dataesr/scientific-paragraphs-categorization huggingface dataset:
The shake algorithm was used during the simulation. The equations of motion were integrated with a 1 fs time step, using the r-RESPA algorithm  [28]  electrostatic forces at a slower 2 fs frequency. Long-range interactions were treated using the particle mesh Ewald approach  [29] , with an 11 Å cut-off. The calculation of forces and motion equations was repeated to generate a trajectory corresponding to a simulation time of 100 ns in which a conformation was recorded every 1 ps, generating a trajectory of 100,000 conformations. The conservation of the secondary structure elements along the MD trajectories was checked using the Timeline plugin in VMD  [30]  (data not shown).
Virtual screening We used the VMS-G software platform  [31]  to perform virtual screening (VS). The docking program used within the platform was GOLD  [32] , which has been recognized as among the best docking software  [33]. The ensemble docking option was selected in GOLD, in order to perform the calculations with all longiborneol synthase stable conformers detected via MD simulation. The use of such conformational ensembles was considered as an improved strategy in structure-based docking procedures  [15]. The choice of the chemical compounds to be screened is also critical. To ensure the VS campaign, two strategies can be applied: Bbrute force^, using millions of compounds, or Bdata-driven^using a chosen limited set. The first strategy requires high computing power but, nevertheless, the bottleneck is the analysis of the enormous number of outputs.
The second approach performs a molecular mining with all the possible chemical information available around the biological target and its neighbors. This strategy results in an optimized reduced collection of compounds that will minimize false positives while maximizing candidate hit rate. We used this latter approach by searching the literature and patents for all compounds already proposed as inhibitors of the synthase superfamily. We clustered the compounds using ChemMine  [34]  in order to extract the most representative compounds and to analyze their chemical diversity. Meanwhile, we analyzed the diversity of the chemical libraries  [35]  proposed by several B A Fig. 2  a Dendrogram showing the longiborneol synthase related proteins in the PDB database. Numbers in red correspond to branch support values according to likelihood-ratio test  [22].
b Protein sequence alignment between the longiborneol synthase (UniProt sequence D1M8S2, top line), and the PDB 1JFA template. Asterisks Strictly conserved residues, colons conservative substitutions, periods semi-conservative substitutions (defined as PAM250 score > 1. 5 and > 0. 5 respectively). Segments with significant similarity are highlighted in yellow. Red letters 1JFA helices as assigned by the define secondary structure of proteins (DSSP) algorithm chemical providers using the ChemicalAssistant software  [36, 37]. Through diversity information, we obtained the most suitable library for the VS experiment in order to find putative longiborneol synthase inhibitors  [38]. In the present work, the selected chemical library was the one providing the largest diversity of compounds (from the Otava supplier)  [39].
Our database contained 15,000 molecules, each one being docked within the longiborneol synthase binding site. We also included in our compound working set all the molecules found in the PDB as known synthase ligands, so, in total, 15,119 compounds were included in the initial docking experiment. The 3D structures of all molecules were obtained using CORINA software  [40]. The compound protonation states and atom names were corrected when needed for pH 7 and compatibility with the GOLD program, respectively, using a homemade script. The binding site was previously characterized using the LIGSITE program  [41]  after performing a structural alignment of all the protein conformers resulted from MD sampling. For each docking, 50 starting ligand conformers were used in GOLD software. In the docking campaign, all the conformers for longiborneol synthase MD were superimposed to estimate the pocket to be used by GOLD.
Starting 3D model Table  1  summarizes the PDB codes of structural templates proposed by the homology modeling. The corresponding cladogram is presented in Fig. 2a , showing the relationship between the proteins. It appears that the closest templates are trichodiene synthase 1YJ4, 1YYQ, 2Q9Y and 1JFA PDB, the first two being a single Y305F point mutation of the native protein, the third a protein-ligand complex, and the last the structure of the free enzyme  [57] [58] [59]. X-rays studies have shown that terpenoid synthases present an important conformational change to cap the active site upon metal binding  [59]. It has been shown  [60]  that the sequence of events catalyzed by this enzyme family starts with binding of the substrate to the open form of the protein in the absence of metal ions, followed by the coordination of the ions and active site closure.
To avoid such a bias in our search for inhibitors, which should bind to the open form of the enzyme, we consequently chose the free form of the trichodiene synthase structure from Fusarium sporotrichoides (1JFA PDB code) as the closest template to start the homology modeling process. The sequence alignment used as input to the MODELLER program is shown in Fig. 2b. This image shows that the sequence identity between the two proteins is quite weak, especially concerning the first domain containing one of the Mg 2+ ion binding sites. According to the literature, three Mg 2+ binding sites are found in the trichodiene synthase family, two corresponding to the DDXXD motif (Mg 2+ A and Mg 2+ C ) and the second to the NDXXSXXXE motif (Mg 2+ B )  [61]. In the longiborneol synthase protein, only the second metal-binding N 241 DXXSXXXE motif positioned at helix H is conserved from the proposed alignment, in good agreement with the contribution of these residues in a consensus sequence identified in all terpene synthase sequences.
Acknowledgments This work is funded by a  National Council for Scientific and Technological Development (CNPq)  grant  400432/2012-9. E. B. is supported by a  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  postdoctoral fellowship (# 51/2013 ). M. U. and K. H. K. receive support from  Biotechnology and Biological Sciences Research Council, UK , Institute Strategy Grants 20: 20®  wheat ( BB/J/ 00426X/1 ).
Crowdsourcing is increasingly utilized for performing tasks in both natural language processing and biocuration. Although there have been many applications of crowdsourcing in these fields, there have been fewer high-level discussions of the methodology and its applicability to biocuration. This paper explores crowdsourcing for biocuration through several case studies that highlight different ways of leveraging 'the crowd'; these raise issues about the kind(s) of expertise needed, the motivations of participants, and questions related to feasibility, cost and quality. The paper is an outgrowth of a panel session held at BioCreative V (Seville, September 9-11, 2015). The session consisted of four short talks, followed by a discussion. In their talks, the panelists explored the role of expertise and the potential to improve crowd performance by training; the challenge of decomposing tasks to make them amenable to crowdsourcing; and the capture of biological data and metadata through community editing.
Introduction Crowdsourcing, a natural evolution of Web technologies, is attracting increased attention in the biocuration and natural language processing communities as a cost-effective way to develop resources for systems evaluation and machine-learning, to perform specific tasks in biocuration and to collect improved data and metadata  (1). Although the use of crowdsourcing technologies is now widespread, especially in natural language processing, the broader discussion of the applicability to biocuration is just now becoming a central topic within the biocuration community. The Pacific Symposium on Biocomputing 2015 held a session entitled 'Crowdsourcing and Mining Crowd Data'  (2). Presentations on crowdsourcing have steadily increased at the annual Biocuration conferences: there were three talks at Biocuration 2014 (https://biocura tion2014.
events. oicr. on. ca/); Biocuration 2015 (http://bio curation2015. big. ac. cn/workshop) included a keynote and a workshop on crowd and community annotation; and at Biocuration 2016 (https://www. isb-sib. ch/events/biocura tion2016/scientific-program) there were two workshops on community curation and a session on 'Crowd and Community Curation' with four talks. The goal of the panel session at BioCreative V was to build on this interest, bringing together the perspectives of the text mining and natural language processing communities with those of curators and bioinformaticians developing curated resources. This perspective piece reviews four case studies to explore broader questions, including: expertise and the kinds of expertise required for different tasks; crowdsourcing applied to biocuration that uses a micro-tasking approach combining automated entity extraction with human judgments on relationships between those entities (so-called 'hybrid curation'); the use of crowdsourcing to verify (and refine) existing network models for disease-related pathways derived from literature curation and transcriptomics data; and the challenges of capturing adequate computable metadata for metagenomics and the need for crowdsourced data.
This is a multi-disciplinary highly complex spacethe goal of this paper is to encourage further research, by exploring possible ways in which crowdsourcing and text mining could be combined to address major challenges for biocuration, in particular, trade-offs in cost, quality, timeliness and ability to recruit people with appropriate expertise for curation tasks. Background: types and approaches to crowdsourcing  Khare et al. (2015)  describe a range of crowd-based approaches, including labor markets for micro-tasking (such as Amazon Mechanical Turk), collaborative editing (wikis), scientific games and community challenges  (1). The 'crowd' involved in these applications ranges from scientists participating in community annotation and evaluation activities to citizen scientists to people participating in crowd labor platforms; these participants differ in expertise and motivation (scientific, entertainment, financial); and the crowdsourced applications differ in intended use, from development of training data to improve algorithms, to validation of curated data and to generation of curated data.
For example, this is true for FoldIt (http://fold. it)  (9) , in which it took a team of players (after hours spent on the game to master the task) only a few weeks to find the solution to the crystal structure of a monomeric retroviral protease (simian AIDS-causing monkey virus), an issue unsolved for over a decade. This example and other language-oriented GWAPs inspired ZombiLingo (http:// zombilingo. org/) (10) , a game in which players, eating 'heads', annotate corpora with dependency syntax-see Figures  2  and 3  below, for examples. Despite the fact that the game features in the initial release were still very basic, the game allowed for the production of > 23 000 annotations in one week, with approximately 84% accuracy on average across the 10 more active players (over 86% if removing the best and worst players)  (11).
Hybrid curation has many potential applications to biomedical curation problems-for example, to extract gene/ mutation associations from the published literature  (15) , or to extract drug indications (relevant to indexing and search operations over a database such as DailyMed) (http://dai lymed. nlm. nih. gov/dailymed/) (16). One of the teams participating in BioCreative V (17) used a hybrid curation approach to extract chemical-induced disease relations for participation in the BioCreative Chemical Disease Relation track. In addition, this approach has been used to create enhanced data sets for machine-learning based systems, with the goal of improving performance of both entity and relation extraction systems in biomedical domains  (18, 19). The attraction of this approach is first, it has the potential to provide cost-effective high throughput curation and second, it greatly simplifies the process of recruiting annotators.
75 per abstract. Of particular note, the throughput was rapid: the drug labeling experiment took 8 h of elapsed time (from time the task was posted to completion) to obtain five independent judgments for each possible indication in a set of 700 drug inserts. Conventional wisdom suggests that biocuration tasks require domain expertise; however, the results of both experiments reveal that when the task is structured as carefully designed micro-tasks, it may be possible to leverage the labor of the crowd to achieve rapid throughput and cost-effective curation, especially by adding a layer of expert checking. These considerations (accuracy, cost and throughput) are the major dimensions in understanding the current curation bottleneck. Manual expert curation is relatively high precision but is not readily scalable and, over the longterm, not affordable as biological databases proliferate and the literature continues to grow exponentially.
Crowdsourcing and higher-level tasks: biological network model verification and enhancement Crowdsourcing is typically used to perform small, highly constrained tasks. In contrast, the sbv IMPROVER project aims at a higher-level task: verifying methods and data used in systems biology using a crowdsourcing approach. Contributions from the crowd on specific topics such as computational methods for gene signature extraction  (24)  or investigation of the concept of species translatability  (25)  are encouraged by the design of challenges (26) in a similar fashion to DREAM challenges (http:// dreamchallenges. org/). To explore crowdsourcing beyond computational tasks, sbv IMPROVER launched the Network Verification Challenge (NVC) to leverage crowdsourcing for the verification and enhancement a set of biological network models.
The substrate of the verification was a set of 50 network models describing important pathways in lung biology ranging in size between 30 and 400 nodes. The set of networks offers a framework for understanding disease and describes the relationships between molecular mechanisms involved in the regulation of biological processes (27). What makes this set of networks different from others such as KEGG  (28)  or Wikipathways  (29)  is that clear network boundaries are set prior to building the networks, in terms of pathways and context (e. g. healthy versus disease or liver versus lung). The building of the networks was as follows: published literature was manually curated to extract causal relationships between entities described in the Biological Expression Language (BEL) and then datasets were used to enhance the networks.
Finally, as an additional incentive much appreciated by the participants, the same top 20 participants were invited to share authorship of a publication that describes the verification and the set of curated networks (27). Importantly, the refined network model set was made available to the scientific community through the causalbionet database (http:// causalbionet. com), so that the activity of the scientists who participated in the challenge benefits them and their peers. The network verification challenge demonstrated the usefulness of crowdsourcing for biological network refinement and verification. It likely would not be, in its current form, a viable option for new network curation, and other initiatives, such as WikiPathways are certainly more appropriate for engaging a larger crowd for this particular task  (29).
Crowdsourcing and curation of metadata for metagenomics Metagenomics is the study of genetic material extracted directly from microbial communities in environmental samples. It enables the survey of the different microorganisms present in any environment (e. g. aquatic, soil, etc. ), and addresses two main questions: who is there and what are they doing? Since metagenomics bypasses the need for culturing, it allows tapping into the uncultured diversity of microbial life. The recent advances in sequencing technology have led to an unprecedented increase of sequencing projects, both for isolate organisms and environmental communities (metagenomics). The Genomes OnLine Database (GOLD)  (32)  is a comprehensive resource for information on genome and metagenome sequencing projects and their associated metadata.
In this respect, metagenomics can be viewed as the interface of organisms/populations and the environment. The integration of the sequence data (i. e. metagenomes) with the contextual data (i. e. environmental metadata), is key for the interpretation and comparative analysis of metagenomes. Accordingly, paraphrasing Dobzhansky's famous quote for evolution, we can say that nothing in metagenomics make sense except in the light of metadata. For isolate organisms, particularly for the type strains of microbes, there are usually sufficiently rich metadata available in the literature, or in the databases of the Global Bioresource Centers (culture collection centers). In this case, crowdsourcing could significantly contribute to the process of metadata curation, provided that there are specific metadata standards for capturing specific sets of metadata fields from the literature to databases.
Recently there have been significant advances in tools to extract taxonomic and environmental data from free text  (33, 34). In light of these advances, capture of metadata might be amenable to curation by a combined text-mining/ crowdsourcing approach as discussed in earlier, or possibly even by gamification. Publishers could play a role, by checking that appropriate data and metadata have been deposited prior to acceptance of an article, but this imposes a significant extra burden on publishers. Given the NIH mandates that allow publications to follow years after the sequences have been submitted, it is difficult to do retrospective metadata capture, although crowdsourcing might enable some of this retrospective annotation. Another possibility would be for the database owner not to release an ID until both data and metadata have been deposited.
Participants may also be recruited at large, with the application itself providing some training and feedback, as for ZombiLingo, where the participant learns a new skill and/or has fun, while the developer of the application can gain large amounts of training data to create better models. Another approach is paid micro-task work, where participants can be easily recruited via a crowd labor platform, such as Amazon Mechanical Turk, with minimal requirements for task-specific expertise. This shows promise for rapid cost-effective collection of data; the main challenges are achieving quality adequate to the intended purpose, scoping the micro-tasks appropriately and minimizing set up costs. One limitation of microtasking is the need to present manageable small tasks that are visually appealing and can be answered within a short period of time.
A well-designed interface can train a task contributor to become a task expert, even when the person is not a domain expert. Recent experiments show that good instructions can be very short, and yet still yield good performance, even on a conceptually complicated task. The results of the sbv IMPROVER project suggest that with this approach, quite high-level goals can be reached. Some areas, such as metagenomics, have glaring gaps in metadata-these would be particularly good application areas for crowdsourcing, provided that the necessary information can be exposed to participants via extraction from journal articles and/or project descriptions. Alternatively, it may be possible to apply the kinds of interfaces developed for micro-tasking to elicit metadata from contributors at the time of data deposit.
Funding BioCreative has been partially funded by  NIH/NIGMS   R13-GM109648-01A1 , by the  National Institutes of Health Intramural Research Program, National Library of Medicine  and by  DOE  grant  DE-SC0010838. ZombiLingo is funded by  Inria  and by the  French Ministry of Culture  through a  DGLFLF grant  to KF. sbv IMPROVER is funded by  Philip Morris International. KBC's work was supported by grants  NIH   2R01 LM008111-09A1 NIH 2R01  and  LM009254-09   NIH  to  Lawrence E. Hunter ,  1R01MH096906-01A1  to  Tal Yarkoni , and  NSF   IIS-1207592  to  Lawrence E. Hunter  and  Barbara Grimpe. Funding for open access charge: The  MITRE Corporation.
Using the Pan-STARRS1 survey, we derive limiting magnitude, spatial completeness and density maps that we use to probe the three dimensional structure and estimate the stellar mass of the so-called Monoceros Ring. The Monoceros Ring is an enormous and complex stellar sub-structure in the outer Milky Way disk. It is most visible across the large Galactic Anticenter region, 120 We estimate its stellar mass density profile along every line of sight in 2×2 degree pixels over the entire 30,000 deg 2 Pan-STARRS1 survey using the previously developed MATCH software. By parsing this distribution into a radially smooth component and the Monoceros Ring, we obtain its mass and distance from the Sun along each relevant line of sight. The Monoceros Ring is significantly closer to us in the South (6 kpc) than in the North (9 kpc).
Conversely, de Jong et al. (2010) used the Sloan Extension for Galactic Understanding and Exploration (SEGUE) stripes which cross the Milky Way plane and have sufficient depth to observe main sequence MR stars. But these stripes only provide relatively localized pictures of the MR. Martin et al. (2006) ;  Conn et al. (2012)  and other works mentioned above have used deep photometry and spectroscopy to measure metallicities, stellar types and velocities in the MR, but a lack of sufficiently deep and wide optical data has prevented us from studying the global structure of the MR with great precision. Without large contiguous datasets near the Galactic plane, even estimating the total mass of the MR has been out of reach. The Panoramic Survey Telescope and Rapid Response System 1 Survey (PS1  Kaiser et al.
2010)  3π dataset is an ideal dataset to study the MR near the plane and produce the first large scale, contiguous map of the MR. PS1 has sufficient depth in the g P1 and r P1 bands to measure the luminosity and color of main sequence stars, particularly the blue edge stars which are plentiful and suffer less from the dwarf/giant degeneracy of redder stars, and it covers approximately 75% of the Galactic plane. Slater et al. (2014)  used the PS1 dataset to take the first large scale contiguous look at the MR and qualitatively compare the MR results to computational models of the MR as a satellite accretion  (Peñarrubia et al. 2005)  and the MR as a disrupted disk  (Kazantzidis et al. 2009). In this paper, we extend the work from Slater et al. (  2014 ) using the CMD-fitting techniques from de  Jong et al.
(2010)  to produce a more quantitative, three dimensional analysis of the MR. We observe the large scale structure of the MR and particularly find major asymmetries in mass and distance in the North and South. We also estimate the total observed mass and extrapolate the total mass of the MR for a variety of models. In Section 2, we discuss the PS1 dataset, its advantages and limitations when mapping the MR. In Sections 3 and 4 we will discuss how we bin stars across the sky and use the MATCH software to measure densities along the line of sight of every 2 × 2 degree (constant area of 4 square degrees) pixel. In Sections 5 and 6 we discuss our techniques for making large, contiguous, 2D and 3D maps of the Milky Way and Monoceros Ring. We estimate the total mass of the Monoceros Ring and in Section 7.