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Fig. 3 – A in Mylabrini diversity and host plants in a Saharan oasis ecosystem with an updated checklist of Meloidae from Algeria (Coleoptera)
Fig. 3 – A, Mylabris impressa; B, Croscherichia litigiosa, C, Croscherichia gilvipes and D, a specimen of Alosimus sp. cfr. viridissimus.
Fig. 2 in Mylabrini diversity and host plants in a Saharan oasis ecosystem with an updated checklist of Meloidae from Algeria (Coleoptera)
Fig. 2 – Blister beetle habitats at Ouled Djellal palm groves; A, Oued El Assel, B, Oued Djdai and C, Saad.
Figures 26 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands
Figures 26 to 31. Bactrocera umbrosa (26), B. penefurva (27), B. neoxanthodes (28), B. xanthodes (29), B. decipiens (30), B. atrisetosa (31). All photos by Steve Wilson, Queensland Museum.
Figure 1 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands
Figure 1. Map of the Pacific Island countries and territories, with area covered by this publication enclosed within the dotted line (courtesy from the Secretariat of the Pacific Community).
Data for "Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives"
<p>Research data supporting the study "Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives".</p> <p>It contains:<br> 1) merged.gdx - data derived from the original scenario database<br> 2) map.csv - mapping of food commodities to food groups used for analysis<br> 3) manure.csv - results on nitrogen input to cropland and N crop fertilization from manure<br> 4) AgMIP_regions.shp - shape file used to make maps<br> 5) Paper_visuals_NCOM.R - R script to analyze and visualize the data. It reproduces the main figures in the paper and the appendix<br> last tested for R Studio 2022.12.0 Build 353, Release (7d165dcf, 2022-12-03) for Windows 10 Pro, 64-bit operating system</p> <p>Instructions:<br> The R code, file 4, reads in files 1, 2 and 3 and generates figures, tables and maps.<br> The directories (line 50 and 58) need to be updated to the location of the data (the current folder). </p> <p> </p>
Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey
<p><strong>Data and file overview:</strong></p> <ol> <li>Kamaru_Path_Analysis_Data.csv</li> <li>Kamaru_Path_Analysis.R</li> <li>Kamaru_Zebra_RSF_Data.csv</li> <li>Kamaru_Zebra_RSF.R</li> </ol> <p><strong>Layers used to build Zebra RSF:</strong></p> <ol> <li>Kamaru_DWater: distance to water</li> <li>Kamaru_DGlade: distance to glade</li> <li>Kamaru_DSettlement: distance to human settlement</li> <li>Kamaru_OPC_Veg: vegetation layer (classes: <em>V. drepanolobium</em>, <em>E. divinorum, </em>others)</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv</strong></p> <ol> <li>Number of variables: 11</li> <li>Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020)</p> <p>(B) Kill_ID: kill identifier.</p> <p>(C) Lion_ID: individual lion pride identifier.</p> <p>(D) Date (Day, Month, Year) when a specific kill occurred.</p> <p>(E) Zebra_kill (1 = kill site, 0 = paired random location).</p> <p>(F). Species: Zebra.</p> <p>(G) Visibility: openness measurement using a rangefinder in (m).</p> <p>(H) Lion_activity: Utilization distributions (UD) of lions.</p> <p>(I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants).</p> <p>(J) zeb.rsf: resource selection function value.</p> <p>(K) zeb.density: zebra density estimated from resource selection functions.</p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv</strong></p> <ol> <li>Number of variables: 10</li> <li>Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> <p>(A) Species: Zebra.</p> <p>(B) Date (Day, Month, Year) for that sighting.</p> <p>(C) Survey: count identifier (Survey 2 to 21).</p> <p>(D) GPS location (X and Y), longitude and latitude of that sighting location.</p> <p>(E) Transect: Transect number.</p> <p>(F) Used: (1= zebra sighting, 0 = paired point).</p> <p>(G) zebra.ct: total number of zebras in each sighting.</p> <p> </p> <p><strong>R CODE</strong></p> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models.</li> </ol> <p><strong>SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R</strong></p> <ol> <li>Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.</li> </ol>
Data from: Leaf metabolic traits reveal hidden dimensions of plant form and function
<p>In this study, we interpreted leaf metabolome variation among 457 tropical and 339 temperate plant species to understand how the metabolome contributes to macroecological variation in plant functioning. Metabolome data were generated using liquid chromatography mass spectrometry, annotated with compound names (where possible), and cross-referenced against chemoinformatics databases to derive metabolite chemical properties. We then compared variation in leaf metabolite chemical properties among species with variation in classical plant functional traits.</p>
Figures 32 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands
Figures 32 to 37. B. strigifinis (32), B. triangularis (33), B. chorista (34), B. cucurbitae (35), Dacus axanus (36), D. solomonensis (37). All photos by Steve Wilson, Queensland Museum.
Figures 20 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands
Figures 20 to 25. B. passiflorae (20), B. psidii (21), B. tinomiscii (22), B. trilineola (23), B. trivialis (24), and B. tryoni (25). All photos by Steve Wilson, Queensland Museum.
Figures 14 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands
Figures 14 to 19. Bactrocera melanotus (14), B. moluccensis (15), B. musae (16), B. obliqua (17), B. papayae (18), B. paramusae (19). Photos by Steve Wilson, Queensland Museum, except for B. melanotus (Gerald McCormak).
Figures 8 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands
Figures 8 to 13. B. dapsiles (8), B. enochra (9), B. facialis (10), B. frauenfeldi (11), B. kirki (12), and B. lineata (13). All photos by Steve Wilson, Queensland Museum.
In silico subcellular targeting predictions for cytosolic aminoacyl tRNA-synthetases (aaRS) in parasitic plants
<p>Eukaryotic nuclear genomes often encode distinct sets of protein translation machinery for function in the cytosol vs. organelles (mitochondria and plastids). This phenomenon raises questions about why multiple translation systems are maintained even though they are capable of comparable functions, and whether they evolve differently depending on the compartment where they operate. These questions are particularly interesting in land plants because translation machinery, including aminoacyl-tRNA synthetases (aaRS), is often dual-targeted to both the plastids and mitochondria. These two organelles have quite different metabolisms, with much higher rates of translation in plastids to supply the abundant, rapid-turnover proteins required for photosynthesis. Previous studies have indicated that plant organellar aaRS evolve more slowly compared to mitochondrial aaRS in other eukaryotes that lack plastids. Thus, we investigated the evolution of nuclear-encoded organellar and cytosolic translation machinery across a broad sampling of angiosperms, including non-photosynthetic (heterotrophic) plant species with reduced rates of plastid gene expression to test the hypothesis that translational demands associated with photosynthesis constrain the evolution of bacterial-like enzymes involved in organellar tRNA metabolism. Remarkably, heterotrophic plants exhibited wholesale loss of many organelle-targeted aaRS and other enzymes, even though translation still occurs in their mitochondria and plastids. These losses were often accompanied by apparent retargeting of cytosolic enzymes and tRNAs to the organelles, sometimes preserving aaRS-tRNA charging relationships but other times creating surprising mismatches between cytosolic aaRS and mitochondrial tRNA substrates. Our findings indicate that the presence of a photosynthetic plastid drives the retention of specialized systems for organellar tRNA metabolism.</p>
Organellar tRNAs in parasitic plant species
<p>Eukaryotic nuclear genomes often encode distinct sets of protein translation machinery for function in the cytosol vs. organelles (mitochondria and plastids). This phenomenon raises questions about why multiple translation systems are maintained even though they are capable of comparable functions, and whether they evolve differently depending on the compartment where they operate. These questions are particularly interesting in land plants because translation machinery, including aminoacyl-tRNA synthetases (aaRS), is often dual-targeted to both the plastids and mitochondria. These two organelles have quite different metabolisms, with much higher rates of translation in plastids to supply the abundant, rapid-turnover proteins required for photosynthesis. Previous studies have indicated that plant organellar aaRS evolve more slowly compared to mitochondrial aaRS in other eukaryotes that lack plastids. Thus, we investigated the evolution of nuclear-encoded organellar and cytosolic translation machinery across a broad sampling of angiosperms, including non-photosynthetic (heterotrophic) plant species with reduced rates of plastid gene expression to test the hypothesis that translational demands associated with photosynthesis constrain the evolution of bacterial-like enzymes involved in organellar tRNA metabolism. Remarkably, heterotrophic plants exhibited wholesale loss of many organelle-targeted aaRS and other enzymes, even though translation still occurs in their mitochondria and plastids. These losses were often accompanied by apparent retargeting of cytosolic enzymes and tRNAs to the organelles, sometimes preserving aaRS-tRNA charging relationships but other times creating surprising mismatches between cytosolic aaRS and mitochondrial tRNA substrates. Our findings indicate that the presence of a photosynthetic plastid drives the retention of specialized systems for organellar tRNA metabolism.</p>
Finding orthologs for aminoacyl tRNA synthetases in parasitic plants
<p>Eukaryotic nuclear genomes often encode distinct sets of protein translation machinery for function in the cytosol vs. organelles (mitochondria and plastids). This phenomenon raises questions about why multiple translation systems are maintained even though they are capable of comparable functions, and whether they evolve differently depending on the compartment where they operate. These questions are particularly interesting in land plants because translation machinery, including aminoacyl-tRNA synthetases (aaRS), is often dual-targeted to both the plastids and mitochondria. These two organelles have quite different metabolisms, with much higher rates of translation in plastids to supply the abundant, rapid-turnover proteins required for photosynthesis. Previous studies have indicated that plant organellar aaRS evolve more slowly compared to mitochondrial aaRS in other eukaryotes that lack plastids. Thus, we investigated the evolution of nuclear-encoded organellar and cytosolic translation machinery across a broad sampling of angiosperms, including non-photosynthetic (heterotrophic) plant species with reduced rates of plastid gene expression to test the hypothesis that translational demands associated with photosynthesis constrain the evolution of bacterial-like enzymes involved in organellar tRNA metabolism. Remarkably, heterotrophic plants exhibited wholesale loss of many organelle-targeted aaRS and other enzymes, even though translation still occurs in their mitochondria and plastids. These losses were often accompanied by apparent retargeting of cytosolic enzymes and tRNAs to the organelles, sometimes preserving aaRS-tRNA charging relationships but other times creating surprising mismatches between cytosolic aaRS and mitochondrial tRNA substrates. Our findings indicate that the presence of a photosynthetic plastid drives the retention of specialized systems for organellar tRNA metabolism.</p>
Commodity canola and seed canola visitation and plant data
<p>Insect-mediated pollination of crops is an important service to agriculture, as increased insect visitation can increase fruit production by increasing pollen deposition. Unfortunately, pollination is often treated as a <span>"</span>black box<span>"</span>, and pollination management suffers from key knowledge gaps that hinder its greater utility, particularly the specific mechanisms underlying the processes of visitation, pollination, and fruit production. We present a causal model that links insect visitation to pollination to three separate components of yield, using field data from two types of canola (<span><em>Brassica</em> <em>napus</em></span>) production systems. Our results demonstrate that yield in commodity canola fields is primarily determined by plant size, and we found no relationship between honey bee (<span><em>Apis</em> <em>mellifera</em></span>) visitation and pollen deposition, or pollen deposition and seed yield. In contrast, yield in seed production canola fields was similarly controlled by plant size, but there was also a strong relationship between alfalfa leafcutting bee (<span><em>Megachile</em> <em>rotundata</em></span>) visitation and pollen deposition, as well as pollen deposition and seed yield. Leafcutting bee visitation in particular strongly increased pollen deposition in seed canola fields, whereas honey bee visitation did not. This model serves as a step towards a dynamic model of pollination services and highlights the relative importance of bee pollination in canola production.</p>
Data and code for: Plants sum and subtract stimuli over different timescales
<p>This repository contains the experimental data presented in "Plants sum and subtract stimuli over different timescales" as well as the Python scripts to reproduce the figures, run simulations based on the discussed model and estimate the memory kernel for individual plant organs.</p>
Fig 5 in Description and key to the fifth-instars of some Cicadas (Hemiptera: Cicadidae) associated with coffee plants in Brazil
Fig 5 Spines along the length of mid tibia. a) Dorisiana viridis; b) Fidicina mannifera; c) Fidicinoides pronoe. Scales = 1 mm.
Fig 2 in Description and key to the fifth-instars of some Cicadas (Hemiptera: Cicadidae) associated with coffee plants in Brazil
Fig 2 Left foreleg of fifth-instars. a) Dorisiana drewseni, inner view; b) D. drewseni, outer view; c) Dorisiana viridis, outer view; d) Fidicina mannifera, outer view; e) Fidicinoides pronoe, outer view; f) Carineta fasciculata, outer view; g) Quesada gigas, outer view. acf. Accessory tooth of femur; apt. Apical tooth of tibia; bt. Blade of tibia; f. Femur; fc. Femoral comb; itf. Intermediate tooth of femur; pbt. Point of blade of tibia; ptf. Posterior tooth of femur; t. Trochanter; ti. Tibia. Scale = 1 mm.
Fig 3 in Description and key to the fifth-instars of some Cicadas (Hemiptera: Cicadidae) associated with coffee plants in Brazil
Fig 3 Abdominal apex in ventral view. a) Dorisiana drewseni, female; b) D. drewseni, male; c) Dorisiana viridis, male; d) Fidicina mannifera, male; e) Fidicinoides pronoe, male; f) Carineta fasciculata, male; g) Quesada gigas, male. Scales = 1 mm.
Fig 1 Fifth-instars. a in Description and key to the fifth-instars of some Cicadas (Hemiptera: Cicadidae) associated with coffee plants in Brazil
Fig 1 Fifth-instars. a) Dorisiana drewseni, general lateral view of body; b) D. drewseni, dorsal view of head and thorax; c) Dorisiana viridis, general lateral view of body; d) D. viridis, dorsal view of head and thorax; e) Fidicina mannifera, general lateral view of body; f) F. mannifera, dorsal view of head and thorax; g) Fidicinoides pronoe, general lateral view of body; h) F. pronoe, dorsal view of head and thorax; i) Carineta fasciculata, general lateral view of body; j) C. fasciculata, dorsal view of head and thorax. Scale = 3 mm.
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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.