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8 results for “SHG”
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake
2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy
<p><strong>General</strong></p> <p>This dataset consists out of multiple Second Harmonic Generation (SHG) microscopy scans of collagen fibers in mice bones. Some mices are diseased with osteogenesis imperfecta (brittle bone).</p> <p>We used this data to investigate the segmentation of uncertain local collagen fiber orientations. The corresponding paper "2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy" is accepted at GCPR 2019.</p> <p><strong>Abstract</strong></p> <p>Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.</p> <p><strong>Links</strong></p> <p>A preprint of the paper is available at <a href="https://arxiv.org/abs/1907.12868">https://arxiv.org/abs/1907.12868</a>.</p> <p>The final publication is available at Springer via <a href="https://doi.org/10.1007/978-3-030-33676-9_26">https://doi.org/10.1007/978-3-030-33676-9_26</a></p> <p>The source code is available at <a href="https://github.com/Emprime/uncertain-fiber-segmentation">https://github.com/Emprime/uncertain-fiber-segmentation</a>.</p> <p><strong>Data description</strong></p> <p>Please read the accompanying paper for more information about the dataset. Please see the source code for more information about the usage of the data.</p> <ul> <li>shg-ce-de: contains the enhanced and denoised scans as image slices, the scans are sorted by mice (wt wildtyp, het ill mice), scan location and individual scan</li> <li>shg-masks: contains the ground truth masks for the three different classes (similar - Green, dissimilar - Red, not of interest - blue)</li> <li>shg-featues: contains the input and gt for the second stage of the proposed two stage segmentation</li> <li>shg-cross-splits: contains the 10 random splits for the 10 fold cross validation</li> <li>logs-prediction: contains the 10 tensorboard logs, weights and predictions for the 10 fold cross validations</li> </ul>
Raw P-SHG data and processing codes for Raoux et al, Light Science and Applications 2023
<p><strong>Raw P-SHG data and processing codes</strong> for Raoux et al, Light Science and Applications 2023</p> <p>Article DOI: 10.1038/s41377-023-01224-0</p> <p>Raw P-SHG data: 10 human corneas</p> <p>Important: crop 20 pixels on left and right sides of all images to remove scanning artefacts and obtain 250 x 250 images</p> <p>voxel size: 1 µm in all directions<br> Channel 0: trans-2PEF<br> Channel 1: epi-2PEF<br> Channel 2: epi-SHG<br> Channel 3: trans-SHG</p>
Highly efficient nanosecond 560 nm source by SHG of a combined Yb-Raman fiber amplifier
<p>Open access data set for the manuscript "Highly efficient nanosecond 560 nm source by SHG of a combined Yb-Raman fiber amplifier" to be published in Optics Express.</p>
Collagen fiber images and centerline annotations based on SHG imaging
<p>The dataset contains images of collagen fibers collected using SHG and manual annotations of the fiber centerlines, from sections of the pancreas, kidney, and breast.</p>
Noncentrosymmetric Lanthanide-Based MOF Materials Exhibiting Strong SHG Activity and NIR Luminescence of Er3+: Application in Nonlinear Optical Thermometry
<p>Optically active luminescent materials based on lanthanide ions attract significant attention due to their unique spectroscopic properties, nonlinear optical activity, and the possibility of application as contactless sensors. Lanthanide metal-organic frameworks (Ln-MOFs) that exhibit strong second-harmonic generation (SHG) and are optically active in the NIR region are unexpectedly underrepresented. Moreover, such Ln-MOFs require ligands that are chiral and/or need multistep synthetic procedures. Here, we show that the NIR pulsed laser irradiation of the noncentrosymmetric, isostructural Ln-MOF materials (MOF-Er<sup>3+</sup> (1) and codoped MOF-Yb<sup>3+</sup>/Er<sup>3+</sup> (2)) that are constructed from simple, achiral organic substrates in a one-step procedure results in strong and tunable SHG activity. The SHG signals could be easily collected, exciting the materials in a broad NIR spectral range, from ≈800 to 1500 nm, resulting in the intense color of emission, observed in the entire visible spectral region. Moreover, upon excitation in the range of ≈900 to 1025 nm, the materials also exhibit the NIR luminescence of Er<sup>3+</sup> ions, centered at ≈1550 nm. The use of a 975 nm pulse excitation allows simultaneous observations of the conventional NIR emission of Er<sup>3+</sup> and the SHG signal, altogether tuned by the composition of the Ln-MOF materials. Taking the benefits of different thermal responses of the mentioned effects, we have developed a nonlinear optical thermometer based on lanthanide-MOF materials. In this system, the SHG signal decreases with temperature, whereas the NIR emission band of Er<sup>3+</sup> slightly broadens, allowing ratiometric (Er<sup>3+</sup> NIR 1550 nm/SHG 488 nm) temperature monitoring. Our study provides a groundwork for the rational design of readily available and self-monitoring NLO-active Ln-MOFs with the desired optical and electronic properties.</p>
TVS, SHG, and MRI in Uterine Niche
ClinicalTrials.gov study NCT07279181. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Data from: Quantification and comparison of anti-fibrotic therapies by polarized SRM and SHG-based morphometry in rat UUO model
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