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The translation represents one of the most crucial processes in the cell. That is why it is often targeted by various regulations. Its initiation phase has a particularly important role in regulatory processes. Initiation of translation usually starts by recognition and binding of canonical eukaryotic initiation factor 4E1 (eIF4E1) to the methylguanosine cap present on the 5' end of the majority of eukaryotic mRNA. The family of 4E translation initiation factors contains two more members - eIF4E2 and eIF4E3. Those two proteins can bind cap structure as well which predetermines it to function in the regulation of translation. Protein eIF4E2 is well known for being a translational repressor in development processes and it takes part in specific miRNA-dependent silencing. It was proven to be able to initiate translation in hypoxia which is consistent with its proposed role in hypoxic tumor cells. The biological roles of the protein eIF4E3 are much less understood. This thesis propounds the picture of the overall functions of all discussed translation initiation factors using cell lines with their overexpression or deletion. Experimental data confirmed the role of the eIF4E2 in the regulation of developmental processes. Cell lines with deleted eIF4E2 and eIF4E3 were characterized based on the influence...

This document is a supplement to the article entitled “The Multidimensional Quality Metric (MQM) Framework: A New Framework for Translation Quality Assessment”, which has been acepted for publication in the upcoming January volume of JoSTrans, the Journal of Specialized Translation. The article is a coauthored project between Dr. Alan K. Melby, Dr. Troy Cox and myself. In this document you will find a preface describing the process of writing the article, an annotated bibliography of sources consulted in my research, a summary of what I learned, and a conclusion that considers the future avenues opened up by this research. Our article examines a new method for assessing the quality of a translation known as the Multidimensional Quality Metric, MQM. In our experiment we set the MQM framework to mirror, as closely as possible, the American Translators Association's (ATA) translator certification exam. To do this we mapped the ATA error categories to corresponding MQM error categories. We acquired a set of 29 student translations and had a group of student raters use the MQM framework to rate these translations. We measured the practicality of the MQM framework by comparing the time required for ratings to the average time required to rate translations in the industry. In addition, we had 2 ATA certified translators rate the anchor translation (a translation that was scored by every rater in order to have a point of comparison). The certified translators' ratings were used to verify that the scores given by the student raters were valid. Reliability was also measured, which found that the student raters were not interchangeable, but that the measurement estimate of reliability was adequate. The article's goal was to determine the extent to which the Multidimensional Quality Metric framework for translation evaluation is viable (practical, reliable and valid) when designed to mirror the ATA certification exam. Overall, the results of the experiment showed that MQM could be a viable way to rate translation quality when operationalized based on the ATA's translator certification exam. This is an important discovery in the field of translation quality, because it shows that MQM could be a viable tool for future researchers. Our experiment suggests that researchers ought to take advantage of the MQM framework because, not only is it free, but any studies completed using the MQM framework would have a common base, making these studies more easily comparable.

Machine translation (MT) is experiencing a renaissance. On one hand, machine translation is becoming more common and used in ever larger scale, on the other hand many translators have an almost hostile attitude towards machine translation programs and those translators who use MT as a tool. Either it is assumed that the MT can never be as good as a human translation or machine translation is viewed as the ultimate enemy of the translator and as a job killer. The article discusses with various examples the limits and possibilities of machine translation. It demonstrates that machine translation can be better than human translations – even if they were made by experienced professional translators. The paper also reports the results of a test that showed that translation customers must expect that even well-known and expensive translation service providers deliver a quality that is on par with poor MT. Overall, it is argued that machine translation programs are no more and no less than an additional tool with which the translation industry can satisfy certain requirements. This abstract was also – as the entire article – automatically translated into English.

Unsupervised image-to-image translation (UNIT) is a challenging task in the image manipulation field, where input images in a visual domain are mapped into another domain with desired visual patterns (also called styles). An ideal direction in this field is to build a model that can map an input image in a domain to multiple target domains and generate diverse outputs in each target domain, which is termed as multi-domain and multi-modal unsupervised image-to-image translation (MMUIT). Recent studies have shown remarkable results in UNIT but they suffer from four main limitations: (1) State-of-the-art UNIT methods are either built from several two-domain mappings that are required to be learned independently or they generate low-diversity results, a phenomenon also known as model collapse. (2) Most of the manipulation is with the assistance of visual maps or digital labels without exploring natural languages, which could be more scalable and flexible in practice. (3) In an MMUIT system, the style latent space is usually disentangled between every two image domains. While interpolations within domains are smooth, interpolations between two different domains often result in unrealistic images with artifacts when interpolating between two randomly sampled style representations from two different domains. Improving the smoothness of the style latent space can lead to gradual interpolations between any two style latent representations even between any two domains. (4) It is expensive to train MMUIT models from scratch at high resolution. Interpreting the latent space of pre-trained unconditional GANs can achieve pretty good image translations, especially high-quality synthesized images (e.g., 1024x1024 resolution). However, few works explore building an MMUIT system with such pre-trained GANs. In this thesis, we focus on these vital issues and propose several techniques for building better MMUIT systems. First, we base on the content-style disentangled framework and propose to fit the style latent space with Gaussian Mixture Models (GMMs). It allows a well-trained network using a shared disentangled style latent space to model multi-domain translations. Meanwhile, we can randomly sample different style representations from a Gaussian component or use a reference image for style transfer. Second, we show how the GMM-modeled latent style space can be combined with a language model (e.g., a simple LSTM network) to manipulate multiple styles by using textual commands. Then, we not only propose easy-to-use constraints to improve the smoothness of the style latent space in MMUIT models, but also design a novel metric to quantitatively evaluate the smoothness of the style latent space. Finally, we build a new model to use pretrained unconditional GANs to do MMUIT tasks.

This paper presents a study on the translation of slang used by drag queens. The study aims to examine how the slang used by drag queens in RuPaul’s Drag Race season 12 is translated into Japanese. The translation strategies used are revealed and the faithfulness of the translation is discussed. Both the original and translated expressions, jargon and words used by drag queens in RuPaul’s Drag Race season 12 were gathered from the first three episodes. The data was then grouped based on the translation strategy and discussed further regarding the effectiveness of the translation strategy and faithfulness of the translation itself with the help of Nida’s theory of dynamic equivalence. The results show that translation by loan, excluding the omission, was the most used translation strategy, but its success hinges on the familiarity of the target audience. Together with substitution, they were the most successful strategies regarding the faithfulness of the translation and the preservation of the slang. Softening, employed to adapt informal and vulgar expressions, risked losing the original slang, and modulation also less successful in preserving it. Paraphrase and explicitation were less successful in retaining the slang nuances but aligned with Nida’s dynamic equivalence which emphasizes the meaning.

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<p>This thesis consists of three related studies presented as three separate manuscripts. The first two comprise part of a larger sequential mixed-methods study with a qualitative and subsequent quantitative (survey) component. The overarching goal of this study was to understand the factors that influence the use of home dialysis, from the perspectives of Canadian nephrologists. The third study was a methodological study (a clinical trial) embedded in the survey, evaluating a novel strategy to incentivize survey responses.</p> <p>In the first paper, we aimed to develop a theoretical framework describing determinants of dialysis modality choice. We selected informants using a maximum-variation sampling strategy, and used in-depth interviews to explore their perspectives. We used a grounded theory-informed analytical approach to construct a taxonomy of barriers and related facilitators to home dialysis use. We triangulated our findings against related published studies and qualitative results from our survey study. This study informed the development of the questionnaire that is the focus of the second study.</p> <p>The second paper describes the development, administration, and results of a 47-item survey measuring Canadian nephrologist perspectives on the relevance of barriers to home dialysis use, and the utility of candidate interventions to overcome them. We used factor analysis to aggregate items into domains, and examined the relationships between respondent and practice characteristics with domain-level scores. Respondents expressed enthusiasm and reluctance towards a number of strategies to optimize home dialysis use. Our findings will guide policy development and further research directed at managing barriers to home dialysis use.</p> <p>The third and final study tests the effectiveness of a promised donation as an incentive for survey completion. We randomized survey recipients to receive standard notifications versus notifications that offered a charitable donation of $40 CAD to the Kidney Foundation of Canada in exchange for returning a completed survey. Contrary to our hypothesis, the intervention was not effective, thus adding to the cumulative evidence that such incentives do not impact on physician response rates.</p> / Master of Science (MSc)

This study assesses the translation accuracy of ChatGPT-4 against two human translators, Donald Keene and Mark Gibeau, focusing on the first 50 sentences of Osamu Dazai's Japanese novel "Ningen Shikkaku" translated into English. In the rapidly advancing field of artificial intelligence, where AI increasingly integrates into fields such as translation traditionally occupied by humans, it examines the effectiveness and reliability of AI incapturing both the literal and figurative meaning of a literary text. A significant gap in the field is the scarcity of comparative studies between AI and human translators, and all the more so in Japanese-English translation. Most existing research on AI translation focuses on European languages or evaluates AI against other machine translation tools. The study employs a translation quality assessment framework based on how erroneous the translations are, where either one or two points are deducted for each error depending on severity to evaluate the accuracy of each translation. The identified error types are grounded on the standardized error marking system utilized by the American Translators Association, and endeavors to provide an objective measure of translation quality. The results of the study show that ChatGPT-4's translation incurred the least number of point deductions, roughly half as many as those of Gibeau and Keene. Gibeau's translation rankedsecond in accuracy, with Keene's trailing closely behind. The results also reveal that Keene's translation errors typically stemmed from altered words and phrases, while Gibeau's translation rather added, intensified, or omitted elements. ChatGPT-4's translation had fewer errors overall, except in relation to literalness. It is discussed that the utility of AI in literary translation varies depending on whether accuracy or aesthetic is most valued. Nevertheless, translators can already at present utilize AI to manage routine tasks and accelerate translation processes, enabling them to concentrate on aspects such as flow, rhythm, and readability.