Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

Simple graphene chemiresistors as pH sensors: fabrication and characterization

We report the fabrication and characterization of a simple gate-free graphene device as a pH sensor. The graphene sheets are made by mechanical exfoliation. Platinum contact electrodes are fabricated with a mask-free process using focused ion beam, and then expanded by silver paint. Annealing is used to improve the electrical contact. The experiment on the fabricated graphene device shows that the resistance of the device decreases linearly with increasing pH values (in the range of 4-10)in the surrounding liquid environment. The resolution achieved in our experiments is approximately 0.3 pH in alkali environment. The sensitivity of the device is calculated as approximately 2 k\Omega /pH. The simple configuration, miniaturized size and the integration ability make graphene-based sensors promising candidates for future micro/nano applications

Serum 25-hydroxyvitamin D levels and the risk of idiopathic central precocious puberty in girls

Introduction: Prior studies have found inconsistent results regarding the relationship between vitamin D status and Idiopathic Central Precocious Puberty (ICPP). Objective: To assess the role of serum 25-hydroxyvitamin D (25 [OH]D) levels in ICPP development. Method: The authors retrospectively collected data from 221 girls with ICPP and 144 healthy girls between January 2017 and December 2019. The participants’ serum 25(OH)D levels were measured using an automatic chemiluminescence method, and the association between serum 25(OH)D levels and the risk of ICPP was assessed using multivariate logistic regression analysis. Odds Ratios (OR) with 95% Confidence Intervals (95% CI) were calculated as effect estimates. Results: Serum 25(OH)D levels in the ICPP group were significantly lower than those in healthy controls (p < 0.001). Multivariate analysis indicated that girls with insufficient vitamin D levels (OR = 0.201; 95% CI 0.094–0.428; p < 0.001) and sufficient vitamin D levels (OR = 0.141; 95% CI 0.053–0.375; p < 0.001) both had a lower risk of ICPP than girls with vitamin D deficiency. Moreover, the authors found that the height (p = 0.014), weight (p = 0.014), breast stage (p = 0.010), mother's height (p < 0.001), and luteinizing hormone/follicle-stimulating hormone ratio (p = 0.010) in girls with ICPP could be associated with levels of vitamin D. Conclusion: This study found that a low serum 25(OH)D level is an independent risk factor for ICPP, and several characteristics of girls with ICPP could be affected by their vitamin D status.

Mathematical modeling of simultaneous carbon-nitrogen-sulfur removal from industrial wastewater

A mathematical model of carbon, nitrogen and sulfur removal (C-N-S) from industrial wastewater was constructed considering the interactions of sulfate-reducing bacteria (SRB), sulfide-oxidizing bacteria (SOB), nitrate-reducing bacteria (NRB), facultative bacteria (FB), and methane producing archaea (MPA). For the kinetic network, the bioconversion of C-N by heterotrophic denitrifiers (NO\ua0→\ua0NO\ua0→\ua0N), and that of C-S by SRB (SO\ua0→\ua0S) and SOB (S\ua0→\ua0S) was proposed and calibrated based on batch experimental data. The model closely predicted the profiles of nitrate, nitrite, sulfate, sulfide, lactate, acetate, methane and oxygen under both anaerobic and micro-aerobic conditions. The best-fit kinetic parameters had small 95% confidence regions with mean values approximately at the center. The model was further validated using independent data sets generated under different operating conditions. This work was the first successful mathematical modeling of simultaneous C-N-S removal from industrial wastewater and more importantly, the proposed model was proven feasible to simulate other relevant processes, such as sulfate-reducing, sulfide-oxidizing process (SR-SO) and denitrifying sulfide removal (DSR) process. The model developed is expected to enhance our ability to predict the treatment of carbon-nitrogen-sulfur contaminated industrial wastewater

Genetic variants in ELOVL2 and HSD17B12 predict melanoma‐specific survival

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single‐nucleotide polymorphisms (SNPs) in 149 genes of the fatty‐acid synthesis pathway with cutaneous melanoma disease‐specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome‐wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses’ Health and Health Professionals Follow‐up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51–0.84 and p = 8.34 × 10−4) and 2.29 (1.55–3.39 and p = 3.61 × 10−5), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies

Peroxisome Proliferator-Activated Receptors Associated with Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is rapidly becoming a major cause of chronic liver disease worldwide. Concurrent to an increase in NAFLD prevalence, there is an increase in the obesity epidemic and the correlated insulin-resistant state. It is a challenge to diagnose NAFLD because many patients are asymptomatic until the later stages of disease. The most common symptoms include fatigue, malaise, and discomfort in the right upper quadrant. The major and most accurate tool to clinically diagnose NAFLD is a liver biopsy, followed by histological analysis. However, this procedure is invasive and often carries a high risk of complications. Currently, there are no officially approved medications for the treatment of NAFLD. Although lifestyle modifications with proper diet and exercise have been shown to be beneficial, this has been difficult to achieve and sustain for many patients. Effective pharmacological treatments are still lacking; therefore, additional research to identify novel drugs is clearly warranted. PPARs are promising drug targets for the management of NAFLD and its related conditions of type 2 diabetes mellitus and cardiovascular disease. In this review, we provide an overview of recent studies on the association of PPARs and NAFLD