This literature review provides an overview of the fabrication and application of biopolymer hybrid particles in dentistry. A total of 95 articles have been included in this review. In the review paper, the common inorganic particles and biopolymers used in dentistry are discussed in general, and detailed examples of inorganic particles (i.e., hydroxyapatite, calcium phosphate, and bioactive glass) and biopolymers such as collagen, gelatin, and chitosan have been drawn from the scientific literature and practical work. Among the included studies, calcium phosphate including hydroxyapatite is the most widely applied for inorganic particles used in dentistry, but bioactive glass is more applicable and multifunctional than hydroxyapatite and is currently used in clinical practice. Today, biopolymer hybrid particles are receiving more attention as novel materials for several applications in dentistry, such as drug delivery systems, bone repair, and periodontal regeneration surgery. The literature published on the biopolymer gel-assisted synthesis of inorganic particles for dentistry is somewhat limited, and therefore, this article focuses on reviewing and discussing the biopolymer hybrid particles used in dentistry.

IntroductionFarmed fish like European seabass (Dicentrarchus labrax) anticipate meals if these are provided at one or multiple fixed times during the day. The increase in locomotor activity is typically known as food anticipatory activity (FAA) and can be observed several hours prior to feeding. Measuring FAA is often done by demand feeders or external sensors such as cameras or light curtains. However, purely locomotor-activity-based FAA may provide an incomplete view of feeding and prefeeding behaviour.MethodsHere, we show that FAA can be measured through passive acoustic telemetry utilising three different approaches and suggest that adding more means to food anticipation detection is beneficial. We compared the diving behaviour, acceleration activity, and temperature of 22 tagged individuals over the period of 12 days and observed FAA through locomotor activity, depth position, and density-based unsupervised clustering (i.e., DBSCAN).ResultsOur results demonstrate that the position- and density-based methods also provide expressions of anticipatory behaviour that can be interchangeable with locomotor-driven FAA or precede it.DiscussionWe, therefore, support a unified framework for food anticipation: FAA should only describe locomotor-driven FAA. Food anticipatory positioning (FAP) should be a term for position-based (P-FAP) and density-based (D-FAP) methods for food anticipation. Lastly, FAP, together with the newly defined FAA, should become part of an umbrella term that is already in use: food anticipatory behaviour (FAB). Our work provides data-driven approaches to each FAB category and compares them with each other. Furthermore, accurate FAB windows through FAA and FAP can help increase fish welfare in the aquaculture industry, and the more approaches available, the more flexible and more robust the usage of FAB for a holistic view can be achieved.

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Instance Segmentation in general deals with detecting, segmenting and classifying individual instances of objects in an image. Underwater instance segmentation methods often involve aquatic animals like fish as the things to be detected. In order to train deep learning models for instance segmentation in an underwater environment, rigorous human annotation in form of instance segmentation masks with labels is usually required, since the aquatic environment poses challenges due to dynamic background patterns and optical distortions.However, annotating instance segmentation masks on images is especially time- and cost-intensive compared to classification tasks.Here we show an unsupervised instance learning and segmentation approach that introduces a novel class, e.g., ""fish"" to a pre-trained Mask R-CNN model using its own detection and segmentation capabilities in underwater images.Our results demonstrate a robust detection and segmentation of underwater fish in aquaculture without the need for human annotations.This proof of concept shows that there is room for novel objects within trained instance segmentation models in the paradigm of supervised learning.

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