Análise dos Media Isabel Ferin Cunha; Ana Teresa Peixinho
Imprensa da Universidade de Coimbra eBooks,
2020
eBook, Book
Odprti dostop
O livro Análise dos Media é uma reedição, revista e atualizada, de uma obra de 2012. Está organizado em seis capítulos, antecedidos por uma Introdução. O primeiro capítulo, “Enquadramentos Teóricos”, ...é um capítulo de natureza epistemológica que situa este campo do saber no âmbito dos Estudos dos Media. No segundo, “Desenhar uma pesquisa sobre os media”, descreve-se o percurso de elaboração de uma pesquisa sobre os media, utilizando como exemplo a cobertura jornalística do tema “corrupção política” em Portugal. O terceiro capítulo, “Métodos na Análise dos Media”, focaliza os métodos utilizados nas pesquisas empíricas sobre os media, com ênfase na análise de conteúdo e nos métodos qualitativos, tais como as análises narrativas, as análises semióticas e as análises de discurso. O quarto capítulo “Os Desafios da Análise dos Media Sociais” pretende dar resposta aos desafios que os investigadores em Ciências Sociais enfrentam face aos media sociais. No quinto capítulo, faz-se uma breve conclusão dos enquadramentos, pressupostos e percursos inerentes
rAAV viral vectors (VV) used for in vivo gene therapy are commonly produced using transient transfection of HEK293 cells. Although this method is well established, challenges remain in achieving high ...VV titer and a high ratio of full-to-empty capsids. Our aim is to build a mechanistic understanding and knowledge of amino acid (AA) consumption to guide the development of advanced manufacturing processes. The resulting model-driven approach is used to design, predict, test, and optimize VV production processes – scaling up and transferring to a continuous process at bioreactor scale.
Media optimization can be a laborous process involving multiple DoEs. Rapid and simple analytical tools at the point-of-use are required to quickly assess key-nutrient consumption and provide feedback to optimize feed and supplement addition strategies. In this study, we performed data-driven media screening and optimization experiments leveraging a fast, at-line amino acid analyser, REBEL.
Utilising mechanistic models for HEK293 rAAV production and at-line spent media analysis for data-driven process intensification and yield improvement by cell culture media and feed optimisation.
For initial experiments, rAAV was produced via transient transfection in a fed-batch 300 mL mini-bioreactor and in the shake flasks. Samples were collected to identify potential AA depletion that may impact capsid titre.
Asn is an important AA building block and constitutes ∼10% of capsid protein composition. Results show clear differences in fluxes of Asn and Asp, which were reported previously as candidate AAs for producing viral particles. Other essential AA also exhibited strong depletion, indicating opportunities to optimize cell culture conditions. With subsequent experiments, the impact of AA addition to the cell culture was assessed with the goal of improving vector titre.
This work is relevant to addressing key challenges of rAAV production platforms via process engineering and optimization of cell culture conditions to scale-up vector production and development of continuous bioprocessing of rAAVs. The technology used uniquely enables at-line AA measurements, leading to deeper insights and informed feeding strategy optimization.
A review of topic modeling methods Vayansky, Ike; Kumar, Sathish A.P.
Information systems (Oxford),
December 2020, 2020-12-00, 20201201, Letnik:
94
Journal Article
Recenzirano
Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; ...however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.
•Reviewed different topic modeling approaches dealing with correlation between topics.•This review will encourage more diversity when performing topic modeling.•The classification of methods in our review is flexible.•Discussed the techniques of optimizing the topic modeling algorithms.•Created and presented a decision tree to select a topic modeling method
As the use of CAR T-cell therapies advances, testing different CAR T-cells, and associating metabolic characteristics to well-performing, desired phenotypes of CAR T-cells enables optimization of CAR ...constructs and manufacturing processes. Vasoactive intestinal peptide (VIP) is an emerging checkpoint pathway for T-cell function. Antagonization of VIP/VIPR axis in T-cells enhances T-cell-mediated anti-tumor function. In this work, we elucidate the mechanisms by which CAR T-cells engineered to secrete potent VIPR antagonistic peptides (CAR/VIPRa) can overcome the immunosuppression of VIP-rich tumor microenvironments.
CAR T-cell manufacturing processes enhanced by characterisation of products through spent media amino acid analysis – correlating metabolism with desired, less-differentiated and less-exhaustive phenotype CAR T-cell populations.
To interrogate if VIPR antagonism impacts metabolic pathways, spent media analysis was performed using 908 Devices REBEL at-line amino acid analyzer to determine overall amino acid usage differences between CAR and CAR/VIPRa T-cells. Spent media analysis revealed differences in the consumption of amino acids such as alanine, glutamate, and serine. This prompted further investigation of the differences in metabolic pathway utilization and alterations in metabolites within these CAR T-cells. LC/MS metabolomics, cellular bioenergetics and RNA sequencing were used to elucidate differences between metabolic states of CAR and CAR/VIPRa T-cells.
We found that when the manufactured cells are antigen-stimulated, CAR/VIPRa T-cells become significantly more energetic. In contrast, CAR T-cells become less energetic and more glycolytic, which are known features of T-cell exhaustion. Further, metabolic assessment in terms of oxygen consumption and amino acid levels in spent media correlated with these findings. CAR T-cell activity, potency, and cytotoxicity assessments showed a correlation with anti-tumor activity. Together, this work demonstrates that distinct metabolic features allow CAR/VIPRa T-cells, when antigen-stimulated, to have enhanced effector functions against tumors.
At-line spent media analysis of amino acids, in combination with other assays and measurements, was used to understand, characterise and optimise CAR T-cell manufacturing process, correlate with cell performance, and develop bioprocess parameters.
Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online ...content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of
stance detection
in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {
Favor
,
Against
,
Neither
}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.
Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German ...federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.
The article examines how the crisis phenomenon is discussed in the Romanian public communication environment. The authors argue that in the current context, where society is marked by multiple ...crises, the term "permacrisis" has become relevant, describing a state of permanent crisis affecting the society at multiple levels. The authors analysed how the topic of crisis has been represented in the Romanian media, focusing on articles published online between 2017 and 2022. Through quantitative and qualitative analysis, the study highlights a significant increase in crisis-related mentions since 2020, in the context of the COVID-19 pandemic. This increase indicates a shift in public perception and media discourse, suggesting the normalisation and acceptance of the crisis as a constant state. Four dominant themes associated with the crisis were identified in media discourse: the economic crisis, the health crisis generated by COVID-19, the domestic political crisis and the crisis generated by the war in Ukraine. These themes illustrate the different facets of the permacrisis in Romania, from the economic and social impact, to political uncertainty and the consequences of pandemics and geopolitical conflicts. The paper highlights the importance of recognising and understanding the phenomenon of permacrisis in Romanian society in order to develop more effective strategies for managing crises and building a more resilient society.
Automated data collection provides tempting opportunities for social sciences and humanities studies. Abundant data accumulating in various digital archives allows more comprehensive, timely and ...cost-efficient ways of harvesting and processing information. While easing or even removing some of the key problems, such as laborious and time-consuming data collection and potential errors and biases related to subjective coding of materials and distortions caused by focus on small samples, automated methods also bring in new risks such as poor understanding of contexts of the data or non-recognition of underlying systematic errors or missing information. Results from testing different methods to collect data describing newspaper coverage of climate change in Finland emphasize that fully relying on automatable tools such as media scrapers has its limitations and can provide comprehensive but incomplete document acquisition for research. Many of these limitations can, however, be addressed and not all of them rely on manual control.
This study delves into the evolving role of generative Large Language Models (LLMs). We develop a data-driven approach to collect and analyse tasks that users are asking to generative LLMs. Thanks to ...the focus on tasks this paper contributes to give a quantitative and granular understanding of the potential influence of LLMs in different business areas. Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic. The combination makes it possible to collect granular tasks of LLMs (NER) and clusters them in business areas (BERTopic). Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM's versatility. The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education. The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.
•The study bridged a research gap in the tasks assigned to generative LLMs.•Utilizing over 3.8 million tweets, we identify and cluster 31,747 unique tasks.•The analysis highlighted six emerging areas of application for generative LMMs.•We propose a research agenda intersecting the six areas with the innovation process.
•Extensive literature review on social media analytics providing clear definitions of the most common used terms.•Discovery of social media data covers a wide interdisciplinary field.•Data quality ...and description of procedure have much room for improvement.•Majority of social media data stems from Twitter, while sentiment and content analysis are the current prevailing methods.
The spread and use of social networks provide a rich data source that can be used to answer a wide range of research questions from various disciplines. However, the nature of social media data poses a challenge to the analysis. The aim of this study is to provide an in-depth overview of the research that analyzes social media data since 2017. An extensive literature review based on 94 papers led to the findings that clear definitions are neither established nor commonly applied. Predominant research domains include marketing, hospitality and tourism, disaster management, and disruptive technology. The majority of analyzed social media data are taken from Twitter. Sentiment and content analysis are the current prevailing methods. Half of the studies include practical implications. Based on the literature review, clear definitions are provided, and future avenues for high-quality research are suggested.
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