Large floating projects have the potential to overcome the challenge of land scarcity in urban areas and offer opportunities for energy and food production, or even for creating sustainable living environments. However, they influence the physical, chemical, biological and ecological characteristics of water bodies. The interaction of the floating platforms affect multiple complex aquatic processes, and the potential (negative/positive) effects are not yet fully understood. Managing entities currently struggle with lack of data and knowledge that can support adequate legislation to regulate future projects.In the Netherlands the development of small scale floating projects is already present for some years (e.g. floating houses, restaurants, houseboats), and more recently several large scale floating photovoltaic plants (FPV) have been realized. Several floating constructions in the Netherlands were considered as case-studies for a data-collection campaign.To obtain data and images from underneath floating buildings, underwater drones were equipped with cameras and sensors. The drones were used in multiple locations to scan for differences in concentrations of basic water quality parameters (e.g. dissolved oxygen, electrical conductivity, algae, light intensity) from underneath/near the floating structures, which were then compared with data from locations far from the influence of the buildings. Continuous data was also collected over several days using multi-parameter water quality sensors permanently installed under floating structures.
Background: Adverse outcome pathway (AOP) networks are versatile tools in toxicology and risk assessment that capture and visualize mechanisms driving toxicity originating from various data sources. They share a common structure consisting of a set of molecular initiating events and key events, connected by key event relationships, leading to the actual adverse outcome. AOP networks are to be considered living documents that should be frequently updated by feeding in new data. Such iterative optimization exercises are typically done manually, which not only is a time-consuming effort, but also bears the risk of overlooking critical data. The present study introduces a novel approach for AOP network optimization of a previously published AOP network on chemical-induced cholestasis using artificial intelligence to facilitate automated data collection followed by subsequent quantitative confidence assessment of molecular initiating events, key events, and key event relationships. Methods: Artificial intelligence-assisted data collection was performed by means of the free web platform Sysrev. Confidence levels of the tailored Bradford-Hill criteria were quantified for the purpose of weight-of-evidence assessment of the optimized AOP network. Scores were calculated for biological plausibility, empirical evidence, and essentiality, and were integrated into a total key event relationship confidence value. The optimized AOP network was visualized using Cytoscape with the node size representing the incidence of the key event and the edge size indicating the total confidence in the key event relationship. Results: This resulted in the identification of 38 and 135 unique key events and key event relationships, respectively. Transporter changes was the key event with the highest incidence, and formed the most confident key event relationship with the adverse outcome, cholestasis. Other important key events present in the AOP network include: nuclear receptor changes, intracellular bile acid accumulation, bile acid synthesis changes, oxidative stress, inflammation and apoptosis. Conclusions: This process led to the creation of an extensively informative AOP network focused on chemical-induced cholestasis. This optimized AOP network may serve as a mechanistic compass for the development of a battery of in vitro assays to reliably predict chemical-induced cholestatic injury.
Lignocellulose biorefining is a promising technologyfor the sustainable production of chemicals and biopolymers.Usually, when one component is focused on, the chemical natureand yield of the others are compromised. Thus, one of thebottlenecks in biomass biorefining is harnessing the maximumvalue from all of the lignocellulosic components. Here, we describea mild stepwise process in a flow-through setup leading to separateflow-out streams containing cinnamic acid derivatives, glucose,xylose, and lignin as the main components from differentherbaceous sources. The proposed process shows that minimaldegradation of the individual components and conservation oftheir natural structure are possible. Under optimized conditions,the following fractions are produced from wheat straw based ontheir respective contents in the feed by the ALkaline ACid ENzyme process: (i) 78% ferulic acid from a mild ALkali step, (ii) 51%monomeric xylose free of fermentation inhibitors by mild ACidic treatment, (iii) 82% glucose from ENzymatic degradation ofcellulose, and (iv) 55% native-like lignin. The benefits of using the flow-through setup are demonstrated. The retention of the ligninaryl ether structure was confirmed by HSQC NMR, and this allowed monomers to form from hydrogenolysis. More importantly, thecrude xylose-rich fraction was shown to be suitable for producing polyhydroxybutyrate bioplastics. The direct use of the xylose-richfraction by means of the thermophilic bacteria Schlegelella thermodepolymerans matched 91% of the PHA produced with commercialpure xylose, achieving 138.6 mgPHA/gxylose. Overall, the ALACEN fractionation method allows for a holistic valorization of theprincipal components of herbaceous biomasses.
