Dual CNN based channel estimation for MIMO-OFDM systems

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low

Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data

A Piezoelectric Immunosensor Using Hybrid Self-Assembled Monolayers for Detection of Schistosoma japonicum

BACKGROUND: The parasite Schistosoma japonicum causes schistosomiasis disease, which threatens human life and hampers economic and social development in some Asian countries. An important lesson learned from efforts to reduce the occurrence of schistosomiasis is that the diagnostic approach must be altered as further progress is made towards the control and ultimate elimination of the disease. METHODOLOGY/PRINCIPAL FINDINGS: Using mixed self-assembled monolayer membrane (mixed SAM) technology, a mixture of mercaptopropionic acid (MPA) and mercaptoethanol (ME) was self-assembled on the surface of quartz crystals by gold-sulphur-bonds. Soluble egg antigens (SEA) of S. japonicum were then cross-linked to the quartz crystal using a special coupling agent. As compared with the traditional single self-assembled monolayer immobilization method, S. japonicum antigen (SjAg) immobilization using mixed self-assembled monolayers exhibits much greater immunoreactivity. Under optimal experimental conditions, the detection range is 1:1500 to 1:60 (infected rabbit serum dilution ratios). We measured several infected rabbit serum samples with varying S. japonicum antibody (SjAb) concentrations using both immunosensor and ELISA techniques and then produced a correlation analysis. The correlation coefficients reached 0.973. CONCLUSIONS/SIGNIFICANCE: We have developed a new, simple, sensitive, and reusable piezoelectric immunosensor that directly detects SjAb in the serum. This method may represent an alternative to the current diagnostic methods for S. japonicum infection in the clinical laboratory or for analysis outside the laboratory

A-6G and A-20C Polymorphisms in the Angiotensinogen Promoter and Hypertension Risk in Chinese: A Meta-Analysis

BACKGROUND: Numerous studies in Chinese populations have evaluated the association between the A-6G and A-20C polymorphisms in the promoter region of angiotensinogen gene and hypertension. However, the results remain conflicting. We carried out a meta-analysis for these associations. METHODS AND RESULTS: Case-control studies in Chinese and English publications were identified by searching the MEDLINE, EMBASE, CNKI, Wanfang, CBM, and VIP databases. The random-effects model was applied for dichotomous outcomes to combine the results of the individual studies. We finally selected 24 studies containing 5932 hypertensive patients and 5231 normotensive controls. Overall, we found significant association between the A-6G polymorphism and the decreased risk of hypertension in the dominant genetic model (AA+AG vs. GG: P=0.001, OR=0.71, 95%CI 0.57-0.87, P(heterogeneity)=0.96). The A-20C polymorphism was significantly associated with the increased risk for hypertension in the allele comparison (C vs. A: P=0.03, OR=1.14, 95%CI 1.02-1.27, P(heterogeneity)=0.92) and recessive genetic model (CC vs. CA+AA: P=0.005, OR=1.71, 95%CI 1.18-2.48, P(heterogeneity)=0.99). In the subgroup analysis by ethnicity, significant association was also found among Han Chinese for both A-6G and A-20C polymorphisms. A borderline significantly decreased risk of hypertension between A-6G and Chinese Mongolian was seen in the allele comparison (A vs. G: P=0.05, OR=0.79, 95%CI 0.62-1.00, P(heterogeneity)=0.84). CONCLUSION: Our meta-analysis indicated significant association between angiotensinogen promoter polymorphisms and hypertension in the Chinese populations, especially in Han Chinese

Uric Acid Induces Renal Inflammation via Activating Tubular NF-κB Signaling Pathway

Inflammation is a pathologic feature of hyperuricemia in clinical settings. However, the underlying mechanism remains unknown. Here, infiltration of T cells and macrophages were significantly increased in hyperuricemia mice kidneys. This infiltration of inflammatory cells was accompanied by an up-regulation of TNF-α, MCP-1 and RANTES expression. Further, infiltration was largely located in tubular interstitial spaces, suggesting a role for tubular cells in hyperuricemia-induced inflammation. In cultured tubular epithelial cells (NRK-52E), uric acid, probably transported via urate transporter, induced TNF-α, MCP-1 and RANTES mRNA as well as RANTES protein expression. Culture media of NRK-52E cells incubated with uric acid showed a chemo-attractive ability to recruit macrophage. Moreover uric acid activated NF-κB signaling. The uric acid-induced up-regulation of RANTES was blocked by SN 50, a specific NF-κB inhibitor. Activation of NF-κB signaling was also observed in tubule of hyperuricemia mice. These results suggest that uric acid induces renal inflammation via activation of NF-κB signaling