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297 results for “pearls”
Caries Prevention Potential of Pearl Powder Versus CPP-ACP on Enamel White Spot Lesions
ClinicalTrials.gov study NCT03973554. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Pearls and Pitfalls in Usage of Plasma-Rich Platelet Graft Versus Dartos Flap in Distal Penile Hypospadias Repair
ClinicalTrials.gov study NCT06808139. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Pearl River Estuary Pollution Project (PREPP)
Measurements made under the Pearl River Estuary Pollution Project (PREPP) near Hong Kong in 2001.
Global gene expression profile for pearl oyster
GEO Series GSE14306. Pinctada maxima. 84 samples. Type: Expression profiling by array.
miRNA expression profile of mantle tissues for pearl oyster, Pinctada fucata
GEO Series GSE145993. Pinctada fucata. 1 samples. Type: Non-coding RNA profiling by high throughput sequencing.
cDNA microarray analysis revealing candidate biomineralizational genes of the pearl oyster, Pinctada fucata martensii
GEO Series GSE70439. Pinctada imbricata. 12 samples. Type: Expression profiling by array.
Analysis of the global gene expression profile for pearl oyster, Pinctada maxima, exposed to organotin (tributyltin)
GEO Series GSE13980. Pinctada maxima. 6 samples. Type: Expression profiling by array.
An integrated transcriptome analysis of immunological responses in pearl sac of pearl mussel Hyriopsis cumingii after mantle implantation
GEO Series GSE123069. Sinohyriopsis cumingii. 10 samples. Type: Expression profiling by high throughput sequencing.
Digital gene expression tag profile of five development stages of pearl sac from pearl oyster Pinctada martensii reveals potential candidates contributing to nacre formation
GEO Series GSE46265. Pinctada imbricata. 5 samples. Type: Expression profiling by high throughput sequencing.
Effects of Lithospermum erythrorhizon on gut health and growth performance of pearl gentian grouper based on Transcriptome
GEO Series GSE235735. Epinephelus fuscoguttatus x Epinephelus lanceolatus. 9 samples. Type: Expression profiling by high throughput sequencing.
Bodhgayā, Bihar. Assemblage of pearls.
<p>Bodhgayā, Bihar. Assemblage of pearls and pieces of shell. British Museum 1892,1103.71 presented by Alexander Cunningham.</p>
The measurements and three operational numerical forecasts of surface wind speed over Pearl River Estuary during 2018–2021.
Open the record for dataset details and reuse information.
Major outputs for source apportionment of fine secondary inorganic aerosol over the Pearl River Delta region using a hybrid method
<p>The results for our work can be downloaded from this URL. The source impact estimates were simulated by CAMx (v6.4) with the module of PSAT in January, April, July, and October 2015. Matlab R2017b was used to perform spatial interpolation, optimization, and statistics calculations.</p>
Artifacts of PEARL
<p>This repository contain the artifacts of a paper titled "Program Repair as a Code Completion Problem: Addressing the Challenge of Accurately Locating Fine-Grained Buggy Code Elements" under review by ICSE'21.</p> <p>The files <strong>prediction_CoCoNut.csv</strong> and <strong>prediction_ManySStuBs4J.csv</strong> illustrate the prediction results of <strong>BEP</strong> on the two datasets, in which the column <em>element_Rank</em> shows the ranking of buggy element and the column <em>rank</em> is for operation path.</p> <p>The file <strong>Pipeline_Results.zip</strong> contains the results of our repair pipeline on the 111 single-token bugs. In each folder, <em>rem.txt</em> corresponds to the buggy line, <em>add.txt</em> corresponds to the correct line, and <em>context.txt</em> corresponds to the whole buggy method. These three files are required inputs for each prediction. <em>query.txt</em> is the content sent to <em>AnyCodeGen </em>and the returned code fragment is stored in <em>result.txt</em>. </p> <p>We also open the cleaned dataset of ManySStuBs4J which only contains single-token bugs in <strong>Single-token_bugs_in_ManySStuBs4J.zip</strong>. In this file, each bug is corresponding to three lines with the same line number in three different files which are <em>rem.txt</em>, <em>add.txt</em>, and <em>context.txt</em>, respectively representing the buggy line, correct line, and the buggy method. This storage style follows the training set of <a href="https://github.com/lin-tan/CoCoNut-Artifact">CoCoNut</a>. Note that we do not release our pre-processed dataset of CoCoNut sinse it is too large. We encourage researchers to process this dataset based on their own needs. Moreover, we do provide the script we used for selecting single-token bugs which is <strong>single-token_selection.py</strong>.</p> <p>We also note there is no visual interface for the newly added bugs in Defects4J-V2.0. We thus provide our script for selecting single-token bugs (<strong>getSingle-token_bugs_from_D4J.py</strong>) from these bugs, making it easy for others to reproduce our experiment. </p> <p><strong>Case Study:</strong></p> <p><strong>PEARL</strong> does not work well on method name-related fix. For the following bug (Closure-10), our BEP model successfully predicts the oracle operation path as No.2.</p> <pre><code class="language-java">- return allResultsMatch(n, MAY_BE_STRING_PREDICATE); + return anyResultsMatch(n, MAY_BE_STRING_PREDICATE);</code></pre> <p>Nonetheless, we have to change this statement into "<strong>return ??</strong>" when querying <strong>AnyCodeGen</strong> due to the format restriction of it. Unfortunately, <strong>AnyCodeGen</strong> cannot synthesize such a detailed method call, thus leading to the failure of PEARL of repairing this bug.</p> <p> </p> <p> </p> <p>The source code of <strong>PEARL</strong> will be open-access upon acceptance.</p>
PEARL
<p>This repository contains the artifacts of PEARL.</p> <p>The files <strong>prediction_CoCoNut.csv</strong> and <strong>prediction_ManySStuBs4J.csv</strong> illustrate the prediction results of <strong>BEP</strong> on the two datasets, in which the column <em>element_Rank</em> shows the ranking of buggy element and the column <em>rank</em> is for operation path.</p> <p>The file <strong>Pipeline_Results.zip</strong> contains the results of our repair pipeline on the 111 single-token bugs. In each folder, <em>rem.txt</em> corresponds to the buggy line, <em>add.txt</em> corresponds to the correct line, and <em>context.txt</em> corresponds to the whole buggy method. These three files are required inputs for each prediction. <em>query.txt</em> is the content sent to <em>AnyCodeGen </em>and the returned code fragment is stored in <em>result.txt</em>. </p> <p>We also open the cleaned dataset of ManySStuBs4J which only contains single-token bugs in <strong>Single-token_bugs_in_ManySStuBs4J.zip</strong>. In this file, each bug is corresponding to three lines with the same line number in three different files which are <em>rem.txt</em>, <em>add.txt</em>, and <em>context.txt</em>, respectively representing the buggy line, correct line, and the buggy method. This storage style follows the training set of <a href="https://github.com/lin-tan/CoCoNut-Artifact">CoCoNut</a>. Note that we do not release our pre-processed dataset of CoCoNut sinse it is too large. We encourage researchers to process this dataset based on their own needs. Moreover, we do provide the script we used for selecting single-token bugs which is <strong>single-token_selection.py</strong>.</p> <p>We also note there is no visual interface for the newly added bugs in Defects4J-V2.0. We thus provide our script for selecting single-token bugs (<strong>getSingle-token_bugs_from_D4J.py</strong>) from these bugs, making it easy for others to reproduce our experiment. </p> <p>Finally, we release our souce code in <strong>source code.zip</strong> and will build a GitHub homepage for PEARL upon acceptance.</p> <p> </p> <p><strong>Case study on the failure of PEARL:</strong></p> <p><strong>PEARL</strong> does not work well on method name-related fix. For the following bug (Closure-10), our BEP model successfully predicts the oracle operation path as No.2.</p> <pre><code>- return allResultsMatch(n, MAY_BE_STRING_PREDICATE); + return anyResultsMatch(n, MAY_BE_STRING_PREDICATE);</code></pre> <p>Nonetheless, we have to change this statement into "<strong>return ??</strong>" when querying <strong>AnyCodeGen</strong> due to the format restriction of it. Unfortunately, <strong>AnyCodeGen</strong> cannot synthesize such a detailed method call, thus leading to the failure of PEARL of repairing this bug.</p> <p> </p> <p><strong>Case study on the high CR of PEARL:</strong></p> <pre><code class="language-java">// Ground-truth patch for Closure-62 if (excerpt.equals(LINE) - && 0 <= charno && charno < sourceExcerpt.length()) { + && 0 <= charno && charno <= sourceExcerpt.length()) { // An overfitting patch generated for Closure-62 by jKali - if (excerpt.equals(LINE) - && 0 <= charno && charno < sourceExcerpt.length()) { + if (true) {</code></pre> <p>The ground-truth patch (also the <strong>PEARL</strong>-generated) and <strong>jKali</strong>-generated patch for the bug Closure-62 are listed. The ground-truth patch changes an operator <strong><</strong> into <strong><=</strong>. <strong>PEARL</strong> first identifies this operator and the operation type to be updated. After obtaining these information, we change the third line to <em>&& 0 <= charno && ??) {</em> and send the whole method to <strong>AnyCodeGen</strong>. After computing the possibilities of possible answers, <strong>AnyCodeGen</strong> returns <em>charno <= sourceExcerpt.length()</em> as the code fragment to replace ``??'' and thus we generate this correct patch.</p> <p>On the contrary, <strong>jKali</strong> is Java implementation of Kali. The operators implemented in it are removal of statements, modification of if conditions to <em>true</em> and <em>false</em> and so on which are too course-grained. In this example, <strong>jKali</strong> identifies the whole conditional statement and modifies the condition to <em>true</em> which leads to the overfitting patch. This case is a vivid example showing that fine-grained buggy element localization can avoid generating overfitting patch.</p>
Monthly Chlorophyll-a and total suspended solids concentrations during August 2002 and July 2020 in the Pearl River Estuary, China
<p>Monthly Chlorophyll-a (Chl-a) and total suspended solids (TSS) concentrations during August 2002 and July 2020 in the Pearl River Estuary, China. Chl-a and TSS were retrieved based on a machine learning algorithm developed by <em>Ma et al.</em> [2022] using satellite imagery from MODerate-resolution Imaging Spectroradiometer (MODIS) onboard Aqua. </p> <p>Reference</p> <p>Ma, C., J. Zhao, B. Ai, S. Sun, and Z. Yang (2022), Machine Learning Based Long‐Term Water Quality in the Turbid Pearl River Estuary, China, <em>Journal of Geophysical Research: Oceans</em>, <em>127</em>(1).</p>
Pearl River ADCP data - momentum redistribution
<p>These three-dimensional velocity data were acquired using a boat-mounted acoustic Doppler current profiler (ADCP) on a series of six consecutive meander bends of the Pearl River, near Bogalusa, Louisiana. Data were collected during campaigns in 2017 and 2018. In this published dataset, there are raw ADCP files, as well as processed data provided within a Matlab .mat file.</p>
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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.