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486 results for “Hierarchical”
A Hierarchical Model of Accelerating Factors to Promote Urban Renewal and Reconstruction of Unsafe and Old Buildings
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Data For: Retrieval Augmented Docking using Hierarchical Navigable Small Worlds
<p>These are the DOCK scores for the DUDE-Z "goldilocks" molecules docked to each of the 43 DUDE-Z proteins used in the paper: Retrieval Augmented Docking using Hierarchical Navigable Small Worlds.</p> <p>The data is saved as a pickle of a python dictionary. The keys are the ZINC IDs of the molecules, and the values are lists where the first entry is the corresponding SMILES string, and the second is a dictionary of DOCK scores for each DUDE-Z receptor. If a receptor does not appear in a particularly molecule's dictionary, it means that the molecule failed to dock to the receptor.</p> <p>{</p> <p>zinc_id1: [SMILES, {receptor1:score, receptor2:score,...} ],</p> <p>zinc_id2: [SMILES, {receptor1:score, receptor2:score,...} ],</p> <p>....</p> <p>}</p>
Hierarchical mesoporous NanoMUV-2 for the selective delivery of macromolecular drugs
<p>Relevant data for publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3TB01819A">10.1039/D3TB01819A</a></p>
Wing with Root Holes - Hierarchical, random and bifurcation tiling with heterogeneity in micro-structures construction via functional composition.
<p>This microstructure has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific wing is a functional composition of trivariate spline tiles inside a macro trivariate shape of a wing. The root tiles have through vertical holes in them.</p> <p>Model is provided in STL format.</p>
Figure 3. Four dendrograms depicting the hierarchical relationship between the nine Chthamalus populations. A in Molecular analysis reveals a cryptic species of Chthamalus (Crustacea: Cirripedia) in the Cape Verde Islands
Figure 3. Four dendrograms depicting the hierarchical relationship between the nine Chthamalus populations. A, Pearson's correlation coefficient with UPGMA amalgamation. B, modified Morisita's similarity coefficient with farthest neighbour amalgamation. C, squared Euclidean distance with minimum variance amalgamation. D, Manhattan distance with nearest neighbour amalgamation.
Data files for the article "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region"
<p>This repository contains R data files required for reproducing the results from the article by Räty et al (2021) "Bayesian hierarchical modeling of sea level extremes in the Finnish coastal region", submitted to Nat. Hazards Earth Syst. Sci. See the README file for more details on the content of the files.</p>
Deficits of hierarchical predictive coding in left spatial neglect
<p>Right brain-damaged patients with unilateral spatial neglect fail to explore the left side of space. Recent EEG and clinical evidence suggests that neglect patients might suffer deficits in predictive coding, i.e. in identifying and exploiting probabilistic associations among sensory stimuli in the environment. To gain direct insights on this issue, we focussed on the hierarchical components of predictive coding. We recorded EEG responses evoked by central, left-side or right-side tones that were presented at the end of sequences of four central tones. Left-side and right-side deviant tones produce a pre-attentive Mismatch Negativity that reflects a lower-order prediction error for the ‘Local’ deviation of the tone at the end of the sequence. Higher-order prediction errors for the frequency of these deviations in the acoustic environment, i.e. ‘Global’ deviation, are marked by the P3 response. We show that when neglect patients are immersed in an acoustic environment characterized by frequent left-side deviant tones, they display no pre-attentive Mismatch Negativity both for left-side deviant tones and infrequent omissions of the last tone, while they have Mismatch Negativity for infrequent right-side deviant tones. In the same condition, neglect patients show no P300 response to ‘Global’ prediction errors for deviant tones, including those in the non-neglected right-side, and omissions. In contrast to this, when right-side deviant tones are predominant in the acoustic environment, neglect patients have pre-attentive Mismatch Negativity both for right-side deviant tones and infrequent omissions, while they display no Mismatch Negativity for infrequent left-side deviant tones. Most importantly, in the same condition neglect patients show enhanced P300 response to infrequent left-side deviant tones, notwithstanding that these tones evoked no pre-attentive Mismatch Negativity. This latter finding indicates that ‘Global’ predictions are independent of ‘Local’ error signals provided by the Mismatch Negativity. These results qualify deficits of predictive coding in the spatial neglect syndrome and show that neglect patients base their predictive behaviour only on statistical regularities that are related to the frequent occurrence of sensory events on the right side of space.</p>
Data for: Inferring spatially-varying animal movement characteristics using a hierarchical continuous-time velocity model
<p>Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially-varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic dataset, then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.</p>
Unraveling hierarchical genetic structure of tea green leafhopper, Matsumurasca onukii, in East Asia based on SSRs and SNPs
<p><em>Matsumurasca onukii</em> (Matsuda, 1952), one of the dominant pests in major tea production areas in Asia, currently is known to occur in Japan, Vietnam, and China, and severely threatens tea production, quality, and international export trade. To elucidate the population genetic structure of this species, 1633 single nucleotide polymorphisms (SNPs) and 18 microsatellite markers (SSRs) were used to genotype samples from 27 sites representing 18 geographical populations distributed throughout the known range of the species in East Asia. Analyses of both SNPs and SSRs showed that <em>M</em>. <em>onukii</em> populations in Yunnan exhibit high genetic differentiation and structure compared to other populations. The Kagoshima (JJ) and Shizuoka (JS) populations from Japan were separated from populations from China by SNPs but clustered with Jinhua (JH), Yingde (YD), Guilin (GL), Fuzhou (FZ), Hainan (HQ), Leshan (CT), Chongqing (CY) and Zunyi (ZY) tea areas in China and the Vietnamese Vinh Phuc (VN) population based on SSR data. On the contrary, CT, CY, ZY, and Shaanxi (SX) populations clustered together based on SNPs, but were separated by SSRs. Both marker datasets identified significant geographic differentiation among the 18 populations. Various environmental and anthropogenic factors, including the geographical barriers to migration, human transport of hosts (<em>Camellia sinesis</em> (L.) O. Kuntze), and adaptability of <em>M. onukii</em> to various climatic zones possibly account for the rapid spread of this pest in Asia. The results demonstrate that SNPs from high-throughput genotyping data can be used to reveal subtle genetic substructure at broad scales in r-strategist insects.</p>
Data for "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"
<div> <p>Additional material to Malfait et al. 2024, subm. "SPH modelling of AGB wind morphology in hierarchical triple systems & comparison to observation of R Aql"</p> <p>This contains input files and final output dumps of the Phantom simulations of this paper.</p> <p>The code used to perform the simulations is available at: <a href="https://github.com/danieljprice/phantom">https://github.com/danieljprice/phantom.</a></p> <p>Splash (<a href="https://github.com/danieljprice/splash">https://github.com/danieljprice/splash</a> ) and Plons (<a href="https://github.com/Ensor-code/plons">https://github.com/Ensor-code/plons</a> ) were used to create figures and plots from this data.</p> <p> </p> </div>
The features of Tissues and Patches for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model"
<p>This repository contains features and corresponding coordinates of patches and tissues extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into The histology images were classified as “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) according to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer”, which corresponds to the division of the training and test sets in the article.<br><br></p> <p>Patches were extracted at 40x objective magnification and 20x objective magnification, respectively, and the corresponding features were extracted by pre-training resnet48, respectively</p> <p>The features of Tissues are thumbnails obtained at 2.5x objective magnification and further extracted by MedSAM after extracting the masks of the tissues.</p>
Cell features for "Datasets for "Predicting microsatellite instabilitiy from histology images with a three-level hierarchical graph fusion model""
<p>This repository contains features and corresponding coordinates of cells extracted from 430 and 326 histologic images from patients with colorectal and gastric cancers from the TCGA cohort (original whole section SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this library are from formalin-fixed paraffin-embedded (FFPE) diagnostic sections (“DX” on the GDC Data Portal). This blog explains this in detail: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing.</strong></p> <p>All SVS slices were pre-processed as follows.</p> <p>According to “Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer” these histology images were categorized into “MSS” (microsatellite stable) or “MSIMUT” (microsatellite unstable or highly mutated) and corresponded to the article dividing the training and test sets.<br><br></p> <p>The features of all cells were extracted by Hovernet and Transnuseg at 40x objective magnification for extraction masking and further feature extraction</p>
Experimental Results for the SoCS 2024 Paper: "Modeling Assistance for Hierarchical Planning: An Approach for Correcting Hierarchical Domains with Missing Actions"
<p>This collection contains all the experimental results produced in the empirical evaluation for the paper "Modeling Assistance for Hierarchical Planning: An Approach for Correcting Hierarchical Domains with Missing Actions", accepted by The 17th International Symposium on Combinatorial Search (SoCS 2024). For a detailed description about this collection, please read the README file. </p>
Fig. 3 in Integrating selection, niche, and diversification into a hierarchical conceptual framework
Fig. 3 Fitness distributions of niche value for three types of species. Species types are based on niche breadth: specialist species with specialist organisms (SPE), generalist species with specialist organisms (GEN-spe), and generalist species with generalist organisms (GEN-gen). Thick lines represent species distributions, whereas dotted lines represent distributions for organisms. Note that specialist species will have higher fitness than generalist species for the niche value where they are adapted for
Fig. 1 in Integrating selection, niche, and diversification into a hierarchical conceptual framework
Fig. 1 Environmental space with niche breadth and value for each species and organism. Two types of species and organisms in terms of niche breadth: specialist or generalist. Note that each specialist species occupy a certain niche value, and their organisms occupy the same niche value. E space means environmental space
Datasets and Hierarchical Labels
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Forest Carbon Modeling Improved through Hierarchical Integration of Pool-Based Measurements
<p>This folder contains data from </p> <ol> <li>measured carbon stocks (carbonpools) from forest inventories</li> <li>carbon stocks (modeled_C_stock_40) estimated by each HDA constraint</li> <li>posterior parameter sets (para_posterior) after burn-in</li> <li>carbon fluxes (modeled_C_HD) estimated by 500 randomly selected posterior parameter sets from each HDA step.</li> <li>carbon stocks (modeled_C_stock_40_DEF) estimated by the default model settings (uninformed)</li> </ol> <p>figures.R to reproduce the figures in the manuscript. </p>
Dataset: The Main Evolutionary Pathways of Massive Hierarchical Triple Stars
<p>This is a reproduction package for the paper "The Main Evolutionary Pathways of Massive Hierarchical Triple Stars" by Kummer et al. (2023). It aims to provide the most important data products and reproduce the Figures of the paper.</p>
Reproduction package for the paper "BH-BH mergers with & without EM counterpart: A model for stable tertiary mass transfer in hierarchical triple systems"
<p>This is a reproduction package for the paper "BH-BH mergers with & without EM counterpart: A model for stable tertiary mass transfer in hierarchical triple systems" by Kummer et al. (2024). It aims to provide the most important data products and reproduce the Figures of the paper.</p> <p> </p>
Data from: A chorus of color: hierarchical and graded information content of rapid color change signals in chameleons
Animals rely on information-rich signals to minimize costs associated with competition. If fighting ability is linked to stable individual attributes (e.g. morphology), the signals that communicate information about such ability should be relatively static. Conversely, the temporal variability of motivation should favor dynamic threat signals that indicate an animal's likelihood of escalating a contest. Though static colors are used by many animals to signal quality or fighting ability, the function of dynamic color change as a social signal has only recently begun to be investigated. Here, we examined the information content of rapid physiological color changes displayed by adult male veiled chameleons Chamaeleo calyptratus during agonistic interactions by conducting experimental trials between live chameleons and standardized, experimentally-controlled robochameleon models. Chameleons reliably communicated motivation with dynamic color displays – individuals that brightened were 14 times more likely to approach the robochameleon than non-brightening individuals. Additionally, chameleons with shorter latencies to maximum stripe brightness had stronger bites, and those displaying brighter, yellower stripes exhibited more aggression. The parallels between dynamic color changes and the vocalizations used to mediate aggressive interactions in other taxa are numerous. The use of particular vocalizations/color changes can signal motivation levels while specific signal elements (e.g. pitch, timing, brightness) may be linked to fighting ability. Because the complexity and potential information content of color signals increases markedly when organisms can display context-specific variation in the expression of these ornaments, the study of dynamic color signals is a field ripe for the investigation of complex visual and signaling strategies.
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.