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3,684 results for “extract”
Arabic Raw Text Corpus (unfiltered, extracted from the Clueweb WARC Files)
<p>The corpus is created from 29,192,662 ClueWeb html. Initially archived in WARC files. Each text file contains in average 30,000 different webpages. The size of the corpus is 18,482,719 terms. </p>
Green synthesis of ZnO nanoparticles using Cocos nucifera leaf extract: Characterization, antimicrobial, antioxidant, and photocatalytic activity
<p>Zinc oxide nanoparticles (ZnO NPs) have been successfully prepared using <em>Cocos nucifera</em> leaf extract and investigated with their antimicrobial, antioxidant, and photocatalytic activity. The structural, compositional, and morphological properties of the NPs were recorded and studied systematically to confirm the synthesis. The aqueous suspension of NPs showed a UV-Vis. absorption maxima of 370 nm indicating primarily its formation. The XRD analysis identified the NPs with hexagonal wurtzite structure with an average particle size of 16.6 nm. The FTIR analysis identified some biomolecules and functional groups in the leaf extract as responsible for the encapsulation and stabilization of ZnO NPs. The EDX analysis showed the desired elemental compositions in the material. A flower-shaped morphology of ZnO NPs was observed by SEM with a grain size of around 15 nm. The optical properties of the NPs were studied by UV-Vis spectroscopy and the bandgap was calculated as 3.37 eV. The Prepared ZnO NPs have demonstrated antimicrobial activity against <em>T. harzianum</em>, and <em>S. aureus</em> with a ZOI (zone of inhibition) of 14 and 10 mm, respectively. The photocatalytic behavior of ZnO NPs showed absorbance degradation at around 640 nm and discolored methylene blue dye after one hour with a degradation maximum of 84.29 %. Thus, the prepared ZnO NPs could be used potentially in antibiotic development, pharmaceutical industries, and as photocatalysts. </p>
Extracting interaction potentials from trajectories: code and a set of 30 simulated contest trajectories
<p>Dataset and code provided as supporting materials for the manuscript "Modeling animal contests based on spatio-temporal dynamics". See the Supporting Information of the manuscript for detailed instructions of use.</p>
Energy extraction from air: structural basis of atmospheric hydrogen oxidation
<p>MD simulation trajectories of HucSL dimer including wild-type and mutant proteins(E15A+I64A; I64A+L112A; E15A+I64A+L112A) in the presence of excess hydrogen and oxygen molecules. Preprint with details of the simulations and results can be found here: https://doi.org/10.1101/2022.10.09.511488.</p>
Data Extraction table for the study Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data
<p>This table contains the data extraction results for the study Machine-based Stereotypes: How Machine Learning Algorithms Evaluate Ethnicity from Face Data. It contains 24 columns and 74 rows.</p>
Data extraction Diabetes cochrane review
<p>This is the data that was extracted using Covidence. please note that some changes to risk of bias assessments were made later on review of the ratings by the lead author, upon discussion and agreement with the raters. These changes are noted directly in the Cochrane review. </p>
Data Extraction Sheet for the Review - 2
<p>Approximately 30 variables will be extracted from the publications that are included in the review. This will include information on the:</p> <ol> <li>Study characteristics <ol> <li>Publication (title, year of publication, author(s) and their affiliation, journal, type of document)</li> </ol> </li> <li>Variables of interest <ol> <li>Concepts that are used for health research on racialised minority groups and how they are operationalised</li> <li>Research methodology and methods used</li> <li>The data used, and how this is collected, and applied</li> </ol> </li> </ol> <p>A full overview of the variables to be extracted can be found in this Data Extraction sheet.</p>
LISTING Extraction Results
<p>The file is in json format and contains a list of entities (which may be known or NIL) with the mentions that have been identified for them. The fields are as follows:</p> <pre><code>idx: Index of the entity name: Name of the entity (None, if NIL-entity) is_nil: Whether the entity is a NIL-entity mentions: (list) page: Name of the page where the mention is found listing: Index of the listing where the mention is found item: Index of the item within the listing where the mention is found text: Mention text</code></pre>
Dataset related to the article "Extraction-Free Absolute Quantification of Circulating miRNAs by Chip-Based Digital PCR"
<p>This record contains raw data related to the article "Extraction-Free Absolute Quantification of Circulating miRNAs by Chip-Based Digital PCR"</p> <p>Circulating microRNAs (miRNA) have been proposed as specific biomarkers for several diseases. Quantitative Real-Time PCR (RT-qPCR) is the gold standard technique currently used to evaluate miRNAs expression from different sources. In the last few years, digital PCR (dPCR) emerged as a complementary and accurate detection method. When dealing with gene expression, the first and most delicate step is nucleic-acid isolation. However, all currently available protocols for RNA extraction suffer from the variable loss of RNA species due to the chemicals and number of steps involved, from sample lysis to nucleic acid elution. Here, we evaluated a new process for the detection of circulating miRNAs, consisting of sample lysis followed by direct evaluation by dPCR in plasma from healthy donors and in the cardiovascular setting. Our results showed that dPCR is able to detect, with high accuracy, low-copy-number as well as highly expressed miRNAs in human plasma samples without the need for RNA extraction. Moreover, we assessed a known myocardial infarction-related miR-133a in acute myocardial infarct patients vs. healthy subjects. In conclusion, our results show the suitability of the extraction-free quantification of circulating miRNAs as disease markers by direct dPCR.</p>
Sargassum sp extract improve Hematological profile of Tilapia fish (Oreochromis niloticus)
<p>Strategies to increase body resistance and prevent disease in cultivation include using vaccines, antibiotics, and probiotics. Today, the use of antibiotics with natural ingredients is becoming a trend. One of the natural ingredients that contain high antioxidants and antibiotics is <em>Sargassum</em> sp. This research was conducted from March to May 2022 at the Biotechnology Laboratory, Faculty of Fisheries and Marine, Universitas Riau. This research was conducted in two stages, namely 1) the sensitivity of extracts of <em>Sargassum</em> sp. and 2) the application of <em>Sargassum</em> sp. extract orally in tilapia (<em>O. niloticus</em>). The results showed that the extract of <em>Sargassum</em> sp. was able to inhibit the growth o<em>f Aeromonas hydrophila </em>bacteria with a clear zone of 6.5-15.0 mm, which is classified as resistant. At doses of 2000, 2500, and 3000 ppm, it did not cause death in fish for 96 hours (LD<sub>50</sub>). Hematological parameters can be a sign of the health status of fish. Tilapia given to the addition of <em>Sargassum</em> sp. with different doses gave an effect between treatments, both after 30 days of rearing and post-test against <em>A.hydrophila</em> bacteria (p<0.05). The results showed that the hematology of fish fed with <em>Sargassum</em> sp. extract was in the normal or healthy range. Healthy tilapia had erythrocyte counts ranging from 1.34-2.11 x10<sup>6</sup> cells/mm<sup>3</sup>, hematocrit 26.17-33.19%, hemoglobin 6.26-11.2 g/dL and total leukocytes 1.01-1.50 x10<sup>4</sup> cells/mm<sup>3</sup> and total erythrocytes 5.88-9.13x10<sup>4</sup> cells/ mm<sup>3</sup>. A dose of 3000 ppm provided the highest health improvement against <em>A.hydrophila</em> bacterial infection</p>
Extracted ANSSI's ASCADv2, AES-128 with affine masking
<p>Extracted version of the ASCADv2 dataset provided by the "Agence nationale de la sécurité des systèmes d'information" (ANSSI). </p> <p>https://www.data.gouv.fr/fr/datasets/ascadv2/</p> <p>Training traces : 250k, Leakages from the masks, and mixed bytes from subbytes inputs, subbytes output and permutations (shape :(250000,4749))</p> <p>Test traces : 50k, Leakages from the masks, and mixed bytes from subbytes inputs, subbytes output and permutations (50000,4749)</p> <p>Attack traces, 50k Leakages from the masks, and all bytes from subbytes inputs, outputs and permutations (50000, 28734)</p> <p>The labels corresponding to the extracted leakages are provided in the dataset. The key is artificially fixed (t = p1 + k_random => t = p2 + k_fixed), since all keys are random in the original dataset. Fixed value : 0x00112233445566778899AABBCCDDEEFF.</p> <p>For more information about the extraction, contact thomas.marquet@aau.at</p>
Construction of microneedle of Atractylodes macrocephala aqueous extract and effect on mammary gland hyperplasia based on intestinal flora
<p><strong><span>Background</span></strong><span>: </span><span>A </span><span>microneedle</span><span> patch loaded with <em>Atractylodes macrocephala</em> water extract was prepared for the treatment of mammary gland hyperplasia to explore the relationship between MGH and intestinal flora. </span></p> <p><strong><span>Materials and methods</span></strong><span>: </span><span>Preparation of the microneedle patch by micromolding method, the prescription of the microneedle was optimized by the Box-Behnken Design response surface test, and the micro-morphology, penetration, toughness, and brittleness were investigated. <em>In vitro</em> release of drug-loaded microneedles was measured by diffusion cell method. The rat model of mammary gland hyperplasia was prepared by the combination of estradiol benzoate-progesterone, and the microneedle patch of <em>Atractylodes macrocephala</em> aqueous extract was used for intervention treatment. The change of levels in E<sub>2</sub>, P, and PRL in rat serum was determined. The intestinal contents of rats were collected and the changes in intestinal flora in MGH rats were analyzed by 16s rRNA high-throughput sequencing. </span></p> <p><strong><span>Results</span></strong><span>: </span><span>The optimized microneedle formula is a PVA concentration of 6.0%, HA concentration of 15.5%, and PVPK30 concentration of 16.0%. The prepared microneedle tip loaded with <em>Atractylodes macrocephala</em> aqueous extract has complete, sharp, and no bubbles and the needle rate of the microneedle array is in the range of 95%~100%. The bending rate of the microneedle is about 12.7%, and it has good flexibility, and the microneedle can puncture 4 layers of Parafilm</span><span>Ⓡ</span><span> membrane smoothly, and the puncture rate is more than 96%. The <em>in vitro</em> release of the microneedle was characterized by rapid release. The results of animal experiments showed that <em>Atractylodes macrocephala</em> aqueous extract microneedle patch could significantly reduce the E<sub>2</sub> level, significantly reduce the PRL level, and significantly increase the P level. At the same time, it can regulate the abundance and diversity of intestinal flora in MGH rats, improve the intestinal flora disorder caused by mammary gland hyperplasia, and balance the community structure. </span></p> <p><strong><span>Conclusion</span></strong><span>: </span><span>The prepared microneedle containing <em>Atractylodes macrocephala</em> aqueous extract has good toughness and brittle strength, can penetrate the skin and enter the dermis, and effectively deliver drugs to play a role in the treatment of mammary gland hyperplasia.</span></p>
dataset of sample papers used for literature review of effect of moringa extract to reduce stunting probability
<p>This dataset contains two files namely:</p> <p>1. Txt file of abstract of all papers used for samples</p> <p>2. CSV file of details of all papers</p> <p> </p> <p>Those two file were downloaded from PubMed</p> <p> </p>
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
<p>The PHEE dataset contains over 5,000 finely annotated pharmacovigilance events from public medical case reports. Two types of events, the adverse events and the potential therapeutic events, are annotated. For each event, we annotate the event trigger and hierarchical arguments. The main arguments (coarse-grained spans) include subject, treatment and effect. Further fine-grained sub-arguments - <em>age</em>, <em>gender</em>, <em>race</em>, <em>number of patients</em> (labelled as population) and <em>preexisting conditions</em> (labelled as subject.disorder) for the subject argument and <em>drug </em>(and their combinations), <em>dosage</em>, <em>frequency</em>, <em>route</em>, <em>time-elapsed</em>, <em>duration</em>,<em> target disorder</em> (labelled as treatment.disorder) for the treatment argument - are then annotated upon main arguments.</p> <p>We provide two formats of data: visualisation-friendly brat-format data and structured json data for the convenience of use.</p> <p> </p>
Satellite remote sensing dataset of Sentinel-2 for phenology metrics extraction from sites in Bulgaria and France
<p><strong>Site Description:</strong></p> <p>In this dataset, there are seventeen production crop fields in Bulgaria where winter rapeseed and wheat were grown and two research fields in France where winter wheat – rapeseed – barley – sunflower and winter wheat – irrigated maize crop rotation is used. The full description of those fields is in the database "In-situ crop phenology dataset from sites in Bulgaria and France" (doi.org/10.5281/zenodo.7875440).</p> <p> </p> <p><strong>Methodology and Data Description:</strong></p> <p>Remote sensing data is extracted from Sentinel-2 tiles 35TNJ for Bulgarian sites and 31TCJ for French sites on the day of the overpass since September 2015 for Sentinel-2 derived vegetation indices and since October 2016 for HR-VPP products. To suppress spectral mixing effects at the parcel boundaries, as highlighted by Meier et al., 2020, the values from all datasets were subgrouped per field and then aggregated to a single median value for further analysis.</p> <p>Sentinel-2 data was downloaded for all test sites from CREODIAS (https://creodias.eu/) in L2A processing level using a maximum scene-wide cloudy cover threshold of 75%. Scenes before 2017 were available in L1C processing level only. Scenes in L1C processing level were corrected for atmospheric effects after downloading using Sen2Cor (v2.9) with default settings. This was the same version used for the L2A scenes obtained intermediately from CREODIAS. </p> <p>Next, the data was extracted from the Sentinel-2 scenes for each field parcel where only SCL classes 4 (vegetation) and 5 (bare soil) pixels were kept. We resampled the 20m band B8A to match the spatial resolution of the green and red band (10m) using nearest neighbor interpolation. The entire image processing chain was carried out using the open-source Python Earth Observation Data Analysis Library (EOdal) (Graf et al., 2022).</p> <p>Apart from the widely used Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we included two recently proposed indices that were reported to have a higher correlation with photosynthesis and drought response of vegetation: These were the Near-Infrared Reflection of Vegetation (NIRv) (Badgley et al., 2017) and Kernel NDVI (kNDVI) (Camps-Valls et al., 2021). We calculated the vegetation indices in two different ways: </p> <p>First, we used <strong>B08</strong> as near-infrared (NIR) band which comes in a native spatial resolution of 10 m. <strong>B08</strong> (central wavelength 833 nm) has a relatively coarse spectral resolution with a bandwidth of 106 nm.</p> <p>Second, we used <strong>B8A</strong> which is available at 20 m spatial resolution. <strong>B8A</strong> differs from B08 in its central wavelength (864 nm) and has a narrower bandwidth (21 nm or 22 nm in the case of Sentinel-2A and 2B, respectively) compared to B08.</p> <p> </p> <p>The High Resolution Vegetation Phenology and Productivity (<strong>HR-VPP</strong>) dataset from Copernicus Land Monitoring Service (CLMS) has three 10-m set products of Sentinel-2: vegetation indices, vegetation phenology and productivity parameters and seasonal trajectories (Tian et al., 2021). Both vegetation indices, Normalized Vegetation Index (NDVI) and Plant Phenology (PPI) and plant parameters, Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and Leaf Area Index (LAI) were computed for the time of Sentinel-2 overpass by the data provider. </p> <p>NDVI is computed directly from B04 and B08 and PPI is computed using Difference Vegetation Index (DVI = B08 - B04) and its seasonal maximum value per pixel. FAPAR and LAI are retrieved from B03 and B04 and B08 with neural network training on PROSAIL model simulations. The dataset has a quality flag product (QFLAG2) which is a 16-bit that extends the scene classification band (SCL) of the Sentinel-2 Level-2 products. A “medium” filter was used to mask out QFLAG2 values from 2 to 1022, leaving land pixels (bit 1) within or outside cloud proximity (bits 11 and 13) or cloud shadow proximity (bits 12 and 14). </p> <p>The <strong>HR-VPP</strong> daily raw vegetation indices products are described in detail in the user manual (Smets et al., 2022) and the computations details of PPI are given by Jin and Eklundh (2014). Seasonal trajectories refer to the 10-daily smoothed time-series of PPI used for vegetation phenology and productivity parameters retrieval with TIMESAT (Jönsson and Eklundh 2002, 2004).</p> <p>HR-VPP data was downloaded through the WEkEO Copernicus Data and Information Access Services (DIAS) system with a Python 3.8.10 harmonized data access (HDA) API 0.2.1. Zonal statistics [’min’, ’max’, ’mean’, ’median’, ’count’, ’std’, ’majority’] were computed on non-masked pixel values within field boundaries with rasterstats Python package 0.17.00.</p> <p> </p> <p>The Start of season date (SOSD), end of season date (EOSD) and length of seasons (LENGTH) were extracted from the annual Vegetation Phenology and Productivity Parameters (<strong>VPP</strong>) dataset as an additional source for comparison. These data are a product of the Vegetation Phenology and Productivity Parameters, see (https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity/vegetation-phenology-and-productivity) for detailed information.</p> <p> </p> <p><strong>File Description:</strong></p> <p>4 datasets:</p> <p>1_senseco_data_S2_B08_Bulgaria_France; 1_senseco_data_S2_B8A_Bulgaria_France; 1_senseco_data_HR_VPP_Bulgaria_France; 1_senseco_data_phenology_VPP_Bulgaria_France</p> <p>3 metadata:</p> <p>2_senseco_metadata_S2_B08_B8A_Bulgaria_France; 2_senseco_metadata_HR_VPP_Bulgaria_France; 2_senseco_metadata_phenology_VPP_Bulgaria_France</p> <p> </p> <p>The dataset files “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” concerns all vegetation indices (EVI, NDVI, kNDVI, NIRv) data values and related information, and metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France” describes all the existing variables. Both “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” have the same column variable names and for that reason, they share the same metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France”.</p> <p>The dataset file “1_senseco_data_HR_VPP_Bulgaria_France” concerns vegetation indices (NDVI, PPI) and plant parameters (LAI, FAPAR) data values and related information, and metadata file “2_senseco_metadata_HRVPP_Bulgaria_France” describes all the existing variables. </p> <p>The dataset file “1_senseco_data_phenology_VPP_Bulgaria_France” concerns the vegetation phenology and productivity parameters (LENGTH, SOSD, EOSD) values and related information, and metadata file “2_senseco_metadata_VPP_Bulgaria_France” describes all the existing variables.</p> <p> </p> <p><strong>Bibliography</strong></p> <p>G. Badgley, C.B. Field, J.A. Berry, Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv. 3 (2017) e1602244. https://doi.org/10.1126/sciadv.1602244.</p> <p>G. Camps-Valls, M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M.D. Mahecha, J. Muñoz-Marí, F.J. García-Haro, L. Guanter, M. Jung, J.A. Gamon, M. Reichstein, S.W. Running, A unified vegetation index for quantifying the terrestrial biosphere, Sci. Adv. 7 (2021) eabc7447. https://doi.org/10.1126/sciadv.abc7447.</p> <p>L.V. Graf, G. Perich, H. Aasen, EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data, Comput. Electron. Agric. 203 (2022) 107487. https://doi.org/10.1016/j.compag.2022.107487.</p> <p>H. Jin, L. Eklundh, A physically based vegetation index for improved monitoring of plant phenology, Remote Sens. Environ. 152 (2014) 512–525. https://doi.org/10.1016/j.rse.2014.07.010.</p> <p>P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens. 40 (2002) 1824–1832. https://doi.org/10.1109/TGRS.2002.802519.</p> <p>P. Jönsson, L. Eklundh, TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci. 30 (2004) 833–845. https://doi.org/10.1016/j.cageo.2004.05.006.</p> <p>J. Meier, W. Mauser, T. Hank, H. Bach, Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions, Comput. Electron. Agric. 169 (2020) 105205. https://doi.org/10.1016/j.compag.2019.105205.</p> <p>B. Smets, Z. Cai, L. Eklund, F. Tian, K. Bonte, R. Van Hoost, R. Van De Kerchove, S. Adriaensen, B. De Roo, T. Jacobs, F. Camacho, J. Sánchez-Zapero, S. Else, H. Scheifinger, K. Hufkens, P. Jönsson, HR-VPP Product User Manual Vegetation Indices, 2022.</p> <p>F. Tian, Z. Cai, H. Jin, K. Hufkens, H. Scheifinger, T. Tagesson, B. Smets, R. Van Hoolst, K. Bonte, E. Ivits, X. Tong, J. Ardö, L. Eklundh, Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ. 260 (2021) 112456. https://doi.org/10.1016/j.rse.2021.112456.</p>
SIMARA: a database for key-value information extraction from full-page handwritten documents
<p>We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents.</p> <p><br> Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction.<br> </p>
sequenced cfDNA samples, extracted DoC for TSSs (HK + PAU) and TFBSs (LYL1 and GRHL2)
<p>File contains CSV files which store depth of coverage values (cfDNA fragment's central 60 bp if length > 60 bp; entire fragment otherwise) extracted from 4 samples (sample BAM files deposited at the European Genome Phenome Archive, accession: <span> </span><a href="https://ega-archive.org/studies/EGAS00001006963">EGAS00001006963</a><span><span>) before and after correction of sample specific GC sequence content bias (GC bias) by GCparagon software using a custom Pysam implementation. The Pysam implementation can read alignment tags for DoC computation which is required for the signal after bias correction.</span></span></p> <p><span><span>Extracted loci are top 10k reported transcription factor binding sites for LYL1 and GRHL2 as well as transcription start sites of housekeeping genes (HK genes) and unexpressed genes as assessed from the Protein atlas (PAU genes).<br> Pysam implementation by Kapidzic Faruk can be found here: </span></span><a href="https://github.com/Faruk-K/pysam">https://github.com/Faruk-K/pysam</a></p> <p>Linked to the <a href="https://github.com/BGSpiegl/GCparagon/tree/including_EGAS00001006963_results-DEV">GCparagon tool code repository</a> which is available on GitHub.</p> <p>For details see publication which is linked to the EGA dataset and the GitHub repository. (not published at moment of submission to Zenodo)</p>
Data Extraction Form
<p>This Excel sheet contains the data extraction and quality assessment for our Systematic Literature Review (SLR) titled "Challenges in Constructing Deep Learning Software - A Systematic Literature Review."</p>
Annona squamosa Leaf Extract Inhibit Migration of Human Cervical Cancer Cells Through MMP-9 Expression
<p> Figure 1. a.<em>Annona squamosa</em> leaf . b.Simplicia powder of <em>Annona squamosa</em> leaves</p> <p>Figure 2. <em>Annona squamosa</em> leaves ethanol extract.</p> <p>Figure 3. FTIR spectrophotometer results of acetogenin compounds of<em> </em><em>Annona squamosa</em> leaves extract</p> <p>Figure 4. The cytotoxic test of HeLa cells at 24 hours. The combination of ASL (<em>Annona squamosa</em> leaf Extract) 12.5 mg/ml + cisplatin was very effective compared with a single therapy. *<em>P</em> < 0.05Figure 4. The cytotoxic test of HeLa cells at 24 hours. The combination of ASL (<em>Annona squamosa</em> leaf Extract) 12.5 mg/ml + cisplatin was very effective compared with a single therapy. *<em>P</em> < 0.05</p> <p>Figure 5. The percentage of living cells of HeLa cells were cultured with different concentrations of <em>Annona squamosa</em> leaf extract.</p> <p>Figure 6. The combination therapy of ASL with cisplatin reduced the expressions of MMP-9 in HeLa cells. a. A significant concentration for cytotoxicity was a concentration of 12.5 mg/mL ASL + 5 μg/mL Cisplatin. b. The number of MMP-9 positive cells. *<em>P< </em>0.005.</p> <p>Figure 7. The combination therapy of ASL and Cisplatin inhibit cell migration. a. HeLa cells were treated with single cisplatin or in combination with ASL 12.5; 25, 50, and a single dose of ASL 75 mg/mL. b. Percentage area of cells undergoing migration. *<em>P</em><0.005.</p> <p> </p> <p> </p>
Individual .ttl files of the PF 1,600 plant extracts
<p>Each file contains the ENPKG data (.ttl) of a given plant extract.</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.