Proteomics analysis of differentially-expressed proteins in uterus of primary dysmenorrhea mice following administration of nuangong zhitong

Purpose: To use label-free proteomic method to investigate the mechanism of action of nuanggong zhitong decoction (NZD) on primary dysmenorrhea (PD). Methods: A mouse model of PD was established through oxytocin administration. The mice were divided into control group (normal mice), model group (PD mice administered normal saline), and treatment group (mice given NZD). The serum levels of PGE2 and PGF2α in the mice were measured by ELISA. The differentially expressed proteins (DEPs) among the three groups were revealed by identifying the proteins that were up-regulated (or down-regulated) in model group and down-regulated (or up-regulated) in the treatment group. The DEPs in the three groups were identified using Nano- HPLC-MS/MS, and their functions were investigated using bioinformatics analyses. The accuracy of proteomics was verified with western blot analysis. Results: Thirty-eight up-regulated and 66 down-regulated DEPs were identified. Bioinformatics analysis revealed that the DEPs were related to immune response, signal conduction, protein binding, and metabolism. STRING analysis indicated a total of 53 DEPs have direct or indirect functional links. Western blot results revealed that levels of Stat1, Rock1, vinculin and vaveolin-1 were consistent with the results of proteomic analysis. Conclusion: These findings provide further insights into the mechanism underlying the protective effects of NZD. Keywords: Primary dysmenorrhea, Uterus, Nuangong zhitong decoction, Vinculin, Caveolin, Differentially expressed proteins (DEPs), Bioinformatic

A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild Images

Limited by the nature of the low-dimensional representational capacity of 3DMM, most of the 3DMM-based face reconstruction (FR) methods fail to recover high-frequency facial details, such as wrinkles, dimples, etc. Some attempt to solve the problem by introducing detail maps or non-linear operations, however, the results are still not vivid. To this end, we in this paper present a novel hierarchical representation network (HRN) to achieve accurate and detailed face reconstruction from a single image. Specifically, we implement the geometry disentanglement and introduce the hierarchical representation to fulfill detailed face modeling. Meanwhile, 3D priors of facial details are incorporated to enhance the accuracy and authenticity of the reconstruction results. We also propose a de-retouching module to achieve better decoupling of the geometry and appearance. It is noteworthy that our framework can be extended to a multi-view fashion by considering detail consistency of different views. Extensive experiments on two single-view and two multi-view FR benchmarks demonstrate that our method outperforms the existing methods in both reconstruction accuracy and visual effects. Finally, we introduce a high-quality 3D face dataset FaceHD-100 to boost the research of high-fidelity face reconstruction. The project homepage is at https://younglbw.github.io/HRN-homepage/.Comment: Accepted by CVPR202

NPA: Neural News Recommendation with Personalized Attention

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation

Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN

FvBck1, a component of cell wall integrity MAP kinase pathway, is required for virulence and oxidative stress response in sugarcane Pokkah Boeng pathogen

Fusarium verticillioides (formerly F. moniliforme) is suggested as one of the causal agents of Pokkah Boeng, a serious disease of sugarcane worldwide. Currently, detailed molecular and physiological mechanism of pathogenesis is unknown. In this study, we focused on cell wall integrity MAPK pathway as one of the potential signaling mechanisms associated with Pokkah Boeng pathogenesis. We identified FvBCK1 gene that encodes a MAP kinase kinase kinase homolog and determined that it is not only required for growth, micro- and macro-conidia production, and cell wall integrity but also for response to osmotic and oxidative stresses. The deletion of FvBCK1 caused a significant reduction in virulence and FB1 production, a carcinogenic mycotoxin produced by the fungus. Moreover, we found the expression levels of three genes, which are known to be involved in superoxide scavenging, were down regulated in the mutant. We hypothesized that the loss of superoxide scavenging capacity was one of the reasons for reduced virulence, but overexpression of catalase or peroxidase gene failed to restore the virulence defect in the deletion mutant. When we introduced Magnaporthe oryzae MCK1 into the FvBck1 deletion mutant, while certain phenotypes were restored, the complemented strain failed to gain full virulence. In summary, FvBck1 plays a diverse role in F. verticillioides, and detailed investigation of downstream signaling pathways will lead to a better understanding of how this MAPK pathway regulates Pokkah Boeng on sugarcane

An efficient double-fluorescence approach for generating fiber-2-edited recombinant serotype 4 fowl adenovirus expressing foreign gene

Recently, the infection of serotype 4 fowl adenovirus (FAdV-4) in chicken flocks has become endemic in China, which greatly threatens the sustainable development of poultry industry. The development of recombinant FAdV-4 expressing foreign genes is an efficient strategy for controlling both FAdV-4 and other important poultry pathogens. Previous reverse genetic technique for generating the recombinant fowl adenovirus is generally inefficient. In this study, a recombinant FAdV-4 expressing enhanced green fluorescence protein (EGFP), FA4-EGFP, was used as a template virus and directly edited fiber-2 gene to develop an efficient double-fluorescence approach to generate recombinant FAdV-4 through CRISPR/Cas9 and Cre-Loxp system. Moreover, using this strategy, a recombinant virus FAdV4-HA(H9) stably expressing the HA gene of H9N2 influenza virus was generated. Chicken infection study revealed that the recombinant virus FAdV4-HA(H9) was attenuated, and could induce haemagglutination inhibition (HI) titer against H9N2 influenza virus at early time points and inhibit the viral replication in oropharynx. All these demonstrate that the novel strategy for constructing recombinant FAdV-4 expressing foreign genes developed here paves the way for rapidly developing attenuated FAdV-4-based recombinant vaccines for fighting the diseases caused by both FAdV-4 and other pathogens