Ferns are important components of plant communities on earth, but their genomes are generally very large, with many redundant genes, making whole genome sequencing of ferns prohibitively expensive and time-consuming. This means there is a significant lack of fern reference genomes, making molecular biology research difficult. The gametophytes of ferns can survive independently, are responsible for sexual reproduction and the feeding of young sporophytes, and play an important role in the alternation of generations. For this study, we selected Adiantum flabellulatum as it has both ornamental and medicinal value and is also an indicator plant of acidic soil. The full-length transcriptome sequencing of its gametophytes was carried out using PacBio three-generation sequencing technology. A total of 354,228 transcripts were obtained, and 231,705 coding sequences (CDSs) were predicted, including 5,749 transcription factors (TFs), 2,214 transcription regulators (TRs) and 4,950 protein kinases (PKs). The transcripts annotated by non-redundant protein sequence database (NR), Kyoto encyclopedia of genes and genomes (KEGG), eukaryotic ortholog groups (KOG), Swissprot, protein family (Pfma), nucleotide sequence database (NT) and gene ontology (GO) were 251,501, 197,474, 193,630, 194,639, 195,956, 113,069 and 197,883, respectively. In addition, 138,995 simple sequence repeats (SSRs) and 111,793 long non-coding RNAs (lncRNAs) were obtained. We selected nine chlorophyll synthase genes for qRT-PCR, and the results showed that the full-length transcript sequences and the annotation information were reliable. This study can provide a reference gene set for subsequent gene expression quantification.
Abstract Background Presently, the field of differential expression analysis of RNA-seq data is still in its infancy with new approaches constantly being proposed. Combining deep neural networks to explore gene expression information in RNA-seq data provides a novel possibility in this field. Results This study focuses on the development of a deep neural network-based RNA-seq gene expression analysis model, named Deep-cloud. Its main advantage is not only the excellent construction of deep learning network models using convolutional neural network and long short-term memory to predict the gene expression of RNA-seq, but also the more in-depth analysis of the differential gene expression between disease and normal groups of transcriptome data by combining the statistical methods of cloud model. Conclusion Comparing the results of this data analysis with those obtained by traditional differential gene analysis software such as DESeq2 and edgeR, the Deep-cloud model further improves the sensitivity of obtaining differential expressed genes. Overall, the proposed Deep-cloud paves a new pathway for the biomedical field.
The knowledge-based product design was analyzed in this paper firstly. Aimed the shortages that the knowledge of the existing system for product design was close and the distributed knowledge couldn't be integrated and shared, a novel distributed knowledge management system framework for product design was proposed. The main layers were studied and the key technologies of system were discussed. Ontology-based knowledge share is proposed. Knowledge ontology and knowledge ontology model are defined in terms of the design knowledge's characteristic. A novel model searching method based on ontology is proposed, where the appropriate parts model was searched through EDCOM array and similarity. Finally a distributed knowledge management system is developed on the basis of prior analysis and about an example about eddy current retarder design based on the prototype system is given.
Abstract Switched inertance hydraulic converters (SIHC) are new digital hydraulic devices which provide an alternative to conventional proportional or servo valve-controlled systems in hydraulic fluid power. SIHCs can adjust and control flow and pressure by means of using digital control signals that do not rely on throttling the flow and dissipation of power, and provide hydraulic systems with high-energy efficiency, good controllability, and insensitivity to contamination. A flow booster is one configuration of SIHCs which can deliver more flow than the supply flow. In this article, the loading effects of SIHCs are investigated by applying a time-varying load on the flow booster. A control system consisting of a PI controller and a switching frequency optimizer was designed to operate a flow booster at its optimal switching frequencies and switching ratios to maximize system efficiency when the load varies. Simulated results showed that the flow booster with the proposed controller has very good dynamic response and can be operated at an average efficiency of 70% with a time-varying load. Compared with only using a PI controller, the proposed controller can improve the overall efficiency by up to 20%. As time-varying loading conditions are commonly found in hydraulic applications, this work constitutes an important contribution to the design and development of high-efficiency SIHCs.
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