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10,767 results for “Contribution”
FIGURE 5 in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 5. Hypoxys ocanensis sp. n. Female genital plates: A—ventral view, B—oblique view. Scale bars = 1 mm.
FIGURE 2 in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 2. Hypoxys striatus sp. n. Male (pygophore): A—dorsal view, B —posterior view, C—ventral view, D—posterolateral view. Female: E—genital plates. Scale bars = 1 mm.
FIGURE 9 in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 9. Female genital plates in oblique view, mediolateral crest swollen on valvifers VIII indicated by the arrow: A— Hypoxys triangularis; B—H. capito; C—H. subrastratus; D—H. myrtacivorus sp. n.; E—H. curvatus sp. n.; F—H. striatus sp. n. Scale bars = 1mm.
FIGURE 8 in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 8. Male genital capsule in posterolateral view, swollen area on inner wall of the pygophore indicated by the arrow: A—Hypoxys triangularis; B—H. capito; C—H. subrastratus; D—H. myrtacivorus sp. n.; E—H. curvatus sp. n.; F—H. striatus sp. n. Scale bars = 1mm.
FIGURE 1 in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 1. Hypoxys myrtacivorus sp. n. Male (pygophore): A—dorsal view, B —posterior view, C—ventral view, D— posterolateral view. Female: E—genital plates. Acronyms: Dr—dorsal rim; Pc—superior process of the genital cup; Pa— parameres; Proc—proctiger; Vr—ventral rim; Vf8—valvifers VIII; Vf9—valvifers IX; Lat 8—laterotergites VIII; Lat 9— laterotergites IX; Va9—valvulae IX; X—tenth segment. Scale bars = 1 mm.
FIGURE 7. Phallus A—H in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 7. Phallus A—H. triangularis; B—H. subrastratus; C—Hypoxys myrtacivorus sp. n. Acronyms: ph—phallotheca; vs—vesica. Scale bars = 1mm.
FIGURE 4 in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 4. Hypoxys curvatus sp. n. Male (pygophore): A—dorsal view, B —posterior view, C—ventral view, D—posterolateral view. Female: E—genital plates. Scale bars = 1 mm.
FIGURE 11. Dorsal and ventral view. A–B H in New species of the Hypoxys triangularis group and the first contribution on the morphology of the internal genitalia of Hypoxys Amyot & Serville, 1843 (Hemiptera: Pentatomidae: Edessinae)
FIGURE 11. Dorsal and ventral view. A–B H. curvatus sp. n.; C–D Hypoxys ocanensis sp. n. Scale bars = 5mm.
How does variation in total and relative abundance contribute to gradients of species diversity?
Patterns of biodiversity provide insights into the processes that shape biological communities around the world. Variation in species diversity along biogeographical or ecological gradients, such as latitude or precipitation, can be attributed to variation in different components of biodiversity: changes in the total abundance (i.e. more-individual effects) and changes in the regional species abundance distribution (SAD). Rarefaction curves can provide a tool to partition these sources of variation on diversity, but first must be converted to a common unit of measurement. Here, we partition species diversity gradients into components of the SAD and abundance using the effective number of species (ENS) transformation of the individual-based rarefaction curve. Because the ENS curve is unconstrained by sample size, it can act as a standardized unit of measurement when comparing effect sizes among different components of biodiversity change. We illustrate the utility of the approach using two datasets spanning latitudinal diversity gradients in trees and marine reef fish, and find contrasting results. Whereas the diversity gradient of fish was mostly associated with variation in abundance (86%), the tree diversity gradient was mostly associated with variation in the SAD (59%). These results suggest that local fish diversity may be limited by energy through the more-individuals effect, while species pool effects are the larger determinant of tree diversity. We suggest that the framework of the ENS-curve has the potential to quantify the underlying factors influencing most aspects of diversity change. --
FIGURES 11–19 in Contribution to the Erebidae fauna of the Great Nicobar Island, India with description of two new species (Lepidoptera: Noctuoidea)
FIGURES 11–19. Genitalia of Erebidae species from the Great Nicobar Island. 11–12, Anoba rigida, male: 11, ventral view, 12, aedeagus; 13–15, Garudinia shompen sp. nov., male (Holotype): 13, ventral view, 14, lateral view, 15, aedeagus; 16–17, Nishada sambara, male: 16, ventral view, 17, aedeagus; 18–19, Teulisna chiloides, male: 18, ventral view, 19, aedeagus. Diagnostic characters indicated with black lines.
FIGURES 1–10 in Contribution to the Erebidae fauna of the Great Nicobar Island, India with description of two new species (Lepidoptera: Noctuoidea)
FIGURES 1–10. Adults of new species and new records of Erebidae from the Great Nicobar Island. 1, Anoba rigida, male, dorsal side; 2, Garudinia shompen sp. nov., male (Holotype), dorsal side; 3, Nishada sambara, male, dorsal side; 4, Teulisna chiloides, male, dorsal side; 5, ditto, female, dorsal side; 6, Plusiodonta nicobarensis sp. nov., male (Holotype), dorsal side; 7, ditto, ventral side; 8, Tamba cosmoloma, male, dorsal side; 9, T. occidinawa, male, dorsal side; 10, Anereuthina renosa, female, dorsal side.
Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?
<p>Input files and data used in the paper "Do high-spin high mass X-ray binaries contribute to the population of merging binary black holes?". MESA models were computed with version 12115 of MESA. COSMIC models new computed with version 3.4. README file included. </p>
Niche differentiation along multiple functional-trait dimensions contributes to high local diversity of Euphorbiaceae in a tropical tree assemblage
<p><span>Understanding the mechanisms that drive community assembly in species-rich tropical forest remains a fundamental challenge in ecology. Here, we integrated multivariate functional trait dimensions, phylogeny, and metabolomics to test fundamental predictions concerning the role of differentiation with respect to abiotic and biotic niche axes in the maintenance of high local diversity of woody plants in the Euphorbiaceae.</span></p> <p><span>We measured 40 functional traits related to resource acquisition, photosynthetic capacity, hydraulic efficiency, and secondary-metabolite profiles generated using untargeted metabolomics in all 26 Euphorbiaceae species in a 20-ha forest dynamics plot in tropical southwestern China. We examined the correlation structure of 40 traits using a trait networking approach. We coupled these traits with variation in soil nutrients, light environment, soil water content and herbivore pressure within the plot to assess niche differentiation in space. We compared phylogenetic signal among </span><span>multivariate trait dimensions and secondary metabolites to assess niche differentiation in evolutionary time.</span></p> <p><span>Network analysis revealed that a small number of traits with high network centrality reflected variation in ecological strategy among the Euphorbiaceae. Using these high-centrality traits, we observed significant functional turnover along environmental gradients defined by light, soil moisture, soil nutrients and leaf herbivory, respectively. Most resource utilization traits showed significant phylogenetic signal, whereas almost all defensive traits lacked phylogenetic signal, including species similarity with respect to plant secondary metabolites.</span></p> <p><span>Synthesis</span><span>.</span><span> </span><span>Our results suggest that resource-utilization traits and the habitat associations play a significant role in the </span><span>niche segregation</span><span> of co-occurring woody plants in the Euphorbiaceae. Secondary metabolites, however, may enhance diversity at a finer spatial scale by allowing closely related species with similar functional traits to partition biotic niche space within shared habitats in tropical</span><span> rainforest.</span></p>
Supplementary Data for the Project Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome
<p><strong>Supplementary Tables for the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"</strong></p> <p>Supplementary Table 1. List of 18 genome-sequenced human gut bacteria with metabolic characteristics that were used for community assembly.</p> <p>Supplementary Table 2. Diet composition.</p> <p>Supplementary Table 3. Species relative abundance.</p> <p>Supplementary Table 4. Gene abundance and expression changes.</p> <p>Supplementary Table 5. Gene pathway enrichment results.</p> <p>Supplementary Table 6. Metabolomics data.</p> <p>Supplementary Table 7. Metabolite fold changes, clustering and model parameters.</p> <p>Supplementary Table 8. Description of the intestinal flux model.</p> <p>Supplementary Table 9. Enzymatic paths between substrates and products.</p> <p>Supplementary Table 10. Pearson's correlation coefficients between potential substrates and products, and metagenomics and metatranscriptomic measurements.</p>
FIGURE 60 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 60. Distribution of Plecoptera in cities of Guangxi. a. Tyloperla bilobata Mo, Wang & Li, 2020. b. Tyloperla courtneyi Stark & Sivec, 2005. c. Tyloperla sinensis Yang & Yang, 1993. d. Cerconychia multiseta Mo, Liu & Li, 2021. e. Styloperla jiangxiensis Yang & Yang, 1990. f. Styloperla spinicercia Wu, 1935.
FIGURE 58 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 58. Distribution of Plecoptera in cities of Guangxi. a. Neoperlops gressitti Banks, 1939. b. Neoperlops triangulatus Mo, Wang, Li & Murányi, 2020. c. Paragnetina insignis Banks, 1939. d. Sinacroneuria obscura Li & Murányi, 2017. e. Sinacroneuria wui (Yang & Yang, 1998). f. Togoperla canilimbata (Enderlein, 1909).
FIGURE 57 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 57. Distribution of Plecoptera in cities of Guangxi. a. Neoperla truncata Wu, 1948. b. Neoperla wui Yang & Yang, 1990. c. Neoperla xuansongae Li & Li, 2013. d. Neoperla yao Stark, 1987. e. Neoperla yentu Cao & Bae, 2007. f. Neoperlops cheni (Wu, 1938).
FIGURE 56 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 56. Distribution of Plecoptera in cities of Guangxi. a. Neoperla shangsiensis Mo, Wang, Li & Murányi, 2021. b. Neoperla shiwandashana Mo, Yao, Wang & Li, 2019. c. Neoperla similidella Li & Wang, 2013. d. Neoperla similidelphina Mo, Li & Murányi, 2021. e. Neoperla tadpolata Li & Murányi, 2014. f. Neoperla transversprojecta Du & Sivec, 2004.
FIGURE 53 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 53. Distribution of Plecoptera in cities of Guangxi. a. Neoperla delphina Li, Mo & Wang, 2020. b. Neoperla falcatata Mo, Wang, Li & Murányi, 2021. c. Neoperla fanjingshana Yang & Yang, 1992. d. Neoperla furcostyla Li & Qin, 2013. e. Neoperla guangxiensis Du & Sivec, 2004. f. Neoperla henana Li, Wu & Zhang, 2011.
FIGURE 59 in Review and contribution to the stoneflies (Insecta: Plecoptera) of Guangxi Zhuang Autonomous Region, China, with new species and new records from South China
FIGURE 59. Distribution of Plecoptera in cities of Guangxi. a. Togoperla condyla Li & DeWalt, 2012. b. Togoperla noncoloris Du & Chou, 1999. c. Togoperla perpicta Klapálek, 1921. d. Togoperla totanigra Du & Chou, 1999. e. Togoperla triangulata Du & Chou, 1999. f. Togoperla tricolor Klapálek, 1921.
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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.