Dataset Information

Key Factors Influencing Rapid Development of Potentially Dune-Stabilizing Moss-Dominated Crusts.

ABSTRACT: Biological soil crusts (BSCs) are a widespread photosynthetic ground cover in arid and semiarid areas. They have many positive ecological functions, such as increasing soil stability, and reducing water and wind erosion. Using artificial technology to achieve the rapid development of BSCs is expected to become a low-cost and highly beneficial ecological restoration measure. In the present study, typical moss-dominated crusts in a region characterized by mobile dunes (Mu Us Sandland, China) were collected, and a 40-day cultivation experiment was performed to investigate key factors, including watering frequency, light intensity and a nutrient addition, which affect the rapid development of moss crusts and their optimal combination. The results demonstrated that watering frequency and illumination had a significant positive effect (P=0.049, three-factor ANOVA) and a highly significant, complicated effect (P=0.000, three-factor ANOVA), respectively, on the plant density of bryophytes, and a highly significant positive effect on the chlorophyll a and exopolysaccharide contents (P=0.000, P=0.000; P=0.000, P=0.000; one-way ANOVA). Knop nutrient solution did not have a significant positive but rather negative effect on the promotion of moss-dominated crust development (P=0.270, three-factor ANOVA). Moss-dominated crusts treated with the combination of moderate-intensity light (6,000 lx) + high watering frequency (1 watering/2 days) - Knop had the highest moss plant densities, while the treatment with high-intensity light (12,000 lx) + high watering frequency (1 watering/2 days) + Knop nutrient solution had higher chlorophyll a contents than that under other treatments. It is entirely feasible to achieve the rapid development of moss crusts under laboratory conditions by regulating key factors and creating the right environment. Future applications may seek to use cultured bryophytes to control erosion in vulnerable areas with urgent needs.

SUBMITTER: Bu C

PROVIDER: S-EPMC4521833 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Key Factors Influencing Rapid Development of Potentially Dune-Stabilizing Moss-Dominated Crusts.

Bu Chongfeng C Zhang Kankan K Zhang Chunyun C Wu Shufang S

PloS one 20150731 7

Biological soil crusts (BSCs) are a widespread photosynthetic ground cover in arid and semiarid areas. They have many positive ecological functions, such as increasing soil stability, and reducing water and wind erosion. Using artificial technology to achieve the rapid development of BSCs is expected to become a low-cost and highly beneficial ecological restoration measure. In the present study, typical moss-dominated crusts in a region characterized by mobile dunes (Mu Us Sandland, China) were ...[more]

PMID: 26230324

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Project description:Background & aimsProgressive fibrosis has been identified as the major predictor of mortality in patients with non-alcoholic fatty liver disease (NAFLD). Several biomarkers are currently being evaluated for their ability to substitute the liver biopsy as the reference standard. Recent clinical studies in NAFLD/NASH patients support the utility of PRO-C3, a marker of type III collagen formation, as a marker for the degree of fibrosis, disease activity, and effect of treatment. Here we establish the healthy reference range, optimal sample handling conditions for both short- and long-term serum storage, and robustness for the PRO-C3 assay.MethodsPRO-C3 was measured in 269 healthy volunteers and in 222 NAFLD patients. Robustness of the PRO-C3 assay was measured according to Clinical and Laboratory Standards Institute standards and included validation of interference, precision, and reagent stability, whilst sample stability was defined for storage at different temperatures and for 3 freeze-thaw cycles. Fibrosis scoring was based on histological assessments and used as a reference for the diagnostic ability of PRO-C3 to discriminate between patients with different levels of fibrosis.ResultsRobustness of the PRO-C3 analysis validated by interference, precision, and reagent stability was found to be within the predefined acceptance criteria. The healthy reference range was determined to be 6.1-14.7 ng/ml. Levels of PRO-C3 were not affected by sex, age, BMI, or ethnicity. Levels of PRO-C3 were able to identify patients with clinically significant fibrosis and advanced fibrosis (AUC = 0.83 (95% CI [0.77-0.88], p <0.0001), and AUC = 0.79 (95% CI [0.73-0.85], p <0.0001), respectively).ConclusionsThe assay proved to be robust and sample stability was found to comply with hospital sample handling requirements. PRO-C3 measured in samples from patients with NAFLD/NASH was diagnostic for significant and advanced liver fibrosis.Lay summaryWe showed that PRO-C3 levels were stable under conditions conforming with hospital sample-handling requirements. We determined a healthy reference range and showed that PRO-C3 levels were not associated with sex, age, BMI, or ethnicity. Finally, we provide further evidence of an association of PRO-C3 with increasing liver fibrosis.

Project description:MotivationDeep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated.ResultsWe analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global coevolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from MSAs alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction.Availability and implementationhttps://github.com/multicom-toolbox/DNCON2/.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Key Factors Influencing Rapid Development of Potentially Dune-Stabilizing Moss-Dominated Crusts.

Publications

Key Factors Influencing Rapid Development of Potentially Dune-Stabilizing Moss-Dominated Crusts.

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