Identifying knowledge important to teach about the nervous system in the context of secondary biology and science education-A Delphi study

Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures

The counting and characterization of neurons in primary cultures have long been areas of significant scientific interest due to their multifaceted applications, ranging from neuronal viability assessment to the study of neuronal development. Traditional methods, often relying on fluorescence or colorimetric staining and manual segmentation, are time consuming, labor intensive, and prone to error, raising the need for the development of automated and reliable methods. This paper delves into the evaluation of three pivotal deep learning techniques: semantic segmentation, which allows for pixel-level classification and is solely suited for characterization; object detection, which focuses on counting and locating neurons; and instance segmentation, which amalgamates the features of the other two but employing more intricate structures. The goal of this research is to discern what technique or combination of those techniques yields the optimal results for automatic counting and characterization of neurons in images of neuronal cultures. Following rigorous experimentation, we conclude that instance segmentation stands out, providing superior outcomes for both challenges.

Mapping lower secondary school students' conceptions of three aspects critical for understanding the nervous system

Understanding the nervous system is an important but perhaps ambitious goal, particularly for students in lower secondary education. It is important because of its' direct role in both mental and physical health, and it is ambitious because instruction focuses on the human nervous system, which is extremely complex, and subject to numerous misconceptions. Despite its' complexity, the science curricula, both nationally and internationally, emphasize an understanding of the system, and not just knowledge of isolated facts. But what does it mean to understand this system, and what content knowledge is critical for understanding it? Unfortunately, the curricula are usually too general to answer these questions, therefore other sources of information are needed. Using the science literature, the present study defines the system level of the nervous system and proposes three basic aspects necessary to understand it: 1) neural circuit architecture, 2) synaptic action, and 3) nerve signal origin. With this background, the aim of the present study is to identify lower secondary school students' conceptions of these three aspects, and to determine how they impact students' understanding of the system. To reach this aim, the study used a questionary which allowed for a mixed method design, and the results show that many students have an immediate conception of the brain as the origin of nerve signals. In addition, many students hold the alternative conceptions that 1) synaptic action is exclusively excitatory, and that 2) neural circuits consists of neurons connected in a chain, one single neuron after another. These alternative conceptions prevent students from understanding the system. Implications for instruction are discussed in the context of conceptual learning theories, and teaching strategies are proposed. Since similar curricula goals and textbook content exist in several countries, the present results may be representative across nations.