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In analyzing semi-continuous data, two-part model is a widely appreciated tool, in which two components are enclosed to characterize the mixing proportion of zeros and the actual level of positive values in semi-continuous data. The primary interest underlying such a model is primarily to exploit the dependence of the observed covariates on the semi-continuous variables; as such, the exploitation of unobserved heterogeneity is sometimes ignored. In this paper, we extend the conventional two-part regression model to much more general situations where multiple latent factors are considered to interpret the latent heterogeneity arising from the absence of covariates. A structural equation is constructed to describe the interrelationships between the latent factors. Moreover, a general statistical analysis procedure is developed to accommodate semi-continuous, ordered and unordered data simultaneously. A procedure for parameter estimation and model assessment is developed under a Bayesian framework. Empirical results including a simulation study and a real example are presented to illustrate the proposed methodology.

AbstractRainfall represents one of the most significant cause of landslide hazards. With runoff in the saturated zone of shallow soil considered, an improved Green–Ampt model was proposed to depict the rainfall infiltration process. Furthermore, a formula for calculating the depth of wetting front was derived, and the slope stability was evaluated accordingly. Finally, a formula for calculating the safety factor of multilayer slopes above different surfaces was obtained via the limit equilibrium method. An illustration is a multilayer colluvial slope in Guangdong Province of China, the results indicated that the downward speed of wetting front decreases gradually within the same layer with continuous rainfall but increases significantly when it extends to the interfaces between the soil layers. Safety factor of the slope decreases sharply at the initial stage of rainfall but then more slowly. Slope failure is more likely to occur along the first interface between the soil layers than along the surface of wetting front. The result from the proposed model is closer to the result of the physical model test than from the existed Green–Ampt model. The improved Green–Ampt model for multilayer slopes can accurately evaluate the infiltration process and related stability of multilayer slopes under rainfall.

In order to develop realistic water resources strategy, we initially analyze the situation of water resources in the United States which present a significant regional difference. Then on the basis of previous studies, we propose the cost formulas of water storage, movement and desalinization. By analyzing the practical application, along with pros and cons of the above three solutions accordingly, we consider that a comprehensive utilization of these measures will be the best water strategy for America to solve the water shortage problem. Later we apply the conclusion to a case study of Southern California, we propose an optimization model which aims to minimize the total cost of the water strategy, and then use Lingo to solve the problem. Results show that 5.946 billion cubic meters of water by transferring is the best way to make up for the water use gap in 2025 and the total cost is 2.94 billion dollars. Finally, we summarize the strengths and weaknesses of our paper. What needs to be emphasized is that our model can also be adapted into the analysis of other states, which shows great flexibility.

In order to solve the problem of monitoring dust concentration, the paper applies the charge induction method in the design of the monitoring sensor. Specifically, the dust concentration can be characterized by the induction voltage of the sensor which is located in the high-voltage alternating electric field. In particular, the paper introduces the structure of the sensor and analyses the basic principles and feasibility of the measuring equipment. Furthermore, couples of simulation test is conducted based on the test platform. According to the test results, the monitoring sensor performs well when the dust concentration is between 0.81g/m3 and 1.99g/m3, in which the maximum measurement error is only 1.8%. Undoubtedly, the method used for the dust concentration monitoring has a wide prospect in industrial application.

Papain and alcalase were successively used to hydrolyze grass carp scales for preparation of crude polysaccharide. Chondroitin sulfate were further purified by DEAE-Sepharose Fast Flow and Sepharose 6 Fast Flow gel column chromatograph, and indentified by IR, NMR, element analysis and chemical analysis. Three polysaccharide fractions including chondroitin sulfate were fistly obtained from grass carp scales. The weight-average molecular weight of chondroitin sulfate was 46168 Da. The contents of various elements (C, H, N, S, O) in chondroitin sulfate were 28.89 %, 3.80 %, 3.36 %, 6.71 % and 47.16 %, respectively. The results of IR, NMR and chemical analysis exhibited that the sulfate group in chondroitin sulfate be O-linked in position 4 of N-acetyl-D-galactosamine.

Recent research data on chondroitin sulfate have suggested that they have many new biological functions such as anti-atherogenesis, anticoagulation, prevention and cure of arthritis, protection and repair of neuron during CNS development, morphogenesis and cell division. They are widely applied to functional food, clinical medicine and biomaterial. However, these bioactivities and applications are dependent on their fine structure with different monosaccharide unit and sulfation patterns. This review provides information on intimate relationship between these biological functions and the structure. Moreover, we overviewed the newly analytical methods about the composition and fine structure of them, including chromatometry, high performance liquid chromatography, mass spectrum and nuclear magnetic resonance before and after enzymolysis.

PANX2 forms large-pore channels mediating ATP release in response to physiological and pathological stimuli. Although PANX2 shows involvements in glioma genesis, the underlying mechanism remains unclear. PANX2 mRNA expression was analyzed via Oncomine and was confirmed via Gene Expression Profiling Interactive Analysis (GEPIA). The influence of PANX2 on overall survival (OS) of glioma was evaluated using LinkedOmics and further assessed through Cox regression analysis. The correlated genes with PANX2 acquired from LinkedOmics were validated through GEPIA and cBioPortal. Protein-protein interaction (PPI) of these genes was then obtained using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with MCODE plug-in. All the PANX2-related genes underwent Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The correlation between PANX2 and cancer immune infiltrates was evaluated via Tumor Immune Estimation Resource (TIMER). A higher expression of PANX2 only revealed a better OS in brain low grade glioma (LGG). PANX2-related genes in LGG functionally enriched in neuroactive ligand-receptor interaction, synaptic vesicle cycle, and calcium signaling. The hub genes from highest module of PPI were mainly linked to chemical synaptic transmission, plasma membrane, neuropeptide, and the pathway of neuroactive ligand-receptor interaction. Besides, PANX2 expression was negatively associated with infiltrating levels of macrophage, dendritic cells, and CD4+ T cells. This study demonstrated that PANX2 likely participated in LGG pathogenesis by affecting multiple molecular pathways and immune-related processes. PANX2 was associated with LGG prognosis and might become a promising therapeutic target of LGG.