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2,649 results for “optics”
Replication Data for: OPTICAL COHERENCE TOMOGRAPHY IDENTIFIES LOWER LABIAL SALIVARY GLAND SURFACE DENSITY IN CYSTIC FIBROSIS
<p>Swept-source optical coherence tomography images of the mucosa of the lower lip acquired in 18 cystic fibrosis patients (CF.zip, includes an XLS file with additional data) and 18 healthy volunteers (HS.zip). Within the volumetric datasets consisting of sets of images the labial salivary glands can be identified.</p> <p>Authors: Nowak JK, Grulkowski I, Karnowski K, Wojtkowski M, Walkowiak J.<br /> When using the data, please always refer to the original paper (currently under review in PLOS ONE; title as specified above).</p> <p>The methodology used to obtain the volumetric datasets is described in:<br /> Grulkowski I, Nowak JK, Karnowski K, Zebryk P, Puszczewicz M, Walkowiak J, Wojtkowski M. Quantitative assessment of oral mucosa and labial minor salivary glands in patients with Sjögren’s syndrome using swept source OCT. Biomedical Optics Express, Vol. 5, Issue 1, pp. 259-274 (2014). DOI: http://dx.doi.org/10.1364/BOE.5.000259</p>
Optical pumping and readout of bismuth hyperfine states in silicon for atomic clock applications
<p>Published in</p> <p>Sci. Rep. 5, 10493 (2015)</p> <p>DOI: 10.1038/srep10493</p>
Involvement of the optic nerve in mutated CSF1R-induced hereditary diffuse leukoencephalopathy with axonal spheroids
<p>Figure legends</p> <p>Figure 1: Family pedigree. The arrow indicates the proband (present patient). Her mother developed a motor disorder at 40 years of age and died at 60 years of age. Her grandparents, father, brothers, sisters, and daughters were not affected.</p> <p> </p> <p>Figure 2: Brain MRI, DWI, DTI, and MRS images. T2/Flair showed multifocal periventricular white matter lesions (A, B, and C), without enhancement (D). DWI shows high-signal intensities in periventricular white matters and corpus callosum (E, F). DTI shows decreased numbers of corpus callosum fibers, while subcortical arcuate fibers are spared (G). MRS shows increased Cho levels, while NAA levels are decreased in the white matter lesions (H, I).</p> <p> </p> <p>Figure 3: Optic nerves on MRI, showing that bilateral optic nerves are injured (red arrows).</p> <p> </p> <p>Figure 4: OCT shows that the right peripapillary retinal nerve fiber layer (pRNFL) is atrophic in the temporal quadrant, and the left pRNFL is thinning in the temporal superior quadrants. Green represents pRNFL thickness, which is within normal limits; yellow represents pRNFL thickness, which is below borderline; red represents pRNFL thickness, which is below normal limits.</p> <p> </p> <p>Figure 5: VEP shows reduced bilateral P100 amplitudes, although P100 latencies are normal in both eyes.</p> <p> </p> <p>Figure 6: Visual fields in the right eye are partially missing in the upper right, lower right, and lower left quadrants, especially in the lower right quadrant. Visual fields in the left eye are partially missing in the four quadrants, especially in the upper left and lower right quadrants.</p> <p> </p> <p>Figure 7: Gene analysis of CSF1R. The sequencing result from exon 18 of CSF1R (NM_005211.3) indicates a heterozygous c.2345 G>A (p.782Arg>His) substitution in the patient.</p>
FIGURE 4 in Optical characterization and redescription of the South Pacific firefly Bourgeoisia hypocrita Olivier (Coleoptera: Lampyridae: Luciolinae)
FIGURE 4. Bourgeoisia hypocrita: Optical characteristics of adult female (black line) and eggs (grey line) methanol extracts. (A) Relative fluorescence intensity (exc. 380 nm) and (B) Relative absorbance. Spectra were smoothed as described in the methods. Dotted lines mark 400, 450 or 500 nm for reference.
FIGURE 5 in Optical characterization and redescription of the South Pacific firefly Bourgeoisia hypocrita Olivier (Coleoptera: Lampyridae: Luciolinae)
FIGURE 5. Bourgeoisia hypocrita: (A) ventral view of male with details on (F) pronotum, (G) head anterior and (H) head anterolateral with arrows indicating eye emargination, and (K) male head ventral. Luciola atra type: (B) ventral view and (C) dorsal view, with (J) details of original type labels. Luciola hypocrita type: (D) dorsal view and (E) dorsal pronotum, with (I) details of original type labels. A, F–H from QM specimen; K from ANIC specimen; B–E, I, J from MNHN specimen. Scale bars are 1 mm
FIGURE 3 in Optical characterization and redescription of the South Pacific firefly Bourgeoisia hypocrita Olivier (Coleoptera: Lampyridae: Luciolinae)
FIGURE 3. Bourgeoisia hypocrita (female): Views in bright field and epifluorescence of (A-B) full individual with light organ in the abdominal section, (C–D) ventral front section, (E–F) ventral back section with close-up (insert) of luminous organ showing autofluorescent cells by transparency, and (G–H) recently laid eggs. Scale bars are 2 mm (A–B), 0.7 mm (C–D), 0.5 mm (E–H) and 0.1 mm for insert.
FIGURE 2 in Optical characterization and redescription of the South Pacific firefly Bourgeoisia hypocrita Olivier (Coleoptera: Lampyridae: Luciolinae)
FIGURE 2. Bourgeoisia hypocrita: Relative intensity emission spectra of bioluminescence for three individuals. Spectra were smoothed as described in the methods. Dotted lines mark 550 nm and 600 nm for reference. Spectra peaks at 575–580 nm and has two shoulders (545–550 nm and 585-605 nm) that can be more or less pronounced.
FIGURE 1 in Optical characterization and redescription of the South Pacific firefly Bourgeoisia hypocrita Olivier (Coleoptera: Lampyridae: Luciolinae)
FIGURE 1. Bourgeoisia hypocrita: Typical time course over 5 min of spontaneous bioluminescence produced by three individuals separately.
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p> <p>Please, refer to the readme.txt for the description.</p>
"Contrast based circular approximation for accurate and robust optic disc segmentation in retinal images" - Code
<p>A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databases, respectively. A simple circular approximation to the optic disc boundary is proposed based on the maximum average contrast between the inner and outer ring of a circle centered on the estimated location. An average overlap coefficient of 0.890 and 0.865 was achieved for the same datasets, outperforming other state of the art methods. The results obtained confirm the advantages of using a simple circular model under non ideal conditions as opposed to more complex deformable models.</p>
Dataset for paper "All-Optical Generation and Time-Resolved Polarimetry of Magnetoacoustic Resonances via Transient Grating Spectroscopy" by Carrara P. et al.
<p>This dataset complements the publication "All-Optical Generation and Time-Resolved Polarimetry of Magnetoacoustic Resonances via Transient Grating Spectroscopy" by Carrara P. et al.</p><p>The data hierarchy is explained in the file "Readme.docx", which also reports relevant metadata.</p>
FIGURE 9 in <p><strong>Nomenclatural and taxonomic problems related to</strong> <strong>the electronic publication of new nomina and nomenclatural acts in zoology, with brief comments on optical discs and on the situation in botany</strong></p>
FIGURE 9. Three documents concerning Gingerich et al. (2009) downloaded from the PLoS website on 18 December 2012: (a) first web page devoted to this work on the website; (b) first page of PDF of work, dated 4 February 2009; (c) comment 2, dated 22 May 2009, accessed through the link "Comments: 2" on top of (a).
FIGURE 10 in <p><strong>Nomenclatural and taxonomic problems related to</strong> <strong>the electronic publication of new nomina and nomenclatural acts in zoology, with brief comments on optical discs and on the situation in botany</strong></p>
FIGURE 10. Three documents concerning Franzen et al. (2009) downloaded from the PLoS website on 18 December 2012: (a) first web page devoted to this work on the website; (b) comment 7, dated 21 May 2009, accessed through the link "Comments: 13" on top of (a); (c) "formal correction", dated 21 July 2009, accessed through the link "Formal correction: 1" on top of (a).
Thermo-optic epsilon-near-zero effects
<p>The data and code used to produce the results in the paper "<i>Thermo-optic epsilon-near-zero effects</i>".</p>
A mode-locked random laser generating transform-limited optical pulses
<p>Ever since the mid-1960's, locking the phases of modes enabled the generation of laser pulses of duration limited only by the uncertainty principle, opening the field of ultrafast science. In contrast to conventional lasers, mode spacing in random lasers is ill-defined because optical feedback comes from scattering centres at random positions, making it hard to use mode locking in transform limited pulse generation. Here the generation of sub-nanosecond transform-limited pulses from a mode-locked random fibre laser is reported. Rayleigh backscattering from decimetre-long sections of telecom fibre serves as laser feedback, providing narrow spectral selectivity to the Fourier limit. The laser is adjustable in pulse duration (0.34-20 ns), repetition rate (0.714-1.22 MHz) and can be temperature tuned. The high spectral-efficiency pulses are applied in distributed temperature sensing with 9.0 cm and 3.3×10⁻³ K resolution, exemplifying how the results can drive advances in the fields of spectroscopy, telecommunications, and sensing.</p>
Chirped Bloch-harmonic oscillations in a parametrically forced optical lattice
<p>Dataset and codes of the publication "Chirped Bloch-harmonic oscillations in a parametrically forced optical lattice" by Usman Ali, Martin Holthaus, and Torsten Meier,<br>published in PHYSICAL REVIEW RESEARCH 5, 043152 (2023)<br>( <a href="https://doi.org/10.1103/PhysRevResearch.5.043152">https://doi.org/10.1103/PhysRevResearch.5.043152</a> )</p>
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
Optical illusion
This is a optical illusion made by M.C. Escher Source: Objaverse 1.0 / Sketchfab
Original research data for the paper "Additive Manufacturing of Polymeric Gradient Index Optics via Grayscale Digital Light Processing Vat Photopolymerization Technology"
<p>[CMOS camera data]</p> <p>[conversion maps]</p> <p>[cure kinetics]</p> <p>[GRIN profiles arbitrary]</p> <p>[MonoPrinter grayscale power density]</p> <p>[MonoPrinter print files]</p> <p>[pictures of arbitrary GRINs]</p> <p>[predicted printing param matrices]</p> <p>[refractive index vs conversion]</p> <p>[working curve]</p>
Optical constants, cross-sections, and supporting Python scripts for Fe L shell XAFS compounds in Corrales et al (2024), accepted to AAS Journals
<p>This Zenodo repository contains the data products and calculations of Corrales et al. (2024), https://arxiv.org/abs/2402.06726 (accepted to AAS Journals)</p> <p> </p> <p><strong>A WORD OF CAUTION</strong></p> <p>The cross-sections presented here have not been shifted in absolute energy scale. One of the key results from Corrales et al. (2024) is that the energy scale calibration for these compounds needs to be revisited. Please proceed with caution when using this information.</p> <p> </p> <h2>Optical Constants</h2> <p>kkcalc_products/ - This folder contains optical constants for the various compounds</p> <p>kkcalc_products/*_input.dat files contain the absorption as measured in Lee et al. (2009) https://ui.adsabs.harvard.edu/abs/2009ApJ...702..970L/abstract). These values are supplied as input to kkcalc (https://ui.adsabs.harvard.edu/abs/2014OExpr..2223628W/abstract, available at https://github.com/benajamin/kkcalc), along with the stoichiometric formula and material density for the compound of interest.</p> <p>kkcalc_products/*_refrac.dat files contain the kkcalc output, i.e., the real and imaginary parts of the complex index of refraction (m). The "Delta" column equals Re(1-m) and the "Beta" column equals Im(m).</p> <p> </p> <h2>Python Scripts</h2> <p>extinction_xsects.py - Calculates the extinction cross-section for an MRN distribution of dust</p> <p>These Python files from github.com/eblur/gastronomy are used by extinction_xsects.py in order to properly scale the mass column density to Fe abundance:</p> <ul> <li>abundances.py</li> <li>molecules.py</li> <li>minerals.py</li> </ul> <p> </p> <h2>Extinction Cross-sections</h2> <p>extinction_xsects/ - This folder contains the results of extinction_xsects.py</p> <p>extinction_xsects/*_FeL.pdf - A plot of the high resolution Fe L shell features</p> <p>extinction_xsects/*_broad.pdf - A plot of the broad band (0.3 - 10 keV), lower resolution cross-sections with 50 eV spacing. These cross-sections include extrapolations for the K and L shell features for other elements in the compounds based on Henke tables (see Watts et al. 2014)</p> <p>extinction_xsects/*_final.pdf - A plot of the consolidated (low resolution broad band and high resolution Fe L shell) extinction cross-sections</p> <p>extinction_xsects/*_xsect.fits - The final cross-section information for each compound, stored as fits file table. The table columns are energy, absorption optical depth, scattering optical depth, and extinction optical depth. All optical depths are scaled to have a total dust mass column of 1e-4 g cm^-2.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.