dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation details All simulations were performed with the SpinDynamica v3. 4. 2 simulation package  [89] , within Mathematica v12. 1. 0. 0. Five cases studies I-V were investigated, each tailored to highlight a particular feature of the EPR formalism. The full set of input parameters both for the spin system and virtual spectrometer is given in Table  4 , along with quantities calculated from those input variables. The calculations were performed in Liouville space in the rotating frame, with an initial spin state given by the equilibrium density operator calculated from the appropriate laboratory Hamiltonian. The RF pulse excitation was simulated using an ideal rotation superoperator, and was followed by signal acquisition during which the density operator evolved under the action of the appropriate Liouvillian.
2A. Pressure-Temperature (P-T) cycling with a warming step above the water ice melting point favors the full reaction and efficient conversion of ice to clathrate hydrate  (Stern et al. , 2003). During the cooling steps, water inside the autoclave was vigorously stirred with a motorized magnetic drive mixer at 15. 3 rpm to ensure mixing in the autoclave and renew fresh surfaces for clathrate formation. The P-T conditions at which the clathrate synthesis was performed are compared to the phase diagrams and phase boundaries for the formation of CH 4 -N 2 clathrates computed with the CSMGem program based on the thermodynamic model developed by  Sloan and Koh (2007)  (Fig. 2B ). Much of the knowledge on hydrate phase equilibria for simple and multicomponent clathrates compiled by  Sloan and Koh (2007)  has been translated into prediction programs capable of determining clathrate equilibrium boundaries in terms of temperature, pressure, structure, and composition.
One of these available programs, developed at the Colorado School of Mines (CSM), is the CSMGem, a second generation hydrate model implemented into a multi-phase Gibbs energy minimization (Gem) program  (Ballard and Sloan Jr, 2002; Ballard, 2002; Ballard and Sloan Jr, 2004; Sloan and Koh, 2007). CSGem model predicts clathrate compositions precisely at the phase equilibrium conditions. We show in Fig. 2  that the pressure and temperature conditions used in our clathrate synthesis experiments (Fig. 2A ) and reached at the end of their synthesis (see Table  1  and Fig. 2B ) are always inside the formation and stability field of CH 4 -N 2 clathrates calculated with CSMGem (Fig. 2B ). For this study, ten CH 4 -N 2 clathrate compositions were synthesized with reactant gas mixtures ranging from 4 mol% to 95 mol% CH 4 in N 2 -CH 4 , as well as pure CH 4 clathrate and pure N 2 clathrate.
The clathrate guest compositions were determined by analyzing in-situ the gas phase issued from the clathrate dissociation, thanks to direct gas injection by a ROLSI micro-sampler/injector to a gas chromatograph coupled to a thermal conductivity detector (GC-TCD). Sloan and Koh (2007). Clathrate phase boundaries are reported as curves. (A) Top part-Example of typical P-T paths of the heating-cooling cycles (indicated by arrows) carried out to synthesize clathrates from a 85 mol% CH 4 reactant gas mixture. Experimental data measured at the beginning (black square), during (yellow, green and purple symbols) and at the end of the synthesis (black diamond) are indicated. Bottom part-Clathrate dissociation step (orange symbols) performed after evacuating the excess gas phase inside the autoclave at the end of the synthesis in order to measure the CH 4 and N 2 gases trapped in the clathrates.
From χCH 4 = 0. 5 in the reactant gas mixture, the sequestration factor of N 2 begins to increase with increasing χCH 4 and for χCH 4 ≥ 0. 8 the mole fraction of N 2 trapped in clathrates becomes higher than that in the reactant phase. At χCH 4 ~ 0. 7, the hydrate and gas phase composition are similar (azeotrope point). Our experimental results show that the presence of methane at high concentration in the reactant gas mixture promotes the trapping of dinitrogen in clathrates. The sequestration factor we obtained are comparable to model predictions for CH 4 , but are different for N 2. As described above, we obtained an almost constant sequestration factor of N 2 for χCH 4 < 0. 5, followed by a rapid increase at high χCH 4 , while model prediction using CSMGem program  (Sloan and Koh, 2007)  results in a progressive decrease with increasing χCH 4.
According to predictions from the thermodynamic model developed by van der Waals and Platteeuw (1959), CH 4 -N 2 double-clathrate hydrate may form as sI or sII structure depending on the composition of the vapor phase, pressure and temperature of the system. At the temperature and pressure conditions we carried out the synthesis of the clathrates, all CH 4 -N 2 clathrate hydrates studied here are predicted theoretically to form the thermodynamically stable cubic structure sI. This theoretical prediction was obtained by running the hydrate formation model with the CSMGem statistical thermodynamics program from  Sloan and Koh (2007). Our Raman results are thus consistent with the model predictions. A N 2 vibration band at 2322 cm -1 (ν1 symmetric N-N stretching mode) is also observed in the Raman spectra and is attributed to nitrogen in clathrate structure (Fig.
A shift of 7 cm -1 of the N 2 ν 1 symmetric stretching band towards the lower frequencies is observed for N 2 in clathrate (  2322 cm -1 ) compared to N 2 in gas phase (2329 cm -1 ). D. Nna-Mvondo et al. Icarus 358 (2021) 114182  4. 33 Å of average cavity radius, respectively, from  Sloan, 2003). Calculations we made using the Sloan hydrate model/CSMGem program predict nitrogen to enter both cavities for all the CH 4 -N 2 clathrates we synthesized  (Sloan and Koh, 2007). Hence, splitting of the N 2 vibrational band into two bands for the clathrate hydrate, as seen for methane, should be expected. Raman experiments for natural and synthetic air N 2 + O 2 hydrates and pure N 2 clathrate have reported also only one single peak for N 2 at ~2326-2322 cm -1 (van  Hinsberg et al. , 1993; Chazallon et al.
Raman quantitative analysis For each spectrum representing a clathrate crystal, we measured the area of the bands of CH 4 , N 2 and H 2 O. For the analysis of the CH 4 double band, we have used the curve decomposition method (or curve-fitting technique) to fit the combined intensity and area of the two peaks. OriginPro 9 data analysis and graphing software was used for performing baseline correction, peak detection, peak deconvolution, peak fitting and peak integration of the spectrum between 2850 and 2950 cm -1 , range where is detected CH 4 double peak. The baseline was initially automatic determined and then additional adjustment was made manually to pick anchor points and subsequently connect them to create the baseline. The baseline was then subtracted from the spectrum before proceeding to the analyzing the peaks and fitting them with curve functions.
Acknowledgments The research leading to this work has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  (FP7/2007-2013 Grant Agreement No. 259285 ). D. N. -M thanks particularly  E. D. Sloan  and  A. K. Sum  for providing their newest version of the CSMGem program software used in this study. D. N. -M. also acknowledges the  LPG Nantes and CNRS  for their support.
MARTY, a public and open-source C++ code. MARTY is the very first independent program able to calculate automatically amplitudes, squared amplitudes and Wilson coefficients at the one-loop level for general BSM models. This type of calculations requires a computer algebra system and could only be done, up to now, using Mathematica, a commercial and closed software for symbolic manipulations. MARTY does not rely on Mathematica since it re-implements its own computer algebra system also in C++, called CSL. MARTY will considerably ease BSM studies as by automating the analytical calculations the main task of the user would be the model building part. Once interfaced with other phenomenological codes in particle physics, MARTY will be incredibly efficient to make detailed predictions for general BSM models automatically.
Up to now -at the one-loop level -symbolic calculations could be done mainly by hand or using Mathematica  [1] , a commercial and closed software dedicated to this task. Several packages written with Mathematica have implemented high-energy physics features. FeynRules  [2]  computes Feynman rules from a BSM Lagrangian, from which FormCalc  [3]  can derive tree-level and one-loop quantities such as transition amplitudes or differential cross-sections. Packages such as FormFlavor  [4]  or FlavorKit  [5, 6]  can also calculate Wilson coefficients using the FormCalc machinery. Many efforts have been made to develop independent computer algebra systems for high-energy physics. LanHEP  [7] , CompHEP  [8]  and CalcHEP  [9]  automate together calculations from the Lagrangian using their own symbolic computation framework.
However Mathematica packages are still the only ones to provide one-loop calculations and Wilson coefficients in BSM scenarios. With MARTY  [10]  we introduce a modern solution to this issue. Having for the first time a unique, free and open-source code fully written in modern C++ (2017 standard) implementing all the theoretical BSM machinery up to the one-loop order including Wilson coefficients and Next to the Leading Order (NLO) calculations, and without depending on any other framework, since MARTY has its own C++ Symbolic computation Library (CSL) to manipulate mathematical expressions.
Design MARTY is composed of three modules (see Figure  1 ). CSL is a pure symbolic calculation library, containing no physics. It includes features allowing to store and manipulate mathematical expressions, in particular with indicial tensors (such as -matrices    ). Then the physics core of MARTY implements model building utilities and all the group theory and quantum field theory features needed to perform one-loop calculations for BSM physics. Finally, GRAFED (Generating and Rendering Application for FEynman Diagrams) generates automatically Feynman diagram layouts, and displays them via a C++/Qt  [11]  application. Every calculation in MARTY is automatic, symbolic, and at the one-loop level. Possible outputs are Feynman rules (from a Lagrangian), transition amplitudes, differential cross-sections, and Wilson coefficients.
These will include renormalization (couplings, masses, fields), Renormalization Group Equations (RGEs) and operator mixings for Wilson coefficients. MARTY generates C++ code automatically, allowing the user to perform phenomenological analyses. Any mathematical expression (MARTY's output typically) may be written as numerical functions in a C++ library, taking as parameter every symbol that has no specified value. These libraries are also built automatically and may be called from a C++ program or directly using Python to facilitate further analysis.
Cross-section at the tree-level Figure  2a  shows the two diagrams contributing to the  +  -→  +  -process at tree-level in the SM. They are both s-channels, with a photon or -boson exchange. The cross-section is thus shaped around two resonances: The photon resonance (  = 0 GeV) and the  one (  ≈ 91. 2 GeV). The full cross-section calculation has been done with MARTY. The bare output is the differential crosssection. The phase space integration is not done automatically but is straightforward to implement in Python from MARTY's output, a numerical function taking external momenta as parameters. Results are compared to analytical formulas calculated by hand, with or without the  contribution. This is shown in Figure  2b. The -resonance appears clearly, with a maximum value   ≈ 2 nb. The symbolic part of the calculation (from the Lagrangian to the squared amplitude) is performed automatically by.
The Minimal Supersymmetric extension of the Standard Model (MSSM), extended gauge models, vector-like quarks are examples of possible BSM implementations. MARTY can be very useful for BSM phenomenology. One of its most significant advantages is to be written as a unique code, not depending on any other framework. Within MARTY every aspects of model building and high-energy physics calculation are under control. This is a unique opportunity for more collaborations to take this code even further, extending it to new models, other simplification methods, or even different types of calculations.
Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called "PPIDM" (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described "CODAC" (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as "Gold-Standard" a set of DDIs extracted from 3D structural databases.
Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27. 5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.
In fact, the interest raised by DDI study is justified by simple combinatorial reasoning. As most proteins are composed of a limited subset of domains, the study of all possible interactions between the hundreds of millions of proteins available in the sequence databases can be advantageously reduced by the study of the possible interactions between the tens of thousands of domains existing in current domain classifications such as Pfam  (17, 929  families in the 2018 release 32;  [1] ) or CDD (Conserved Domain Database, 52,910 conserved domain models in the v3. 17 version;  [2]. It is therefore of utmost importance to enumerate all plausible DDIs which could explain all the PPIs described so far and which could be used to predict interactions which have not yet been described. Today, well established DDI resources are based on experimental evidence such as 3D structures extracted from the Protein Data Bank (PDB).
This is the case of 3did (updated every year;  [3] [4] [5] ), KBDOCK (last release in 2016;  [6] ), iPfam (last release in 2013;  [7] ) and INSTRUCT (last release in 2013;  [8] ). However the DDI content of these databases does not account for all PPIs described so far. For example, it has been estimated in 2015 that DDIs extracted from 3did only cover around 20% of PPIs from the STRING database  [9]. For this reason, many computational methods have been proposed for inferring DDIs from the domain composition of proteins involved in PPIs. The pioneer work by Sprinzak and Margalit in 2001 used a simple correlation measure of domain occurrence in PPIs  [10]. It was rapidly extended and improved in quite diverse manners. A first group of methods uses probabilistic or statistical approaches including measures of the interaction probability between two domains  [11, 12]  or involving expectation maximization algorithm to maximize a certain likelihood function over a given interactome  [13] [14] [15] , enrichment calculation  [16] , sometimes with the help of functional (Gene Ontology) annotations  [17, 18]  or with complex network neighborhood coefficients  [9].
Other methods exploit the properties of DDIs related to sequence and gene evolution, such as the concepts of domain fusion  [19] , phylogenetic profiling  [20] , relative co-evolution of domain pairs  [21, 22] , correlated mutations  [23] , or combination of these properties  [24]. Another group of methods aims to explain a given set of PPIs with a minimum set of DDIs using various optimization methods such as Integer Linear Programming for parsimonious explanation  [25] [26] [27] , genetic algorithm  [28] , or parameter-dependent selection  [29]. Finally, other authors use PPI and non PPI datasets to develop machine learning approaches such as random forest framework  [30, 31] , discriminant feature selection methods  [32] , or formal concept analysis  [33]. The various datasets of inferred DDIs generated by all these methods do not overlap perfectly well, mainly because they rely on various non overlapping protein interactomes such as DIP (Database of Interacting Proteins;  [34] ), IntAct  [35] , HPRD (Human Protein Reference Database;  [36] ) or STRING  [37].
Thus, several attempts have tried to merge a certain number of the produced DDI datasets and compute a score for each DDI depending on its presence in the composing resources. The most famous integrated resource is certainly the DOMINE database, created in 2008 and updated in 2011  [38, 39]. In its last available release (2011), DOMINE has merged not less than 12 sources of inferred DDIs with two sources of DDIs derived from 3D structures, namely iPfam (2007 release) and 3did (2010 release), leading to a dataset of 26,219 DDIs involving 5,140 distinct Pfam domains. The UniDomInt  [40]  and IDDI (Integrated DDI;  [41] ) resources are other initiatives aimed at merging well-established sets of structural DDIs with sets of inferred DDIs from various methods (9 and 20 sets, respectively). The DIMA3.
0 database (Domain Interaction MAp;  [23] ) integrates more than 5,800 structurally known DDIs from iPfam and 3did databases and 46,900 DDIs computed with four of the methods listed above. The fact that different methods infer the same DDIs can be considered as an argument for DDI reliability. Indeed, in the DOMINE resource, an index quantifying the percentage of overlap (POI) between the twelve sources has been defined and each DDI has received a score calculated as the sum of the POIs of all the sources inferring this DDI. Depending on their score and on their participation in the same biological process (according to the Lee's dataset;  [17] ), DDIs are classified as high, medium or low confidence. Resources such as UniDomInt and IDDI use slightly different methods to assess consistency between the DDIs datasets  [40, 41].
The extreme heterogeneity of methods for inferring DDIs and the absence of regular updates for most of the generated datasets have lead to the absence of any available large-scale updated repository of scored DDIs, reflecting the multiple PPI resources available today. Such a repository could be advantageous in studying PPIs at the domain level. Therefore, we were encouraged to develop a generic new method capable of exploiting multiple PPI datasets for the automatic inference of DDIs. The method is called PPIDM (for Protein-Protein Interaction Domain Miner) and is inspired both by the statistical approaches described above and by the CODAC method, that we previously designed to infer protein domain annotations  [42]. CODAC uses a tri-partite graph setting and a Gold-Standard of existing associations to infer all possible associations between two sets of objects, assuming the objects share a similar neighborhood in a third set of objects.
In PPIDM, the two sets of objects are sets of domains and the third set is a set of PPIs. In this paper, we first describe the PPIDM algorithm and its application to seven datasets of PPIs using a Gold-Standard of 7,254 DDIs derived from 3did and KBDOCK. We then compare the inferred DDIs (84,552 pairs of Pfam domains) with inferred DDIs in DOMINE, revealing a large percentage of newly inferred DDIs in PPIDM. We also analyse the overlap with the 2017 to 2020 releases of 3did, revealing that the DDIs inferred by PPIDM overlap with 3, 239 (46%) DDIs present in the 3did 2020 release (not counting the Gold-Standard), including 800 newly observed DDIs that were not known in 3did when PPIDM was generated. We also study the coverage of PPIs from curated (IMEx) or non curated (STRING) databases by PPIDM DDIs and we compare with DOMINE to the advantage of PPIDM.
In Fig  1 , the X and Y sets are named D L and D R , respectively. By construction, D L and D R are overlapping but distinct sets of domains encompassing the domains present in the left and right proteins of PPIs, respectively. The third bipartite graph G 3 ðX; Y; E 3 Þ is initialized with a set of edges representing DDIs observed in structural databases. Our Gold-Standard set of edges E 3 pertains from the intersection of the KBDOCK and 3did databases. The output of the PPIDM algorithm is E � 3 , which will contain an enriched set of DDIs weighted by their neighborhood similarity score. The adjacency matrices M X of X in Z and M Y of Y in Z are built and cosine similarities are computed between each row of M X and each row of M Y , representing the neighborhood similarity score of the corresponding two domains in the PPI dataset Z.
Combining multiple datasets One original feature of the PPIDM approach is to be designed for handling multiple data sources of PPIs (Z in our tripartite graph model). When more than one PPI datasource is used, a tripartite graph is built for each data source d and processed separately to calculate its respective cosine similarity matrix C d. All matrices are then combined in a unique consensus matrix merging all domain subsets considered in all the graphs. Whenever there is no data for a given pair (x, y) in an input graph G d , the corresponding score C d x;y is set to zero. The cosine similarity scores are then combined as a non-zero weighted average (over all non-zero scores) to give a consensus similarity matrix, CS. Weight determination is performed using Receiver-Operator-Characteristic (ROC) analysis as for information retrieval system when one wishes to retrieve positive documents as first ranked, i.
Algorithm 1 summarizes all this procedure that allows PPIDM to handle multiple data sources of PPIs to infer DDIs. We finally classify our DDIs into Gold, Silver, and Bronze categories using the p-values calculated for each DDI in each input databases. Gold DDIs are those with a non-zero score in at least half of the data sources and that display a significant p-value in all these data sources, Silver DDIs are those with a non-zero score in less than half of the data sources while displaying a significant p-value in all these data sources, and Bronze are the remaining DDIs.
Application to PPI sources In this study, seven PPI sources have been used: IntAct and MINT  [46] , DIP  [34] , HPRD  [36] , and BioGRID  [47, 48]  are manually curated databases of physical interaction between proteins; the very extensive STRING database  [37]  contains both physical and predicted interactions between proteins; the SIFTS database  [49, 50]  allows to access to PPIs present as complexes between PDB chains in the PDB. Thus, these seven PPI databases together provide a comprehensive combination of all available protein interactions and are appropriate for our PPIDM approach. Note that we retrieve all available interactions from these databases and we do not discriminate between stable and transient interactions. Flat data files of IntAct, DIP, MINT, HPRD, BioGRID, STRING, SIFTS, KBDOCK, 3did and UniProt (all releases as of February 2017), were downloaded and parsed using in-house Python scripts.