Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Data from: Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning

Abstract Precise segmentation of coronary arteries in non-contrast Computed Tomography (CT) scans plays an important role in the assessment of the coronary artery disease, where it is the key component for evaluating the Calcium Score (Agatston et al. 1990). In the paper by Bujny et al. (2024), a deep-learning approach for high-precision segmentation of coronary arteries in non-contrast CT was proposed along with a novel method for generating Ground Truth (GT) test data (test-GT) via manual registration of high-resolution coronary tree models obtained based on contrast CT with the non-contrast CT scans. In this dataset, we present the inferences of the neural network model together with the corresponding test-GT samples, based on 6 CT scans from the openly available OrCaScore dataset (Wolterink et al. 2016). The geometrical models included in the dataset can be used both for inspection of the proposed deep learning model and for testing of new non-contrast coronary vessel segmentation approaches, which is a unique opportunity since, to the best of our knowledge, manual generation of GT for non-contrast coronary artery segmentation was not addressed so far due to very challenging character of this particular segmentation task.   Methods Manual Generation of test-GT The geometric models of coronary arteries used for the evaluation of the proposed neural network model were generated according to the manual mesh-to-image registration process as described by Bujny et al. (2024). In this approach, the high-resolution coronary artery masks obtained based on contrast CT scans are manually aligned with the corresponding non-contrast CT images using tools available in the open-source 3D computer graphics software, Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>). To ease the manual alignment process, specialized add-ons for medical image processing such as Cardiac add-on for Blender of Graylight Imaging (<a href="https://graylight-imaging.com/3d-modelling/">https://graylight-imaging.com/3d-modelling/</a>) can be used, as well. The STL models in this dataset were manually generated by a medical expert with 4 years of experience. Segmentation of Coronary Arteries using a Deep Learning Model For each of the cases presented in this dataset, we run an inference of an nnU-Net (Isensee et al. 2021) model trained according to the process described in our paper (Bujny et al. 2024). Since we use a standard nnU-Net, which utilizes a sliding window approach for processing of the CT scan, the context information within a patch is limited, which can lead to some false-positive detections. To mitigate this problem, we additionally post-process the inferences by eliminating small vessel fragments of less than 50 [mm^3] volume and structures outside of pericardium, which we segment using another nnU-Net model, SegTHOR (Lambert et al. 2020). The resulting geometric models are stored using the STL format and presented as green masks in the HTML reports with an embedded viewer based on the K3D-jupyter library (<a href="https://k3d-jupyter.org/">https://k3d-jupyter.org/</a>).   Dataset organization The root folder contains 6 folders whose names correspond to the CT scans from the OrCaScore dataset (Wolterink et al. 2016). In each of the folders, there are the following 4 files available: <ul> <li>‘manualGT_rater1.stl’ – high-resolution STL model of coronary arteries obtained via manual alignment of the geometric model segmented in contrast CT with the corresponding non-contrast CT scan by the first rater. A sample belonging to the test-GT set (Bujny et al. 2024).</li> <li>‘manualGT_rater2.stl’ – corresponding test-GT sample by the second rater.</li> <li>‘ML.stl’ – post-processed inference of the nnU-Net ML model in the STL format.</li> <li>‘report.html’ – interactive HTML report consisting of a manually-aligned test-GT sample (red mask), the ML segmentation based on the non-contrast CT scan (green mask), and selected slices of the non-contrast CT scan. The reports contain the relevant information related to the scanning device and present the main segmentation quality metrics for the ML model inference.</li> </ul>

opencc-by-4.0Nov 2021View details →

Calcium channel model

This dataset contains two data tables, include "channel_model.mat", "U_potential_3D_e(60).mat". The first data table is used to construct 2D and 3D calcium channel models. The model is constructed based on the model in the Ref. (Corry et al., 2001). The second data table gives the potential distribution in 3D calcium ion channels for Brownian dynamics calculation of ion transport in calcium ion channels.

opencc-by-4.0Mar 2015View details →

Fig. 4 in Use of otolith strontium:calcium and zinc:calcium ratios as an indicator of the habitat of Percophis brasiliensis Quoy & Gaimard, 1825 in the southwestern Atlantic Ocean

Fig. 4. Discriminant analysis of the otolith Sr:Ca and Zn:Ca ratios for Percophis brasiliensis. Plot of the first two discriminant functions for each age group (a-d). An association was observed between data for ER and SMG, which were separated from data for AUCFZ. Triangles: ArgentineUruguayan Common Fishing Zone (AUCFZ), stars: San Matías Gulf (SMG) and black circles: El Rincón (ER).

opencc-by-4.0Mar 2015View details →

Fig. 2 in Use of otolith strontium:calcium and zinc:calcium ratios as an indicator of the habitat of Percophis brasiliensis Quoy & Gaimard, 1825 in the southwestern Atlantic Ocean

Fig. 2. Variation of Sr:Ca (a) and Zn:Ca (b) ratios of Percophis brasiliensis separated by age for the three sampling sites. Different letters indicate statistical significant differences among age groups (years) for each sampling site (p<0.05).

opencc-by-4.0Mar 2015View details →

Fig. 3 in Use of otolith strontium:calcium and zinc:calcium ratios as an indicator of the habitat of Percophis brasiliensis Quoy & Gaimard, 1825 in the southwestern Atlantic Ocean

Fig. 3. Relationship between otolith Sr:Ca and Zn:Ca ratios (mmol mol-1) for Percophis brasiliensis from three areas. Data for ER and SMG tended to cluster, while data for AUCFZ tended to disperse. Separation of data of AUCFZ and ER-SMG is observed. Triangles: Argentine-Uruguayan Common Fishing Zone (AUCFZ), stars: San Matías Gulf (SMG) and black circles: El Rincón (ER).

Figure 6 in Effect of calcium on Pseudomonas aeruginasa and Bacillus cereus metabolites

Figure 6. Pyorubin production of P. aeruginosa (●), grown in NB medium under static conditions at 37 °C.

Figure 4. H O in Effect of calcium on Pseudomonas aeruginasa and Bacillus cereus metabolites

Figure 4. H O diameter of B. cereus (○) and P. aeruginosa (●), 2 2 grown in NB medium under static conditions at 37 °C.

Figure 3. Las B in Effect of calcium on Pseudomonas aeruginasa and Bacillus cereus metabolites

Figure 3. Las B activity of B.cereus (○) andP.aeruginosa (●), grown in NB medium under static conditions at 37 °C.

Figure 2 in Effect of calcium on Pseudomonas aeruginasa and Bacillus cereus metabolites

Figure 2. Biofilm levels ofB.cereus (○) and P.aeruginosa (●), grown in NB medium under static conditions at 37 °C.

Figure 1 in Effect of calcium on Pseudomonas aeruginasa and Bacillus cereus metabolites

Figure 1. Amylase unit activity of B. cereus (○) and P. aeruginosa (●), grown in NB medium under static conditions at 37 °C.

opencc-by-4.0Jul 2024View details →

Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)

Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain

opencc-by-sa-4.0Jul 2024View details →

Rawdata for publication: Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR

This is the raw data set for the publication: Observation of Transient Prenucleation Species of Calcium Carbonate by DNP-Enhanced NMR by Martins Balodis, Yu Rao, Gabriele Stevanato, Matthias Kellner, Josephine Meibom, Mattia Negroni, Bradley F. Chmelka, Lyndon Emsley . https://pubs.acs.org/doi/10.1021/acs.jpclett.4c01588

opencc-by-4.0May 2017View details →

Fig. 67. Problematic calcium phosphatic sclerites Fomitchella acinaciformis Missarzhevsky, 1977 in Terreneuvian stratigraphy and faunas from the Anabar Uplift, Siberia

Fig. 67. Problematic calcium phosphatic sclerites Fomitchella acinaciformis Missarzhevsky, 1977, from early Cambrian Emyaksin Formation, eastern flank of the Anabar Uplift, Siberia, Russia; sample 5a/18.5, section 96-5a. A SMNH X5962. B. SMNH X5964. C. SMNH X5966. D. SMNH X5967. E. SMNH X5963. F. SMNH X5965. G. SMNH X5968. A1, A3, C, D2, G, lateral; A2, D1, oblique apertural; B, F, oblique apical views; A4, E2, outer surface with fibres at apertural margin. Scale bar 100 μm (A4, E2), 250 μm (A3), 500 μm (A1, A2, B–D, E1, F, G).

opencc-by-4.0May 2017View details →

Fig. 66. Problematic calcium phosphatic sclerites Fomitchella aff. acinaciformis Missarzhevsky, 1977 in Terreneuvian stratigraphy and faunas from the Anabar Uplift, Siberia

Fig. 66. Problematic calcium phosphatic sclerites Fomitchella aff. acinaciformis Missarzhevsky, 1977 (A–E), Fomitchella acinaciformis Missarzhevsky, 1977 (F, G), and Fomitchella sp. (H), from early Cambrian Emyaksin Formation, eastern flank of the Anabar Uplift, Siberia, Russia; samples 5a/10.5 (A, B, E), 5a /17.5 (C, F–H), 5a/9 (D), section 96-5a. A–H. SMNH X5954–5961, respectively. A1, oblique apical; A3, C2, D2, H3, apical; A2, B, C3, D1, E1, F, G2, G3, H1, H2, lateral; E2, apertural; G1, oblique apertural views; C1, close-up of apertural margin with fibres (note two inserted sclerites). Scale bar 50 μm (C1), 250 μm (A1, B, C2, C3, E, H), 500 μm (A2, A3, D, F, G).

Fig. 6 in Calcium phosphate preservation of faecal bacterial negative moulds in hyaena coprolites

Fig. 6. SEM images of coprolites of the hyaenid Lycyaena chaeretis (Gaudry, 1861) from La Roma 2 (Upper Miocene, Spain). A. Spherical and elongated voids present in the fine calcium phosphate precipitated around the microspherulites (white arrows) (Zone X) (RO-2008-117). B, C. Small voids resembling rod-shaped bacteria (white arrows), differing from the microspherulites (black arrows) in their smaller size (RO-2008-3). D. TEM of an ultrathin section, showing negative moulds resembling rod-like bacteria (white arrows) in the fine calcium phosphate material (RO-2008-117).

Fig. 4 in Calcium phosphate preservation of faecal bacterial negative moulds in hyaena coprolites

Fig. 4. SEM images of coprolites of the hyaenid Lycyaena chaeretis (Gaudry, 1861) from La Roma 2 (Upper Miocene, Spain). A. Calcite crystals inside a void likely produced by gas arising from digestive processes (RO-SSC). B. Matrix composed of microspherulites 1–3 μm in diameter (RO-SSC). C. Polished sections examined in backscattered detection mode, showing the thin-walled structure of the microspherulites (white arrows) (RO-2008-117). D. Microspherulites embedded in a fine-grained calcium phosphate precipitate; the brighter zones indicate areas enriched in Na and Cl (white arrows) (RO-2008-117).

Fig. 1. A in Calcium phosphate preservation of faecal bacterial negative moulds in hyaena coprolites

Fig. 1. A. Location of the La Roma 2 site (modified from van Dam et al. 2001). B. General stratigraphic section of the La Roma 2 site (modified from Alcalá 1994).

Fig. 3 in Calcium phosphate preservation of faecal bacterial negative moulds in hyaena coprolites

Fig. 3. Photomicrographs showing thin sections of a coprolite (RO-2008-9a) of the hyaenid Lycyaena chaeretis (Gaudry, 1861) from La Roma 2 (Upper Miocene, Spain). A. Section of the coprolite. B–E. Homogeneous zone (Zone X). B. Quartz inclusion, probably introduced from the surrounding sediment. C. A void, probably produced by gas, in the homogeneous zone (Zone X), with no filling and showing no corroded margins. D. Limit between the homogeneous zone (Zone X) (right) and the central hole (left) (Zone Z) (the rounded shapes are artefacts caused by the consolidation of the sample). E. Thin outer rim of the homogeneous zone (Zone X), showing a more compact phosphatic margin (orange, on the left). F–H. Heterogeneous zone (Zone Y). F. Bone fragment altered by digestive acids in the heterogeneous zone (Zone Y), showing the presence of iron in the surrounding phosphatic matrix. G. Voids and cracks within the heterogeneous zone (Zone Y), showing margin corrosion and iron precipitation partially replacing the original phosphatic matrix. H. Calcite-filled voids and shrinkage cracks in the heterogeneous zone (Zone Y).

Fig. 2. X in Calcium phosphate preservation of faecal bacterial negative moulds in hyaena coprolites

Fig. 2. X-Ray diffractograms of seven coprolites of the hyaenid Lycyaena chaeretis (Gaudry, 1861) from the locality of La Roma 2 (Upper Miocene, Spain). Image generated from XPowder Ver. 2004.04.46 PRO.