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17 results for “ear dataset”
Zellige example dataset: inner ear organoid epithelium
<p><strong>Inner ear organoid at day 14 of culture. </strong></p> <p>The z-stack image encompasses half of the spherical organoid including two distinct and superimposed surfaces that correspond to the basal side of the epithelium and the apical junctional network. It was acquired with a confocal microscope (Nikon A1HD25) equipped with a Nikon Plan-Apochromat 25x lens (NA=1.05). Pixel size 0.690 µm, z step >1 µm. This dataset contains both the ground-truth height maps and the height maps generated with Zellige. The Zellige parameters used are:</p> <p><span class="math-tex">\(T_{A}=5, T_{otsu}=12, S_{min}=5, \sigma_{xy}=2, \sigma_{z}=1, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.8, T_{OSE2}=0.1, R_{2}=10, C_{2}=0.8.\)</span></p> <p>Nota: to compare the ground truth height map with the Zellige height map, one first needs to substrat 1 to all values of the Zellige height map.</p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p>
EARS-NWC Metop-B dataset March 9, 2020, 08:00-08:19 UTC
<p>EARS-NWC data, Metop-B, March 9th, 08:00 UTC to 08:19 UTC</p> <p> </p>
Wheat-Ears-Detection-Dataset
<p>Dataset from the Ear density estimation from high resolution RGB imagery using deep learning technique paper</p> <p> <strong>Highlights</strong><br> - 236 high resolution images (6000*4000)<br> - Wheat ears annotated with a bounding box<br> - 30729 ears identified<br> - Spatial resolution (GSD) of 0.13mm/pixel<br> - Two images for each microplots <br> - 20 contrasted genotype with 6 replicated growth in two environment</p> <p>contact : simon.madec@inra.fr </p> <p> </p> <p> </p> <p> </p> <p> </p>
An Occlusion and Pose Sensitive Image Dataset for Black Ear Recognition
<p><strong>RESEARCH APPROACH</strong></p> <p>The research approach adopted for the study consists of seven phases which includes as shown in Figure 1:</p> <ol> <li>Pre-acquisition</li> <li>data pre-processing</li> <li>Raw images collection</li> <li>Image pre-processing</li> <li>Naming of images</li> <li>Dataset Repository</li> <li>Performance Evaluation</li> </ol> <p>The different phases in the study are discussed in the sections below.</p> <p> </p> <p><strong>PRE-ACQUISITION</strong></p> <p>The volunteers are given brief orientation on how their data will be managed and used for research purposes only. After the volunteers agrees, a consent form is given to be read and signed. The sample of the consent form filled by the volunteers is shown in Figure 1.</p> <p>The capturing of images was started with the setup of the imaging device. The camera is set up on a tripod stand in stationary position at the height 90 from the floor and distance 20cm from the subject.</p> <p> </p> <p><strong>EAR </strong><strong>IMAGE ACQUISITION</strong></p> <p>Image acquisition is an action of retrieving image from an external source for further processing. The image acquisition is purely a hardware dependent process by capturing unprocessed images of the volunteers using a professional camera. This was acquired through a subject posing in front of the camera. It is also a process through which digital representation of a scene can be obtained. This representation is known as an image and its elements are called pixels (picture elements). The imaging sensor/camera used in this study is a Canon E0S 60D professional camera which is placed at a distance of 3 feet form the subject and 20m from the ground. </p> <p>This is the first step in this project to achieve the project’s aim of developing an occlusion and pose sensitive image dataset for black ear recognition. (OPIB ear dataset). To achieve the objectives of this study, a set of black ear images were collected mostly from undergraduate students at a public University in Nigeria.</p> <p> </p> <p>The image dataset required is captured in two scenarios:</p> <p>1. uncontrolled environment with a surveillance camera</p> <ol> </ol> <p>The image dataset captured is purely black ear with partial occlusion in a constrained and unconstrained environment.</p> <p> </p> <p>2. controlled environment with professional cameras</p> <p>The ear images captured were from black subjects in controlled environment. To make the OPIB dataset pose invariant, the volunteers stand on a marked positions on the floor indicating the angles at which the imaging sensor was captured the volunteers’ ear. The capturing of the images in this category requires that the subject stand and rotates in the following angles 60<sup>o</sup>, 30<sup>o</sup> and 0<sup>o</sup> towards their right side to capture the left ear and then towards the left to capture the right ear (Fernando <em>et al.,</em> 2017) as shown in Figure 4. Six (6) images were captured per subject at angles 60<sup>o</sup>, 30<sup>o</sup> and 0<sup>o</sup> for the left and right ears of 152 volunteers making a total of 907 images <strong><em>(five volunteers had 5 images instead of 6, hence f</em></strong><strong><em>olders 34, 22, 51, 99 and 102 contain 5 images).</em></strong></p> <p>To make the OPIB dataset occlusion and pose sensitive, partial occlusion of the subject’s ears were simulated using rings, hearing aid, scarf, earphone/ear pods, etc. before the images are captured.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>CONSENT FORM</strong></p> <p>This form was designed to obtain participant’s consent on the project titled: <strong>An Occlusion and Pose Sensitive Image Dataset for Black Ear Recognition</strong><strong> (OPIB)</strong>. The information is purely needed for academic research purposes and the ear images collected will curated anonymously and the identity of the volunteers will not be shared with anyone. The images will be uploaded on online repository to aid research in ear biometrics.</p> <p>The participation is voluntary, and the participant can withdraw from the project any time before the final dataset is curated and warehoused.</p> <p>Kindly sign the form to signify your consent.</p> <p><strong><em>I consent to my image being recorded in form of still images or video surveillance as part of the OPIB ear images project.</em></strong></p> <p><strong>Tick as appropriate:</strong></p> <p><strong>GENDER</strong> Male Female</p> <p><strong>AGE</strong> (18-25) (26-35) (36-50)</p> <p> </p> <p>………………………………..</p> <p>SIGNED</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Figure 1</strong>: Sample of Subject’s Consent Form for the OPIB ear dataset</p> <p> </p> <p><strong>RAW IMAGE COLLECTION</strong></p> <p>The ear images were captured using a digital camera which was set to JPEG because if the camera format is set to raw, no processing will be applied, hence the stored file will contain more tonal and colour data. However, if set to JPEG, the image data will be processed, compressed and stored in the appropriate folders.</p> <p> </p> <p><strong>IMAGE PRE-PROCESSING </strong></p> <p>The aim of pre-processing is to improve the quality of the images with regards to contrast, brightness and other metrics. It also includes operations such as: cropping, resizing, rescaling, etc. which are important aspect of image analysis aimed at dimensionality reduction. The images are downloaded on a laptop for processing using MATLAB.</p> <p> </p> <p><strong>Image Cropping</strong></p> <p>The first step in image pre-processing is image cropping. Some irrelevant parts of the image can be removed, and the image Region of Interest (ROI) is focused. This tool provides a user with the size information of the cropped image. MATLAB function for image cropping realizes this operation interactively by waiting for a user to specify the crop rectangle with the mouse and operate on the current axes. The output images of the cropping process are of the same class as the input image.</p> <p><strong>Naming of OPIB Ear Images</strong></p> <p>The OPIB ear images were labelled based on the naming convention formulated from this study as shown in Figure 5. The images are given unique names that specifies the subject, the side of the ear (left or right) and the angle of capture. The first and second letters (SU) in the image names is block letter simply representing subject for subject 1-to-n in the dataset, while the left and right ears is distinguished using L1, L2, L3 and R1, R2, R3 for angles 60<sup>0</sup>, 30<sup>0</sup> and 0<sup>0</sup><sub>, </sub>respectively as shown in Table 1.</p> <p> </p> <p><strong>Table 1: Naming Convention for OPIB ear images</strong></p> <table align="center"> <tbody> <tr> <td> <p>NAMING CONVENTION</p> </td> </tr> <tr> <td> <p>Label</p> <p>Degrees 60<sup>0</sup> 30<sup>0</sup> 0<sup>0</sup></p> </td> </tr> <tr> <td> <p>No of the degree 1 2 3</p> </td> </tr> <tr> <td> <p>Subject 1 indicates (first image in dataset) SU<sub>1</sub></p> </td> </tr> <tr> <td> <p>Subject n indicates (last image in dataset) SU<sub>n</sub></p> </td> </tr> <tr> <td> <p>Left Image 1 L 1</p> <p>Left image n L n</p> <p>Right Image 1 R 1</p> <p>Right Image n R n</p> </td> </tr> <tr> <td> <p>SU1L<sub>1</sub> SU1R<sub>I</sub></p> <p>SU1L<sub>2</sub> SU1R<sub>2</sub> </p> <p>SU1L<sub>3</sub> SU1R<sub>3</sub></p> </td> </tr> </tbody> </table> <p> </p> <p><strong>OPIB EAR DATASET EVALUATION</strong></p> <p>The prominent challenges with the current evaluation practices in the field of ear biometrics are the use of different databases, different evaluation matrices, different classifiers that mask the feature extraction performance and the time spent developing framework (Abaza <em>et al.</em>, 2013; Emeršič <em>et al.,</em> 2017).</p> <p>The toolbox provides environment in which the evaluation of methods for person recognition based on ear biometric data is simplified. It executes all the dataset reads and classification based on ear descriptors.</p> <p> </p> <p><strong>DESCRIPTION OF OPIB EAR DATASET</strong></p> <p>OPIB ear dataset was organised into a structure with each folder containing 6 images of the same person. The images were captured with both left and right ear at angle 0, 30 and 60 degrees. The images were occluded with earing, scarves and headphone etc. The collection of the dataset was done both indoor and outdoor. The dataset was gathered through the student at a public university in Nigeria. The percentage of female (40.35%) while Male (59.65%). The ear dataset was captured through a profession camera Nikon D 350. It was set-up with a camera stand where an individual captured in a process order. A total number of 907 images was gathered.</p> <p>The challenges encountered in term of gathering students for capturing, processing of the images and annotations. The volunteers were given a brief orientation on what their ear could be used for before, it was captured, for processing. It was a great task in arranging the ear (dataset) into folders and naming accordingly.</p> <p> </p> <p><strong>Table 2</strong>: Overview of the OPIB Ear Dataset</p> <table align="left"> <tbody> <tr> <td> <p>Location</p> </td> <td> <p>Both Indoor and outdoor environment</p> </td> </tr> <tr> <td> <p>Information about Volunteers</p> </td> <td> <p>Students</p> </td> </tr> <tr> <td> <p>Gender</p> </td> <td> <p>Female (40.35%) and male (59.65%)</p> </td> </tr> <tr> <td> <p>Head Side Left and Right</p> </td> <td> <p>Side Left and Right</p> </td> </tr> <tr> <td> <p>Total number of volunteers</p> </td> <td> <p>152</p> </td> </tr> <tr> <td> <p>Per Subject images</p> </td> <td> <p>3 images of left ear and 3 images of right ear</p> </td> </tr> <tr> <td> <p>Total Images</p> </td> <td> <p>907</p> </td> </tr> <tr> <td> <p>Age group</p> </td> <td> <p>18 to 35 years</p> </td> </tr> <tr> <td> <p>Colour Representation</p> </td> <td> <p>RGB</p> </td> </tr> <tr> <td> <p>Image Resolution</p> </td> <td> <p>224x224</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset of acoustic intensity vector measurements around an upscaled ear model
<p>A dataset of acoustic vector (particle velocity vector and scalar sound pressure) measurements of the sound field around an upscaled model of an ear. Data collected in July 2022 at the Aalto Acoustics Lab in Espoo, Finland.</p> <p>See the companion paper at AES for information about the contents of the dataset, measurement methodology, and example scripts.</p> <p>See the companion repository <a href="https://github.com/aaron-geldert/upscaled-ear-model-scripts">github.com/aaron-geldert/upscaled-ear-model-scripts</a> for example MATLAB scripts using the dataset.</p> <p>Correspondence should be directed to <a href="mailto:aarongeldert@gmail.com?subject=RE%20Big%20Ear%20Dataset%20(Zenodo)">Aaron Geldert (aarongeldert@gmail.com)</a>. <br> </p>
Contrasted-Fertilization Wheat Ear Dataset 2020
<p><strong>Overview</strong></p> <p>This dataset contains 701 wheat RGB images in which all the ears have been manually labelled with bounding boxes. Its primary function is to provide training or test data for deep learning models aiming at the detection of wheat ears. In terms of diversity, it integrates wheat images from two varieties, at all the key development stages from heading to maturity, and for four or eight contrasted nitrogen fertilization management. </p> <p><strong>Field experiments and image acquisition</strong></p> <p>Images were acquired during the 2020 season in two trial fields located in the Hesbaye area, Belgium. The images were captured by two RGB cameras in nadir position. Those cameras were positioned on the cantilever beam to avoid shadows from the phenotyping platform. The auto-exposure algorithm was tuned to prevent, as possible, image saturation. All the details regarding the field experiments and the image acquisition can be found in the related paper: "Dandrifosse S., Ennadifi E., Carlier A., Gosselin B., Dumont B. & Mercatoris B., 2022. Deep learning for wheat ear segmentation and ear density measurement : From heading to maturity. Comput. Electron. Agric. 199(June), DOI:10.1016/j.compag.2022.107161."</p> <p><strong>Image pre-processing</strong></p> <p>2048 x 2560 pixel images were acquired in the field, but each of them was converted to four square sub-images of 1024 x 1024 pixels. The labelled images in this dataset are the sub-images.</p> <p><strong>Ear bounding boxes</strong></p> <p>The dataset contains a total of 77657 bounding boxes stored in csv files as Python-style lists of [xmin, ymin, width, height]</p>
Lim_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Liza Lim, The Heart's Ear for flute/piccolo, clarinet and string quartet (1997), excerpt
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Lim_dataset_PoD </strong>contains eight files:</p> <ul> <li>Lim_01_ReadMe.pdf</li> <li>Lim_02_data.xlsx (processed data for this excerpt)</li> <li>Lim_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Lim_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Lim_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Lim_04_audio.mp3 to display correctly]</li> <li>Lim_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Lim_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Lim_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
test images dataset for ear tracking example
<p>Test images for ear tracking example, including parameters about these images</p>
Figure 2. Haplotype median joining network estimated from dataset 1, comprising 76 in Phylogeography and evolutionary lineage diversity in the small-eared greater galago, Otolemur garnettii (Primates: Galagidae)
Figure 2. Haplotype median joining network estimated from dataset 1, comprising 76 samples of partial cytochrome b (402 bp).
Metabolomic Dataset - EAR study
<p>Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects millions of people worldwide, and early detection is crucial for effective treatment and management. Metabolomics, the study of small molecules in biological systems, has the potential to provide important insights into the early diagnosis and treatment of RA. One promising area of metabolomics research in RA is the identification of early biomarkers. By analyzing metabolic changes that occur in the serum of RA patients naïve to treatment we have identified a panel of 3 metabolites: glyceric acid, lactic acid, and 3-hydroxyisovaleric acid as the best metabolite combination for early RA diagnosis (ERA) identifying patients with 96.7% of certainty outperforming the classical anti-cyclic citrullinated peptide marker by 2.9% and the C-reactive protein inflammatory marker by 15.4%. This early detection could allow for early intervention and treatment, potentially preventing the progression of the disease. Additionally, the deregulated metabolites have been associated with alterations in pathways related to amino acid metabolism such as aminoacyl-tRNA biosynthesis and the metabolism of serine, glycine, and phenylalanine that can be used to identify potential targets for therapeutic intervention.</p>
EarSet: A Multi-Modal In-Ear Dataset
<p>EarSet aims at providing the research community with a novel, multi-modal, dataset, which, for the first time, will allow studying of the impact of body and head/face movements on both the morphology of the PPG wave captured at the ear, as well as on the vital signs estimation. To accurately collect in-ear PPG data, coupled with a 6 degrees-of-freedom (DoF) motion signature, we prototyped and built a flexible research platform for in-the-ear data collection. The platform is centered around a novel ear-tip design which includes a 3-channel PPG (green, red, infrared) and a 6-axis (accelerometer, gyroscope) motion sensor (IMU) co-located on the same ear-tip. This allows the simultaneous collection of spatially distant (i.e., one tip in the left and one in the right ear) PPG data at multiple wavelengths and the corresponding motion signature, for a total of 18 data streams. <br> Inspired by the Facial Action Coding Systems (FACS), we consider a set of potential sources of motion artifact (MA) caused by natural facial and head movements. Specifically, we gather data on 16 different head and facial motions - head movements (nodding, shaking, tilting), eyes movements (vertical eyes movements, horizontal eyes movements, brow raiser, brow lowerer, right eye wink, left eye wink), and mouth movements (lip puller, chin raiser, mouth stretch, speaking, chewing).<br> We also collect motion and PPG data under activities, of different intensities, which entail the movement of the entire body (walking and running). Together with in-ear PPG and IMU data, we collect several vital signs including, heart rate, heart rate variability, breathing rate, and raw ECG, from a medical-grade chest device.</p> <p>With approximately 17 hours of data from 30 participants of mixed gender and ethnicity (mean age: 28.9 years, standard deviation: 6.11 years), our dataset empowers the research community to analyze the morphological characteristics of in-ear PPG signals with respect to motion, device positioning (left ear, right ear), as well as a set of configuration parameters and their corresponding data quality/power consumption trade-off. We envision such a dataset could open the door to innovative filtering techniques to mitigate, and eventually eliminate, the impact of MA on in-ear PPG. We ran a set of preliminary analyses on the data, considering both handcrafted features, as well as a DNN (Deep Neural Network) approach. Ultimately, we observe statistically significant morphological differences in the PPG signal across different types of motions when compared to a situation where there is no motion. We also discuss a 3-classes classification task and show how full-body motions and head/face motions can be discriminated from a still baseline (and among themselves). These preliminary results represent the first step towards the detection of corrupted PPG segments and show the importance of studying how head/face movements impact PPG signals in the ear. </p> <p>To the best of our knowledge, this is the first in-ear PPG dataset that covers a wide range of full-body and head/facial motion artifacts. Being able to study the signal quality and motion artifacts under such circumstances will serve as a reference for future research in the field, acting as a stepping stone to fully enable PPG-equipped earables.</p>
ear-EEG dataset for AAD task in Multiple-speaker environment
<div> <p>Please cite these original paper where this dataset was presented:</p> <p>YJ Yan, X Xu, H Zhu, P Tian, ZS Ge, X Wu, J Chen "Auditory Attention Decoding in Four-Talker Environment with EEG" which was accepted in INTERSPEECH 2024 and we will update more details after the closing of INTERSPEECH 2024.</p> <p>X. Xu, B.Wang, Y. Yan, X.Wu, and J. Chen, “A DenseNet-Based Method for Decoding Auditory Spatial Attention with EEG,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, Korea, Republic of: IEEE, Apr. 2024, pp. 1946–1950.</p> <p>H. Zhu <em>et al</em>., "Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment," <em>ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</em>, Hyderabad, India, 2025, pp. 1-5, doi: 10.1109/ICASSP49660.2025.10890810.</p> <p>The details of the setup for the experiment was mentioned in the original paper.</p> </div> <p><em>[1] Z. Fu, X. Wu, and J. Chen, “Congruent audiovisual speech enhances </em><em>auditory attention decoding with EEG,” Journal of Neural </em><em>Engineering, vol. 16, no. 6, p. 066033, Nov. 2019.</em></p> <p><em>[2] P. J. Sch ̈afer, F. I.</em><em> Corona-Strauss, R. Hannemann, S. A. Hillyard, </em><em>and D. J. Strauss, “Testing the Limits of the Stimulus Reconstruction </em><em>Approach: Auditory Attention Decoding in a Four-</em><em>Speaker Free Field Environment,” Trends in Hearing, vol. 22, p. </em><em>233121651881660, Jan. 2018.</em></p> <p><em>[3] T. Qu, Z. Xiao, M. Gong, Y. Huang, X. Li, and X. Wu, </em><em>“Distance-Dependent Head-Related Transfer Functions Measured </em><em>With High Spatial Resolution Using a Spark Gap,” IEEE Transactions </em><em>on Audio, Speech, and Language Processing, vol. 17, no. 6, </em><em>pp. 1124–1132, Aug. 2009.</em></p> <p><em><br>In our code repository https://github.com/zhl486/Ear_EEG_code.git , we have provided the envelopes of the experimental stimuli. If you require the original speech stimuli for auditory attention decoding (AAD) tasks, please feel free to contact us via email 2301111611@stu.pku.edu.cn and provide a brief explanation of your research needs.<br></em></p>
Imperial College Ear-EEG Dataset for Auditory Attention Decoding
<p>This repository contains the ear-EEG data and the speech material that were used in our publication "Decoding of Selective Attention to Speech From Ear-EEG Recordings" [1].</p> <p>The data are stored in the archive "icl_earEEG_dataset.zip", which contains:</p> <ul> <li>audio/ - this directory contains the original speech material from each EEG trial.</li> <li>eeg_data.h5: this file contains the unprocessed EEG recordings from each trial, sampled at the original rate of 256 Hz.</li> </ul> <p>The onsets of the EEG and speech material are already aligned - all you need to do is resample the audio (or the features derived therefrom) to the same sample rate as the EEG (256 Hz) in order to obtain time-aligned signals. </p> <p>If you use this dataset for your research, please cite this repository as well as the following article:</p> <p>[1] M. Thornton, D. Mandic, T. Reichenbach, "Decoding of Selective Attention to Speech From Ear-EEG Recordings". <em>submitted</em>. arXiv:2401.05187.</p> <div> <div> <div></div> </div> </div>
Figure 3. Phylogenetic tree estimated using BEAST from dataset 1 in Phylogeography and evolutionary lineage diversity in the small-eared greater galago, Otolemur garnettii (Primates: Galagidae)
Figure 3. Phylogenetic tree estimated using BEAST from dataset 1 (cytochrome b) and node-calibrated using the fossil record.
Ear to the ground - Dataset
Open the record for dataset details and reuse information.
Dataset raw data related to article "In-depth genetic and molecular characterization of Diaphanous Related Formin 2 (DIAPH2) and its role in the inner ear"
<p>This record contains the raw data (Exome sequencing) related to article "In-depth genetic and molecular characterization of DIAPH2 as a novel candidate gene for hearing loss"</p> <p><strong>ABSTRACT </strong></p> <p>Hereditary hearing loss is characterized by an extreme genetic heterogeneity. Nowadays, whole-exome sequencing represents a reasonably cost-effective approach for both mutational screening of known deafness genes and novel disease-gene identification. Here, we investigate the role of Diaphanous-related formin 2 (DIAPH2), coding for a protein involved in actin filament elongation, in nonsyndromic hearing loss. Using whole-exome sequencing, we found a predicted pathogenic missense variant at a conserved site in <em>DIAPH2</em>, which segregated with nonsyndromic X-linked hearing loss in an Italian family. Our immunohistochemical studies indicated that the mouse ortholog protein Diaph2 is expressed during development in the cochlea, specifically in the actin-rich stereocilia of the sensory outer hair cells. <em>In-vitro</em> studies showed a possible functional impairment of the mutant DIAPH2 protein upon RhoA-dependent activation. Finally, <em>Diaph2</em> knock-out and knock-in mice were generated by CRISPR/Cas9 technology and auditory brainstem response measurements performed at 4, 8 and 14 weeks. However, no hearing impairment was detected, possibly due to functional redundancy/compensation by other diaphanous proteins. Our findings indicate that <em>DIAPH2</em> may play a role in the inner ear; further studies are however needed to clarify the contribution of <em>DIAPH2</em> to deafness.</p> <p> </p>
Dataset related to article "In-depth genetic and molecular characterization of Diaphanous Related Formin 2 (DIAPH2) and its role in the inner ear"
<p>This record contains data (Table S1 to S4, Figure S1 to S12, File S1) related to article “In-depth genetic and molecular characterization of Diaphanous Related Formin 2 (<em>DIAPH2</em>) and its role in the inner ear” . </p> <p>Specifically, the record contains the following supplementary information to the article:</p> <p>Table S1. Coding sequence coverage of known autosomal recessive and X-linked NSHL-causing genes in exome data.</p> <p>Table S2. Prioritized variants shared between affected siblings III1 and III3.</p> <p>Table S3. CNVs shared between affected siblings III1 and III3.</p> <p>Table S4. Primers used for genetic screening of candidate genes/variants.</p> <p>Figure S1. Analysis of auditory brainstem evoked potentials in proband III3.</p> <p>Figure S2. Expression of mouse <em>Diaph2 </em>mRNA in P4 organ of Corti by RT-PCR. </p> <p>Figure S3. Diaph2 expression in E14.5 and E16.5 wild-type mouse cochlea.</p> <p>Figure S4. Evaluation of Diaph2 expression in whole-mount mouse cochleas.</p> <p>Figure S5. Diaph2 expression in P7 and P14 wild-type mouse cochlea.</p> <p>Figure S6. <em>In-silico</em> analysis of the impact of c.868A>G variant on <em>DIAPH2</em> pre-mRNA splicing.</p> <p>Figure S7. <em>In-vitro</em> analysis of the impact of c.868A>G variant on <em>DIAPH2</em> pre-mRNA splicing.</p> <p>Figure S8. <em>In-vitro</em> characterization of the effect of the c.868A>G variation on splicing using a <em>DIAPH2</em>minigene spanning exons 6 to 9.</p> <p>Figure S9. Analysis of <em>DIAPH2</em> exon 8 splicing in blood from NSHL3 family subjects.</p> <p>Figure S10. <em>In-vivo</em> analysis of <em>Diaph2</em> exon 8 splicing in the mouse cochlea.</p> <p>Figure S11. Pathogenicity prediction of the p.I290V missense variant with 8 commonly used software.</p> <p>Figure S12. DIAPH2 immunolocalization studies in basal conditions.</p> <p>File S1. Supplementary Methods</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.