dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational docking tools can be used to generate a large number of poses that are subsequently filtered and scored based on their capacity to describe the experimental data. This strategy has been applied to specific cases  (27) (28) (29) (30)  and has been implemented and systematically benchmarked in a few computational methods that combine SAXS and docking for the structural modeling of proteinprotein complexes, such as pyDockSAXS  (31) , FoXSDock  (32)  or HADDOCK  (33). Among them, we previously reported the first of such methods, pyDockSAXS  (31) , which provided a 2-fold increase in the success rate for the prediction of protein complexes as compared to that of the individual approaches based on energy-based docking or SAXS data alone  (31). Here, a server that makes pydock-SAXS available is described.
MATERIALS AND METHODS The pyDockSAXS method integrates SAXS data and py-Dock energy-based scoring  (16)  to determine the structure of a protein-protein complex from its components. This integrative method uses FTDock to generate 10 000 rigid-body docking poses, which are re-scored by a combination of pyDock energy and the χ value defining the goodness of fit to the SAXS data computed with CRYSOL 2. 8  (34) : pyDockSAXS = E pyDock + w c • χ CRYSOL , ( 1 ) where w c is a parameter that was previously optimized on 62 cases of the protein-protein docking benchmark 2. 0, using synthetic SAXS data obtained from the complex structures after adding noise. The structural modeling capabilities of the server have been validated on 81 complexes of the Protein-Protein Benchmark 4. 0 (35) which were not present in the previous training of the scoring function, using SAXS data synthetically obtained from the complex structure after adding noise.
We considered only complexes in which the molecular mass did not significantly vary between the unbound and the complex structures, as previously described  (31). Figure  1  shows the predictive success rates obtained in this benchmark. The pyDockSAXS server identifies an acceptable docking model (i. e. with ligand RMSD < 10 Å from the reference structure after superimposing the receptor molecules) within the top 10 predictions in 25. 9% of the cases (as compared to 13. 6% success rate when using energy-based scoring alone) (Figure  1 ). This is a similar improvement as that previously reported for the stand-alone version on the benchmark 2. 0  (31). SAXS-based scoring is sensitive to large conformational changes between the unbound structures (used in docking) and the bound state (from which SAXS data are derived).
We have also successfully validated the server on experimental systems of interest. As an example, we have applied pyDockSAXS to rebuild the structure of the Alvinella pompejana Cu,Zn superoxide dismutase homo-dimer (PDB 3F7L), using the X-ray coordinates of one monomer (chain A)  (36)  and the experimental SAXS data deposited in Biosis database  (37). This complex presents a spherical shape, which is challenging for modeling based only on SAXS data  (31). Thus, it represents an excellent case to test the robustness of the method. The server finds a near-native docking solution as rank 1, and additional acceptable solutions within the top 10 docking models. Actually, six of the top 10 docking models were within (or slightly above) acceptance criteria in CAPRI (Figure  2 ). However, the other four docking models (not shown in Figure  2 ) were significantly far from the correct orientation, which indicates that docking results in blind conditions should always be considered with caution.
After manually filtering the results from the py-DockSAXS server to keep only the docking poses with the residues Adx C80 and Cc C28 within 10-Å distance in order to describe the cross-linked complex, a model similar to the NMR structure (PDB 2JQR) was found within the top 10 pyDockSAXS docking poses. The other nine of the top 10 docking poses showed large variability in the mutual orientation between the two molecules. Interestingly, without using the SAXS data, this near-native solution would not have been identified within the top 10 docking poses. This example highlights the capacities of integrating SAXS data with computational docking, and the power that additional residue-specific information has to enrich final solutions. However, while pyDockSAXS provides a reduced set of models that typically includes one or several correct solutions, the existence of high-scoring incorrect models could complicate the identification of the correct assembly.
Input The user is requested to upload the structure files for the two interacting proteins in the Protein Data Bank (PDB) format  (40). The choice of molecules as receptor or ligand is arbitrary, although for the sake of efficiency it is recommended to set the receptor as the largest molecule. The user can specify the exact chains that will be included for modeling. Incomplete residues are rebuilt with SCWRL 3. 0  (41). At present, cofactors are not considered in the calculations but this possibility will be implemented in future versions of the server. In addition, the server expects a file with the SAXS experimental curve compatible with CRYSOL software. Thus, it should be a plain-text file where the first line is a title ignored by the software and the following lines are composed by three columns of numerical data separated by blanks or commas, which represent momentum transfer, scattering intensity and experimental error, respectively.
If experimental errors are not specified, they are automatically estimated by CRYSOL (2% of intensity values). All input file formats are described in the help section of the server. Users can customize some CRYSOL execution parameters. At present, the available options for CRYSOL calculations implemented in pyDockSAXS are: (i) the use or not of constant subtraction and (ii) to specify different angular units of the SAXS experimental data provided. Other parameters such as the number of spherical harmonics are set to their standard values that have been proven to provide accurate estimation of theoretical SAXS curves. The option of specifying a rigid-body docking set from previous pyDockWeb  (42)  executions has also been implemented for the convenience of advanced users. This option allows the user to upload pre-filtered rigid-body docking poses to be evaluated by the server.
Implementation The implementation of the web server is based on a threecomponents architecture: (i) a web front end that acts as user input source and makes results available to display and download when job is completed, (ii) a relational database where the job information is stored and (iii) a back end application which periodically polls the database for queued user projects and schedules jobs for parallel calculation of pyDockSAXS using the Slurm batch queuing system (slurm. schedmd. com). The web front end has been implemented using the web2py (www. web2py. com) free and open source web framework, and has been tested in all major modern web browsers. In addition, it adapts fluently to mobile devices screens. The back end application has been written in Python version 2. 7 with the use of external libraries as numpy and matplotlib.
The relational database has been designed and implemented using MySQL (www. mysql. com). The pyDockSAXS method is part of the pyDock software version 3 and calls internally CRYSOL software to evaluate the fitness of each of the predicted protein-protein complexes to the SAXS experimental data. The server runs on a multi-user cluster with access to two nodes composed of 16 cores (4 Intel Xeon E5620 Quad Core at 2. 4 GHz) and 32 cores (2 AMD Opteron Abu Dhabi 6376 cpus), respectively, with 11 TB of total available disk space and 256 GB of physical memory.
CONCLUSIONS AND FUTURE DEVELOPMENT The motivation behind the pyDockSAXS web server was to provide access to the scientific community to the efficient pyDockSAXS method, which integrates SAXS experimental data with pyDock protein-protein scoring energy for improved structural predictions of protein-protein complexes. The pyDockSAXS web server is an on-going project that will implement new features according to the future scientific community feedback. In the next upgrade, cofactors, ions and other non-peptidic molecules will be able to be considered during calculations. We also plan to implement a filter by symmetry, for the use on homo-meric complexes, and an extended input to analyze docking sets from different docking methods.
FUNDING Programa Estatal I+D+i , the  Spanish Ministry of Economy and Competitiveness  [ BIO2013-48213-R  to J. F. -R. ]; Agence Nationale de la Recherche  [ SPIN-HD-ANR-CHEX-2011  and  ATIP-Avenir Program  to P. B. ];  FPU Fellowship , the  Spanish Ministry of Science and Innovation  [ BES-2011-045634  to B. J. G. ]. Funding for open access charge:  Spanish Ministry of Economy and Competitiveness  [ BIO2013-48213-R] ;  Agence Nationale de la Recherche  [ SPIN-HD-ANR-CHEX-2011 ].
Creation of Intact Femur Models A set of 109 3D femur models (75 male and 34 female, 43-106 years, voxel size: 0. 488x0. 488x1. 5mm-0. 7422x0. 7422x0. 97mm) were generated from slices obtained from CT scan data, following manual segmentation of cross sectional images using ScanIP software (Simpleware Ltd. , UK). The average weight, average height and BMI for females were 73. 02±11. 93 kg, 1611±65. 83 mm and 28. 19±4. 78 kg. m 2 , respectively compared to 88. 07±16. 5 kg, 1773. 5±98. 29 mm and 27. 89±3. 92 kg. m 2 for males. The material properties were automatically assigned in ScanIP, assuming a linear relationship between the apparent density of the bones ( ) and the Hounsfield unit from the CT scan. The elastic modulus of bone to apparent density relationship was of the form E = 6850ρ 1.
Femur Implantation and Finite Element Contact Analysis The cementless FURLONG EVOLUTION® collarless titanium alloy (E=105GPA, ν=0. 3) femoral short stem design (JRI Orthopaedics Ltd, UK) was used in this study. The 126° or 133° CCD angle stem, with either a low or high offset, has a distal size ranging from 6 to 17mm; this forms a full implant system of 48 stems. Table  1  shows the values of 7 geometrical parameters defined for each of the 12 available stems available for this study. The optimal implant configuration (CCD angle, femoral offset, neck length, canal width) and size were automatically selected to best approximate the 3D morphology of each femur. To this end, a script was developed that first compared CCD angles, neck lengths and offsets of both femur and stem but also minimised the difference between the femur canal width (2cm ρ above the lesser trochanter) against the stem proximal width.
The stem was placed such that its axes matched those of the femoral shaft and neck. Finally, the stem was positioned such that the distance between centers of femoral and implant heads was minimum. Figure  1  displays the process of automated anatomic measurement and implant selection and match process. Another script was developed that can automatically export the bone and stem geometries to +CAD software (Simpleware Ltd. , UK) for Boolean operations and generation of the reconstructed hip. Note that for each specific stem, a unique cutter was used for removal of the head and creation of the femoral cavity. The implanted femur was then meshed in ScanIP (Simpleware Ltd. , UK) using tetrahedral elements, see Table  A1  in Appendix A for meshing parameters. A typical mesh contained 68050 nodes and 284155 elements for the bone and 20510 nodes and 81425 elements for the implant.
Note that for each of the 109 finite element models considered in this work, each node was associated with a unique density value. Therefore, for each of the 7 Gruen zones, the average density was computed by simply dividing the total density over all nodes in the zone by the total number of nodes. The set of processed micromotion metrics were therefore used to construct surrogate models for each metric using ''Kriging'', a regression methodology based on Bayesian processes  (Deutsch and Journel, 1992; Rasmussen and Williams, 2006; Bah et al. , 2011)  that is particularly suitable for highly nonlinear responses and optimisation studies. Correlation charts were drawn to measure the possible correlation between considered anatomy and implant design parameters and micromotion metrics using modeFRONTIER multi-objective and multi-disciplinary optimization software.
Results Table  2  shows the mean and standard deviation of measured anatomical parameters that were used for selection and positioning of stem designs. The highest standard deviations were found in the vertical and medial offsets and CCD angle. The application of the ''stem positioning'' script revealed that that 7 out of 12 stem designs were automatically selected for the 109 considered femurs: a 126° CCD stem with low offset (sizes 12 and 13) and high offset (size 13); and a 133° CCD stem with low offset only (sizes 6, 12 and 17). The stem design that fitted most femurs (36 male and 20 female) was 33mm wide with a 126° CCD angle, a medial offset of 40. 9mm, a vertical offset of 26. 4mm and a 13mm distal stem diameter. The FE simulations revealed that during walking, the maximum and average implant micromotions across the subpopulation were 100±7µm and 7±5µm with ranges [15µm, 350µm] and [1µm, 25µm], respectively.
Acknowledgements The authors would like to acknowledge Cetim (Research Contract  12028/01-RP009755 ), the  EPSRC Impact Acceleration Account (IAA) Knowledge Transfer Secondments UK  for supporting this research (Grant Number  EP/E057705/1 ), for  Southampton University General Hospital  for providing parient CT scans and  JRI Orthopaedics Ltd (Sheffield, UK)  for supplying Furlong Evolution® femoral stems.
Specifically, we used the EyeMusic Visualto-auditory SSD, which conveys whole-scene visual information via audition (see  (Abboud, Hanassy, Levy-Tzedek, Maidenbaum, & Amedi, 2014 , 2012)  for more information) with a dedicated zooming-in mechanism developed for the purpose of this study (and subsequently integrated into the EyeMusic's software following the success of this experiment, see below). Users tapped a touch tablet to select and focus on a smaller sub-area within the image (referred to here as the "scanning window"), which is transformed into an audio signal with the EyeMusic's maximal resolution (in this experiment 24 × 40), thus ultimately enabling a finer-grained perception of the whole. The idea of using a zooming-in method with SSDs is not entirely new. A zooming-in mechanism which sonifies a zoomed-in version of the center of the image instead of the full image was added to The vOICe  (Meijer, 1992 (Meijer, , 2015) )  SSD, but to the best of our knowledge has not been used for research in general or for exploring integration in particular.
, 2012). It is important to note that the EyeMusic does not require any additional hardware beyond standard headphones and can be downloaded at no cost as an iOS App (http://tinyurl. com/oe8d4p4) and for Android on Google Play (http://tinyurl. com/on6lz4e), thereby increasing the availability and decreasing the cost for future potential users. For this experiment, the EyeMusic program was adapted to include a "zoom" mode. In this mode, it was possible to select a specific area in a picture that was sonified at a higher resolution. The size of the scanning window around the selected point was fixed to 1/16 of the original picture, which was down-sampled to a resolution of 24 × 40 pixels (in the EyeMusic 2 it is down-sampled to a resolution of 30 × 50 pixels). For points close to the edge, the scanning window was snapped to the grid.
Acknowledgments Authors wish to thank  Eliana Harow  and  Idit Lozowick-Gabay  for their help in running the experiment, and  Sami Abboud  for inspiring this work. This work was supported by The  European Research Council  Grant ( 310809 ), the  Israel Science Foundation  (grant no. 1684/08 ), a  James S. McDonnel Foundation scholar award  (no. 220020284 ), The Edmond and  Lily Safra Center for Brain Sciences (ELSC) Vision center, The Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Center of Ben-Gurion University of the Negev.
Furthermore, synthetic biology allows the redesign of BGCs for effective heterologous expression in preengineered hosts, which will ultimately empower the construction of standardized high-throughput platforms for natural product discovery  10, 11. In this changing research environment, there is an increasing need to access all the experimental and contextual data on characterized BGCs for comparative analysis, for function prediction and for collecting building blocks for the design of novel biosynthetic pathways. For this purpose, it is paramount that this information be available in a standardized and systematic format, accessible in the same intuitive way as, for example, genome annotations or protein structures. Currently, the situation is far from ideal, with.
information on natural product biosynthetic pathways scattered across hundreds of scientific articles in a wide variety of journals; it requires in-depth reading of papers to confidently discern which of the molecular functions associated with a gene cluster or pathway have been experimentally verified and which have been predicted solely on the basis of biosynthetic logic or bioinformatic algorithms. Although some valuable existing manually curated databases have data models in place to store some of this information  12, 13, 14  , all are specialized towards certain subcategories of BGCs and include just a limited number of parameters defined by the interests of a subset of the scientific community. To enable the future development of databases with universal value, a generally applicable community standard is required that specifies the exact annotation and metadata parameters agreed upon by a wide range of scientists, as well as the possible types of evidence that are associated with each variable in publications and/or patents.
Such a standard will be of great value for the consistent storage of data and will thus alleviate the tedious process of manually gathering information on BGCs. Moreover, a comprehensive data standard will allow future data infrastructures to enable the integration of multiple types of data, which will generate new insights that would otherwise not be attainable. The Genomic Standards Consortium (GSC)  15  (Box 1) previously developed the Minimum Information about any Sequence (MIxS) framework  16. This extensible 'minimum information' standardization framework includes the Minimum Information about a Genome Sequence (MIGS)  17  and the Minimum Information about a MARKer gene Sequence (MIMARKS)  16  standards. MIxS is a flexible framework that can be expanded upon to serve a wide variety of purposes.
The GSC facilitates the community effort of maintaining and extending MIxS, and stimulates compliance among the community. Here, we introduce the "Minimal Information about a Biosynthetic Gene cluster" (MIBiG) specification as a coherent extension of the GSC's MIxS standards framework. MIBiG provides a comprehensive and standardized specification of BGC annotations and gene cluster-associated metadata that will allow their systematic deposition in databases. Through a community annotation of BGCs that have been experimentally characterized and described in the literature during previous decades, we have constructed an MIBiG-compliant seed dataset. Moreover, a large part of the research community has committed to continue submitting data on newly characterized gene clusters in the MIBiG format in the future.
Design of the MIBiG standard The MIBiG standard covers general parameters that are applicable to each and every gene cluster as well as compound type-specific parameters that apply only to specific classes of pathways (Fig. 1 ). Notably, the standard has been designed to be suitable for biosynthetic pathways from any taxonomic origin, including those from bacteria, archaea, fungi and plants. The general parameters cover important data items that are universally applicable. First, they include identifiers of the publications associated with the characterization of the gene cluster, so that the full description of the experimental results that support the entire entry can be accessed easily. The second key group of general parameters describes the associated genomic locus (or loci) and its accession numbers and coordinates, as deposited in or submitted to one of the databases of the International Nucleotide Sequence Database Collaboration (INSDC): the DNA Data Bank of Japan (DDBJ), the European Nucleotide Archive (EBI-ENA) or GenBank, all of which share unified accession numbers.
The INSDC accession numbers are also used to link each MIBiG entry (which is given a separate MIBiG accession number) and its annotations to the corresponding nucleotide sequence(s) computationally; hence, a GenBank/ENA/DDBJ submission of the underlying nucleotide sequence is always required to file a MIBiG submission. The third group of general parameters describes the chemical compounds produced from the encoded pathway, including their structures, molecular masses, biological activities and molecular targets. Additionally, these parameters allow documentation of miscellaneous chemical moieties that are connected to the core scaffold of the molecule (but synthesized independently) and the genes associated with their biosynthesis; this will facilitate the design of tools for the straightforward comparison of such 'sub-clusters', which are frequently present in different variants across multiple parent BGCs.
Finally, there is a group of general parameters describing experimental data on genes and operons in a gene cluster, including gene knockout phenotypes, experimentally verified gene functions and operons verified by techniques such as RNA-seq. Beside the general parameters, the MIBiG standard contains dedicated class-specific checklists for gene clusters encoding pathways to produce polyketides, nonribosomal peptides (NRPs), ribosomally synthesized and post-translationally modified peptides (RiPPs), terpenes, saccharides and alkaloids. These include items such as acyltransferase domain substrate specificities and starter units for polyketide BGCs, release/cyclization types and adenylation domain substrate specificities for NRP BGCs, precursor peptides and peptide modifications for RiPP BGCs, and glycosyltransferase specificities for saccharide BGCs.
Addressing key research needs Adoption of the MIBiG standard will allow the straightforward collation of all annotations and experimental data on each BGC, which would otherwise be dispersed across multiple scientific articles and resources. Moreover, there are at least three additional key ways in which MIBiG will facilitate new scientific and technological developments: it will enable researchers to systematically connect genes to chemistry (and vice versa), to better understand secondary metabolite biosynthesis and the compounds produced in their ecological and environmental context, and to effectively use synthetic biology to engineer newly designed BGC configurations underpinned by an evidence-based parts registry (Fig. 3 ). First, the comprehensive dataset generated through MIBiG-compliant submissions will enable researchers to systematically connect genes and chemistry.