Predicting the status of sediment ecosystems around commercial fish farms from taxonomic and functional profiles

The sediments around commercial fish farms are regularly monitored regarding the environmental condition. If the environmental condition is not sufficient, a quarantine period is imposed on the fish farms. It is therefore important that the environmental state is mapped quickly and with good precision. Currently, this is determined manually from inspection of sediment samples by expert taxonomists, who determine an environmental index based on macrofauna. The AQUAeD project (2021-2025) – On-site monitoring of aquaculture impact on the environment by open-source nanopore eDNA analysis – aims to replace the current environmental monitoring analyses with digital DNA based solutions, as well as moving the analyses to the facilities to achieve fast and accurate results. This thesis is a part of the AQUAeD project, and the data used was from 16S sequencing. From the 16S data, taxonomic profiles can be made, and the essential aim of this thesis is to predict the ecosystem status from this. However, the taxonomic diversity present in sediments is significant, with numerous organisms being unidentified and lacking names within the existing taxonomy. From the metagenome data, functional profiles can be derived. This entails coding genes and categorizing them into functional groups such as EC functions, KO functions and MetaCyc pathways. Then functional profiles can be constructed accordingly. An indicator of the ecosystem status is the nEQR values, which is what has been predicted in this thesis for both the taxonomic and functional profiles. The results from this shows that the predictions are good for both taxonomic and functional profiles, but also that the functional profiles do not give better predictions. Rather, they are very similar to each other. In this thesis, AI (Artificial Intelligence) was used to assist with the coding, as well as finding sources for the background information in the introduction of this thesis. The AI instruments used in this thesis was ChatGPT and Perplexity. Sedimentene rundt kommersielle fiskeoppdrettsanlegg blir jevnlig overvåket med tanke på miljøtilstanden. Hvis miljøtilstanden ikke er tilstrekkelig, blir det pålagt karantenetid for oppdrettsanleggene. Det er derfor viktig at miljøtilstanden kartlegges raskt og med god presisjon. For øyeblikket blir dette bestemt gjennom inspeksjon av sedimentprøver utført av eksperter på taksonomi, som fastsetter en miljøindeks basert på makrofauna. AQUAeDprosjektet (2021-2025) – Overvåkning av akvakulturens påvirkning på miljøet ved hjelp av open source for nanopore eDNA analyse – har som mål å erstatte de nåværende miljøovervåkningsanalysene med digitale DNA-baserte løsninger, samt å flytte analysene til fiskeoppdrettsanleggene for å oppnå raske og nøyaktige resultater. Denne avhandlingen er en del av AQUAeD-prosjekter, og dataene som ble brukt var fra 16S sekvensering. Fra 16S dataene kan det lages taksonomiske profiler, og det essensielle målet med denne avhandlingen er å forutsi økosystemets tilstand fra dette. Den taksonomiske mangfoldigheten i sedimentene er derimot betydelig, med mange organismer som ikke er identifisert og som mangler navn innenfor den eksisterende taksonomien. Fra metagenomdataene kan funksjonelle profiler utledes. Dette innebærer kodende gener og kategorisering av dem i funksjonelle grupper som EC funksjoner, KO funksjoner og MetaCyc pathways. Videre kan funksjonelle grupper konstrueres. En indikator på økosystemets tilstand er nEQR-verdier, som er det som har blitt predikert i denne avhandlingen for både de taksonomiske og funksjonelle profilene. Resultatene fra dette viser at prediksjonene er gode for både taksonomiske og funksjonelle profiler, men de funksjonelle profilene gir heller ikke bedre prediksjoner. Tvert imot er de veldig like hverandre. I denne avhandlingen ble KI (Kunstig Intelligens) brukt til å hjelpe med kodingen, samt å finne kilder til bakgrunnsinformasjonen i innledningen til denne avhandlingen. KI-instrumentene som ble brukt i denne avhandlingen var ChatGPT og Perplexity

Hedonic and incentive signals for body weight control

Here we review the emerging neurobiological understanding of the role of the brain’s reward system in the regulation of body weight in health and in disease. Common obesity is characterized by the over-consumption of palatable/rewarding foods, reflecting an imbalance in the relative importance of hedonic versus homeostatic signals. The popular ‘incentive salience theory’ of food reward recognises not only a hedonic/pleasure component (‘liking’) but also an incentive motivation component (‘wanting’ or ‘reward-seeking’). Central to the neurobiology of the reward mechanism is the mesoaccumbal dopamine system that confers incentive motivation not only for natural rewards such as food but also by artificial rewards (eg. addictive drugs). Indeed, this mesoaccumbal dopamine system receives and integrates information about the incentive (rewarding) value of foods with information about metabolic status. Problematic over-eating likely reflects a changing balance in the control exerted by hypothalamic versus reward circuits and/or it could reflect an allostatic shift in the hedonic set point for food reward. Certainly, for obesity to prevail, metabolic satiety signals such as leptin and insulin fail to regain control of appetitive brain networks, including those involved in food reward. On the other hand, metabolic control could reflect increased signalling by the stomach-derived orexigenic hormone, ghrelin. We have shown that ghrelin activates the mesoaccumbal dopamine system and that central ghrelin signalling is required for reward from both chemical drugs (eg alcohol) and also from palatable food. Future therapies for problematic over-eating and obesity may include drugs that interfere with incentive motivation, such as ghrelin antagonists