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Figure 4 from: Fu Y, Fang X-L, Wang X-H, Shen M, Xiao Y-L (2022) Corynoneura Winnertz species from Hunan Province, Oriental China, delineated with morphological and 16S rDNA data (Diptera, Chironomidae). ZooKeys 1082: 87-102. https://doi.org/10.3897/zookeys.1082.73019
Figure 4 Corynoneura incuria sp. nov., female imago. A wing B hind tibial apex C thorax D genitalia.
Figure 2 from: Fu Y, Fang X-L, Wang X-H, Shen M, Xiao Y-L (2022) Corynoneura Winnertz species from Hunan Province, Oriental China, delineated with morphological and 16S rDNA data (Diptera, Chironomidae). ZooKeys 1082: 87-102. https://doi.org/10.3897/zookeys.1082.73019
Figure 2 Corynoneura gibbera sp. nov., male imago. A wing B tentorium and cibarial pump C antenna D thorax E hind tibial apex F hypopygium, dorsal view G, H hypopygium, ventral view.
Figure 1 from: Fu Y, Fang X-L, Wang X-H, Shen M, Xiao Y-L (2022) Corynoneura Winnertz species from Hunan Province, Oriental China, delineated with morphological and 16S rDNA data (Diptera, Chironomidae). ZooKeys 1082: 87-102. https://doi.org/10.3897/zookeys.1082.73019
Figure 1 Corynoneura enormis sp. nov., male imago. A wing B tentorium and cibarial pump C apex of antenna D legs E hypopygium, dorsal view F hypopygium, ventral view.
data for "Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms"
<p>These data were used in article "Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms" (https://doi.org/10.3390/agronomy12010183)</p>
Data-driven approach for the delineation of the irritative zone in epilepsy in MEG
<p>The reliable identification of the irritative zone (IZ) is a prerequisite for the correct clinical evaluation of medically refractory patients affected by epilepsy. Given the complexity of MEG data, visual analysis of epileptiform neurophysiological activity is highly time consuming and might leave clinically relevant information undetected. We recorded and analyzed the interictal activity from seven patients affected by epilepsy (Vectorview Neuromag), who successfully underwent epilepsy surgery (Engel >= II). We visually marked and localized characteristic epileptiform activity (VIS). We implemented a two-stage pipeline for the detection of interictal spikes and the delineation of the IZ. First, we detected candidate events from peaky ICA components, and then clustered events around spatio-temporal patterns identified by convolutional sparse coding. We used the average of clustered events to create IZ maps computed at the amplitude peak (PEAK), and at the 50% of the peak ascending slope (SLOPE). We validated our approach by computing the distance of the estimated IZ (VIS, SLOPE and PEAK) from the border of the surgically resected area (RA). We identified 25 spatiotemporal patterns mimicking the underlying interictal activity (3.6 clusters/patient). Each cluster was populated on average by 22.1 [15.0-31.0] spikes. The predicted IZ maps had an average distance from the resection margin of 8.4 ± 9.3 mm for visual analysis, 12.0 ± 16.5 mm for SLOPE and 22.7 ±. 16.4 mm for PEAK. The consideration of the source spread at the ascending slope provided an IZ closer to RA and resembled the analysis of an expert observer. We validated here the performance of a data-driven approach for the automated detection of interictal spikes and delineation of the IZ. This computational framework provides the basis for reproducible and bias-free analysis of MEG recordings in epilepsy.</p>
Boundary Delineator for Martian Crater Instances with Geographic Information and Deep Learning
<p>Detecting impact craters on the Martian surface is a critical component to studying Martian geomorphology and planetary evolution. Accurately determining impact crater boundaries, which are distinguishable geomorphic units, is important work in geological and geomorphological mapping. The Martian topography is more complex than that of the Moon, making the accurate detection of impact crater boundaries challenging. Currently, most techniques concentrate on replacing impact craters with circles or points. Accurate boundaries are more challenging to identify than simple circles. Therefore, a boundary delineator for Martian crater instances (BDMCI) using fusion data was proposed. First, optical image, digital elevation model (DEM), and slope of elevation difference after filling the DEM (called slope of EL_Diff, to highlight the boundaries of craters) were used to combined. Second, a benchmark dataset with annotations for accurate impact crater boundaries was created, and sample regions were chosen using geospatial prior knowledge and an optimization strategy for the proposed BDMCI framework. Third, the multiple models were fused to train at various scales using deep learning. To repair patch junction fractures, several postprocessing methods were devised. The proposed BDMCI framework was also used to expand the catalog of Martian impact craters between 65°S and 65°N. This study provides a reference for identifying terrain features and demonstrates the potential of deep learning algorithms in planetary science research.</p>
Fig. 2 in Comparative molecular and morphological species delineation of Halammohydra Remane, 1927 (Hydrozoa)-with the description of four new species
Fig. 2 Light microscopy images of Halammohydra species. a H. vermiformis with a 3 times longer tentacle (arrowhead) in the subaboral whorl. b Magnification of aboral cone in a showing a thickening at the tentacle bases of the subaboral tentacles (arrowhead). c H. octopodides with 2 times longer tentacle (arrowhead). d H. coronata with 1.5 times longer tentacle (arrowhead) in the subaboral whorl. ab, aboral tentacles; ac, aboral cone; ao, adhesive organ; gt, gastric tube; sub, subaboral tentacles
Fig. 3 in Comparative molecular and morphological species delineation of Halammohydra Remane, 1927 (Hydrozoa)-with the description of four new species
Fig. 3 Light microscopy images of Halammohydra adherens. a Smaller variant with less tentacles. b Larger variant with more tentacles. c Magnification of a tentacle of a with a few elongated nematocysts (arrowheads). d Magnification of the tentacle of c filled with elongated nematocysts (arrowheads). gt, gastric tube; mo, mouth opening
Fig. 15 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 15 Phorcus articulatus (Lamarck 1822). a PCA plot of PC1 vs. PC2 for genus Phorcus. Single specimen of Phorcus articulatus separates from all other species. b Representative specimen of Ph. articulatus from this study. c One syntype of Ph. articulatus (INVE 51532). Scale bars 5 mm
Fig. 13 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 13 Phorcus mutabilis (Philippi, 1846). a PCA plot of PC1 vs. PC3 from genus Phorcus. Phorcus mutabilis (olive) splits into two morphotypes: one from Croatia and one from Crete. b Representative specimen of Ph. mutabilis (Croatia) from this study. c Representative specimen of Ph. mutabilis (Crete) from this study. d Specimen (black arrow) showing Ph. richardi morphology, but Ph. mutabilis COI barcode. From this study. Scale bars 5 mm
Fig. 9 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 9 Steromphala rarilineata (Michaud, 1829). a PCA plot of PC1 vs. PC3 from genus Steromphala. Steromphala rarilineata (violet) is separated but very near St. adriatica and St. divaricata. b Representative specimen of St. rarilineata from this study. Scale bar 5 mm
Fig. 7 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 7 Steromphala adansonii (Payraudeau 1826). a PCA plot of PC1 vs. PC2 of genus Steromphala. Single specimen of Steromphala adansonii (black) clusters near St. umbilicaris. b Representative specimen of St. adansonii from this study. Scale bar 5 mm
Fig. 6 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 6 Steromphala umbilicaris (Linnaeus, 1758) and Steromphala nebulosa (Philippi, 1848). a PCA plot of PC1 vs. PC2 of genus Steromphala. Steromphala umbilicaris (green) separates well from all other species. There is no overlap between specimens from this study and the type material. One individual (Linné 24) nests within St. adriatica. Steromphala nebulosa (grey) nests at the edge of St. umbilicaris. b Representative specimen of St. umbilicaris from this study. c Representative specimen of St. nebulosa from this study. d: Designated lectotype for St. umbilicaris located at LSL (LSL.504). Scale bars 5 mm
Fig. 8 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 8 Steromphala adriatica (Philippi, 1844). a PCA plot of PC1 vs PC2 of genus Steromphala. Steromphala adriatica (red) is separated from all but St. divaricata. One individual of Steromphala umbilicaris (Linné
Fig. 3 in Revision of the genus complex Gibbula: an integrative approach to delineating the Eastern Mediterranean genera Gibbula Risso, 1826, Steromphala Gray, 1847, and Phorcus Risso, 1826 using DNA-barcoding and geometric morphometrics (Vetigastropoda, Trochoidea)
Fig. 3 RAxML based phylogenetic reconstruction (GTRGAMMA model and 1000 rapid bootstrap replicates). COI barcodes were used to identify species and reconstruct relationships between species and genera.
FIGURE 1 in Molecular delineation of the Agave Red Worm Comadia redtenbacheri (Lepidoptera: Cossidae)
FIGURE 1. Last instar Comadia redtenbacheri larvae in rhizome tissue of Agave salmiana.
FIGURE 2 in Molecular delineation of the Agave Red Worm Comadia redtenbacheri (Lepidoptera: Cossidae)
FIGURE 2. Female Comadia redtenbacheri.
FIGURE 3 in Checklist of the Red Sea Fishes with delineation of the Gulf of Suez, Gulf of Aqaba, endemism and Lessepsian migrants
FIGURE 3. Map of the Gulf of Suez and the Gulf of Aqaba.
FIGURE 1 in Checklist of the Red Sea Fishes with delineation of the Gulf of Suez, Gulf of Aqaba, endemism and Lessepsian migrants
FIGURE 1. Map of the Indian Ocean showing the locality of the Red Sea.
Supplementary material 2 from: Raupach MJ, Hannig K, Morinière J, Hendrich L (2019) About Notiophilus Duméril, 1806 (Coleoptera, Carabidae): Species delineation and phylogeny using DNA barcodes. Deutsche Entomologische Zeitschrift 66(1): 63-73. https://doi.org/10.3897/dez.66.34711
: Data type: Substitution saturation plot
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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.