Selection of optimal primer pairs in 16S rRNA gene sequencing is a pivotal issue in microorganism diversity analysis. However, limited effort has been put into investigation of specific primer sets for analysis of the bacterial diversity of aging flue-cured tobaccos (AFTs), as well as prediction of the function of the bacterial community. In this study, the performance of four primer pairs in determining bacterial community structure based on 16S rRNA gene sequences in AFTs was assessed, and the functions of genes were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). Results revealed that the primer set 799F–1193R covering the amplification region V5V6V7 gave a more accurate picture of the bacterial community structure of AFTs, with lower co-amplification levels of chloroplast and mitochondrial genes, and more genera covered than when using the other primers. In addition, functional gene prediction suggested that the microbiome of AFTs was involved in kinds of interested pathways. A high abundance of functional genes involved in nitrogen metabolism was detected in AFTs, reflecting a high level of bacteria involved in degrading harmful nitrogen compounds and generating nitrogenous nutrients for others. Additionally, the functional genes involved in biosynthesis of valuable metabolites and degradation of toxic compounds provided information that the AFTs possess a huge library of microorganisms and genes that could be applied to further studies. All of these findings provide a significance reference for researchers working on the bacterial diversity assessment of tobacco-related samples.Electronic supplementary materialThe online version of this article (10.1186/s13568-018-0713-1) contains supplementary material, which is available to authorized users.
Purpose
Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reduction (MAR) techniques segment out the metal regions and estimate the corrupted projection data by various interpolation methods. However, interpolations are not accurate and introduce new artifacts or even deform the teeth in the reconstructed image. This work presents a new strategy to take advantage of the power of deep learning for metal artifact reduction.
Method
The analysis first uses coarse reconstructions from simulated locally interpolated data affected by metal fillings as a starting point. A deep learning network is then trained using the simulated data and applied to practical data. Thus, an easily implemented three‐step MAR method is formed: Firstly, use the acquired projection data to create a preliminary image reconstruction with linearly interpolated data for the metal‐related projections. Secondly, a deep learning network is used to remove the artifacts from the linear interpolation and recover the nonmetal region information. Thirdly, the method adds the ROI reconstruction of the metal regions. The structures behind the shading artifacts in the direct filtered back‐projection (FBP) reconstruction can be partially recovered by interpolation‐based MAR (I‐MAR) with the network further correcting for interpolation errors. The key to this method is that the linear interpolation reconstruction errors can be easily simulated to train a network and the effectiveness of the network can be easily generalized to I‐MAR results in real situations.
Results
We trained a network with a simulation dataset and validated the network against a separate simulation dataset. Then, the network was tested using simulation data that did not overlap with the training/validation datasets and real patient datasets. Both tests gave encouraging results with accurate tooth structure recovery and few artifacts. The relative root mean square error and structure similarity index method indexes were significantly improved in the tests. The method was also evaluated by two experienced dentists who gave positive evaluations.
Conclusions
This work presents a strategy to build a transferable learning from simulations to practical systems for metal artifact reduction using a supervised deep learning method. The system transforms the MAR analyses to an interpolation‐artifact reduction problem to recover structural details from the coarse interpolation reconstruction. In this way, training data from simulations with ground truth labels can easily model the similar features in real data with I‐MAR as the bridge. The network can seamlessly optimize both simulations and real data. The whole method is easily implemented with little computational cost. Test results demonstrated that this is an effective MAR method applicable to practical dental CT systems.
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