GSK3 Inhibitor-BIO Regulates Proliferation of Immortalized Pancreatic Mesenchymal Stem Cells (iPMSCs)

<div><h3>Background</h3><p>The small molecule 6-bromoindirubin-30-oxime (BIO), a glycogen synthase kinase 3 (GSK3) inhibitor, is a pharmacological agent known to maintain self-renewal in human and mouse embryonic stem cells (ESCs). However, the precise role of GSK3 in immortalized pancreatic mesenchymal stem cells (iPMSCs) growth and survival is not completely understood at present.</p> <h3>Results</h3><p>To determine whether this molecule is involved in controlling the proliferation of iPMSCs, we examined the effect of BIO on iPMSCs. We found that the inactivation of GSK3 by BIO can robustly stimulate iPMSCs proliferation and mass formation as shown by QRT-PCR, western blotting, 5-Bromo-2-deoxyuridine (BrdU) immunostaining assay and tunel assay. However, we did not find the related roles of BIO on β cell differentiation by immunostaining, QRT-PCR assay, glucose-stimulated insulin release and C-peptide content analysis.</p> <h3>Conclusions</h3><p>These results suggest that BIO plays a key role in the regulation of cell mass proliferation and maintenance of the undifferentiated state of iPMSCs.</p> </div

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

A novel recombinant serotype 4 fowl adenovirus expressing fiber-2 protein of duck adenovirus 3

Recently, the highly pathogenic serotype 4 fowl adenovirus (FAdV-4) and duck adenovirus 3 (DAdV-3) were outbroken and widespread, causing substantial economic losses to the duck industry. Therefore, there is an urgent need to generate a recombinant genetic engineering vaccine candidate against both FAdV-4 and DAdV-3. In this study, a novel recombinant FAdV-4 expressing the Fiber-2 protein of DAdV-3, designated as rFAdV-4-Fiber-2/DAdV-3, was generated based on CRISPR/Cas9 and Cre-LoxP systems. Indirect immunofluorescence assay (IFA) and western blot (WB) showed that the Fiber-2 protein of DAdV-3 in rFAdV-4-Fiber-2/DAdV-3 was expressed successfully. Moreover, the growth curve revealed that rFAdV-4-Fiber-2/DAdV-3 replicated efficiently in LMH cells and even showed a stronger replication ability compared to the wild type FAdV-4. The generation of the recombinant rFAdV-4-Fiber-2/DAdV-3 provides a potential vaccine candidate against both FAdV-4 and DAdV-3

A Mobile WSN Sink Node Using Unmanned Aerial Vehicles: Design And Experiment

In this paper, we construct a new type of mobile wireless data sinking platform for data collection based on unmanned aerial vehicle (UAV) technology, which aims to address the increasing demand for wireless sensor network (WSN) distribution in different monitoring areas and enlarge the coverage for various application scenarios. A wireless environmental monitoring system is firstly studied, and then wireless communication capacity and data collection experiments are performed. The communication capacity test results show that when the RF modules operate with a transmission power above 1 dBm and a communication distance below 100 m, the UAV wireless sink node can maintain a high quality communication data link. Additionally, an outdoor data collection experiment is performed using this UAV platform within a mountainous area. In this outdoor experiment, the data analysis results show that the validity rate of the environmental data that is obtained from the WSN cluster head node on the ground is higher than 92%, and most of the missing data results from WSN communication failures. This experiment proves the feasibility of introducing UAV as a sink node in a clustering WSN. The overall contributions of this paper can provide guidance for building a UAV cooperative WSN system in future

Hybrid Fake Information Containing Strategy Exploiting Multi-Dimensions Data in Online Community

It is well-established that, in the past few years, internet users have rapidly increased. Meanwhile, various types of fake information (such as fake news or rumors) have been flooding social media platforms or online communities. The effective containing or controlling of fake news or rumor has drawn wide attention from areas such as academia to social media platforms. For that reason, numerous studies have focused on this subject from different perspectives, such as employing complex networks and spreading models. However, in the real online community, misinformation usually spreads quickly to thousands of users within minutes. Conventional studies are too theoretical or complicated to be applied to practical applications, and show a lack of fast responsiveness and poor containing effects. Therefore, in this work, a hybrid strategy exploiting the multi-dimensional data of users and content was proposed for the fast containing of fake information in the online community. The strategy is mainly composed of three steps: the fast detection of fake information by continuously updating the content comparison dataset according to the specific hot topic and the fake contents; creating spreading force models and user divisions via historical data, and limiting the propagation of fake information based on the content and user division. Finally, an experiment was set up online with BBS (Bulletin Board System), and the acquired results were analyzed by comparison with other methods in different metrics. From the extracted results, it has been demonstrated that the proposed solution clearly outperforms traditional methods

A Hotspot Information Extraction Hybrid Solution of Online Posts’ Textual Data

Online posts have gradually become a major carrier of network public opinion in social media, and the social network hotspots are the important basis for the study of network public opinion. Therefore, it is significant to extract hotspots for monitoring Internet public opinion from online posts textual big data. However, the current hotspot extraction methods are focused on the users’ features that are based on textual big data with spam and low-quality content. Meanwhile, these methods seldomly consider the time span of posts and the popularity of users. Accordingly, this article presents a hotspots information extraction hybrid solution of online posts’ textual data. Firstly, a filtering strategy to obtain more high-quality textual data is designed. Secondly, the topic hot degree is presented by considering the average number of replies and the popularity of the participant. Thirdly, an improved co-word analysis technology is used to search the same topic posts and Bisecting k-means clustering algorithm using repliers’ popularity and key posts are designed for studying and monitoring the hotspots of online posts in a valid big data environment. Finally, the proposed algorithms are verified in experiments by extracting the hotspots of online posts from the dataset. The results show that the data filtering strategy can help to obtain more valuable information and decrease the computing time. The results also demonstrate that the proposed solution can help to obtain hotspots comparing the traditional methods, and the hot degree can reflect the trend of the online post by comparing the traditional methods

Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs

In recent years, UAVs (Unmanned Aerial Vehicles) have been widely applied for data collection and image capture. Specifically, UAVs have been integrated with wireless sensor networks (WSNs) to create data collection platforms with high flexibility. However, most studies in this domain focus on system architecture and UAVs’ flight trajectory planning while event-related factors and other important issues are neglected. To address these challenges, we propose a cloud-assisted data gathering strategy for UAV-based WSN in the light of emerging events. We also provide a cloud-assisted approach for deriving UAV’s optimal flying and data acquisition sequence of a WSN cluster. We validate our approach through simulations and experiments. It has been proved that our methodology outperforms conventional approaches in terms of flying time, energy consumption, and integrity of data acquisition. We also conducted a real-world experiment using a UAV to collect data wirelessly from multiple clusters of sensor nodes for monitoring an emerging event, which are deployed in a farm. Compared against the traditional method, this proposed approach requires less than half the flying time and achieves almost perfect data integrity

Analysis and Applications of GlobeLand30: A Review

Abstract: GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed