Chiral Antioxidant-based Gold Nanoclusters Reprogram DNA Epigenetic Patterns

Epigenetic modifications sit ‘on top of’ the genome and influence DNA transcription, which can force a significant impact on cellular behavior and phenotype and, consequently human development and disease. Conventional methods for evaluating epigenetic modifications have inherent limitations and, hence, new methods based on nanoscale devices are needed. Here, we found that antioxidant (glutathione) chiral gold nanoclusters induce a decrease of 5-hydroxymethylcytosine (5hmC), which is an important epigenetic marker that associates with gene transcription regulation. This epigenetic change was triggered partially through ROS activation and oxidation generated by the treatment with glutathione chiral gold nanoclusters, which may inhibit the activity of TET proteins catalyzing the conversion of 5-methylcytosine (5mC) to 5hmC. In addition, these chiral gold nanoclusters can downregulate TET1 and TET2 mRNA expression. Alteration of TET-5hmC signaling will then affect several downstream targets and be involved in many aspects of cell behavior. We demonstrate for the first time that antioxidant-based chiral gold nanomaterials have a direct effect on epigenetic process of TET-5hmC pathways and reveal critical DNA demethylation patterns

Application of Fractal Dimensions and Fuzzy Clustering to Tool Wear Monitoring

Monitoring of metal cutting tool wear states is a key technology for automatic, unmanned and adaptive machining. As tool wear increases, the vibration signals of cutting tool become more and more irregular in the turning processes. The degree of tool wear can be indirectly monitored according to these changes of vibration signals. In order to quantitatively describe these changes, fractal theory and fuzzy clustering method were introduced into the cutting tool wear monitoring area. Firstly, wavelet de-noising method was used to reduce the noise of original signals, and eliminate the effect of noise on fractal dimensions. Secondly, the fractal dimensions based on fractal theory were got from the de-noised signals, including box dimension, information dimension, and correlation dimension. Finally, the relationship between the fractal dimensions and tool wear states was studied; the affinities between the known and unknown states can be obtained through fuzzy c-mean clustering algorithm; tool wear states can be recognized by those affinities based on fractal dimensions. The experiment results demonstrate that wavelet de-noising method can efficiently eliminate the effect of noise on fractal dimensions, and tool wear states can be real-timely and accurately recognized through the fuzzy clustering analysis on fractal dimensions. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.188

Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period

Every financial crisis has caused a dual shock to the global economy. The shortage of market liquidity, such as default in debt and bonds, has led to the spread of bankruptcies, such as Lehman Brothers in 2008. Using the data for the ETFs of the S&P 500, Nasdaq 100, and Dow Jones Industrial Average collected from Yahoo Finance, this study implemented Deep Learning, Neuro Network, and Time-series to analyze the trend of the American Stock Market in the post-COVID-19 period. LSTM model in Neuro Network to predict the future trend, which suggests the US stock market keeps falling for the post-COVID-19 period. This study reveals a reasonable allocation method of Long Short-Term Memory for which there is strong evidence.Comment: 9 pages, 4 figures, Submitted to the Cambridge University Press Journa

User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction

In this paper, we investigate whether is it possible to train a neural network directly from user inputs. We consider this approach to be highly relevant for applications in which the point of optimality is not well-defined and user-dependent. Our application is medical image denoising which is essential in fluoroscopy imaging. In this field every user, i.e. physician, has a different flavor and image quality needs to be tailored towards each individual. To address this important problem, we propose to construct a loss function derived from a forced-choice experiment. In order to make the learning problem feasible, we operate in the domain of precision learning, i.e., we inspire the network architecture by traditional signal processing methods in order to reduce the number of trainable parameters. The algorithm that was used for this is a Laplacian pyramid with only six trainable parameters. In the experimental results, we demonstrate that two image experts who prefer different filter characteristics between sharpness and de-noising can be created using our approach. Also models trained for a specific user perform best on this users test data. This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.Comment: Accepted on BVM 2019; Extended ArXiv Version with additional figures and detail

“Keep it simple, scholar”: an experimental analysis of few-parameter segmentation networks for retinal vessels in fundus imaging

Purpose With the recent development of deep learning technologies, various neural networks have been proposed for fundus retinal vessel segmentation. Among them, the U-Net is regarded as one of the most successful architectures. In this work, we start with simplification of the U-Net, and explore the performance of few-parameter networks on this task. Methods We firstly modify the model with popular functional blocks and additional resolution levels, then we switch to exploring the limits for compression of the network architecture. Experiments are designed to simplify the network structure, decrease the number of trainable parameters, and reduce the amount of training data. Performance evaluation is carried out on four public databases, namely DRIVE, STARE, HRF and CHASE_DB1. In addition, the generalization ability of the few-parameter networks are compared against the state-of-the-art segmentation network. Results We demonstrate that the additive variants do not significantly improve the segmentation performance. The performance of the models are not severely harmed unless they are harshly degenerated: one level, or one filter in the input convolutional layer, or trained with one image. We also demonstrate that few-parameter networks have strong generalization ability. Conclusion It is counter-intuitive that the U-Net produces reasonably good segmentation predictions until reaching the mentioned limits. Our work has two main contributions. On the one hand, the importance of different elements of the U-Net is evaluated, and the minimal U-Net which is capable of the task is presented. On the other hand, our work demonstrates that retinal vessel segmentation can be tackled by surprisingly simple configurations of U-Net reaching almost state-of-the-art performance. We also show that the simple configurations have better generalization ability than state-of-the-art models with high model complexity. These observations seem to be in contradiction to the current trend of continued increase in model complexity and capacity for the task under consideration