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FIGURE 3 in An unusual new genus of istiodactylid pterosaur from China based on a near complete specimen
FIGURE 3. Cervical column of ELDM 1000. Cervicals are numbered. Scale bar is 50 mm. Abbreviations throughout are as follows: cc, cranial crest; co, coracoid; cv, cervical vertebrae; mn, mandible; sc, scapula; sf, soft tissues; sk, skull.
FIGURE 2 in An unusual new genus of istiodactylid pterosaur from China based on a near complete specimen
FIGURE 2. Cranium of ELDM 1000. The block arrow points to a fossil fish. Scale bar is 50 mm. Abbreviations throughout are as follows: co, coracoid; cv, cervical vertebrae; hy, hyoids; mn, mandible; naof, nasoantorbital fenestra; sk, skull.
Fig. 1 in First complete mitogenome sequence of Korean Gloydius ussuriensis (Viperidae: Crotalinae)
Fig. 1. Maximum likelihood (ML) phylogenetic tree constructed with 37 viper mitogenomes, based on 13 concatenated protein-coding genes. Achalinus meiguensis (FJ424614) was used as an outgroup species and bootstrap values are shown at the nodes. The mitogenome of Gloydius ussuriensis (OR680782) was determined in this study.
Fig. 1. Sample completeness curve for a in Abundance and diversity of beneficial and pest arthropods in buckwheat on blueberry and vegetable farms in north Florida
Fig. 1. Sample completeness curve for a vegetable farm (dark shaded area, 38 samples) and blueberry farm (light shaded area, 19 samples) in Suwannee County, Florida. The solid line is the interpolated insect family-level richness and the dashed line is the extrapolated richness. The shaded areas represent 95% confidence intervals. Sampling effort is standardized based on the sample completeness (the proportion of sampled families to predicted families) at each sampling level, with the circle representing final sampling completeness of 38 samples taken at the vegetable farm and the triangle representing final sampling completeness of 19 samples taken at the blueberry farm.
Fig. 6 in Comparative Analysis of Complete Mitogenomes of Two Gobies and Their Phylogenetic Implication.
Fig. 6. Termination-associated sequences (TAS), conserved sequence blocks (CSB-1, CSB-2, and CSB-3) and central conserved sequences (CSB-D) and GTGGG box in control region of two Oxyurichthys species mitogenomes.
Fig. 8 in Comparative Analysis of Complete Mitogenomes of Two Gobies and Their Phylogenetic Implication.
Fig. 8. Phylogenetic trees of goby derived from Maximum Likelihood (ML) method based on 13 PCGs + 2 rRNAs. The numbers at nodes are ultrafast bootstrap values. GenBank accession numbers are placed in front of species names.
Fig. 7 in Comparative Analysis of Complete Mitogenomes of Two Gobies and Their Phylogenetic Implication.
Fig. 7. Phylogenetic trees of goby derived from Bayesian Inference (BI) method based on 13 PCGs + 2 rRNAs. The numbers at nodes are posterior probability values. GenBank accession numbers are placed in front of species names.
Fig. 5 in Comparative Analysis of Complete Mitogenomes of Two Gobies and Their Phylogenetic Implication.
Fig. 5. The putative origin of L-strand replication (OL) of Oxyurichthys ophthalmonema (a) and Oxyurichthys microlepis (b).
Fig. 7 in Comprehensive Analysis of the Jellyfish (Goette, 1886) (Semaeostomeae: Pelagiidae) with Description of the Complete rDNA Sequence.
Fig. 7. Nucleotide divergences of the cnidarians 18S and 28S rDNAs (datasets used in Table 1) based on corrected p-distances. Genetic distances between each paired sequence were calculated by the Kimura 2-parameter model, where a total of 16 cnidarian species were compared. Statistical analysis showed that the 18S rDNA divergences were significantly different from those of 28S rDNA (Student t-test, P <0.05, N = 66).
Fig. 6 in Comprehensive Analysis of the Jellyfish (Goette, 1886) (Semaeostomeae: Pelagiidae) with Description of the Complete rDNA Sequence.
Fig. 6. Phylogenetic relationships of the family Pelagiidae, including the genera Chrysaora, Pelagia and Sanderia, inferred from 18S rDNA (A), 28S rDNA (B) and morphological characters (C), which were redrawn from Fig. 95 in Morandini and Marques (2010). Phylogenetic trees of the rDNAs were constructed using the maximum-likelihood (ML) algorithms with the GTR+G model. A jellyfish Cyanea capillata (the family Cyaneidae) was used as the outgroup. Additional Bayesian trees generated similar branch patterns. The first and second numbers at the nodes display bootstrap proportions (BP) and posterior probabilities (PP) obtained in the ML and Bayesian analyses, respectively. Branch lengths are proportional to the scale given. Thick lines represent congruent branches between 18S and 28S, and morphological systematics.
Fig. 4. A in Comprehensive Analysis of the Jellyfish (Goette, 1886) (Semaeostomeae: Pelagiidae) with Description of the Complete rDNA Sequence.
Fig. 4. A dot matrix comparison of rDNA sequences between Chrysaora pacifica (KY 212123) and Aurelia coerulea (EU276014). Color scale bars represent consecutive sequence length of some regions detected similarly between the two sequence pairs. The open boxes in matrices indicate rDNA coding regions such as 18S, 5.8S, and 28S.
Fig. 2. A in Comprehensive Analysis of the Jellyfish (Goette, 1886) (Semaeostomeae: Pelagiidae) with Description of the Complete rDNA Sequence.
Fig. 2. A schematic representation of the single unit of rDNA (A), and GC content (%), nucleic acid distribution (% thymine), sequence complexity, and entropy (dS) in 100-bp windows across the entire rDNA nucleotides of Chrysaora pacifica (B). In the full rDNA (A), solid boxes indicate the ribosomal RNA genes and thin lines represent ITS or IGS. Nucleotide sequences in length and GC composition of each locus are represented near a line by calculation from a single unit of rDNA. The putative transcription start site is represented by an arrow; solid inverted-triangles represent sub-repeats in IGS.
Fig. 5. Phylogenetic relationships between jellyfishes within the order Semaeostomeae inferred from nearly complete 18S in Comprehensive Analysis of the Jellyfish (Goette, 1886) (Semaeostomeae: Pelagiidae) with Description of the Complete rDNA Sequence.
Fig. 5. Phylogenetic relationships between jellyfishes within the order Semaeostomeae inferred from nearly complete 18S rDNA (A) and partial 28S rDNA sequences (B) with maximum-likelihood (ML) algorithms. ML analyses of 18S and 28S were used as the nucleotide substitution model of GTR+G. Two hydrozoans (Hydractinia echinata and Podocoryne carnea for 18S rDNA; Astrohydra japonica and Melicertissa sp. for 28S) were included as the outgroups. Additional Bayesian analysis generated similar topology of the tree compared with the ML tree. Posterior probabilities (PP) from the analyses were incorporated into the ML tree to support the strength of each branch. The first and second numbers at the nodes display bootstrap proportions (BP) (> 50%) in ML and PP (> 0.50) in Bayesian, respectively. Branch lengths are proportional to the scale given. *Represents controversial species names, because they were suspected as different species by Bayha et al. (2017).
Fig. 1 in Comprehensive Analysis of the Jellyfish (Goette, 1886) (Semaeostomeae: Pelagiidae) with Description of the Complete rDNA Sequence.
Fig. 1. Live Chrysaora pacifica in natural habitat: basolateral (A and B), lateral (C) and apical view (D).
Fig. 1 in Phylogenetic position of the freshwater fish trypanosome, Trypanosoma ophiocephali (Kinetoplastida) inferred from the complete small subunit ribosomal RNA gene sequence
Fig. 1 The neighbor-joining tree of aquatic trypanosomes constructed from complete small subunit ribosomal RNA (SSrRNA) sequences indicating the systematic position of T. ophiocephali and phylogenetic relationships among the aquatic trypanosomes whose sequences are available. T. lewisi, T. theileri, and T. avium are taken as the outgroup. Bootstrap values are shown for the maximum parsimony/neighborjoining/Bayes analyses
Fig. 1 in Complete mitochondrial genome of Nyctalus aviator and phylogenetic analysis of the family Vespertilionidae
Fig. 1. Neighbor Joining (NJ) phylogeny of Vespertilionidae was inferred from concatenated nucleotide sequences of 13 mitogenomic protein-coding genes. Node labels indicate the bootstrap values. GenBank accession numbers for the sequences are indicated next to species designations.
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Figure 3. (Top): Illustration of the therapy selection main menu. This enables the user to select one of three options for the therapy. Stimuli sequence selectors; (Bottom): (a) Short distance – complete visual field; (b) Short distance – macular; (c) Middle-long distance.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>distance therapies for the prescribed time suggested by the ophthalmologist.<br> Note that the complete visual field therapy stimulates different parts in the entire visual field<br> whereas macular therapy stimulate only a small part of the visual field, only the first 10° of vision<br> range. In contrast, middle-long distance therapies are not developed inside the device; instead the<br> patient must sit watching a wall, where the stimuli will be presented. Figure 3 (Bottom) shows the<br> sequence selectors for the three different cases. The therapist will choose a desired number of<br> sequences according to the results of the examination to each patient; hence it is completely patient<br> dependent.<br> Once the therapist finishes the particular design of the stimuli sequence, the software<br> automatically displays a window where he can save the customized patient-specific details for future<br> use as a text file.</p>
Ecological and Forest Inventory of Catalonia: Complete Plant Trait Dataset for Imputation Assessment
<p>Plant trait and forest data were retrieved from the Ecological and Forest Inventory of Catalonia (IEFC), carried out between 1988 and 1998 (Gracia et al. 2000‒2004). The subset of the IEFC was limited to 13 study species. Forest structure, lithology and sampling information for each plot were retrieved from the IEFC database. Climate data were obtained from the Climatic Digital Atlas of Catalonia, with a spatial resolution of 180 m (Ninyerola et al. 2000).</p> <p>We selected five plant traits (leaf mass per area, LMA, mg cm<sup>-2</sup>; leaf nitrogen per unit mass, <em>N</em><sub><em>mass</em></sub>, %mass; maximum tree height, <em>H</em><sub><em>max</em></sub><em>,</em><sub><em> </em></sub>m; wood density, WD, gm cm<sup>-3</sup>; leaf biomass to sapwood area ratio, <em>B</em><sub><em>L</em></sub><em>:A</em><sub><em>S</em></sub>, t m<sup>-2</sup>) that are used to describe major plant functional strategies. The auxiliary variables we considered were species identity, a set of climatic variables (mean annual temperature, annual thermal amplitude, both in °C), a set of forest structure variables (total aboveground biomass [T ha<sup>-1</sup>] and stem density [stems ha<sup>-1</sup>]), a set of topographical variables (county, elevation [m.a.s.l.], slope [°] and aspect), lithology (calcareous, non-calcareous or undetermined) and sampling month.</p>
Complete numerical solutions for "Inference of ecological and social drivers of human brain-size evolution" by Mauricio González-Forero and Andy Gardner
<p>This zip file contains the complete numerical solutions across the parameter sweep over the P parameters for the six cases considered.</p> <p>/1RatioForm/ -> solutions for power competence.<br> /2DiffForm/ -> solutions for exponential competence.</p> <p>/1RatioForm/1BenchmarkFromSimpleInitialGuess -> solution for the step 1 of initialization (section 5 of the SI).<br> /1RatioForm/2Benchmark/ -> solution for the step 2 of the initialization (section 5 of the SI).<br> /1RatioForm/3AdditiveCoop/ -> solutions across the P parameter combinations for additive cooperation.<br> /1RatioForm/4MultCoop/ -> solutions across the P parameter combinations for multiplicative cooperation.<br> /1RatioForm/5SubMultCoop/ -> solutions across the P parameter combinations for submultiplicative cooperation.</p> <p>The contents of /2DiffForm/ are analogous.</p> <p>The P vector is written in these files in the order (etas,etac,etaC,etag), where<br> etas -> P1<br> etac -> P3<br> etaC -> P2<br> etag -> P4</p> <p>The files 1runNotesACMC.pdf and 2runNotesSC.pdf contain the tree structure of the parameter sweep, specifying which parameter combination was used as the resident and which combinations converged to an uninvadable strategy (those with a checkmark).</p> <p>The file 3runNotesMaternalCareOptimization.pdf contains the 10 parameter combinations that yielded the best adult fit, which then were subject to variation in the parameter phi to find the combination that yielded the best ontogenetic fit.</p> <p>The file 4runNotesDuplicates.pdf gives the parameter combinations that were not run because they are equivalent to other parameter combinations.</p> <p>Running [T,N1,run,Tshort,N1short,runShort]=etaCombinations in Matlab and typing run.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i (where i is a number 1,2,...) for PC-AC, EC-MC, PC-SC, and EC-SC. The meaning of "next" is explained in step 4 of the parameter sweep (section 5 of the SI). Typing runShort.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i for PC-MC and EC-AC.</p> <p>The terminal folders contain the solutions and have the following files:<br> brainNashDeep.m -> the master file launching the iteration of best responses.<br> brainMainDeep.m -> the file launching one iteration solving the optimal control problem to find best response.<br> brainMainTestRunDeep.m -> runs a test to check if there are infeasibility warnings.<br> brainContinuous.m -> specifies the dynamic constraints.<br> brainEndpoint.m -> specifies the terminal constraints.<br> parameters.m -> specifies the parameter values and rescales them to rescale units as specified in section 5 of the SI.<br> getSolution.m -> extracts solution.<br> plots.m -> plots solutions over best response iterations.<br> brainPlot.m -> plots solution of a given best response iteration.<br> guessDeep.mat -> initial guess and resident used.<br> solutionNashDeep.mat -> solutions over best response iterations.<br> solutionDeep.mat -> solution of last best response iteration.</p> <p> </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.