A novel EB-1/AIDA-1 isoform, AIDA-1c, interacts with the Cajal body protein coilin

BACKGROUND: Cajal bodies (CBs) are nuclear suborganelles that play a role in the biogenesis of small nuclear ribonucleoproteins (snRNPs), which are crucial for pre-mRNA splicing. Upon nuclear reentry, Sm-class snRNPs localize first to the CB, where the snRNA moiety of the snRNP is modified. It is not clear how snRNPs target to the CB and are released from this structure after their modification. Coilin, the CB marker protein, may participate in snRNP biogenesis given that it can interact with snRNPs and SMN. SMN is crucial for snRNP assembly and is the protein mutated in the neurodegenerative disease Spinal Muscular Atrophy. Coilin knockout mice display significant viability problems and altered CB formation. Thus characterization of the CB and its associated proteins will give insight into snRNP biogenesis and clarify the dynamic organization of the nucleus. RESULTS: In this report, we identify a novel protein isoform of EB-1/AIDA-1, termed AIDA-1c, that interacts with the CB marker protein, coilin. Northern and nested PCR experiments reveal that the AIDA-1c isoform is expressed in brain and several cancer cell lines. Competition binding experiments demonstrate that AIDA-1c competes with SmB' for coilin binding sites, but does not bind SMN. When ectopically expressed, AIDA-1c is predominantly nuclear with no obvious accumulations in CBs. Interestingly, another EB-1/AIDA-1 nuclear isoform, AIDA-1a, does not bind coilin in vivo as efficiently as AIDA-1c. Knockdown of EB-1/AIDA-1 isoforms by siRNA altered Cajal body organization and reduced cell viability. CONCLUSION: These data suggest that specific EB-1/AIDA-1 isoforms, such as AIDA-1c, may participate in the regulation of nucleoplasmic coilin protein interactions in neuronal and transformed cells

Discovery of Dependency Tree Patterns for Relation Extraction

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese

This paper explores the nature of linguistic synaesthesia in the auditory domain through a corpus-based lexical semantic study of near synonyms. It has been established that the near synonyms 聲 sheng “sound ” and 音 yin “sound ” in Mandarin Chinese have different semantic functions in representing auditory production and auditory perception respec-tively. Thus, our study is devoted to test-ing whether linguistic synaesthesia is sensi-tive to this semantic dichotomy of cognition in particular, and to examining the relation-ship between linguistic synaesthesia and cog-nitive modelling in general. Based on the cor-pus, we find that the near synonyms exhibit both similarities and differences on synaesthe-sia. The similarities lie in that both 聲 and音 are productive recipients of synaesthetic trans-fers, and vision acts as the source domain most frequently. Besides, the differences exist in se-lective constraints for 聲 and 音 with synaes-thetic modifiers as well as syntactic functions of the whole combinations. We propose that the similarities can be explained by the cogni-tive characteristics of the sound, while the dif-ferences are determined by the influence of the semantic dichotomy of production/perception on synaesthesia. Therefore, linguistic synaes-thesia is not a random association, but can be motivated and predicted by cognition.

Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network

In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (CycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions. Specifically, we propose the CycleGAN detector by constructing a bidirectional loop of two modified least squares generative adversarial networks (LS-GAN). The forward LS-GAN learns to model the transmission process, while the backward LS-GAN learns to detect the received signals. By optimizing the cycle-consistency of the transmitted and received signals through this loop, the proposed method is trained online and semi-supervisedly using both the pilots and the received payload data. As such, the demand on labelled training dataset is considerably controlled, and thus the overhead is effectively reduced. Numerical results show that the proposed CycleGAN detector achieves better performance in terms of both bit error-rate (BER) and achievable rate than existing semi-blind deep learning (DL) detection methods as well as conventional linear detectors, especially when considering signal distortion due to the nonlinearity of power amplifiers (PA) at the transmitter