Suchergebnisse

Abstract
Background
The soil P leaching change point (CP) has been widely used to evaluate soil P leaching risk. However, an automation calculation method for soil P leaching CP value, and an effective risk grading method performed for classifying soil P leaching risk evaluation have not been developed.

Results
This study optimized the calculation process for soil P leaching CP value with two different fitting models. Subsequently, based on the Python programming language, a computation tool named Soil Phosphorus Leaching Risk Calculator (SPOLERC) was developed for soil P leaching risk assessment. SPOLERC not only embedded the calculation process of the soil P leaching CP value, but also introduced the single factor index (SFI) method to grade the soil P leaching risk level. The relationships between the soil Olsen-P and leachable P were fitted by using SPOLERC in paddy soils and arid agricultural soils in the Xingkai Lake Basin, and the results showed that there was a good linear fitting relationship between the soil Olsen-P and leachable P; and the CP values were 59.63 and 35.35 mg Olsen-P kg−1 for paddy soils and arid agricultural soils, respectively. Additionally, 32.7, 21.8, and 3.64% of arid agricultural soil samples were at low risk, medium risk, and high risk of P leaching, and 40.6% of paddy soil samples were at low risk.

Conclusions
SPOLERC can accurately fit the split-line model relationship between the soil Olsen-P and leachable P, and greatly improved the calculation efficiency for the soil P leaching CP value. Additionally, the obtained CP value can be used for soil P leaching risk assessment, which could help recognize key area of soil P leaching.

In this study, we investigated hepatitis C virus (HCV) molecular epidemiology and evolutionary dynamics. Both E1 and NS5B sequences were characterized in 379 of 433 patients in southern China and classified into five major subtypes: 1b in 256 patients, 6a in 67 patients, 2a in 29 patients, 3a in 14 patients, and 3b in 13 patients. Using the E1 sequences obtained, along with those from other studies using samples from China, we inferred the HCV epidemic history by means of coalescence strategies. Five Bayesian skyline plots (BSPs) were estimated for the five subtypes. They concurrently highlighted the rapid growth in the HCV-infected population size from 1993 to 2000, followed by an abrupt slowing. Although flanked on both sides by variable population sizes, the plots showed distinct patterns of rapid HCV growth. Coincidently, 1993 to 2000 was a period when contaminated blood transfusions were common in China due to a procedural error in an officially encouraged plasma campaign. The abrupt slowing in 1998 to 2000 corresponded to the central government outlawing paid blood donations in 1998. Using a parametric model, the HCV population growth rates were estimated during 1993 to 2000. It was revealed that the 6a rate was the highest, followed by those of 1b, 2a, 3b, and 3a. Because these rates differed significantly (P < 1e−9) from each other, they may help explain why 6a is increasingly prevalent in southern China and 1b is predominant nationwide. These rates are approximately 10-fold higher than those reported elsewhere. These findings suggested that during the plasma campaign, certain barriers to efficient viral transmission were removed, allowing wide HCV dissemination.