dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we quantified the SPD-1 region in embryos co-depleted of ZEN-4 and KLP-19. In these embryos the width of the SPD-1 signal was also increased (Suppl Fig. 7 ) This data shows that KLP-19 regulates the size of the antiparallel microtubule overlap and thus narrows the distribution of SPD-1, confirming a role for the C. elegans KLP-19 in the spindle midzone in addition to its previously reported role during congression  (Powers et al. , 2004). Based on previous publications we assumed that depletion of SPD-1 should reduce or prevent the localization of KLP-19 to the midzone in C. elegans. However, this is inconsistent with the observed effect on microtubule overlap and the different phenotypes upon double depletion. To follow up on this observation we generated a worm strain expressing GFP tagged KLP-19 using CRISPR technology.
Spinning disk confocal fluorescence imaging Embryos for live-imaging experiments were obtained by dissecting gravid adult hermaphrodites in M9 buffer (42 mM Na2HPO4, 22 mM KH2PO4, 86 mM NaCl, and 1 mM MgSO4). One-cell embryos were mounted on slides with 2% agarose pad, overlaid with a 22 × 22-mm coverslip, and imaged at room temperature on a widefield fluorescence microscope (ECLIPSE Ti2; Nikon) equipped with a CFI Apo TIRF 60× 1. 45 NA oil immersion lens and a CCD camera (iXon 897 Ultra EMCCDs) or on a 3i VIVO spinning-disc confocal microscope (Axio Examiner. Z1; Zeiss) equipped with Zeiss Plan-Apochromat 63x/1. 40 oil microscope objective, 6 laser lines and a Hamamatsu ORCA-Flash4. 0 scientific CMOS camera (Hamamatsu) for detection. The microscope was controlled by Nikon NIS-Elements software (Nikon).
Widefield Imaging for Oscillations. Embryos were observed at the spindle plane at 23°C using a Zeiss Axio Imager upright microscope (Zeiss, Oberkochen, Germany) modified for long-term time-lapse. First, extra anti-heat and ultraviolet filters were added to the mercury lamp light path. Secondly, to decrease the bleaching and obtain optimal excitation, we used an enhanced transmission 12-nm bandpass excitation filter centred on 485 nm (AHF analysentechnik, Tübingen, Germany). We used a Plan Apochromat 100/1. 45 NA (numerical aperture) oil objective. Images were acquired with an Andor iXon3 EMCCD (electron multiplying charge-coupled device) 512 × 512 camera (Andor, Belfast, Northern Ireland) at 33 frames per second and using Solis software.
Centrosome tracking and fluctuation analysis The tracking of labelled centrosomes and analysis of trajectories were performed by a custom tracking software developed using Matlab (The Mathworks)  (Pecreaux et al. , 2006; Pécréaux et al. , 2016). Tracking of -20°C methanol-fixed γ-tubulin labelled embryos indicated accuracy to 10 nm. We bandpass filtered the time traces between 0. 1 and 1. 1 Hz using the robust local regression algorithm to remove mean, residual drift, avoid any contribution from spindle elongation and remove high-frequency noise due to the tracking algorithm. The standard deviation of this filtered track corresponded to the filtered standard deviation  (Pécréaux et al. , 2016).
Laser ablation and FRAP The FRAP experiment was conducted using a Yokogawa CSU-W1 SoRa dual cam spinning disk confocal. The microscope is equipped with an Acal BFi UV Opti-Microscan point scanner that we used for the FRAP experiment. This system is integrated with Nikon NIS Elements software for seamless experimental setup and data acquisition. The movies were acquired with a 60 × 1. 27 NA water objective and 2. 8× SoRa magnifier with 100 ms exposure times and 250 ms intervals (4 frames/s). For laser ablation the intensity of the ablation laser was increased to achieve severing of the spindles. FRAP was calculated with a combination of Fiji  (Schindelin et al. 2012 ) and MATLAB (MATLAB and Statistics Toolbox Release 2012, The MathWorks, Nitick, USA). Time-lapse images of spindles expressing GFP:: -tubulin and mCherry::histone (corresponding to chromosomes) were realigned in a routine for matching, rotation, and translation using Rigid Body of Fiji's StackReg plug-in, so that the random displacement of the spindle due to the spontaneous motion of the worm was corrected.
Quantification of electron tomography data We used the IMOD software package (http://bio3d. colorado. edu/imod) for the calculation of electron tomograms  (Kremer et al. , 1996). We applied the Amira software package for the segmentation and automatic tracing of microtubules  (Stalling et al. , 2005). For this, we used an extension to the filament editor of the Amira visualization and data analysis software  (Redemann et al. , 2014 (Redemann et al. , , 2017;; Weber et al. , 2012). We also used the Amira software to stitch the obtained 3D models in z to create full volumes of the recorded spindles  (Redemann et al. , 2017; Weber et al. , 2012). The automatic segmentation of the spindle microtubules was followed by a visual inspection of the traced microtubules within the tomograms. Correction of the individual microtubule tracings included manual tracing of undetected microtubules, connection of microtubules from section to section, and deletions of tracing artifacts (e.
Interaction analysis For the detection of possible interactions in 3D, the Interaction and Distance modules of the SpindleAnalysis Toolbox were used in Amira. Interactions computes possible microtubule intersections. An intersection is defined as a part of a microtubule that is closer to another microtubule than given a threshold (here 100nm). In detail, for each point of a microtubule, the distance to another microtubule is computed. Based on these distances, all parts of the microtubule are detected that lie closer to the other microtubule than the threshold. For each part an interaction is generated and stored in a spread sheet. For each interaction the approximated angle and the interaction start, end and length is sored, In addition, we defined the maximum angle of 90 and the minimal interaction length of 100nm to filter the interactions.
Forward-propagating SHG was collected through an oil-immersion condenser (1. 4, Nikon) with a 425/30 nm filter (FF01-425/30-25, Semrock). Two-photon fluorescence was imaged with a non-descanned detection scheme with an emission filter appropriate for green-fluorescent-protein (GFP)-labeled tubulin in C. elegans (FF01-520/5-25, Semrock, Rochester, NY). Both pathways contained short-pass filters (FF01-650/SP-25, Semrock) to block the fundamental laser wavelength. Image analysis was performed with MATLAB (The MathWorks, Natick MA), and ImageJ (National Institutes of Health, Bethesda, MD.
Mitotracker Timelapse fluorescent image sequences were processed in Python using nd2reader (https://github. com/Open-Science-Tools/nd2reader), OpenCV (https://pypi. org/project/opencvpython/), and scipy (https://scipy. org) along with other standard packages for data analysis. Multi-channel images were converted to 8-bit per channel and then split by spindle and DNA channels. Embryos remained in a relative fixed position during observation. Outlines of individual embryos were determined by creating a median projection of low background fluorescence of the GFP microtubule signal along the time axis, followed by watershed segmentation. The embryo outlines were used to create masks to eliminate all fluorescent microtubule and chromatid signals external to the individual embryo. To identify the absolute position of spindle poles and chromatids, a gaussian blur with a 3x3 kernel was applied to each channel at each timepoint, followed by binary thresholding using the top 25% (191-255) and top 50% (127-255) signal intensity for spindle poles and chromatid, respectively.
Each line in the final kymograph reflects a sum intensity projection of 10 pixels along the y-axis (+/-5 pixel above/below the reference axis) of each rotated image. Enhanced kymographs were created from the original kymographs by applying standard adaptive thresholding and skeletonization to visualize the trajectories of spindle pole centers and chromatids' leading edges. The Python code is available here: https://github. com/uvarc/mitosisanalyzer. Tracked data was used to plot distances between centrosomes and chromosomes at a specific time or during anaphase. Segregation rates were calculated from the displacement during first 40s after the beginning of anaphase. Plot of pole-to-pole distance throughout anaphase in control embryos (green), after spd-1 (RNAi) (blue), klp-19/ spd-1 (RNAi) (black) and spd-1/ gpr-1/2 (RNAi) (grey).
Acknowledgements We are extremely grateful to the  Ariel Pani lab  for providing Crispr/Cas transgenesis plasmids and tools, as well as with the help of setting the injections for transgenesis. As well as the use of their microscopes for ablation and FRAP experiments. Some strains were provided by the  Caenorhabditis Genetics Center (CGC) , which is funded by  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD010440 ;  University of Minnesota ). The strain  TH169  is a kind gift from  Prof A. A. Hyman. The Redemann lab was supported by  1R01GM144668-01   NIH NIGMS. MM was supported by  UVA's Wagner Fellowship ; LC was supported by a  « Ligue Nationale Contre le Cancer » fellowship. We also acknowledge the committees 22 & 35 of " Ligue regional contre le cancer " and the  ANR MICENN  ( ANR-22-CE45-0016 ).
d = Center to center distance between two microtubules, li = length of interaction, β= orientation angle between the microtubules. D. Plot of the interaction length li between microtubules of the different microtubule subclasses for microtubules with d= 100nm, minimum li = 100nm and b = 90° for 3 different datasets throughout anaphase, chromosome distance as an indicator for progression through anaphase is provided. Error bars are sem. E. Polarity plot of microtubules in the spindle throughout anaphase obtained by the combination of SHG and TP microscopy. 0 on the x-axis represents the spindle center. Spindle length is indicated as a measure of progression throughout anaphase. F. Polarity plot of microtubules in the spindle throughout anaphase after gpr-1/2 (RNAi) obtained by SHG and TP microscopy.
The SARS-CoV-2 main protease (M pro ) crystal structure was obtained using the Protein Data Bank (PDB). Using the SwissADME web server, the bioactive compounds' drug-likeness was assessed, and AutoDock was employed for the molecular docking with the M pro. The Proteins Plus and Protein-Ligand Interaction Profiler web servers were used to analyse the docked complexes. Furthermore, the admetSAR website was utilized to predict the ligands' absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties.
Protein target selection The main protease (M pro ) identified in SARS-CoV-2 with Protein Data Bank (PID) number 6lu7 was used as the target protein in this study. The protein data bank (http:// www. rcsb. org) maintained by the Research Collaboratory of Structural Bioinformatics (RCSB)  [31] , a widely used database, was used to obtain the three-dimensional (3D) crystallographic structure of this target protein. The Main Protease (M pro ) of the SARS-CoV-2 is depicted graphically in Fig. 2.
Protein target preparation The 3D structure of the target protein obtained for the Protein Data Bank (PDB) was prepared and cleaned using the UCSF-Chimera (version 1. 13. 1) software  [32]. The protein was cleaned by eliminating the co-crystallized ligand and any accompanying water molecules, resulting in a refined receptor. Hydrogen charges were subsequently added to the protein, and it was further subjected to minimization for molecular docking purposes. Finally, the prepared protein structure was saved in PDB format.
Ligands optimizations and molecular docking The Python prescription (PyRx) software  [35] , which contained the Autodock, was used for the molecular docking of bioactive compounds and the target protein to predict the binding affinity. The Open Babel integration within PyRx (version 0. 8) was employed to optimize the ligands and prepare them for docking. The 3D structure of the downloaded bioactive compounds and the control medication were uploaded into the software for molecular docking. Subsequently, the ligands were converted to the AutoDock ligand format Protein Data Bank, Partial Charge (Q), and Atom Type (T) (PDBQT). The molecular docking was carried out using AutoDock. To define the protein's active site, a grid box was adjusted with specific dimensions (x: 17. 9373, y: 8. 5847, z: 63.
Molecular interaction analysis A graphical user interface (GUI) software called PyMOL© molecular graphics (version 2. 4, 2010, Schrödinger LLC)  [37]  was used for protein-ligand complex formation and the molecular interaction of the ligands. The complexes generated were saved in PDB format and stored for further analysis. The molecular interactions of the complexes are analysed using Protein-Ligand Interaction Profiler (https:// plip-tool. biotec. tu-dresd en. de/ plip-web/ plip/ index)  [38]  and Proteins Plus online server (https:// prote ins. plus)  [39, 40].
The PyRx software was utilized to determine the binding affinities of bioactive compounds with the target protein M pro. Furthermore, a web-based application was employed to predict the pharmacokinetic properties of these bioactive substances. Our study assessed the drug-likeness characteristics of bioactive compounds and the control medication by applying Lipinski's rule of five. Lipinski's rule of five, which considers a medication's biological and pharmacological properties, helps determine a compound's potential effectiveness as an orally administered medication. After the screening process, a total of twenty-three (23) compounds were identified that either complied with Lipinski's rule of five or had only one violation, except for Rutin and the control medication Remdesivir, which had three and two violations, respectively, as indicated in Table  1.
Conversely, three bioactive compounds have LogP values exceeding 5, suggesting favourable interactions in nonpolar environments. The target protein (M pro ) was docked against the 23 bioactive compounds from four African plants as part of this study's molecular docking evaluation using PyRx software. M pro , found in SARS-CoV-2, is a cysteine protease whose structure depicts a homodimer consisting of two protomers with a specific substrate-binding site. The substrate-binding site consists of cysteine and histidine amino acids (His41 and Cys145) that control the catalytic activity of SARS-CoV-2  [45]. M pro has been validated as the protein target for promising natural compounds to interact with and enable the development of medications for COVID-19 because of the vital role it plays in SARS-CoV-2 replication and the production of polypeptide synthases inside the host, as well as the fact that no homolog of the protein can be found in humans  [46].
Furthermore, it can adapt to changes in I/O behavior, which makes IO-SETS a more robust approach. Dorier et al. [6]  analyzed the benefits of either delaying or interrupting an application when it is interfering with another one. The authors conceptualized a software layer, CALCioM, that allows for inter-application communication and gathers information from all levels of the I/O stack. Such a layer could be used to implement our IO-SETS method. From their analysis, they pointed that the bigger the difference in size between applications, the less effective the exclusive approach is. However, it was found to be the best option when applications have similar I/O requirements. That observation, that both exclusive and shared access have advantages, is the main motivation behind our work. This has also been documented by Jeannot et al.
The authors would like to thank  Emmanuel Jeannot  and  Arnaud Legrand  for useful discussions. Experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr). This work was supported in part by the  French National Research Agency (ANR)  in the frame of  DASH  ( ANR-17-CE25-0004 ), by the  Project Région Nouvelle Aquitaine   2018-1R50119  " HPC scalable ecosystem " and by the " Adaptive multitier intelligent data manager for Exascale (ADMIRE) " project, funded by the  European Union 's  Horizon 2020 JTI-EuroHPC Research and Innovation Programme  (grant  956748 ).
In the vertical, the configuration possesses 75 levels, with thickness increasing from 1 m at the surface to 200 m at depth  (Storkey et al. , 2018). We use the z * vertical coordinate adapted to the ice shelf so that all cells between the surface and the ice shelf base are masked at initialization and the effect of the ice shelf on friction and pressure gradient is calculated  (Madec and NEMO System Team, 2019; Mathiot et al. , 2017). The bathymetry used is derived from the Earth TOPOgraphy version 2 dataset (ETOPO2v2;  NOAA, 2006)  with information for the extension under the ice shelves based on the International Bathymetric Chart of the Southern Ocean (IBSCO;  Arndt et al. , 2013). For the calculation of the thermodynamic properties of seawater, NEMO uses the Thermodynamic Equation Of Seawater -2010 (TEOS-10), giving results in conservative temperature and absolute salinity, which, for the purposes of this study, were converted to potential temperature and practical salinity in order to facilitate comparison of the model results with observations and known signatures of water masses.
Initial conditions For all simulations, global ocean properties were initialized using the 1981-2010 climatology of World Ocean Atlas 2013 (WOA2013;  Locarnini et al. , 2013; Zweng et al. , 2013)  as this dataset is used for the IPSL climate model and so was a convenient choice. This climatology does not, however, extend under the Antarctic ice shelves, and so in order to provide somewhat realistic initial conditions underneath FRIS, LCIS and RIS, we employed an idealized regional configuration of each ice shelf. For this we created a NEMO test case using a closed domain, with temperature and salinity restoring at the boundaries; 75 vertical layers; and a resolution, time step and bathymetry corresponding to those of eORCA1. The domain for each of the 3 configurations included just the ice shelf and adjacent continental shelf and slope and so were reasonably low-cost and fast to run in order to perform sensitivity experiments.
2f ). WOA bottom temperatures and salinities for the Ross Sea indicate a strong east-west gradient in properties across the continental shelf (Fig. 3b  and c ). Conditions in the southwest reveal the cold and salty signature of HSSW likely formed in the Terra Nova Bay polynya and the Ross Polynya. Intrusions of CDW at the eastern portion of the RIS front can be seen by warm signatures of up to 1 • C (Fig. 3b ) and fresher bottom salinities (Fig. 3c ). Hydrological and current meter data presented by  Budillon et al. (2003)  reported that HSSW dominates bottom properties within the troughs connected to the Joides Basin, and ISW dominates in the Challenger Trough (see locations of bathymetric features in Fig. 1b ), thus indicating a western intensified anticlockwise circulation cell under RIS.
Comparison with ice shelf front CTD observations The differences in circulation patterns and in thermohaline properties that result from opening the RIS and FRIS cavities documented above do not elucidate whether or not we have reduced biases and improved the realism of shelf waters in eORCA1. For this, a direct comparison with in situ observations is necessary. Due to the remote location of these ice shelves and the harsh conditions associated with obtaining hydrographic samples in these areas, there are limited observations, and so optimally interpolated atlases such as WOA or ocean reanalysis products like SOSE miss important local features or seasonal variability. For comparison purposes, we have consequently selected CTD data from research cruises that have sampled transects across the front of the ice shelves and extracted the model data corresponding to the approximate ship's track using PAGO, a tool to analyse gridded ocean datasets  (Deshayes et al.
, 2014). For FRIS we use two CTD sections across the ice shelf front undertaken in 1980 and 1995 on board the RV Polarstern by the Alfred Wegener Institute  (Rohardt et al. , 2016; Janout et al. , 2021). The location of the section selected in NEMO to approximately overlay the CTD transects can be seen as a dotted magenta line in Fig. 1a. The output from NEMO corresponding to the same months and same equivalent year (for the second cycle of CORE forcing) in the simulation was selected for both closed-cavity (prescribed freshwater flux) and open-cavity (FRIS, LCIS and RIS) runs. A comparison between the CTD data and NEMO can be seen in Fig. 6a  to f for January 1980 and Fig. 6g  to l for February to March 1995. In terms of surface waters, NEMO does not capture the fine-scale horizontal variability and overestimates the subsurface salinity.
3  of  Janout et al. (2021)  and so should be kept in mind for comparison. Opening the FRIS cavity overall improves the thermohaline properties at the ice shelf front, most notably by spreading out the pool of HSSW from the Ronne Depression (e. g. Fig. 6k ) across the continental shelf (e. g. Fig. 6l ) and by facilitating the production and thus outflow of ISW within Filchner Trough (Fig. 6c  and i ). The CTD sections used for comparison along the front of RIS were obtained through the World Ocean Circulation Experiment Database  (Boyer et al. , 2018)  and correspond to cruises undertaken on board the RVIB Nathaniel B. Palmer in 2000 (cruise id: US010404;  Smethie and Jacobs, 2005)  and in 2007 (cruise id: US034357). Data were extracted from the eORCA1 simulation corresponding to the dates of these cruises and the approximate ship track across the ice shelf front (dotted magenta line in Fig.
Code and data availability. The NEMO ocean model code is available via an open software license from the NEMO website (https: //www. nemo-ocean. eu, last access: February 2021). The NEMO output for the Weddell Sea and the Ross Sea (focus of this study), as well as the namelists used, bathymetry, ice shelf draft, freshwater input and initial condition files, is available via the data repository stored at https://doi. org/10. 5281/zenodo. 7561767  (Hutchinson et al. , 2023). Some example scripts for data extraction, calculations and plotting can also be found in this repository. The World Ocean Atlas hydrographic data of  Locarnini et al. (2019)  and  Zweng et al. (2019)  can be found at https://www. nodc. noaa. gov/OC5/woa18/woa18data. html (last access: February 2021) and Southern Ocean State Estimate data of  Mazloff et al.
(2010)  can be accessed at http://sose. ucsd. edu/sose_stateestimation_data_ 05to10. html. The mixed-layer-depth data from  Sallée et al. (2021b)  can be accessed at https://doi. org/10. 5281/zenodo. 5776180. The CTD transects used for comparisons across the ice shelf front for FRIS 1980 and 1995 can be respectively found at https://doi. org/10. 1594/PANGAEA. 860066  (Rohardt et al. , 2016)  and here https://folk. uib. no/ngfso/Data/CTD/ (last access: January 2022). The RIS CTD data from the 2000 (US010402) and https://doi. org/10. 5194/gmd-16-3629-2023 Geosci. Model Dev. , 16, 3629-3650, 2023.