dataesr/scientific-paragraphs-categorization huggingface dataset:
VACCELERATE Interactive Educational Cards The educational cards enhance general knowledge and address the misinformation about COVID-19 vaccines, vaccine trials, and COVID-19 vaccination, as well as voluntary trial participation of children and adults. Each set of educational cards represents an educational aim and comprises a specific number of cards and target groups, as shown in Table  1. The interactive educational cards (Multimedia Appendix 1) have been prepared with the help of 2 graphic designers and under the guidance of a pediatrician and several VACCELERATE participants. The educational cards include a QR code with a URL for promoting educational material on the one side and additional text script and characters on the other. The URL can be a website (eg, the one showed on the VACCELERATE website  [.
Promotional Video The promotional video (see Multimedia Appendix 1) presents short, recorded videos by the VACCELERATE work package leaders and the National Coordinator's pictures to promote, inform, and describe the VACCELERATE Volunteer Registry through specific text scripts. The aim of the video is the inclusiveness and representation of all participating European Union countries and European Union-associated countries of the VACCELERATE Consortium. Currently, we have active representation from 17 countries (Austria, Belgium, Cyprus, Czech Republic, Germany, Greece, Ireland, Israel, Italy, Hungary, Lithuania, the Netherlands, Norway, Portugal, Spain, Sweden, and Turkey) in the VACCELERATE Volunteer Registry.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  101037867 ; VACCELERATE). The produced educational and promotional tools were developed as part of the  VACCELERATE Consortium. For further information, please refer to the official website of the VACCELERATE network [29]. The research leading to these results was conducted as part of the  VACCELERATE consortium. For further information please refer to [29].
Results The C403A mutation abrogates OMA1-dependent OPA1 cleavage in a mouse sarcoma model OMA1 processing is recapitulated in Fig 1A  and  based on a 3D model of the mouse 59-kD pre-pro-OMA1 protein available in the AlphaFold database (https://alphafold. ebi. ac. uk/). This immature form undergoes further proteolysis during mitochondrial insertion (see Fig  1  legend ). Upon depolarization induced by the carbonyl cyanide m-chlorophenyl hydrazine (CCCP) uncoupling drug, the mature 40-kD L-OMA1 isoform is autocatalytically cleaved at the C-terminal end to generate the 35-kD S-OMA1. This isoform is catalytically active on its target proteins, undergoes further C-terminus cleavage, and is unstable  (Baker et al, 2014; Zhang et al, 2014; Alavi, 2021). As shown in yeast, this activation process might involve redox changes in cysteines 272 and 332, corresponding to cysteines 403 and 461 in mouse OMA1  (Bohovych et al, 2019).
OMA1-deficient cells were previously shown to be resistant to apoptosis because of the reduced leakage of cytochrome c  (Jiang et al, 2014; Gilkerson et al, 2021). We treated CTRL and C403A cells second peptide approximately in the region 443-452, in turquoise  (Baker et al, 2014; Zhang et al, 2014). (B, C, D, E, F, G) Western blot analysis of OMA1 (B, C, D) and OPA1 (E, F, G) proteins prepared from  WT, unedited, and C403A total MCA205 cell (panels B and E)  or mitochondrial extracts (panels D and G) exposed for 1 h or not to the uncoupling agent CCCP. Quantification included data obtained from analysis of two WT and four C403A samples (source data), as shown in panels C for OMA1 and F for OPA1 using actin or TOMM20 as control cell or mitochondrial protein (n = 2). Mann-Whitney test; * P < 0.
05. (H) BN-PAGE analysis of native proteins prepared from CTRL and C403A cell mitochondrial extracts. MIB and MICOS complex composition was analyzed using anti-MIC60, anti-MIC19, and anti-OPA1 antibodies (n = 2). Source data are available for this figure. In contrast, the proportion of stress-induced cell necrosis was higher in C403A than in CTRL cells  (Fig S2F and G). The disorganization in the crista structure is a consequence of OMA1 deficiency  (Viana et al, 2021). To define qualitative changes in mitochondrial and crista organization, we performed a transmission electron microscopy (TEM) analysis on in vitro-grown, untreated, or CCCP-stressed cell lines. As shown in Fig  2H , CTRL cell lines illustrated the mitochondrial distribution, electron density, and crista ultrastructure observed under untreated conditions.
Cells were treated for 1 h with CCCP and fixed for TEM acquisition. Scale bars: 200 nm (n = 2). Quantification was performed on 16 independent fields obtained from CTRL and C403A MCA205 cells. (I) We scored MCS (panel I) per field in untreated or CCCP-treated conditions. (J) Additional analyses were performed to evaluate crista area (panel J). Mann-Whitney test; ****P < 0. 0001, ***P < 0. 001, and **P < 0. 01. Source data are available for this figure. cross-presentation of tumor antigen  (Xu et al, 2017). Furthermore, the XCR1 + cDC1 cell subset cross-presents antigen and primes antitumor CD8 + cytotoxic lymphocytes  (Ferris et al, 2020). To test the contribution of these processes to the control of tumor growth in immunocompetent mice, we grafted CTRL and C403A tumor cells in Xcr1 DTA mice that lack cDC1 cells  (Wohn et al, 2020)  We then characterized by flow cytometry the immune infiltrate at days 12 (Fig S3E ) and 21  (Fig 4E)  after grafting.
The consequences of mtDNA release in tumor microenvironment vary depending on the nature of infiltrating immunocytes, associated with the neutrophil-mediated worsening of tumor progression  (Singel et al, 2019)  or, reciprocally, with the stimulation of the cross-priming potential of cDC1 cells for the development of anti-tumor cytotoxic CD8 cells  (Xu et al, 2017). Our results support this latter hypothesis because over time, edited tumors were enriched in NK1. 1 + and CD8 + lymphocytes, and furthermore, in the absence of cDC1 cells, C403A clones grew in immunocompetent mice. In addition, the presence of mutant tumor cells in a chimeric tumor slowed down the growth of the non-edited clones and was associated with the accumulation of an immune infiltrate. The relevance of our findings in a mouse sarcoma model was questioned by exploring the levels of expression of the transcripts coding for OMA1 and OPA1 in a large series of STS with complex genomics, documented in various databases.
Cells were transfected with jetOPTIMUS reagent (Polyplus) following the manufacturer's instructions. For PE2 experiments, cells were transfected with 2 µl of jetOPTIMUS, 1,5 μg of PE2 plasmid (#132775; Addgene), 500 ng of pegRNA plasmid (#132777; Addgene), and 200 ng of peGFP-C1 vector plasmid (Clontech). The transfected cells were collected after 72 h of culture and GFPpositive cells sorted in single clones in 96-well plates. After genomic DNA extraction (QIAGEN), clones were screened by PCR (Supplemental Data 2) for the presence of mutation. 12% of the clones were positive for the mono-allelic mutation and confirmed by sequencing  (Fig S1E). For PE3, cells were transfected with 2 µl of jetOPTIMUS, 1,5 μg of PE2 plasmid, 500 ng of pegRNA plasmid, and 200 ng of pLKO-GFP-sgRNA plasmid. The transfected cells were collected after 72 h of culture and GFP-positive cells sorted in single clones in 96-well plates.
Analysis of OMA1 and OPA1 expressions in soft tissue sarcoma clinical samples We analyzed our database  (Bertucci et al, 2022)  including clinicopathological and normalized gene expression data of clinical STS samples gathered from 16 public data sets (Table  S4 ). All samples were from an operative specimen of previously untreated primary tumors. The gene expression profiles had been generated using DNA microarrays or RNA sequencing. Because our mouse model represented a sarcoma with complex genomics, our analysis was limited to the 921 cases of STS with complex genomics. The most frequent pathological types were LMS and UPS, and 56% of cases were high-risk CINSARC. OMA1 and OPA1 mRNA expressions were analyzed as discrete variables (high versus. low) using their mean expression level of the whole series as cutoff.
Flow cytometry Cell death was evaluated using the LIVE/DEAD Fixable Blue flow cytometry, and unspecific labeling was prevented by incubation with anti-CD16/CD32 mAb in PBS/2 mM EDTA for 30 min. Cell surface antibody labeling was performed in FACS buffer for 1 h at 4°C. For intracellular staining, the Foxp3/Transcription Factor Staining Buffer Set (eBioscience) was used. Cells were analyzed on BD LSR Symphony or Fortessa (Becton Dickinson). Data analysis was done using FlowJo 10. 8 software. To evaluate mitochondrial depolarization and mitochondrial mass, cells were stained with MDR (10 nM) and MitoTracker Green (100 nM) for 20 min at 37°C (Thermo Fisher Scientific) in serum-free RPMI. For staining of cultured cells, we used 100 nM MDR. CD8 and CD4 T cells were quantified within CD45 + CD11b -NK1.
1 -cells, and tumor-associated macrophages, within CD11b + Ly6G -NK1. 1 -Ly6C low MHCII + CD64 + F4/80 + cells. Annexin V/SYTOX Blue assay 3 × 10 5 CTRL and C403A cells were seeded in a six-well plate. After 24 h of 1 µM staurosporine (used as a positive control) or 1 µM bortezomib stimulation, cells were harvested and stained for 20 min with 1 µM SYTOX Blue in PBS. Apoptotic cells were stained with Annexin PE for 20 min and immediately analyzed on FACSCalibur/Canto II. Data analysis was done using FlowJo 10. 8 software.
Structure prediction The mouse OMA-1 amino acid sequences were obtained from ENSEMBL (ENSMUSG00000035069). With the advent of powerful AI implementations, the AlphaFold Protein Structure Database (https://alphafold. ebi. ac. uk/) and DeepMind's Colab notebook  (Jumper et al, 2021)  have been used for structure prediction. Figures representing structural renderings were generated with the PyMOL Molecular Graphics System (version 2; Schr ödinger, LLC).
Immunoblotting Cell lysates were prepared in ice-cold RIPA buffer, supplemented with a protease inhibitor cocktail. Samples were centrifuged at 10,000g for 5 min at 4°C and supernatants collected. Protein concentrations were determined using the Pierce BCA assay. Laemmli buffer was supplemented with β-mercaptoethanol and incubated for 5 min at 95°C. 5 μg of total proteins was loaded on a 4-12% SDS-PAGE run at 150 V and then transferred to PVDF membranes during 1 h at 100 V. Membranes were saturated in PBS/ Tween/5% BSA overnight at 4°C, followed by incubation with either mouse anti-OMA-1, anti-OPA1, anti-DELE1, anti-TOM20, and antiactin antibodies at 4°C (see Supplemental Data 1). Membranes were washed and incubated with HRP-linked goat anti-rabbit IgG or HRPlinked goat anti-mouse IgG for 1 h at RT.
Immunofluorescence analysis C403A and CTRL MCA205 cells were seeded in eight-well Nunc Lab-Tek plates (Thermo Fisher Scientific) and labeled with MDR at 100 nM at 37°C in prewarmed DMEM/F-12 without phenol red for 30 min. Airyscan imaging was performed using a commercial Zeiss confocal microscope LSM 880 equipped with an Airyscan module (Carl Zeiss AG), and images were taken with a 63x/1. 40 NA M27 Plan-Apochromat oil objective. In this mode, the emitted light was projected onto an array of 32 sensitive GaAsP detectors, arranged in a compound eye fashion. MDR-labeled samples were excited with a 633-nm beam, and emission was recorded using BP 570-620 + LP 645 filters. Images were processed using Zen Black 2. 3 software. For TOMM20 staining, cells were seeded on glass coverslips, treated with 20 µM CCCP for 45 min, then fixed in PFA 4% for 10 min, and permeabilized in PBS/3% Triton/ 10% DKS (donkey serum) for 30 min.
Mitochondria were stained overnight using a rabbit anti-TOMM20 antibody (186734-1/500; Abcam) and revealed with Alexa Fluor 594-AffiniPure Donkey Anti-Rabbit IgG. Coverslips were prepared using DAPI-containing Fluoromount. The Airyscan processing performs filtering, deconvolution, and pixel reassignment to improve SNR. The filtering (the Wiener filter associated with deconvolution) was set to the default filter setting of 6. 1 in 2D. Microscopy images were analyzed using Imaris 5. 104 software. To model MDR-labeled mitochondria in 3D, we used the plugin "structure. " The threshold was positioned at the curve inflexion point. The mitochondrial volume of fragmented mitochondria varies between 0. 08 and 0. 53 μm 3 , and that of fused mitochondria is set over 0. 53 μm 3. To quantify fission potential, we calculated the mitochondrial fission index as log 10 (å 0.
Statistics analysis Sample size was designed to minimize the number of individual experimental units (mice or samples), and obtain informative results and appropriate material for downstream analysis. This represents five mice per group, and experiments were typically performed 2-3 times as stated in figure legends. Error bars represent the standard error of the mean. GraphPad Prism 9 software was used for statistical significance assessment. The Gaussian distribution was tested using the D'Agostino-Pearson omnibus normality test. When passing the normality test, a t test was used. Otherwise, a Mann-Whitney U test was used. Differences were considered to be statistically significant when ****P < 0. 0001, ***P < 0. 001, **P < 0. 01, and * P < 0. 05.
Acknowledgements The  INCA   PLBIO19-015  grant supported  R Miallot  funding and the work realized in the teams of  P Naquet  and  J-Y Blay. We thank the  Cancerop ôle Provence-Alpes-C ôte d'Azur , the  National Cancer Institute , and the  CRISPR Screen Action  for the support brought to the realization of this research project. We thank the core facilities involved in this project: CIML flow cytometry, CIML animal facilities, ImagImm ( Roxane Fabre ), the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and the  Plateforme d'Imagerie Vasculaire et de Microscopie Intravitale  at the C2VN. The electron microscopy experiments were performed on the  PiCSL-FBI core facility (IBDM, AMU-Marseille ), a member of the  France-BioImaging National Research Infrastructure  ( ANR-10-INBS-0004 ).
This way, they leveraged the linear mechanisms embedded in the resolvent operator associated with noise generation and modelled only the remaining nonlinearities coming from the forcing. Other studies have used a wavepacket source model for the near-field velocity correlations paired with Lighthill's analogy to predict jet noise  (Huerre & Crighton 1983; Cavalieri et al. 2011 Cavalieri et al. , 2012;; Cavalieri & Agarwal 2014; Maia et al. 2019; da Silva, Jordan & Cavalieri 2019). In this study, we use a compressible LES database of turbulent jets at different Mach numbers and operating conditions to develop a source model for jet noise. A numerical high-fidelity database is required since the analysis involves using both the state and forcing data, and the latter are not accessible with sufficient spatial and temporal resolution via state-of-the-art measurement techniques.
The advantage of the proposed strategy is that it leverages the versatility of the resolvent framework similar to  Pickering et al. (2021b)  to systematically identify the dominant noise-generation mechanisms. Different from their study, here, we start by analysing the forcing data to identify the noise-generating part and then drive the resolvent operator with this refined forcing to predict the acoustic field. Given the inefficiency of turbulence to generate noise, such an identification significantly contributes to the robustness of the final empirical model, as it retains only the essential information from the forcing data. In summary, the new achievements of the present work are: (i) performing resolvent-based prediction using forcing obtained from an LES database for a high-Reynolds-number turbulent jet; (ii) collective use of linear analysis tools as a novel The paper is organised as follows: the mathematical framework for resolvent analysis and RESPOD is revisited in § 2.
Resolvent-based extended spectral proper orthogonal decomposition One of the goals of this study, and indeed one of the broader goals of jet-noise modelling, is to obtain simplified representations or models of the nonlinear interactions that underpin jet noise. One such approach is to search for a useful rank reduction. A known trait of turbulent jets and their sound is the marked difference in complexity between the turbulence and acoustic fields. This difference implies that, given a discretised turbulent jet database, it may be possible to represent the acoustic field using a compact basis with a small number of vectors, yielding a low-rank system, while a substantially larger basis will be required to define the turbulent fluctuations, and thus the forcing, in the near field, yielding a high-rank system.
Large-eddy simulation database The numerical database used in this study to develop an empirical forcing model consists of LES of four subsonic jets, one at jet Mach number, M j U j /c j = 0. 4, with no flight effect and others at M j = 0. 9 with or without flight effect. We use three other LES databases at M j = 0. 7 with or without flight effect and at M j = 0. 8 without flight effect to test the model. The LES was conducted using the unstructured flow solver 'Charles'  (Brès et al. 2017). In all cases, the jets are isothermal and ideally expanded. Two of the cases at M j = 0. 9 contain a flight stream at M ∞ = 0. 15 and 0. 3, respectively. All of the jets are turbulent thanks to a synthetic forcing applied inside the nozzle, generating a fully turbulent boundary layer at the jet exit  (Brès et al.
(2018)  for the cases M04Mc00 and M09Mc00 and by  Maia et al. (2022)  for the remaining cases. The M09Mc00 case is publicly available as part of a database for reduced-complexity modelling of fluid flows  (Towne et al. 2022). The case M04Mc00 contains both the state and the forcing data while others contain only the state data. The forcing data, once the state data are stored, are obtained via the procedure devised by  Towne (2016)  and summarised in algorithm 1. The extraction of the forcing data is restricted to the M04Mc00 case, as aliasing effect in the database (see the Appendix and  Karban et al. 2022b  for a detailed discussion) increase with Mach number, rendering resolvent-based prediction using forcing data unreliable for the remaining flow cases. Both the state and forcing data are Fourier transformed using blocks containing 512 snapshots in time with an overlap ratio of 75 %.
Resolvent analysis Resolvent-based prediction of the response using the forcing data is achieved via a custom resolvent analysis code  (Bugeat et al. 2019). The code uses the finite-volume method to solve the linearised N-S equations decomposed into azimuthal Fourier modes. The  q(m) = R (m) f (m) , (3. 3) where R (m) (iωI -A (m) ) -1. In practice, the response, q(m) , to a given forcing, f (m) , is computed by solving the linear system L (m) q(m) = f (m) , (3. 4) where L (m) = R (m) -1 = (iωI -A (m) ) is a sparse linear operator. The resolvent code solves (3. 4) via lower-upper (LU) decomposition using the PETSc library  (Balay et al. 1997). Once the LU decomposition is performed, (3. 4) can be solved efficiently for numerous forcing vectors. This allowed us to calculate at once the responses to different Fourier realisations of the forcing, which then are used to compute the power spectral density (PSD) of the resulting acoustic field via Welch averaging.
When calculating the response of the modified resolvent operator to a RESPOD forcing mode as in (2. 16), we simply replaced the vector on the right-hand side of (3. 4) with B χ ( p). In all computations, a sponge zone is placed within x/D = [20, 30] and r/D = [6, 12] at the downstream and outer ends of the domain, respectively, to avoid spurious reflections. The domain is extruded to 12D in the radial direction to accommodate the top sponge region. A single damping function of the form 1 -exp κ (η -η s ) 2 (η s -η max ) 2 1 -exp(κ) (3. 5) is used to damp fluctuations in both x and r directions, where κ = 4, η denotes either the x or r coordinate, η s denotes the beginning of the sponge zone and η max denotes the domain end position. Further details can be found from  Bugeat et al. (2019). The original code was written based on conservative variables, while the LES forcing database was generated using the primitive-like variable set, q = [ν, u, p], as discussed in § 2.
Identification of the acoustically efficient forcing components The goal of this study is to extract acoustically relevant forcing components that underpin the noise-generation mechanisms in subsonic jets and to propose an empirical model for these components. Identification of the noise-generating part of the forcing prior to empirical modelling is crucial for the model to yield robust acoustic predictions. To achieve this, we proceed as follows. Using the database M04Mc00, which contains both the state, q, and the forcing, f , we first conduct the analysis outlined in § 2 based on RESPOD to identify the acoustically active forcing components. We limit the study to mechanisms associated with noise generation at low polar angles, which we refer to as downstream noise. We then discuss how to further decompose the low-rank forcing associated with the acoustic field to extract the part which satisfies the acoustic matching criterion  (Ffowcs Williams 1963; Crighton 1975).
This underconvergence observed in the forcing modes does not pose a problem in the following analysis. The SPOD modes of the response and the RESPOD modes of the forcing are computed using Fourier realisations of response and forcing that exactly correspond to the same time window. In the ideal case of an error-free database, (2. 3) is therefore satisfied for each pair of response-forcing realisations. So, no matter how underconverged the forcing data are, the structures generating the converged acoustic field are, by construction, ensured to be contained in the forcing mode seen in figure  4. As discussed in § 2. 2, the first RESPOD forcing mode contains all structures correlated with the leading SPOD mode. This indicates that the remaining forcing that amounts to 99 % of the total forcing energy can generate only 25 % of the total acoustic energy.
A similar discrepancy is observed for the rank-5 prediction, which recovers 80 % of the acoustic energy while it should generate 99 %. This is due to the errors contained in the LES database, causing a loss in the correlation information between the response and the forcing. Despite all the limitations of the existing database as discussed in the Appendix, we see that it is still possible to define a rank-1 forcing that can generate most of the downstream noise. In what follows, we further decompose the rank-1 forcing obtained by RESPOD to extract the acoustically active forcing components which drive the leading SPOD mode of the acoustic pressure seen in figure  4.
04 %, while it generates ∼ 75 % of the total acoustic energy in the downstream region for a frequency range St = [0. 4, 1. 0] at M j = 0. 4. The analysis above identifies the forcing subspace that actively contributes to noise generation in the M04Mc00 case. As discussed earlier, this data-driven approach is only applicable for this case, since the errors contained in the LES database prevent us from 965 A18-18  extracting reliable forcing data at higher Mach numbers. In the following section, we will discuss how to develop an empirical model for these forcing structures and extend it to other flow cases.