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opencc-by-4.0May 2017View details →

Udayagiri, Madhya Pradesh. Cave 6, guardian figure.

Udayagiri, Madhya Pradesh. Cave 6, guardian figure, probably 5th century. As documented in 11/2012.

opencc-by-4.0Jun 2017View details →

PONE-D-17-12804R1 (TRACMIT) dataset for Figures 3D, 4A, 4B, 4C

Dataset for Figures 3D, 4A, 4B and 4C of the manuscript TRACMIT: an effective pipeline for tracking and analyzing cells on micropatterns through mitosis PONE-D-17-12804R1

opencc-by-4.0Nov 2024View details →

Model output and figure code for "Tradeoffs Between Vertical and Lateral Resilience in a Salt Marsh Restoration Model"

Simulation output data from COLT_Restorations model stored on the Community Surface Dynamics Modeling System public model repository at <a href="https://csdms.colorado.edu/wiki/Model:COLT_Restorations">https://csdms.colorado.edu/wiki/Model:COLT_Restorations</a> and on a GitHub public repository at<a href="https://github.com/mbbarksdale/CoLT_Restorations"> https://github.com/mbbarksdale/CoLT_Restorations</a>. These data were processed and graphed with model code that can also be found in a GitHub public repository at <a href="https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures">https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures</a>.

opencc-by-4.0Oct 2024View details →

Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"

Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article: <blockquote> <a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent.</code></a> Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> The outputs are obtained by running the Jupyter notebooks in the notebooks/preconditioned_sgd directory in the GitHub repository (release v0.2): <blockquote> <a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the particles_file/my_particles_8.star file in the attached archive.</div>

opencc-by-4.0Aug 2024View details →

Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"

Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves.  <h2> </h2> <h2>Interactive Figure Data</h2> Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII). clouds_{line_name}.csv: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight. transfer_function_velocity_{line_name}.csv: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity. transfer_function_tau_{line_name}.csv: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days. transfer_function_{line_name}.csv: A CSV file containing the transfer function Ψ.   <h2>Model-Related Figures</h2> fitplot_low.pdf: Same as Figure 4 in the publication, but for the low state. fitplot_high.pdf: Same as Figure 4 in the publication, but for the high state. geoplot_low.pdf: Same as Figure 5 in the publication, but for the low state. geoplot_high.pdf: Same as Figure 5 in the publication, but for the high state. lagplot_low.pdf: Same as Figure 6 in the publication, but for the low state. lagplot_high.pdf: Same as Figure 6 in the publication, but for the high state.    <h2>Spectral Reduction Method Comparison</h2> spec_decomp_pyqsofit.pdf: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels. input_method_comp.pdf: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.   <h2>Published Value Comparison</h2> pubval_table.pdf: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024).    <h2>Joint Posterior Analysis</h2> joint_line_posterior_table.pdf: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species.    <h2>Virial Factor Analysis</h2> fcomp.pdf: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states. fcorr_table.pdf: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.

Figure 2 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time

Figure 2. Cicada timetree built by BEAST v1.X, applying 1,534 bp COI sequence. OUTs with isolate number: our own analyzed specimens shown in Table 1, and others: from GenBank/DDJB. In outgroup Hemiptera; #: analyzed family by Johnson et al. (2018); % analyzed family by Misof et al. (2014). Inserted figure: Base substitution rate (= rate median shown at each node; substitutions per site per million year; s/s/myr) vs age (= posterior age shown at each node) diagram. Red approximate curve with its formula was drawn by Excel function, with the intersection for the curve = 0.0128 s/s/myr, the rate median shown on Tracer.

Figure 4 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time

Figure 4. Number of base changes of transition and tansversion vs corrected pairwide distance diagram for whole mitochondrial gene.

Figure 1 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time

Figure 1. Simplified cicada timetree built by BEAST v1.X, applying a 1,534 bp in maximum COI sequence. Inserted figure: Base substitution rate (= ratemedian shown at each node; substitutions per siteper millionyear; s/s/ myr) vsage (= posterior age shown at each node) diagram. Red approximate curve with its formula was drawn by an Excel function, with the intersection for the curve = 0.0128 s/s/myr, the rate median shown on Tracer.

Figure 3 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time

Figure 3. Cicada timetree built by BEAST v1.X, applying 1,534 bp COI and 874 bp 18S rRNA sequences. OUTswith isolate number: our own analyzed specimens shown in Table 1, and others: from GenBank/DDJB. In outgroup Hemiptera; #: analyzed family by Johnson et al. (2018); % analyzed family by Misof et al. (2014). Inserted figure: Base substitution rate (= rate median shown at each node; substitutions per site per million year; s/s/myr) vs age (= posterior age shown at each node) diagram. Red approximatecurve with its formulawas drawn by Excel function, with the intersection for the curve = 0.0114 s/s/myr, the rate median shown on Tracer. Note that this rate is a little slower than thatsolely of COI in Figures 1 and 2, reflecting slowerrate of 18S rRNAthan COI (see Osozawa et al. 2017a).

Figure 1 in The invasive ant Solenopsis invicta is established in Europe

Figure 1. Location, genetic analysis, and modeling of the potential spread of the new alien population of S. invicta. (A) The invaded area in Sicily is marked with a star. The directions of wind trajectories starting from (forward) and arriving at (backward) the study area are indicated as percentages over the total time frame. Main commercial hubs on the island are highlighted. Inset map summarizes the records retrieved for the species alien and native ranges. (B) Nuptial flight recorded in January 2023. (C) Satellite view of the study area (37.055N, 15.267E) and ant nest positions (Data S1A). (D) Haplotype network of mitochondrial sequences. The three main haplotypes are annotated. Colors indicate the sample origin and the sizes of the circles represent the number of samples. (E) Worldwide frequency of the H5 haplotype recorded in Italy, highlighting possible introduction sources. (F) Bars represent suitable area estimated for the species partitioned by land use category (green: % of total area of Europe; blue: % of total area of that category). (G) Ensemble model map prediction under current and future (H) environmental conditions. (I) Future trends of predicted suitable area (% of total area).

Figure 6 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 6. Moth (A); coffee leaf damage (B and C); caterpillar at the beginning of pupal stage (D); and the characteristic X-shaped cocoon of coffee tree miner (E).

Figure 1 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 1. Rosette with coffee berry borer attack symptom (A); and detail of the pest hole in the crown region of the fruit (B).

Figure 9 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 9. Orthezia colony (Praelongorthezia praelonga) on coffee leaves (A); leaf covered with dark- colored fungus, commonly referred to as sooty mold (B); and coffee plant with high defoliation caused by the pest (C).

Figure 7 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 7. Flower bud (A), branches (B) and rosette (C) of the coffee tree infested with citrus mealybug.

Figure 3 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 3. Prorops nasuta - Uganda wasp (A); C. stephanoderis - Ivory Coast Wasp (B); and C. hyalinipennis (C).

Figure 12 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 12. Young form and pupa of the honeydew moth caterpillar and damages the coffee tree rosette.

Figure 4 in INTEGRATED PEST MANAGEMENT IN CONILON COFFEE

Figure 4. Parasitism stages of the Ivory Coast Wasp, Cephalonomia stephanoderis from the coffee berry borer larva (top of the figure) and the coffee berry borer pupa (bottom). Source: Benassi (1996).

FIGURE 5 U in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 5 U-Net implementation. The architecture of the used convolutional neural network (CNN) is an implementation of U-Net. It consists of two parts: two 3×3 convolutions followed by 2×2 max pooling and two 3×3 convolutions followed by 2×2 upconvolutions. Dropout was added to avoid overfitting. As a final step a 1×1 convolution is applied, resulting in an output map with two classes.

FIGURE 6 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 6 Network performance evaluation. High true positive rate (TPR) and low false positive rate (FPR) values for training (blue) and testing data (red) indicate the network's high generalizability.

FIGURE 10 in Automated segmentation of insect anatomy from micro-CT images using deep learning

FIGURE 10 Application of pipeline for other insect species. The brain textures of various insect species can be very similar to those of ants, facilitating the prediction by the network even without pretraining on specific insect brain scans. (a) Raw image of wasp head (original 1000 × 1000 px) and (b) its prediction without postprocessing (original 520 × 520 px), indicating satisfactory identification of the borders of the brain area. (c) 2D image of praying mantis head (520 × 520 px) and (d) the prediction of its brain area without postprocessing. Even though the network overpredicts some small pixel islands, it excludes from its prediction areas of the muscles, fibers, and cuticle.