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Figure 10 in Karyotypes of water scavenger beetles (Coleoptera: Hydrophilidae): new data and review of published records
Figure 10. Karyotypes of Enochrus (Lumetus). A–C, Enochrus quadripunctatus, mitosis, midgut. D–K, Enochrus fuscipennis, mitosis, midgut (H–I, specimens from Denmark, Rømø Island with the karyotypes indicating their hybrid origin). L–N, meiotic metaphase I from testes (L, Enochrus quadripunctatus, UK: East Walton, Norfolk; M–N, Enochrus fuscipennis, Denmark: Rømø Island). O–P, Enochrus fuscipennis, testes, mitotic metaphase from the same specimens as in (H–I). A, B, D, E, H, I, J, M–P, without treatment. C, F, G, K, L, C-banded.
Figure 9 in Karyotypes of water scavenger beetles (Coleoptera: Hydrophilidae): new data and review of published records
Figure 9. Karyotypes of Enochrus (Lumetus), mitosis from embryos. A, Enochrus bicolor. B–C, Enochrus ochropterus. D–E, Enochrus testaceus. F–K, Enochrus halophilus. A, B, D, F, H, I, without treatment. C, E, G, K, C-banded. Habitus figures: (L) Enochrus (Lumetus) testaceus; (M) Enochrus (Lumetus) halophilus.
Figure 8 in Karyotypes of water scavenger beetles (Coleoptera: Hydrophilidae): new data and review of published records
Figure 8. Mitotic karyotypes of the Enochrinae. A, Cymbiodyta marginella, embryo. B–D, European usual-looking species of Enochrus (Methydrus) from embryos: (B) Enochrus affinis; (C) Enochrus coarctatus; (D–E) Enochrus nigritus. F–G, unusual species assigned at the moment to Enochrus (Methydrus): (F) Enochrus morenae, midgut; (G) Enochrus sauteri, midgut. H, Enochrus (s.s.) melanocephalus, embryo. A–G, without treatment. H, C-banded. Habitus figures: (I) Cymbiodyta marginella; (J) Enochrus morenae.
Figure 17 in Karyotypes of water scavenger beetles (Coleoptera: Hydrophilidae): new data and review of published records
Figure 17. Mitotic karyotypes of Cercyon from midgut. (A) Cercyon marinus; (B–E) Cercyon lateralis; (F–G) Cercyon obsoletus; (H) Cercyon impressus; (I–K) Cercyon haemorrhoidalis; (L–N) Cercyon melanocephalus. A, B, D, F, H, I, K, L, N, without treatment. C, E, G, J, M, C-banded. Habitus figures: (O) Cercyon marinus; (P) Cercyon impressus; (Q) Cercyon haemorrhoidalis, from Fikáček (2019).
Figure 15 in Karyotypes of water scavenger beetles (Coleoptera: Hydrophilidae): new data and review of published records
Figure 15. Karyotypes of the Coelostomatini and Protosternini. A–C, Coelostoma orbiculare, embryo (A, with B-chromosomes; B–C, without B-chromosomes). D–F, Dactylosternum flavicorne, embryo. G, Dactylosternum corbetti, mitosis, midgut. J–K, Protosternum abnormale, meiotic nuclei from testes. A–F, J, K, without treatment. G, C-banded. Habitus figures: (H) Coelostoma orbiculare; (I) Dactylosternum corbetti; (L) Protosternum abnormale, from Fikáček et al. (2018).
Fig. 1 in Checklist of the Rove Beetles (Coleoptera: Staphylinidae) of South Carolina, Based on Published Records
Fig. 1. States and regions with lists of species of Staphylinidae. Red = state list; blue = regional list; green = unpublished list. See text for details.
All data support published article "Function of four tryptophan residues on Cel7A catalytic efficiency: insight into cellulase screening strategy based on natural cellulose or cellulose analogs"
<p>There is a high level of conservation of tryptophans within the active site architecture of the cellulase family, whereas the function of the four tryptophans in the catalytic domain of Cel7A is unclear. By mutating four tryptophan residues in the catalytic domain of Cel7A from <em>Penicillium piceum</em> (PpCel7A), the binding affinity between PpCel7A and <em>p-</em>nitrophenol-D-cellobioside (<em>p</em>NPC) was reduced as determined by Michaelis–Menten constants, molecular dynamics simulations, and fluorescence spectroscopy. Furthermore, PpCel7A variants showed a reduced level of cellobiohydrolase activity against cellulose analogs or natural cellulose. Therefore, it could be concluded four tryptophan residues in Cel7A played a critical role in substrate binding. Mutagenesis results indicated that the W390 stacking interactions at the -2 site played an essential role in facilitating substrate distortion to the -1 site. As soon as the function was altered, the mutation would inevitably affect the catalytic activity against the natural substrate. Interestingly, no clear relationship was found between the cellobiohydrolase activity of PpCel7A variants against <em>p</em>NPC and Avicel. <em>p</em>NP contains many electrophilic groups that may result in overestimation of the binding constant between tryptophan residues and <em>p</em>NPC in comparison to the natural substrate. Consequently, screening improved cellulase using cellulose analogs would divert attention from the target direction for lignocellulose biorefinery. Clarifying mechanism of catalytic diversity on the natural cellulose or cellulose analogs may give better insight into cellulase screening and selecting strategy.</p>
All data support the published articel "Loop-optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade-off through cross-strategy between machine learning and the B-factor analysis"
<p><em>Trichoderma reesei</em> endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from<em> T.reesei</em> without reducing its activity counteracting the activity-stability trade-off. A cross-strategy combination of machine learning and B-factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single-site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild-type EGs. Furthermore, the mechanism explained mutant variants had lower RMSD values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity-stability trade-off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross-strategy between machine learning and B-factor analysis improved superior enzyme activity-stability performance, which integrated structure-dependent and sequence-dependent information.</p>
Supplements and raw data for article to be published in Open Linguistics
<p>Supplements A, B and C of article (docx and pdf versions) :</p> <p>Emmanuel Cartier, Alexander Onysko*, Esme Winter-Froemel, Eline Zenner, Gisle Andersen, Béryl Hilberink-Schulpen, Ulrike Nederstigt, Elizabeth Peterson, and Frank van Meurs (2022). Linguistic repercussions of COVID-19:A corpus study on four languages, Open Linguistics 2022; 8:1-16.</p> <p>A supporting web exploration interface is available here : <a href="https://tal.lipn.univ-paris13.fr/neoveille/html/covid19_project/html/data_exploration.php">Link to web interface</a></p> <p>Raw data for the supplements and the web interface are here :</p> <p><a href="https://zenodo.org/api/files/d633542e-cdbc-4f4b-a43b-5bf7895b8a7c/raw_data_virus_names.tar.gz">raw_data_virus_names.tar.gz</a> : the raw data of the virus names (with a file for all languages, and a file per language).</p> <p><a href="https://zenodo.org/api/files/d633542e-cdbc-4f4b-a43b-5bf7895b8a7c/raw_data_virus_associated_words.tar.gz">raw_data_virus_associated_words.tar.gz</a> : the raw data of the virus names associated words (with a file for all languages, and a file per language).</p>
FIGURE 4 in Critical evaluation of two published descriptions of the same species of Ceratozamia (Zamiaceae) from northern Oaxaca, Mexico, and the formal synonymization of C. martinezii under C. aurantiaca
FIGURE 4. Leaflet vein comparison: (A) conspicuous, translucent veins of Ceratozamia zoquorum Pérez-Farr., Vovides & Iglesias (2001: 175); (B) inconspicuous, slightly translucent, light green veins of C. aurantiaca. Both leaflets were taken from old, mature leaves.
FIGURE 2 in Critical evaluation of two published descriptions of the same species of Ceratozamia (Zamiaceae) from northern Oaxaca, Mexico, and the formal synonymization of C. martinezii under C. aurantiaca
FIGURE 2. Leaflet texture comparison: (A) papyraceous leaflet of Ceratozamia miqueliana H.Wendl. (1854: 68) bends easily; (B) coriaceous leaflet of C. aurantiaca cracks upon bending. Both leaflets were taken from old, mature leaves.
FIGURE 1 in Critical evaluation of two published descriptions of the same species of Ceratozamia (Zamiaceae) from northern Oaxaca, Mexico, and the formal synonymization of C. martinezii under C. aurantiaca
FIGURE 1. Ceratozamia aurantiaca at Fairchild Tropical Botanic Garden in FL, USA: (A) large adult plant growing in full sun; (B) closeup of characteristic orange emerging leaves.
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
FIGURE 1 in Names validly published in Kalanchoe (Crassulaceae subfam. Kalanchooideae) by Louis-René 'Edmond' Tulasne in 1857, with reference to names proposed in sched. by Hilsenberg and Bojer for two Malagasy species, and with notes on the type of the name K. tubiflora
FIGURE 1. Kalanchoe pinnata. In Tulasne (1857: 148) K. pinnata was included under the name Bryophyllum calycinum (see main text for a discussion). A. Leaf with plantlets developing along the margin. B. Inflorescence. Photographs: Gideon F. Smith.
FIGURE 1. Kalanchoe porphyrocalyx. A in A reassessment of combinations previously proposed or published for Kalanchoe subg. Alatae (Crassulaceae subfam. Kalanchooideae), and its valid publication at the rank of subgenus
FIGURE 1. Kalanchoe porphyrocalyx. A. Close-up of foliage and flowers. B. Corolla segments viewed from the mouth, i.e., from above. Note the burgundy red sepals. Both photographs by Gideon F. Smith.
FIGURE 2. Kalanchoe uniflora. A. Growing epiphytically with the thin stems typically descending. B in A reassessment of combinations previously proposed or published for Kalanchoe subg. Alatae (Crassulaceae subfam. Kalanchooideae), and its valid publication at the rank of subgenus
FIGURE 2. Kalanchoe uniflora. A. Growing epiphytically with the thin stems typically descending. B. Flowers and leafy stems in lateral view. Both photographs by Gideon F. Smith.
Clinical Study Reports published by the European Medicines Agency 2016-2018
<p>Clinical Study Reports published by the European Medicines Agency 2016-2018</p>
Raw Data for manuscript published at Nanomaterials, entitled: Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes
<p>.dm3 TEM, STEM and EELS raw data</p>
Data and code for Keller et al. (2023) "Links between large igneous province volcanism and subducted iron formations," published in Nature Geoscience
<p>Data and computer code used to generate results for Keller et al. (2023) "<em>Links between large igneous province volcanism and subducted iron formations</em>." A readme file in the folder gives a description for each file. Data may also be accessed from the article link on the publisher's website.</p>
Data underlying the study on 'Lability and the rigidification of word order. Evidence from Early Middle English', published in 'Linguistics'
<p>see the publication for a description of the data</p>
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.