Determinants and Impacts of the Relative Use of Depository Receipts and Euro Convertible Bonds by High-tech Corporations: An Empirical Study

This paper adopts Taiwan's high-tech companies as the sample to address and examine four new determinants of various foreign financing instruments and test their impacts on the issuing firms. Our empirical findings are consistent with the following notions. First, the firms with higher foreign holding and foreign investment will be likely to adopt foreign financing policy. Moreover, the firms with higher stock dividend payment in Taiwan will adopt both of ECB (Euro convertible bond) and DR (depository receipt). Firm managers with better education background will prefer DR. Second, the use of DR can effectively decrease the volatility of stock returns but also pronounce a negative influence on the mean of stock returns. In contrast, the use of ECB can effectively increase the mean but can not significantly decrease the volatility.

Korean Red Ginseng Suppresses Metastasis of Human Hepatoma SK-Hep1 Cells by Inhibiting Matrix Metalloproteinase-2/-9 and Urokinase Plasminogen Activator

Korean red ginseng and ginsenosides have been claimed to possess wide spectrum of medicinal effects, of which anticancer effect is one. The present study was undertaken to investigate the antimetastatic effect of Korean red ginseng on human hepatoma as well as possible mechanisms. The inhibitory effect of the water extract of Korean red ginseng (WKRG) on the invasion and motility of SK-Hep1 cells was evaluated by the Boyden chamber assay in vitro. Without causing cytotoxicity, WKRG exerted a dose-dependent inhibitory effect on the invasion and motility, but not adhesion, of highly metastatic SK-Hep1 cells. Zymography analyses revealed significant downregulating effects on MMP-2, MMP-9, and uPA activities in SK-Hep1 cells. Western blot analyses also showed that WKRG treatment caused dose-dependent decreases in MMP-2 and MMP-9 protein expressions. Moreover, WKRG increased the levels of TIMP-1, TIMP-2, and PAI-1. The present study not only demonstrated that invasion and motility of cancer cells were inhibited by WKRG, but also indicated that such effects were likely associated with the decrease in MMP-2/-9 and uPA expressions of SK-Hep1 cells

Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.Comment: 23 pages, 6 figures, 1 tabl

Chromatin signature of widespread monoallelic expression

In mammals, numerous autosomal genes are subject to mitotically stable monoallelic expression (MAE), including genes that play critical roles in a variety of human diseases. Due to challenges posed by the clonal nature of MAE, very little is known about its regulation; in particular, no molecular features have been specifically linked to MAE. In this study, we report an approach that distinguishes MAE genes in human cells with great accuracy: a chromatin signature consisting of chromatin marks associated with active transcription (H3K36me3) and silencing (H3K27me3) simultaneously occurring in the gene body. The MAE signature is present in ∼20% of ubiquitously expressed genes and over 30% of tissue-specific genes across cell types. Notably, it is enriched among key developmental genes that have bivalent chromatin structure in pluripotent cells. Our results open a new approach to the study of MAE that is independent of polymorphisms, and suggest that MAE is linked to cell differentiation. DOI: http://dx.doi.org/10.7554/eLife.01256.00

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. IV. Hβ\beta Time Lags and Implications for Super-Eddington Accretion

We have completed two years of photometric and spectroscopic monitoring of a large number of active galactic nuclei (AGNs) with very high accretion rates. In this paper, we report on the result of the second phase of the campaign, during 2013--2014, and the measurements of five new H$\beta$ time lags out of eight monitored AGNs. All five objects were identified as super-Eddington accreting massive black holes (SEAMBHs). The highest measured accretion rates for the objects in this campaign are $\dot{\mathscr{M}}\gtrsim 200$, where $\dot{\mathscr{M}}= \dot{M}_{\bullet}/L_{\rm Edd}c^{-2}$, $\dot{M}_{\bullet}$ is the mass accretion rates, $L_{\rm Edd}$ is the Eddington luminosity and $c$ is the speed of light. We find that the H$\beta$ time lags in SEAMBHs are significantly shorter than those measured in sub-Eddington AGNs, and the deviations increase with increasing accretion rates. Thus, the relationship between broad-line region size ($R_{_{\rm H\beta}}$) and optical luminosity at 5100\AA, $R_{_{\rm H\beta}}-L_{5100}$, requires accretion rate as an additional parameter. We propose that much of the effect may be due to the strong anisotropy of the emitted slim-disk radiation. Scaling $R_{_{\rm H\beta}}$ by the gravitational radius of the black hole, we define a new radius-mass parameter ($Y$) and show that it saturates at a critical accretion rate of $\dot{\mathscr{M}}_c=6\sim 30$, indicating a transition from thin to slim accretion disk and a saturated luminosity of the slim disks. The parameter $Y$ is a very useful probe for understanding the various types of accretion onto massive black holes. We briefly comment on implications to the general population of super-Eddington AGNs in the universe and applications to cosmology.Comment: 53 pages, 12 figures, 7 tables, accepted for publication in The Astrophysical Journa

Density Functional Theory Study of Pt_3M Alloy Surface Segregation with Adsorbed O/OH and Pt_3Os as Catalysts for Oxygen Reduction Reaction

Using quantum mechanics calculations, we have studied the segregation energy with adsorbed O and OH for 28 Pt_3M alloys, where M is a transition metal. The calculations found surface segregation to become energetically unfavorable for Pt_3Co and Pt_3Ni, as well as for the most other Pt binary alloys, in the presence of adsorbed O and OH. However, Pt_3Os and Pt_3Ir remain surface segregated and show the best energy preference among the alloys studied for both adsorbed species on the surface. Binding energies of various oxygen reduction reaction (ORR) intermediates on the Pt(111) and Pt_3Os(111) surfaces were calculated and analyzed. Energy barriers for different ORR steps were computed for Pt and Pt_3Os catalysts, and the rate-determining steps (RDS) were identified. It turns out that the RDS barrier for the Pt_3Os alloy catalyst is lower than the corresponding barrier for pure Pt. This result allows us to predict a better ORR performance of Pt_3Os compared to that of pure Pt

Seasonal variations of C-1-C-4 alkyl nitrates at a coastal site in Hong Kong: Influence of photochemical formation and oceanic emissions

Five C-1-C-4 alkyl nitrates (RONO2) were measured at a coastal site in Hong Kong in four selected months of 2011 and 2012. The total mixing ratios of C-1-C-4 RONO2 (Sigma 5RONO2) ranged from 15.4 to 143.7 pptv with an average of 65.9 +/- 33.0 pptv. C-3-C-4 RONO2 (2-butyl nitrate and 2-propyl nitrate) were the most abundant RONO2 during the entire sampling period. The mixing ratios of C-3-C-4 RONO2 were higher in winter than those in summer, while the ones of methyl nitrate (MeONO2) were higher in summer than those in winter. Source analysis suggests that C-2-C-4 RONO2 were mainly derived from photochemical formation along with biomass burning (58.3-71.6%), while ocean was a major contributor to MeONO2 (53.8%) during the whole sampling period. The photochemical evolution of C-2-C-4 RONO2 was investigated, and found to be dominantly produced by the parent hydrocarbon oxidation. The notable enrichment of MeONO2 over C-3-C-4 RONO2 was observed in a summer episode when the air masses originating from the South China Sea (SCS) and MeONO2 was dominantly derived from oceanic emissions. In order to improve the accuracy of ozone (O-3) prediction in coastal environment, the relative contribution of RONO2 from oceanic emissions versus photochemical formation and their coupling effects on O-3 production should be taken into account in future studies. (C) 2017 Elsevier Ltd. All rights reserved

Theory of Variable Fuzzy Sets for Artificial Emotions Prediction

Emotions have a very important impact on human’s beliefs, motivations, actions, and physical states. Emotions predicting and its application in intelligent system can improve the interaction between humans and machines. Current research in artificial emotion focuses on how to measure, calculate, or compute it. However, the transfer of emotion is often too complicated to present full emotion states and changes. This paper combines with emotional dimension and theory of variable fuzzy sets to present a predicting artificial emotion model and shows illustrated example of it. This study shows that any raw data from input can be computed with variable fuzzy set. It provides a mathematical method for representing emotion quantitative, gradual qualitative, and mutated qualitative change. This framework improves calculation methods and mechanisms, closer to real emotional changes