dataesr/scientific-paragraphs-categorization huggingface dataset:
Model building and refinement The atomic model of RIPK2 1-317 in complex with XIAP BIR2 154-240 was obtained by fitting with rigid-body refinement the existing crystal structures of both proteins. For RIPK2 1-317 , we firstly fit the structure of active RIPK2 (PDB ID: 5NG0)  (Pellegrini et al, 2017)  which displays RIPK2 residues from 5 to 310. For XIAP BIR2, we used the crystal structure 4J3Y, chain C  (Lukacs et al, 2013). We firstly fit the two structures in the cryo-EM map, using ChimeraX  (Pettersen et al, 2021). After removal of waters, ions, and nucleotide analogue, we then proceeded with rigid-body refinement in Phenix  (Liebschner et al, 2019). In Coot, we mutated back to cysteine residues 202 and 212 in the BIR2 domain, manually adjusted loop 210-214, built the αLhelices (residues 299-312, Fig  3D ) using as template the structure of RIPK2-ponatininb (PDB ID: 4C8B)  (Canning et al, 2015) , and deleted the regions obviously outside of the density and the N-termini βstrand interaction.
Alphafold2 For the Alphafold2 calculations, we used Local ColabFold  (Jumper et al, 2021; Evans et al, 2022 Preprint; Mirdita et al, 2022)  installed on an in house server. To compute the complex structure of XIAP BIR2 with RIPK2 (residues 154-240, 1-317 respectively) we used three cycles, model "multimer-2" and 5 models. Neither increasing the number of cycles nor changing module improved the prediction. We applied the same calculation to RIPK2-cIAP1 BIR2 and RIPK2-cIAP2 (Fig  S12C ). As for the RIPK2-XIAP BIR2 complex, the prediction does not show any significant interaction between the two proteins.
Acknowledgements We thank  Katrin Rittinger  ( Francis Crick Institute, UK ) for the kind gift of plasmids. We acknowledge  Alice Aubert  and  Martine Pelosse  for support in using the Eukaryotic Expression Facility at the  EMBL in Grenoble. We acknowledge  Sarah Schneider  and  Wojtek Galej  for support in using the EM facility at the  EMBL in Grenoble. We thank the  European Synchrotron Radiation Facility  for provision of beam time on CM01. We acknowledge the EM platform in HD for further data collections not here reported, but project connected. We thank the IT assistant of the  IBS-EMBL  shared processing cluster. This work used the platforms of the Grenoble Instruct-ERIC Center (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Data Availability Coordinates for RIPK2-XIAP BIR2 complex are in the protein data bank PDB with accession code 8AZA. The cryo-EM map has accession code EMD-15757. The NMR chemical shift assignments of XIAP BIR2 AG 124-240 have been deposited with the Biological Magnetic Resonance Bank (BMRB) under accession number 51600.
1D Double Beam Numerical Simulations Having performed analysis on the 1-D system, we then developed code (written in MATLAB) to visualize and supplement our analytical results. For simplicity, the nondimensionalized 1D double beam system with and without damping was simulated with no nonlinear effects. The numerical solution of the system is found through numerical integration of the time dependent Fourier coefficients, A n (t) and B n (t), of v 1 (x, t) and v 2 (x, t) respectively, v 1 (x, t) = ∞ n=1 A n (t) sin(nπx) , v 2 (x, t) = ∞ n=1 B n (t) sin(nπx). (5. 1).
The powerful new infrared capabilities of JWST enable embedded young star clusters throughout nearby galaxies to be identified and studied statistically. Complete samples of embedded clusters from PHANGS-JWST, combined with the PHANGS Hubble Space Telescope (HST), Atacama Large Millimeter/submillimeter Array (ALMA), and MUSE data sets, will allow basic open issues in star and cluster formation to be addressed, such as the fraction of star formation that occurs in clusters (cluster formation efficiency), timescales for the clearing of the natal dust and gas, the conditions that lead to cluster formation, and the dependence of these parameters on galactic environment. In this pilot study, we identify candidate embedded clusters in NGC 7496 using PHANGS-JWST NIRCam and PHANGS-HST UV-optical imaging.
Source Detection and Photometry To obtain positions and photometry for our set of 12 prototype embedded clusters, as well as search for embedded clusters in a more systematic way, we use routines in the PHOTUTILS  (Bradley et al. 2022 ) Astropy package for photometry. First, we use the peak-finding algorithm find_peaks to detect sources in the F335M image. The level of the background varies significantly across the image. So, in order to estimate local background levels, we use the function SExtractorBackground to build a 2D model of the background. Then, we search for local maxima in square regions that are 11 pixels on a side. To derive accurate source positions, we use the function centroid_quadratic, which determines the centroid by fitting 2D quadratic polynomials. We extract photometry from a circular aperture with a radius of 0 093 (3 NIRCam shortwavelength pixels) that encloses more than 50% of the energy for a point source (0 073) in the F335W filter.
We also plot the LF of the combined cluster sample, i. e. , PHANGS-HST clusters plus the 67 embedded candidates (shown with green triangles). The LF for the full HST+JWST cluster sample (green) has a best-fit power-law index of -2. 01 in F200W and -1. 77 for F300M (shown as the green lines in each panel). The fit over only the PHANGS-HST clusters (orange) gives very similar index values, -1. 91 in F200W and -1. 73 for F300M (orange lines). On the other hand, while there are only a few data points before our embedded cluster distribution (red points) flattens at magnitudes fainter than M F200W -8, the bright points suggest a behavior similar to the optically selected clusters. The LF in the F300M filter, however, appears to be steeper than the PHANGS-HST clusters. This difference could be due to the imposed selection criteria that involve cuts using the F300M filter (Section 3.
Such techniques are needed to produce SEDs appropriate for constraining the physical properties of these clusters through the fitting of stellar population and dust models. Ultimately, a complete census of star clusters from PHANGS-HST and JWST will enable basic unsolved problems in star formation and cluster evolution to be addressed through new constraints on the initial cluster mass function, star cluster formation efficiencies, and feedback timescales associated with the clearing of the natal gas and dust. The authors would like to thank the anonymous referee for constructive comments that helped improve the quality of this work. This work is based on observations made with the NASA/ESA/CSA JWST and NASA/ESA Hubble Space Telescopes. The data were obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc.
Molar ratios of PCPEs and domains to CPIII-Long are indicated above the gel and enhancement factors are reported below the gel. The gel is representative of n = 3 independent experiments. c Quantification of CPIII-Long (360 nM) processing by BMP-1 (16. 5 nM) in the presence of increasing amounts of PCPE-1 and PCPE-2 (0-2. 2 µM). Analysis was by SDS-PAGE (reducing conditions) on a 15% acrylamide gel with Sypro Ruby staining and quantification of individual substrate bands (means ± SD; n = 3 independent experiments). d Superposition of a model of PCPE-2 CUB domains (green) on the X-ray structure (PDB code 6FZV) of the complex between the CUB domains of PCPE-1 (orange) and C-propeptide III (blue). Calcium ions are represented as cyan spheres. e PCPE-2 binding to immobilized miniprocollagen III (435 RU).
Sections were examined with a Jeol 1400JEM 120 kV transmission electron microscope (Tokyo, Japan), equipped with a Gatan Orius 600 camera on wide field position and Digital Micrograph software v1. 7 (Gatan Inc). Fibril diameters were analyzed with ImageJ v1. 53 on 2-6 TEM images/mouse for skin and tendon and up to 10 images/mouse for heart (4-8 mice/genotype). Diameters were derived from Feret diameters for skin fibrils, cross-sectional areas for heart fibrils and diameters of the smallest inscribed circles for tendons. The scripts of the macros developed to measure fibril diameters in hearts and tendons are available in Supplementary Methods.
The pattern of Indian ink dots created the macroscopic pattern needed to analyze the experiments with Digital Image Correlation in post treatment using CorrelManuV software (v1. 681). Prior to the experiment, the dimensions of each sample were measured using a caliper. Tensile tests were performed at a strain rate around 0. 5% s -1 , without any stops, until rupture of the sample. During each test, the displacement of the grips and the force were recorded every second. The force was divided by the initial section of the sample to obtain the nominal stress. Stretch was determined either through the machine or through the optical measurement, averaged on the whole sample. As reported  70  , the optical stretch varies proportionally to the machine stretch (and, in mean, is equal) and we used it as the true one.
Cleavage assays All cleavage assays with protein substrates were performed at 37 °C in 50 mM HEPES pH 7. 4, 5 mM CaCl 2 and 0. 02% βOG. The NaCl concentration could vary between assays in the range 0. 15 M-0. 25 M but was kept consistent between comparable conditions. All other concentrations and incubation times were as indicated in figure legends. Analysis was by SDS-PAGE with 4-20% polyacrylamide Criterion gels (BioRad), unless otherwise stated, and Coomassie Blue, Instant Blue (Euromedex) or Sypro Ruby (Merck) staining. For Sypro Ruby staining, a maximum amount of 250 ng of substrate/lane was used to stay in the linear range and gels were analyzed with a Typhoon FLA9500 scanner (blue laser, LPG emission filter; FLA9500 software, Cytiva). Uncropped gels are available in Supplementary Fig.
16  or as a Source Data file. When applicable, quantification of protein band intensities was made with the ImageQuant TL software v8. 2 (Cytiva). Enhancement factors for mini-procollagens and CPIII-Long were calculated as the following ratio: [intensity of Intermediate 1 (1 chain cleaved) + 2 x intensity of Intermediate 2 (2 chains cleaved) + 3 x intensity of Cpropeptide] / [3 x (sum of intensities of all cleavage products + procollagen)] and normalized to the BMP-1 alone condition. Cleavage of the fluorogenic peptide Mca-YVADAPK(Dnp)-OH (20 µM, Covalab) was monitored in the same buffer conditions  26. Fluorescence was recorded for 20 min (excitation: 320 nm; emission: 405 nm) in a Tecan Infinite M1000 fluorimeter equipped with the iControl software v1. 10 and the slope of the curve was used to derive BMP-1 activity.
SPR experiments Surface plasmon resonance experiments were run with a Biacore T200 apparatus (Cytiva) equipped with the Biacore T200 control software (v3. 2. 1). Protein ligands were covalently immobilized on Series S sensor chips CM5 (Cytiva) by amine coupling chemistry using the reagents included in the Amine coupling kit (Cytiva). Ligands were diluted in 10 mM sodium acetate pH 5 for mini-procollagen III, C-propeptide III and BMP-1 or in 10 mM HEPES pH 7. 4 or 8. 0 for PCPE-2. SPR signals were recorded simultaneously on a control channel where the same immobilization procedure was applied, except for the presence of protein ligands. Soluble analytes were injected at 30 or 50 µl/min at 25 °C after dilution in running buffer (10 mM HEPES pH 7. 4, 0. 15 M NaCl, 5 mM CaCl 2 and 0. 05% P20) for 90 or 120 s, either in high performance or dual inject modes.
In co-injection experiments, the first binding partner was injected for 60 s followed by the injection of the two co-injected partners, as indicated, for another 60 s. In all cases, regeneration was achieved with 2 M guanidinium chloride. The proteins used for these experiments were the same as for activity assays except for PCPE-1 which was used as the native form with no His tag in SPR analyses, as described  44. BMP-1 hydroxamate inhibitor (UK 383,367) was from Merck. Finally, best fits of the data in kinetic and steady-state modes were obtained with the Biacore T200 evaluation software (v3. 2. 1). Both the 1:1 binding (A + B ↔ AB) and heterogenous ligand (A + B ↔ AB and A + B' ↔ AB' with B and B' representing two different presentations/conformations of the immobilized ligand) models were tested and when a significantly better fit of the data was obtained with the heterogenous complex model, the latter was selected to compute kinetic and steady-state constants.
Circular dichroism CD measurements were carried out using 1-mm path length quartz cells in an Applied Photophysics Chirascan instrument, calibrated with aqueous d-10-camphorsulfonic acid. Proteins (0. 2 mg/ml) were analyzed at 25 °C in 10 mM Tris•HCl pH 7. 4, 0. 15 M NaCl, 5 mM CaCl 2. The spectra were measured with a wavelength increment of 0. 2 nm, an integration time of 1 s and a bandpass of 1 nm. Spectra were processed, baseline-corrected, smoothed and converted with the Chirascan software. Spectral units were expressed as the mean molar ellipticity per residue.
After three washes in PBS containing 0. 05% Tween 20, horseradish peroxidase (HRP)-coupled secondary antibodies were added for 1 h at room temperature. Proteins of interest were detected by chemiluminescence with Amersham ECL Select Western blotting detection reagent (Cytiva) using the Fusion FX camera system equipped with the FusionCapt Advance FX7 16. 16b software (Vilber Lourmat). When applicable, quantification of band intensities was performed with the FusionCapt Advance FX7 16. 16b software or the Image-Quant TL software v8. 2 (Cytiva). Uncropped immunoblots are available in Supplementary Fig. 16  or as a Source Data file. Human and mouse C-propeptides of procollagen I were detected with the LF41 antibody  71  which was a kind gift of Dr. Larry W. Fisher (NIH, Bethesda, USA; dilution 1/1000).
RNA isolation and quantitative real-time PCR Total RNA from primary cells was isolated using the RNeasy Plus Mini kit (Qiagen) or the Nucleospin RNA kit (Macherey-Nagel) according to the manufacturer's instructions. RNA from mouse skin was extracted using the RNeasy Fibrous Tissue Mini kit (Qiagen). Quality and quantity of the isolated RNA were determined with a Nanodrop 2000. RNA (500 ng) was then reverse-transcribed to cDNA using the First Strand cDNA Synthesis Kit (ThermoFisher Scientific) or the PrimeScript RT-PCR kit (Takara). Quantitative real-time PCR (qRT-PCR) analyses were performed with a CFX96 Real-Time system equipped with the CFX Manager Software software (BioRad) or a Rotor-Gene Q system equipped with the Rotor-Gene Q software v2. 3. 5 (Qiagen). qRT-PCR reactions were made using the iQ SYBR Green Supermix (Bio-Rad) or the FastStart Universal SYBR Master mix (Roche Applied Science) according to the manufacturers' recommendations with the primers described in Supplementary Table  1  (amplification efficiency > 90%).
PCPE-2 structure modeling and sequence alignments Sequence alignments were performed using Clustal Omega (via the Uniprot web server) and rendering using Clustal Omega or ESPript 3. 0 (https://espript. ibcp. fr), based on Uniprot sequences Q15113 for PCPE-1 and Q9UKZ9 for PCPE-2 and on structural information extracted from PDB entry 6FZV  34. Protein structures were drawn using UCSF Chimera (https://www. cgl. ucsf. edu/chimera) or ChimeraX (http://www. rbvi. ucsf. edu/chimerax). YASARA (http://www. yasara. org) was used for homology modeling of PCPE-2 and computing of rmsd.
Acknowledgements We thank the staff of the  PBES animal facility (SFR Biosciences, Lyon, France ,  UAR3444/CNRS ,  US8/INSERM ,  ENS de Lyon, UCBL)  for breeding and housing the mice. We are grateful to  Jacques Brocard  and  Christophe Chamot  ( PLATIM facility, Lyon Multiscale Imaging Center and SFR Biosciences ) and to  Denis Ressnikoff  and  Bruno Chapuis  ( CIQLE facility, Lyon Multiscale Imaging Center and SFR Santé-Lyon Est ) for their help with the quantitative analysis of collagen fibrils. We acknowledge the contribution of  Virginie Gueguen-Chaignon  from the  Protein Science Facility of the SFR Biosciences  for circular dichroism experiments and for the kind gift of the HRV 3C proteases. We also thank  Fernando Lopez-Casillas  ( Universidad Nacional Autonoma de Mexico, Instituto de Fisiologia Celular, Mexico ) for providing recombinant betaglycan and  David Hulmes  ( LBTI ,  CNRS-University of Lyon ) for critical reading of the manuscript.
This resulted in a cut-off wavelength of 20 mm. The laboratory 𝐴𝐴 ⟨𝑆𝑆 2 𝑥𝑥 ⟩ thus encapsulates the gravity wave spectrum. Because of the orientation of the wave gauges, wave-slopes were only measured in the upwind direction, and crosswind components were therefore omitted. We also use data from a fifth wave type that was studied during the MATE19 campaign. This fifth wave type consists of a JONSWAP  (Hasselmann et al. , 1973)  wave configuration, generated by the wavemaker (f p = 0. 9 Hz for U 10 = 0 m s -1 ), and exposed to the same U 10 8-20 m s -1 range as the other four wave types. These data were not included in  Bruch et al. (2021)  because the more complex wave type required longer meteorological, hydrodynamic and aerosol sampling durations, thus preventing to accurately estimate the vertical sea spray flux.
As shown in Figure  5a , a wide range of conditions were met, with foremast (gray line) and main mast (black line) wind speeds spanning 1-20 m s -1. Gaps in the data correspond to when the aerosol probes were not operating. The air (solid yellow curve) and the water (blue solid curve) temperature are represented in Figure  5c. As a result of the small average air-sea temperature gradients, weak air-sea heat fluxes and neutral atmospheric conditions are assumed. Relative humidity was measured continuously during the campaign, though issues with the main sensor resulted in some data gaps toward the end of the campaign. At 28 m above the MWL, the average RH over the entire campaign was equal to 73%. Following classical humidity profiles at sea, the average RH at the heights of L1 and L2 can be expected to be closer to 80%.
When inspecting the relationships between concentration and wind speed, we note an increasing overestimation by the CSASP-200 relative to the CSASP-100-HV for increasing wind speeds. Furthermore, though not graphically highlighted in Figure  6b , we find no significant difference between stationary and mobile stations for the CSASP-100-HV, but concentrations measured by the CSASP-200 were consistently higher at mobile stations, by a factor of 2. 5 for true wind speeds lower than 13 m s -1 , despite the correction applied to CSASP-200 sample volumes (cf. Section 3. 2. 1 and Appendix A). The discrepancy may still be caused by distortions by the CSASP-200 inlet, and the fact that the airflow used for the sample volume correction was not directly measured in front of the inlet during SUMOS. As a precautionary measure, the data collected by the CSASP-100-HV is used for the remainder of the study.
Modeling Sea Spray Transport With the Meso-NH Model With the urgent need for more accurate sea spray models, the wide range of environmental conditions offered by the SUMOS data set provides a unique opportunity to validate wind speeds and sea spray modeled by Meso-NH using B21A and B21B SSGFs. Comparison between measurements and numerical simulations is made over 31 SUMOS sample stations encapsulated in the 23 day long Meso-NH simulation period, beginning on 10 February, and ending on 2 March 2021.
On Using Laboratory 𝑨𝑨 ⟨𝑺𝑺 𝟐𝟐 𝒙𝒙 ⟩ in the Field The necessity to consider the integral part of the gravity wave spectrum for a complete multiscale representation of the free surface geometry motivated the study of laboratory 𝐴𝐴 ⟨𝑆𝑆 2 𝑥𝑥 ⟩ by  Bruch et al. (2021). In the present study, whilst the multiscale 𝐴𝐴 ⟨𝑆𝑆 2 𝑥𝑥 ⟩ drives sea spray generation, the assumed unique dependence on wind speed (cf. Section 2) reduces SSGF sensitivity to environmental characteristics such as sea state. The influence of wave type is shown from MATE19 laboratory data (cf. Figure  2 , Section 2), but further study is required to understand the effects of non-linear wave-wave interactions on wave-slope variance, as the different wave scales between the laboratory and the field may change how they interact. As suggested by  Plant (1982)  and  Donelan (2001) , wave-wave non-linear interactions may modulate the contribution of different wave components to 𝐴𝐴 ⟨𝑆𝑆 2 𝑥𝑥𝑥𝑥𝑥 ⟩.
Acknowledgments We acknowledge  CNRS-CNES  for funding the SUMOS research cruise. We also wish to thank the  Service Informatique Pytheas  for their support and the availability of the  OSU-Pytheas  super computer necessary for the Meso-NH numerical simulations, as well as  Quentin Rodier  at the  Centre National de Recherches Météorologiques   (CNRM). We are grateful for the sponsorship by the  Agence Innovation Défense (AID-DGA)  under contract  2018-60-0038  and the  Region SUD  contract  2018-06085. This work also benefitted from the  MATRAC  research project sponsored by  ANR-ASTRID  under contract  ANR-18-ASTR-0002.
Data Availability Statement The databases used for the present study are made available  (Bruch, Christophe, et al. , 2023; Bruch, Peter, et al. , 2023). The corresponding links to the data are https://www. odatis-ocean. fr/en/data-and-services/data-access/ (SUMOS-SA aerosol field measurements) and https://doi. org/10. 34930/4bd472f3-683a-4b0c-ba9c-c9c1f20bff8a (SUMOSModSpray23 Meso-NH simulation outputs). A video supplement showing horizontal distributions of sea spray concentrations simulated using Meso-NH and the B21B SSGF is available at: https:// www. youtube. com/watch?v=IRrRdmycfdY.
The procedure used to construct a reference database is detailed. It is obtained exploiting FPM reconstruction algorithm to explore images around optimal focal plane with virtual Z-stacking calculations and selecting those with adequate contrast and focus. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartments visibility and to compensate any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of Plasmodium falciparum detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.
In this article we develop a deep neural network that takes advantage of intensity and phase images (in the same vein of  [15] ) combined to virtual z-stacking. The bi-modal DNN is trained to reinforce the contrast and sharpness of targeted sub-cellular compartments. To this end, z-stacking is realized virtually by calculating images at finely-spaced planes of focus with a reconstruction algorithm. Images of the highest quality are then isolated to construct a reference database for the DNN training. Here, the optimal focal plane is selected depending on the modality. We demonstrate quantitatively the impact of the DNN from an automated diagnosis point of view. For this, the use-case of Plasmodium falciparum detection in blood smears is undertaken as an example. It is chosen, noting that the problem of parasites detection with the required sensitivity and specificity has hitherto not been solved satisfactorily using blood smear images only  [16].