Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software

<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: - Dynamic_PEFC_data.h5 # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z - liquid_simulation.h5 # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). - phasetran_simulation.h5 # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: - astra_nonrigid.zip # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. - reconstruction_PEFC.py # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * !!! Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. - reconstruction_simulation.py # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>

opencc-by-4.0Nov 2018View details →

Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition

This repository contains code to accompany the manuscript titled Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition by Carly D. Kenkel and Line K. Bay   In this study, we used a phylogenetically controlled design to investigate the role of vertical symbiont transmission, an evolutionary mechanism predicted to enhance cooperation and holobiont fitness of reef-building corals. Six species of coral, three vertical transmitters and their closest horizontally transmitting relatives, were fragmented and subjected to a two-week thermal stress experiment. Symbiont cell density, photosynthetic function and translocation of photosynthetically fixed carbon between symbionts and hosts were quantified to assess changes in physiological metrics of fitness and cooperation. Amplicon sequencing of the Symbiodinium ITS-2 locus was used to investigate differences in symbiont community composition among focal species. We did not observe universally higher levels of cooperation in vertically transmitting species. However, the reduction in cooperation at the onset of bleaching was marginally associated with symbiont community diversity. Analysis of ITS2 amplicon sequence data suggest that it may not be vertical transmission per se that influences host-symbiont cooperation, but genetic uniformity of the symbiont community. Repository contents: <ul> <li> TraitDataAnalysis.R: Annotated R script for generating figures and re-creating statistical analyses <ul> <li> RsquaredGLMM.R: Accessory R script for running RsquaredGLMM analyses, called by TraitDataAnalysis.R </li> <li> NSF_RunningPam.csv: Input file for statistical analysis. Contains photophysiological data. Column headers are as follows: <ul> <li> Tank: Number of experimental tank in which experimental coral fragment was held </li> <li> Treatment: short-hand notation for sample treatments (e.g. ctrl1-5 = control temperature, genotypes 1-5) </li> <li> Water: source sump for temperature controlled water jackets for each set of treatment tanks </li> <li> Position: numerical rack position of coral fragment within experimental treatment tank </li> <li> Species: Coral species (Amil=A. millepora, Maqe=M. aequituberculata, Gast=G. astreata, Gach=G. acrhelia, Plob=P. lobata, Gcol=G. columna) </li> <li> Genotype: source colony origin of individual coral fragments within species </li> <li> Temp: experimental temperature treatment (ctrl: 27°C ; heat: 31°C) </li> <li> Treat: whether experimental corals received C14-labeled bicarbonate (bicarb), artemia or were sampled separately for Gene Expression Analysis (not presented in this manuscript) </li> <li> EQY: Effective quantum yield of Symbiodinium photosystem II as measured using PAM fluorometry </li> <li> Date: Actual calendar date of measure </li> <li> Transmission: coral symbiont transmission mode </li> <li> Reef: reef site of original coral collection </li> <li> Date: experimental date of measure </li> </ul> </li> <li> TraitData.csv: Input file for statistical analysis. Contains all physiological trait data. <ul> <li> Includes columns as described above for the Running_Pam file in addition to columns containing raw trait data as described in the manuscript. </li> </ul> </li> <li> TraitData_DaysAsCols.csv: Reformatted input file with trait data split by sampling day across columns </li> </ul> </li> <li> DADA2Analysis.R: Annotated R script for generating figures and running ITS2 amplicon analyses <ul> <li> GeoSymbio_ITS2_LocalDatabase_verForPhyloseq.fasta: FASTA file of the GeoSymbio ITS2 reference database <a href="https://sites.google.com/site/geosymbio/">https://sites.google.com/site/geosymbio/</a>, formatted for use with the R prograom Phyloseq </li> <li> SeqVars_6Feb.fasta: FASTA file of identified sequence variants resulting from DADA2 analysis </li> <li> OutputDADA_6Feb.csv: Counts of sequence variants by sample </li> <li> Raw FASTQ paired end read files can be downloaded from NCBI's SRA: PRJNA338365 </li> </ul> </li> </ul>

opencc-by-4.0Sep 2024View details →

1D cell trajectories as studied in "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference"

Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. Here, we present trajectories of MDA-MB-231 breast cancer cells and MCF-10A breast epithelial cells. Cells were confined to 1D using fibronectin lanes and exposed to three different treatments, namely the actin polymerisation inhibitor Latrunculin A (LatA), the ROCK inhibitor Y-27632 (Y27) and a control. Each csv file contains a number of 24h long trajectories of cells corresponding to the name of the file. The column names are: `traject_id`: The trajectories are numbered, starting from 0 in each file. `time (h)`: Time in h, starting at 0h for each trajctory and ending at 24h with a temporal resolution of 2min. `x_front`: Position of the cell's front. `x_nucleus`: Position of the cell's nucleus, where x_nucleus=0 for the first time point of the trajectory `x_rear`: Position of the cell's rear. The data was analysed in our study "Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference". Further information can be found there.

opencc-by-4.0Oct 2024View details →

Stacks of microCT Scans, Cell size, weight, volume and thallus size data supporting the paper 'Mechanical regulation of tissue flatness in Marchantia'

<div> <div> <div> These data are the supporting elements to the following paper: 'Mechanical regulation of tissue flatness in Marchantia' </div> </div> </div>  .tif files contain MicroCT (MCT) scans of 16-day-old Marchantia polymorpha thalli. Three genotypes were analysed here: fer-2 mutant (from Mecchia et al., 2022), FER-OE #9 (proMpEF1::MpFERONIA-mCitrine trangenic line 9) from Mecchia et al., 2022), and Tak-1 (WT line). These plants were grown in 3 different media: Gamborgh B5 + vitamins and 0.6, 1.2 and 2.5% agar, and one stress condition consisting of the adjunction of a thin PDMS film at 4, to mimich external mechanical stimulus (only performed on thalli grown on 1.2% agar). MicroCT scans were performed at the faculity of odontology of Université Paris-Cité (Plateform imagerie du vivant) with the technical support of Lotfi Slimani and Baptiste Casel. https://piv.u-paris.fr/micro-ct-haute-resolution/  All files already have embeded scales. Each file name consists of a unique ID number in the following form: P+<LETTER>+<NUMBER>-<CONDITION> -LETTER: One letter = one imaging session -NUMBER: Individual and Genotype: 33-40 -> Tak1; 200-207->fer-2; 41-49 -> FER-OE -CONDITION : AGAR0.6/AGAR2.5/PDMS. Absence of condition indicates growth on standard medium (1.2% agar). PDMS indicated growth on standard medium and supplementation of a topping PDMS film at day 4)   -Volume data were calculated from MicroCT scans -thallus projected surfaces were calculated from MicroCT scans <a href="https://zenodo.org/api/records/13981438/draft/files/Lambda%20curvature%20calculation.ipynb/content" target="_blank" rel="noopener noreferrer">-Lambda curvature calculation.ipynb</a> is suited for MorphographX mesh exported .txt files.

opencc-by-4.0Oct 2021View details →

Potential Cell Lysis Mechanisms for B. subtilis

Description: A list of potential cell lysis mechanisms for Bacillus subtilis, cured and organized as part of the partnership between iGEM UANL and UNILA teams in the design of a Kill Switch for B. subtilis. For more information on both designs, check out the projects wikis: iGEM UNILA LatAm 2021: https://2021.igem.org/Team:UNILA_LatAm iGEM FCB-UANL 2021: https://2021.igem.org/Team:FCB-UANL Columns: <ul> <li>CRISPR: CRISPR systems developed for B. subtilis <ul> <li>CRISPR Variant: Cas variant used in the system</li> <li>Editing/Disruption Efficiency: System efficiency evaluated for multiple or single editions</li> <li>Mechanisms: operating mechanism</li> <li>Application: Main objective for the developed system</li> <li>B. subtilis considerations: Particularities regarding the development of the system in B. subtilis</li> <li>Year: year of publication</li> </ul> </li> <li>TA Systems: Toxin-Antitoxin systems developed for B. subtilis  <ul> <li>Toxin-Antitoxin: Which antitoxins and toxins involved in the system  </li> <li>Type: TA System Type</li> <li>Mechanism: operating mechanism</li> <li>Application: Main objective for the developed system</li> </ul> </li> <li>Sporulation: Control of sporulation events in B. subtilis  <ul> <li>Spores Control</li> <li>Type</li> <li>Mechanism: operating mechanism</li> <li>Application: Main objective for the developed system</li> </ul> </li> <li> iGEM Applications: Kill Switch designs for B. subtilis since 2014. <ul> <li>Kill Switch System: Design description of Kill switch systems</li> <li>Type: System components</li> <li>Mechanism: operating mechanism</li> <li>Application: Main objective for the developed system</li> <li>Test: Type of assessment performed by the team</li> <li>Parts Registry: Parts submitted to the Registry of Standard Biological Parts.</li> </ul> </li> </ul> Remote Access: <a href="https://docs.google.com/spreadsheets/d/1PM7NS5JbfVLgjsa6s7L8yqBj_G1A5mSnKCPaeGmkIwg/edit#gid=0">Potential Cell Lysis Mechanisms for B. subtilis</a>

opencc-by-4.0Oct 2024View details →

The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability

This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research.

opencc-by-4.0Aug 2022View details →

F-actin Imaging of Mechanically Compressed Pseudostratified Human Bronchial Epithelial Cells

Overview This dataset includes immunofluorescence microscopy of pseudostratified airway epithelial cells stained for F-actin. Each field of view consists of 3 images visualizing the apical cell boundaries, basal cell boundaries, and basal cell stress fibers. Cell Culture and Treatment Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction. Cells were fixed (4% PFA for 30 minutes) at 24, 48, or 72 hours after mechanical compression. Two transwells were collected per condition and timepoint. Immunofluorescence Imaging Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. Dataset Each image file contains <ul> <li>Timepoint: 24, 48, or 72 hours after treatment</li> <li>Condition & Well: control (C) or mechanical compression (P) followed by a number indicating the well (1 or 2)</li> <li>Unique Z-stack ID: 3-digit number</li> <li>Miscellaneous note: MIP or MIP_ROI</li> <li>Region of Interest: apical cell boundaries (ACB), basal cell boundaries (BCB), or basal stress fibers (SF)</li> </ul> For example, these 3 maximum intensity projection images came from the same z-stack/field of view. They are from the 72 hour timepoint, mechanical compression, well #2, z-stack #147: <ul> <li>72hr_P2_147_MIP_ACB.tif: apical cell boundaries</li> <li>72hr_P2_147_MIP_ROI_BCB.tif: basal cell boundaries</li> <li>72hr_P2_147_MIP_ROI_SF.tif: basal stress fiber</li> </ul>

opencc-by-4.0Aug 2022View details →

Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells

Overview This dataset includes phase contrast time-lapse imaging of in vitro pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers. Cell Culture and Treatment Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14). Phase Contrast Time-Lapse Imaging At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. The image resolution is 0.586 µm/pixel. Immunofluorescence Imaging Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. The image resolution is 0.293 µm/pixel. Dataset Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes: <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> Phase contrast time-lapse filenames contain <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> Immunofluorescence image filenames contain: <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: 1-5</li> <li>Region of Interest: apical cell boundaries (ACB), basal cell boundaries (BCB), or basal stress fibers (SF)</li> </ul> Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales. Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).

dryad40/100

Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)

Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.

opencc-zeroApr 2024View details →

opencc-by-4.0Jun 2024View details →

Accompanying data for the paper "Accounting for the mechanical response of the cell membrane during the uptake of random nanoparticles"

<h2>Contributions</h2> <ul> <li>Iaquinta Sarah did contribute to the first draft edition, the development of the theoretical background of the algorithms and their implementation</li> <li>Khazaie Sharam did contribute to the revision and edition of the draft, and to the development of the theoretical background of the algorithms</li> <li>Jacquemin Frédéric did contribute to the project management and to the revision of the article.</li> <li>Fréour Sylvain did contribute to the project management and to the revision of the article.</li> </ul> <h2>Funding sources</h2> i-Site NExT : Grant/Award Number: ANR-16-IDEX-0007, Région Pays de la Loire and CNRS (French National Centre for Scientific Research). <h2>Data structure and information</h2> <ul> <li>code - <code>np_uptake source and data directory</code> <ul> <li>workflow - <code>scripts to reproduce figures</code></li> <li>np_uptake - <code>source code producing results and figures</code> <ul> <li>figures - <code>utility module to produce figures</code></li> <li>model - <code>see detailed description below</code></li> <li>metamodel_implementation - <code>see detailed description below</code></li> <li>sensitivity_analysis - <code>see detailed description below</code></li> </ul> </li> </ul> </li> </ul> <h3>Detailed description</h3> <h4>Abstract</h4> In order to improve the efficiency of the delivery of cancer treatments to cancer cells, the cellular uptake of nanoparticles (NPs), used as drug delivery systems, is numerically investigated through a mechanical approach. The objective is to optimize the NP's mechanical and geometrical properties to enhance their entry into cancer cells while avoiding benign ones. In previous studies, these properties are modeled as constant during the process of cellular uptake. However, recent observations of the displacement of the membrane's constituents towards the region in the cell membrane where the uptake of the NPs takes place show that the mechanical properties of the membrane vary during this process. Reason for writing The important contribution of adhesion to the wrapping process is already well documented in literature. It is therefore crucial to model this parameter properly as the conclusions made with a constant adhesion model may not be accurate compared to reality. Methodology Based on the existing knowledge on the reaction of membrane constituents to interaction with NPs, a 3-parameter sigmoidal function, accounting for the delay, amplitude, and speed of the reaction, has been used to model the evolution of adhesion. A variance-based sensitivity analysis has then been performed in order to quantify the influence of these parameters on the outputs of the model. Results It was found that the introduction of a variable adhesion tends to alter the predictions of endocytosis of NPs. The contribution of the amplitude and delay is respectively 0.32 and 0.43 times as important as that of the NP's aspect ratio, which is the prominent parameter. The influence of the slope of the transition is the least important parameter and does not appear to contribute to endocytosis. Implications Hence, models of the cellular uptake of NPs should use a variable, instead of constant, adhesion in order a representative as possible of the behavior of the cell membrane. The predictions are different from those obtained using a model with constant adhesion. <h4>Code</h4> This repository is divided into 4 folders: <ul> <li> model: contains the code used to compute the total variation of energy of the interface between a circular NP and a membrane by accounting for the mechanical accommodation of the latter. This folder also contains the routine to determine the final wrapping phase of the system. </li> <li> metamodel_implementation: contains a script to check for the representativeness of the dataset used to create a metamodel, a script to create Kriging and PCE metamodels using the Openturns opensource library, and a routine to validate the metamodel that has just been created; </li> <li> sensitivity_analysis: contains a script that allows to create samples based on the Kriging metamodels that have been created and exported as .pkl files in the metamodel folder. These samples are then used to the apply sensitivity algorithms. The user can choose among the various sensitivity algorithms provided by Openturns. For PCE metamodels, a routine is implemented to directly get the Sobol indices from the coefficients of the PCE metamodel. The indices can be plotted through plot routines; </li> <li> figures: contains a utils script to display the graphs and save them as PNG files with consistency. </li> </ul>

FIGURE 5 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 5. Simplified overview of factors that influence the preservation and destruction of the cellular, soft tissue, and mineral content of bone. Diagenesis of these materials is more complex than is shown here. Additional factors also have influence, and multiple levels and modes of preservation and destruction may occur in different regions of a single bone (see text for details).

FIGURE 4 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 4. The geologic column according to science vs. YEC ideology. Time periods are not shown to scale. The dates according to science are from radiometric dating (Schmitz, 2020). The dates according to YEC ideology are based on biblical genealogies (Jones, 2016). YEC identifications of Paleozoic, Mesozoic, and pre-Quaternary Cenozoic strata as Flood deposits (e.g., Clarey, 2020; Oard and Carter, 2021) are based on misinterpretations of geologic data (Senter, 2011; Willoughby, 2016; Prothero, 2017; Senter, 2019).

FIGURE 2 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 2. Microstructure of bone matrix. A. Part of a collagen molecule, showing its triple helical structure (based on figure 2 of Bella (2016), with modifications), with each of the three helices shown in a different color: black, dark gray, and light gray. B. A collagen microfibril and associated bone mineral crystallites, showing that the microfibril consists of five staggered collagen molecules and that the crystallites form between the tips of the collagen molecules in the microfibrils (based on figure 1d of Alexander et al. (2012), with modifications). C. Part of a collagen fibril, showing that bone mineral crystallites form both within microfibrils (unshaded crystallites) and between microfibrils (shaded crystallites).

FIGURE 1 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 1. Cells and soft tissues from bones of the hadrosaurid dinosaur Edmontosaurus annectens, from the Standing Rock Hadrosaur Site (SHRS) in South Dakota (Upper Cretaceous: Maastrichtian). The images are reprinted from figure 2 of Cretaceous Research vol. 99, Ullmann et al., "Patterns of soft tissue and cellular preservation in relation to fossil bone tissue structure and overburden depth at the Standing Rock Hadrosaur Site, Maastrichtian Hell Creek Formation, South Dakota, USA" (2019), with permission from Elsevier. A. Osteocyte from fragment of ossified tendon. B. Osteocyte from caudal vertebra SRHS-DU-220. C. Blood vessels with spherical structures in the lumen, from metatarsal SHRS-DU-274. D. Blood vessel (right) and sheets of CBM (lower left) from fragment of ossified tendon. E. Sheet of CBM with embedded osteocytes, from metatarsal SHRS-DU-274.

FIGURE 3 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 3. Histology of bone. A. Macroscopic view of compact and spongy bone in a cross-section of the humerus of a domestic cow (Bos taurus). B. Arrangement of microstructures in compact and spongy bone. C. Human compact bone viewed through a compound microscope, with cells boiled away and voids filled with black ink, to make lacunae and canaliculi stand out.

FIGURE 6 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 6. Recrystallization of bone mineral. Note that through geologic time, the crystallite has become enlarged, and many of its original ions have been replaced by other ions from groundwater. Here, ions are not shown to scale with respect to each other or to the size of the crystallite. For details on relative abundances of the various ions in fossil bone, see Hubert et al. (1996); Kiseleva et al. (2019); Ullman et al. (2021); Schroeter et al. (2022); and Ullmann et al. (2022). REE = rare earth elements.

FIGURE 7 in Cells and soft tissues in fossil bone: A review of preservation mechanisms, with corrections of misconceptions

FIGURE 7. Spherical objects in a blood vessel from fossil bone, and items with which such structures have been hypothetically identified. The scale bar applies to A, C, and the smaller version of the image in B. The correct identity of the spherical objects in blood vessels of fossil bone remains unknown. A. Spherical objects in a blood vessel from fossil bone of the theropod dinosaur Beipiaosaurus inexpectus, from the Yixian Formation of Liaoning, China (Lower Cretaceous: Barremian–Aptian). This image is used with the permission of the journal PeerJ. It is from figure 2C of "Putative fossil blood cells reinterpreted as diagenetic structures," PeerJ, vol. 9: e12651, Korneisel et al. (2019). B. Pyrite framboids, shown to scale with A and C (left) and enlarged (right). This image is used with the permission of the journal PALAIOS. It is from figure 1 of "Rapid formation of framboidal sulfides on bone surfaces from a simulated marine carcass fall," PALAIOS, vol. 30: 327-334, Vietti et al. (2015). C. Red blood cells of the crocodilian species Caiman yacare (spectacled caiman). This image is reprinted by permission from Springer, from figure 1A of "Hepatozoon caimani Carini, 1909 (Adeleina: Hepatozoidae) in wild population of Caiman yacare Daudin, 1801 (Crocodylia: Alligatoridae), Pantanal, Brazil," Parasitology Research, vol. 116: 1907-1916 (2017).