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397 results for “Supplementary table”
Supplementary tables master's thesis Carolin Mattausch 2023
<p>These table contain information about ChIP-seq files from different sources used in Carolin Mattausch's master's thesis "Integration of transcriptome and epigenome for comparative analysis of various chondrocyte subtypes" conducted November 2022 to June 2023 in the Department of Developmental Biology at the Center of Medical Biotechnology, University Duisburg-Essen, under the supervision of Prof. Dr. Andrea Vortkamp, Dr. Manuela Wuelling, and Christoph Neu.</p> <p><strong>Supplementary table 4</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from <a href="https://www.encodeproject.org/">ENCODE</a>. These files were used in in comparison of H3K27me3 level between different murine and human tissues.</p> <p><strong>Supplementary table 5</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from <a href="https://egg2.wustl.edu/roadmap/web_portal/">Roadmap</a>. These files were used in in comparison of H3K27me3 level between different murine and human tissues.</p> <p><strong>Supplementary table 6</strong>: Metadata, experiment, and file accession numbers for ChIP-seq files downloaded from <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus</a>. These files were used in in comparison of H3K27me3 level between different murine and human tissues.</p>
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel's lambda model
<p class="MsoNormal"><span>Phylogenetic generalized least squares (PGLS) regression is widely used to detect evolutionary correlations. In contrast to the equal treatment of analyzed traits in conventional correlation methods such as Pearson and Spearman's rank tests, we must designate one trait as the independent variable and the other as the dependent variable. However, in our PGLS regression analyses (using Pagel's <em>λ</em> model) of both empirical and simulated datasets, switching independent and dependent variables yielded many conflicting results. A serious problem with PGLS regression that has not been noticed before is that selecting an inappropriate trait as the dependent variable will often result in an error. To assess correlations in simulated data, we established a gold standard by analyzing changes in traits along phylogenetic branches. Next, we tested seven potential criteria for dependent variable selection: log-likelihood, Akaike information criterion, <em>R</em><sup>2</sup>, <em>p</em>-value, Pagel's <em>λ</em>, Blomberg et al.'s <em>K</em>, and the estimated <em>λ</em> in <a name="_Hlk136010442"></a>Pagel's <em>λ</em> model. We determined that the last three criteria performed equally well in selecting the dependent variable and were superior to the other four. For practicality, we suggest using the trait with a higher <em>λ</em></span><span> or <em>K</em> </span><span>value as the dependent variable in future PGLS regressions. In analyzing the evolutionary relationship between two traits, we should designate the trait with a stronger phylogenetic signal as the dependent variable even if it could logically assume the cause in the relationship.</span></p>
Supplementary Tables - Re-analysis of hepatitis B virus integration sites reveals potential new loci associated with oncogenesis in hepatocellular carcinoma
<p>------------------------------------------------------------</p> <p><strong>Supplementary Tables</strong></p> <p>supplementary_table.xlsx</p> <ul> <li>T2T-CHM13_human</li> <li>T2T-CHM13_hbv</li> <li>GRCh38_human</li> <li>GRCh38_hbv</li> <li>GRCh_human_annotation</li> <li>meta</li> </ul> <p>------------------------------------------------------------</p> <p><strong>Re-analysis of hepatitis B virus integration sites reveals potential new loci associated with oncogenesis in hepatocellular carcinoma</strong><br> <a href="https://doi.org/10.5501/wjv.v12.i3.209">https://doi.org/10.5501/wjv.v12.i3.209</a></p><br> <p><strong>BACKGROUND</strong></p> <p>Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC). HBV DNA can get integrated into the hepatocyte genome to promote carcinogenesis. However, the precise mechanism by which the integrated HBV genome promotes HCC has not been elucidated.</p> <p><strong>AIM</strong></p> <p>To analyze the features of HBV integration in HCC using a new reference database and integration detection method.</p> <p><strong>METHODS</strong></p> <p>Published data, consisting of 426 Liver tumor samples and 426 paired adjacent non-tumor samples, were re-analyzed to identify the integration sites. Genome Reference Consortium Human Build 38 (GRCh38) and Telomere-to-Telomere Consortium CHM13 (T2T-CHM13 (v2.0)) were used as the human reference genomes. In contrast, human genome 19 (hg19) was used in the original study. In addition, GRIDSS VIRUSBreakend was used to detect HBV integration sites, whereas high-throughput viral integration detection (HIVID) was applied in the original study (HIVID-hg19).</p> <p><strong>RESULTS</strong></p> <p>A total of 5361 integration sites were detected using T2T-CHM13. In the tumor samples, integration hotspots in the cancer driver genes, such as TERT and KMT2B, were consistent with those in the original study. GRIDSS VIRUSBreakend detected integrations in more samples than by HIVID-hg19. Enrichment of integration was observed at chromosome 11q13.3, including the CCND1 pro-moter, in tumor samples. Recurrent integration sites were observed in mitochondrial genes.</p> <p><strong>CONCLUSION</strong></p> <p>GRIDSS VIRUSBreakend using T2T-CHM13 is accurate and sensitive in detecting HBV integration. Re-analysis provides new insights into the regions of HBV integration and their potential roles in HCC development.</p>
Kinematic Evolution of the Tangra Yumco Rift, South-Central Tibet: Supplementary Data Tables
<h4>We investigate rifting during continental collision in southern Tibet by testing kinematic models for two classes of rifts: Tibetan rifts are defined as >150 km in length and crosscut the Lhasa Terrane, and Gangdese rifts are <150 km long and isolated within the high topography of the Gangdese Range. Discerning rift kinematics is a crucial step towards understanding rift behavior and evolution that has been historically limited. We evaluate spatiotemporal trends in fault displacement and extension onset in the Tangra Yumco (TYC) rift and several nearby Gangdese rifts, and examine how contraction and rift exhumation relate to evolution of the Gangdese drainage divide. Igneous U-Pb and zircon (U-Th)/He (ZHe) results indicate rift footwall crystallization between ~59-49 Ma and cooling between ~60-4 Ma, respectively, with ZHe ages correlating with sample latitude. Samples from Gangdese latitudes (~29.4-29.8°N) yield predominantly Oligocene-early Miocene ages, whereas samples north of ~29.8°N yield both late Miocene-Pliocene ages and Paleocene-Eocene ages. Thermal history models indicate two-stage cooling, with initially slow cooling followed by accelerated cooling during late Miocene-Pliocene time. From spatial distributions of ZHe ages we interpret: (1) ~28-16 Ma ages from Gangdese latitudes reflect exhumation along contractional structures, (2) ~8-4 Ma ages reflect rift-related exhumation, and (3) ~60-48 Ma ages indicate these samples experienced lesser rift exhumation. Our data are consistent with a segment linkage evolution model for the TYC rift, withinteractions between rifts and contractional structures likely influencing the evolution of topography and location of the Gangdese drainage divide since Miocene time.</h4>
Supplementary tables for: Dependent variable selection in phylogenetic generalized least squares regression analysis under Pagel’s lambda model
Open the record for dataset details and reuse information.
Framepool supplementary data and tables
<p>Supplementary data for the Framepooling models.</p>
Thesis-Supplementary Tables
<p>The uploaded excel file contains all the supplementary tables for Marwa Almosailleakh Doctoral Thesis tilted '' Insights into cellular and molecular mechanisms of normal and malignant hematopoiesis from mouse models'' </p> <p> </p>
Supplementary Table S1: KEGG pathway enrichment
<p>This is a dataset excel file directly related to my manuscript (MBO3_2019_11_0405.R1). The dataset excel file is a detailed result of the KEGG pathway enrichment analysis. </p>
Supplementary data (table with explanation)
<p>Homology analysis data generated by package DNASTAR MegaAlign 5.00. Sequence data were obtained from HCV (n=29 HCV sequeces) detected in the dialysis unit of a tertiary care teaching medical center in Assam. </p>
Supplementary table with homology analysis of twenty nine HCV sequences ((5'UTR-Core region) )
<p>It is a supplementary data table with homology analysis of 29 HCV sequences ((5’UTR-Core region) obtained from a dialysis unit of a tertiary care teaching hospital of Northeast India (Assam). It is a part (supplementary data) of the manuscript titled - "Circulation of an atypical hepatitis C virus (HCV) strain in a dialysis unit in northeast India" </p>
Supplementary Material - Table 1: MAG summary
<p><strong>Supplementary Table 1. Details on taxonomic classification, genes of interest, completeness, redundancy, and abundance of MAGs obtained in this study. </strong>This file relates to the preprint https://www.biorxiv.org/content/10.1101/2020.09.22.307553v1, "Enrichment of novel <em>Verrucomicrobia, Bacteroidetes</em> and <em>Krumholzibacteria</em> in an oxygen-limited, methane- and iron-fed bioreactor inoculated with Bothnian Sea sediments".</p>
Supplementary Table 1 - GISAID Accession Numbers of Samples analysed in the first and second waves of SARS-CoV-2 cases in Irish hospitals
<p>The Supplementary Table 1 contains the GISAID accession numbers of samples sequenced in the context of the AIID biobank in the Republic of Ireland during the first and second wave of SARS-CoV-2 cases in hospitals of Dublin.</p>
Supplementary Tables S1 - S6
<p>Supplementary Material for publication - Environmental DNA as a tool to reconstruct catch composition for longline fisheries vessels.</p> <p>S1: Brine tanks, species and samples collected per vessel</p> <p>S2: Summary statistics per sequencing run</p> <p>S3: Number of raw reads for non-target taxa and the taxa present in control samples</p> <p>S4: Number of raw reads per target species</p> <p>S5: ASV species assignment with bootstrap values</p> <p>S6: Raw ASV counts per sample</p>
Supplementary Tables for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Tables for the Preprint entitled: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv. </p> <p><strong>Table S1. </strong>Detailed information on the strawberry fields included in this study.</p> <p><strong>Table S2</strong>. Detailed information on the weather stations used to retrieve temperature and precipitation data used in this study </p> <p><strong>Table S3. </strong>Strawberry samples analyzed in this study with symptoms resembling strawberry green petal phytoplasma disease during both growing seasons studied here.</p> <p><strong>Table S4.</strong> The geographic location of all the strawberry green petal phytoplasma disease cases reported to the provincial laboratory in expertise in diagnostic and phytopathology in the last decade.</p> <p><strong>Table S5.</strong> Leafhopper species and the number of specimens per species analyzed by phytoplasma-specific PCR to detect the presence of the pathogen.</p> <p><strong>Table S6.</strong> Detailed information on the leafhoppers incubated with strawberry plants during the phytoplasma transmission assays.</p> <p><strong>Table S7.</strong> Detailed information on <em>Macosteles quadrilineatus</em> used to study the leafhopper microbiome.</p> <p><strong>Table S8. </strong>Detailed information on the insecticides used by strawberry growers during both grow seasons included in the study and those treatments selected for further statistic analyses.</p> <p><strong>Table S9. </strong>Identification and number of leafhopper species captured in strawberry fields in each geographic region screened in this study.</p> <p><strong>Table S10. </strong>Detailed information of diversity indexes Shannon and Simpson calculated using the data collected in this study.</p> <p><strong>Table S11.</strong> Fixed days and temperature values used during leafhopper populations modelling.</p> <p><strong>Table S12.</strong> Detailed information on the taxonomy of the phytoplasma strain SbGPQ affecting strawberry plants in eastern Canada by hybridization and illumine sequencing and by PCR amplification, cloning and Sanger sequencing.</p> <p><strong>Table S13.</strong> Detailed information on <em>Macosteles quadrilineatus</em> microbiome including OTUs, reads, and metadata information.</p> <p><strong>Table S14.</strong> Detailed information on the core microbiome for <em>Macosteles quadrilineatus</em> captured during each growing season and in common for all the leafhoppers analyzed during this study.</p> <p><strong>Table S15. </strong><span>BIC values for models selection. </span></p>
Supplementary Tables for "Cormohipparion cappadocium, a new species from the Late Miocene of Yeniyaylacık, Türkiye, and the emergence of Western Eurasian Hipparion bioprovinciality"
<p>Supplemetary Tables for the paper:</p> <p><em>Cormohipparion cappadocium</em>, a new species from the Late Miocene of Yeniyaylacık, Türkiye, and the emergence of Western Eurasian Hipparion bioprovinciality<em> </em></p> <p>Raymond Louis Bernor, Majid Mirzaie Ataabadi, Oksan Basoglu, Omar Cirilli, Ferhat Kaya, Cesur Pehlevan, Mansoureh Niknahad, Mohammad Reza Vaziri, Ahmad Lotfabad Arab</p> <p>Supplementary Table 1: Measurements for Yeniyaylacık <em>Cormohipparion cappadocium, </em>Sinap <em>Cormohipparion sinapensis</em> and <em>Cormohipparion kecigibi</em>, Maragheh <em>Hipparion gettyi </em>and <em>Hipparion </em>aff. <em>gettyi.</em></p> <p>Supplementary Table 2: Character State Analysis of Yeniyaylacık <em>Cormohipparion cappadocium </em>compared to <em>Hippotherium, Hipparion </em>and other <em>Cormohipparion </em>species.</p> <p>Supplementary Table 3: Variance components and loadings’ distribution for Principal Component Analyses on crania, third metacarpals and third metatarsals discussed in the text.</p>
Supplementary Files (Figures and Tables)
<p>Supplementary files for publication: </p> <p>Figure S1: Stacked barplots of the mean relative abundances of the most abundant phyla. The x-axis labels refer to soft coral (CrS), hard coral (CrH), sea cucumber (SeC), flatworm (Flt), chiton (Cht), algae (Alg), the sponges Ptilocaulis sp. (SpT), Aaptos lobata (SpA), Cinachyrella sp. (SpC), and Paratetilla sp. (SpP), seawater (Wat), and sediment (Sed). </p> <p>Figure S2: Ordination showing the first two axes of the Principal Coordinates Analysis (PCO). a. total microbial community, b. virulence factors. Light grey symbols represent total microbial community or VFs with the symbol size representing their abundance (number of sequence reads). Symbol codes refer to: soft coral (CrS), hard coral (CrH), sea cucumber (SeC), flatworm (Flt), chiton (Cht), algae (Alg), the sponges Ptilocaulis sp. (SpT), Aaptos lobata (SpA), Cinachyrella sp. (SpC), and Paratetilla sp. (SpP), seawater (Wat), and sediment (Sed).</p> <p>Figure S3: Procrustes analysis comparing putative pathogens versus a. total microbial community, b. antibiotic resistance genes (ARGs) and c. virulence factor (Vfs) composition (arrowbase indicates the corresponding positions of the samples in the total microbial community or ARGs or VFs ordination (de Voogd et al. 2019) while arrowheads indicate the corresponding positions of the samples in the putative pathogens ordination). While d. represents the procrustes analysis comparing total microbial community versus a. ARGs (in this case: arrowbase indicates the corresponding positions of the samples in ARGs ordination while arrowheads indicate the corresponding positions of the samples in the total microbial community ordination). Symbol codes refer to: soft coral (CrS), hard coral (CrH), sea cucumber (SeC), flatworm (Flt), chiton (Cht), algae (Alg), the sponges Ptilocaulis sp. (SpT), Aaptos lobata (SpA), Cinachyrella sp. (SpC), and Paratetilla sp. (SpP), seawater (Wat), and sediment (Sed).</p> <p>Tables S1-S11.</p>
Supplementary Table 1 Interview flow and description of activities
<p>Supplementary table 1</p>
Supplementary Table 1-9 of the manuscript: Magmatic Cl-H2O contents, fluid extraction and porphyry fertility: Evidence from zircon and its apatite inclusions
<p><strong><span>Table DR1</span></strong><span> Major element composition of biotite from the three intrusions in the ZOF</span></p> <p><strong><span>Table DR2</span></strong><span> Laser Raman spectra and trace element compositions of zircon grains from the Cretaceous intrusions in the ZOF</span></p> <p><strong><span>Table DR3</span></strong><span> Major element composition of plagioclase grains from the Cretaceous intrusions in the ZOF</span></p> <p><strong><span>Table DR4</span></strong><span> Sr isotopic composition of the plagioclase from the Cretaceous intrusions in the ZOF</span></p> <p><strong><span>Table DR5</span></strong><span> Zircon Lu-Hf isotopic composition of the Sifang granodiorite, Luoboling granodiorite porphyry and Zhongliao granodiorite</span></p> <p><strong><span>Table DR6 </span></strong><span>Zircon water contents and O isotopic compositions by SIMS of the Cretaceous intrusions in the ZOF</span></p> <p><strong><span>Table DR7 </span></strong><span>Major and volatile element composition of the zircon-host apatite from the Cretaceous intrusions in the ZOF </span></p> <p><strong><span>Table DR8</span></strong><span> The top 20 best fitting runs</span></p> <p><strong><span>Table DR9</span></strong><span> Summarization of the salinity calculations</span></p>
Supplementary tables for Geophysical Research Letters_2021GL096049
<p>Data associated with the publication "Significant increase of continental freeboard during the early Paleoproterozoic: Insights from metasediment-derived granites"</p>
Supplementary Table S1 (raw data) of "Filtration extraction method using microfluidic channel for measuring environmental DNA "
<p>Supplementary Table S1 (all raw data) of "Filtration extraction method using microfluidic channel for measuring environmental DNA ". Each data of the validation experiment; Experiment 1-4 was located in different sheets..</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.