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2,031 results for “Transformation”
Figure 3 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 3. Lysilla laciniata (holotype, AM W199626): A, B, progressively closer ventral views of the anterior end; C, closeup view of prostomium to anteriormost segments, dorsal view; arrows point to prostomial process. Polycirrus nonatoi: D (paratype, MZUSP 1243), anterior end, ventral view; E, F (holotype, MZUSP 1213), anterior end, ventral and left ventrolateral views, respectively. Polycirrus papillosus: G–I (holotype, MZUSP 1216), G, entire worm; H, I, anterior end, dorsal and ventral views, respectively; *segment 2; J (paratype, MZUSP 1244), anterior end, ventral view. Numbers refer to segments; ll, lower lip; P, basal part of prostomium; P* or *, distal part of prostomium; ul, upper lip. Scale bars: A, 1 mm; B, 0.4; C, D, 0.3 mm; E, F, H, J, 0.2 mm; G, 0.5 mm; I, 0.1 mm.
Figure 2 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 2. Amaeana apheles (holotype, AM W5239): A, anterior end, dorsal view; arrow points to prostomial mid-dorsal process; B, anterior end, ventral view. Hauchiella renilla (holotype, AM W199607): C, D, progressively closer ventral views of anterior end. Biremis blandi (holotype, USNM 47976): E, F, entire worm, ventral and dorsal views, respectively. Enoplobranchus sanguineus: G (syntype, YPM 40569), H (syntype, YPM 40568), anterior end, ventral views; I (syntype, YPM 40568), anterior end, dorsal view; J (syntype, YPM 181), mid-body parapodia. Numbers refer to segments; ll, lower lip; P, basal part of prostomium; *, distal part of prostomium; ul, upper lip. Scale bars: A–C, 0.4 mm; D, 0.2 mm; E–F, 5 mm; G–J, 1 mm.
Figure 1 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 1. Strict consensus tree from Nogueira et al. (2013: fig. 21), indicating phylogenetic relationships among Terebelliformia family-level clades.
Figure 5 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 5. Polycirrus "clavatus" (MZUSP 1221): A–C, anterior end, ventral, dorsal, and left lateral views, respectively. Rhinothelepus occabus: D (paratype, AM W201904), anterior end, right lateral view; E (holotype, AM W201903), closeup view of the prostomium and anterior segments; unspecified arrow points to eyespots. Rhinothelepus lobatus: F (holotype, AM W5234), anterior end, left lateral view. Glossothelepus mexicanus (holotype, LACM–AHF Poly 1449): G, anterior end, dorsal view. Decathelepus wambira (paratype AM W1749): H, anterior end, right lateral view. Parathelepus collaris (specimen BMNH 1983.1696): I, anterior end, ventral view. Numbers refer to segments; unspecified arrows point to nephridial/ genital papillae; ll, lower lip; P, basal part of prostomium; *, distal part of prostomium; PP, prostomial process; ul, upper lip. Scale bars: A–C, I = 0.2 mm; D = 0.15 mm; E–F = 0.5 mm; G = 0.4 mm; H = 0.25 mm.
Figure 10 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 10. Enoplobranchus sanguineus (syntype, YPM 181): A, notochaetae; C, notopodium from mid-body segment. Decathelepus ocellatus (holotype, AM W6782): B, uncini, segment 18. Polycirrus sp. Hawaii (LACM-AHF): D, posterior end, frontal view; H, posterior end, dorsolateral view. Polycirrus nonatoi (paratype, MZUSP 1243): E, posterior end, frontal view. Polycirrus "clavatus" (MZUSP 1221): F, posterior end, right dorsolateral view. Amaeana trilobata (AM W7050): G, posterior end, left dorsolateral view. Lysilla laciniata (holotype, AM W199626): I, posterior end, right lateral view. Scale bars: A, C, 30 μm; B, 6 μm; D, 50 μm; E, G–H, 100 μm; F, 200 μm; I, 0.3 mm.
Figure 4 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 4. Amaeana apheles (AM W10864): A, anterior end, dorsal view; E, close-up view of the anterior end, right lateral view. Lysilla pacifica (AM W199622): B, anterior end, right lateral view; F, G, close-up views of the anterior end, ventral and dorsal views, respectively; unspecified arrows point to basal part of prostomium and segment 1. Lysilla bilobata (AM W199514): C, anterior end, ventral view; D, left side notopodia. Numbers refer to segments; ll, lower lip; P, basal part of prostomium; *, distal part of prostomium; PP, prostomial process; ul, upper lip. Scale bars: A, 0.5 mm; B–C, G, 0.4 mm; D, 0.2 mm; E–F, 0.3 mm.
Figure 14 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 14. One of the 54 minimum-length cladograms, showing transformation series for selected characters (see also Figs 13, 15–16) that contribute to the non-monophyly of Polycirrus, Lysilla, and Amaeana.
Figure 13 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 13. One of the 54 minimum-length cladograms, showing transformation series for selected characters (see also Figs 14–16) that contribute to the non-monophyly of Polycirrus, Lysilla, and Amaeana.
Figure 12 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 12. One of the 54 minimum-length cladograms, showing transformation series for characters potentially relevant to defining Polycirridae. A–C and A′–C′ refer to respective alternative transformation series.
Figure 8 in An assessment of the status of Polycirridae genera (Annelida: Terebelliformia) and evolutionary transformation series of characters within the family
Figure 8. Lysilla bilobata (AM W199514): A, base of notochaetae. Amaeana apheles: B, C (AM W10864), D (AM W5384): notochaetae. Amaeana trilobata (AM W7050): E, notochaetae. Glossothelepus mexicanus (holotype, LACM–AHF Poly 1449): F, notochaetae, segment 6. Scale bars: A, E–F, 10 μm; B, C, 6 μm; D, 20 μm.
A mode-locked random laser generating transform-limited optical pulses
<p>Ever since the mid-1960's, locking the phases of modes enabled the generation of laser pulses of duration limited only by the uncertainty principle, opening the field of ultrafast science. In contrast to conventional lasers, mode spacing in random lasers is ill-defined because optical feedback comes from scattering centres at random positions, making it hard to use mode locking in transform limited pulse generation. Here the generation of sub-nanosecond transform-limited pulses from a mode-locked random fibre laser is reported. Rayleigh backscattering from decimetre-long sections of telecom fibre serves as laser feedback, providing narrow spectral selectivity to the Fourier limit. The laser is adjustable in pulse duration (0.34-20 ns), repetition rate (0.714-1.22 MHz) and can be temperature tuned. The high spectral-efficiency pulses are applied in distributed temperature sensing with 9.0 cm and 3.3×10⁻³ K resolution, exemplifying how the results can drive advances in the fields of spectroscopy, telecommunications, and sensing.</p>
Data from: Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding
<p>Wildfires and climate change are having transformative effects on vegetation composition and structure, and post-fire management may have long-lasting impacts on ecosystem reorganization. Post-fire aerial seeding treatments are commonly used to reduce runoff and soil erosion, but little is known about how seeding treatments affect native vegetation recovery over long periods of time, particularly in type-converted forests which have been dramatically transformed by the effects of repeated, high-severity fire. In this study, we analyze and report on a rare long-term (23-year) dataset that documents vegetation dynamics following a 1996 post-fire aerial seed treatment and subsequent 2011 high-severity reburn in a dry conifer forest of northern New Mexico in the southwestern United States. Repeated surveys between 1997 – 2019 of 49 permanent transects were used to test for differences in vegetation cover, richness, and diversity between seeded and unseeded areas, and to characterize the development of seeded and unseeded vegetation communities through time and across gradients of burn severity, elevation, and soil-available water capacity. Post-fire seeding led to a clear and sustained divergence in herbaceous community composition. Seeded plots had much higher cover of non-native graminoids, primarily Bromus inermis, a likely contaminant in the seed mix. High-severity reburning in all plots in 2011 reduced native graminoid cover by half at seeded plots compared to both pre-fire levels and to plots that were unseeded following the initial 1996 fire. In addition, increased fire severity was associated with increased non-native graminoid cover and reduced native graminoid cover, native species richness, and species diversity. This study documents a fire-driven ecosystem transformation from a former conifer forest into a shrub-grass system, reinforced by aerial seeding of grasses and high-severity reburning. This unique long-term dataset illustrates that post-fire seeding carries significant risk of unwanted non-native species invasions that persist through subsequent fires – indicating that alternative post-fire management actions merit consideration to better support native ecosystem resilience in the face of emergent climate change and increasing disturbance. Lastly, this study highlights the importance of long-term monitoring of post-fire vegetation dynamics, as short-term assessments will miss key elements of the full complexity of ecosystem responses to fire and post-fire management actions.</p>
SAMPLER representations of FFPE TCGA-lung WSIs using tile-level features of the MMIL-Transformer model
<p>Here we provide single-scale SAMPLER representations of the FFPE TCGA-lung (LUAD and LUSC) WSIs using tile-level features provided in https://github.com/hustvl/MMIL-Transformer. To learn more about SAMPLER please visit https://github.com/TheJacksonLaboratory/SAMPLER.</p><p>The SAMPLER representations are provided as a single python pickle file. This pickle file contains a dictionary where each key is a WSI ID and each entry is the SAMPLER representation of the WSI.</p>
Numerical direct scattering transform for breathers
<p>Data and numerical codes used in the paper "Numerical direct scattering transform for breathers" by I. Mullyadzhanov, A. Gudko, R. Mullyadzhanov, & A. Gelash (2023). The paper is accepted for publication in the Proceedings of the Royal Society A journal.</p>
Graph Fourier transform for spatially resolved omics representation and analyses
<p>The file is a CODEX image of a human tonsil. They contain:</p> <ol> <li>Whole image: entire tissue image of human tonsil.</li> <li>Raw image data: six field of view (FOV) are deposited in this figure</li> <li>Processed data: the pixel-level data of six FOVs are deposited in the Seurat object and H5AD file.</li> </ol> <p><span>The following anti-bodies were used for CODEX profiling:</span></p> <p><span>BCL-2 (124, Novus Biologicals), CCR6 (polyclonal, Novus Biologicals), CD11b (EPR1344, Abcam), CD11c (EP1347Y, Abcam), CD15 (MMA, BD Biosciences), CD16 (D1N9L, Cell Signaling Technology), CD162 (HECA-452, Novus Biologicals), CD163 (EDHu-1, Novus Biologicals), CD2 (RPA-2.10, Biolegend), CD20 (rIGEL/773, Novus Biologicals), CD206 (polyclonal, R&D Systems), CD25 (4C9, Cell Marque), CD30 (BerH2, Cell Marque), CD31 (C31.3+C31.7+C31.10, Novus Biologicals), CD4 (EPR6855, Abcam), CD44 (IM-7, Biolegend), CD45 (B11+PD7/26, Novus Biologicals), CD45RA (HI100, Biolegend),<span> </span>CD45RO (UCH-L1, Biolegend), CD5 (UCHT2, Biolegend), CD56 (MRQ-42, Cell Marque), CD57 (HCD57, Biolegend), CD68 (KP-1, Biolegend), CD69 (polyclonal, R&D Systems), CD7 (MRQ-56, Cell Marque), CD8 (C8/144B, Novus Biologicals), collagen IV (polyclonal, Abcam), cytokeratin (C11, Biolegend), EGFR (D38B1, Cell Signaling Technology), FoxP3 (236A/E7, Abcam), granzyme B (EPR20129-217, Abcam), HLA-DR (EPR3692, Abcam), IDO-1 (D5J4E, Cell Signaling Technology), LAG-3 (D2G4O, Cell Signaling Technology), mast cell tryptase (AA1, Abcam), MMP-9 (L51/82, Biolegend), MUC-1 (955, Novus Biologicals), PD-1 (D4W2J, Cell Signaling Technology), PD-L1 (E1L3N, Cell Signaling Technology), podoplanin (D2-40, Biolegend), T-bet (D6N8B, Cell Signaling Technology), TCR β (G11, Santa Cruz Biotechnology), TCR-γ/δ (H-41, Santa Cruz Biotechnology), Tim-3 (polyclonal, Novus Biologicals), Vimentin (RV202, BD Biosciences), VISTA (D1L2G, Cell Signaling Technology), α-SMA (polyclonal, Abcam), and β-catenin (14, BD Biosciences). </span></p>
Sulfate affinity controls phosphate sorption and the proto-transformation of schwertmannite
<p><span>Schwertmannite is a metastable sulfate-rich ferric iron Fe(III) (oxyhydr)oxide and a common mineral in acid mine drainage sites and acid sulfate soils. Schwertmannite is </span><span>also used as a sorbent in various industrial applications, including phosphate removal for water treatment and environmental remediation. Phosphate sorption to schwertmannite, however, is complex and likely involves ligand exchange for inner- and outer-spherically coordinated sulfate groups, both on the surface and in the tunnel structure of the mineral. Here, we investigated phosphate sorption, concomitant sulfate release and their impact on the structure of schwertmannite as a function of pH and phosphate concentration. Kinetic and equilibrium batch experiments with synthetic schwertmannite were carried out at pH 3, 6, and 8, and the solid-phase was analyzed using </span><span>scanning electron microscopy, Mössbauer spectroscopy, infra-red spectroscopy and </span><span>X-ray diffraction spectroscopy</span><span>. We found a </span><span>strong correlation between phosphate sorption and sulfate release, with both following a two-step sorption model.</span><span> </span><span>K</span><span>inetics of phosphate sorption and sulfate release <span>were faster at more alkaline </span>pH</span><span>. M</span><span>aximum phosphate sorption was found at pH 6</span><span> (1.7 mmol PO<sub>4</sub><sup>3-</sup> g<sup>-1</sup>) , which decreased to 1.5 and 1.2 mmol PO<sub>4</sub><sup>3-</sup> g<sup>-1</sup> at pH 3 and 8, respectively. Fourier transform infrared spectroscopy revealed a shift from inner- to outer-spherical coordination of sulfate with increasing pH. <sup>57</sup>Mössbauer analyses of schwertmannite indicated a proto-transformation of schwertmannite at neutral to alkaline pH, characterized by a rise in a partially ordered sextet area. This change was interpreted as an increase in crystallinity resulting from the transition from Fe-SO<sub>4</sub> to Fe-O domains. This</span><span> </span><span>pH-induced </span><span>proto-transformation was inhibited in the presence of phosphate.</span><span> </span><span>We concluded that the phosphate sorption rate and maximum as well as the proto-transformation of schwertmannite were strongly affected by the mineral’s affinity for sulfate. Sorption to schwertmannite should primarily be regarded as a competitive exchange reaction between the <span>sorbing</span> oxyanion and the bound sulfate. This resulted in the highest phosphate sorption at circumneutral pH, a stark contrast to non-sulfate-containing </span><span>Fe(III) (oxyhydr)oxides</span><span>, where phosphate sorption is highest at acidic pH. </span><span>Our results are important for a fundamental understanding of the sorption properties of schwertmannite in phosphate-rich environments as they point towards the central role of sulfate coordination for phosphate immobilization.</span><span> </span></p>
Temporal Event Knowledge Graphs transformed from Object-Centric Event Logs
<p>In the paper"Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs", we introduced and formalized temporal Event Knowledge Graphs (tEKGs) and presented an algorithm to transform Object-Centric Event Logs (OCEL) 2.0 into tEKGs. Data sets are the results of transforming OCEL 2.0 log files. The source of the generated dump files are as follows:</p> <ol> <li>ContainerLogistics.neo4j.dum : <a href="../records/8428084">Link to the source data</a></li> <li>OrderManagement.neo4j.dump: <a href="../records/8428112">Link to the source data</a></li> <li>Procure-To-Payment.neo4j.dump: <a href="../records/8412920">Link to the source data</a></li> </ol> <p>The version of the dump files is <strong>5.12.0</strong>. Additionally, for restoring dump files inside Neo4j, you need to enter the username and password, which is indicated below:</p> <p><strong>Username</strong>: neo4j</p> <p><strong>Password</strong>: 12345678</p> <p> </p>
Fig. 4 in Increase in isoflavonoid content in Glycine max cells transformed by the constitutively active Ca independent form of the AtCPK1 gene
Fig. 4. Expression of the G. max genes (a) - of the upstream enzymes - 4- coumarate:CoA ligase (4CL), (b) – of the downstream enzymes - isoflavones synthase (IFS) and the final step of daidzein and genistein biosynthesis - 2- hydroxyisoflavanone dehydratase (HID) and (c) - enzymes involve in prenilation of daidzein and coumestrol - isoflavone dimethylallyltransferase (IDT1) and coumestrol 4-dimethylallyltransferase (C4DT) in the control callus culture (Gm) and callus line transformed with the constitutively active AtCPK1 (GmCa1 and GmCa2). Data (mean ± standard error) represent measurements of three independent replicates from two different RNA isolations and are presented as relative expression levels normalized to the expression of the G. max housekeeping genes. Different letters above the bars indicate statistically significant differences of means (P <0.05), Fisher's LSD.
Fig. 2. A in Increase in isoflavonoid content in Glycine max cells transformed by the constitutively active Ca independent form of the AtCPK1 gene
Fig. 2. A representative HPLC-UV profile of the AtCPK1-transformed callus culture of G. max – GmCa2 (A) and the control callus culture Gm (B). The callus tissue extracts recorded at 254 nm. Peak numbers correspond to each of the identified components and are listed in Table 3.
Fig. 3 in Increase in isoflavonoid content in Glycine max cells transformed by the constitutively active Ca independent form of the AtCPK1 gene
Fig. 3. Content (mg/g DW) of isoflavones-aglycones (a) and their glucosides and malonyl-glucosides derivatives (b–d) and prenylated isoflavones (e) in the control calli (Gm) and the AtCPK-transformed callus cultures - GmCa1 and GmCa2. Data are presented as the mean ± SE from four subcultures (biological replicates) with two technical replicates for each experiment. Different letters above the bars indicate statistically significant differences of means (P <0.05), Fisher's LSD.
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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.