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.
EEG mu rhythms (8-13. Hz) recorded at fronto-central electrodes are generally considered as markers of motor cortical activity in humans, because they are modulated when participants perform an action, when they observe another's action or even when they imagine performing an action. In this study, we analyzed the time-frequency (TF) modulation of mu rhythms while participants read action language ("You will cut the strawberry cake"), abstract language ("You will doubt the patient's argument"), and perceptive language ("You will notice the bright day"). The results indicated that mu suppression at fronto-central sites is associated with action language rather than with abstract or perceptive language. Also, the largest difference between conditions occurred quite late in the sentence, while reading the first noun, (contrast Action vs. Abstract), or the second noun following the action verb (contrast Action vs. Perceptive). This suggests that motor activation is associated with the integration of words across the sentence beyond the lexical processing of the action verb. Source reconstruction localized mu suppression associated with action sentences in premotor cortex (BA 6). The present study suggests (1) that the understanding of action language activates motor networks in the human brain, and (2) that this activation occurs online based on semantic integration across multiple words in the sentence.
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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).
The expanding world’s population challenges the way we produce and supply food. The ever-increasing production of food and its subsequent generated biomass forms immense risks to the environment and, eventually, public health. Aside from developing innovative food production methods (hydroponics, non-toxic pesticides, resistant species), the generation of waste biomass remains a challenge. Large volumes of food waste are processed in animal food, biofuel or used as a composting source, while these by-products are valuable sources of bioactive compounds (BACs). The processing of fruits and vegetables generates a variety of biomass such as peels, seeds and pulp that contain high-value compounds such as polyphenols. These BACs are implemented in pharmaceutical products or food supplements for their beneficial influence on human health, such as antioxidant or anti-inflammatory properties. The valorization and extraction of these compounds originating from agricultural waste streams is a key strategy for recycling and reusing food waste and, subsequently, reducing the environmental impact caused by waste streams. Additionally, the ability to further process food waste into valuable compounds can provide an extra source of income for the agricultural sector, supporting local economies. Local pharmaceutical companies are interested in developing methods to extract BACs from local sources since the current market is strongly dependent on the Asian market. Phytopharma finds the production of local food supplements crucial for the local circular economy and their sustainable business. During this project, the consortium partners will investigate sustainable extraction methods of BACs from local waste streams (duurzame chemie: bronnen en grondstoffen). More specifically Zuyd, CHILL and Phytopharma will pursue the “green” extraction of quercetin (BACs) from locally sourced onion waste. The partners will explore various extraction and purification methods needed to evaluate a potentially sustainable business model. Furthermore, the bioavailability of quercetin will be enhanced by encapsulation.
