18 research outputs found

    Surgical ciliated cyst of the mandible after orthognathic surgery: a case report with review of the literature

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    Background : Surgical ciliated cysts, also known as postoperative maxillary cysts or implantation cysts, occur mainly in the posterior maxilla after radical maxillary sinus surgery; they rarely develop in the mandible. They are thought to occur when the sinonasal epithelium is infiltrated by a surgical instrument during surgery or as a result of transplantation of bone or cartilage with respiratory epithelium attached. Case presentation : We report a case in which a surgical ciliated cyst developed in the anterior part of the mandible, presumably as a result of bimaxillary orthognathic surgery and genioplasty performed 24 years earlier. We then review the few similar cases reported in the literature. Conclusion : Surgical ciliated cysts in the mandible are extremely rare, but they could occur after simultaneous surgery on the maxilla and mandible, even decades later. To prevent surgical ciliated cysts in the mandible, we recommend that the surgical instruments, especially the saw blade used during bimaxillary surgery, be new or cleaned and that previously placed plates and screws be removed at an appropriate time

    Probing Majorana Neutrinos at the CMS

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    Observing a Lepton Number Violation process (Delta L = 2) is a clear way to establish the Majorana nature of the neutrino mass. We search for a signature of same-sign dileptons and two jets from heavy neutrino decay. The latest search results for heavy Majorana neutrinos at the LHCusing the CMS detector will be presented

    Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards

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    In this paper, we consider stochastic multi-armed bandits (MABs) with heavy-tailed rewards, whose p-th moment is bounded by a constant nu_p for 1<p<=2. First, we propose a novel robust estimator where information about nu_p is not required in prior, while other existing robust estimators demand the constant nu_p as prior information. We show that an error probability of the proposed estimator decays exponentially fast. Using this estimator, we propose a perturbation-based exploration strategy and develop a regret analysis scheme that provides upper and lower regret bounds for a general perturbation by revealing the relationship between the regret and the cumulative density function of the perturbation. From the proposed analysis scheme, we obtain gap-dependent and gap-independent upper and lower bounds of various perturbations. We also find the optimal hyperparameters for each perturbation, which can achieve the minimax optimal regret bound with respect to total rounds. In simulations, the proposed estimator shows favorable performance compared to existing robust estimators for various pp values and, for MAB problems, the proposed perturbation strategy outperforms existing exploration methods

    Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling

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    In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The proposed uncertainty acquisition can be done with a single forward path without Monte Carlo sampling and is suitable for real-time robotics applications. Then, we show that it can be decomposed into explained variance and unexplained variance where the connections between aleatoric and epistemic uncertainties are addressed. The properties of the proposed uncertainty measure are analyzed through three different synthetic examples, absence of data, heavy measurement noise, and composition of functions scenarios. We show that each case can be distinguished using the proposed uncertainty measure and presented an uncertainty-aware learning from demonstration method for autonomous driving using this property. The proposed uncertainty-aware learning from demonstration method outperforms other compared methods in terms of safety using a complex real-world driving dataset

    Lysine-cyclodipeptide-based polyamidoamine microparticles: Balance between the efficiency of copper ion removal and degradation in water

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    A key requirement for materials that adsorb pollutants in aqueous media is the balance between efficiency and biodegradation owing to rising microplastic pollution. Hyperbranched polyamidoamine-based microhydrogel particles from ethylene diamine (EDA) monomer demonstrate high absorbance activity for removing heavy metal ions, yet are vulnerable to hydrolysis. Here, we copolymerize lysine diketopiperazine (L-DKP) and EDA with N,N'-methylenebisacrylamide via a Michael addition reaction-mediated inverse suspension polymerization to obtain highly efficient Cu2+-absorbing materials with controlled degradation in aqueous media. When the L-DKP content is increased, which replaces EDA, degradation is typically prevented at the cost of absorption capacity. At optimal L-DKP content (20 mol% per fed diamine monomers), the microparticle exhibits a performance of 159 Cu2+-mg/g, which is comparable to that of the EDA-only microparticles, but with higher degradation resistance, as only 38 wt% was lost at 37 degrees C after two weeks. During the hydrolysis of microparticles without L-DKP, the absorbed Cu2+ ions were released, polluting the aquatic environment. In the presence of L-DKP, Cu2+ ions were significantly retained within the working time. In contrast to synthetic microbeads such as polystyrene, accidently leaked L-DKP-based microparticles decompose within six months. These results provide an industrial, environment-friendly, and long-lasting absorbent for water purification.11Nsciescopu

    Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots

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    In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, including the well-known standard Shannon-Gibbs (SG) entropy, using an additional real-valued parameter, called an entropic index. By controlling the entropic index, we can generate various types of entropy, including the SG entropy, and a different entropy results in a different class of the optimal policy in Tsallis MDPs. We also provide a full mathematical analysis of Tsallis MDPs. Our theoretical result enables us to use any positive entropic index in RL. To handle complex and large-scale problems such as learning a controller for soft mobile robot, we also propose a Tsallis actor-critic (TAC). For a different type of RL problems, we find that a different value of the entropic index is desirable and empirically show that TAC with a proper entropic index outperforms the state-of-the-art actor-critic methods. Furthermore, to alleviate the effort for finding the proper entropic index, we propose a linear scheduling method where an entropic index linearly increases as the number of interactions increases. In simulations, the linear scheduling shows the fast convergence speed and a similar performance to TAC with the optimal entropic index, which is a useful property for real robot applications. We also apply TAC with the linear scheduling to learn a feedback controller of a soft mobile robot and shows the best performance compared to other existing actor critic methods in terms of convergence speed and the sum of rewards. Consequently, we empirically show that the proposed method efficiently learns a controller of soft mobile robots

    Propolis Suppresses UV-Induced Photoaging in Human Skin through Directly Targeting Phosphoinositide 3-Kinase

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    Propolis is a resinous substance generated by bees using materials from various plant sources. It has been known to exhibit diverse bioactivities including anti-oxidative, anti-microbial, anti-inflammatory, and anti-cancer effects. However, the direct molecular target of propolis and its therapeutic potential against skin aging in humans is not fully understood. Herein, we investigated the effect of propolis on ultraviolet (UV)-mediated skin aging and its underlying molecular mechanism. Propolis suppressed UV-induced matrix metalloproteinase (MMP)-1 production in human dermal fibroblasts. More importantly, propolis treatment reduced UV-induced MMP-1 expression and blocked collagen degradation in human skin tissues, suggesting that the anti-skin-aging activity of propolis can be recapitulated in clinically relevant conditions. While propolis treatment did not display any noticeable effects against extracellular signal-regulated kinase (ERK), p38, and c-jun N-terminal kinase (JNK) pathways, propolis exerted significant inhibitory activity specifically against phosphorylations of phosphoinositide-dependent protein kinase-1 (PDK1) and protein kinase B (Akt). Kinase assay results demonstrated that propolis can directly suppress phosphoinositide 3-kinase (PI3K) activity, with preferential selectivity towards PI3K with p110α and p110δ catalytic subunits over other kinases. The content of active compounds was quantified, and among the compounds identified from the propolis extract, caffeic acid phenethyl ester, quercetin, and apigenin were shown to attenuate PI3K activity. These results demonstrate that propolis shows anti-skin-aging effects through direct inhibition of PI3K activity

    Biodegradable chito-beads replacing non-biodegradable microplastics for cosmetics

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    The global ban on plastic microbeads for personal care products has forced researchers to find sustainable alternatives. However, current biodegradable microbeads rarely offer competitive qualities such as those related to the mechanical properties, stability, and toxicity of their degraded products. Herein, we synthesized ‘chito-beads’, which can satisfy the aforementioned requirements, and evaluated their practical usage, biodegradability, and phytotoxicity. Chito-beads were made into uniform spherical microbeads with a diameter of 280 μm through the reacetylation of chitosan – a renewable polymer from crustacean waste via an inverse emulsion system. Chito-beads exhibit a higher cleansing efficiency than conventional polyethylene microbeads, with a hardness of 128 MPa. Furthermore, they can be used to remove potentially toxic elements and are stable and functional in commercial cleansing. The used chito-beads were fully degraded in soils without any toxicity to the model plants. Our alternative can be used as competitive and environmentally friendly microparticles in sustainable daily necessities.11Ysciescopu

    Differences in the Electrochemical Performance of Pt-Based Catalysts Used for Polymer Electrolyte Membrane Fuel Cells in Liquid Half- And Full-Cells

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    © A substantial amount of research effort has been directed toward the development of Pt-based catalysts with higher performance and durability than conventional polycrystalline Pt nanoparticles to achieve high-power and innovative energy conversion systems. Currently, attention has been paid toward expanding the electrochemically active surface area (ECSA) of catalysts and increase their intrinsic activity in the oxygen reduction reaction (ORR). However, despite innumerable efforts having been carried out to explore this possibility, most of these achievements have focused on the rotating disk electrode (RDE) in half-cells, and relatively few results have been adaptable to membrane electrode assemblies (MEAs) in full-cells, which is the actual operating condition of fuel cells. Thus, it is uncertain whether these advanced catalysts can be used as a substitute in practical fuel cell applications, and an improvement in the catalytic performance in real-life fuel cells is still necessary. Therefore, from a more practical and industrial point of view, the goal of this review is to compare the ORR catalyst performance and durability in half- and full-cells, providing a differentiated approach to the durability concerns in half- and full-cells, and share new perspectives for strategic designs used to induce additional performance in full-cell devices.11Nsciescopu