The present study is targeted at investigating the effects of long intergenic non-protein coding RNA 847 (LINC00847) on the malignant biological behaviors of laryngeal squamous cell carcinoma (LSCC) cells, and the mechanisms. Quantitative real-time PCR and Western blotting were conducted for detecting the expressions of LINC00847, microRNA-181a-5p (miR-181a-5p) and zinc finger E-box binding homeobox 2 (ZEB2) in LSCC cell lines and tissue samples. BrdU, cell counting kit-8, scratch wound healing, Transwell and flow cytometry assays were utilized for detecting cell proliferation, migration, invasion, and cell cycle progression. Dual-luciferase reporter gene, RNA binding protein immunoprecipitation (RIP), and RNA pull-down assays were utilized to investigate the interaction among LINC00847, miR-181a-5p, and ZEB2. The subcellular location of LINC00847 was determined by RNA fluorescence in situ hybridization (RNA-FISH) assay. Tumor growth was evaluated using a xenograft model of nude mice. It was revealed that LINC00847 expression was increased in LSCC tissues, and its high expression was associated with lymph node metastasis and poor differentiation. LINC00847 was mainly located in the cytoplasm of LSCC cells, and LINC00847 overexpression promoted LSCC cell proliferation, migration, invasion, and accelerated the cell cycle progression while knocking down LINC00847 had the opposite effects in vitro and inhibited the tumor growth in vivo . LINC00847 directly targeted miR-181a-5p and negatively modulated miR-181a-5p expression. ZEB2 was a target gene of miR-181a-5p, and was positively and indirectly modulated by LINC00847. Our data suggest that LINC00847 promotes LSCC progression by regulating the miR-181a-5p/ZEB2 axis.

Recently, there has been increasing interest in applying graph theory to the quantitative analysis of brain functional networks, while phase synchronization (PS) analysis has been demonstrated to be a useful method to infer functional connectivity with multichannel neural signals, e.g., electroencephalogram (EEG). In this paper, we focus on the case that the number of channels in EEG data is not adequate for the use of graph theory analysis. The degree of network-links (DNLs), an index based on the PS analysis of all the EEG wave pairs, is proposed to study the relevant and the overall characteristics of the brain. With the help of a novel division to the frequency range 0.5–30 Hz, we analyze the DNLs in different frequency bands of the EEG signals. As a comparison, a frequency band analysis of the relative power spectrum is conducted. The results demonstrate that when the cerebral infarction (CI) patients and normal control people are analyzed, there is a need for the reasonable length of EEG data to quantify the differences between different dynamical systems; under a reasonable data length, the frequency band (19–24 Hz) yields the best accuracy for diagnosing CI, which lies within the classical beta band (13–30 Hz); furthermore, only in the 19–24 Hz band, as for the values of relative power spectrum, in each EEG channel, there presents a similar relationship between the CI group and control group. The experimental results suggest that 19–24 Hz should be the optimal range for the diagnosis of CI, further the DNLs calculated within this band serve as an assist indicator in the CI diagnosis.