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Рис. 3–6. Coelorinchus idiolepis sp. nov., гоΛотип, ΔетаΛи строения: 3 — гоΛова, виΔ сверху; 4 — ditto, виΔ снизу; 5 — чешуйный покров верха гоΛовы; 6 — абΔоминаΛьная обΛасть. Обозначения: А — гоΛый участок верхней поверхности рыΛа; B — меΔиаΛьный назаΛьный гребень; C — затыΛочный гребень; D — перипрокт и анус. Масштаб: 3, 4 — 30 мм (Λинейка общая); 5 — 15 мм; 6 — 25 мм Figs. 3–6. Coelorinchus idiolepis sp. nov., holotype, structural details: 3 — head, dorsal view; 4 — ditto, ventral view; 5 — squamation of snout and top of head; 6 — abdominal region. Symbols: А — scaleless area on snout; B — medial nasal ridge; C — occipital ridge; D — periproct and anus. Scale bars: 3, 4 — 30 mm (common bar); 5 — 15 mm; 6 — 25 mm in Coelorinchus From The Hawaiian-Emperor Seamount Chain (The Pacific Ocean) (Teleostei, Gadiformes, Macrouridae)
Рис. 3–6. Coelorinchus idiolepis sp. nov., гоΛотип, ΔетаΛи строения: 3 — гоΛова, виΔ сверху; 4 — ditto, виΔ снизу; 5 — чешуйный покров верха гоΛовы; 6 — абΔоминаΛьная обΛасть. Обозначения: А — гоΛый участок верхней поверхности рыΛа; B — меΔиаΛьный назаΛьный гребень; C — затыΛочный гребень; D — перипрокт и анус. Масштаб: 3, 4 — 30 мм (Λинейка общая); 5 — 15 мм; 6 — 25 мм Figs. 3–6. Coelorinchus idiolepis sp. nov., holotype, structural details: 3 — head, dorsal view; 4 — ditto, ventral view; 5 — squamation of snout and top of head; 6 — abdominal region. Symbols: А — scaleless area on snout; B — medial nasal ridge; C — occipital ridge; D — periproct and anus. Scale bars: 3, 4 — 30 mm (common bar); 5 — 15 mm; 6 — 25 mm
Figs. 10-14 in Dorsal chaetotaxy of neotropical species supports a basal position for the genus Lepidonella among scaled Paronellidae (Collembola, Entomobryoidea)
Figs. 10-14. Lepidonella zeppelinii sp. nov.; (10) Dorsal chaetotaxy of abdomen 4; (11) Trochanteral organ; (12) Metathoracic claw complex; (13) Dens and mucro; (14) Detail of mucro.
Fig. 5 in Influence of environmental factors and sessile biota on vagile epibionts: The case of amphipods in marinas across a regional scale Abstract
Fig. 5: RDA graphic showing the similarity between the amphipod community present in each marina and its relationship with the sessile community (CHI = Chipiona, AME = Puerto. América, BAR = Barbate, LIN= La Línea, FUE = Fuengirola, ALM = Almería; Green = Atlantic; Orange = Mediterranean).
Fig. 4 in Influence of environmental factors and sessile biota on vagile epibionts: The case of amphipods in marinas across a regional scale Abstract
Fig. 4: Composition (percentage of total volumen) of sessile species present in the pontoons of each marina (CHI = Chipiona, AME = Puerto. América, BAR = Barbate, LIN= La Línea, FUE = Fuengirola, ALM = Almería. Numbers represents the three pontoons). The group "Others" includes "Other ascidians" and "Filiform algae".
Fig. 3 in Influence of environmental factors and sessile biota on vagile epibionts: The case of amphipods in marinas across a regional scale Abstract
Fig. 3: Two dimensional MDS plot based on Bray–Curtis similarity matrix on the square root-transformed amphipod abundance data. Dashed lines indicate SIMPROF results (P<0.05).
Fig. 2 in Influence of environmental factors and sessile biota on vagile epibionts: The case of amphipods in marinas across a regional scale Abstract
Fig. 2: Number of species (S), abundance (N) and Shannon-Wiener diversity values (H') of the amphipod community (Mean ± standard error). *P <0.05; **P <0.01.
◂Fig. 5 Cells of phylogenetically related strains (light microscopy). a Thecate cell in dorsal view. b Thecate cell in ventral view, note the sulcus extending onto the epitheca (arrow). c Putatively necrotic, thecate cell. d Thecate cell with one bulge on the epitheca (arrow), note that this was the only such cell among thousands of inspected cells. e, f Coccoid cells, apparently without thecae. g Two thecate cells enclosed in the parental theca. h Two connected, immotile cells enclosed in the parental thecae. j Lid of epitheca in dorsal-apical view (mirrored), composed of plates 2′‒4′, all intercalary plates and plates 2′′‒6′′. l‒m Same opened theca in ventral view (l) and dorsal view (m), note the sulcus extending onto the epitheca (arrow), the dorsal opening and all apical and all intercalary plates and plates 3′′‒5′′ remaining with the hypotheca. n Chloroplasts (as inferred from autofluorescence), note the space occupied by the nucleus. Plate labelling follows the Kofoidean notation, n′: apical plate; n′′: precingular plate; n′′′: postcingular plate; na: anterior intercalary plate. Scale= 10 µm in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 5 Cells of phylogenetically related strains (light microscopy). a Thecate cell in dorsal view. b Thecate cell in ventral view, note the sulcus extending onto the epitheca (arrow). c Putatively necrotic, thecate cell. d Thecate cell with one bulge on the epitheca (arrow), note that this was the only such cell among thousands of inspected cells. e, f Coccoid cells, apparently without thecae. g Two thecate cells enclosed in the parental theca. h Two connected, immotile cells enclosed in the parental thecae. j Lid of epitheca in dorsal-apical view (mirrored), composed of plates 2′‒4′, all intercalary plates and plates 2′′‒6′′. l‒m Same opened theca in ventral view (l) and dorsal view (m), note the sulcus extending onto the epitheca (arrow), the dorsal opening and all apical and all intercalary plates and plates 3′′‒5′′ remaining with the hypotheca. n Chloroplasts (as inferred from autofluorescence), note the space occupied by the nucleus. Plate labelling follows the Kofoidean notation, n′: apical plate; n′′: precingular plate; n′′′: postcingular plate; na: anterior intercalary plate. Scale= 10 µm
◂Fig. 3 Historically described phenotypical variations and yet undiscovered deviations in the plate pattern of P. volzii. b–c, f, i, l–m Light microscopy, a, d–e, g–h, k scanning electron microscopy. a–f Newly identified deviations a–b plate 4′′ pentagonal in strains a GeoM*793; b GeoM*788; c plate 2a split (strain GeoK*024); d plates 2′′ and 3′′ fused (strain GeoM*866); e plates 1′′′ and 1′′′′ fused (strain GeoM*788); f plates 1a and 3′ fused (strain GeoM*788). g–m Historic infraspecific taxa; g P. guestrowiense forma lineatum (strain GeoM*866); h P. guestrowiense forma compressum (strain GeoM*866); i P. guestrowiense subvar. originale (strain GeoK*024); k P. volzii var. cinctiforme (strain GeoM*793); l P. volzii var. simplex (strain GeoM*789); m P. volzii forma complexum (strain GeoM*793). Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, split or fused plates are indicated by asterisks. Scale bar= 10 µm. U A= 15 kV in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 3 Historically described phenotypical variations and yet undiscovered deviations in the plate pattern of P. volzii. b–c, f, i, l–m Light microscopy, a, d–e, g–h, k scanning electron microscopy. a–f Newly identified deviations a–b plate 4′′ pentagonal in strains a GeoM*793; b GeoM*788; c plate 2a split (strain GeoK*024); d plates 2′′ and 3′′ fused (strain GeoM*866); e plates 1′′′ and 1′′′′ fused (strain GeoM*788); f plates 1a and 3′ fused (strain GeoM*788). g–m Historic infraspecific taxa; g P. guestrowiense forma lineatum (strain GeoM*866); h P. guestrowiense forma compressum (strain GeoM*866); i P. guestrowiense subvar. originale (strain GeoK*024); k P. volzii var. cinctiforme (strain GeoM*793); l P. volzii var. simplex (strain GeoM*789); m P. volzii forma complexum (strain GeoM*793). Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, split or fused plates are indicated by asterisks. Scale bar= 10 µm. U A= 15 kV
PSYCHOMETRIC PROPERTIES OF THE PROSOCIAL BEHAVIOR SCALE IN SAMPLE INDONESIAN ADOLESCENTS
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A three-weight surface modeling approach for optimizing small-scale population disaggregation
<p><span>In recent decades, gridded population data at fine scales has become a popular data source for assessing and monitoring the Sustainable Development Goals (SDGs). However, current population disaggregation methods are facing challenges in generating high-precision population grids for small areas with limited data. To fill this gap, we proposed a lightweight population gridding method that combines basic dasymetric mapping and point-based surface modeling, named three-weight surface modeling. In this method, there are three weights designed to describe the population spatial heterogeneity from different perspectives. The first weight is building-volume weight, which is equivalent to the preliminary results of population assignment based on building volume information. The second weight, POI-center weight, incorporates POI categories and aggregation patterns to express the centers with high population density, which is calculated based on the neighborhood accumulation rule of Spearman's correlation coefficients between POIs and population size. The third weight called POI-distance weight, indicates different rates of population decay with distance from high-density centers. The three-weight surface model allows us to dynamically adjust the parameters so as to correct the building-volume weight according to the remaining two POI-related weights for a more accurate population surface. After analyzing the census population and the disaggregation results of 544 villages in three counties (Huishui, Luodian and Pingtang) in southern Guizhou Province, China, we found that the customized three-weight model constructed using the local parameter groups demonstrated better accuracy performance compared to separate dasymetric mapping or point-based surface modeling. Meanwhile, the 10-m population grid generated by the local parameter model (LPTW-POP) exhibited higher resolution and lower errors (RMSE, MAE and MRE) than widely used gridded population datasets like LandScan, WorldPop and GHS-POP.</span></p>
Figs. 9-12 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 9-12. Silica-scaled chrysophytes from Mt. Sinbul wetland. 9. Mallomonas papillosa, 10, 11. M. papillosa var. monilifer, 12. M. portaeferreae var. reticulata. Scale bars = 1 μm.
Figs. 13-15 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 13-15. Silica-scaled chrysophytes from Mt. Sinbul wetland, 13. Mallomonas striata, 14. M. tonsurata, 15. Whole cell of M. dimorphus sp. nov. Scale bars = 13, 14 (1 μm), 15 (5 μm).
Figs. 1-4 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 1-4. Silica-scaled chrysophytes from Mt. Sinbul wetland. 1. Mallomonas calceolus, 2. M. crassisquama, 3. M. elongata, 4. M. lelymene. Scale bars = 1 μm.
Figs. 25-30 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 25-30. Silica-scaled chrysophytes from Mt. Sinbul wetland. 25. Scales of Synura petersenii, 26. Scales of S. sphagnicola, 27. Scales of S. spinosa, 28. Plate and spine scales of Chromophysomonas trioralis, 29. Plate and spine-scales of Chrysosphaerella brevispina, 30. Several scales with short and long spine of Paraphysomonas vestita, Scale bars = 1 μm.
Figs. 21-24 in Silica-scaled chrysophytes from Mt. Sinbul wetland in South Korea
Figs. 21-24. Mallomonas alpestris sp. nov., 21. Whole cell with two needle shaped bristles, 22. Collar apical scales, 23. Body scales and rear scales with short stout spine, 24. Showing the shield with 9-10 transverse ribs and papillae of 3-4 rows and anterior flange with a row of papillae (arrow). Scale bars = 21 (5 μm), 22-24 (1 μm).
Fig. 1 in New record of commensal scale worms, Arctonoe vittata (Grube, 1855) and Hyperhalosydna striata (Kinberg, 1856) (Polychaeta: Polynoidae) from Korean waters
Fig. 1. Arctonoe vittata (Grube, 1855), NIBRIV0000326189. A, Anterior end without elytra, dorsal view (arrow indicates dorsal cirrophore); B, Anterior end, ventral view; C, anterior elytron pairs; D, Posterior end, dorsal view; E, Cirrigerous parapodium from segment 6, anterior view; F, Notochaeta from segment 6; G, Superior neurochaeta from segment 6; H, Inferior neurochaeta from segment 6; I, Posterior end, ventral view; J, Posterior segments with nephridial papillae, ventral view. Scale bar: A-D = 0.5 mm; E, I, J = 0.2 mm; F-H = 0.05 mm.
2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 7/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains a sample of the input data for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
evolution of urban scaling BR
<p>This is the data used for the paper. Evolution if urban scaling: evidences for Brazil</p>
FIGURE 6. Dzumacoccus baylaci spec. n in A new genus and species of felt scale (Hemiptera: Coccomorpha: Eriococcidae) from New Caledonia on an endemic species of Casuarinaceae
FIGURE 6. Dzumacoccus baylaci spec. n., mounted first-instar nymph.
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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.