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1,003 results for “darkness”
Data for dating in the dark: Elevated substitution rates in cave cockroaches (Blattodea: Nocticolidae) have negative impacts on molecular date estimates
<p>Rates of nucleotide substitution vary substantially across the Tree of Life, with potentially confounding effects on phylogenetic and evolutionary analyses. A large acceleration in mitochondrial substitution rate occurs in the cockroach family Nocticolidae, which predominantly inhabit subterranean environments. To evaluate the impacts of this among-lineage rate heterogeneity on estimates of phylogenetic relationships and evolutionary timescales, we analysed nuclear ultraconserved elements (UCEs) and mitochondrial genomes from nocticolids and other cockroaches. Substitution rates were substantially elevated in nocticolid lineages compared with other cockroaches, especially in mitochondrial protein-coding genes. This disparity in evolutionary rates is likely to have led to different evolutionary relationships being supported by mitochondrial genomes and UCE loci. Furthermore, analyses using relaxed-clock models inferred much deeper divergence times compared with a flexible local clock. Our phylogenetic analysis of UCEs, which is the first genome-scale study to include all nine major cockroach families, unites Corydiidae and Nocticolidae and places Anaplectidae as the sister lineage to the rest of Blattoidea. We uncover an extraordinary level of genetic divergence in Nocticolidae, including two highly distinct clades that separated ~115 million years ago despite both containing representatives of the genus <em>Nocticola</em>. The results of our study highlight the potential impacts of high among-lineage rate variation on estimates of phylogenetic relationships and evolutionary timescales.</p>
Photometry of outer Solar System objects from the Dark Energy Survey I: photometric methods, light curve distributions and trans-Neptunian binaries - data release
<p>This repository contains the full data release for the 814 outer Solar System objects found in the Dark Energy Survey.</p> <p>A full description of the object search is described in <a href="http://(https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022)</a>, and a full description of the photometric processing is described in<a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230403017B/abstract"> Bernardinelli et al (2023)</a>. If you use these files, we ask you to cite the corresponding papers.</p> <p>The FITS table `y6_des_tnos_color.fits` contains both the orbital elements and the colors for each object. The full description of the orbital element information is given in Table 3 of <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..258...41B/abstract">Bernardinelli et al (2022</a>). In addition to these, the table also includes the mean absolute magnitudes in each band, as well as mean <span class="math-tex">\((g-r, r-i, r-z)\)</span> colors and their corresponding covariance matrix, and the 68% confidence interval for the lightcurve amplitude.</p> <p>Inside the `fluxes` directory, the complete photometric record for each object is included in a `hdf5` file (for each object), and the MCMC chains for their fluxes and LCAs. The three Jupyter Notebooks included in the `notebooks` directory explains the columns and how to use these files to reproduce the results of the paper. Inside this directory there are also additional files needed to reproduce the code.</p> <p>The `binary` directory has the MCMC chains for their mutual orbits (in `.npy` files), as well as the astrometric and photometric record for the binary measurements. Another `README` file is included in that directory with a detailed explanation.</p>
Ultrafast dark-field X-ray microscopy to image strain wave propagation
<p>This dataset includes DF-XRM data obtained at an X-FEL source.<br> The sample is a diamond single crystal with a strain wave propagating through the center, which is imaged in a stroboscopic fashion.<br> This data originated the LCLS, see <a href="https://arxiv.org/abs/2211.01042">arXiv:2211.01042</a> for a description of the experiment.<br> Run 538 contains a time series with 1 ns between each step, run 540 contains a time series with 72 ns steps (one full period) while run 536 contains a rocking curve.</p> <p>Each run is divided into hdf5 files with 2000 shots each. Each hdf5 file has then been zipped using the BZIP2 algorithm to reduce the overall size.</p> <p>Each run contains header information with the status of the X-FEL beam and the laser (the laser excites the strain wave). The shots where the X-FEL beam is off should be used to subtract the detector background. Run 536 additionally contains motor positions for the goniometer.</p> <p>Please note: for run 538 and 540 there are ~120 shots at each position. However, the timing tool is unreliable, giving a ±1 ns error, and to analyze this data the correct time of each shot must be first identified. This can be done by looking at the position of the strain wave.<br> <br> Using python, the data may be unziped, opened, and visualized in the following way:</p> <pre><code class="language-python">import zipfile import numpy as np import h5py #unzip with zipfile.ZipFile(f'538_0.zip', 'r') as zip_ref: zip_ref.extractall('') with h5py.File(f'538_0.hdf5', 'r') as f: # print contents of file for key in f.keys(): print(key, f[key].shape, f[key].dtype) # approximate the detector background mask = np.array(f[b'lightStatus_xray']) == 0 Images_DF_Xray_OFF = np.array(f['images_dark_field_arm'][mask,:,:]) df_noise = np.median(Images_DF_Xray_OFF, axis = 0) # extract desired frames ims = np.array(f['images_dark_field_arm'][120*1:120*2]) im = np.average(ims, axis = 0) # plot the detector background and the average of the selected frames import matplotlib.pyplot as plt fig, axes = plt.subplots(1,2, figsize = (4, 12), dpi = 300) axes[0].imshow(df_noise, vmin = np.percentile(df_noise,1), vmax = np.percentile(df_noise,99)) axes[1].imshow(im-df_noise, vmin = np.percentile(im-df_noise,1), vmax = np.percentile(im-df_noise,99))</code></pre> <p> </p>
Astrometric Calibration and Performance of the Dark Energy Spectroscopic Instrument Focal Plane
<p>Supplementary material to DESI's publication Astrometric Calibration and Performance of the Dark Energy Spectroscopic Instrument Focal Plane.</p> <p>Figure 6: Astrometric trends<br> trend.dat</p> <p>Figure 7: Deformation residuals<br> gfa-south-west.dat</p> <p>Figure 10: Turbulence<br> e2c.dat<br> b2c.dat</p> <p>Figure 11: Turbulence Corrected<br> turbcorr.dat</p> <p>Figure 13: Quiver residuals<br> quiver-R.dat</p> <p>Figure 14: Dither offsets<br> dither20220518-R.dat</p> <p>Figure 15: Chromatic residuals<br> chromatic.dat</p> <p>Figure 16: Temperature dependence<br> tempscale.dat<br> </p>
Twin Supersymmetric Dark Matter in Light of the First LZ Results
<p>We review the status of dark matter (DM) candidates in supersymmetric Twin Higgs models in light of the first results of the LUX-ZEPLIN (LZ) experiment. We found that, for twin bino-dominated DM, the new results strengthened the lower bound on the higgsino mass. However, a large part of the parameter space consistent with natural electroweak symmetry breaking is still allowed. In the case of twin-stau DM, the new results imply that, if the thermal abundance of the twin-stau LSP fits the observed density of DM, the twin stau cannot have a large left-handed component anymore.</p>
Charged Dark Matter in Supersymmetric Twin Higgs models
<p>Supersymmetric Twin Higgs models ameliorate the fine-tuning of the electroweak scale originating from the heavy scalar top partners required by the non-discovery of them at the Large Hadron Collider. If the Lightest Supersymmetric Particle resides in the twin sector, it may play the role of dark matter even if it is charged under twin gauge interactions. We show that the twin stau is a viable candidate for charged dark matter, even if the twin electromagnetic gauge symmetry is unbroken, with thermal relic abundance that naturally matches the observed dark matter abundance. A wide parameter space satisfies all the experimental constraints including those on dark matter self-interactions. Twin stau dark matter can be observed in future direct detection experiments such as LUX-ZEPLIN. The stau has a mass in the range of 300-500 GeV, and in the minimal scenario, has a decay length long enough to be observed as a disappearing track or a long-lived particle at the Large Hadron Collider.</p>
GreenSCENT logo dark background
<p>GreenSCENT logo for dark background to disseminate the project</p>
Coin of Basil II the Dark.
Coin of Basil II the Dark. Monetary unit: denga. Denomination: 1. Year of issue: 1446-1462. Material: silver. Vasily II Vasilyevich Dark (1425-1462), Grand Duke of Moscow and Vladimir, son of Grand Duke Vasily I Dmitrievich, grandson of Dmitry Donskoy. During the feudal war, Vasily II was captured and blinded (1446) by Dmitry Shemyaka (hence the nickname "Dark"). In the early 1450s, he regained the Moscow throne, having won. Монета Василия II Темного. Денежная единица: денга. Номинал: 1. Год выпуска: 1446-1462 годы. Материал: серебро. Василий II Васильевич Темный (1425-1462), великий князь Московский и Владимирский, сын великого князя Василия I Дмитриевича, внук Дмитрия Донского. В ходе феодальной войны Василий II был захвачен и ослеплён (1446) Дмитрием Шемякой (отсюда прозвище «Тёмный»). В начале 1450-х вернул себе московский трон, одержав победу. Source: Objaverse 1.0 / Sketchfab
Dark Blade New
I have some troble with animated old Drak Blade, So I did few changes to make more easy to make animated our cutie Drak Blade This one have even more better topology that's why I can work wiht rig and skin so easy in this level. Maybe Nameless one need some changes because he has some topology to old Drak Blade. I will uplode the new Nameless one too I hope you like this one Source: Objaverse 1.0 / Sketchfab
Visual Activity Evoked by Infrared in Humans After Dark Adaptation
ClinicalTrials.gov study NCT02909985. IPD Sharing: NO. Countries: 1. Publications: 14.
Vitamin A Palmitate Supplementation in People With Age-Related Macular Degeneration (and Without Reticular Pseudodrusen) and Delayed Dark Adaptation
ClinicalTrials.gov study NCT03478865. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Subject Reported Outcomes With Use of Adapalene 0.3% - Benzoyl Peroxide (BPO) 2.5% in Dark Skin Acne
ClinicalTrials.gov study NCT02932267. IPD Sharing: Not stated. Countries: 3. Publications: 1.
Efficacy of Polyphenols From Milk and Dark Chocolate
ClinicalTrials.gov study NCT00513344. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Vitamin A Palmitate Supplementation in Patients With Reticular Pseudodrusen (RPD) and Delayed Dark Adaptation
ClinicalTrials.gov study NCT03478878. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Concealed by darkness: interactions between predatory bats and nocturnally migrating songbirds illuminated by DNA sequencing
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Dark diversity in temperate forests of northeastern China: drivers and implications
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A “Dirty” Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils
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Genome assembly and annotation of the Dark-branded Bushbrown butterfly Mycalesis mineus (Nymphalidae: Satyrinae)
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Under cover of darkness: Refuge from artificial light at night may mitigate risks to stranded seabirds
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Earth-scattering induced modulation data for low-threshold dark matter experiments
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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)
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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.