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309 results for “multidimensional”
Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Deformation simulation results of Capriccio method coupled systems for conducting comparative one- and multidimensional studies on the coupling of the finite element method with particle-based techniques
<p>readme_3Dresults.txt</p> <p><br> <strong>Description</strong>:</p> <p>This readme explains the content and path structure of the results obtained from a<br> deformation test conducted on slightly different MD-FE coupled systems performing the<br> Capriccio method in a three-dimensional space within the associated project thesis [1],<br> published on the following dataset: <a href="https://doi.org/10.5281/zenodo.7924367">https://doi.org/10.5281/zenodo.7924367</a></p> <p>Furthermore, input files and parameters as well as potential tables required to reproduce<br> the obtained data are provided as well.</p> <p>The molecular dynamics (MD) part is executed in LAMMPS and the finite element (FE) method<br> part by a MATLAB script as described in Section 4.1 of [1]. The whole setup of the 3D<br> models is elaborated in Section 4.2 of [1]. A discussion of some results is given in<br> Chapter 6 of [1] in the context of assessing their comparability with the corresponding 1D<br> model.</p> <p><br> <strong>Context</strong>:</p> <p>[1] L. Laubert, "Establishing a framework for conducting comparative one- and<br> multidimensional studies on the coupling of the finite element method with<br> particle-based techniques", Project Thesis, Friedrich-Alexander-Universität<br> Erlangen-Nürnberg (FAU), 2023.</p> <p><br> <strong>Contact</strong>:</p> <p>Lukas Laubert<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universiät Erlangen-Nürnberg<br> Egerlandstraße 5<br> 91058 Erlangen</p> <p><br> <strong>License</strong>:</p> <p>Creative Commons Attribution Non Commercial 4.0 International</p> <p><br> <strong>Path structure and files</strong>:</p> <p>- The ZIP compressed files each contain a folder containing all simulation files as well as<br> postprocessing variables:<br> * /FE_data/ contains all output files after each FE simulation in each iteration step<br> * /MD_data/ contains all output files after each MD simulation in each iteration step<br> * /input_files/ contains the input FE model "cgps_dpd_c_1_2000.inp", the MD particle<br> configurations "cgps_dpd_c_1_2000.data", the AP particle coordinates <br> "cgps_dpd_c_1_2000.ac" as well as further Abaqus CAE FE files that<br> can be used to adapt the present FE model<br> * /input_parameters/ contains the parameter dataset; "Capriccio.prm" is the main parameter<br> dataset, whose adaptations lead to similar adjustments in the other parameter files<br> * "Capriccio_FEMD_main_meggie_WZ.sh" is a shell script for executing simulations<br> * "job.out" is an output protocol that documents the progress of the simulations<br> * "Job.err" is an error protocol that documents detected errors during the simulations<br> * "log.lammps" logs MD parameter sets<br> * "meta.info" provides version information of used softwares among few other information<br> * "next_job.info" documents the next load step and iteration step that is to be executed<br> when simulation jobs are restarted on the used computation cluser<br> * **_workspace_vars.mat comprises a set of postprocessing variables obtained by executing a<br> postprocessing script provided by Capriccio group</p> <p>- "md_dpd_main-CBpot-writeobs-sandw.in" is an input script that further defines and loads<br> MD simulation parameter</p> <p>- ***_table are potential tables applied during the MD simulations<br> * "Angle_table" lists the angle bending potential<br> * "Bond_table" lists the bond potentials<br> * "Nonbond_table" lists the non-bonded interaction potential</p>
Supplementary data for multidimensional drivers of mercury distribution in global surface soils
<p>Global distribution of Hg in surface soils predicted by a machine learning method. </p> <p>Random forest modeling.</p> <p>Latitude 1 degree by longitude 1 degree.</p>
Dataset: Multidimensional surrogate modelling for Airborne TDEM data
<p>DataHF contains the synthetic data for a SkyTEM 304 TDEM system, flying at 40 m height for a two-layered model with an interface at an angle, described in parameters.csv, via 3D simulations. DataLF contains the 1D data (without angle) with a 1D analytical forward model.</p> <p>Look out for the published PhD dissertation "Improving Airborne Time-Domain Electromagnetic Imaging with Applications to Groundwater Salinity Mapping" for a description of the dataset in Chapter 5.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
<p>Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal underlying abiotic and biotic drivers of community dynamics. Here we present a model-based predictive framework to quantify multidimensional trait distributions of communities (community trait niches), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait niches and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
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Data from: Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi
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Multidimensional scaling informed by F-statistic: Visualizing microbiome for inference
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Multidimensional plasticity of phenology: Assessing the effects of population density on plastic responses of breeding time to temperature
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Data from: Multidimensional plasticity in the Glanville fritillary butterfly: larval performance is temperature, host and family specific
<p>Variation in environmental conditions during development can lead to changes in life-history traits with long-lasting effects. Here, we study how variation in temperature and host plant, i.e. the consequences of potential maternal oviposition choices, affects a suite of life-history traits in pre-diapause larvae of the Glanville fritillary butterfly. We focus on offspring survival, larval growth rates and relative fat reserves, and pay specific attention to intraspecific variation in the responses (GxExE). Globally, thermal performance and survival curves varied between diets of two host plants, suggesting that host modifies the temperature impact, or <i>vice versa</i>. Additionally, we show that the relative fat content has a host-dependent, discontinuous response to developmental temperature. This implies that a potential switch in resource allocation, from more investment in growth at lower temperatures to storage at higher temperatures, is dependent on the larval diet. Interestingly, a large proportion of the variance in larval performance is explained by differences among families, or interactions with this variable. Finally, we demonstrate that these family-specific responses to the host plant remain largely consistent across thermal environments. Altogether, the results of our study underscore the importance of paying attention to intraspecific trait variation in the field of evolutionary ecology.</p>
Multidimensional framework for furthering open access
<p>The spreadsheet provides a multidimensional framework for furthering open access to research output. Combining three dimensions (i.e. aspects of open access, actors involved and the levels at which actions can be taken) results in a multidimensional framework that can inform future developments. Vertically, the different aspects of open access are projected. Horizontally, the five “levels of engagement” are presented for each of the different actors relevant for open access in the Dutch context. The framework can be used in various ways. For instance one could fill it with current actions/policies. But one could also use it to prioritize or plan future actions. This file set contains both the template and the versions filled with current actions relevant for researchers in the Nederlands in PDF format, in spreadsheet format and as presentation slides.</p>
Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry
<p>Carbon stable isotope ratios (δ<sup>13</sup>C) for protein fractions extracted from a mixture of cultured cells of <em>Allochromatium vinosum </em>DSM 180 and <em>Synechocystis</em> sp. PCC6803, as well as from extracts of each pure culture.</p> <p>Citation: Mohr W, Tang T, Sattin SR, Bovee RJ, Pearson A. (2014) Protein stable isotope fingerprinting (P-SIF): Multidimensional protein chromatography coupled to stable isotope-ratio mass spectrometry. Analytical Chemistry 86, 8514-8520.</p> <p>Contact: Ann Pearson (pearson@eps.harvard.edu)</p>
Data from: Latest Ordovician (Hirnantian) brachiopod faunal lists used for non-matric multidimensional scaling (NMDS) and network analyses
<p><span>A total of 107 brachiopod genera of Hirnantian age among 42 localities worldwide are compiled into a binary dataset (Table S1; presence =1, absence = 0). The majority of the faunal lists was derived from the well-screened Hirnantian brachiopod faunal data of Rong et al. (2020). In this study, the Hirnantian faunal lists are updated for the following localities: </span><span>Anticosti Island, eastern Canada; </span><span>Edgewood region, American Mid-Continent; </span><span>Mackenzie Mountains, northwestern Canada. D</span><span>etailed discussions on these faunal update and references are provided in the main paper (section on Paleobiogeography of the Mackenzie Mountains Hirnantian fauna). </span></p>
Data from: multidimensional beta-diversity across local and regional scales in a Chinese subtropical forest: the role of forest structure
<p>Beta-diversity, or the spatio-temporal variation in community composition, can be partitioned into turnover and nestedness components in a multidimensional framework. Forest structure, including comprehensive characteristics of vertical and horizontal complexity, strongly affects species composition and its spatial variation. However, the effects of forest structure on beta-diversity patterns in multidimensional and multiple-scale contexts are poorly understood. Here, we assessed beta-diversity at local (a 20-ha forest dynamics plot) and regional (a plot network composed of 19 1-ha plots) scales in a Chinese subtropical evergreen broad-leaved forest. We then evaluated the relative importance of forest structure, topography, and spatial structure on beta-diversity and its turnover and nestedness components in taxonomic, functional, and phylogenetic dimensions at local and regional scales. We derived forest structural parameters from both unmanned aerial vehicle light detection and ranging (UAV LiDAR) data and plot inventory data. Turnover component dominated total beta-diversity for all dimensions at the two scales. With the exception of some components (taxonomic and functional turnover at the local scale; functional nestedness at the regional scale), environmental factors (i.e., topography and forest structure) contributed more than pure spatial variation. Explanations of forest structure for beta-diversity and its component patterns at the local scale were higher than those at the regional scale. The joint effects of spatial structure and forest structure influenced component patterns in all dimensions (except for functional turnover) to some extent at the local scale, while pure forest structure influenced taxonomic and phylogenetic nestedness patterns to some extent at the regional scale. Our results highlight the importance and scale dependence of forest structure in shaping multidimensional beta-diversity and its component patterns. Clearly, further studies need to link forest structure directly to ecological processes (e.g., asymmetric light competition and disturbance dynamics) and explore its roles in biodiversity maintenance.</p>
Sensitivity-enhanced multidimensional solid-state NMR spectroscopy by optimal-control-based transverse mixing sequences
<p>The dataset here contains the raw data and pulse programs used for the publication "Sensitivity-enhanced multidimensional solid-state NMR spectroscopy by optimal-control-based transverse mixing sequences" submitted to JACS.</p> <p>All data is in a native Bruker TopSpin format. Data is organized in folders corresponding to Figures of the original publication. Detailed description is included in the file description.txt.</p> <p>We reccomend to visit our website optimal-nmr.net for additional information about optimal control methods applied to pulse sequence development for solid-state magic-angle-spinning NMR studies of proteins.</p>
Database of Tailings Deposits and Multidimensional Poverty in Chile
<p>Exploratory database that links the multidimensional poverty averages of municipalities, together with the number of Mine Tailings they own. </p>
Characterizing multidimensional cellular physiological states with quantitative three-dimensional shape descriptors for cell membranes
<h3>Supplementary dataset and code for the article "<em>Characterizing Cellular Physiological States with Three-Dimensional Shape Descriptors for Cell Membranes</em>"</h3> <p>CShaper Dataset.zip: The 3D cell regions reused from the previously published article <a href="https://doi.org/10.1038/s41467-020-19863-x">https://doi.org/10.1038/s41467-020-19863-x</a>.</p> <p>Cell Shape Descriptors - Code & Data.zip: The code (exemplifed by embryo Sample04) and data (including embryo Sample04-Sample20) of 12 3D shape descriptors for all 3D cell regions in the <em>CShaper</em> dataset.</p> <p>GUI.zip: The user-friendly software <em>Shape Descriptor Tool</em> is a Graphical User Interface based on <em>Matlab</em> for calculating 12 shape descriptors for a 3D cell region (exemplified by embryo Sample20, time point 14, ABpl cell in the <em>CShaper</em> dataset). The instruction guidebook is included in the Supplementary Material of the article.</p>
Data from: Experimental evolution of halophiles: rapid divergence along a multidimensional niche
<p>This data was collected during the study entitled "Experimental evolution of halophiles: rapid divergence along a multidimensional niche."</p> <p>We explored multidimensional niche breadth evolution among two halophilic species, an archaeon (<em>Halobacterium salinarum</em>) and a bacterium (<em>Salinibacter ruber</em>). We propagated each species in rich and poor media for 60 generations and measured associated changes across novel conditions. In particular, we isolated the effects of selection history on axes of salinity and resource abundance, documenting whether shifts in niche breadth are context-dependent.</p> <p>Growth curves were generated via daily measurements over 5 days (4 replicate populations per treatment), with the area under the curve used as a proxy for absolute fitness in a given environment. Relative fitness was calculated as the fitness of the derived populations (following 10 transfers in their respective environment) divided by the fitness of the ancestral populations. </p>
The multidimensional nutritional niche of fungus-cultivar provisioning in free-ranging colonies of a neotropical leafcutter ant
<p>Foraging trails of leafcutter colonies are iconic scenes in the Neotropics, with ants collecting freshly-cut plant fragments to provision a fungal food crop. We hypothesized that the fungus-cultivar's requirements for macronutrients and minerals govern the foraging niche breadth of <i>Atta colombica </i>leafcutter ants. Analyses of plant fragments carried by foragers showed how nutrients from fruits, flowers, and leaves combine to maximize cultivar performance. While the most commonly foraged leaves delivered excess protein relative to the cultivar's needs, <i>in vitro </i>experiments showed that the minerals P, Al, and Fe may expand the leafcutter foraging niche by enhancing the cultivar's tolerance to protein-biased substrates. A suite of other minerals reduces cultivar performance in ways that may render plant fragments with optimal macronutrient blends unsuitable for provisioning. Our approach highlights how the nutritional challenges of provisioning a mutualist can govern the multidimensional realized niche available to a generalist insect herbivore. </p>
Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study
<p>Simple Summary: Pain is one of the most common and debilitating symptoms in cancer patients. A clinical peculiarity of cancer pain is the breakthrough cancer pain (BTcP), which is defined as a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The NP-BTcP occurs in the absence of any specific activity. In this paper, we addressed the topic through a mathematical approach to provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management. Abstract: Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting—Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)’ vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.