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FIGURE 1 in A new species of Conocephalus (Orthoptera: Tettigoniidae: Conocephalinae) from Argentina with extraordinarily broad-band ultrasound calling song
FIGURE 1. Live individuals of Conocephalus tuyu in situ (from Aguas Verdes): A. male (January 2022); B, C. female (February 2023).
FIGURE 2 in A new species of Conocephalus (Orthoptera: Tettigoniidae: Conocephalinae) from Argentina with extraordinarily broad-band ultrasound calling song
FIGURE 2. Conocephalus tuyu (male holotype): A. lateral, B. dorsal view, C. head in dorsal view, D. tegmina (mirror of left tegmen damaged in posterior left corner), E. cerci in dorsal view (right cercus piercing epiproct, right stylus of subgenital plate missing), F. same ventral, vertical scale bars 1 mm.
Data from: Yellowtail damselfish Chrysiptera parasema can associate predation risk with the acoustic call of a heterospecific damselfish following pairing with conspecific alarm cues
<p>The ability to detect and respond to the presence of predation risk is under intense selection, especially for small-bodied fishes that coexist with predators. Fish use visual, olfactory, and auditory cues to assess predation risk. Damselfishes (Pomacentridae) use auditory vocalizations during inter- and intrasexual interactions, but it is not known if they can use vocalizations in the context of predator-prey interactions. Here, we test if yellowtail damselfish, <em>Chrysiptera parasema</em>, can learn to associate the territorial vocalization of heterospecific humbug damselfish <em>Dascyllus aruanus</em> with predation risk. In conditioning trials of yellowtail damselfish we played the territorial call of humbug damselfish while introducing either blank water (control treatment) or chemical alarm cue derived from damaged skin of conspecific yellowtail damselfish. In conditioning trials, fish exposed to alarm cue increased activity and spent more time in the water column relative to fish that received the control treatment. After a single conditioning trial, conditioned fish were exposed again to the territorial call. Fish conditioned with the call + alarm cue increased activity and time in the water column relative to fish that had been conditioned with the control treatment. These data indicate associative learning of an auditory stimulus with predation risk in a species that regularly uses auditory signaling in other contexts. Recordings of conditioning and test trials failed to detect any acoustic calls produced by test fish in response to the perception of predation risk. Thus, although yellowtail damselfish can associate risk with auditory stimuli, we found no evidence that they produce an alarm call.</p>
Mecopoda Call Diversity work Meghalaya
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Replication package for the paper: Harnessing Test Call Structures for Improved Fault Localization Effectiveness
<p>This repository contains the replication package for the paper <strong>"Harnessing Test Call Structures for Improved Fault Localization Effectiveness"</strong>. The package includes the necessary scripts, data, and instructions to reproduce the results presented in the study, focusing on the effectiveness of Spectrum-Based Fault Localization (SBFL) using the Barinel algorithm.</p> <h3>Description of Folders and Files:</h3> <ul> <li><strong>algorithms/</strong>: Contains the SBFL algorithms implemented for this study. Note that only the Barinel algorithm was used in the presented results.</li> <li><strong>base/</strong>: Contains the scripts needed for calculating spectra for SBFL.</li> <li><strong>D4J/</strong>: Contains the Defects4J projects. These need to be unpacked using the appropriate scripts provided in this folder.</li> <li><strong>changeset.json</strong>: Contains metadata about changesets for the projects analyzed in this study.</li> <li><strong>main.py</strong>: The main script for calculating ranks and metrics for the selected projects and heuristics.</li> <li><strong>SBFL ranks.csv</strong>: Contains the SBFL ranks calculated from the analysis.</li> </ul> <h2>Requirements</h2> <p>To run the scripts, you will need:</p> <ul> <li><strong>Python 3.9</strong></li> <li><strong>Defects4J</strong>: The <code>D4J</code> folder should contain unpacked Defects4J projects. Please ensure you have followed the instructions in the <code>D4J</code> folder to unpack the projects accordingly.</li> <li><strong>Required Python packages</strong> (install using <code><a href="http://localhost:63343/markdownPreview/1636345610/markdown-preview-index-2076127249.html?_ijt=mqjd55qrfb1m1q15g8bm74pk1u#"></a>pip</code>): <code><a href="http://localhost:63343/markdownPreview/1636345610/markdown-preview-index-2076127249.html?_ijt=mqjd55qrfb1m1q15g8bm74pk1u#"></a>pip install -r requirements.txt</code></li> <li><strong>Prepare the Defects4J Projects</strong>: <ul> <li>Navigate to the <code>D4J</code> directory.</li> <li>Run the provided scripts to unpack the necessary Defects4J projects.</li> <li>Ensure that each project folder is structured properly to be used by the <code>main.py</code> script.</li> </ul> </li> </ul> <h2>Running the Main Script</h2> <p>The main analysis script is <code>main.py</code>, which calculates the SBFL ranks using the Barinel algorithm based on the selected heuristics.</p> <h3>Usage</h3> <p>To run the main script:</p> <div></div> <pre><code><a href="http://localhost:63343/markdownPreview/1636345610/markdown-preview-index-2076127249.html?_ijt=mqjd55qrfb1m1q15g8bm74pk1u#"></a>python main.py </code></pre> <h3>Parameters and Settings:</h3> <ul> <li><strong>Projects and Ranges</strong>: The projects are defined in the <code>projects</code> list, with their respective bug ranges in the <code>ranges</code> list. The script iterates over these projects and bug IDs to calculate SBFL ranks.</li> </ul> <h3>Outputs:</h3> <ul> <li>The script outputs the ranks and coverage metrics directly to the console. Results can be redirected or saved as needed.</li> <li>It uses the <code>Ranks.RankContainer</code> class to calculate and print the minimum suspiciousness ranks for the selected metrics.</li> </ul>
FIGURE 1 in Reproductive and defensive calls of Hydrolaetare schmidti (Anura: Leptodactylidae) from the Amazonian savanna of Amapá in northern Brazil
FIGURE 1. Spectrogram and oscillogram of (A–B) distress calls and (C) advertisement call of Hydrolaetare schmidti from the Amazonian savanna of Amapá in northern Brazil. (A) Typical distress call with clear harmonic structure and (B) distress call with pulsatile structure and without clear harmonic intervals. Male vouchers: CECC 3421 (A and C) and CECC 3422 (B). Calls were produced on different time and frequency scales, because of their distinct temporal and spectral characteristics.
Figure 4 in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 4. Best models of each general structure. Model 6 (M6) for linear and 13 (M13) for sinusoidal structure. I = relative calling intensity, t = time. The solid lines represent the predictions of the models; the points represent the data acquired from the ARS.
Figure 3 in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 3. Rose-diagram of anuran assemblage relative calling intensity. The length and colour (light = low, dark = high) of the bars from the centre indicate the intensity of vocalisation for the temporal section. A Rayleigh z test for unimodal orientation was performed, resulting in significant evidence of non-uniform distribution of vocalisations across the year (P = 0.0303).
Figure 1 in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 1. Study site location and land use, natural protected area of Nahá, Ocosingo, Chiapas, México.
Figure 2. a in Calling phenology of anurans in a tropical rainforest in South Mexico: testing predictive models
Figure 2. a) Calling anuran species recorded at temporary sections (7 months). The horizontal axis corresponds to temporary sections and the vertical to species. b) Average temperature T � (° C) (continuous line) and accumulated rainfall Ra (dashed line) by temporary section (32 in 7 months). Both graphs share the horizontal axis, where breaks indicate recording gaps, and the numbers are the days of the months covered.
FIGURE 3 in Mikrischyrum musicum, a new katydid species from montane rainforest in southern Ecuador with complex pure-tone calling song (Orthoptera: Tettigoniidae: Pseudophyllinae: Platyphyllini)
FIGURE 3. Calling song of Mikrischyrum musicum: A. oscillogram of complete pulse train (20°C, holotype, recording cbt-019s04r03), B. first group of four pulses of A in higher resolution (and photo of holotype staged at day), C. linear spectrogram, D. zero-crossing analysis of one pulse, E. field recording together with two other katydid species, the arrow points at first pulse of the M. musicum sequence (13°C, cbt019x01r01, 14 August 1998), F. spectrogram taken from this recording with the distinctive peak of the new species, G. spectrogram of an unknown katydid species, showing the M. musicum peak too, H. spectrogram of an undescribed species of Aemasia (also Platyphyllini, song known from collected males, Braun 2002: Figs. 6.2.4, 6.3.3), based on the second call right after the end of the M. musicum sequence.
FIGURE 2 in Mikrischyrum musicum, a new katydid species from montane rainforest in southern Ecuador with complex pure-tone calling song (Orthoptera: Tettigoniidae: Pseudophyllinae: Platyphyllini)
FIGURE 2. Mikrischyrum musicum: A. male habitus, B. male subgenital plate in ventral view, C. male left cercus in dorsal view, D. male pronotum in dorsal view, E. ovipositor of female (pencil drawings done in October 2003, after holotype and a paratype).
FIGURE 1 in Mikrischyrum musicum, a new katydid species from montane rainforest in southern Ecuador with complex pure-tone calling song (Orthoptera: Tettigoniidae: Pseudophyllinae: Platyphyllini)
FIGURE 1. Mikrischyrum musicum: A. male holotype in dorsal view, B. same in lateral view, C. female paratype to same scale as B (specimen cbt019s01).
FIGURE 6. Assa advertisement calls from A–B in A new hip-pocket frog from mid-eastern Australia (Anura: Myobatrachidae: Assa)
FIGURE 6. Assa advertisement calls from A–B) Assa wollumbin sp. nov. calls with nine and seven notes (Wollumbin, NSW) (Temperature 19.5 to 22.0 oC) (R.185956–60). (C–D) A. darlingtoni calls with 15 (Border Ranges National Park, NSW (Temperature 16 to 17.0 oC) and 13 (Conondale Ranges National Park, Qld. Temperature 18.2 oC). Waveforms (upper panel) display amplitude (y-axis) against time (x-axis, seconds); spectrograms (lower panel) display call frequency (y-axis) and intensity (degree of shading) against time (x-axis, seconds).
FIGURE 10 in Two new species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda: Hydrobiidae) from springs in the Rio Grande watershed in Texas
FIGURE 10. Scanning electron micrographs of radula of Pyrgulopsis rubra sp. nov. A = portion of radular ribbon; B–C = central radular teeth; D = lateral teeth and inner marginal tooth; E = lateral, inner, and outer marginal teeth. Scale bars A = 20 µm; B–E = 10 µm.
FIGURE 11. Type localities for new species. A. P in Two new species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda: Hydrobiidae) from springs in the Rio Grande watershed in Texas
FIGURE 11. Type localities for new species. A. P. harrymilleri sp. nov., Vasquez Spring, Presidio Co., TX. Photo by B. Schwartz. B. P. rubra sp. nov., Palo Amarillo Springs, Presidio Co., TX. Photo by K. Perez.
FIGURE 8 in Two new species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda: Hydrobiidae) from springs in the Rio Grande watershed in Texas
FIGURE 8. Shells and anatomical features of Pyrgulopsis rubra sp. nov. A–C. holotype, Vasquez Spring, ANSP A483288, height=2.79 mm, width=2.01 mm. D–E. Penial anatomy illustrating glandular features. Pf=Penial filament, Tg=terminal gland, Tl=terminal lobe, Vg=ventral gland, Dp=penial gland, Dg1=dorsal gland 1, Dg2=dorsal gland 2, Dg3=dorsal gland 3. D. Penis, dorsal surface. E. Penis, ventral surface. F. Animal with shell removed to show dark reddish pigmentation.
FIGURE 9 in Two new species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda: Hydrobiidae) from springs in the Rio Grande watershed in Texas
FIGURE 9. Scanning electron micrographs of shells (A–B), protoconch (C–D), and opercula (E = outer side; F = inner side) of Pyrgulopsis rubra sp. nov. Scale bars A–B = 500 µm; C = 100 µm; D = 50 µm; E–F = 200 µm.
FIGURE 7 in Two new species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda: Hydrobiidae) from springs in the Rio Grande watershed in Texas
FIGURE 7. Scanning electron micrographs of radula of Pyrgulopsis harrymilleri sp. nov. A = portion of radular ribbon; B = central radular teeth; C = lateral and inner marginal teeth; D = outer marginal teeth. Scale bars = 10 µm.
FIGURE 5 in Two new species of Pyrgulopsis Call & Pilsbry, 1886 (Mollusca: Caenogastropoda: Hydrobiidae) from springs in the Rio Grande watershed in Texas
FIGURE 5. Shells and anatomical features of Pyrgulopsis harrymilleri sp. nov. A–C. holotype, Vasquez Spring, ANSP A483285, height=2.2 mm, width=1.74 mm. D–E. Penial anatomy illustrating glandular features. Pf=Penial filament, Tg=terminal gland, Tl=terminal lobe, Vg=ventral gland, Dp=penial gland, Dg1=dorsal gland 1, Dg2=dorsal gland 2, Dg3=dorsal gland 3. D. Penis, dorsal surface. E. Penis, ventral surface. F. Animal with shell mostly removed to show dark gray-black pigmentation.
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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.