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1,764 results for “Fungi”
Figures 4–6 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 4–6. Immature states of Edessa leucogramma. 4) Third-instar nymphs of Edessa leucogramma. a) Dorsal habitus. b) Ventral habitus. 5) Fourth-instar nymphs of Edessa leucogramma. a) Dorsal habitus. b) Ventral habitus. 6) Fifth-instar nymphs of Edessa leucogramma on host.
Figures 7–9 in First report of the stinkbug Edessa leucogramma (Perty) (Hemiptera: Heteroptera: Pentatomidae: Edessinae) attacking Handroanthus chrysanthus (Jacq.) S.O. Grose (Bignoniaceae), with descriptions of the adult and immatures and notes on associated fungi and protozoa
Figures 7–9. Adults of Edessa leucogramma. 7) Adult male of Edessa leucogramma. 8) Female external genitalia and sternites. 9) Internal reproductive system of the male. a) Dissected genitalia. b) Illustration of male genitalia: (Be.) ejaculatory bulb; (Dej.) ejaculatory duct; (Dr.) distal region; (GlsAc.) accessory glands; (Vd.) deferent ducts; (Tes.) testis exhibiting four testicular follicles; (Tues.) testicular follicle; (Pr.) proximal region.
Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly
<p>Symbiosis with arbuscular mycorrhizal (AM) fungi improves plant nutrient capture from the soil, yet there is limited knowledge about the diversity, structure, functioning, and assembly processes of AM fungi-related microbial communities. Here, 16S rRNA gene sequencing and metagenomic sequencing were used to detect bacteria in the rhizosphere of <em>Lotus japonicus</em> inoculated with and without AM fungi, and the <em>L. japonicus</em> mutant <em>ljcbx</em> (defective in symbiosis) inoculated with AM fungi in southern grassland soil. Our results show that AM symbiosis significantly increased bacterial diversity and promoted deterministic processes of bacterial community construction, suggesting that mycorrhizal symbiosis resulted in the directional enrichment of bacterial communities and established a stable rhizosphere bacterial community. AM fungi promoted the enrichment of nine bacteria, including <em>Ohtaekwangia</em>, <em>Niastella</em>, <em>Gemmatimonas</em>, <em>Devosia</em>, <em>Sphingomonas</em>, <em>Novosphingobium</em>, <em>Opitutus</em>, <em>Lysobacter</em>, <em>Brevundimonas</em>, which are positively correlated with NPK-related parameters. Through a functional identification experiment, we found that six of these genera, including <em>Brevundimonas</em>, <em>Lysobacter</em>, <em>Ohtaekwangia</em>, <em>Sphingomonas</em>, <em>Devosia</em>, and <em>Gemmatimonas</em>, demonstrated the ability to mineralize organophosphate and dissolve inorganic phosphorus, nitrogen, and potassium. Our study revealed that AM fungi can regulate rhizosphere bacterial community assembly and attract specific rhizosphere bacteria to promote soil nutrient turnover in southern grasslands.</p>
Dataset from the analysis of biological activity of endophytic strain Serratia quinivorans KP32, the expression of biocontrol-related genes and the activity of antioxidant enzymes in bacterial cells treated with pathogenic fungi filtrates
<p>This dataset contains the data from the analyses published in the article entitled "Genetic Determinants of Antagonistic Interactions and the Response of New Endophytic Strain <i>Serratia quinivorans</i> KP32 to Fungal Phytopathogens" in the International Journal of Molecular Sciences (https://doi.org/10.3390/ijms232415561). The data consist of results collected for studies on the antifungal activity of KP32 strain towards four fungal phytopathogens, results of primer efficiency determination and studies on the expression of genes potentially involved in biocontrol after treatment of KP32 strain with the fungal phytopathogens filtrates. Additionally, absorbances from activity tests for catalase (CAT) and superoxide dismutase (SOD) in the strain treated with fungal pathogens are included.</p>
Data from: Ectomycorrhizal fungi are more sensitive to high soil nitrogen levels in forests exposed to nitrogen deposition
<p>Ectomycorrhizal fungi are essential for nitrogen cycling in many temperate forests and responsive to anthropogenic nitrogen addition, which generally, decreases host carbon allocation to the fungi. In the boreal region, however, ectomycorrhizal fungal biomass has been found to correlate positively with soil nitrogen availability. Still, responses to anthropogenic input, for instance through atmospheric deposition, are commonly negative.</p> <p>To elucidate whether variation in nitrogen supply affects ectomycorrhizal fungi differently depending on geographical context, we investigated ectomycorrhizal fungal communities along two fertility gradients across nemo-boreal forests with similar ranges in soil N/C ratios and inorganic nitrogen availability but located in regions with contrasting rates of nitrogen deposition.</p> <p>Ectomycorrhizal biomass and community composition remained relatively stable across the nitrogen-gradient with low atmospheric nitrogen deposition, but biomass decreased, and the community changed more drastically, with increasing nitrogen availability in the gradient subjected to higher rates of nitrogen deposition. Moreover, potential activities of enzymes involved in ectomycorrhizal mobilisation of organic nitrogen decreased as N/C ratios increased.</p> <p>In forests with low external input, we propose that stabilising feedbacks in tree-fungal interactions maintain ectomycorrhizal fungal biomass and communities even in highly fertile soils. In contrast, anthropogenic nitrogen input seems to impair ectomycorrhizal functions.</p>
Ant handling changes myrmecochore seed coat microbiomes and alters diversity of seed-borne plant pathogenic fungi
<p>The putative benefits to seeds in myrmecochory (ant-mediated seed dispersal) are often cast in a reward context. However, microbes have been mostly overlooked as seed mortality agents in myrmecochory, as have potential treatments provided by ant-handling. We investigated the effects of ant handling on the diversity of seed coat fungal communities of three myrmecochorous plant species. Ant-handling altered measures of both alpha and beta diversity of fungal communities. Ant-handled seeds harbored different overall fungal communities and plant pathogen communities than non-ant-handled seeds. The myrmecochore pathogenic fungal community showed high dissimilarity (high pairwise community turnover) between ant-handled and control seeds, while beta diversity measures for ant-handled seeds and seeds with manually-removed elaiosomes were less dissimilar. Ant handling may offer an additional benefit to myrmecochorous seeds via the reduction of the seed coat pathogenic community, which may be driven by elaiosome removal or as a byproduct of ant cleaning behaviors and chemical secretions. </p>
A minimal Fanconi Anemia complex in Early Diverging Fungi
<p>This dataset provides all the raw data used in the<strong> A minimal Fanconi Anemia complex in Early Diverging Fungi </strong>manuscript including:</p> <p><strong>Supplementary Table S1.</strong></p> <p>All protein identifiers, genomic assemblies, transcriptomic datasets, hypergeometric test values</p> <p><strong>Supplementary Materials SM1</strong></p> <p>The figures of phylogenetic trees for FA proteins.</p> <p><strong>Supplementary Dataset SD1.</strong></p> <p>The phylogenetic trees for FA proteins as a newick file format.</p>
Figs 1, 2. Aspergillus flavus. 1 in Symptomatology of termite Coptotermes curvignathus Holmgren (Rhinotermitidae) after fungi infection of Aspergillus flavus
Figs 1, 2. Aspergillus flavus. 1 – colony on Potato Dextrose Agar (PDA); 2 – morphology of conidia: (1) vesicles; (2) metula; (3) fialid; (4) conidiospores; (5) conidiophore.
Figs 3–6 in Symptomatology of termite Coptotermes curvignathus Holmgren (Rhinotermitidae) after fungi infection of Aspergillus flavus
Figs 3–6. Body surface morphology of Coptotermes curvignathus infected with Aspergillus flavus. 3 – negative control; 4 – 1st day after application; 5 – 3rd day; 6 – 7th day.
Figure 7 in Updates on the classification and numbers of marine fungi
Figure 7: Rhytidhysteron bruguierae. (A–C) Appearance of hysterothecia on host. (D, E) Vertical section through hysteriothecium. (F, G) Cells of peridium. (H, I) Pseudoparaphyses, asci, and ascospores. (I) Stained with lactophenol cotton blue. (J–O) Ascospores. Scale bars: (A) = 1 mm, (B–E) = 200 μm, (F–I) = 50 μm, (J–O) = 5 μm.
Figure 6 in Updates on the classification and numbers of marine fungi
Figure 6: Phylogram generated from maximum likelihood (ML) analysis based on combined LSU, ITS, and act1 sequence data representing Sarocladiaceae (Hypocreales). Thirty-seven strains are included in the combined analyses which comprised 1753 characters (504 characters for LSU, 478 characters for ITS, 771 characters for act1) after alignment. Acremonium variecolor strains CBS 130360 and FMR 11141 in Bionectriaceae (Hypocreales) were used as the outgroup taxa. The best scoring RAxML tree with a final likelihood value of −8526.836 is presented. The matrix had 435 distinct alignment patterns, 356 parsimony-informative, 73 singleton sites, and 1332 constant sites. Estimated base frequencies were as follows: A = 0.216, C = 0.298, G = 0.265, T = 0.221; substitution rates: AC = 2.09479, AG = 3.08268, AT = 2.09479, CG = 1.00000, CT = 10.05849, GT = 1.000000; gamma distribution shape parameter α = 0.574. Bootstrap support values for ML equal to or greater than 75 % are given above the nodes (left side). Bayesian posterior probabilities (BYPP) equal to or greater than 0.95 are given above the nodes (right side). Ex-type strains are in bold and newly generated sequences are in blue.
Figure 5 in Updates on the classification and numbers of marine fungi
Figure 5: Colony of Remispora submersa (MUM 20.48) in malt extract agar (MEA) for 7 days at 25 °C: (A) obverse, (B) reverse.
Figure 8 in Updates on the classification and numbers of marine fungi
Figure 8: Savoryella sarushimana. (A–C) Holoblastic conidiogenous cells. (D–J) Variously shaped conidia. Note the proliferating conidia in (D, H). Scale bar = 10 μm.
Figure 3 in Updates on the classification and numbers of marine fungi
Figure 3: Randomized axelerated maximum likelihood (RAxML) phylogenetic tree based on a combined analysis of the 28S and 18S rRNA sequence data. Bootstrap support values for ML (>70 %) are given above each branch. The newly transferred isolate is shown in blue. The tree is rooted to Ceratocystis adiposa CCFC212707.
Figure 4 in Updates on the classification and numbers of marine fungi
Figure 4: Monosporascus cannonballus. (A) Oil globules realised from squashed ascospore. (B) Ascus with single globose ascospore. (C) Thick-walled ascospore. (D) Ascus with three globose ascospores. (E, F) Released ascospores around the projecting neck of the ascoma. Scale bars: (A–D) = 20 μm, (E–F) = 300 μm.
Figure 2 in Updates on the classification and numbers of marine fungi
Figure 2: Microascus cinereus. (A–I) Asci. (J–N, P) Ascospores. (O, Q, R) Anamorphic Scopulariopsis cinerea of Microascus cinereus. Scale bars: (A–D, K, M, P–Q) = 20 μm, (E–J, L, N–O, R) = 10 μm.
Figure 1 in Updates on the classification and numbers of marine fungi
Figure 1: Microascus trigonosporus. (A–E) Perithecia with spore cirrus. (F) Young ascus. (G, H, O) Mature asci. (I–L) Ascospores. (M, N) Anamorphic Scopulariopsis trigonosporus of Microascus trigonosporus. Scale bars: (A–E) = 100 μm, (G, H, O) = 20 μm, (F, I–N) = 10 μm.
The data for: LCR in fungi display functional groups and are depleted in positively charged amino-acids
<p>Abstract</p> <p>The dataset consists of a TAB formatted table (Main_Dataset.tsv), that integrates information about protein domains (pfam_scan, GO terms), low complexity regions (SEG), signal peptides (SignalP), and transmembrane elements (TMHMM) for each of the analysed proteins within 183 fungal proteomes. The Main Dataset has been designed to ease further searches with Linux bash commands, for e.g. sorting and subsetting by the aforementioned traits. This resource can be used by users interested in detailed annotation of particular protein families, sets of organisms, low complexity regions, types of proteins (for instance transmembrane proteins). Thanks to its simple and clear format, it may also be easily enriched with additional data.</p> <p>Data types:</p> <p>Main_Dataset.tsv is a TSV table with protein annotations</p> <p>Estimate of dataset size:</p> <p> 245MB<br><br>Readme file:</p> <p>Main_Dataset.tsv is a TSV table with the following columns:</p> <ol> <li> <p>Assembly ID from NCBI</p> </li> <li> <p>Protein ID (NCBI accession)</p> </li> <li> <p>Protein length</p> </li> <li> <p>Presence of protein domains; Boolean</p> </li> <li> <p>symbolic localization of protein domains; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>number of transmembrane elements predicted with TMHMM</p> </li> <li> <p>total length of transmembrane elements</p> </li> <li> <p>Symbolic localization of transmembrane elements; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>Presence of signal peptide; Boolean</p> </li> <li> <p>Total number of LCR</p> </li> <li> <p>Total length of LCR</p> </li> <li> <p>Symbolic localization of LCR; 3 bins: N-termini (0-0.25 of protein length), middle (0.25-0.75 of protein length), and C-term (0.75-1 protein length)</p> </li> <li> <p>Symbolic localization of LCR; 10 bins scaled to sum up to total protein length</p> </li> <li> <p>LCR sequences in the N-terminal part of protein, separated by a comma</p> </li> <li> <p>LCR sequences in the middle part of protein, separated by a comma</p> </li> <li> <p>LCR sequences in the C-terminal part of the protein, separated by a comma</p> </li> <li> <p>Pfam domains overlapping with LCRs (>80% of LCR length)</p> </li> <li> <p>Pfam domains in protein (ordered by domain start)</p> </li> <li> <p>GO terms based on Pfam domains obtained by mapping on pfam2go, separated with the pipe symbol '|'</p> </li> </ol> <p> </p> <p>Acknowledgements</p> <p>This work was supported by National Science Centre grants (#2021/41/B/NZ2/02426 to AM, #2019/35/D/NZ2/03411 to K.S).</p>
Progressive degradation of acetylated wood by the brown rot fungi Coniophora puteana and Rhodonia placenta
<p>This dataset contains measurement data from the following publication: Belt, T.; Awais, M. (2025) Progressive degradation of acetylated wood by the brown rot fungi <em>Coniophora puteana</em> and <em>Rhodonia placenta</em>. Wood Science and Technology 59:13. https://doi.org/10.1007/s00226-024-01620-8. The experimental methods are described in brief below; further details can be found in the publication.</p> <p>Scots pine sapwood samples were acetylated in neat acetic anhydride to 9% or 18% weight percent gain (WPG) or left untreated to act as reference. The samples were exposed to brown rot decay in a stacked-sample decay test conducted in test tubes. Each replicate tube received 6 reference or acetylated samples stacked on top of each other (sample positions 1-6 from top to bottom) over nutrient agar inoculated with <em>Coniophora puteana</em> (N = 7) or <em>Rhodonia placenta</em> (N = 5). The different wood-fungus combinations were incubated for different durations until the visible fungal mycelium reached the top of the topmost block in one replicate tube. The samples were weighed before modification, after modification, at the end of the decay test, and after the decay test to determine their WPG, moisture content at the end of the decay test, and mass loss due to decay. After decay, 15 samples from every wood-fungus combination were selected for acetyl content measurement and FTIR spectroscopy.</p> <p>Acetyl contents were measured on powdered samples by saponification followed by HPLC quantification of the liberated acetic acid. FTIR spectra were measured in the 400-4000 cm<sup>-1</sup> spectral range in triplicate on powdered samples using an ATR accessory. For further analysis, the spectra were cut to 800-1800 cm<sup>-1</sup> range, baseline corrected (third degree polynomial subtraction), smoothed (Savitzky-Golay procedure with a second-order polynomial and a 15-point window), and normalised (unit vector normalisation).</p> <p>The "Mass data and acetyl content.csv" -file gives the sample identifiers (ID, sample type, test fungus, tube number, sample position) and measured mass data of all samples, and the acetyl contents of the selected samples. Masses m<sub>init</sub>, m<sub>mod</sub>, m<sub>wet</sub>, and m<sub>dec</sub> are the initial unmodified dry mass, the modified dry mass, the decaying wet mass, and the decayed dry mass, respectively. Acetyl contents (%) are given on a decayed wood basis.</p> <p>The “Unprocessed FTIR spectra_reference”, “Unprocessed FTIR spectra_9%”, and “Unprocessed FTIR spectra_18%” -files contain the unprocessed FTIR spectra of the reference, 9% WPG and 18% WPG samples (see the "Mass data and acetyl content.csv" -file for sample identifiers). The “Preprocessed FTIR spectra_reference”, “Preprocessed FTIR spectra_9%”, and “Preprocessed FTIR spectra_18%” -files in turn contain the preprocessed FTIR spectra of reference, 9% WPG and 18% WPG samples.</p>
Fungi ecomorphological trait data
<p>Ecomorphological trait data for fungi derived from the following sources:</p> <p>Bodensteiner, P., Binder, M., Moncalvo, J.M., Agerer, R. and Hibbett, D.S., 2004. Phylogenetic relationships of cyphelloid homobasidiomycetes. Molecular Phylogenetics and Evolution, 33(2), pp.501-515. <a href="https://doi.org/10.1016/j.ympev.2004.06.007">https://doi.org/10.1016/j.ympev.2004.06.007 </a></p> <p>Diederich, P., Lawrey, J.D., Ertz, D., 2018. The 2018 classification and checklist of lichenicolous fungi, with 2000 non-lichenized, obligately lichenicolous taxa. The Bryologist 121, 340–425. <a href="https://doi.org/10.1639/0007-2745-121.3.340">https://doi.org/10.1639/0007-2745-121.3.340 </a></p> <p>Hassett, B., Vonnahme, T., Peng, X., Jones, E. and Heuzé, C. (2020) Global diversity and geography of planktonic marine fungi. Botanica Marina, Vol. 63 (Issue 2), pp. 121-139. <a href="https://doi.org/10.1515/bot-2018-0113">https://doi.org/10.1515/bot-2018-0113 </a></p> <p>He, M.-Q., Zhao, R.-L., Hyde, K.D., Begerow, D., Kemler, M., Yurkov, A., McKenzie, E.H.C., Raspé, O., Kakishima, M., Sánchez-Ramírez, S., Vellinga, E.C., Halling, R., Papp, V., Zmitrovich, I.V., Buyck, B., Ertz, D., Wijayawardene, N.N., Cui, B.-K., Schoutteten, N., Liu, X.-Z., Li, T.-H., Yao, Y.-J., Zhu, X.-Y., Liu, A.-Q., Li, G.-J., Zhang, M.-Z., Ling, Z.-L., Cao, B., Antonín, V., Boekhout, T., da Silva, B.D.B., De Crop, E., Decock, C., Dima, B., Dutta, A.K., Fell, J.W., Geml, J., Ghobad-Nejhad, M., Giachini, A.J., Gibertoni, T.B., Gorjón, S.P., Haelewaters, D., He, S.-H., Hodkinson, B.P., Horak, E., Hoshino, T., Justo, A., Lim, Y.W., Menolli, N., Mešić, A., Moncalvo, J.-M., Mueller, G.M., Nagy, L.G., Nilsson, R.H., Noordeloos, M., Nuytinck, J., Orihara, T., Ratchadawan, C., Rajchenberg, M., Silva-Filho, A.G.S., Sulzbacher, M.A., Tkalčec, Z., Valenzuela, R., Verbeken, A., Vizzini, A., Wartchow, F., Wei, T.-Z., Weiß, M., Zhao, C.-L., Kirk, P.M., 2019. Notes, outline and divergence times of Basidiomycota. Fungal Diversity 99, 105–367. <a href="https://doi.org/10.1007/s13225-019-00435-4">https://doi.org/10.1007/s13225-019-00435-4 </a></p> <p>Hosaka, K., Bates, S.T., Beever, R.E., Castellano, M.A., Colgan III, W., Domínguez, L.S., Nouhra, E.R., Geml, J., Giachini, A.J., Kenney, S.R. and Simpson, N.B., 2006. Molecular phylogenetics of the gomphoid-phalloid fungi with an establishment of the new subclass Phallomycetidae and two new orders. Mycologia, 98(6), pp.949-959. <a href="https://doi.org/10.1080/15572536.2006.11832624">https://doi.org/10.1080/15572536.2006.11832624</a></p> <p>Riccioni C, Belfiori B, Rubini A, Bucci G, Ianigro M (2024). Dataset of endophytic and forest fungi (IBBR-CNR-FABI-01). Consiglio Nazionale delle Ricerche, Istituto di Bioscienze e BioRisorse (CNR-IBBR). Occurrence dataset <a href="https://doi.org/10.15468/arjsuy">https://doi.org/10.15468/arjsuy</a> accessed via GBIF.org on 2025-01-14.</p> <p>Wijayawardene, N.N., Hyde, K.D., Rajeshkumar, K.C., Hawksworth, D.L., Madrid, H., Kirk, P.M., Braun, U., Singh, R.V., Crous, P.W., Kukwa, M., Lücking, R., Kurtzman, C.P., Yurkov, A., Haelewaters, D., Aptroot, A., Lumbsch, H.T., Timdal, E., Ertz, D., Etayo, J., Phillips, A.J.L., Groenewald, J.Z., Papizadeh, M., Selbmann, L., Dayarathne, M.C., Weerakoon, G., Jones, E.B.G., Suetrong, S., Tian, Q., Castañeda-Ruiz, R.F., Bahkali, A.H., Pang, K.-L., Tanaka, K., Dai, D.Q., Sakayaroj, J., Hujslová, M., Lombard, L., Shenoy, B.D., Suija, A., Maharachchikumbura, S.S.N., Thambugala, K.M., Wanasinghe, D.N., Sharma, B.O., Gaikwad, S., Pandit, G., Zucconi, L., Onofri, S., Egidi, E., Raja, H.A., Kodsueb, R., Cáceres, M.E.S., Pérez-Ortega, S., Fiuza, P.O., Monteiro, J.S., Vasilyeva, L.N., Shivas, R.G., Prieto, M., Wedin, M., Olariaga, I., Lateef, A.A., Agrawal, Y., Fazeli, S.A.S., Amoozegar, M.A., Zhao, G.Z., Pfliegler, W.P., Sharma, G., Oset, M., Abdel-Wahab, M.A., Takamatsu, S., Bensch, K., de Silva, N.I., De Kesel, A., Karunarathna, A., Boonmee, S., Pfister, D.H., Lu, Y.-Z., Luo, Z.-L., Boonyuen, N., Daranagama, D.A., Senanayake, I.C., Jayasiri, S.C., Samarakoon, M.C., Zeng, X.-Y., Doilom, M., Quijada, L., Rampadarath, S., Heredia, G., Dissanayake, A.J., Jayawardana, R.S., Perera, R.H., Tang, L.Z., Phukhamsakda, C., Hernández-Restrepo, M., Ma, X., Tibpromma, S., Gusmao, L.F.P., Weerahewa, D., Karunarathna, S.C., 2017. Notes for genera: Ascomycota. Fungal Diversity 86, 1–594. <a href="https://doi.org/10.1007/s13225-017-0386-0">https://doi.org/10.1007/s13225-017-0386-0</a></p>
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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.