Perovskite-based Catalysts for Direct Ethanol Fuel Cells

Utilizing a screening strategy featuring energy efficient and rapid solution combustion (SC) synthesis technique, and the high throughput NuVant system, a library of conductive perovskites was synthesized and tested as anode electrode in the direct ethanol fuel cell (DEFC) conditions. It was found that a variety of Ru-based perovskites showed considerable electro-catalytic activity for ethanol oxidation. Further, it was demonstrated that the perovskite-platinum catalysts with low noble metal loading prepared directly in by SC method exhibit comparable performance with standard Pt-Ru alloy

Myosin Va binding to neurofilaments is essential for correct myosin Va distribution and transport and neurofilament density

The identification of molecular motors that modulate the neuronal cytoskeleton has been elusive. Here, we show that a molecular motor protein, myosin Va, is present in high proportions in the cytoskeleton of mouse CNS and peripheral nerves. Immunoelectron microscopy, coimmunoprecipitation, and blot overlay analyses demonstrate that myosin Va in axons associates with neurofilaments, and that the NF-L subunit is its major ligand. A physiological association is indicated by observations that the level of myosin Va is reduced in axons of NF-L–null mice lacking neurofilaments and increased in mice overexpressing NF-L, but unchanged in NF-H–null mice. In vivo pulse-labeled myosin Va advances along axons at slow transport rates overlapping with those of neurofilament proteins and actin, both of which coimmunoprecipitate with myosin Va. Eliminating neurofilaments from mice selectively accelerates myosin Va translocation and redistributes myosin Va to the actin-rich subaxolemma and membranous organelles. Finally, peripheral axons of dilute-lethal mice, lacking functional myosin Va, display selectively increased neurofilament number and levels of neurofilament proteins without altering axon caliber. These results identify myosin Va as a neurofilament-associated protein, and show that this association is essential to establish the normal distribution, axonal transport, and content of myosin Va, and the proper numbers of neurofilaments in axons

Comparative assessment of gasification based coal power plants with various CO2 capture technologies producing electricity and hydrogen

Seven different types of gasification-based coal conversion processes for producing mainly electricity and in some cases hydrogen (H2), with and without carbon dioxide (CO2) capture, were compared on a consistent basis through simulation studies. The flowsheet for each process was developed in a chemical process simulation tool “Aspen Plus”. The pressure swing adsorption (PSA), physical absorption (Selexol), and chemical looping combustion (CLC) technologies were separately analyzed for processes with CO2 capture. The performances of the above three capture technologies were compared with respect to energetic and exergetic efficiencies, and the level of CO2 emission. The effect of air separation unit (ASU) and gas turbine (GT) integration on the power output of all the CO2 capture cases is assessed. Sensitivity analysis was carried out for the CLC process (electricity-only case) to examine the effect of temperature and water-cooling of the air reactor on the overall efficiency of the process. The results show that, when only electricity production in considered, the case using CLC technology has an electrical efficiency 1.3% and 2.3% higher than the PSA and Selexol based cases, respectively. The CLC based process achieves an overall CO2 capture efficiency of 99.9% in contrast to 89.9% for PSA and 93.5% for Selexol based processes. The overall efficiency of the CLC case for combined electricity and H2 production is marginally higher (by 0.3%) than Selexol and lower (by 0.6%) than PSA cases. The integration between the ASU and GT units benefits all three technologies in terms of electrical efficiency. Furthermore, our results suggest that it is favorable to operate the air reactor of the CLC process at higher temperatures with excess air supply in order to achieve higher power efficiency

Multi-agent modeling of the South Korean avian influenza epidemic

<p>Abstract</p> <p>Background</p> <p>Several highly pathogenic avian influenza (AI) outbreaks have been reported over the past decade. South Korea recently faced AI outbreaks whose economic impact was estimated to be 6.3 billion dollars, equivalent to nearly 50% of the profit generated by the poultry-related industries in 2008. In addition, AI is threatening to cause a human pandemic of potentially devastating proportions. Several studies show that a stochastic simulation model can be used to plan an efficient containment strategy on an emerging influenza. Efficient control of AI outbreaks based on such simulation studies could be an important strategy in minimizing its adverse economic and public health impacts.</p> <p>Methods</p> <p>We constructed a spatio-temporal multi-agent model of chickens and ducks in poultry farms in South Korea. The spatial domain, comprised of 76 (37.5 km × 37.5 km) unit squares, approximated the size and scale of South Korea. In this spatial domain, we introduced 3,039 poultry flocks (corresponding to 2,231 flocks of chickens and 808 flocks of ducks) whose spatial distribution was proportional to the number of birds in each province. The model parameterizes the properties and dynamic behaviors of birds in poultry farms and quarantine plans and included infection probability, incubation period, interactions among birds, and quarantine region.</p> <p>Results</p> <p>We conducted sensitivity analysis for the different parameters in the model. Our study shows that the quarantine plan with well-chosen values of parameters is critical for minimize loss of poultry flocks in an AI outbreak. Specifically, the aggressive culling plan of infected poultry farms over 18.75 km radius range is unlikely to be effective, resulting in higher fractions of unnecessarily culled poultry flocks and the weak culling plan is also unlikely to be effective, resulting in higher fractions of infected poultry flocks.</p> <p>Conclusions</p> <p>Our results show that a prepared response with targeted quarantine protocols would have a high probability of containing the disease. The containment plan with an aggressive culling plan is not necessarily efficient, causing a higher fraction of unnecessarily culled poultry farms. Instead, it is necessary to balance culling with other important factors involved in AI spreading. Better estimations for the containment of AI spreading with this model offer the potential to reduce the loss of poultry and minimize economic impact on the poultry industry.</p

Semantic integration to identify overlapping functional modules in protein interaction networks

<p>Abstract</p> <p>Background</p> <p>The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.</p> <p>Results</p> <p>We have developed novel metrics, called semantic similarity and semantic interactivity, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. We presented a flow-based modularization algorithm to efficiently identify overlapping modules in the weighted interaction networks. The experimental results show that the semantic similarity and semantic interactivity of interacting pairs were positively correlated with functional co-occurrence. The effectiveness of the algorithm for identifying modules was evaluated using functional categories from the MIPS database. We demonstrated that our algorithm had higher accuracy compared to other competing approaches.</p> <p>Conclusion</p> <p>The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.</p

The Myosin Va Head Domain Binds to the Neurofilament-L Rod and Modulates Endoplasmic Reticulum (ER) Content and Distribution within Axons

The neurofilament light subunit (NF-L) binds to myosin Va (Myo Va) in neurons but the sites of interaction and functional significance are not clear. We show by deletion analysis that motor domain of Myo Va binds to the NF-L rod domain that forms the NF backbone. Loss of NF-L and Myo Va binding from axons significantly reduces the axonal content of ER, and redistributes ER to the periphery of axon. Our data are consistent with a novel function for NFs as a scaffold in axons for maintaining the content and proper distribution of vesicular organelles, mediated in part by Myo Va. Based on observations that the Myo Va motor domain binds to intermediate filament (IF) proteins of several classes, Myo Va interactions with IFs may serve similar roles in organizing organelle topography in different cell types

Osteopontin induces growth of metastatic tumors in a preclinical model of non-small lung cancer

Osteopontin (OPN), also known as SPP1 (secreted phosphoprotein), is an integrin binding glyco-phosphoprotein produced by a variety of tissues. In cancer patients expression of OPN has been associated with poor prognosis in several tumor types including breast, lung, and colorectal cancers. Despite wide expression in tumor cells and stroma, there is limited evidence supporting role of OPN in tumor progression and metastasis. Using phage display technology we identified a high affinity anti-OPN monoclonal antibody (hereafter AOM1). The binding site for AOM1 was identified as SVVYGLRSKS sequence which is immediately adjacent to the RGD motif and also spans the thrombin cleavage site of the human OPN. AOM1 efficiently inhibited OPNa binding to recombinant integrin αvβ3 with an IC50 of 65 nM. Due to its unique binding site, AOM1 is capable of inhibiting OPN cleavage by thrombin which has been shown to produce an OPN fragment that is biologically more active than the full length OPN. Screening of human cell lines identified tumor cells with increased expression of OPN receptors (αvβ3 and CD44v6) such as mesothelioma, hepatocellular carcinoma, breast, and non-small cell lung adenocarcinoma (NSCLC). CD44v6 and αvβ3 were also found to be highly enriched in the monocyte, but not lymphocyte, subset of human peripheral blood mononuclear cells (hPBMCs). In vitro, OPNa induced migration of both tumor and hPBMCs in a transwell migration assay. AOM1 significantly blocked cell migration further validating its specificity for the ligand. OPN was found to be enriched in mouse plasma in a number of pre-clinical tumor model of non-small cell lung cancers. To assess the role of OPN in tumor growth and metastasis and to evaluate a potential therapeutic indication for AOM1, we employed a KrasG12D-LSLp53fl/fl subcutaneously implanted in vivo model of NSCLC which possesses a high capacity to metastasize into the lung. Our data indicated that treatment of tumor bearing mice with AOM1 as a single agent or in combination with Carboplatin significantly inhibited growth of large metastatic tumors in the lung further supporting a role for OPN in tumor metastasis and progression