Hieu Chi Dam

Alternative splicing produces structural and functional changes in CUGBP2

BackgroundCELF/Bruno-like proteins play multiple roles, including the regulation of alternative splicing and translation. These RNA-binding proteins contain two RNA recognition motif (RRM) domains at the N-terminus and another RRM at the C-terminus. CUGBP2 is a member of this family of proteins that possesses several alternatively spliced exons.ResultsThe present study investigated the expression of exon 14, which is an alternatively spliced exon and encodes the first half of the third RRM of CUGBP2. The ratio of exon 14 skipping product (R3δ) to its inclusion was reduced in neuronal cells induced from P19 cells and in the brain. Although full length CUGBP2 and the CUGBP2 R3δ isoforms showed a similar effect on the inclusion of the smooth muscle (SM) exon of the ACTN1 gene, these isoforms showed an opposite effect on the skipping of exon 11 in the insulin receptor gene. In addition, examination of structural changes in these isoforms by molecular dynamics simulation and NMR spectrometry suggested that the third RRM of R3δ isoform was flexible and did not form an RRM structure.ConclusionOur results suggest that CUGBP2 regulates the splicing of ACTN1 and insulin receptor by different mechanisms. Alternative splicing of CUGBP2 exon 14 contributes to the regulation of the splicing of the insulin receptor. The present findings specifically show how alternative splicing events that result in three-dimensional structural changes in CUGBP2 can lead to changes in its biological activity.

Data mining for materials design: A computational study of single molecule magnet

We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn(4+)Mn3(3+) single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn(4+) and Mn(3+) ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results.

Machine learning reveals orbital interaction in materials

We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM. Graphical Abstract

Novel mixture model for the representation of potential energy surfaces

We demonstrate that knowledge of chemical physics on a materials system can be automatically extracted from first-principles calculations using a data mining technique; this information can then be utilized to construct a simple empirical atomic potential model. By using unsupervised learning of the generative Gaussian mixture model, physically meaningful patterns of atomic local chemical environments can be detected automatically. Based on the obtained information regarding these atomic patterns, we propose a chemical-structure-dependent linear mixture model for estimating the atomic potential energy. Our experiments show that the proposed mixture model significantly improves the accuracy of the prediction of the potential energy surface for complex systems that possess a large diversity in their local structures.

Combining classifiers based on OWA operators with an application to word sense disambiguation

This paper proposes a framework for combining classifiers based on OWA operators in which each individual classifier uses a distinct representation of objects to be classified. It is shown that this framework yields several commonly used decision rules but without some strong assumptions made in the work by Kittler et al. [7]. As an application, we apply the proposed framework of classifier combination to the problem of word sense disambiguation (shortly, WSD). To this end, we experimentally design a set of individual classifiers, each of which corresponds to a distinct representation type of context considered in the WSD literature, and then the proposed combination strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard, and compared to previous studies.

Learning structure-property relationship in crystalline materials: A study of lanthanide–transition metal alloys

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 μB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

Correlation between charge transfer and exchange coupling in carbon-based magnetic materials

Several forms of carbon-based magnetic materials, i.e. single radicals, radical dimers, and alternating stacks of radicals and diamagnetic molecules, have been investigated using density-functional theory with dispersion correction and full geometry optimization. Our calculated results demonstrate that the C31H15 (R4) radical has a spin of ½. However, in its [R4]2 dimer structure, the net spin becomes zero due to antiferromagnetic spin-exchange between radicals. To avoid antiferromagnetic spin-exchange of identical face-to-face radicals, eight alternating stacks, R4/D2m/R4 (with m = 3-10), were designed. Our calculated results show that charge transfer (Δn) between R4 radicals and the diamagnetic molecule D2m occurs with a mechanism of spin exchange (J) in stacks. The more electrons that transfer from R4 to D2m, the stronger the ferromagnetic spin-exchange in stacks. In addition, our calculated results show that Δn can be tailored by adjusting the electron affinity (Ea) of D2m. The correlation between Δn,...

The Knowledge-Bridging Process in Software Offshoring from Japan to Vietnam

The role of coordinators in highly knowledge‐intensive international business has been increasingly portrayed as filling the communication, cross‐cultural, social gaps; or facilitators of knowledge sharing and knowledge transfer. However, they do not mention the process coordinators cooperate with partners to create new knowledge; whereas a critical issue is to facilitate the collaboration among partners in international and cross‐cultural context. Our study bridges this gap by studying the knowledge‐bridging process of bridge System Engineers (bridge SEs) in the software offshore development context. Our analysis pointed out that beside required knowledge, bridge SEs utilized background of long term residence or study abroad; and “bridging‐knowledge” to adjust communication contents before information is sent from one side to another. Based on the theoretical work of Nonaka and Takeuchi (1995) and empirical evidence of bridge SEs in Software Offshoring from Japan to Vietnam; we further develop the model of Knowledge‐bridging process to show how the bridge SEs, the vendor and the client interact to create technological, business, bridging knowledge and decrease the cultural gaps.

Simulation-based data mining solution to the structure of water surrounding proteins

What is structure of water surrounding proteins remains as one of fundamental unsolved problems of science. Methods in biophysics only provide qualitative description of the structure and thus clarifying the collective phenomena of a huge number of water molecules is still beyond intuition in biophysics. We introduce a simulation-based data mining approach that quantitatively model the structure of water surrounding a protein as clusters of water molecules having similar moving behavior. The paper presents and explains how the advances of AI technique can potentially solve this challenging data-intensive problem.

Weighted combination of classifiers for word sense disambiguation based on dempster-shafer theory

This paper proposes a framework for weighted combination of classifiers for word sense disambiguation based on Dempster-Shafer theory of evidence. First, by taking the confidence of individual classifiers into account, weighted com- bination of individual classifiers corresponding to distinct repre- sentations of context of a polysemous word is formulated based on Dempsters rule of combination. Second, the developed model is improved by an adaptive strategy of weight determination and a ranking-and-combination scheme. The experiment conducted for four polysemous words, namely interest, line, serve ,a ndhard, shows significantly better results in comparison with previous studies on the same datasets.