Statistical properties of volatility in fractal dimension and probability distribution among six stock markets - USA, Japan, Taiwan, South Korea, Singapore, and Hong Kong

This study examines the statistical properties of volatility. Fractal dimension, probability distribution and two-point volatility correlation are used to measure and compare volatility among six different markets for the 12-year period from Jan. 1 1990 to Dec. 31 2001. New York market is found to be the strongest among the six in terms of market efficiency. Moreover, the Tokyo and Singapore markets are found to be very similar in fractal dimension and probability distribution, but different in their resistance to volatility : Tokyo has a higher ability to dissipate volatility. This phenomenon implies that the Tokyo market is more efficient than the Singapore market. The Hong Kong market is similar to the Singapore market in its ability to dissipate volatility. Meanwhile, the Taiwanese and Korean markets are the two most volatile markets among the six. Notably, the Taiwanese market is weaker than the Korean market in dissipating volatility.Volatility, fractal dimension, probability distribution.

Multipotent vascular stem cells contribute to neurovascular regeneration of peripheral nerve.

BackgroundNeurovascular unit restoration is crucial for nerve regeneration, especially in critical gaps of injured peripheral nerve. Multipotent vascular stem cells (MVSCs) harvested from an adult blood vessel are involved in vascular remodeling; however, the therapeutic benefit for nerve regeneration is not clear.MethodsMVSCs were isolated from rats expressing green fluorescence protein (GFP), expanded, mixed with Matrigel matrix, and loaded into the nerve conduits. A nerve autograft or a nerve conduit (with acellular matrigel or MVSCs in matrigel) was used to bridge a transected sciatic nerve (10-mm critical gap) in rats. The functional motor recovery and cell fate in the regenerated nerve were investigated to understand the therapeutic benefit.ResultsMVSCs expressed markers such as Sox 17 and Sox10 and could differentiate into neural cells in vitro. One month following MVSC transplantation, the compound muscle action potential (CMAP) significantly increased as compared to the acellular group. MVSCs facilitated the recruitment of Schwann cell to regenerated axons. The transplanted cells, traced by GFP, differentiated into perineurial cells around the bundles of regenerated myelinated axons. In addition, MVSCs enhanced tight junction formation as a part of the blood-nerve barrier (BNB). Furthermore, MVSCs differentiated into perivascular cells and enhanced microvessel formation within regenerated neurovascular bundles.ConclusionsIn rats with peripheral nerve injuries, the transplantation of MVSCs into the nerve conduits improved the recovery of neuromuscular function; MVSCs differentiated into perineural cells and perivascular cells and enhanced the formation of tight junctions in perineural BNB. This study demonstrates the in vivo therapeutic benefit of adult MVSCs for peripheral nerve regeneration and provides insight into the role of MVSCs in BNB regeneration

Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders

An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure of speech in an unsupervised manner. A previous study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for VC. How- ever, VAE using other types of spectral features such as mel- cepstral coefficients (MCCs), which are related to human per- ception and have been widely used in VC, have not been prop- erly investigated. Instead of using one specific type of spectral feature, it is expected that VAE may benefit from using multi- ple types of spectral features simultaneously, thereby improving the capability of VAE for VC. To this end, we propose a novel VAE framework (called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple objectives in order to constrain the behavior of the learned encoder and de- coder. Experimental results demonstrate that the proposed CD- VAE framework outperforms the conventional VAE framework in terms of subjective tests.Comment: Accepted to ISCSLP 201

Phase Distribution and Phase Correlation of Financial Time Series

Scaling, phase distribution and phase correlation of financial time series are investigated based on the Dow Jones Industry Average (DJIA) and NASDAQ 10-minute intraday data for a period from Aug. 1 1997 to Dec. 31 2003. The returns of the two indices are shown to have nice scaling behaviors and belong to stable distributions according to the criterion of Levy's alpha stable distribution condition. A novel approach catching characteristic features of financial time series based on the concept of instantaneous phase is further proposed to study phase distribution and correlation. The analysis of phase distribution concludes return time series fall into a class which is different from other non-stationary time series. The correlation between returns of the two indices probed by the distribution of phase difference indicates there was a remarkable change of trading activities after the event of 911 attack, and this change persisted in later trading activities.Phase Distribution, High Frequency Data, Scaling Analysis, Levy Distribution, Stock Market, Frequency Variant

Separation Enhancement of Mechanical Filters by Adding Negative Air Ions

The purpose of this work is to combine negative air ions (NAIs) and mechanical filters for removal of indoor suspended particulates. Various factors, including aerosol size (0.05-0.45 μm), face velocity (10 and 20 cm/s), species of aerosol (potassium chloride and dioctyl phthalate), relative humidity (30% and 70%), and concentrations of NAIs (2 ´ 104, 1 ´ 105, and 2 ´ 105 NAIs/cm3) were considered to evaluate their effects on the aerosol collection characteristics of filters. Results show that the aerosol penetration through the mechanical filter is higher than that through the mechanical filters cooperated with NAIs. This finding implies that the aerosol removal efficiency of mechanical filters can be improved by NAIs. Furthermore, the aerosol penetration through the mechanical filters increased with the aerosol size when NAIs were added. That is due to that the aerosol is easier to be charged when its size gets larger. The results also indicate the aerosol penetration decreased with the NAIs concentration increased. Reversely, aerosol penetration through the mechanical filters increased with the face velocity under the influence of NAIs. The aerosol penetration through the filter with NAIs was no affected with relative humidity. Finally, The penetration through the filter with NAIs against solid aerosol was lower than that against liquid aerosol

Predicting RNA-binding residues from evolutionary information and sequence conservation

Abstract Background RNA-binding proteins (RBPs) play crucial roles in post-transcriptional control of RNA. RBPs are designed to efficiently recognize specific RNA sequences after it is derived from the DNA sequence. To satisfy diverse functional requirements, RNA binding proteins are composed of multiple blocks of RNA-binding domains (RBDs) presented in various structural arrangements to provide versatile functions. The ability to computationally predict RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments. Results The proposed prediction framework named “ProteRNA” combines a SVM-based classifier with conserved residue discovery by WildSpan to identify the residues that interact with RNA in a RNA-binding protein. Although these conserved residues can be either functionally conserved residues or structurally conserved residues, they provide clues on the important residues in a protein sequence. In the independent testing dataset, ProteRNA has been able to deliver overall accuracy of 89.78%, MCC of 0.2628, F-score of 0.3075, and F0.5-score of 0.3546. Conclusions This article presents the design of a sequence-based predictor aiming to identify the RNA-binding residues in a RNA-binding protein by combining machine learning and pattern mining approaches. RNA-binding proteins have diverse functions while interacting with different categories of RNAs because these proteins are composed of multiple copies of RNA-binding domains presented in various structural arrangements to expand the functional repertoire of RNA-binding proteins. Furthermore, predicting RNA-binding residues in a RNA-binding protein can help biologists reveal important site-directed mutagenesis in wet-lab experiments.</p