Association of single nucleotide polymorphisms (SNPs) with gastric cancer susceptibility and prognosis in population in Wuwei, Gansu, China.

BACKGROUND: Gastric cancer (GC) is the sixth most common cancer. China is one of the most frequent GC occurred countries, and Wuwei, Gansu, is one of the highest incidence area in China. Possible biomarkers of GC susceptibility and prognosis among the population in Wuwei are urgently needed. METHODS: All participants in this study were recruited from the Wuwei Cancer Hospital in Gansu, including 303 patients diagnosed with GC and 200 non-cancer controls. DNA was extracted for further single nucleotide polymorphisms (SNP) genotyping. All SNPs were firstly screened by additive logistic regression model then selected SNPs were subjected to univariate Cox regression analysis and multivariate Cox regression analysis for their associations with GC occurrence. RESULTS: The results showed that 31 SNPs were significantly related to the incidence of GC in Wuwei, Gansu, China. Genotype rs4823921 was significantly related to the overall survival of GC patients and AC/AA genotype of rs4823921 polymorphism was significantly associated with an increased risk of GC in Wuwei population. CONCLUSIONS: Thirty-one SNPs were significantly related to the incidence of GC in Wuwei and rs4823921 genotype AC/AA was significantly associated with poor prognosis of GC patients in Wuwei, Gansu.

Blockchain-Enabled Asynchronous Federated Learning in Edge Computing.

The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.

Residual Spatial and Channel Attention Networks for Single Image Dehazing.

Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate estimations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.