dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, it provides a simple sanity-check procedure and the possibility of building LK proofs. This paper is organized as follows. Section 2 defines basic concepts and extends the usual definition of expansion trees to accommodate polarities. Section 3 explains how to extract the necessary information from both formats and how it is then used to build expansion trees. Section 4 presents the results of the transformation applied to a database of proofs in the considered formats. It also discusses the advantages of having the proofs as expansion trees. Section 5 discusses some related work and, finally, Section 6 concludes the paper pointing to future work.
Importing GAPT 1  is a framework for proof transformations implemented in the programming language Scala. It supports different proof formats, such as LK (with or without equality) for first and higher order logic, Robinson's resolution calculus  [11] , the schematic calculus LKS  [4]  and, more recently, expansion trees. It provides various algorithms for proofs, such as reductive cut-elimination  [5] , cut-elimination by resolution  [2] , cut-introduction  [6] , Skolemization, and translations between the proof formats. GAPT also comes with prooftool  [3] , an interactive proof visualization tool supporting all these formats. VeriT and leanCoP are automated theorem provers that produce unsatisfiability (in the shape of a resolution refutation) and connection proofs respectively. Both output the proof objects to a structured text file, having in common the fact that all inferences are listed with the operands and the conclusion.
Connection proofs Connection calculi is a set of formalisms for deciding first-order classical formulas which consists on connecting unifiable literals of opposite polarities from the input. Proof search in these calculi is characterized as goal-oriented and, in general, non-confluent. LeanCoP 5  is a connection based theorem prover that implements a series of techniques for reducing the search space and making proof search feasible  [10]. Although its strategy is incomplete, it achieves very good performance in practice. For this work, leanCoP 2. 2 was used. It can be obtained from the CASC24 competition website  6  or, alternatively, executed online at SystemOnTPTP  7. Given an input problem (a set of axioms and conjectures in the language of first-order logic), leanCoP will negate the axioms, skolemize the formulas and translate them into a disjunctive normal form (DNF).
Results We were able to import as expansion trees all the 142 proof objects provided to us by the veriT team, and all but one under one minute. The expansion sequents generated have been used as input for the cut-introduction algorithm  [6]  and some of their features (e. g. high number of instances) have motivated improvements to the algorithm. As for leanCoP, our database consists of 3043 proofs of problems from the TPTP library  [12]. Of those, we can successfully import 1224 as expansion sequents. Some errors still occur while parsing and matching (e. g. our generated clauses do not have the same literal ordering as the clauses in the proof file), but we are working to increase the success rate. Getting proofs from various theorem provers in the shape of expansion sequents allows us to do a number of interesting things.
Related Work Other projects and tools also address the issues of proof visualization and checking. For proofs in the TPTP language in particular, there is IDV  [13] , which provides an interactive interface for manipulating the DAG representing a derivation. This tool focuses solely on visualization of proofs in the TPTP format. Our work aims on a more general framework, of which visualization is only a small part. We are also capable to import different proof objects, not only those in the TPTP language. As for proof checking,  [7]  proposes a check of leanCoP proofs in HOL Light while  [1]  shows how to check SAT and SMT proofs using Coq. The former paper involved re-implementing leanCoP's kernel in HOL Light, which differs a lot from our approach of simply parsing the outputs of theorem provers.
2 ). Modeling, in turn, offers the ability to formalize meta-study findings and explore interactions between the causes and consequences of land change in ways not possible with current metastudy techniques. 1  Importantly, these two modes of meta-study and modeling integration e in the presence or absence of established theory e are mutually exclusive paths. The same meta-study cannot be used to both design and validate a model in order to maintain independence of model inputs and outputs. Unlike case study data where a dataset can be partitioned into calibration data and validation data components  (Peterson et al. , 2011, pp. 55) , such partitioning is often not possible with meta-studies because it is likely impossible for anyone other than the meta-study author to trace the contribution of each case study to synthesized results.
Acknowledgments This paper began as the topic of a workshop titled " From Meta-Analysis to Modeling: Understanding Local Land Change Globally ", which was organized at the  Second Global Land Project (GLP)  (http://www. globallandproject. org)Open Science Meeting held in Berlin, Germany from March 19e31, 2014. All of the workshop participants contributed in some form to the concepts presented in this paper. In addition, the participation of NM and VS in the workshop was supported by the  GLOBE  Project (NSF # CNS-1125210 ). NM was also supported by the  National Socio-Environmental Synthesis Center (SESYNC)  through a grant from the  U. S. National Science Foundation  (#  DBI-1052875 ) to the  University of Maryland. PV, JvV and CO acknowledge funding support from  ERC  Grant  ERC-Stg-2012-311819  ' GLOLAND '.
SOCIAL RECOMMENDATION APPROACH Since the context of our work concerns the social networks for learning, our approach  (Tadlaoui, 2014)  illustrated in Figure  1  is based on 1) data that describes users and stored in their profiles, 2) data on different types of links between users and between the groups to which they belong and 3) feedbacks on results of exercises made by learners. All these data will be used to make recommendations to learners. Figure  2  illustrates the overall principle of our approach. Each user of the system is described with some information that characterizes her/his profile and is linked with friendships links with other system users. Users can view educational resources and they can also evaluate the quality and usefulness of the resources they have already seen.
EVALUATION To evaluate our approach, we have wanted first to use datasets extracted from existing educational recommender systems. Among these datasets, we can mention Mendeley  (Jack et al. 2010) , MACE (Wolpers and Niemann 2010) and APOSDLE  (Beham et al. 2010 ). After studying this type of datasets, we found it impossible to use them to evaluate our model. The data they provide do not contain all the data we need to conduct the evaluation, such as social relationships between users and resource evaluations in terms of utility. We have considered the possibility to complete these datasets with missing information but the whole new modified dataset can be incoherent and it can make our simulation wrong. To address this problem, we established a process for studying the feasibility of our approach, evaluate that it proposes relevant resources and compare it with other existing approaches.
Simulation on a Created Dataset For the first step of the evaluation we have made a simulation on a dataset that we have created. This evaluation helped us, to test the algorithms related to our formulas to evaluate their effectiveness and to refine them. We have developed a prototype in Java that calculates the similarities between users and calculates and displays the three lists of recommendation that we propose in our approach. The dataset that we have created contains all the information that our model needs. It uses the information about 10 users and 9 resources. This dataset contains mainly 1) user characteristics such as age, preferences. 2) social relationships between users and 3) evaluation values that users attribute to educational resources.
Simulation on ACCEL Dataset The dataset extracted from the ACCEL platform can help us to evaluate our approach. It can give us a complementary evaluation compared to the icraa platform since the context of use and the type of users are not the same between these two systems. The problem with this platform is that it does not contain the functionality of evaluating resources and the functionality of declaring friend relationships. We are working with the ACCEL team to incorporate these functionalities on their platform. All their users will use these new functionalities and after some weeks of use we will extract a dataset which contains information that we need for our evaluation.
In aerospace Engineering, the accurate simulation of ice accretion is a key element to increase flight safety and avoid accidents related to icing effects. The icing code developed in the NSMB solver is based on an Eulerian formulation for droplets tracking, an iterative Messinger model using a modified water runback scheme for ice thickness calculation and mesh deformation to track the ice/air interface through time. The whole process is parallelized with MPI and applied with chimera grids.
The key element of ice accretion is the determination of impingement collection efficiency. While a Lagrangian formulation requires a statistical averaging process to evaluate it, the determination of this quantity in Eulerian formulation is direct. In addition, the resolution of droplet equations in the Eulerian frame open up access to the large panel of computational fluid dynamics accelerating techniques. For reasons stated above, the advantages of choosing Eulerian framework are obvious. In this paper we present the icing code developed in the structured compressible and multi-block Navier-Stokes solver NSMB  [1]. Supercooled water droplets field and impingement areas are obtained by means of an Eulerian approach. The ice thickness is calculated based on an iterative Messinger model upgraded by an improved water runback scheme for three-dimensional and multi-block ice accretion simulations.
Results: A selection of stable, potential reference genes was carried out in silico from RNA-seq based transcriptomic data obtained from T. versatilis. A dozen functionally unrelated candidate genes were analysed by RT-qPCR assays over more than 30 relevant culture conditions. By using geNorm, we showed that most of these candidate genes had stable transcript levels in most of the conditions, from growth environments to conidial germination. The overall robustness of these genes was explored further by showing that any combination of 3 of them led to minimal normalization bias. To extend the relevance of the study beyond T. versatilis, we challenged their stability together with sixteen other classically used genes such as β-tubulin or actin, in a representative sample of about 100 RNA-seq datasets.
These datasets were obtained from 18 phylogenetically distant filamentous fungi exposed to prevalent experimental conditions. Although this wide analysis demonstrated that each of the chosen genes exhibited sporadic up-or down-regulation, their hierarchical clustering allowed the identification of a promising group of 6 genes, which presented weak expression changes and no tendency to up-or down-regulation over the whole set of conditions. This group included ubcB, sac7, fis1 and sarA genes, as well as TFC1 and UBC6 that were previously validated for their use in S. cerevisiae. Conclusions: We propose a set of 6 genes that can be used as reference genes in RT-qPCR data normalization in any field of fungal biology. However, we recommend that the uniform transcription of these genes is tested by systematic experimental validation and to use the geometric averaging of at least 3 of the best ones.
Good practice in data normalization for gene expression analysis therefore relies on the identification, experimental validation and use of several reference genes. In filamentous fungi, such efforts have been observed with recent publications dedicated to the validation of suitable reference genes under specific experimental contexts  [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45]. In Zhou's work  [39] , cypB and crzA were evaluated because of their stability in transcriptomic datasets. Similarly, Kim and Yun  [46]  selected 8 reference genes from transcriptomic data available with Fusarium graminearum. Such an approach was an exception, as most often, authors have evaluated more classic "housekeeping genes" encoding for example actin, glyceraldehyde-3-phosphate dehydrogenase or β-tubulin, which are still and too frequently used as single, nonvalidated reference genes.
During the course of the RNA-seq based transcriptomic analysis of the industrial strain T. versatilis exposed to wheat straw, it was found that most of the classical reference genes exhibited expression changes in the presence of this lignocellulosic substrate (unpublished data). This finding prompted the formulation of a list of putative reference genes and validation of their expression stability in T. versatilis cultivated under more than 30 different relevant conditions, following the MIQE guidelines for robust and reliable RT-qPCR expression data acquisition and treatment. Finally, 90 RNA-seq based transcriptomic datasets from 18 phylogenetically distant filamentous fungi were scrutinized, including datasets from industrially important or model species as well as plant or animal interacting fungi, to demonstrate that some of the candidate genes suitable for T.
For each sample, a ValidPrime Assay (VPA), consisting of a pair of primers that bind to a non-transcribed intergenic region identified from RNA-seq data, was also included to detect and quantify the presence of contaminating gDNA  [47]. The primers for the VPA were; 5′ACCGAATGGCACCGA GTTGG 3′ and 5′AATGGAGGAAGCGTGCCGTG 3′. As gDNA contamination rarely exceeded 1%, the RT-qPCR data were directly analysed using the CFX Manager software (Bio-Rad).
15, the addition of the (n + 1) th gene has a significant effect on normalization quality and should preferably be included for calculation of a reliable normalization factor. In-silico analysis of RNA-seq data Three RNA-seq datasets from the industrial T. versatilis were at our disposal (unpublished data) and were prepared from: 1) growth of the mycelium on MM for 48 h (reference condition); 2) transfer of the water-rinsed mycelium to MM with ball-milled wheat straw 1% (w/v) as carbon source and sampling after 24 h; 3) direct addition of glucose at 1% final concentration to the mycelium exposed to wheat straw, and sampling after 5 h. These RNA-seq data were used for the pre-selection of stable genes (fold change (FC) equal to one, see Additional file 2) after calculating the FC as follow: RPKM (Reads Per Kilobase of exon model per Million mapped reads) value in the sample of interest / RPKM in the reference condition, for each gene.
Similarly, FC for candidate reference genes were calculated from RNA-seq data publicly available at the NCBI GEO database  [48, 49]. To identify the homologues of T. versatilis selected reference genes in the different fungi, a standard protein BLAST (blastp) using the amino-acid sequence from T. versatilis was performed against protein databases, specifying the organism. Each homologous sequence was then used for a reciprocal BLAST against the T. versatilis database in order to confirm the accuracy of the result. The detailed list of locus tags for each gene in every fungus is available in the Additional file 3. For each GOI in these studies, the ratio between the expression in a condition of interest and the expression in the control condition was calculated.
Results and discussion Selection of candidate reference genes from T. versatilis RNA-seq datasets In a preliminary study on the industrial Talaromyces versatilis strain IMI378536, RNA-seq data were generated to analyse the transcriptome of this filamentous fungus on wheat straw (unpublished data, property of ADISSEO SAS). An in silico screen for genes that showed no differential expression between glucose and wheat straw, was used to select about a hundred genes with fold-changes close to one, indicating stability of transcript levels under those conditions. From this preselection, genes were discarded because of anti-sense transcription, alternative splicing events, as well as very low expression level (RPKM below 15). The design of primers and their experimental validation by RT-qPCR were then performed on a residual list of 20 candidate genes taking care to avoid their participation in similar cellular functions to minimise the risk of co-regulation under culture conditions of interest.
This is most likely the reason why these different algorithms could not propose clear-cut, identical classifications of these genes. Still using geNorm, the optimal number of reference genes required for accurate normalization in the "all conditions" set (Figure  2B ) was evaluated. Vandesompele and coworkers  [33]  recommended a cut-off value at 0. 15 for the pairwise variation value (V n/(n+1) ), below which the inclusion of an additional gene does not result in a significant improvement of the normalization. According to this criterion, the V 2/3 and V 3/4 values indicated that three genes, i. e. R2 (ubcB), R10 (sac7) and R6 (psm1), were sufficient for accurate normalization of transcript levels in any of the samples examined.
2 and 32-fold upon transfer from glucose to thio-gentiobiose and arbocel, respectively, whereas the transfer to C-starved medium did not cause any significant change. Different regulatory patterns were obtained using single reference genes for normalization. The use of R3, the worst gene according to geNorm classification, led to no-significant change of abf-B2 transcript level upon transfer to thio-gentiobiose, slightly over-estimated induction on arbocel (64-fold), and indicated that this gene was repressed by 3-fold upon C-starvation. The use of β-tub also modified the expression pattern but in contrast to R3, it allowed concluding that abf-B2 was induced by 9 and 7-fold upon transfer to thio-gentiobiose and C-starvation, respectively. Similar conclusions could be drawn by analysing xynC.
Expression data collection from phylogenetically distant filamentous fungi To demonstrate the suitability of some of the 12 putative reference genes for RT-qPCR analysis in fungi, RNA-seq based transcriptomic datasets from 18 phylogenetically distant filamentous fungi were interrogated, exploiting web resources such as the GEO portal  [48, 49]. These datasets covered model fungi  [55, [59] [60] [61] [62] , biotechnologically important organisms  [50, 51, 54] , agronomically relevant fungi such as symbiotic organisms or plant pathogens (  [8, 11, 12, [47] [48] [49]  and personal communications), and human pathogens  [52, 53]. This collection corresponded to 90 independent datasets, most of them in triplicates and harvested from a broad variety of experimental conditions, e. g. exposure to stress, nutritional source utilisation, fungihost interactions or development stages (see Additional file 5 for further details).
(C) Quantile plot of the normalization bias values for each NF. The normalization bias (Log (base 2) ) is represented on the X axis and the same colour code used in (B) was applied. The quantile fractions are represented on the Y axis. (♦) NF (R12,R7,R8) , (☐) NF (R4,R9,R1) , (◯) NF (R5,R11,R3) , (+) NF (R3) and (×) NF (β-tub). Figure  5  (See legend on next page. ) by B. graminis, extensive transcriptomic changes were observed for most of the genes. A similar situation was observed during different development stages of S. macrospora, where most of the reference genes were either down-or up-regulated. These two datasets strengthened the conclusion that even the best reference genes will never escape sporadic differential expression, so that validation of their stability is highly recommended prior to their use for normalization in each new project.
Even though genes in the red class could be considered as a very promising set of reference genes for normalization purposes in fungi, they too may present unexpected regulatory changes in specific contexts. An interesting case-study was indeed found in this work with two of these most promising reference genes, i. e. R4 (fis1) and Sc4 (UBC6), which were strongly down-regulated in C. higginsianum (See figure on previous page. ) Figure  5  Heat map of RNA-seq based expression data of putative reference genes, collected from 18 phylogenetically distant fungi. For each RNA-seq dataset (study), we calculated for each gene fold changes (FC) as the ratio between the expression in a condition of interest and the expression in the condition that was defined as the control in this study. Each line represents a condition of interest, each column a gene of interest (corresponding names of the genes are given in Figure  6 ).
e. signal transduction and secretion, respectively. Unfortunately, this is not the case for R2 (ubcB, Ubiquitin-protein ligase activity) and Sc4 (UBC6, ER-associated protein catabolic process), which belong to a similar functional category and hence may show undesirable co-regulation. Therefore, the use of both of them as references genes should be discouraged, although both could be evaluated. It is unlikely that expression of most of these promising reference genes will be found to be unstable simultaneously in projects. If this was the case, it will require identifying and validating new genes. Transcriptomic data obtained from distant organisms studied under comparable conditions, or from the organism of interest cultivated in conditions as diverse as possible, could be collected. This strategy turned out to be successful in our hands for pre-selection of appropriate reference genes, even from a very limited set of transcriptomic data as starting material.
The automated identification of suitable reference genes by the use of tools such as RefGenes  [30]  might be useful, taking care to focus on functionally unrelated candidates, provided   5  (Legend as in Figure  1 ). The extreme values amongst outliers are marked with a red asterisk. The colours of the boxes relate the classes that were determined from the HAC (see Figure  7 ). From the top to the bottom, we listed the new candidates ('R series'), the classical reference genes ('C series') and the putative references homologous to S. cerevisiae genes (Sc1─Sc4). The right panel resumes the type of distribution (normal or not), average, median and interquartile for each gene. that transcriptomic data that are targeted by this tool are generalised to all published datasets, including those that have been produced from filamentous fungi.
Conclusions Starting with a restricted set of 3 RNA-seq based transcriptomic datasets from T. versatilis cultivated under extremely different growth conditions, a list of 12 stable genes that belong to different functional classes was selected. The stability of transcript levels in more than thirty conditions of interest such as varied nutritional sources, stress exposure or time course analysis of conidial germination was examined. Three genes, R2 (ubcB), R10 (sac7) and R6 (psm1), were certified as the best reference genes for accurate normalization of expression levels in T. versatilis. Ideally, the optimal number of genes relies on the stepwise inclusion of additional reference genes until the time when this supplementary gene may not improve, nor worsen the normalization factor  [33]. The overall good stability of the 12 candidates selected allowed us to show that any combination of 3 of them resulted in very similar normalized fold-change values and minimal normalization bias, even with the least stable genes on the list.