Mutation and Lineage Analysis of DNMT3A in BCR-ABL1-negative Chronic Myeloproliferative Neoplasms

SummaryIn addition to the JAK2 V617F mutation, somatic mutation in DNMT3A has been described in BCL-ABL1-negative myeloproliferative neoplasms (MPNs). We have screened for DNMT3A exon 23 mutations in 130 adult Taiwanese patients with chronic phase myeloproliferative neoplasms. Only one somatic DNMT3A R882H mutation was identified in one JAK2 V617F mutation-positive essential thrombocythemia patient (1/91, 1%). Both mutations were detected in the CD34+-, CD19+-, peripheral blood mononuclear cell- and granulocyte-enriched fractions, but were not detected in the CD3+-enriched fraction by lineage analysis. Our findings suggest that DNMT3A mutation is not prevalent in MPNs, and further study is needed to clarify its role in the molecular pathogenesis of myeloproliferative neoplasms

Serotonin receptor HTR6-mediated mTORC1 signaling regulates dietary restriction-induced memory enhancement

Dietary restriction (DR; sometimes called calorie restriction) has profound beneficial effects on physiological, psychological, and behavioral outcomes in animals and in humans. We have explored the molecular mechanism of DR-induced memory enhancement and demonstrate that dietary tryptophan-a precursor amino acid for serotonin biosynthesis in the brain-and serotonin receptor 5-hydroxytryptamine receptor 6 (HTR6) are crucial in mediating this process. We show that HTR6 inactivation diminishes DR-induced neurological alterations, including reduced dendritic complexity, increased spine density, and enhanced long-term potentiation (LTP) in hippocampal neurons. Moreover, we find that HTR6-mediated mechanistic target of rapamycin complex 1 (mTORC1) signaling is involved in DR-induced memory improvement. Our results suggest that the HTR6-mediated mTORC1 pathway may function as a nutrient sensor in hippocampal neurons to couple memory performance to dietary intake

The local linear M-estimator with a robust initial estimate

[[abstract]]In the field of nonparametric regression, the local linear M-estimator (LLM; Fan and Jiang 1999) is proposed to adjust for the unrobustness of the local linear estimator (LLE; Fan 1992, 1993). In practice, the LLM is often computed using Newton method together with an initial estimate produced by the LLE. However, by the unrobustness of the LLE, such initial estimate might be far from the global minimizer of M function. In this case, the Newton method might provide an incorrect solution for the LLM. To improve the drawback, a robust initial estimate for Newton method is proposed. Simulation results show that our robust initial estimate is useful when using Newton method to find a solution for the LLM.[[notice]]補正完畢[[journaltype]]國

On Study of the Relationship between TCRI and Technical Inefficiency

[[abstract]]In this paper, a stochastic frontier model with firmspecific technical inefficiency effects in a panel framework (Battese and Coelli, 1995) is used to examine whether a firm with better Taiwan Corporate Credit Risk Index (TCRI) provided by Taiwan Economic Journal attains higher technical efficiency. A special design matrix of discrete ordinal variables is used to study the effects of TCRI on technical inefficiency for firms in Taiwan under the two control variables, firm's age and size. Our empirical result shows that a firm with better TCRI generally has higher technical efficiency. Combining the result with the fact that economic-based efficiency measures are reasonable indicators of the long-term health and prospects of firms (Baek and Pagán, 2002), we conclude that TCRI is a good credit risk proxy for firms in Taiwan.[[conferencetype]]國際[[conferencedate]]20110226~20110228[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Singapor

Predicting issuer credit ratings using a semiparametric method

This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model.Industry effect Issuer credit rating Market-driven variable Ordered linear probit model Ordered semiparametric probit model

Amlexanox Blocks the Interaction between S100A4 and Epidermal Growth Factor and Inhibits Cell Proliferation.

The human S100A4 protein binds calcium, resulting in a change in its conformation to promote the interaction with its target protein. Human epidermal growth factor (EGF) is the target protein of S100A4 and a critical ligand of the receptor EGFR. The EGF/EGFR system promotes cell survival, differentiation, and growth by activating several signaling pathways. Amlexanox is an anti-inflammatory and anti-allergic drug that is used to treat recurrent aphthous ulcers. In the present study, we determined that amlexanox interacts with S100A4 using heteronuclear single quantum correlation titration. We elucidated the interactions of S100A4 with EGF and amlexanox using fluorescence and nuclear magnetic resonance spectroscopy. We generated two binary models (for the S100A4-EGF and S100A4-amlexanox complexes) and observed that amlexanox and EGF share a similar binding region in mS100A4. We also used a WST-1 assay to investigate the bioactivity of S100A4, EGF, and amlexanox, and found that amlexanox blocks the binding between S100A4 and EGF, and is therefore useful for the development of new anti-proliferation drugs

Predicting bankruptcy using the discrete-time semiparametric hazard model

The usual bankruptcy prediction models are based on single-period data from firms. These models ignore the fact that the characteristics of firms change through time, and thus they may suffer from a loss of predictive power. In recent years, a discrete-time parametric hazard model has been proposed for bankruptcy prediction using panel data from firms. This model has been demonstrated by many examples to be more powerful than the traditional models. In this paper, we propose an extension of this approach allowing for a more flexible choice of hazard function. The new method does not require the assumption of a parametric model for the hazard function. In addition, it also provides a tool for checking the adequacy of the parametric model, if necessary. We use real panel datasets to illustrate the proposed method. The empirical results confirm that the new model compares favorably with the well-known discrete-time parametric hazard model.Discrete-time hazard model, Local likelihood, Out-of-sample error rate, Panel data, Semiparametric model,

A semiparametric method for predicting bankruptcy

Bankruptcy prediction methods based on a semiparametric logit model are proposed for simple random (prospective) and case-control (choice-based; retrospective) data. The unknown parameters and prediction probabilities in the model are estimated by the local likelihood approach, and the resulting estimators are analyzed through their asymptotic biases and variances. The semiparametric bankruptcy prediction methods using these two types of data are shown to be essentially equivalent. Thus our proposed prediction model can be directly applied to data sampled from the two important designs. One real data example and simulations confirm that our prediction method is more powerful than alternatives, in the sense of yielding smaller out-of-sample error rates. Copyright © 2007 John Wiley & Sons, Ltd.

Functional Assay.

<p>(A) A431 cells were treated with 10 nM EGF, 10 nM EGF + 5 μM mS100A4, 10 nM EGF + 25 μM mS100A4, 10 nM EGF + 50 μM mS100A4, or 10 nM EGF + 100 μM mS100A4, and cell proliferation was assessed using the WST-1 assay. The relative cell counts after treatment with mS100A4 are plotted as the fold induction, with serum-free medium and AG1478 as the controls (lanes 7–11). The data are expressed as the mean ± SD of 3 independent experiments. (B) Effects of amlexanox on mS100A4-mediated cell proliferation and EGFR signaling. A431 cells were treated with 10 nM EGF, 10 nM EGF + 100 μM mS100A4, 10 nM EGF + 100 μM mS100A4 + 5 μM amlexanox, 10 nM EGF + 100 μM mS100A4 + 10 μM amlexanox, or 10 nM EGF + 100 μM mS100A4 + 50 μM amlexanox. Cell proliferation was analyzed after 48 h. (C) Left panel: A431 cells were serum starved for 24 h and then incubated with or without 10 nM EGF in the presence or absence of 100 nM mS100A4 or 1 μM amlexanox for 1 h. The cell lysate was extracted from each treatment, and 1 mg of cell lysate was immunoprecipitated with an anti-EGFR antibody (Biovision). The amounts of immunoprecipitated EGF and EGFR were examined by Western blotting (upper plot). The amounts of phosphorylated and total EGFR in cell lysate were subsequently detected by Western blotting. Alpha-tubulin was used as an internal control (lower plot). Right panel: The quantitative result of EGFR-associated EGF was shown from two independent experiments.</p