dataesr/scientific-paragraphs-categorization huggingface dataset:
Slides were then washed several times with PBS and incubated with secondary antibodies diluted in NGS/Triton/ PBS 1 h at room temperature. Slides were mounted on coverslips with Dako fluorescent mounting medium (S3023) and examined using an Apotome fluorescence and a Confocal LSM 880 Airyscan microscopes (Zeiss, France). For each sciatic nerve, 9-10 fields were captured and the percentage of mSC (GFP and MBP positive cells), nmc (GFP and GFAP positive cells) and axons (GFP and NF positive cells) transduced by AAV vectors among all transduced cells were calculated using Zen software (Zeiss, France). (INM, MRI-COMET). Samples were post-fixed with osmium 0. 5% and K4FeCN6 0. 8% 24 h at room temperature in the dark, went through dehydration in successive ethanol baths and embedding in Epoxy resin (EMbed-812 Embedding Kit, Electron Microscopy Sciences, France) using a Leica AMW machine.
They were finally incubated 36 h in an oven at 60 °C. Nerves were cut with Leica Reichert Ultracut S ultramicrotome into semi-thin sections of 700 nm-1 µm. Sections were stained with Toluidine Blue and imaged using an AxioScan slide scanner (Zeiss, France). The total number of myelinated axons, the mean axon diameter (100 axons per nerve randomly distributed throughout the entire section) and the g-ratio (axon diameter over the full fiber diameter) were measured using ImageJ software and GRatio plugin. Cell culture and transfection. The efficiency of sh1 and sh2 was examined in vitro using Schwann cell lines from mouse (MSC80)  66  and rat (RT4-D6P2T). 500 000 cells per well were seeded in a 6-well-plate in Dulbecco's Modified Eagle Medium (DMEM) (Gibco/Thermo Fisher, France) with 10% Fetal Bovine Serum (FBS) (Gibco/Thermo Fisher, France) and 1% Penicillin Streptomycin (Gibco/Thermo Fisher, France).
Gene expression was measured in cell lysates using a chemoluminescent substrate of galactosidase (GalactoStar kit, Invitrogen). Neutralizing factor titer corresponds to the last dilution leading to the inhibition of more than 50% of the standard AAV2/9 transduction (100% corresponds to the maximum transduction obtained with the standard AAV2/9 alone). Five rats injected with AAV2/9-ctr. sh and fives CMT1A rats injected with AAV2/9-ctr. sh were randomly analyzed for the detection of AAV2/9 neutralizing factors. Statistical analysis. Data were analyzed with Graphpad Prism version 7 (Graphpad Software) and expressed as the mean ± standard error of the mean (SEM) or ± standard deviation (SD) as indicated in the Figures legends. Statistical differences between mean values were tested using one-way or two-way ANOVA analysis followed by Tukey's, Dunnett's or Sidak's multiple comparison test, two sided, as indicated in the Figures legends.
Acknowledgements We thank the SMARTY platform ( RAM -Animal facility network of Montpellier ) for expert care of animals and help for behavioral tests ( Anne-Laure Bonnefont ), the  Electron Microscopy platform (Chantal Cazevieille) ,  Montpellier Ressources Imaging  ( Hassan Boukhaddaoui ),  CEA MIRCen Institut  ( CEA Fontenay aux Roses, France ), CPV vector core and Gene Therapy Immunology core from the  INSERM UMR 1089, University of Nantes  (https://umr1089. univ-nantes. fr/facilities-cores/). We also are grateful to  Klaus-Armin Nave  and  Mickael Sereda  for their gift of CMT1A rat. Funding: This work has been supported by  European Research Council  grant ( FP7-IDEAS-ERC  project  311610 ) and  E-Rare program  (project  CMT-NRG ,  11-040 ) to NT, Labex EPIGENMED through a fellowship to SG and by  Droguerie Mercury S.
Data availability All data supporting the findings of this study are provided within the paper and its supplementary information. Materials are available upon request. The source data underlying Tables 1, 2 and 3, Figs. 2, 3, 4c, e-g, 5a-d  and 6 , and Supplementary Figs. S6a-c , S7, S9 and Supplementary Table  S1  are provided as a Source Data file. All exact p-values are available in the Source Data File. Source data are provided with this paper.
Isolation, mass spectrometry, and cryo-EM of mitoribosomes To analyze the C. reinhardtii mitoribosome, mitochondria were purified and used for mitoribosome isolation following a procedure based on sucrose density gradient separation (see Methods) (Fig. S1 ). Collected fractions were systematically analyzed by nano-LC MS/MS (Table  S1 ) and screened by cryo-EM to determine their composition. This approach allowed us to identify fractions containing the two mitoribosome subunits which were subsequently used for data collection (Fig. S1 ). The proteomic analysis identified putative Chlamydomonas-specific r-proteins that were then confirmed by the corresponding cryo-EM reconstructions. Following image processing and extensive particle sorting, reconstructions of both dissociated subunits were obtained.
Cryo-electron microscopy data collection The single particle data collection was performed on a Talos Arctica instrument (Thermofisher Company) at 200 kV using the SerialEM software for automated data acquisition. Data were collected at a nominal underfocus of -0. 5 to -2. 5 µm at a magnification of 36,000 X yielding a pixel size of 1. 13 Å for the SSU and 45,000 X yielding a pixel size of 0. 9 Å for the LSU. Micrographs were recorded as movie stack on a K2 direct electron detector (GATAN Company), each movie stack were fractionated into 65 frames for a total exposure of 6. 5 sec corresponding to an electron dose of 45 ē/Å 2.
Electron microscopy image processing Drift and gain correction and dose weighting were performed using MotionCorr2  (Zheng et al. , 2017). A dose weighted average image of the whole stack was used to determine the contrast transfer function with the software Gctf  (Zhang, 2016). The following process has been achieved using RELION 3. 0  (Zivanov et al. , 2018). Initial analyses were performed in CryoSPARC  (Punjani et al. , 2017)  to asses sample composition and to generate ab-initio cryo-EM map. After reference-free 2D classification, for the LSU 346,994 particles were extracted and used for 3D classification into 6 classes (Fig. S2 ). Abinitio cryo-EM reconstruction generated in CryoSPARC was low-pass filtered to 30 Å, and used as an initial reference for 3D classification. 2 subclass depicting high-resolution features was selected for refinement with 101,291 particles.
Structure building and model refinement The atomic model of the LSU of C. reinhardtii was built into the high-resolution maps using Coot, Phenix and Chimera. Atomic models from E. coli (PDB: 5KCR) and A. thaliana mitoribosome (PDB: 6XYW) were used as starting points for protein identification and modelisation as well as rRNA modelisation. The online SWISS-MODEL  (Waterhouse et al. , 2018)  service was used to generate initial models for bacterial and mitochondria conserved r-proteins. Models were then rigid body fitted to the density in Chimera  (Pettersen et al. , 2004 ) and all subsequent modeling was done in Coot  (Emsley et al. , 2010). Extensions were built has polyalanine and mutated to the adequate sequences. Chlamydomonasspecific proteins for which no model could be generated were first built entirely as polyalanine, then the sequence-from-map PHENIX tool  (Liebschner et al.
Cryo-electron tomography data acquisition Cellular tomograms were acquired on a 300 kV Titan Krios microscope (FEI), equipped with a Gatan post-column energy filter (968 Quantum) and a direct detector camera (K2 summit, Gatan) operated in movie mode at 12 frames per second. Tilt series were recorded using SerialEM software  (Mastronarde, 2005)  with 2° tilt increments from -60° to +60° (in two halves separated at either 0° or -20°), an object pixel size of 3. 42 Å, a defocus of -4 to -5. 5 μm, and a total accumulated dose of <100 e-/Å.
Cryo-electron tomography data processing Movies from the K2 detector were motion corrected with MotionCor2  (Zheng et al. , 2017). Using IMOD software, tilt series were aligned with patch-tracking, and tomograms were reconstructed with weighted back projection. Out of ~130 tomograms, 47 tomograms containing mitochondria were selected. An initial structure of the C. reinhardtii mitoribosome was obtained by manually picking 104 mitoribosomes from 27 tomograms following template-free alignment by spherical harmonics  (Chen et al. , 2013). The initial map was then used as a template for automated template matching on 47 tomograms with a voxel size of 2. 1 nm using PyTOM  (Hrabe et al. , 2012). To reduce-false positives, the highest correlation peaks of the resulting 6-D cross-correlation function localized in the mitochondrial matrix were manually inspected in UCSF Chimera  (Pettersen et al.
The obtained map was used as template for automated template-matching on tomograms with a voxel size of 2. 1 nm using Pytom, and the highest correlation-peaks were then manually inspected in Chimera to remove false-positives. Cytosolic 80S ribosomes were localized in one tomogram by filtering EMDB-1780  (Armache et al. , 2010)  to 40 Å and using it as a template for template matching on deconvolved tomogram (tom_deconv; https://github. com/dtegunov/tom_deconv) using Pytom. Subvolumes were extracted for the 2,100 highest correlation peaks, of which 1,700 subvolumes were classified as ribosomes by unsupervised, autofocused 3D classification  (Chen et al. , 2014).
Proteomic analyses of C. reinhardtii mitoribosome composition Mass spectrometry analyses of the total, mitochondrial and ribosomal fractions of C. reinhardtii were done at the Strasbourg-Esplanade proteomic platform and performed as previously  (Waltz et al. , 2019). In brief, proteins were trypsin digested, mass spectrometry analyses and quantitative proteomics were carried out by nanoLC-ESI-MS/MS analysis on a QExactive+ (Thermo) mass spectrometer. Data were searched against the UniProtKB (Swissprot+trEMBL) database restricted to the C. reinhardtii taxonomy with a target-decoy strategy (UniProtKB release 2020_03, taxon 3055, 31246 forward protein sequences), Proteins were validated respecting FDR<1% (False Discovery Rate) and quantitative labelfree analysis was performed through in-house bioinformatics pipelines.
Artificial miRNA C. reinhardtii strain generation and analyses Artificial microRNAs constructs were created according to  (Molnar et al. , 2009). The oligonucleotides were designed using the WMD3 Web MicroRNA Designer software v3. 2 (wmd3. weigelworld. org) and genome release Chlamydomonas CDS reinhardtii 281 v5. 6. cds (Phytozome) (Table  S3 ). The oligonucleotides were annealed, phosphorylated, and ligated into a SpeI-digested pChlamiRNA2 containing the ARG7 gene as a selection marker. The resulting plasmids were linearized and transformed into Chlamydomonas CC-4351 strain by the Neon® Transfection System (LifeTechnologies) according to the GeneArt® MAX Efficiency® Transformation protocol for Algae (LifeTechnologies Cat#A24229). Cells with integrated plasmid were selected on TAP plates without arginine.
d'Avenir' (ANR-11-LABX-0057_MITOCROSS) to PG and a Agence Nationale de la Recherche (ANR) grant [MITRA, ANR-16-CE11-0024-02] and [DAMIA, ANR-20-CE11-0021] to YH and PG. Additional funding was provided by the Max Planck Society and the Helmholtz Zentrum München.
Acknowledgments This work has benefitted from the facilities and expertise of the  Biophysical and Structural Chemistry platform (BPCS)  at  IECB ,  CNRS UMS3033 , Inserm  US001 ,  University of Bordeaux. We thank,  J. Chicher  and  P. Hamman  of the Strasbourg Esplanade proteomic analysis for the proteomic analysis. The mass spectrometry instrumentation was funded by the  University of Strasbourg ,  IdEx "Equipement mi-lourd " 2015. We thank  Miroslava Schaffer  for help with FIB milling and cryo-ET acquisition. We thank  Jürgen Plitzko  and  Wolfgang Baumeister  for access to cryo-EM instrumentation and support. We are grateful to  Pr. Claire Remacle  ( University of Liège ) for her kind gifts of strains and antibodies. This work was supported by a  European Research Council   Starting Grant  (TransTryp ID: 759120 ) to YH, by the  LabEx consortium 'MitoCross ' in the framework of the  French National Program  'Investissement.
Data Availability The cryo-EM maps of C. reinhardtii mitoribosome have been deposited at the Electron Microscopy Data Bank (EMDB): EMD-XXXX for the LSU, EMD-XXXX for the head of the SSU, EMD-XXXX for the body of the SSU and EMD-XXXX for the subtomogram averaging of the whole ribosome. The corresponding atomic models have been deposited in the Protein Data Bank (PDB) under the accession XXXX for the LSU and XXXX for the SSU. Mass spectrometric data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD024708 and 10. 6019/PXD024708. RNAseq data were deposited in the NCBI Gene Expression Omnibus under accession number GSE171125.
Uniprot ID ATP Synthase (subunit 19. 5 kDa) Q6QAY3 ATP Synthase (subunit 5) A8J3U9 ATP Synthase (subunit 36. 3 kDa) A8JCE9 Complex I (subunit 39 kDa) Q6V506 Complex I (subunit 24 kDa) Q6V9B3 Complex I (subunit 49 kDa). d'Avenir' (ANR-11-LABX-0057_MITOCROSS) to PG and a Agence Nationale de la Recherche (ANR) grant [MITRA, ANR-16-CE11-0024-02] and [DAMIA, ANR-20-CE11-0021] to YH and PG. Additional funding was provided by the Max Planck Society and the Helmholtz Zentrum München. A 10% sucrose concentration 30% 4 260nm 280nm 3 5 6 Abs 2 1 Respiratory Mitochondrial Mitochondrial Monosomes cyto complexes SSU LSU & mito B Protein 1 2 3 4 5 6 180 154 106 65 20 13 165 158 95 50 8 9 146 169 90 50 20 17 35 141 66 18 4 0 21 60 35 14 2 0 29 106 61 13 2 0 0 15 51 29 14 7 0 22 83 43 32 16 5 37 83 51 50 19 0 30 90 45 41 10 0 0 16 11 8 0 0 8 30 11 14 6 1 8 50 95 58 34 0 0 11 26 23 5 0 3 23 47 40 12 0 0 5 11 11 4 0 0 11 38 35 9 0 0 6 43 33 13 0 0 2 0 0 10 0 0 6 2 5 26 3 1 2 3 1 10 0 0 0 0 2 13 0 0 0 0 5 14 0 0 0 0 0 13 4 20 65 35 22 12 0 1 75 15 6 1 6 43 119 58 50 31 2 21 47 23 22 15 7 77 190 112 81 28 9 25 93 166 127 65 0 4 39 80 49 31 8 0 36 93 39 11 10 18 79 132 76 38 6 4 67 112 76 44 4 14 70 133 73 31 0 2 39 66 63 31 0 0 7 31 15 4 0 5 21 62 39 12 2 0 3 44 78 18.
) Mitochondria qPCR 346,994 ATACCGGTAGCGCAAGCGAATG 445,469 TGACCTTACCGCCGATTGTCTG rrnL5 Mitochondria qPCR AAGGTAGAGTCTGCGTTGAACACC TTCCAAGCACAGTGGCTGCTTC rrnL6 Mitochondria qPCR ACGTTCTGGAACAATTACGCTGA GGGCTTACGGCCATGTTATCCA rrnL7 Final particle images (no. ) Mitochondria qPCR 101,291 ACGTTTAATTGGCGTCCTTG 40,131 AGCTGCATAGGGTCTCTTCG rrnL8 rrnS1 Map resolution (Å) Mitochondria Mitochondria qPCR qPCR 2. 93 CGCCTATGCACAGTTAGCC GTTTGGTGCTGGCTCAGCTTTC 4. 19 4. 47 TGACAACAAGTGTACCAGAGGTTT ATTACGCACTCGTGCGCTTAATC rrnS3 FSC threshold Mitochondria qPCR 0. 143 TTCAAATCTATAACGCCTGGTG 0. 143 0. 143 GCTGCCAATTGCTTTGATTT rrnS4 rtl Map resolution range (Å) Mitochondria Mitochondria qPCR qPCR 2. 7 -7 ACGGTCGAGCAAGTCATCATGG TACGCCGACAGTACATCCAG 3. 8 -10 TGGTATCCAACTTGTGGGTGTCC TACCAAAACCAGGACGGAAG dT Refinement RT TTTTTTTTTTTTTTTTTTTTTTT Initial model used (PDB code) 5KCR, 6GAW, 6XYW, 5MRC Map sharpening B factor (Å 2 ) -50.
The SP142 IHC assay is approved by the US Food and Drug Administration (FDA) to identify patients with mTNBC for treatment with AþnP using a !1% expression cutoff for PD-L1 on IC  (14). Although SP142 is clinically validated, other commercially available PD-L1 IHC assays, including Dako 22C3 (Dako; Carpinteria, CA, USA) and VENTANA SP263  (15, 16) , are widely used in laboratories worldwide for TNBC and non-TNBC indications. However, these assays use scoring algorithms different from SP142 and may differ in analytical concordance and/or clinical utility for patients with TNBC treated with AþnP. Comparative assay data provide value in guiding clinical decision making and treatment guidelines globally. In this post hoc, exploratory substudy of IMpassion130, we investigated analytical concordance and clinical utility among the VENTANA SP142, VENTANA SP263, and Dako 22C3 PD-L1 IHC assays.
Patients and Treatment IMpassion130 (NCT02425891) is an international, randomized, double-blind, placebo-controlled, phase III study evaluating first-line AþnP vs PþnP in patients with unresectable locally advanced or mTNBC  (2). Eligibility criteria and methodology are described elsewhere  (2). Patients were randomly assigned 1:1 to atezolizumab 840 mg or placebo every 2 weeks plus nab-paclitaxel 100 mg/m 2  on days 1, 8, and 15 of every 28-day cycle intravenously until disease progression (per Response Evaluation Criteria in Solid Tumors version 1. 1) or intolerable toxicity. IMpassion130 was conducted in accordance with Good Clinical Practice guidelines and the Declaration of Helsinki. Protocol approval was obtained from independent review boards or ethics committees for each site; all patients provided written informed consent.
Clinical outcomes in the BEP were not unequivocally comparable with those in the overall population. Further, it was not possible to validate the 22C3 IC 1% cutoff compared with the other assays and cutoffs because there are no formal training programs for this nonstandard evaluation, reducing the precision of this analysis. In 2020, the KEYNOTE-355 study showed that first-line pembrolizumab plus chemotherapy was beneficial in a mTNBC PD-L1þ population per 22C3 CPS !10, leading to FDA approval of the combination in the United States  (1, (4) (5) (6) (7). CPS !10 represents the cutoff identified in our harmonization analysis, wherein concordance remained subpar between SP142 IC !1% and 22C3 CPS !10; thus, these data are of interest given the changes in TNBC testing landscape. Tumor tissue source differences between KEYNOTE-355 (all samples were metastatic) and IMpassion130 (both primary and metastatic samples), as well as lack of reader-validated precision on the CPS 10 cutoff (pathologists in this study were not trained by the vendor for the TNBC indication based on 22C3 using CPS), may account for interstudy variations in CPS !10 prevalence and outcomes.
Methods: We performed a retrospective cohort study of sepsis patients in four ICU databases in North America, Europe, and Asia. We developed an intuitive vascular leak index (VLI) and explored the relationship between VLI and in-hospital death and fluid balance using generalized additive models (GAM). Results: Using a GAM, we found that increased VLI is associated with an increased risk of in-hospital death. Patients with a VLI in the highest quartile (Q4), across the four datasets, had a 1. 61-2. 31 times increased odds of dying in the hospital compared to patients with a VLI in the lowest quartile (Q1). VLI Q2 and Q3 were also associated with increased odds of dying. The relationship between VLI, treated as a continuous variable, and in-hospital death and fluid balance was statistically significant in the three datasets with large sample sizes.
Study population We conducted a retrospective analysis of patients from the eICU, MIMIC-III, AmsterdamUMCdb, and SNUH databases  [16] [17] [18] [19] [20]. Details on the patient cohort contained in each dataset are included in Additional file 1, and the relevant Institutional Review Board information for the de-identified data is at the end of the article. Our study population includes patients diagnosed with sepsis by ICD9 and Angus criteria in the eICU-CRD and MIMIC-III databases  [21]. In AmsterdamUMCdb, diagnosis of sepsis at admission, diagnosis of other severe infections, the use of antibiotics not for prophylaxis after surgery, and finally the presence of sepsis cultures were all used to identify sepsis patients. In the SNUH database, due to data constraints, we only used diagnosis of sepsis at admission to identify sepsis patients.
In addition to our variables of interest, we identified potential confounders that would account for a patients' physical characteristics, such as age and sex. We accounted to disease severity and comorbidity burden with the Acute Physiology and Chronic Health Evaluation (APACHE) IV score and the Charlson Comorbidity Index (CCI) in eICU, the Oxford Acute Severity of Illness Score (OASIS) and the Elixhauser Comorbidity Score in MIMIC III, APACHE II in AmsterdamUMC, and APACHE II in SNUH. Comorbidity information was not available for the AmsterdamUMC and SNUH databases.
Population description In Table  1 , we show how we derived our final patient cohort from our inclusion and exclusion criteria. The demographics, clinical characteristics, and outcomes for the patients in the four databases are presented in Table  2  and Additional file 1: Table  S1  for the fluid balance and in-hospital death outcomes, respectively. Covariates and outcomes stratified by VLI quartiles (for both the in-hospital death and fluid balance populations), in-hospital death, and fluid balance quartiles are presented in Additional file 1: Figs. S1-S4, respectively. The final distribution of VLI for each database for the in-hospital death cohort is presented in Additional file 1: Fig. S5. VLI is left-skewed with a small number of patients having positive leak indices. A positive leak index indicates that a patient's hematocrit increased over time despite the administration of fluids.
Key findings We developed an equation to identify patients with increased vascular leak and found that a higher VLI was associated with a higher fluid balance between 36 and 84 h of ICU care and a higher risk of in-hospital death in patients with negative VLI. The results for the patients with positive VLI varied across the databases due to considerations that will be discussed in the limitations section. Our analyses were replicated in several datasets from North America, Europe, and Asia, suggesting that the relationships observed are robust. Our results suggest a causal relationship between VLI and risk for death according to updated Bradford Hill rules  [25]. We found a dose-response relationship between the quartiles of VLI and risk for death. Our observations are consistent across several databases from around the world, and they fit well with current models of biological plausibility with respect to the severity of vascular leak, organ dysfunction, fluid administration, and risk for death.
Limitations Limitations common to studies that use electronic medical record databases include mistakes in charting data that must be input manually. Errors in collecting fluid data are especially prevalent given the amount of different fluid inputs that a patient receives and the difficulty of accurately measuring urine output. In addition, we found that most patients have a negative VLI. That is, the hematocrit decreased over time as they received IV fluids. In some patients, however, we observed an increase in hematocrit over the study period, even as the patients received IV fluids. Because we sought to exclude patients who received blood component transfusions, the reasons for the observed increase in hematocrit are uncertain. Due to the nature of our equation, patients with a rising hematocrit have the highest VLI and are classified in VLI Q4.
We recognize that body surface area may be an unreliable surrogate  [39]. Nevertheless, we sought an index that uses easily available data in order to facilitate bedside use, especially in low-or middle-income settings where more sophisticated monitoring may not be available. Future work should validate our VLI prospectively. In the different hospitals that represent these four databases, fluid administration practices were quite different. In addition, depending on fluid administration practices, 36-84 h fluid balance and death can be significantly dependent on fluid balance in the first 36 h. This makes it difficult to cleanly describe the relationship between VLI and 36-84 h fluid balance. Finally, while the patient population size was sufficiently large in the eICU, MIMIC, and Amsterda-mUMC databases, the SNUH database had a very small final cohort size.