dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: MSP was funded with a public Ph. D. grant from the  French National Research Agency (ANR)  as part of the  Investissement d'Avenir program , through the  Initiative Doctorale Interdisciplinaire (IDI)  2015 project funded by the  Initiative d'Excellence (IDEX) Paris-Saclay ,  ANR-11-IDEX-0003-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We produced segmentations of the membrane bilayers  (Martinez-Sanchez et al. , 2014) , based on which elements connecting two adjacent disks are defined, and employed the Pyto software package  (Lučić et al. , 2016)  to segment densities of these elements (Figure  2 -figure supplement 1A). Here, we customized the original Pyto workflow by applying an additional mask prior to the segmentation to separate artificially continuous connectors (Figure  2 -figure supplement 1B, C) by water shedding  (Meyer, 1994). The result of the automated segmentation method was compared to a manual segmentation (Figure  2 -figure supplement 2C). Over 90% of the connectors were picked by both methods and the error of the determined coordinates was below 2 nm. Therefore, the automated segmentation allowed for quantitative analysis of connector abundance and arrangement in 3D.
Due to the clear structural flexibility of the connectors observed in the raw data (Figure  2A-C ), we could not obtain further structural information by alignment, classification, and averaging of connector subvolumes (Figure  2  The second key structural feature of ROS architecture is the high curvature at the disk rims. There, the two membrane bilayers of a disk are connected via a hairpin-like structure, with a 12 nm radius of curvature. To elucidate its organization in 3D, subvolumes were extracted at the disk periphery in the conventional dataset (WTconv), aligned and averaged (Figure  3 -figure supplement 1). The resulting average showed three densities (Video 7) inside the disk lumen. In 3D, they form an ordered assembly of repeats organized in three interconnected rows, creating a continuous belt along the disk rim (focused on the central row in Figure  3A , Video 8).
Isosurface representation of the disk rim subvolume average for the peripheral rows (CW + CCW). Initially, the whole average is shown. Later, the peripheral row (PR) with its contact to the dentral density rows (CD) is shown in solid grey while the signal of the whole disk rim is displayed in transparent grey similar to Figure  3  Video 10. Isosurface representation of the disk rim subvolume average for the peripheral rows (PR). This is the same average as in Video 9, but at higher threshold emphasizing the density which links the repeats within the PR on the outside of the disk rim scaffold. Mammalian ROS contain two abundant protein complexes, which localize to the disk rim and harbor large disk luminal domains; namely, PRPH2-ROM1 tetramers and ABCA4. To clarify the identity of the scaffold protein, we analyzed the disk rims in VPP datasets of WT mice (WTVPP) and of homozygote ABCA4 knockout mice (Abca4 -/-VPP).
Cryo-transmission electron microscopy and tomography Cryo-transmission electron microscopy observations were performed using a Titan Krios operated at 300 kV (Thermo Fisher Scientific). This microscope was equipped with a field-emission gun, a quantum post-column energy filter (Gatan, Pleasanton, USA), and a Volta phase plate (VPP, Thermo Fisher Scientific)  (Danev et al. , 2014). Bidirectional tilt-series were collected using SerialEM software  (Mastronarde, 2005)  between ± 50° or ± 60° starting at 20° with a tilt increment of 2° and a total exposure dose of ~100 e -/Å 2. The individual projection images were recorded as movies (dose fractionation mode) on a K2 Summit (Gatan) direct electron detector camera operated in counting mode with an image pixel size of 2. 62 Å. The exposure dose for the projection at 0° doseα=0 was 1.
Tomographic tilt-series were collected using standard automated acquisition procedures. All datasets are listed in Table  1. Prior to the acquisition of the tilt-series, montage images at lower magnification (pixel size ~2 nm) were taken of the entire lamella. The montage tiles were aligned using the IMOD (version 4. 10. 18) (Kremer et al. , 1996)  command "justblend". Each lamella contained several ROS. In some cases, the ROS ultrastructure was partially distorted. As the distortions were locally confined, tilt-series were exclusively recorded in areas with ROS unperturbed by the sample preparation (Figure  1 -figure supplement 2A). For each mouse strain and acquisition scheme, data was collected on samples derived from at least three different mice.
Processing of tilt-series Prior to tilt-series alignment, the projection images were corrected for beam-induced motion with MotionCor2  (Zheng et al. , 2017). For the conventional dataset (no VPP and non-zero defocus), the CTF parameters of the projections were determined with Gctf  (Zhang, 2016). Prior to tomogram reconstruction the projections were CTF-corrected with the IMOD function 'ctfphaseflip' and dose-filtered as described in  (Grant and Grigorieff, 2015)  with a MatLab implementation for tilt-series  (Wan et al. , 2017). Data acquired with VPP in focus was not CTF-corrected. Tilt-series alignment and tomographic reconstructions were performed using the IMOD  (Mastronarde and Held, 2017)  software package (version 4. 10. 18). Platinum particles originating from the protective platinum layer which were deposited over the lamella surface during FIB-milling served as fiducials (Figure  1 -figure supplement 2B).
This was then used to mask disk membranes with their apparent thickness of ~6. 8 nm in raw tomograms. Additionally, these masks defined the borders of the cytosolic gap between disks which a connector must bridge. The cytosolic voxels between the membrane masks were normalized separately for each membrane pair to a mean value of zero and a standard deviation of one. This extinguished gradients in the grey value distribution throughout a tomogram caused by heterogeneous lamella thickness and compensated for contrast differences between tomograms. To pick the connectors, the Pyto software package was used  (Lučić et al. , 2016). The original Pyto workflow segments connectors between the membranes of adjacent disks by evaluating all cytosolic voxels between the membrane masks as described below.
First, a binary mask was created that is one for all voxels with grey value below gmax, and elsewhere zero. Second, this binary mask was subjected to watershed transform  (Fernand Meyer Algorithm (Meyer, 1994)  implemented in MatLab) with "catchment basins" filled from the center between the two membranes. Third, a volume with the watershed lines set to zero and elsewhere one was multiplied with the binary mask. The resulting mask was applied to the original tomographic volume. Then Pyto was used to segment connectors in the masked tomogram. A sketch of the customized Pyto workflow and its processing steps applied to the data for one membrane pair are depicted in Figure  2 -figure supplement 1B and C, respectively. The threshold ramp for the original and the customized Pyto workflow was always started at the minimum grey value gmin of -2 and ended at maximum grey value gmax of -0.
Subvolume averaging Initially, binned subvolumes were extracted from dose-weighted and, if possible, CTF-corrected tomograms. The initial alignments were performed with scripts based on TOM, AV3 and Dynamo as described in  (Schur et al. , 2016; Wan et al. , 2017). The alignment references were exclusively derived from the data itself and low-pass filtered to 30 Å. To describe the orientation of subvolumes within the tomograms, triplets of Euler angles in "ZXZ" convention were used, comprising the angles Phi, Theta, and Psi. Phi is the angle of the first in-plane rotation around the z-axis. Theta describes the second rotation around the new x-axis, and Psi the third rotation around the new z-axis. Classification of 3D subvolumes and the final alignments were performed in RELION (version 3) with 2x binned or unbinned subvolumes.
For the WTconv dataset, unbinned subvolumes were extracted with Warp  (Tegunov and Cramer, 2019). Warp automatically generates a CTF model for each subvolume which is needed for RELION  (Tegunov and Cramer, 2019). For VPP tomograms a simple "fan"-shaped CTF model  (Bharat et al. , 2015)  was created which was one for all information-containing slices in Fourier space, and zero elsewhere. For the grey value representation of subvolume averages, the scale was inverted compared to the raw tomograms. Therefore, density in slices through subvolume averages appears bright, translating into a high grey value. Slices of subvolume averages were depicted in IMOD  (Kremer et al. , 1996) , while isosurface representations and subvolume positions within the context of a tomograms were displayed in UCSF chimera  (Pettersen et al.
py" script was used as part of the PySeg package  (Martinez-Sanchez et al. , 2020). A cylindrical mask focused the classification on the cytosol between disks. Only a fraction of the subvolumes was assigned to classes with a clear connector-like density (40% and 6% for disk rim and disk interior connectors; The high rate of putative false positives indicated by the classification of the connector subvolumes suggests, that our segmentation approach is error prone. Most likely, because the segmentation algorithm cannot distinguish two densities in close proximity protruding from opposite disks into the cytosol from an actual connector. On the other hand, we obtain classes with elongated densities that appear to link neighboring disks. Therefore, we assume that the two types of connectors indeed exist, yet at lower concentration than the initial segmentation determined (Figure  2H ).
Unbinned subvolumes (pixel size = 2. 62 Å, box size = 128 3 ) were extracted and processed in RELION. For this step, the tomograms of the WTconv dataset were preprocessed in Warp (version 1. 0. 9). Instead of using the entire preprocessing capabilities implemented in Warp, the motion corrected, non-CTF-corrected, and non-dose-filtered projections of the tilt-series were imported into Warp with the corresponding tilt-series alignment files. In a first step, the CTF parameters were calculated for each projection in Warp. The patch size for the CTF estimation was set to 512 x 512 pixels 2 , the spatial frequency range used for the fit was between 34 Å and 12 Å and the defocus value was searched within ± 2 µm of the tilt-series' target defocus. In a second step, the CTF was estimated for the whole tilt-series taking the tilted geometry of the individual projections into account.
The averages obtained by processing two independent half-sets of the data were used to calculate Fourier shell correlation (FSC) curves  (Bharat and Scheres, 2016). The global resolution was estimated as the spatial frequency where the FSC drops to 0. 143 according to the "gold-standard"  (Rosenthal and Henderson, 2003) , and according to the more conservative threshold of 0. 5  (Hrabe et al. , 2012). The final density maps were sharpened with a B-factor of -400 Å 2. The angular distributions of the final averages are depicted in For WTconv further processing steps were applied. The output of the alignment with the classified subvolumes was imported into the M software (version 1. 0. 9, (Tegunov et al. , 2020) ). M performed a refinement of the tilt-series alignment. The default refinement parameters were used with an image and volume warp grid of 3x3 and 2x2x2x10, respectively.
Furthermore, particle positions and stage angles were refined but not the CTF estimate. Afterwards, all subvolumes before classification were re-extracted from tomograms with refined tilt-series alignment and processed by the same RELION pipeline as used before running M. To elucidate the repeat length and the offset between the repeats of the peripheral and the central rows of density, three cuboid masks were defined around the luminal densities, one for each row, in the disk rim average of the WTconv dataset. The voxels inside the masks were then averaged perpendicular to the rows (Figure  3 -figure supplement 2C). The three resulting 1D intensity profiles were fitted to sine functions of the following shape: y(x) = y0 + A sin(w0 x + p). The repeat length λ is related to w0 by: λ = Ps 2π/w0.
The offset between the rows ∆λ as: ∆λ = (pPR -pCD)/2π. Here, pPR and pCD denote the phase shift of the peripheral and central rows, respectively, and the pixel size Ps) of the subvolume average was 0. 262 nm. Furthermore, the peripheral rows of the WTconv dataset were analyzed. To generate the initial extraction points, the unbinned coordinates of the central row were modified. First, the peripheral rows were centered and rotated to adopt a similar orientation as the central row before. Additionally, clockwise (CW) and counterclockwise (CCW) rows were aligned to each other. For the CW row, the orientation of CD was rotated by 63° around the y-axis. The CCW row required a 180° rotation of the CD orientation around the z-axis, followed by 63° around the y-axis (Figure  3   The density which links repeats of the peripheral row is highlighted by orange arrow heads.
Acknowledgements We are grateful to  Z. Dong  for providing the Abca4 -/-mice, to  A. Martinez-Sanchez  and  V. Lučić  for assisting with the segmentation tools, to  W. Wan  and  S. Khavnekar  for streamlining tilt-series preprocessing, to  W. Wan  and  F. Beck  for advice on subvolume averaging, and to  P. Erdmann ,  G. Pfeifer  and  M. Schaffer  for support on the electron microscopes. This work was supported by grants  CIFAR program Molecular architecture of Life for WB and KP , by funding from the  National Institutes of Health :  R01EY030873  (KP),  R01EY030912  (KP), and unrestricted grants from  Research to Prevent Blindness  to the Departments of.
Data availability The subvolume averages of ROS disk rims are deposited in the Electron Microscopy Data Bank (EMDB) under the accession codes: EMD-13321, EMD-13322, EMD-13323 and EMD-13324 (see also Table  1 ). Two representative 4x binned tomograms for each acquisition scheme or mouse strain were deposited in the Electron Microscopy Public Image Archive (EMPIAR). Ophthalmology at UCI. MP acknowledges the support of the Max-Planck Institute of Biochemistry and JM the support from EMBL.
(E-G) Statistical analysis of 7000 connectors from 5 tomograms of the WTVPP dataset. Histograms are shown of nearest neighbor distances (E), connector length (F) and mean grey value (G). Arrowheads above the histograms indicate the median values. (H) Connector density per µm 2 of total disk membrane (Error bars: one standard deviation). P values were calculated according to the two-sample Kolmogorov-Smirnov test.
Our results demonstrate that ECRA-AHSV4 vaccines are capable of inducing both Th1 and Th2 immune responses. Further, the induction of Treg cytokine profiles in two ponies was associated with lower antibody responses in these animals. We also assessed the Th1 and Th2 responses of animals vaccinated with multivalent ECRA-AHSV vaccines of two different cocktails, thus each animal has been vaccinated only once with each vaccine strain. Only few animals demonstrated induction of low level of Th1-immune responses and only one animal, Blue, showed increased TNFa secretion on day 31 post vaccinations (data not shown). However, none developed Th2 or Treg-mediated responses. Note that the vaccine strains are different for prime and boost doses. Altogether, these data are consistent with both inactivated and live virus vaccine immune responses  [21].
Study Populations The Humanitas Research Hospital Ethics Committee approved the study. Written informed consent was obtained from each participant. The study was conducted by EuroMDS consortium (ClinicalTrials. gov identifier: NCT04174547). We analyzed an international retrospective cohort of 2,043 patients affected with primary MDS according to 2016 WHO criteria  2  and an independent cohort of 318 patients prospectively diagnosed at Humanitas Research Hospital, Milan, Italy (Data Supplement 1, online only).
Statistical Methods Detailed methods are reported in the Data Supplement 1. Bradley-Terry models are used to estimate timing of mutation acquisition and to assess the prognostic value of clonal versus subclonal mutations. 12 5] [16]  Bayesian networks allow to infer the structure of conditional dependencies among mutations, that is, how the presence of a given mutation influences the probability of the others (causality). 5] [16]  Patients are clustered based on genomic components identified by Dirichlet processes. Multivariate logistic regression analysis is applied to compare clinical and hematological characteristics among different groups. Survival analyses are performed with Kaplan-Meier method, and differences between groups are evaluated by log-rank test. To carry out the analysis, R package available online  17  is used.
Knowledge Generated We provided evidence form a large, international database that MDS could be classified into eight distinct subtypes according to specific genomic features. These subgroups do not correlate with morphological categories defined by current WHO classification and displayed significantly different clinical phenotypes and outcome. By integrating clinical and genomic variables, we created a novel prognostic model that generated personally tailored predictions of survival. Relevance Comprehensive gene sequencing of patients with MDS is becoming increasingly accessible and routine. The integration of clinical data with diagnostic genome profiling improves the accuracy of currently available prognostic scores. Such information will support complex decision-making process in these patients.
Genomic Landscape in Myelodysplastic Syndromes Detailed results of this section are reported in the Data Supplement 1. We studied 2,043 patients with MDS from EuroMDS consortium (Data Supplement 1). Normal karyotype is reported in Accordingly, 58% of patients show only clonal mutations, whereas 42% have evidence for both clonal and subclonal mutations (Data Supplement 1). No significant differences in survival between clonal and subclonal mutations for the majority of the investigated genes are observed, highlighting the importance of including information on subclonal mutations in the predictive model (Data Supplement 1).
Methodology To understand and appreciate the landscape of plastic pyrolysis, we analyzed published research using the PRISMA model or the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses"  [13]. The PRISMA method has helped to standardize literature review and bibliographic analysis, as indicated in our previous research  [14]  and summarized in Fig. 1. Adopting PRISMA requires following four systematic steps-identification, screening, eligibility, and qualitative synthesis-and completing a final checklist. We focused our review and analysis on peer-reviewed journal articles with keywords related to plastic pyrolysis published within 2001-2020. Initially, we searched both the Web of Science (WoS) and Scopus databases using "TS" or "Title-Abs-Key" as a field tags searching, respectively.
The query employed in both case was ((("plastic waste" or "polymer waste" or polyolefin* or polyethylene or polypropylene or polystyrene or "polyvinyl chloride" or "poly(ethylene terephthalate)") and ("thermal pyrolysis" or "catalytic pyrolysis" or "thermal cracking" or "catalytic cracking" or "thermolysis" or "thermocatalytic"))). The timespan selected was 2001-2020 and the quotation marks used before ("") were necessary to extract documents with exact phrases and to avoid lemmatization and synonyms. The Boolean operator "or" and "and" were required to ensure the appearance of at least one term between parenthesis. An initial search with the same queries and restricted to only articles, proceedings, and reviews produced 1201 documents from WoS and 1284 documents from Scopus. A majority of the documents and all of the top cited papers (Table  1 ) were the same for both databases.
We chose to focus our analysis and review solely on the results from WoS for three reasons. First, the top two papers in the results from Scopus did not relate to pyrolysis and would have been removed in the screening stage. Second, we noticed discrepancies in the citation counts for many of the specific articles with generally higher citation counts in Scopus, which would have impacted the accuracy of our analysis made by data-mining software (VOSViewer) of authors, country, and journals and the coconcurrence map. Finally, WoS is a thorough database with the largest number of indexed journals and conference proceedings (~12,000) and covers nearly 150 research disciplines. Our decision to use only results from WoS for our study was further validated after we completed our meta-analysis and review and realized that, of the 670 articles that were found to be eligible based on review of titles and abstract, 422 of these documents were in the results from Scopus, suggesting that we had not limited our results in any way and, instead, gained some clarity in the meta-analysis with regards to citation counts.
On November 5, 2020, all original data was extracted from the online version of the WoS database (indexes: Science Citation Index-Expanded) by selecting "marked list" function and exporting the data in "plain text" format file. A keyword co-occurrence map was generated using VOSviewer based on the extracted publications from WoS. A thesaurus file was used to remove duplicates and to standardize the variations of the spelling for authors name or keywords which refer to the same author or concept, respectively. The resulting 1201 documents were then screened using the WoS function so as NOT to exclude papers related to "carbon based materials" OR "nanotube*" OR "CNT*" or "algae" OR "asphalt". After this screening phase, we had 897 documents. In the next step, eligibility, we read titles and abstracts to eliminate publications related to a range of topics adjacent to pyrolysis, including but not limited to the following areas: hydrothermal treatment of plastic, plastic life-cycle assessments, and thermochemical treatments of solid waste.
In other words, in-situ catalyst regeneration occurred, leading to prolonged catalyst lifetime and hence H 2 production. These findings demonstrate the possibility of H 2 or syngas production from catalytic pyrolysis of plastic waste in a CO 2 atmosphere, and more studies are needed to verify such potential. To illustrate the number of studies conducted using different reactor types, a density visualization map (Fig. 7 ) was generated using VOS-Viewer based on 601 of the articles in our dataset. It is noted that the fluidized bed reactors (represented by the intense red color) are the most studied reactor type with 45 % (270 documents), followed by batch or semi-batch reactors with 20 % (118 documents), and conical spouted bed reactors featured in 15 % of all analyzed documents. Other systems such as microwave  [107]  or plasma reactors  [108]  allow for process intensification and the possibility to improve heat and mass transfer phenomena, thus offering better control of kinetics and reducing side-product formation.
Limitations All articles filtered and selected for this analysis and review were retrieved from WoS database, which could neglect some influential documents. Additionally, documents related to the pyrolysis of tire, rubber or urban solid waste and combinations are not considered in this study. The search query was designed with the intention to recover all the scientific documents closely related to the subject addressed; however, the authors know that is possible that several influential articles or journals could have been inadvertently ignored. Also, a deeper analysis including other databases such as Scopus, Google Scholar could be useful to give a better compression and reinforce the findings highlighted in this paper.