A Healthy Dietary Pattern Reduces Lung Cancer Risk: A Systematic Review and Meta-Analysis

Background: Diet and nutrients play an important role in cancer development and progress; a healthy dietary pattern has been found to be associated with several types of cancer. However, the association between a healthy eating pattern and lung cancer risk is still unclear. Objective: Therefore, we conducted a systematic review with meta-analysis to evaluate whether a healthy eating pattern might reduce lung cancer risk. Methods: We identified relevant studies from the PubMed and Embase databases up to October 2015, and the relative risks were extracted and combined by the fixed-effects model when no substantial heterogeneity was observed; otherwise, the random-effects model was employed. Subgroup and publication bias analyses were also performed. Results: Finally, eight observational studies were included in the meta-analysis. The pooled relative risk of lung cancer for the highest vs. lowest category of healthy dietary pattern was 0.81 (95% confidence interval, CI: 0.75–0.86), and no significant heterogeneity was detected. The relative risks (RRs) for non-smokers, former smokers and current smokers were 0.89 (95% CI: 0.63–1.27), 0.74 (95% CI: 0.62–0.89) and 0.86 (95% CI: 0.79–0.93), respectively. The results remained stable in subgroup analyses by other confounders and sensitivity analysis. Conclusions: The results of our meta-analysis suggest that a healthy dietary pattern is associated with a lower lung cancer risk, and they provide more beneficial evidence for changing the diet pattern in the general population

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD

A Spatial Downscaling Approach for WindSat Satellite Sea Surface Wind Based on Generative Adversarial Networks and Dual Learning Scheme

Sea surface wind (SSW) is a crucial parameter for meteorological and oceanographic research, and accurate observation of SSW is valuable for a wide range of applications. However, most existing SSW data products are at a coarse spatial resolution, which is insufficient, especially for regional or local studies. Therefore, in this paper, to derive finer-resolution estimates of SSW, we present a novel statistical downscaling approach for satellite SSW based on generative adversarial networks and dual learning scheme, taking WindSat as a typical example. The dual learning scheme performs a primal task to reconstruct high resolution SSW, and a dual task to estimate the degradation kernels, which form a closed loop and are simultaneously learned, thus introducing an additional constraint to reduce the solution space. The integration of a dual learning scheme as the generator into the generative adversarial network structure further yield better downscaling performance by fine-tuning the generated SSW closer to high-resolution SSW. Besides, a model adaptation strategy was exploited to enhance the capacity for downscaling from low-resolution SSW without high-resolution ground truth. Comprehensive experiments were conducted on both the synthetic paired and unpaired SSW data. In the study areas of the East Coast of North America and the North Indian Ocean, in this work, the downscaling results to 0.25° (high resolution on the synthetic dataset), 0.03125° (8× downscaling), and 0.015625° (16× downscaling) of the proposed approach achieve the highest accuracy in terms of root mean square error and R-Square. The downscaling resolution can be enhanced by increasing the basic blocks in the generator. The highest downscaling reconstruction quality in terms of peak signal-to-noise ratio and structural similarity index was also achieved on the synthetic dataset with high-resolution ground truth. The experimental results demonstrate the effectiveness of the proposed downscaling network and the superior performance compared with the other typical advanced downscaling methods, including bicubic interpolation, DeepSD, dual regression networks, and adversarial DeepSD

Parasite-Derived Proteins for the Treatment of Allergies and Autoimmune Diseases

The morbidity associated with atopic diseases and immune dysregulation disorders such as asthma, food allergies, multiple sclerosis, atopic dermatitis, type 1 diabetes mellitus, and inflammatory bowel disease has been increasing all around the world over the past few decades. Although the roles of non-biological environmental factors and genetic factors in the etiopathology have been particularly emphasized, they do not fully explain the increase; for example, genetic factors in a population change very gradually. Epidemiological investigation has revealed that the increase also parallels a decrease in infectious diseases, especially parasitic infections. Thus, the reduced prevalence of parasitic infections may be another important reason for immune dysregulation. Parasites have co-evolved with the human immune system for a long time. Some parasite-derived immune-evasion molecules have been verified to reduce the incidence and harmfulness of atopic diseases in humans by modulating the immune response. More importantly, some parasite-derived products have been shown to inhibit the progression of inflammatory diseases and consequently alleviate their symptoms. Thus, parasites, and especially their products, may have potential applications in the treatment of autoimmune diseases. In this review, the potential of parasite-derived products and their analogs for use in the treatment of atopic diseases and immune dysregulation is summarized

Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting