A Comprehensive Four-Quark Interpretation of D_s(2317), D_s(2457) and D_s(2632)

The recently observed new member of the charm-strange family D_s(2632) which has a surprisingly narrow width is challenging our theory. D_s(2317) and D_s(2457) which were observed earlier have similar behaviors and receive various theoretical explanations. Some authors use the heavy hadron chiral effective theory to evaluate heavy-light quark systems and obtain a reasonable evaluation on the masses of D_s(2317) and D_s(2457). An alternative picture is to interpret them as four-quark or molecular states. In this work, we are following the later and propose a unitive description for all the three new members D_s(2632), D_s(2317) and D_s(2457) and at least, so far our picture is consistent with the data.Comment: 6 page

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Family, learning environments, learning approaches, and student outcomes in a Malaysian private university

This paper presents the quantitative findings from a mixed methods study of students and faculty at a private medical university in Malaysia. In particular, the relationships among students’ individual characteristics, general self-efficacy, family context, university and classroom learning environments, curriculum, approaches to learning, and measures of students’ academic achievement, self-directed learning readiness and mental health at the student level. Data were collected from 392 students attending a private medical university in Malaysia. The findings from the partial least square path (PLSPATH) suggest that: (a) parental involvement continues to impact and influence student learning process, and related student outcomes, at the university level, and (b) a surface approach to learning is related to poor quality processes and outcomes and a deep approach to learning is related to high quality processes and outcomes

On Kernel Formulas and Dispersionless Hirota Equations

We rederive dispersionless Hirota equations of the dispersionless Toda hierarchy from the method of kernel formula provided by Carroll and Kodama. We then apply the method to derive dispersionless Hirota equations of the extended dispersionless BKP(EdBKP) hierarchy proposed by Takasaki. Moreover, we verify associativity equations (WDVV equations) in the EdBKP hierarchy from dispersionless Hirota equations and give a realization of associative algebra with structure constants expressed in terms of residue formula.Comment: 30 pages, minor corrections, references adde