dataesr/scientific-paragraphs-categorization huggingface dataset:
The OASIS-MCT coupler is developed by the CERFACS (Toulouse, France) and the CNRS (Paris, France). It is distributed under the Lesser GNU General Public License (LGPL). OASIS-MCT_2. 0 can be downloaded from the its official website: https: //portal. enes. org/oasis (Valcke, 2013). The Coral job submission tool is developed by the CISM in the UCLouvain (Louvain-la-Neuve, Belgium) and is distributed under the Creative Commons CC0 1. 0 Universal license. The ocean-ice-sheet coupling interface also relies on the Climate Data Operators (CDO, Schulzweida, 2019) version 1. 9. 5, developed at the Max-Planck-Institute for Meteorology (see https: //code. mpimet. mpg. de/projects/cdo/, last access: 24 August 2021), and released under the terms of the GNU General Public License v2 (GPL). NetCDF Operators (NCO; see  Zender, 2008) version.
Background: Over the last decade, we have observed in microbial ecology a transition from gene-centric to genome-centric analyses. Indeed, the advent of metagenomics combined with binning methods, single-cell genome sequencing as well as high-throughput cultivation methods have contributed to the continuing and exponential increase of available prokaryotic genomes, which in turn has favored the exploration of microbial metabolisms. In the case of metagenomics, data processing, from raw reads to genome reconstruction, involves various steps and software which can represent a major technical obstacle. Methods: To overcome this challenge, we developed SnakeMAGs, a simple workflow that can process Illumina data, from raw reads to metagenome-assembled genomes (MAGs) classification and relative abundance estimate.
It integrates state-of-the-art bioinformatic tools to sequentially perform: quality control of the reads (illumina-utils, Trimmomatic), host sequence removal (optional step, using Bowtie2), assembly (MEGAHIT), binning (MetaBAT2), quality filtering of the bins (CheckM, GUNC), classification of the MAGs (GTDB-Tk) and estimate of their relative abundance (CoverM). Developed with the popular Snakemake workflow management system, it can be deployed on various architectures, from single to multicore and from workstation to computer clusters and grids. It is also flexible since users can easily change parameters and/or add new rules. Results: Using termite gut metagenomic datasets, we showed that SnakeMAGs is slower but allowed the recovery of more MAGs encompassing more diverse phyla compared to another similar workflow named ATLAS.
To face this problem, several workflows such as MetaWRAP,  12  its Snakemake version called SnakeWRAP,  13  ATLAS  14  and more recently MAGNETO,  15  have been developed to automatically reconstruct genomes from metagenomes. However, these workflows contain various modules and perform more tasks than only generating MAGs. For instance, they will taxonomically assign the metagenomic reads, create gene catalogs or perform functional annotations. They rely on numerous dependencies, require significant computational resources and regenerate a lot of outputs which are not essential to most research projects. To simplify this procedure and make it more accessible while remaining efficient, reproducible and biologically relevant, we developed with the popular Snakemake workflow management system,  16  a configurable and easy-to-use workflow called SnakeMAGs to reconstruct MAGs in just a few steps.
Implementation The workflow has been developed with the workflow management system Snakemake v7. 0. 0  16  based on the Python language. Snakemake enables reproducible and scalable data analyses as well as an independent management of the required software within a workflow. SnakeMAGs is composed of two main files: The Snakefile, named "SnakeMAGs. smk", contains the workflow script. It is divided into successive rules which correspond to individual steps. Our workflow includes a total of 17 distinct rules. Each rule requires input files and relies on a single software installed independently when starting the workflow in a dedicated conda v4. 12. 0 environment. At the end of each rule, output files will be generated in a dedicated folder, as well as a log file (stored in the logs folder) summarizing the events of the software run and a benchmark file (stored in the benchmarks folder) containing the central processing unit (CPU) run time, the wall clock time and the maximum memory usage required to complete the rule.
Operation The minimal system requirements to run the workflow will depend on the size of the metagenomic dataset. Small datasets (e. g. the test files provided on the GitHub repository) have been successfully analyzed on a workstation with an Intel Xeon Silver 4210, 2. 20GHz (10 cores/20 threads) processor and 96GB of RAM. Larger datasets should be processed on cluster computing or within a high-performance infrastructure. For instance, performance evaluation of publicly available metagenomes (see below) was performed on a computer cluster under CentOS Linux release 7. 4. 1708 distribution with Slurm 18. 08. 7, on a node possessing an Intel Xeon CPU E7-8890 v4, 2. 20GHz (96 cores/192 threads) and 512 GB RAM. SnakeMAGs integrates a series of bioinformatic tools to sequentially perform from Illumina raw reads: quality filtering of the reads with illumina-utils v2.
Use cases To demonstrate the benefits and potential of our workflow, we compared it to another Snakemake workflow named ATLAS v2. 9. 1. 14 To produce a fair comparison, ATLAS was run with the MEGAHIT assembler, without co-binning and dereplicating only 100% similar MAGs. To test these two workflows, we downloaded and analyzed ten publicly available termite gut metagenomes (accession numbers: SRR10402454; SRR14739927; SRR8296321; SRR8296327; SRR8296329; SRR8296337; SRR8296343; DRR097505; SRR7466794; SRR7466795) from five studies  [25] [26] [27] [28] [29]  and belonging to ten different termite species. SnakeMAGs requires only a limited number of inputs files: the raw metagenomic reads in FASTQ format from the 10 above-mentioned metagenomes, a FASTA file containing the adapter sequences, 43 a YAML configuration file specifying the variable names, paths and computational resource allocations (available on the GitHub repository and on Zenodo), and here since we worked with host-associated metagenomes a FASTA file containing the termite genome sequences.
Discussion Using metagenomic datasets from the gut of various termite species, our analyses revealed that while being slower, SnakeMAGs allowed the recovery of more MAGs encompassing more diverse phyla compared to ATLAS, another similar Snakemake workflow. More importantly our results showed that SnakeMAGs was able to recover MAGs encompassing the major bacterial phyla found in termite guts,  32, 33  and that some of these phyla were not recovered by ATLAS. Indeed, taxa belonging to Verrucomicrobiota,  34  Planctomycetota,  33, 35  Synergistota, 36 Elusimicrobiota 37 and Acidobacteriota  38, 39  have been repeatedly found in the gut of various termite species. As such, they would represent relevant targets for genome-centric analyses of the termite gut microbiota. Although we found no significant difference between the relative abundance of the MAGs belonging to phyla recovered by SnakeMAGs only and the MAGs recovered by both workflows, it is worth mentioning that Verrucomicrobiota, Planctomycetota, Synergistota, Elusimicrobiota and Acidobacteriota are usually less abundant than Spirochaetota, Firmicutes and Bacteroidota which are dominant phyla in termite gut,  32, 33  and that have been recovered by both workflows in our study.
Therefore, it would suggest that SnakeMAGs is not restricted to the most abundant taxa. Altogether, we showed that SnakeMAGs has the potential to retrieve quantitatively more genomic information from metagenomes but also to extract genomic features of biological interest. Thanks to the inherent flexibility of Snakemake, SnakeMAGs offers the possibility to the users to easily tune the parameters of the workflow (e. g. resource allocations for each rule, options of a specific tools) to adapt their analysis to the datasets and to the computational infrastructure. Additionally, advanced users will have the opportunity to edit or add new rules to the workflow. Regarding the future of SnakeMAGs, several avenues will be considered for the next versions of the workflow. Firstly, the workflow could give more freedom to the users by offering the choice of different tools to perform the same task (e.
g. different trimming, assembly or binning software). Secondly, with the current emergence of metagenomic datasets generated with long-read DNA sequencing,  40  it might be relevant to adjust our workflow for longread sequencing technology by including specific bioinformatic tools for this technology. 41 Meanwhile, since the majority of the metagenomic datasets have been and are still currently generated with Illumina short-read technology, SnakeMAGs can be widely used to explore the genomic content of various ecosystems via metagenomics.
Source data Termite genome references used for removing host sequences and their Bowtie2 index are available at: https://zenodo. org/record/6908287#. Y1JLANJBzUR The termite gut metagenomes analyzed in the present study are available on NCBI with the following accession numbers: SRR10402454; SRR14739927; SRR8296321; SRR8296327; SRR8296329; SRR8296337; SRR8296343; DRR097505; SRR7466794; SRR7466795. The authors describe SnakeMAGs as a dedicated pipeline for the compositional binning of contigs generated from Illumina sequencing reads obtained from microbial communities. It integrates the various steps in the process of comparative analyses in metagenomes, starting with the assembly of reads generated from community DNA, to the compositional binning of the assembled contigs into high-quality metagenome assembled contigs (MAGs).
Underlying data The main advantage of Snake is that it is highly task-specific to the generation of highquality MAGs from complex communities. It therefore can save considerable time and resources by avoiding steps in processing data (e. g. extensive functional or taxonomic annotations) that are irrelevant to the researcher's objective. I would like the authors to expand on how their pipeline controls for chimeric sequences, since these may lead to an artefactual inflation of MAGs. 1. As a termite researcher, I am aware of the many quirks that termite gut microbiomes present, and the limitations of many data processing pipelines and databases when it comes to addressing those quirks. I was glad that the authors used this challenging community for their benchmarks, and impressed at the contrast between the results from SnakeMAGs and ATLAS.
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Entomology, Microbial Ecology, Microbial systematics, Symbiosis, Bioinformatics, Evolution I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Vincent Hervé The authors describe SnakeMAGs as a dedicated pipeline for the compositional binning of contigs generated from Illumina sequencing reads obtained from microbial communities. It integrates the various steps in the process of comparative analyses in metagenomes, starting with the assembly of reads generated from community DNA, to the compositional binning of the assembled contigs into high-quality metagenome assembled contigs (MAGs). I believe this pipeline represents a valuable addition to the community. I found the manuscript was easy to read and have the following comments for the authors: 1. The main advantage of Snake is that it is highly task-specific to the generation of highquality MAGs from complex communities. It therefore can save considerable time and resources by avoiding steps in processing data (e.
In summary, a few MAGs generated by both workflows did not pass the GUNC quality criteria. Importantly, this new analysis shows that including GUNC does not change the major outcome of our comparison with ATLAS: SnakeMAGs produces more MAGs and more diverse MAGs than ATLAS. This analysis has now been included in the revised version of the manuscript. 2. As a termite researcher, I am aware of the many quirks that termite gut microbiomes present, and the limitations of many data processing pipelines and databases when it comes to addressing those quirks. I was glad that the authors used this challenging community for their benchmarks, and impressed at the contrast between the results from SnakeMAGs and ATLAS. I will emphasize further that the apparent inability of ATLAS to report phyla such as Planctomycetota and Elusimicrobia, or reconstruct fewer MAGs from Fibrobacterota or Desulfobacterota, is not trivial.
For instance, ATLAS is faster, although relying on more dependencies. What benefit do SnakeMAGs have in this regard? Despite being condensed, SnakeMAGs does appear to skip certain necessary stages to produce better MAGs, including: Using software that controls for chimera, such as metaMIC 1 , to fix contigs obtained after assembly; 1. Including multiple binning systems which may seem like a delay but ensures the best bin for each species and even offers a better resolution at the strain level. For instance, using a next-generation binner, such as semiBin or VAMB, then afterward clustering the MAGs on an ANI basis to assure a higher resolution of the species found. Several of these techniques even permit the use of long reads, which may ultimately be advantageous; 2. Since we now know that some of the species binned occasionally display under or overestimated completeness due to bias in the calculations using USCG, run a quality check of the bins using a more comprehensive approach than CheckM, for example, implementing software that addresses contamination, such as GUNC, or even more advanced updates of previous systems that now incorporate machine learning, such as CheckM2 , which does not present bias towards a taxonomy.
# Methods I want to congratulate the authors for the open scientific component. The databases, code, and software were all presented in an organized manner. The documentation reads quite well and appears to be crucial component of your work. Both the tool's installation and usage are quite well-explained and simple. Reply -We thank Reviewer 1 for these positive comments. As an adept of collaborative research, I saw that your GitHub lacked some unit tests that would have allowed other contributors to repair bugs that the authors had little interaction with or had previously overlooked without jeopardizing the distribution. That is not a question in this assessment, but it may represent a future enhancement. Reply -We thank Reviewer 1 for this suggestion. We will fully consider it for future enhancement.
Meanwhile, users have the opportunity to open Issues on the GitHub page to report any bug or request. Additionally, the test files provided on GitHub allow the user to test our workflow before running real and large datasets. I understand the authors' desire to keep the pipeline short to avoid several dependencies. However, it doesn't appear that the main pipeline is more effective or addresses that issue. For instance, ATLAS is faster, although relying on more dependencies. What benefit do SnakeMAGs have in this regard? Reply -Our main goal was to design a simple and minimalist workflow, so a non-specialist can easily bin MAGs without having to choose among (or test and compare) the myriads of software currently available to perform the different steps of our workflow. We believe that the strength of SnakeMAGs in this regard is its simplicity.
Using SnakeMAGs, a non-specialist user just needs to follow general instructions, while using ATLAS, such a user need to make decisive choices of various tools at many steps of the workflow, an approach that can easily become quite confusing. Despite being condensed, SnakeMAGs does appear to skip certain necessary stages to produce better MAGs, including: Using software that controls for chimera, such as metaMIC  1  , to fix contigs obtained after assembly; 1. Including multiple binning systems which may seem like a delay but ensures the best bin for each species and even offers a better resolution at the strain level. For instance, using a next-generation binner, such as semiBin or VAMB, then afterward clustering the MAGs on an ANI basis to assure a higher resolution of the species found.
Therefore, we believe that termite gut metagenomes constitute relevant datasets to test our workflow. The quality criteria used by authors to classify good genomes (>50% completeness and <10% contamination according to CheckM) is quite loose and not widely adopted. Parks et al. (2017)  2  -one of the first works using MAGs and also where checkM was presented -used an estimated quality ≥50 (defined as completeness -5 × contamination). In the present work, if a genome is in the bottom line, would have an estimated quality of 0, which is far from the threshold Parks adopted. Other criteria are also quite important, e. g. the number of contigs, N50, and ambiguous base pairs. I recommend authors reassess these results using a more strict quality parameter and then report if their results are better or comparable to the other pipelines.
Zenodo. Reconstruction of prokaryotic genomes from ten termite gut metagenomes using two distinct workflows: SnakeMAGs and ATLAS: https://doi. org/10. 5281/zenodo. 7661004. 43 -SnakeMAGs_config. yaml (The configuration file used to analyze the 10 termite gut metagenomes with SnakeMAGs) -ATLAS_config. yaml (The configuration file used to analyze the 10 termite gut metagenomes with ATLAS) -MAGs_SnakeMAGs. zip (A zipped folder containing the genomes of the 65 MAGs reconstructed with SnakeMAGs) -MAGs_ATLAS. zip (A zipped folder containing the genomes of the 37 MAGs reconstructed with ATLAS) -taxonomic_assignment_MAGs. csv (A text file containing the taxonomic assignment of all the MAGs reconstructed by both workflows) Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4.
This work was supported by the  FEDER grant InFoBioS n°EX011185  (région  Val de Loire, France) , the  Centre National de la Recherche Scientifique (CNRS)  and the  Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE). Ph. D. work of  Nachida Tadrent  (Ph. D. scholarship) was funded by the  University of Tours. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements. The authors are grateful to the  ORCHIDEE group  for their kind help with the model. The authors are very grateful to the FLUXNET communities for their efforts with respect to making the sites and collecting data. The authors also thank  Xiaoni Wang  for providing the ORCHIDEE PFT fraction at each flux site. Financial support. This research has been supported by the  European Commission ,  Horizon 2020 Framework Programme  (4C (grant no. 821003 )) and the  Agence Nationale de la Recherche (CLAND  (grant no. 16-CONV-0003 )). Review statement. This paper was edited by  Jatin Kala  and reviewed by two anonymous referees.
consent was provided by each patient to participate in the study (ClinicalTrials. gov NCT00367003, IDE G060028). Patients were recruited based on strict inclusion and exclusion criteria [described in  Riva-Posse et al. (2018) ], with baseline Hamilton Depression Rating Scale (HDRS-17) greater than 19. Therapeutic DBS at bilateral SCCwm was initiated one month after surgical implantation (Week C01) and maintained continuously for six months (Week C24). Experiments involving LFP±EEG were performed immediately before therapeutic DBS initiation, with the final three patients (the EEG subcohort) having simultaneous LFP and EEG on the day of therapy initiation (Figure  1A ). OffTarget electrodes (Figure  1 ) that are not situated in the SCCwm target are stimulated only for this experimental session and not used therapeutically.
Remote DO Identification Under the hypothesis that DOs are also evoked in regions downstream of stimulated tracts, we searched for DOs across 256-channel EEG in the single LFP-DO+ patient with a full LFP and EEG dataset (Patient 4). Filtered 256-channel dEEG is analyzed in the low-frequency 0 to 10 Hz range for (Figure  10A ), with max low-frequency power across all time segments calculated for all channels. The top 10% of channels are identified (Figure  10B  red channels) and an example frontal.
LFP-DOs Are Neural and Stereotyped We observed LFP-DOs in four out of six patients, primarily under OffTarget (non-therapeutic) stimulation (Figure  4 ). The DOs, when present, were present across six months of active therapy with slightly modified characteristics, like shortened evolution time, changes in intra-regime fundamental frequency dynamics, and harmonic structure (Figures  4C, D ). DOs are observed in both a high-power intraoperative data acquisition system (Figure  4A , Supplementary Figure  1 ) as well as the prototype Activa PC+S TM (Figures  4B-D ). All patients who exhibited LFP-DOs with the Activa PC+S TM and also had high performance intraoperative LFP recordings (Patients 3 and 4) exhibited DOs in both systems, strengthening the likelihood that LFP-DOs are not device-related artifact.
Supplementary Fig. S4  displays coherent x-ray diffraction measurements of NP300 for different gas compositions. All the diffraction patterns display well defined streaks indicating the faceted nature of the NP surface. The patterns are all asymmetric, reflecting strain distributions in NP300. Interestingly, large changes are observed in the vicinity of the 111 Bragg peak during stoichiometric CO oxidation (see the first fringes in Supplementary Fig. S4c ). These changes arise from local structural variations and suggest that the structure of the Pt particle strongly evolves during CO oxidation. Phase retrieval and faceting. The reconstructed shape of the Pt NP300 particle is displayed in Fig. 1b-f. Phase retrieval was carried out on the diffraction data using the PyNX package  30  (see Methods section).
Methods Growth. Pt nanocrystals were prepared by the solid-state dewetting of a 30-nm thin Pt film for 24 h at 1100 ∘ C in air. The Pt film was deposited on α-Al 2 O 3 (sapphire) with an electron beam evaporator. The Pt nanocrystals have their c-axis oriented along the [111] direction normal to the (0001) sapphire substrate. A standard photolithography method was employed to prepare a patterned layer of photoresist on sapphire prior to the electron beam evaporation of Pt. The lithographic processing route ensured that a number of dewetted Pt particles are wellseparated from their neighbours and that only one crystallite is irradiated by the incoming x-ray beam. Average lattice strain calculation. The 3D datasets of the reciprocal space maps (RSMs) of the NP give enough information to retrieve the average strain of the measured (111) atomic planes as a function of the gas composition (Ar, O 2 or CO).
The average strain is related to the variation of the d-spacing of the (  111  111 is calculated from the measured q 111 scattering vector: d meas: 111 = 2π/∥ q 111 ∥ = 2π/ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi q 2 x þ q 2 y þ q 2 z q , where q x , q y and q z are the coordinates of the q 111 scattering vector. The conversion of pixels from the detector images into q 111 coordinates was done using the xrayutilities package  48. The q 111 coordinates were determined by taking the centre of mass of the 3D datasets, while the FWHM of the 111 Pt diffraction patterns along the Q y direction was obtained by performing a Gaussian fit of the 3D data sets. Bragg coherent diffraction imaging. The BCDI experiment was performed at a beam energy of 8 keV (wavelength of 1.