dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Large sets of protein-protein interaction data coming either from biological experiments or predictive methods are available and can be combined to construct networks from which information about various cell processes can be extracted. We have developed an in silico approach based on these information to model the biogenesis of multiprotein complexes in the yeast Saccharomyces cerevisiae. Results: Firstly, we have built three protein interaction networks by collecting the protein-protein interactions, which involved the subunits of three complexes, from different databases. The protein-protein interactions come from different kinds of biological experiments or are predicted. We have chosen the elongator and the mediator head complexes that are soluble and exhibit an architecture with subcomplexes that could be functional modules, and the mitochondrial bc 1 complex, which is an integral membrane complex and for which a late assembly subcomplex has been described.
Proteomic experiments often provide incomplete datasets due to the difficulty for detecting some PPI and to the asymmetry of the some experimental assays. To compensate for these missing data, predicted information that combined structural and non-structural information can be added  [9]. All PPI involved in a same biological process can be depicted as a network or graph, where each protein is a node and each interaction an edge between two nodes. Most of these networks are too large to be understandable. Biological information can be then extracted by the means of visualisation tools for the basis of the network representation  [10, 11]  and by using network analysis tools and clustering algorithms. From PPI interaction networks obtained by integration of information coming from different sources  [12, 13]  (PPI, literature, expression, evolution, genomic context), functional modules can be identified  [14] [15] [16]  and complexes can be predicted  [15, [17] [18] [19] [20] [21].
To model the assembly of a complex, it is not relevant to add genetic information as they reflect functional relationships between proteins and not structural interactions. By taking into account these elements, we developed a strategy that uses protein interaction networks to identify subcomplexes. By querying different databases, we built a non-redundant and un-weighted protein interaction network for each complex. This network is composed of interactions between all single proteins of the complex, named protein subunits, and proteins interacting with any of them. During the biogenesis, protein subunits assemble together to compose subcomplexes that are intermediaries of the entire complex. To identify them, we apply a clustering strategy that groups the subunits having the most interactors in common in the network.
1. Starting from the list of the subunits which are the components of the studied complex (listed in the input file) and from databases containing all the experimental and predicted PPI of Saccharomyces cerevisiae (see Methods), the first step of ISIPS consists of the identification of proteins interacting with the subunits of the complex. The second step of ISIPS consists of an aggregative clustering of the subunits or subcomplexes of the complex based on the PPI network built in the first step. The principle of the clustering is to progressively aggregate the subunits or the subcomplexes having the most interactors in common. The formula of the distance allowing the aggregation of subunits or subcomplexes and the details on the clustering algorithm are given in the Methods section; the result of the clustering is depicted by a tree.
Characteristics of the network Our method was applied to three multiprotein complexes of Saccharomyces cerevisiae that are composed of subcomplexes or functional modules whose subunit compositions are known: the elongator complex (Elp complex), the mediator head complex and the bc1complex of the respiratory chain. The list of the subunits of each of these complexes is given in Table  1. From this list, our script ISIPS. R generates the network of the protein interactions of each complex. These PPI are experimental or predicted physical interactions (see Methods) and self interactions of a protein are removed. The network of the Elp complex that is composed of six subunits (Elp1 to Elp6) contained 116 proteins and 197 interactions. The seven subunits of mediator head (Med6, Med8, Med11, Med17, Med18, Med20, Med22)  [28]  belong to a network composed of 452 proteins and 802 interactions.
Identification of the subcomplexes The results of ISIPS on the identification of the tested subcomplexes presented above are very satisfying. Elp complex is known to be composed of two modules, {Elp1, Elp2, Elp3} and {Elp4, Elp5 and Elp6}; this second module is known to form a dimer that makes a    hexameric ATPase  [23]. Our script leads to a hierarchical tree with two groups of three subunits that correspond to the two functional modules (Fig. 3 ). Moreover, in this tree, the subunit Elp2 is separated from the subcomplex Elp1-Elp3, as showed in  [23]. For the mediator head module, the result of ISIPS is also in broad agreement with the subcomplexes identified biologically  [26, 27]. In our model, Med20 is found alone. This result is consistent with the 3D structure of the complex (Fig. 4b ) where: Med20 is the most external protein.
False PPI can also be introduced by different kinds of experiments  [7]. They cannot be filtered out because all databases do not provide confidence scores and when those are available, they are not computed in the same manner  [12, 33, 34]. Moreover, it is important to note that two interacting proteins detected by "physical" methods are just identified in a same molecular assembly and are not systematically in contact. Then, many interactions are due to the steric overcrowding and to some proteins that are more abundant and/or more stable and/or more stiky than others. The pseudo-Jaccard index used as proximity score to cluster the subunits is well adapted to take these kind of drawbacks into account (see section methods).
Robustness of the algorithm to the update of the databases It is important to note that due to the high number of false positive and false negative PPI in the databases, each update can change the protein interaction network of the complex of interest. Some interactions can be added and other removed, which may change the probability of two subunits (or subcomplexes) to form a subcomplex and then alter the results of the clustering process. The in silico models of the assembly of three complexes (Elp Mediator head and bc1) were not affected by the update of the databases during the 18 months of this study, despite changes in the networks. That would not be the case if the network was clustered with graph partition methods based on the density (MCODE  [35]  and ClusterOne  [36] ) or on the modularity (Tfit  [37] ).
Choice of the cut-off of confidence score of the interactions from PrePPI database and impact on the subcomplexes identified with ISIPS To complement the PPI network, predicted PPI from the PrePPI database  [38, 39]  are added to the experimental PPI. We choose this database because it integrates a high number of criteria combined to provide confidence scores. When the biogenesis of the complex is partially known, a cut-off on the predicted PPI score can be adjusted to remove the less reliable PPIs and at the same time to keep PPIs known to modify the biogenesis of the studied complex. The default cut-off of the database PrePPI is equal to 0. 5. For the Elp complex, the tree representing the distance between the subunits of the complex is always the same irrespective to the cutoff on the PrePPI score because the number of the added interactions remains low (Table  1 ).
Weakness of our method We saw previously that some subcomplexes of the bc1 complex identified with ISIPS, contradict the biological models described in the literature: The subunit Cob is found alone when it is associated with Qcr7 and Qcr8 in the biological models  [30] [31] [32]. This result may be explained by the fact that mitochondrially-encoded Cob is the most hydrophobic subunit of the bc1 complex (8 transmembrane segments) and might be more difficult to identify in mass spectrometry experiments. Indeed, we remarked that in the databases it does not interact with the same proteins as the other subunits. Another issue with the bc1 complex is the fact that some subunits of the complex are more stable than others. The subunits inside the membrane are less stable because if they are not quickly assembled, they are degraded because they can disrupt the cohesion of the membrane.
Construction of the protein-protein interaction network To build the network, we used the interactions involving the subunits of the complex of interest from the 14 th release of iRefIndex  [46]  that collects data from eight primary databases. We split the file (in our case: 559,292. mitab. 04072015) into eight "database" files stored in a unique folder. Within each database file, Taxon A and B must be the same, else the corresponding interactions are removed. We also used the most recent version of some frequently updated databases such as BioGRID  [47]  and Intact  [33]. In addition, we used the PrePPI database  [39]  a repository that provides predicted or experimental PPI with associated confidence scores. Indeed, we needed to add predicted PPIs because some subunits of protein complexes do not have any partner in common with the other subunits and therefore would not be included in the model.
The PPI involving the proteins of the studied complex are downloaded from the site: https://honiglab. c2b2. columbia. edu/PrePPI/ and those having a confidence score greater than a cut-off (0. 5 by default) are stored in a file in the same format as the other database files (see Additional file 2: Table  S1 ). To construct and cluster the PPI network of the complex of interest, we developped the R script ISIPS. R which workflow is described in Fig. 1. This script inputs a list of Uniprot identifiers (ID) of the proteins subunits of the complex of interest, stored in the "inputlist. txt" file. The script also requires specifying the directory containing the previously mentioned databases. First, we search for interactions involving the proteins of the studied complex (predicted or experimentally identified by physical methods) and we discard self interactions.
Results obtained with the ISIPS method The script generates different types of results: 1) The distance matrices computed at each step of the clustering are stored successively in a file. 2) The result of the clustering, stored in a file having a linear format where two aggregated subunits or group of subunits are written between parentheses and separated by a comma. The distance between the two aggregated elements is indicated after the elements and separated by a colon. The groups of subunits (or subunit alone) are ordered according to the progress of their formation, from the left to the right. 3) A graphical representation of proximities between the subunits and/or subcomplexes. We choose to represent the results of our clustering method by a rooted dendrogram (the root is the final complex), drawn horizontally in order to have enough space to display the labels representing the subunits.
Instantiating the selection of far candidates The pluggable method farCandidatesXX(p) can be instantiated in three different manners, with the procedures farCandidatesFarFromClose(p), far-CandidatesCloseToFar(p) and farCandidatesMixed(p), shown in Algorithms 3, 4, and 6. -farCandidatesFarFromClose(p) (Algorithm 3) implements the far-fromclose strategy discussed in Section 2. 2: the local node p first selects one of its close neighbors q close (line 2), and returns the far neighbors of q close , Γ far (q close ), as new candidates to update Γ far (p). In addition, the procedure pushes towards q close the far neighbors of p, as nodes far from p are likely to lay far from q close as well. The receipt of the corresponding far message is handled by the code shown in Algorithm 5. -farCandidatesCloseToFar(p) (Algorithm 4) implements the close-tofar strategy presented above: this time, p picks one of its current far neighbors q far , and returns the close neighbors of q far , Γ close (q far ) in order to improve Γ far (p).
Evaluation We evaluate our framework using the simulator PeerSim  [23] , and compare its behavior against a basic greedy epidemic protocol (Algorithm 1) that uses a negative similarity metric (Equation  1 ). We term this baseline solution Far From Far. We are essentially interested in two aspects of our solution: (i) its convergence, i. e. how fast our framework is able to converge to a good KFN graph, and (ii) its scalability, i. e. how does this convergence speed evolve with growing network sizes. The code used for our experiments can be found on-line at https://gitlab. inria. fr/ASAP/HyFN.
Survey data collection Coordinators of the three training programs provided us with the graduates' contact information. Recruitment was initiated in   2 ). We invited graduates to complete an online questionnaire via Internet links to the webbased open-source software LimeSurvey TM version 1. 92 þ  [25]  and we sent a reminder email one week later. In addition, we contacted graduates by phone to verify if they had received the email inviting them to participate in the survey. Paper copies of the questionnaire were made available for graduates who were interested in participating in the survey but did not have access to the Internet, or preferred to fill out a paper copy. Seven participants chose to complete the paper version of the questionnaire, and members of the research team manually entered these data into the LimeSurvey TM database.
Interview data collection We conducted individual semi-structured interviews in French with 20 RT graduates (see Figure  2 ) who had completed the survey and were currently employed as RTs. We invited only working RTs to participate in an interview as we wished to gain further understanding of RTs' perspectives related to their current occupational activities. To maximize the number of interviewees, we used a convenience sampling strategy. We subsequently invited by phone or email all individuals who indicated in their survey (component 1) that they were willing to participate in an interview. Characteristics of the interview participants are presented in Table  1. The interviews were conducted in Haiti by francophone members of the research team (VC and ND in 2014, and EJ in 2015). Interviews ranged from 23 to 67 minutes (mean of 42 minutes) in duration and were transcribed by a member of the research team.
Work responsibilities and roles of interviewees included direct clinical treatment, as well as data collection. Hands on   interventions discussed included muscle strengthening, joint mobilizations, stretching, gait training, balance training, prosthetic training, and massage therapy. Also, interviewees identified arterial tension measurement, speech therapy, wheelchair and seat positioning, washing patients, and patient education as being part of their responsibilities. Collection of data, such as through household surveys, was a task discussed by five of the interviewees, and four described this as their main responsibility. These interviewees reported that they expected to have more clinical responsibilities in the future; however, their current tasks were not specific to the RT training they received.
Limitations As study data were collected over a 13-month period, several limitations noted during early phases of data collection were addressed as the study progressed. Challenges were noted related to understanding of some questions in the questionnaire and during the interviews. All of the participants' first language was Creole and both the survey and interviews were completed in French. In order to address this, the survey and interview guide were reviewed by several Haitians and expatriate rehabilitation professionals involved in the training of RTs in Haiti. This led to the reformulation or retraction of several questions to enhance clarity. Miscommunication regarding the purpose of the interview also occurred in some cases. It appeared that some interviewees hoped that participating in the research interview might lead to an offer of employment, and this might have distorted some of their answers despite efforts by the interviewer to clarify the purpose of the interview.
Acknowledgements We are grateful to the participants for giving their time and sharing their experiences, as well as to the coordinators and leadership of the three RT training programs for supporting this project. We thank the  Handicap International team ,  Laure Ancellin  (masseur-kin esith erapeute),  Franshy P. Dorcimil  (MD),  Johanne Eliacin  (PT),  Denise Marcajoux  (PT),  Joseph Emmanuel Jr Philippe  (PT), and  Axelle Senelonge  (OT), for providing feedback on provisional versions of the survey and interview guide. We also thank members of the  Global Health, Ethics and Rehabilitation Works-in-Progress group at McGill University  for helpful comments on an earlier version of this manuscript. This study was completed as part of the Master's research projects in the professional Master's programs in OT and PT at  McGill University.
Simulation of Angular-Resolved Magnetic Measurements In order to further analyze the agreement between the single-crystal SQUID magnetic measurements and the PND experiments, we tentatively simulated the dependence of the magnetic susceptibility versus the rotation angle (Φ) using the cluster susceptibility tensor ↔ χ obtained from PND. To do this, we implemented a Fortran 90 routine called CalcM. The source code of the program and more information about the algorithm are given in the Supplementary Materials. Basically, this program computes the magnetic moment for a user-defined set of magnetic field orientations → H, magnetic susceptibility tensor ↔ χ for one molecule, and the symmetry equivalent positions in the considered lattice. More precisely, if we call C i the transformation matrices that link the n symmetry equivalent positions in the lattice to the asymmetric unit, the measured magnetic moment for the bulk can be expressed as: m = 1 n ∑ n i=1 C -1 i × ↔ χ × C i → H • → H → H • → H , (1) where we assumed n = 1 accounts for the asymmetric unit.
Flipping ratio measurements were performed at 10 K on the polarized neutron diffractometer 5c1 at LLB-Orphée (Saclay, France) on a single crystal of pyramidal shape (3 × 2 × 1. 5) mm 3 , for three different orientations of the crystal with respect to the vertical applied magnetic field (see Table  1 ). The same sample was used on the 5c2 diffractometer. From the magnetization curve at 10 K, it appears magnetization remains linear with field at 2 T, whatever the field orientation with respect to the crystal lattice. Therefore, data collection for the magnetic susceptibility tensor determination were performed at 10 K under 2 T.
Unpolarized Neutron Diffraction A complementary unpolarized neutron diffraction data collection was performed on the 4-circle diffractometer 5c2 at LLB-Orphée, on the same sample as used for 5c1, cooled to 20 K in a fridge, in order to determine the thermal parameters at low temperature. The details of the data collection are reported in Table  2. The cell parameters from  [3]  were used for the data collection at 20 K. The integrated nuclear intensities were measured for 91 reflections only, which had previously been observed by PND on 5c1. Due to the low number of observations, the position parameters of all atoms were fixed to their room temperature values as obtained from X-ray diffraction, and only isotropic thermal parameters were refined. The JANA program  [14]  was used for the refinement.
It can be noticed that the maximal χT values for the rotation around the axis The results of angle-resolved susceptibility measurements on single crystal are displayed in Figure  4 , in the form of the product of the magnetic susceptibility with temperature (χT) versus the rotation angle (Φ). The crystal was successively rotated around the  For each set of data we also measured the dependence of magnetization with the magnetic field at each maximum and minimum, at 10 K and up to 2 T, which is the magnetic field used during the PND study. For rotation around the axis (Figure  5a ), the field dependence of magnetization for the two maxima ( // and // ) superimpose as may be expected, and magnetization continuously increases up to 4. 4 μB at 2 T, while, for the minimum ( // * ), it increases up to 3.
Local Susceptibility Tensor from PND The paramagnetic susceptibility tensor of the crystal results from the sum of the local For each set of data we also measured the dependence of magnetization with the magnetic field at each maximum and minimum, at 10 K and up to 2 T, which is the magnetic field used during the PND study. For rotation around the → c axis (Figure  5a ), the field dependence of magnetization for the two maxima (  For each set of data we also measured the dependence of magnetization with the magnetic field at each maximum and minimum, at 10 K and up to 2 T, which is the magnetic field used during the PND study. For rotation around the axis (Figure  5a ), the field dependence of magnetization for the two maxima ( // and // ) superimpose as may be expected, and magnetization continuously increases up to 4.
Local Susceptibility Tensor from PND The paramagnetic susceptibility tensor of the crystal results from the sum of the local susceptibility tensors of each molecular cluster in the lattice, which are related by the symmetry operations of the space group. There are four clusters with two different molecular orientations in the cell and, therefore, the magnetic proper axes of the crystal (bulk) may not coincide with the molecular cluster proper axes: ↔ χ crystal = 4 ∑ sym ↔ χ cluster ( 10 ) By the successive application of a magnetic field on a single crystal of Ni4, along three orthogonal directions, we managed to collect three experimental flipping ratio datasets. This enables the refinement of the six components of the local (molecular) cluster tensors, χ cluster ij , which are reported in the first row of Table  3.
The directions of the cluster magnetic axes are displayed in Figure  6 , altogether with the magnetic ellipsoid of the cluster. Table  3. Susceptibility tensor components in (μ B /T) from PND for the cluster, individual ions, and antiferromagnetic pairs. As a footnote: the respective goodness-of-fits in the combined refinement are relative to the three different datasets. → i , → j , → k ) frame are provided in Table χ ↔ χ cluster = ∑ Ni l=1,4 ↔ χ l Ni + W F + W AF. (12) This tensor can be rewritten as a sum of "effective tensors" ↔ χ l Ni which include the perturbation due to the antiferromagnetic exchange between Ni(II) ions belonging to Ni-Ni pairs and the ferromagnetic exchange interactions between the Ni atoms along the diagonal of the Ni 4 O 4 cube. This approximation is justified by the weakness of the magnetic exchange interactions in the cluster, as shown by the coupling constants deduced from magnetic measurements, J F = 8(1) cm -1 and J AF = -3(1) cm -1 from  [11] : ↔ χ cluster = ∑ Ni l=1,4 ↔ χ l Ni.
( 13 ) The six components χ ij for each Ni ion were first refined, on the joint basis of the three datasets, but only those for which the obtained value was larger than 1. 5 times the error bar were included in the final refinement. The refinement of the local susceptibility tensors ↔ χ l Ni (with l = 1, 4) evidences a strong similarity between the Ni1 and Ni4 susceptibilities on one side and between Ni2 and Ni3 on the other side as showed by the reported components in Table  1. This suggests that the two Ni atoms belonging to a same antiferromagnetic (AF) pair, (Ni1, Ni4) and (Ni2, Ni3), respectively, have similar magnetic behaviors with respect to the applied magnetic field. Furthermore, the refined eigenvalues of each Ni local tensor, reported in Table  2 , show that the Ni1 and Ni4 tensors are more anisotropic than those of Ni2 and Ni3.