Domain Translation Research Articles

A mixed method study investigating the key translational competencies acquired during a challenge-based course

BackgroundThe translational domain is a complex subfield of the biomedical life sciences focused on bridging the gap between scientific research and clinical application, with the ultimate goal of improving patient care through healthcare innovations. Professionals in this field, ranging from researchers to clinicians and industry experts, require specific core competencies. These include communication, collaboration, boundary crossing, innovation, and the ability to integrate diverse scientific domains.MethodsWe investigated the translational competencies acquired most by life science students who participated in a six-month challenge-based course at a Dutch graduate school. Seventeen students were part of the 2021 cohort, supervised by five biomedical experts serving as their clients. Quantitative and qualitative student survey data (n = 10), and semi-structured expert interview data (n = 4) were collected after completion of the course in 2021. Two existing translational competency frameworks were used to categorize and rank the acquired competencies, highlighting those most developed and relevant during the translational challenge-based course.ResultsCommunication was the most listed competency category by both students and experts, and a new competency category was identified named Self-Development Tools, which included competencies related to decision-making, reflection, feedback, and creative thinking. Student data also showed that the course influenced their choice in pursuing a translational career path.ConclusionsThis study provides insight into key translational competencies acquired by students during a six-month challenge-based course and insight into competencies that may be part of their continued education after graduation from both student and expert perspectives. These findings are relevant for both educators and prospective employers in the translational domain as they apply a novel ranking to the existing literature on translational competencies and elaborate on how to prepare life sciences graduates towards a translational career.

A deep neural network model for classifying pharmacy practice publications into research domains

BackgroundPharmacy practice faculty research profiles extend beyond the clinical and social domains, which are core elements of pharmacy practice. But as highlighted by journal editors in the Granada Statements, there is no consensus on these terms. Four domains (clinical, education, social & administrative, and basic & translational) of pharmacy practice faculty research are proposed. ObjectivesTo develop a classifier for categorizing pharmacy practice faculty publications into four proposed domains, and to compare the model with zero-shot performances of state-of-the-art, general purpose large language models (gpLLMs). MethodsOne thousand abstracts from 2018 to 2021 documents published by pharmacy practice faculty were reviewed, labelled and used to screen and finetune several Bidirectional Encoders Representations from Transformers (BERT) models. The selected model was compared with zero-shot performances of 7 state-of-the-art gpLLMs including ChatGPT-4o, Gemini-1.5-Pro, Claude-3.5, LLAMA-3.1 and Mistral Large, using 80 randomly selected abstracts from 2023 publications labelled with ≥80% consensus by all authors. Classification metrics included F1, recall, precision and accuracy, and reproducibility was measured with Cohen's kappa. A use case was demonstrated by testing the null hypothesis that the research domain distribution of faculty publications was independent of the pandemic. ResultThe model – Pharmacy Practice Research Domain Classifier (PPRDC) produced a 5-fold stratified cross-validation metrics of 89.4 ± 1.7, 90.2 ± 2.2, 89.0 ± 1.7, and 95.5 ± 0.6, for F1, recall, precision and accuracy, respectively. PPRDC produced perfectly reproducible classifications (Cohen's kappa = 1.0) and outperformed zero-shot performances of all gpLLMs. F1 scores were 96.2 ± 1.6, 92.7 ± 1.2, 85.8 ± 3.2, and 83.1 ± 9.8 for education, clinical, social, and translational domains, respectively. ConclusionsPPRDC (https://sadeosun-pprdc.streamlit.app) performed better than gpLLMs in this abstract classification task. Among several other impacts, PPRDC opens a new frontier in bibliometric studies; it will also advance the goals of the Grenada Statements by aiding authors and journal editors in journal selection and article prioritization decisions, respectively.

Evaluating the feasibility and utility of machine translation for patient education materials written in plain language to increase accessibility for populations with limited english proficiency

BackgroundPopulations with limited English proficiency (LEP) face comprehension barriers with health information as navigating healthcare systems involves encountering health information that is written at high reading grade levels, utilizes complex medical jargon, and unfamiliar or abstract terms and concepts. Despite the serious consequences of language discordance there is limited funding available for the translation of patient education material in the public healthcare setting. In response to the imperative need to provide equal access to patient education materials to all patients, regardless of English language proficiency, some have raised the feasibility of leveraging machine translation software. This study investigates the feasibility and utility of using machine translation (Google Translate) to translate patient education materials written in plain language. MethodsA sample of 5 patient education pamphlets were selected for inclusion based on their high usage and importance. These were assessed for their readability and translated by both human translators and using Google Translate into Spanish, Portuguese, Punjabi, Simplified Chinese, and Vietnamese. Medical translators conducted blinded appraisal of both sets of translations on four domains. ResultsSpanish and Vietnamese language pamphlets achieved the highest overall scores. There were significant differences between human and machine translation in favour of the former for all of the languages, although machine translation scored above 3/5 in 90 % of the domains tested. There was no correlation between readability scores and translation scores. DiscussionGoogle Translate performs well in multiple translation domains despite its continued inferiority relative to professional human translation. The high scoring of machine translated pamphlets, particularly in the most crucial domain of severity however, points to its potential adoption in a limited capacity in healthcare settings, with processes in place, like pre-screening for high-risk content that may pose a threat to patient well-being.

An STM study on the diffusion of O atoms on a CO-covered Ru(0001) surface—The role of domain boundaries

We investigate tracer diffusion at the domain boundaries in an adsorption layer, an effect that corresponds to grain boundary diffusion in 3D polycrystalline solids. Experiments were performed on adsorbed O atoms on a Ru(0001) surface in a layer of CO molecules. The CO molecules form a (3x3)R30∘ structure which displays translational domains. High-speed scanning tunneling microscopy (STM) was used to image the motion of the O atoms. The data show that single O atoms preferentially move along the domain walls which in the STM movies appear as disordered, fluctuating stripes between the ordered domains. The diffusion coefficient of the O atoms is one order of magnitude higher than the diffusion coefficient in the ordered domains. By comparison with previous experiments on completely disordered CO layers, it is concluded that the diffusion is similarly promoted by the enhanced fluctuations in the disordered domain walls.

What makes a medical translator?

Advancements in healthcare and the rise of patient-centredness have increased workloads for professional medical translators. In parallel, new and emerging technologies are impacting translators’ output and workflows, though investigations into the medical translation domain remain scarce. This article presents the results of an international survey on 165 professional medical translators to identify trends concerning their socio-demographic and professional profiles, the textual- and process-related challenges of translating medical texts aimed at patients as laypersons, and their use of technology, specifically Computer-Assisted Translation (CAT) tools and Automatic Speech Recognition (ASR). Results show that the medical translators surveyed have decades-long experience in translation and come primarily from a linguistic background. They find specialised terminology and delivery deadlines as most challenging when translating patient-facing medical texts. Finally, most medical translators in our survey use CAT tools when translating or post-editing, although a small minority also integrate ASR tools into their workflows.

The Emotional Face of Language: Sentiment Analysis of English and Korean Subtitles on Barbie

Barbie: A Touch of Magic, Eposide 5th, an English and Korean subtitled Barbie cartoon, is used in this study to investigate the relationship between language culture and emotions. Finding out how language and culture impact emotional expression and what this means for language acquisition, emotional intelligence development, and cross-cultural communication are the key objectives. An in-depth analysis was done on the emotional intonations that change between the Korean and English subtitles, as well as any possible implications for language learning. The study emphasizes the intricate connection between language and society as well as the significance of including affective components in language instruction materials. The results imply that when learning a language and acquiring cross-cultural communication skills, the degree to which an emotion is communicated matters. The study offers helpful data that can be utilized in the domains of communication techniques, translation, and language instruction. It also emphasizes how important cultural sensitivity is for developing good communication and empathy skills. The purpose of this study is to advance knowledge of the relationship between language and culture and establish the framework for further investigation into this field.

Revolutionising Translation Technology: A Comparative Study of Variant Transformer Models - BERT, GPT, and T5

Recently, transformer-based models have reshaped the landscape of Natural Language Processing (NLP), particularly in the domain of Machine Translation (MT). this study explores three revolutionary transformer models: Bidirectional Encoder Representations from Transformers (BERT), Generative Pretrained Transformer (GPT), and Text-to-Text Transfer Transformer (T5). The study delves into their architecture, capabilities, and applications in the context of translation technology. The study begins by discussing the evolution of machine translation from rule-based to statistical machine translation and finally to transformer models. The models have distinct architectures and purposes which pushed the limits of MT and have been instrumental in revolutionising the field. The study found significant contributions of the models in the advancement of NLP tasks including translation technology. Using comparative approach, the study further elaborates on each model’s design and utility. BERT is strong in excelling in tasks requiring a deep understanding of the context. GPT is excellent for tasks such as text generation, translation and creative writing. While the strengths of T5 is text-to-text framework by simplifying the taskspecific architectures, making it easy to perform different NLP tasks. Recognising these models’ unique features allows translators to select the best one for particular translation tasks and adjust them for better accuracy, fluency, and cultural relevance in translations. The study concludes that the models bridge language barriers, improve cross-cultural communication and pave way for more accurate and natural translations in the future. The study also points out that language processing models are continually evolving but understanding BERT, GPT, and T5’s specific features is key for ongoing development in translation technology.

Unveiling Translation's Role in Soft Power: The Case of the TEDA Project

Literary translation has been one of the most intriguing fields of study in terms of translation studies. Like other forms of translation, it encompasses not only linguistic transfer but also cultural transfer. Considering that cultural elements and literary features are prominent in literary texts, it is no surprise that literary translation gained significant ground in translation research. Despite the challenges faced in the process of literary translation, its contributions to literature, readers, and intercultural understanding outweigh. Apart from these, translation's role in fostering a country's soft power should not be underestimated. As a resource reflecting a nation's culture with abundant cultural elements, literary translation is one of the most substantial domains of translation that can have a pivotal role in soft power. In parallel with all these contributions of translation, the promotion of literature through translation on a global scale comes into prominence. Promoting literature belonging to less prevalent languages is even more crucial as the authors of such literature need to show more effort to make their voices heard in broader geographies. To fulfill this goal, translation projects that provide such an opportunity for authors play a significant role. From this point of view, this study aims to present a clear framework of literary works translated from Turkish literature into Spanish language within the scope of TEDA project funded by the Turkish Ministry of Culture and Tourism. The literary works translated into Spanish are presented based on elements such as the authors, translators, publishing houses, literary genres, countries, years, and titles of the books in the source language and target language. The results reveal that there is a remarkable amount of effort to make Turkish literature recognized in Spanish-speaking regions; however, there is still a long way to go as the number of translations and diversification in terms of authors, translators, and literary genres are still limited and translations of the book titles sometimes could not avoid stereotypes about Turkish culture.

Lost in Untranslatability: Ishvara, Allah and Interfaith Dialogue

While translation plays a vital role in bridging intercultural gaps, it struggles to convey the exact meaning of certain ideas due to the unique characteristics and structures inherent in each language and the underlying social context. This difficulty is pronounced when translating between the language pair Hindi and Urdu, which, despite both originating from Khari Boli, have diverged significantly under the influences of Hinduism and Islam. In an Indian social context, the Arabic-origin Urdu word Allah is often equated with the Sanskrit-origin Hindi word Ishvara. However, this translation is problematic and can cause confusion because the Hindu idea of the divine, Ishvara, is fundamentally different from the Islamic concept of Allah. Building upon the theory of Sanskrit non-translatability proposed by Malhotra and Babaji, this paper argues for the existence of cultural untranslatability in the domain of Urdu-Sanskrit translation. Using a case study approach for the terms Ishvara and Allah, the paper concludes that specific religious terms should not be translated and makes the case that preserving precise linguistic categories is essential for meaningful inter-faith engagement.

Lost in Untranslatability: Ishvara, Allah and Interfaith Dialogue

While translation plays a vital role in bridging intercultural gaps, it struggles to convey the exact meaning of certain ideas due to the unique characteristics and structures inherent in each language and the underlying social context. This difficulty is pronounced when translating between the language pair Hindi and Urdu, which, despite both originating from Khari Boli, have diverged significantly under the influences of Hinduism and Islam. In an Indian social context, the Arabic-origin Urdu word Allah is often equated with the Sanskrit-origin Hindi word Ishvara. However, this translation is problematic and can cause confusion because the Hindu idea of the divine, Ishvara, is fundamentally different from the Islamic concept of Allah. Building upon the theory of Sanskrit non-translatability proposed by Malhotra and Babaji, this paper argues for the existence of cultural untranslatability in the domain of Urdu-Sanskrit translation. Using a case study approach for the terms Ishvara and Allah, the paper concludes that specific religious terms should not be translated and makes the case that preserving precise linguistic categories is essential for meaningful inter-faith engagement.

The effect of parental age on child development at 36 months: Insights from the Japan environment and children's study

BackgroundThe impact of parent-childbearing age on child development at 36 months of age is controversial. AimsWe used data from a large cohort study with multiple imputation and mediation analyses of variables. Methods and ProceduresA total of 72,606 parent–child pairs from the Japan Environment and Children's Study were included in the study. Parents' ages were categorized into five groups. We used five domains of the Japanese translation of the Ages and Stages Questionnaire, Third Edition (J-ASQ-3). Scores below the cutoff value at 36 months were defined as developmental delays in each domain. We used three logistic analysis models. In Model 3, we analyzed maternal and paternal age using other variables and covariates. Outcomes and ResultsThe outcome was a developmental delay in the five domains of J-ASQ-3. In Model 3, ORs for the developmental delay scores regarding parental age were significantly associated with all five domains of J-ASQ-3. The mediation analysis showed a significant mediation interaction effect for mothers but localized for fathers. ConclusionAdvanced paternal and maternal ages were associated with developmental delay in children. Awareness of the risks of childbearing at an advanced age is crucial. What’s the paper addsThis manuscript used data from a large cohort study with multiple imputation and mediation analyses. With these analyses, we identified the pure effect of advanced parental age on their children’s development at 36 months.

Pre-service Teachers' Perspectives on the Integration of Technology in Future Education: Between English Education and Ethics Education Majors

The current study aimed to investigate the perceptions of pre-service teachers regarding the integration of advanced technologies into future education and to examine whether these perceptions differ significantly based on the pre-service teachers' academic majors. The participants consisted of 110 pre-service teachers majoring in Ethics education and English education at a university in Korea's Gyeongsang Province. A questionnaire comprising 59 items was administered, addressing aspects such as interest in the utilization of advanced technologies, resistance to their use, concerns about their application, and expectations for the future of education over the next decade. Statistical analyses, including a paired t-test, an independent <i>t</i>-test, frequency analysis, and analysis of variance, were employed to examine differences based on variables of gender and major. </br>The study revealed statistically significant disparities in the perceptions of pre-service teachers from distinct majors, particularly between English education majors and ethics education majors, concerning the incorporation of advanced technologies in education. English education majors demonstrated notably high awareness, particularly in the domain of neural machine translation. Conversely, ethics education majors demonstrated a lower inclination to embrace the use of advanced technologies in their instructional practices. Additionally, ethics education majors expressed the belief that the role of teachers would become more pivotal in classes integrating these technologies. Considering these research outcomes, it becomes evident that future education for pre-service teachers should underscore comprehensive training in both digital technology education and the development of competencies in areas where artificial intelligence cannot replace the role of educators.

Cross-Language Translation and Comparative Study of English Literature Using Machine Translation Algorithms

Cross-language processing in English literature involves the translation and analysis of literary texts from English into other languages or vice versa. This multidimensional task encompasses various aspects, including language translation, cultural adaptation, and literary interpretation. Through cross-language processing, literary works originally written in English can reach a wider audience, enabling individuals from diverse linguistic backgrounds to access and appreciate the richness of English literature. This paper presents an innovative approach to language processing tasks through the integration of Ant Swarm Domain Statistical Machine Learning (ASDS-ML). Leveraging principles of swarm intelligence and statistical learning techniques, ASDS-ML offers a robust framework for addressing challenges in language translation and classification. In the domain of translation, ASDS-ML demonstrates promising results in achieving accurate and nuanced translations across diverse language pairs, while also exhibiting adaptability to varying linguistic contexts. Furthermore, ASDS-ML showcases its effectiveness in text classification tasks, accurately categorizing instances across multiple classes with high precision and recall. In language translation tasks, ASDS-ML achieves an average BLEU score of 0.85 across multiple language pairs, outperforming baseline methods by 10%. Additionally, in text classification tasks, ASDS-ML achieves an average accuracy of 0.92 across ten different classes, surpassing existing approaches by 5%.

Global respiratory health priorities at the beginning of the 21st century.

Respiratory health has become a prevailing priority amid the diverse global health challenges that the 21st century brings, due to its substantial impact on individuals and communities on a global scale. Due to rapid advances in medicine, emerging knowledge gaps appear along with new challenges and ethical considerations. While breakthroughs in medical science can bring about encouraging possibilities for better treatments and interventions, they also lead to unanswered questions and areas where further research is warranted. A PubMed search on the topic "global respiratory health priorities" between the years 2000 and 2023 was conducted, which returned 236 articles. Of these, 55 were relevant and selected for inclusion in this article. The selection process took into account literature reviews, opinions from expert groups and careful analysis of existing gaps and challenges within the field; our selection encompasses specific infectious and noninfectious respiratory conditions in both adults and children. The global respiratory health priorities identified were selected on the basis that they have been recognised as critical areas of investigation and potential advancement and they span across clinical, translational, epidemiological and population health domains. Implementing these priorities will require a commitment to fostering collaboration and knowledge-sharing among experts in different fields with the ultimate aim to improve respiratory health outcomes for individuals and communities alike.

Deciphering linguistic and cultural hurdles‎ in ‎English-Arabic media translation: Insights from the BBC ‎online news articles

Media language encompasses literary and ‎scientific elements, technical terminology, and cultural references. Effectively conveying its message to the target audience from diverse cultural and linguistic backgrounds can be challenging. This highlights some of the subtleties of media language that might add to translation difficulties. Media translation is a complex process, which involves information, knowledge, and analysis from a wide range of disciplines, including art, economics, literature, politics and science. In translating media content, accuracy in communicating cultural allusions, technical terms, and nuances ‎ to the intended audience is equally crucial as translating plain text from one language to another. This study explores the linguistic and cultural barriers that may occur while translating English-Arabic online media, with a focus on findings from BBC Online News articles. It attempts to further contribute to a deeper understanding of media translation by focusing on bridging the linguistic and cultural divide while translating English to Arabic in online media. The results of this study suggest that a translator with advanced ‎proficiency and a thorough comprehension of the subtle linguistic and cultural ‎differences that exist ‎in both languages is required to provide high-quality ‎English to Arabic translation. In other ‎‎words, skilled translators who can ‎understand and incorporate the linguistic and cultural quirks of both ‎‎languages, creating a seamless connection ‎between the translation and media ‎domains, are essential for efficient media translation.

Study on Dynamic Characteristics of Vibration System with Two Degrees of Freedom and Multiple Rigid Constraints

A dynamic model of a vibration system with two degrees of freedom and multiple rigid constraints is constructed, the dynamics constraints of the segmented smooth vibration system under the multi-body impact coupling condition are given. Based on the cell mapping principle, a two-parameter co-simulation is carried out with two important parameters of excitation frequency and constraint gap as control variables. The model types and distribution rules of the periodic motion of the system response on the [Formula: see text]-whole two-parameter plane are obtained. The correlation between the dynamic response of the system and the model parameters is analyzed, and the evolution and bifurcation characteristics of fundamental periodic vibrations in the low-frequency small-gap parameter domain of the two-degree-of-freedom multi-rigid constrained vibration system are discussed. The diversity and regularity of subharmonic shock vibrations in the translational domain of the lingual domain are found.

Effect of immersion time on the structural order of the self-assembled monolayers of terphenylthiols on the gold surface: Coexistence of different molecular adlayer phases

By the example of terphenylthiolate (TPT) films on Au(111), we show by using a high-resolution scanning tunneling microscope (STM) that a long immersion time at room temperature results in the formation of distinct polymorphism. At least three different structural phases namely, α, β, and γ are found to coexist on the gold surface. Two of these phases (α and β) have not been previously detected in the STM images obtained for TPT monolayers prepared for shorter exposure times. The α phase is characterized by the commensurate (4×25)R9.7° structure, with an area per molecule of 34.7 Å2. Whereas the structure of the β phase can be described either by a semi-rectangular (25×213)R7°unit cell or an oblique (25×217)R23° unit cell with a molecular footprint of 33.4 Å2. The third γ phase can be assigned either as a (2×8)or (23×2)structure with a molecular area of 28.8 Å2. The γ phase was found to contain a high-density of translational domains. Unlike shorter immersion times, our results showed that prolonged exposure of the gold substrate to a TPT thiol solution (190 h) leads to the formation of loosely packed monolayers where molecules are largely tilted downward away from surface normal, contrasting with the fully saturated monolayers in which the (23×3)R30°superstructure with an average areal molecular density of 21.6 Å2 was observed in previous studies but, unexpectedly, it is absent in our current results.

Ribosome-targeting antibiotic control NLRP3-mediated inflammation by inhibiting mitochondrial DNA synthesis

While antibiotics are designed to target bacteria specifically, most are known to affect host cell physiology. Certain classes of antibiotics have been reported to have immunosuppressive effects, but the underlying mechanisms remain elusive. Here, we show that doxycycline, a ribosomal-targeting antibiotic, effectively inhibited both mitochondrial translation and nucleotide-binding domain and leucine-rich repeat-containing protein 3 (NLRP3) inflammasome-mediated caspase-1 activation and interleukin-1β (IL-1β) production in bone-marrow-derived macrophages (BMDMs). In addition, knockdown of mitochondrial methionyl-tRNA formyltransferase (Mtfmt), which is rate limiting for mitochondrial translation, also resulted in the inhibition of NLRP3 inflammasome-mediated caspase-1 activation and IL-1β secretion. Furthermore, both doxycycline treatment and Mtfmt knockdown blocked the synthesis of mitochondrial DNA (mtDNA) and the generation of oxidized mtDNA (Ox-mtDNA), which serves as a ligand for NLRP3 inflammasome activation. In addition, in vivo results indicated that doxycycline mitigated NLRP3 inflammasome-dependent inflammation, including lipopolysaccharide-induced systemic inflammation and endometritis. Taken together, the results unveil the antibiotics targeting the mitoribosome have the ability to mitigate NLRP3 inflammasome activation by inhibiting mitochondrial translation and mtDNA synthesis thus opening up new possibilities for the treatment of NLRP3-related diseases.

The Application of Computer-Aided Under-Resourced Language Translation for Malay into Kadazandusun

A computer-aided language translation using a Machine translation (MT) is an application performed by computers (machines) that translates one natural language to another. There are many online language translation tools, but thus far none offers a sequence of text translations for the under-resourced Kadazandusun language. Although there are web-based and mobile applications of Kadazandusun dictionaries available, the systems do not translate more than one word. Hence, this paper aims to present the discussion of the preliminary translation of Malay to Kadazandusun. The basic word-to-word with dictionary alignment translation based on Direct Machine Translation (DMT) is selected to begin the exploration of the translation domain where DMT is one of the earliest translation methods which relies on the word-to-word approach (sequence-to-sequence model). This paper aims to investigate the under-resourced language and the task of translating from the Malay language to the Kadazandusun language or vice versa. This paper presents the application and the process as well as the results of the system according to the basic Kadazandusun word arrangement (Verb-Subject-Object) and its translation quality using the Bilingual Evaluation Understudy (BLEU) score. Several phases are involved during the process, including data collection (word pair translation), preprocessing, text selection, translation procedures, and performance evaluation. The preliminary language translation approach is proven to be capable of producing up to 0.5 BLEU scores which indicate that the translation is readable, however, requires post-editing for better comprehension. The findings are significant for the quality of the under-resourced language translation and as a starting point for other machine translation methodologies such as statistical or deep learning-based translation.

Exploring Composite Indexes for Domain Adaptation in Neural Machine Translation

Domain adaptation in neural machine translation (NMT) tasks often involves working with datasets that have a different distribution from the training data. In such scenarios, k-nearest-neighbor machine translation (kNN-MT) has been shown to be effective in retrieving relevant information from large datastores. However, the high-dimensional context vectors of large neural machine translation model result in high computational costs for distance computation and storage. To address this issue, index optimization techniques have been proposed, including the use of inverted file index (IVF) and product vector quantization (PQ), called IVFPQ. In this paper, we explore the recent index techniques for efficient machine translation domain adaptation and combine multiple index structures to improve the efficiency of nearest-neighbor search in domain adaptation datasets for machine translation task. Specifically, we evaluate the effectiveness when combining optimized product quantization (OPQ) and hierarchical navigable small-world (HNSW) indexing with IVFPQ. Our study aims to provide insights into the most suitable composite index methods for efficient nearest-neighbor search in domain adaptation datasets, with a focus on improving both accuracy and speed.