Human Translation Research Articles

Investigation in yeast of novel variants in mitochondrial aminoacyl-tRNA synthetases WARS2, NARS2, and RARS2 genes associated with mitochondrial diseases.

Aminoacyl-transfer RiboNucleic Acid synthetases (ARSs) are essential enzymes that catalyze the attachment of each amino acid to their cognate tRNAs. Mitochondrial ARSs (mtARSs), which ensure protein synthesis within the mitochondria, are encoded by nuclear genes and imported into the organelle after translation in the cytosol. The extensive use of next generation sequencing (NGS) has resulted in an increasing number of variants in mtARS genes being identified and associated with mitochondrial diseases. The similarities between yeast and human mitochondrial translation machineries make yeast a good model to quickly and efficiently evaluate the effect of variants in mtARS genes. Genetic screening of patients with a clinical suspicion of mitochondrial disorders through a customized gene panel of known disease-genes, including all genes encoding mtARSs, led to the identification of missense variants in WARS2, NARS2 and RARS2. Most of them were classified as Variant of Uncertain Significance. We exploited yeast models to assess the functional consequences of the variants found in these genes encoding mitochondrial tryptophanyl-tRNA, asparaginyl-tRNA, and arginyl-tRNA synthetases, respectively. Mitochondrial phenotypes such as oxidative growth, oxygen consumption rate, Cox2 steady-state level and mitochondrial protein synthesis were analyzed in yeast strains deleted in MSW1, SLM5, and MSR1 (the yeast orthologues of WARS2, NARS2 and RARS2, respectively), and expressing the wild type or the mutant alleles. Pathogenicity was confirmed for most variants, leading to their reclassification as Likely Pathogenic. Moreover, the beneficial effects observed after asparagine and arginine supplementation in the growth medium suggest them as a potential therapeutic approach.

Human versus Neural Machine Translation Creativity: A Study on Manipulated MWEs in Literature

In the digital era, the (r)evolution of neural machine translation (NMT) has reshaped both the market and translators’ workflow. However, the adoption of this technology has not fully reached the creative field of literary translation. Against this background, this study aims to explore to what extent NMT systems can be used to translate the creative challenges posed by idioms, specifically manipulated multiword expressions (MWEs) found in literary texts. To carry out this pilot study, five manipulated MWEs were selected from a fantasy novel and machine-translated (English > Spanish) by four NMT systems (DeepL, Google Translate, Bing Translator, and Reverso). Then, each NMT output as well as a human translation are assessed by six professional literary translators by using a human evaluation sheet. Based on these results, the creativity obtained in each translation method was calculated. Despite the satisfactory performance of both DeepL and Google Translate, HT creativity was highly superior in almost all manipulated MWEs. To the best of our knowledge, this paper not only contributes to the ongoing study of NMT applied to literature, but it is also one of the few studies that delve into the almost unexplored field of assessing creativity in neural machine-translated MWEs.

Does Translation Technology Affect Translators' Performance? A Meta-Analysis

Translation technologies, including computer-assisted translation (CAT) tools, translation memory (TM) systems, and machine translation (MT), are increasingly utilized in professional translation workflows and training. However, the effects of these technologies on translators' performance remain inconclusive. This meta-analysis examines the overall impact of translation technologies on translator performance by synthesizing data from 12 experimental studies published between 2000 and 2023. The study investigates the effectiveness of translation technologies compared to traditional translation methods. The findings reveal a significant positive effect size, indicating that translation technologies have the potential to improve translators’ performance relative to purely human translation. The integration of advanced interactive CAT systems and post-editing MT demonstrates larger advancements compared to basic TM match retrieval. Moreover, experienced professional translators derive greater benefits from incorporating technologies than student translators, highlighting the importance of leveraging automation capabilities alongside human expertise. However, the study identifies significant heterogeneity among the studies, influenced by factors such as translation direction. Translators translating into their native language exhibit greater advancements, emphasizing the advantages of technologies that strengthen fluency in the target language.

Research on Optimized Path of Machine Translation from the Perspective of Cognitive Approach - Collaborative Development of Human Translation and Machine Translation

Research on Optimized Path of Machine Translation from the Perspective of Cognitive Approach - Collaborative Development of Human Translation and Machine Translation

Innovative methods for integrating translation memory and CAT tools: Enhancing intelligent support in human translation processes

This paper explores the innovative integration of Translation Memory (TM) and Computer-Assisted Translation (CAT) tools to enhance translation efficiency, consistency, and quality in multinational organizations. By adopting a user-centric interface design, modular system architecture, and cloud-based deployment, the integrated system addresses diverse translation needs while ensuring data security and privacy. Key features include real-time translation suggestions powered by machine learning algorithms, seamless access to TM databases, and real-time collaboration tools. The paper discusses implementation strategies, challenges, and solutions, highlighting the importance of user training and continuous improvement. A case study demonstrates significant improvements in translation speed, accuracy, and user satisfaction, underscoring the potential of advanced translation technologies to transform translation workflows. The findings provide valuable insights into best practices for successful implementation and optimization of TM and CAT tools in complex, large-scale environments.

القفاز الناطق الذكي بيد واحدة للصم و البكم

The sign language is an effective means of communication between the deaf and dumb communities and the general public, but it requires human translators. This paper describes the development of a smart speaking glove with one-hand deaf and Dumb individuals using Arabic sign language. The glove bridges the communication gap between those with hearing and speech problems and those who don't understand sign language. It accurately converts sign language motions into audio and text, enhancing dignity, independence, and social inclusion. The glove, made of flexible sensors and a six-dimensional motion tracking device, allows deaf individuals to communicate with regular people via a smartphone, reducing the gap between them. It has focused on six major scenarios based on sentences and words of foodstuff and welcome signs. In cooperation with the Zawia Center for the Deaf and Dumb, every scenario was evaluated. The glove performed well when tested for side bending, resistance, voltage, and angle.

BIO 300 Attenuates Whole Blood Transcriptome Changes in Mice Exposed to Total-Body Radiation.

Development of radiation medical countermeasures under the U.S. Food and Drug Administration Animal Rule requires the capability to translate an effective animal-to-human drug dose. One method of human dose translation is using a biomarker and determining drug doses that modulate the biomarker to the desired level. BIO 300 Oral Powder (BIO 300) is a prophylactic radiation medical countermeasure that is currently being developed following the Animal Rule. The present study aimed to identify biomarkers that can be used for human dose conversion by conducting transcriptomics of whole blood collected from BIO 300-treated CD2F1 mice in the presence and absence of total-body irradiation (TBI). Unirradiated mice were treated with vehicle or 50, 100, or 200 mg/kg BIO 300, and irradiated mice were treated with 200 mg/kg or BIO 300 or vehicle prior to TBI. Whole-blood samples were collected after the last dose of the drug and after irradiation. RNA sequencing demonstrated 100 and 200 mg/kg of BIO 300 doses caused significantly more differential gene expression at 48 h after drug dose compared to 50 mg/kg of BIO 300 (7648, 7680, and 55 significantly differently expressed genes, respectively). Interestingly, following TBI, there were no significantly differentially expressed genes between vehicle- and BIO 300-treated mice. Despite the lack of significant changes in gene expression, the transcriptomic profiles in both groups indicated differential changes in signaling pathways. Pathway analysis of the transcriptome profile from vehicle-treated/TBI mice revealed that many inflammatory signaling pathways were activated in these animals. Signaling pathways enriched in BIO 300-treated/TBI mice were involved in cellular stress and immune response and were predicted to be inhibited. In all, four signaling pathways of interest were identified that were differentially enriched in irradiated animals treated with BIO 300: pathogen-induced cytokine storm signaling, S100 family signaling, pulmonary fibrosis idiopathic signaling, and wound-healing signaling. These pathways should be explored to identify potential biomarkers of BIO 300 that can be used for human dose translation.

Mild cold stress at ambient temperature elevates muscle calcium cycling and exercise adaptations in obese female mice.

Housing temperature is a critical regulator of mouse metabolism and thermoneutral housing can improve human translation. However, the impact of housing temperature on the ability of wheel running to rescue the detrimental effect of diet-induced obese mice is currently not fully understood. Lean or obese female mice were housed at standard ambient temperature (22℃) or thermoneutrality (30℃) with/without access to running wheels. The metabolic phenotype was investigated using glucose tolerance tests, indirect calorimetry, and body composition. Molecular muscle adaptations were measured using immunoblotting, qPCR, and spectrophotometric/fluorescent assays. Obese female mice housed at 22°C showed lower adiposity, lower circulating insulin levels, improved glucose tolerance, and elevated basal metabolic rate compared to 30°C housing. Mice exposed to voluntary wheel running exhibited a larger fat loss and higher metabolic rate at 22°C housing compared to thermoneutrality. In obese female mice, glucose tolerance improved after exercise training independent of housing temperature. Independent of diet and training, 22°C housing increased skeletal muscle sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) activity. Additionally, 22°C-housing elevated the induction of training-responsive muscle proteins in obese mice. Our findings highlight that housing temperature significantly influences adiposity, insulin sensitivity, muscle physiology, and exercise adaptations in diet-induced obese female mice.

Navigating Ethics in AI-Driven Translation for a Human-Centric Future

General Background: The rapid growth of AI technology is reshaping various industries, including translation, bringing both opportunities and ethical challenges. Specific Background: AI's integration into translation processes promises automation and efficiency but raises concerns about the role of human translators, translation quality, and cultural nuances. Additionally, issues like machine bias and privacy preservation are emerging as critical ethical considerations. Knowledge Gap: While much attention has been given to AI's technical advancements, there is a lack of comprehensive research addressing the ethical implications of AI in translation and its impact on professional roles and requirements. Aims: This study aims to critically review the literature on AI's impact on the translation profession and explore the ethical challenges arising from AI integration. It seeks to provide recommendations for ethical translation practices in an AI-enhanced environment. Results: Through in-depth interviews with professional translators, educators, and industry experts, the study identified key themes such as transparency in AI systems, the responsibility of human translators, bias in AI systems, and the impact on human translators. Novelty: This research offers a unique perspective by combining insights from diverse stakeholders and emphasizing ethical considerations in AI-assisted translation. It proposes an action-oriented approach to addressing ethical concerns to ensure the survival of the translation profession in an AI-driven society. Implications: The findings serve as valuable guidance for translators, educators, and policymakers, highlighting the need for ethical frameworks and guidelines specific to AI integration in translation. This study underscores the importance of balancing AI advancements with ethical standards to achieve high-quality, culturally sensitive translations. Highlight: Ethical Considerations: Address bias and privacy in AI-assisted translation. Human Expertise: Maintain human involvement for quality and cultural nuance. Future Research: Enhance ethical practices with diverse, comprehensive studies. Keyword: AI integration, translation ethics, machine bias, human translators, cultural sensitivity

A pilot study evaluating dosing tolerability of 17α-estradiol in male common marmosets (Callithrix jacchus).

17α-estradiol extends healthspan and lifespan in male mice without significant feminization or deleterious effects on reproductive function, making it a candidate for human translation. However, studies in animal models that more accurately replicate human physiology are necessary to establish 17α-estradiol dosing standards for clinical trials. This study evaluated the tolerability of 17α-estradiol treatment in the common marmoset over a short treatment duration. We found that male marmosets tolerated two dosing regimens (0.37-0.47 or 0.62-0.72 mg/kg/day) as evidenced by the absence of gastrointestinal distress, changes in vital signs, or overall health conditions. 17α-estradiol treatment mildly decreased body mass, adiposity, and glycosylated hemoglobin, although these changes were not statistically significant in most instances. However, neither dose of 17α-estradiol elicited feminization in our study, thereby suggesting that optimized dosing regimens may provide health benefits without feminization in primates. Additional studies are needed to determine if longer duration treatments would also be nonfeminizing and elicit significant health benefits, which would aid in developing dosing regimens targeting healthy aging in humans.

Un-Stroking the Spanish Translations (and Synonyms) of “Stroke”: A Comparative Analysis

Within the field English-Spanish medical translation we encounter a noteworthy list of difficulties, arising from the fact that medical English oftentimes tends to use either a single term (or a phrase) lexicalizing each concept. Considering the increasing relevance of “stroke” nowadays, we aimed at researching the Spanish renderings (and synonyms) for this issue in order to see whether the English language provides us with more or less terms and phrases. By using institutional and professional sources, in this paper, we first delve into the etymology of “stroke” in English language, as well as the stroke types and subtypes, recording a total of 10 lexical items and phrases. Then, we research into the Spanish counterparts. By referring to computer-aided translation, as well as human translation, later on we carry out the English-Spanish contrast. Overall, the findings unveil how Spanish lexical and phrase items (n=33) triple the English ones available (n=10).

Enhancement of English-Bengali Machine Translation Leveraging Back-Translation

An English-Bengali machine translation (MT) application can convert an English text into a corresponding Bengali translation. To build a better model for this task, we can optimize English-Bengali MT. MT for languages with rich resources, like English-German, started decades ago. However, MT for languages lacking many parallel corpora remains challenging. In our study, we employed back-translation to improve the translation accuracy. With back-translation, we can have a pseudo-parallel corpus, and the generated (pseudo) corpus can be added to the original dataset to obtain an augmented dataset. However, the new data can be regarded as noisy data because they are generated by models that may not be trained very well or not evaluated well, like human translators. Since the original output of a translation model is a probability distribution of candidate words, to make the model more robust, different decoding methods are used, such as beam search, top-k random sampling and random sampling with temperature T, and others. Notably, top-k random sampling and random sampling with temperature T are more commonly used and more optimal decoding methods than the beam search. To this end, our study compares LSTM (Long-Short Term Memory, as a baseline) and Transformer. Our results show that Transformer (BLEU: 27.80 in validation, 1.33 in test) outperforms LSTM (3.62 in validation, 0.00 in test) by a large margin in the English-Bengali translation task. (Evaluating LSTM and Transformer without any augmented data is our baseline study.) We also incorporate two decoding methods, top-k random sampling and random sampling with temperature T, for back-translation that help improve the translation accuracy of the model. The results show that data generated by back-translation without top-k or temperature sampling (“no strategy”) help improve the accuracy (BLEU 38.22, +10.42 on validation, 2.07, +0.74 on test). Specifically, back-translation with top-k sampling is less effective (k=10, BLEU 29.43, +1.83 on validation, 1.36, +0.03 on test), while sampling with a proper value of T, T=0.5 makes the model achieve a higher score (T=0.5, BLEU 35.02, +7.22 on validation, 2.35, +1.02 on test). This implies that in English-Bengali MT, we can augment the training set through back-translation using random sampling with a proper temperature T.

Human eRF1 Translation Regulation

Eukaryotic translation release factor eRF1 is an important cellular protein that plays a key role in translation termination, nonsense-mediated mRNA decay (NMD), and readthrough of stop codons. The amount of eRF1 in the cell influences all these processes. The mechanism of regulation of eRF1 translation through an autoregulatory NMD-dependent expression circuit has been described for plants and fungi, but the mechanisms of regulation of human eRF1 translation have not yet been studied. Using reporter constructs, we studied the effect of eRF1 mRNA elements on its translation in cell-free translation systems and HEK293 cell culture. Our data indicate the absence of an NMD-dependent autoregulatory circuit for human eRF1 expression. We found that the translation of the eRF1 coding sequence is most strongly influenced by the 5′ untranslated region of eRF1 mRNA and the start codon of the upstream open reading frame. According to the transcription start database, eRF1 mRNA is characterized by high heterogeneity of the transcription start and a variable 5' untranslated region in length. In addition, the start codon of the CDS in eRF1 mRNA is located within the known translational regulator of short 5' untranslated regions (TISU), which also stimulates mRNA transcription of genes with high transcription start heterogeneity. We hypothesize that regulation of human eRF1 synthesis occurs at both the transcriptional and translational levels. At the transcription level, the length of the eRF1 5' untranslated region and the number of the upstream open reading frames in it are regulated. This regulation in turn, regulates the production of eRF1 at the translation level.

Strategies of Translating Euphemistic Expressions from Arabic into English: A Comparative Study of Artificial Intelligence Models with Human Translation

Euphemism is used to soften rude expressions or words. Translating euphemisms is a very challenging task for human translators and will be more difficult for AI applications. Almost all previous studies have clustered around studying the efficiency, accuracy, coherence and cohesion of AI translation in comparison to human translation. However, these studies have not tackled the strategies used in both types of translations. Consequently, this study aims to identify the strategies utilized by AI Models with human translation when translating euphemistic expressions from Arabic into English. Additionally, it aims to evaluate how these methods influence the translation accuracy of both human translators and AI models in both directions of translation (Arabic to English and English to Arabic). The researchers have utilized some euphemistic expressions taken from the Holy Quran, Prophetic Hadiths, and some previous studies. These expressions have been inputted into AI models. The outcomes of these AI models are compared to Human translation, using Baker’s (2013) translation strategies. Researchers have analysed the collected data quantitatively. The results revealed that translation by a more general word and translation by cultural substitution strategies were mostly used by human translation whereas translation using a loan word strategy is mostly used by AI models. The findings also showed that human translators outperform AI models regarding cultural substitution. It has been found that the direction of language plays a crucial role in AI outcomes, as they perform better when translating from English into Arabic.

The Datasets of Human and AI Translation

The Datasets of Human and AI Translation

Thinking solidarity and translation together: Towards a new definition of solidarity

In a cross-disciplinary roundtable discussion on the feminist politics of translation,Judith Butler (Nagar et al., 2017, p. 113)aptly notes that “there can be no solidarity without translation, and certainly no global solidarity”. This article is the first dedicated attempt to definesolidarity from within translation studies: it redefines the concept to foreground the role of translation, understood as a hermeneutic, interpersonal and semiotic process, in solidarity development. The article is also the first to bring together scholarship on solidarity from philosophy, political science and sociology, on the one hand, and translation studies, on the other. I show that insights from translation studies supplement scholarship on solidarity in the other disciplines, where translation is largely overlooked. I also apply analytical categories from those disciplines to discussions of solidarity in translation studies and demonstrate the de factodifferent understandings of solidarity behind translation scholars’ use of the term, thus initiating a discipline-wide theoretical conversation on the concept. Moreover, upon noticing that the roleof translation in generating solidarity is not captured in any existing definition, I redefine solidarity as a sense of interconnection and commonality in difference –developed through caring, careful and inherently incomplete translation –that pursues inclusion and impels action towards common good. Finally, I signal how the definition can inform our understanding of the interlingual human translator as an active agent, adept at forging and facilitating solidarity.

Cross-gender hijacking of high achievers by translation students: English to Polish

ABSTRACT In Polish, where different inflectional paradigms apply to female and male names, attributing male gender to female referents such as eminent scientists and authors that pass unnoticed in English becomes visible in scholarly as well as popular scientific texts, exposing the society’s gender bias. While gender bias in machine translation has been widely observed and criticised, it is believed that human translation would eliminate factual mistakes of this type. Yet false attributions of male gender to eminent female linguists found in Polish scholarly texts suggest the contrary. A translation experiment conducted with a group of graduate students of translation was conducted in order to test the presence and strength of the bias, differences between male and female student translators, and the impact of earlier teaching on assigning gender in translation from English to Polish. Both male and female students frequently attributed the male gender to linguistic scholars even in the presence of strong evidence to the contrary, and after prior learning. Male students’ choices were slightly but significantly more biased. The conclusions pertain to the social consequences of the bias and the need to include this topic in translation training while attending more strongly to students’ research and sociocultural competence.

Qualities of Literary Machine Translation: A Corpus-based Case Study

The research assesses and compares the translation performance of two popular machine translation systems, GPT-4o and Youdao AI Translate, in translating into English ten Chinese prose essays excerpted from Selected Modern Chinese Essays 2 by Zhang Peiji. The goal is to discover their linguistic features and investigate how well they can perform in this translation. Through a corpus-based analysis, the research explores the STTR and word/ sentence length of their translations and conducts both automated and human evaluations on their translation quality. It reveals that GPT-4o exhibits higher lexical variety and both the two machine translation systems tend to produce more and shorter sentences than the human translation does. Both of them perform surprisingly well in the translation, as they get relatively high BLUE scores yet low TER scores, as well as high adequacy and fluency rates. Our evaluation results also show that Youdao AI Translate displays generally better performance than GPT-4o in the translation of Chinese-to-English literary texts, and they can complement each other to achieve even better performance, though a certain amount of errors are still present in both of their translations.

An Active Inference Agent for Modeling Human Translation Processes.

This paper develops an outline for a hierarchically embedded architecture of an artificial agent that models human translation processes based on principles of active inference (AIF) and predictive processing (PP). AIF and PP posit that the mind constructs a model of the environment which guides behavior by continually generating and integrating predictions and sensory input. The proposed model of the translation agent consists of three processing strata: a sensorimotor layer, a cognitive layer, and a phenomenal layer. Each layer consists of a network of states and transitions that interact on different time scales. Following the AIF framework, states are conditioned on observations which may originate from the environment and/or the embedded processing layer, while transitions between states are conditioned on actions that implement plans to optimize goal-oriented behavior. The AIF agent aims at simulating the variation in translational behavior under various conditions and to facilitate investigating the underlying mental mechanisms. It provides a novel framework for generating and testing new hypotheses of the translating mind.

The human mitochondrial translation factor TACO1 alleviates mitoribosome stalling at polyproline stretches.

The prokaryotic translation elongation factor P (EF-P) and the eukaryotic/archaeal counterparts eIF5A/aIF5A are proteins that serve a crucial role in mitigating ribosomal stalling during the translation of specific sequences, notably those containing consecutive proline residues (1,2). Although mitochondrial DNA-encoded proteins synthesized by mitochondrial ribosomes also contain polyproline stretches, an EF-P/eIF5A mitochondrial counterpart remains unidentified. Here, we show that the missing factor is TACO1, a protein causative of a juvenile form of neurodegenerative Leigh's syndrome associated with cytochrome c oxidase deficiency, until now believed to be a translational activator of COX1 mRNA. By using a combination of metabolic labeling, puromycin releaseand mitoribosome profiling experiments, we show that TACO1 is required for the rapid synthesis of the polyproline-rich COX1 and COX3 cytochrome c oxidase subunits, while its requirement is negligible for other mitochondrial DNA-encoded proteins. In agreement with a role in translation efficiency regulation, we show that TACO1 cooperates with the N-terminal extension of the large ribosomal subunit bL27m to provide stability to the peptidyl-transferase center during elongation. This study illuminates the translation elongation dynamics within human mitochondria, a TACO1-mediated biological mechanism in place to mitigate mitoribosome stalling at polyproline stretches during protein synthesis, and the pathological implications of its malfunction.