Aims
CLARITY is a novel technique which enables three‐dimensional visualization of immunostained tissue for the study of circuitry and spatial interactions between cells and molecules in the brain. In this study, we aimed to compare methodological differences in the application of CLARITY between rodent and large human post mortem brain samples. In addition, we aimed to investigate if this technique could be used to visualize Lewy pathology in a post mortem Parkinson's brain.MethodsRodent and human brain samples were clarified and immunostained using the passive version of the CLARITY technique. Samples were then immersed in different refractive index matching media before mounting and visualizing under a confocal microscope.ResultsWe found that tissue clearing speed using passive CLARITY differs according to species (human vs. rodents), brain region and degree of fixation (fresh vs. formalin‐fixed tissues). Furthermore, there were advantages to using specific refractive index matching media. We have applied this technique and have successfully visualized Lewy body inclusions in three dimensions within the nucleus basalis of Meynert, and the spatial relationship between monoaminergic fibres and Lewy pathologies among nigrostriatal fibres in the midbrain without the need for physical serial sectioning of brain tissue.ConclusionsThe effective use of CLARITY on large samples of human tissue opens up many potential avenues for detailed pathological and morphological studies.
In this paper, we explore the challenges experienced by a group of Primary 5 to 6 (age 12–14) students as they engaged in a series of problem-solving tasks through block-based programming. The challenges were analysed according to a taxonomy focusing on the presence of computational thinking (CT) elements in mathematics contexts: preparing problems, programming, create computational abstractions, as well as troubleshooting and debugging. Our results suggested that the challenges experienced by students were compounded by both having to learn the CT-based environment as well as to apply mathematical concepts and problem solving in that environment. Possible explanations for the observed challenges stemming from differences between CT and mathematical thinking are discussed in detail, along with suggestions towards improving the effectiveness of integrating CT into mathematics learning. This study provides evidence-based directions towards enriching mathematics education with computation.
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