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Mixed chirality α-helix in a stapled bicyclic and a linear antimicrobial peptide revealed by X-ray crystallography
<p>The upload contains additional primary data associated with the publication <a href="https://doi.org/10.1039/D1CB00124H">https://doi.org/10.1039/D1CB00124H</a>, including raw data in the original file format whenever possible.</p> <p>Data content: HPLC-MS, CD, Vesicle leakage, Molecular Dynamics, Crystallography (primary electron density maps) and Supporting Information.</p>
Figure 4 in Horn scaling relationships in three species of Bledius Leach 1819 (Insecta: Coleoptera: Staphylinidae) show no indication of fitting non-linear allometric models
Figure 4. Scaling relationship between log pronotum width and log horn length in Bledius tricornis (Herbst, 1784). The slope is 1.166 (SE = 0.093) and the linear model has an R2 of 0.78.
Figure 3 in Horn scaling relationships in three species of Bledius Leach 1819 (Insecta: Coleoptera: Staphylinidae) show no indication of fitting non-linear allometric models
Figure 3. Scaling relationship between log pronotum width and log horn length in Bledius frisius Lohse, 1978. The slope is 1.816 (SE = 0.136) and the linear model has an R2 of 0.78.
Figure 1 in Horn scaling relationships in three species of Bledius Leach 1819 (Insecta: Coleoptera: Staphylinidae) show no indication of fitting non-linear allometric models
Figure 1. (a) Bledius spectabilis Kraatz, 1857 schematic diagram of pronotal measurements taken and habitus. (b) Bledius spectabilis at Chesil Fleet, Dorset, UK (photograph by Steve Trewhella). HL = horn length; PW = pronotum width.
Figure 2 in Horn scaling relationships in three species of Bledius Leach 1819 (Insecta: Coleoptera: Staphylinidae) show no indication of fitting non-linear allometric models
Figure 2. Scaling relationship between log pronotum width and log horn length in Bledius spectabilis Kraatz, 1857. The slope is 1.447 (SE = 0.096) and the linear model has an R2 of 0.68.
FIGURES – 0. Shape and position of pseudocyphellae in several Ramalina species. 6. orbicular pseudocyphellae and laminal soralia (R. chiguarensis), scale = 1.3 mm. 7. Orbicular pseudocyphellae (R. cochlearis), scale = 0.4 mm. 8. Ellipsoid pseudocyphellae (R. santanensis), scale = 0.3 mm. 9. Linear pseudocyphellae (R. tenuissima), scale = 0.6 mm. 10. Flattened pseudocyphellae (R. maegdefraui), scale = 61.5 µm. in The genus Ramalina Acharius (Ascomycota, Lecanoromycetes, Ramalinaceae) in northern South America
FIGURES – 0. Shape and position of pseudocyphellae in several Ramalina species. 6. orbicular pseudocyphellae and laminal soralia (R. chiguarensis), scale = 1.3 mm. 7. Orbicular pseudocyphellae (R. cochlearis), scale = 0.4 mm. 8. Ellipsoid pseudocyphellae (R. santanensis), scale = 0.3 mm. 9. Linear pseudocyphellae (R. tenuissima), scale = 0.6 mm. 10. Flattened pseudocyphellae (R. maegdefraui), scale = 61.5 µm.
FIGURE. Examples of interpetiolar stipules: A) distinct, filiform, laciniate, glabrous (E. peplis), B) distinct, linear-subulate, glabrous (E. glyptosperma), C) connate, deltate, dentate, glabrous (E. serpens susbp. serpens), D) connate, deltate fimbriate, hairy (E. nutans), E) connate, deltate, lacerate, hairy (E. prostrata). in Synopsis of Euphorbia section Anisophyllum (Euphorbiaceae) in Italy, with an insight on variation of distribution over time in Tuscany
FIGURE. Examples of interpetiolar stipules: A) distinct, filiform, laciniate, glabrous (E. peplis), B) distinct, linear-subulate, glabrous (E. glyptosperma), C) connate, deltate, dentate, glabrous (E. serpens susbp. serpens), D) connate, deltate fimbriate, hairy (E. nutans), E) connate, deltate, lacerate, hairy (E. prostrata).
Figure 9 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 9. Wireframe visualization of allometric shape change along the least squares regression line of Procrustes coordinates on log centroid size. Grey landmarks represent the average configuration amongst all specimens, whereas black landmarks represent the approximate extreme of variation (1.2 log centroid size units) in the direction of the smallest specimens, which have proportionally larger heads and eyes.
Figure 1. Leporinus cylindriformis, MCZ 20430 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 1. Leporinus cylindriformis, MCZ 20430, holotype, 188.0 mm standard length; Brazil, Pará, Rio Xingu at Porto de Moz. Image © President and Fellows of Harvard College.
Figure 10 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 10. Wireframe visualization of variation along the allometrically corrected principal components one (PC1), two, and three from geometric morphometric analysis. Grey landmarks represent the configuration of the average specimen, black landmarks represent one approximate extreme of variation on that axis. The deformation on PC1 represents 0.07 units, that on PC2 represents 0.04 units, and that on PC3 represents 0.03 units. Percentages indicate the proportion of total variance amongst the Procrustes residuals explained by each axis.
Figure 7 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 7. Reduced major axis regression of principal component one (PC1) scores from traditional linear morphometrics on log standard length for species of Leporinus discussed in text. Trendline represents a universal regression that does not take species membership into account; tests for equivalence of slope and intercept as reported in Tables 2 and 3 estimate a separate regression line for each putative species.
Figure 12 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 12. Geographical distribution of examined specimens of Leporinus amazonicus, Leporinus apollo sp. nov., Leporinus cylindriformis, Leporinus niceforoi, Leporinus cf. niceforoi, and Leporinus sp. Some symbols represent more than one collection locality.
Figure 3 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 3. Leporinus apollo sp. nov., FMNH 116827, holotype, 111.1 mm standard length; Suriname, Saramacca, Coppename River, Sidonkrutu, sand island and channel.
Figure 8 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 8. Scatterplot of principal components one and two from geometric morphometric analysis without allometric correction for species of Leporinus discussed in text. Both axes show significant correlations with log centroid size.
Figure 11 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 11. Scatterplot of (A) principal components one and two and (B) principal components one and three from geometric morphometrics after allometric correction. Polygons represent convex hulls surrounding nominal species of Leporinus.
Figure 6 in Dealing with allometry in linear and geometric morphometrics: a taxonomic case study in the Leporinus cylindriformis group (Characiformes: Anostomidae) with description of a new species from Suriname
Figure 6. Scatterplot of principal component two (PC2) versus one from traditional linear morphometrics for species of Leporinus discussed in text. PC1 is an allometric vector describing size and shape variation, whereas PC2 is essentially size-free. Polygons indicate convex hulls.
Data for "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks"
<p>This data is related to the manuscript "A Comparison of Linear Solvers for Resolving Flow in Three-Dimensional Discrete Fracture Networks". It contains meshes, boundary conditions, and medium properties for discrete fracture networks used to test fluid flow solvers.</p>
Synthetic data set to evaluate and benchmark the performance of multiple linear regression algorithms in Scikit-Learn and SANElib
<p>The datasets respresent different numbers of columns and rows to measure the scalability of linear regression algorihms in terms of columns and rows.</p>
Trifurcate structure of oxygen band EMIC waves excited in a warm magnetospheric plasma: Linear theory
<p>Simulation data of "Trifurcate structure of oxygen band EMIC waves excited in a warm magnetospheric plasma: Linear theory"</p>
Attack time analysis in dynamic attack trees via integer linear programming
<p>Code and data corresponding to the paper "Attack time analysis in dynamic attack trees via integer linear programming"</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)
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.