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FIGURE 15 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 15. Principal Components Analysis for anterior cervicals of Australian xenopsarian specimens (including previously described QM F3567 from Sachs (2004) and Opallionectes andamookaensis from Kear (2006), but excluding polycotylid QM F12719) and non-Australian elasmosaurids using shape variables (HI, BI, BHI). Data for QM F3567 and RM FR271 from Sachs (2004); Opallionectes andamookaensis from Kear (2005a); Elamosaurus platyurus, Thalassomedon haningtoni, Callawayasaurus colombiensis and Cm Zfr 115 from O'Keefe and Hiller (2006); Aristonectes quiriquinensis from Otero et al. (2014); Albertonectes vanderveldei from Kubo et al. (2012); Vegasaurus molyi from O'Gorman el. (2015); Tuarangisaurus keyesi from Hiller et al. (2017); AMNH FARB 1495, AMNH FARB 5835, Styxosaurus snowii, and AMNH FARB 2554 from Otero (2016); Aristonectes parvidens from O'Gorman (2016a); Kawanectes lafquenianus from O'Gorman (2016b); Lagenanectes richterae from Sachs et al. (2017) and Jucha squalea from Fischer et al. (2020).
FIGURE 3 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 3. Specimen QM F12719. A. Dorsal vertebra, anterior view. B. Dorsal vertebra, lateral view. C. Cervical vertebra, anterior view. D. Cervical vertebra, ventral view showing foramina subcentralia (f.s.). E. Cervical vertebra, dorsal view showing foramen on neural arches. Scales shown on figure.
FIGURE 9 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 9. Specimen QM L39 – anterior cervicals. A. Lateral view with prominent ridge. B. Anterior view. C. Ventral view with foramina subcentralia. D. Anterior view. E. Lateral view showing rib facet. F. Ventral view showing foramina subcentralia. Scales shown on figure.
FIGURE 12 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 12. Normalised vertebral position (cervicals 0-1; dorsals 1-2; caudals 2-3) plotted against vertebral length index (VLI) for Australian plesiosaurians and non-Australian elasmosaurids. Data for QM F3567 and RM FR271 from Sachs (2004); Opallionectes andamookaensis from Kear (2005a); Elamosaurus platyurus, Thalassomedon haningtoni, Callawayasaurus colombiensis, and Cm Zfr 115 from O'Keefe and Hiller (2006); Vegasaurus molyi from O'Gorman el. (2015); AMNH FARB 1495, AMNH FARB 5835, and AMNH FARB 2554 from Otero (2016); Aristonectes parvidens from O'Gorman (2016a); Kawanectes lafquenianus from O'Gorman (2016b); Lagenanectes richterae from Sachs et al. (2017), and Jucha squalea from Fischer et al. (2020).
FIGURE 6 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 6. Specimen RM FR269. A. Cervical vertebra, anterior view with weakly fused neural arches and neural spine. B. Cervical vertebrae, lateral view with rib facets borne wholly on the centrum. C. Cervical vertebra, ventral view showing paired foramina subcentralia (f.s.). D. Dorsal vertebra, anterior view with rib facets (diapophyses) borne wholly on neural arches. Scales shown on figure.
FIGURE 16 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 16. Taxon/specimen average plot for anterior cervicals of Australian plesiosauromorph specimens and nonAustralian elasmosaurids. Data for QM F3567 and RM FR271 from Sachs (2004); Opallionectes andamookaensis from Kear (2005a); Elamosaurus platyurus, Thalassomedon haningtoni, Callawayasaurus colombiensis and Cm Zfr 115 from O'Keefe and Hiller (2006); Aristonectes quiriquinensis from Otero et al. (2014); Albertonectes vanderveldei from Kubo et al. (2012); Vegasaurus molyi from O'Gorman el. (2015); Tuarangisaurus keyesi from Hiller et al. (2017); AMNH FARB 1495, AMNH FARB 5835, Styxosaurus snowii, and AMNH FARB 2554 from Otero (2016); Aristonectes parvidens from O'Gorman (2016a); Kawanectes lafquenianus from O'Gorman (2016b); Lagenanectes richterae from Sachs et al. (2017) and Jucha squalea from Fischer et al. (2020).
FIGURE 5 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 5. Specimen QM F12934. A. Caudal vertebra showing chevron facets; B. Caudal vertebra showing rib facet, lateral view. C. Sacral vertebra showing rib facets borne partly on centrum and partly on neural arch, lateral view; D. Sacral vertebra, anterior view. E. Posterior cervical showing rib facet, lateral view. F. Anterior cervical showing foramina subcentralia (f.s.), ventral view. G. Anterior cervical, lateral view. H. Dorsal vertebra, anterior view. Scales shown on figure.
FIGURE 11. A-C. Specimen F171282 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 11. A-C. Specimen F171282/QM ISO. A. Lateral view showing lateral ridge. B. Anterior view, showing neural arch fused to the centrum. C. Ventral view showing paired foramina subcentralia. D-F. QM Specimen PL (unregistered). D. Anterior cervical, lateral view showing ridge. E. Anterior cervical, ventral view showing paired foramina subcentralia. F. Anterior cervical, anterior view, showing part of neural arches fused to the centrum. Scales shown on figure.
FIGURE 14 in Taxonomic utility of Early Cretaceous Australian plesiosaurian vertebrae
FIGURE 14. Plot for vertebral length index (VLI) against breadth index (BI) for Australian plesiosaurians and nonAustralian elasmosaurids. Data for QM F3567 and RM FR271 from Sachs (2004); Opallionectes andamookaensis from Kear (2005a); Elamosaurus platyurus, Thalassomedon haningtoni, Callawayasaurus colombiensis, and Cm Zfr 115 from O'Keefe and Hiller (2006); Aristonectes quiriquinensis from Otero et al. (2014); Albertonectes vanderveldei from Kubo et al. (2012); Vegasaurus molyi from O'Gorman el. (2015); Tuarangisaurus keyesi from Hiller et al. (2017); AMNH FARB 1495, AMNH FARB 5835, Styxosaurus snowii, and AMNH FARB 2554 from Otero (2016); Aristonectes parvidens from O'Gorman (2016a); Kawanectes lafquenianus from O'Gorman (2016b); Lagenanectes richterae from Sachs et al. (2017) and Jucha squalea from Fischer et al. (2020).
Extracting Ridge and Valley Lines in Mountainous Areas from Airborne Lidar Data by Utilizing Line Feature Strength
<p><strong><span>Background</span></strong><strong><span>:</span></strong><span> </span><span>DEMs (digital elevation models) are very important in many fields, such as in Geomatics and in water conservation of mountainous areas etc. Geomorphic feature lines are necessary data for the topography interpolation and computation from DEMs.</span></p> <p><strong><span>Methods</span></strong><strong><span>:</span></strong><span> </span><span>Instead of the parameter space, we propose a novel automatic extraction of Geomorphic feature lines in the feature space from discrete airborne LiDAR (Light detection and ranging) data by TVM (tensor voting method) developed originally for image data in this article. A tensor field for discrete airborne LiDAR points is first established and then utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line growing method based on the local maximum line feature strength is proposed in the article.</span></p> <p><strong><span>Results</span></strong><strong><span>:</span></strong><span> </span><span>Compared with the general line growing that is based on a certain threshold, our line growing method is quite effective, in particular for the extraction of primary and minor ridge and valley lines in mountainous areas.</span></p> <p><strong><span>Conclusions</span><span>:</span></strong><span> </span><span>The method presented in this paper is fast and automated and can furnish operators with a wealth of detailed information about minor line features. This will enable the extraction of ridge and valley lines tailored to specific requirements. It is no doubt that the method developed here can be generalized to a large amount of Lidar data.</span></p>
Fig. 3 in Limited gene flow among Cydia pomonella (Lepidoptera: Tortricidae) populations in two isolated regions in China: Implications for utilization of the SIT
Fig. 3. Dendrogram generated by NJ analysis representing the genetic distance among populations of Cydia pomonella. The topology was tested by bootstrap analysis with 1,000 replicates. The scale bar represents 2.0% genetic distance. The sampling locations include JinTa (JJT), YinDa (JYD), XiDong (JXD) and ZongZai (JZZ) of Jiuquan Region, and Luo Tuocheng (ZLT), NiJiaying (ZNJ), MinYong (ZMY) and XiaoMan (ZXM) of Zhangye Region.
Fig. 1 in Limited gene flow among Cydia pomonella (Lepidoptera: Tortricidae) populations in two isolated regions in China: Implications for utilization of the SIT
Fig. 1. Sampling regions and locations of Cydia pomonella in Hexi Corridor of China. (I) map of China, the Hexi Corridor is indicated in the dash box; (II) enlarged map of Hexi Corridor, the 2 sampling regions are indicated as (A) Jiuquan region and (B) Zhangye region; (III) enlarged map of the sampling regions and sampling locations. The sampling locations includes JinTa (JJT), YinDa (JYD), XiDong (JXD), and ZongZai (JZZ) of the Jiuquan Region, and Luo Tuocheng (ZLT), NiJiaying (ZNJ), MinYong (ZMY) and XiaoMan (ZXM) of the Zhangye Region.
Fig. 2 in Limited gene flow among Cydia pomonella (Lepidoptera: Tortricidae) populations in two isolated regions in China: Implications for utilization of the SIT
Fig. 2. Bayesian clustering analysis by use of STRUCTURE, which indicates the presence of 2 clusters. Proportion of membership coefficient for 8 Cydia pomonella populations falling into the 2 clusters is depicted by 2 different shades of gray, respectively. The sampling locations include JinTa (JJT), YinDa (JYD), XiDong (JXD) and ZongZai (JZZ) of Jiuquan Region, and Luo Tuocheng (ZLT), NiJiaying (ZNJ), MinYong (ZMY) and XiaoMan (ZXM) of Zhangye Region.
Fig. 1 in Utilization of an introduced weed biological control agent, Megamelus scutellaris (Hemiptera: Delphacidae), by a native parasitoid
Fig. 1. Kalopolynema ema (Hymenoptera: Mymaridae) adults that emerged from eggs of Megamelus scutellaris (Hemiptera: Delphacidae), a biological control agent of waterhyacinth, Eichhornia crassipes. Female (lef) and male (right). Photos taken by Jeremiah Foley, USDA-ARS Invasive Plant Research Laboratory.
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 6. Comparison on resource utilization.
<p>Figure 6 shows resource utilization in different system loads and as shown in it, in ICDA<br> resource utilization is more efficient than other methods especially in higher system load which is<br> due to tradeoff and sharing factors.</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 4. Sharing and tradeoff factor effect on resource utilization
<p>In Figure 4 we consider tradeoff and sharing factors in providers. The result illustrates that<br> by using these factors providers improve resource utilization. Higher resource utilization motivates<br> more providers to participate in the cloud and also enables the cloud market to handle more<br> consumers which influences market efficiency.</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 10. Detailed accuracy separated by classes and a confusion matrix which belongs to the dataset of Coiflet 1 applied by our main method (ANNSVM)
<p>With regard to accuracy values of each class as presented in Figure 10, we observed that the accuracy of the two-dimensional chart class was the lowest (i.e., 0.875), while others were over 0.9. Results here suggested that both the bar and pie classes have their own unique characteristics, as opposed to the 2Dchart class. For example, the graph images that contained some rectangles were individually categorized in the bar graph class. A similar phenomenon occurred for circles in the pie chart class. In contrast, the 2Dchart class contained mixed types of graphs; hence, the graph characteristics belonging to the 2Dchart class varied. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 8. Simulation of Coiflet 1 (PyWavelets discussion group, 2008), analyzing as one-dimensional images
<p>Using only the wavelet coefficients was inadequate for classification. For example, for the pie chart, we obtained large wavelet coefficients located in the low-frequency domain; however, if we changed a circle in the pie chart to other shapes, such as a radar chart, the wavelet transformation gave results that were similar to those of the original pie chart. The Hough transformation can solve this problem since it detects the shapes of objects</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7b. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT
<p>From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain. </p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7c. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT
<p>From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain. </p>
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.