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FIGURE 3. Rhopalaea idoneta n in A new species of the genus Rhopalaea (Class: Ascidiacea) from the Red Sea
FIGURE 3. Rhopalaea idoneta n. sp. A. Atrial siphon, scale bar 1 mm B. Pigment dots in between the atrial siphonal lobes, scale bar 1mm. C. Left side, scale bar 1cm. Arrows pointing out to the longitudinal muscles.
FIGURE 4. Rhopalaea idoneta n in A new species of the genus Rhopalaea (Class: Ascidiacea) from the Red Sea
FIGURE 4. Rhopalaea idoneta n. sp. A. Dorsal tubercle B. Neural ganglion C. Dorsal lamina D. Stigmata shape. Scale bar 1mm. Photo: A. Gur
FIGURE 5. Rhopalaea idoneta n in A new species of the genus Rhopalaea (Class: Ascidiacea) from the Red Sea
FIGURE 5. Rhopalaea idoneta n. sp. rectal region. Arrows pointing out to the genital papilla and anus.
FIGURE 9 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 9. Oral pedicellariae, showing species-specific variation among (A) Acanthaster benziei sp. nov., (B) A. planci, (C) A. mauritiensis, and (D) A. cf. solaris.
FIGURE 7 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 7. Typical latero-oral spines, showing species-specific variation among (A) Acanthaster benziei sp. nov., (B) A. planci, (C) A. mauritiensis, and (D) A. cf. solaris.
FIGURE 6 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 6. Typical primary spines, showing species-specific variation among (A, B) Acanthaster benziei sp. nov., (C) A. planci, (D) A. mauritiensis, and (E) A. cf. solaris.
FIGURE 5 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 5. Typical colouration of Acanthaster benziei sp. nov. (A) GW4081 (Paratype, hiding during the day under a crevice), Al-Lith, Saudi Arabia, (photo credit: Oliver Voigt), (B–D) Thuwal Reefs, Saudi Arabia (photo credit: Gert Wörheide). Approximate diameter of specimens is 25–30 cm.
FIGURE 4 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 4. Acanthaster benziei sp. nov. (holotype GW4202) (A-B) Aboral spines, (C-F) oral spines and (G-H) pedicellariae of four adult specimens of Acanthaster benziei sp. nov.: (A) Primary spines, (B) Secondary spines, (C) Latero-oral spines, (D) Circumoral spines, (E) Oral spines, (F) Subambulacral spines, (G) Aboral pedicellariae, (H) Oral pedicellariae.
FIGURE 2 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 2. Ethanol-preserved specimens of the type series. GW4202 (A) is the holotype, all the others (B–D) are paratypes. Note that individual GW4266 is a juvenile specimen. Size of labels 6 × 2 cm.
FIGURE 1 in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 1. Illustrations of pedicellariae and spines (A–C). A: Subambulacral spines, top: bundle with two equally long spines each, bottom: bundle with four unequal long spines; B: Aboral pedicellariae with two valves; C: Close up of part of one arm of paratype GW4081, showing three different spine types indicated by arrows: latero-oral spines (dark blue), oral spines (yellow), subambulacral spines (turquoise). The actinal pedicellariae (red) are associated with the first row of oral spines.
FIGURE 3. A in A new species of crown-of-thorns sea star, Acanthaster benziei sp. nov. (Valvatida Acanthasteridae), from the Red Sea
FIGURE 3. A: Indo-Pacific 'Acanthaster planci' species complex, COI ML tree based on the haplotype alignment of Vogler et al. (2008), supplemented with five samples from Israel (see text for details) extracted from full mitochondrial genome sequences from Yuasa et al. (2021) and four sequences of the type series of Acanthaster benziei sp. nov. (GW4xxx, highlighted in bold). The tree was rooted with Acanthaster brevispinus (accession number AB231476), showing the deep divergence among, and little diversity within, species/geographic clades. ML bootstrap values are above branches, bootstrap values of the NJ clustering of haplotypes below branches. B: Geographic distribution of COI-barcoded clades and of type localities of names (Figure 1 from Haszprunar et al. 2017): red—Red Sea (RS) species; blue—Southern Indian Ocean (SIO) species (A. mauritiensis); yellow—Northern Indian Ocean (NIO) species (A. planci); green—Pacific Ocean (PO) species (A. cf. solaris). Location of type localities of nominal Acanthaster species: asterisk—A. planci; cross—A. echinites; triangle—A. solaris, square—A. mauritiensis; circle—A. ellisii pseudoplanci; "?" - the type locality of A. ellisii was not specified: in South American waters of the East Pacific.
Microfluidic device videos of malaria infected red blood cells
<p>These videos contain malaria-infected red blood cells moving through microfluidic devices.</p>
FIGURE. Hypothetical relationships within Ammoglochin section; modified, after Parent (1974). Green—parental species; red— putative stabilised hybrids. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Hypothetical relationships within Ammoglochin section; modified, after Parent (1974). Green—parental species; red— putative stabilised hybrids.
Large-scale annotation dataset for cell/tissue segmentation in H&E-stained images : anti-CD235a (red blood cells)
<p><strong>LICENSE</strong></p> <p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (<strong>CC-BY-NC-SA 4.0</strong>)</p> <p>For non-commercial use, please use the dataset under CC-BY-NC-SA.<br> If you would like to use the dataset for commercial purposes, please contact us (ishum-prm@m.u-tokyo.ac.jp).</p> <p>A Tar.gz file contains the following files:</p> <p>- HE image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_HE.png</p> <p>- Mask image file: {antigen}_{celltype}_{slideID}_{posx}_{posy}_mask.png</p> <p>Each image file is 984x984 px.</p> <p>posX and posY are the leftmost position in WSI coordinate.</p> <p>Mask files store binary segmentation mask (background : 0, target : 1)</p> <p> </p> <p>A csv file contains the following information:</p> <p>antigen : Antibodies for this antigen were used to create the segmentation mask.</p> <p>filename: filename of image or mask file.</p> <p>train_val_test : train, validation, or test sample in the paper.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Daisuke Komura, Takumi Onoyama, Koki Shinbo, Hiroto Odaka, Minako Hayakawa, Mieko Ochi, Ranny Rahaningrum Herdiantoputri, Haruya Endo, Hiroto Katoh, Tohru Ikeda, Tetsuo Ushiku, Shumpei Ishikawa,<br> Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists, Patterns, Volume 4, Issue 2, 2023, 100688, https://doi.org/10.1016/j.patter.2023.100688.</p>
Piezo1 as a force-through-membrane sensor in red blood cells
<p>Piezo1 is the stretch-activated Ca<sup>2+</sup> channel in red blood cells that mediates homeostatic volume control. Here we study the organization of Piezo1 in red blood cells using a combination of super-resolution microscopy techniques and electron microscopy. Piezo1 adopts a nonuniform distribution on the red blood cell surface, with a bias towards the biconcave "dimple". Trajectories of diffusing Piezo1 molecules, which exhibit confined Brownian diffusion on short timescales and hopping on long timescales, also reflect a bias towards the dimple. This bias can be explained by "curvature coupling" between the intrinsic curvature of the Piezo dome and the curvature of the red blood cell membrane. Piezo1 does not form clusters with itself, nor does it co-localize with F-actin, Spectrin or the Gardos channel. Thus, Piezo1 exhibits the properties of a force-through-membrane sensor of curvature and lateral tension in the red blood cell.</p>
Tube locations of the red imported fire ant (RIFA)
<p><span><i>Solenopsis invicta</i> Buren, also known as the red imported fire ant (RIFA), has had a large negative impact on human and livestock health. However, few studies have further investigated the influence of human land use, which is an important factor affecting the habitats of insects, on the expansion of RIFAs. In addition, there is a lack of knowledge of the empirical associations between RIFA diffusion and land use within countries. Therefore, the objectives of this study were to provide an approach to delineate the areas of RIFA infestations and explore how land-use influences the spatiotemporal diffusion of <i>S. invicta</i>. We used RIFA data from 2008 to 2015 from the RIFA surveillance system, which was conducted by the National RIFA Control Center in Taiwan. Two regions in Taiwan with different RIFA infestation levels were investigated. The ordinary kriging method was applied to show the spatial intensity of RIFAs and the extreme distance estimator method was applied to determine the critical dispersal distances which showed the distance of the highest probability of RIFAs in two consecutive years. In addition, network analyses were used to identify RIFA invasion routes between land-use types. Finally, bivariate <span>local indicators of spatial association</span> were used to capture the invasion process in time and space. The results showed paddy fields, main roads, and warehouses were identified as the top three land-use types of diffusion sources. On average, the critical RIFA dispersal distances were 600 and 650 m in two consecutive years in high- and low-infestation regions, respectively. Finally, RIFAs were likely to diffuse between main roads and warehouses in the low-infestation region. Therefore, it is suggested that RIFA control activities be implemented at least 600 m from the observed spot. Additionally, control activities should be conducted on the identified three land-use types of diffusion sources in the high-infestation region, and the roadsides between main roads and warehouses in the low-infestation region to prevent the accidental spread of RIFAs.</span></p>
ReD&T_Phase1Round 1 dataset
<p>This is the anonymised data from round 1 of the dataset.</p> <p>The project is using the Delphi method to redesign a new D&T curriculum. In this first round, D&T teachers have been asked to opine whether they agree with 26 controversial questions.</p> <p>This is the dataset from the first round.</p>
Data for: Multiple antipredator behaviors in red-tailed monkeys reveal spatially distinct landscapes of fear
<p>Foraging opportunity and predation risk act as opposing influences on an animal's habitat use. "Landscapes of fear" (LOF), whereby one predicts the spatial distribution of predators or perceived predator presence using prey responses, are an important tool for modeling this conflict. LOF models examining perceived predation risk are often generated using a single behavioral metric, even though individuals can respond to predation pressure with multiple potential behaviors. Here, we expanded traditional LOF approaches by measuring three antipredator behaviors in wild red-tailed monkeys (<em>Cercopithecus ascanius</em>): aggregation, alarm calling, and vigilance. We predicted that each behavior would reveal spatially explicit regions of high risk, as each behavior may attend to different aspects of perceived predation risk. The use of different behaviors may depend upon factors such as vegetation type, age/sex class of an individual, and which other antipredator behaviors are being exhibited by group members. We collected data on two troops of monkeys in the Issa Valley, Tanzania for over 19 months and conducted 3,189 group follows. We found that vegetation type varied in its effect on antipredator behavior. Monkeys conducted more antipredator behavior in more open vegetation types compared to more closed, riparian forests. The LOF models generated for each behavior mapped distinct and predominantly non-overlapping spatial regions of perceived predation risk, which was replicated across the two groups. This suggested that monkeys responded differently across their home range to specific perceived risks. Such spatially explicit behavior may indicate vegetation-specific predation risk or unique trade-offs in antipredator behavior throughout a heterogenous habitat.</p>
Red‐backed shrike (Lanius collurio) versus common cuckoo (Cuculus canorus): An example of ineffective cuckoo–hawk mimicry
<p><span>The red-backed shrike (<em>Lanius collurio</em>) used to be one of the most common hosts of the common cuckoo (<em>Cuculus</em> <em>canorus</em>). Nevertheless, during the last 30 years, there is increasing evidence from Central Europe that the occurrence of cuckoo chicks in shrike nests has become scarcer, and that in some locations they have disappeared completely. Multiple hypotheses have been suggested to explain this abandonment. Here, we test the hypothesis that shrikes vigorously attack adult cuckoos, potentially resulting in ineffective parasitism. Adult common cuckoos resemble in appearance the Eurasian sparrowhawk (<em>Accipiter</em> <em>nisus</em>), a common predator of small passerines. One hypothesis presumes that the cuckoo has evolved this mimicry to avoid attack by small passerines when searching for their nests. Our results show that shrikes defending their nests attacked cuckoos very vigorously, more often and more intensively than they did sparrowhawks. In the presence of a sparrowhawk dummy, parent shrikes only produced alarm calls and flew over the dummy. This suggests that cuckoo-hawk mimicry is ineffective in the case of shrikes and that they attack them much more often than they do any other presented intruder. Therefore, this activity could possibly result in the abandonment of shrikes as potential hosts for cuckoos.</span></p>
Data for: Assessment of the accuracy of counting large ungulate species (red deer Cervus elaphus) with UAV-mounted thermal infrared cameras during night flights
<p>Unmanned Aerial Vehicles (UAVs) are increasingly used in wildlife surveying, including estimation of population densities. It is essential that we evaluate and test new survey methods to guide optimal sampling strategies. This study aimed to assess the accuracy of using a UAV-mounted thermal infrared (TIR) camera to count red deer <em>Cervus elaphus</em> populations, and how this was influenced by flight season, height and velocity, in order to help guide future census design. We flew 57 flights across a captive population of red deer in a 13 ha deer park enclosure of semi-natural habitat, representative of the species' range in northern Germany. Flights and image assessments were performed with no prior knowledge of actual population size. Accuracy was quantified by comparing real population size (known only to deer park staff) and independently estimated population sizes from UAV TIR images. Accuracy was significantly influenced by ecological season (early and late winter, spring and early summer) and height. Across all seasons, lower flights (100 m) performed better than higher ones (120 m), with lower flights in early winter and early summer being on average accurate to within 1% of actual population counts. For the season where we had the largest range of temperatures between flights (late winter) we found that accuracy was highest when temperatures were lowest. Flights were also able to identify all five stags (defined as a male deer ≥2 years old) present in early summer, but not in spring. Deer appeared to avoid the landing/take-off area, but there were no noted behavioural responses to drones flying over animals when at constant height and velocity during surveys. Our results indicate that UAV-mounted TIR camera have the potential to accurately count populations of large ungulate species, but that flight season, height and potentially temperature need to be taken into account to maximise accuracy. This approach has the potential to be scaled up to more accurately estimate densities of wild populations compared to existing approaches.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.