No abstract

Despite the substantial progress in understanding pathogenesis of schizophrenia, the causal treatment of a full range of symptoms (positive, negative, and cognitive) is still missing. One reason for this is the fact that traditional model organisms (rodents and zebrafish) do not enable the modeling of specific language-based symptoms typical for schizophrenia, such as verbal hallucinations, disorganized speech, and delusions, and their relation to other schizophrenia symptoms. Hence, development of new effective treatments requires animal models of high sociability, advanced forms of communication and intelligence, and their measurable expressions, such as verbal speech and spatial orientation (Langova et al., 2020). To this end, we identified a new suitable candidate animal species of weakly electric fish Gnathonemus petersii (G. petersii), which are abound with a high level of intelligence, various forms of communication and

No abstract

The availability of ground-truth flow field is crucial for quantitative evaluation of any optical flow computation method. The fidelity of test data is also important when artificially generated. Therefore, we generated an artificial flow field together with an artificial image sequence based on real-world sample image. The presented framework benefits of a two-layered approach in which user-selected foreground was locally moved and inserted into an artificially generated background. The background is visually similar to input sample image while the foreground is extracted from original and so is the same. The framework is capable of generating 2D and 3D image sequences of arbitrary length. Several examples of the version tuned to simulate real fluorescent microscope images are presented. We also provide a brief discussion.

No abstract