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Objective: As a result of the development of microarray technologies, gene expression levels of thousands of genes involved in a given biological process can be measured simultaneously, and it is important to study their temporal behavior to understand their mechanisms. Since the dependence between gene expression levels over time for a given gene is often too complicated to model parametrically, sparse functional data analysis has received an increasing amount of attention for analyzing such data. Methods: We propose a new functional mixed-effects model for analyzing time-course gene expression data. Specifically, the model groups individual functions with heterogeneous smoothness. The proposed method utilizes the mixed-effects model representation of penalized splines for both the mean function and the individual functions. Given noninformative or weakly informative priors, Bayesian inference on the proposed models was developed, and Bayesian computation was implemented by using Markov chain Monte Carlo methods. Results: The performance of our new model was studied by two simulation studies and illustrated using a yeast cell cycle gene expression dataset. Simulation results suggest that our proposed methods can outperform the previously used methods in terms of the mean integrated squared error. The yeast gene expression data application suggests that the proposed model with two latent groups should be used on this dataset. Conclusion: The new Bayesian functional mixed-effects model that assumes multiple groups of functions with different smoothing parameters provides an enhanced approach to analyzing timecourse gene expression data.

Silica nanoparticles (SiO2 NPs) synthesized by the Stober method were used as drug delivery vehicles. Doxorubicin hydrochloride (DOX·HCl) is a chemo-drug absorbed onto the SiO2 NPs surfaces. The DOX·HCl loading onto and release from the SiO2 NPs was monitored via UV-VIS and fluorescence spectra. Alternatively, the zeta potential was also used to monitor and evaluate the DOX·HCl loading process. The results showed that nearly 98% of DOX·HCl was effectively loaded onto the SiO2 NPs’ surfaces by electrostatic interaction. The pH-dependence of the process wherein DOX·HCl release out of DOX·HCl-SiO2 NPs was investigated as well. For comparison, both the free DOX·HCl molecules and DOX·HCl-SiO2 NPs were used as the labels for cultured cancer cells. Confocal laser scanning microscopy images showed that the DOX·HCl-SiO2 NPs were better delivered to cancer cells which are more acidic than healthy cells. We propose that engineered DOX·HCl-SiO2 systems are good candidates for drug delivery and clinical applications.

In this paper, we study semantic role labelling (SRL), a subtask of semantic parsing of natural language sentences and its application for the Vietnamese language. We present our effort in building Vietnamese PropBank, the first Vietnamese SRL corpus and a software system for labelling semantic roles of Vietnamese texts. In particular, we present a novel constituent extraction algorithm in the argument candidate identification step which is more suitable and more accurate than the common node-mapping method. In the machine learning part, our system integrates distributed word features produced by two recent unsupervised learning models in two learned statistical classifiers and makes use of integer linear programming inference procedure to improve the accuracy. The system is evaluated in a series of experiments and achieves a good result, an $F_1$ score of 74.77%. Our system, including corpus and software, is available as an open source project for free research and we believe that it is a good baseline for the development of future Vietnamese SRL systems., Accepted to the VNU Journal of Science

Lexicon is an important resource in natural language processing (NLP), as it provides NLP systems with lexical information at different levels, from morphology to semantics. For Vietnamese, lexical resources are available for several basic tools such as word segmentation, part-of-speech tagging and syntactic parsing. In this paper, we discuss the construction of a lexicon enriched with syntactic and semantic information, based on an existing computational lexicon for Vietnamese. This lexicon is designed to serve for a syntactic and semantic parser using the tree adjoining grammar (TAG) formalism.

In this work, we propose to use distributed word representations in a greedy, transition-based dependency parsing framework. Instead of using a very large number of sparse indicator features, the multinomial logistic regression classifier employed by the parser learns and uses a small number of dense features, therefore it can work very fast. The distributed word representations are produced by a continuous skip-gram model using a neural network architecture. Experiments on a Vietnamese dependency treebank show that the parser not only works faster but also achieves better accuracy in comparison to a conventional transition-based dependency parser.

This paper presents a method for automatically inducing the parts-ofspeech of the Vietnamese language from a large text corpus. We first build a classbased bigram language model using several statistical algorithms assigning words to classes based on their ability to combine with neighbouring words.We then show that this model is able to extract word classes that have the flavor of either syntactically based or semantically based groupings of Vietnamese words, which are the long disputed approaches among the Vietnamese linguistic community. Finally, the quality of word clusters is quantitatively evaluated when word cluster features are used to improve the accuracy of a statistical part-of-speech tagger for Vietnamese.

This paper presents the construction and evaluation of a deep syntactic parser based on Lexicalized Tree-Adjoining Grammars for the Vietnamese language. This is a complete sys- tem integrating necessary tools to process Vietnamese text, which permits to take as input raw texts and produce syntactic struc- tures. A dependency annotation scheme for Vietnamese and an algorithm for extracting dependency structures from derivation trees are also proposed. At present, this is the first Vietnamese parsing system capable of producing both constituency and dependency analyses with encouraging performances: 69.33% and 73.21% for constituency and dependency analysis accuracy, respectively.

Held in conjunction with ACL-IJCNLP 2009; International audience; Treebank is an important resource for both research and application of natural language processing. For Vietnamese, we still lack such kind of corpora. This paper presents up-to-date results of a project for Vietnamese treebank construction. Since Vietnamese is an isolating language and has no word delimiter, there are many ambiguities in sentence analysis. We systematically applied a lot of linguistic techniques to handle such ambiguities. Annotators are supported by automatic labeling tools and a tree-editor tool. Raw texts are extracted from Tuoi Tre (Youth), an online Vietnamese daily newspaper. The current annotation agreement is around 90 percent.

International audience; no abstract

International audience; Only very recently have Vietnamese researchers begun to be involved in the domain of Natural Language Processing (NLP). As there does not exist any published work in formal linguistics nor any recognizable standard for Vietnamese word definition and word categories, the fundamental tasks for automatic Vietnamese language processing, such as part-of-speech tagging, parsing, etc., are very difficult tasks for computer scientists. The fact that all necessary linguistic resources have to be built from scratch by each research team is a real obstacle to the development of Vietnamese language processing. The aim of our projects is thus to build a common linguistic database that is freely and easily exploitable for the automatic processing of Vietnamese. In this paper, we present our work on creating a Vietnamese lexicon for NLP applications. We emphasize the standardization aspect of the lexicon representation. We especially propose an extensible set of Vietnamese syntactic descriptions that can be used for tagset definition and morphosyntactic analysis. These descriptors are established in such a way as to be a reference set proposal for Vietnamese in the context of ISO subcommittee TC 37/SC 4 (Language Resource Management).

Polymeric nanoparticles (NPs) prepared from poly(ethylene glycol)-block-poly (e-caprolactone) (PEG–PCL) were fabricated by the modified nanoprecipitation method with and without sonication to entrap docetaxel (Doc) and curcumin (Cur). NPs were characterized in terms of morphology, size distribution, zeta potential, encapsulation efficiency and cytotoxicity. The particles have a ~45–80 nm mean diameter with a spherical shape. The cellular uptake of the NPs was observed after 2 and 4 h of incubation by fluorescence of curcumin loaded with docetaxel. The cell viability was evaluated by an [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay on the Hela cell line. Doc and Doc–Cur NPs had higher cytotoxicity and a much lower IC50 value compared with free Doc or Cur after 24 and 48 h of incubation. Doc and Cur incorporated into the PEG–PCL NPs had the highest cytotoxicity in comparison with all other NPs and may be considered as an attractive and promising drug delivery system for cancer treatment.

Drug delivery to cancer cells in chemotherapy is one of the most advanced research topics. The effectiveness of the current cancer treatment drugs is limited because they are not capable of distinguishing between cancer cells and normal cells so that they kill not only cancer cells but also normal ones. To overcome this disadvantage by profiting from the differences in physical and chemical properties between cancer and normal cells, nanoparticles (NPs) delivering a drug are designed in a specific manner such that they can distinguish the cancer cells from the normal ones and are targeted only to the cancer cells. Currently, there are various drug delivery systems with many advantages, but sharing some common disadvantages such as difficulty with controlling the size, low encapsulation capacity and low stability. With the development and success of DNA nanotechnology, DNA strands are used to create effective drug delivery NPs with precisely controlled size and structure, safety and high stability. This article presents our study on drug encapsulation in DNA nanostructure which loaded docetaxel and curcumin in a desire to create a new and effective drug delivery system with high biological compatibility.