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372 results for “functional group”
Fig. 2 in Two new species of Vaejovis (Scorpiones: Vaejovidae) belonging to the mexicanus group from Aguascalientes, Mexico, with comments on the homology and function of the hemispermatophore
Fig. 2. Sinistral hemispermatophore, cleared of soft tissue by hand. Contralateral aspect (left) and lateral aspect (right). A. Vaejovis aguazarca Díaz-Plascencia and Gonz´alez-Santill´an sp. nov. B. Vaejovis aquascalentensis Ch´avez-Samayoa and Gonz´alez-Santill´an sp. nov. C. Vaejovis tenamaztlei Contreras-F´elix, Francke and Bryson, 2015. The tridimensional boxes are enclosing the total extension of the capsular distal carina, including the basal and distal sections delimited by the presence of the laminar hook. Abbreviations: Ac, Axial carina, Cdc, Capsular distal carina. Lac, Laminar antero-basal constriction, Lap, Laminar antero-distal process. Llc, laminar latero-distal crest.
Fig. 6 in Two new species of Vaejovis (Scorpiones: Vaejovidae) belonging to the mexicanus group from Aguascalientes, Mexico, with comments on the homology and function of the hemispermatophore
Fig. 6. Coxal region, genital operculum, and pectines under UV light. Left side (6) and right side (♀). A. Vaejovis aguazarca Díaz-Plascencia and Gonz´alez-Santill´an sp. nov. B. Vaejovis aquascalentensis Ch´avez-Samayoa and Gonz´alez-Santill´an sp. nov. C. Vaejovis tenamaztlei Contreras-F´elix, Francke and Bryson, 2015.
Raw data to "A Quantum Chemical Study on the Evolution of Sulfur Functional Groups During Char Burnout"
<p>This data is a supplement to the publication entitled "A Quantum Chemical Study on the Evolution of Sulfur Functional Groups During Char Burnout".</p>
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb.</p> <p>The total disk space for pore Tet6Di12 alone is 1.5Gb.</p>
The ambient noise cross-correlation function and teleseismic events, the Love-wave phase velocities, Rayleigh-wave phase and group velocities and H/V, Vsv and Vsh model in the study region (CNCC)
<p>The cross-correlation functions form EOBSArray (COR_CNCC), The Love-wave phase velocities (Love_phase), Rayleigh-wave phase (Rayleigh_phase) and group velocities (Rayleigh_group) and H/V (HtoV),and 3-D Vsv and Vsh model and related uncertainties (Vsv_Vsh_model) in the study region (CNCC) are archieved in the file named CNCC_data.7z.</p>
Fig. 3 in Structure and Composition of Dung Beetle Assemblages (Coleoptera: Scarabaeidae) in a Livestock Ranch in Central Uruguay: Responses of Functional Groups and Species to Local Habitats and Trophic Resources
Fig. 3. Dominance/diversity curves based on a) abundance and b) biomass of the dung beetle species of Scarabaeidae in three habitats in Puntas de Sauce Maciel, Florida, Uruguay. PHS = pasture on humid soil; PDS = pasture on dry soil; EP = Eucalyptus plantation.
Fig. 1. a in Structure and Composition of Dung Beetle Assemblages (Coleoptera: Scarabaeidae) in a Livestock Ranch in Central Uruguay: Responses of Functional Groups and Species to Local Habitats and Trophic Resources
Fig. 1. a) Sample completeness curves as a function of dung beetle abundance in three study habitats in Puntas de Sauce Maciel, Florida, Uruguay, b) Coverage-based rarefaction (solid lines) and extrapolation (dash above endpoint for PHS) sampling curves for species richness of the dung beetle data from the three habitats. PHS = pasture on humid soil; PDS = pasture on dry soil; EP = Eucalyptus plantation.
Fig. 2 in Structure and Composition of Dung Beetle Assemblages (Coleoptera: Scarabaeidae) in a Livestock Ranch in Central Uruguay: Responses of Functional Groups and Species to Local Habitats and Trophic Resources
Fig. 2. Diversity profiles for dung beetle assemblages in three habitats in Puntas de Sauce Maciel, Florida, Uruguay as a function of Hill numbers (q) with 95% confidence intervals (shaded areas). PHS = pasture on humid soil; PDS = pasture on dry soil; EP = Eucalyptus plantation.
Fig. 4 in Structure and Composition of Dung Beetle Assemblages (Coleoptera: Scarabaeidae) in a Livestock Ranch in Central Uruguay: Responses of Functional Groups and Species to Local Habitats and Trophic Resources
Fig. 4. Detrended correspondence analysis ordination of dung beetle species sampled with baited pitfall traps in three different habitats in Puntas de Sauce Maciel, Florida, Uruguay. PHS = pasture on humid soil; PDS = pasture on dry soil; EP = Eucalyptus plantation.
Figure 6 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 6. Dorsal view of third-instar larval head capsule (left half) of Chrysoperla bolti sp. nov. The drawing represents the typical condition seen in 18 individuals from two populations in northern Kyrgyzstan. The colour insert is a typical mature third-instar larva of the species.
Figure 3 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 3. Frequency distributions of volley period measured in solo vibrational songs recorded at 25 ± 1° C from individuals of Chrysoperla shahrudensis sp. nov. (red/grey bars), Chrysoperla bolti sp. nov. (blue/ dark grey bars), and Chrysoperla adamsi (green/light grey bars). Measurements were calculated as individual averages; N = number of individuals from which individual averages were calculated.
Figure 5 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 5. Dorsal view of third-instar larval head capsule (left half) of Chrysoperla shahrudensis sp. nov. The drawing represents the typical condition seen in 24 individuals from two populations in Iran. The colour insert is a typical mature third-instar larva of the species.
Figure 7 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 7. Maximum likelihood phylogram of the cryptic species of the Chrysoperla carnea- group, based on 4630 bp of mitochondrial DNA sequence from the protein-coding genes ND2, COI, COII and ND5. The tree was inferred under the General Time Reversible model, using a discrete gamma distribution to model evolutionary rate variation while allowing some sites to remain invariant (GTR+G + I as implemented in MEGA v.7.0, Kumar et al. 2016). Numbers at the branch points are support values from 150 bootstrap replicates; branch lengths are proportional to the number of substitutions per site. Specimen numbers and geographical locations are shown for each operational taxonomic unit (twig) on the tree. The three songconvergent species discussed in the text are highlighted in colour: Chrysoperla shahrudensis in red, Chrysoperla bolti in blue and Chrysoperla adamsi in green. Thumbnail oscillographs of 12 s of song phenotype for 13 common species are placed on the figure at their approximate vertical positions on the phylogram.
Figure 4 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 4. Adult of (a) Chrysoperla. shahrudensis sp. nov., non-diapausing green form; (b) C. shahrudensis, diapausing sandy-brown form, and (c) Chrysoperla bolti sp. nov., all-season green form.
Figure 2 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 2. Oscillographs of duetting interactions. (a) 12 s of a typical duet between a male and a female of Chrysoperla shahrudensis sp. nov., showing the partners politely exchanging single volleyplus-rumble units or shortest repeated units (SRUs). (b) 12 s of a typical duet between a male and a female of Chrysoperla bolti sp. nov., showing the partners politely exchanging single SRUs. (c) 71 s oscillograph showing a female of C. shahrudensis duetting with a conspecific for the first 24 s, with C. bolti for the next 25 s, and with Chrysoperla adamsi (North America) for the last 22 s. In each of those three duets, the female being tested is capable of synchronizing precisely with the prerecorded playback signal. As discussed in the text, C. shahrudensis shows no such duetting response to playbacks of songs of any carnea-group species other than C. bolti and C. adamsi. All duets were recorded at 25 ± 1°C.
Figure 1 in Distinctive but functionally convergent song phenotypes characterize two new allopatric species of the Chrysoperla carnea-group in Asia, Chrysoperla shahrudensis sp. nov. and Chrysoperla bolti sp. nov. (Neuroptera: Chrysopidae)
Figure 1. Oscillographs (lower of each pair of traces) and sonographs (upper traces) of (a) a solo vibrational song of Chrysoperla shahrudensis sp. nov. from northern Iran and (b) a solo vibrational song of Chrysoperla bolti sp. nov. from Kyrgyzstan, Asia, illustrating convergent song phenotypes in these two Asian species. Each song was recorded at 25 ± 1°C and is drawn to the same time scale of 12 s. Start, middle, end, and rumble sections of an individual volley are marked by the arrows, while duration and period of the primary volley are bracketed.
Functional groups in piscivorous fishes
<p>Piscivory is a key ecological function in aquatic ecosystems, mediating energy flow within trophic networks. However, our understanding of the nature of piscivory is limited; we currently lack an empirical assessment of the dynamics of prey capture, and how this differs between piscivores. We therefore conducted aquarium-based performance experiments, to test the feeding abilities of 19 piscivorous fish species. We quantified their feeding morphology, striking, capturing, and processing behaviour. We identify two major functional groups: grabbers and engulfers. Grabbers are characterised by horizontal, long-distance strikes, capturing their prey tail first, and subsequently processing their prey using their oral jaw teeth. Engulfers strike from short distances, from high angles above or below their prey, engulfing their prey, and swallowing their prey whole. Based on a meta-analysis of 2,209 published in situ predator-prey relationships in marine and freshwater aquatic environments, we show resource partitioning between grabbers and engulfers, with grabbers feeding on relatively larger prey than engulfers. Our results provide a functional classification for piscivorous fishes, delineating patterns which transcend habitats, that may help explain size structures in fish communities.</p>
Pharmulator™ Module: Quantifying Functional Groups and Its Applications in Drug Design
<p><strong>MS-Excel-1.xlsx:</strong> It contains SMILES codes of the training and test sets along with their GHS classification </p> <p><strong>MS-Excel-2.xlsx: </strong>It contains<strong> </strong>SMILES codes of the 8993 chemicals and their experimental aqueous solubility parameters and FGs, Morgan and MACCS-based structural descriptor values</p> <p><strong>MS-Excel-3.xlsx: </strong>It contains the DrugBank IDs, SMILES codes and number of FG occurrences (binary string) of the 2356 drug molecules</p> <p><strong>MS-Excel-4.xlsx:</strong> It contains SMILES codes as well as FGs descriptors for the entire training sets, balanced training sets and final test sets</p> <p><strong>MS-Excel-5.xlsx:</strong> It contains SMILES codes as well as Morgan descriptors for the entire training sets, balanced training sets and final test sets</p> <p><strong>MS-Excel-6.xlsx:</strong> It contains SMILES codes as well as MACCS descriptors for the entire training sets, balanced training sets and final test sets</p>
Dominance of contrasting fungal functional groups influence nutrient cycling across four Japanese cool-temperate forest soils - Data repository
<p>Soil data related to the publication <strong>Dominance of contrasting fungal functional groups influence nutrient cycling across four Japanese cool-temperate forest soils.</strong></p>
Social Skills Group Training ("KONTAKT") for Children and Adolescent With High-functioning Autism Spectrum Disorders
ClinicalTrials.gov study NCT01854346. IPD Sharing: Not stated. Countries: 1. Publications: 5.
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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.