Naphthylacetic acid (NAA) was used to increase the tuberous root yield of Rehmannia glutinosa, but the differences between its NAA-treated and control tuberous roots (NT and CG) and the regulatory mechanism of NAA effect remain unclear. In order to investigate them, NTs and CGs were used as materials, and both yield-related indices were measured; the metabolomics and transcriptomics were used to capture differentially accumulated metabolites (DAM) and to validate them via mining differentially expressed genes (DEGs), respectively. The effects of NAA treatment: increased NT mass per plant by 21.14%, through increasing the number of roots and increasing the mean root diameter; increased catalpol content by 1.2234% (p < 0.05); up-regulated 11DAMs and 596DEGs; and down-regulated 18 DAMs and 517DEGs. In particular, we discovered that NAA regulated its DAMs and biomass via 10 common metabolic pathways, and that the number of NAA-down-regulated DAMs was more than that of NAA-up-regulated DAMs in its tuberous root. Furthermore, HPLC validated the changes of several DAMs and 15 DEGs (4CL, ARF, CCoAOMT, ARGOS, etc.) associated with the yield increase and DAMs were verified by RT-qPCR. This study provided some valuable resources, such as tuberous root indices, key genes, and DAMs of Rehmannia glutinosa in response to NAA for distinguishing the CGs from NTs, and novel insights into the regulatory mechanism of NAA effects on both at the transcriptomic and metabolomic levels, so it will lay a theoretical foundation for NAA-regulated plant yield and quality, and provide references for prohibiting the uses of NAA as a swelling agent in medicinal tuber plants in China.
Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g., Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and Implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp Contact chen@haplox.com
Rehmannia glutinosa root contains many compounds with important medicinal properties and nutritional benefits, but only more than 140 compounds have been reported so far. Many other compounds and their accumulation and metabolic networks during its development remain unclear. In order to clarify them, its metabolic profiles at three different developmental stages were analyzed using untargeted LC-MS analysis. Multivariate analysis revealed that 434 metabolites differently accumulated in its different stages, suggesting different change trends. The metabolites having the same trend share common metabolic pathways, the metabolites showing increasing contents during its development have medical and nutritional values, and some mature root-specific metabolites may be better candidates for its quality control; 434 metabolites were mapped onto 111 KEGG pathways including 62 enzymes, whose increasing and decreasing patterns were shown during its development. Some metabolites complicatedly interacted with some enzymes and the top-10 pathways enriched from 111 KEGG pathways in network analysis. These findings extended the dataset of its identified compounds, and revealed that its development and quality were associated with the accumulation of different metabolites. Our work will lay the foundation for the better understanding of its chemical constituents, quality and developmental mechanism.
Mycoplasma hyopneumoniae is the etiological agent of swine enzootic pneumonia (EP), which resulting in considerable economic losses in pig farming globally. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is a major tool for gene expression studies. However, no internal reference genes for normalization of RT-qPCR data of M. hyopneumoniae have been reported. The aim of this study was to screen the most stable genes for RT-qPCR analysis in M. hyopneumoniae under different conditions. Therefore, a total of 13 candidate internal reference genes ( rpoC, Lipo, sgaB, oppB, hypo621, oppF, gyrB, uvrA, P146, prfA, proS, gatB , and hypo499 ) of M. hyopneumoniae filtered according to the reported quantitative proteomic analysis and the 16S rRNA internal reference gene frequently used in other bacteria were selected for RT-qPCR analysis. The mRNAs from different virulence strains (168, 168 L, J, NJ, and LH) at five different growth phases were extracted. The corresponding cycle threshold (Ct) values of the 25 reverse transcribed cDNAs using the 14 candidate genes were determined. Different internal reference genes or combinations were then screened for expression stability analysis using various statistical tools and algorithms, including geNorm, BestKeeper, and NormFinder software, to ensure the reliability of the analysis. Through further comprehensive evaluation of the RefFinder software, it is concluded that the gatB gene was the most suitable internal reference gene for samples of the different virulence strains in different growth phases for M. hyopneumoniae , followed by prfA, hypo499 , and gyrB .
The genomic DNAs were extracted by CTAB method from the young leaves of 16 individuals of Rehmannia Glutinosa Libosh.f.Hueichingensis(Chao et Schih) Hsiao cultivar 85-5,whose purities and sizes were tested by UV method and agrose gel electrophoresis,respectively.Genetic diversity within cultivar 85-5 was assessed by RAPD and ISSR Markers.Three RAPD primers and two ISSR primers were selected from twenty-four RAPD ones and from forty-three ISSR ones to generate DNA fingerprints of these 16 individuals.Seven fragments were amplified with the three RAPD primers,four of which were polymorphic.Polymorphic percentage was 57.14%.Seventeen fragments were amplified with the two ISSR primers,eleven of which were polymorphic.Polymorphic percentage was 64.7%.The results showed that genetic difference occurred within cultivar 85-5,but genetic diversity was lower than that among different Rehmannia Glutinosa Libosh cultivars.
Though Shandong Province has achieved brilliant success in the construction of energy sources, there are still a few problems in the exploitation and utilization of energy sources. This paper advances some corre-sponding countermeasures against these problems.
Cartoon-style pictures can be seen almost everywhere in our daily life. Numerous applications try to deal with cartoon pictures, a dataset of cartoon pictures will be valuable for these applications. In this paper, we first present ToonNet: a cartoon-style image recognition dataset. We construct our benchmark set by 4000 images in 12 different classes collected from the Internet with little manual filtration. We extend the basal dataset to 10000 images by adopting several methods, including snapshots of rendered 3D models with a cartoon shader, a 2D-3D-2D converting procedure using a cartoon-modeling method and a hand-drawing stylization filter. Then, we describe how to build an effective neural network for image semantic classification based on ToonNet. We present three techniques for building the Deep Neural Network (DNN), namely, IUS: Inputs Unified Stylization, stylizing the inputs to reduce the complexity of hand-drawn cartoon images; FIN: Feature Inserted Network, inserting intuitionistic and valuable global features into the network; NPN: Network Plus Network, using multiple single networks as a new mixed network. We show the efficacy and generality of our network strategies in our experiments. By utilizing these techniques, the classification accuracy can reach 78% (top-1) and 93%(top-3), which has an improvement of about 5% (top-1) compared with classical DNNs.
Human parametric models can provide useful constraints for human shape estimation to produce more accurate results. However, the state-of-art models are computational expensive which limit their wide use in interactive graphics applications. We present PROME (PROjected MEasures) - a novel human parametric model which has high expressive power and low computational complexity. Projected measures are sets of 2D contour poly-lines that capture key measure features defined in anthropometry. The PROME model builds the relationship between 3D shape and pose parameters and 2D projected measures. We train the PROME model in two parts: the shape model formulates deformations of projected measures caused by shape variation, and the pose model formulates deformations of projected measures caused by pose variation. Based on the PROME model we further propose a fast shape estimation method which estimates the 3D shape parameters of a subject from a single image in nearly real-time. The method builds an optimize problem and solves it using gradient optimizing strategy. Experiment results show that the PROME model has well capability in representing human body in different shape and pose comparing to existing 3D human parametric models, such as SCAPE[Anguelov et al. 2005] and TenBo[Chen et al. 2013], yet keeps much lower computational complexity. Our shape estimation method can process an image in about one second, orders of magnitude faster than state-of-art methods, and the estimating result is very close to the ground truth. The proposed method can be widely used in interactive applications such as virtual try-on and virtual reality collaboration.
The genetic diversity of 23 R.glutinosa germplasm resources was analyzed by SRAP to accurately identify R.glutinosa and provide the basis for its breeding.The results showed that: 1) 310 polymorphic loci were amplified from 13 pairs of primers(23.8 polymorphic loci per one pair of primers) and the polymorphic primer ratio reached 91.71%.The Jaccard genetic similarity coefficient was 0.335~0.703;2) 23 R.glutinosa varieties could be divided into four groups;3) The result from principle component analysis accorded with that from cluster analysis.
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