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2,170 results for “clouds”
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
<p>Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes. Automatic rib segmentation methods can speed up the process through rib measurement and visualization. However, prior arts mostly use in-house labeled datasets that are publicly unavailable and work on dense 3D volumes that are computationally inefficient. To address these issues, we develop a labeled rib segmentation benchmark, named RibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. For ground truth generation, we used existing morphology-based algorithms and manually refined its results. Then, considering the sparsity of ribs in 3D volumes, we thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation. The proposed method achieves state-of-the-art segmentation performance (Dice<span class="math-tex">\(\approx95\%\)</span>) with significant efficiency (<span class="math-tex">\(10\sim40\times\)</span> faster than prior arts). The RibSeg dataset, code, and model in PyTorch are available at <a href="https://github.com/M3DV/RibSeg">https://github.com/M3DV/RibSeg</a>.</p> <p> </p> <p><strong>Note:</strong> This repository provides rib segmentation ("RibFrac31-rib-seg.nii.gz") and centerline ("RibFrac31-rib-cl.nii.gz") <em>annotations</em> for 490 cases in RibFrac dataset. Please download the corresponding CT <em>images </em>("RibFrac31-image.nii.gz") at <a href="https://ribfrac.grand-challenge.org/">https://ribfrac.grand-challenge.org/</a> (1-click registration is needed via <em>"Join"</em>).</p>
Fig. 5 in Head in the clouds: a new dwarf frog species of the Physalaemus signifer clade (Leptodactylidae, Leiuperinae) from the top of the Brazilian Atlantic Forest
Fig. 5. Spectrogram (above) and oscillogram (below) of advertisement calls. A–C. Three populations of Physalaemus maculiventris (Lutz, 1925). A. Paranapiacaba (FNJV 31815). B. São Paulo (FNJV 31988). C. Salesópolis (FNJV 33756). – D. P. araxa sp. nov. from Ibitirama (FNJV 34633).
Supplementary material: Fundamental watersheds and altitudinal tripartition of the mountain cloud forests from Northwestern Argentina (Yungas)
<p>Vegetation zonation along the slopes of a mountain, from low to high altitude, is a well-known pattern in landscape ecology. A similar picture is also described in the field of freshwater ecology. Rivers and streams show different types of habitats between upland and downland site locations. We studied both segmentations of altitudinal gradient in a subtropical hotspot of biodiversity, namely the mountainous rainforest <span>of Yungas from Northwestern Argentina. We assessed the agreement between vegetation stratification (</span>fog grasslands, cloud montane forest and low montane rainforest<span>) and the tripartition of assemblages of mayflies (an ancient group of aquatic insects). The products we offer here accompany the paper of Biotropica entitled </span><em>The inter-forest line could be the master key to track biocoenotic effects of climate change in a subtropical forest</em>.</p> <p>We established a pair of altitudinal cutoffs in each of the watersheds from Argentinean Yungas. They correspond to the upper and lower limits of the middle layer, and they were obtained through an optimization task. The objective function was to maximize the overlap between the extent of such a middle layer and the area classified as cloud montane forest. The upper limit approaches to the treeline, whereas the lower limit is expected to fit the inter-forest line. Interestingly enough, assemblages of aquatic insects can be ordinated along altitudinal transects, but the main distinction occurs at either side of the hypothetical inter-forest line rather than the treeline. This finding is consistent throughout the study area. Map of the three altitudinal floors, as well as the different watersheds, are available as raster files. </p>
FIGURE 1 in Zadbimyia, a new genus of asynaptine Porricondylinae (Diptera: Cecidomyiidae) with twenty-two new species from the cloud forest of Costa Rica
FIGURE 1. Habitus of male adult Zadbimyia sp., most of wing setae omitted. Scale 1.0 mm.
Figure 6 from: Maveety S, Browne R, Erwin T (2011) Carabidae diversity along an altitudinal gradient in a Peruvian cloud forest (Coleoptera). ZooKeys 147: 651-666. https://doi.org/10.3897/zookeys.147.2047
Figure 6 - Non-Metric Multidimensional Scaling (NMDS) using the Bray Curtis Similarity Index for collections made actively (diamond) and passively (circle). Values for each altitude are grouped by ovals.
Figure 5 from: Maveety S, Browne R, Erwin T (2011) Carabidae diversity along an altitudinal gradient in a Peruvian cloud forest (Coleoptera). ZooKeys 147: 651-666. https://doi.org/10.3897/zookeys.147.2047
Figure 5 - Species accumulation curves when all altitudes and collection types (active or passive) are combined (n = 1924). Brackets represent 95% confidence intervals.
Figure 2 from: Maveety S, Browne R, Erwin T (2011) Carabidae diversity along an altitudinal gradient in a Peruvian cloud forest (Coleoptera). ZooKeys 147: 651-666. https://doi.org/10.3897/zookeys.147.2047
Figure 2 - Species accumulation curves for 5 diversity indices when all altitudes and collection types (active or passive) are combined (n = 1924).
Figure 4 from: Maveety S, Browne R, Erwin T (2011) Carabidae diversity along an altitudinal gradient in a Peruvian cloud forest (Coleoptera). ZooKeys 147: 651-666. https://doi.org/10.3897/zookeys.147.2047
Figure 4 - Percentage of rare species at five altitudinal sites. Black bars represent the taxonomic rarity index and gray bars represent the ecological rarity index (see text for definitions of rarity).
Figure 3 from: Maveety S, Browne R, Erwin T (2011) Carabidae diversity along an altitudinal gradient in a Peruvian cloud forest (Coleoptera). ZooKeys 147: 651-666. https://doi.org/10.3897/zookeys.147.2047
Figure 3 - Number of species at five altitudes when all altitudes and collection types (active or passive) are combined (n = 1924).
Figure 4 from: César E, Bandala VM, Montoya L, Ramos A (2018) A new Gymnopus species with rhizomorphs and its record as nesting material by birds (Tyrannideae) in the subtropical cloud forest from eastern Mexico. MycoKeys 42: 21-34. https://doi.org/10.3897/mycokeys.42.28894
Figure 4 Gymnopusnidus-avis: a Hymenial trama elements b Thick- and thin-walled hyphae from stipe medullary tissue c Hyphae and terminal elements of pileipellis (holotype). Scale bars: 10 µm (a–c).
Figure 3 from: César E, Bandala VM, Montoya L, Ramos A (2018) A new Gymnopus species with rhizomorphs and its record as nesting material by birds (Tyrannideae) in the subtropical cloud forest from eastern Mexico. MycoKeys 42: 21-34. https://doi.org/10.3897/mycokeys.42.28894
Figure 3 Gymnopusnidus-avis: a Spores b Basidia c Cheilocystidia d Pileipellis (holotype). Scale bars: 5 µm (a); 10 µm (b–d).
Figure 1 from: César E, Bandala VM, Montoya L, Ramos A (2018) A new Gymnopus species with rhizomorphs and its record as nesting material by birds (Tyrannideae) in the subtropical cloud forest from eastern Mexico. MycoKeys 42: 21-34. https://doi.org/10.3897/mycokeys.42.28894
Figure 1 Phylogenetic relationships within Gymnopus species inferred from the ITS sequence dataset by maximum likelihood method (ML). Tree with the highest log likelihood (-3619.93). Initial tree(s) for the heuristic search were obtained automatically by applying Neighbour-Joining and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with superior log likelihood value. The bootstrap values and Bayesian posterior probabilities (obtained after Bayesian inference) are indicated on the tree branches (BS/BPP). Sequences obtained in this study are in bold.
High genetic diversity but spatially restricted genetic connectivity in a Tropical Montane Cloud Forest tree (Magnolia schiedeana)
Magnolias are representative shrub and tree species of the Tropical Montane Cloud Forest (TMCF) in Mexico, which is one of the most endangered ecosystems due to anthropogenic landscape degradation and climate change. Magnolia schiedeana is a vulnerable and restricted endemic TMCF species, occurring in scattered TMCF fragments in the Sierra Madre Oriental. In this study, we investigated spatial patterns of genetic diversity across the M. schiedeana species range and assessed whether gene flow was influenced by topographic and environmental factors. We sampled eleven remnant populations across the species range and genotyped 180 individuals at eight nuclear microsatellite loci. Our data showed a strong spatial structure in five genetic clusters, which match the patchy geographic distribution of M. schiedeana. Contemporary migration rates between these genetic clusters were very low. Levels of genetic diversity within populations were moderate to high, and no evidence of inbreeding was found. Our results from landscape genetic analysis showed that isolation by distance, rather than topography or environmental variation, was the main process shaping genetic differentiation. Urgent actions are needed to protect M. schiedeana populations as most of them persist isolated within agricultural and urban landscapes. Ex-situ management actions would be crucial to ensure the conservation of genetically diverse germplasm for the in-situ propagation where it has gone locally extinct.
Supporting Information for the Journal Article "High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference treatment"
<p>This dataset contains the supporting information for the journal article "High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference treatment". It consists of two files:</p> <ul> <li>"structures.json", containing 3335 unique structures found in the exploration, and</li> <li>"energies.json", containing 2227 elementary steps and its energies found in the exploration</li> </ul> <p>In the file "structures.json", the following keys are used to characterize a structure:</p> <ul> <li> _id: the ID of the structure</li> <li>nAtoms: the total number of atoms in the structure</li> <li>atoms: the XYZ coordinates of the atoms in atomic units</li> <li>charge: the total charge of the structure</li> <li>multiplicity: the spin multiplicity 2S+1 of the structure</li> <li>label: the position of the structure on the minimum energy pathway</li> </ul> <p>In the file "energies.json", there are two major fields: structures and reactions.</p> <p>In the field "structures", the keys are:</p> <ul> <li>id: the ID of structure (can be linked to XYZ coordinates in "structures.json")</li> <li>label: the position of the structure on the minimum energy pathway</li> <li>energy_dft: PBE-D3BJ/def2-SVP total energy in atomic units</li> <li>energy_hf: HF/cc-pVDZ total energy in atomic units</li> <li>energy_ccsd: CCSD/cc-pVDZ total energy in atomic units</li> <li>energy_ccsd_t: CCSD(T)/cc-pVDZ total energy in atomic units</li> <li>energy_gibbs_correction: PBE-D3BJ/def2-SVP Gibbs free energy correction in atomic units</li> </ul> <p>In the field "reactions", the keys are:</p> <ul> <li>id: the ID of the elementary step</li> <li>lhs: the structure IDs on the left-hand side of the reaction</li> <li>rhs: the structure IDs on the right-hand side of the reaction</li> <li>type: regular or barrierless</li> <li>barrierless: binary key to indicate whether the elementary step has a barrier</li> <li>ts: ID of the transition state structure</li> <li>reaction: the ID of the reaction to which the elementary step belongs</li> </ul> <p>Due to the nature of exploration, certain structures can be explored from multiple elementary steps. Therefore, duplicated structures are common in the reaction exploration. In "structures.json", we de-duplicated the structures and only list the unique ones. The coordinates (in bohr) and global attributes like spin multiplicity and charge are provided. </p> <p>In "energies.json", each "step" represents an elementary step. 1260 of the steps are barrierless, while 967 of the steps have a barrier. The structures keys cited in "lhs", "rhs", and "ts" are before de-duplication. The "id" in each structure key points to the unique, de-duplicated structure in "structures.json".</p> <p>The main catalytical pathways (figure 9) can be found in the dataset with the following elementary_step IDs:</p> <ul> <li>62066d2fb8e290f3afb607e2</li> <li>6206704db8e290f3afb607e3</li> <li>62067db7b8e290f3afb607e4</li> <li>620a6d49b8e290f3afb607e5</li> <li>620a6dbeb8e290f3afb607e6</li> <li>620a6e2fb8e290f3afb607e7</li> <li>620a6e78b8e290f3afb607e8</li> <li>620a7f01b8e290f3afb607e9</li> <li>620a7f54b8e290f3afb607ea</li> </ul>
A long-term monthly dataset of cloud fraction over the Arctic based on multiple satellite products using cumulative distribution function matching and Bayesian maximum entropy
<p>The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties is an effective way to produce more spatiotemporally complete and accurate CF data record. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method to produce a syncretic 1°×1° CF dataset in the Arctic during 2000-2020. The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM. The fused CF product is more consistent with the active satellite data GEWEX-CALIPSO and the ground-based observation data CRU TS4.05.</p>
Single column 1D radiative transfer simulations for a case study of low-level-stratus clouds in the central Arctic during PS106
<p>The collection of datasets published contain the input parameters and output simulations from a single column 1D radiative transfer simulations using the <strong>R</strong>apid <strong>R</strong>adiative <strong>T</strong>ransfer <strong>M</strong>odel for <strong>G</strong>eneral Circulation Model (GCM) applications (RRTMG).</p> <p>The simulations are focused on a selected case study of low-level-stratus clouds during the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing observations, which were synergistically used with the Cloudnet algorithm to derive macro and microphysical properties of clouds. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.</p>
FIGURE 2 in A new species of planthopper from Costa Rica in the genus Herpis (Hemiptera: Derbidae) from palms in the cloud forest
FIGURE 2. Male adult habitus, Herpis circumsoros sp. n. (A) lateral view; (B) dorsal view.
FIGURE 1 in A new species of planthopper from Costa Rica in the genus Herpis (Hemiptera: Derbidae) from palms in the cloud forest
FIGURE 1. Habitat and locality of Herpis circumsoros sp. n.
Cross-scale assessment of the impacts of chronic seasonal drought and episodic typhoons on subtropical montane cloud forests
<p>The data that support the findings of this study.</p>
OpenCorpora Cloud
<p>Sense-annotated corpus for Russian in Linguistic Linked Open Data cloud</p>
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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)
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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.