Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to huge domain gap between real-world and synthetic LR ones obtained by generators. To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. Our Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes. Experiments on two synthetic and two real-world datasets demonstrate that, our SCGAN outperforms the state-of-the-art methods on recovering the face structures/details and quantitative metrics for real-world face SR. The code will be publicly released at https://github.com/HaoHou-98/SCGAN

Expression and correlation of PBRM1 and P53 in clear cell carcinoma of kidney

Prevalence of clear cell renal cell carcinoma (ccRCC) among human population though common among adults, it occurs in children and young adults as well. The prognostic value of P53 expression in ccRCC is well known. Recently, PBRM1 has also acquired attention for its prognostic and predictive value in ccRCC. Here, we investigated the expression and correlation of PBRM1 and P53 in ccRCC. Renal tissues were collected from 70 patients who have undergone radical nephrectomy for clear cell carcinoma of the kidney in our hospital and 24 healthy volunteers for the study. We used immunohistochemical approach to determine the expression of PBRM1 and P53 in clear cell carcinoma of the kidney and normal kidney tissues and to analyze the correlation between them. Clinicopathological parameters and prognosis of patients were also studied. The positive expression rate of PBRM1 in clear renal cell carcinoma tissues was significantly higher (62.86%) compared to the normal renal tissues 8.33%. Similarly, positive expression rate of P53 in clear renal cell carcinoma tissues was 40%, while it was no expression in normal renal tissues. The expression level of PBRM1 was correlated with pathological grade and clinical stage of ccRCC patients, but not with age, sex and tumor size. P53 and expression levels were independent of age, sex, tumor size, pathological grade, and clinical stage of patients with clear cell carcinoma of the kidney. The 5-year survival rate of PBRM1 positive expression patients was 40.91% significantly lower than that of PBRM1 negative expression patients (84.62%), whereas in P53 it was 50 and 61.90%, respectively. Clinical stage, pathological grade and PBRM1 were all independent risk factors affecting the prognosis of patients with clear cell carcinoma of the kidney. Overall, the results suggest that PBRM1 is positively correlated with P53 in clear cell carcinoma of kidney (r=0.781, P=0.012). PBRM1 and P53 are both highly expressed in ccRCC and play an important role in the development of the disease. PBRM1 can also be used as an independent risk factor affecting the prognosis of ccRCC patients

Expression and correlation of PBRM1 and P53 in clear cell carcinoma of kidney

717-721Prevalence of clear cell renal cell carcinoma (ccRCC) among human population though common among adults, it occurs in children and young adults as well. The prognostic value of P53 expression in ccRCC is well known. Recently, PBRM1 has also acquired attention for its prognostic and predictive value in ccRCC. Here, we investigated the expression and correlation of PBRM1 and P53 in ccRCC. Renal tissues were collected from 70 patients who have undergone radical nephrectomy for clear cell carcinoma of the kidney in our hospital and 24 healthy volunteers for the study. We used immunohistochemical approach to determine the expression of PBRM1 and P53 in clear cell carcinoma of the kidney and normal kidney tissues and to analyze the correlation between them. Clinicopathological parameters and prognosis of patients were also studied. The positive expression rate of PBRM1 in clear renal cell carcinoma tissues was significantly higher (62.86%) compared to the normal renal tissues 8.33%. Similarly, positive expression rate of P53 in clear renal cell carcinoma tissues was 40%, while it was no expression in normal renal tissues. The expression level of PBRM1 was correlated with pathological grade and clinical stage of ccRCC patients, but not with age, sex and tumor size. P53 and expression levels were independent of age, sex, tumor size, pathological grade, and clinical stage of patients with clear cell carcinoma of the kidney. The 5-year survival rate of PBRM1 positive expression patients was 40.91% significantly lower than that of PBRM1 negative expression patients (84.62%), whereas in P53 it was 50 and 61.90%, respectively. Clinical stage, pathological grade and PBRM1 were all independent risk factors affecting the prognosis of patients with clear cell carcinoma of the kidney. Overall, the results suggest that PBRM1 is positively correlated with P53 in clear cell carcinoma of kidney (r=0.781, P=0.012). PBRM1 and P53 are both highly expressed in ccRCC and play an important role in the development of the disease. PBRM1 can also be used as an independent risk factor affecting the prognosis of ccRCC patients

NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising

Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS prior. In this paper, we take one step forward by introducing a pixel-level NSS prior, i.e., searching similar pixels across a non-local region. This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance. With the introduced pixel-level NSS prior, we propose an accurate noise level estimation method, and then develop a blind image denoising method based on the lifting Haar transform and Wiener filtering techniques. Experiments on benchmark datasets demonstrate that, the proposed method achieves much better performance than previous non-deep methods, and is still competitive with existing state-of-the-art deep learning based methods on real-world image denoising. The code is publicly available at https://github.com/njusthyk1972/NLH.Comment: 14 pages, 9 figures, 10 tables, accept by IEEE TI

STAR: A Structure and Texture Aware Retinex Model

© 2020 IEEE. Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent γ ) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with γ \u3e 1, while the texture map is generated by been shrank with γ \u3c 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at https://github.com/csjunxu/STAR

A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma

In this study, we analyzed the clinical significance of ferroptosis-related genes (FRGs) in 32 cancer types in the GSCA database. We detected a 2-82% mutation rate among 36 FRGs. In clear cell renal cell carcinoma (ccRCC; n=539) tissues from the The Cancer Genome Atlas database, 30 of 36 FRGs were differentially expressed (up- or down-regulated) compared to normal kidney tissues (n=72). Consensus clustering analysis identified two clusters of FRGs based on similar co-expression in ccRCC tissues. We then used LASSO regression analysis to build a new survival model based on five risk-related FRGs (CARS, NCOA4, FANCD2, HMGCR, and SLC7A11). Receiver operating characteristic curve analysis confirmed good prognostic performance of the new survival model with an area under the curve of 0.73. High FANCD2, CARS, and SLC7A11 expression and low HMGCR and NCOA4 expression were associated with high-risk ccRCC patients. Multivariate analysis showed that risk score, age, stage, and grade were independent risk factors associated with prognosis in ccRCC. These findings demonstrate that this five risk-related FRG-based survival model accurately predicts prognosis in ccRCC patients, and suggest FRGs are potential prognostic biomarkers and therapeutic targets in several cancer types

High C1QTNF1 expression mediated by potential ncRNAs is associated with poor prognosis and tumor immunity in kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) originates from proximal tubular cells and is the most common subtype of renal cell carcinoma. KIRC is characterized by changes in lipid metabolism, and obesity is a risk factor for it. C1q And TNF Related 1 (C1QTNF1), a novel adipokine and member of the C1q and TNF-related protein (CTRP) family, has been shown to affect the progression of various cancers. However, the role of C1QTNF1 in KIRC has not been studied.Methods: The Wilcoxon rank sum test was used to analyze the expression of C1QTNF1 in KIRC tissues and normal tissues. The relationship between clinicopathological features and C1QTNF1 levels was also examined by logistic regression and the Wilcoxon rank sum test. In addition, the effect of C1QTNF1 on the prognosis of KIRC patients was analyzed by Kaplan-Meier (KM). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the potential signaling pathways and biological functions of differential genes. A nomogram was constructed to predict the prognosis of KIRC patients. Spearman correlation analysis was performed to determine the association between C1QTNF1 expression and immune cell infiltration and immune checkpoint genes. The upstream miRNAs and lncRNAs of C1QTNF1 were predicted by the ENCORI online tool. Finally, we examined the proliferation, invasion, and migration abilities of KIRC cells after C1QTNF1 knockdown.Results: The expression of C1QTNF1 in KIRC tissues was significantly higher than in normal renal tissues. Patients with higher C1QTNF1 expression had a poor prognosis, a finding supported by Kaplan-Meier survival analysis. C1QTNF1 expression was significantly correlated with TNM and pathologic stages, age, and gender (p < 0.05). The C1QTNF1 expression level was significantly correlated with immune cell infiltration and immune checkpoint genes in KIRC. Additionally, high C1QTNF1 expression was associated with poor prognosis in stage I and II, T1 and T2, T3 and T4, N0, and M0 patients (HR > 1, p < 0.05). The calibration diagram shows that the C1QTNF1 model has effective predictive performance for the survival of KIRC patients. Knockdown of C1QTNF1 inhibited KIRC cell proliferation, cell migration, and cell invasion. In addition, CYTOR and AC040970.1/hsa-miR-27b-3p axis were identified as the most likely upstream ncRNA-related pathways of C1QTNF1 in KIRC.Conclusion: In conclusion, our study suggests that high expression of C1QTNF1 is associated with KIRC progression and immune infiltration. The increased expression of C1QTNF1 suggests a poor prognosis in KIRC patients

The Protective Effects of Ciji-Hua’ai-Baosheng II Formula on Chemotherapy-Treated H22 Hepatocellular Carcinoma Mouse Model by Promoting Tumor Apoptosis

Ciji-Hua’ai-Baosheng II Formula (CHB-II-F) is a traditional Chinese medical formula that has been shown in clinical practice to relieve side effects of chemotherapy and improve quality of life for cancer patients. In order to understand the mechanism of its protective effects on chemotherapy, mice with transplanted H22 hepatocellular carcinoma were employed in this study. Ninety-two mice were injected subcutaneously with H22 HCC cell suspension into the right anterior armpit. After mice were treated with 5-fluorine pyrimidine (5-FU), they were divided into six groups as untreated group, 5-FU group, 5-FU plus Yangzheng Xiaoji Capsule group and three groups of 5-FU plus different concentrations of CHB-II-F. Twenty mice were euthanized after 7 days of treatment in untreated and medium concentration of CHB-II-F groups and all other mice were euthanized after 14 days of treatment. Herbal components/metabolites were analyzed by UPLC-MS. Tumors were evaluated by weight and volume, morphology of light and electron microscope, and cell cycle. Apoptosis were examined by apoptotic proteins expression by western blot. Four major components/metabolites were identified from serum of mice treated with CHB-II-F and they are β-Sitosterol, Salvianolic acid, isobavachalcone, and bakuchiol. Treatment of CHB-II-F significantly increased body weights of mice and decreased tumor volume compared to untreated group. Moreover, CHB-II-F treatment increased tumor cells in G0-G1 transition instead of in S phase. Furthermore, CHB-II-F treatment increased the expression of pro-apoptotic proteins and decreased the expression anti-apoptotic protein. Therefore, CHB-II-F could improve mice general condition and reduce tumor cell malignancy. Moreover, CHB-II-F regulates apoptosis of tumor cells, which could contribute its protective effect on chemotherapy