dataesr/scientific-paragraphs-categorization huggingface dataset:
There are several possible solutions for this such as incorporating more analysis into the PLM software or developing new open CAD formats that support parametric components such as STP AP242 7. The approach taken by the Paramak is to provide a parametric creation of non parametric STP files with a permissive licensing model. The combination of permissive licensing and parametric studies allows automated geometry creation and analysis to be carried out on potentially tens of thousands of designs in parallel. Cloud bursting together with Cloud computing can provide the computing resources for such a study. Traditional licensing models where the costs scale with number of parallel sessions can result in significant costs implications for such a spike in compute capacity. Depending on the number of parallel sessions required these licensing costs can become significant.
Software practices employed The source-code is under version control and openly available via Github 9 under a permissive MIT licence. The Paramak Python package is distributed via PyPi 10 and there are plans to incorporate a Conda distribution in the future. A containerised build environment is distributed via Dockerhub 11 containing a pre-built environment with all the required dependencies. The code is documented with diagrams and examples on ReadTheDocs 12 which makes use of extensive Docstrings within the code. The code has been internally reviewed by a Research Software Engineer internal to UKAEA and also undergoing a professionally reviewed by an external company PullRequest 13. Continuous integration has been implemented using CircleCI  14  to run a broad range of unit tests and integration tests.
The test suite also covers use of the parametrically generated CAD in neutronics simulations using DAGMC  15  and OpenMC  16. This helps ensure the geometry made is suitable for use in neutronics analysis. Github Actions have been utilised from an early stage for automating several aspects of the code distribution, packaging and static code analysis. Github Actions have been used for employing code style guides (PEP8), updating the PyPi package distribution and automatically building and uploading new Dockerhub images with each new version of the code. The decision to open-source the Paramak code was a key enabler that allowed use of the previously mentioned platforms and in turn allowed the code to grow and improve rapidly. Additionally, the open-source nature of the project has facilitated contributions from outside the organisation, as demonstrated by the wide author list and contributors on to the Github repository.
Conclusion The Paramak code has been introduced and the motivation for facilitating a data science approach to geometry construction has been discussed. Several benefits of the open-source approach have been realised during the project. The number of Parametric Components has grown to the level where simplified reactor models can be constructed. Reactor models can be encoded to encapsulate design decisions which allow the required user inputs to be limited. This is demonstrated by the three example models presented in the paper and reinforced by additional Parametric Reactor models contained in the documentation  12. There are currently six different Parametric Reactors for users to create. Due to the structure of the code, it is straightforward to inherit existing reactors and modify specific parts of their design to extend the reactor family to accommodate additional features or parameters of Parametric Reactors.
The current parametric models provided in the Paramak are relatively simple but it is also possible to make more complex models when provided with a design. The Paramak has been used within UKAEA to create models of several spherical tokamak configurations and has also been used to reproduce a SPARC like design based on the diagrams in 17. The outputs of the Paramak are CAD models which are useful in fusion analysis disciplines such as Finite Element Analysis, neutronics, visualisation and even cost models which often require CAD files as an input. Due to the use of modern software practices (continuous integration and containerisation), the software is able to test the CAD generated in neutronics analysis and demonstrate the compatibility of the geometry in use. The software employs modern software practices such as automatic documentation generation (ReadTheDocs)  12  , package distribution services (PyPi) 10 and can be containerised 11.
2020; (accessed January 4, 2021). Reference Source 10. PyPi -The Python Package Index: Paramak v0. 2. 0 PyPi distribution. 2020; (accessed January 4, 2021). Reference Source 11. Dockerhub, containerized distribution via Dockerhub of the Paramak. 2020; (accessed November 30, 2020). Reference Source 12. ReadTheDocs: Paramak API documentation and example usage. 2020; (accessed November 30, 2020). Reference Source 13. PullRequest: Code Review as a Service. 2020; (accessed November 30, 2020). /doi. org/10. 5256/f1000research. 31218. r80436 © 2021 Liang J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Jingang Liang Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China The article describes an open-source python package Paramak which facilitates rapid production of 3D CAD models of fusion reactors and helps parametric studies on geometry configurations for reactor prototyping.
It worked well and as expected. I have no suggestions for improvement of the manuscript. Is the rationale for developing the new software tool clearly explained? Yes Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yuefeng Qiu Institute for Neutron Physics and Reactor Technology, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Acknowledgements The authors would also like to thank  Dr Lidija Pasuljevic Shimwell ,  Helen Gale ,  Stephanie Ellis ,  Colin Billingsley  and  Linda Billingsley  for their support. The authors would like to thank  Simon McIntosh  for the provision of examples when calculating the coordinates of the TF coils. The authors would like to thank  Lyal Avery  and the rest of the team at  PullRequest  for initiating the software review. The authors would also like to thank all the CadQuery developers and in particular  Adam Urbańczyk ,  Jeremy Wright  and  Dave Cowden.
Simulation Environment The simulations are carried out in AMoDeus, an open-source software tool designed to conduct research on mobility-on-demand systems, specifically on different operating policies  [25]. It is based on the agent-and activity-based transport simulation framework MATSim  [45] , which allows for accurate simulation of road network dynamics. Its queuingnetwork based traffic simulator makes it possible to conduct large scale parametric studies with high spatial resolution. Furthermore, the framework allows to simulate complex traveler decisions and supply-demand interactions in a dynamic and iterative way. While this ability has recently been demonstrated in the context of AMoD  [26, 46, 47] , our research on parking policies only focuses on static demand (i. e. , no decision-marking by the travelers) to study the effects of parking policies individually.
] [8]  They implement algorithms such as Random Forest, Neural Networks or Principal Component Analysis and are based on sequence data. They advocate to show good prediction results with sensitivity up to 81. 05% and specificity up to 95. 91% for O-GlcNAcPred-II. However, these numbers depend on the underlying test set which is different for each tool, making the results hard to compare. In order to be able to properly evaluate the performances of these predictors, we decided to build a large dataset of currently available experimentally proven O-GlcNAcylated sites and to test the performance of the three tools on this new dataset. We show here that the predictions are not as efficient as expected. Thus, we decided to use the dataset to develop a new prediction tool for O-GlcNAcylation sites, expanding upon the use of sequence information by including structural parameters.
Dataset Creation The dataset was constructed with data from experimentally proven O-GlcNAcylated sites of 236 mammal proteins from the UniProt reviewed database (Swiss-Prot) with the following research: "annotation: (type: carbohyd o glcnac evidence: experimental) AND reviewed: yes". 9 o complete this set, we also retrieved experimental data from PTM-ssMP  10  and from the results of Deracinois etal. 11 In these sets, we rejected the sites found by sequence homology. The negative sites were taken from the same sequences, considering serines and threonines which were not described as O-GlcNAcylated. We curated the full set, removing non-mammal sequences. We also removed redundant sequences. After curation, 565 O-GlcNAcylated sites (positive sites) and 40,271 non O-GlcNAcylated sites (negative sites) were gathered.
Evaluation of O-GlcNAcylation Prediction Software We evaluated the performance of three prediction tools, which are YinOYang, O-GlcNAcpred II and OGTsite,  [6] [7] [8]  using the sequences of the reduced dataset. The results of predictions of each of them were then compared to experimental data. A prediction was considered as • false positive (FP) if not identified experimentally, • true positive (TP) if validated experimentally, • false negative (FN) if not predicted but proven experimentally, • true negative (TN) if not predicted and not proven experimentally. We further calculated the specificity, the sensitivity, the precision (also called Positive Predictive Value (PPV)), the Negative Predictive Value (NPV), the False Discovery Rate (FDR) and the accuracy of each prediction tool: • sensitivity is the percentage of unmissed positive sites and corresponds to the number of positive sites correctly classified among all positive ones (TP/(TP+FN)), • specificity is the percentage of unmissed negative sites and corresponds to the number of TN among all negative ones (TN/(TN+FP)), • precision (or PPV) corresponds to the chance to predict a site as positive and be correct (TP/(TP+FP)), • False Detection Rate (FDR) is the contrary, namely the chance to be wrong when predicting a positive site (1-PPV), • NPV is the same as PPV but for negative sites (TN/ (TN+FN)), • accuracy corresponds to the proportion of correctly predicted sites whether they are positive or negative ((TN+TP)/(TN+FN+TP+FP)).
Features Sequence -Structural and Polarity Classification First of all, each amino acid around the sites in a window of -/+10 residues in the sequence were translated into size (Table  1A ) and polarity (Table  1B ) classes with Python v3. 6 scripts. For the size class, we chose to focus on the nature and the length of each residue totaling 8 classes, whereas for the polarity class, we considered physicochemical properties of amino acids, totaling 9 classes. We calculated the proportions of each class at each position in the -/+10 windows and compared them to a random composition of residues of all mammal sequences (reviewed only) retrieved from UniProt (82,495 sequences). The Chi Square tests were performed after calculating the number of individuals from the proportions. For the case of the random set, these values are theoretical and correspond to the number of individuals that would have been observed.
Sequence -Flexibility Prediction The flexibility of each site was predicted with the DynaMine tool that only requires a sequence as input. 12  S 2 score is provided for each residue. It is lying between 0 and 1 where score inferior to 0. 69 is considered as flexible, superior to 0. 8 considered as rigid and between these two values there is a twilight zone called context dependent. The results were parsed with a homemade Python v3. 6 script to extract the flexibility score of each site, for positive and negative data, and depending on the nature of the site (serine or threonine).
Sequence -Secondary Structure and Angles Predictions For every site, secondary structures were predicted using two software: SPIDER3 and PSSpred, run locally. 13, 14 ecause the predictions of both were very close to each other, the results were only retrieved from SPIDER3 and stored in a file with all the other parameters. φ and ψ angles of residues in a -3/+2 window were also retrieved from SPIDER3 predictions, classified as follows: β-strands (-160° < phi < -50° and 100° < psi < 180°), α-helix (-160° < phi < -50° and -60° < psi < 20°) and other.
Structure -Models To calculate the accessibility of each site, a structure for each protein was needed. As all proteins do not have an available structure in the Protein Data Bank or structures at site locations were missing, we modeled all of them with I-TASSER v5. 1 (default parameters),  15  installed and run locally on the HPC cluster of the Mesocenter of the University of Lille. We chose I-TASSER, which combines threading and de novo modeling, because it was ranked as the best structure modeling server according to the Critical Assessment of Techniques for Protein Structure Prediction (CASP) for rounds 7 to 14. [16] [17] [18]  Because the tool is limited to 1500 amino acids, we used a sequence window of 1500 residues for those cases where the sequence is longer, ensuring the maximum number of residues at either side of the O-GlcNAcylation site.
Structure -Accessibility to Solvent NAccess v2. 1. 1  19  was used with default parameters to compute the accessibility to solvent of the hydroxyl group of serines or threonines. ElNémo (v10/18/2018)  20  was used to create 10 normal modes, from -100 to 100 perturbations by step of 20, from each model obtained by I-TASSER: the 10 largest modes with 10 structures for each of them were kept to render the elasticity of the mode, which produced 100 structures per protein. Then, a homemade Python 3. 6 script was written to launch NAccess on each model and their modes to calculate the accessibility. The maximum of accessibility for the 100 structures was conserved for each site.
Machine Learning To train and test our model, we divided our dataset into two sets: a training set which represents 80% of the dataset and a testing set which represents the remaining 20% of the dataset. As the amount of positive data is much lower than the amount of negative data, we performed an oversampling of the training set and an undersampling of the testing set. Oversampling and undersampling were done with an algorithm for non-continuous data called Random Over Sampling Examples (R package "ROSE", v0. 0. 3). 21 e also tested on real proportions data (1. 4% O-GlcNAcylated vs 98. 6% non O-GlcNAcylated) in order to compare the results with the undersampled testing set (Figure  1 ). We used three types of machine learning algorithms: • Random Forest (R package "randomForest", v4. 6. 14).
22 To optimize the parameterization, we trained data on different numbers of trees: once a plateau was found, the first value of this plateau was chosen. Each tree has nodes to test different features. The number of variables tested at each node was chosen to get the best predictions. The finally selected parameters are: ntree = 200 (number of trees), mtry = 3 (number of variables tested at each division). • Gradient Boosting Tree (GBT) ("xgboost" R package "xgboost", v1. 1. 1. 1)  23 that uses decision trees like Random Forest but including a new variable which is residuals. 24 The difference between residuals and the real value to predict is used in the algorithm. • Support Vector Machine (SVM) is an algorithm essentially based on prediction of two classes (R package "e1071", v1. 7. 3). 25  Each data value is set in a matrix with as many dimensions as features.
90 • Secondary structure: 0, 1 or 2 where 0 is not structured, 1 is alpha helix and 2 is beta strand • Presence of a proline in +1: 0 or 1 (no or yes) • Secondary structure according to phi and psi angles (0, 1 or 2) • Nature of the site: 0 or 1 where 0 is serine and 1 threonine To test the different algorithms we compared the methods according to the sensitivity and the PPV. We ran each algorithm ten times with ten randomly shuffled datasets (80% to train and 20% to test) and computed the statistics for these ten runs. To ensure the added value of each feature, MRMD V3. 0 26 was used regarding its five methods: PageRank, LeaderRank, TrustRank, Hist_a and Hist_h. Everything in this section was done with R v3. 6. 3.
OGT Partners Analysis The OGT partners were retrieved from the IMex database through the PSICQUIC  27  service inside the Cytoscape  28, 29  v3. 7 software for network visualization and analysis. The enrichment analysis in molecular function of the Gene Ontology was performed with ClueGO v2. 5. 7  30  and the EBI GOA (v23/07/2020). The selection criteria were: • Statistical Test Used = Enrichment/Depletion (Two- sided hypergeometric test) • Correction Method Used = Bonferroni step down • Min GO Level = 2 • Max GO Level = 6 • Cluster #1 • Sample File Name = Network selection: ManuallyAddedOrModifiedIDs • Min number of Genes = 3 • Min Percentage of Genes = 3. 0 • GO Fusion = false • GO Group = true • Kappa Score Threshold = 0. 4 • Over View Term = SmallestPValue • Group By Kappa Statistics = true • Initial Group Size = 1.
Evaluation of Available Prediction Tools on a Newly Built Dataset We built a new dataset with only experimentally proven O-GlcNAcylated sites, ignoring sites identified by homology in order to avoid inclusion of false positives (see Materials and Methods). We obtained a dataset of 565 O-GlcNAcylation sites and 40,271 serine or threonine residues that are not O-GlcNAcylated (data set provided in the git repository). This means that only ~1. 4% of all S and T residues of O-GlcNAcylated proteins are O-GlcNAcylated. We refer to O-GlcNAcylated sites as positive data and non O-GlcNAcylated sites as negative data. Because the YinOYang and O-GlcNAcPred-II prediction tools are limited in terms of protein sequence size, we built a reduced dataset that we used to run each tool. The results of our evaluation are listed in Table  2.
These results show that, although acceptable values are obtained by the tools for the specificity, the sensitivity and the accuracy, the values are lowered with respect to those previously published  [6] [7] [8]  when applied to our new dataset. However, these criteria remain limited in determining which sites are truly O-GlcNAcylated. Another indicator such as the precision, also called Positive Predictive Value (PPV), is more useful. PPV is the chance that a positive prediction is right. In our analysis, we observed that the PPV is very low for any tool, the best one showing only 8. 68% (most stringent YinOYang), which means that a site predicted as positive has less than 9% chances to be really O-GlcNAcylated. The Negative Predictive Value (NPV) is the chance that a negatively predicted site is factually not O-GlcNAcylated.
Analysis of Secondary and Tertiary Structures Around the Sites To predict the backbone flexibility, we used the DynaMine software. 12 Figure  6  shows that only 15. 27% of all the O-GlcNAcylated sites are predicted in rigid regions, 43. 7% in flexible regions and 41. 03% in contextdependent regions. These proportions are very similar to  the negative dataset with, respectively, 15. 78%, 45. 13% and 39. 09%. By making the distinction between serine and threonine, we find that serine residues are more often found in flexible regions than the threonine ones, independently of whether a site is (positive) or is not (negative) glycosylated and on the contrary that less serines are found in rigid regions. But here, the positive and negative distributions are so close that this feature is not discriminative enough.
Accordingly, modified sites should be globally accessible to solvent. As a consequence, we computed the accessibility to solvent of the O-GlcNAcylated sites. This calculation required the three-dimensional structure of each protein. However, not all proteins of our dataset possess a structure listed in the Protein DataBank (PDB), and even for those for which a structure was available, the area which contained the sites was often missing, undoubtedly due to the intrinsic flexibility of sites prone to O-GlcNAcylation. Therefore, we decided to build modeled structures. Because for a part of the proteins any good template was available, we used I-TASSER. We then calculated the accessibility of all sites in the models using NAccess. 19 We also computed normal modes for each structure with the ElNémo software to take into account the potential elasticity of the molecule, which may improve the accessibility of each site.
Integration of the Features in Machine Learning Algorithms We studied different aspects of the sites, comparing parameters between the O-GlcNAcylated sites and non O-GlcNAcylated sites of our dataset, from the primary to tertiary structure. None of these parameters are sufficient to differentiate positive from negative data despite some tendencies. Consequently, to find a way to predict O-GlcNAcylation sites, we used them as features in three different types of machine learning (ML) algorithms. We decided not to include the accessibility since it appeared to be unreliable and moreover relies on models of tertiary structures and not on resolved ones. The three types of algorithms we chose are Random Forest (RF), Gradient Boosting Tree (GBT) and Support Vector Machine (SVM),  22, 24, 33  the latter being split into 4 variants (linear, polynomial, radial basis and sigmoidal).
96 We further tried SVM because it is not based on trees and it is a well-known classifier in bioinformatics that has already been used for O-GlcNAcylation prediction. 8, 34 ur dataset was divided into two: one for training and one for testing. Following Box and Meyer (1986),  35  we used 80% of the data for training and the remaining 20% for testing. As the amount of positive and negative data in the set differs by three orders of magnitude, this could bias the training step. To counter this fact, we used an oversampling method, which consists of adding points in the space representation (created by the features) of positive data to make the amount of positive data equal to the amount of negative data  5  for the training (32,217 sites). And we used an undersampling method which consists of choosing randomly the same number of negative data in the set as positive data for the testing (113 sites).
Because there is only a limited amount of experimentally proven data available, the training of an algorithm to predict sites is very hard. Some of the currently available tools therefore use the prediction of O-GlcNAcylated sites by homology. This provides more data for the training, but the data are intrinsically biased, which may explain the poor results we get when running them on our dataset. Precisely, regarding the results of the different tools, we pointed out that the statistical quantities used to compare their efficiency are not as significant as they should be. Looking at the biological problem, the statistical measurements used here must be improved by taking into account the number of false positives. Sensitivity is a major statistical measurement to see if some positive sites are missed which is interesting when data are balanced.