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Abstract Background The Ecotropic viral integration site 5 (Evi5) is recognized as a potential oncogene and a cell cycle regulator. Evi5 regulates the abundance of Emi1, an inhibitor of the anaphase-promoting complex/cyclosome, to govern mitotic fidelity. Evi5 has been shown to be dysregulated in several cancer types. However, the expression and biological function of Evi5 in human laryngeal squamous cell carcinoma (LSCC) are still unknown. Methods Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing was used to generate Evi5 knockout (KO) LSCC cells. The proliferation and cell cycle distribution of LSCC cells was determined. The effect of Evi5 on LSCC tumor growth in vivo was studied in a tumor xenograft model in mice. The interaction between Evi5 and c-Myc was detected by immunoprecipitation (IP) assay. Luciferase assay was used to determine the transcriptional activity of c-Myc. Results Here, we show that Evi5 controls LSCC tumorigenesis via the stabilization of c-MYC oncogene. CRISPR-mediated knockout (KO) of Evi5 decreased the proliferation and decreased colony formation ability of LSCC cells. Knockout of Evi5 caused increased G1 phase and decreased S phase cells. In the tumor-bearing nude mice, The transplanted tumors originated from Evi5-KO TU212 cells were significantly decreased when compared with control TU212 cells. At the molecular level, we found that Evi5 interacted with c-MYC and Evi5 antagonized E3 ligase FBXW7-mediated ubiquitination and degradation of c-Myc protein, and promoted c-Myc-dependent transactivation. Conclusion Given the critical role of c-Myc in tumorigenesis, our data suggest that Evi5 is a potential therapeutic target in LSCC, and inhibition of Evi5 should be a prospective strategy for LSCC therapy.

Liver cancer is one of the most common malignant tumors. In Asia, liver cancer often develops on a background of cirrhosis caused by chronic hepatitis. The procedure of hepatitis, cirrhotic nodules, dysplastic nodules, and then hepatocellular carcinoma is the most common liver cancer evolutionary process. Judging the stage of hepatic nodules in the evolution process and taking intervention measures are critical for reducing the incidence of liver cancer. In this paper, a more accurate support vector machine (SVM) classification algorithm, LFOA-F-SVM, was proposed for radiomics to classify hepatic nodules from 120 patients into four categories based on dynamic enhanced magnetic resonance images. The algorithm uses radius-margin-based F-SVM, and combines the fruit fly optimization algorithm (FOA) of Levy flight (LF) strategy to optimize the parameters. To verify the effectiveness of the method, five UCI classification data sets (hearts, Parkinson’s disease, iris, wine and zoo) were added and compared with SVM, PSO-SVM, FOA-SVM, F-SVM. The results showed that LFOA-F-SVM has the highest classification accuracy in six data sets compared to the other methods. And in the hepatic nodules data set, the classification precision and recall are relatively high.

Objective: In this study, we compared the enhancement of blood vessels and liver parenchyma on enhanced computed tomography (CT) of the upper abdomen with two concentrations of contrast media (400 and 300 mg I/mL) based on similar iodine delivery rate (IDR) of 0.88 and 0.9 g I/s and iodine load of 450 mg I/kg.Methods: We randomly assigned 160 patients into two groups: iomeprol 400 mg I/mL (A group) and iohexol 300 mg I/mL (B group). The CT attenuation values of the main anatomical structures in the two groups with different scanning phases were measured and the image quality of the two groups was analyzed and compared. The peak pressure and local discomfort (including fever and pain) during contrast medium injection were recorded.Results: The mean attenuation value of the abdominal aorta was 313.6 ± 29.6 in the A group and 322.4 ± 30.1 in the B group during the late arterial phase (p = 0.8). Meanwhile, the mean enhancement values of the portal vein were 176.2 ± 19.3 and 165.9 ± 24.5 in the A and B groups, respectively, during the portal venous phase (p = 0.6). The mean CT values of liver parenchyma were 117.1 ± 15.3 and 108.8 ± 18.7 in the A and B groups, respectively, during the portal venous phase (p = 0.9). There was no statistical difference in image quality, peak injection pressure (psi), and local discomfort between the two groups (p > 0.05).Conclusion: When a similar IDR and the same iodine load are used, CT images with different concentrations of contrast media have the same subjective and objective quality, and can meet the diagnostic needs.

The left myocardium is the strongest cardiac muscle, representing the ability of heart to pump arterial blood throughout the body. Quantitative analysis of the contractive motion of left ventricle (LV) stands an important approach to diagnose cardiovascular diseases such as myocardial infarction. This paper utilized a biomechanical model describing the material of left myocardium to reconstruct the displacement field of LV. The mechanical model was used as an interpolation term, which was incorporated into the Bayesian estimation framework with the displacement field observed by cardiac cine magnetic resonance (CCMR) image, and the equation of displacement field was solved by finite element method. Functional parameters of LV were compared between the weak (46 cases) and normal (55 cases) ejection fraction groups of LV in the experiment. Significant differences in radial and circumferential displacement and velocity, strain and strain rate were observed between the two groups (p < 0.001), which proved that the method could effectively distinguish whether the motion of LV is normal or not. The experimental results are also highly correlated with the functional parameters of LV measured by CVI software, indicating that the method will facilitate the diagnosis of cardiovascular diseases.

In this paper, a new method for fine brain functional parcellation based on resting-state functional magnetic resonance imaging (rs-fMRI) data was proposed. The method combines the t-distribution stochastic neighbor embedding (t-SNE) and automatic spectral clustering (ASC) algorithms. First, correlation analyses are conducted between the time courses of the brain region to be parcellated and the whole brain. Second, t-SNE is used to extract the high-dimensional functional connectivity patterns. Last, the number of clusters is automatically determined by the ASC algorithm, and to divide the brain region of interest to generate the fine brain subregions. The results of simulated seed regions proved that the method proposed had higher accuracy than the commonly-used spectral clustering and spectral clustering with principal component analysis. Moreover, the method was successfully applied to parcellate the parahippocampal gyrus into 3 functional subregions in the left and right hemispheres. In conclusion, the algorithm combining t-SNE and ASC is an effective method for fine brain functional parcellation and construction of functional brain atlas.

Intelligent learning technologies are often applied within the educational industries. While these technologies can be used to create learning experiences tailored to an individual student, they cannot address students’ affect accurately and quickly during the learning process. This paper focuses on two core research questions. How do students regulate affect and what are the processes that affect regulation? First, this paper reviews the affect regulation methods and processes in an intelligent learning environment based on affective transition and affect compensation. This process, along with affect analysis, affect regulation, intelligent agents, and an intervention strategy can be used to analyze specific affect regulation methods and improve the affective regulation system. Seventy-two 7th grade students were randomly placed into an experimental condition that used Betty’s Brain, an intelligent tutoring system (ITS), or a classroom control. A lag sequence analysis and a multinomial processing tree analysis of video data captured at 25-minute intervals revealed significant differences in affect transitions frequencies between the two groups. Based on the results of the above analyses and after-class interviews, we found that Betty’s Brain was able to promote effective affect-regulation strategies to students in the domain of forest ecosystems.

Bioaugmentation with degrading bacteria can improve the treatment of nicotine-containing tobacco industrial wastewater effectively. However, the transient and extremely high feeding of pollutants may compromise the effectiveness of the bioaugmented reactors. The effect of transient nicotine shock loads on the performance of Pseudomonas sp. HF-1 bioaugmented SBRs were studied. The results showed that, under 500–2500 mg/L of transient nicotine shocks, all the reactors still could realize 100% of nicotine degradation in 4 days of recovery, while the key nicotine degradation enzyme HSP hydroxylase increased in expression. Though the dramatic increase of activities of ROS, MDA, SOD, and CAT suggested that transient nicotine shock loads could induce oxidative stress on microorganisms in activated sludge, a decrease to control level demonstrated that most of the microorganisms could resist 500–1500 mg/L of transient nicotine shock under the protection from strain HF-1. After 8 cycles of recovery, high ROS level and low TOC removal in high transient shock reactors implied that 2000–2500 mg/L of transient nicotine shock was out of its recovery of strain HF-1 bioaugmented system. This study enriched our understanding on highly efficient nicotine-degrading strain bioaugmented system, which would be beneficial to tobacco waste or wastewater treatment in engineering.