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In the field of studies on the "Neural Synapses" in the nervous system, its experts manually (or pseudo-automatically) detect the bio-molecule clusters (e.g., of proteins) in many TIRF (Total Internal Reflection Fluorescence) images of a fluorescent cell and analyze their static/dynamic behaviors. This paper proposes a novel method for the automatic detection of the bio-molecule clusters in a TIRF image of a fluorescent cell and conducts several experiments on its performance, e.g., mAP @ IoU (mean Average Precision @ Intersection over Union) and F1-score @ IoU, as an objective/quantitative means of evaluation. As a result, the best of the proposed methods achieved 0.695 as its mAP @ IoU = 0.5 and 0.250 as its F1-score @ IoU = 0.5 and would have to be improved, especially with respect to its recall @ IoU. But, the proposed method could automatically detect bio-molecule clusters that are not only circular and not always uniform in size, and it can output various histograms and heatmaps for novel deeper analyses of the automatically detected bio-molecule clusters, while the particles detected by the Mosaic Particle Tracker 2D/3D, which is one of the most conventional methods for experts, can be only circular and uniform in size. In addition, this paper defines and validates a novel similarity of automatically detected bio-molecule clusters between fluorescent cells, i.e., SimMolCC, and also shows some examples of SimMolCC-based applications. [ABSTRACT FROM AUTHOR]

In the field of studies on the “Neural Synapses” in the nervous system, its experts manually (or pseudo-automatically) detect the bio-molecule clusters (e.g., of proteins) in many TIRF (Total Internal Reflection Fluorescence) images of a fluorescent cell and analyze their static/dynamic behaviors. This paper proposes a novel method for the automatic detection of the bio-molecule clusters in a TIRF image of a fluorescent cell and conducts several experiments on its performance, e.g., mAP @ IoU (mean Average Precision @ Intersection over Union) and F1-score @ IoU, as an objective/quantitative means of evaluation. As a result, the best of the proposed methods achieved 0.695 as its mAP @ IoU = 0.5 and 0.250 as its F1-score @ IoU = 0.5 and would have to be improved, especially with respect to its recall @ IoU. But, the proposed method could automatically detect bio-molecule clusters that are not only circular and not always uniform in size, and it can output various histograms and heatmaps for novel deeper analyses of the automatically detected bio-molecule clusters, while the particles detected by the Mosaic Particle Tracker 2D/3D, which is one of the most conventional methods for experts, can be only circular and uniform in size. In addition, this paper defines and validates a novel similarity of automatically detected bio-molecule clusters between fluorescent cells, i.e., SimMolCC, and also shows some examples of SimMolCC-based applications.

The two cell-adhesion molecules, Neurexin-1 and Neuroligin-1, are essential for synapse function in neural networks. Interruptions to the function of these two proteins are associated with predestination for cognitive disorders, such as Autism spectrum disorder (ASD). In this paper, we study the effects of genetic missense mutations located on the cell interface between NRXN-1 and NLGN-1 on the development of neurodevelopmental disorders by identifying and determining the effects of mutations on bond strengths between NRXN-1 and NLGN-1 on the synaptic interface by building a computational platform. This was done by mapping all ASD-associated point mutations found in a web-based database on the computationally modeled structure of NRXN-1, then determining which mutations interact with NLGN-1. Then, the binding strength of the two proteins without the influence of the mutations was compared to the binding strength with the mutations so that the effects of the mutation were established. Based on our structural models and calculations of binding free energy, we found that these mutations can change the binding affinity of Neurexin-neuroligin interaction. Thus, we suggest that the changes in bond strength between Neurexin and Neuroligin at neural synapses can affect neural circuits, leading to autism-associated symptoms. [ABSTRACT FROM AUTHOR]