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FIG. 2 in A morphological re-evaluation of the taxonomic status of Satureja L. (Lamiaceae, Nepetoideae, Mentheae) from flora of Iran
FIG. 2. — Habit and close-up of inflorescence of Satureja L. species from flora of Iran: A, S. kermanica Payandeh, Bordbar & Mirtadz. (Bordbar 1956); B, C, S. khuzistanica Jamzad (Mirtadzadini 3805); D, S. khuzistanica (Mirtadzadini 3804); E, S. macrantha C.A.Mey. (Bordbar 1970); F, S. macrantha (Mirtadzadini 2036); G, S. macrosiphonia Bornm. (Mirtadzadini & Bordbar 5156); H, S. macrosiphonia Bornm. (Mirtadzadini & Bordbar 4157); I, S. mutica Fisch. & C.A.Mey. (Mirtadzadini 3822); J, S. mutica (Mirtadzadini 3832); K, S. sahendica Bornm. (Mirtadzadini 1984).
FIG. 1 in A morphological re-evaluation of the taxonomic status of Satureja L. (Lamiaceae, Nepetoideae, Mentheae) from flora of Iran
FIG. 1. — Habit and close-up of inflorescence of Satureja L. species from flora of Iran: A, B, S. avromanica Maroofi (Mirtadzadini 3029); C, D, S. bachtiarica Bunge (Mirtadzadini & Bordbar 4151); E, S. bachtiarica (Bordbar 1995); F, S. bachtiarica (Bordbar 1944); G, S. longiflora Boiss. & Hausskn. (Mirtadzadini 2031); H, S. longiflora (Bordbar 2032); I, S. longiflora (Advay 4155); J, S. longiflora (Mirtadzadini & Bordbar 4153); K, S. intermedia (Mirtadzadini 1980); L, M, S. isophylla Rech.f. (Mirtadzadini 1978).
FIGURE 6 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 6. Suggested head positions of Spinosaurus relative to water. A) when dipping the snout in the water to forage while leaving the naris above the waterline as per the wading model. Head angle of 45 degrees based on Schade et al. (2020) for Irritator. B) while lying submerged, keep the naris and orbit clear of the water while minimising the amount of head that is exposed as per Arden et al. (2019), C) fully submerged as if coming up for air and trying to expose only the nares to breathe. Scale bar equals 1 m.
FIGURE 9 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 9. Line drawings of mid caudal vertebrae and chevrons of assorted reptiles (all in left lateral view) compared to A) Spinosaurus (Ibrahim et al., 2020a). Taxa with known or inferred signaling structures linked to their elongate neural spines (top row), B) Bagaceratops (Tereschenko and Singer, 2013), the sail-finned lizards C) Hydrosaurus* and D) Basiliscus* (courtesy of Jeroen Costeseque), E) the drepanosaur Drepanosaurus (redrawn from Sues, 2019), F) the chameleon Trioceros (courtesy of Steven Huskey), and those which show adaptations for aquatic locomotion (bottom row), G) a juvenile specimen of the crocodylian Tomistoma* (courtesy of Mathew Wedel), H) the phytosaur Mystriosuchus (Renesto and Lombardo, 1999) I) the mosasaur Mosasaurus (modified from Lindgren et al., 2011), J) the sea snake Pelamis (modified from Lindgren et al., 2011), and K) diapsid Hovasaurus* (redrawn from Sues, 2019). Scale bars are A) 200 mm, B) 20 mm, C) 10 mm, D) 10 mm, E) 20 mm, F) 10 mm, G), 20 mm, H) 20 mm, I) 100 mm, J) 2 mm K) 20 mm.
FIGURE 8 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 8. Depth of water required for Spinosaurus to avoid the considerable effects of wave drag. Even with the hind limbs lifted up, the animal is nearly 3 m in dorsoventral height so to avoid wave drag (fully submerged by over 3.5 m) the water would need to be close to 6 m in depth for Spinosaurus to swim efficiently. This is a minimum and the real value is likely to be higher (see text for details). Outline modified from Ibrahim et al. (2020a) and scale bar equals 1 m.
FIGURE 4 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 4. Graph of theropod ungual curvature vs ungual length. The inset shows how the curvature of the unguals was measured. In lateral view a line AB is drawn between the ungual tip and the base. This is bisected by a perpendicular line until it contacts the ungual at point C. Lines are drawn from A to C and A to B and the internal angle measured. Unguals of Spinosaurus are in red, a further specimen attributed to a spinosaur is in yellow, and individual specimens are abbreviated as follows: Ab, abelisaurid; Ac, Acrocanthosaurus; Ai, Alioramus; Al, Allosaurus; Ca, Caudipteryx; Ce, ceratosaur; Co, Compsognathus; Di, Dilophosaurus; Ga, Gaulicho; Gg, Gigantoraptor; Gl, Gallimimus; Gu, Guanlong; Ha, Halszkaraptor; Ju, Juravenator; Ki, Kileskus; Li, Limusaurus; Mj, Majungasaurus; Sc, Spectrovenator; Sd, spinosaurid; Sn, Sinraptor; Sp, Spinosaurus; SB, Spinosaurus B; Tt, Tyrannotitan; Ty, Tyrannosaurus.
FIGURE 3 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 3. Graphs of various skull measurements to show the relationship between skull shape for different ecotypes. The red point is Spinosaurus, yellow are other spinosaurids, green are terrestrial taxa, pale blue are semi-aquatic and dark blue, fully aquatic animals. Least squares regressions are given for the terrestrial, semi-aquatic and aquatic datasets (the various spinosaurids were not included in these calculations), and the R2 values for these regressions are given.
FIGURE 2 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 2. Principal Components Analysis of various measurements of the skull rescaled to skull length. Principal Component 1 (83.5% of variance) plotted against Principal Component 2 (13.7% of variance), plotted using eigenvalue scale. The red point is Spinosaurus, yellow are other spinosaurids, green are terrestrial taxa, pale blue are semi-aquatic, and dark blue, fully aquatic animals. Silhouettes are from PhyloPic.org and color-coordinated with the lines of the convex hulls for the groups of taxa they represent: the red Suchomimus (representing Spinosauridae; red Xs), the light green Allosaurus (representing non-spinosaurid Theropoda; open light green circles), and the orange Paleorhinus (representing phytosaurs: light brown pluses) are by Scott Hartman; blue Peloneustes (representing Plesiosauria: solid dark blue circles) by Nobu Tamura; dark green Varanus (representing terrestrial lepidosaurs: green asterisks) and dark brown Crocodylus (representing Crocodyliformes: dark brown pluses) by Steven Traver. Additional taxa plot include thallatosuchians (solid light blue circles), the mosasauroid Plotosaurus (blue asterisk), the nothosauroid Lariosaurus (solid aqua circle), and freshwater semi-aquatic lepidosaurs (open orange squares). The inset shows a reptile skull and how measurements were taken for the data used here and in Figure 3.
FIGURE 1 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 1. Skeleton in a standing posture as if dip fishing in water following the wading model, and in a swimming posture (based on Ibrahim et al., 2020a) following the pursuit predator model. A non-exhaustive set of lines of evidence as described in the text are indicated by arrows that either directly support either model (white arrow), are ambiguous or do not contradict the model (grey arrow), or actively contradict the model (black arrow). Key traits are as follows: A) laterally compressed skull, B) nares position, C) mechanical jaw performance, D) orbit position, E) neck stiffness and posture, F) non-hydrodynamic shape, G) instability in water, H) sub-anguilliform locomotion, I) thin caudal neural spines, J) tail propulsion, K) distal tail flexibility, L) low swimming efficiency, M) somewhat reduced hind limbs, N) enlarged 1st toe, O) pachyostosis, P) pneumatic elements, Q) forelimbs not reduced, R) neck ventriflexion, S) quadrate shape, T) head posture (as determined for Irritator), U) isotopic data from teeth, V) tooth enamel ridges, W) rostral sensory system. Skeleton modified from the original by Genya Masukawa (used with permission) and scaled to the size of the neotype. Scale bar is 1 m.
FIGURE 5 in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 5. Comparison of skull shape of Spinosaurus and Baryonyx scaled to the same size. The two are very similar, which although this may be expected from their shared evolutionary history would suggest that they fundamentally forage in similar ways for similar prey, which contradicts the idea that one is an aquatic specialist. Not to scale.
FIGURE 7. A in Evaluating the ecology of Spinosaurus: Shoreline generalist or aquatic pursuit specialist?
FIGURE 7. A) Skull of a stork (Leptoptilos - scale bar is 100 mm) with a posteriorly retracted naris allowing them to forage while keeping the nares free of the water as in B) showing Ephipporhynchus senegalensis feeding. Although proportionally much further back here than in Spinosaurus, the absolute distance of the naris from the anterior tip of the jaw is less in the stork. C) Skull of crocodylian (Crocodylus - scale bar is 100 mm) with dorsally positioned naris allowing them to rest with minimal exposure of the head as in D) Crocodylus niloticus resting at the surface (image courtesy of Jonathan J. Meisenbach).
FIG. 5 in A morphological re-evaluation of the taxonomic status of Satureja L. (Lamiaceae, Nepetoideae, Mentheae) from flora of Iran
FIG. 5. — Distribution map of Satureja L. species in Iran: A, S. macrantha C.A.Mey.; B, S. macrosiphonoa (Coss.) Maire; C, S. sahendica Bornm.; D, S. mutica Fisch. & C.A.Mey. (circle), S. longiflora Boiss. & Hausskn. (square).
FIG. 4 in A morphological re-evaluation of the taxonomic status of Satureja L. (Lamiaceae, Nepetoideae, Mentheae) from flora of Iran
FIG. 4. — Distribution map of Satureja L. species in Iran: A, S. bachtiarica Bunge; B, S. avromanica Maroofi (circle), S. hortensis L. (naturally grows) (square); C, S. kermanica Payandeh, Bordbar & Mirtadz. (circle), S. intermedia C.A.Mey. (square); D, S. khuzistanica Jamzad (circle), S. isophylla Rech.f. (square).
Figure 5 in Evaluation of vertical and horizontal changes in community structure of zooplankton in a deep dam lake
Figure 5. Tree diagram resulting from average linkage clustering using UPGMA method on the zooplankton community data reported during study period.
Evaluating the Prevalence and Impact of Chronic Ankle Insta-bility in Collegiate Athletes across Various Sports: A Cross-Sectional Study.
<p>In the cross-sectional study "Evaluating the Prevalence and Impact of Chronic Ankle Instability in Collegiate Athletes across Various Sports," researchers sought to quantify how widespread chronic ankle instability (CAI) is among collegiate athletes and to determine its effects on athletic performance and training. The study involved 385 athletes from diverse sports, including basketball, soccer, track and field, swimming, and other sports, and revealed that CAI was present in nearly half of the participants. The investigation highlighted significant disparities in prevalence rates among different sports, with basketball and soccer athletes exhibiting higher rates of CAI. Furthermore, the study assessed the impact of CAI on several performance metrics such as training intensity, performance scores, and recovery times, finding that athletes with CAI generally showed lower performance levels and longer recovery times. These results emphasize the need for targeted preventive and rehabilitative strategies to mitigate the effects of CAI and support athlete health and performance.</p>
Genome editing of LKB1 gene by CRISPR/Cas9 in lung cancer cells and evaluation of its role in metformin and cisplatin response.
<pre>LKB1 is an important upstream inhibitor of the mTOR/S6Ks pathway. Loss of LKB1 is often associated with cancer, including in A549 lung cancer cell line, boosting the transformation of pre-malignant neoplasic cells. Metformin, a natural compound derived from Galega officinalis, is mainly used as a treatment for diabetes mellitus type 2 (DM2), but recently, it has been associated to lower incidence of cancer. One of the main mechanisms is by activation of AMPK through different pathways, including LKB1 activation. Activation of AMPK inhibits the mTOR pathway, protein synthesis and thus cell growth. The combination of metformin and cisplatin, a main chemothepeutic drug for lung cancer, has shown to improve treatment of cancer cells to cisplatin and also to hinder the cisplatin associated resistance common in lung cancer. Here we aim to use the CRISPR/Cas9 technology to correct the mutation presented in A549 lung cancer cells and improve their response to metformin or to the combination between metformin and cisplatin. Besides, we will assess the mTOR signaling pathway status, as well as cell growth, proliferation, cell cycle and sensitivity to apoptosis induction by cisplatin. This project may be useful as a proof of principle that in the future therapies must consider the genomic background of cancer cells before administration of a specific anticancer drug.<br><br></pre>
ASDF Evaluation Dataset + Corner Clamp Base
<p>Evaluation data for ASDF and Corner Clamp Base Training Data</p> <p> </p> <blockquote> <p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p> </blockquote>
Raw data for the article entitled ''Evaluation of in-plane architecture in a thermo-electrochemical cell with nanostructured and porous Sb:SnO2 electrodes''
<p>Raw data for the plots in the article entitled ''Evaluation of in-plane architecture in a thermo-electrochemical cell with nanostructured and porous Sb:SnO2 electrodes''.</p>
Multi-domain evaluation of a latest generation combustion engine: focusing on sound quality perception
<p>Dataset for conference paper "Multi-domain evaluation of a latest generation combustion engine: focusing on sound quality perception"</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</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.