Antitumor effect of salidroside on mice bearing HepA hepatocellular carcinoma

Salidroside, a phenylpropanoid glycoside extracted from Rhodiola rosea L., has antiproliferative effects on tumour cells in mice. However it’s antitumor mechanism remains largely unknown. In this study, 4 groups of mice bearing hepatocarcinoma cells were given treatment with vehicle alone, cyclophosphamide (25 mg/kg, i.p.) and salidroside, either 100 or 200 mg/kg (p.o.) for 14 days. The morphology of tumour specimens was analysed by transmission electron microscopy. Apoptotic cells in sections of mouse tumour tissue were analysed using an in situ apoptosis kit. The expression of Bcl-2, Bax and caspase 3 mRNA were examined with RT-PCR. The results showed that the tumour weights in groups 100 or 200 mg/kg/day of salidroside were reduced significantly (45.34 and 52.48% respectively), compared to vehicle groups. Salidroside increased apoptotic cells index, e.g. in 200 mg/kg group, it was four times higher compared to the control group. Even more, treatment with salidroside decreased Bcl-2 mRNA expression and increased Bax and caspase 3 mRNA expressions. These indicated that the antitumor mechanism of salidroside may induce tumour cell apoptosis in mice by triggering the mitochondrial-dependent pathway and activation of caspase 3

Inhibition effects of paeonol on mice bearing EMT6 breast cancer through inducing rumor cell apoptosis

Paeonol, a phenolic component from the root bark of Paeonia moutan, has been identified to possess antitumor effects on mice bearing EMT6 breast cancer in our previous studies. However, the underlying mechanisms remain unknown. In the present study the molecular mechanisms of paeonol were further investigated in EMT6 mice model. The results showed that treatment of mice with 175 and 350 mg/kg/day of paeonol significantly inhibited the growth of the EMT6 tumor in mice, and induced tumor cell apoptosis which were demonstrated by light microscopy after hematoxylin and eosin staining and apoptosis analysis by flow cytometry. In addition, compared with the control group, paeonol increased the number of tumor cells in G0/G1 phase but decreased the number of cells in S and G2/M phase. Paeonol treatment (350 mg/kg body weight) also resulted in a decrease of Bcl-2 and an increase in Bax and caspase-3 expressions, which were demonstrated by immunohistochemical and western blot analysis. These results indicate that the antitumor effects of paeonol might be associated with arresting tumor cells in the G0/G1 phase, inducing cell apoptosis and regulation of the expression of Bcl-2, Bax and activation of caspase-3

Joint Multi-Label Attention Networks for Social Text Annotation

We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semantic-based loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time

Energy-Water Balance and Ecosystem Response to Climate Change in Southwest China

It is important to highlight energy-water balance and ecosystem response to climate changes. The change of water-energy balance and ecosystem due to climate change will affect the regional ecological and human living significantly, especially in Southwest China which is an ecologically fragile area. This chapter presents the retrieval methodology of parameters (reconstruction of vegetation index, land cover semi-automatic classification, a time series reconstruction of land surface temperature based on Kalman filter and precipitation interpolation based on thin plate smoothing splines), time-series analysis methodology (land cover change, vegetation succession and drought index) and correlate analysis methodology (correlation coefficient and principal component analysis). Then, based on the above method, remote sensing data were integrated, a time series analysis on a 30-year data was used to illustrate the water-energy balance and ecosystem variability in Southwest China. The result showed that energy-water balance and ecosystem (ecosystem structures, vegetation and droughts) have severe response to climate change

Automated Social Text Annotation With Joint Multilabel Attention Networks

Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d ) has the potential to further improve the performance of JMAN but at the cost of substantial memory and warrants further study

Knowledge Base Enrichment by Relation Learning from Social Tagging Data

There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We propose a supervised learning method to discover subsumption relations from tags. The key to this method is quantifying the probabilistic association among tags to better characterise their relations. We further develop an algorithm to organise tags into hierarchies based on the learned relations. Experiments were conducted using a large, publicly available dataset, Bibsonomy, and three popular, human-engineered or data-driven knowledge bases: DBpedia, Microsoft Concept Graph, and ACM Computing Classification System. We performed a comprehensive evaluation using different strategies: relation-level, ontology-level, and knowledge base enrichment based evaluation. The results clearly show that the proposed method can extract knowledge of better quality than the existing methods against the gold standard knowledge bases. The proposed approach can also enrich knowledge bases with new subsumption relations, having the potential to significantly reduce time and human effort for knowledge base maintenance and ontology evolution

Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.

The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended.IMPORTANCE The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile

2-[(2-Hy­droxy-4-meth­oxy­benzyl­idene)aza­nium­yl]benzoate monohydrate

In the title compound, C15H13NO4·H2O, the Schiff base exists in a zwitterionic form and a bifurcated intra­molecular N—H⋯(O,O) hydrogen bond generates two S(6) rings. The dihedral angle between the two benzene rings is 25.8 (2)°. The crystal structure is stabilized by inter­molecular O—H⋯O hydrogen bonds

Motivations for self-archiving on an academic social networking site:A study on researchgate

© 2019 ASIS & T This study investigates motivations for self-archiving research items on academic social networking sites (ASNSs). A model of these motivations was developed based on two existing motivation models: motivation for self-archiving in academia and motivations for information sharing in social media. The proposed model is composed of 18 factors drawn from personal, social, professional, and external contexts, including enjoyment, personal/professional gain, reputation, learning, self-efficacy, altruism, reciprocity, trust, community interest, social engagement, publicity, accessibility, self-archiving culture, influence of external actors, credibility, system stability, copyright concerns, additional time, and effort. Two hundred and twenty-six ResearchGate users participated in the survey. Accessibility was the most highly rated factor, followed by altruism, reciprocity, trust, self-efficacy, reputation, publicity, and others. Personal, social, and professional factors were also highly rated, while external factors were rated relatively low. Motivations were correlated with one another, demonstrating that RG motivations for self-archiving could increase or decrease based on several factors in combination with motivations from the personal, social, professional, and external contexts. We believe the findings from this study can increase our understanding of users' motivations in sharing their research and provide useful implications for the development and improvement of ASNS services, thereby attracting more active users