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Fig. 1 in Morphological keys to advance the understanding of protostrongylid biodiversity in caribou (Rangifer spp.) at high latitudes
Fig. 1. Dorsal spine larva of Parelaphostrongylus andersoni at 400× magnification in differential interference contrast, depicting important anatomical features.
Fig. 10 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description
Fig. 10. Drawing of the oral field of a larva of Gracixalus quangi from Hoa Binh Province (IEBR 4333b) in development stage 35: natural view on top, schematic drawing below). Drawing by C. Niggemann.
Fig. 9 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description
Fig. 9. Drawing of a larva of Gracixalus quangi from Hoa Binh Province (IEBR 4333b) in development stage 35 in dorsal and lateral view. Drawing by C. Niggemann.
Fig. 8 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description
Fig. 8. Advanced developmental stages of Gracixalus quangi from Hoa Binh Province (development stages indicated). Photo credit T. Ziegler and T.D. Tran.
Fig. 6 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description
Fig. 6. Early developmental stages of Gracixalus quangi from Hoa Binh Province (development stages indicated). Photo credit C.T. Pham.
Fig. 3 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description
Fig. 3. Adult couple of Gracixalus quangi from Hoa Binh Province (male above, female below). Photo credit C.T. Pham.
Fig. 11 in First record of Gracixalus quangi Rowley, Dau, Nguyen, Cao & Nguyen, 2011, from Hoa Binh Province, Vietnam, including the first documentation of advanced larval stages and an extended tadpole description
Fig. 11. Larva of Gracixalus quangi from Hoa Binh Province (IEBR 4333b) in development stage 35 in life (photo taken 4 May 2014). Photo credit T. Ziegler.
Data of the publication: Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing
<p>Data corresponding to the figures of the publication "Recent Advances in Rare Earth Doped Inorganic Crystalline Materials for Quantum Information Processing" by N. Kunkel and Ph. Goldner (https://doi.org/10.1088/1361-648X/aa529a). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls
<p><strong>Dataset description</strong></p> <p>The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.</p> <p><strong>Meta Information</strong></p> <p>The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.</p> <p><strong>Recording Procedure</strong></p> <p>We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:</p> <ul> <li>Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}</li> <li>Ask the participant to read out “The North Wind and the Sun”</li> <li>Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”</li> <li>Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.</li> <li>Test executor ends call by farewell (on-hook signal triggered).</li> </ul> <p><strong>Annotation Scheme</strong></p> <p>For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:</p> <p>SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18</p> <p>with</p> <ul> <li>SI as subject identification in the form ID<em>NN</em>, <em>N</em> in [0, 9]</li> <li>HS as the health status label (hc or pd accordingly)</li> <li>HYR as the expert assessed H&Y scale rating</li> <li>UPDRS II-5 as the according expert peer-reviewed score</li> <li>UPDRS III-18 as the according expert assessed score</li> </ul> <p>For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.</p> <p><strong>Appendix</strong></p> <p>North Wind and the Sun (Orthographic Version):</p> <p>“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”</p> <p>BNC – Tech. Engin. Computer applications in geography snippet:</p> <p>“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”</p>
ViSAPy-generated test data from Lee JH., et al. Advances in Neural Information Processing Systems 30 (NIPS 2017), pp4002--4012
<p>This dataset corresponds to the simulated test data for spike-sorting algorithms in Figure 3 of:</p> <p>Lee, Jin Hyung and Carlson, David E and Shokri Razaghi, Hooshmand and Yao, Weichi and Goetz, Georges A and Hagen, Espen and Batty, Eleanor and Chichilnisky, E.J. and Einevoll, Gaute T. and Paninski, Liam. YASS: Yet Another Spike Sorter. Advances in Neural Information Processing Systems 30 (NIPS 2017). Editors I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett, year 2017, pp4002-4012.<br> publisher: Curran Associates, Inc. URL http://papers.nips.cc/paper/6989-yass-yet-another-spike-sorter.pdf</p>
Advance and retreat of glaciers during the end of the Little Ice Age in Europe
<p>Input data and model results of Huss&Förster (2019), L'avancée et le recul des glaciers pendant le Petit Age Glaciaire. Actes du colloque du Giétro, 14./15.juin, 2018. In: Annales Valaisannes, publication of the SHVR.</p> <p>Refer to file header for the description of all variables.</p> <p> </p>
Figure 2. Advanced meront showing a in An ultrastructural study on the merogonic stages of Goussia senegalensis (Faye, 1988) Diouf and Toguebaye, 1993 (Apicomplexa, Coccidia) from the liver of Pagellus bellottii (Pisces, Teleostei)*
Figure 2. Advanced meront showing a nucleus (N). Db = dense body, Er = endoplasmic reticulum, and HcN = host cell nucleus. Scale: 2.6 µm.
New advance in land controlled-source audio-frenquency magnetotellurics exploration: measurement at a shortened transmitter-receiver offset and three-dimensional inversion
<p><span>Ambient noise significantly affects the quality of land controlled-source audio-frequency magnetotellurics (CSAMT) data. Shortening the transmitter–receiver offset can enhance the raw data signal-to-noise ratios (S/N). However, most of CSAMT explorations still collect field data using a large separation. To enhance the S/N of the raw electric and magnetic field and the corresponding Cagniard apparent resistivity, we propose shortening offset to 2–4 km during CSAMT fieldwork and inverting apparent resistivity rather than only electric field. We tested this approach with three experimental models with varying offsets, simulating 3D responses. Result showed that longer offsets predominantly produce data anomalies in the far-field zone, whereas shorter offsets shift these anomalies toward the transition zone with some in the far-field or near-field zones. Shortened-offset responses exhibit stronger electric and magnetic fields. Synthetic datasets with different noise levels were generated for inversion analysis. The results indicate that shortening the offset, particularly in noisy environments, improves data S/N. Inversion results reveal that a shortened CSAMT transmitter–receiver offset allows for data collection with most anomalies in the transition-field zone, facilitating accurate prediction of the true model in the 3D inversion. In low-noise environments, inversion results from data with a shortened offset are comparable to those with a long offset. However, in high-noise environments, the shortened-offset approach yields more accurate results due to improved data S/N. Field data inversion from Yanqing, China, further confirms the effectiveness of our scheme. The shortened-offset approach achieves higher S/N datasets and inversion results that align with actual geological structures.</span></p> <p> </p>
Appendix Figures A.1 - A.15 of the paper "Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data". This work has been accepted for publication in the journal Astronomy & Astrphysics (A&A) on September 24, 2024.
<p><strong>Figure captions:</strong></p> <p> </p> <p><strong>Fig. A.1.</strong> Heatmap with the number of common stars (true positives) labeled as binary for the five methods used.</p> <p> </p> <p><strong>Fig. A.2.</strong> Heatmap with the number of common stars (true negatives) labeled as single for the five methods used.</p> <p> </p> <p><strong>Fig. A.3.</strong> Color-magnitude diagrams, showing the 2815 stars of our sample from Sect. 3. Colors indicate the label predictions by SOM (left panel) and CNN (right panel).</p> <p> </p> <p><strong>Fig. A.4.</strong> K-S test comparing radial SOM (black) and CNN (blue).</p> <p> </p> <p><strong>Fig. A.5.</strong> Spectra of the star "LAMOSTJ112914.11+471501.7" (blue color) and in the background (gray color) the 35 stars classified as binary by Solano et al. (2022) with VOSA tools.</p> <p> </p> <p><strong>Fig. A.6.</strong> Spectra of the star "HD14829" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.7.</strong> Spectra of the star "Feige98" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.8.</strong> Spectra of the star "PG0304+184" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.9.</strong> Spectra of the star "PG1510+635" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).</p> <p> </p> <p><strong>Fig. A.10.</strong> Cluster 0 of spectra (blue color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.11.</strong> Cluster 4 of spectra (brown color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.12.</strong> Cluster 1 of spectra (yellow color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.13.</strong> Cluster 3 of spectra (red color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.14.</strong> Cluster -1 of spectra (pink color) with the other spectra in the background (gray color).</p> <p> </p> <p><strong>Fig. A.15.</strong> Cluster 2 of spectra (green color) with the other spectra in the background (gray color).</p>
Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging
<p>This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository.</p> <p>The artifacts amount to the following:</p> <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Acer pseudoplatanus</em> sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a <em>Pinus sylvestris </em>sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>
Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework
<h1><strong><span>Data Description and File Structure:</span></strong></h1> <p>This data repository contains the raw data collected across all the experiments describe from the paper entitled “Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework”. These are available as CSV files under the appropriate directories.</p> <p>Three main folders are found in this repository:</p> <ul> <li><code><strong>1FA-single-factor-auth/</strong></code> <ul> <li>Contains raw data from experiments using the Single-Factor Authentication (1FA) framework, where only on-chain consensus validation (OCV) is applied without the off-chain peer verification (OPV) phase.</li> </ul> </li> <li><code><strong>2FBC_two-factor-blockchain/</strong></code> <ul> <li>Includes data from experiments employing the Two-Factor Blockchain Consensus (2FBC) framework, which integrates both off-chain peer verification (OPV) and on-chain consensus validation (OCV) phases for enhanced security. This also contains the baseline results.</li> </ul> </li> <li><code><strong>BB_blockchain-base/</strong></code> <ul> <li>Stores the experimental data from the Blockchain Base (BB) framework, where a basic blockchain model was used without the multi-factor authentication features of 1FA or 2FBC. Most data points here are obtained from the work of Strobel et al. (2023) in their work, <u>doi/10.1126/scirobotics.abm4636</u></li> </ul> </li> </ul> <p>Under each directory, we have the following folders:</p> <ul> <li><code><strong>exp_1/</strong></code> <ul> <li>Contains data from scalability experiments, where swarm size was increased within a fixed 3.6 m² arena to evaluate the framework’s performance as the number of robots grows.</li> </ul> </li> <li><code><strong>exp_2/</strong></code> <ul> <li>Includes data from accuracy tests that varied the percentage of white tiles in the environment to assess the framework's ability to reach accurate consensus under different conditions.</li> </ul> </li> <li><code><strong>exp_3a/</strong></code> <ul> <li>Stores data from robustness experiments focused on testing the swarm's resilience to different numbers of Byzantine robots within the network.</li> </ul> </li> <li><code><strong>exp_3b/</strong></code> <ul> <li>Contains data from experiments evaluating the robustness of the swarm when subjected to various Byzantine attack types, testing the framework’s ability to handle adversarial behaviors.</li> </ul> </li> <li><code><strong>exp_4/</strong></code> <ul> <li>Holds data from the resource efficiency experiments, which measured the computational resource usage (CPU, RAM, and blockchain size) during a prolonged 10-hour swarm operation.</li> </ul> </li> </ul> <p>Each experiment configuration is carried out in 20 repetitions.</p> <h3><strong><em>Experiment 1 (exp_1):</em></strong></h3> <ul> <li><code><strong>8rob-2byz/</strong></code> Data for scalability experiments with 8 robots, 2 of which are Byzantine.</li> <li><code><strong>16rob-4byz/</strong></code> Data for scalability experiments with 16 robots, 4 of which are Byzantine.</li> <li><strong><code>24rob-6byz/</code> </strong>Data for scalability experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for scalability experiments with 48 robots, 12 of which are Byzantine.</li> </ul> <h3><strong><em>Experiment 2 (exp_2):</em></strong></h3> <ul> <li><code><strong>24rob-5floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 5% white floor tiles.</li> <li><code><strong>24rob-25floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 25% white floor tiles.</li> <li><strong><code>24rob-45floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 45% white floor tiles.</li> <li><strong><code>24rob-75floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 75% white floor tiles.</li> </ul> <h3><strong><em>Experiment 3a (exp_3a):</em></strong></h3> <ul> <li><strong><code>24rob-0byz/</code> </strong>Data for robustness experiments with 24 robots and no Byzantine robots.</li> <li><strong><code>24rob-3byz/</code> </strong>Data for robustness experiments with 24 robots and 3 Byzantine robots.</li> <li><code><strong>24rob-6byz/</strong></code> Data for robustness experiments with 24 robots and 6 Byzantine robots.</li> <li><strong><code>24rob-9byz/</code> </strong>Data for robustness experiments with 24 robots and 9 Byzantine robots.</li> </ul> <h3><strong><em>Experiment 3b (exp_3b):</em></strong></h3> <ul> <li><strong><code>24rob-6byz-1style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 1 or 0% white tile estimate</li> <li><strong><code>24rob-6byz-2style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 2 or 100% white tile estimate</li> <li><strong><code>24rob-6byz-3style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 3 or attack from a Bernoulli distribution</li> <li><code><strong>24rob-6byz-4style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 4 or attack from a Uniform distribution</li> <li><code><strong>24rob-6byz-5style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 5 or flooding</li> <li><strong><code>24rob-6byz-6style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 6 or eavesdropping</li> </ul> <h3><strong><em>Experiment 4 (exp_4):</em></strong></h3> <ul> <li><strong><code>8rob-2byz/</code> </strong>Data for resource efficiency experiments with 8 robots, 2 of which are Byzantine.</li> <li><strong><code>16rob-4byz/</code> </strong>Data for resource efficiency experiments with 16 robots, 4 of which are Byzantine.</li> <li><code><strong>24rob-6byz/</strong></code> Data for resource efficiency experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for resource efficiency experiments with 48 robots, 12 of which are Byzantine.</li> <li><strong><code>72rob-18byz/</code> </strong>Data for resource efficiency experiments with 72 robots, 18 of which are Byzantine.</li> <li><strong><code>96rob-24byz/</code> </strong>Data for resource efficiency experiments with 96 robots, 24 of which are Byzantine.</li> <li><strong><code>120rob-30byz/</code> </strong>Data for resource efficiency experiments with 120 robots, 30 of which are Byzantine.</li> </ul> <h3><strong>Relevant Files:</strong></h3> <ul> <li><code><strong>block.csv</strong></code> Contains information about each blockchain block generated during the experiment, including block number, size, timestamp, and the number of transactions. The TELAPSED column indicates the time elapsed since the previous block was generated.</li> <li><code><strong>estimate.csv</strong></code> Stores the estimates collected by each robot during the simulation. Each entry includes the time of the estimate and the estimated percentage of white tiles in the arena.</li> <li><code><strong>sc.csv</strong></code> Contains information on smart contract interactions, including the mean estimate across robots, vote counts, and whether consensus was achieved (C?).</li> <li><strong><code>extra.csv</code></strong> Records additional performance metrics during the experiments, including CPU and RAM usage, as well as the size of the blockchain data folder.</li> </ul> <h3><strong>Relevant Data Fields:</strong></h3> <ul> <li><code><strong>ID</strong></code> The identifier assigned to each robot participating in the experiment. It remains constant across all entries for a particular robot.</li> <li><code><strong>TIME</strong></code> The timestamp (in seconds) at which the data was recorded. This is relative to the start of the simulation.</li> <li><code><strong>TELAPSED</strong></code> Indicates the time elapsed between blocks or events, recorded in seconds.</li> <li><code><strong>TIMESTAMP </strong></code>Represents the Unix timestamp when a blockchain block was generated, denoting the actual system time.</li> <li><code><strong>BLOCK </strong></code>The blockchain block number created by the system during the simulation. This value increments as new blocks are added.</li> <li><code><strong>SIZE</strong></code> The size of each block in bytes, indicating the data storage requirement of each blockchain entry.</li> <li><strong><code>ESTIMATE</code> </strong>The estimate provided by the robot, representing the percentage of white tiles detected in the arena.</li> <li><strong><code>MEAN</code> </strong>The mean estimate across the swarm, as calculated on-chain via the smart contract.</li> <li><code><strong>VOTECOUNT</strong></code> Total number of estimates submitted to the smart contract for consensus validation.</li> <li><code><strong>VOTEOKCOUNT</strong></code> The number of valid votes that passed the validation process (e.g., not flagged as outliers).</li> <li><strong><code>C?</code> </strong> A Boolean value indicating whether consensus has been achieved for a given block of estimates.</li> <li><code><strong>CPU</strong></code> Percentage of CPU utilization, showing the computational load on the robot during the simulation.</li> <li><code><strong>RAM</strong></code> The amount of RAM used by each robot during the experiment, measured in percent or bytes.</li> <li><code><strong>KB</strong></code> The size of the blockchain data folder, measured in kilobytes (KB). This indicates how much data was stored by the blockchain system during the experiment.</li> </ul>
Dataset for article: Emerging materials and technologies for advancing bioresorbable surgical meshes
<p>The dataset contains all the data discussed in the related paper: Mechanical and biological performances of bioresorbable meshes, clinical complications associated with meshes. Separate folders include data on ideal surgical meshes (Tables), advanced manufactering technologies such as 3D and 4D printing, methods of surface modifications in jpg files. </p> <p> </p>
Matrix Framework for Analysis: Building a Matrix from Advanced Projects
<p>This raw dataset is part of a Framework Global - Matrix that enables a comprehensive and holistic assessment, simplifying comparisons and guiding decision-makers in selecting the alternatives that offer the greatest value in terms of financial return, social and environmental impact, and regulatory compliance. To streamline the analysis and comparison of indicators, the author utilized categories linked to Sustainable Competitiveness and the World Business Council for Sustainable Development (WBCSD). As a result, the indicators contribute to the functionality of the matrix, facilitating the identification of key projects related to the desired area and enabling future application of the data in sustainability-focused initiatives.</p>
VISION Invited lecture - Advances in familial pancreatic cancer
<p>Recording and presentation of the invited lecture that took place online on 28 October 2020 - <strong>Dr Julie Earl - Advances in familial pancreatic cancer.</strong></p> <p>The prognosis of patients diagnosed with pancreatic cancer (PC) is dismal with a 5 year survival rate of around 5% as the majority of patients present with advanced disease. Very few risk factors have been identified, although there is good evidence to suggest that smoking, obesity, a family history of pancreatic cancer, pancreatitis and diabetes increase pancreatic cancer risk. Sporadic PC occurs worldwide at an approximate frequency of 1 in 10,000 people. However, the risk of developing PC increases according to the number of affected family members, the standard incidence ratio is 4.6 with one affected family member to 32 with three affected family members. Familial pancreatic cancer (FPC) is defined as a family with at least one pair of affected first degree relatives and an estimated 4-10% of pancreatic cancers diagnosed have a familial background. Approximately 10–13% of FPC families carry germline mutations in BRCA2, PALB2, ATM, CHEK2, CDKN2A, Lynch syndrome mismatch repair genes, Fanconi anaemia related genes and PRSS1 and SPINK2 (hereditary pancreatitis), among others. The understanding of genetic basis of hereditary pancreatic cancer has important implications for the identification of true high-risk individuals in order to optimise secondary screening strategies.</p>
Advanced Glycation End-products are Retained in Decellularized Muscle Matrix Derived from Aged Skeletal Muscle
<p>Advanced glycation end-products (AGEs) accrue on skeletal muscle collagen in old age, stiffening the matrix and increasing inflammation. Whether decellularized biomaterials derived from aged muscle would suffer from increased AGE cross-links is unknown. We hypothesized that DMM from old muscle would have increased collagen, collagen cross-linking, stiffness, and AGEs. We isolated, decellularized, and characterized gastrocnemii of 1-month, 2-month, and 20-month old C57BlJ6 mice to determine age-dependent changes to collagen in muscle and decellularized muscle matrix (DMM). Total hydroxyproline and soluble hydroxyproline after proteinase K digestion were measured to assay collagen levels and cross-linking, respectively. Muscle fiber and DMM stiffness was determined using atomic force microscopy (AFM). AGE ELISAs were used to test AGE levels, and the effect of AGE cross-link breaker ALT-711 on DMM was tested. We determined age-dependent increases in collagen amount, cross-linking, and general stiffness are retained on DMM. DMM from old muscle was stiffer compared to younger groups according to shifts in AFM modulus distribution. We measured an increase in AGE-specific cross-links with old age in whole muscle and observed that these changes are countered in DMM by AGE cross-link breaker ALT-711. Future study investigating and countering the biological effects of old age on DMM is warranted.</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.