dataesr/scientific-paragraphs-categorization huggingface dataset:
Atomic scattering factors are retrieved from a combination of tabulated spherical contributions and refined multipole parameters. This means that in multipole modelling both the molecular geometry, including atomic displacement parameters, and electron-density parameters are obtained by refinement against the measured structure factors. Alternatively, multipole parameters can be transferred from databanks (either constructed from theoretical calculations or averaged over experimental multipole refinements) and fixed during the refinement of positions and anisotropic displacement parameters  (Dittrich et al. , 2005; Dadda et al. , 2012; Ba ˛k et al. , 2011). By virtue of their construction, multipole databanks are suitable for the refinement of peptide and protein crystal structures with nonspherical atomic form factors  (Jelsch et al.
, 2000; Dittrich et al. , 2010). Beyond multipole modelling, there are methods that make direct use of quantum-mechanical wavefunctions to model experimental diffraction data by taking into account atomic nonsphericity, and these are discussed in the following paragraphs. These methods belong to the emerging field of quantum crystallography (QCr)  (Grabowsky et al. , 2017 (Grabowsky et al. , , 2020;; Genoni et al. , 2018). The majority of QCr methods have to date been exclusively implemented and run in the software Tonto  (Jayatilaka & Grimwood, 2003). The software lama-GOET presented in this work is a graphical user interface (GUI) for Tonto to make its full capability more easily accessible. Therefore, lamaGOET acts as an interface for the three quantum-crystallographic methods described below.
, 2016). In other words, lamaGOET allows interfacing Tonto and Gaussian directly. This gives access to quantum-mechanical methods otherwise not available, without detriment of any functionality in the original software Tonto. HAR-ELMO. A limitation of HAR is the fact that it requires a Hartree-Fock (HF) or density-functional-theory (DFT) computation before each refinement step, so that it is computationally expensive. Hence, it cannot be readily applied to larger systems such as macromolecules or compounds containing heavy elements. However,  Meyer & Genoni (2018)  have recently constructed a library of extremely localized molecular orbitals (ELMOs). These molecular orbitals are strictly localized on small molecular units, i. e. atoms, bonds and functional groups. For this reason, they are easily transferable from molecule to molecule  (Meyer, Guillot, Ruiz-Lopez & Genoni, 2016; Meyer, Guillot, Ruiz-Lopez, Jelsch & Genoni, 2016) , allowing the quick reconstruction of wavefunctions and electron densities of proteins through instantaneous transfer of ELMOs from the databank with the software ELMOdb  (Meyer & Genoni, 2018).
At present, the databank includes ELMOs for all the naturally encoded amino acids. Therefore, for coordination compounds and corresponding ligands, ELMOs have to be calculated once before the transfer. It was recently demonstrated that the new HAR-ELMO method allows one to perform refinements that produce H-atom parameters as accurate and precise as those resulting from neutron diffraction or original HAR for small molecules  (Malaspina et al. , 2019). For HAR-ELMO applications, lamaGOET interfaces the ELMOdb software  (Meyer & Genoni, 2018)  with Tonto  (Malaspina et al. , 2019). The ELMOdb software takes care of the rapid generation of wavefunctions from ELMO building blocks, lamaGOET transfers these wavefunctions to Tonto, and Tonto carries out the Hirshfeld atom partitioning and crystallographic least-squares refinement.
Although other fragment approaches have recently been developed  (Zheng et al. , 2020; Bergmann et al. , 2020) , to the best of our knowledge, computer programs 2 of 9 Lorraine A. Malaspina et al. lamaGOET  HAR-ELMO within lamaGOET is currently the only available method that has been used to refine a protein with quantum crystallographically derived nonspherical atomic scattering factors beyond multipole database techniques  (Malaspina et al. , 2019). X-ray constrained wavefunction (XCW) fitting. XCW fitting  (Jayatilaka, 1998; Jayatilaka & Grimwood, 2001; Grimwood & Jayatilaka, 2001)  allows for the fitting of molecular orbital coefficients to measured structure factors. Whereas HAR and HAR-ELMO as well as multipole database techniques derive nonspherical atomic form factors theoretically and refine only coordinates and displacement parameters, in the XCW strategy the electron density is refined via the fitting of molecular orbitals.
lamaGOET: platform, availability and use Traditionally, a myriad of different stand-alone utilities have been used by the crystallographic community, mostly in the Fortran programming language. However, more importantly, most crystallographic software can be run using a commandline interpreter. Therefore, interfacing different software can be easily achieved in command language. The lamaGOET interface started as a small bash script to perform a specific job, namely a HAR using Gaussian for the SCF calculations and Tonto for the refinements. Its utilities and functions rapidly increased, making it a tool for broad use in quantum crystallography. The latest version of lamaGOET is still written in bash, which makes it easy for users to read and understand the code. This also allows easy transferability across different operating systems.
The script can be run on Linux and MacOS platforms using the native command-line interpreter. The prerequisites to run lamaGOET are usually default in any bash interpreter. These are gawk, zenity and coreutils. The increasing number of features and options led to the implementation of the graphical interface using gtkdialog, which is a GUI-creation utility that can be used with an arbitrary interpreter. By running the installation script provided with lamaGOET, all these dependencies are automatically installed, including gtkdialog. The lamaGOET script encourages code reuse and distribution and is subject to the GNU Public License. lamaGOET was written by LAM (lamaGOET = Lorraine A. Malaspina Gaussian Orca ELMO Tonto)  (Malaspina, 2020)  and can be obtained free of charge at http://www. tinyurl.
com/lamaGOET (source code is also available at https://github. com/lomalaspina/lamaGOET). Once the installation script has been run successfully, the GUI can be called by typing lamaGOET inside the chosen command-line interpreter from the folder where the result files are to be written. As input, a file which contains the initial geometry and crystal information of the structure is needed; this can be a CIF or a Protein Data Bank (PDB) file if the job uses the ELMO libraries. In addition, a reflection file in free format is needed as input for all quantum-crystallographic treatments. A flowchart visualizing the way in which lama-GOET enables HAR and HAR-ELMO by interfacing different quantum-mechanical software with Tonto is given in Fig. 1(a). Moreover, the procedure of an XCW fitting with lamaGOET is visualized in Fig.
1(b). Full explanations of all available functions and options to be chosen for the quantumcrystallographic examples discussed in Section 3 are given in the supporting information (Section 1). There are a few special versions of lamaGOET. In Windows, it can be run using different available X servers or the GNU environment. It has been successfully tested on Windows platforms using the MinGW, MobaXterm and Cygwin tools. For use on computer clusters or supercomputers, two separate scripts are available for download. The first contains the graphical user interface (called GUI-lamaGOET), which should be placed on the user's local machine. The second reads the inputs provided in the GUI and runs the script (RUN-lamaGOET). It should be placed on the computer cluster. Versions using the Torque (PBS) and the YARN (through spark.
cmd) queuing systems are available at the github page https://github. com/ lomalaspina/lamaGOET. Tonto is available at https://github. com/dylanjayatilaka/tonto. The github page provides stepby-step tutorials on how to install and compile Tonto for all different operating systems. We recommend to use gfortran-8 for the Tonto compilation on a Linux system. ELMOdb is a stand-alone program  (Meyer & Genoni, 2018 ) independent of lamaGOET. It allows the automatic transfer of ELMOs from the available ELMO databanks  (Meyer & Genoni, 2018)  to target polypeptide/protein structures. It requires a PDB file as input, which is analysed by the program one residue at a time. For each residue, ELMOdb processes every single fragment by retrieving the orbitals to be transferred and by defining the atomic triads that are necessary to define the matrices for the rotation/ transfer of the ELMOs to the target structure  (Meyer, Guillot, Ruiz-Lopez & Genoni, 2016; Meyer, Guillot, Ruiz-Lopez, Jelsch & Genoni, 2016; Meyer & Genoni, 2018).
The current version of the ELMO libraries covers all the possible fragments for the 20 naturally encoded amino acids in all their possible protonation states and forms (N-terminal, C-terminal and nonterminal) and the water molecule. The stored ELMOs are available in different standard basis sets of quantum chemistry [6-31G, 6-31G(d, p), 6-311G, 6-311G(d, p) and cc-pVDZ]. In addition, ELMOdb has the option of reading customized ELMOs expanded on any basis set and corresponding to particular fragments, ligands or solvent molecules that may constitute the systems under examination. These tailor-made ELMOs must be preliminarily computed on appropriate model molecules and then stored in a suitable directory where the program can retrieve them when necessary. ELMOdb finally provides an output file with general information on the performed computation, along with a binary file for the final rotated ELMOs, a binary file incorporating the associated ELMO one-electron density matrix, and a Gaussian-formatted checkpoint file that is used to perform subsequent analyses or calculations.
The ELMOdb program and the ELMO libraries are currently available free of charge by sending a request to the main developer of the software (Alessandro. Genoni@ univ-lorraine. fr) (Meyer & Genoni, 2018 ). In the course of a HAR-ELMO treatment (Fig. 1 ), lamaGOET reads the initial or iteratively refined geometry and passes it to ELMOdb for the automatic transfer and rotation of molecular orbitals, together with information on tailor-made residues (if present). lamaGOET then reads the formatted checkpoint file output from ELMOdb and passes it on to Tonto for the leastsquares (LS) refinement. The lamaGOET interface also facilitates the generation of grid files in Gaussian cube-file format within Tonto for different properties. These grid files are generated from the resulting binary wavefunction files written by Tonto.
These binary files are generated through lamaGOET regardless of the software selected for the wavefunction-calculation step. At the moment, all Tonto-generated grid files will contain only atoms of the asymmetric unit which are within the unit-cell dimensions. Therefore, in many cases, pieces of molecules will be omitted for the calculation of cubes. To avoid this problem, lamaGOET offers the user the option to set the size, origin and orientation of the grid file manually. The next paragraph illustrates a short example of properties that can be calculated and plotted; further instructions on how to use this option can be found in the supporting information (at the end of Section 2 and in Figs. S5  and S9 ). In the ammonia crystal structure, only a third of the molecule is symmetry independent.
We performed HAR of ammonia based on experimental data taken from  Boese et al. (1997). We used Tonto at the HF/def2-TZVP level of theory with the option to auto-complete the structure as described in the supporting information. The corresponding CIF is deposited with the Cambridge Structural Database (CSD) under CCDC deposition No. 1987830 and is also provided as supporting information to this article. Fig. 2  shows the related representations of the deformation density distribution, of the distribution of the negative Laplacian of the electron density and of the Becke88 exchange-correlation potential based on the Kohn-Sham orbitals calculated in the final HAR geometry. In the deformation density and the negative Laplacian, valence-shell charge concentrations (purple, blue) signify bonding and non-bonding effects, here the covalent N-H bonds and the nitrogen lone pair, respectively.
The exchange-correlation potential is less structured, but it does show the presence of the nitrogen lone pair by a deviation from sphericity into the lone-pair direction. Although unrelated to QCr, another option available inside the lamaGOET GUI is the possibility of setting up theoretical geometry optimizations of structures in Gaussian using a field of self-consistent Hirshfeld point charges within a defined cluster radius. Unlike the previous steps, this is a fully theoretical approach that allows the user to perform isolatedmolecule optimizations with the influence of the environment. The idea is similar to that of the software baerlauch  (Dittrich, Pfitzenreuter & Hu ¨bschle, 2012) , where crystal structures are used to provide input files consisting of explicit clusters of molecules for Gaussian optimizations, but in lamaGOET the environment is considered implicitly via symmetry-generated cluster charges, not explicitly as in baerlauch.
Conclusions In this paper, we have demonstrated the usefulness and capabilities of the quantum-crystallographic interface lama-GOET. It facilitates Hirshfeld atom refinement, HAR-ELMO, X-ray constrained wavefunction fitting, the representation of properties on grids and the generation of a symmetry-generated crystallographic cluster of point charges for further theoretical calculations. At present, it is the only software that allows HAR-ELMO. lamaGOET is conceptually meant to be an interface for crystallographers who aim to work with Tonto for quantum-crystallographic applications and want to make the most of Tonto's vast functionality, or even extend it with external quantum-mechanical software. lamaGOET will be maintained and expanded in this direction. In this sense, the development of lamaGOET is different from recent HAR  developments in Olex2  (Fugel et al.
, 2019). When working on embedded networks, the proximity facet is embedded in the vector space. It is then possible to focus on the detection of subspaces containing genes that have a high activity. Following this line, we propose AMINE (Active Module Identification through Network Embedding), a new and efficient method for active module detection based on Node2vec  (Grover and Leskovec, 2016). Our method uses a greedy approach to build increasingly large clusters of nodes based on the similarity of their encoding vectors and to evaluate them according to a metric taking into account the activity of the contained nodes. On artificially generated datasets, the effectiveness of the method compares well with several recent algorithms. On real datasets, AMINE allows to complement the results obtained with classical approaches by identifying new groups of genes of great interest.
Evaluation of AMINE on artificial data generated by Robinson et al. (2017) Many studies dealing with the identification of active modules have tested their methods on datasets generated by themselves and which are, at times, difficult to reproduce. Robinson et al. (2017)  are among the few to give access to all materials used to test the MRF method they proposed. These materials contain the graph itself, the p-values associated with the nodes and the modules to be identified. It gives us the opportunity to apply our method on exactly the same data. The simulated experiment used to evaluate the MRF method  (Robinson et al. , 2017)  consists of a set of 1000 scale free graphs, each containing 1000 vertices associated with values simulated from a standard uniform distribution. In this dataset, there are three modules to discover (the hit modules), each containing 10 vertices.
To simulate the fact that the vertices belonging to these modules represent differentially expressed genes, and are therefore associated with low p-values, these vertices are assigned simulated values from a truncated Gaussian distribution with mean 0 and standard deviation equals to 10 -6. Robinson et al. (2017)  compared their MRF method to NePhe  (Wang et al. , 2009) , Knode  (Cornish and Markowetz, 2014)  and BioNet  (Beisser et al. , 2010)  and report that MRF gives the best performance in term of recall. Since it is known that there are exactly 30 true hits in the dataset, the authors rate the different methods by considering only the proportion of true hits in each hit list of size 30. We ran AMINE on these data to detect the three most significant modules. Since our method does not consider the size of expected modules as an input parameter, it can identify modules of various sizes.
When considering only the recall criterion (proportion of hit nodes identified) as Robinson et al. did, AMINE is disadvantaged when it identifies small modules and favored when it identifies large modules. In order to have a fair comparison between AMINE and MRF, we apply the following rule: if the number of genes belonging to the three identified modules is greater than 30, then only 30 genes randomly chosen are considered. In the case where the three identified modules contain less than 30 genes, the recall obtained by our method is underestimated. Despite this disadvantage, the predictions made by AMINE obtained a slightly better recall than the one obtained by MRF (Fig. 1A , last boxplot ; the detail of the evaluation performed by  Robinson et al. can be found in Fig. 7  of their article  (Robinson et al.
, 2017) ). The recall without truncation (number of true positive divided by the number of hit nodes), the precision (number of true positive divided by the number of nodes identified) and the F1 score (harmonic mean of precision and recall) were also plotted on the same figure. The median value of the F1 score is 0. 76 while the minimum and maximum values are 0. 31 and 0. 94 respectively. The total number of genes in the three identified modules range from 18 to 46 with a median of 28 (Fig. 1B ). This means that AMINE is able to identify modules close to the ground truth (although slightly smaller) without the need to specify their size a priori.
S2  for an example of a generated dense network). The performance of AMINE was compared with the methods COSINE  (Ma et al. , 2011) , BioNet  (Beisser et al. , 2010) , GiGA  (Breitling et al. , 2004 ) and a baseline consisting in simply picking the genes with the lowest p-values. The results are shown in Fig. 2  for networks comprising 1000 vertices and modules of size 10 and in Fig. 3  for networks with 1000 vertices and modules of size 20. We can observe that identifying module on denser network is a process much more complicated as the median F1 score drops from 0. 76 on a sparse graph to values just above 0. 5, depending of the size of the module. The scores of the other methods are much lower in the case of modules of size 10 ; some even score lower than the baseline. In the case of COSINE and BioNet, these poor results come with the prediction of large modules whose size exceeds 100 genes (Fig.
2B  and Fig. 3B ). For modules of size 20, the F1 scores of GiGA and AMINE are very close (Fig. 3B ). However, it should be noted that GiGA uses a parameter that determines the maximum size of the module to be identified. In our experiment, we have set the maximum size to the size of the module to be identified, which, of course, helps the procedure. As shown in Fig. 2C  and Fig. 3C , for GiGA, the size of the identified modules is always below the maximum. For AMINE, without any indication on the size of the modules being searched, we can see that the method predicts modules with a median size close to the ground truth size. This is a significant advantage of our method since biologists do not know in advance the size of the modules they are looking for.
Scalability of the method To test to what extent our method is able to scale to larger networks, we applied it to an artificial network of 10,000 vertices, that was generated using the same parameters previously defined. The processing time increases from 1 minute for a dense network of 1,000 nodes to 30 minutes for a dense network of 10,000 nodes. This processing time is acceptable as real biological networks are close to this size. The distribution of the F1 scores obtained by AMINE as well as the distribution of the size of the identified modules are shown in supplementary Fig. S3 -S4. For modules of size 10, the median F1 is in agreement with that obtained on networks of size 1,000. The overall performance is however less good because it is penalized by the fact that, in many cases, the module is completely missed (which explains why the second quartile starts at zero in the leftmost boxplot of supplementary Fig.
Validation using a real gene expression dataset In order to test the ability of AMINE to identify relevant biological functions, we downloaded from Gene Expression Omnibus a dataset relative to a study aimed at characterizing processes and genes associated to metastatic spreading in pancreatic ductal adenocarcinoma (PDAC). With a 5-year survival that has not significantly evolved for 30 years despite progresses in anticancer therapies (<6%), PDAC is a cancer with one of the bleakest prognoses of the most fatal cancers. In the study of  Chiou et al. (2017) , the authors compared two populations of primary PDAC cells according to the expression of HMGA2, a gene associated to a high metastatic potency and poor outcome in several cancers, including PDAC. Gene expression AMINE profiling was carried out from six pairs of HMGA2+/HMGA2-cell populations, each pair originating from PDAC primary tumors spontaneously generated in a genetically engineered PDAC mice model (PKC mice).
The profiling generated 193 statically relevant modules (with associated p-values < 0. 05) and we decided to focus on the five ones figuring at the top of the list to verify whether they were relevant to metastatic process in PDAC from an ontological point of view. The functions and signaling pathways associated to these five modules were explored using STRING database  (Szklarczyk et al. , 2019)  and are shown in Table  1  and Fig. 4.
Extra cellular Matrix (ECM) organization and ECM cell interaction A hallmark of PDAC is a pronounced collagen-rich fibrotic extracellular matrix (ECM) produced by fibroblasts and cancer cells, known as the desmoplastic reaction. The neoplastic epithelium exists within a dense stroma, which is recognized as a critical mediator of disease progression through direct effects of ECM on cancer cells  (Hosein et al. , 2020). Interestingly, three out of the five modules generated by AMINE were associated to ECM matrix organization (module 1 and 4) and cell interaction with ECM (module 3). Module 1 was more specifically linked to collagen fibril organization, one of the major constituent of PDAC ECM. Indeed, Collagen contributes to tumor cell aggressiveness, metastatic process and chemoresistance  (Shields et al.
More interestingly, hypoxia-, VEGF-and HIF-1-associated pathways were repressed in PDAC cells in which BLIMP1, one of the most overexpressed gene in HMGA2+ cell subpopulation, was silenced  (Chiou et al. , 2017). Altogether, these results validate our methods since non-oriented analysis of genes deregulated in pro-metastatic PDAC cells by AMINE retrieves gene modules involved in highly relevant functions in the context of the disease.