Outcomes of robotic-assisted laparoscopic prostatectomy versus open prostatectomy in surgical intervention of localized prostate cancer

An informed consent conversation regarding robotic-assisted laparoscopic prostatectomy versus open prostatectomy in patients with localized prostate cancer

Rapid toxicity assessment of six antifouling booster biocides using a microplate-based chlorophyll fluorescence in Undaria pinnatifida gametophytes

Biocides of antifouling agents can cause problems in marine ecosystems by damaging to non-target algal species. Aquatic bioassays are important means of assessing the quality of water containing mixtures of contaminants and of providing a safety standard for water management in an ecological context. In this study, a rapid, sensitive and inexpensive test method was developed using free-living male and female gametophytes of the brown macroalga Undaria pinnatifida. A conventional fluorometer was employed to evaluate the acute (48 h) toxic effects of six antifouling biocides: 4,5-Dichloro-2-octyl-isothiazolone (DCOIT), diuron, irgarol, medetomidine, tolylfluanid, zinc pyrithione (ZnPT). The decreasing toxicity in male and female gametophytes as estimated by EC50 (effective concentration at which 50% inhibition occurs) values was: diuron (0.037 and 0.128 mg l(-1), respectively) > irgarol (0.096 and 0.172 mg l(-1), respectively) > tolylfluanid (0.238 and 1.028 mg l(-1), respectively) > DCOIT (1.015 and 0.890 mg l(-1), respectively) > medetomidine (12.032 and 12.763 mg l(-1), respectively). For ZnPT, 50% fluorescence inhibition of U. pinnatifida gametophytes occurred at concentrations above 0.4 mg l(-1). The Undaria method is rapid, simple, practical, and cost-effective for the detection of photosynthesis-inhibiting biocides, thus making a useful tool for testing the toxicity of antifouling agents in marine environments

Feature Map for Quantum Data: Probabilistic Manipulation

The kernel trick in supervised learning signifies transformations of an inner product by a feature map, which then restructures training data in a larger Hilbert space according to an endowed inner product. A quantum feature map corresponds to an instance with a Hilbert space of quantum states by fueling quantum resources to ML algorithms. In this work, we point out that the quantum state space is specific such that a measurement postulate characterizes an inner product and that manipulation of quantum states prepared from classical data cannot enhance the distinguishability of data points. We present a feature map for quantum data as a probabilistic manipulation of quantum states to improve supervised learning algorithms.Comment: 5 pages, 4 figures, 1 tabl

The Public-Private Partnerships and the Fiscal Soundness of Local Governments in Korea

This paper studies the risks associated with local finance in Korea by identifying the financial status of each local government, including the financial burdens of PPP projects, and examined governmental future burdens related to PPP projects. We reviewed all fiscal burdens associated with projects, such as, for BTL (Build-Transfer-Lease) types of projects, facility lease and operating expenses, and, for the BTO (Build-Transfer-Operate) types of projects, construction subsidies that are paid at the construction stage, MRG (Minimum Revenue Guarantee) payments and the government’s share of payment. Furthermore, we compared the annual expenditures of local governments on PPP projects against their annual budgets and checked if the 2% ceiling rule could be applied

Oleate Prevents Palmitate-Induced Atrophy via Modulation of Mitochondrial ROS Production in Skeletal Myotubes

Accumulation of saturated fatty acids contributes to lipotoxicity-related insulin resistance and atrophy in skeletal muscle. Conversely, unsaturated fatty acids like docosahexaenoic acid were proven to preserve muscle mass. However, it is not known if the most common unsaturated oleate will protect skeletal myotubes against palmitate-mediated atrophy, and its specific mechanism remains to be elucidated. Therefore, we investigated the effects of oleate on atrophy-related factors in palmitate-conditioned myotubes. Exposure of myotubes to palmitate, but not to oleate, led to an induction of fragmented nuclei, myotube loss, atrophy, and mitochondrial superoxide in a dose-dependent manner. Treatment of oleate to myotubes attenuated production of palmitate-induced mitochondrial superoxide in a dose-dependent manner. The treatment of oleate or MitoTEMPO to palmitate-conditioned myotubes led to inhibition of palmitate-induced mRNA expression of proinflammatory (TNF-α and IL6), mitochondrial fission (Drp1 and Fis1), and atrophy markers (myostatin and atrogin1). In accordance with the gene expression data, our immunocytochemistry experiment demonstrated that oleate and MitoTEMPO prevented or attenuated palmitate-mediated myotube shrinkage. These results provide a mechanism indicating that oleate prevents palmitate-mediated atrophy via at least partial modulation of mitochondrial superoxide production

Tradeoff of generalization error in unsupervised learning

Finding the optimal model complexity that minimizes the generalization error (GE) is a key issue of machine learning. For the conventional supervised learning, this task typically involves the bias-variance tradeoff: lowering the bias by making the model more complex entails an increase in the variance. Meanwhile, little has been studied about whether the same tradeoff exists for unsupervised learning. In this study, we propose that unsupervised learning generally exhibits a two-component tradeoff of the GE, namely the model error and the data error -- using a more complex model reduces the model error at the cost of the data error, with the data error playing a more significant role for a smaller training dataset. This is corroborated by training the restricted Boltzmann machine to generate the configurations of the two-dimensional Ising model at a given temperature and the totally asymmetric simple exclusion process with given entry and exit rates. Our results also indicate that the optimal model tends to be more complex when the data to be learned are more complex.Comment: 15 pages, 7 figure

Determinants of Competitive Advantage for Sport Firms: Using Public Big Data in Korea

This study examines the determinants of competitive advantage with respect to economic performance of sport firms. Logit regressions estimated dependent variables of economic performance measures based on sales per capita of firms. Determinants of competitive advantage were estimated by efficiency indicators, organization characteristic indicators, and industry classification indicators. Increase in efficiency was a significant determinant of competitive advantage as well as organizational type, size of human resource, diversification of products, and sales growth rate. Operationalizing competitive advantage as outperforming the market average and better than the top 10%, the logit regression model provides means for sport firms to analyze industry data to evaluate their own performance. In particular, including efficiency estimates showed practical significance for market analysis