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69 results for “feature selectivity”
FIGURE 23. Selected morphometric features for 91 in Systematic revision of the flatfish genus Peltorhamphus Günther, 1862 (Teleostei: Pleuronectiformes: Rhombosoleidae), including description of a new species from Southeastern New Zealand, with biological and ecological summaries for the species
FIGURE 23. Selected morphometric features for 91 specimens of Peltorhamphus latus 43.0–133.4 mm SL. A–D. Body depth (BD), Head length (HL), Head width (HW), and Ocular-side pectoral fin (OSP) expressed as percent of SL versus SL (in mm), respectively. E–J. Dorsal head width (DHW), Snout length (SNL), Eye diameter (ED), Interorbital width (IO), Upper jaw length (UJL), and Eye to upper mouth distance (EUM) expressed as percent of HL versus HL (in mm), respectively.
FIGURE 19. Selected morphometric features for 77 in Systematic revision of the flatfish genus Peltorhamphus Günther, 1862 (Teleostei: Pleuronectiformes: Rhombosoleidae), including description of a new species from Southeastern New Zealand, with biological and ecological summaries for the species
FIGURE 19. Selected morphometric features for 77 specimens of Peltorhamphus tenuis 56.0–158 mm SL. A–D. Body depth (BD), Head length (HL), Head width (HW), and Ocular-side pectoral fin (OSP) expressed as percent of SL versus SL (in mm), respectively. E–J. Dorsal head width (DHW), Snout length (SNL), Eye diameter (ED), Interorbital width (IO), Upper jaw length (UJL), and Eye to upper mouth distance (EUM) expressed as percent of HL versus HL (in mm), respectively.
FIGURE 9. Selected morphometric features for 86 in Systematic revision of the flatfish genus Peltorhamphus Günther, 1862 (Teleostei: Pleuronectiformes: Rhombosoleidae), including description of a new species from Southeastern New Zealand, with biological and ecological summaries for the species
FIGURE 9. Selected morphometric features for 86 specimens of two colour morphs of Peltorhamphus novaezeelandiae with size range from 42.9 to 395 mm SL. A–D. Body depth (BD), Head length (HL), Head width (HW), and Ocular-side pectoral fin (OSP) expressed as percent of SL versus SL (in mm), respectively. E–J. Dorsal head width (DHW), Snout length (SNL), Eye diameter (ED), Interorbital width (IO), Upper jaw length (UJL), and Eye to upper mouth distance (EUM) expressed as percent of HL versus HL (in mm), respectively.
Data from: Applications of random forest feature selection for fine-scale genetic population assignment
Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest, and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP dataset for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each dataset, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic datasets to improve assignment for management and conservation of exploited populations.
Data from: Compensatory selection for roads over natural linear features by wolves in northern Ontario: implications for caribou conservation
Woodland caribou (Rangifer tarandus caribou) in Ontario are a threatened species that have experienced a substantial retraction of their historic range. Part of their decline has been attributed to increasing densities of anthropogenic linear features such as trails, roads, railways, and hydro lines. These features have been shown to increase the search efficiency and kill rate of wolves. However, it is unclear whether selection for anthropogenic linear features is additive or compensatory to selection for natural (water) linear features which may also be used for travel. We studied the selection of water and anthropogenic linear features by 52 resident wolves (Canis lupus x lycaon) over four years across three study areas in northern Ontario that varied in degrees of forestry activity and human disturbance. We used Euclidean distance-based resource selection functions (mixed-effects logistic regression) at the seasonal range scale with random coefficients for distance to water linear features, primary/secondary roads/railways, and hydro lines, and tertiary roads to estimate the strength of selection for each linear feature and for several habitat types, while accounting for availability of each feature. Next, we investigated the trade-off between selection for anthropogenic and water linear features. Wolves selected both anthropogenic and water linear features; selection for anthropogenic features was stronger than for water during the rendezvous season. Selection for anthropogenic linear features increased with increasing density of these features on the landscape, while selection for natural linear features declined, indicating compensatory selection of anthropogenic linear features. These results have implications for woodland caribou conservation. Prey encounter rates between wolves and caribou seem to be strongly influenced by increasing linear feature densities. This behavioral mechanism - a compensatory functional response to anthropogenic linear feature density resulting in decreased use of natural travel corridors - has negative consequences for the viability of woodland caribou.
Dataset from: "Selective weighting of action-related feature dimensions in visual working memory"
<p>Dataset from the following publication:</p> <p>Heuer, A. & Schubö, A. (2016). Selective weighting of action-related feature dimensions in visual working memory. <em>Psychonomic Bulletin & Review.</em> doi:10.3758/s13423-016-1209-0</p>
Bożepole Małe, feature 589, selection 1
Częściowa rekonstrukcja fragmentu naczynia ceramicznego (cykl łużycko-pomorski) z ob. 589 na stan. 14 w miejscowosci Bożepole Małe, woj. pomorskie. Zabytek z badań Archeobaltica (https://www.archeobaltica.pl/). Partial reconstruction of Late Bronze/Early Iron Age (lausatian-pomeranian cycle) pottery vessel from feature 589. Archaeological site: Bożepole Małe 14, Pomerania, northern Poland. Excavation by Archeobaltica (https://www.archeobaltica.pl/). Source: Objaverse 1.0 / Sketchfab
DELVE: Feature selection for preserving biological trajectories in single-cell data
<p>Contains preprocessed single-cell data and metadata for feature selection. Preprocessed adata objects can be accessed using the <em>read_h5ad</em> function in anndata. Also contains the source data files for reproducing the Figures and Supplementary Figures referenced in the manuscript.</p> <ul> <li>The RPE iterative indirect immunofluorescence imaging dataset (adata_RPE.h5ad) from Ref. (<a href="https://doi.org/10.1016/j.cels.2021.10.007">https://doi.org/10.1016/j.cels.2021.10.007</a>) was originally downloaded from the Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4525425">https://doi.org/10.5281/zenodo.4525425</a>).</li> <li>The PDAC iterative indirect immunofluorescence imaging datasets (listed below) were originally downloaded from the Zenodo repository (<a href="https://doi.org/10.5281/zenodo.7860332">https://doi.org/10.5281/zenodo.7860332</a>). <ul> <li>adata_PDAC_BxPC3_control.h5ad</li> <li>adata_PDAC_CFPAC_control.h5ad</li> <li>adata_PDAC_HPAC_control.h5ad</li> <li>adata_PDAC_MiaPaCa_control.h5ad</li> <li>adata_PDAC_Pa01C_control.h5ad</li> <li>adata_PDAC_Pa02C_control.h5ad</li> <li>adata_PDAC_Pa16C_control.h5ad</li> <li>adata_PDAC_PANC1_control.h5ad</li> <li>adata_PDAC_UM53_control.h5ad</li> </ul> </li> <li>The CD8+ T cell differentiation dataset (adata_CD8.h5ad) from Ref. (<a href="https://doi.org/10.1016/j.cels.2021.10.007">https://doi.org/</a><a href="https://doi.org/10.1126/sciimmunol.aaz6894">10.1126/sciimmunol.aaz6894</a>) was originally downloaded from the Gene Expression Omnibus under the accession code GSE131847 (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131847</a>).</li> <li>The definitive endoderm differentiation dataset (adata_DE.h5ad) contains multiplexed single-cell RNA sequencing data profiling the differentiation of human embryonic stem cells into the definitive endoderm. </li> </ul>
Data for DualNetGO: A Dual Network Model for Protein Function Prediction via Effective Feature Selection
<p>Data used in the paper, including annotation files, graph embeddings from TransformerAE, and protein attributes for both human and mouse, and for cafa3 data. Extract and place them in the <em>data </em>folder.</p>
Automated Selection and Ordering Video Clip based on Tonal Tension and Frame Features
<p>Dataset, videos, and code of article submitted to EvoMUSART 2025.</p>
Figure 4. Selected skull features. A, B in The phylogeny of charadriiform birds (shorebirds and allies) - reassessing the conflict between morphology and molecules
Figure 4. Selected skull features. A, B, cranium (ventrolateral view) of A, Haematopus ostralegus (Haematopodidae) and B, Burhinus oedicnemus (Burhinidae). C, D, caudal section of orbita of C, H. ostralegus and D, Sterna paradisaea (Sternidae). E, cranium of Rostratula benghalensis (Rostratulidae) in ventral view. F–I, os lacrimale/os ectethmoidale complex of F, Sterna paradisaea (Sternidae); G, Larus fuscus (Laridae); H, Alca torda (Alcidae); and I, Pluvianus aegyptius (Pluvianidae). J–M, basicranial area (ventral view) of J, Sterna paradisaea (Sternidae); K, Chionis minor (Chionidae); L, Haematopus ostralegus (Haematopodidae); and M, Burhinus oedicnemus (Burhinidae). Abbreviations: bpt, processus basipterygoideus; ccv, concavity next to condylus medialis of quadratum; cdf, foramen caudal of foramen nervi maxillomandibularis; fnm, foramen nervi maxillomandibularis; for, foramen formed by duct of nasal gland; hlp, hook-like projection on os pterygoideum; mpA, maxillopalatine strut A; orb, processus orbitalis of os lacrimale; plp, processus lateralis parasphenoidalis; smt, processus suprameaticus; unc, os uncinatum; zyg, processus zygomaticus. Figures not to scale.
Data from: Pop-out search instigates beta-gated feature selectivity enhancement across V4 layers
<p>Visual search is a work-horse for investigating how attention interacts with processing of sensory information. Attentional selection has been linked to altered cortical sensory responses and feature preferences (i.e., tuning). However, attentional modulation of feature selectivity during search is largely unexplored. Here we map the spatiotemporal profile of feature selectivity during singleton search. Monkeys performed search where a pop-out feature determined the target of attention. We recorded laminar neural responses from visual area V4. We first identified "feature columns" which showed preference for individual colors. In the unattended condition, feature columns were significantly more selective in superficial relative to middle and deep layers. Attending a stimulus increased selectivity in all layers but not equally. Feature selectivity increased most in the deep layers, leading to higher selectivity in extragranular layers as compared to the middle layer. This attention-induced enhancement was rhythmically gated in phase with the beta-band local field potential. Beta power dominated both extragranular laminar compartments, but current source density analysis pointed to an origin in superficial layers, specifically. While beta-band power was present regardless of attentional state, feature selectivity was only gated by beta in the attended condition. Neither the beta oscillation nor its gating of feature selectivity varied with microsaccade production. Importantly, beta modulation of neural activity predicted response times, suggesting a direct link between attentional gating and behavioral output. Together, these findings suggest beta-range synaptic activation in V4's superficial layers rhythmically gates attentional enhancement of feature tuning in a way that affects the speed of attentional selection.</p>
The density of anthropogenic features explains seasonal and behaviour-based functional responses in selection of linear features by a social predator
<p>Anthropogenic linear features facilitate access and travel efficiency for predators, and can influence predator distribution and encounter rates with prey. We used GPS collar data from eight wolf packs and characteristics of seismic lines to investigate whether (1) ease-of-travel or (2) access to areas presumed to be preferred by prey best explained seasonal selection patterns of wolves near seismic lines, and whether the density of anthropogenic features led to functional responses in habitat selection. At a broad scale, wolves showed evidence of habitat-driven functional responses by exhibiting greater selection for areas near low-vegetation height seismic lines in areas with low densities of anthropogenic features. We highlight the importance of considering landscape heterogeneity and habitat characteristics, and the functional response in habitat selection when investigating seasonal behaviour-based selection patterns. Our results support behaviour in line with search for primary prey during summer and fall, and ease-of-travel during spring, while patterns of selection during winter aligned best with ease-of-travel for the less-industrialized foothills landscape, and with search for primary prey in the more-industrialized boreal landscape. These results highlight that time-sensitive restoration actions on anthropogenic features can affect the probability of overlap between predators and threatened prey within different landscapes.</p>
Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data
<p>This repository contains the code and data for reproducing the results in the paper 'Per-Run Algorithm Selection with<br> Warm-starting using Trajectory-based Features'</p> <p>The file 'figure_generation.ipnb' contains the code used to create the heatmaps shown in the paper.</p> <p>The file 'Raw_data' contains the raw performance data, for both running A1 and recording all relevant features and for running the switching algorithms for each of the 6 algorithms in the portfolio while recording only performance. The processed version of the A2 data into relative performance is available in 'perf_relative'.</p> <p>The file 'data-collection' contains the code used to run the switching algorithms with the used warmstarting procedures.</p> <p>The file 'compute_features.ipnyb' contains the code used to extract the timeseries features, and those features themselves are included in 'TS_Features'.</p> <p>The file 'AlgorithmSelector' contains the scripts and data used to create the algorithm selector.</p> <p>The files 'selector_5_as_f' and 'selector_ng_f' contain the results of running the final models on the test-instances and nevergrad functions respectively.</p>
Blinded Predictions and Post-hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
<p>Training and test datasets and scripts for training models.</p>
BPM 14, feature 574, selection
Fragment naczynia sitowatego z obiektu 574 na stanowisku Bożepole Małe 14, woj. pomorskie badanego przez archeobaltica.pl. Potsherd (I don't know English name for this type) from excavations on Bożepole Małe 14 archaeological site located in Pomerania, northern Poland. Source: Objaverse 1.0 / Sketchfab
Selecting Theorem Prover Configurations Based on Problem Features
<p>This archive contains the raw evaluation results and scripts associated with the experiment described in the bachelor thesis "Selecting Theorem Prover Configurations Based on Problem Features" by Yingdi Xie (Vrije Universiteit Amsterdam).</p>
Preliminary Report on Computed Tomography Radiomics Features as biomarkers to Immunotherapy selection in Lung Adenocarcinoma Patients
<p>We uploaded all radiomics metrics extracted for 88 patients enrolled in the study: Preliminary Report on Computed Tomography Radiomics Features as biomarkers to Immunotherapy selection in Lung Adenocarcinoma Patients.</p> <p>Each radiomic feature is described in the Appendix of the manuscript.</p> <p>Moreover Overall survival (OS) and Progression free survival (PFS) for each patient is provided.</p>
Data from: Pop-out search instigates beta-gated feature selectivity enhancement across V4 layers
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Data from: Applications of random forest feature selection for fine-scale genetic population assignment
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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.