dataesr/scientific-paragraphs-categorization huggingface dataset:
In Sect. 4. 2 of that paper, the authors propose to start for each position at the most extreme velocities and iterate over the velocity channels until the first position-velocity pixel above a certain threshold is found. They find this method to be the most robust among those they tested on synthetic PV data where the true YSO mass was known. We apply this method to the PV diagrams in Fig. 9 , to estimate the highest velocity as a function of the radial distance to the central YSO (black diamonds) and to finally fit the result with a function of Keplerian tangential velocity, according to Eq. (3). For the fitting procedure, we use the python package astropy. modeling 6 (Astropy Collaboration et al. 2013). We add the parameters v 0 ≈ v LSR and r 0 to the model function to account for deviations in the local standard of rest (LSR) and for the resolution-limited position of the central object.
Acknowledgements. We thank the referee for the suggestions, which significantly clarified the analysis and structure of the paper. FB, HB, AA and JCM acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Consolidator Grant   CSF-648505. Furthermore, the team thanks  IRAM  and its staff for conducting the large program CORE. RK acknowledges financial support via the  Emmy Noether Research Group on Accretion Flows and Feedback in Realistic Models of Massive Star Formation  funded by the  German Research Foundation (DFG)  under grant no. KU 2849/3-1  and  KU2849/3-2. DS acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : " Building a Habitable Earth " ( SE 1962/6-1 ). ASM is partially supported by the  German Research Foundation (DFG)  through grant  SFB956  (subproject  A6 ).
Although the main feature of the operating system is that it is open source, i. e. everyone can download it for free and modify it, it is still subject to licensing (without a license fee), mostly under the Apache Software License. 10 In Google's business model the revenue flows not from the licenses, but from the consumers' use of proprietary applications -such as Gmail, Google Maps, and Google Search. 11 The second issue is whether this market also includes other operating systems for mobile devices such as iOS (owned by Apple). iOS is different from Android because it is proprietary to the manufacturer of the mobile devices and is used exclusively on those devices. 12 Therefore iOS as the second most popular operating system in Europe  13  is not a substitute for Google's product from the demand side perspective.
The third defined relevant market is the "app stores for the Android mobile operating system". 18 The Commission considers that specific apps (such as Google Play) which serve as a store for other apps constitute a separate market. Such a conclusion could follow from the fact that Google Play faces direct competition from other products with the same functionality -to allow customers to download Android-compatible mobile applications. 19 Nevertheless, the authority should provide convincing data to prove whether those apps are sufficiently different from any other apps and therefore not substitutable for the users and the device manufacturers. As explained below, this particular market is needed for the assessment of the anticompetitive tying. In conclusion, even if the definitions of the relevant markets raise some concerns, the dominance of Google is most likely not going to be disproved as the estimations of the market shares by the Commission are well supported by statistical data.
The conduct of the company can again be characterised by tying but, unlike in the Microsoft case, here the tying product is the application (Play Store) and the tied product is the operating system (Google's Android version). However, the big difference between the cases lies in the open source character of Android. In the context of the antitrust rules the possibility for any manufacturer to create its own independent version of the operating systems neutralises any real dominance of Google and diminishes the risk of anticompetitive abuse. 45 Google is proudly highlighting this feature of Android and claims that the system is "spurring this competition and choice, lowering prices and increasing choice for everyone". 46 e issue with the open source trait deserves a closer examination. Without going into very technical details, it is enough to point out that Android consists of a kernel (based on the PC operating system Linux), libraries, a Java platform, and applications.
Some parts of the Linux that is included in Android are not free, as well as some of the libraries and many of the applications. This makes the source code of the Android version, which is licensed as free software, anti-fragmentation agreements ensure that apps work across all sorts of different Android devices. 52 In essence, the company claims that by inducing OEMs to install Google's version of Android it provides a guarantee to the users for reliable and smooth operation of the applications. This matter is of a very technical character and needs to be addressed by experts, but it is not hard to accept that if each manufacturer uses a different "fork" this might lead to higher costs for ensuring the compatibility by the developers, as well as to some discrepancies in the functionality of the systems.
Google's position would have to be supported by a very solid proof as the standards of the Court of Justice for the justification of a tying practice on reasons of "technical efficiency" are very high. In the Microsoft case the attempt of the company to prove that disintegration of Windows Media Player from the Windows operating system would create a series of problems to the detriment of consumers, software developers, and internet site creators was firmly rejected. 53 The Court did not accept the arguments for the integration of the two products as long as it is technically feasible for them to be marketed separately, which is also the case with Google -the company does not claim that Play Store can work properly only under Google's own Android version, but rather points at the risk of mobile devices running different Android forks in parallel.
PCPE-1 expression is upregulated in fibrosis  [18, 19]. PCPE-1 comprises two complement, sea urchin protein Uegf, BMP-1 (CUB) domains  [20]  and a netrin-like (NTR) domain  [21]. Although neither an X-ray nor an NMR structure is available for full-length PCPE-1, X-ray structure of CUB1-CUB2 domains (PDB ID: 6FZV, 2. 7 Å) in a complex with C-propeptide of procollagen  [22]  and NMR structure of the NTR domain (PDB ID: 1UAP) are available  [23]. In the structure of the active CUB1-CUB2 fragment of PCPE-1 bound to the C-propeptide trimer of procollagen III (CPIII), two Ca 2+ ions participate in the formation of the interface between the CUB1-CUB2 domains and the procollagen III molecule  [22]. Often, CUB domains bind Ca 2+ , and Ca 2+ coordination involves acidic amino acid residues (i. e. , Tyr-Glu-Asp-Asp motif)  [24].
the MD approach identified from one to three binding sites, one of which being consistent through all five repetitions of MD simulations. The fact that these methods did not agree with MD simulations could be due to conformational changes of negatively charged amino acid side chains during the MD simulation, allowing them to come close to each other and to coordinate calcium ions. FoldX and IonCom used static structures, which prevents the dynamics required for the coordination of Ca 2+. For CUB1-CUB2 domains, all methods were consistent and predicted two Ca 2+ binding sites identical to those found in the CUB1-CUB2 domain complexed with procollagen (PDB ID: 6FZV). This means that CUB1-CUB2 domains in PCPE-1 could be already prebound to Ca 2+ ions when the interaction with the procollagen is established.
The experimental evidence that the NTR domain binding to HP is dependent on divalent cations  [28]  leads to the hypothesis that Ca 2+ ions could potentially bind within the interface of the NTR-HP complex. GAG binding. For this, we used two Ca 2+ binding sites corresponding to the X-ray structure (PDB ID: 6FZV) of the CUB1-CUB2 domain, and the weak Ca 2+ binding site predicted in the NTR domain (Figure  5 ). Major differences in both positive and negative electrostatic potential shape were observed for the NTR domain. This is not only explained by the direct effect of Ca 2+ positive charge but also by the fact that E405, E406 and N407 were moved closer to each other to coordinate the cation, which also affects the topology of the isosurface. For CUB1-CUB2 domains, the largest positively charged patch of the potential isosurface was not noticeably affected by the presence Ca 2+ ions.
Surface Plasmon Resonance (SPR) Binding Assays The SPR measurements were performed on a BIAcore 3000 instruments (GE Healthcare, Uppsala, Sweden), and the data were analtzed with the BIAevaluation 3. 1 Software (GE Healthcare, Uppsala, Sweden) as previously described  [28]. Inhibition assays of PCPE-1 binding to HP and HS by HP oligosaccharides (from dp2 up to dp8, generous gift of Rabia Sadir and Hugues Lortat-Jacob, Institut de Biologie Structurale, Grenoble, France) were carried out as previously described  [28]. Briefly, HP (Sigma, St Quentin Fallavier, France) and HS (Celsus Laboratories Inc, Cincinnati, OH, USA) from porcine intestinal mucosa were biotinylated and captured on streptavidin previously immobilized on a CM4 sensor chip (GE Healthcare, Uppsala, Sweden). Human recombinant PCPE-1 (1 µM)  [28]  was incubated with HP oligosaccharides (5 µg/mL) in 10 mM Hepes pH 7.
Protein Structures The structure of the N-terminal CUB1-CUB2 domains was obtained from the X-ray structure of CUB1-CUB2 fragment of PCPE-1 bound to the C-propeptide trimer of procollagen III (PDB ID: 6FVZ, 2. 7 Å). In this structure, the residues 33-275 are resolved (here and further, the numeration of the sequence corresponds to the UniProtKB ID: Q15113). However, the structure of the linker (151-157) between two CUB domains was not determined due to its flexibility  [22]. The structure of the NTR domain (313-442) was also obtained from the PDB (PDB ID: 1UAP, 1st NMR model)  [23]. The structures of the linker between two CUB domains (151-157) as well as the interdomain linker (276-312) were built in xLeap module of AMBER16, refined using an MD approach and then used for modeling the full-length structure of PCPE-1 with a CG MD approach as described in the Section 3.
Electrostatic Potential Calculations The PBSA approach as implemented in AmberTools within AMBER16 package  [50]  was used to calculate electrostatic potential isosurfaces corresponding to the analyzed proteins. This method proved to be successful for GAG binding site prediction on an extensive protein-GAG dataset from the PDB  [31]. The default value for the grid spacing of 1. 0 Å and ff99SB force field parameters were used. The electrostatic potential isosurfaces were analyzed in VMD  [51]. For visualization, such values of positive and negative electrostatic potential were chosen for each molecular system so that the data were as informative as possible for GAG binding site propensities.
Fragment-Based Approach In order to dock longer HP to the full-length PCPE-1 protein, which is unfeasible for the AD3 protocol described above due to the limitation of the available number of the degrees of freedom and, in general, because of the computationally very expensive conformational sampling required for simulations with such ligands, a fragment-based docking approach we recently developed was applied  [64]. In brief, first, HP dp3 of both types, IdoA(2S)-GlcNS(6S)-IdoA(2S) and GlcNS(6S)-IdoA(2S)-GlcNS(6S), were docked using the AD3 docking procedure described in Section 3. 5. 1. Then, all 1000 solutions for each HP dp3 fragments were used to assemble ~10 5 dp11 GAG chains applying the approach standard parameters [64]. This was followed by the refinement and all-atom conversion of the chains with a slight modification in the original scripts to avoid the RMSD-based selection of the best fitting structures compared to the experimental ones due to the lack of proper atomistic experimental PCPE-1/GAG complex structures.
All-Atom MD Simulations and MM-GBSA Free Energy Calculations All-atom molecular dynamics (MD) simulations of the PCPE-1, PCPE-1/Ca 2+ and PCPE-1/Ca 2+ /GAG complexes obtained by molecular docking were performed with the use of the AMBER16 MD package  [50]. Periodic boundary conditions in a truncated octahedron TIP3P water box with at least 8 Å distance from the solute to the periodic box border were used. Na + and Cl -monovalent counterions were used to neutralize the system. ff99SB force field parameters for protein  [65]  and the GLYCAM06  [66]  for GAGs were used, respectively. Prior to MD production runs, two energy-minimization steps were performed: first, 500 steepest descent cycles and 1000 conjugate-gradient cycles with harmonic force restraints on solute (10 kcal/mol/Å 2 ), then, 3000 steepest-descent cycles and 3000 conjugate-gradient cycles without restraints.
After the minimization, the system was heated up to 300 K for 10 ps with harmonic force restraints on solute (10 kcal/mol/Å 2 ), equilibrated for 100 ps at 300 K and 10 5 Pa in isothermal isobaric ensemble (NTP). This was followed by a 100 ns MD production run in the same NTP ensemble. The SHAKE algorithm, 2 fs time integration, 8 Å cutoff for non-bonded interactions, and the particle mesh Ewald method were used. The trajectories were analyzed using the cpptraj module of AMBER Tools  [46]. Free-energy calculations and per-residue energy decomposition were done using molecular mechanics-generalized born surface area (MM-GBSA) model igb = 2  [67]  for protein-GAG and protein-Ca 2+ complexes for the parts of the trajectory where convergence in terms of RMSD was obtained. The obtained energy values account explicitly for the enthalpy and implicitly for the solvent entropy.
Ca 2+ Ion Position Prediction We applied and compared several approaches to predict the Ca 2+ binding sites on the surface of PCPE-1 and its domains. Prior to applying these approaches to PCPE-1, we analyzed their performance for the complex between an annexin V and HP, where Ca 2+ ions are known to be stable and to contribute to GAG binding (PDB ID: 1G5N, 2. 7 Å)  [35]. In this structure, 9 Ca 2+ were resolved.
FoldX and IonCom We used the scripts of FoldX available at http://foldx. embl. de and online ion ligand binding site prediction tool IonCom at https://zhanglab. ccmb. med. umich. edu/IonCom for Ca 2+ binding site predictions. FoldX represents a tool with an implemented empirical force field developed for effective evaluation of the contribution of mutations on the stability, folding and dynamics of proteins and nucleic acids  [70, 71]. As an output FoldX yields the positions of predicted ions on the surface of the protein. In contrast, IonCom utilizes ab initio training and template-based information to output a list of protein residues potentially involved in ion binding  [72]. Both programs were used with the default parameters.
After the submission deadline for predictions, a subset of these targets accumulated experimental annotations over time, either as a consequence of new publications about these proteins or the biocuration work updating the annotation databases. The members of this set of proteins were used as benchmarks for evaluating the participating computational methods, as the function was revealed only after the prediction deadline. CAFA2 expanded the challenge founded in CAFA1. The expansion included the number of ontologies used for predictions, the number of target and benchmark proteins, and the introduction of new assessment metrics that mitigate the problems with functional similarity calculation over concept hierarchies such as Gene Ontology  [28]. Importantly, we provided evidence that the top-scoring methods in CAFA2 outperformed the top-scoring methods in CAFA1, highlighting that methods participating in CAFA improved over the 3-year period.
Much of this improvement came as a consequence of novel methodologies with some effect of the expanded annotation databases  [26]. Both CAFA1 and CAFA2 have shown that computational methods designed to perform function prediction outperform a conventional function transfer through sequence similarity  [25, 26]. In CAFA3 (2016-2017), we continued with all types of evaluations from the first 2 challenges and additionally performed experimental screens to identify genes associated with specific functions. This allowed us to provide unbiased evaluation of the term-centric performance based on a unique set of benchmarks obtained by assaying Candida albicans, Pseudomonas aeruginosa, and Drosophila melanogaster. We also held a challenge following CAFA3, dubbed CAFA-π, to provide the participating teams another opportunity to develop or modify prediction models.
The head-to-head comparisons in MFO, BPO, and CCO between the top 5 CAFA3 and CAFA2 methods are shown in Fig. 1. CAFA3 and CAFA1 comparisons are shown in Additional file 1: Figure  S1. We first observe that, in effect, the performance of baseline methods  [25, 26]  has not improved since CAFA2. The Naïve method, which uses the term frequency in the existing annotation database as a prediction score for every input protein, has the same F max performance using both annotation databases in 2014 (when CAFA2 was held) and in 2017 (when CAFA3 was held), which suggests little change in term frequencies in the annotation database since 2014. In MFO, the BLAST method based on the existing annotations in 2017 is slightly but significantly better than the BLAST method based on 2014 training data. In BPO and CCO, however, the BLAST based on the later database has not outperformed its earlier counterpart, although the changes in effect size (absolute change in F max ) in both ontologies are small.
Second, methods that perform direct function transfer based on sequence similarity do not necessarily benefit from a larger training dataset. Although the performance observed in our work is also dependent on the benchmark set, it appears that the annotation databases remain too sparsely populated to effectively exploit function transfer by sequence similarity, thus justifying the need for advanced methodology development for this problem. Head-to-head comparisons of the top 5 CAFA3 methods against the top 5 CAFA2 methods show mixed results. In MFO, the top CAFA3 method, GOLabeler  [23] , outperformed all CAFA2 methods by a considerable margin, as shown in Fig. 2. The rest of the 4 CAFA3 top methods did not perform as well as the top 2 methods of CAFA2, although only to a limited extent, with little change in F max.
Of the top 12 methods ranked in MFO, 7 are from CAFA3, 5 are from CAFA2, and none from CAFA1. Despite the increase in database size, the majority of function prediction methods do not seem to have improved in predicting protein function in MFO since 2014, except for 1 method that stood out. In BPO, the top 3 methods in CAFA3 outperformed their CAFA2 counterparts, but with very small margins. Out of the top 12 methods in BPO, 8 are from CAFA3, 4 are from CAFA2, and none from CAFA1. Finally, in CCO, although 8 out of the top 12 methods over all CAFA challenges come from CAFA3, the top method is from CAFA2. The differences between the top-performing methods are small, as in the case of BPO. The performance of the top methods in CAFA2 was significantly better than of those in CAFA1, and it is interesting to note that this trend has not continued in CAFA3.
Protein-centric evaluation The protein-centric evaluation measures the accuracy of assigning GO terms to a protein. This performance is shown in Figs. 3  and 4. We observe that all top methods outperform the baselines with the patterns of performance consistent with CAFA1 and CAFA2 findings. Predictions of MFO terms achieved the highest F max compared with predictions in the other two ontologies. BLAST outperforms Naïve in predictions in MFO, but not in BPO or CCO. This is because sequence similarity-based methods such as BLAST tend to perform best when transferring basic biochemical annotations such as enzymatic activity. Functions in biological process, such as pathways, may not be as preserved by sequence similarity, hence the poor BLAST performance in BPO. The reasons behind the difference among the three ontologies include the structure and complexity of the ontology as well as the state of the annotation database, as discussed previously  [26, 31].
Evaluation via molecular screening Databases with proteins annotated by biocuration, such as UniProt knowledge base and UniProt Gene Ontology Annotation (GOA) database, have been the primary source of benchmarks in the CAFA challenges. New to CAFA3, we also evaluated the extent to which methods participating in CAFA could predict the results of genetic screens in model organisms done specifically for this project. Predicting GO terms for a protein (proteincentric) and predicting which proteins are associated with a given function (term-centric) are related but different computational problems: the former is a multi-label classification problem with a structured output, while the latter is a binary classification task. Predicting the results of a genome-wide screen for a single or a small number of functions fits the term-centric formulation.
Biofilm formation In March 2018, there were 3019 annotations to biofilm formation (GO:0042710) and its descendent terms across all species, of which 325 used experimental evidence codes. These experimentally annotated proteins included 131 from the Candida Genome Database  [34]  for C. albicans and 29 for P. aeruginosa, the 2 organisms that we screened. Of the 2746 genes we screened in the Candida albicans colony biofilm assay, 245 were required for the formation of wrinkled colony biofilm formation (Table  1 ). Of these, only 5 were already annotated in UniProt: MOB, Fig. 7  Heatmap of similarity for the top 10 methods in CAFA1, CAFA2, and CAFA3. Similarity is represented by Euclidean distance of the prediction scores from each pair of methods, using the intersection set of benchmarks in the "Top methods have improved from CAFA2 to CAFA3, but improvement was less dramatic than from CAFA1 to CAFA2" section.
Red indicates the ratio is greater than one while blue indicates the ratio is less than one. Only the top five keywords ranked by ratio are shown. The larger the ratio, the more difference there is between the F max -weighted and the equal-weighted EED1 (DEF1), and YAK1, which encode proteins involved in hyphal growth, an important trait for biofilm formation  [35] [36] [37] [38]. Also, NUP85, a nuclear pore protein involved in early phase arrest of biofilm formation  [39]  and VPS1, contributes to protease secretion, filamentation, and biofilm formation  [40]. Of the 2063 proteins that we did not find to be associated with biofilm formation, 29 were annotated with the term in the GOA database in C. albicans. Some of the proteins in this category highlight the need for additional information to GO term annotation.
Using this assay, we identified 3 genes involved in the perception of wasp exposure and 12 genes involved in the long-term memory. For details on the target selection and fly assay, see  [29]. None of the 29 genes had an existing annotation in the GOA database. Because no genome-wide screen results were available, we did not release this as part of the CAFA-π and instead relied only on the transfer of methods that predicted the "long-term memory" at least once in D. melanogaster from CAFA3. Results from this assessment were more promising than our findings from the genome-wide screens in microbes (Fig. 10 ). Certain methods performed well, substantially exceeding the baselines.