Posterpresentatie gegeven tijdens bezoek SBE (Samenwerkende Bedrijven Eemsdelta) aan de Hanzehogeschool
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PURPOSE: The purpose of this review article is to describe characteristics of auditory processing disorders (APD) by evaluating the literature in which children with suspected or diagnosed APD were compared with typically developing children and to determine whether APD must be regarded as a deficit specific to the auditory modality or as a multimodal deficit.METHOD: Six electronic databases were searched for peer-reviewed studies investigating children with (suspected) APD in comparison with typically developing peers. Relevant studies were independently reviewed and appraised by 2 reviewers. Methodological quality was quantified using the American Speech-Language-Hearing Association's levels of evidence.RESULTS: Fifty-three relevant studies were identified. Five studies were excluded because of weak internal validity. In total, 48 studies were included, of which only 1 was classified as having strong methodological quality. Significant dissimilarities were found between children referred with listening difficulties and controls. These differences relate to auditory and visual functioning, cognition, language, reading, and physiological and neuroimaging measures.CONCLUSIONS: Methodological quality of most of the incorporated studies was rated moderate due to the heterogeneous groups of participants, inadequate descriptions of participants, and the omission of valid and reliable measurements. The listening difficulties of children with APD may be a consequence of cognitive, language, and attention issues rather than bottom-up auditory processing.
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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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The current electroencephalography study investigated the relationship between the motor and (language) comprehension systems by simultaneously measuring mu and N400 effects. Specifically, we examined whether the pattern of motor activation elicited by verbs depends on the larger sentential context. A robust N400 congruence effect confirmed the contextual manipulation of action plausibility, a form of semantic congruency. Importantly, this study showed that: (1) Action verbs elicited more mu power decrease than non-action verbs when sentences described plausible actions. Action verbs thus elicited more motor activation than non-action verbs. (2) In contrast, when sentences described implausible actions, mu activity was present but the difference between the verb types was not observed. The increased processing associated with a larger N400 thus coincided with mu activity in sentences describing implausible actions. Altogether, context-dependent motor activation appears to play a functional role in deriving context-sensitive meaning.
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Dit document bevat in grote lijnen de opzet van ZAP. Binnen Zernike Advanced Processing (ZAP) wordt de mogelijkheid gecreëerd voor het uitvoeren van opschalingsexperimenten op het gebied van bioprocestechnologie en groene chemie.
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After fifty years of research there is still debate on the concept of auditory processing disorders (APD). We conducted a systematic review to examine the characteristics associated with APD. The purpose of this study is to decide whether APD can be regarded as a unique and identifiable clinical entity.
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Social media firestorms pose a significant challenge for firms in the digital age. Tackling firestorms is difficult because the judgments and responses from social media users are influenced by not only the nature of the transgressions but also by the reactions and opinions of other social media users. Drawing on the heuristic-systematic information processing model, we propose a research model to explain the effects of social impact (the heuristic mode) and argument quality and moral intensity (the systematic mode) on perceptions of firm wrongness (the judgment outcome) as well as the effects of perceptions of firm wrongness on vindictive complaining and patronage reduction. We adopted a mixed methods approach in our investigation, including a survey, an experiment, and a focus group study. Our findings show that the heuristic and systematic modes of information processing exert both direct and interaction effects on individuals’ judgment. Specifically, the heuristic mode of information processing dominates overall and also biases the systematic mode. Our study advances the literature by offering an alternative explanation for the emergence of social media firestorms and identifying a novel context in which the heuristic mode dominates in dual information processing. It also sheds light on the formulation of response strategies to mitigate the adverse impacts resulting from social media firestorms. We conclude our paper with limitations and future research directions.
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The purpose of this study was to investigate relations between goal orientations, information processing strategies and development of conceptual knowledge of pre-vocational secondary education students (n=719; 14 schools). Students' preferences for certain types of goals and information processing strategies were examined using questionnaires. Conceptual knowledge was investigated by having students create concept maps before and after a learning project. Structural analyses showed that student preferences for mastery and performance goals positively affected their preferences for the use of deep and surface information processing strategies. Preferences for work avoidance goals negatively influenced preferences for deep and surface processing. Use of surface information processing strategies negatively affected the development of conceptual knowledge. Remarkably, no relation was found between students' preferences for deep processing strategies and development of conceptual knowledge.
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The preference of students in competence-based Pre-Vocational Secondary Education (PVSE) for information processing strategies and the development of their body of knowledge were measured in a study that was carried out with 31 participants. The students' information processing strategies were measured by means of semi-structured interviews, questionnaires and think-aloud sessions. 26 of the 31 participants had a preference for surface processing strategies when working in workplace simulation. The other 5 students preferred deep learning. The learning environment appeared to elicit this surface level processing. The development of the body of knowledge of the students was measured by means of the concept mapping technique. For most students, an improvement of the body of knowledge took place in the course of the project in workplace simulation that was researched. Their knowledge became more elaborate and better organized. No significant relations between information processing strategies and the development of the concept maps could be found for the students participating in the research.
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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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