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196 results for “Spatial map”
reference genome used for scRNA-seq mapping with CellRanger in the method spatial-scERA
<p>The modified <em>Drosophila </em>melanogaster (dm6) reference genome used for the mapping with CellRanger in the method paper about spatial-scERA</p> <p>The genome is composed of the original genome from EnsembleMetazo website (BDGP6.46.110). An addition of 26 chromosomes (one for the plasmid construct and 25 for the tested enhancer sequences) is also present to allow for the mapping of mRNAs comming from our constructs. </p>
Spatially mapping the baseline and bisphenol-A exposed Daphnia magna lipidome using desorption electrospray ionisa-tion - mass spectrometry
<p>Data from desorption electrospray ionisation - mass spectrometry of <em>Daphnia magna</em> tissue section from control and daphnids exposed to 5 ppm of bisphenol A over their 7<sup>th</sup> adult instar, specifically four sampling points; 8, 24, 48 and 72 h after the 6<sup>th</sup> brood, as well as data acquired from a blank DESI slide.</p> <p>Data is provided in the form of *imzML files with the associated *.ibd of the same name.</p>
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae- in New Findings Of White Clawed Crayfish, Austropotamobius Pallipes (Decapoda, Astacidae), And Peculiarities Of Its Spatial Distribution In Neretvica (Bosnia And Herzegovina)
Maps of depths are created for the site of 50 m length. Flow types are turbulent, broken standing waves, unbroken standing waves, and rippled. The average width was 8 m and varied from 5.5 to 12 m. Bed elements included bars, rocks, and step/pools. The average depth was 0.35 m, with a maximum of 0.6 m. The average velocity was 0.4 m/s, with a maximum of 1.2 m/s (figs 10). Distribution of bottom habitats at the locations with the crayfish are as follows: megalital — 5 %, macrolithal — 30 %, mesolithal — 25 %, microlithal — 15 %, psammal — 15 %, CPOM — 10 %. Assessment by hydrobiological parameters showed that the presence of Lyngbya and Oscillatoria, as well as the increase of the number of Oligochae-
Рис. 3. Карта-схема пространственного распределениЯ биомассы Macoma balthica. Fig. 3. A schematic map of the spatial distribution of the Macoma balthica biomass. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис. 3. Карта-схема пространственного распределениЯ биомассы Macoma balthica. Fig. 3. A schematic map of the spatial distribution of the Macoma balthica biomass.
Development of a desorption electrospray ionization –multiple-reaction-monitoring mass spectrometry (DESI-MRM) workflow for spatially mapping oxylipins in pulmonary tissue
<p>Data from desorption electrospray ionization mass spectrometry – multiple-reaction-monitoring mass spectrometry (DESI-MRM) analysis of oxylipins in guinea pig lung tissue following<em> in vivo</em> exposure to house dust mite extract.</p> <p>Data are provided as Waters *.raw data folders, each incuding an 'Analyte .txt' file, which is generated from processing within MassLynx (Waters). The 'ion_library.txt' file includes details about the MRM transitions and is required for processing the data with quantMSImageR (<span><a href="https://github.com/targeted-lipidomics/quantMSImageR"><span>https://github.com/targeted-lipidomics/quantMSImageR</span></a></span><span>).</span></p>
A framework for spatial map generation using acoustic echoes for robotic platforms
<div> <div> <div> <div> <div> <div> <p>In this work, we present a framework for constructing a spatial map of an indoor environment using the concept of echolocation. More specifically, we propose a non-linear least squares (NLS) estimator which is combined with a spatial filtering technique, e.g., beamforming, to estimate both the time-of-arrival (TOA) and direction-of-arrival (DOA) of the acoustic echoes. The proposed framework is complemented with an echo detector to classify a spurious estimate and an acoustic reflector, i.e., a wall. Based on these estimators, we propose two algorithms that complement existing range sensors and aid robotic platforms in acoustic reflector localization and mapping: single-channel localization and mapping (ScLAM) and a multi-channel localization and mapping (McLAM). Compared to commonly used sensors, such as lidar, cameras and ultrasonic sensors, our proposed model-based approach can detect transparent surfaces that are typically found in an office environment and could work in audible frequency ranges. A proof-of-concept robotic platform was built to test our algorithms. According to our evaluation, both qualitative and quantitative experiments reveal that the proposed methods can detect an acoustic reflector up to a distance of 1.5 m at a signal-to-diffuse-noise ratio (SDNR) of 0 dB in a simulated environment and 10 dB in a real environment with an accuracy of 80%.</p> </div> </div> </div> </div> </div> </div>
Fig. 3. Spatial 2D in Nir Raman Scattering For The Study Of Biochemical Features Of The Human Skin Epidermis And A Skin Surface Micro-Mapping In Vitro
Fig. 3. Spatial 2D image of the human epidermis surface: A) an optical image; B) mapping scheme; C) micro–Raman signal intensity map.
Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"
<p>Supplementary Datasets and Movies for the Paper <br><strong>Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax</p> <p>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a></p> <p> </p> <p><strong>Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022.</strong> Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details.</p> <p><strong>Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence.</strong> The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.</p> <p> </p> <p><strong>CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study:</strong></p> <p>Parkfield_2022_NLL-SSST-coherence_20231201A.csv<br>Parkfield_2022_NLL-SSST-coherence_20231201A.hyp</p> <p>AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv<br>AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp</p> <p>Anchorage_2018_NLL-SSST-coherence_20231125A.csv<br>Anchorage_2018_NLL-SSST-coherence_20231125A.hyp</p> <p> </p>
Figure 1. Map showing the localities where F in Effects of genetic relatedness, spatial distance, and context on intraspecific aggression in the red wood ant Formica pratensis (Hymenoptera: Formicidae)
Figure 1. Map showing the localities where F. pratensis colonies were sampled for the analysis of genetic relatedness and tested for their aggressive behavior towards each other. The numbers denote the localities. 1: Balaban village (N 41°49ʹ18ʺ, E 27°40ʹ44ʺ) containing three nests; B1, B2, and B3, 2: Asilbeyli village (N 41°39ʹ32ʺ, E 27°13ʹ50ʺ), one nest (As), 3: Ulukonak village (N 41°39ʹ35ʺ, E 27°01ʹ52ʺ) one nest (U), 4: Doğanköy village (N 41°56ʹ12ʺ, E 26°41ʹ20ʺ) one nest (D), and 5: Ahmetler village (N 42°00ʹ37ʺ, E 27°11ʹ12ʺ), three nests; Ah1, Ah2, and Ah3.
Fig. 4 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 4. Transmission electron micrographs showing the tegument and capsule walls of metacercariae of Cardiocephaloides longicollis. A and F illustrate the capsule wall of monocyst and multicyst metacercariae; B and I represent diagrams of monocyst and multicyst metacercariae showing the location of the following TEM micrographs. C – E Longitudinal section through the capsule wall and tegument of a monocyst. G, H, J-M Longitudinal section through the inner capsule wall and tegument of a multicyst metacercaria. D, E, J-M Detail of necrotic material accumulated on the capsule wall surrounding the metacercaria. K, Detail of inner capsule walls merging together within a multicyst. CW, capsule wall; F, fibrocyte; Gx, glycocalyx; GxF, glycocalyx filaments; ICW, inner capsule wall; M, metacercaria; M1- M3 number of metacercaria in a multicyst; MA, macrophage; Mt, metacercarial tegument; N, nucleus; NC, necrotic cells. Head arrows indicate glycocalyx filaments, asterisks (*) outside of the cyst, (**) inside of the cyst, (***) inside of the cyst when encysted with more than one capsule wall. Scale bars: D, E = 1 μm; C, G, H, J, L, M = 5 μm; K = 10 μm.
Fig. 3 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 3. Occupation of the fish brain by Cardiocephaloides longicollis in fresh (A, B) and histological samples (C–F). Cardiocephaloides longicollis metacercariae within (A) the PGZ and (B) the medulla oblongata in experimentally-infected fish one month after infection. Asterisks indicate the position of metacercariae. Cardiocephaloides longicollis metacercariae are found at 6 dpi in the tectal ventricle (C), and as they grow (D, 21 dpi; E, 8 mpi; F, 15 mpi) they occupy larger part of the tectal ventricle, and also the PGZ. The representations of brains indicate the sections and positions (yellow square) where metacercariae have been found. Legend: TeO striped, cerebellum in dots and Mo squared. ICL, inferior cerebellar lobe; Mo, medulla oblongata; PGZ, periventricular gray zone of optic tectum; TeO, tectum opticum; TV, tectal ventricle. Scale bars: A = 300 μm; B = 450 μm; C–F = 200 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 1. Distribution of metacercariae of Cardiocephaoides longicollis in the different fish brain regions, i.e., olfactory bulbs (Olf-B), olfactory lobes (Olf-L), optic lobe region (Op-L), inferior and superior cerebellar lobes (ICL, SCL), medulla oblongata (Mo), and spinal cord (SC). Metacercarial distribution in different fish species sampling locations are provided. N, number of infected brains used for metacercarial distribution; P, prevalence (based on total number of fish, see Table 1); MI, mean intensity. Note that the number of metacercariae in the brain of fish from the marine pond is based only of half brain (see Materials and methods).
Fig. 2 in Mapping a brain parasite: occurrence and spatial distribution in fish encephalon
Fig. 2. Variation in the number of metacercariae of Cardiocephaloides longicollis encysted in different fish groups. Box plots represent the median number of metacercariae per brain region, upper and lower quartile (box) with maximum and minimum ranges (whiskers). Olfactory bulbs (Olf-B), olfactory lobes (Olf-L), optic lobe region (Op-L), inferior and superior cerebellar lobes (ICL, SCL), medulla oblongata (Mo), and spinal cord (SC). Y-axis is represented in logarithmic scale, and dots represent jittered raw data.
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Visualizing phage infection
<p>We staged infections at four multiplicities of infection (MOI 0, 0.01, 0.1, and 1), and took snapshots every ten minutes over a 40-minute period. We designed FISH probes targeting the non-coding strand of the <em>gp34</em> gene, which encodes a tail fiber protein and quantified cells with 5 or more MGE spots, less than 5 spots, and no spots</p>
Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'
<p>Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article: </p> <p>Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405</p> <p>The .zip file has the following folders:</p> <p>1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents</p> <p>2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction</p> <p>3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil texture prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil texture prediction</p>
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections (Revision data)
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the <strong>revisions</strong> of manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections", which has been processed for revision (2020-01-31) by PLOS Biology.</p> <p>The files are in *.zip format and the naming follows thenumbering of the manuscript figures, which will be found in bioRxiv.org (<strong>ID#: BIORXIV/2020/938571</strong>). Each *.zip files contains a document with the description of all the provided files.</p>
Spatial mapping of root systems reveals diverse strategies of soil exploration and resource contest in grassland plants
<p>1. When foraging and competing for belowground resources, plants have to coordinate the behaviour of thousands of root tips in a manner similar to that of eusocial animal colonies. While well described in animals, we know little about the spatial behaviour of plants, particularly at the level of individual roots.</p> <p>2. Here, we employed statistical methods previously used to describe animal ranging behaviour to examine root system overlap and the efficiency of root positioning in eight grassland species grown in monocultures and mixtures along a gradient of neighbour densities.</p> <p>3. Species varied widely in their ability to distribute roots efficiently, with the majority of species showing significant root aggregation at very fine spatial scales. Extensive root system overlap was observed in species mixtures, indicating a lack of territoriality at the level of whole root systems. However, with increasing density of competitors, several species withdrew roots from the periphery of foraging ranges and increased intraplant root aggregation in the remaining area, which may indicate consolidation of foraging areas under competitive pressure.</p> <p>4. Several species exhibited responses consistent with resource contest in species mixtures where encounters with competitors' roots triggered increased root aggregation at the expense of foraging efficiency. Such responses only occurred in mixtures of species with comparable competitive abilities but were absent in asymmetric species combinations.</p> <p>5. Synthesis. Combining fine-scale measurement of plant root distributions with spatial statistics yields new insights into plant behavioural strategies with significant potential to impact resource foraging efficiency and productivity.</p>
Data from: Retinotopic-like maps of spatial sound in primary 'visual' cortex of blind human echolocators
The functional specialisations of cortical sensory areas were traditionally viewed as being tied to specific modalities. A radically different emerging view is that the brain is organized by task rather than sensory modality, but it has not yet been shown that this applies to primary sensory cortices. Here we report such evidence by showing that primary 'visual' cortex can be adapted to map spatial locations of sound in blind humans who regularly perceive space through sound echoes. Specifically, we objectively quantify the similarity between measured stimulus maps for sound eccentricity and predicted stimulus maps for visual eccentricity in primary 'visual' cortex (using a probabilistic atlas based on cortical anatomy) to find that stimulus maps for sound in expert echolocators are directly comparable to those for vision in sighted people. Furthermore, the degree of this similarity is positively related with echolocation ability. We also rule out explanations based on top-down modulation of brain activity – e.g. through imagery. This result is clear evidence that task-specific organization can extend even to primary sensory cortices, and in this way is pivotal in our reinterpretation of the functional organisation of the human brain.
Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning
Marine spatial planning (MSP) should assist managers in guiding human activities towards sustainable practices and in minimizing user-conflicts in our oceans. A necessary first step is to quantify spatial patterns of marine assemblages in order to understand the ecosystem's structure, function, and services. However, the large spatial scale, high economic value, and density of human activities in near-shore habitats often makes quantifying this component of marine ecosystems especially daunting. To address this challenge, we developed an assessment method that employs abiotic proxies to rapidly characterize marine assemblages in near-shore benthic environments with relatively high resolution. We evaluated this assessment method along 300 km of the State of Maine's coastal shelf (< 100m depth)—a zone where high densities of buoyed lobster traps typically preclude extensive surveys by towed sampling gear (i.e., otter trawls). During the summer months of 2010-2013, we implemented a stratified-random survey using a small remotely operated vehicle that allowed us to work around lobster buoys and to quantify all benthic megafauna to species. Stratifying by substrate, depth, and coastal water masses, we found that abiotic variables explained a significant portion of variance (37- 59%) in benthic species composition, diversity, biomass and economic value. Generally, the density, diversity, and biomass of assemblages significantly increased with the substrate complexity (i.e., from sand-mud to ledge). The diversity, biomass and economic value of assemblages also decreased significantly with increasing depth. Lastly demersal fish densities, sessile invertebrate densities, species diversity, and assemblage biomass increased from east to west, while the abundance of mobile invertebrates and economic value decreased, corresponding mainly to the contrasting water-mass characteristics of the Maine Coastal Current system (i.e., summertime current direction, speed, and temperature). Integrating modeled predictions with existing GIS layers for abiotic conditions allowed us to scale up important assemblage attributes to define key foundational ecological principles of MSP and to find priority regions where some bottom-disturbing activities would have minimal impact to benthic assemblages. We conclude that abiotic proxies can be strong forcing functions for the assembly of marine communities and therefore useful tools for spatial extrapolations of marine assemblages in congested (heavily used) near-shore habitats.
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.