156 research outputs found
Transcription factors control apple pathogen response
No full textFull text is available to authenticated members of The University of Auckland only.The apple pathogen Venturia inaequalis can infect Malus x domestica, causing an economically damaging disease, scab. Knowledge of how the recognition of a potential pathogen by an R gene product is translated into an effective resistance response in apple is limited. One apple accession, carrying the Rvi5 R gene (Rvi5), is resistant to V. inaequalis. In contrast, the commercial variety Royal Gala is highly susceptible while other accessions, such as those carrying the R gene Rvi8 (Rvi8) are partly resistant to infection caused by V. inaequalis. Transcription factors (TFs) are a vital class of proteins which interact with DNA binding domains to manipulate gene transcription and, therefore, affect gene expression. Some TF families, such as WRKYs, MYB, bHLHs have important functions in plant immunity. One of the functions of TFs is triggering expression of pathogenesis-related (PR) genes in response to attempted infection. This thesis describes the generation of NGS data based on a V. inaequalis inoculation experiment on the resistant accession Rvi5. This shows the global gene expression during resistance including the expression of specific TFs which were dramatically activated by attempted infection. Based on these data and the construction of phylogenetic trees, candidates from the WRKY family of plant TFs were selected for further analysis including WRKY7, WRKY9, WRKY11, WRKY18, WRKY40, WRKY75a and WRKY75b. PR1a, PR2, PR4, PR5 and PR8 were selected as the PR gene candidates. qPCR was employed to validate the NGS results. Functional experiments of TFs and PR genes proved there was a direct link between specific combinations and that there was evidence for interaction between individual WRKY TFs. In contrast to the NGS and qPCR data from Rvi5, qPCR expression profiles of TFs and PR genes in either the partly resistant Rvi8 or the susceptible ‘Royal Gala’ showed a very different expression profile. Expression of the selected TF and PR gene candidates was not activated by infection of V.inaequalis. From this work, it appears that expression of these TFs is vital in apple immunity and that they activate PR genes during the resistance response
Bioinformatics and Network Pharmacology Identify the Therapeutic Role of Guominkang in Allergic Asthma by Inhibiting PI3K/Akt Signaling
No full textHonglei Zhang,1,2,* Haiyun Zhang,1– 3,* Lei Wang,4 Yihang Zhang,1,2 Linhan Hu,1,2 Juntong Liu,1,2 Yumei Zhou,1 Ji Wang1 1National Institute of TCM Body Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People’s Republic of China; 2College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People’s Republic of China; 3Dalian Women and Children’s Medical Group, Dalian, 116000, People’s Republic of China; 4Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan, Hubei, 430065, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yumei Zhou; Ji Wang, National Institute of TCM body constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People’s Republic of China, Email [email protected]; [email protected]: As a classical regulating formula, Guominkang (GMK) has been extensively employed in clinical practice to treat the allergic asthma (AA) and alleviate allergy symptoms, however, the underlying mechanism remains elusive. The aim of this study was to explored the mechanism of action through which GMK combats AA.Methods: Potential target genes for the compounds were identified from the database and subjected to functional enrichment analysis. Subsequently, a protein-protein interaction (PPI) network was constructed in order to screen the core target and confirmed by molecular docking. An asthma model was further developed in mice and airway hyperresponsiveness and lung pathological changes were observed following drug administration. The expression of PI3K and AKT proteins in lung tissues was then detected by Western blotting. Subsequently, the GSE104468 data were normalised and visualised using the R language, compared to the PI3K-Akt pathway gene set to identify overlapping genes, constructed a PPI network and analysed correlations between genes.Results: 267 compounds and 475 disease-relevant GMK targets have been obtained, primarily in the areas of chemokine binding, drug binding, and PI3K-Akt pathway modulation. Molecular docking simulations revealed that predicted targets (PI3K, TNF, IL6, AKT1, SRC, TP53, and STAT3) could be closely bonded with component of GMK. According to in vivo experiments, GMK could reduce mucus obstruction and airway inflammation (P < 0.05), decrease airway hyperresponsiveness (P < 0.05), and inhibited the PI3K-Akt pathway (P < 0.05). After normalising the genes in the dataset between AA and healthy individuals, GO showed that 388 DEGs were associated with PI3K/AKT signaling pathway. The PPI network showed that the overlapping gene were located in the centre of asthma-associated network and that exhibited a correlation with the PI3K-Akt signaling pathway.Conclusion: Based on our findings, GMK potentially acts via the PI3K/Akt pathway and alleviates allergic symptoms in AA.Keywords: allergic diseases, traditional Chinese medicine, network pharmacology, PI3K/Akt signaling pathwa
A tensorflow based feature learning method application in fault detecting of tract motor
No full textGlobal and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition
No full textDue to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn high-quality discourse relation representations between two arguments. Recent methods tend to integrate the whole hierarchical information of senses into discourse relation representations for multi-level sense recognition. Nevertheless, they insufficiently incorporate the static hierarchical structure containing all senses (defined as global hierarchy), and ignore the hierarchical sense label sequence corresponding to each instance (defined as local hierarchy). For the purpose of sufficiently exploiting global and local hierarchies of senses to learn better discourse relation representations, we propose a novel GlObal and Local Hierarchy-aware Contrastive Framework (GOLF), to model two kinds of hierarchies with the aid of multi-task learning and contrastive learning. Experimental results on PDTB 2.0 and PDTB 3.0 datasets demonstrate that our method remarkably outperforms current state-of-the-art models at all hierarchical levels.</p
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning
No full textContrastive learning has been demonstrated to be effective in enhancing
pre-trained language models (PLMs) to derive superior universal sentence
embeddings. However, existing contrastive methods still have two limitations.
Firstly, previous works may acquire poor performance under domain shift
settings, thus hindering the application of sentence representations in
practice. We attribute this low performance to the over-parameterization of
PLMs with millions of parameters. To alleviate it, we propose PromCSE
(Prompt-based Contrastive Learning for Sentence Embeddings), which only trains
small-scale \emph{Soft Prompt} (i.e., a set of trainable vectors) while keeping
PLMs fixed. Secondly, the commonly used NT-Xent loss function of contrastive
learning does not fully exploit hard negatives in supervised learning settings.
To this end, we propose to integrate an Energy-based Hinge loss to enhance the
pairwise discriminative power, inspired by the connection between the NT-Xent
loss and the Energy-based Learning paradigm. Empirical results on seven
standard semantic textual similarity (STS) tasks and a domain-shifted STS task
both show the effectiveness of our method compared with the current
state-of-the-art sentence embedding models. Our code is publicly avaliable at
https://github.com/YJiangcm/PromCSEComment: 15 pages, 3 figures, Findings of EMNLP 202
End-to-end QoS performance analysis of wireless mesh network on RM-AODV routing protocol
No full textGlobal and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition
No full textDue to the absence of explicit connectives, implicit discourse relation
recognition (IDRR) remains a challenging task in discourse analysis. The
critical step for IDRR is to learn high-quality discourse relation
representations between two arguments. Recent methods tend to integrate the
whole hierarchical information of senses into discourse relation
representations for multi-level sense recognition. Nevertheless, they
insufficiently incorporate the static hierarchical structure containing all
senses (defined as global hierarchy), and ignore the hierarchical sense label
sequence corresponding to each instance (defined as local hierarchy). For the
purpose of sufficiently exploiting global and local hierarchies of senses to
learn better discourse relation representations, we propose a novel GlObal and
Local Hierarchy-aware Contrastive Framework (GOLF), to model two kinds of
hierarchies with the aid of multi-task learning and contrastive learning.
Experimental results on PDTB 2.0 and PDTB 3.0 datasets demonstrate that our
method remarkably outperforms current state-of-the-art models at all
hierarchical levels. Our code is publicly available at
https://github.com/YJiangcm/GOLF_for_IDRRComment: 15 pages, 9 figures, Findings of ACL 202
- …
