1,720,960 research outputs found
Identification of Differential Gene Expression in Glioma Patients
No full textDuring the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas - both tumoral and peritumoral (area surrounding the tumor) - using a computational cluster. First, the data was downloaded onto the cluster from the Gene Expression Omnibus, then aligned using a batch script. Following this, a table of gene counts for each file was created using another batch script, then downloaded onto a GitHub repository for analysis using RStudio’s DeSeq2 package. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for specific targeting
Identification of Differential Gene Expression in Peritumoral and Tumoral Glioma Cells
No full textDuring the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas, separated by tumoral and peritumoral (area surrounding the tumor) cells. The data was analyzed using RStudio software with PCA, correlation, and logistic regression testing in order to determine the statistical variances between cell types. A statistical model was also created using R in order to predict whether a given gene is a tumoral or peritumoral gene. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for gene therapy targeting
Identification of Differential Gene Expression in Glioma Patients
No full textDuring the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas - both tumoral and peritumoral (area surrounding the tumor) - using a computational cluster. First, the data was downloaded onto the cluster from the Gene Expression Omnibus, then aligned using a batch script. Following this, a table of gene counts for each file was created using another batch script, then downloaded onto a GitHub repository for analysis using RStudio’s DeSeq2 package. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for specific targeting
Identification of Differential Gene Expression in Peritumoral and Tumoral Glioma Cells
No full textDuring the translation phase of the cell cycle, the exons, or coding regions, of the mRNA transcript are joined together to create the relevant proteins for the cell, while the introns, or non-coding regions, are cut out of the transcript. The amount of produced protein can be measured using RNA sequencing techniques, namely differential gene expression analysis. This study focuses on performing RNA sequencing analysis using publicly available genomic data for gliomas, separated by tumoral and peritumoral (area surrounding the tumor) cells. The data was analyzed using RStudio software with PCA, correlation, and logistic regression testing in order to determine the statistical variances between cell types. A statistical model was also created using R in order to predict whether a given gene is a tumoral or peritumoral gene. The survival rate for glioma patients is very low and this type of brain cancer is especially aggressive and resistant to treatment. Therefore, applications of this study include identification of specific proteins present in tumoral RNA that aid in glioma progression for gene therapy targeting
The Effects of Spoken Language on Emitted Respiratory Aerosol Concentration
No full textMany airborne diseases, such as COVID-19 and flu, are spread through aerosol droplets emitted by the human respiratory system. Public health officials utilized a variety of disease mitigation methods throughout the recent COVID-19 pandemic that focused on reducing the spread of respiratory droplets that carry pathogens, such as wearing face masks and increasing distance between individuals. Several studies have already proven that the concentration of aerosol particles emitted through the respiratory tract increases as the loudness of speech and proximity to the particle counter increases; however, the differences in concentration based on the language the participant is speaking has yet to be studied in detail. For this study, preliminary data was collected by having the participant sit in front of a laptop and follow the instruction on the monitor. In this study the concentration of respiratory aerosol particles and physiological data such as photoplethysmogram (PPG) and electrodermal activity (EDA) are collected during normal breathing and reading the passages. Same passage is translated to Hindi and Spanish, and the subject is instructed to repeat the experiments 5 times. Based on our preliminary results, there is very little difference between the total concentration of respiratory particles emitted when speaking English and speaking Hindi or Spanish, but there is the difference in total concentration between simply breathing and speaking
The Effects of Spoken Language on Emitted Respiratory Aerosol Concentration
No full textMany airborne diseases, such as COVID-19 and flu, are spread through aerosol droplets emitted by the human respiratory system. Public health officials utilized a variety of disease mitigation methods throughout the recent COVID-19 pandemic that focused on reducing the spread of respiratory droplets that carry pathogens, such as wearing face masks and increasing distance between individuals. Several studies have already proven that the concentration of aerosol particles emitted through the respiratory tract increases as the loudness of speech and proximity to the particle counter increases; however, the differences in concentration based on the language the participant is speaking has yet to be studied in detail. For this study, preliminary data was collected by having the participant sit in front of a laptop and follow the instruction on the monitor. In this study the concentration of respiratory aerosol particles and physiological data such as photoplethysmogram (PPG) and electrodermal activity (EDA) are collected during normal breathing and reading the passages. Same passage is translated to Hindi and Spanish, and the subject is instructed to repeat the experiments 5 times. Based on our preliminary results, there is very little difference between the total concentration of respiratory particles emitted when speaking English and speaking Hindi or Spanish, but there is the difference in total concentration between simply breathing and speaking
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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