Natural Text Research Articles

An Epistemological Approach to The Translation of Tenses and Aspects in English-French and French-English Contexts

This paper delves into the complexities of translating tenses and aspects, particularly in English-French and French-English contexts, advocating for an epistemological approach that entails a meticulous examination of source language (SL) tense and aspectual elements, as well as considerations of situational context, textual nature (e.g., direct speech, narrative, retrospection, anticipation) and pragmatics. The concept of tense inherently encompasses aspect, which delineates the structure of actions conveyed by verbs. Aspect may denote completeness or incompleteness, iteration or punctuality, semelfactivity, telicity, atelicity, frequency, and permanence, among other qualities. Employing a qualitative method, this paper first outlines the theoretical underpinnings of the notions discussed, followed by concrete examples. The study reveals that tense, originally derived from the concept of time, is intricately linked with aspect and action. Aspect, moreover, extends beyond linguistic realms to encompass philosophical (e.g., Aristotelian aspect) and metalinguistic dimensions. A cross-linguistic examination of tense and aspect unveils multifaceted relationships encompassing lexical, philosophical, semantic, syntactic, pragmatic, and strategic dimensions, necessitating further elucidation.

Особливості поширення раннього християнства у римській провінції Африка

The purpose of the article is to research the first origins of Christianity in the Roman province of Africa, to establish the geographical and chronological origin of early Christian doctrine in this region, and to determine the nature of its initial development. The research methodology is based on the principle of historicism and social approach. The article uses chronological, comparative historical approach, and analytical methods. The scientific novelty consists in the wide use of various source: written (early apologetic, hagiographic), archaeological and epigraphic, linguistic comparison of written sources, as well as the involvement of the latest historiographical research on Christianity in Roman Africa. Conclusions. Roman Africa underwent its initial Romanization after victory of Rome in the Punic Wars, and Proconsular Africa became a province of the Roman state in the first century B.C. The extensive trade network of Proconsular Africa with the Eastern and Western provinces of the Roman Empire contributed to the rapid urbanization and economic growth of the region. The constant trade and economic communication of the province made it possible for early Christianity to spread to the area. The first references to Christians date back to the second half of the second century AD and are typically written Christian texts of an urbanized nature. The Latin and Greek versions of these sources indicate the initial bilingualism of the early Christian ecclesia of Africa. The spread of Christianity in Africa came from both the Roman church and the churches of the Eastern provinces, and from there thanks to the Jewish trade diaspora. The beginning of the third century AD is characterized by the rapid development of the church in Africa and the work of the early Christian apologist Tertullian, who left behind a large written heritage. The earliest references to Christians in Proconsular Africa also indicate sporadic conflict between pagan society and local authorities against Christians.

From virtual assistant to writing mentor: Exploring the impact of a ChatGPT-based writing instruction protocol on EFL teachers’ self-efficacy and learners’ writing skill

Language teaching is a highly emotional profession that can affect the teachers’ well-being and learners’ achievement. However, studies have yet to explore the potential of positive psychology interventions and artificial intelligence (AI) tools to promote the psycho-emotional aspects of second language (L2) teachers and learners. Further, studies regarding the effectiveness of AI in promoting the learners’ language skills could have been expansive. Responding to these gaps, researchers chose ChatGPT, an AI-powered chatbot capable of generating natural and coherent texts, as a potential tool to foster positive emotions and interactions between Iranian English language teachers ( n = 12) and learners ( n = 48) in the L2 writing context. We operationalized ChatGPT in a three-phased writing instruction protocol (CGWIP): (1) a planning phase, where teachers used ChatGPT to brainstorm ideas and generate outlines for each session; (2) an instruction phase, where teachers used ChatGPT to engage the learners in writing process, analyse and reflect on their drafts, and (3) an assessment phase, where teachers used ChatGPT to simulate IELTS writing exam and provided detailed and constructive feedback to the learners. We further tested the effectiveness of CGWIP on teachers’ self-efficacy and learners’ writing skills before and after a 10-week instruction program. The Independent Samples t-test results showed that CGWIP significantly enhanced teachers’ self-efficacy compared to the control group. Also, the results of One Way ANCOVA revealed that CGWIP significantly improved learners’ writing skills and that these effects persisted over time. The study implied that the protocol can nurture teachers’ efficiency by helping them in various aspects of L2 writing instruction, including brainstorming, revising, providing feedback, and assessment, which in turn, improves learners’ writing skills.

A simplified classification computational model of opinion mining using deep learning

Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.

A face retrieval technique combining large models and artificial neural networks

SummaryMultimodal sentiment analysis is a popular research direction in the field of affective computing. It extends unimodal‐based sentiment analysis to the environment based on multimodal information exchange. Face retrieval is the most critical technology in the current multimodal sentiment analysis field. Traditional methods for face retrieval rely on large amounts of data to train the matching relationship between text and face images. This approach suffers from several issues, including high data acquisition costs, insufficient or poor quality training data, and high computational costs for training models. To address the aforementioned issues, this paper proposes a face retrieval technique that merges a large language model with a visual base model. This technique explores the use of zero samples for training, eliminating the need to collect data and train the model to achieve face retrieval. As a result, it significantly reduces the cost requirements of data and computational power. First, an accurate natural text of face description is generated using a large language model based on cue engineering based on face‐independent features. Second, the text and images are encoded using a visual base model and mapped into the same multidimensional space for matching. The experimental results indicate that this method performs well on the CelebA dataset. Extracting 31 different sets of face feature descriptions from the CelebA dataset for retrieval achieves 72% on top1 accuracy and 93% on top3 accuracy. Compared to traditional methods, the method proposed in this paper achieves considerable face retrieval accuracy without collecting text image pairs of data and without training the model.

Speech competence of judges' professional decisions during martial law

The article defines the importance of speaking competence of judges to ensure compliance with the requirements of current laws and regulations. Special attention is paid to the use of the Ukrainian language in practical judicial activity under the new circumstances of the administration of justice under martial law. The purpose of the article is to confirm modern approaches to deepening the speech competence of specialists in the field of law during martial law. Studying research in the field of speech competence of lawyers has shown that scientists consider various aspects of language expertise of law enforcement officers. The relevance of this research topic is due to the importance of analysis from the point of view of the level of speech competence of court decisions, especially during martial law. To conduct a speech assessment, the texts of four decisions of courts of different jurisdictions during the martial law of 2023, which are freely available, and which were randomly selected, were analyzed. The texts of the resolutions are written in business language with the necessary lexical legal content, clear and balanced. Summarized information on selected court cases according to developed criteria for speech assessment: the city of the court and its level, the date of the decision, statistical characteristics of the content by the number of words, signs and lines, grammatical, spelling, punctuation, stylistic, lexical and speech errors determined by the text of a technical nature. Identified errors are summarized and recommendations are given for the use of correct language forms according to Ukrainian spelling. Increasing the level of speech competence of specialists in the field of law in business writing is one of the important tasks of higher education, which requires the constant attention of scientific and pedagogical workers in the organizational, tactical provision of law enforcement activities during martial law.

Features of Teaching Written Speech in Russian Language Lessons in a Kyrgyz School (Based on the Example of 7th Class)

The article discusses the features of teaching writing in Russian as a second language lessons. Definitions of the concepts “writing” and “written speech” in scientific and methodological literature are given. Particular attention in the article is paid to the methods and means of developing written speech in Russian language lessons in a Kyrgyz school. For this purpose, requirements for teaching written speech are given in the Subject Standard for the Russian language for schools with Kyrgyz as the language of instruction. Considerable attention is paid to the analysis of the textbook “Russian Language” for the 7th grade of schools with the Kyrgyz language of instruction in order to identify a system of gradual formation of skills to write texts of a productive nature. The given examples of tasks aimed at developing written speech in accordance with the requirements of the Subject Standard for the Russian language for the Kyrgyz school indicate that the textbook provides for step-by-step work on the formation of students’ written speech. A variety of exercises are aimed at creating texts of a productive nature (short reasoning, descriptive text based on the text read, texts based on a given beginning or end). The skill of creating a written statement of a productive nature is important in the process of developing a student’s communicative competence. Mastery of written speech is a prerequisite for full communication in the target language (Russian), therefore it is necessary to develop the skills and abilities of written speech consistently and constantly. Teaching productive written speech in the process of learning Russian as a second language allows you to develop cognitive and linguistic skills and form the written communicative competence of students.

The Interconnection of Substance, Structure and Function in High Systemicity Texts

The purpose of the study is to establish the texts substance properties, to identify the features of its relationship with text structure and function and also to prove that the exclusion of substance as the most important, but not obvious for the current state of science, aspect of the system leading to semiotic distortion which cannot be improved. The study was carried out on the material of texts of different etiologies - religious and artistic, united by a functionally determined ultra-high degree of their levels consistency. The relevance of the work is determined by its significance in the development of a systematic method, the formation of the methodological foundations of systemic philology and the semiotics of culture. The systemic linguistics ideas developed in the works of W. von Humboldt, A.A. Potebnya, I.A. Baudouin de Courtenay, I.I. Sreznevsky, G.P. Melnikov, and the systemology developed by philosophers (K.F. Samokhvalov, G.P. Shchedrovitsky) and representatives of the natural and exact sciences (P.K. Anokhin, V.A. Engelgardt, L.V. Krushinsky, R.O. Bartini) did not have a direct impact on the systematic research of texts. Awareness of the significance of substance in the understanding of highly systemic texts begins to take shape in the exegesis and philosophy of art. Philology, focusing its interest mainly on fiction texts, contributes to the development of a systematic method with the emergence and development of ideas about the text dominant (yu.N. Tynyanov, R.O. yakobson), the diversity of its units (y. Mukarzhovsky), the discovery of the author’s image supercategory (V.V. Vinogradov), the identification of the method of estrangement (V.B. Shklovsky) and the universalization of it as a principle of assimilation of reality (V.B. Shklovsky, L.A. Novikov, M.L. Novikova). The comparison of different nature texts substance properties - against the background of the properties of the substance of language - allows us significantly refines the text substance understanding as the extreemly important system aspect.

Method to assess the trustworthiness of machine coding at scale

Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing much larger and broader datasets to be analyzed than is practical with human coders. Existing work that uses these tools, however, does not investigate norms that allow for trustworthy quantitative claims without full reliance on cross-checking with human coding, which defeats the purpose of using these automated tools. Here we propose a four-part method for making such claims with supervised natural language processing: evaluating a trained model, calculating statistical uncertainty, calculating systematic uncertainty from the trained algorithm, and calculating systematic uncertainty from novel data sources. We provide evidence for this method using data from two distinct short response survey questions with two distinct coding schemes. We also provide a real-world example of using these practices to machine code a dataset unseen by human coders. We offer recommendations to guide physics education researchers who may use machine-coding methods in the future. Published by the American Physical Society 2024

On Text Understanding Modeling by Means of Artificial Intelligence Systems

Purpose of reseach is to conduct a comparative analysis of texts generated by an artificial intelligence system in the process of processing source text in natural language and counter texts that are the result of human understanding of the source literary text. Methods. To achieve the goal and objectives of the study, the author used an experimental technique to conduct a comparative analysis of the denotational structures of counter texts. Participants in the experiment (7 4th year students of the Faculty of Additional Education “Translator in the field of professional communications”, 3 associate professors of the Department of Foreign Languages of South- West State University) assessed the success of recreating the semantic structure of the text of 10 counter texts of different nature - generated by AI and humans. Results. The results of the experiment indicate that the completeness of the semantic content of the generated text does not depend on the structure of the source text. Modern methods of semantic text processing by an AI system make it possible to obtain the output of full-fledged text works created taking into account the rules and norms of natural language.AI systems successfully recreate the denotational structure of the text and reconstruct the syntactic structure. Conclusion. Access to large databases allows you to train a neural network on large text corpora, which results in an increase in the accuracy and variability of the lexical units and constructs used. The accuracy of conveying the semantic content of the text varies. It depends on the degree of text compression - the higher it is, the less accuracy can be, because the neural network is unable to classify denotational connections for relevance to the underlying meaning. The degree of accuracy in conveying semantic content is determined by the success / failure of understanding the deep hidden meaning, which is determined by the understanding of the linguistic and extralinguistic context. The ability to recognize the situation model recreated in the source text is the key to understanding the hidden meaning. The AI system can recreate the surface denotational structure of the text quite correctly and accurately, but is not able to construct a model of the situation at this stage of development.

Collaborative Encoding Method for Scene Text Recognition in Low Linguistic Resources: The Uyghur Language Case Study

Current research on scene text recognition primarily focuses on languages with abundant linguistic resources, such as English and Chinese. In contrast, there is relatively limited research dedicated to low-resource languages. Advanced methods for scene text recognition often employ Transformer-based architectures. However, the performance of Transformer architectures is suboptimal when dealing with low-resource datasets. This paper proposes a Collaborative Encoding Method for Scene Text Recognition in the low-resource Uyghur language. The encoding framework comprises three main modules: the Filter module, the Dual-Branch Feature Extraction module, and the Dynamic Fusion module. The Filter module, consisting of a series of upsampling and downsampling operations, performs coarse-grained filtering on input images to reduce the impact of scene noise on the model, thereby obtaining more accurate feature information. The Dual-Branch Feature Extraction module adopts a parallel structure combining Transformer encoding and Convolutional Neural Network (CNN) encoding to capture local and global information. The Dynamic Fusion module employs an attention mechanism to dynamically merge the feature information obtained from the Transformer and CNN branches. To address the scarcity of real data for natural scene Uyghur text recognition, this paper conducted two rounds of data augmentation on a dataset of 7267 real images, resulting in 254,345 and 3,052,140 scene images, respectively. This process partially mitigated the issue of insufficient Uyghur language data, making low-resource scene text recognition research feasible. Experimental results demonstrate that the proposed collaborative encoding approach achieves outstanding performance. Compared to baseline methods, our collaborative encoding approach improves accuracy by 14.1%.

Эксплицитная и имплицитная семантика термина «жанр» в юрислингвистическом дискурсе

The paper addresses the issue related to the functioning of terminological elements in the metalanguage of legal linguistics. The study aims to identify the semantic features of the term “genre” functioning in legal-linguistic discourse. The scientific novelty of the study lies in the fact that the term “genre” has become the subject of research taking into consideration its functioning in a new scientific discourse, i.e., legal-linguistic discourse, for the first time. The term “genre” is quite often encountered in theoretical texts, as well as in research materials, i.e., in texts of a practical nature. The meanings embedded in the term “genre” are examined and analyzed, specifically those from explanatory and specialized linguistic dictionaries. The results of the study show that a new semantic charge has been discovered in legal-linguistic discourse, which is not recorded in any of the existing definitions to date, namely, the meaning of the method of analyzing linguistic material, providing the opportunity for interpreting not only explicit but also implicit semantic charge of the data presented for analysis.

Text Localization and Enhancement of Mobile Camera based Complex Natural Bilingual Text Scene Images

In this paper, we proposed a text localization and enhancement of the Mobile camera based complex natural scene images using fixed coordinates for removal of unwanted non text regions and making it localized. The morphological erosion technique is applied for the smoothness of foreground text regions. Here, the experimental working nature of the proposed method is first localized color input image is converted into a gray level, then a median filter is applied for the removal of noises. After that, the input image is further converted into binary image. In addition to this, morphological dilation and erosion operations also applied for connecting the broken pixels. Further, the connected component segmentation technique is applied to extract the foreground text regions from the binary input image. The real time input samples are collected by using mobile camera from the various categories of natural scenes of monolingual/bilingual text such as Stone, Wall, Poster and Iron-based. A total of 140 real time samples of natural scene text images are considered for the experimental setup. The experimental results shown that 132 real time sample images are localized and then obtained the accuracy of 94.24%. Further, bilingual text regions are enhanced using hybrid approach and obtain the segmentation accuracy of 83.17%. The novelty of the paper is working on the real time data set and it contains a mixture of printed and handwritten natural scene text images.

KONCEPTUALIZACIJA DETINjSTVA U DETINjSTVU – PEDESETOGODIŠNjA BAŠTINA

Childhood – a children’s literature journal (Detinjstvo – časopis o književnosti za decu) has been published continuously since 1975 in Novi Sad in the Zmaj’s Children’s Games (Zmajeve dečje igre) edition. During its half a century of existence, six editors-in-chief and many more editorial teams have changed. On the pages of Childhood, texts of different nature were published, but all of them were dedicated to children’s literature and childhood, examining their various aspects. This text shows how criticism, evaluation of works of literature for children, actually evaluates the recipient itself, that is a child. It is also shown how the attitude towards the language of the literary work reflects the understanding of the child’s overall development, not only speech development. Insights into childhood and the child in older literature (for example, medieval) and oral tradition lead to a conclusion about the position of the child during the historical development of society. The image of childhood and children in different genres and in the works of different writers is most common on the pages of Childhood. It goes from the vision of an “ideal times” in which the “ornament of the world” flourishes, to the representation of a lonely child, left to itself and the terror of a consumer society in which it has the status of a “suppressed group”. The paper provides a broad framework that includes the concept of a child and childhood in a series of variations that are difficult to encompass. In such a widely covered synchronic and diachronic understanding of the child and childhood, in the Yugoslav, South Slavic and Serbian regions, but also in constant communication with general movements and knowledge, in the texts of different preoccupations (program texts, literary criticism and presentations, didactic texts), concepts of the child as an unfinished being and the child as an ideal and idealized projection of humanity are clearly observed, but also observed are the general value system of the culture, the national culture above all, and the culture of Western civilization with its essentially ambivalent attitude towards the child and childhood.

Harnessing AI to Generate Indian Sign Language from Natural Speech and Text for Digital Inclusion and Accessibility

Harnessing AI to Generate Indian Sign Language from Natural Speech and Text for Digital Inclusion and Accessibility

CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing

The whole cycle for manufacturing aerospace thin-walled shells is a lengthy and sophisticated process. A large amount of quality-related data exists within and between processes, involving many types of quality defects and influencing factors. However, there are ambiguous causal associations among quality-related data affecting the shape-properties of the shell. Also, the coupling of long processes and multiple factors makes it hard to analyze the main factors that affect the quality defects in shell manufacturing. In this paper, taking into account the advantages of causal Scientology and the large language model (LLM), we propose an industrial structure causal knowledge-enhanced large language model for the cause analysis of quality defects in aerospace product manufacturing. To reinforce the causal associations among quality-related data deriving from manufacturing documents (product defect survey sheets, quality inspection, and maintenance reports), a structure causal graph-based sum-product network (SCG-SPN) model is designed to model machining quality-related knowledge and eliminate pseudo-association confounding factors by doing an intervention. Thus, a causal quality-related knowledge graph (CQKG) with high-quality causal associations is constructed. With this, to provide a trustworthy guarantee in responding to quality problem solving, we construct a quality-related prompt dataset with multi-round conversations based on CQKG. Then, a novel P-tuning that adapts to utilize external CQKG instructions is designed to fine-tune an open-source ChatGLM base model. Based on this, a causal knowledge graph-augmented LLM, named CausalKGPT, is developed to enable reasoning and responding to quality defects in both Chinese and English. It uses natural text descriptions related to quality defects as input and takes a quality-related causal knowledge graph as an additional corpus. Finally, the case study shows that the CausalKGPT performs with more expertise and reliability in responding to quality question solving of aerospace shell manufacturing than the classic commercial models like ChatGPT and GPT4. The results indicate that the proposed method may provide a trustworthy guide in assisting workers to analyze quality defects in aerospace products.

AN ITALIAN TRAVELLER DISCOVERING BOSNIA

The theme of this paper is the image of Bosnia by Luchino Dal Verme (1838- 1911), a politician, diplomat, geographer and traveller, who in 1903 set off from Italy on a journey across the Adriatic Sea and on to Bosnia. He left a testimony of his experience in the text ‘An Excursion to Bosnia’, published in the Nuova Antologia in 1903.
 The aim of the paper is to identify odeporic elements in a text of a hybrid nature with elements of geography, history and literature and to show that the perception of this region of the Balkans changed at the beginning of the 20th century. From the wild, primitive and hostile land of the 19th century travellers, the image offered by Luchino Dal Verme changes radically. This affirms the importance of this traveller’s profile, a cultured and informed man. With a considerable cultural background, he observes and accepts the Other and his land, without conditioning.

Learner-Centered Analysis in Educational Metaverse Environments: Exploring Value Exchange Systems through Natural Interaction and Text Mining

Amid Education 4.0 and the Fourth Industrial Revolution, we explore the integration of self-directed learning within the metaverse. This study envisions empowered learners, merging the metaverse’s immersive potential with self-directed learning. Using text mining and co-occurrence analysis for student responses prompts questions about their preference over traditional methods. Text mining extracts nuanced insights from open-ended responses, surpassing structured data. Co-occurrence analysis reveals hidden concept relationships, enhancing student engagement and understanding. Beyond XR, the metaverse encompasses avatars, virtual experiences, and value systems. Educators navigate this landscape with text mining, shaping value exchange through engaging content. Integrating real-world experiences in the metaverse merges immersion and personalized learning. Challenges include data precision and semantic intricacies in co-occurrence graphs. Future solutions involve real- time adaptability and sentiment analysis for holistic insights into learner emotions. This study envisions a synergy of self- directed learning and the metaverse, bridging digital and physical realms. Learners navigate interconnected experiences, fostering autonomy. Uncovering the metaverse’s potential contributes to education for digitally adept learners.

Blue Eyes Technology: The Secret Behind ChatGPT’s Human-Like Abilities

ChatGPT is a powerful language model that can generate natural and coherent text responses based on a given context. In this paper, we explore how ChatGPT can be used as a Blue Eyes Technology, a term that refers to the use of artificial intelligence to create human-like interfaces that can see, hear, and speak. We present several use cases where ChatGPT can leverage its voice and image capabilities to assist users with various tasks, such as language translation, education, entertainment, and customer service. We conclude that ChatGPT is a promising tool for enhancing human-computer interaction and creating more personalized and immersive experiences. Keyword: Data Acquisition Unit, Central System Unit, Blue Eyes technology

Athletic Training Students’ Perceptions of Translating Knowledge From Classroom to Clinical Practice

Context As the health care landscape evolves, athletic training students (ATSs) must gain the skills necessary to effectively translate new evidence into practice to optimize patient outcomes. Knowledge translation is an iterative process that promotes the application of new knowledge by users, but little is known about how students perceive this translation process during athletic training education. Objective To explore ATSs’ perceptions of knowledge translation. Design Cross-sectional. Setting Web-based survey with open-ended questions. Patients or Other Participants A total of 255 professional ATSs accessed the survey; 168 students (118 women, 48 men, 2 missing; age = 22 ± 2.3 years) completed the entire survey (69.5% completion rate). Data Collection and Analysis A Web-based survey composed of 6 open-ended questions was distributed to ATSs via a recruitment e-mail forwarded by the Commission on Accreditation of Athletic Training Education-accredited athletic training program director on behalf of the research team. Due to the textual nature of the data, we used a multiphased, multianalyst data analysis approach guided by the consensual qualitative research tradition to analyze responses to the open-ended items. Self-reported topics that ATSs translated into clinical practice were categorized into the preestablished practice analysis domains identified by the Board of Certification. Results Topics within the examination, assessment, and diagnosis domain were most frequently reported by respondents. In addition to the topics translated, 3 themes emerged during data analysis. Students discussed their perceptions regarding the (1) application of knowledge gained in clinical practice, (2) challenges prohibiting successful knowledge translation, and (3) strategies to promote the knowledge translation process. Conclusions Findings from this study suggest a need to promote synergy between faculty and preceptors to ensure that didactically taught knowledge is aligned with what students are encountering during clinical experiences. Incorporating established knowledge translation strategies may help ATSs bridge the knowledge-to-practice gaps they perceive as current challenges inhibiting successful translation of new knowledge into the clinical environment.