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FIGURES 18–21. Figures 18–20. Plectrocnemia brevis McLachlan 1871, 5 in The larva of Plectrocnemia renetta Malicky 1975 (Trichoptera, Polycentropodidae), including a discriminatory matrix to the larvae of Plectrocnemia Stephens 1836 species of Greece
FIGURES 18–21. Figures 18–20. Plectrocnemia brevis McLachlan 1871, 5th instar larva: 18, abdominal segment IX, ventral (ip = innermost primary seta; op = outermost primary seta; p = intermediate primary setae); 19, abdominal segment IX, dorsal, posterolateral setal trio (is = inner secondary seta; os = outer secondary seta; p = primary seta); 20, head and prothorax, dorsal (dotted lines = half-trapezoidal pattern of spots on posterior frontoclypeus, about 85°). Figure 21. P. c. conspersa (Curtis 1834), 5th instar larva, head, dorsal (dotted curved line = shallow arc pattern of spots on posterior frontoclypeus, about 85°). Scale bars: 0.5 mm.
text-fig. 54. Reduced consensus tree of the pruned data matrix after the deletion of Shuvosaurus, Segisaurus, and Poekilopleuron. Named nodes: 1, Saurischia; 4, Herrerasauridae; 5, Neotheropoda; 6, Coelophysoidea; 8, Coelophysidae; 9, Liliensternus', 12, Ceratosauria; 13, Abelisauroidea; 14, etanurae; 16, Camosauria; 17, Spinosauroidea; 20, Allosauroidea; 24, Coelurosauria; 26, Coeluridae; 27, Compsognathinae; 30, Tyrannosauroidea; 34, Maniraptora; 37, Deinonychosauria. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 54. Reduced consensus tree of the pruned data matrix after the deletion of Shuvosaurus, Segisaurus, and Poekilopleuron. Named nodes: 1, Saurischia; 4, Herrerasauridae; 5, Neotheropoda; 6, Coelophysoidea; 8, Coelophysidae; 9, Liliensternus', 12, Ceratosauria; 13, Abelisauroidea; 14, etanurae; 16, Camosauria; 17, Spinosauroidea; 20, Allosauroidea; 24, Coelurosauria; 26, Coeluridae; 27, Compsognathinae; 30, Tyrannosauroidea; 34, Maniraptora; 37, Deinonychosauria.
text-fig. 53. Strict consensus tree resulting from the analysis of the pruned data matrix with 51 taxa. Numbers at the nodes indicate bootstrap support values in branches that have more than 50 per cent support. The consensus tree is based on 5544 trees of 652 steps (CI 0-42, RI 0-748, RCI 0-314). in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 53. Strict consensus tree resulting from the analysis of the pruned data matrix with 51 taxa. Numbers at the nodes indicate bootstrap support values in branches that have more than 50 per cent support. The consensus tree is based on 5544 trees of 652 steps (CI 0-42, RI 0-748, RCI 0-314).
ai-matrix NCF
<p>Large files in ai-matrix.</p>
ai-matrix BERT_PyTorch
<p>ai-matrix BERT_PyTorch pretrained model, converted from TensorFlow model, <strong>uncased_L-24_H-1024_A-16</strong></p>
Fig. 1 in Cytoplasmic UV-R Absorption in an Integumentary Matrix (tunic) of Photosymbiotic Ascidian Colonies
Fig. 1. Transmittance (%) of 250-800 nm light for the bandpass filters U340 (thick line) and CT330/20 (thin line) measured with a spectrophotometer Genesis 10S UV-Vis.
Fig. 5 in Cytoplasmic UV-R Absorption in an Integumentary Matrix (tunic) of Photosymbiotic Ascidian Colonies
Fig. 5. Tunic cells in the surface tunic of Diplosoma virens. A, Bladder tunic cells. B, Tunic net cells (= myocyte sensu Mackie and Singla, 1987). C-E, Other types of tunic cells with vacuoles containing electron-dense materials or clear vesicles. Scale bars: 5 µm in A, 2 µm in B-E.
Fig. 4. A in Cytoplasmic UV-R Absorption in an Integumentary Matrix (tunic) of Photosymbiotic Ascidian Colonies
Fig. 4. A, Histological cross-section of the Diplosoma virens colony showing surface tunic (su) and middle layer (md). B and C, UV images of the surface tunic of D. virens (band-pass filter: U340). D and E, a pair of UV images of the surface tunic of D. simile (band-pass filter: U340 for D, U340 + CT330/20 for E). Arrows indicate some cell-like objects absorbing UV-R. Arrowhead in C indicates Prochloron cells. bl, tunic bladder cell; bs, branchial sac; cc, cloacal cavity; st, stomach. Scale bars: 100 µm in A, 50 µm in B-E.
Fig. 3 in Cytoplasmic UV-R Absorption in an Integumentary Matrix (tunic) of Photosymbiotic Ascidian Colonies
Fig. 3. Pair images of cross-sections of Diplosoma virens (A, B) and D. simile (C, D) observed with an oblique epi-illumination of white- LED (A, C) and a transmission light filtered with U340 (B, D). The colonies have a three-layer structure: surface tunic (su), middle layer (md), and basal tunic (ba). Rectangles in A and B indicate an aggregate of pigmentary tunic cells. Green areas are cloacal cavities filled with Prochloron cells. em, embryo; fe, feces in rectum; os, oral siphon; st, stomach. Scale bars = 0.5 mm.
Fig. 2 in Cytoplasmic UV-R Absorption in an Integumentary Matrix (tunic) of Photosymbiotic Ascidian Colonies
Fig. 2. Absorption spectra of the surface tunic of Diplosoma virens (A) and D. simile (B). Spectra from four specimens are shown for each species. A prominent absorption peak is present at approximately 325 nm in each spectrum.
Input files for MD simulations of VP40 matrix protein dimer-dimer structure for WT, G198R, and G201R
<p>For AA simulations, the inp files need to have the path for the toppar folder and the corresponding pdb/psf files need to be renamed.</p>
Data and code for "Tensor product random matrix theory"
<p>The data files and python scripts to generate Figure 3 of the manuscript "Tensor product random matrix theory" are uploaded.</p> <ul> <li>In the 'scripts' folder there are two python files. The file 'SFF.py' was used to generate the dataset. The file 'plot.py' generates Figure 3.</li> <li>In the 'processed data' folder there are .csv files with the data used to generate Figure 3.</li> <li>In the 'figures' folder the generated figure is included.</li> <li>In the 'additional plots' folder, three data sets and corresponding plots not incorporated in the paper are included.</li> </ul>
A simple framework for agent-based modeling with extracellular matrix: Simulation results
<h1>Data for "A simple framework for agent-based modeling with extracellular matrix"</h1> <p> </p> <div>Metzcar, J., Duggan, Ben S., Fischer, B., Murphy, M., Heiland, R., Macklin, P. A simple framework for agent-based modeling with extracellular matrix. bioRxiv. doi: 10.1101/2022.11.21.514608</div> <div> </div> <div><a href="https://www.biorxiv.org/content/10.1101/2022.11.21.514608">Link to preprint</a></div> <div> <p>This repository contains the data for each subfigure (and video) in the preprint cited and linked abvoe, as well as the original figures and videos themselves. We have tried to include original model file (<code>PhysiCell_settings.xml</code> or other <code>*.xml</code> file) used to generate each results with each file set as well as the code (see python scripts) to make images and videos that appear in the pre-print - both within the files and base modules and examples in a separate folder.</p> <p>The data can be regenerated using release 2.0, <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.1">2.1</a>, and <a href="https://github.com/PhysiCell-Models/collective-invasion/releases/tag/v2.2.1">2.2.1</a>. Release 2.1 is recommended for reproducing more exactly the results for the fibrosis, invasive carcinoma, and series of leader-follower results as the results archived here were produced using a set of two random number generators (one from PhysiCell and one from BioFVM). 2.2.1, used to produce the invasive cellular front results, consolidates the use of random number generators to just one (the PhysiCell one). As such, in 2.2.1, the random number generator seed may need changed to produce stochastic replicates, even when multithreading.</p> </div> <p> </p> <div>The following file sets are in this download:</div> <ul> <li>Fig2_SM_3_simple_tests.zip <ul> <li>Has results for Figure 2 and SM Figure 3</li> </ul> </li> <li>Fig3_fibrosis.zip <ul> <li>Results from fibrosis simulation (originally Figure 3, now Figure 4)</li> </ul> </li> <li>Fig4_invasive_carcinoma.zip <ul> <li>Results from invasive carcinoma simulation (originally Figure 4, now Figure 5)</li> </ul> </li> <li>Fig5_collective_migration_initial_tests.zip <ul> <li>Results from initial leader follower model development (originally Figure 5, now Figure 6)</li> </ul> </li> <li>Fig6_instant_remodeling.zip <ul> <li>Results from instant remodeling leader follower model scenariods (originally Figure 6, now Figure 7)</li> </ul> </li> <li>Fig7b_leader_follower.zip <ul> <li>Results from leader-follower collective migration scenario (origianlly Figure 7b, now Figure 8b)</li> </ul> </li> <li>images_and_vidoes_for_paper.zip <ul> <li>has all the images, videos, and some figures from the main body of the paper and the supplmental material. Updated in this version.</li> </ul> </li> <li>Invasive_cellular_front.zip <ul> <li>Has results for each ECM scenario: random, parallel, and perpindicular orientations and mixed ECM conditions. New to this data repository.</li> </ul> </li> <li>python_imaging.zip <ul> <li>Base modules and examples for producing images (tested on Python 3.9). Updated in in this verison</li> </ul> </li> <li>SM_Fig_4b_leader_follower_decreased_remodeling.zip</li> <li>stochastic_replicates.zip <ul> <li>Contains stochastic replicates for the collective migration, fibrosis, and invasive carcinoma models and source code for all.</li> </ul> </li> <li>stochastic_replicates_invasive_cellular_front.zip <ul> <li>Contains stochastic replicates for the invasive cellular front scenarios. New to this data repository.</li> </ul> </li> </ul>
60Mn beta decay into 60Fe - Ex-Eg matrix
<p>Raw data from the beta decay of 60Mn into 60Fe. The y axis corresponds to excitation energy (Ex) and the x axis to gamma-ray energy (Eg). Units on both axis is keV. Measurement done at the National superconducting Cyclotron Laboratory at Michigan State University with the SuN detector. The data format is in "root". </p>
Direct electrochemical reduction of graphene oxide thin film for aptamer-based selective and highly sensitive detection of matrix metalloproteinase 2
<p>Simple and low-cost biosensing solutions are suitable for point-of-care applications aiming to overcome the gap between scientific concepts and technological production. To compete with sensitivity and <a title="Learn more about selectivity from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/selectivity">selectivity</a> of golden standards, such as <a title="Learn more about liquid chromatography from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/liquid-chromatography">liquid chromatography</a>, the functionalization of biosensors is continuously optimized to enhance the signal and improve their performance, often leading to complex chemical assay development. In this research, the efforts are made on optimizing the methodology for <a title="Learn more about electrochemical reduction from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/electrochemical-reaction">electrochemical reduction</a> of <a title="Learn more about graphene oxide from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/chemistry/graphene-oxide">graphene oxide</a> to produce thin film-modified gold electrodes. Under the employed specific conditions, 20 cycles of <a title="Learn more about cyclic voltammetry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/cyclic-voltammetry">cyclic voltammetry</a> (CV) are shown to be optimal for superior electrical activation of <a title="Learn more about graphene oxide from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/graphene-oxide">graphene oxide</a> into electrochemically <a title="Learn more about reduced graphene oxide from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/materials-science/reduced-graphene-oxide">reduced graphene oxide</a> (ERGO). This platform is further used to develop a <a title="Learn more about matrix metalloproteinase 2 from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mmp2">matrix metalloproteinase 2</a> (MMP-2) biosensor, where specific anti-MMP2 <a title="Learn more about aptamers from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/aptamer">aptamers</a> are utilized as a biorecognition element. MMP-2 is a protein which is typically overexpressed in tumor tissues, with important roles in tumor invasion, metastasis as well as in tumor <a title="Learn more about angiogenesis from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/angiogenesis">angiogenesis</a>. Based on impedimetric measurements, we were able to detect as low as 3.32 pg mL<sup>−1</sup> of MMP-2 in PBS with a dynamic range of 10 pg mL<sup>−1</sup> – 10 ng mL<sup>−1</sup>. Further experiments with real blood samples revealed a promising potential of the developed sensor for direct measurement of MMP-2 in complex media. High specificity of detection is demonstrated – even to the closely related <a title="Learn more about enzyme from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/enzyme">enzyme</a> MMP-9. Finally, the potential of reuse was demonstrated by signal restoration after experimental detection of MMP-2.</p>
Dataset used in "Deamidation analysis of Therapeutic Drugs using Matrix-assisted Laser Desorption Ionization Mass Spectrometry and A Novel Algorithm QuanDA"
<p><span>Overview</span></p> <p><span>Theoretical MALDI spectra of peptides, both before and after deamidation, are crucial for the analysis of real samples. QuanDA is a method utilizing non-negative least squares to calculate the percentage of deamidation during the aging process of standard peptides and therapeutic drugs.</span></p> <p><span>Dataset Description</span></p> <p><span>The `data` directory contains example datasets specifically designed for analyzing peptide deamidation:</span></p> <p><span>D.txt: Simulated spectra of the pure synthetic peptide Pep-D.</span></p> <p><span>N.txt: Simulated spectra of the pure synthetic peptide Pep-N.</span></p> <p><span>Asn2Asp18: Contains 10 measurements from a sample comprising 10% Pep-N and 90% Pep-D.</span></p> <p><span>Asn18Asp2: Comprises 10 measurements from a sample with 90% Pep-N and 10% Pep-D.</span></p> <p><span>Peptide Sequences: Pep-N: YTHQGLSSPVTKSFNRGE; Pep-D: YTHQGLSSPVTKSFDRGE</span></p> <p><span>These synthetic peptides are utilized to investigate deamidation behavior.</span></p> <p><span>Somatostatin Dataset: Contains data from actual samples analyzed over 0-13 days under physiological conditions (pH 7.4, 37°C) and storage conditions (pH 4, 4°C). This dataset includes theoretical values before and after deamidation.</span></p>
MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing
<p><span>This is the dataset for the NCST project "</span><span>MOVES-Matrix 3.0: On-Road Energy and Emission Modeling with High-Performance Supercomputing</span><span>"</span></p> <p> </p> <p><span>The Georgia Tech research team developed MOVES-Matrix 3.0 based on the EPA's MOVES3 (version 3.1.0) energy use and emission rate model by running MOVES3 thousands of times on the PACE supercomputing cluster across all combinations of input variables and storing the output as lookup tables.<span> </span>MOVES-Matrix 3.0 allows on-road energy consumption and emissions modeling to be conducted more than 800 times faster than running MOVES, while it generates the exact same results, as verified in this report. <span> </span>MOVES-Matrix 3.0 was designed similarly to its predecessor, MOVES-Matrix 2014, but required extensive code modifications to accommodate changes in the MOVES3 environment (including a shift from MySQL to MariaDB and incorporation of new vehicle source sub-types and operating parameters). <span> </span>The review of the fuel and I/M scenarios indicated that MOVES3 now defines 122 modeling regions, as compared with 109 regions in MOVES 2014b (different matrices need to be developed each modeling region).<span> </span>The development of matrices for each modeling region takes approximately 15-20 days on the PACE supercomputing cluster given our assigned resources (compared with only 5-7 days to develop matrices for MOVES 2014). <span> </span>A case study of 3,000 roadway links using Atlanta's matrices confirmed that MOVES-Matrix 3.0 produces the exact same energy consumption and emissions results as MOVES3, but execution modules operate 800 times faster using MOVES-Matrix lookups than running MOVES for any single run.</span></p>
intraspecific the to correspond. outgroups bold: grey in in Values; retzii. ) pb Microphis 605 ( : alignment red in; . I nov . subunit sp oxydase arrakisae c Microphis cytochrome: blue the in; on torrentius based) Microphis p-distances: green uncorrected in; (nicoleae matrix distance Microphis : Pairwise yellow In. . 2 divergence TABLE in A new freshwater pipefish species (Syngnathidae: Microphis) from the Sunda shelf islands, Indonesia
intraspecific the to correspond. outgroups bold: grey in in Values; retzii. ) pb Microphis 605 ( : alignment red in; . I nov . subunit sp oxydase arrakisae c Microphis cytochrome: blue the in; on torrentius based) Microphis p-distances: green uncorrected in; (nicoleae matrix distance Microphis : Pairwise yellow In. . 2 divergence TABLE
FIGURE 3. Gap data matrix for 28S D2 in Molecular relationships of the Australian Ennominae (Lepidoptera: Geometridae) and implications for the phylogeny of the Geometridae from molecular and morphological data
FIGURE 3. Gap data matrix for 28S D2 sequences of geometrid species used to create a phylogeny for the Australian Geometridae. (0) gap absent; (1) gap present; () inapplicable.
FIGURES 8–13 in Redescription of the last instar larva of Stenophylax fissus (McLachlan 1875) and the DNA barcode of Stenophylax crossotus McLachlan 1884 (Trichoptera Limnephilidae) with an updated diagnostic matrix of the known larvae of Stenophylax species of the Iberian Peninsula
FIGURES 8–13. Larva of Stenophylax fissus (McLachlan 1875). (8) Thorax and abdominal segment I, dorsal; pronotal setae, white circle; metanotal setae between sa2 sclerites, blue circle. (9) Right legs, posterior faces. (10) Right legs, anterior faces; spines of ventral edge of meso- and metathoracic femora, blue circles; (11) right foreleg and midleg, posterior faces; dorsoproximal and dorsodistal setae of forefemur, black arrows, and of mesothoracic femur, red arrows. (12) Habitus, right lateral; start and end of lateral fringe, red and black arrows, respectively. (13) Terminal abdominal segments, right lateral; end of lateral fringe, black arrow. All figures scale: 1 mm.
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.