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Figure 3 in New records and new host associations of genus Platygaster Latreille (Hymenoptera: Platygastridae) from India
Figure 3. Plataster luteipes Buhl (Female). A. Habitus B. Strobilanthus – leaf gall C. Strobilanthus – leaf gall with the inducer Cecidomyiid; D. and E. Platygaster narendrani Ushakumari (Female) D. Habitus E. stem galls of Luffa cylindrical (L.) M. Roem.
Figure 2 in New records and new host associations of genus Platygaster Latreille (Hymenoptera: Platygastridae) from India
Figure 2. Platygaster neostriatitergitis Veenakumari and Buhl sp. nov. (Female). A. Habitus B. Head font view C. Mesosoma lateral view D. Antenna E. Mesosoma dorsal view F. Head dorsal view G. Metasoma dorsal view H. Forewing.
Figure 1 in New records and new host associations of genus Platygaster Latreille (Hymenoptera: Platygastridae) from India
Figure 1. Platygaster cf. achterbergiana Buhl (Female). A. Habitus B. Mesosoma dorsal view C. Mesosoma lateral view D. Antenna E. Head font view F. Head dorsal view G. Forewing H. Mesosoma dorsal view.
Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
<p>Antibiotic resistance genes (ARGs) benefit host bacteria in environments containing corresponding antibiotics, but it is less clear how they are maintained in environments where antibiotic selection is weak or sporadic. In particular, few studies have measured the effect of ARGs on host fitness in the absence of direct selection or determined if any costs are fixed or depend on the host strain, perhaps marking some ARG-host combinations as reservoirs that can maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in 11 diverse <em>Escherichia spp</em>. strains. Three ARGs (blaTEM-116, cat, and dfrA5, encoding resistance to β-lactams, chloramphenicol, and trimethoprim, respectively) imposed an overall cost but all ARGs had an effect in at least one host strain, reflecting a significant strain interaction effect. A simulation predicts these interactions cause the success of ARGs to depend on available host strains, and, to a lesser extent, for successful host strains to depend on the ARGs present in a community. These results indicate the importance of considering ARG effects over different host strains, especially the potential of reservoir strains that allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.</p>
A model of within-host interactions between host resources, macroparasite infection and immune response
<p>This project was designed to mathematically investigate the of different host parasite-mitigation strategies on host condition. The R code herein comprises:</p> <ul> <li>An ODE model of within-host interactions between a macroparasite (e.g. helminth) infection, host resource levels and host immune response, and the consequent effects on host condition. In brief, resources are ingested and utilised by the host, leading to inceased condition. The host is infected by a parasite, which matures and establishes within the host; both age stages cause harm to the host, decreasing host condition. The presence of the parasite stimulates an immune response, which can either target larval or adult parasites (a resistance strategy), or ameliorate the harm they cause (a tolerance strategy). The host can also reduce resource intake in order to also reduce ingestion of parasite infective stages (an avoidance strategy). Resistance responses have an associated immunopathology, in that the immune response also harms the host.</li> <li>Code to plot model trajectories over time.</li> <li>Code to plot multiple trajectories as a heat map, in which the x-axis is time and the y-axis is a parameter representing the host investment in its parasite-mitigation strategy.</li> <li>Code to calculate the optimum host investment for each strategy, over various sets of parameter values, as determined by maximising mean host condition over a given timeframe, and to plot the output.</li> <li>The same are also provided for an ODE model in which the total immune response is allocated between the two resistance responses and tolerance (a combined strategy). The optimisation code optimises both the total investment in immune repsonse, and how much is allocated to the three different individual strategies.</li> </ul> <p>The model and results are described in detail in the associated manuscript. We also provide here the simulated datasets in which the optimum host investments were calculated over a range of different parameter values, as these take several hours to run on a standard desktop computer..</p>
Profiling phage-host interactions between Skunavirus receptor binding proteins and lactococcal cell wall polysaccharide structures
Open the record for dataset details and reuse information.
Fig. 5 in Molecular characteristics of representatives of the genus Brachylecithum Shtrom, 1940 (Digenea, Dicrocoeliidae) with comments on life cycle and host specificity
Fig. 5 Comparison of morphometric characters of B. glareoli and B. lobatum. a Ratio of body length to vitellaria length, b distance between oral and ventral suckers, c testis area, and d vitellaria length
Fig. 4 in Molecular characteristics of representatives of the genus Brachylecithum Shtrom, 1940 (Digenea, Dicrocoeliidae) with comments on life cycle and host specificity
Fig. 4 Bayesian analysis of partial sequence 28S rDNA + partial sequence cox1 data of nine members of the Brachylecithum genus. Tree constructed with MrBayes using the GTR + G model for 28S rDNA and HKY + G for cox1. The analysis was run for one million generations, with 250,000 generations as burn-in. Scale bars: number of substitutions per site. Nodal support is indicated as Bayesian posterior probabilities. Host species are provided in parentheses. Outgroup— Lyperosomum collurionis
Fig. 2 in Molecular characteristics of representatives of the genus Brachylecithum Shtrom, 1940 (Digenea, Dicrocoeliidae) with comments on life cycle and host specificity
Fig. 2 Bayesian analysis of partial sequences of the 28S rDNA gene of 16 members of Dicrocoeliidae. The tree constructed with MrBayes using the GTR + G model. The analysis was run for one million generations, with 250,000 generations as burn-in. Scale bars: number of substitutions per site. Nodal support is indicated as Bayesian posterior probabilities. Host species are provided in parentheses. Outgroup—M. magellanica (Opecoelidae)
Fig. 3 in Molecular characteristics of representatives of the genus Brachylecithum Shtrom, 1940 (Digenea, Dicrocoeliidae) with comments on life cycle and host specificity
Fig. 3 Bayesian analysis of the partial mitochondrial proteincoding gene cox1 (data as amino acids) derived from nine isolates of Brachylecithum spp. Tree constructed using the HKY + G model. The analysis was run for two million generations; 500,000 generations were discarded as burn-in. The branch-length scale indicates the number of substitutions per site. Nodal support is indicated as Bayesian posterior probabilities. Host species are provided in parentheses. Outgroup— Lyperosomum collurionis
Fig. 1 in Molecular characteristics of representatives of the genus Brachylecithum Shtrom, 1940 (Digenea, Dicrocoeliidae) with comments on life cycle and host specificity
Fig. 1 Larval stages of Brachylecithum lobatum from Cepaea hortensis. a Sporocyst, b cercaria and metacercaria hatching from the cyst, c encysted metacercaria, and d cercaria, free metacercaria, cysts with metacercaria, and fragment of a sporocyst
Fig. 4 in The marine myxosporean Sigmomyxa sphaerica (Thélohan, 1895) gen. n., comb. n. (syn. Myxidium sphaericum) from garfish (Belone belone (L.)) uses the polychaete Nereis pelagica L. as invertebrate host
Fig. 4 Phylogenetic affinities of S. sphaerica among related members of the marine clade of Myxosporea. S. sphaerica is closest related to Ellipsomyxa spp., and these two genera represent a sister group to M. queenslandicus incertae sedis in a well-supported clade. Other Myxidium spp. in the marine clade are not closely related to S. sphaerica, including M. laticurvum (JN033229, new sequence) and M. bergense from the type host P. virens in Norway (JN033231, new sequence). All new sequences in bold. Clade support values: upper, MrBayes posterior probabilities (in percent); middle, maximum likelihood bootstrap (N=100) support values (Paup); lower, maximum parsimony (Mega)
Fig. 1 in The marine myxosporean Sigmomyxa sphaerica (Thélohan, 1895) gen. n., comb. n. (syn. Myxidium sphaericum) from garfish (Belone belone (L.)) uses the polychaete Nereis pelagica L. as invertebrate host
Fig. 1 Plasmodia and myxospores of S. sphaerica from the gallbladder of B. belone. a Plasmodium (flattened) without visible indication of sporogony, showing distinction between ecto- and endoplasm. b Sporulated plasmodium (flattened) showing spores in valvular view, vacuolate appearance and refractive granules. Note that any polar capsule lengths taken in valvular view may be erroneously short due to their oblique orientation in the spores. c Spore in sutural view. d, e Spores as seen in the focal plane of one polar capsule, showing polar filament coils and the valvular extensions associated with the protruding part of the capsules. Scale bars a, b 10 μm, c, d 5 μm
Fig. 3 in The marine myxosporean Sigmomyxa sphaerica (Thélohan, 1895) gen. n., comb. n. (syn. Myxidium sphaericum) from garfish (Belone belone (L.)) uses the polychaete Nereis pelagica L. as invertebrate host
Fig. 3 Actinospores of S. sphaerica in naturally infected N. pelagica from northern Øresund, Denmark. Interference contrast, to same scale. a Apical and lateral views of free actino-spores. b Lateral views showing the three nuclei of the shell valve cells (arrows) and the two nuclei of the sporoplasm cells (arrowheads). Scale bar 5 μm
Fig. 5 in The marine myxosporean Sigmomyxa sphaerica (Thélohan, 1895) gen. n., comb. n. (syn. Myxidium sphaericum) from garfish (Belone belone (L.)) uses the polychaete Nereis pelagica L. as invertebrate host
Fig. 5 Schematic illustration of the life cycle of S. sphaerica. The polychaete N. pelagica acts as the invertebrate hosts and the garfish B. belone acts as the fish hosts. a Actinospore, b myxospore. Not to scale
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Figure 1 Micromegistus bakerion Scarites subterraneus.a in New records of Micromegistus bakeri, Trägårdh 1948 (Acari: Mesostigmata: Parantennulidae), a mite symbiotic on carabid beetles, and notes on the species' distribution and host specificity
Figure 1 Micromegistus bakerion Scarites subterraneus.a – Dorsal and ventral view of infested S. subterraneus. b – The anterior ventral side of M. bakeriinfestedS. subterraneus. c – Adult and larvalM. bakeri.
Figure 3 in New records of Micromegistus bakeri, Trägårdh 1948 (Acari: Mesostigmata: Parantennulidae), a mite symbiotic on carabid beetles, and notes on the species' distribution and host specificity
Figure 3 Distribution map of Micromegistus bakeriand Scarites spp. in North America. The star indicates the location of the specimens collected in the present study. Squares indicate localities ofM. bakeridocumented in the literature (Trägårdh 1948; Nickel and Elzinga 1970; McDaniel and Bolen
Figure 2 in New records of Micromegistus bakeri, Trägårdh 1948 (Acari: Mesostigmata: Parantennulidae), a mite symbiotic on carabid beetles, and notes on the species' distribution and host specificity
Figure 2 Examples of photographic records of mites (putatively identified as M. bakeri) on Scarites spp., available on the citizen science websites BugGuide and iNaturalist. Note how only one or no mites are visible in the dorsal images, while one to many are visible in the lateral and ventral images. 2a – by lazarus via iNaturalist, used under a CC BY 4.0 license. 2b–2c by Bert Harris and Breanna Couey, respectively, via iNaturalist, used under CC BY-NC 4.0 licenses.
Fig. 3 in Black cherry as a host plant for stink bugs (Hemiptera: Pentatomidae) in agroecosystems in Georgia, USA
Fig. 3. Mean number of Chinavia hilaris adults and nymphs detected per scout sample in black cherry tree in 2016 (A) and 2018 (B). Fl = flowering; Gr = green fruit; Pi = pink fruit; Rd = red fruit; Pu = purple fruit; Go = fruit gone.
ScienceDex guides
Understand access before you commit
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.