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Fig. 77. Cantharomyces spp. A–C. C. numidicus Maire. A, C. Mature thalli. B in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 77. Cantharomyces spp. A–C. C. numidicus Maire. A, C. Mature thalli. B. Appendage basal cell in detail. – D–F. C. orientalis Speg. Mature thalli. – G. C. platystethi Thaxt. Mature thallus with labelled antheridium (an). Scale bars: 50 µm. Photographs from slides ZMUC C-F-122850 (A), ZMUC C-F-122935 (B–C), ZMUC C-F-122482 (D), ZMUC C-F-122831 (E), ZMUC C-F-123217 (F), ZMUC C-F-124182 (G).
Fig. 43. Laboulbenia spp. A–B. L. rougetii Mont. & C.P.Robin. A. Mature thallus. B in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 43. Laboulbenia spp. A–B. L. rougetii Mont. & C.P.Robin. A. Mature thallus. B. Two young thalli showing antheridia (an). – C –D. L. slackensis Cépède & F.Picard. In D, the black and constricted septum separating the basal cell of outer appendage from outermost branch is labelled (arrow). – E–F. L. sphaerii Santam. E. A pair of two immature thalli. F. Mature thallus from holotype. Both images with undivided cells III and IV being labelled. Scale bars: 50 µm. Photographs from slides ZMUC C-F-124117 (A–B), ZMUC C-F-124219 (C), ZMUC C-F-122634 (D), ZMUC C-F-124098 (E), BCB SS1047b (holotype) (F).
Fig. 44. Laboulbenia spp. A–E. L. stenolophi Speg. Mature thalli except fig. D which represents a in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 44. Laboulbenia spp. A–E. L. stenolophi Speg. Mature thalli except fig. D which represents a detail of the inner appendage. A and C show the perithecial ventral prominence formed by the junction of outer wall cell tiers w1 and w2 (arrows). B. Cell IV subdivision is shown (arrow). A–B. From Stenolophus mixtus. C. From Stenolophus teutonus. D–E. From Acupalpus flavicollis. – F–G. L. stilicicola Speg. F. Mature thallus. G. Upper part of an immature thallus showing an elongate trichogyne (tr). – H–I. L. thaxteri Cépède & F.Picard. Mature thalli. Scale bars: A–C, E–I = 50 µm; D = 25 µm. Photographs from slides ZMUC C-F-122606 (A), ZMUC C-F-123150 (B), ZMUC C-F-122707 (C), ZMUC C-F-122974 (D–E), ZMUC C-F-123727 (F–G), ZMUC C-F-123102 (H–I).
Fig. 70. Rickia spp. A–C. R. huggertii Balazuc. A–B. Mature thalli. C in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 70. Rickia spp. A–C. R. huggertii Balazuc. A–B. Mature thalli. C. Immature thallus showing the trichogyne scar (ts) and the antheridium (an). – D. R. peYerimhoffii Maire. Mature thallus. – E. R. proteini T.Majewski. Immature thallus at left and mature thallus on the right. Scale bars: A–B, D–E = 50 µm; C = 20 µm. Photographs from slides ZMUC C-F-122781 (A–B), ZMUC C-F-122862 (C), ZMUC C-F-122656 (D), ZMUC C-F-122485 (E).
Fig. 60. Stigmatomyces spp. A. S. limosinae Thaxt. Mature thallus. – B–E. S. majewskii H.L in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 60. Stigmatomyces spp. A. S. limosinae Thaxt. Mature thallus. – B–E. S. majewskii H.L.Dainat, Manier & Balazuc. B–C. Mature thalli. D. Primary appendage in detail with labelled spinous process from the original spore apex (sx). E. Perithecial tip in detail. – F–H. S. platensis Speg. Mature thalli showing protrusion of cell VII (arrows). Scale bars: A–C, E–H = 50 µm; D = 25 µm. Photographs from slides ZMUC C-F-123718 (A), ZMUC C-F-123705 (B, D–E), ZMUC C-F-123731 (C), ZMUC C-F-123713 (F–H).
Fig. 29. Laboulbenia spp. A. L. cristata Thaxt. Two mature thalli. – B–C. L in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 29. Laboulbenia spp. A. L. cristata Thaxt. Two mature thalli. – B–C. L. curtipes Thaxt. Mature thalli. Scale bars: 50 µm. Photographs from slides ZMUC C-F-122471 (A), ZMUC C-F-123641 (B–C).
Fig. 27. Laboulbenia spp. A–B. L. clivinalis Thaxt. A. Mature thallus. B in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 27. Laboulbenia spp. A–B. L. clivinalis Thaxt. A. Mature thallus. B. Three immature thalli showing the oblique and blackened septum separating basal and suprabasal cells of outer appendage (arrows), a diagnostic character. – C. L. collae T.Majewski. Mature thallus. Scale bars: 50 µm. Photographs from slides ZMUC C-F-122501 (A–B), ZMUC C-F-123503 (C).
Fig. 7. Hydrophilomyces spp., mature thalli. A–B. H in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 7. Hydrophilomyces spp., mature thalli. A–B. H. coneglianensis Speg. Thallus from female (A) and from male hosts (B). – C. H. digitatus F.Picard. – D–E. H. gracilis T.Majewski. – F–G. H. hamatus T.Majewski. Abbreviations: acc = accessory cell; bf = buffer cells. Scale bars: 50 µm. Photographs from slides ZMUC C-F-122709 (A), ZMUC C-F-124088 (B), ZMUC C-F-124274 (C), ZMUC C-F-124043 (D–F), and ZMUC C-F-124042 (G).
Fig. 14. A–B. Idiomyces peyritschii Thaxt. Mature thalli. – C–L in Laboulbeniomycetes (Fungi, Ascomycota) of Denmark
Fig. 14. A–B. Idiomyces peyritschii Thaxt. Mature thalli. – C–L. Symplectromyces vulgaris (Thaxt.) Thaxt. C. Mature thallus from Quedius umbrinus with labelled cells IIa, IIb, and III. D. Mature thallus from Quedius xanthopus. E. Mature thallus from Quedius mesomelinus. F–G. Details of appendages with seriated antheridia (G, in poor condition is from lectotype). H–J. Thalli from Quedius boopoides, with detail of dark cell VI in H (arrow). I. Mature thallus from lectotype slide. K. Ascospore. L. Very young thallus showing ascospore original spinous apex, the sx (arrow). Scale bars: A–B, G–H, J, L = 50 µm; C–E, I = 100 µm; F, K = 25 µm. Photographs from slides ZMUC C-F-122507 (A–B), ZMUC C-F-122498 (C), ZMUC C-F-122679 (D, K), ZMUC C-F-123742 (E–F), FH6581 (lectotype) (G, I), ZMUC C-F-123075 (H, J), ZMUC C-F-123521 (L).
Figure 6 – Streptocarpus salesianorum. A. Habit. B. Inflorescence. C. Corolla. D. Mature capsule. E in Five new species of Streptocarpus (Gesneriaceae) from Katanga, D.R. Congo
Figure 6 – Streptocarpus salesianorum. A. Habit. B. Inflorescence. C. Corolla. D. Mature capsule. E. Hairs on the ovary. From E.Hofmann S 1799. Illustration by Eberhard Fischer.
Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination
<p>These are the<strong> processed</strong> BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a>. The <strong>raw</strong> sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).</p> <p> </p> <p><strong>Code</strong></p> <p>Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2022">found on GitHub</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>File: WU368_kim_et_al_nature_2022_meta.tsv</p> <p>Notes:</p> <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul> <p> </p> <p><strong>Information on the 2099 recombinant mAbs generated in this study</strong></p> <p>File: WU368_kim_et_al_nature_2022_mabs.tsv</p> <p>Notes on columns:</p> <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul> <p> </p> <p><strong>Processed BCR data - heavy chains</strong></p> <p>File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv</p> <p><em>Analysis was based on heavy chain-based clonal inference.</em></p> <p>Notes on columns:</p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login"><em>iReceptor</em></a> use these. Non-standard columns are noted below.</p> <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`isotype`: IGH[ADEGM].</li> <li>`cdr3`: CDR3 nucleotide sequence.</li> <li>`cdr3_length`: CDR3 nucleotide sequence length.</li> <li>`cdr3_aa`: CDR3 amino acid sequence.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`donor`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., <em>Nature</em>, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul> <p> </p> <p><strong>Processed BCR data - light chains</strong></p> <p>File: WU368_kim_et_al_nature_2022_bcr_light.tsv</p> <p><em>Light chains were not used for heavy chain-based clonal inference or analysis.</em></p> <p> </p> <p><strong>Processed transcriptomics data</strong></p> <p>Files:</p> <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> <p>Notes:</p> <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> <p>In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.</p>
Fig. 1 in Pecular Features Of Hematopoiesis In The Liver Of Mature And Immature Green Frogs (Pelophylax Esculentus Complex)
Fig. 1. Smear-imprint of the liver of immature green frog: a — pigment cells; b — erythroblasts; c — undifferentiated blast, erythroblast and eosinophilicmyelocyte; d — erythroblast and medullocell neutrophil. Pappenheim staining, ×200.
Data from: Seasonality, body size and maturation time in the neotropical grasshopper Sphenarium histrio across an altitudinal gradient
<p>In insects, male mating success and female fecundity usually increase with body size. However, natural selection favors faster maturation, reducing the risk of pre-reproductive death when the reproductive season is short in habitats located at high altitudes or far from the equator. Also, if males that mature earlier than females under these conditions increase their mating opportunities, protandry may evolve in their populations. Nonetheless, since body size is strongly correlated with maturation time in insects, a faster sexual maturation is reached at the expense of having a small body size. We analyzed the differences in the adult body size of males and females of the grasshopper Sphenarium histrio in three sites across an altitudinal gradient in southern Mexico. We also evaluated the possibility of protandry in these sampling sites using a common garden experiment. Male and female grasshoppers collected from low altitude sites in the field and reared in the laboratory were larger than those from a high altitude, suggesting genetic differentiation. Grasshoppers from a high altitude hatched earlier, had a shorter development time, presented fewer instars, and were smaller than grasshoppers from the other sampling sites. Moreover, development time in the three sampling sites was shorter in males than in females, suggesting protandry. Interestingly, the males from the three sites showed similar growth rates, but the females from low and high altitudes, respectively, had the fastest and slowest growth rates. In general, the adaptive value of the evolution of protandry has been focused on males. However, it may be that the growth rates of females in these sites could modify the degree of protandry as a response to their risk of pre-reproductive death and the potential benefits associated with multiple matings.</p> <p>The xlsx file contains the data for all the statistical analyses.</p>
Reproductive maturity in boreal trees, Northwest Territories, Canada
<p>In boreal North America, much of the landscape is covered by fire-adapted forests dominated by serotinous conifers. For these forests, reductions in fire return interval could limit reproductive success, owing to insufficient time for stands to reach reproductive maturity i.e., to initiate cone production. Improved understanding of the drivers of reproductive maturity can provide important information about the capacity of these forests to self-replace following fire. Here, we assessed the drivers of reproductive maturity in two dominant and widespread conifers, semi-serotinous black spruce and serotinous jack pine. Presence or absence of female cones were recorded in approximately 15,000 individuals within old and recently burned stands in two distinct ecozones of the Northwest Territories (NWT), Canada. Our results show that reproductive maturity was triggered by a minimum tree size threshold rather than an age threshold, with trees reaching reproductive maturity at smaller sizes where environmental conditions were more stressful. The number of reproductive trees per plot increased with stem density, basal area, and at higher latitudes (colder locations). The harsh climatic conditions present at these higher latitudes, however, limited the recruitment of jack pine at the treeline ecotone. The number of reproductive black spruce trees increased with deeper soils, whereas the number of reproductive jack pine trees increased where soils were shallower. We examined the reproductive efficiency i.e., the number of seedlings recruited per reproductive tree, linking pre-fire reproductive maturity of recently burned stands and post-fire seedling recruitment (recorded up to 4 years after the fires) and found that a reproductive jack pine can recruit on average three times more seedlings than a reproductive black spruce. We suggest that the higher reproductive efficiency of jack pine can explain the greater resilience of this species to wildfire compared with black spruce. Overall, these results help link life history characteristics such as reproductive maturity to variation in post-fire recruitment of dominant serotinous conifers.</p>
FIGURES 12, 13 in Descriptions of the Mature Larvae of Three Australian Ground-Nesting Bees(Hymenoptera: Colletidae: Diphaglossinae and Neopasiphaeinae)
FIGURES 12, 13. SEM micrographs of predefecating larva of Trichocolletes orientalis. 12. Front of head. 13. Close-up of mouthparts, noting huge down-curved labral tubercles and uncertain arrangement of spicules on maxilla as well as clear arrangement of labial palpi laterad of only slightly projecting salivary opening.
FIGURES 2, 3 in Descriptions of the Mature Larvae of Three Australian Ground-Nesting Bees(Hymenoptera: Colletidae: Diphaglossinae and Neopasiphaeinae)
FIGURES 2, 3. Diagrams of mature larva of Leioproctus wanni. Head, frontal and lateral views. Scale bar = 2 cm.
FIGURES 9–11 in Descriptions of the Mature Larvae of Three Australian Ground-Nesting Bees(Hymenoptera: Colletidae: Diphaglossinae and Neopasiphaeinae)
FIGURES 9–11. Diagrams of predefecating larva of Trichocolletes orientalis Batley and Houston. 9. Entire larva, lateral view. Scale bar = 2 cm. 10, 11. Head, frontal and lateral views.
FIGURES 21, 22 in Descriptions of the Mature Larvae of Three Australian Ground-Nesting Bees(Hymenoptera: Colletidae: Diphaglossinae and Neopasiphaeinae)
FIGURES 21, 22. Microphotograph of head and body, lateral views, of a live postdefecating larva of Paracolletes crassipes, revealing texture and color as well as shape of integument.
FIGURES 14–16 in Descriptions of the Mature Larvae of Three Australian Ground-Nesting Bees(Hymenoptera: Colletidae: Diphaglossinae and Neopasiphaeinae)
FIGURES 14–16. Diagrams of postdefecating larva of Paracolletes crassipes Smith. 14. Full body, lateral view. 15, 16. Head, frontal and approximate lateral views.
FIGURES. 4–8 in Descriptions of the Mature Larvae of Three Australian Ground-Nesting Bees(Hymenoptera: Colletidae: Diphaglossinae and Neopasiphaeinae)
FIGURES. 4–8. SEM micrographs of mature larva of L. wanni. 4, 5. Head frontal view and only approximate lateral view (note both left and right antennae visible). 6. Frontal view of mouthparts showing: (a) conspicuous pattern of spicules on labrum between labral tubercles; (b) elongate maxillary palpi; (c) recessed labial palpi; and (d) recessed and somewhat obscure salivary opening lacking lips. 7. SEM micrograph of left side of metasomal segments 5–9, showing projecting spiracles of segments 6–8. 8. Close-up of projecting spiracle.
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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.