dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We wish to thank  Lilith Faucheux ,  Melissa Saichi ,  Fanny Coffin ,  Philippe Hupé ,  Franck Perez ,  Sebastian Amigorena ,  Yong-Jun Liu  and  Fivos Soumelis  for insightful comments and discussions, the  Institut Curie Flow Cytometry facility (Z. Maciorowsky) , the  Institut Curie Affymetrix facility  ( D. Gentien ). This work was supported by funding from  Institut National de la Santé et la Recherche Médicale (Inserm) , the  Institut Curie, Agence Nationale pour la Recherche (ANR) ,  Fondation pour la Recherche Médicale (FRM) , the  European Research Council  ( ERC starting grant   281987 ),  ANR-10-IDEX-0001-02   PSL ,  ANR-11-LABX-0043 ,  CIC IGR-Curie 1428  for V. S. EMBO, Institut Curie post-doctoral fellowships to ICL. ANRS  and  ARC  fellowships to MG and La ligue contre le cancer doctoral fellowship to LMR.
Different facets of the module can be described using semantic annotations, e. g. , input and output parameters, task and quality of service (QoS). In practice, however, we observe that most of semantic annotations that are available are confined to the description of the domain of input and output parameters of modules. Annotations specifying the behavior of the module, as to the task it performs, are rarely specified. Indeed, the number of modules that are semantically described with concepts that describe the behavior of the module lags well behind the number of modules that are semantically annotated in terms of the domains of the input and output parameters, e. g. , in BioTools. Even when they are available, annotations that describe the behavior of the module tend to give a general idea of the task that the module implements, and fall short in describing the specifics of its behavior.
For example, the modules in BioTools, which is a registry that provides information about data preparation modules, are described using terms such as merging and retrieving. While such terms provide a rough idea of what a module does, they do not provide the user with sufficient information to determine if a it is suitable for the data preparation at hand. The failure in crisply describing the behavior of scientific modules should not be attributed to the designers of task ontologies. Indeed, designing an ontology that captures precisely the behavior of modules, without increasing the difficulty that the human annotators who use such ontologies may face thereby compromising the usability of the ontology, is challenging. To overcome this issue, we examine in this paper a solution that utilizes semantic annotations describing the inputs and outputs of modules together with data examples that characterize modules' behavior as ingredients for the discovery of data preparation modules.
It is worth noting that a number of systems have been developed recently to facilitate data preparation tasks, including Trifacta 4  , NADEEF  [3] , Tamer  [8]  and VADA  [6]. These systems come with a number of functionalities that covers, amongst other things, format transformation, data deduplication and data repair. They are primarily targeted for end-users (be they domain expert or not), who would like to use a GUI to clean a single tabular dataset (mainly in relational form or CSV). In our work, we target scientists who wish to programatically process one or multiple datasets, in any format (relational, CSV, text, JSON, etc). The paper is structured as follows. We start by introducing background information regarding data examples and how they are generated for characterizing modules based on retrospective provenance of modules' executions (in Sect.
A source of information that is used for partitioning is the semantic annotations used to describe module parameters. Indeed, the input and output parameters of many scientific modules are annotated using concepts from domain ontologies  [7]. In its simple form, an ontology can be viewed as a hierarchy of concepts. For example, Fig. 2  illustrates a fragment of the myGrid domain ontology used for annotating the inputs and output parameters of bioinformatics modules  [4]. The concepts are connected together using the subsumption relationship, e. g. , ProteinSequence is a sub-concept of BiologicalSequence, which we write using the following notation: ProtSequence BioSequence. Such a hierarchy of concepts can be used to partition the domain of parameters. To generate data examples that characterize the behavior of a module m, m is probed using input instances from a pool, the instances of which cover the concepts of the ontology used for annotations.
To illustrate the approach we adopt for this purpose, consider the candidate module getAccession and getAccessionOfSimilarProtein (see Fig. 3 ). These two modules consume a protein name and output a protein accession, and are characterized by one data example each because the concepts ProteinName and ProteinAccession are leaf nodes in the ontology used for annotation. The feedback supplied by the user to annotate the data examples δ 1 and δ 2 illustrated in Fig. 3  shows that δ 1 is expected and δ 2 is unexpected. Therefore, δ 1 is a true positive for the module getAccession, and δ 2 is a false positive for the module getAccessionOfSimilarProtein. Now, to know whether δ 1 is a false negative for the module getAccessionOfSimilarProtein, we will need to invoke getAccessionOfSimilarProtein using the input value specified in δ 1 , i.
e. , Chorion protein S36. Intuition Behind Feedback Learning. Using the solution that we adopt, we do not need to invoke getAccessionOfSimilarProtein. To do so, we slightly modify the process by which data examples are constructed to cover the partitions of input parameters presented in  [2]  and overviewed in Sect. 2. 1. Specifically, when selecting input values for data examples to cover a given partition, i. e. , semantic domain, c, the same input value v (in c) is used in all those data examples. For example, using this method, the data examples used to characterize the two modules getAccession and getAccessionOfSimilarProtein will have the same input value. Figure  4  illustrates the data examples δ 1 and δ 3 specified using this method to characterize such modules. Consider that the user supplies the feedback instance annotating the data example δ 1 as expected (see Fig.
4 ). Given this feedback instance, we do not have to invoke the module getAccessionOfSimilarProtein using the input value specified in δ 1 to know if δ 1 is a false negative for getAccessionOfSimilarProtein. Indeed, the data example δ 3 shows that the output produced by getAccessionOfSimilarProtein using the same input value as that used in δ 1. Given that the output values of δ 1 and δ 3 are different, we can make the following inferences: i) δ 1 is a false negative for getAccessionOfSimilarProtein, moreover, ii) δ 3 is unexpected, and is, therefore, a false positive for getAccessionOfSimilarProtein. This last inference can be made because the modules that we consider are deterministic. Therefore, the fact that δ 1 is expected implies that δ 3 is unexpected. Note that if δ 3 had the same output value as δ 1 , then we would have inferred that δ 3 is expected and is, therefore, a true positive for getAccessionForSimilarProtein.
Concluding Remarks To assess the performance of the discovery strategy described in the previous section, we ran an experiment to identify the amount of feedback required to detect the modules that are relevant to users' needs. We also examined the error in the F-score estimates computed for candidate modules based on user feedback. To perform a systematic sweep of the parameters of the experiment, we use a synthetic dataset that we created for this purpose. We also used real-world bioinformatic modules. The result of this experiment showed that users can effectively discover scientific modules using a small number of feedback instances. A particularly interesting result that we empirically showed is that the number of feedback instances that the user needs to provide to identify the module that meets the requirement, and more generally a ranking that meets his/her expectations, is small even in the cases where the number of data examples describing the behavior of the modules is large.
The model here has two internal variables (volume fraction of martensite and the accumulative plastic strain). It is based on the assumption of the local multiplicative split of the deformation gradient into elastic and inelastic parts with a total Lagrangian formulation. The inelastic deformation gradient splits also into a transformation and a plastic parts. The developed model is implemented into the commercial software Matlab. The results obtained for thermomechanical loadings are discussed and, a good agreement with.
The inelastic part was then decomposed into contributions for phase transformation F tr and plastic slip F pl. The model constitutive equations are implemented in Matlab software. Obtained results are compared to the experimental data and numerical results of  Khalil et al. (2012 Khalil et al. ( , 2013)). Results of the comparison shows the relevance of the proposed model. Section 2, presents an overview of the basic kinematic framework introducing the adopted local decomposition of the gradient deformation. Section 3 is dedicated to the development of the constitutive model, beginning with the definition of the Helmholtz free energy and the inner potential energy to construct the Lagrangian. Then using the second law of thermodynamics, the loading functions were defined for each behavior.
Constitutive equations: Resolution algorithm The constitutive equations are solved with the Newton-Raphson method combined to the return mapping technique (elastic predictor-inelastic corrector procedure) using the Matlab software. An exponential integration scheme is adopted to update the inelastic deformation gradient. Deformation gradients F n+1 ine = exp(∆D n+1 ine )F n ine (44) F = F el F ine F ine = F tr F pl Strain quantities C = F T F C ine = F T ine F ine E = 1 2 (C -I) E ine = 1 2 (C ine -I) H = 1 2 ln(C) Stress quantities S = µC -1 ine + (λ ln (J) -µ) C -1 M = µ 2 C -1 ine CC -1 ine + 1 2 (λ ln (J) -µ) C -1 ine σ = 1 J FSF T Loading functions Υ m tr = Λ χ dr -Λ tr,m y Υ a tr = -Λ χ dr -Λ tr,a y Υ a pl = Λ γ dr -Λ pl y Kuhn-Tucker conditions on the loading functions Υ m tr ≤ 0, χ ≥ 0, χΥ m tr = 0 Υ a tr ≤ 0, -χ ≥ 0, -χΥ a tr = 0 Υ a pl ≤ 0, γ ≥ 0, γΥ a pl = 0 Kuhn-Tucker conditions on the state variables 1 ≥ 0, 1 χ = 0 2 ≥ 0, 2 (1 -χ) = 0 Flow rules D ine = D tr + D pl D tr = χε tr max N tr D pl = (1 -χ) γN pl where ∆D = D∆t.
Reminding that for any invertible matrix B, the tensor exponential satisfies exp (B) -1 = exp(-B). Using this latter relation along with equations (  1 ) and (  44 ) leads to: F n+1 el = F trial el exp(-∆D n+1 ine ) ( 45 ) where F trial el = ∆FF n el , and ∆F is an incremental deformation gradient, for a local perturbation. The incremental form of the global deformation gradient can be given as: F n+1 = ∆FF n el F n tr F n pl exp(-∆D n+1 ine ) (46) The incremental form for the temperature evolution is defined as follows: T n+1 = T n + ∆T ( 47 ) Using Matlab software, the first step defines the known initial states. In the second step, iterative scheme based on the return mapping procedure allows to identify which mechanisms are activated and to determine the increment for χ and γ solutions of the system in table  1.
To date, there is only a limited dataset of thermodynamic properties for germanate minerals  (Pokrovski and Schott, 1998; Shtenberg et al. , 2017) , and there is no thermodynamic data available for any type of aluminogermanate mineral. Recent advances in models for estimating thermodynamic properties of clays can be applied to Ge-bearing clays  (Blanc et al. , 2015) ; while new developments in tracer-isotope tracking in reaction path and reactive transport codes permit testing the partitioning of germanium and silicon into clays and waters using the synthetic thermodynamic data  (Druhan et al. , 2013; Steefel et al. , 2014). In this study, we calculate the equilibrium fractionation coefficient for reaction (1) using an ideal solid-solution model based on available Ge/Si data and an independent method that predicts the thermodynamic properties of clays.
Therefore, the distribution coefficient (or equilibrium fractionation factor) D 0 Ge Si is given by: Ge K sp Si K sp = Ge/Si 2 fluid Ge/Si 2 clay = D 0 Ge Si (6) note that we have defined the distribution coefficient D 0 Ge Si as the inverse of the squared partitioning coefficient, which would be: K 2 D = R solid R fluid = 1 D 0 Ge Si (where R represents the Ge/Si ratio). Also, the activity coefficients of germanic acid Ge(OH) 4 and silicic acids Si(OH) 4 are almost identical and that neither acid is significantly dissociated. The equilibrium constant for the hydrolysis of kaolinite was obtained from the Thermoddem database  (Blanc et al. , 2012)  (Table  3 ). We favor the use of this database because is internally consistent, and it is the most updated compilation of classical databases (e.
15 K, P = 0. 1 MPa) and the equilibrium constant for Ge-kaolinite hydrolysis (Equation  2 ) is calculated using G f °for Ge-kaolinite and the same data for aqueous species as above  (Pokrovski and Schott, 1998; Blanc et al. , 2012). Note that the calculation of the Gibbs free energy of formation ( G f °) follows the convention adopted by frequently used databases-such as SUPCRT92, SUPCRTBL and Thermoddem  (Johnson et al. , 1992; Blanc et al. , 2012; Zimmer et al. , 2016) -in which it differs in scale from the so called "apparent" Gibbs energy by a constant given by the entropies of the constituent elements  (Berman, 1988; Dolejs, 2013). The free aqueous species and constituent oxides data used here is from Thermoddem and it is consistent with the values used by  Blanc et al. (2015)  for their parametrization.
g. Zhu et al. , 2020). In this section we seek to determine which type of formulation can predict results that are in plausible agreement with observations from natural systems. We have set up a batch dissolution and precipitation model using the geochemical reactive transport code CrunchFlow  (Steefel et al. , 2015). The model is initialized with a singlemineral porous media consisting of a solid solution between albite (NaAlSi 3 O 8 ) and Gealbite (NaAlGe 3 O 8 ), represented by NaAl(Si (1-x) Ge x ) 3 O 8 with a Ge/Si ratio of 1. 5 µmol/mol  (Lugolobi et al. , 2010) , thus with a Ge activity a Ge = 4. 5 ⇥ 10 6. Note here, that by using a single mineral solid-solution for the dissolving phase, we have assumed that Ge-Si fractionation during dissolution of feldspars does not occur, which is consistent with the general observation that Ge/Si in streams are controlled by precipitation and dissolution of secondary minerals  (Murnane and Stallard, 1990; Froelich et al.
, 1995; Cama et al. , 2002; Marty et al. , 2015)  and non-linear-TST with n 1 = 0. 5 formulations for dissolution with a weaker dependence on the affinity term  (Nagy et al. , 1991; Devidal et al. , 1997; Yang and Steefel, 2008)  and assumed these are valid for precipitation (Table  5  in  Marty et al. , 2015). Additionally, we also compare these results with rate laws derived explicitly for kaolinite precipitation: the first is a the parametrization derived from experimental data  (Nagy et al. , 1991; Yang and Steefel, 2008)  by  Marty et al. (2015)  (their Table  8 ) and the second is the experimental formulation by  Yang and Steefel (2008) , which describes no back-reaction close to equilibrium. The rate constants (k f ) used here were obtained directly from  Marty et al. (2015)  or as distributed with the CrunchFlow package for those from  Yang and Steefel (2008).
e. Q kaolinite = Si K sp. Equilibrium constants (K sp ) used for albite and kaolinite are from the Thermoddem database (Table  3 ). For Ge-kaolinite, the equilibrium constant was determined in section 2. 1 for G f °(Ge kaolinite) = 3130 kJ/mol, which corresponds to an equilibrium fractionation coefficient D 0 Ge Si = 4 ⇥ 10 4 (Figure  2 ).
Implications for modeling silicate weathering. Geochimica et Cosmochimica Acta 271, 132-153. doi:10. 1016/j. gca. 2019. 12. 024. Zimmer, K. , Zhang, Y. L. , Lu, P. , Chen, Y. Y. , Zhang, G. R. , Dalkilic, M. ,Zhu, C. , 2016. SUPCRTBL: A revised and extended thermodynamic dataset and software package of SUPCRT92. Computers & Geosciences 90, 97-111. doi:10. 1016/j. cageo. 2016. 02. 013.
Evolutionary relationship between Dop and PafA To give some insight into the evolutionary history of Dop and PafA a phylogenetic analysis was performed. Initially, taxa bearing Dop and PafA homologous sequences were identified, via alignment of the M. smegmatis strain MC 2 155 Dop and PafA sequences against the refseq_protein database using BLASTP searches. The analysis confirmed that Dop and PafA are largely conserved across the Actinobacteria and Nitrospirae phyla. Homologous sequences of one or both proteins were also detected very sporadically in a few draft genomes within other phyla, like the candidate division NC10, Armatimonadetes, Verrucomicrobia, Nitrospinae, Firmicutes and Proteobacteria. A single copy of a homolog to both Dop and PafA was identified in some Planctomycetes species and further used as an external group for construction of a maximum likelihood phylogenetic tree.
To reliably obtain this, we used the highest quality sequences that also represent the maximum diversity of bacteria having a complete pupylation pathway. We thus selected only complete genomes of the RefSeq database (https://www. ncbi. nlm. nih. gov) available in February 2019. Given the massive number of genomes available, a reduction of the dataset was done by selecting randomly only one genome per Actinobacteria family, and per species for the other phyla. The resulting tree built with Dop and PafA sequences indicated that Dop and PafA form two distinct and statistically well supported clusters that originated from an ancient duplication event (Fig. 2 ). The Planctomycetes paralogous proteins share about 29-31% identity with both Actinobacteria and Nitrospirae PafA and Dop proteins, and their sequence partially aligns with the Dop-loop (MAFFT alignment in Sup.
Phylogenetic analysis The hmmsearch program of the HMMER 3. 2. 1 software  (Eddy 2011; Mistry et al. 2013)  and the hidden Markov model (HMM) profiles TIGR03688 and TIGR03686 available in TIGRFAM database  (Haft 2003)  were initially used to extract Dop and PafA orthologous proteins respectively. However, we later observed incongruencies in alignments and concluded that the profiles were not discriminative enough to clearly distinguish both paralogs. We thus built HMM profiles in this study with the hmmbuild program using Dop and PafA sequences of model organisms. These 20-30 sequences, aligned using the MAFFT v7. 313 software  (Katoh and Standley 2013) , represent several phyla and were unambiguously annotated using the MicroScope annotation platform as Dop or PafA  (Vallenet et al. 2009; Vallenet et al.
2017). Built HMM profiles and alignments are given in supplementary materials. For each genome, only the most significant hit was retained, setting an expectation E-value threshold of 1e-100. One copy of Dop and PafA was recovered from each genome, aligned using MAFFT and the --lensi option for higher accuracy, and trimmed with the Gblocks software with less stringent parameters  (Castresana 2000). A Maximum-Likelihood tree was built with the IQ-TREE software  (Nguyen et al. 2015)  and the model LG+F+R5 for describing amino-acid evolution, selected using ModelFinder  (Kalyaanamoorthy et al. 2017 ) and the BIC criterion. 200 replicates of a non-parametric bootstrap approach were conducted to test the robustness of the tree topology. All known proteins in the γglutamyl-cysteine synthetases families were too divergent to be used here as an external group.
Identification of uniquely conserved positions Using enzyme function initiative-enzyme similarity tool (EFI-EST)  (Gerlt et al. 2011; Gerlt et al. 2015)  web server (http://enzymefunction. org), 2,689 sequences belonging to the Pupligase/deamidase family were collected from InterPro  (Finn et al. 2017 ) (IPR004347, IPR022279, IPR022366) and used to generate a sequence-similarity network (SSN)  (Atkinson et al. 2009; Brown and Babbitt 2014). The resulting SSN was plotted and analyzed using Cytoscape  (Shannon et al. 2003; Smoot et al. 2011). The sequences that were clustered together under alignment score of 100 (377 Dop sequences and 285 PafA sequences) were used separately to create a multiple sequence alignment (MSA) using Jalview  (Waterhouse et al. 2009; Troshin et al. 2011)  (http://jalview.
Structural alignment Molecular graphics and analyses were performed with the UCSF Chimera package  (Pettersen et al. 2004 )   The numbers for the residues that were chosen for subsequent mutagenesis are colored red. ATP is colored grey. A quantitative distribution of the highly and uniquely conserved positions is displayed in the table, referring to Dop and PafA of M. smegmatis. The residues referred to in the text are surrounded by a yellow square and marked by an asterisk. FIGURE LEGENDS In addition, a sequence logo of the alpha-loop is presented for each enzyme  (Crooks 2004). Polar, green; neutral, purple; basic, blue; acidic, red; hydrophobic, black.
web server (http://consurf. tau. ac. il/2016) to compute evolutionary conservation for each amino acid based on the phylogenetic relations between sequences. Finally, a sequence alignment from a structural superposition of the solved Dop A. cellulolyticus [PDB: 4B0R] (Özcelik et al. 2012) and PafA C. glutamicum [PDB: 4BJR] ( Barandun et al. 2013).
Hazard analysis using bowtie method We used the Graphical interface for reliability forecasting (GRIF) software to develop the different bowtie models  [33, 34]. The bowties represented in Figs. 14-16 includes risk scenarios of bad control for each approach, respectively. We identify causes that could lead to hazard and their effects. The middle node of each bowtie represents the same unwanted event which is losing control by leader. The use of bowtie method assists to: -Visualize the outcomes of STPA (the frequent causes for each architecture and hazards); -Offer further details about the causal factors obtained by STPA; -Define the existing safety barriers for each structure; -Finally assess the hazards using the risk classification matrix by combining the frequency of the unwanted event obtained from the frequencies of causes with the severity of each hazard.
1 subunit  6, 16. Additionally, we introduced new concepts based on the use of specifically labeled isoleucine-methyl groups to simplify ssNMR data, inspired by earlier progress based on methyl-labeling in solution state NMR  17, 18. This allowed for the collection of unambiguous long-range distance restraints within and between subunits of the tail tube  19. However, due to the large size and inherent heterogeneity of this system the amount of data collected from these experiments does not suffice for a confident structure calculation. In this work, we set out to expand the labeling strategy for long-range distance restraints to further methyl groups, as well as to integrate the NMR data with a 3. 5-6 Å cryo-EM map for hybrid structure calculation. Additionally, the dynamics of the system are assessed by combining relaxation data from ssNMR with variances in the cryo-EM map to create a model for tail tube bending.