Aperture Synthesis Observations of CO, HCN, and 89GHz Continuum Emission toward NGC 604 in M 33: Sequential Star Formation Induced by Supergiant Hii region

We present the results from new Nobeyama Millimeter Array observations of CO(1-0), HCN(1-0), and 89-GHz continuum emissions toward NGC 604, known as the supergiant H ii region in a nearby galaxy M 33. Our high spatial resolution images of CO emission allowed us to uncover ten individual molecular clouds that have masses of (0.8 -7.4) 10$^5$M_{\sun } and sizes of 5 -- 29 pc, comparable to those of typical Galactic giant molecular clouds (GMCs). Moreover, we detected for the first time HCN emission in the two most massive clouds and 89 GHz continuum emission at the rims of the "H${\alpha}$ shells". Three out of ten CO clouds are well correlated with the H${\alpha}$ shells both in spatial and velocity domains, implying an interaction between molecular gas and the expanding H ii region. Furthermore, we estimated star formation efficiencies (SFEs) for each cloud from the 89-GHz and combination of H${\alpha}$ and 24-${\mu}$m data, and found that the SFEs decrease with increasing projected distance measured from the heart of the central OB star cluster in NGC 604, suggesting the radial changes in evolutionary stages of the molecular clouds in course of stellar cluster formation. Our results provide further support to the picture of sequential star formation in NGC604 initially proposed by Tosaki et al. (2007) with the higher spatially resolved molecular clouds, in which an isotropic expansion of the H ii region pushes gases outward and accumulates them to consecutively form dense molecular clouds, and then induces massive star formations.Comment: 23 pages, 8 figures, accepted for publication in Ap

Predicting mostly disordered proteins by using structure-unknown protein data

BACKGROUND: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences. RESULTS: When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred (long)), its sensitivity was 0.834 for disordered proteins, which is 0.052–0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036–0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5%–10% disordered sequences, 1.46% for the proteins with 10%–20% disordered sequences and 16.57% for proteins with 20%–40% disordered sequences. CONCLUSION: The proposed method, which utilizes the information of structure-unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness

MetaVM: A Transparent Distributed Object System Supported by Runtime Compiler

MetaVM is a distributed object system for Java virtual machine. It allows programmers to deal with remote objects in the same way they do local objects. Therefore, it can provide a single machine image to programmers. We implemented a runtime compiler of Java bytecode to provide the facilities. The runtime compiler generates a native code which can handle remote objects beyond the network besides the local objects. The compiler uses semantic expansion, which is a technique that changes the original semantics of a Java bytecode. Keywords: distributed object system, network transparency, Java Just-In-Time compiler