Critical Transitions in Plant-Pollinator Systems Induced by Positive Inbreeding-Reward-Pollinator Feedbacks.
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ABSTRACT:
SUBMITTER: Huang H
PROVIDER: S-EPMC6976937 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A recent genome-wide association study (GWAS) identified a significant single-nucleotide polymorphism (SNP) for trait-positive emotion at rs322931 on chromosome 1, which was also associated with brain activation in the reward system of healthy individuals when observing positive stimuli in a functional magnetic resonance imaging (fMRI) study. In the current study, we aimed to further validate the role of variation at rs322931 in reward processing. Using a similar fMRI approach, we use two paradigms that elicit a strong ventral striatum (VS) blood oxygen-level dependency (BOLD) response in a sample of young, healthy individuals (N=82). In the first study we use a similar picture-viewing task to the discovery sample (positive>neutral stimuli) to replicate an effect of the variant on emotion processing. In the second study we use a probabilistic reversal learning procedure to identify reward processing during decision-making under uncertainly (reward>punishment). In a region of interest (ROI) analysis of the bilateral VS, we show that the rs322931 genotype was associated with BOLD in the left VS during the positive>neutral contrast (PROI-CORRECTED=0.045) and during the reward>punishment contrast (PROI-CORRECTED=0.018), although the effect of passive picture viewing was in the opposite direction from that reported in the discovery sample. These findings suggest that the recently identified GWAS hit may influence positive emotion via individual differences in activity in the key hubs of the brain's reward system. Furthermore, these effects may not be limited to the passive viewing of positive emotional scenes, but may also be observed during dynamic decision-making. This study suggests that future studies of this GWAS locus may yield further insight into the biological mechanisms of psychopathologies characterised by deficits in reward processing and positive emotion.
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Project description:BackgroundDisease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems.MethodsIn this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state.ResultsThe proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called "dark genes" that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis.ConclusionsThe proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.
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Project description:BackgroundDelayed initiation and early discontinuation of treatment due to limited availability and accessibility of services may often result in people with mild or moderate mental disorders developing more severe disorders, leading to an increase in demand for specialised care that would be expected to further restrict service availability and accessibility (due to increased waiting times, higher out-of-pocket costs, etc.).MethodsWe developed a simple system dynamics model of the interaction of specialised services capacity and disease progression to examine the impact of service availability and accessibility on the effectiveness and efficiency of mental health care systems.ResultsModel analysis indicates that, under certain conditions, increasing services capacity can precipitate an abrupt, step-like transition from a state of persistently high unmet need for specialised services to an alternative, stable state in which people presenting for care receive immediate and effective treatment. This qualitative shift in services system functioning results from a 'virtuous cycle' in which increasing treatment-dependent recovery among patients with mild to moderate disorders reduces the number of severely ill patients requiring intensive and/or prolonged treatment, effectively 'releasing' services capacity that can be used to further reduce the disease progression rate. We present an empirical case study of tertiary-level child and adolescent mental health services in the Australian state of South Australia demonstrating that the conditions under which such critical transitions can occur apply in real-world services systems.ConclusionsPolicy and planning decisions aimed at increasing specialised services capacity have the potential to dramatically increase the effectiveness and efficiency of mental health care systems, promoting long-term sustainability and resilience in the face of future threats to population mental health (e.g., economic crises, natural disasters, global pandemics).
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Project description:BackgroundAnimal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing.ResultsHere we present a vocabulary of terms and a data model for sharing plant-pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant-pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant-pollinator interactions.ConclusionsThe adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant-pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant-pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms.
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