Application of multiplex realtime PCR detection for hemorrhagic fever syndrome viruses.

BACKGROUND: Multiplex real-time PCR is a quick and cost effective method for detection of various gene simultaneously. HFSV (Hemorrhagic Fever Syndrome Virus) is a newly emerging infectious disease because of globalization and climate change. We tried to develop a molecular diagnostic technique for various causative viruses and evaluate its usefulness for improving public health. METHODS: Molecular diagnostic test method that qualitatively detects viruses causing viral hemorrhagic fevers hired Taq-Man Real-time RT-PCR technique. The Ct value was experimentally observed three or more times at the RNA concentration before and after the detection limit. After designing a multiplex real-time RT-PCR test for target gene of selected 17 viruses, the detection limit for each target and the presence or absence of cross-reaction and interference reaction were evaluated to determine its availability. RESULTS: Six kinds of viruses, including Crimean-Congo hemorrhagic fever virus, Omsk hemorrhagic fever virus, Sabia virus, Chapare virus, Yellow fever virus, and Variola virus (A4L gene, B12R gene), were able to confirm the detection limit of 0.5 copies/µl, and other Ebola virus, Marburg virus, Rift Valley fever virus, Kyasanur Forest disease virus, Junin virus, Guanarito virus, Machupo virus, Chikungunya virus, Hantavirus, Dengue virus types 1-4, and Lassa virus (L gene, GPC gene), and 11 kinds of viruses, the detection limit was confirmed at 5 copies/µl. No cross-reaction or interference between detected genes was observed. CONCLUSION: The virus test method developed through this study using multiplex is expected to be used for public health and quarantine as a test method that can be used when a hemorrhagic fever virus of unknown cause is introduced.

A spectral graph convolution for signed directed graphs via magnetic Laplacian.

Signed directed graphs contain both sign and direction information on their edges, providing richer information about real-world phenomena compared to unsigned or undirected graphs. However, analyzing such graphs is more challenging due to their complexity, and the limited availability of existing methods. Consequently, despite their potential uses, signed directed graphs have received less research attention. In this paper, we propose a novel spectral graph convolution model that effectively captures the underlying patterns in signed directed graphs. To this end, we introduce a complex Hermitian adjacency matrix that can represent both sign and direction of edges using complex numbers. We then define a magnetic Laplacian matrix based on the adjacency matrix, which we use to perform spectral convolution. We demonstrate that the magnetic Laplacian matrix is positive semi-definite (PSD), which guarantees its applicability to spectral methods. Compared to traditional Laplacians, the magnetic Laplacian captures additional edge information, which makes it a more informative tool for graph analysis. By leveraging the information of signed directed edges, our method generates embeddings that are more representative of the underlying graph structure. Furthermore, we showed that the proposed method has wide applicability for various graph types and is the most generalized Laplacian form. We evaluate the effectiveness of the proposed model through extensive experiments on several real-world datasets. The results demonstrate that our method outperforms state-of-the-art techniques in signed directed graph embedding.