Hemodynamic evaluation using four-dimensional flow magnetic resonance imaging for a patient with multichanneled aortic dissection

The hemodynamic function of multichanneled aortic dissection (MCAD) requires close monitoring and effective management to avoid potentially catastrophic sequelae. This report describes a 47-year-old man who underwent endovascular repair based on findings from four-dimensional (4D) flow magnetic resonance imaging of an MCAD. The acquired 4D flow data revealed complex, bidirectional flow patterns in the false lumens and accelerated blood flow in the compressed true lumen. The collapsed abdominal true lumen expanded unsatisfactorily after primary tear repair, which required further remodeling with bare stents. This case study demonstrates that hemodynamic analysis using 4D flow magnetic resonance imaging can help understand the complex pathologic changes of MCAD

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

ZIKV infection activates the IRE1-XBP1 and ATF6 pathways of unfolded protein response in neural cells.

BACKGROUND: Many viruses depend on the extensive membranous network of the endoplasmic reticulum (ER) for their translation, replication, and packaging. Certain membrane modifications of the ER can be a trigger for ER stress, as well as the accumulation of viral protein in the ER by viral infection. Then, unfolded protein response (UPR) is activated to alleviate the stress. Zika virus (ZIKV) is a mosquito-borne flavivirus and its infection causes microcephaly in newborns and serious neurological complications in adults. Here, we investigated ER stress and the regulating model of UPR in ZIKV-infected neural cells in vitro and in vivo. METHODS: Mice deficient in type I and II IFN receptors were infected with ZIKV via intraperitoneal injection and the nervous tissues of the mice were assayed at 5 days post-infection. The expression of phospho-IRE1, XBP1, and ATF6 which were the key markers of ER stress were analyzed by immunohistochemistry assay in vivo. Additionally, the nuclear localization of XBP1s and ATF6n were analyzed by immunohistofluorescence. Furthermore, two representative neural cells, neuroblastoma cell line (SK-N-SH) and astrocytoma cell line (CCF-STTG1), were selected to verify the ER stress in vitro. The expression of BIP, phospho-elF2α, phospho-IRE1, and ATF6 were analyzed through western blot and the nuclear localization of XBP1s was performed by confocal immunofluorescence microscopy. RT-qPCR was also used to quantify the mRNA level of the UPR downstream genes in vitro and in vivo. RESULTS: ZIKV infection significantly upregulated the expression of ER stress markers in vitro and in vivo. Phospho-IRE1 and XBP1 expression significantly increased in the cerebellum and mesocephalon, while ATF6 expression significantly increased in the mesocephalon. ATF6n and XBP1s were translocated into the cell nucleus. The levels of BIP, ATF6, phospho-elf2α, and spliced xbp1 also significantly increased in vitro. Furthermore, the downstream genes of UPR were detected to investigate the regulating model of the UPR during ZIKV infection in vitro and in vivo. The transcriptional levels of atf4, gadd34, chop, and edem-1 in vivo and that of gadd34 and chop in vitro significantly increased. CONCLUSION: Findings in this study demonstrated that ZIKV infection activates ER stress in neural cells. The results offer clues to further study the mechanism of neuropathogenesis caused by ZIKV infection

Hydrostatic pressure effects on the static magnetism in Eu(Fe0.925_{0.925}Co0.075_{0.075})2_{2}As2_{2}

The effects of hydrostatic pressure on the static magnetism in Eu(Fe$_{0.925}$Co$_{0.075}$)$_{2}$As$_{2}$ are investigated by complementary electrical resistivity, ac magnetic susceptibility and single-crystal neutron diffraction measurements. A specific pressure-temperature phase diagram of Eu(Fe$_{0.925}$Co$_{0.075}$)$_{2}$As$_{2}$ is established. The structural phase transition, as well as the spin-density-wave order of Fe sublattice, is suppressed gradually with increasing pressure and disappears completely above 2.0 GPa. In contrast, the magnetic order of Eu sublattice persists over the whole investigated pressure range up to 14 GPa, yet displaying a non-monotonic variation with pressure. With the increase of the hydrostatic pressure, the magnetic state of Eu evolves from the canted antiferromagnetic structure in the ground state, via a pure ferromagnetic structure under the intermediate pressure, finally to a possible "novel" antiferromagnetic structure under the high pressure. The strong ferromagnetism of Eu coexists with the pressure-induced superconductivity around 2 GPa. The change of the magnetic state of Eu in Eu(Fe$_{0.925}$Co$_{0.075}$)$_{2}$As$_{2}$ upon the application of hydrostatic pressure probably arises from the modification of the indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between the Eu$^{2+}$ moments tuned by external pressure.Comment: 9 pages, 6 figure