dataesr/scientific-paragraphs-categorization huggingface dataset:
Other potential enhancements could focus on nonlinear workflows, the integration of the AIMe registry  [43]  into the certification process of FeatureCloud applications, and reducing Docker dependency by also supporting other secure containerization systems such as Singularity  [44]. To address the problem of data harmonization and preprocessing of different formats at different sites, it may be useful to add a federated database with a common ontology to the FeatureCloud controller  [45]. Through this, the problem of different data formats between sites is solved, as the input data for workflows can be directly created from the database. Integrating local data into this database can be performed using predefined Extract-Transform-Load scripts for the most common data formats and standards.
Acknowledgments The  FeatureCloud  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  826078. This publication reflects only the authors' view, and the  European Commission  is not responsible for any use that may be made of the information it contains. This work was further supported by the  German Federal Ministry of Education and Research (BMBF) -funded  German Network for Bioinformatics Infrastructure (de. NBI)  Cloud within the de. NBI ( 031A532B ,  031A533A ,  031A533B ,  031A534A ,  031A535A ,  031A537A ,  031A537B ,  031A537C ,  031A537D , and  031A538A ). This work was further supported by the  German BMBF  within the framework of " CLINSPECT-M"  (grant  FKZ161L0214A ). This work was partly developed as part of the  Center for Female Artificial Intelligence  project funded by the  German BMBF  under grant  01IS21079.
This work was further funded by the  German BMBF  under grant  16DTM100A. JKP was funded by the  Bavarian State Ministry of Education and the Arts  in the framework of the  Bavarian Research Institute for Digital Transformation  (bidt, grant  LipiTUM ). This study uses data from the  Survey of Health, Aging and Retirement in Europe  (SHARE) Wave 8 (refer to the study by  Börsch-Supan  et al [38] for methodological details). The SHARE data collection has been funded by the  European Commission, Directorate-General for Research and Innovation (DG RTD)  through  FP5  ( QLK6-CT-2001-00360 ),  FP6  ( SHARE-I3 :  RII-CT-2006-062193 ,  COMPARE :  CIT5-CT-2005-028857 , and  SHARELIFE :  CIT4-CT-2006-028812 ),  FP7  ( SHARE-PREP :  GA N°211909 ,  SHARE-LEAP :  GA N°227822 ,  SHARE M4 :  GA N°261982 , and  Data Service Infrastructure for the Social Sciences and Humanities (DASISH) :  GA N°283646 ), and  Horizon 2020  ( SHARE-DEV3 :  GA N°676536 ,  SHARE-COHESION :  GA N°870628 ,  SERISS :  GA N°654221 ,  SSHOC :  GA N°823782 , and  SHARE-COVID19 :  GA N°101015924 ); and by  DG Employment, Social Affairs & Inclusion  through  VS 2015/0195 ,  VS 2016/0135 ,  VS 2018/0285 ,  VS 2019/0332 , and  VS 2020/0313.
Data Availability The Survey of Health, Aging and Retirement in Europe (SHARE) data are distributed by SHARE-European Research Infrastructure Consortium (ERIC) to registered users through the SHARE Research Data Center. We used only data from the 8 waves  [38]. Except for the SHARE data, all our data sets, including the Indian Liver Patient Dataset  [34] , Breast Invasive Carcinoma data set  [35] , Boston data set  [37] , and Diabetes data set  [36] , and scripts used for our evaluation results are available in our GitHub repository  [47]. To increase interpretation and reproducibility, we followed the minimum information about clinical artificial intelligence modeling (ML-CLAIM) reporting standard (Norgeot et al  [48] ). The filled-out ML-CLAIM clinical checklist is also available in our GitHub repository.
Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments.
This leads to a situation where it is unclear how appropriate and successful individual methodologies used for fragment growing actually are. In this work we will describe our fragment growing workflow FastGrow based on the Ray Volume Matrix (RVM) shape descriptor  [18] , a pharmacophoric interaction description, and JAMDA geometry optimization  [19]. Its features will be statistically evaluated on a previously reported data set of fragment growing steps extracted from crystallographic data  [18]  and compared to DOCK, a well-known, open source docking suite  [20]. Furthermore, we will demonstrate FastGrow's capabilities in the context of an FBDD campaign on the target DYRK1A (Dual Specificity Tyrosine-phosphorylation-regulated Kinase 1A). DYRK1A is a kinase implicated in various forms of cancer, neurodegenerative disease, and Down's Syndrome.
Ray volume matrix pose generation FastGrow is primarily based on the Ray Volume Matrix (RVM) shape descriptor  [18]. RVM shape screening is a fast way to generate accurate poses for thousands of fragments in a few seconds. In short, it uses a shape description symmetric to both pockets and fragments, to perform rapid comparisons and orient fragment conformations in a binding site. The input is a pre-calculated fragment database and a fragment bound to its target. The RVM is very fast and generates accurate poses, but it is limited to shape comparison.
Ensemble flexibility An input fragment can be positioned in multiple aligned binding sites so that it can be simultaneously screened against a database of fragments. This means multiple conformations of amino acid side chains and even backbone movements can be mimicked when generating the growing hits. When a fragment is scored against an ensemble only the best score of a fragment with respect to a member of the ensemble is used to position it on the hit list. This means that if a fragment conformation clashes with an amino acid side chain in three out of four conformations of the binding site, then the score of the fourth non-clashing, and by implication highest scoring, binding site will be used.
Comparison to docking To establish FastGrow in the larger context of structurebased tools, it was compared to the well-known docking program DOCK  [20]. DOCK is a high-profile  [28]  open-source suite of tools that has been validated in similar scenarios  [20, 29]. The pose re-prediction capabilities of DOCK version 6. 9 were compared to FastGrow using the cross-growing set in two configurations: a full flexible cross/re-docking of the ligand to be grown and a fixed anchor docking that receives the common core of the two ligands as an input, just as FastGrow does. The protein-ligand complexes for docking were prepared in the same way as in the internal FastGrow workflow, which involves removing all crystal waters as well as molecules that clash with the input core from the binding site. Binding sites were re-protonated using protoss  [30] , which replaces the protonation scheme that is used by the PDBbind refined set.
The active site for docking was defined as all atoms within 15Å of the native ligand. The binding site was chosen to be rather large so as to avoid any stability issues at its edges for the sphere generation. Spheres were generated by either the sphgen version included in the source code distribution or sphgen_cpp  [31]  when necessary. Those spheres within 10Å of the native ligand were selected for docking. Docking grids were generated using the GRID implementation that was provided with DOCK. Unless otherwise specified, all parameters were set to defaults originating either from the software package, DOCK publications  [20, 29] , and/or the DOCK fans mailing list  [32]. The scripts that were used to automate this process are available at https:// github. com/ rarey lab/ dock_ scrip ts. Only the actual call to DOCK after all input data had been pre-calculated was included in runtime measurements.
DYRK1A case study The first part of the DYRK1A case study simulated a fragment hit optimization from a micromolar fragment (PDB code: 7A4R) to a nanomolar ligand (PDB code: 7A5N). Three fragment growings were performed that roughly corresponded to three areas of optimization in the publication by Walmsley et al. [21]. The three fragment growings were performed with FastGrow and with DOCK by full re-docking, as well as anchored docking. The generated poses were compared and discussed. The second part of the DYRK1A case study described screening libraries of fragments with FastGrow. The idea of this enrichment study was to measure how well FastGrow could pick out fragments known to be very active from a collection of generic fragments. Multiple high affinity ligands produced by collaborators at Vernalis and Servier contain a 2,6-diaminopyridine moiety.
The diaminopyridine fragment from PDB code: 7AJW was extended by a collection of known active fragments generated from Servier/Vernalis ligands, as well as a collection of property matched generic fragments created by BioSolveIT  [34]. Three workflows were used for growing: FastGrow's pose scoring, FastGrow with restrained JAMDA optimization, and anchored docking with DOCK. All methods had to generate a pose for all fragments before these poses could then be scored. The workflow was designed to measure both the quality of the pose generation, as well as the screening power. Only fragments for which all methods could generate a pose were included in the statistics. After all fragments had been scored, the enrichment of all workflows was first compared to the enrichment achievable by sorting with "Rule of Three" properties  [35] , which served as the baseline, and then to each other.
8 ± 6. 8% , it was not significant. The somewhat small effect may in part be a symptom of the comparatively few test cases. Water replacement does not seem to confer a significant general advantage and may be more useful in specific systems. Figure  3  shows a side chain of a quinolinone-6-sulfonamide derivative crystallized in PDB code: 6MA4 being grown into 6MA5. The purely shape-based growing without water replacement did not know about any of the interactions the carboxylic acid could make with the backbone and turned it away. Using the waters in the binding site led to the side chain fully stretching out and interacting with the backbone.
Ensemble flexibility Ensemble test cases with at least two binding site conformations, not including the reference PDB, could be generated for 246 cross-growing test cases. Almost half of the ensemble test cases (120) contained five binding site conformations. 29 ensemble cases had only two binding site conformations. Except for five test cases, the average all atom RMSD of the ensemble binding sites to the SIENA Fig. 3  Growing with and without water replacement. The purely shape-based growing is to the left and the one using water replacement is to the right. The binding site is from PDB code: 6MA5. The ligand of 6MA4 in gray is aligned to the binding site and used as a reference for RMSD calculation. The grown ligands are in a darker blue query was between 0 and 1. 5 Å distributed around a mean RMSD of 0.
74 Å. Automatically generated binding site ensembles did not show a strong statistical improvement of the pose generation performance ( 69. 3 ± 5. 7% vs. 75. 2 ± 5. 4% , see Sect. 6 of the Supplementary Information). It is well-known that ensembles tend to cause false positive hits in docking  [39]  and while many systems profited from ensembles, a similar amount generated poses with higher RMSDs. When used appropriately, ensemble flexibility can make a significant difference  [40]. Conformational changes of binding site residues may obstruct growing of a ligand bound in a different structure of that binding site. Figure  4  shows a shikimate precursor mimicking ligand from PDB code: 3N76 that was grown into 3N7A and an ensemble of 4B6O and 4KIU. FastGrow could not grow the phenolic substituent of 3N76 into the groove that was defined by 3N7A.
4  Growing in a binding site with a highly flexible loop  [41]. To the left in purple is PDB code: 3N7A. To the right are 4KIU in orange and 4B6O in blue. All are structures of the 3-dehydroquinate dehydratase. The conserved TYR24 is in light blue. The ligand atoms in darker blue were grown by FastGrow in the 4KIU/4B6O ensemble, which was not possible in 3N7A alone. The reference ligand from 3N76 is in gray Fig. 5  Performance comparison of DOCK in a "Cross Docking" and an "Anchored Docking" to FastGrow with or without subsequent restrained JAMDA optimization. The error bars are 95% confidence intervals would have rejected  [18]. Restrained JAMDA optimization then resolved these clashes with minor geometry adjustments. The permissiveness of the pose generation may have been able to sample the sometimes uncomfortable fit of a non-native ligand better than a more clash sensitive system.
Growing a ligand from a fragment Walmsley et al. discovered fragment 1 (PDB code: 7A4R) as one of their micromolar hits (DYRK1A cKi 1. 5 M) from a fragment screening that was performed on DYRK1A using the Vernalis fragment library  [21, 46, 47]. Toward the end of the publication they focused on compound 34 (PDB code: 7A5N), a nanomolar ligand (DYRK1A IC 50 7 nM) that they described as "[. ] a potent, in vivo-tolerated, selective inhibitor of DYRK1A kinase"  [21]. Both fragment 1 and compound 34 can be seen in Fig. 6. Both fragment 1 and compound 34 exhibited canonical hinge binding  [48]  and compound 34 retained the central ring core of fragment 1. Compound 34 could therefore be grown from fragment 1 by exchanging the amine close to the hinge to a methyl, growing into the salt-bridge region, and using the vector defined by the carbonyl at the ring of fragment 1 to address the glycine loop.
A hydrogen position at the pyrrole substructure of the pyrrolopyrimidine could be used to grow into the salt-bridge Fig. 6  Fragment 1 and compound 34 in a structure of DYRK1A. The structure and fragment 1 coordinates in gray are from PDB code: 7A4R and compound 34 in orange comes from PDB code: 7A5N and is aligned to the binding site using SIENA  [27]. The cartoon for the residues GLU160 to ASP178 was removed for clarity as it is in Walmsley et al. [21]. All 3D molecule images were made with the NGL viewer  [38]  Fig. 7  Amine to methyl exchange of fragment 1. Fragment 1 from PDB code: 7A4R is in grey. To the left are poses that were generated by DOCK cross-docking in light green and anchored docking in yellow. To the right is the pose that was generated by FastGrow in a darker blue region.
The pyridine nitrogen of the aminopyridine to be placed there was expected to interact with LYS188, while the amine was expected to interact with GLU203. Figure  8  shows the poses generated by the three methods. Using an anchored docking or a cross-docking configuration led to significantly different poses in this case. The cross-docking generated a pose very close to the eventual orientation the aminopyridine has in compound 34 (i. e. , PDB code: 7A5N). Despite receiving the coordinates of the full methylated fragment 1 as an input, the anchored docking optimized its pose out of the pocket. This could have been an overreaction of the underlying scoring function and optimization to minor clashes. Cross-docking did find a good pose, proving that both the scoring function and optimization could support one, however some strong effect perceived by the optimization led to a poorer pose for anchored docking.
All methods initially placed the amine of the aminopyridine away from GLU203. There are structures that support this as at least a reasonable position. Weber et al. modeled the ligand of PDB code: 7AJ2 with multiple conformations one of which has an aminopyridine that points away from GLU203. Most structures reported by Walmsley et al. and Weber et al. , however, were modeled so that the aminopyridine pointed toward GLU203. We could achieve a pose more consistent with the other structures by using the position of the water HOH603 to estimate a reasonable position for the amine to interact with GLU203. HOH603 was then removed as usual in the growing process. We could also use the position of the amine in a different structure to achieve the same result. A search point with the correct type at that position guided FastGrow to place the amine near GLU203.
Fragment growing enrichment A number of ligands from Walmsley et al. [21]  and Weber et al. [22]  had common cores that could be used to grow Fig. 8  Growing the aminopyridine into the salt-bridge region. The core of methylated fragment 1 that was used as an input to FastGrow and anchored docking is in grey. To the left are the poses that were generated by DOCK cross-docking in green and anchored docking in yellow. To the right are two poses produced by FastGrow in darker blue. The pose with the amine towards GLU203 was guided by placing a search point at the position of HOH603 Fig. 9  Poses of compound 34 that were grown from fragment 1 in PDB code: 7A4R. In green and yellow are the DOCK cross-docking and anchored docking poses, respectively. The FastGrow pose is in a darker blue. The compound 34 reference structure aligned to 7A4R is in orange other ligands.
The diaminopyridine moiety of the ligand in PDB code: 7AJW was used as the growing seed or core for the enrichment case study. Figure  10  shows the ligand of 7AJW with the diaminopyridine highlighted in green. Any fragment growing from this core would initially point directly to the hinge region. Many ligands of DYRK1A, especially those from Weber et al. had a conserved diaminopyridine moiety. Filtering all diaminopyridine-containing ligands down to only the ones with affinities of less than 10 nM resulted in 77 ligands. These 77 ligands were fragmented at the bond to the diaminopyridine. The Rule of Three  [35]  properties of these fragments were inspected to detect outliers far outside of the property distribution. Nine fragments were discarded as outliers. There was a large group of fragments with exactly the same TPSA, which had to be downsampled to avoid biasing the screening.
To substantiate this we repeated the experiment with all terms of the pose scoring function set to zero except for clash. Figure  S7  shows that the pose scoring function with just clash performed comparably to the other scoring functions. 3D clash, and by extension shape complementarity, seemed to be a very discriminative property in this particular system, which leads to the high performance of all three of these methods. Table  2  shows the time it takes for each of the three methods to generate poses for all fragments in the enrichment screening and score them. There was a factor five difference in speed between DOCK anchored docking and FastGrow with restrained JAMDA optimization. There was a factor Fig. 10  The fragment screening enrichment core generated from PDB code: 7AJW. The diaminopyridine many of the ligands in the data set have in common is in light green.
The bond between the gray and the green substructures is cut and the green structure used for growing. The growing direction will therefore be towards the hinge region Fig. 11  ROC curves and AUCs of DOCK anchored docking, Fast-Grow with JAMDA, the FastGrow pose scoring function and sorting by descending molecular weight. The annotated errors on the AUCs describe a 95% confidence interval  1 3  500 difference in speed between anchored docking and using FastGrow's pose scoring function. Of the 2 s FastGrow and its pose scoring function spent screening, half was spent in the input-output operations of extracting 2693 fragments from the screening database and writing the hits. The hundred-fold increase in runtime between the simple FastGrow pose scoring function, which performed competitively in this case study, and the more sophisticated scoring functions was disproportional to the apparent gain in performance.
Anchored docking with DOCK encountered stability issues in the case study, which could not happen with FastGrow, due to its restrained optimization and the inherent stability of the JAMDA scoring function  [19]. We could also demonstrate FastGrow's pose generation being guided by including external interaction information in the query. The enrichment case study on DYRK1A demonstrated a scenario that could largely be solved by clash or in other words shape complementarity. A general statistical evaluation should however also include examples that require electrostatic complementarity in addition to shape complementarity. Building up a balanced dataset of such cases without biasing it by the methods to be evaluated is a significant challenge and beyond the scope of this work. A more general analysis will be necessary to address the lingering questions of how to incorporate shape and interactions into a scoring function of appropriate complexity.
In contrast to their application in the modern/Pleistocene realm, application in Palaeozoic or Precambrian glacial landscapes are nascent, but offer great potential. As an example, traditional aerial photograph and field-based mapping of subhorizontal plateaux exposing Late Ordovician glacial strata in North Africa  (Beuf et al. , 1971)  was enhanced using satellite image interpretation (e. g. ,  Moreau et al. , 2005; Le Heron et al. , 2006; Le Heron and Craig, 2008; Moreau, 2011; Girard et al. , 2012; Deschamps et al. , 2013) , and additional basin-scale subsurface datasets  (Deschamps et al. , 2013; Battaler et al. , 2019). The satellite mapping approach thus allowed for mapping of much larger areas than previously possible with aerial photographs. Here, satellite imagery applied to large scale (hundreds to thousands of kilometres) mapping of deep-time ice sheets is possible owing to 1) the desert landscape and 2) a general lack of tectonic deformation in the Saharan Basins.
The workflow to produce the images is straightforward, and is explained in detail in Le Heron et al. (  2019a ), but can be summarized as follows. Photographs are taken from an UAV with approximately 60% overlap, and are imported and aligned automatically in Agisoft Metashape. From these, point clouds are generated, a mesh is built from triangulating the points, and texture added. The result is a high quality 3D outcrop model. For mapping purposes an orthomosaic is then generated together with a digital elevation model (DEM). These data are then layered and multiplied together in QGIS, allowing the power of both elevation data and photography to be harnessed and incorporated into a single image.
Most of the examples given in this paper highlight the value of mapping in terrains characterised by arid to semi-arid climate. The absence of vegetation in these settings allows for more lucid interpretations. Nevertheless, the future is bright for the mapping of the deep time record in vegetated terrains. In many regions of the world, the rapid expansion of freely available (or at least low cost) LiDAR datasets has facilitated rapid advances in the mapping of Quaternary landforms (e. g. ,  Thorndycraft et al. , 2016). Allowing the interpreter to "see" the geomorphology concealed beneath dense vegetation, these data will likely have major application to the interpretation of the pre-Pleistocene record in coming years.