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203 results for “Adaptive morphology”
What the heart wants: adaptive significance of cordate leaf morphology in Arnica (Asteraceae)
We studied how the leaf inclination of basal leaves of two species, heartleaf arnica (Arnica cordifolia Hook.) and broadleaf arnica (Arnica latifolia Bong.) varied with canopy cover in the Greater Yellowstone Ecosystem, Wyoming, USA in July and August, 2022. Basal leaves of heartleaf arnica possess cordate leaf bases while those of broadleaf arnica do not, leading to potential biomechanical limitations of the latter to persist in shaded forest understories. Leaf inclination was measured as the angle (degrees) between the petiole and leaf planes of basal leaves for each species; cordateness was measured as the ratio of leaf length on either side of the petiole insertion point in basal leaves of heartleaf arnica. Data collection are complete.
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
<p>We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection".</p> <p>A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class.</p> <p> </p> <p><strong>DATA:</strong></p> <p><strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: <a href="https://zenodo.org/record/5514180#.Y6SM7y-B2_w">https://zenodo.org/record/5514180</a> . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class).</p> <p><strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class).</p> <p>SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5</p> <p>DeCALS: decals.zip</p> <p><strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field.</p> <p>SDSS (wide filed): the same data as in 2)</p> <p>SDSS (Strip 82 deep field): sdss_stripe82.zip</p> <p>All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: <a href="https://github.com/deepskies/DeepAstroUDA">https://github.com/deepskies/DeepAstroUDA</a> .</p>
Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds
<p>Natal dispersal—the movement from birthplace to breeding location—is often considered the most significant dispersal event in an animal's lifetime. Natal dispersal distances may be shaped by a variety of intrinsic and extrinsic factors, and remain poorly quantified in most groups, highlighting the need for indices that capture variation in dispersal among species.</p> <p>In birds, it is hypothesized that dispersal distance can be predicted by flight efficiency, which can be estimated using wing morphology. However, the use of morphological indices to predict dispersal remains contentious and the mechanistic links between flight efficiency and natal dispersal are unclear.</p> <p>Here, we use phylogenetic comparative models to test whether hand-wing index (HWI, a morphological proxy for wing aspect ratio) predicts natal dispersal distance across a global sample of 114 bird species. In addition, we assess whether HWI is correlated with flight usage in foraging and daily routines.</p> <p>We find that HWI is a strong predictor of both natal dispersal distance and a more aerial lifestyle.</p> <p>Our results support the use of HWI as a valid proxy for relative natal dispersal distance, and also suggest that evolutionary adaptation to aerial lifestyles is a major factor connecting flight efficiency with patterns of natal dispersal.</p>
Figure 6. A–F in Chrysopetalidae (Annelida: Phyllodocida) from the Senghor Seamount, north-east Atlantic: taxa with deep-sea affinities and morphological adaptations
Figure 6. A–F: Arichlidon gathofi, 7-segmented larva, Carolina, West Atlantic, USNM 186017. A, Entire larva, dorsal view; B, ventral view of A; C, notopodium segment IV; D, notopodium segment VI (figs A, C, after Watson Russell, 1987: Figs 28.4, 6: as 'new genus 1'). E, A. gathofi, adult, mid-body notopodium, detail median fascicle; F, mid-body neuropodium with epitokous swimming neurochaetae (figs E, F after Watson Russell, 2000: Figs 1D, 5A). Scalebars: A, 200µm; B, 350µm; C-E, 40 µm; F, 100µm.
Figure 5. A–F in Chrysopetalidae (Annelida: Phyllodocida) from the Senghor Seamount, north-east Atlantic: taxa with deep-sea affinities and morphological adaptations
Figure 5. A–F: Arichlidon reyssi 6-segmented larva, Arcachon, NE Atlantic, NTM 25385; A, D–F: slide preparations. A, Entire larva, dorsal view; B, anterior end, dorsal, left side detail (transitory chaetae drawn in part); C, anterior end, ventral view, left side detail; D, detail of anterior end of fig. 5A; E, notopodium segment IV; F, neuropodia segments IV and V. Scalebars: A, 50µm; B-C, 100µm; D-F, 10 µm.
Figure 4. A–B in Chrysopetalidae (Annelida: Phyllodocida) from the Senghor Seamount, north-east Atlantic: taxa with deep-sea affinities and morphological adaptations
Figure 4. A–B: Arichlidon reyssi, adult, Senghor Seamount, NMS.Z.2013.160.09, slide preparations. A, Anterior end; B, mid-body notopodium from anterior end. Scalebars: A, 100 µm; B, 50µm.
Figure 2. A–D in Chrysopetalidae (Annelida: Phyllodocida) from the Senghor Seamount, north-east Atlantic: taxa with deep-sea affinities and morphological adaptations
Figure 2. A–D: Thrausmatos senghorensis sp. nov., Senghor Seamount, SMF 22963. A, Anterior end, dorsal view, slide preparation; B, anterior end, dorsal view; C, mid-body parapodium, slide preparation; D, detail of superior-most neurochaeta (asterisked in fig. 2C). Scalebars: A-C, 100 µm; D, 10µm.
Figure 3. A–B in Chrysopetalidae (Annelida: Phyllodocida) from the Senghor Seamount, north-east Atlantic: taxa with deep-sea affinities and morphological adaptations
Figure 3. A–B: Dysponetus caecus, Senghor Seamount, SMF 22964. A, Anterior end, dorsal view, slide preparation; B, neuropodium XII with superior swimming neurochaetae. Scalebars: A-B, 100µm.
Figure 1. A in Chrysopetalidae (Annelida: Phyllodocida) from the Senghor Seamount, north-east Atlantic: taxa with deep-sea affinities and morphological adaptations
Figure 1. A, Map of Senghor Seamount, located in the Cape Verde Archipelago, NE Atlantic. Data extracted from Smith and Sandwell (1997); dataset created by A. Dale (SAMS). B, Senghor Seamount with the location of transects. Data and map created by Thor Hansteen and Alexander Schmidt, GEOMAR. (A, B, reproduced from Chivers et al., 2013.)
Fig. 8 in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 8 Mitochondrial genome map of Veneriserva pygoclava. The inner ring presents the GC content graph. The complete, fully annotated mitochondrial genome is accessible via the NCBI GenBank database under the accession number: OR449961
Fig. 7 in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 7 Maximum likelihood (ML) tree of Dorvilleidae. The tree depicts the phylogenetic relationships within Dorvilleidae inferred through concatenated 16S, COI, Cytb, 18S, and H3 sequences. Bootstrap support values are provided for each node. Nodes with complete support are indicated with an asterisk (*); values below 50% are not shown. Branches of parasitic/symbiotic species highlighted in blue. Haplotype network for 5 Veneriserva pygoclava specimens is shown next to the tree
Fig. 6 in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 6 Epidermal ultrastructure of Veneriserva pygoclava. A–D TEM images of the epidermis revealing the presence of dense, modified microvilli (mv) that cover the body surface. A mucosecretory gland cell (gl) is discernable in A. D shows details of a multi-ciliated epidermal cell. B depicts the microvilli (mv) covering the cuticle (cu). Note the inflated tips of the microvilli and the electron-dense droplets. E Apically the epidermal cells display an abundance of transport vesicles (v). Arrowheads mark the branching microvilli piercing through the cuticle in all images. Abbreviations—ci cilia, m mitochondria, nc nucleus
Fig. 3 in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 3 µCT visualization of parasites within Aphrodita longipalpa. A 3D rendering of parasites shown within the projection of the host body. B–D Virtual dissections of surface renderings, showing crosssections of the host across three consecutive body regions, from anterior to posterior. Raw image data from the micro-CT stack, illustrating a horizontal section through the host (E) and a sagittal section (F). Head of the juvenile parasite is magnified to display the prominent jaws in white. Abbreviations—ja jaws, ne nephridia, pha pharynx. Female Veneriserva pygoclava is shown in yellow or with yellow arrowheads and the juvenile V. pygoclava in blue or with blue arrowheads
Fig. 4 AZAN-stained paraffin histology. A in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 4 AZAN-stained paraffin histology. A Histological cross-section of a juvenile Aphrodita longipalpa featuring an endoparasitic immature Veneriserva pygoclava (denoted by a star). B Longitudinal section of V. pygoclava, highlighting the absence of a through gut, and continuous uninterrupted mesenteries. C–H Cross-sections through the anterior region of V. pygoclava, showing the muscularized pharynx with jaws culminating in blind termination at section G. Abbreviations—ac acicula, br brain, df dorsal felt, el elytra, ja jaws, mo mouth, ne nephridium, pha pharynx, vnc ventral nerve cord
Fig. 2 Parasite abundance and distribution statistics. A in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
Fig. 2 Parasite abundance and distribution statistics. A total of 58 Aphrodita longipalpa were dissected and examined for parasite presence. The upper horizontal bars graphically depict the proportional parasitism rates and the corresponding distribution among male, female, and juvenile parasites, along with various cohabitation configurations. The box plots show the relationship between host size and the occurrence of parasites, presented collectively and then individually for female, male, and juvenile parasites
◂Fig. 5 Gametogenesis in male and female Veneriserva pygoclava. A–D Semi-thin histological sections of female Veneriserva pygoclava, stained with toluidine blue. A Cross-section of a female Veneriserva. B Close-up of large mature oocytes without discernible nurse cells. C Developing oocytes attached to mesenteries (mes), and oogonia proliferating from the ventral side of the dorsal blood vessel (bv). D Details of vitellogenic oocytes and nurse cells. Arrowheads indicate brownstained yolk platelets and yolk bodies. E Live sperm cells captured in a light micrograph. F–G Cross-sections of male Veneriserva. Note the absence of a gut in the cross-sections. Abbreviations—ac acicula, acr acrosome, bv blood vessel, coe coelomic cavity, mes mesentery, nc nurse cell, nn nurse cell nucleus, nu sperm cell nucleus, Oo oocyte, on oocyte nucleus, sp spermatogonia, vnc ventral nerve cord in Hardly Venus's servant-morphological adaptations of Veneriserva to an endoparasitic lifestyle and its phylogenetic position within Dorvilleidae (Annelida)
◂Fig. 5 Gametogenesis in male and female Veneriserva pygoclava. A–D Semi-thin histological sections of female Veneriserva pygoclava, stained with toluidine blue. A Cross-section of a female Veneriserva. B Close-up of large mature oocytes without discernible nurse cells. C Developing oocytes attached to mesenteries (mes), and oogonia proliferating from the ventral side of the dorsal blood vessel (bv). D Details of vitellogenic oocytes and nurse cells. Arrowheads indicate brownstained yolk platelets and yolk bodies. E Live sperm cells captured in a light micrograph. F–G Cross-sections of male Veneriserva. Note the absence of a gut in the cross-sections. Abbreviations—ac acicula, acr acrosome, bv blood vessel, coe coelomic cavity, mes mesentery, nc nurse cell, nn nurse cell nucleus, nu sperm cell nucleus, Oo oocyte, on oocyte nucleus, sp spermatogonia, vnc ventral nerve cord
Mandible morphology as a tool to investigate origin, adaptation and stress in invasive alien species. First insights into Callosciurus erythraeus in Europe
<p>When an alien species is introduced in a new area, the number of founding individuals affects the severity of the population bottleneck, hence the new population may be distinctively different, both genetically and phenotypically, from the parent population from which it is derived. In this study we investigated the variation in shape and size of the mandible among and within three populations of the invasive Pallas’s squirrel, a tree squirrel native to SE Asia and introduced in Italy, Belgium and France. Significant differences in both size and shape of the mandible were found among all population pairs, with France being the most distinct. French squirrels showed a larger and slender mandible with a broad angular process, a restricted condyle, and a backward-oriented coronoid process. The Italian and the Belgian population differ at a lesser extent, the Italian squirrels having a lower coronoid process, a broader angular apophysis, and a restricted condyle. s. Size explained 15% of the total shape variation, but the orientation of allometric trajectories did not reveal any significant difference among populations. French squirrels showed the highest fluctuating asymmetry (both size and shape) of the right versus the left mandible, the Italians the highest directional asymmetry. Results are discussed in terms of different selective pressures in the invaded areas related to functionally mastication, and possible factors affecting fluctuating and directional asymmetry. The hypothesis of the classic mandibular two-module organization of rodent mandible (alveolar region vs ascending ramus) was confirmed both before and after correcting for size.</p>
Fig. 2 in Genetic and morphological differentiation among populations of the narrowly endemic and karst forest-adapted Pilea pteridophylla (Urticaceae)
Fig. 2 Morphological variation among individuals of Pilea pteridophylla sampled along its distribution range in the tropical karst forest of southern Mexico. Plot of individual scores for the first two components of the principal component analysis using morphological data. Coloured symbols represent the two populations recognized for the species: red circles, Tabasco; and blue circles, Chiapas. Ellipses correspond to the 95% confidence intervals estimated for each population. The lines represent the dispersion of the individuals within each population
Fig. 3 in Genetic and morphological differentiation among populations of the narrowly endemic and karst forest-adapted Pilea pteridophylla (Urticaceae)
Fig. 3 Statistical parsimony networks of rps16-trnQ, trnL-trnF and rps16-trnQ + trnL-trnF dataset using the gaps as missing data. Coloured symbols represent the two populations recognized for the species: red circles, Tabasco; and blue circles, Chiapas. Open-white circles represent the number of mutational steps between haplotypes. The size of the circles is proportional to the frequency of each haplo-
Fig. 1 in Genetic and morphological differentiation among populations of the narrowly endemic and karst forest-adapted Pilea pteridophylla (Urticaceae)
Fig. 1 Mountain karst forests of Mexico and the studied species Pilea pteridophylla A. K. Monro (Urticaceae). A Geographic distribution of the Mountain karst forests of Mexico. B Individual from the Chiapas population. C Individual from the Tabasco population
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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)
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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.