dataesr/scientific-paragraphs-categorization huggingface dataset:
All X-ray diffraction experiments were carried out at Synchrotron SOLEIL (St-Aubin, France) on Proxima-2A beamline (Table  S1 ). Measurements were performed at 100 K. A first crystal without osmium was used to collect a native dataset at λ = 0. 9801 Å with a useful resolution limit of 1. 75 Å (data processed with XDS  [44]  and scaled with Aimless  [45] ). From an osmium containing crystal, 8 datasets (400° span, 4000 images each) were collected at osmium peak absorption λ = 1. 1397 Å, at different kappa angles. Merging all of them was mandatory to get a final unique dataset at 2. 2 Å resolution with sufficient quality for successful phasing (data processed with XDS and XSCALE). Preliminary phases were determined using the SAD Pipeline implemented in PHASER CCP4i  [46]  and the partial results were enhanced by ARP/wARP  [47].
The resulting model was further improved with the native dataset at 1. 75 Å, using PHENIX  [48]  and COOT  [49]  (Table  S1 ). The asymmetric unit contained a single OrfG64-204 monomer. Several regions were unobserved in the electron density when contoured at 1. 2 σ, especially at the N-terminal end. These regions were assumed to be disordered and were not included in the model. The final model contained residues Asp92-Val133 + Val139-Ile168 + Glu176-Ala204, i. e. 70 % of the 143 amino acids of the OrfG64-204 subdomain. It is worth noting an important blob of density in the pocket delineated by Tyr106-Phe107, Arg140-Ser143, Tyr164, Glu166 and Phe202 (Fig. S2 ). This blub could be assigned to none of the compounds used for protein crystallization. It probably resulted from a significant affinity for a ligand present in the protein expression cells or in the successive media used to prepare the protein sample.
The final OrfG64-204 structure was deposited in the Protein Data Bank with ID 6zgn. All three-dimensional structure representations were obtained using PYMOL (Schrödinger, LLC). Sequence alignment, structure comparison and interface analysis. Protein sequences were retrieved from NCBI and compared using clustalW from MEGA7  [50]. Phylogenetic trees were built with MEGA7 by using the Maximum Likelihood method based on the Tamura-Nei model  [51]  without outgroup. The first step of structure comparison was conducted with both DALI  [30]  and PDBefold  [31]  to fetch a complete list of VirB8 structures (26 PDB entries). From them, a non-redundant set of 16 structures was conserved (2cc3, 3ub1, 3wz3, 3wz4, 4akz, 4ec6, 4jf8, 4kz1, 4lso, 4mei, 4nhf, 4o3v, 5aiw, 5cnl, 5i97, 6iqt) and added to the new OrfG64-204.
Then the mTM-align server  [52]  based on the robust TM-align method  [53]  performed the structural alignment and produced the structure-based "phylogenetic tree". PyMOL and an associated homemade python script were used to emphasize visually structure differences and similarities (available upon request). Briefly, a color gradient was applied to each position depending on the rmsd value calculated on Cα carbons of structurally aligned amino acids at this position. Conserved or similar residues were also automatically highlighted. Furthermore, the superposition of all VirB8 monomers produced by the mTM-align server was used to compare oligomers interfaces and to visually identify interesting features.
To examine crosslinked products, 15 µL of SDS-PAGE loading buffer (4X) were added and 10 µL from each reaction was analyzed by 12% SDS-PAGE for OrfG64-331 and 15% SDS-PAGE for OrfG64-204 and OrfG223-331. TcpC superfamily including OrfG. This tree was inferred by using the Maximum Likelihood method based on the JTT matrix-based model  [54]. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The tree is drawn to scale, with branch lengths measured as the number of substitutions per site. All positions containing gaps and missing data were eliminated. There was a total of 284 positions in the final dataset. FIGURE LEGENDS Evolutionary analyses were conducted using MEGA7  [51]. with its amino acids colored according to the quality of the superposition observed within a set of 17 selected structures.
For each position, a gradient from violet to red is used depending on the low to high values of the root mean square calculated on the distance between all the possible pairs of the 17 superposed Cα carbons at this position (grey means that mTM-Align found at least one structure impossible to align). The amino acid shown with lines (Val191 in OrfG) and localised in β4 facing α1 is the only one whose side chain shares a similar nature in all structures, while there is no conserved residue. The whole set of structures (PDB ids 2cc3, 3ub1(limited to TcpC104-231), 3wz3, 3wz4, 4akz, 4ec6, 4jf8, 4kz1, 4lso, 4mei, 4nhf, 4o3v, 5aiw, 5cnl, 5i97, 6iqt, 6zgn) represented as a wire using the same color-code (B) reveal resemblances and differences in their NTF2-like folds. Residues of TraM and TcpC involved in the trimer assemblies are highlighted, in orange in one partner and in blue in the facing monomer: white letters with dark background are for residues involved in hydrogen bonds in all interface instances in the asymmetric units, while dark letters with lighter background are involved in contact in at least one interface (medium background when at least 50% of the residue is buried in the contact, light background if less than 50% is buried).
ACKNOWLEDGMENTS We thank the members of the  DynAMic  and  CRM2  laboratories for insightful discussions;  Laurence Hotel ,  Emilie Piotrowski ,  Louise Thiriet ,  Stéphane Bertin ,  Johan Staub  and  Anthony Gauthier  for technical assistance;  Nicolas Soler  and  Yvonne Roussel  for valuable discussions; Emilie Robert  and  Jean-Michel Girardet  from  ASIA  platform for encouragements. SEC-MALS analysis were conducted using MiniDAWN TREOS II mounted on FPLC system (AKTA purifier) available on the  ASIA platform (Université de Lorraine-INRAE , https://a2f. univlorraine. fr/asia/). The  Soleil Synchrotron radiation facility  is acknowledged for beamline allocation. This work was funded by the  National Research Institute for Agriculture, Food and Environment INRAE , the  Université de Lorraine  and the  A2F scientific pole.
The conventional approach to search-model identification in molecular replacement (MR) is to screen a database of known structures using the target sequence. However, this strategy is not always effective, for example when the relationship between sequence and structural similarity fails or when the crystal contents are not those expected. An alternative approach is to identify suitable search models directly from the experimental data. SIMBAD is a sequenceindependent MR pipeline that uses either a crystal lattice search or MR functions to directly locate suitable search models from databases. The previous version of SIMBAD used the fast AMoRe rotation-function search. Here, a new version of SIMBAD which makes use of Phaser and its likelihood scoring to improve the sensitivity of the pipeline is presented.
These include cases where only distant, low-sequence-identity homologues can be identified, which can often be too structurally divergent from the target. Even where high-sequence-identity homologues are available, they may have crystallized in different conformational states and hence prove too structurally distinct to succeed. Another possibility is that a contaminant protein has crystallized in place of the target protein. An alternative approach adopted by some developments is to perform a brute-force search of the entire PDB  (Stokes-Rees & Sliz, 2010; Hatti et al. , 2016). SIMBAD  (Simpkin et al. , 2018)  provides a novel, sequence-independent method to identify search models for MR by performing a rotationfunction search on every structure in the MoRDa database  (Vagin & Lebedev, 2015)  against the experimental diffraction data.
Therefore, in the distributed version of SIMBAD a default threshold of 7 will be used. Additionally, the improved Matthews coefficient  (Kantardjieff & Rupp, 2003)  implemented in the cell-content analysis module of Phaser was used to predict the molecular weight (MW) of the target structure prior to the rotation search. We reasoned that search models close to this predicted value would be more likely to give a solution. Therefore, search models were placed in ascending order using the equation jMW predicted À MW search j: ð1Þ This increased the likelihood that a suitable search model would be tested early in the search and therefore results in earlier termination. However, at this time this method is only suited to crystal structures containing one molecule in the asymmetric unit. This is discussed further in Section 4.
Ensemble generation Ensembles were generated for each entry in the MoRDa database using the ensembling procedure described by  Keegan et al. (2018). Here, the sequence from each MoRDa domain was used to identify suitable homologues from the PDB using phmmer  (Eddy, 2011). MrBUMP includes a set of redundancy-reduced derivatives of the PDB sequence database for use in the phmmer search. For SIMBAD, ensembles are generated from the 100% database (i. e. no models are removed based on sequence redundancy). Phmmer returns a list of matches with scores based on sequence alignment to indicate how similar they are to the target sequence. MrBUMP uses a phmmer score threshold of 20 to eliminate unrelated proteins and homologues that are likely to be too dissimilar to be used as suitable search models in MR.
This score is constructed by inferring residue probabilities from a standard 20 Â 20 substitution score matrix, plus two additional parameters for position-independent gapopen and gap-extend probabilities (for further details, see the HMMER user manual; http://eddylab. org/software/hmmer/ Userguide. pdf). To construct each ensemble, we took a maximum of five structures matching our MoRDa entry. If no suitable homologues could be found, the original single model was used instead. This was the case for 2024 out of 81 716 entries in the MoRDA database. The database we created therefore contains a mixture of single structures and ensembles, and it will henceforth be referred to as the 'ensemble database' to distinguish it from the database containing only single structures. Once a set of suitable homologues from the PDB has been selected, MrBUMP performs homologue modification to try and improve the chance of successful MR.
This is performed by comparing the information provided by the alignment of the target sequence with the matching sequences found by phmmer. In this case the target sequence is that of the MoRDa domain. Modifications include the truncation of side chains and the trimming away of loops. This is performed using the Sculptor application (Bunkoczi  & Read, 2011) , which modifies the homologues based on the provided alignment. The final step is to align the edited structures into an ensemble. This alignment is performed using GESAMT  (Krissinel, 2012; Krissinel & Uski, 2017). Once aligned, MrBUMP puts the resulting ensembles through a truncation procedure to remove the more variable regions and thereby identify a structurally conserved common core. For testing, an ensemble database was generated from the version of the MoRDa database released on 12 March 2016.
Testing In previous work describing SIMBAD  (Simpkin et al. , 2018) , a test set of 25 structures that had recently been deposited was compiled to assess the ability of SIMBAD to solve novel structures. This test set contained a wide range of resolutions, copies in the asymmetric unit, space groups, monomer sizes and secondary-structure types. Given that the true structures were known for our test cases, GESAMT  (Krissinel, 2012; Krissinel & Uski, 2017)  was used to identify the most structurally similar entries in the MoRDa database. Default options for GESAMT were used, including a requirement that the alignment of the target to the model covered at least 70% of both the target and the model. MR was performed using these structures to identify the maximum number of solutions possible. In 19 out of the 25 cases (76%) the MoRDa database contained sufficiently similar homologues to solve the target.
This work showed that it was beneficial to increase the r. m. s. d. estimate with the size of the search model. We therefore employed a fixed sequence identity in place of a fixed r. m. s. d. to benefit from this function. A predicted sequence identity of 70% was employed to screen the single models in the MoRDa database. This choice led to the solution of 15 of the 25 test cases (60%), a significant improvement on the ten cases solved using the AMoRe-based method. Unexpectedly, a suitable search model could not be found for PDB entry 5lu3, a case that was solved in the AMoRe search. 3. 1. 3. Phaser using ensembles. A database of ensembles was generated, each derived from an entry in the MoRDa database. Given that Phaser generates a set of weighted structure factors based on the variation in an ensemble, we reasoned that ensembles might compensate for a poor initial estimate of r.
m. s. d. by downweighting the more variable parts in our search models  (Keegan et al. , 2018). A high (70%) and a low (30%) predicted sequence identity were selected in order to sample alternative initial r. m. s. d. values. Using ensembles with the higher estimate yielded an additional two solutions; 17 of the 25 test cases (68%) were solved. The lower estimate was only able to match the singlemodel performance; 15 of the 25 test cases (60%) were solved. A comparison of these results is shown in Fig. 2 , with additional details in Table  1. Only two cases (PDB entries 5hxg and 5khl) remained that were known to be solvable using the best available homologues in the MoRDa database, but which were not solved by SIMBAD in any run. Given the importance of the error estimate in the likelihood-enhanced rotation search, the experiment was repeated using the true r.
m. s. d. value between the targets and the best available search models (1. 61 A ˚for PDB entry 5hxg and 1. 12 A ˚for PDB entry 5khl). This gave maximum RFZ values of 4. 52 and 4. 29, respectively. These values fell below the noise level observed in a typical SIMBAD run and therefore it was unlikely that these search models would have been carried through into the full MR step. We further explored this by observing the rotation scores using sequence-identity values ranging from 10% to 100% in increments of 10%. However, this failed to improve upon the scores given above. We attribute the poor rotation scores to the fact that the best available search models have r. m. s. d. s of 1. 61 and 1. 12 A from the true structure and they constitute 37% (one domain from a two-domain dimer) and 47% (one of two domains) of the total scattering content of the target crystals.
Both are also in high symmetry space groups, P3 (PDB entry 5hxg) and Figure 2 Venn diagram comparing the number of solutions and the average r. m. s. d. of successful search models obtained from the 19 test cases (known to be solvable) using the default AMoRe rotation search and the Phaser rotation search in several differing configurations. As mentioned in the text, two of the 19 cases were not solved in any run even when using the best available search model. Not shown here are the six cases that we were unable to solve using the closest available search model.
Comparative rankings Improving the success rate of SIMBAD relies on distinguishing the signal of a correct solution from the noise. It follows that in addition to solving more cases, a successful method will be more likely to rank a good search model highly. Fig. 3  shows a three-dimensional bar graph of ranking versus method versus PDB code for the 19 test cases known to be solvable. In general, Phaser returns more solutions and ranks these solutions higher in the search than AMoRe.
Discussion The implementation of the Phaser fast rotation search in SIMBAD has proved to be significantly better at detecting suitable search models than the version using AMoRe. The use of ensembles in SIMBAD has helped to further increase the sensitivity of the rotation function when screening the MoRDa database. This has allowed us to obtain greater RFZ values for suitable search models and therefore increase the likelihood of a solution and early termination. PDB entry 5uba was only able to be solved using Phaser with 70% predicted sequence identity and ensemble search models. In this instance it seemed that it was not the ensembling but the redefined domain boundaries that allowed the correct search model to be identified. Fig. 4  shows a comparison between the full MoRDa domain and the MrBUMPderived subdomain.
Efficiency improvements Whilst moving to this procedure is computationally more expensive, it is tolerably fast when run on a 100-core cluster  (2. 6 GHz, Intel Xeon E5-2640). With crystallographic software moving onto the cloud  (Krissinel et al. , 2018) , such clusters are becoming more readily accessible to users. Additionally, the implementation of an early-termination function (LLG > 60 and TFZ > 8 and/or R factors of <0. 45) allows SIMBAD to bypass much of the computational expense if a clear solution is detected. Indeed, applying this to our test set resulted in an $35% reduction in average time taken (27. 8 to 18. 2 h). Additionally, when comparing the nine cases that solved with both AMoRe and Phaser using single search models, the early-termination function allowed Phaser to identify solutions in a lower average time (9.
1 versus 10. 8 h). We postulated that increasing the sensitivity of the rotation search may result in even faster run times by way of the earlytermination function. SIMBAD had previously been modified to include an additional translation step to increase the sensitivity when screening ensembles derived from metagenomic databases  (Simpkin et al. , 2019). A few of the cases that took longer to run were tested with this method and demonstrated significant time reductions. For example, the time taken to find a solution for PDB entry 5uqf decreases to 5 h from the previous 54 h (both performed on a 100-core cluster). Future work will seek to further explore any efficiency advantages that this method might confer.
Future developments A key future development is to improve the method by which ensembles are generated for SIMBAD. The current strategy uses sequence to identify suitable homologues for ensemble generation, whereas searching the PDB for homologues according to structural similarity (as assessed by programs such as GESAMT) may yield better results. So, for example, searching the PDB based on sequence will fail to distinguish between alternate conformations, for example Rand T-states in allosterically regulated enzymes. Processing and ensembling a mixture of such conformational states is likely to hamper structure solution. The method for creating the ensembles may also be improved upon. In the current version of MrBUMP, the 'seed' model obtained from the MoRDa database is not modified in line with its homologues.
Background The vision-based system consists of the processing of images acquired by the on-board camera. Today, you can find some software libraries to carry out this process, such as Torch3vision, VXL (Vision something Libraries), JavaVIS, LIT-lib, and OpenCV (Open source Computer Vision library)  [32]. After some studies and tests, OpenCV was selected to carry out the image processing. It presents the most complete solutions for the problems that we face processing the images acquired by the robot. OpenCV   [33] , etc. In our case, its main drawback is that it cannot be executed on the robot. That is why we need to send the images from the robot to a server to process them and calculate the orders for the robot.
Platform used The implementation of the system is based on the platform developed by the authors in a previous work  [26] [27] [28]  with some modifications. Figure  3  shows a diagram of the platform in the laboratory. The system is composed of Khepera IV robots that run software that grants communication and operation. The other component is a server that processes the images acquired by the Khepera IV robot and calculates the velocities to control it. As mentioned before, this software has been developed in Python and uses the OpenCV library to work with the images. The wireless communication between the robot and the system is carried out with a WI-FI router. On the right side, the server is represented by the yellow dashed line. The system works as follows, where the numbers represent the order in which the task is executed:.
Implementing cascade classifiers To train the classifiers, existing databases of "positive" and "negative" images can be used, or customized databases can be created. These image training sets can be created using OpenCV. In our case, the "negative" image set was created using the on-board camera of the robot. Figure  6  shows on the left side, 4 examples of "negative" images obtained by the robot. To provide correct training, these images must not contain the object that wants to be classified as positive. The set of negative images for this experiment was composed of approximately 1400 images. Figure  6  shows on the right side, 4 examples of the positive images acquired by the robot. In this case, the object that wants to be detected is the signal of the airport, which is included in all positive images.
Code of the server The following code segment shows the implementation at the server side. This code has been developed with Python. An important detail is that the server reads the images acquired by the robot using the TCP connection created and the application MJPG-streamer that is running at the robot. Note that some lines have been omitted for space reasons. From lines 1 to 5, certain tasks are carried out: the variables are defined; the client socket is created and connected; and the trained cascade classifiers are loaded. After that, the main loop starts. From lines 9 to 20, the image from the robot is obtained and conditioned. From lines 24 to 29, the cascade of classifiers is applied to the obtained image. The result of this operation is that only one classifier must be true. If not, the traffic signal is detected with the classifiers, which means that the track must be followed.
Given that most studies suggesting adaptation in this gene have relied on population-genetic methodologies, which pool the statistics across all sites, a comparison of sites uncovered by the MutSel-M3 * model with previous results is not possible. As with the analyses of the b-GLOBIN data set, much more work will be required to determine the plausibility of these new results on the ADH data set. In addition to the aforementioned potential model violations, with a sampling across Drosophila, which have high effective population sizes, features such as uneven codon usage can become highly pronounced  (Powell and Moriyama 1997) , potentially misleading inferences of site-specific adaptation as well. As a hypothetical example, suppose that the codon TTG is used almost exclusively for encoding leucine, and that GTG is similarly strongly favored for encoding valine.
Empirical Studies A more detailed examination, ideally combined with experimental corroborations, of the sites uncovered by the model is pressing, and hopefully based on far more than the hand-full of data sets of the present study. This would help build our empirical understanding how the model behaves in a variety of different contexts  (Moutinho et al. 2019; Slodkowicz and Goldman 2020). We hope to apply the model on a few thousand genes from the OrthoMamm database  (Scornavacca et al. 2019 ) in a first step, before engaging broader applications across varied taxanomic contexts.