A method to calculate design tide levels on the basis of numerical model of tidal current and its application

In order to determine the design tide levels in the areas without measured tide level data, especially in the areas where it is difficult to measure tidal levels, a calculation method based on a numerical model of tidal current is proposed. The essentials of the method are described, and its application is illustrated with an example. The results of the application show that the design tide levels calculated by the method are close to those determined by long-time measured tide level data, and its calculation precision is high, so it is feasible to use the method to determine the design tide levels in the areas.

Integrating multi-omics data of childhood asthma using a deep association model

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity.The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma.To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model,we proposed a novel deep association model(DAM)and corresponding efficient analysis framework.First,the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information,thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises.Second,the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels.Third,our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module,which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels.Using DAM,we deeply analyzed the transcriptome and methylation data of childhood asthma.The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset,by ablation experiment and comparison with many baseline methods from clinical and biological studies.The DAM-induced diagnostic model can achieve a prediction AUC of o.912,which is higher than that of many other alternative methods.Meanwhile,relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels.As an interpretable machine learning approach,DAM simultaneously considers the non-linear associations among samples and those among biological features,which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complexdiseases.

Decomposition of Phragmites australis rhizomes in artificial land-water transitional zones （ALWTZs） and management implications

Rhizomes are essential organs for growth and expansion of Phragmites australis. They function as an important source of organic matter and as a nutrient source, especially in the artificial land-water transitional zones （ALWTZs） of shallow lakes. In this study, decomposition experiments on 1- to 6-year-old R australis rhizomes were conducted in the ALWTZ of Lake Baiyangdian to evaluate the contribution of the rhizomes to organic matter accumulation and nutrient release. Mass loss and changes in nutrient content were measured after 3, 7, 15, 30, 60, 90, 120, and 180 days. The decomposition process was modeled with a composite exponential model. The Pearson correlation analysis was used to analyze the relationships between mass loss and litter quality factors. A multiple stepwise regression model was utilized to determine the dominant factors that affect mass loss. Results showed that the decomposition rates in water were significantly higher than those in soil for 1- to 6-year-old rhizomes. However, the sequence of decomposition rates was identical in both water and soil. Significant relationships between mass loss and litter quality factors were observed at a later stage, and P-related factors proved to have a more significant impact than N-related factors on mass loss. According to multiple stepwise models, the C/P ratio was found to be the dominant factor affecting the mass loss in water, and the C/N and C/P ratios were the main factors affecting the mass loss in soil. The combined effects of harvesting, ditch broadening, and control of water depth should be considered for lake administrators.