Twisted Courant algebroids and coisotropic Cartan geometries

In this paper, we show that associated to any coisotropic Cartan geometry there is a twisted Courant algebroid. This includes in particular parabolic geometries. Using this twisted Courant structure, we give some new results about the Cartan curvature and the Weyl structure of a parabolic geometry. As more direct applications, we have Lie 2-algebra and 3D AKSZ sigma model with background associated to any coisotropic Cartan geometry

Genome-wide comparison of microRNAs and their targeted transcripts among leaf, flower and fruit of sweet orange

BACKGROUND: In plants, microRNAs (miRNAs) regulate gene expression mainly at the post-transcriptional level. Previous studies have demonstrated that miRNA-mediated gene silencing pathways play vital roles in plant development. Here, we used a high-throughput sequencing approach to characterize the miRNAs and their targeted transcripts in the leaf, flower and fruit of sweet orange. RESULTS: A total of 183 known miRNAs and 38 novel miRNAs were identified. An in-house script was used to identify all potential secondary siRNAs derived from miRNA-targeted transcripts using sRNA and degradome sequencing data. Genome mapping revealed that these miRNAs were evenly distributed across the genome with several small clusters, and 69 pre-miRNAs were co-localized with simple sequence repeats (SSRs). Noticeably, the loop size of pre-miR396c was influenced by the repeat number of CUU unit. The expression pattern of miRNAs among different tissues and developmental stages were further investigated by both qRT-PCR and RNA gel blotting. Interestingly, Csi-miR164 was highly expressed in fruit ripening stage, and was validated to target a NAC transcription factor. This study depicts a global picture of miRNAs and their target genes in the genome of sweet orange, and focused on the comparison among leaf, flower and fruit tissues. CONCLUSIONS: This study provides a global view of miRNAs and their target genes in different tissue of sweet orange, and focused on the identification of miRNA involved in the regulation of fruit ripening. The results of this study lay a foundation for unraveling key regulators of orange fruit development and ripening on post-transcriptional level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-695) contains supplementary material, which is available to authorized users

Two Small Molecules Restore Stability to a Sub-population of the Cystic Fibrosis Transmembrane conductance Regulator with the Predominant Disease-causing Mutation

Cystic fibrosis (CF) is caused by mutations that disrupt the plasma membraneexpression, stability, and function of the cystic fibrosis transmembrane conductance regulator (CFTR) Cl- channel. Two small molecules, the CFTR corrector lumacaftor and the potentiator ivacaftor, are now used clinically to treat CF, although some studies suggest that they have counteracting effects on CFTR stability. Here, we investigated the impact of these compounds on the instability of F508del-CFTR, the most common CF mutation. To study individual CFTR Cl- channels, we performed single-channel recording, whereas to assess entire CFTR populations, we used purified CFTR proteins and macroscopic CFTR Cl- currents. At 37 °C, low temperature–rescued F508del-CFTR more rapidly lost function in cell-free membrane patches, and showed altered channel gating andcurrent flow through open channels. Compared with purified wild-type CFTR, the full-length F508del-CFTR was about 10 °C less thermostable. Lumacaftor partially stabilized purified full-length F508del-CFTR and slightly delayed deactivation of individual F508del-CFTR Cl- channels. By contrast, ivacaftor further destabilized full-length F508del-CFTR and accelerated channel deactivation. Chronic (prolonged) co-incubation of F508del-CFTR– expressing cells with lumacaftor and ivacaftor deactivated macroscopic F508del-CFTR Cl-currents. However, at the single-channel level, chronic co-incubation greatly increased F508del-CFTR channel activity and temporal stability in most, but not all, cell-free membrane patches. We conclude that chronic lumacaftor andivacaftor co-treatment restores stability in a small sub-population of F508del-CFTR Cl- channels, but that the majority remain destabilized. The fuller understanding of these effects and the characterization of the small F508del-CFTR subpopulation might be crucial for CF therapy development

Human Promoter Recognition Based on Principal Component Analysis

This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combined frequency matrices, and principal components (PCs) of each matrix are generated to construct the new feature space. ANN built classifiers are used to test the discriminability of each feature space. Finally, the 3 and 5-mer feature matrix is selected as the context feature in this model. Two proposed schemes of HPR-PCA model are discussed and the implementations of sub-modules in each scheme are introduced. The context features selected by PCA are III used to build three promoter and non-promoter classifiers. CpG-island modules are embedded into models in different ways. In the comparison, Scheme I obtains better prediction results on two test sets so it is adopted as the model for HPR-PCA for further evaluation. Three existing promoter prediction systems are used to compare to HPR-PCA on three test sets including the chromosome 22 sequence. The performance of HPR-PCA is outstanding compared to the other four systems

Construction of Transformer Fault Diagnosis and Prediction Model Based on Deep Learning

The current intelligent diagnosis and prediction methods for transformer faults are prone to low diagnostic accuracy and insufficient trend prediction ability when the fault categories are imbalanced. Therefore, a fault diagnosis and prediction model for transformers was constructed using a deep learning framework. The fault diagnosis model was constructed using a focus loss stack sparse noise reduction autoencoder on the deep learning framework. The prediction model was constructed by fusing long and short term memory networks on the basis of tree structure Parzen optimization, and the two models were validated. The results obtained through validation of the diagnostic model indicate that, when the actual hidden layer is set to 3 and the quantity of neurons is 58, the model accuracy during training and testing reaches 97.5% and 92.5% respectively. After adding 0.001 times the Gaussian white noise, the model accuracy was significantly lifted, so this study set the Gaussian noise coefficient to 0.001. In the comparison with baseline models, the actual classification ability of the research model samples is strong, significantly improving the fault diagnosis ability. In the validation of the prediction model, the three error index values of the research model in the single prediction step of CH4 concentration were 0.0699, 0.0540, and 0.8481%, respectively, and proved to be were lower than in the case of the baseline model. The three error values in the two-step prediction are 0.0194, 0.0161, and 0.6535%, which are also lower than in case of the baseline model. Overall, the diagnosis and prediction model proposed in this paper can provide real-time future numerical predictions of dissolved gas analysis and monitoring data in transformer oil. Furthermore, the research outilnes the future development trend of monitoring and measurement through application of tensor flow deep learning framework in transformer fault diagnosis. The attained prediction results are innovative, and could well complete the purpose of actual transformer fault diagnosis and early warning

Ultracapacitor character analysis and its application in unified power quality conditioner as energy storage system

This dissertation focuses on the Ultracapacitor (UCAP) character analysis and its application in Unified Power Quality Conditioner (UPQC) as an Energy Storage System (ESS) for improved UPQC performance. It includes three parts as described below. The first part is Paper I. The UCAP is a popular choice for the ESS because of its distinct characters. In the application UCAP\u27s transient behavior need to be studied for design purpose. Usually these transient characters are not shown in the product data sheet. In this paper UCAP frequency analysis is performed, and based on the test data the equivalent model of the UCAP is built. The fitting result shows that using multi-level ladder circuit can perfectly fit the UCAP transient characters. The second part is Paper II. A UPQC is to compensate both source voltage sag and load current imperfections in power distribution system. With the UCAP based Energy Storage System, the UPQC has an optimized power flow between UPQC and system during the transit state, also the UPQC\u27s serial and shunt part has an improved performance with Ucap. The impact of UCAP model on its control and simulation is analyzed. From the analysis, UCAP based Energy Storage System can well fulfill the requirement of UPQC to provide high active power during transit time and improve the UPQC overall performance. The third part is Paper III. Conventionally the PI control method is applied in UPQC, including DVR and APF part. With ESS the H∞ control method is applied in UPQC. This paper shows that the two methods have their own advantages and disadvantages. In the practical application the control method can be chosen by considering the different transit states --Abstract, page iv