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FIGURE 2 A–D in A new species of Aculus mite (Acari: Eriophyidae), a potential biocontrol agent for Australian swamp stonecrop, Crassula helmsii (Crassulaceae)
FIGURE 2 A–D. Scanning electron micrographs of Aculus crassulae sp. nov. A. Two mites presented in dorsal view (showing mid-dorsal ridge towards posterior); B. Ventral view of female showing coxigenital region; C. Detailed view of male coxigenital region; D. Detailed view of tarsal empodium.
Metal−Organic Frameworks as Efficient Oral Detoxifying Agents
<p>Poisoning and accidental oral intoxication are<br> major health problems worldwide. Considering the insufficient<br> efficacy of the currently available detoxification treatments, a<br> pioneering oral detoxifying adsorbent agent based on a single<br> biocompatible metal−organic framework (MOF) is here<br> proposed for the efficient decontamination of drugs commonly<br> implicated in accidental or voluntary poisoning. Furthermore, the<br> in vivo toxicity and biodistribution of a MOF via oral<br> administration have been investigated for the first time. Orally<br> administered upon a salicylate overdose, this MOF is able to<br> reduce the salicylate gastrointestinal absorption and toxicity more<br> than 40-fold (avoiding histological damage) while exhibiting exceptional gastrointestinal stability (<9% degradation), poor<br> intestinal permeation, and safety.</p>
Simulation results of two agent-based models of logistics systems
<p>The data set contains the simulation results of the two different agent-based models of logistics systems: <br> 1. A medical treatment facility (MTF) model, consisting of agents representing wounded soldiers and fixed sites representing medical facilities.<br> 2. A ship fueling (SF) simulation, consisting of agents representing fuel transport ships and fixed sites representing fuel-using bases.</p> <p>Folders in folder "MTF" are related to the MTF model.</p> <p>Files in folder "casualty_rateX" are related to the case with casualty rate of X new casualties per time step, where X = 30, 50, 70, . . . , 330.</p> <p>Each file "RunN_casualtyX_fullness.txt" has the "fullness" (defined as the ratio of the number of patients at a site to the total patient capacity of that site) of each site for each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Each file "RunN_casualtyX_dow.txt" has the total number of Dead Of Wounds that occur in all sites in each time step, where the run number N = 1, 2, 3, . . . , 100.</p> <p>Folders in folder "SF" are related to the SF model.</p> <p>Files in folder "siteMaxX" are related to the case with an initial (and maximum) site fuel value of X units, where X = 25, 50, 75, . . . , 200.</p> <p>Each file "assetTowedFuelHistory_siteMaxX_N.txt" has the number of towed fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "assetUseFuelHistory_siteMaxX_N.txt" has the number of onboard fuel units for each asset for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p> <p>Each file "siteHistory_siteMaxX_N.txt" has the number of fuel units at each site for each time step for run number N, where N = 1, 2, 3, . . . , 100.</p>
FIGURE 2 in Colletotrichum xishanense as a new etiological anthracnose agent on Fagopyrum gracilipes in Yunnan Province, China
FIGURE 2. Phylogenetic tree derived from maximum parsimony analysis of the combined apmat-gs sequences, using Colletotrichum xanthorrhoeae BRIP 45094 as outgroup. Maximum parsimony bootstrap values (MP>50%) and Bayesian posterior probabilities (BI>0.95) are shown above or below the branches. Type strains are in bold.
FIGURE 3 in Colletotrichum xishanense as a new etiological anthracnose agent on Fagopyrum gracilipes in Yunnan Province, China
FIGURE 3. Colletotrichum xishanense (453-2-3-1, ex-holotype): A. Front (left) and back (right) views of 7-d-old PDA culture; B. Symptoms on leaves of Fagopyrum gracilipes; C–D. Conidiogenous cells; E. Conidia; F–I. Appressoria. A. On PDA. C–I. On OA. Scale bars: C–I = 10 μm.
FIGURE 1 in Colletotrichum xishanense as a new etiological anthracnose agent on Fagopyrum gracilipes in Yunnan Province, China
FIGURE 1. Phylogenetic tree derived from maximum parsimony analysis of the combined act-tub2-cal-chs-1-gapdh-ITS sequences, using Colletotrichum boninense CBS 123755 as outgroups. Maximum parsimony bootstrap values (MP>50%) and Bayesian posterior probabilities (BI>0.95) are shown above or below the branches. Type strains are in bold.
Fig. 5 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Fig. 5. Maximum-likelihood gene tree of Bangasternus, Rhinocyllus, and Larinus cytochrome c oxidase subunit I sequences. Black circles indicate 100% support with SH-aLRT ratios/ultrafast bootstrapping and gray circles indicate greater than 70% support. Dark red labels depict identified specimens from the William F. Barr Entomological Museum at the University of Idaho.
Figs. 2–4 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Figs. 2–4. Adult Bangasternus fausti (sensu Colonnelli and Whitehead 1990) from British Columbia, Canada. 2) Lateral habitus; 3) Dorsal habitus; 4) Male genitalia, dorsal view. Specimen in Figs. 2–3 from Peachland, British Columbia, Canada (49.739669, −119.769512), collected 27 June 2022 on Centaurea diffusa. Specimen in Fig. 4 from Kelowna, British Columbia, Canada (49.90889, −119.49167), collected 11 June 2022 on C. diffusa (specimen code Bf068). Photos by C. L. Cock.
Fig. 1. Search effort for Bangasternus between 2020–2022 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Fig. 1. Search effort for Bangasternus between 2020–2022. Symbol color represents presence or absence of B. fausti: white depicts presence, green depicts iNaturalist observations, and unfilled depicts absence. White triangles depict sites that had B. fausti and at least two species of Centaurea whereas unfilled triangles depict sites that had no B. fausti but had both C. diffusa and C. stoebe ssp. micranthos. Photo of B. fausti on C. diffusa by N. G. Earley. Figure prepared in QGIS version 3.16.6 with basemaps from https://github.com/stamen/terrain-classic and https://github.com/CartoDB/ basemap-styles.
Arctic Ocean Biodiversity 2011: temp- pipe separated agents
experiment, to get around harvest error
Structure Databases: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites
<p>Structures and energies associated with the paper: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites</p>
A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
Exploring agent interaction patterns in the comment sections of fake and real news
<p>The code and dataset provided for the paper <em>"Exploring agent interaction patterns in the comment sections of fake and real news"</em> contain all information about fake and real news, as well as the complete code for constructing comment networks and user network experiments.</p>
Exploring agent interaction patterns in the comment sections of fake and real news
<p>The code and dataset provided for the paper <em>"Exploring agent interaction patterns in the comment sections of fake and real news"</em> contain all information about fake and real news, as well as the complete code for constructing comment networks and user network experiments.</p>
Cellular population data (Agent-based model CRC)
Open the record for dataset details and reuse information.
Data and Scripts for "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals"
<p>This contains the data and graphing scripts necessary to recreate all figures in the paper "Timing matters in Macrophage / CD4+ T cell interactions: An agent-based model comparing Mycobacterium tuberculosis host-pathogen interactions between latently infected and naïve individuals". Supplemental Material for the paper is also provided here. Please refer to the README.md for instructions on how to use. The model can be found at: https://github.itap.purdue.edu/ElsjePienaarGroup/LTBINaiveinvitroModel/ along with the uncalibrated parameter files and scripts to run on HPCs.</p>
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
Figure 1 in Description and biological parameters of Ooencyrtus isabellae Guerrieri and Noyes sp. nov. (Hymenoptera: Chalcidoidea: Encyrtidae), a potential biocontrol agent of Zophiuma butawengi (Heller) (Hemiptera: Fulgoromorpha: Lophopidae) in Papua New Guinea
Figure 1. Ooencyrtus isabellae Guerrieri and Noyes. Female: (A) antenna; (B) base of forewing; (C) mandible; (D) hypopygium; and. (E) ovipositor. Male: (F) antenna; and (G) genitalia.
Figure 5 in Comparative analysis of the karyotype sensitivities of Apodemus flavicollis and laboratory mice to DNA-damaging agents
Figure 5. Polyploid metaphase in bone marrow cell of A. flavicollis after Mitomycin C treatment. Some of the chromosomes are also damaged. Pericentric inversions and fragments are observed.
FIGURE 42 in Acarine biocontrol agents Neoseiulus californicus sensu Athias-Henriot (1977) and N. barkeri Hughes (Mesostigmata: Phytoseiidae) redescribed, their synonymies assessed, and the identity of N. californicus (McGregor) clarified based on examination of types
FIGURE 42. Neoseiulus californicus sensu Athias-Henriot (1977). Variation in the shape of the spermathecae of specimens from: (a, b) slide lot #23 (Table 1); (c, d) lot #22; (e, f) lot #28; (g, h) lot #25; (i, j) lot # 30; (k, l) lot #34; (m–o) lot #35; (p, q) lot #36; (r, s) lot #39; (t, u) lot #37; (v) lot #41; (w) lot #42; (x) lot #45; (y) holotype of N. chilenensis, lot #43 (image is the result of a montage (merging) of photos taken by Diana Goernert, Senckenberg Museum of Natural History Görlitz). All figures at the same scale, shown in (a). Legend: AU—Australia, CA—California (USA), CAN—Canada, CH—Chile, FL— Florida (USA), KE—Kenya, SK—South Korea.
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.