TGFβ-Treated Placenta-Derived Mesenchymal Stem Cells Selectively Promote Anti-Adipogenesis in Thyroid-Associated Ophthalmopathy

Orbital fibroblasts (OFs) in thyroid-associated ophthalmopathy (TAO) are differentiated from pre-adipocytes and mature adipocytes; increased lipid and fat expansion are the major characteristics of ophthalmic manifestations. Human placental mesenchymal stem cells (hPMSCs) were reported to immunomodulate pathogenesis and suppress adipogenesis in TAO OFs. Here, we prepared transforming growth factor β (TGFβ, 20 ng/mL)-treated hPMSCs (TGFβ-hPMSCs) in order to enhance anti-adipogenic effects in vitro and in TAO mice. TAO OFs were grown in a differentiation medium and then co-cultured with hPMSCs or TGFβ-hPMSCs. TAO OFs were analyzed via quantitative real-time polymerase chain reaction, Oil red O staining, and western blotting. The results showed that TGFβ-hPMSCs reduced the expression of adipogenic, lipogenic, and fibrotic genes better than hPMSCs in TAO OFs. Moreover, the adipose area decreased more in TAO mice injected with TGFβ-hPMSCs compared to those injected with hPMSCs or a steroid. Further, TGFβ-hPMSCs inhibited inflammation as effectively as a steroid. In conclusion, TGFβ-hPMSCs suppressed adipogenesis and lipogenesis in vitro and in TAO mice, and the effects were mediated by the SMAD 2/3 pathways. Furthermore, TGFβ-hPMSCs exhibited anti-inflammatory and anti-fibrotic functions, which suggests that they could be a new and safe method to promote the anti-adipogenic function of hPMSCs to treat TAO patients.     

Creation of Faceted Polyhedral Microgels from Compressed Emulsions

Compressed monodisperse emulsions in confined space exhibit highly ordered structures. The influence of the volume fraction and the confinement geometry on the organized structures is investigated and the mechanism by which structural transition occurs is studied. Based on the understanding of ordering behavior of compressed emulsions, a simple and high‐throughput method to fabricate monodisperse polyhedral microgels using the emulsions as the template is developed. By controlling the geometry of the confined spaces, a variety of shapes such as hexagonal prism, Fejes Toth honeycomb prism, truncated octahedron, pyritohedron, and truncated hexagonal trapezohedron are implemented. Moreover, the edge sharpness of each shape is controllable by adjusting the drop volume fraction. This design principle can be readily extended to other shapes and materials, and therefore provides a useful means to create polyhedral microparticles for both fundamental study and practical applications.     

Optimizing Chain Topology of Bottle Brush Copolymer for Promoting the Disorder-to-Order Transition

The order-disorder transitions (ODT) of core-shell bottle brush copolymer and its structural isomers were investigated by dissipative particle dynamics simulations and theoretically by random phase approximation. Introducing a chain topology parameter λ which parametrizes linking points between M diblock chains each with N monomers, the degree of incompatibility at ODT ((χN)ODT; χ being the Flory–Huggins interaction parameter between constituent monomers) was predicted as a function of chain topology parameter (λ) and the number of linked diblock chains per bottle brush copolymer (M). It was found that there exists an optimal chain topology about λ at which (χN)ODT gets a minimum while the domain spacing remains nearly unchanged. The prediction provides a theoretical guideline for designing an optimal copolymer architecture capable of forming sub-10 nm periodic structures even with non-high χ components.     

Composition pattern oriented tag extraction from short documents using a structural learning method

With the rapid growth of web, automatic tagging that detects informative terms from a document becomes an important problem for information aggregation and sharing services. In particular, automatic tagging for short documents becomes more interesting as many users are increasingly publishing information through social media services which encourage users to create the documents of short length. In this paper, we propose a novel automatic tagging model for short text documents from social media services, following the framework of supervised learning. We redefine traditional frequency-based term features so that they can address the properties of the documents created under length limitation and consider sequential dependencies between successive terms in a document based on a structural support vector machine. In addition, our proposed approach incorporates composition patterns by which users put informative terms into their documents. Extensive experiments have been conducted to validate the presented approach, and it was found that the proposed term features were effective for extracting tags, and the tag extractor trained by considering the sequential dependencies and composition patterns achieved superior performance results over the existing alternative methods.     

Multiagent reinforcement learning applied to a chase problem in a continuous world

Reinforcement learning (RL) is one of the methods of solving problems defined in multiagent systems. In the real world, the state is continuous, and agents take continuous actions. Since conventional RL schemes are often defined to deal with discrete worlds, there are difficulties such as the representation of an RL evaluation function. In this article, we intend to extend an RL algorithm so that it is applicable to continuous world problems. This extension is done by a combination of an RL algorithm and a function approximator. We employ Q-learning as the RL algorithm, and a neural network model called the normalized Gaussian network as the function approximator. The extended RL method is applied to a chase problem in a continuous world. The experimental result shows that our RL scheme was successful. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Experimental investigation on the CO2 laser cutting of soda-lime glass

Journal of Mechanical Science and Technology - This study reports on complete glass cutting using a single CO2 laser beam with a low power of several tens of watts. In this study, the morphological...     

Intermittent Hypoxia Increased the Expression of DBH and PNMT in Neuroblastoma Cells via MicroRNA-375-Mediated Mechanism

Sleep apnea syndrome (SAS), characterized by recurrent episodes of oxygen desaturation and reoxygenation (intermittent hypoxia (IH)), is a risk factor for hypertension and insulin resistance. We report a correlation between IH and insulin resistance/diabetes. However, the reason why hypertension is induced by IH is elusive. Here, we investigated the effect of IH on the expression of catecholamine-metabolizing enzymes using an in vitro IH system. Human and mouse neuroblastoma cells (NB-1 and Neuro-2a) were exposed to IH or normoxia for 24 h. Real-time RT-PCR revealed that IH significantly increased the mRNA levels of dopamine β-hydroxylase (DBH) and phenylethanolamine N-methyltransferase (PNMT) in both NB-1 and Neuro-2a. Western blot showed that the expression of DBH and PNMT in the NB-1 cells was significantly increased by IH. Reporter assays revealed that promoter activities of DBH and PNMT were not increased by IH. The miR-375 level of IH-treated cells was significantly decreased relative to that of normoxia-treated cells. The IH-induced up-regulation of DBH and PNMT was abolished by the introduction of the miR-375 mimic, but not by the control RNA. These results indicate that IH stress increases levels of DBH and PNMT via the inhibition of miR-375-mediated mRNA degradation, potentially playing a role in the emergence of hypertension in SAS patients.