in Sommers et al. (Methods). These results can be found in Supplementary Table  S3. In general, we found that while there was clearly a significant overlap between highly expressed and active miRNAs, relatively few of the highly expressed miR-NAs were also found to be active by the methods we tested. For example, of the top 10 motifs returned, MixMir identified three exact seed sequences corresponding to highly expressed miRNAs.
All methods perform well for most miRNA transfections, but MixMir performs the best for let-7b In addition to testing MixMir on our mouse Dicer-KO T conv data, we also tested our algorithm on miRNA transfection data from human cell lines, to demonstrate that our results are not particular to the mouse microarray dataset. We tested both microarray and quantitative protein expression data obtained from Selbach et al. (20)  (see Methods), and compared our results to those obtained from the same data using miReduce, cWords, Sylamer and the simple linear model (Table  3 ). These data extend our analysis to a very different technology, from microarrays to pSILAC, and from mouse to human. We found that nearly all methods were able to find the exact seed sequence for nearly all of the quantitative proteomics datasets, with the exception being that Sylamer ranked the seed sequence of miR-30a fourth rather than first.
This is an expected result because unlike the Dicer-KO scenario where many miRNAs were perturbed, the transfection experiment perturbs one miRNA very strongly and therefore is expected to produce much less noisy expression data. Here our analysis demonstrates that the performance of MixMir extends from the complicated T conv dataset considered earlier to other simpler datasets as well. In addition, we found that MixMir was able to find as the most significant motif the exact seed sequence (or in the case of let-7b, an offset seed sequence) of the transfected miRNA for each of the microarray experiments at 32 h post, while in several cases the other methods had difficulty doing so. In particular, the other statistical methods had difficulty identifying both seed and offset matches in the let-7b experiment.
When compared to the miRNAs that are downregulated at both E15. 5 and E16. 5, we found that most methods were able to find either an exact or offset seed match to let-7d either as the first or second motif returned, with the exception of the linear model, which performed worse. Overall, MixMir ranked true miRNA seeds higher than the other methods in both E15. 5 and E16. 5 datasets (Table  5 ). Most notably, MixMir found both miR-34b and miR-34c in the top ranked motifs at E15. 5, which no other method was able to do. We also performed a separate analysis of motif ranks and miRNA matches for E15. 5 and E16. 5 separately, as some miRNAs were found to be significantly downregulated at one stage and not at another--namely, there were more such miRNAs at E16. 5, as expected. We found sim-e135 Nucleic Acids  Research, 2014, Vol.
42, No. 17  PAGE 10 OF 13 ilar results in this analysis, in particular that MixMir consistently found biologically significant miRNAs, with performance comparable to miReduce for both time points. cWords and the linear model were comparable for E16. 5 only (Supplementary Table  S8 ). Thus, testing the different methods on additional biological datasets confirms the improvement of MixMir over previous methods. We believe that the most important use of miRNA motif finding methods is to find a small number of miRNAs that are most important in a particular cell type for further experimental validation, since usually there are very few miRNAs that are active in a cell type  (24). Therefore, we view the ability of MixMir to improve the predictions by a small number of motifs to be a significant result that is a feature of the biological properties of the miRNA system.
DISCUSSION In conclusion, we have presented MixMir, a novel method for miRNA motif discovery from sequence and gene expression data. Our method corrects for pairwise sequence similarities between 3' UTRs that could confound a motif finding algorithm in a way that is fundamentally different from previous approaches to this problem (e. g. cWords, Sylamer). We applied MixMir to a microarray dataset from WT and Dicer-KO mouse CD4+CD25-T cells (T conv cells) collected by one of the authors. Since Dicer is required for miRNA biogenesis, we expect that Dicer-KO cells do not contain any miRNAs and indeed this point was validated by quantitative PCR for selected miRNAs, showing a greater than 90% decrease in the knockout (unpublished results). We found that MixMir was more accurate in finding active miRNAs in these cells than three other similar published methods, miReduce, cWords and Sylamer, as well as a simple linear regression model we used as a baseline for comparison.
We validated our computational predictions using two independent biological datasets consisting of miRNA expression measurements in this cell type quantified by either miRNA microarrays or single molecule imaging using the nCounter system (Nanostring Technologies). Importantly we found that miRNA activity was highly but not perfectly correlated with miRNA abundance in the cells, so it is not sufficient to simply measure miRNA expression levels in a cell type to determine the miRNAs that play the largest role in shaping global gene expression in those cells. For example, as in similar analyses for transcription factors, miRNAs could be highly abundant but not highly active in repressing mRNA expression due to their subcellular localization or the presence of competing RNA species that could sequester the miRNAs from their mRNA targets  (39).
Another possibility is that miRNAs may have differential efficiency of loading into the RISC complex or of targeting mRNAs, and certain mRNAs may not be efficiently repressed by miRNAs due to the presence of either stable RNA secondary structures occluding the miRNA-binding site or the binding of additional trans-acting factors. An interesting biological finding from our analysis is that the miRNAs that we found to be the most active in T conv cells were in fact exactly the miRNAs that were more differentially expressed between these cells and CD4+ CD25+ T cells (T reg), based on previously published data from the same cell type  (22). To confirm the performance of MixMir on additional datasets, we tested MixMir against the other methods on five miRNA transfection experiments in HeLa cells, using both microarray and pSILAC quantitative proteomics data previously published by Selbach et al.
Most significantly, MixMir6 was able to correctly implicate significant hexamer motifs associated with both known miRNAs and with highly expressed miRNAs in our dataset, as indicated using the area under the truncated receiver operating characteristic. In particular, on the datasets we tested, MixMir performed better than current state-of-the-art methods of motif discovery, miReduce, cWords and Sylamer (5-7) over the entire range of sensitivity settings considered and on several different types of data. Notably, both cWords and Sylamer correct for 3' UTR length and compositional biases. Overall, cWords performed better than Sylamer, but exhibited strong AU bias in the datasets we examined. These results suggest that background nucleotide composition similarity can strongly affect the ability of a linear model to uncover true motifs, but also that the way in which we correct for background composition can dramatically alter the results.
Unlike Sylamer and cWords, MixMir utilizes the expression fold change values instead of just the ranks. Additionally, MixMir makes pairwise comparisons of the entire 3' UTR sequences, thus performing a more direct comparison of sequence context, rather than comparing motif versus background composition within each 3' UTR like the other methods. The MixMir software is freely available online, and utilizes FaST-LMM, a fast MLM solver that can be obtained freely online (see the MixMir README file with the software for details). One limiting factor in our approach is the total amount of 3' UTR sequence available to construct e135 Nucleic Acids Research, 2014, Vol. 42, No. 17 PAGE 12 OF 13 large correlation matrices for long kmers. Increasing the kmer length to 7mers or higher would make the correlation matrix very sparse and difficult to estimate accurately.
Another drawback of MixMir is its relative computational inefficiency: we exhaustively analyzed all 6mers but if we wanted to exhaustively analyze all 7-or 8mers, the runtime and memory requirements for FaST-LMM would make the computation too inefficient for practical use in our experience. However, miReduce suffers from a similar problem of computational inefficiency for values of k greater than about 6, so this is not an issue unique to MixMir. Finally, we note that our MLM approach is not limited to solving the miRNA motif discovery problem. Like the RE-DUCE software, MixMir can potentially also be applied to other regulatory element motif detection problems, such as transcription factor and RNA-binding protein motif prediction, by varying the type of sequence input and gene expression fold change input.
Background The GPU enhanced Neuronal Networks (GeNN) framework  [1, 2]  was introduced in 2011 to facilitate the efficient use of graphical processing units (GPUs) as accelerators for neuronal network simulations, in particular as part of computational neuroscience investigations. GeNN is based substantially on code generation for the NVIDIA CUDA application programming interface. Code generation provides decisive advantages over stand-alone simulators in that (i) code can be optimized both for every individual model and for the specific GPU hardware detected at compile time, and (ii) code generation allows to provide a practically limitless number of pre-defined models while the generated simulation code remains as small and efficient as possible. While GeNN is an important step towards facilitating the use of GPU acceleration for computational neuroscience applications it has been designed with expert users in mind.
Results We have created prototype interfaces from SpineCreator using the SpineML markup language and from Brian 2 by modifying the code generation facilities within Brian 2 to generate valid GeNN input files. In both cases, a middleware was created that takes the model descriptions that were either available in SpineML (as generated by SpineCreator) or the internal representation in Brian 2 and translate them into the three main code parts necessary to run a GeNN simulation: (i) Neuron, synapse and network definitions including variables, parameters, code to integrate dynamical equations and properties of the connectivity, (ii) Code that runs the GeNN generated code to execute the simulation and (iii) code that regulates the transfer of information from SpineCreator/Brian 2 to GeNN and vice versa (e.
In particular, its virtues have been illustrated in the context of the CalcHep  [115] [116] [117] [118] , FeynArts/FormCalc  [119] [120] [121] [122] [123] [124] , MadGraph  [125] [126] [127] [128] [129] , Sherpa  [130, 131]  and Whizard  [132, 133]  programs. Furthermore, it also offers the possibility to translate any particle physics model in terms of a Python library under the so-called Universal Feyn-Rules Output (UFO) format  [134]. Previously designed model formats for Monte Carlo programs are usually imposing restrictions on the color and/or Lorentz structures and the number of external legs that are allowed in the interaction vertices so that the UFO has been conceived to overcome such limitations. It is therefore suitable for effective field theories that often contain vertices with uncommon Lorentz structures.
Employing the various interfaces linking Feyn-Rules to Monte Carlo event generators, the effects of those operators can be further studied at colliders. Care must however be taken with the choice of the Monte Carlo generator to employ for this purpose, as most of them have strong requirements on the allowed Lorentz structures for the vertices 1. To avoid such limitations and be capable of probing any of the 39 considered operators, we make use, for the few examples of Section 4, of the MadGraph 5 program  [129]  linked to the UFO  [134]  version of the model presented above.
Interactions between the Higgs boson and the electroweak gauge bosons As first examples of a possible usage of our implementation, we focus on the restricted set of operators implying modifications of the hV V couplings between one single Higgs boson and a gauge-boson pair. New physics effects possibly arising in such interactions have been largely investigated in pioneering and more recent works prior the discovery of a Higgs boson by the ATLAS and CMS experiments  [5-18, 20, 22-24, 26, 87-89, 91, 92, 95, 96, 143-145]. Within the last two years, they have been reassessed in the light of newer LHC data including the Higgs results  [28-30, 34, 37, 45, 100-105, 146]. We reproduce and extend, in the rest of this subsection, some of these results by employing the MadGraph 5 package  [129] , using our FeynRules  [106, 113]  implementation to generate the necessary UFO model files  [134].
Conclusions The Higgs discovery, together with the characterization of its properties and quantum numbers, sets the ground for the approach taken in this paper. With the absence of any evidence for new physics, we have followed the path of effective field theories to describe beyond the Standard Model effects in the Higgs sector. We have first formulated in extensive details the Higgs Effective Lagrangian that we have employed, limiting ourselves to dimension-six operators involving the Higgs and/or gauge bosons and adopting the basis and conventions of Refs. [93, 97]. Next, we have implemented this Lagrangian in the framework of FeynRules, a Mathematica package interfaced to several sophisticated Monte Carlo tools. Our implementation allows to study, in particular, various differential distributions related to processes involving one or several Higgs bosons in the context of the LHC collisions.
Acknowledgments The authors are grateful to  Fabio Maltoni  and  Kentarou Mawatari  for enlightening discussions during all the phases of this project. The work of VS has been supported by the  Science Technology and Facilities Council (STFC)  under grant number  ST/J000477/1 , while the work of  AA  and BF has received partial support from the  Theorie-LHC France initiative  of the  CNRS/IN2P3.
The development of a system is usually based on shared and accepted requirements. Hence, to be largely understood by the stakeholders, requirements are often written in natural language (NL). However, checking requirements completeness and consistency requires having them in a formal form. In this article, we focus on user requirements describing a system behaviour, i. e. its behavioural rules. We show how to transform behavioural rules identified from NL requirements and represented within an OWL ontology into the formal specification language Maude. The OWL ontology represents the generic behaviour of a system and allow us to bridge the gap between informal and formal languages and to automate the transformation of NL rules into a Maude specification.
Similarly, in  [7] , the authors use SBVR as an intermediate representation to transform NL business rules into semi-formal models such as UML. The tool NL2Alloy  [1]  also uses SBVR as a pivot representation to generate Alloy 6  code from NL constraints. To our knowledge, only NL2Alloy proposes a complete chain of transformation from NL to formal specifications, but it does not perform formal verifications on the intermediate representations to validate it. Indeed, verifying extracted information needs formal knowledge representation and inference mechanisms. However, controlled natural languages as SBVR or semi-formal representation models as UML often lack validation mechanisms and inference engines. These shortcomings have led many researchers to explore the transformation of SBVR or UML into languages such as OWL and SWRL  [6, 10]  or as Maude  [3].
We propose an OWL-DL ontology based on description logics as an intermediate representation. We use this ontology to guide the automatic identification of behavioural rules from NL requirements analysis and to represent them formally  [8]. Behavioural rules are represented in the ontology in order to be transformed into a formal specification language. Indeed, OWL allows us to check the consistency and the completeness of the modelled rules. However, it cannot represent state evolution or sequential rules application. Hence, to simulate and validate the whole system behaviour, we propose to transform the ontology model into a formal specification Maude. In this article, we focus on the ontology conception choices and the transformation process that enable us to automate the production of formal specifications and to maintain the link between NL requirements and their formal representation.
This distinction is pertinent for both NL requirement analysis and the automatic ontology translation into the formal language Maude. Based on that, we define two high level concepts to represent a system behaviour: Component (C C ⊆ C) and Type (C T ⊆ C) (cf. figure  1 ). Representing the system behaviour requires taking into account the dynamic aspects of its operation. Thus, we modelled two super-properties in the ontology: 1) Relation for describing an interaction between two components of the system; 2) Attribute for describing a characteristic of a component, defined as follows: (P ) ⊆ (I C [C C ] ∪ I C [C T ]) × (I C [C C ] ∪ I C [C T ] ∪ V). We also distinguish two types of attributes: dynamic attribute whose value may evolve over the time, as the balance of a bank account; static attribute whose value is not set to change, such as a bank account ID.
The formal specification language Maude Maude 13  enables to describe the dynamic of a system, i. e. its state changes, and provides different tools for checking it. The state space of a system is represented by a signature Σ that defines sorts (i. e. types) of constants and variables manipulated by Maude and operators that will act upon the manipulated data and by a set of equations E built between terms using the signature. Within Maude, the evolution of the system state is described by rewriting rules of the form R : t → t , where t and t are terms formed on the signature. Rewriting rules rewrite each term of the left hand side of the rule into a term of the right hand side. The rewriting mechanism allows for specification animation and verification of certain properties as the reachability or the non-reachability of particular states.
Maude defines an object-oriented module that offers an object-oriented syntax which is well adapted for concurrent systems, using sets of objects, and a communication mechanism based on message transmission between objects. We use it as a target module for the transformation of the ontology model. In an object-oriented module, objects are of the form <O : C|a 1 : v 1 ,. , a n : v n > with O the object identifier, C the object class, a i (i ∈ 1. n) its attribute names and v i (i ∈ 1. n) the corresponding attribute values. Messages represent the dynamic interaction between objects. They have the form msg M es : Oid, T 1 ,. , T k → M sg. with msg a keyword, M es the message name, Oid the type of the recipient object and T i (i ∈ 1. k) the types of the message arguments. The state of a system, called configuration, corresponds to a multiset of objects and messages.
Conclusion We proposed an approach for behavioural rules representation and formalization from user requirements written in natural language. The core of this approach is an OWL-DL ontology that encompasses the general behaviour of a system. The ontology is used as a pivot representation as it defines a framework for guiding the identification of behavioural rules and allows us to implement an automated transformation of them into a formal specification in Maude. We described an application of our approach on the domain of smart spaces and showed how representing the behaviour of smart space by a Maude specification enabled us to check its consistency and completeness.
The quality of SSTs in this reanalysis is in good agreement with the observations  (Simmons et al. , 2006). However, due to the small size and short lifetimes of medicanes, the quality of the reanalysis data is compromised by the coarse time and space resolution. Most reanalysis datasets are available in 6 h intervals, whereas medicanes occur in short time scales (ranging from 12 h to 90 h). Fine scale feedback associated with airsea interactions can influence the temporal and spatial structure of medicanes. A fully Figures.