dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). Herein lies the risk of jeopardising reproducibility, defined as the ability to consistently obtain similar results across multiple measurements using the same methodology  (Renard et al. , 2020). Machine and deep learning segmentation strategies form promising alternatives for automatic segmentation, optimising parameters for high accuracy performance on the training dataset and ensuring effective generalisation to other datasets within the same problem class. However, transitioning towards automatic segmentation, while promising, is not trivial. Successful automation of segmentation methods still requires an initial investment of time and resources for skill acquisition and understanding needed to fine-tune models and adapt the workflow to the specific dataset at hand. A good example of this is the intrinsic dependency of deep learning segmentation on the ground truth data input and the selection of hyperparameters during the training process.
, 2022). For our case study, by using the chemical theoretical molar reaction as a guiding principle, we can train the segmentation algorithm to identify and accurately outline the volumes of different mineral phases at various stages of the dehydration process. This adaptive learning process, driven by the theoretical molar reaction, could maintain high accuracy and robustness of the segmentation algorithm throughout the dehydration process. In addition to this promising integration of techniques, two key areas of potential advancement lie in the development of unsupervised segmentation approaches and the use of time as a parameter to learn from. Unsupervised learning can dramatically reduce the time and effort required for data annotation, accelerating analysis, and enabling the exploration of larger datasets  (Mahdaviara et al.
, 2023). While data from before and after a scan in a time series may provide extra information that can be leveraged to better segment complex datasets. 4D data pose a unique challenge and opportunity for these unsupervised methods, as leveraging temporal information can significantly improve the quality and consistency of the segmentation. In this work, we have demonstrated the potential of deep learning methods in the segmentation of 4D synchrotron X-ray tomographic images, particularly in the context of metamorphic rock transformations. We successfully overcame the inherent challenge of accurately segmenting all mineral phases and the pore network in an operando dataset, consisting of around 50 tomograms for each experimental setting, by using a robust and efficient deep learning-based workflow.
Our deep learning algorithm, trained on just 13 representative slices, generated a reliable segmentation, substantiating the versatility and power of such approaches. Conversely to the conventional external calibration techniques, we achieved validation of the segmentation accuracy by employing the metamorphic reactions themselves as an internal standard. We found the errors between the theoretical and segmented volumes from our time-series experiments to be consistently within the 2% confidence intervals of the theoretical curves. This facilitates extracting quantitative information, such as mineral growth rate and pore size variations, from segmented CT images during a reaction. The implementation of a 2D U-net architecture for segmentation and the utilisation of Random Forest-obtained labelled data as input demonstrated how machine learning can efficiently process large datasets and provide robust results even under challenging conditions.
Acknowledgements. This research was funded through  NERC  standard grant  NE/T001615/1. We would like to thank  Federica Marone  and  Christian Schlepütz  at  TOMCAT beamline at PSI  for the fantastic support and assistance during beamtime. Oliver Plümper ,  Hamed Amiri , and  Alireza Chogani  from  Utrecht University  for invaluable help during the long days at the beamtime. Finally, we are very grateful to  John Wheeler  for discussions during the drafting of this paper. We are sincerely grateful to  Object Research Systems (ORS) Inc ( Montreal, Canada)  for granting us a free-of-charge Non-Commercial licence of their software Dragonfly.
Acknowledgements The research in this manuscript was supported by  NIH   T32 Training  Grants  GM068411  and  AI1049815  to J. C. ; the  Montpellier University of Excellence  ( ANR-16-IDEX-0006 ) program to P. E. and L. B. ;  NIH   AI127370  and  AI50698  to J. M. ; and  NSF   RAPID 2033354  and  NIH   GR530882  to D. F. We thank members of the  Fu and Ghaemmaghami Labs  for comments on this manuscript; the  Mass Spectrometry Resource Lab at the University of Rochester ; and the  Clinical Proteomics Platform, CHU Montpellier.
PCR amplification and 16s rDNA sequencing Supernatant of extracted DNA was used as PCR template for 16s rDNA gene amplification with the primers 27f (5 0 -AGAGTT TGATCMTGGCTCAG-3 0 ) and 1492 R (5 0 -GGTTACCTTGTTAC-GACTT-3 0 ) and a high-fidelity polymerase. The PCR was performed as follow: denaturation step at 98 C during 30 s, 30 cycles of amplification step at 98 C for 30 s, 55 C for 30 s and 72 C for 30 s, a final elongation step at 72 C for 1 min. The PCR amplification product was purified and sequenced (Microsynth, Switzerland). The sequence was compared with the sequences available in the NCBI and EZbiocloud databases. Mega 11 was used for phylogenetic tree construction.
Data analysis Cumulated H 2 production was analysed with modified Gompertz equation (Eq. (  1 ))  [28]. With nls package of Rstudio, model factors were determined: latency period of biogas production of the strain (L), maximum production of H 2 (R max ), and maximum productivity (H 0 ). Then, the duration to produced 95% of the H 2 was calculated (t 95 ) according to Eq. (  2 ). H ¼ H0*exp À exp Rmax*e H0 *ðL À tÞ þ 1 (1) t 95 ¼ H0 Rmax*e *ð1 À lnðÀlnð0:95ÞÞÞ þ L (2) 3.
F1 and CW showed a TOC/N of 10. 6 and 82. 2 respectively, which illustrates a deficiency in nitrogen, mainly for CW. This deficiency is consistent with data of the literature  [29]. Both of the substrates showed limitation of iron, which is a key element for the hydrogenase activity. The only organic acid in presence for both wastes is the lactic acid, with respectively 0. 46 g L À1 for F1 and 2. 71 g L À1 for CW. All these characteristics are consistent with the origin of the effluent. Whey is obtained after the acid coagulation of milk. The liquid phase separated from the curd is therefore concentrated in lactose and lactic acid but depleted in proteins. F1 is a mixture of tap water and sodium hydroxide used for cleaning production tanks and pipes, and is therefore fairly low in organic matter but highly alkaline.
4 ) 3 7 1 e3 8 3 43 sp. 1. 9 e e e C. sp. 1. 10 þ þ e Nd: not detected. i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 9 ( 2 0 2 From the ten isolated colonies, only one strain, named C. sp. 1. 3, shows a high H 2 production without the production of unwanted gas such as H 2 S in all tested conditions. For these reasons, the strain C. sp. 1. 3 was selected as the best candidate for H 2 production. The 16S rRNA gene of the strain C. sp. 1. 3 was sequenced and blasted in the NCBI and EzBioCloud databases for sequence comparison. Both databases showed that C. sp. 1. 3 share 99. 69% identity with Clostridium beijerinckii DSM 791. The neighbour-joining phylogenetic tree (Fig. 1) was constructed using 16S rRNA sequences of the closest Clostridium species as revealed by NCBI and EzBioCloud databases, supplemented with 16S rRNA sequences of sequenced Clostridium beijerinckii strains.
It clearly shows that C. sp. 1. 3 fell in the cluster of Clostridium beijerinckii strains, suggesting that the isolate should belong to Clostridium beijerinckii. The genome of C. sp. 1. 3 was sequenced and deposited in the GenBank database with the accession number GCA_029715615. 1. The draft genome was 5,652,128 bp, consisting of 233 contigs. The longest scaffolds were 284,810 bp and the N50 length was 71,898 bp. Annotation predict a total of 5022 CDS, 76 tRNA and 3 rRNA. The genomic distance of the C. sp. 1. 3 isolate with 11 other sequenced C. beijerinckii species (Supplementary Table.
Acknowledgements We acknowledge funding support from the  Institut Pasteur ,  Agence Nationale de la Recerche (Laboratoire d ' Exellence Revive, Investissement d'Avenir ;  ANR-10-LABX-73  to ST and  ANR-21-CE13-0005 MUSE  to GC),  Association Française contre les Myopathies  (Grant # 20510  to ST and # 23201  to GC), and the  Centre National de la Recherche Scientifique. M. K. was supported by a  Post-Doctoral Fellowship  from  Uehara Memorial Foundation  (Grant#  202231020 ). We gratefully acknowledge the  UtechS Photonic BioImaging (Imagopole) , C2RT,  Institut Pasteur , supported by the  French National Research Agency (France BioImaging ;  ANR-10-INSB-04 ; Investments for the Future) for support in conducting this study. We thank  Dounia Bouragba  and  Sayna Miri  for initial contributions to this study.
Acknowledgement This study was supported by the  Amada Foundation  ( AF-2021003-A3 ). Additionally, the measurements using synchrotron XRD were part of the research results obtained with the aid of  JSPS KAKENHI ( JP20H02484 ) and were carried out under proposal numbers  2020A1097  and  2022A1385  at the large synchrotron radiation facility SPring-8. We would like to express our gratitude to  Dr. Kentaro Kajiwara  of  JASRI  and the following alumni and current members of the  Solid Mechanics Laboratory at Tottori University  for their dedicated efforts in conducting the experiments:  Daiki Fujita ,  Daiki Kondo ,  Tomoya Takahashi ,  Yuya Ueda ,  Taichi Shimizu ,  Homiyo Sato , and  Hidena Yokota. The test steel and stress-strain curves obtained using JIS13B tensile test specimens were provided by  Ms.
To align the HasR barrel in the outer membrane, co-ordinates were taken from the MemProtMD database  26  and used for alignment to ensure the lipid headgroups were in the correct position relative to the protein. The location of the HasB helix in the inner membrane was estimated by using the location of the tryptophan residues, given there was no structural information available for this section  38. Simulations involving HasR and HasB were set up as follows; once the proteins had been inserted into the membranes, any overlapping lipids were removed prior to energy minimisation. The CHARMM-GUI webserver was used to generate the necessary topology and parameter files for all models and structures reported in this study.
Acknowledgement: This work was funded by the  Institut Pasteur , the  Centre National de la Recherche Scientifique (CNRS) , the  French Agence Nationale de la Recherche ANR HemeStockExchange , the  Fondation pour la Recherche Médicale  (Equipe FRM 2017M. DEQ20170839114). We thank  Christophe Thomas  from the platform of production and purification of recombinant proteins at the  Institut Pasteur  for bacterial cell production in fermentors, the platform of molecular biophysics at the  Institut Pasteur  for providing access to the microcalorimeter and  Loïc Hellio  for HasB sample preparation. We thank  Riccardo Pellarin  for helpful discussions.
Catchment dataset Data from 325 catchments around France  (Chauveau et al. , 2011)  were used in order to i) generalize the conclusions drawn from this study  (Gupta et al. , 2014)  and ii) possibly identify links between catchment characteristics and specific behaviours of transformations. These catchments were chosen for the low human impact on the precipitation-streamflow relationship and for the low rate of missing streamflow data (< 0. 5 %) over the period of interest. Moreover, the catchments are spread throughout France (Figure  2 ), thus representing a wide variety of meteorological and hydrological conditions. Precipitation and temperature data were retrieved from the Météo-France SAFRAN reanalysis  (Vidal et al. , 2010). Streamflow data were retrieved from the French HydroPortail database (https://hydro.
Acknowledgements. The  SCHAPI and Météo-France  are thanked for providing the hydro-meteorological dataset. Laurent Strohmenger  and  Vazken Andréassian  are thanked for their feedback on this study. 22 https://doi. org/10. 5194/egusphere-2023-775 Preprint. Discussion started: 5 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Cells and Transfections The MCF10A cell line was cultured in DMEM/F12 medium (Gibco) supplemented with 5% horse serum (Sigma), 100 ng/mL cholera toxin (Sigma), 20 ng/mL epidermal growth factor (Sigma), 0. 01 mg/mL insulin (Sigma), 500 ng/mL hydrocortisone (Sigma) and 100 U/mL penicillin/streptomycin (Gibco). Human vimentin ORF (GenBank KU178388. 1) was amplified from a vector provided by Dr. Alexander Minin (Institute of Protein Research of Russian Academy of Science). The PCR product was cloned into the home-made MXS CAG Blue SV40pA PGK Puro bGHpA between the Fsel and Ascl restriction sites and sequenced (Eurofins Genomics, Ebersberg, Germany) to ensure, that no mutations appeared after amplification. MCF10A cells were transfected using Lipofectamine 3000 (Invitrogen). Two days after transfection, 1 µg/mL Puromycin was added.
Antibodies For Western blots, the following antibodies were used: CYFIP2 (Sigma SAB2701081), Tubulin clone DM1A (Sigma T9026), ARPC1A (Sigma #HPA004334), Arpin home-made (Dang, Nature), p62/DCTN4 clone H-4 (Santa Cruz Biotechnology). For immunofluorescence anti-Laminin V, clone D4B5 (Merck). For both applications, Anti-Vimentin, clone V9 (sc-6260, Santa Cruz) was used. Secondary goat anti-mouse and anti-rabbit antibodies conjugated with Alexa Fluor 488, 555 and 647 used for immunofluorescence were from Life Technologies. Secondary goat anti-mouse and anti-rabbit antibodies conjugated with alkaline phosphatase used for Western blots were from Promega. Western Blots Cells were lysed in 50 mM Hepes, pH7. 7, 150 mM NaCl, 1 mM CaCl2, 1% NP40, 0. 5% Na Deoxycholate, and 0. 1% SDS supplemented with a protease inhibitor cocktail (Roche).
Acknowledgements We thank  Dmitry Guschin  and  Pierre Mahou  for excellent technical assistance,  Alexander Minin  for providing vimentin cDNA. This work was supported by grants from  Agence Nationale de la Recherche  ( ANR-20-CE13-0016  to AMG and NBD and  ANR-22-CE13-0041  to AMG),  Fondation ARC pour la Recherche sur le Cancer  ( ARC PJA 2021 060003815  to AMG), from  Institut National du Cancer  ( INCA_11508  for AMG and IB). We thank the  Polytechnique Bioimaging Facility  for confocal microscopy partly supported by  Région Ile-de-France  (interDIM) and  Agence Nationale de la Recherche  ( ANR-11-EQPX-0029 Morphoscope2 ,  ANR-10-INBS-04   France BioImaging ).
Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour.
While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.
Introduction Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their success relies on training data that is available in programmatically accessible and standardized format  1. For example in structural biology, Protein Data Bank (PDB)  2  has enabled revolutionary tools that can predict protein structures with unprecedented accuracy  3. However, the lack of smart guidelines and community-consensus best-practices for data sharing are limiting the development of such databanks and AI applications in many fields  1. This is the case in biomolecular modeling, where vast amount of data is already available for proteins, lipids, nucleic acids, and carbohydrates, but tools that enable applications of these data for data-driven applications are not yet available  4.
Here we propose a cost-effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications: an overlay databank. We demonstrate the practical relevance of such approach for understanding cellular membranes by incorporating available lipid bilayer simulations into the NMRlipids Databank. Importantly, the basic principles of an overlay databank can be applied to simulation data of any other biomolecules (that are becoming available in increasing amounts  4  ), as well as to any other field where the development of AI-based tools is limited by the lack of data access and community best-practices -such as the assignment of NMR spectra  5. Incentives to share data can be further accelerated by combining overlay databanks with an open collaboration approach (see Ref.
Here we present the NMRlipids Databank -a community-driven, open-for-all database featuring programmatic access to atom-resolution MD simulations of lipid bilayers. To demonstrate its advantages over existing approaches, we build a machine learning model that predicts membrane properties from its lipid composition and show how information on rare phenomena that are beyond the scope of standard MD simulation investigations can be gleaned from the Databank. In addition, we demonstrate the immediate relevance of NMRlipids Databank in extending the scope of MD simulations to new fields: Using a data-driven approach, we are able to analyse how anisotropic diffusion of water depends on membrane properties; this benefits understanding in magnetic resonance imaging (MRI)  11  and pharmacokinetics  12  , where MD simulations have, until now, been rarely applied.
Furthermore, the Databank performs automatic quality evaluation of membrane simulations, which facilitates the selection of best-performing models for each given application and accelerates the development of simulation parameters and methodology. Notably, the overlay-databank and open-collaboration approaches would facilitate the collecting of community-contributed data and providing programmatic access to them also in fields other than membrane simulations. For example, substantial biomolecular MD simulation data are already available but in scattered locations and formats  4. The technical advances presented here will immediately benefit making these data programmatically accessible. Furthermore, the overlay databank configuration will benefit a broad range of fields where access to training data is a bottleneck for building AI-based tools.
NMRlipids overlay databank delivers access to MD simulations of membranes composed of the biologically most abundant lipids NMRlipids Databank is a community-driven catalogue containing atomistic MD simulations of biologically relevant lipid membranes emerging from the NMRlipids open collaboration  6, [13] [14] [15] [16]. It has been designed to improve the Findability, Accessibility, Interoperability, and Reuse (FAIR)  17  of MD simulation data, most importantly the output trajectories and necessary information to their reuse. The NMRlipids Databank is constructed using the NMRlipids project protocol, in which all the content is openly accessible throughout the project  6. Currently, the NMRlipids Databank contains 765 simulation trajectories with the total length of approximately 0. 4 ms.
Single-component lipid membranes and binary mixtures are currently most abundant in the NMRlipids Databank, yet mixtures with up to five lipid types are available. For available mixtures, see Fig. 1E. The distribution of lipids among the available simulations, shown in Fig. 1B , roughly resembles the biological relative abundance of different lipid types, with phosphatidylcholine (PC) being the most common followed by cholesterol, phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylglycerol (PG), phosphatidylinositol (PI), and other lipids, depending on organism and organelle  7. Abbreviations and full names of all lipids present in the Databank are listed in the NMRlipids databank documentation  18. Force fields used in simulations cover all the essential parameter sets commonly used in lipid simulations, see Fig.
1C  and Table  S5 , including also united atom and polarizable force fields. Therefore, the averages calculated over the Databank can be considered as mean predictions from available lipid models (average over force field parameters) for an average cell membrane (average over lipid compositions). The overlay structure of the NMRlipids Databank, illustrated in Fig. 1A , is designed to enable efficient upcycling of MD simulations for data-driven and machine learning applications with minimal investment on new infrastructure. Raw simulation data in the Data layer can be stored in any publicly available location with long term stability and with permanent links to the data, such as digital object identifiers (DOIs), such as Zenodo (zenodo. org). The Databank layer (github. com/NMRlipids/Databank) is the core of the Databank containing all the relevant information about the simulations: links to the raw data, relevant metadata describing the systems, universal naming conventions for lipids and their atoms, quality evaluation of simulations against experimental data, and the computer programs to create the entries and to analyse the five basic properties extracted from all simulations (area per lipid, C-H bond order parameters, X-ray scattering form factors, membrane thickness, and equilibration times of principal components).
Also the values for these five basic properties are stored in the Databank layer. The Application layer is composed of repositories and tools that read information from the Databank layer for further analyses. Because the Application layer does not interfere with the Databank layer, it can be freely extended by anyone for a wide range of purposes. This is demonstrated here with two examples: the NMRlipids Databank graphical user interface (NMRlipids Databank-GUI) at databank. nmrlipids. fi and a repository exemplifying novel analyses utilizing NMRlipids Databank as discussed below (github. com/NMRlipids/DataBankManuscript). A more detailed description of the NMRlipids Databank structure is available in the supplementary information.
NMRlipids Databank-GUI: graphical access to the MD simulation data NMRlipids Databank-GUI, available at databank. nmrlipids. fi, provides easy access to the NMRlipids Databank content through a graphical user interface (GUI). Simulations can be searched based on their molecular composition, force field, temperature, membrane properties, and quality; the search results are ranked based on the simulation quality as evaluated against experimental data when available. Membranes can be visualized, and properties between different simulations and experiments compared. The NMRlipids Databank-GUI enables rapid surveying of what simulation data is available, selection of the best available simulations for specific systems based on ranking lists, and comparisons of basic properties between different types of membranes.