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252 results for “in vitro analysis”
Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro
<p>This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).</p>
The Supplementary Material for the article entitled "Comparative analysis of global transcriptomes in nontyphoidal Salmonella clinical isolates from pediatric patients with and without bacteremia after infecting human intestinal epithelium in vitro"
<p>The Supplementary Material (Additional files 1-5, including Table S1-S4 and Figure S1) for this article.</p> <p> </p> <p><strong>Table S1.</strong> Upregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S2.</strong> Downregulated genes in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S3. </strong>The enriched GO terms in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Table S4. </strong>The enriched KEGG pathways in Group B versus Groups A and C+D.</p> <p> </p> <p><strong>Figure S1. </strong>The enriched GO terms and KEGG pathways in Group B relative to Group A. Bar charts show the enriched GO terms (A) and the enriched KEGG pathways (B) by significance power. Color of bars indicate power of significance and length in x axes of bar indicate number of annotated genes in the particular term of pathway. Cnetplots show the relationship between GO term (C) and KEGG pathways (D). Dot size representing GO terms and KEGG pathways indicates number of significantly changed and its annotated genes. The GO terms or KEGG pathways connected through their common and annotated genes. </p>
Figure 3 in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 3. Photomicrography of exfoliated oral mucosa cells with: (A) karyorrhexis; (B) karyolysis; (C) micronucleus; (D) binucleation; and (E) macronucleus. Magnification X1000.
Figure 2 in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 2. Cytotoxic effect of tiliroside (H. velutina) against RBC; (C-) Negative control (erythrocytes 0.5%), (C+) Positive control (1% Triton X-100). P <0.05 (*), P <0.01(**) and P <0.001 (***) versus positive control.
In vivo and in vitro electrochemical impedance spectroscopy analysis of acute and chronic intracranial electrodes
<p>Invasive intracranial electrodes are used in both clinical and research applications for recording and stimulation of brain tissue, providing essential data in acute and chronic contexts. The impedance characteristics of the electrode–tissue interface (ETI) evolve over time and can change dramatically relative to pre-implantation baseline. Understanding how ETI properties contribute to the recording and stimulation characteristics of an electrode can provide valuable insights for users who often do not have access to complex impedance characterizations of their devices. In contrast to the typical method of characterizing electrical impedance at a single frequency, we demonstrate a method for using electrochemical impedance spectroscopy (EIS) to investigate complex characteristics of the ETI of several commonly used acute and chronic electrodes. We also describe precise modeling strategies for verifying the accuracy of our instrumentation and understanding device–solution interactions, both in vivo and in vitro. Included with this publication is a dataset containing both in vitro and in vivo device characterizations, as well as some examples of modeling and error structure analysis results. These data can be used for more detailed interpretation of neural recordings performed on common electrode types, providing a more complete picture of their properties than is often available to users.</p>
Design and in vitro realization of carbon-conserving photorespiration - Computational Analysis
<p>Our aim is to develop a framework for modeling C3 photosynthesis in mesophyll cells that allows us to compare native photorespiration with engineered photosynthetic shunts. In particular, we want to model conditions that are most relevant to agricultural crops, i.e. a range of light intensities and both ambient and low CO2 intercellular airspace concentrations. Here we presented our computational analysis based on pathSeekR, the stoichiometric-kinetic model, kinetic models of photorespiration shunts and pathSeekR pathway architectures. </p>
In vivo and in vitro electrochemical impedance spectroscopy analysis of acute and chronic intracranial electrodes
Open the record for dataset details and reuse information.
Dataset and Data analysis "Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?"
<p>Data Analysis for the publication 10.1002/jbio.202000264.</p> <p>It is divided in three different folders describing three different part of the data analysis:</p> <p><strong>A. DATA TREATMENT RAMAN (Folder 1)</strong></p> <p><em>1. Import data using the Import_Raman script.<br> 2. Plot Spectra and integrate DOX band<br> Figure 1A<br> Figure 1B<br> 3. PCA<br> Figure 1D<br> Figure 1C<br> SM 1<br> 4. PLS<br> Figure 1F<br> Figure 1E</em></p> <p><strong>B. ANALYSIS OF IR DATA AND MULTIMODAL IR-RAMAN OF DOX UPTAKE (Folder 2)</strong></p> <p><em>1 Load Data IR<br> 2 Exploratory Analysis IR<br> Figure 2A<br> 3 PCA <br> SM 2<br> 4. Partial Least Squares vs time<br> Figure 2C<br> Figure 2B<br> 5. Partial Least Squares vs Raman Signal<br> Figure 2E<br> Figure 2D<br> 6. Make Averages and clean up Data for DATA Fusion<br> IR<br> Raman<br> 7. 2DCORR<br> Figure 3B<br> 8. MCR_ALS WITH DATA FUSION<br> Fitting of the concentration of Raman using the method in [9].<br> MCR-ALS<br> Figures 4 A, B and C</em></p> <p> </p> <p><strong>C. SIMULATION (Folder 3)</strong></p> <p><em>1. Load Raman DATA<br> 2. Simulate Raman DAta<br> 3. Load and simulate IR Data<br> 4. 2D corr<br> Figure 3A</em></p> <p> </p> <p>. Each folder contains a .mlx with the data analysis performed. Figures numbering corresponds to the one found in the article.</p>
Figure 1. Kaempferol-3-O in Analysis of the toxicological and pharmacokinetic profile of Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside - Tiliroside: in silico, in vitro and ex vivo assay
Figure 1. Kaempferol-3-O-β-D-(6"-E-p-coumaryl) glucopyranoside – tiliroside.
Time series analysis of tegument ultrastructure of in vitro transformed miracidium to mother sporocyst of the human parasite Schistosoma mansoni
<p>Here is a compilation of all the Scanning Electron Microscopy pictures at our disposal regarding the in vitro transformation of miracidia to mother sporocysts of <em>Schistosoma mansoni</em>. These datas were partially published in:</p> <p><a href="https://doi.org/10.1016/j.actatropica.2023.106840">https://doi.org/10.1016/j.actatropica.2023.106840</a></p> <p> </p>
Stone Size on Endoscopic View as a Predictor of Successful Stone Retrieval During Flexible Ureteroscopy: An in Vitro Analysis
<p>Raw data for study: Stone Size on Endoscopic View as a Predictor of Successful Stone Retrieval During Flexible Ureteroscopy: An in Vitro Analysis</p>
Development of a flow chamber system for the reproducible in vitro analysis of biofilm formation on implant materials
<p>The data provided are the original data from the microscopic investigation of oral bacterial biofilms. Bacteria were stained with a life/dead staining. Viable cells are represented in red, non-viable cells are shown in green. The biofilms were gained by 50 stacks each with a CLSM. The biofilms were formed in a flow chamber system with a flow velocity of 100µL/min over 24-72 hours. The biofilms were grown on tintanium discs.</p>
Data for "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection"
<p>Once decompressed, the file contains a folder which contains:</p> <ul> <li>The files "s100_Nth.fasta" (where "N" is 5, 6, 7 or 8), which are the output of the SELEX experiment described in the paper with DOI: <a href="https://doi.org/10.1002/cbic.201900265">10.1002/cbic.201900265</a>. They are standard fasta files, and the descriptor of each sequence is of the form "seqX-Y", where "X" is an increasing label, and "Y" is the number of times "seqX" has been obtained (number of counts of "seqX").</li> <li>The file "Aptamer_Exp_Results.csv", which contains the sequences tested experimentally for the paper "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection" (preprint available at https://doi.org/10.1101/2022.03.12.484094), with the following experimental results for each sequence: (i) whether the sequence was able to bind thrombin ('B' for binders, 'NB' for non-binders); (ii) the thrombin exosite used for binding ('I' for exosite I, 'II' for exosite II, 'n/a' for sequences not tested).</li> </ul> <p>Examples of usage of the data are available at https://github.com/adigioacchino/RBMsForAptamers.</p>
Global Transcriptomic Analysis of Topical Sodium Alginate Protection Against Peptic Damage in An In Vitro Model of Treatment-Resistant Gastroesophageal Reflux Disease
<p>PA= pepsin + Acid; "Sham + PA" means "Pretreatment + Treatment"</p> <p><span>Breakthrough symptoms </span>are thought to occur in roughly half of <span>all </span>gastroesophageal reflux disease (GERD) patients despite maximal acid suppression (proton pump inhibitor, PPI) therapy. Topical alginates have recently been shown to enhance mucosal defense against acid-pepsin insult during GERD. We aimed to examine potential alginate protection of transcriptomic changes in a cell culture model of PPI recalcitrant GERD. Immortalized normal-derived human esophageal epithelial cells underwent pretreatment with commercial alginate-based anti-reflux medications (Gaviscon Advance or Gaviscon Double Action), a matched-viscosity placebo control, or pH 7.4 buffer (sham) alone for 1 minute, followed by exposure to pH 6.0+pepsin or buffer alone for 3 minutes. RNA sequencing was conducted, and Ingenuity Pathway Analysis was performed with a false discovery rate of ≤0.01, and absolute fold-change of ≥<span>1.3. Pepsin-acid exposure disrupted gene expressions associated with epithelial barrier function, chromatin structure</span>, carcinogenesis, and inflammation<span>. Alginate formulations demonstrated protection by mitigating these changes and promoting extracellular matrix repair, downregulating proto-oncogenes, and enhancing tumor suppressor expression. </span>These data suggest molecular mechanisms by which alginates provide topical protection against injury during weakly acidic reflux and support a potential role for alginates in prevention of GERD-related carcinogenesis.</p>
Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis
<p>Associations between leguminous plants and symbiotic nitrogen-fixing rhizobia are a classic example of mutualism between a eukaryotic host and a specific group of prokaryotic microbes. Although this symbiosis is in part species-specific, different rhizobial strains may colonise the same nodule. Some rhizobial strains are commonly known as better competitors than others, but detailed analyses that aim to predict rhizobial competitive abilities based on genomes are still scarce. Here, we performed a bacterial <em>genome-wide association (GWAS) analysis to define the </em>genomic determinants related to the competitive capabilities in the model rhizobial species <em>Sinorhizobium meliloti.</em> For this, 13 tester strains were GFP-tagged and assayed <i>vs.</i> 3 RFP-tagged reference competitor strains (<em>Rm1021, AK83, and BL225C) in a</em> <i>Medicago sativa</i> nodule occupancy test. Competition data and strain genomic sequences were employed to build a model for GWAS based on <i>k</i>-mers. Among the <i>k</i>-mers with the highest scores, 51 <i>k</i>-mers mapped on the genomes of four strains showing the highest competition phenotypes (> 60% single strain nodule occupancy; GR4, KH35c, KH46 and SM11) <i>vs.</i> BL225C. These <i>k</i>-mers were mainly located on the symbiosis-related megaplasmid pSymA, specifically on genes coding for transporters, proteins involved in the biosynthesis of cofactors and proteins related to metabolism (e.g., fatty acids). The same analysis was performed considering the sum of single and mixed nodules obtained in the competition assays <em>vs. </em>BL225C, retrieving <i>k</i>-mers mapped on the genes previously found and on <i>vir</i> genes. Therefore, the competition abilities seem to be linked to multiple genetic determinants and comprise several cellular components.</p>
Underlying data for: Apical microleakage evaluation for different endodontic sealers by spectrophotometric analysis: an in vitro study
<p>Underlying data for: Apical microleakage evaluation for different endodontic sealers by spectrophotometric analysis: an in vitro study</p>
STROBE checklist for In vitro Apical microleakage evaluation for different endodontic sealers by spectrophotometric analysis: an observational study
<p>STROBE checklist for: In vitro Apical microleakage evaluation for different endodontic sealers by spectrophotometric analysis: an observational study</p>
Dataset for study on In vitro analysis of the effects of analgesics on the motility of female adult Onchocerca volvulus worms
<p>Dataset on the effects of acetaminophen, ibuprofen and aspirin on the motility of Onchocerca volvulus parasites. The measurements were done with the WormAssay Software over the course of 7 days.</p>
Fig. 2 in Comparative transcriptome analysis infers bulb derived in vitro cultures as a promising source for sipeimine biosynthesis in Fritillaria cirrhosa D. Don (Liliaceae, syn. Fritillaria roylei Hook.) - High value Himalayan medicinal herb
Fig. 2. (A–E) Differential gene expression analysis in comparative F. roylei transcriptome: (A) Heat-map showing differential gene expression in the bulb (PKW) vs callus (PK2); bulb (PKW) vs in vitro regenerated plantlets (PK1) and callus (PK2) vs in vitro regenerated plantlets (PK1); (B) Venn diagram represents the differential gene expression in PKW vs PK1; PKW vs PK2; PK2 vs PK1, (C–E) Volcano plots represents the differential gene expression in PKW vs PK1; PKW vs PK2; PK2 vs PK1 as colour description image, where p-value & log2 fold-change in red colour represents genes with log2 fold-change cut off 2 and p-value <=0.05; p-value in blue colour represents genes with no cut off on log2 fold-change and p-value <=0.05. Whereas, log2 fold-change in green colour represents genes with fold-change cut off 2 but no p-value cut off and non-significant (NS) in grey colour represents genes with no filter on log2 fold-change and p-value, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Comparative transcriptome analysis infers bulb derived in vitro cultures as a promising source for sipeimine biosynthesis in Fritillaria cirrhosa D. Don (Liliaceae, syn. Fritillaria roylei Hook.) - High value Himalayan medicinal herb
Fig. 1. (A–F) Functional annotations and unigenes classification of comparative F. roylei transcriptome: (A) Unigenes annotation with top 15 different plant species; (B) Top 5 pathway representation as per Kyoto Encyclopedia of Genes and Genomes; (C) Gene Ontology classification under the cellular component, molecular function, and biological process categories; (D) COG (Cluster of Orthologous Groups of proteins) classification into nine different categories; (E) Unigenes classification into major transcription factor families; (F) Gene family and sub-family classification using TAIR database.
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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)
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.