China and the crisis : global power, domestic caution and local initiative

Even though the global crisis had a quick and dramatic impact on Chinese exports, the Chinese government responded with a range of policy responses that have helped maintain high rates of growth. This success has helped propel China to the centre of global politics, accelerating what many perceive to be a power shift from the West to China. But these gains were achieved by reversing policy in previous years designed to make a fundamental shift in China‟s mode of development, and have highlighted the problems associated with making such a transition. At the moment that many are looking at the Chinese "model" as a potential alternative to the Washington Consensus, one of the consequences of the crisis is to further question the long term efficacy of this "model" in China itself

Investigation of the Spin Density Wave in NaxCoO2

Magnetic susceptibility, transport and heat capacity measurements of single crystal NaxCoO2 (x=0.71) are reported. A transition to a spin density wave (SDW) state at Tmag = 22 K is observable in all measurements, except chi(ac) data in which a cusp is observed at 4 K and attributed to a low temperature glassy phase. M(H) loops are hysteretic below 15 K. Both the SDW transition and low temperature hysteresis are only visible along the c-axis. The system also exhibits a substantial (~40%) positive magnetoresistance below this temperature. Calculations of the electronic heat capacity gamma above and below Tmag and the size of the jump in C indicate that the onset of the SDW brings about the opening of gap and the removal of part of the Fermi surface. Reduced in-plane electron-electron scattering counteracts the loss of carriers below the transition and as a result we see a net reduction in resistivity below Tmag. Sodium ordering transitions at higher temperatures are observable as peaks in the heat capacity with a corresponding increase in resistivity.Comment: 14 pages, 6 figure

Semi-supervised protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.</p> <p>Results</p> <p>In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.</p> <p>Conclusion</p> <p>Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.</p

A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins

Subcellular locations of proteins are important functional attributes. An effective and efficient subcellular localization predictor is necessary for rapidly and reliably annotating subcellular locations of proteins. Most of existing subcellular localization methods are only used to deal with single-location proteins. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. To better reflect characteristics of multiplex proteins, it is highly desired to develop new methods for dealing with them. In this paper, a new predictor, called Euk-ECC-mPLoc, by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and hybridizing gene ontology with dipeptide composition information, has been developed that can be used to deal with systems containing both singleplex and multiplex eukaryotic proteins. It can be utilized to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centrosome, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome, (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole. Experimental results on a stringent benchmark dataset of eukaryotic proteins by jackknife cross validation test show that the average success rate and overall success rate obtained by Euk-ECC-mPLoc were 69.70% and 81.54%, respectively, indicating that our approach is quite promising. Particularly, the success rates achieved by Euk-ECC-mPLoc for small subsets were remarkably improved, indicating that it holds a high potential for simulating the development of the area. As a user-friendly web-server, Euk-ECC-mPLoc is freely accessible to the public at the website http://levis.tongji.edu.cn:8080/bioinfo/Euk-ECC-mPLoc/. We believe that Euk-ECC-mPLoc may become a useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying subcellular locations of eukaryotic proteins

Measurement of Neutrino-Electron Scattering Cross-Section with a CsI(Tl) Scintillating Crystal Array at the Kuo-Sheng Nuclear Power Reactor

The $\bar{\nu}_{e}-e^{-}$ elastic scattering cross-section was measured with a CsI(Tl) scintillating crystal array having a total mass of 187kg. The detector was exposed to an average reactor $\bar{\nu}_{e}$ flux of $\rm{6.4\times 10^{12} ~ cm^{-2}s^{-1}}$ at the Kuo-Sheng Nuclear Power Station. The experimental design, conceptual merits, detector hardware, data analysis and background understanding of the experiment are presented. Using 29882/7369 kg-days of Reactor ON/OFF data, the Standard Model(SM) electroweak interaction was probed at the squared 4-momentum transfer range of $\rm{Q^2 \sim 3 \times 10^{-6} ~ GeV^2}$. The ratio of experimental to SM cross-sections of $\xi =[ 1.08 \pm 0.21(stat)\pm 0.16(sys)]$ was measured. Constraints on the electroweak parameters $(g_V , g_A)$ were placed, corresponding to a weak mixing angle measurement of \s2tw = 0.251 \pm 0.031({\it stat}) \pm 0.024({\it sys}) . Destructive interference in the SM \nuebar -e process was verified. Bounds on anomalous neutrino electromagnetic properties were placed: neutrino magnetic moment at \mu_{\nuebar}< 2.2 \times 10^{-10} \mu_{\rm B} and the neutrino charge radius at -2.1 \times 10^{-32} ~{\rm cm^{2}} < \nuchrad < 3.3 \times 10^{-32} ~{\rm cm^{2}}, both at 90% confidence level.Comment: 18 Figures, 7 Tables; published version as V2 with minor revision from V

Two Birds with One Stone? Possible Dual-Targeting H1N1 Inhibitors from Traditional Chinese Medicine

The H1N1 influenza pandemic of 2009 has claimed over 18,000 lives. During this pandemic, development of drug resistance further complicated efforts to control and treat the widespread illness. This research utilizes traditional Chinese medicine Database@Taiwan (TCM Database@Taiwan) to screen for compounds that simultaneously target H1 and N1 to overcome current difficulties with virus mutations. The top three candidates were de novo derivatives of xylopine and rosmaricine. Bioactivity of the de novo derivatives against N1 were validated by multiple machine learning prediction models. Ability of the de novo compounds to maintain CoMFA/CoMSIA contour and form key interactions implied bioactivity within H1 as well. Addition of a pyridinium fragment was critical to form stable interactions in H1 and N1 as supported by molecular dynamics (MD) simulation. Results from MD, hydrophobic interactions, and torsion angles are consistent and support the findings of docking. Multiple anchors and lack of binding to residues prone to mutation suggest that the TCM de novo derivatives may be resistant to drug resistance and are advantageous over conventional H1N1 treatments such as oseltamivir. These results suggest that the TCM de novo derivatives may be suitable candidates of dual-targeting drugs for influenza.National Science Council of Taiwan (NSC 99-2221-E-039-013-)Committee on Chinese Medicine and Pharmacy (CCMP100-RD-030)China Medical University and Asia University (CMU98-TCM)China Medical University and Asia University (CMU99-TCM)China Medical University and Asia University (CMU99-S-02)China Medical University and Asia University (CMU99-ASIA-25)China Medical University and Asia University (CMU99-ASIA-26)China Medical University and Asia University (CMU99-ASIA-27)China Medical University and Asia University (CMU99-ASIA-28)Taiwan Department of Health. Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004)Taiwan Department of Health. Cancer Research Center of Excellence (DOH100-TD-C-111-005

Transforming Growth Factor-β1 Suppresses Hepatitis B Virus Replication by the Reduction of Hepatocyte Nuclear Factor-4α Expression

Several studies have demonstrated that cytokine-mediated noncytopathic suppression of hepatitis B virus (HBV) replication may provide an alternative therapeutic strategy for the treatment of chronic hepatitis B infection. In our previous study, we showed that transforming growth factor-beta1 (TGF-β1) could effectively suppress HBV replication at physiological concentrations. Here, we provide more evidence that TGF-β1 specifically diminishes HBV core promoter activity, which subsequently results in a reduction in the level of viral pregenomic RNA (pgRNA), core protein (HBc), nucleocapsid, and consequently suppresses HBV replication. The hepatocyte nuclear factor 4alpha (HNF-4α) binding element(s) within the HBV core promoter region was characterized to be responsive for the inhibitory effect of TGF-β1 on HBV regulation. Furthermore, we found that TGF-β1 treatment significantly repressed HNF-4α expression at both mRNA and protein levels. We demonstrated that RNAi-mediated depletion of HNF-4α was sufficient to reduce HBc synthesis as TGF-β1 did. Prevention of HNF-4α degradation by treating with proteasome inhibitor MG132 also prevented the inhibitory effect of TGF-β1. Finally, we confirmed that HBV replication could be rescued by ectopic expression of HNF-4α in TGF-β1-treated cells. Our data clarify the mechanism by which TGF-β1 suppresses HBV replication, primarily through modulating the expression of HNF-4α gene

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.</p> <p>Results</p> <p>The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.</p> <p>Conclusions</p> <p>The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at <url>http://biomine.ece.ualberta.ca/MODAS/</url>.</p

Vegetation forcing modulates global land monsoon and water resources in a CO2-enriched climate

The global monsoon is characterised by transitions between pronounced dry and wet seasons, affecting food security for two-thirds of the world’s population. Rising atmospheric CO2 influences the terrestrial hydrological cycle through climate-radiative and vegetation-physiological forcings. How these two forcings affect the seasonal intensity and characteristics of monsoonal precipitation and runoff is poorly understood. Here we use four Earth System Models to show that in a CO2-enriched climate, radiative forcing changes drive annual precipitation increases for most monsoon regions. Further, vegetation feedbacks substantially affect annual precipitation in North and South America and Australia monsoon regions. In the dry season, runoff increases over most monsoon regions, due to stomatal closure-driven evapotranspiration reductions and associated atmospheric circulation change. Our results imply that flood risks may amplify in the wet season. However, the lengthening of the monsoon rainfall season and reduced evapotranspiration will shorten the water resources scarcity period for most monsoon regions

Modern microwave methods in solid state inorganic materials chemistry: from fundamentals to manufacturing

No abstract available