Potential Clinical Applications for Human Pluripotent Stem Cell-Derived Blood Components

The ability of human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) to divide indefinitely without losing pluripotency and to theoretically differentiate into any cell type in the body makes them highly attractive cell sources for large scale regenerative medicine purposes. The current use of adult stem cell-derived products in hematologic intervention sets an important precedent and provides a guide for developing hESC/iPSC based therapies for the blood system. In this review, we highlight biological functions of mature cells of the blood, clinical conditions requiring the transfusion or stimulation of these cells, and the potential for hESC/iPSC-derivatives to serve as functional replacements. Many researchers have already been able to differentiate hESCs and/or iPSCs into specific mature blood cell types. For example, hESC-derived red blood cells and platelets are functional in tasks such as oxygen delivery and blood clotting, respectively and may be able to serve as substitutes for their donor-derived counterparts in emergencies. hESC-derived dendritic cells are functional in antigen-presentation and may be used as off-the-shelf vaccine therapies to stimulate antigen-specific immune responses against cancer cells. However, in vitro differentiation systems used to generate these cells will need further optimization before hESC/iPSC-derived blood components can be used clinically

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design

Eriodictyol attenuates spinal cord injury by activating Nrf2/HO-1 pathway and inhibiting NF-κB pathway

Purpose: To investigate the effect of eriodictyol on spinal cord injury (SCI) and its underlying mechanism of action.Methods: Thirty Sprague-Dawley rats were assigned to sham, SCI, and eriodictyol-treated groups (SCI + Eri; 10, 20, and 50 mg/kg). Moderate spinal cord contusion injury was induced to model SCI. Locomotor recovery was assessed based on Basso, Beattie, and Bresnahan (BBB) score. Pain wasevaluated by paw withdrawal threshold (PWT) and latency (PWL), and spinal cord water content was measured. Tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), and interleukin-6 (IL-6) expression were determined by enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Immunoassay was used to determine malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase (GSH-PX) levels while Western blotting was employed to evaluate nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and phosphorylated NF-κB (p-NF-κB) levels.Results: Eriodictyol elevated BBB score, PWT, and PWL in SCI rats but reduced spinal cord water content (p < 0.05). Eriodictyol treatment down-regulated TNF-α, IL-1β, IL-6, and MDA, whereas SOD, GSH, and GSH-PX levels were elevated (p < 0.05). Eriodictyol administration increased Nrf2 and HO-1 levels but reduced p-NF-κB/NF-κB.Conclusion: This study provides a potential therapy to promote long-term functional recovery following SCI. Keywords: Spinal cord injury, Eriodictyol, Nrf2/HO-1 pathway, NF-κB signaling pathway, Polymerase chain reaction, Basso, Beattie and Bresnahan scor

Event detection and user interest discovering in social media data streams

Social media plays an increasingly important role in people’s life. Microblogging is a form of social media which allows people to share and disseminate real-life events. Broadcasting events in microblogging networks can be an effective method of creating awareness, divulging important information and so on. However, many existing approaches at dissecting the information content primarily discuss the event detection model and ignore the user interest which can be discovered during event evolution. This leads to difficulty in tracking the most important events as they evolve including identifying the influential spreaders. There is further complication given that the influential spreaders interests will also change during event evolution. The influential spreaders play a key role in event evolution and this has been largely ignored in traditional event detection methods. To this end, we propose a user-interest model based event evolution model, named the HEE (Hot Event Evolution) model. This model not only considers the user interest distribution, but also uses the short text data in the social network to model the posts and the recommend methods to discovering the user interests. This can resolve the problem of data sparsity, as exemplified by many existing event detection methods, and improve the accuracy of event detection. A hot event automatic filtering algorithm is initially applied to remove the influence of general events, improving the quality and efficiency of mining the event. Then an automatic topic clustering algorithm is applied to arrange the short texts into clusters with similar topics. An improved user-interest model is proposed to combine the short texts of each cluster into a long text document simplifying the determination of the overall topic in relation to the interest distribution of each user during the evolution of important events. Finally a novel cosine measure based event similarity detection method is used to assess correlation between events thereby detecting the process of event evolution. The experimental results on a real Twitter dataset demonstrate the efficiency and accuracy of our proposed model for both event detection and user interest discovery during the evolution of hot events.N/