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741 results for “Decay”
Transcriptomic analysis reveals the mechanism of watercore occurrence and its accelerating fruit decay
GEO Series GSE164987. Pyrus pyrifolia. 6 samples. Type: Expression profiling by high throughput sequencing.
Capturing early gene expression dynamics during wood decay by brown rot fungus Rhodonia placenta
GEO Series GSE193915. Rhodonia placenta. 40 samples. Type: Expression profiling by high throughput sequencing.
Embryonic lumenogenesis is controlled by selective mRNA decay triggered by LIN28A relocation [miCLIP]
GEO Series GSE169549. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing; Other.
Nonsense-mediated decay of alternative precursor mRNA splicing variants is a major determinant of the eukaryotic steady state transcriptome
GEO Series GSE41432. Arabidopsis thaliana. 12 samples. Type: Expression profiling by high throughput sequencing.
Global decay of regulatory chromatin architecture promotes rebalancing of gene expression during colorectal cancer development
GEO Series GSE207954. Homo sapiens. 104 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
The nonsense-mediated mRNA decay (NMD) pathway safeguards telomeres in pluripotent stem cells
GEO Series GSE300187. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Genomewide measurements of neural-specific mRNA decay in Drosophila embryos
GEO Series GSE67512. Drosophila melanogaster. 15 samples. Type: Expression profiling by array.
m6A sites in the coding region trigger translation-dependent mRNA decay (YTHDF2 iCLIP2)
GEO Series GSE248574. Homo sapiens. 8 samples. Type: Other.
RNA decay defines the therapeutic response to transcriptional perturbation in leukemia [MACseq_K562]
GEO Series GSE229307. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Pumilio-Homology Domain protein APUM9 regulates seed dormancy via decapping-dependent mRNA decay
GEO Series GSE104860. Arabidopsis thaliana. 4 samples. Type: Expression profiling by high throughput sequencing.
Nonsense-mediated mRNA decay in Tetrahymena is EJC independent and requires a protozoa-specific nuclease
GEO Series GSE90899. Tetrahymena thermophila. 7 samples. Type: Expression profiling by high throughput sequencing.
Barcode decay Lineage Tracing, BdLT-Seq, unravels lineage-linked transcriptome plasticity [scRNAseq_BdLTseq_HA1ER_F12_clone_3]
GEO Series GSE223489. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
4EHP and GIGYF1/2 induce translation-coupled messenger RNA decay [Wildtype and ZNF598-null]
GEO Series GSE149279. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Raw Data to 'Decay and recurrence of non-Gaussian correlations in a quantum many-body system', arXiv:2003.01808
<p><strong>Absorption images</strong> representing the raw data for <strong>arXiv:2003.01808</strong>, Nature Physics (2021). <a href="https://doi.org/10.1038/s41567-020-01139-2">https://doi.org/10.1038/s41567-020-01139-2</a></p> <p>'scan5100.zip' contains the raw data for figure 2.</p> <p>'scan8679.zip', 'scan8685.zip', and 'scan8696.zip' contain the raw data for the left, middle, and right subplot of figure 3 respectively.</p> <p>The absorption images are numbered consecutively.</p> <p>The first image (for all scans) is taken in the direction of the double-well (DW) separation with our 'transverse' imaging system. The measurement is performed before ramping up the DW barrier. The image is taken after 11 ms time of flight (TOF). Two images are taken. One image with atoms ('1-atomcloud.tif'), and a second image to record the intensity of the imaging beam without atoms ('1-withoutatoms.tif').</p> <p>The subsequent images record the interference fringes for the different evolution times (again always pairs '-atomcloud.tif' and '-withoutatoms.tif'). They are taken with our 'vertical' imaging system after 15.6 ms TOF. The imaging direction is perpendicular to the weakly confined direction of the clouds as well as the DW separation.</p> <p>The recorded evolution times for scan 5100 are -2 ms (right before ramping the DW barrier up), 0 ms (right after the DW barrier is ramped up), and then in 3 ms steps until 18 ms. I.e., '2-atomcloud.tif' corresponds to -2 ms, '9-atomcloud.tif' corresponds to 18 ms. This completes the 'first repeat'. The next picture '10-atomcloud.tif' then belongs to the 'second repeat' and is again taken with the 'transverse' imaging system. The picture '11-atomcloud.tif' is then again taken with the 'vertical' imaging and corresponds to -2 ms. And so forth.</p> <p>In the same way, the pictures for the other scans are ordered, only the recorded evolution times are different. The evolution times are (in ms):</p> <p>scan 8679: -1.2, 0, 2.5, 5, 20 in steps of 2.5 until 32.5<br> scan 8685: -1.3, 0, 2.5, 5, 20 in steps of 2.5 until 32.5<br> scan 8696: -1.6, 0, 2.5, 5, 20 in steps of 2.5 until 32.5</p> <p>The first time always corresponds to right before the DW barrier is ramped up. 0 is always right after the barrier was ramped up.</p> <p>Note that for scan 8696 only the repeats 1 to 193, 215 to 246, 262 to 281, and 295 to 330 have been used. For the other repeats the digital micromirror device (DMD) shaping the optical dipole potential has failed.</p> <p>In addition to the data a matlab script is provided, illustrating how to extract the two dimensional atomic density from the absorption images. It contains all relevant parameters of the imaging systems.</p>
Data from: Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria
Understanding the extreme variation among bacterial genomes remains an unsolved challenge in evolutionary biology, despite long-standing debate about the relative importance of natural selection, mutation, and random drift. A potentially important confounding factor is the variation in mutation rates between lineages and over evolutionary history, which has been documented in several species. Mutation accumulation experiments have shown that hypermutability can erode genomes over short timescales. These results, however, were obtained under conditions of extremely weak selection, casting doubt on their general relevance. Here, we circumvent this limitation by analyzing genomes from mutator populations that arose during a long-term experiment with Escherichia coli, in which populations have been adaptively evolving for >50,000 generations. We develop an analytical framework to quantify the relative contributions of mutation and selection in shaping genomic characteristics, and we validate it using genomes evolved under regimes of high mutation rates with weak selection (mutation accumulation experiments) and low mutation rates with strong selection (natural isolates). Our results show that, despite sustained adaptive evolution in the long-term experiment, the signature of selection is much weaker than that of mutational biases in mutator genomes. This finding suggests that relatively brief periods of hypermutability can play an outsized role in shaping extant bacterial genomes. Overall, these results highlight the importance of genomic draft, in which strong linkage limits the ability of selection to purge deleterious mutations. These insights are also relevant to other biological systems evolving under strong linkage and high mutation rates, including viruses and cancer cells.
The supporting data for the paper "Synergistic Enhancement of LSTM Time Series Prediction via Companion Strategy and Decay Operator-Improved Aquila Optimization"
<p>数据生成程序</p> <p>该数据集是使用 The Investor's Exchange API 生成的,脚本会定期获取标准普尔 500 指数中所有公司的历史股价。详细说明和脚本可以在 GitHub 存储库中找到。该数据每5年更新一次,最近一次更新于2018年2月。</p> <p> </p> <p>数据处理方法和步骤</p> <p>数据处理的主要步骤包括:</p> <p> </p> <p>数据采集:使用 API 获取每只股票的历史数据,存储在.csv文件中。</p> <p>数据清理:删除重复条目,纠正格式错误,确保数据完整性。</p> <p>数据合并:将单个股票数据合并到一个大.csv文件中,以便于使用。</p> <p>数据验证:通过检查时间序列的连续性和完整性来验证数据的准确性。</p> <p>使用的设备和工具</p> <p>数据采集工具:Python 脚本</p> <p>数据处理工具:用于数据清洗和处理的 Pandas 库</p> <p>数据存储:CSV文件格式</p> <p>时间和地理范围</p> <p>时间范围:数据涵盖过去 5 年的历史股票价格,最新更新于 2018 年 2 月。</p> <p>地理范围:数据涵盖标准普尔500指数中的所有公司,主要是美国市场数据。</p> <p>时间和空间分辨率</p> <p>时间分辨率:每日数据,每个交易日一条记录。</p> <p>空间分辨率:无地理空间分辨率;数据按公司分组。</p> <p>表格数据</p> <p>条目总数:条目总数取决于标准普尔500指数中的公司数量和总交易日数。</p> <p>行标题和列标题:</p> <p>日期:交易日期格式为yy-mm-dd</p> <p>开盘价:开盘价(美元)</p> <p>最高价:当日最高价(美元)</p> <p>最低价:当日最低价格(美元)</p> <p>收盘价:收盘价(美元)</p> <p>交易量:成交股数</p> <p>名称:以股票代码的名义</p> <p>缺失数据</p> <p>数据集在某些交易日可能缺少数据,主要是由于非交易日(例如节假日)或API数据采集过程中的临时网络问题。这些缺失的数据通常不会影响整体分析结果。</p> <p> </p> <p>数据错误</p> <p>由于数据源是第三方 API,因此数据错误的可能性很低。如果发现错误,通常是由于 API 数据采集过程中的临时网络问题造成的。数据清理过程旨在最大限度地减少和纠正这些错误。</p> <p> </p> <p>数据文件说明</p> <p>数据文件类型:</p> <p>all_stocks_5yr.csv:包含所有股票的合并数据文件。</p> <p>individual_stocks_5yr文件夹:包含每个股票的单个.csv文件。</p> <p>文件内容和格式:文件采用 CSV 格式,每个文件包含日期、开盘价、最高价、最低价、收盘价、成交量和股票名称列。</p> <p>文件大小:文件大小取决于特定股票的交易数据量,通常从几MB到几十MB不等。</p> <p>文件格式说明</p> <p>数据以通用的 CSV 格式存储,可以使用 Excel、Notepad++ 或任何支持 CSV 文件的工具打开和查看。对于进一步的数据处理和分析,可以使用 Python Pandas 库。</p> <p> </p> <p>总结</p> <p>该数据集提供过去5年标准普尔500指数中所有公司的历史股价数据,包括开盘价、最高价、最低价、收盘价、交易量等详细信息。它适用于各种财务数据分析和建模应用。数据通过 API 获取并处理,以确保准确性和完整性。</p>
Figure 2 from: Aballay F, Arriagada G, Flores G, Centeno N (2013) An illustrated key to and diagnoses of the species of Histeridae (Coleoptera) associated with decaying carcasses in Argentina. ZooKeys 261: 61-84. https://doi.org/10.3897/zookeys.261.4226
Figure 2 - Saprininae, schematic. Habitus, ventral view (taken from Lackner 2010).
Fuzeon Viral Decay Pilot Study
ClinicalTrials.gov study NCT00334022. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Relationship Between Tooth Decay and Trabecular Bone
ClinicalTrials.gov study NCT04608526. IPD Sharing: NO. Countries: 1. Publications: 0.
This Research Study Examines the Effects of Cannabidiol-infused Candy on Reducing the Bacteria Causing Tooth Decay, When Comparing it to a Sugar Free Candy on Adults.
ClinicalTrials.gov study NCT06301113. IPD Sharing: NO. Countries: 1. Publications: 0.
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.