dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Model equations. Low-frequency microturbulence in weakly collisional magnetised plasmas is appropriately described within the gyrokinetic framework  23. The GYSELA code  26  solves the governing coupled gyrokinetic: B Ã ks ∂ F s ∂t þ ∇ Á B Ã ks dx G dt F s þ ∂ ∂v Gk B Ã ks dv Gk dt F s ¼ B Ã ks rhs ð Þ ð2Þ rhs ¼ C þ S þ D À νM mat ðr; θÞ½ F s À G cold À γ K F s À F FÀD 1 þ h F s À F FÀD i hF FÀD i ! ð3Þ and quasineutrality: Δn e ¼ ρ þ 1 n e 0 ∑ s Z s ∇ ? Á n s 0 BΩ s ∇ ? ϕ ð4Þ T e e Δn e ¼ ϕ À λ 1 À M SOL ðrÞ Â Ã hϕi FS À M mat ðr; θÞ À M wall ðrÞ Â Ã ϕ bias À λΛ M SOL ðrÞ À M mat ðr; θÞ Â Ã ðT e À T b:c: e Þ ð5Þ equations for the guiding-centre distribution function F s of ion species s, evolved with no separation between equilibrium and perturbation in five-dimensional guiding-centre space (x G , v G∥ , μ) and time.
A shortfall is robustly found without limiter, even when considereing variations in T e /T i ratio, safety factor q, magnetic shear s and density gradient. These results are reported in detail elsewhere  37. Linear stability analysis. Linear stability analysis of the poloidally symmetric and limited GYSELA profiles is performed using the initial value framework of the GKW code  12  , based on the gradient-driven and local (flux-tube) approximations. A typical example is shown in Fig. 7. Growth rates for the most unstable poloidal wavenumbers k θ ρ i = 0. 6 in poloidally symmetric (Case-2 and Case-3) and limited (Case-1) configurations are estimated in GKW. The salient conclusions are as follow, with details further provided in ref. 37  : in poloidally symmetric Case-2 and Case-3, GKW finds the edge to be marginally stable at vanishing E × B shear: γ lin ≈ 0.
Acknowledgements The authors acknowledge stimulating discussions with  P. H. Diamond  and participants at the 2019 and 2021 " Festival de Théorie" in Aix-en-Provence. This work has been carried out within the framework of the  EUROfusion Consortium  and was supported by the  EUROfusion Theory and Advanced Simulation Coordination (E-TASC) initiative  under the  TSVV (Theory, Simulation, Verification, and Validation)  " L-H transition and pedestal physics " project ( 2019-2020 ) and TSVV " Plasma Particle/Heat Exhaust: Gyrokinetic/Kinetic Edge Codes " ( 2021-2025 ). It has received funding from the  Euratom  research and training programme 2014-2018 and  2019-2020  under grant agreement No  633053. The authors gratefully acknowledge funding from the  European Commission   Horizon 2020 research and innovation programme  under Grant Agreement no.
824158  ( EoCoE-II ). The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This research was supported in part by the  National Science Foundation  under Grant no. NSF PHY-1748958. We acknowledge  PRACE  for awarding us access to  Joliot-Curie  at  GENCI@CEA, France, and MareNostrum at Barcelona Supercomputing Centre (BSC), Spain.
, 2017). As controls, a third set of naïve cultures were left uninfected and processed for scRNA-seq alongside cultures at days 5 and day 9 post-infection. At each collection point, a target of approximately 10,000 individual hepatocytes were partitioned into droplets prior to the generation of RNA libraries. Droplets contained primers with unique nucleotide sequences (cellular barcodes) that were incorporated into the transcript sequence during the reverse transcription step, allowing for the identification of each transcript's cellular origin. After sequencing the single-cell libraries, we mapped the resulting reads onto a customized reference transcriptome containing human  (Ensembl, v103)  and P. vivax PVP01 (PlasmoDB, v51) information. The P. vivax PVP01 transcript information included the nucleotides encoding for the gene-flanking untranslated regions (UTRs)  (Siegel, Chappell et al.
, 2022; Sà et al. , 2020). Furthermore, the median number of UMIs per schizont (6905) was also greater in the current dataset versus other reports. The median number of genes detected per hypnozoite (519) and median number of UMIs per hypnozoite (672) were consistent with other single-cell reports assessing non-replicating, uninucleate stages  (Bogale et al. , 2021; Real et al. , 2021; Ruberto et al. , 2021a) , including recent single-cell assessments of P. vivax sporozoites  (Ruberto et al. , 2021b).
, 2015)  and in vitro  (Roth, Maher et al. , 2018). More specifically, they displayed positive PvUIS4 staining for an intact parasitophorous vacuole membrane and a distinctive prominence. These forms have been shown to activate in vivo  (Mikolajczak et al. , 2015)  as well as in vitro  (Maher et al. , 2020). Additionally, they have been shown to be susceptible to treatment with primaquine in combination with chloroquine and ionophores, implying they were alive to begin with  (Maher et al. , 2021). At the molecular level, we did not find consistencies between our data and the transcriptomic signatures of "abortive" P. berghei liver forms reported by  Afriat and colleagues (2021). For example, orthologous expression of genes encoding for HSP90, HSP70, and HOP-three markers defining abortive parasites in their dataset-displayed greater expression in the schizont cluster relative to hypnozoites.
Computational analysis of single-cell RNA sequencing data Quality control checks on the raw sequencing data were performed using FASTQC  (Andrews, 2010). In a manner similar to other host-pathogen single-cell studies  (Ren et al. , 2021; Szabo et al. , 2021) , a multi-species transcriptome index containing the transcriptomes for P. vivax P01 (PlasmoDB. org, version 51) and H. sapiens GRCh8 (Ensembl, version 103) was created using the 'ref' function in kbpython (0. 26. 3)-a python package that wraps the kallisto | bustools single-cell RNA-seq workflow  (Bray et al. , 2016; Melsted et al. , 2021). The P. vivax P01 transcriptome file used for the generation of the multi-species index contained gene regions encoding for UTRs  (Siegel, Chappell et al. , 2020). Next, using the same package, reads were pseudoaligned and counted using the 'count' function with parameters suitable for sequencing data generated from version 3 of 10x Genomics' 3' gene expression technology: kb count -i human_Pv_index.
Data integration and low dimensional reduction Filtered, normalized data matrices were integrated in a manner described in in the Seurat (version 4) vignette, Introduction to scRNA-seq integration, available on the Sajita Lab's website (https://satijalab. org/seurat/articles/integration_introduction. html). Briefly, integration features were identified in each replicate using the 'SelectIntegrationFeatures' function with the nFeatures parameter set to 3000. Next, we used 'PrepSCTIntegration' to prepare object list containing the 3000 features for integration. We then used 'FindIntegrationAnchors' to identify a set of anchors between datasets with the following parameters specified: normalization. method = "SCT", anchor. features = "features", reference = naïve hepatocytes day 5). Last, using these anchors, the six datasets were integrated using the 'IntegrateData' function with the following parameters specified: normalization.
Gene Ontology and Reactome enrichment analyses Enriched biological processes and pathways were determined using gProfiler  (Raudvere et al. , 2019)  with the following options selected: ordered query, statistical domain scope-only annotated genes, significance threshold-g:SCS threshold, user threshold-0. 05, Gene Ontology-GO biological processes, No electronic GO annotations, biological pathways-Reactome. Transcripts that displayed significant changes in expression in infected hepatocytes versus non-infected hepatocytes were used as input. EnrichmentMap (v 1. 1. 0)  (Merico et al. , 2010)  for CytoScape (v 3. 8. 2)  (Shannon et al. , 2003)  was used to create the enrichment networks. Significantly enriched GO biological processes and Reactome pathways (adjusted p value < 0. 05) were used as input.
Contact for reagent and resource sharing Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Dennis Kyle (email: dennis. kyle@uga. edu). -Silva et al. , 2022) , sporozoites  (Ruberto et al. , 2021b) , and blood-stage parasites  (Sà et al. , 2020). Replicates in sporozoite and blood-stage datasets represent unique 10x Genomics' scRNA-seq library preparations. Blood-stage scRNA-seq metrics were obtained after realignment of sequencing data to an updated P. vivax transcriptome including UTRs as performed previously  (Ruberto et al. , 2021b). N = number of single-cell transcriptomes assessed for each sample. Light blue dashed horizontal line: median number genes detected or UMIs in schizonts obtained in the current dataset; teal dashed horizontal line: median number genes detected or UMIs in hypnozoites obtained in the current dataset.
Acknowledgements We thank the patients of  Mondulkiri Province, Cambodia , for participating in this study. We thank the  Institut Pasteur du Cambodge's  field site manager ( Saorin Kim ) for logistical assistance, insectary staff ( Makara Pring ,  Koeun Kaing ,  Nora Sambath ) for the An. dirus mosquito colony maintenance, and laboratory staff ( Eakpor Piv ,  Chansophea Chhin ,  Sreyvouch Phen ,  Chansovandan Chhun ,  Sivcheng Phal ,  Baura Tat ) for assistance with the mosquito dissections and the in vitro assays. High-content imaging data was produced in part by the  Biomedical Microscopy Core at the University of Georgia , supported by the  Georgia Research Alliance. A. R. J. , B. B. and I. M. acknowledge support from the  Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support Scheme.
This work was also supported by the  Agence Nationale de la Recherche  ( ANR-17-CE13-0025  to A. A. R and I. M. ); an  Australian National Health and Medical Research Council Leadership Fellowship  ( APP1194330  to A. R. J. ); the  Bill & Melinda Gates Foundation  ( OPP1023601  to D. E. K. ); and  Medicines for Malaria Venture  ( RD/2017/0042  to B. W. and A. V. ,  RD/16/1082  and  RD/15/0022  to S. P. M. and D. E. K. ). A. A. R. , S. P. M. , A. V. , I. M. , B. W. and D. E. K conceived and designed the study; A. A. R, S. P. M. and A. V. performed the experiments; A. A. R. , C. B. , B. B. and C. J. curated the data; A. A. R. , S. P. M. , C. B. , B. B. , A. R. J. and C. J. performed the formal analyses; S. P. M. , A. V. and I. M. , B. W. and D. E. K.
Data availability All raw sequencing data have been deposited in the European Nucleotide Archive at European Molecular Biology Laboratory European Bioinformatics Institute (www. ebi. ac. uk/ena/) under accession number PRJEB52293. Scripts containing the code used to process the single-cell RNA seq data are available on GitHub at: https://github. com/AnthonyRuberto/Pv_LS_singleCell. Archived scripts (Shell and R) and output files as at time of publication will be made available on Zenodo.
Crosshair targeting workflow The entire workflow is summarised in Figure  4 , with details in the methods (section 6. 7). In brief, the resin block was first trimmed manually close to the sample, with a rectangular face in its surface. This block face (which can be any shape with four corners, and straight lines in between) provided the reference to define the block coordinate frame (Figure  S5E ). Next, the sample was imaged with X-ray at the highest resolution possible (around one micron isotropic voxel size is feasible on lab-based micro-CT systems). From this X-ray image, two planes were defined -one on the flat block surface, and one on the target plane using the Crosshair plugin (see the Crosshair section below and Figure  2A ). Then, the corners of the block surface were marked in the software, as these were used to determine where cutting would begin and the distance required.
Crosshair To make this workflow accessible to all, we developed the open-source Fiji  [17]  plugin Crosshair (https://github. com/automated-ultramicrotomy/crosshair) which allows user-friendly measurement of X-ray images, and access to the calculations described above (Figure  5  and Supplemental Figure  S8 ). It uses BigDataViewer  [18]  to allow browsing of large images in arbitrary 2D slices, and the ImageJ 3D Viewer  [19]  to allow browsing in 3D. Crosshair allows the planes and points required for the targeting workflow (Figure  4 ) to be created and viewed interactively. It also allows the orientation of the block to be marked, and visualised in 2D and 3D. To make selection of solutions easier, Crosshair also has a 'microtome mode' which allows a simple representation of the ultramicrotome to be manipulated in 3D (Figure  5B ).
This registration was completed with another Fiji plugin we created -RegistrationTree -that is also freely available on github: https://github. com/K-Meech/RegistrationTree. This plugin is a wrapper around two existing registration software, BigWarp  [23]  and elastix  [24, 25] , allowing easy passing of images from one software to the other, and smooth use of very large images with elastix. It is standalone, so it can also be used for general registration of large images, independent of the Crosshair workflow. Once the images were registered, we calculated a series of angle and distance accuracy measures using the built-in features in the Crosshair plugin (Figures  6 7 , and see methods section 6. 9). Comparison of the target plane and trimmed block surface showed that all samples hit their target of cutting through both anterior dorsal cirrus, and provided similar Platynereis crosssections to those predicted from the X-ray (Figure  7 ).
This is useful for methods such as FIB-SEM that cannot image large block surfaces. This would require precise, automated East-West movement of the knife (as already implemented on the RMC system), and expansion of the targeting calculations and Crosshair plugin. In summary, this targeting workflow offers a first step towards fully automated targeting of structures within resin blocks for EM. By making it simpler, and faster, to target a specific plane of interest we can achieve higher throughput, and shorter overall acquisition times. Also, by providing a user-friendly software for this process, we lower the barrier for newcomers to get started with 3D targeting. By providing the motorised ultramicrotome plans and software fully open-source, we hope that this work will enable more EM labs to rapidly target regions of interest for EM.
one modification was made to the ultramicrotome itself -a hole was drilled into the cutting arm at a precisely vertical position (Supplemental Figure  S1A ). This fits a small pin that was added to the back of the arc piece (Supplemental Figure  S1B ) to keep the arc piece precisely vertical. A new sample holder/arc and knife holder were purchased from Leica, and modified with FAUL-HABER DC motors for each axis (Figure  1B, D ). These motors have their own driver module that uses serial communication RS232 (12V). A cable with a converter (RS232 to UART) is used to communicate with a Raspberry Pi running a Node-RED (https://nodered. org/) program for user interface and motor control. The Raspberry Pi is connected to a Raspberry Pi 7 inch touchscreen display, and a StromPi3 battery. The battery is designed to hold up the shutdown process for one second and allow Linux to shut down properly, avoiding damage to the SD memory card that holds the operating system.
The setup uses a desktop power supply (that provides 12V), and connects to the StromPi3 battery module. The StromPi3 then provides the 5V needed for the Raspberry Pi. All electronics are housed in a case with an adjustable arm for good screen visibility / ease of use. This allows the motor positions to be controlled and monitored easily by interacting with the touchscreen. The Node-RED user interface 'flow' json is available here: https://github. com/automated-ultramicrotomy/embl-system/blob/main/node-red/nod e-red-flow. json. Once Node-RED is installed, the flow can be loaded by selecting 'import' from the menu, and providing this json file. This also requires the following nodes to be installed: node-red-dashboard (https://flows. nodered. org/node/node-red-dashboard), and node-red-node-serialport (https://flows.
The retrofitted five motors were controlled by a Trinamic TMCM-6110 6-axis stepper motor driver board, which was linked via USB to a touchscreen PC. The graphic user interface (GUI), as shown in Figure  S1D , was developed with Labview and installed onto this PC, allowing users to control the motions via keyboard/mouse, touchscreen or even a joystick. The stepper motors can provide 51,200 microsteps per full 360º revolutions, which is equivalent to an accuracy of up to 0. 00703125º per microstep. Diagrams, CAD files and software for this system are available on the following github repository:.
e. the plane must be vertical. Using the sympy  [26]  python package to help with rearrangement this gave: sin(θ T ) sin(θ tr ) sin(θ IT ) + cos(θ IT ) cos(θ tr ) cos(θ IK + θ to ) + cos(θ R ) cos(θ T ) -sin(θ IT ) cos(θ tr ) cos(θ IK + θ to ) + sin(θ tr ) cos(θ IT ) + sin(θ R ) cos(θ T ) sin(θ IK + θ to ) cos(θ tr ) = 0 As θ IT , θ IK , θ to and θ tr were constants for a particular targeting run, we could replace these with the A-I constants defined above to make the expression easier to work with. (AI + H) sin(θ T ) + (-AF + G) cos(θ R ) cos(θ T ) + E sin(θ R ) cos(θ T ) = 0 (-AF + G) cos(θ R ) cos(θ T ) + E sin(θ R ) cos(θ T ) = -(AI + H) sin(θ T ) (-AF + G) cos(θ R ) + E sin(θ R ) = -(AI + H) sin(θ T ) cos(θ T ) tan(θ T ) = -AF + G -AI -H cos(θ R ) + E -AI -H sin(θ R ) This meant that the final solution for the sample tilt was: θ T = arctan -AF + G -AI -H cos(θ R ) + E -AI -H sin(θ R ) (2) This solution could of course be simplified further by combining these constants together to give something of the form: θ T = arctan C 1 cos(θ R ) + C 2 sin(θ R ) In practice, we used the form with constants A-I, as this allowed the same constants to be used for this and the knife solution below.
Crosshair Crosshair is a Fiji  [17]  plugin written in Java, and is freely available from the github repository: https://github. com/automated-ultramicrotomy/crosshair. A user guide is also available: https://github. com/automated-ultramicrotomy/crosshair/wiki, along with a video tutorial of the entire workflow demonstrated on the Leica system: https://www. youtube. com/ watch?v=OM712ECthGg. It can also be easily installed via a Fiji update site (instructions in the github repository). [18]  for viewing images in 2D, and the ImageJ 3D Viewer  [19]  for viewing images in 3D. It also builds on a number of functions from Christian Tischer's imagej-utils repository (https://github. com/embl-cba/imagej-utils).
RegistrationTree RegistrationTree is a Fiji  [17]  plugin written in Java, and is freely available from the github repository: https://github. com/K-Meech/RegistrationTree. A user guide is also available: https://github. com/K-Meech/RegistrationTree/wiki. It can be easily installed via a Fiji update site (instructions in the github repository). RegistrationTree is a wrapper around BigWarp  [23]  and elastix  [24, 25]  registration software. It allows easy passing of images between the two software tools, as well as easing the integration of large images with elastix. Users can build arbitrary trees of affine transformations to register their images together, and visualise/compare these easily. RegistrationTree uses BigDataViewer  [18]  for 2D visualisation, and BigDataViewer style file formats (HDF5 and N5) to allow very large images to be viewed efficiently on a normal laptop.
It builds on top of Christian Tischer's elastixWrapper  [27] (https://github. com/embl-cba/elastixWrapper) and imagej-utils repository (https://github. com/embl-cba/imagej-utils). Also, the open-source repositories for image-transform-converters (https://github. com/image-trans form-converters/image-transform-converters) and bigdataviewer-playground (https: //github. com/bigdataviewer/bigdataviewer-playground).
Targeting workflow The targeting workflow is summarised in Figure  4. We expand on those steps in detail here, in the form of a step by step protocol. They are also described in detail in the Crosshair user guide (https://github. com/automated-ultramicrotomy/crosshair/wiki), and in a video tutorial demonstrated on the Leica system: https://www. youtube. com/watch?v=OM712ECthGg. Note that there are some differences between the motorised Leica and RMC systems that are highlighted throughout the workflow. Trim block -Trim the block at the ultramicrotome with a diamond knife. A flat surface must be made that has 4 corners and straight lines in between. Make sure the sides of the block face are trimmed deep enough, so the knife will always touch part of this face first, and not other parts of the block (even if the solution requires a more extreme set of angles).
X-ray imaging Samples a-e were imaged with a Bruker SkyScan 1272, with an isotropic voxel size of 1 micron. The resin blocks were mounted onto small pins with wax for the X-ray, and then removed from these pins for all further steps at the ultramicrotome. The log files with the specific settings for each sample are provided on EMPIAR alongide the images: https://www. ebi. ac. uk/em piar/EMPIAR-11055/. Imaging each sample took 2 hours to complete. Fiji was then used to convert the X-ray images to 8-bit, flip them vertically (Image -Transform -Flip Vertically) and crop to remove empty areas outside the resin block. Samples f-j were imaged with a Versa 510 X-ray microscope (Zeiss). Aluminium pins were mounted to a Versa 510 sample holder (Figure  S11C ), and imaged at a voltage of 40 kV, power of 3 W.
This is calculated as the shortest distance between a point on the before target plane (near to the centre of the region of interest), and the after block surface. Note that therefore this measure will vary depending on where this point was placed within the target plane. See here for the calculation scripts: https://github. com/automated-ultramicrotomy/cros shair/blob/master/src/main/java/de/embl/schwab/crosshair/targetingaccuracy/Acc uracyCalculator. java. For all samples, all accuracy settings and accuracy measures files are available on BioStudies: https://www. ebi. ac. uk/biostudies/studies/S-BSST845.
Acknowledgments We thank the  Electron Microscopy Core Facility (EMBL)  for access to the instrumentation for electron microscopy and the  Arendt lab (EMBL Heidelberg ) for providing the Platynereis dumerilii samples. We also thank  Christian Tischer  ( Centre for Bioimage Analysis, EMBL Heidelberg ) for his help with creating the Fiji plugins, and the Mechanical Workshop at  The Francis Crick Institute  for their mechanical expertise and practical assistance during the development.
Data availability All X-ray images are available on EMPIAR with id 'EMPIAR-11055': https://www. ebi. ac. uk/empiar/EMPIAR-11055/. All files for the accuracy measures tests (Crosshair settings and solutions, accuracy measures, registration metadata, registration accuracy measures) and calibration of the angular rotation of the ultramicrotomes are available on BioStudies with id 'S-BSST845': ebi. ac. uk/biostudies/studies/S-BSST845. Diagrams, CAD files and software for the two ultramicrotome systems are available in the following github repositories: https://github. com/automated-ultramicrotomy/embl-system and https://github. c om/automated-ultramicrotomy/crick-system. The Crosshair Fiji plugin is available with tutorials and documentation at the following github repository: https://github. com/automat ed-ultramicrotomy/crosshair, along with a video tutorial on YouTube for the Leica system: https://www.