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Layer VASO in visual system
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Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis
<p>Underlying dataset and analysis tests of the results described in the article "Systemic Treatment with Cigarette Smoke Extract Affects Zebrafish Visual Behaviour, Intraocular Vasculature Morphology and Outer Segment Phagocytosis".</p>
Xiao et al, Oligodendrocyte Precursor Cells Sculpt the Visual System by Regulating Axonal Remodeling [Dataset]
<p>Raw data from the behavior and imaging experiments of Xiao et al., Nature Neuroscience 2022. For additional details about the acquisition of each part of the dataset, refer to the methods section of the paper. From this dataset, using the published code, all the figures relative to the imaging in the optic tectum can be generated and all the cumulative statistics for the behavioural assays run with Stytra recomputed.</p> <p> </p> <p><strong>Organisation of the dataset</strong></p> <p>This dataset is organised in the following subdirectories:<br> - <em>freely_swimming</em>: contains the data for the freely swimming experiments quantifying motor activity in the various ablated groups. It contains subfolders of groups, each of which contains the Stytra raw data directories for all fish of that group. Refer to `Stytra` and `bouter` documentation for further details about the files.<br> - <em>OMR</em>: contains the data for the quantification of OMR reflex across different spatial frequencies. It contains subfolders for the control and ablated group, each of which contains the Stytra raw data directories for all fish of that group.<br> - <em>receptive_field_imaging</em>: contains the imaging data for the receptive field estimation. Subfolders contains, for each individual fish (both ablated and controls are pooled in the same directory):<br> - stytra raw output from the experiment<br> - data_from_suite2p_unfiltered.h5: `flammkuchen`-loadable `.h5` file that contains the raw fluorescent trace<br> - anatomy.mask: `flammkuchen`-loadable mask file saved by the `pypra` tool that was used for segmenting the tectum, delimiting the region of the tectum<br> The folder contains an additional file, `manual_alignment_offsets.h5`, where the offsets of the manual morphing across fish were saved.</p>
Three-systems for visual numerosity: A single case study
<p>MOT TASK</p> <p>Each file refers to a session with a particular condition. It is spelt out in the file name the number of objects to track as well as the total amount of objects on the screen</p> <p>Within each file it is found a variable called “MatriceRisultati”. Which contains:</p> <ol> <li>Number of targets to follow</li> <li>Number of correct answers</li> <li>Number of trials at the condition</li> <li>Percent correct responses (i.e. value_2 / value_3)</li> </ol> <p> </p> <p>NECKLACE – DISTANCE TASK</p> <p>Each file contains raw data for each session. All the data are store in a variable called RESP, which contains parameters for each trial.</p> <p>The crucial columns are</p> <ol> <li>Inter dot distance in the reference (in pixels – typically 1 pixel =~0.03 cm)</li> <li>Interdot distance in the test (in pixels)</li> <li>Subject choice to the question “which contains closer dots”</li> </ol> <p>For analysis one has to draw a psychometric curve (i.e. a cumulative gaussian) that fits the data of column 3, as a function of interdot distance (column 2). Varinat may include dividing column 2 by column 1 (so to have the ratio between test and reference) and run the psychometric curve on such normalized dimension</p> <p> </p> <p>Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.</p> <p> </p> <p>Numerosity discrimination</p> <ul> <li>The relevant columns in the matrix ‘a’ contain the following information:</li> <li>1<sup>st</sup>: Numerosity</li> <li>2<sup>nd</sup>: Log10 Numerosity</li> <li>3<sup>rd</sup>: Response </li> </ul> <p> </p> <p>Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.</p> <p> </p> <p>Numerosity estimation</p> <ul> <li>The relevant columns in the matrix ‘ContengoRisultati’ contain the following information:</li> <li>1<sup>st</sup>: Numerosity</li> <li>2<sup>nd</sup>: Response</li> </ul> <p> </p> <p>Further explanation of the other columns (seeds for generating the stimuli) can be obtained from the authors.</p> <p> </p> <p> </p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
<p><span><span>In our daily life we often make complex actions comprised of linked movements, such as reaching for a cup of coffee and bringing it to our mouth to drink. Recent work has highlighted the role of such linked movements in the formation of independent motor memories, affecting the learning rate and ability to learn opposing force fields. In these studies, distinct prior movements (lead-in movements) allow adaptation of opposing dynamics on the following movement. Purely visual or purely passive lead-in movements exhibit different angular generalization functions of this motor memory as the lead-in movements are modified, suggesting different neural representations. However, we currently have no understanding of how different movement kinematics (distance, speed or duration) affect this recall process and the formation of independent motor memories. Here we investigate such kinematic generalization for both passive and visual lead-in movements to probe their individual characteristics. After participants adapted to opposing force fields using training lead-in movements, the lead-in kinematics were modified on random trials to test generalization. For both visual and passive modalities, recalled compensation was sensitive to lead-in duration and peak speed, falling off away from the training condition. However, little reduction in force was found with increasing lead-in distance. Interestingly, asymmetric transfer between lead-in movement modalities was also observed, with partial transfer from passive to visual, but very little vice versa. Overall these tuning effects were stronger for passive compared to visual lead-ins demonstrating the difference in these sensory inputs in regulating motor memories. Our results suggest these effects are a consequence of state estimation, with differences across modalities reflecting their different levels of sensory uncertainty arising as a consequence of dissimilar feedback delays. </span></span></p>
Heterogeneity of synaptic connectivity in the fly visual system
<p>Source data of the paper Cornean, Molina-Obando et al. 2024, Nature Communications. This work contains an analysis of synaptic connectivity in the Drosophila system, focusing on the presynaptic circuitry of three medulla interneurons, Tm9, Tm1, and Tm2.<br>Synaptic connectivity was analyzed using the FAFB dataset (Zheng et al. 2018 Cell) and the Flywire connectome (Schlegel et al. 2023 bioRxiv, Dorkenwald et al. 2023 bioRxiv), as well as expansion microscopy. This analysis is supplement by some functional analysis using in vivo 2-photon calcium imaging. <br><br>Connectomics data used for this study are provided as .xlsx and .text files containing raw and processed data. <br>Expansion microscopy are uploaded as .tiff files containing raw data, as well as .nrrd and .csv files containing processed data.<br>Calcium imaging data are provided at .mat files containing both raw and processed data, as well as .xml files with information about the experimental protocol.</p><p>Please find all relevant information to use the code in the README files.</p><p>The code to analyze the data, either written in Matlab or Python, is found at https://github.com/silieslab/Cornean_Molina-Obando_etal_2024.git</p>
Figure 3. Data visualization-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Visual servoing is an approach to control motion of a helicopter using information feedback<br> from a camera mounted on it. For their tremendous potential applications in various areas including<br> environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively<br> studied in robotics and control in recent years. However, the research advance in dynamic control of<br> small helicopters is limited due to highly coupled non- linear dynamics and the existence of various<br> uncertain- ties. Many people studied controller design based on a Publisher Item Identifier.<br> linearized or simplified model, but the controllers developed under linearized models cannot<br> guarantee dynamic stability rigorously. Another effort is application of modern non-linear control<br> theory to helicopter control because small helicopter are good test beds for sophisticated control<br> techniques for their small size and highly coupled dynamics [4].</p>
Examples of ancient Near Eastern artifacts imaged and visualized with PLD system
<p><strong>From top to bottom: a coin, a cylinder seal impression, impressions on the bottom of a funerary cone, and a cuneiform tablet. For each artifact four visualizations were generated with the PLD MLR viewer. From left to right: coloor, shaded, automated sketch, normal map. References for the objects: Greek silver coin: o.i. 522 (©️ KU Leuven Art Collection); Modern impression Old Akkadian cylinder seal: O.861 (©️ Art & History Museum, Brussels - RMAH); Old Egyptian funerary cone: E.3984 (©️ Art & History Museum, Brussels); Old Akkadian cuneiform tablet: O.95 (©️ Art & History Museum, Brussels).</strong></p>
Comparing 2D and Augmented Reality Visualizations for Microservice System Understandability: Protocol And Dataset
<p>Comparing 2D and Augmented Reality Visualizations for Microservice System Understandability: Dataset.</p> <p>This dataset includes:</p> <ol> <li>The tools executable files and JSON representations.</li> <li>The tools screenshots.</li> <li>The participants classification data.</li> <li>Questionnaire Form.</li> <li>Training Materials.</li> <li>Testing Tasks.</li> </ol> <p> </p> <p><strong>Our Paper is at ICPC conference with title</strong>: Comparing 2D and Augmented Reality Visualizations for Microservice System Understandability: A Controlled Experiment</p>
Asymmetry in kinematic generalization between visual and passive lead-in movements are consistent with a forward model in the sensorimotor system
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Experimental Data for: Hierarchical Software Landscape Visualization for System Comprehension: A Controlled Experiment
<p>In many enterprises the number of deployed applications is constantly increasing. Those applications - often several hundreds - form large software landscapes. The comprehension of such landscapes is frequently impeded due to, for instance, architectural erosion, personnel turnover, or changing requirements. Therefore, an efficient and effective way to comprehend such software landscapes is required. The current state of the art often visualizes software landscapes via flat graph-based representations of nodes, applications, and their communication.</p> <p>In our ExplorViz visualization, we introduce hierarchical abstractions aiming at solving typical system comprehension tasks fast and accurately for large software landscapes. To evaluate our hierarchical approach, we conduct a controlled experiment comparing our hierarchical landscape visualization to a flat, state-of-the-art visualization. In addition, we thoroughly analyze the strategies employed by the participants and provide a package containing all our experimental data to facilitate the verifiability, reproducibility, and further extensibility of our results.</p> <p>We observed a statistically significant increase of 14 % in task correctness of the hierarchical visualization group compared to the flat visualization group in our experiment. The time spent on the system comprehension tasks did not show any significant differences. The results backup our claim that our hierarchical concept enhances the current state of the art in landscape visualization.</p> <p>This package contains our experimental data.</p>
Opsin data from: Multiple axes of visual system diversity in Ithomiini, an ecologically diverse tribe of mimetic butterflies
<p><span>The striking structural variation seen in arthropod visual systems can be explained by the overall quantity and spatio-temporal structure of light within habitats coupled with developmental and physiological constraints. However, little is currently known about how fine-scale variation in visual structures arise across shorter evolutionary and ecological scales. In this study, we characterise patterns of interspecific (between species), intraspecific (between sexes) and intraindividual (between eye regions) variation in the visual system of four ithomiine butterfly species. These species are part of a diverse 26-Myr-old Neotropical radiation where changes in mimetic colouration are associated with fine-scale shifts in ecology, such as microhabitat preference. By using a combination of selection analyses on visual opsin sequences, in-vivo ophthalmoscopy, micro-computed tomography (micro-CT), immunohistochemistry, confocal microscopy, and neural tracing, we quantify and describe physiological, anatomical, and molecular traits involved in visual processing. Using these data, we provide evidence of substantial variation within the visual systems of Ithomiini, including: i) relaxed selection on visual opsins, perhaps mediated by habitat preference, ii) interspecific shifts in visual system physiology and anatomy, and iii) extensive sexual dimorphism, including the complete absence of a butterfly-specific optic neuropil in the males of some species. We conclude that considerable visual system variation can exist within diverse insect radiations, hinting at the evolutionary lability of these systems to rapidly develop specialisations to distinct visual ecologies, with selection acting at both the perceptual, processing, and molecular level.</span></p>
CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System
<p>Abstract— Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at outdoor large scale scenes. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars’ unique features. Based on the unique scan pattern of Livox lidars, we propose an automatic lidarcamera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more accurate mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub. (https://github.com/ISEE-Technology/CamVox)</p> <p>This contains our dataset in SUSTech campus with loop closure (CamVox.bag) and the Lidar-camera Synchronization ARM(stm32) code (synchronization.zip ).</p>
Figure 6 in The remarkable visual system of a Cretaceous crab
Figure 6. Reconstruction of the extinct Callichimaera perplexa Renditiondepicting C. perplexa swimming aftera male commashrimp Eobodotria muisca (Cumacea), courtesy of Masato Hattori.
Figure 4 in The remarkable visual system of a Cretaceous crab
Figure 4. Boxplots of the mean and standard deviation of the ratio of eye diameter to carapace length in brachyuran crabs Species of brachyuran crabs are color coded by family as follows: Callichimaeridae, black; Grapsidae, orange; Ocypodidae, blue; Geryonidae, gray; Varunidae, green; Portunidae, red; Calappidae, pink; Oregoniidae, yellow; and Raninidae, purple. Bold font indicates extinct taxa. Outliers indicated by open circles.
Figure 5 in The remarkable visual system of a Cretaceous crab
Figure 5. Visual acuity and eye parameter of Callichimaera (A) Interommatidial angles AǪ of C. perplexa and marine and terrestrial arthropods reflecting visual acuity. (B) Eye parameter (P) values in C. perplexa and marine and terrestrial arthropods that correspond to decreasing environmental luminosity. Raw values and PhyloPic attributions are provided in the supplement (Tables S3 and S4).
Figure 2 in The remarkable visual system of a Cretaceous crab
Figure 2. Exceptional preservation of eyes in the Cretaceous crab Callichimaera perplexa (A–E) Adult individual (IGM p881209a); (A) SEM close-up of frontal region; (B, D) details of the layered optical lobe in left eye in oblique (B) and dorsal (D) views; (C–E) same views with colored optical lobe neuropils, i.e., lamina (green), medulla (blue), lobula (red), and putative axon bundles (yellow). (F, H–M) juvenile individual (IGM p881220) from which (G) regionalization, as well as inter-ommatidial angle, and eye parameter were calculated (Figure 5). (F) SEM image of the eye indicating the three regions where the average facet diameters were estimated (orange, green, and blue shading) in (G). White boxes indicate two different regions with facets of different shape and packing, i.e., hexagonal (H) and squarish (I); (J–M) Close-up SEM images of Hand I, showing the outline of the two underlying corneagenous cells of hexagonal (K) and squarish (M) facets. Images A, D–E, and F modified after Luque et al. (2019b).
Figure 3 in The remarkable visual system of a Cretaceous crab
Figure 3. Growth rate of eyes in living brachyuran crabs relative to carapace length compared with that in Callichimaera perplexa Panels are ordered by decreasing slope from top left to bottom right. Species of brachyuran crab are color coded by family as follows: Callichimaeridae, black; Grapsidae, orange; Ocypodidae, blue; Geryonidae, gray; Varunidae, green; Portunidae, red; Calappidae, pink; Oregoniidae, yellow; and Raninidae, purple. All measurements are recorded in millimeters. P, pelagic; B, benthic.
Figure 1 in The remarkable visual system of a Cretaceous crab
Figure 1. Selected fossil arthropods with large eyes and neural tissue preservation, including Callichimaera perplexa (A and B) The radiodont Lyrarapax unguispinus, YKLP 13305, from the early Cambrian Chengjiang biota, China (images courtesy of Gregory Edgecombe, modified from Cong et al., 2014). (C–D) The bivalved arthropod Odaraia, ROM 60746, from the early Cambrian of the Burgess Shale, Canada (images by Jean-Bernard Caron, courtesy of Javier Ortega-Hernández, modified from Ortega-Hernández, 2015). (E and F) The fuxianhuiid Fuxianhuia protensa, YKLP 15006, from the early Cambrian Chengjiang biota, China (images courtesy of Gregory Edgecombe, modified from Ma et al., 2012). (G and H) Callichimaera perplexa, from the Cretaceous of Colombia; (G) schematic reconstruction of anterior carapace and eyes with the optic lobe of H; (H) specimen IGM p881208 (images by Javier Luque, modified from Luque et al., 2019b). Abbreviations and colors in G: ab?, putative axon bundles (yellow); co, corneal eye; es, eyestalk (light gray); La, lamina (green); Lo, lobula (red); Me, medulla (blue); Re, retina (dark gray);?, undetermined tissue (light orange).
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.