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8,460 results for “MONITORING”
Ultrasound-responsive theranostic platform for real-time monitoring and efficient thrombolysis in tPA-resistant thrombi
GEO Series GSE254810. Rattus norvegicus. 6 samples. Type: Expression profiling by high throughput sequencing.
In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing
GEO Series GSE101900. Mus musculus. 92 samples. Type: Expression profiling by high throughput sequencing.
Pol III transcription machinery occupancy monitoring upon rapamycin treatment
GEO Series GSE3468. Saccharomyces cerevisiae. 8 samples. Type: Genome binding/occupancy profiling by genome tiling array.
The HtrA chaperone monitors sortase-assembled pilus biogenesis in E. faecalis
GEO Series GSE261583. Enterococcus faecalis. 33 samples. Type: Expression profiling by high throughput sequencing.
Identification of genes that are modulated by BET inhibitors in cancer cells to identify robust pharmacodynamic marker for monitoring target engagement of BET family bromodomain inhibitors in tumors a
GEO Series GSE89932. Homo sapiens. 36 samples. Type: Expression profiling by array.
Generation and characterization of a mouse line for monitoring translation in dopaminergic neurons.
GEO Series GSE99927. Mus musculus. 4 samples. Type: Expression profiling by array; Other.
In vitro and in vivo monitoring of valproic acid effects on gene expression signatures in adult acute myeloid leukemia
GEO Series GSE32240. Homo sapiens. 43 samples. Type: Expression profiling by array.
Monitoring Homology Search during DNA Double-Strand Break Repair in vivo
GEO Series GSE44844. Saccharomyces cerevisiae. 140 samples. Type: Genome binding/occupancy profiling by genome tiling array.
Caenorhabditis elegans as an environmental monitor using DNA microarray analysis.
GEO Series GSE2970. Caenorhabditis elegans. 6 samples. Type: Expression profiling by array.
Monitoring the mating-type-driven transcriptomes of Aspergillus fumigatus
GEO Series GSE113404. Aspergillus fumigatus. 8 samples. Type: Expression profiling by high throughput sequencing.
Monitoring of functional gene responses to ERD (enhanced reductive dechlorination) from four TCE-contaminated sites
GEO Series GSE28606. Methylosinus; Dehalococcoides; Shewanella; Pseudomonas; Mycobacterium; Geobacter; groundwater metagenome; Nitrosomonas; Thauera; Dehalobacter; Sulfurospirillum; Desulfitobacterium. 30 samples. Type: Genome variation profiling by array.
Monitoring of functional gene responses to biostimulation from TCE-contaminated sites using a functional microarray
GEO Series GSE28609. Geobacter; Methylosinus; Dehalobacter; Sulfurospirillum; Shewanella; Mycobacterium; Thauera; Desulfitobacterium; groundwater metagenome; Pseudomonas; Nitrosomonas; Dehalococcoides. 33 samples. Type: Genome variation profiling by array; Expression profiling by array.
Monitoring rapid degradation of NANOG reveals UTP15 maintains pluripotency by regulating nascent transcripts
GEO Series GSE276879. Mus musculus. 30 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments in HuH-7 cells
GEO Series GSE128320. Homo sapiens. 2 samples. Type: Other.
Monitoring the 5′UTR landscape reveals isoform switches to drive translational efficiencies in cancer [CAGE-seq]
GEO Series GSE201308. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
MinION-in-ARMS: Nanopore sequencing to expedite barcoding of specimen-rich macrofaunal samples from Autonomous Reef Monitoring Structures
<p>DNA basecalling, demultiplexing information and sequence barcodes</p>
Air Quality Monitoring Protocol Amendment
<p>PM2.5 concentrations reported under standard and actual conditions</p>
Analytical Validation of Aptamer-Based Serum Vancomycin Monitoring Relative to Automated Immunoassays
<p>***ABSTRACT***</p> <p>_________________________</p> <p>The practice of monitoring therapeutic drug concentrations in patient biofluids can significantly improve clinical outcomes while simultaneously minimizing adverse side effects. A model example of this practice is vancomycin dosing in intensive care units. If dosed correctly, vancomycin can effectively treat methicillin-resistant staphylococcus aureus (MRSA) infections. However, it can also induce nephrotoxicity or fail to kill the bacteria if dosed too high or low, respectively. Although undeniably important to achieve effectiveness, therapeutic drug monitoring remains inconvenient in practice, due primarily to the lengthy process of sample collection, transport to a centralized facility, and analysis using costly instrumentation. Adding to this workflow is the possibility of backlogs at centralized clinical laboratories, which is not uncommon and may result in additional delays between biofluid sampling and concentration measurement, which can negatively affect clinical outcomes. Here we explore the possibility of using point-of-care, electrochemical aptamer-based (E-AB) sensors to minimize the time delay between biofluid sampling and drug measurement. Specifically, we conducted a clinical agreement study comparing the measurement outcomes of E-AB sensors to the benchmark automated competitive immunoassays for vancomycin monitoring in serum. Our results demonstrate that E-ABs are selective for free vancomycin—the active form of the drug—over total vancomycin. In contrast, competitive immunoassays measure total vancomycin, including both protein-bound and free drug. Accounting for these differences in a pilot study consisting of 85 clinical samples, we demonstrate that the E-AB vancomycin measurement achieved a 95% positive correlation rate with the benchmark immunoassays. Therefore, we conclude that E-AB sensors could provide clinically useful stratification of patient samples at trough sampling to guide effective vancomycin dose recommendations.</p> <p> </p> <p>***README FILE ***</p> <p>_________________________</p> <div>Project: Analytical Validation of Aptamer-Based Serum Vancomycin Monitoring Relative to Automated Immunoassays</div> <div>Date Accepted: December 5th, 2023</div> <div>DOI: </div> <div>Contact: Netz Arroyo, netzarroyo@jhmi.edu</div> <div> </div> <div>ORGANIZATION</div> <div> </div> <div>All files in this repository are sorted based on their corresponding figure in the associated manuscript</div> <div>and supplementary information document (both found at DOI above). Organization and files found in each figure folder </div> <div>are as follows:</div> <div> </div> <div>- 'Figure_1':</div> <div>- Final .svg image for Figure 1.</div> <div> </div> <div>- 'Figure_2':</div> <div>-"Figure_2.png"</div> <div>- PNG image of Figure 2. Figure panels were generated as detailed in the following entries. Figure was compiled in Adobe Illustrator. </div> <div>-"swv trace.svg"</div> <div>- SVG file for Panel C. Generated using Igor Pro v8 from data in file "SWV Traces.pxp".</div> <div>-"ON&OFF.svg"</div> <div>- SVG file for Panel D . Generated using Igor Pro v8 from data in file "Signal ON&OFF.pxp". </div> <div>-"ratio box whisker plot.svg"</div> <div>- SVG file for Panel E. Generated using Igor Pro v8. </div> <div>-"calibration.svg"</div> <div>- SVG file for Panel F. Generated using Igor Pro v8. </div> <div>-Data: "SWV Traces.xlsx"</div> <div>"SWV Traces.csv"</div> <div>"SWV Traces.pxp" </div> <div>Data was obtained from sheet "SWV curves" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format.</div> <div>-Data: "Signal ON&OFF.xlsx"</div> <div>"Signal ON&OFF.csv"</div> <div>"Signal ON&OFF.pxp" </div> <div>Data was obtained from sheet "signal-on&signal-off" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format. Regression to the Hill equation</div> <div>was performed in Igor Pro v8, constraining rate=1.</div> <div>-Data: "Dose response curve box plot.xlsx"</div> <div>"Dose response curve box plot.csv"</div> <div>"Dose response curve box plot.pxp" </div> <div>Data was obtained from sheet "Curve fitting_Ratio(SUM_MEA)" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format. Box and whisker plot was generated using</div> <div>Igor Pro v8.</div> <div>-Data: "Day1-Day6 Dose Response Curve.xlsx"</div> <div>"Day1-Day6 Dose Response Curve.csv"</div> <div>"Day1-Day6 Dose Response Curve.pxp" </div> <div>Data was obtained from sheet "Curve fitting_Ratio(SUM_MEA)" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. Data in all files are identical, except for file format. Nonlinear regression to the Hill equation</div> <div>was generated using Igor Pro v8. No variables were constrained.</div> <div> </div> <div>-'Figure_3':</div> <div>- DATA FOR FIGURE 3 WAS OBTAINED FROM THE "Ratiometric response" SHEET OF THE FILE "Confidential_ZiO Health Stability Test Data" RECIEVED 12:08, NOVEMBER 8 2023, FROM FRANCESCA NAPOLI.</div> <div>- DATA FROM FIELDS "Average response to 12.5 mg/L for chips tested in specific week" AND "SD response to 12.5 mg/L for chips tested in specific week" (ROWS 39 AND 40) , WITH ASSOCIATED TIMEPOINT IDENTIFIERS, WERE EXTRACTED AND USED AS RECIEVED</div> <div>- IN ROWS 41 AND 42, DATA FROM THE 0.1 MG/L CONCENTRATION POINT (ROW 13) WAS EXCTRACTED AND PROCESSED IN THE SAME WAY AS "Average response to 12.5 mg/L for chips tested in specific week" AND "SD response to 12.5 mg/L for chips tested in specific week" (I.E, AVERAGE AND STANDARD DEVIATION OF THREE OR FOUR CHIPS PER WEEK)</div> <div>- TIMEPOINT IDENTIFIERS WERE CONVERTED TO NUMERIC AND GIVEN THE TITLE "Time" and UNIT "Weeks"</div> <div>- FIELD "Average response to 12.5 mg/L for chips tested in specific week" WAS TITLED "12.5 mg/L Sensor response ratio" AND GIVEN UNIT "360Hz/20Hz"</div> <div>- FIELD "SD response to 12.5 mg/L for chips tested in specific week" WAS TITLED "12.5 mg/L SD" AND GIVEN UNIT "360Hz/20Hz"</div> <div>- THE SAME WAS DONE FOR THE 0.1 MG/l CONCENTRATION DATA</div> <div>- DATA FILE USED FOR MAKING THE FIGURE IS "2023_11_08_Chip_Stability.pxp"</div> <div>- BOTH O.1 MG/L AND 12.5 MG/L WERE SUBJECTED TO LINEAR REGRESSION ANALYSIS IN IGOR V8</div> <div> </div> <div>-'Figure_4':</div> <div>- Final PNG image for Figure 4.</div> <div> </div> <div>-'Figure_5':</div> <div>- "2023_11_07_Fig5.png"</div> <div>- Final .PNG file used for Figure 5. Panels were arranged and Mean/StDev numbers were added in Adobe Illustrator.</div> <div>- "EAB_diff.svg"</div> <div>-.svg file used for panel A of Figure 5. Generated in Igor v8.</div> <div>- "EAB_pct_aveplot.svg"</div> <div>-.svg file used for panel C of Figure 5. Generated in Igor v8.</div> <div>- "ImA_diff.svg"</div> <div>-.svg file used for panel B of Figure 5. Generated in Igor v8.</div> <div>- "ImA_pct_aveplot.svg"</div> <div>-.svg file used for panel D of Figure 5. Generated in Igor v8.</div> <div>- Data: "2023_07_10_Differentials.xlsx"</div> <div>"2023_07_10_Differentials.csv"</div> <div>"2023_07_10_Differentials.pxp"</div> <div>Data was obtained from sheet "Predict con.c" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. </div> <div>-'Figure_6':</div> <div>- "2023_07_12_Fig6.png"</div> <div>- Final .PNG file used for Figure 6. Panels were arranged and statistics valued added in Adobe Illustrator.</div> <div>- "serum.svg"</div> <div>-.svg file used for panel A of Figure 5. Generated in Igor v8 from Table 0.</div> <div>- "Filtrate smaller points.svg"</div> <div>-.svg file used for panel C of Figure 5. Generated in Igor v8 from Graph 0.</div> <div>- "Filtrate_insert.svg"</div> <div>-.svg file used for panel B of Figure 5. Generated in Igor v8 from Graph 5.</div> <div>- "Bland-Altman.svg"</div> <div>-.svg file used for panel D of Figure 5. Generated in Igor v8 from Graph 1.</div> <div>- "Bland-Altman insert.svg"</div> <div>-.svg file used for panel D of Figure 5. Generated in Igor v8 from Graph 1.</div> <div>- "Rplot0_png.png"</div> <div>-.png file of Bangdiwala plot. Generated in Rstudio by code mentioned below.</div> <div>- "Agreement plot code.txt"</div> <div>-Code used to generate Bangdiwala agreement plot using Rstudio. </div> <div>- Data: "Concentrations_all.xlsx"</div> <div>"Concentrations_all.csv"</div> <div>Data was obtained from sheet "Predict con.c" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. </div> <div>- Data: "Bland-Altman.xlsx"</div> <div>"Bland-Altman.csv"</div> <div>Data was obtained from sheet "Bland–Altman Plot" of Excel file "ZiO-JHU_March 2023_Data processing Updates" recieved 10:37 EST, June 2 2023, from Yu Liu. </div> <div>Data was used as recieved, and imported to Igor Pro v8 for plotting. </div> <div>- Data: "ROC-input data for agreement plot.xlsx"</div> <div>"ROC-input data for agreement plot.csv"</div> <div>Data was recieved 12:30 EST, December 7 2023, from Francesca Napoli.</div> <div>- Data: "Fig6.pxp"</div> <div>Table 0: all samples from "Concentrations_all.xlsx"</div> <div>Table 1: average and difference values for EA-B filtrate samples vs immunoassay filtrate samples from "Bland-Altman.xlsx", as well as upper and lower 95% CI boundaries</div> <div>Table 2: samples sorted by concentration and filtered by immunoassay values <15mg/L and <10mg/L.</div> <div>Graph 0: plot of immunoassay vs E-AB serum concentration values from Table 0.</div> <div>Graph 1: plot of immunoassay vs E-AB filtrate concentration values from Table 0.</div> <div>Graph 2: plot of average vs difference values from Tablel 1.</div> <div>Graph 4: plot of filtered immunoassay vs E-AB filtrate concentration values <10mg/L from Table 3.</div> <div>Graph 5: plot of filtered immunoassay vs E-AB filtrate concentration values <15mg/L from Table 3.</div> <div> </div> <div> </div> <div>NOTES</div> <div> </div> <div>(1) Raw voltammetry data is the property of ZiO Health, and is therefore not available for public release. All data presented here were extracted from raw voltammetric traces by ZiO Health,</div> <div>and transfered to the Arroyo lab for further analysis. </div> <div> </div> <div>(2) RStudio is available at: https://posit.co/products/open-source/rstudio/</div> <div> </div> <div>(3) Igor Pro is available at: https://www.wavemetrics.com/</div> <div> </div>
Microseismic monitoring of tailings dams - signal and noise evaluation
<p>The dataset originates from the manuscript titled "Microseismic Monitoring of Tailings Dams: Evaluation of Signal and Noise Sources," currently under submission to Seismica.</p> <p>The database is organized into four distinct categories, each serving a specific purpose:</p> <ol> <li> <p>Signal: This category comprises common signals, which are likely to be observed at various stations worldwide. It includes data from both blast events and natural seismic occurrences;</p> </li> <li> <p>Site-specific Signal: These signals are unique to the specific location or a single station. They primarily consist of data obtained from pulse tests conducted at the site;</p> </li> <li> <p>Noise: Within this category, common noise sources are documented, which are also anticipated to be observed at other stations globally. The noise sources encompass mechanical disturbances such as those from backhoes, drills, excavators, and trucks. Additionally, data related to natural phenomena such as lightning, thunder, and combinations thereof (lightning accompanied by thunder) are also cataloged here;</p> </li> <li> <p>Site-specific Noise: This category comprises noise sources that are specific to the particular location or a single station. The primary source identified here is digitizer interference.</p> </li> </ol>
Plastic monitor time resolution at proton energies between 62 and 225 MeV
<p>Data of the paper "A fast plastic scintillator for low intensity proton beam monitoring" (DOI: 10.1109/TRPMS.2024.3498959)</p> <p>https://arxiv.org/abs/2411.07877</p>
ScienceDex guides
Understand access before you commit
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.