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RNA-seq</title> <meta name="description" content="Search results for: single-cell RNA-seq"> <meta name="keywords" content="single-cell RNA-seq"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="single-cell RNA-seq"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 11</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: single-cell RNA-seq</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Functional Variants Detection by RNAseq</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raffaele%20A.%20Calogero">Raffaele A. Calogero</a> </p> <p class="card-text"><strong>Abstract:</strong></p> RNAseq represents an attractive methodology for the detection of functional genomic variants. RNAseq results obtained from polyA+ RNA selection protocol (POLYA) and from exonic regions capturing protocol (ACCESS) indicate that ACCESS detects 10% more coding SNV/INDELs with respect to POLYA. ACCESS requires less reads for coding SNV detection with respect to POLYA. However, if the analysis aims at identifying SNV/INDELs also in the 5’ and 3’ UTRs, POLYA is definitively the preferred method. No particular advantage comes from ACCESS or POLYA in the detection of fusion transcripts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fusion%20transcripts" title="fusion transcripts">fusion transcripts</a>, <a href="https://publications.waset.org/abstracts/search?q=INDEL" title=" INDEL"> INDEL</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA-seq" title=" RNA-seq"> RNA-seq</a>, <a href="https://publications.waset.org/abstracts/search?q=WES" title=" WES"> WES</a>, <a href="https://publications.waset.org/abstracts/search?q=SNV" title=" SNV"> SNV</a> </p> <a href="https://publications.waset.org/abstracts/57993/functional-variants-detection-by-rnaseq" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57993.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">288</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10</span> RNAseq Reveals Hypervirulence-Specific Host Responses to M. tuberculosis Infection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gina%20Leisching">Gina Leisching</a>, <a href="https://publications.waset.org/abstracts/search?q=Ray-Dean%20Pietersen"> Ray-Dean Pietersen</a>, <a href="https://publications.waset.org/abstracts/search?q=Carel%20Van%20Heerden"> Carel Van Heerden</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Van%20Helden"> Paul Van Helden</a>, <a href="https://publications.waset.org/abstracts/search?q=Ian%20Wiid"> Ian Wiid</a>, <a href="https://publications.waset.org/abstracts/search?q=Bienyameen%20Baker"> Bienyameen Baker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The distinguishing factors that characterize the host response to infection with virulent Mycobacterium tuberculosis (M.tb) are largely confounding. We present an infection study with two genetically closely related M.tb strains that have vastly different pathogenic characteristics. The early host response to infection with these detergent-free cultured strains was analyzed through RNAseq in an attempt to provide information on the subtleties which may ultimately contribute to the virulent phenotype. Murine bone marrow-derived macrophages (BMDMs) were infected with either a hyper- (R5527) or hypovirulent (R1507) Beijing M. tuberculosis clinical isolate. RNAseq revealed 69 differentially expressed host genes in BMDMs during comparison of these two transcriptomes. Pathway analysis revealed activation of the stress-induced and growth inhibitory Gadd45 signaling pathway in hypervirulent infected BMDMs. Upstream regulators of interferon activation such as and IRF3 and IRF7 were predicted to be upregulated in hypovirulent-infected BMDMs. Additional analysis of the host immune response through ELISA and qPCR included the use of human THP-1 macrophages where a robust proinflammatory response was observed after infection with the hypervirulent strain. RNAseq revealed two early-response genes (IER3 and SAA3) and two host-defence genes (OASL1 and SLPI) that were significantly upregulated by the hypervirulent strain. The role of these genes under M.tb infection conditions are largely unknown but here we provide validation of their presence with use of qPCR and Western blot. Further analysis into their biological role under infection with virulent M.tb is required. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=host-response" title="host-response">host-response</a>, <a href="https://publications.waset.org/abstracts/search?q=Mycobacterium%20tuberculosis" title=" Mycobacterium tuberculosis"> Mycobacterium tuberculosis</a>, <a href="https://publications.waset.org/abstracts/search?q=RNAseq" title=" RNAseq"> RNAseq</a>, <a href="https://publications.waset.org/abstracts/search?q=virulence" title=" virulence"> virulence</a> </p> <a href="https://publications.waset.org/abstracts/59133/rnaseq-reveals-hypervirulence-specific-host-responses-to-m-tuberculosis-infection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59133.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">210</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9</span> Identification of Genomic Mutations in Prostate Cancer and Cancer Stem Cells By Single Cell RNAseq Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wen-Yang%20Hu">Wen-Yang Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranli%20Lu"> Ranli Lu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Maienschein-Cline"> Mark Maienschein-Cline</a>, <a href="https://publications.waset.org/abstracts/search?q=Danping%20Hu"> Danping Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Larisa%20Nonn"> Larisa Nonn</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshi%20Shioda"> Toshi Shioda</a>, <a href="https://publications.waset.org/abstracts/search?q=Gail%20S.%20Prins"> Gail S. Prins</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Genetic mutations are highly associated with increased prostate cancer risk. In addition to whole genome sequencing, somatic mutations can be identified by aligning transcriptome sequences to the human genome. Here we analyzed bulk RNAseq and single cell RNAseq data of human prostate cancer cells and their matched non-cancer cells in benign regions from 4 individual patients. Methods: Sequencing raw reads were aligned to the reference genome hg38 using STAR. Variants were annotated using Annovar with respect to overlap gene annotation information, effect on gene and protein sequence, and SIFT annotation of nonsynonymous variant effect. We determined cancer-specific novel alleles by comparing variant calls in cancer cells to matched benign cells from the same individual by selecting unique alleles that were only detected in the cancer samples. Results: In bulk RNAseq data from 3 patients, the most common variants were the noncoding mutations at UTR3/UTR5, and the major variant types were single-nucleotide polymorphisms (SNP) including frameshift mutations. C>T transversion is the most frequently presented substitution of SNP. A total of 222 genes carrying unique exonic or UTR variants were revealed in cancer cells across 3 patients but not in benign cells. Among them, transcriptome levels of 7 genes (CITED2, YOD1, MCM4, HNRNPA2B1, KIF20B, DPYSL2, NR4A1) were significantly up or down regulated in cancer stem cells. Out of the 222 commonly mutated genes in cancer, 19 have nonsynonymous variants and 11 are damaged genes with variants including SIFT, frameshifts, stop gain/loss, and insertions/deletions (indels). Two damaged genes, activating transcription factor 6 (ATF6) and histone demethylase KDM3A are of particular interest; the former is a survival factor for certain cancer cells while the later positively activates androgen receptor target genes in prostate cancer. Further, single cell RNAseq data of cancer cells and their matched non-cancer benign cells from both primary 2D and 3D tumoroid cultures were analyzed. Similar to the bulk RNAseq data, single cell RNAseq in cancer demonstrated that the exonic mutations are less common than noncoding variants, with SNPs including frameshift mutations the most frequently presented types in cancer. Compared to cancer stem cell enriched-3D tumoroids, 2D cancer cells carried 3-times higher variants, 8-times more coding mutations and 10-times more nonsynonymous SNP. Finally, in both 2D primary and 3D tumoroid cultures, cancer stem cells exhibited fewer coding mutations and noncoding SNP or insertions/deletions than non-stem cancer cells. Summary: Our study demonstrates the usefulness of bulk and single cell RNAseaq data in identifying somatic mutations in prostate cancer, providing an alternative method in screening candidate genes for prostate cancer diagnosis and potential therapeutic targets. Cancer stem cells carry fewer somatic mutations than non-stem cancer cells due to their inherited immortal stand DNA from parental stem cells that explains their long-lived characteristics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prostate%20cancer" title="prostate cancer">prostate cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=stem%20cell" title=" stem cell"> stem cell</a>, <a href="https://publications.waset.org/abstracts/search?q=genomic%20mutation" title=" genomic mutation"> genomic mutation</a>, <a href="https://publications.waset.org/abstracts/search?q=RNAseq" title=" RNAseq"> RNAseq</a> </p> <a href="https://publications.waset.org/abstracts/193081/identification-of-genomic-mutations-in-prostate-cancer-and-cancer-stem-cells-by-single-cell-rnaseq-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193081.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> The Importance of including All Data in a Linear Model for the Analysis of RNAseq Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roxane%20A.%20Legaie">Roxane A. Legaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Kjiana%20E.%20Schwab"> Kjiana E. Schwab</a>, <a href="https://publications.waset.org/abstracts/search?q=Caroline%20E.%20Gargett"> Caroline E. Gargett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Studies looking at the changes in gene expression from RNAseq data often make use of linear models. It is also common practice to focus on a subset of data for a comparison of interest, leaving aside the samples not involved in this particular comparison. This work shows the importance of including all observations in the modeling process to better estimate variance parameters, even when the samples included are not directly used in the comparison under test. The human endometrium is a dynamic tissue, which undergoes cycles of growth and regression with each menstrual cycle. The mesenchymal stem cells (MSCs) present in the endometrium are likely responsible for this remarkable regenerative capacity. However recent studies suggest that MSCs also plays a role in the pathogenesis of endometriosis, one of the most common medical conditions affecting the lower abdomen in women in which the endometrial tissue grows outside the womb. In this study we compared gene expression profiles between MSCs and non-stem cell counterparts (‘non-MSC’) obtained from women with (‘E’) or without (‘noE’) endometriosis from RNAseq. Raw read counts were used for differential expression analysis using a linear model with the limma-voom R package, including either all samples in the study or only the samples belonging to the subset of interest (e.g. for the comparison ‘E vs noE in MSC cells’, including only MSC samples from E and noE patients but not the non-MSC ones). Using the full dataset we identified about 100 differentially expressed (DE) genes between E and noE samples in MSC samples (adj.p-val < 0.05 and |logFC|>1) while only 9 DE genes were identified when using only the subset of data (MSC samples only). Important genes known to be involved in endometriosis such as KLF9 and RND3 were missed in the latter case. When looking at the MSC vs non-MSC cells comparison, the linear model including all samples identified 260 genes for noE samples (including the stem cell marker SUSD2) while the subset analysis did not identify any DE genes. When looking at E samples, 12 genes were identified with the first approach and only 1 with the subset approach. Although the stem cell marker RGS5 was found in both cases, the subset test missed important genes involved in stem cell differentiation such as NOTCH3 and other potentially related genes to be used for further investigation and pathway analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20expression" title="differential expression">differential expression</a>, <a href="https://publications.waset.org/abstracts/search?q=endometriosis" title=" endometriosis"> endometriosis</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20model" title=" linear model"> linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=RNAseq" title=" RNAseq"> RNAseq</a> </p> <a href="https://publications.waset.org/abstracts/36190/the-importance-of-including-all-data-in-a-linear-model-for-the-analysis-of-rnaseq-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36190.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Deciphering the Action of Neuraminidase in Glioblastoma Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nathalie%20Baeza-Kallee">Nathalie Baeza-Kallee</a>, <a href="https://publications.waset.org/abstracts/search?q=Rapha%C3%ABl%20Berg%C3%A8s"> Raphaël Bergès</a>, <a href="https://publications.waset.org/abstracts/search?q=Victoria%20Hein"> Victoria Hein</a>, <a href="https://publications.waset.org/abstracts/search?q=St%C3%A9phanie%20Cabaret"> Stéphanie Cabaret</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeremy%20Garcia"> Jeremy Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Abiga%C3%ABlle%20Gros"> Abigaëlle Gros</a>, <a href="https://publications.waset.org/abstracts/search?q=Emeline%20Tabouret"> Emeline Tabouret</a>, <a href="https://publications.waset.org/abstracts/search?q=Aur%C3%A9lie%20Tchoghandjian"> Aurélie Tchoghandjian</a>, <a href="https://publications.waset.org/abstracts/search?q=Carole%20Colin"> Carole Colin</a>, <a href="https://publications.waset.org/abstracts/search?q=Dominique%20Figarella-Branger"> Dominique Figarella-Branger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glioblastoma (GBM) contains cancer stem cells that are resistant to treatment. GBM cancer stem cell expresses glycolipids recognized by the A2B5 antibody. A2B5, induced by the enzyme ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyl transferase 3 (ST8Sia3), plays a crucial role in the proliferation, migration, clonogenicity, and tumorigenesis of GBM cancer stem cells. Our aim was to characterize the resulting effects of neuraminidase that remove A2B5 in order to target GBM cancer stem cells. To this end, we set up a GBM organotypic slice model; quantified A2B5 expression by flow cytometry in U87-MG, U87-ST8Sia3, and GBM cancer stem cell lines, treated or not by neuraminidase; performed RNAseq and DNA methylation profiling; and analyzed the ganglioside expression by liquid chromatography-mass spectrometry in these cell lines, treated or not with neuraminidase. Results demonstrated that neuraminidase decreased A2B5 expression, tumor size, and regrowth after surgical removal in the organotypic slice model but did not induce a distinct transcriptomic or epigenetic signature in GBM CSC lines. RNAseq analysis revealed that OLIG2, CHI3L1, TIMP3, TNFAIP2, and TNFAIP6 transcripts were significantly overexpressed in U87-ST8Sia3 compared to U87-MG. RT-qPCR confirmed these results and demonstrated that neuraminidase decreased gene expression in GBM cancer stem cell lines. Moreover, neuraminidase drastically reduced ganglioside expression in GBM cancer stem cell lines. Neuraminidase, by its pleiotropic action, is an attractive local treatment against GBM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20stem%20cell" title="cancer stem cell">cancer stem cell</a>, <a href="https://publications.waset.org/abstracts/search?q=ganglioside" title=" ganglioside"> ganglioside</a>, <a href="https://publications.waset.org/abstracts/search?q=glioblastoma" title=" glioblastoma"> glioblastoma</a>, <a href="https://publications.waset.org/abstracts/search?q=targeted%20treatment" title=" targeted treatment"> targeted treatment</a> </p> <a href="https://publications.waset.org/abstracts/171854/deciphering-the-action-of-neuraminidase-in-glioblastoma-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Single Cell and Spatial Transcriptomics: A Beginners Viewpoint from the Conceptual Pipeline</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leo%20Nnamdi%20Ozurumba-Dwight">Leo Nnamdi Ozurumba-Dwight</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Messenger ribooxynucleic acid (mRNA) molecules are compositional, protein-based. These proteins, encoding mRNA molecules (which collectively connote the transcriptome), when analyzed by RNA sequencing (RNAseq), unveils the nature of gene expression in the RNA. The obtained gene expression provides clues of cellular traits and their dynamics in presentations. These can be studied in relation to function and responses. RNAseq is a practical concept in Genomics as it enables detection and quantitative analysis of mRNA molecules. Single cell and spatial transcriptomics both present varying avenues for expositions in genomic characteristics of single cells and pooled cells in disease conditions such as cancer, auto-immune diseases, hematopoietic based diseases, among others, from investigated biological tissue samples. Single cell transcriptomics helps conduct a direct assessment of each building unit of tissues (the cell) during diagnosis and molecular gene expressional studies. A typical technique to achieve this is through the use of a single-cell RNA sequencer (scRNAseq), which helps in conducting high throughput genomic expressional studies. However, this technique generates expressional gene data for several cells which lack presentations on the cells’ positional coordinates within the tissue. As science is developmental, the use of complimentary pre-established tissue reference maps using molecular and bioinformatics techniques has innovatively sprung-forth and is now used to resolve this set back to produce both levels of data in one shot of scRNAseq analysis. This is an emerging conceptual approach in methodology for integrative and progressively dependable transcriptomics analysis. This can support in-situ fashioned analysis for better understanding of tissue functional organization, unveil new biomarkers for early-stage detection of diseases, biomarkers for therapeutic targets in drug development, and exposit nature of cell-to-cell interactions. Also, these are vital genomic signatures and characterizations of clinical applications. Over the past decades, RNAseq has generated a wide array of information that is igniting bespoke breakthroughs and innovations in Biomedicine. On the other side, spatial transcriptomics is tissue level based and utilized to study biological specimens having heterogeneous features. It exposits the gross identity of investigated mammalian tissues, which can then be used to study cell differentiation, track cell line trajectory patterns and behavior, and regulatory homeostasis in disease states. Also, it requires referenced positional analysis to make up of genomic signatures that will be sassed from the single cells in the tissue sample. Given these two presented approaches to RNA transcriptomics study in varying quantities of cell lines, with avenues for appropriate resolutions, both approaches have made the study of gene expression from mRNA molecules interesting, progressive, developmental, and helping to tackle health challenges head-on. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transcriptomics" title="transcriptomics">transcriptomics</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA%20sequencing" title=" RNA sequencing"> RNA sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20cell" title=" single cell"> single cell</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial" title=" spatial"> spatial</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression." title=" gene expression."> gene expression.</a> </p> <a href="https://publications.waset.org/abstracts/134742/single-cell-and-spatial-transcriptomics-a-beginners-viewpoint-from-the-conceptual-pipeline" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134742.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Analysis of Differentially Expressed Genes in Spontaneously Occurring Canine Melanoma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simona%20Perga">Simona Perga</a>, <a href="https://publications.waset.org/abstracts/search?q=Chiara%20Beltramo"> Chiara Beltramo</a>, <a href="https://publications.waset.org/abstracts/search?q=Floriana%20Fruscione"> Floriana Fruscione</a>, <a href="https://publications.waset.org/abstracts/search?q=Isabella%20Martini"> Isabella Martini</a>, <a href="https://publications.waset.org/abstracts/search?q=Federica%20Cavallo"> Federica Cavallo</a>, <a href="https://publications.waset.org/abstracts/search?q=Federica%20Riccardo"> Federica Riccardo</a>, <a href="https://publications.waset.org/abstracts/search?q=Paolo%20Buracco"> Paolo Buracco</a>, <a href="https://publications.waset.org/abstracts/search?q=Selina%20Iussich"> Selina Iussich</a>, <a href="https://publications.waset.org/abstracts/search?q=Elisabetta%20Razzuoli"> Elisabetta Razzuoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Katia%20Varello"> Katia Varello</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorella%20Maniscalco"> Lorella Maniscalco</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20Bozzetta"> Elena Bozzetta</a>, <a href="https://publications.waset.org/abstracts/search?q=Angelo%20Ferrari"> Angelo Ferrari</a>, <a href="https://publications.waset.org/abstracts/search?q=Paola%20Modesto"> Paola Modesto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Human and canine melanoma have common clinical, histologic characteristics making dogs a good model for comparative oncology. The identification of specific genes and a better understanding of the genetic landscape, signaling pathways, and tumor–microenvironmental interactions involved in the cancer onset and progression is essential for the development of therapeutic strategies against this tumor in both species. In the present study, the differential expression of genes in spontaneously occurring canine melanoma and in paired normal tissue was investigated by targeted RNAseq. Material and Methods: Total RNA was extracted from 17 canine malignant melanoma (CMM) samples and from five paired normal tissues stored in RNA-later. In order to capture the greater genetic variability, gene expression analysis was carried out using two panels (Qiagen): Human Immuno-Oncology (HIO) and Mouse-Immuno-Oncology (MIO) and the miSeq platform (Illumina). These kits allow the detection of the expression profile of 990 genes involved in the immune response against tumors in humans and mice. The data were analyzed through the CLCbio Genomics Workbench (Qiagen) software using the Canis lupus familiaris genome as a reference. Data analysis were carried out both comparing the biologic group (tumoral vs. healthy tissues) and comparing neoplastic tissue vs. paired healthy tissue; a Fold Change greater than two and a p-value less than 0.05 were set as the threshold to select interesting genes. Results and Discussion: Using HIO 63, down-regulated genes were detected; 13 of those were also down-regulated comparing neoplastic sample vs. paired healthy tissue. Eighteen genes were up-regulated, 14 of those were also down-regulated comparing neoplastic sample vs. paired healthy tissue. Using the MIO, 35 down regulated-genes were detected; only four of these were down-regulated, also comparing neoplastic sample vs. paired healthy tissue. Twelve genes were up-regulated in both types of analysis. Considering the two kits, the greatest variation in Fold Change was in up-regulated genes. Dogs displayed a greater genetic homology with humans than mice; moreover, the results have shown that the two kits are able to detect different genes. Most of these genes have specific cellular functions or belong to some enzymatic categories; some have already been described to be correlated to human melanoma and confirm the validity of the dog as a model for the study of molecular aspects of human melanoma. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=animal%20model" title="animal model">animal model</a>, <a href="https://publications.waset.org/abstracts/search?q=canine%20melanoma" title=" canine melanoma"> canine melanoma</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression" title=" gene expression"> gene expression</a>, <a href="https://publications.waset.org/abstracts/search?q=spontaneous%20tumors" title=" spontaneous tumors"> spontaneous tumors</a>, <a href="https://publications.waset.org/abstracts/search?q=targeted%20RNAseq" title=" targeted RNAseq"> targeted RNAseq</a> </p> <a href="https://publications.waset.org/abstracts/141871/analysis-of-differentially-expressed-genes-in-spontaneously-occurring-canine-melanoma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141871.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> Persistent Ribosomal In-Frame Mis-Translation of Stop Codons as Amino Acids in Multiple Open Reading Frames of a Human Long Non-Coding RNA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leonard%20Lipovich">Leonard Lipovich</a>, <a href="https://publications.waset.org/abstracts/search?q=Pattaraporn%20Thepsuwan"> Pattaraporn Thepsuwan</a>, <a href="https://publications.waset.org/abstracts/search?q=Anton-Scott%20Goustin"> Anton-Scott Goustin</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20Cai"> Juan Cai</a>, <a href="https://publications.waset.org/abstracts/search?q=Donghong%20Ju"> Donghong Ju</a>, <a href="https://publications.waset.org/abstracts/search?q=James%20B.%20Brown"> James B. Brown</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two-thirds of human genes do not encode any known proteins. Aside from long non-coding RNA (lncRNA) genes with recently-discovered functions, the ~40,000 non-protein-coding human genes remain poorly understood, and a role for their transcripts as de-facto unconventional messenger RNAs has not been formally excluded. Ribosome profiling (Riboseq) predicts translational potential, but without independent evidence of proteins from lncRNA open reading frames (ORFs), ribosome binding of lncRNAs does not prove translation. Previously, we mass-spectrometrically documented translation of specific lncRNAs in human K562 and GM12878 cells. We now examined lncRNA translation in human MCF7 cells, integrating strand-specific Illumina RNAseq, Riboseq, and deep mass spectrometry in biological quadruplicates performed at two core facilities (BGI, China; City of Hope, USA). We excluded known-protein matches. UCSC Genome Browser-assisted manual annotation of imperfect (tryptic-digest-peptides)-to-(lncRNA-three-frame-translations) alignments revealed three peptides hypothetically explicable by 'stop-to-nonstop' in-frame replacement of stop codons by amino acids in two ORFs of the lncRNA MMP24-AS1. To search for this phenomenon genomewide, we designed and implemented a novel pipeline, matching tryptic-digest spectra to wildcard-instead-of-stop versions of repeat-masked, six-frame, whole-genome translations. Along with singleton putative stop-to-nonstop events affecting four other lncRNAs, we identified 24 additional peptides with stop-to-nonstop in-frame substitutions from multiple positive-strand MMP24-AS1 ORFs. Only UAG and UGA, never UAA, stop codons were impacted. All MMP24-AS1-matching spectra met the same significance thresholds as high-confidence known-protein signatures. Targeted resequencing of MMP24-AS1 genomic DNA and cDNA from the same samples did not reveal any mutations, polymorphisms, or sequencing-detectable RNA editing. This unprecedented apparent gene-specific violation of the genetic code highlights the importance of matching peptides to whole-genome, not known-genes-only, ORFs in mass-spectrometry workflows, and suggests a new mechanism enhancing the combinatorial complexity of the proteome. Funding: NIH Director’s New Innovator Award 1DP2-CA196375 to LL. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20code" title="genetic code">genetic code</a>, <a href="https://publications.waset.org/abstracts/search?q=lncRNA" title=" lncRNA"> lncRNA</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20non-coding%20RNA" title=" long non-coding RNA"> long non-coding RNA</a>, <a href="https://publications.waset.org/abstracts/search?q=mass%20spectrometry" title=" mass spectrometry"> mass spectrometry</a>, <a href="https://publications.waset.org/abstracts/search?q=proteogenomics" title=" proteogenomics"> proteogenomics</a>, <a href="https://publications.waset.org/abstracts/search?q=ribo-seq" title=" ribo-seq"> ribo-seq</a>, <a href="https://publications.waset.org/abstracts/search?q=ribosome" title=" ribosome"> ribosome</a>, <a href="https://publications.waset.org/abstracts/search?q=RNAseq" title=" RNAseq "> RNAseq </a> </p> <a href="https://publications.waset.org/abstracts/90989/persistent-ribosomal-in-frame-mis-translation-of-stop-codons-as-amino-acids-in-multiple-open-reading-frames-of-a-human-long-non-coding-rna" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">235</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Transcriptome Sequencing of the Spleens Reveals Genes Involved in Antiviral Response in Chickens Infected with Castv</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sajewicz-Krukowska%20Joanna">Sajewicz-Krukowska Joanna</a>, <a href="https://publications.waset.org/abstracts/search?q=Doma%C5%84ska-Blicharz%20Katarzyna"> Domańska-Blicharz Katarzyna</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarasiuk%20Karolina"> Tarasiuk Karolina</a>, <a href="https://publications.waset.org/abstracts/search?q=Marzec-Kotarska%20Barbara"> Marzec-Kotarska Barbara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Astroviral infections pose a significant problem in the poultry industry, leading to multiple adverse effects such as decreased egg production, breeding disorders, poor weight gain, and even increased mortality. Commonly observed chicken astrovirus (CAstV) was recently reported to be responsible for "white chicks syndrome" associated with increased embryo/chick mortality. The CAstV-mediated pathogenesis in chicken occurs due to complex interactions between the infectious pathogen and the immune system. Many aspects of CAstV-chicken interactions remain unclear, and there is no information available regarding gene expression changes in the chicken's spleen in response to CAstV infection. We aimed to investigate the molecular background triggered by CAstV infection. Ten 21-day-old SPF White Leghorn chickens were divided into two groups of 5 birds each. One group was inoculated with CAstV, and the other was used as the negative control. On 4th dpi, spleen samples were collected and immediately frozen at -70°C for RNA isolation. We analysed transcriptional profiles of the chickens' spleens at the 4th day following infection using RNA-seq to establish differentially expressed genes (DEGs). The RNA-seq findings were verified by quantitative real-time PCR (qRT-PCR). A total of 31959 transcripts were identified in response to CAstV infection. Eventually 45 DEGs (p-value<0.05; Log2Foldchange>1)were recognized in the spleen after CAstV infection (26 upregulated DEGs and 19 downregulated DEGs). qRT-PCR performed on 4 genes (IFIT5, OASL, RASD1, DDX60) confirmed RNAseq results. Top differentially expressed genes belonged to novel putative IFN-induced CAstV restriction factors. Most of the DEGs were associated with RIG-I–like signalling pathway or, more generally, with an innate antiviral response(upregulated: BLEC3, CMPK2, IFIT5, OASL, DDX60, IFI6, and downregulated: SPIK5, SELENOP, HSPA2, TMEM158, RASD1, YWHAB). The study provided a global analysis of host transcriptional changes that occur during CAstV infection in vivo and proved the cell cycle in the spleen and immune signalling in chickens were predominantly affected upon CAstV infection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chicken%20astrovirus" title="chicken astrovirus">chicken astrovirus</a>, <a href="https://publications.waset.org/abstracts/search?q=CastV" title=" CastV"> CastV</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA-seq" title=" RNA-seq"> RNA-seq</a>, <a href="https://publications.waset.org/abstracts/search?q=transcriptome" title=" transcriptome"> transcriptome</a>, <a href="https://publications.waset.org/abstracts/search?q=spleen" title=" spleen"> spleen</a> </p> <a href="https://publications.waset.org/abstracts/141921/transcriptome-sequencing-of-the-spleens-reveals-genes-involved-in-antiviral-response-in-chickens-infected-with-castv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141921.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> RNA-Seq Analysis of the Wild Barley (H. spontaneum) Leaf Transcriptome under Salt Stress</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Bahieldin">Ahmed Bahieldin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Atef"> Ahmed Atef</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamal%20S.%20M.%20Sabir"> Jamal S. M. Sabir</a>, <a href="https://publications.waset.org/abstracts/search?q=Nour%20O.%20Gadalla"> Nour O. Gadalla</a>, <a href="https://publications.waset.org/abstracts/search?q=Sherif%20Edris"> Sherif Edris</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20M.%20Alzohairy"> Ahmed M. Alzohairy</a>, <a href="https://publications.waset.org/abstracts/search?q=Nezar%20A.%20Radhwan"> Nezar A. Radhwan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20N.%20Baeshen"> Mohammed N. Baeshen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20M.%20Ramadan"> Ahmed M. Ramadan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hala%20F.%20Eissa"> Hala F. Eissa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sabah%20M.%20Hassan"> Sabah M. Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nabih%20A.%20Baeshen"> Nabih A. Baeshen</a>, <a href="https://publications.waset.org/abstracts/search?q=Osama%20Abuzinadah"> Osama Abuzinadah</a>, <a href="https://publications.waset.org/abstracts/search?q=Magdy%20A.%20Al-Kordy"> Magdy A. Al-Kordy</a>, <a href="https://publications.waset.org/abstracts/search?q=Fotouh%20M.%20El-Domyati"> Fotouh M. El-Domyati</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20K.%20Jansen"> Robert K. Jansen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wild salt-tolerant barley (Hordeum spontaneum) is the ancestor of cultivated barley (Hordeum vulgare or H. vulgare). Although the cultivated barley genome is well studied, little is known about genome structure and function of its wild ancestor. In the present study, RNA-Seq analysis was performed on young leaves of wild barley treated with salt (500 mM NaCl) at four different time intervals. Transcriptome sequencing yielded 103 to 115 million reads for all replicates of each treatment, corresponding to over 10 billion nucleotides per sample. Of the total reads, between 74.8 and 80.3% could be mapped and 77.4 to 81.7% of the transcripts were found in the H. vulgare unigene database (unigene-mapped). The unmapped wild barley reads for all treatments and replicates were assembled de novo and the resulting contigs were used as a new reference genome. This resultedin94.3 to 95.3%oftheunmapped reads mapping to the new reference. The number of differentially expressed transcripts was 9277, 3861 of which were uni gene-mapped. The annotated unigene- and de novo-mapped transcripts (5100) were utilized to generate expression clusters across time of salt stress treatment. Two-dimensional hierarchical clustering classified differential expression profiles into nine expression clusters, four of which were selected for further analysis. Differentially expressed transcripts were assigned to the main functional categories. The most important groups were ‘response to external stimulus’ and ‘electron-carrier activity’. Highly expressed transcripts are involved in several biological processes, including electron transport and exchanger mechanisms, flavonoid biosynthesis, reactive oxygen species (ROS) scavenging, ethylene production, signaling network and protein refolding. The comparisons demonstrated that mRNA-Seq is an efficient method for the analysis of differentially expressed genes and biological processes under salt stress. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electron%20transport" title="electron transport">electron transport</a>, <a href="https://publications.waset.org/abstracts/search?q=flavonoid%20biosynthesis" title=" flavonoid biosynthesis"> flavonoid biosynthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=reactive%20oxygen%20species" title=" reactive oxygen species"> reactive oxygen species</a>, <a href="https://publications.waset.org/abstracts/search?q=rnaseq" title=" rnaseq"> rnaseq</a> </p> <a href="https://publications.waset.org/abstracts/42511/rna-seq-analysis-of-the-wild-barley-h-spontaneum-leaf-transcriptome-under-salt-stress" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42511.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">392</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Genetic Dissection of QTLs in Intraspecific Hybrids Derived from Muskmelon (Cucumis Melo L.) and Mangalore Melon (Cucumis Melo Var Acidulus) for Shelflife and Fruit Quality Traits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Virupakshi%20Hiremata">Virupakshi Hiremata</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratnakar%20M.%20Shet"> Ratnakar M. Shet</a>, <a href="https://publications.waset.org/abstracts/search?q=Raghavendra%20Gunnaiah"> Raghavendra Gunnaiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Prashantha%20A."> Prashantha A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Muskmelon is a health-beneficial and refreshing dessert vegetable with a low shelf life. Mangalore melon, a genetic homeologue of muskmelon, has a shelf life of more than six months and is mostly used for culinary purposes. Understanding the genetics of shelf life, yield and yield-related traits and identification of markers linked to such traits is helpful in transfer of extended shelf life from Mangalore melon to the muskmelon through intra-specific hybridization. For QTL mapping, 276 F2 mapping population derived from the cross Arka Siri × SS-17 was genotyped with 40 polymorphic markers distributed across 12 chromosomes. The same population was also phenotyped for yield, shelf life and fruit quality traits. One major QTL (R2 >10) and fourteen minor QTLs (R2 <10) localized on four linkage groups, governing different traits were mapped in F2 mapping population developed from the intraspecific cross with a LOD > 5.5. The phenotypic varience explained by each locus varied from 3.63 to 10.97 %. One QTL was linked to shelf-life (qSHL-3-1), five QTLs were linked to TSS (qTSS-1-1, qTSS-3-3, qTSS-3-1, qTSS-3-2 and qTSS-1-2), two QTLs for flesh thickness (qFT-3-1, and qFT-3-2) and seven QTLs for fruit yield per vine (qFYV-3-1, qFYV-1-1, qFYV-3-1, qFYV1-1, qFYV-1-3, qFYV2-1 and qFYV6-1). QTL flanking markers may be used for marker assisted introgression of shelf life into muskmelon. Important QTL will be further fine-mapped for identifying candidate genes by QTLseq and RNAseq analysis. Fine-mapping of Important Quantitative Trait Loci (QTL) holds immense promise in elucidating the genetic basis of complex traits. Leveraging advanced techniques like QTLseq and RNA sequencing (RNA seq) is crucial for this endeavor. QTLseq combines next-generation sequencing with traditional QTL mapping, enabling precise identification of genomic regions associated with traits of interest. Through high-throughput sequencing, QTLseq provides a detailed map of genetic variations linked to phenotypic variations, facilitating targeted investigations. Moreover, RNA seq analysis offers a comprehensive view of gene expression patterns in response to specific traits or conditions. By comparing transcriptomes between contrasting phenotypes, RNA seq aids in pinpointing candidate genes underlying QTL regions. Integrating QTLseq with RNA seq allows for a multi-dimensional approach, coupling genetic variation with gene expression dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=QTL" title="QTL">QTL</a>, <a href="https://publications.waset.org/abstracts/search?q=shelf%20life" title=" shelf life"> shelf life</a>, <a href="https://publications.waset.org/abstracts/search?q=TSS" title=" TSS"> TSS</a>, <a href="https://publications.waset.org/abstracts/search?q=muskmelon%20and%20Mangalore%20melon" title=" muskmelon and Mangalore melon"> muskmelon and Mangalore melon</a> </p> <a href="https://publications.waset.org/abstracts/183389/genetic-dissection-of-qtls-in-intraspecific-hybrids-derived-from-muskmelon-cucumis-melo-l-and-mangalore-melon-cucumis-melo-var-acidulus-for-shelflife-and-fruit-quality-traits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183389.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">54</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); 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