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Supplemental Drawings for Cross Linking Workshop/Camp

Supplemental Drawings for Cross Linking Workshop/Camp   Unless specially referenced, all images are available for use/modification by attendees of educational camps

Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'

This is the supplemental data for the manuscript titled ‘Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’ submitted to Ultramicroscopy. Motivation: Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO2-WO3 nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts. Authors and funding: The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG. <pre> </pre> Dataset description: We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data. training_data.zip contains the training data. In the 3D case, the subfolder mask contains the ground truth segmentation masks; the subfolder reconstruction contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder HAADF contains the 2D-projection images. In both cases, the subfolder json contains the annotation. Each file within the json folder provides for each image or reconstruction the following information: ·        aggregat_no: image id, the number of the corresponding image file ·        particle_position_x: list of particle position x-coordinates in nm ·        particle_position_y: list of particle position y-coordinates in nm ·        particle_position_z: list of particle position z-coordinates in nm ·        particle_radius: list of volume equivalent particle radii in nm ·        particle_type: list of particle types, 1: TiO2, 2: WO3 ·        particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron ·        rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation) ·        deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation). ·        cluster_index: list of cluster indices for each particle ·        initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged ·        fractal_dimension: the intended fractal dimension of the aggregate ·        fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities) ·        fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities) ·        fractal_prefactor: fractal prefactor ·        mixing_ratio_intended: the intended mixing ratio (fraction of WO3 particles) ·        mixing_ratio_true: the realised mixing ratio (fraction of WO3 particles) ·        mixing_ratio_volume: the realised mixing ratio (fraction of WO3 volume) ·        mixing_ratio_weight: the realised mixing ratio (fraction of WO3 weight) ·        particle_1_rho: density of TiO2 used for the calculations ·        particle_1_size_mean: mean TiO2 radius ·        particle_1_size_min: smallest TiO2 radius ·        particle_1_size_max: largest TiO2 radius ·        particle_1_size_std: standard deviation of TiO2 radii ·        particle_1_clustersize: average TiO2 cluster size ·        particle_1_clustersize_init: average TiO2 cluster size of primary clusters (before merging into larger clusters) ·        particle_1_clustersize_init_intended: intended TiO2 cluster size of primary clusters ·        particle_2_rho: density of WO3 used for the calculations ·        particle_2_size_mean: mean WO3 radius ·        particle_2_size_min: smallest WO3 radius ·        particle_2_size_max: largest WO3 radius ·        particle_2_size_std: standard deviation of WO3 radii ·        particle_2_clustersize: average WO3 cluster size ·        particle_2_clustersize_init: average WO3 cluster size of primary clusters (before merging into larger clusters) ·        particle_2_clustersize_init_intended: intended WO3 cluster size of primary clusters ·        number_of_primary_particles: number of particles within the aggregate ·        gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities) ·        gyration_radius_weighted: gyration radius of the aggregate (including particle densities) ·        mean_coordination: mean total coordination number (particle contacts) ·        mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material) ·        mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material) ·        material_1: the name of the first material (TiO2) ·        material_2: the name of the second material (WO3) ·        radius_equiv: list of area equivalent particle radii (in projection) in nm ·        k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0) ·        polygons: list of polygons that surround the particle (COCO annotation) ·        bboxes: list of particle bounding boxes ·        aggregate_size: projected area of the aggregate translated into the radius of a circle in nm ·        n_pix: number of pixel per image in horizontal and vertical direction (squared images) ·        pixel_size: pixel size in nm ·        image_size: image size in nm ·        add_poisson_noise: 1 if poisson noise was added, 0 otherwise ·        frame_time: simulated frame time (required for poisson noise) ·        dwell_time: dwell time per pixel (required for poisson noise) ·        beam_current: beam current (required for poisson noise) ·        electrons_per_pixel: number of electrons per pixel ·        dose: electron dose in electrons per Å2 ·        add_scan_noise: 1 if scan noise was added, 0 otherwise ·        beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise) ·        scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise) ·        add_focus_dependence: 1 if a focus effect is included, 0 otherwise ·        add_partial_coating: 1 if some TiO2 particles were coated by a thin WO3 film, 0 otherwise. ·        data_format: data format of the images, e.g. uint8 For 3D reconstructions, the following information is added: ·        add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise. ·        add_projection_noise: 1 if noise was added to projection angles, 0 otherwise. ·        N_SIRT: number of SIRT iterations for the 3D-reconstruction. ·        proj_angles: list of angles used for the projection directions of the tilt series in rad. For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder COCO. For training, validation and test data there is a file annotation_COCO.json that includes all information required for the CNN training. For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script. The zip archive models.zip includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file 2D_projection/logs/fit/20240209-095952/iter_60000.pth. These weights can be loaded with the jupyter-notebook 2D_projection_prediction.ipynb. Furthermore, a configuration file, which is required by the notebooks, is stored as 2D_projection/logs/fit 20240209-095952/config_file.py. In the 3D case, network weights are stored in 3D_reconstruction/model/weights_best.h5. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook 3D_reconstruction_prediction.ipynb. The zip archive experiment_measurement.zip includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN. The zip archive experiment_prediction.zip includes predictions and visualizations obtained by the 2D and 3D CNNs. The zip archive simulation_measurement.zip includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks. The zip archive simulation_prediction.zip includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process. In the zip archive code.zip, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed. ·        2D_projection_training.py: This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox. ·        3D_reconstruction_training.py: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package. ·        2D_projection_prediction.ipynb: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data. ·        3D_reconstruction_prediction.ipynb: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data. ·        Evaluation_experiment.m: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations. ·        Evaluation_simulation.m: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations. ·        particle_detection_functions.py: This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments. ·        ASTRA_CM_plot_results.m: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions. ·        ASTRA_CM_particle_detection.m: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.   There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening. The dataset was generated to discriminate between TiO2 and WO3 nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO2 and WO3 but there is no guarantee.

Supplemental Figures - Detection of SARS-CoV-2-Specific Secretory IgA and Neutralizing Antibodies in the Nasal Secretions of Exposed Seronegative Individuals

Figure S1: Flow diagram of exposed seronegative cohort. Figure S2: SARS-CoV-2-specific neutralization activity at Days 1 and 8 relative to enrollment in exposed seronegative nasal SIgA positive and infected participants. NPS SARS-CoV-2-specific neutralization activity is shown for exposed seronegative and infected participants with normalized OD490 at Day 1 and Day 8, respectively. Sample sizes (N) are indicated in parentheses. Wilcoxon signed-rank tests were used to determine if the median SARS-CoV-2 nasal SIgA neutralization activity differed significantly. A two-tailed p < 0.05 was considered significant.

opencc-by-4.0Dec 2023View details →

Supplement to: Evidence for Low-pressure Crustal Anatexis During the Northeast Atlantic Break-up

These are the supplementary files for Evidence for Low-pressure Crustal Anatexis During the Northeast Atlantic Break-up, submitted to AGU G-cubed on December 18, 2023. They include (1) a PDF file of all the supplementary figures (S1-S9) and references in the AGU, G-cubed journal format, and (2) an Excel file that includes all supplementary tables (S1-S7).

April 7, 2024 (v1) Image Open Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 5 relative to Figure 7 - Figure Supplement 4

Source data file relative to Figure 7 – figure supplement 4 Panel A Raw image of agarose gel showing the 2 alternative mRNAs encoding for ArhGEF11 in control animals (left track, control) and after injection of the MO at the one cell stage (right track, +MO at 2 and 5ng). The source data includes the raw files (native format .scn and open source format .tiff) as well as a pdf file showing both the full scale image and the cropped image selected for the figure.

Supplemental Data for Marie Vogel's "Life is Plastic"-Poster, Roscoff 2024

Open the record for dataset details and reuse information.

opencc-zeroJun 2024View details →

dryad36/100

Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models

Ecological niche models (ENMs) are a powerful tool in ecological research and conservation planning. Since ENMs provide probability maps of suitable areas under environmental change, they may assist in designing conservation actions and addressing conservation priorities. However, ENMs are usually implemented by learning the species climatic preferences from their current geographic distribution, which leaves them vulnerable to the issue of niche truncation issues, as if comes with non-climatic limits to the current species distribution posed by e.g. anthropic activities and settlements, and is bound to assume that species are at equilibrium with their environments. These problems might be alleviated by the inclusion of fossil occurrences, which refer to moments during species evolution when such limits were absent, and a larger fraction of the species fundamental niche was probably explored. Here, we combined current and fossil occurrence data for 38 medium-large mammal species of conservation concern to assess the influence of the fossil record on ENM predictions under future climate change scenarios. We found that ignoring or including fossil data yields consistent trends in terms of predicted range increase/decrease. Yet, although adding fossil data invariably results in increased niche width, estimates of range change magnitude improved for just one half only of the species. These results suggest that most species might be in non-equilibrium with their environment, and that the inclusion of fossil data may be crucial to the better understanding of species climatic requirements, hence for designing effective conservation strategies.

Supplemental material (movies) to article J. Phys. Complex. 5 (2024) 035003

Movie 1: additional description.— The fluid flow’s projection onto the xz plane (that is, the screen plane) corresponds to the planar cellular flow — cf. the stream function given by Eq. (8) of the main text. We emphasize that what we show is the projection of the swimmers’ three-dimensional dynamics onto the xz plane (where the convective cells are visible). The settling parameter is given by Λ = 0.1 for both passive and swimming particles. The other parameters for the run-and-tumble swimmers are v = 0.5 and δτ = 0.1. Movie 2: additional description.— The remarks on projections made in the description of Movie 1 also apply here.

openmit-licenseJun 2024View details →

Supplement - Geologic Map

For lithology, we used the geologic maps of Colton (1978) and Kellogg et al. (2008) digitizing for the study region where necessary; GIS version found here in Shapefile format.

openmit-licenseJun 2024View details →

Supplement - Hedge's G Tables

Physiographic variables were summarized by lithology and trail type. We conducted basic statistical analysis using Hedge’s G calculator to determine the effect size of our process domain datasets (Lakens 2013). The effect size tells us how statistically different the data is from one another. The Hedge’s G analysis for effect size indicated two of the process domains datasets for the lithology were not significantly different from one another. The concavity and TPI-100m datasets showed little variability among the data while the slope dataset showed the most variability indicated several lithologies with a large effect size (the effect size for the datasets was a small effect = 0.2; medium effect= 0.5; large effect 0.8. ; Table 4). Hedge’s G tables are found in this repository.

openmit-licenseJun 2024View details →

Supplement - Physiographic Variable Raster Data

To link physiographic variables to lithology and trail characteristics (Objective 2), we created process domain maps and used principal component analysis considering physiographic variables such as slope, topographic position index (TPI), and concavity as a function of lithology and trail type. All metrics were calculated using ArcGIS Pro and lidar collected 2013 by McKim & Creed Inc. for City of Boulder. High-resolution lidar data can be useful in determining different types of processes, ranging from slow creep to landslides (Booth et al. 2009; Booth et al. 2013) and variables like slope highlight areas where mass movements are likely to occur. Similarly, concavity, or landscape curvature, indicates where advective versus diffusive processes occur, correlated to convex versus concave landscapes, respectively (Dietrich and Perron 2006; Sweeney et al., 2015). Concave processes are dominated by diffusive movement (slope-dependent transport; Dietrich and Perron 2006). These processes are competing against one another on the hillslope and give rise to diffusion-dominated ridges and advection-dominated valleys (Dietrich and Perron 2006; Sweeney et al., 2015). The TPI is a landform classification used to determine roughness indices like valleys and ridges in the study area. The TPI was calculated at different resolutions (5-m, 10-m, 50-m, 100-m) to see if different hillslope attributes were identifiable at the different scales. To calculate the TPI, the mean for each resolution was subtracted from the Digital Elevation Model (DEM). Derived physiographic variables and GIS data products can be found in this repository.

Code and Data Supplement for Using feature importance as exploratory data analysis tool on earth system models

This contains: <ul> <li>Code for all analyses in</li> <li>E3SM data</li> </ul> For the paper Using feature importance as exploratory data analysis tool on earth system models.

opencc-zeroJun 2024View details →

dryad36/100

Supplementing a grain diet with insects instead of fruits sustains the body condition of an omnivorous bird

Omnivores utilize dietary sources that differ in nutrients, hence dietary limitations due to environmental change or habitat alteration could cause nutrient limitations, and thus deterioration of body condition if omnivory is obligate. We investigated how the body condition of the omnivorous Village Weaver Ploceus cucullatus, which forages predominantly on grains, responds to the supplementation of its grain diet with insects instead of fruits. Forty wild‐caught weavers held in aviaries were fed a combination of grains and fruits, or grains and insects ad libitum for 8 weeks. We determined diet preference by recording the number of birds on each diet option per minute for 1 h and the amount of food left over after 3 hours of foraging. Fortnightly, we assessed indices of body condition including body mass, pectoral muscle, and fat scores, packed cell volume (PCV), and hemoglobin concentration (HBC). We modeled the number of foragers, food left over, and body condition indices as functions of diet while accounting for time (weeks) and sex effects. Grains were the preferred diet, but males ate more fruits and insects than females. Weavers fed on grains and fruits lost body and pectoral muscle mass and accumulated less fat than those fed on grains and insects. This effect was sex‐dependent: females supplemented with fruits lost more pectoral muscle mass than males of the same group and males but not females, supplemented with insects accumulated more fat reserve than those supplemented with fruits. PCV and HBC did not differ between diets but increased over the 8 weeks. Village Weavers are likely obligate rather than facultative omnivores, with insects being a more nutritive supplement than fruits. Nutrient limitations arising from environmental change or habitat alteration could impair body condition and affect physiological function to environmental seasonality in obligate omnivores like the weavers.

Supplemental Data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."

All supplemental data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud." Abstract: We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based deployment demonstrates several key advantages over alternative strategies: it enables optimal resource provision to any workflow that comprise of multiple applications with diverse computing needs; it provides protection against application-agnostic out-of-memory (OOM) errors or spot instance interruptions; and it reduces efforts required for workflow development, optimization, and management because it runs workflows with minimal or no code modifications. Proof-of-concept experiments show that ACS avoided both under- and over-provisioning in monolithic single-container deployment. Despite being deployed as a single container, it achieved comparable resource utilization efficiency as optimized Nextflow-managed, multi-modular workflows. Analysis of over 18,000 workflow runs demonstrated that ACS can effectively reduce workflow failures by two-thirds. These findings suggest that ACS frees developers from navigating the complexity of deploying robust workflows and rightsizing compute resources in the cloud, leading to significant reduction in workflow development time and savings in cloud computing costs. Contains the following directories: Fig2-bbtools: running metrics for BBTools Fig3-rna-seq: running metrics for RNA-Seq Fig4-Job_records: meta data and running metrics of 18,000+ jobs

Data Supplement to Impedance Examples (Version 1.1)

These are the output data files as used to generate the figures in "Reconstructing the shape and material parameters of dissipative obstacles using an impedance model" by Travis Askham and Carlos Borges

Genomic analysis of hyperparasitic viruses associated with entomopoxviruses; supplemental data

This data set includes newick files and pdb files used to generate figures in the publication 'Genomic analysis of hyperparasitic viruses associated with entomopoxviruses'.

opencc-by-4.0Jul 2024View details →

Supplemental Table. Historical cohort of 560 well-described papillary craniopharyngiomas reported in the medical literature (560c).

Table listing the historical cohort of 560 well-described/illustrated individual papillary craniopharyngiomas reported in the medical literature, including their corresponding references.

Supplemental Data for "Identifying novel variants of small molecules through database search of mass spectra"

Supplemental Dataset 1 contains GNPS dataset information for the large scale search of GNPS vs Pubchem. Supplemental Dataset 2a contains the top scoring exact mode hit for each spectrum against PubChem and COCONUT. Files are split into "*chunk*" files of up to 10 million records each. Supplemental Dataset 2b contains the top scoring variable mode hit for each spectrum against COCONUT. Supplemental Dataset 3 contains mass spectra provided by Waters Corporation to analyze impurities of Imatinib.

Supplemental Material for "Slow-to-fast Transition and Shear Localization in Accelerating Creep of Clayey Soil"

<div> Dataset and plotter for "Slow-to-fast Transition and Shear Localization in Accelerating Creep of Clayey Soil" by Chengrui Chang. This dataset contains all 40 fluid-injection shear experiments on clayey soil. Each .mat file includes raw data and constrained parameters for the accelerating creep. A spreadsheet summarizes the experimental protocols and constrained parameters. </div> <div>Article information:</div> <div>Chang, C., Noda, H., Xu, Q., Huang, D., & Yamaguchi, T. (2024). Slow‐to‐fast transition and shear localization in accelerating creep of clayey soil. Geophysical Research Letters, 51, e2024GL111839. <a href="https://doi.org/10.1029/2024GL111839">https://doi.org/10.1029/2024GL111839</a></div>