MicroRNAs in the Pathogenesis of Ankylosing Spondylitis and their Clinical Implication.

Ankylosing spondylitis (AS) is a chronic and progressive immunoinflammatory disease, which mainly affects the spine and sacroiliac joints and shows a high rate of late disability. Inflammation, bone destruction, and new bone formation are typical pathological changes of AS. AS is dominated by inflammation at the early stage. While bone destruction and heterotopic ossification, the two contradictory manifestations of AS, occur at a later stage and reflect the imbalance between osteogenesis and osteoclastogenesis in AS patients. Till now, the pathogenesis of AS remains unclear. MicroRNAs (miRNAs) are a class of highly conserved single-stranded noncoding RNAs (ncRNAs) with a length of about 22 bases characterized by temporal sequence and tissue specificity. MiRNAs are key modulators in bone formation, resorption, remodeling and regeneration by regulating the immune responses and the differentiation and functions of osteoblasts, osteoclasts and chondrocytes. The present review summarizes the roles and potential mechanisms of miRNAs' involvement in AS by regulating immuno-inflammatory responses, bone destruction, heterotopic ossification, cell death and autophagy, and the involved signaling including the Wnt/ß-catenin and BMP/Smads pathways. In addition, the feasibility of miRNAs as diagnostic biomarkers and therapeutic targets for AS are also discussed.

Multiple imputation of maritime search and rescue data at multiple missing patterns.

Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns. Probability density curves and overimputation diagnostics were used to explore the effects of multiple imputation. The results showed that the Data Augmentation (DA) algorithm had the characteristics of high operation efficiency and good imputation effect, but the algorithm was not suitable for data imputation when there was a high data missing rate. The EMB algorithm effectively restored the distribution of datasets with different data missing rates, and was less affected by the missing position; the EMB algorithm could obtain a good imputation effect even when there was a high data missing rate. Overimputation diagnostics could not only reflect the data imputation effect, but also show the correlation between different datasets, which was of great importance for deep data mining and imputation effect improvement. The Expectation-Maximization with Bootstrap (EMB) algorithm had a poor estimation effect on extreme data and failed to reflect the dataset's variability characteristics.

Glutathione S-transferase influences the fecundity of Schistosoma japonicum.

The aim of this study was to investigate the effect of Schistosoma japonicum glutathione S-transferase (SjGST) on the developmental stages of the parasite. We found that the mRNA levels of GST were higher in schistosomula obtained from the host and the eggs than that in other developmental stages. SjGST was mainly distributed in the egg shells, teguments of the worms, and part of the parenchyma of the worms. GST knockdown with RNA interference in S. japonicum worms resulted in a silencing rate higher than 80%. The egg reduction rate (18%) and abnormal egg ratio (28%) were significantly higher (P < 0.05) in the GST-silenced group than in the negative control group. These results indicate that SjGST plays an important role in the fecundity of S. japonicum, specifically in egg formation.

A systematic assessment of watershed-scale nonpoint source pollution during rainfall-runoff events in the Miyun Reservoir watershed.

The assessment of peak flow rate, total runoff volume, and pollutant loads during rainfall process are very important for the watershed management and the ecological restoration of aquatic environment. Real-time measurements of rainfall-runoff and pollutant loads are always the most reliable approach but are difficult to carry out at all desired location in the watersheds considering the large consumption of material and financial resources. An integrated environmental modeling approach for the estimation of flash streamflow that combines the various hydrological and quality processes during rainstorms within the agricultural watersheds is essential to develop targeted management strategies for the endangered drinking water. This study applied the Hydrological Simulation Program-Fortran (HSPF) to simulate the spatial and temporal variation in hydrological processes and pollutant transport processes during rainstorm events in the Miyun Reservoir watershed, a drinking water resource area in Beijing. The model performance indicators ensured the acceptable applicability of the HSPF model to simulate flow and pollutant loads in the studied watershed and to establish a relationship between land use and the parameter values. The proportion of soil and land use was then identified as the influencing factors of the pollution intensities. The results indicated that the flush concentrations were much higher than those observed during normal flow periods and considerably exceeded the limits of Class III Environmental Quality Standards for Surface Water (GB3838-2002) for the secondary protection zones of the drinking water resource in China. Agricultural land and leached cinnamon soils were identified as the key sources of sediment, nutrients, and fecal coliforms. Precipitation volume was identified as a driving factor that determined the amount of runoff and pollutant loads during rainfall processes. These results are useful to improve the streamflow predictions, provide useful information for the identification of highly polluted areas, and aid the development of integrated watershed management system in the drinking water resource area.

Uncertainty analysis for an effluent trading system in a typical nonpoint-sources-polluted watershed.

Conventional effluent trading systems (ETSs) between point sources (PSs) and nonpoint sources (NPSs) are often unreliable because of the uncertain characteristics of NPSs. In this study, a new framework was established for PS-NPS ETSs, and a comprehensive analysis was conducted by quantifying the impacts of the uncertainties associated with the water assimilative capacity (WAC), NPS emissions, and measurement effectiveness. On the basis of these results, the uncertain characteristics of NPSs would result in a less cost-effective PS-NPS ETS during most hydrological periods, and there exists a clear transition occurs from the WAC constraint to the water quality constraint if these stochastic factors are considered. Specifically, the emission uncertainty had a greater impact on PSs, but an increase in the emission or abatement uncertainty caused the abatement efforts to shift from NPSs toward PSs. Moreover, the error transitivity from the WAC to conventional ETS approaches is more obvious than that to the WEFZ-based ETS. When NPSs emissions are relatively high, structural BMPs should be considered for trading, and vice versa. These results are critical to understand the impacts of uncertainty on the functionality of PS-NPS ETSs and to provide a trade-off between the confidence level and abatement efforts.

Evaluating the impacts of soil data on hydrological and nonpoint source pollution prediction.

Soil data are one key input for most hydrological and nonpoint source (H/NPS) models, and quantifying the error transmission from soil data to H/NPS predictions is of great importance. In this study, two typical soil datasets were compared using the Soil and Water Assessment Tool (SWAT) in a typical mountainous watershed, the Three Gorges Reservoir Region, China. Besides, the effects of soil data resolution were evaluated, and the error transmission from soil data to watershed management strategy was assessed. The results indicate that model outputs are not sensitive to changes of soil data resolution but the choice of soil data greatly impacts the application of watershed models, in terms of the goodness-of-fit indicator, predicted data and related uncertainty. This soil data-induced error would be inevitably magnified from the flow simulation to the NPS prediction stage. This study could indicate that the choice of soil data will lead to significant differences in management schemes for specific pollution periods. These results provide information on the impacts of soil data on the functionality of watershed models and valuable information for the appropriateness of each soil database.

Using site-specific soil samples as a substitution for improved hydrological and nonpoint source predictions.

Soil databases are one of the most important inputs for watershed models, and the quality of soil properties affects how well a model performs. The objectives of this study were to (1) quantify the sensitivity of model outputs to soil properties and to (2) use site-specific soil properties as a substitution for more accurate hydrological and nonpoint source (H/NPS) predictions. Soil samples were collected from a typical mountainous watershed in China, and the impacts of soil sample parameters on H/NPS predictions were quantified using the Soil and Water Assessment Tool (SWAT). The most sensitive parameters related to predicting flow, sediment, and total phosphorus (TP) mainly were the soil hydrological, the channel erosion processes, and the initial soil chemical environment, respectively. When the site-specific soil properties were used, the uncertainties (coefficient of variation) related to predicting the hydrology, sediment and TP decreased by 75∼80 %, 75∼84 %, and 46∼61 %, respectively. Based on changes in the Nash-Sutcliff coefficient, the model performance improved by 4.9 and 19.45 % for the hydrological and sediment model, accordingly. However, site-specific soil properties did not contribute to better TP predictions because of the high spatial variability of the soil P concentrations across the large watershed. Thus, although site-specific soil samples can be used to obtain more accurate H/NPS predictions, more sampling sites are required to apply this method in large watersheds.

Targeting priority management areas for multiple pollutants from non-point sources.

The control of multiple pollutants from non-point sources is very difficult because their loss potentials are not consistent on the same spatial distributions. In this research, an innovative approach was established for multiple-pollutant priority management areas (MP-PMAs). In the new framework, the MP-PMA approach focused on the sensitive areas that contributed a variety of pollutants instead of a specific targeted pollutant by integrating a watershed model and a Pareto-based multi-criteria evaluation approach. Based on the results, multiple levels of MP-PMAs were established with respect to the corresponding requirements of clean water statutes. Compared to traditional separate strategies, the MP-PMA approach would lead to more cost-effective watershed management because those moderate-level PMAs for specific targeted pollutant might be the high-level MP-PMAs. With respect to spatial distribution, the MP-PMA approach provided more accurate target results for the high-level PMAs, especially among the headwater areas. From a scientific view, the MP-PMA approach provides an integrated suggestion for the placement and removal potentials of best management practices at the watershed scale.