Restructured fungal community diversity and biological interactions promote metolachlor biodegradation in soil microbial fuel cells.

Soil microbial fuel cells (MFCs) provide an inexhaustible electron acceptor for the removal of metolachlor and in situ biocurrent stimulation for fungal activity was investigated. The metolachlor degradation rates enhanced by 33%-36% upon the introduction of electrodes after 23 d. In closed MFCs, the abundance of Mortierella as the most dominant genus increased to 43%-54% from 17% in the original soil, whereas those of Aphanoascus and Penicillium decreased to 0.24%-0.39% and 0.38-0.72% from 14% to 11%, respectively. Additionally, a 10-fold amplification of unique OTUs was observed, mainly from increase on the electrode surface. The different treatments were clustered, especially samples near the cathode. The linear discriminant analysis showed that Aphanoascus fulvescens acted as a biomarker between the original and treated soils. The co-occurrence networks demonstrated that Mortierella universally competed for growth with coexisting species while Cladosporium exhibited the most affiliations with species from the 36 other genera present. The correlation analysis indicated that the species associated with degradation belonged to Mortierella, Kernia, Chaetomium and Trichosporon, while the species associated with electrogenesis were Debaryomyces hansenii and Mortierella polycephala. Importantly, this study is the first to reveal fungal community structure in soil MFCs with degrading pollutants and producing electricity.

Smooth bootstrap methods for analysis of longitudinal data.

In analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the 'sandwich' method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain 'bootstrapped' realizations of the parameter estimates for statistical inference. Our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage. We applied the proposed method to a data set from a clinical trial of antibiotics for leprosy.