Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).
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Purpose: Most speech-language pathologists (SLPs) working with children with developmental language disorder (DLD) do not perform language sample analysis (LSA) on a regular basis, although they do regard LSA as highly informative for goal setting and evaluating grammatical therapy. The primary aim of this study was to identify facilitators, barriers, and needs related to performing LSA by Dutch SLPs working with children with DLD. The secondary aim was to investigate whether a training would change the actual performance of LSA. Method: A focus group with 11 SLPs working in Dutch speech-language pathology practices was conducted. Barriers, facilitators, and needs were identified using thematic analysis and categorized using the theoretical domain framework. To address the barriers, a training was developed using software program CLAN. Changes in barriers and use of LSA were evaluated with a survey sent to participants before, directly after, and 3 months posttraining. Results: The barriers reported in the focus group were SLPs’ lack of knowledge and skills, time investment, negative beliefs about their capabilities, differences in beliefs about their professional role, and no reimbursement from health insurance companies. Posttraining survey results revealed that LSA was not performed more often in daily practice. Using CLAN was not the solution according to participating SLPs. Time investment remained a huge barrier. Conclusions: A training in performing LSA did not resolve the time investment barrier experienced by SLPs. User-friendly software, developed in codesign with SLPs might provide a solution. For the short-term, shorter samples, preferably from narrative tasks, should be considered.
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Worldwide, pupils with migrant backgrounds do not participate in school STEM subjects as successfully as their peers. Migrant pupils’ subject-specific language proficiency lags behind, which hinders participation and learning. Primary teachers experience difficulty in teaching STEM as well as promoting required language development. This study investigates how a professional development program (PDP) focusing on inclusive STEM teaching can promote teacher learning of language-promoting strategies (promoting interaction, scaffolding language and using multilingual resources). Participants were five case study teachers in multilingual schools in the Netherlands (N = 2), Sweden (N = 1) and Norway (N = 2), who taught in primary classrooms with migrant pupils. The PDP focused on three STEM units (sound, maintenance, plant growth) and language-promoting strategies. To trace teachers’ learning, three interviews were conducted with each of the five teachers (one after each unit). The teachers also filled in digital logs (one after each unit). The interviews showed positive changes in teachers’ awareness, beliefs and attitudes towards language-supporting strategies. However, changes in practice and intentions for practice were reported to a lesser extent. This study shows that a PDP can be an effective starting point for teacher learning regarding inclusive STEM teaching. It also illuminates possible enablers (e.g., fostering language awareness) or hinderers (e.g., teachers’ limited STEM knowledge) to be considered in future PDP design.
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This study analyze data from three national contexts in which teachers worked with the same teaching materials and inquiry classroom activities, investigating teachers’ use of strategies to promote interaction and scaffolding when participating in a professional development program. The data material is collected from three case studies from the Netherlands, Norway, and Sweden, respectively. Each case is from a teaching unit about green plants and seed sprouting. In one lesson in this unit, students were involved in planning an experiment with sprouting seeds, and this (similar) lesson was videotaped in three national settings. The main research question is, as follows: How do primary teachers use questions to scaffold conceptual understanding and language use in inquiry science activities? The data analysis shows that teachers ask different kind of questions such as open, closed, influencing and orienting questions. The open, orienting questions induce students to generate their own ideas, while closed orienting and influencing questions often scaffold language and content-specific meaning-making. However, both open, closed, orienting and influencing questions can scaffold student language and conceptual understanding. Often, teacher questions scaffold both language content-specific meaning-making at the same time. The study shows the subtle mechanisms through which teachers can use questions to scaffold student science literacy and thereby including them in classroom interaction.
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Objective:Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications.Methods:We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale.Results:Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance ().Conclusion:The model could support automated screening tools which can be used by journals to draw the authors’ attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.
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In order to optimize collaboration between Speech and Language Therapists (SLTs) and parents of children with Developmental Language Disorders (DLD), our aim was to study what is needed for SLTs to transition from the parent-as-therapist aide model to the FCC model and optimal collaborate with parents. Chapter 2 discusses the significance of demystifying collaborative working by making explicit how collaboration works. Chapter 3 examines SLTs’ perspectives on engaging parents in parent-child interaction therapy, utilizing a secondary analysis of interview data. Chapter 4 presents a systematic review of specific strategies that therapists can employ to enhance their collaboration with parents of children with developmental disabilities. Chapter 5 explores the needs of parents in their collaborative interactions with SLTs during therapy for their children with DLD, based on semi-structured interviews. Chapter 6 reports the findings from a behavioral analysis of how SLTs currently engage with parents of children with DLD, using data from focus groups. Chapter 7 offers a general discussion on the findings of this thesis, synthesizing insights from previous chapters to propose recommendations for practice and future research.
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Background: Early detection and remediation of language disorders are important in helping children to establish appropriate communicative and social behaviour and acquire additional information about the world through the use of language. In the Netherlands, children with (a suspicion of) language disorders are referred to speech and hearing centres for multidisciplinary assessment. Reliable data are needed on the nature of language disorders, as well as the age and source of referral, and the effects of cultural and socioeconomic profiles of the population served in order to plan speech and language therapy service provision. Aims: To provide a detailed description of caseload characteristics of children referred with a possible language disorder by generating more understanding of factors that might influence early identification. Methods & Procedures: A database of 11,450 children was analysed consisting of data on children, aged 2–7 years (70% boys, 30% girls), visiting Dutch speech and hearing centres. The factors analysed were age of referral, ratio of boys to girls, mono‐ and bilingualism, nature of the language delay, and language profile of the children. Outcomes & Results:Results revealed an age bias in the referral of children with language disorders. On average, boys were referred 5 months earlier than girls, and monolingual children were referred 3 months earlier than bilingual children. In addition, bilingual children seemed to have more complex problems at referral than monolingual children. They more often had both a disorder in both receptive and expressive language, and a language disorder with additional (developmental) problems. Conclusions & Implications: This study revealed a bias in age of referral of young children with language disorders. The results implicate the need for objective language screening instruments and the need to increase the awareness of staff in primary child healthcare of red flags in language development of girls and multilingual children aiming at earlier identification of language disorders in these children.
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Explicit language objectives are included in the Swedish national curriculum for mathematics. The curriculum states that students should be given opportunities to develop the ability to formulate problems, use and analyse mathematical concepts and relationships between concepts, show and follow mathematical reasoning, and use mathematical expressions in discussions. Teachers’ competence forms a crucial link to bring an intended curriculum to a curriculum in action. This article investigates a professional development program, ‘Language in Mathematics’, within a national program for mathematics teachers in Sweden that aims at implementing the national curriculum into practice. Two specific aspects are examined: the selection of theoretical notions on language and mathematics and the choice of activities to relate selected theory to practice. From this examination, research on teacher learning in connection to professional development is proposed, which can contribute to a better understanding of teachers’ interpretation of integrated approaches to language and mathematics across national contexts.
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ion of verb agreement by hearing learners of a sign language. During a 2-year period, 14 novel learners of Sign Language of the Netherlands (NGT) with a spoken language background performed an elicitation task 15 times. Seven deaf native signers and NGT teachers performed the same task to serve as a benchmark group. The results obtained show that for some learners, the verb agreement system of NGT was difficult to master, despite numerous examples in the input. As compared to the benchmark group, learners tended to omit agreement markers on verbs that could be modified, did not always correctly use established locations associated with discourse referents, and made characteristic errors with respect to properties that are important in the expression of agreement (movement and orientation). The outcomes of the study are of value to practitioners in the field, as they are informative with regard to the nature of the learning process during the first stages of learning a sign language.
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