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Raw NMR FID data of cryptoporic acid derivatives isolated from Cryptoporus volvatus fruiting bodies
<p>This is a NMR FID data of cryptoporic acid derivatives isolated from fruiting bodies of <em>Cryptoporus volvatus</em> (Polyporaceae).</p> <p>Isolation and structural elucidation of these compounds will be reported in the article titled "Species prioritization based on spectral dissimilarity: A case study of polyporoid fungi species", which is currently under revision.</p>
16S gene and ASV sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana
<p>Data from "Induction of antibiotic specialized metabolism by co-culturing in a collection of phyllosphere bacteria" by Qi et al. </p> <p>- 16S gene sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana in FASTA format. </p> <p>- Filtered OTU (ASV) table across all samples </p> <p>- ASV sequences</p>
Supplementary Materials for Genomic insight into Pediococcus acidilactici HN9, a potential probiotic strain isolated from the traditional Thai-style fermented beef Nhang
<p>Figure S1: Subsystem information of the HN9 based on the RAST annotation server, <br> Figure S2: Phylogenetic analysis of Pediococcus acidilactici HN9 and other strains at the species level, Figure S3: Sequence alignment of Enterolysin A from Pediococcus pentosaceus ATCC 25745 with Enterolysin A identified in the HN9, <br> Figure S4: Identification of bacteriocin-encoding genes from all bacterial strains in the genus Pediococcus, <br> Table S1: The information of strain used in this study, <br> Table S2: Metadata of all strains used in this study.</p>
Phylogenetic and Spatial Distribution of Evolutionary Isolation and Threat in Turtles and Crocodilians (Non-Avian Archosauromorphs)
The origin of turtles and crocodiles and their easily recognized body forms dates to the Triassic. Despite their long-term success, extant species diversity is low, and endangerment is extremely high compared to other terrestrial vertebrate groups, with ~ 65% of ~25 crocodilian and ~360 turtle species now threatened by exploitation and habitat loss. Here, we combine available molecular and morphological evidence with machine learning algorithms to present a phylogenetically-informed, comprehensive assessment of diversification, threat status, and evolutionary distinctiveness of all extant species. In contrast to other terrestrial vertebrates and their own diversity in the fossil record, extant turtles and crocodilians have not experienced any mass extinctions or shifts in diversification rate, or any significant jumps in rates of body-size evolution over time. We predict threat for 114 as-yet unassessed or data-deficient species and identify a concentration of threatened crocodile and turtle species in South and Southeast Asia, western Africa, and the eastern Amazon. We find that unlike other terrestrial vertebrate groups, extinction risk increases with evolutionary distinctiveness: a disproportionate amount of phylogenetic diversity is concentrated in evolutionarily isolated, at-risk taxa, particularly those with small geographic ranges. Our findings highlight the important role of geographic determinants of extinction risk, particularly those resulting from anthropogenic habitat-disturbance, which affect species across body sizes and ecologies.
Data from: Long-term persistence of monotypic dengue transmission in small size isolated populations, French Polynesia, 1978-2014
<p>Understanding the transition of epidemic to endemic dengue transmission remains a challenge in regions where serotypes co-circulate and there is extensive human mobility. French Polynesia, an isolated group of 72 inhabited islands, distributed among five geographically separated subdivisions, has recorded mono-serotype epidemics since 1944, with long inter-epidemic periods of circulation. Laboratory confirmed cases have been recorded since 1978, enabling exploration of dengue epidemiology under monotypic conditions in an isolated, spatially structured geographical location. A database was constructed of confirmed dengue cases, geolocated to island for a 35-year period. Statistical analyses of viral establishment, persistence and fade-out as well as synchrony among subdivisions were performed. Seven monotypic and one heterotypic dengue epidemic occurred, followed by low-level viral circulation with a recrudescent epidemic occurring on one occasion. Incidence was asynchronous among the subdivisions. Complete viral die-out occurred on several occasions with invasion of a new serotype, but also in the absence of any novel serotype. Island population size had a strong impact on the establishment, persistence and fade-out of dengue cases and endemicity was estimated achievable only at a population size in excess of 175 000. Despite island remoteness and low population size, dengue cases were observed somewhere in French Polynesia almost constantly, in part due to the spatial structuration generating asynchrony among subdivisions. Long-term persistence of dengue virus in this group of island populations may be enabled by island hopping, although could equally be explained by a reservoir of sub-clinical infections on the most populated island, Tahiti.</p>
Supporting materials for manuscript entitled 'Ultrasonic Isolation of Buried Pipes'
<p>This set contains data in support of some of the figures appearing in an open access manuscript entitled 'Ultrasonic Isolation of Buried Pipes' that is published at the Journal of Sound of Vibration (DOI:10.1016/j.jsv.2015.10.018).</p> <p>The data set contains ultrasonic guided wave attenuation measurements performed on a full-scale experimental rig, and model simulations output obtained using the software Disperse. The data set contains three files. Details on the specific data is provided within an extra file ('readme' file).</p>
Fig. 4. A–E. Uronemita filificum Kahl, 1931. F–I. Uronema marinum Dujardin, 1841. J–N. Pleuronema setigerum Calkins, 1902. A–C, F–H, J–L in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 4. A–E. Uronemita filificum Kahl, 1931. F–I. Uronema marinum Dujardin, 1841. J–N. Pleuronema setigerum Calkins, 1902. A–C, F–H, J–L. In vivo. D–E, M–N. After silver impregnation. A, F, J. Ventral views of typical individuals. B–C, G–H, K–L. Different individuals, showing variation in body shape, arrow in (B) shows the conspicuous apical plate, arrows in (C, H) mark contractile vacuoles. D, I, M–N. Detailed structures of buccal area, arrow in (M) indicates the ring-like posterior end of M2a, arrowheads in (M) mark preoral kineties. E. Ventral view, arrowheads show somatic kineties. Abbreviations: M1, 2, 3 = membranelle 1, 2 and 3; M2a = the anterior part of membranelle 2; M2b = the posterior part of membranelle 2; Ma = macronucleus; PM = paroral membrane. Scale bars: A–B = 20 μm; F, H, N = 10 μm; G = 5 μm, J–L = 30 μm.
Fig. 3. A–D in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 3. A–D. Pseudocohnilembus hargisi Evans & Thompson, 1964. E–J. Parauronema cf. longum Song, 1995. A–C, E–I. In vivo. D, J. After silver impregnation. A, E. Ventral views of typical individuals, arrow in (A) shows caudal cilia. B–C, F–G. Different individuals, showing varying body shapes, arrowheads in (F) mark somatic kineties. D, J. Detailed structure of the buccal area. H. Ventral view, arrow refers to dumbbell-shaped crystals. I. Posterior end, arrow marks caudal cilium. Abbreviations: M1, 2, 3 = membranelle 1, 2 and 3; PM = paroral membrane; Sc = scutica. Scale bars: A–B = 15 μm; C, E = 40 μm; G = 60 μm.
Fig. 5. All reported populations for the following species. A. Uronema marinum Dujardin, 1841. B in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 5. All reported populations for the following species. A. Uronema marinum Dujardin, 1841. B. Pseudocohnilembus hargisi Evans & Thompson, 1964. C. Metanophrys similis Song et al., 2002. D. Pleuronema setigerum Calkins, 1902. E. Uronemita filificum Kahl, 1931.
Fig. 2. A–D in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 2. A–D. Mesanophrys cf. carcini Small & Lynn in Aescht, 2001. E–G. Metanophrys similis Song et al., 2002. A–C, E–F. In vivo. D, G. After silver impregnation. A, E. Ventral views of typical individuals, arrow in (E) shows caudal cilia. B, F. Different individuals, showing different body shapes. C. Individual in morphogenesis. D, G. Detailed structure of the buccal area. Abbreviations: M1, 2, 3 = membranelle 1, 2 and 3; Ma = macronucleus; PM = paroral membrane. Scale bars: 30 μm.
Fig. 1. Sampling map. A in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 1. Sampling map. A. Chewacla Creek, Auburn, Alabama (32º36′56″ N, 85º28ʹ58″ E). B. Orange Beach, Alabama (30º16′44″ N, 87º33′35″ E).
Overcoming the Dichotomy: New Insights into the Genomic Diversity of Open and Isolated European Populations
<p>The dataset includes autosomal data of 227 individuals of 9 Italian populations (Aosta, Benetutti, Carloforte, Lessinia Cimbrians, North Sardinia, Sappada, Sauris, Sulcis Iglesiente and Timau) genotyper with the GenoChip 2.0.</p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 1
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 1 OF 2 ***</strong></p> <p><strong>Download part 2 here:</strong> <a href="../records/7882857">https://zenodo.org/records/7882857 </a> (now available!) </p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.</p> <p>____________________________</p> <p>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
- Sternite 1 without a pair of submedian tubercles, tubercles present on sternites 2–4 only (a); general coloration pale to striking yellow with isolated black markings (b) …………………………………7 7. Apex of subtegular ridge concave, laterally flanged (A) ……………………………………………8 - Apex of subtegular ridge convex, without lateral flange (a) ……………………………………9 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Sternite 1 without a pair of submedian tubercles, tubercles present on sternites 2–4 only (a); general coloration pale to striking yellow with isolated black markings (b) …………………………………7 7. Apex of subtegular ridge concave, laterally flanged (A) ……………………………………………8 - Apex of subtegular ridge convex, without lateral flange (a) ……………………………………9
StemGMD: A Large-Scale Audio Dataset of Isolated Drum Stems for Deep Drums Demixing - part 2
<p>We introduce StemGMD, a new large-scale dataset of isolated drum stems that builds upon the extensive MIDI collection found in <a href="https://magenta.tensorflow.org/datasets/groove">Magenta's Groove MIDI Dataset (GMD)</a>.</p> <p>GMD is a 13.6-hour corpus of expressive drum performances executed by ten drummers on a Roland TD-11 electronic drum kit. It contains 1150 MIDI files along with the corresponding full-kit audio mixtures.</p> <p>As a first step in creating StemGMD, we mapped the 22 different MIDI pitches found in the original files onto nine canonical instruments through the reduction scheme proposed in J. Gillick, A. Roberts, J. Engel, D. Eck, and D. Bamman, "Learning to groove with inverse sequence transformations," in International Conference on Machine Learning (ICML), vol. 97, 2019, pp. 2269–2279.</p> <p>Each of the nine resulting MIDI channels was manually synthesized as a 16-bit/44.1 kHz stereo WAV file using ten realistic-sounding acoustic drum kits sourced from the <a href="https://support.apple.com/en-me/guide/logicpro/lgsi2fb2509e/mac">Logic Pro X sample libraries</a>, i.e., Bluebird, Brooklyn, Detroit Garage, East Bay, Heavy, Motown Revisited, Portland, Retro Rock, Roots, and SoCal.</p> <p>As a result, StemGMD contains 1224 hours of audio, which correspond to more than 136 hours of full-kit mixtures. Moreover, StemGMD also contains single hits for each of the drum pieces at ten different velocities ranging from 30 to 127.</p> <p>To the best of our knowledge, StemGMD is the largest publicly available dataset of drums to date. Moreover, it is the first collection of single-instrument clips from all nine pieces in a canonical drum kit, making it well-suited for training deep drums demixing models.</p> <p> </p> <p><strong>*** THIS IS PART 2 OF 2 ***</strong></p> <p><strong>Download part 1 here:</strong> <a href="../records/7860223">https://zenodo.org/records/7860223 </a></p> <p>After downloading both parts, run <strong>unzip_StemGMD.sh</strong> to build the dataset from the split archive files.<br>Once unzipped, StemGMD will take just over <strong>1.13 TB </strong>of memory.</p> <p> </p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0) License</a>.<br><br>_____________________________<br><br>We employed the dataset in our paper titled "Toward Deep Drum Source Separation," published in Pattern Recognition Letters. </p> <div> <div> <div> <div> <p>Please, cite this work as: A. I. Mezza, R. Giampiccolo, A. Bernardini, and A. Sarti, "Toward Deep Drum Source Separation," Pattern Recognition Letters, vol. 183, pp. 86-91, 2024, doi: 10.1016/j.patrec.2024.04.026.</p> <pre>@article{mezza2024, title = {Toward deep drum source separation}, author = {Alessandro Ilic Mezza and Riccardo Giampiccolo and Alberto Bernardini and Augusto Sarti}, journal = {Pattern Recognition Letters}, volume = {183}, pages = {86-91}, year = {2024}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.04.026} }</pre> </div> </div> </div> </div>
Figure 4 in A new dwarf boa (Serpentes, Booidea, 'Tropidophiidae') from the Early Oligocene of Belgium: a case of the isolation of Western European snake faunas
Figure 4. Trunk/caudal transition in Falseryx petersbuchi, in left lateral view: A, posterior trunk vertebra (BSP, 1976 XXII 6127); B, fusion of last trunk, 1st cloacal and 2nd cloacal vertebrae (BSP, uncatalogued); C, anterior caudal vertebra (SMNS, 57898-3). Abbreviations: h, hypapophysis; hk, haemal keel; ls, lymphapophyses; pd, paradiapophysis; pl, pleurapophysis. A and C from Szyndlar & Rage (2003: fig. 27C, P).
Figure 1 in A new dwarf boa (Serpentes, Booidea, 'Tropidophiidae') from the Early Oligocene of Belgium: a case of the isolation of Western European snake faunas
Figure 1. Holotype middle trunk vertebra of Falseryx neervelpensis sp. nov. (IRSNB R 240), in right lateral (A), left lateral (B), dorsal (C), ventral (D), anterior (E) and posterior (F) views. Abbreviations: cd, condyle; ct, cotyle; d, diapophysis; hk, haemal keel; lf, lateral foramen; na, neural arch; nc, neural canal; ns, neural spine; p, parapophysis; pd, paradiapophysis (diapophysis + parapophysis); po, postzygapophysis; poa, postzygapophyseal articular surface; pr, prezygapophysis; pra, prezygapophyseal articular surface; prp, prezygapophyseal process; sf, subcentral foramen; sg, subcentral groove; sr, subcentral ridge; z, zygosphene; zy, zygantrum.
Figure 2 in A new dwarf boa (Serpentes, Booidea, 'Tropidophiidae') from the Early Oligocene of Belgium: a case of the isolation of Western European snake faunas
Figure 2. Trunk vertebrae of Falseryx neervelpensis sp. nov. A–C, anterior trunk vertebra (IRSNB R 239), in right lateral (A), anterior (B) and dorsal (C) views; D–F, middle trunk vertebra (IRSNB R 241), in left lateral (D), dorsal (E) and ventral (F) views; G–K, posterior trunk vertebra (IRSNB R 237), in right lateral (G), dorsal (H), ventral (I), anterior (J) and posterior (K) views; L–P, one of the final trunk vertebrae (IRSNB R 238), in left lateral (L), dorsal (M), ventral (N), anterior (O) and posterior (P) views. Abbreviation: h, hypapophysis.
Rapid diversification of gray mangroves (Avicennia marina) driven by geographic isolation and extreme environmental conditions in the Arabian Peninsula
<p><span>Biological systems occurring in ecologically heterogeneous and spatially discontinuous habitats provide an ideal opportunity to investigate the relative roles of neutral and selective factors in driving lineage diversification. The gray mangroves (<em>Avicennia marina</em>) of Arabia occur at the northern edge of the species' range and are subject to variable, often extreme, environmental conditions, as well as to historic large fluctuations in habitat availability and connectivity resulting from Quaternary glacial cycles. Here, we analyze fully sequenced genomes sampled from 20 locations across the Red Sea, the Arabian Sea, and the Persian/Arabian Gulf (PAG) to reconstruct the evolutionary history of the species in the region and to identify adaptive mechanisms of lineage diversification. Population structure and phylogenetic analyses revealed marked genetic structure and highly supported clades among and within the seas surrounding the Arabian Peninsula. Demographic modeling showed times of divergence consistent with recent periods of geographic isolation and low marine connectivity during glaciations, revealing the presence of (cryptic) glacial refugia in the Red Sea and the PAG. Significant migration was detected within the Red Sea and the PAG, and across the Strait of Hormuz to the Arabian Sea, suggesting gene flow upon secondary contact among Arabian mangrove populations. Genetic‐environment association analyses revealed high levels of adaptive divergence and detected signs of multi-loci local adaptation driven by temperature extremes and hypersalinity. These results support a process of rapid diversification resulting from the combined effects of historical factors and ecological selection and reveal mangrove peripheral environments as relevant drivers of lineage diversity.</span></p>
ScienceDex guides
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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.