dataesr/scientific-paragraphs-categorization huggingface dataset:
Financial support. The authors received funding from the French state aid managed by the ANR under the "Investissements d'avenir" program (grant no. ANR-11-IDEX-0004-17-EURE-0006) and from the European Union's Horizon 2020 research and innovation program (grant no. 101003469) for the "eXtreme events: Artificial Intelligence for Detection and Attribution" (XAIDA) project. Review statement. This paper was edited by Po-Lun Ma and reviewed by two anonymous referees.
Acknowledgements. This study benefited from the ESPRI computing and data center (https://mesocentre. ipsl. fr,last access:  12 September 2022 ), which is supported by  CNRS ,  Sorbonne Unihttps://doi. org/10. 5194/gmd-15-7051-2022 Geosci. Model Dev. , 15,  7051-7073 , 2022.
(2020) Cyclone segmentation CAM5. 1 (mod: 25 km) Integrated vapor trans-DeepLabv3+ port, integrated water vapor, vorticity, wind vectors at 10 m and at 850 hPa, sea level pres- sure Pang et al. (2021) Cyclone detection Satellite images from Vis DCGAN and YOLOv3 NII Shi et al. (2022) Extratropical cyclone ERA5 (rea) Top net thermal radi-Single shot detector detection ation, mean sea level pressure, vorticity https://doi. org/10. 5194/gmd-15-7051-2022 Geosci. Model Dev. , 15, 7051-7073, 2022 database (HURDAT2; available from http://www. nhc. noaa. gov/data/#hurdat, last access: 12 September 2022; Landsea.
In this article, we propose to group similar pull-requests together into clusters so that each cluster is assigned to the same reviewer or the same reviewing team. This proposal allows saving reviewing efforts and time. Method: To do so, we first extract descriptive textual information from pull-requests content to link similar pull-requests together. Then, we employ the extracted information to find similarities among pull-requests. Finally, machine learning algorithms (K-Means clustering and agglomeration hierarchical clustering algorithms) are used to group similar pull-requests together. Results: To validate our proposal, we have applied it to twenty popular repositories from public dataset. The experimental results show that the proposed approach achieved promising results according to the well-known metrics in this subject: precision and recall.
Introduction In a social coding platforms such as GitHub, contributors (developers) frequently use Pull-Request (PR) mechanisms to submit their code changes to reviewers or owners of a given software project (repository)  [1, 2]. These changes include development activities (e. g. , adding new functional features)  [3, 4] , fixing errors in an existing project  [5]  or for improvements (in terms of performance, usability, reliability, and so on). The contributors are volunteers, who are distributed geographically around the world, and they implicitly collaborate together to work on a repository  [1]. Indeed, each contributor independently clones or forks the original repository and makes their changes to that fork  [6]. These changes could be useful to merge to the original repository. Therefore, the contributor creates a PR to package and submit their changes to core reviewers of the original repository.
The content of these submitted PRs passes through several rounds of code reviews and discussions. At last, the core reviewers decide which PRs should be merged to the upstream repository (original repository) while other PRs are rejected  [7]. The PR mechanism is useful to keep the contributors up to date about the changes made to the original repository and to manage these changes. In open-source projects, it speeds up the growth of repositories through distributed development. For example, Rails (https://github. com/rails/rails/ pulls) repository has more than 26,557 PRs. Although the PR mechanism is useful in GitHub, it has own drawbacks. The nature of parallel and distributed development using PR mechanism allows many contributors to submit PRs that perform similar or same development activities  [8].
In addition, the GitHub policy for managing the PRs review aggravates the problem of missing the synchronization among development activities submitted by contributors. In GitHub, there are two algorithms to automatically assign PRs to reviewers. The first one is "the round-robin algorithm chooses reviewers based on who's received the least recent review request, focusing on alternating between all members of the team regardless of the number of outstanding reviews they currently have" (https://docs. github. com/en/github/setting-up-and-managingorganizations-and-teams/managing-code-review-assignment-for-your-team). The second one is "the load balance algorithm chooses reviewers based on each member's total number of recent review requests and considers the number of outstanding reviews for each member.
The load balance algorithm tries to ensure that each team member reviews an equal number of PRs in any 30 day period" (https://docs. github. com/en/github/setting-up-and-managing-organizations-andteams/managing-code-review-assignment-for-your-team). Following these algorithms, a group of similar PRs (e. g. , n-PR) will be assigned to n reviewers and the reviewing efforts will be redundant n times especially when these PRs are assigned to plain reviewers. However, when such a cluster is assigned to one team or reviewer, the reviewing effort and time of (n-1) reviewers will be saved and the updating efforts and time by contributors will be reduced as well. Clustering similar PRs together and then submitting them to reviewers as clusters have the following benefits: (1) help to detect duplicated parts (duplication in parts of code changes) among PRs, (2) help to expedite code changes reviewing, and thus lead to low latency for processing PRs, (3) mitigate the limitation of heuristic-duplicate PRs detection techniques.
These repositories are included in a public dataset (https: //github. com/whystar/MSR2018-DupPR/blob/master/dup_prs. md). We evaluate the effectiveness in different scenarios: (i) in case of the number of PRs is more than or equal to the number of reviewers, (ii) in case of the number PRs is less than the number of reviewers. The experimental results show that the proposed approach achieved promising results according to the well-known metrics in this subject: Precision and Recall. Furthermore, the experimental results show that the proposed approach is efficient to save the time and efforts of reviewers. As a summary, our proposal makes the following contributions: 1. An automatic approach to cluster similar PRs together using two supervised and unsupervised ML algorithms considering the number of reviewers or repository's owner preferences.
Pull-Request Mechanism Managing changes in software development can be challenging in terms of continuous, parallel, and distributed development  [16]. GitHub implements a model to manage these changes called pull-request (PR)  [17]. PR lets other contributors know, discuss and review the potential changes before these changes are accepted (merged) or refused in a repository on GitHub. A common PR flow is depicted in Figure  1. When contributors need to add new features or fix existing bugs in the main repository. First of all, rather than contributes to the original repository, the contributors fork the repository and create a branch to keep changes organized and separated from the original repository. Then, the contributors can safely add their changes (commits) and test them in their own branch.
Similar Pull-Requests PRs that are aiming to contribute congruous features or fix congruous bugs in the same GitHub repository are called similar PRs  [19]. Similar PRs may be submitted to review in parallel time by different reviewers. This will cause redundant reviewing time and efforts. Moreover, it will complicate the collaboration process. Therefore, it is useful to assign similar PRs to the same reviewer to be able to decide which the PR is more suitable on effective time and effort. When contributors create a new PR, they should provide a title of the PR, a description that describes in details the contributions in the PR, and commits that contain the contributions  [20]. Reviewers can detect similar PRs after examining these PR contents. Figure  2  shows three similar PRs located in nodejs/node (https://github.
In  [21] , Li et al. have proposed an approach to improve their previous work  [9]  to detect duplicate PRs in GitHub. Their approach combines textual similarity (title and description similarities) and change similarity (changed files and changed lines of code) to retrieve a top-k ranked list of PRs that are the most similar to the new coming PR. This list represents candidate duplicate PRs. The experimental results show that the combined similarities achieved best performance instead of using each similarity separately. The combined similarities help to identify 83% of the duplicate PRs compared with 54. 8% using only textual similarity and 78. 2% only change similarity. The main concern in works of  Li et al. ( [9, 21] ) is how to adjust the similarity threshold or top-k items in order to retrieve only similar PRs and only those PRs.
Recommending Code Reviewers for PRs For improving the quality of code reviewing in social coding (e. g. , GitHub), many techniques are proposed to recommend relevant reviewers for opened PRs. In  [27] , Lipcak et al. make a large-scale study on these techniques and structure them into four categories based on the features and algorithms that they use: (1) heuristic-based techniques, (2) social network-based techniques, (3) machine learning-based techniques, and (4) hybrid techniques. All these techniques do not cluster similar PRs together and do not assign PRs as clusters to the reviewer but they assign each PR individually to reviewers. These techniques are as follows:.
Heuristic-Based Techniques The techniques of this category rely on historical data and heuristics to recommend proper reviewers for opened PRs. In  [28] , Balachandran proposed a recommendation technique called ReviewBot. This technique is based on the assumption that the reviewer who in the past reviewed the same lines of code changes in the emerging PR should review this emerging PR. In  [29] , Thongtanunam et al. proposed a recommender technique called RevFinder based on the similarity between files path of previously PR and files of emerging PR. In  [30] , Xia et al. proposed a technique called TIE to improve the RevFinder technique by combining textual content of code review (PR) and similarity of file paths. CHREV  [31]  and WRC  [32]  are two recommender techniques based on the reviewers' expertise (past reviewing).
Machine Learning-Based Techniques Machine learning-based techniques refer to the use of ML algorithms to recommend proper reviewers by building a learning model based on the training dataset  [36] [37] [38] [39] [40] [41]. There is one typical approach in this category proposed by Jeong et al. [42]. This approach uses a Bayesian network classifier and processes a set of patch features (e. g. , patch content and patch meta-data) to predict suitable reviewers and accept patches (PRs).
Parsing and Preprocessing Pull-Requests Each PR in the GitHub project must have a title, description, and list of changed files. In this step, we parse open PRs of a given repository to extract these three pieces of information using GitHub GraphQL API (https://docs. github. com/en/graphql/overview/about-thegraphql-api). This information includes implicit useful information about PRs objectives and context. The description may include a code snippet to exactly refer to and detail the buggy code. Figure  2  shows three similar PRs with similar titles and descriptions. Furthermore, each PR provides a list of changed files where a developer made their contribution or change. We consider that if two or more PRs share the same file paths, they have a high chance to be related and work on the same functionality or tackle the same issue.
For example, Figure  2  shows three PRs changed the file eslintrc. The standard preprocessing tasks in natural language processing (NLP) are applied to manipulate the extracted information from each PR (title, description and changed files list). These tasks are  [23, 45] : removing punctuation marks and special characters, tokenization, stemming, and stop words removal. In the first task, we remove numbers, all punctuation marks, brackets, parenthesis, and other special characters. An appropriate regular expression has been used to perform such a cleaning task. In the second task, we convert textual sentences into stream of tokens. We applied here different tokenization strategies. The text of title and description is split into many tokens while the path of each changed file is treat a single token.
Dataset To evaluate our approach, we should apply it on a ground truth dataset. Unfortunately, in this subject, there is no benchmark to support our evaluation task. Furthermore, there are no GitHub repositories with predefined similar PRs clusters in each repository. Therefore, we experiment the proposed approach on repositories with clusters of duplicate PRs as each duplicate cluster is also a similar cluster with 100% similarity. We have applied the proposed approach on a public dataset of duplicate PRs, called DupPR (https://github. com/whystar/MSR2018-DupPR), established by Yu et al. [53]. The dataset includes 2323 pairs of duplicate PRs extracted from 26 popular software projects in GitHub. Moreover, these pairs were automatically identified and then manually verified. Most duplicate PRs in DupPR are in pairs.
Conclusions and Future Work In this article, we propose An automatic approach to cluster open similar PRs together of a given repository using two supervised and unsupervised ML algorithms considering the number of reviewers or repository's owner preferences. These algorithms are K-means clustering and agglomeration hierarchical clustering. Such clustering helps to submit similar pull-requests to the same reviewer and thus saves reviewing time and effort. We first extract textual information from PRs to link similar PRs together. Then, the extracted information is used to find similarities among PRs. Finally, the textual similarity helps ML algorithms to group similar PRs together. To evaluate our proposed approach, we have run it on twenty popular repositories from a public dataset. The experimental results show that the proposed approach is efficient in identifying relevant clusters.
's work outperformed Li et al. 's work by (16-21%) in terms of the Recall metric. Wang et al. [5] proposed an approach to improve the work of Ren et al. by considering the creation time of PRs in combination with the nine features identified by Ren et al. They assume that when the creation times of two PRs are close to each other, they are most likely to be duplicated. Firstly, they extract ten features from the training dataset used by Ren et al. The values of these features are used to train the AdaBoost classifier. Finally, for new coming PR, the classifier decides whether this new PR duplicate with other existing one. The experiments showed that Wang et al. improve the performance of Ren et al. 's work by 14. 36% and 11. 93% in terms of F1-score metric.
DSMC simulations and comparison with experimental results Numerical simulations have been carried out using the direct Monte Carlo method, a stochastic method, solving the flow fields in transitional regime i. e. from continuum low density regime to free molecule regime. For this purpose, the well-known DS3V software developed by Prof. Graeme A. Bird have been used  [43, 44]. The main objective is to determine the accommodation parameters between the gaz flow and the waverider surface for our experimental conditions and analyze how they are related to rarefaction effects. The comparison criterion between the numerical and experimental results are the drag and lift forces with the purpose to obtain numerical results as close as possible to those of the experiments. We have selected the DS3V program by many reasons.
The first one is the avaibility as it is free to download from the Bird website (http://www. gab. com. au). This program was designed to be able to run on personal computers with Microsoft windows. Finally, the use of DS3V is quite simple for a beginner as it uses a set of menus which creates files containing all the simulation information for post processing. However, one disadvantages associated with the DS3V codes is the creation/importation of the 3D geometry. Indeed, DS3V does not have an integrated geometry package and does not accept basic 3D CAD files. The geometry file have to be a Raw Triangle files with a series of x, y and z coordinates which form a 'triangle' and when put together they form a mesh. To design the waverider, we have used the software Rhinoceros 7, because it can create triangular mesh with coordinates associated to each triangle apex.
Acknowledgements This work is fully financed by the  Agence Nationale de la Recherche  for the project  APHYRA  ( APHYRA  - 19-ASTR-0014-01 )). PhD is supported by the  French National Research Agency (ANR)  as part of the  Programme d'Investissement d'Avenir (LabEx CAPRYSSES ; Grant No  ANR-11-LABX-0006-01 ).
In 2021, the release of AlphaFold2 -the DeepMind's machine-learning protein structure prediction program -revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy.
Structure comparison using the Dali server may return structural homologs  (Holm, 2020). Here are shown the results obtained for the Tal C-terminal domain (trimeric assembly) from the L. casei phage J-1. (B) AlphaFold2 is becoming an essential part of any structural biology project. AlphaFold2 can synergize with experimental approaches in that predicted structures can be used to determine domain boundaries for recombinant protein production, to solve crystal structures by molecular replacement, and to interpret cryoEM 3D reconstructions. Moreover, AlphaFold2 can be the only way to get structural models of challenging samples, which can then be used as reliable templates for functional characterization. biological questions  (Goulet and Cambillau, 2021; Tunyasuvunakool et al. , 2021). In particular, the AlphaFold Protein Structure Database (https://www.
alphafold. ebi. ac. uk), developed by DeepMind and EMBL-EBI, offers open access to structure predictions for the human proteome, for proteins from key model organisms (e. g. , Mus musculus, Escherichia coli, Arabidopsis thaliana), and for proteins from organisms related to global health (Mycobacterium tuberculosis, Staphylococcus aureus, Trypanosoma cruzi)  (Tunyasuvunakool et al. , 2021; Varadi et al. , 2022). The number of structures in the AlphaFold database is expected to increase at a dizzying rate, with more than 100 million structures that should be available this year to include most representative sequences from the UniRef90 data set  (Varadi et al. , 2022). However, AlphaFold2 is not substituting structural biologists. It is instead becoming a key step of any structural biology project, providing structures that must be properly analyzed.
Abstract In the context of global warming, the melting of arctic permafrost raises the threat of a reemergence of microorganisms some of which were shown to remain viable in ancient frozen soils for up to half a million years. In order to evaluate this risk, it is of interest to acquire a better knowledge of the composition of the microbial communities found in this understudied environment. Here we present a metagenomics analysis of 12 soil samples from Russian Arctic and subarctic pristine areas: Chukotka, Yakutia, and Kamchatka, including 9 permafrost samples collected at various depths. These large datasets (9. 2 10 11 total bp) were assembled  (525,313 contigs > 5kb) , their encoded protein contents predicted, then used to perform taxonomical assignments of bacterial, archaeal, and eukaryotic organisms, as well as DNA viruses.
The retrieved core segments were pushed out from the core barrel with air pressure only. The core segments were visually described and immediately stored in plastic bags or core foil and sealed tightly with duct tape. The wrapped and sealed cores were put into opaque and thermally insulated hard case expanded polypropylene (EPP) boxes (thermoboxes) and stored outside, where they were kept frozen (or refroze slowly if the sediment material was originally unfrozen) at outside temperature around -10 to -20°C. During the entire transportation, the core material was kept in the dark and frozen in the thermo-boxes. Temperatures below freezing during the entire storage and transport chain were recorded with a temperature data logger in one of these boxes from the drilling site to the laboratories in Potsdam, Germany.
DNA Sequencing, assembly, and annotation Each metagenomic sample was sequenced with DNA-seq paired-end protocol on Illumina HiSeq4000 platform at the French National Sequencing Center "Genoscope" (https://jacob. cea. fr) producing datasets of 2x150bp read length, except for sample B (2x100bp read length) (Table  S1 ). Sequencing libraries were prepared as previously described in  (Alberti et al, 2017)  using an 'on O R I G I N A L U N E D I T E D M A N U S C R I P T beads' protocol. Briefly, when available, 250 ng of genomic DNA were sonicated using the E210 Covaris instrument (Covaris, Inc. , USA) and the NEBNext DNA Modules Products (New England Biolabs, MA, USA) were used for end-repair, 3`-adenylation and ligation of NextFlex DNA barcodes (Bioo Scientific Corporation). After two consecutive 1x AMPure XP clean ups, the ligated product were amplified by 12 cycles PCR using Kapa Hifi Hotstart NGS library Amplification kit (Kapa Biosystems, Wilmington, MA), followed by 0.
6x AMPure XP purification. Finally, libraries were quantified by qPCR (MxPro, Agilent Technologies, USA) and libraries profiles were evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Reads were quality checked with FASTQC (v0. 11. 7)  (Andrews 2014). Identified contaminants were removed and remaining reads were trimmed on the 3' end using 30 as quality threshold with BBTools (v38. 07) (BBMap). Assemblies of filtered datasets were performed using MEGAHIT (v1. 1. 3) (Li et al. 2015)  with the following options: --k-list 33,55,77,99,127 --min-contig-len 1000. All filtered reads were then mapped to the generated scaffolds using bowtie2  (Langmead and Salzberg 2012)  with the --very-sensitive option. The mapping of the contigs was used to calculate the mean coverage of each contig.
To ensure a reliable taxonomic assignation, only scaffolds longer than 5kb were considered and submitted to metaGeneMark (v3. 38)  (Zhu, Lomsadze and Borodovsky 2010)  with the default metagenome-style model. The predicted genes were then compared to the Refseq database  (Li et al. 2021)  using the Diamond version of blastP (v0. 9. 31)  (Buchfink, Xie and Huson 2015)  with an E-value < 10 -5 , identity percentage threshold of 35 and retaining only the best hits. The taxonomy of each scaffold was then inferred with a custom-made script applying a Last Common Ancestor (LCA) method. At each rank, recursively, the taxonomy was conserved only if over half of the annotated genes presented the same keyword. Unclassified and ambiguous contigs were screened against Refseq with Diamond blastX (--sensitive option and E-value < 10 -5 ), in order to detect exons.