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The problems of acid mine drainage (AMD) in coal mine acidic wastewaters arise from a range of sources, including severe pollution with heavy metals and SO42- and difficulties during treatment. Based on the ability of Maifan stone to adsorb heavy metals and the dissimilatory reduction of SO42- by sulfate-reducing bacteria (SRB), Maifan stone-sulfate-reducing bacterium-immobilized particles were prepared via immobilization techniques using Shandong Maifan stone as the experimental material. The effects of Maifan stones containing SRB on mitigating AMD were investigated by constructing Dynamic Column 1 with Maifan stone-sulfate-reducing bacterium-immobilized particles and by constructing Dynamic Column 2 with SRB mixed with Maifan stones. By the use of adsorption isotherms, adsorption kinetics, a reduction kinetics model and X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies, the mechanism by which Maifan stone-sulfate-reducing bacterium-immobilized particles mitigate AMD was revealed. The results showed that the total effect of Maifan stone-sulfate-reducing bacterium-immobilized particles on AMD was better than that of biological Maifan stone carriers. The highest rates for the removal of Fe2+, Mn2+, and SO42- in AMD were 90.51%, 85.75% and 93.61%, respectively, and the pH value of the wastewater increased from 4.08 to 7.64. The isotherms for the adsorption of Fe2+ and Mn2+ on Maifan stone-sulfate-reducing bacterium-immobilized particles conformed to the output of the Langmuir model. The adsorption kinetics were in accordance with Lagergren first-order kinetics, and the kinetics for the reduction of SO42- conformed to those of a first-order reaction model.

BackgroundThe wide adoption of electronic health records (EHR) system has provided vast opportunities to advance health care services. However, the prevalence of missing values in EHR system poses a great challenge on data analysis to support clinical decision-making. The objective of this study is to develop a new methodological framework that can address the missing data challenge and provide a reliable tool to predict the hospital readmission among Heart Failure patients.MethodsWe used Gaussian Process Latent Variable Model (GPLVM) to impute the missing values. Specifically, a lower dimensional embedding was learned from a small complete dataset and then used to impute the missing values in the incomplete dataset. The GPLVM-based missing data imputation can provide both the mean estimate and the uncertainty associated with the mean estimate. To incorporate the uncertainty in prediction, a constrained support vector machine (cSVM) was developed to obtain robust predictions. We first sampled multiple datasets from the distributions of input uncertainty and trained a support vector machine for each dataset. Then an optimal classifier was identified by selecting the support vectors that maximize the separation margin of a newly sampled dataset and minimize the similarity with the pre-trained support vectors.ResultsThe proposed model was derived and validated using Physionet MIMIC-III clinical database. The GPLVM imputation provided normalized mean absolute errors of 0.11 and 0.12 respectively when 20% and 30% of instances contained missing values, and the confidence bounds of the estimations captures 97% of the true values. The cSVM model provided an average Area Under Curve of 0.68, which improves the prediction accuracy by 7% as compared to some existing classifiers.ConclusionsThe proposed method provides accurate imputation of missing values and has a better prediction performance as compared to existing models that can only deal with deterministic inputs.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are an important class of genetic marker for target gene mapping. As of yet, there is no rapid and effective method to identify SNPs linked with agronomic traits in rapeseed and other crop species. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrate a novel method for identifying SNP markers in rapeseed by deep sequencing a representative library and performing bulk segregant analysis. With this method, SNPs associated with rapeseed pod shatter-resistance were discovered. Firstly, a reduced representation of the rapeseed genome was used. Genomic fragments ranging from 450-550 bp were prepared from the susceptible bulk (ten F2 plants with the silique shattering resistance index, SSRI 0.90), and also Solexa sequencing-produced 90 bp reads. Approximately 50 million of these sequence reads were assembled into contigs to a depth of 20-fold coverage. Secondly, 60,396 'simple SNPs' were identified, and the statistical significance was evaluated using Fisher's exact test. There were 70 associated SNPs whose -log(10)p value over 16 were selected to be further analyzed. The distribution of these SNPs appeared a tight cluster, which consisted of 14 associated SNPs within a 396 kb region on chromosome A09. Our evidence indicates that this region contains a major quantitative trait locus (QTL). Finally, two associated SNPs from this region were mapped on a major QTL region. CONCLUSIONS/SIGNIFICANCE: 70 associated SNPs were discovered and a major QTL for rapeseed pod shatter-resistance was found on chromosome A09 using our novel method. The associated SNP markers were used for mapping of the QTL, and may be useful for improving pod shatter-resistance in rapeseed through marker-assisted selection and map-based cloning. This approach will accelerate the discovery of major QTLs and the cloning of functional genes for important agronomic traits in rapeseed and other crop species.

The strong association between self-reported sleep difficulties and depressive symptoms is well documented. However, individuals who suffer from depressive symptoms could potentially interpret the values attached to a subjective scale differently from others, making comparisons of sleep difficulties across individuals with different depressive symptoms problematic. The objective of this study is to determine the existence and magnitude of reporting heterogeneity in subjective assessment of sleep difficulties by those who have depressive symptoms. We implement an online survey using Visual Analogue Scales and anchoring vignettes to study the comparability of subjective assessments of sleep difficulties among college students in Switzerland (N = 1, 813). Using multivariate linear regressions and double-index models, our analysis shows that reporting heterogeneity plays only a marginal role in moderating the association between sleep difficulties and depression, irrespective of the severity of the depressive symptoms of the individuals. This suggests that unadjusted comparisons of self-reported sleep difficulties between college students are meaningful, even among individuals with depressive symptoms.

To improve interpersonal comparability of self-reported measures, anchoring vignettes are increasingly collected in surveys and modeled as the hierarchical ordered probit (HOPIT) model. This paper-based on the idea of psychological distance-relaxes the assumption of vignette equivalence in the HOPIT by allowing for heteroscedasticity in respondents' perceptions of vignettes. Particularly, we assume that respondents who are more similar to a vignette are more familiar with the condition described and therefore are capable of forming a more precise perception of the vignette. We show evidence in favor of this extended HOPIT through Monte Carlo simulations and an application concerning self-reported vision difficulty from the WHO Study on Global Aging and Adult Health (SAGE).