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23 results for “digital organism”
Alteromonas Digital Organism Databases
<p>This is the home of the database for the <i>Alteromonas</i> Digital Organism, one of C-CoMP's collaborative efforts. This database was created in anvi'o (anvio-dev) primarily by Michelle DeMers (Massachusetts Institute of Technology) and Rogier Braakman (Massachusetts Institute of Technology), with significant help from members of the Meren Lab (A. Murat Eren, Iva Veseli, and Matthew Schechter) and Moran Lab (Zac Cooper and Mary Ann Moran). This version upload consists of:</p><p>Alteromonas_Pangenome_v2.1.1.md: A reproducible workflow that details the additions made to this version of the pangenome since v2.1.0.</p><p>Alteromonas2.1.1dbs.tar.gz: Collection of all updated contigs databases and genomes storage database.</p><p>external-genomes-v2.txt: Text file consisting of a list of the genomes used in this digital organism with ID, strain, and source information.</p><p>Alteromonas2.1.1pangenome.db.tar.gz: Compressed file containing the <i>Alteromonas</i> pangenome (digital organism).</p><p>Alteromonas2.1.1pangenomefiles.tar.gz: Compressed directory containing any information that anvi'o created when forming the pangenome.</p><p>Alteromonas2.1.1ANI.tar.gz: Compressed directory containing all output files from assessing genome similarity.</p><p>bayesian_2_1_concatenated_proteins*: Concatenated core protein files made from bayesian core gene sets.</p><p>RAxML_*boots: New RAxML tree artifacts produced for this version of the pangenome, with bootstrap values. Includes midpoint rooted tree.</p><p>layer_orders.txt: Tab-delimited file used to import newick tree into the pangenome.</p><p>view.txt: Tab-delimited file containing isolate and strain names.</p>
Dataset Literature Review Digital Forensic and Organization & Administration
<p>Bahwa data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci digital forensik dan Organization & Administration</p>
The phenotypic plasticity of an evolving digital organism
<p>Data set and R code used in the analysis reported in the manuscript entitled "<em>The phenotypic plasticity of an evolving digital organism</em>", which has been accepted in Proc. R. Open. Sci. for publication.</p>
Quantifying Progress: Metrics and Indicators for Measuring Digital Transformation Maturity in Organizations
<p><span>As organizations increasingly embark on digital transformation journeys, the need for effective metrics and indicators to measure progress and maturity becomes paramount. This paper investigates the development and application of metrics for quantifying digital transformation maturity in organizations. Through an extensive review of literature and examination of case studies, the paper identifies key dimensions and stages of digital maturity. It proposes a framework encompassing both quantitative and qualitative metrics that can be used to assess an organization's digital transformation journey. The paper explores challenges associated with defining meaningful metrics and offers insights into adapting measurement frameworks to diverse organizational contexts. By addressing this critical gap in the literature, the paper aims to provide practitioners, researchers, and decision-makers with a valuable resource for evaluating and benchmarking digital transformation progress, fostering a more nuanced understanding of the multifaceted nature of organizational digital maturity.</span></p>
Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study
<p><span>In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by </span><span>leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (</span><span>Δ</span><span>x=117 ± 4 </span><span>μ</span><span>m, </span><span>Δ</span><span>t=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks.<br><br>Dataset A: <span>Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted </span><span>α</span><span>-actinin, Connexin43 and DAPI immunostainings </span><span>for Cellular Potts Model optimization. During data processing, cell shape<span>s (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized.<br>Dataset C corresponds to FluoVolt recordings <span>(3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples)</span>. </span></span></span></p>
Digital Design and Discovery of Biological Metal-Organic Frameworks for Gas Signaling
<p>This repository contains the structures, features and compositions of Bio-hMOFs database.</p> <ol> <li>Fragments and Composition: Contains the building block fragments used to generate the Bio-hMOFs.</li> <li>Structures-CIFs: Contains the structures of Bio-hMOFs</li> <li>Geometric and RACs: Contains the geomtric and RACs features of Bio-hMOFs</li> <li>Adsorption Capacity: Contains the adsorption uptake of NO and CO adsorption simulated under 298K under 1 bar and 10 bar.</li> <li>Mechanical Properties; Computed mechanical properties</li> </ol>
Assessment and Digital-health Based Intervention on Subclinical Organ Damage and Cardiovascular Risk in Chinese
ClinicalTrials.gov study NCT05435898. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Data from: Ontogeny tends to recapitulate phylogeny in digital organisms
Biologists have long debated whether ontogeny recapitulates phylogeny and, if so, why. Two plausible explanations are that (i) changes to early developmental stages are selected against because they tend to disrupt later development and (ii) simpler structures often precede more complex ones in both ontogeny and phylogeny if the former serve as building blocks for the latter. It is difficult to test these hypotheses experimentally in natural systems, so we used a computational system that exhibits evolutionary dynamics. We observed that ontogeny does indeed recapitulate phylogeny; traits that arose earlier in a lineage's history also tended to be expressed earlier in the development of individuals. The relative complexity of traits contributed substantially to this correlation, but a significant tendency toward recapitulation remained even after accounting for trait complexity. This additional effect provides evidence that selection against developmental disruption also contributed to the conservation of early stages in development.
THE IMPACT OF DIGITAL TECHNOLOGY IN OPERATION OF ECONOMIC ORGANIZATION
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Data from: Different evolutionary paths to complexity for small and large populations of digital organisms
A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large or small asexual populations tend to evolve greater complexity. We find that both small and large—but not intermediate-sized—populations are favored to evolve larger genomes, which provides the opportunity for subsequent increases in phenotypic complexity. However, small and large populations followed different evolutionary paths towards these novel traits. Small populations evolved larger genomes by fixing slightly deleterious insertions, while large populations fixed rare beneficial insertions that increased genome size. These results demonstrate that genetic drift can lead to the evolution of complexity in small populations and that purifying selection is not powerful enough to prevent the evolution of complexity in large populations.
Data from: Examining community stability in the face of mass extinction in communities of digital organisms
Digital evolution is a computer-based instantiation of Darwinian evolution in which short self-replicating computer programs compete, mutate, and evolve. It is an excellent platform for addressing topics in long-term evolution and paleobiology, such as mass extinction and recovery, with experimental evolutionary approaches. We evolved model communities with ecological interdependence among community members, which were subjected to two principal types of mass extinction: a pulse extinction that killed randomly, and a selective press extinction involving an alteration of the abiotic environment to which the communities had to adapt. These treatments were applied at two different strengths, along with unperturbed control experiments. We examined how stability in the digital communities was affected from the perspectives of division of labor, relative shift in rank abundance, and genealogical connectedness of the community's component ecotypes. Mass extinction that was due to a Strong Press treatment was most effective in producing reshaped communities that differed from the pre-treatment ones in all of the measured perspectives; weaker versions of the treatments did not generally produce significant departures from a Control treatment; and results for the Strong Pulse treatment generally fell between those extremes. The Strong Pulse treatment differed from others in that it produced a slight but detectable shift towards more generalized communities. Compared to Press treatments, Pulse treatments also showed a greater contribution from re-evolved ecological doppelgangers rather than new ecotypes. However, relatively few Control communities showed stability in any of these metrics over the whole course of the experiment, and most did not represent stable states (by some measure of stability) that were disrupted by the extinction treatments. Our results have interesting, broad qualitative parallels with findings from the paleontological record, and show the potential of digital evolution studies to illuminate many aspects of mass extinction and recovery by addressing them in a truly experimental manner.
Data from: Selective press extinctions, but not random pulse extinctions, cause delayed ecological recovery in communities of digital organisms
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Data from: Context matters: sexual signaling loss in digital organisms
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Data from: Different evolutionary paths to complexity for small and large populations of digital organisms
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Data from: Ontogeny tends to recapitulate phylogeny in digital organisms
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Data from: Ecological and mutation-order speciation in digital organisms
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Data from: Genetically integrated traits and rugged adaptive landscapes in digital organisms
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Data from: Examining community stability in the face of mass extinction in communities of digital organisms
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Synchronization of growth and self-organizing digit pattern by WNT signaling [RNAseq]
GEO Series GSE275783. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing.
Synchronization of growth and self-organizing digit pattern by WNT signaling [ChIPseq]
GEO Series GSE275782. Mus musculus. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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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.