Expression, regulation and putative nutrient-sensing function of taste GPCRs in the heart

G protein-coupled receptors (GPCRs) are critical for cardiovascular physiology. Cardiac cells express >100 nonchemosensory GPCRs, indicating that important physiological and potential therapeutic targets remain to be discovered. Moreover, there is a growing appreciation that members of the large, distinct taste and odorant GPCR families have specific functions in tissues beyond the oronasal cavity, including in the brain, gastrointestinal tract and respiratory system. To date, these chemosensory GPCRs have not been systematically studied in the heart. We performed RT-qPCR taste receptor screens in rodent and human heart tissues that revealed discrete subsets of type 2 taste receptors (TAS2/Tas2) as well as Tas1r1 and Tas1r3 (comprising the umami receptor) are expressed. These taste GPCRs are present in cultured cardiac myocytes and fibroblasts, and are enriched in myocytes, which we corroborated using in situ hybridization. Tas1r1 gene-targeted mice (Tas1r1Cre/Rosa26tdRFP) strikingly recapitulated these data. In vivo taste receptor expression levels were developmentally regulated in the postnatal period. Intriguingly, several Tas2rs were upregulated in cultured rat myocytes and in mouse heart in vivo following starvation. The discovery of taste GPCRs in the heart opens an exciting new field of cardiac research. We predict that these taste receptors may function as nutrient sensors in the heart.

Investigation of the effects of various types of wind turbine generators on power-system stability

It has become more and more demanding to investigate the impacts of wind farms on power system operation as ever-increasing penetration levels of wind power have the potential to bring about a series of dynamic stability problems for power systems. This paper undertakes such an investigation through investigating the small signal and transient stabilities of power systems that are separately integrated with three types of wind turbine generators (WTGs), namely the squirrel cage induction generator (SCIG), the doubly fed induction generator (DFIG), and the permanent magnet generator (PMG). To examine the effects of these WTGs on a power system with regard to its stability under different operating conditions, a selected synchronous generator (SG) of the well-known Western Electricity Coordinating Council (WECC three-unit nine-bus system and an eight-unit 24-bus system is replaced in turn by each type of WTG with the same capacity. The performances of the power system in response to the disturbances are then systematically compared. Specifically, the following comparisons are undertaken: (1) performances of the power system before and after the integration of the WTGs; and (2) performances of the power system and the associated consequences when the SCIG, DFIG, or PMG are separately connected to the system. These stability case studies utilize both eigenvalue analysis and dynamic time-domain simulation methods.

A new approach for evaluating the benefits of smart grid development

In this paper, the inherent mechanism of benefits associated with smart grid development is examined based on the Pressure-State-Response (PSR) model from resource economics. The emerging types of technology brought up by smart grid development are taken as pressures. The improvements of the performance and efficiency of power system operation are taken as states. The effects of smart grid development on society are taken as responses. Then, a novel method for evaluating social benefits in energy saving and CO2 emission reduction from smart grid development is presented. Finally, the benefits in a province in northwest China is carried out by employing the developed evaluation system, and reasonable evaluation results are attained.

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Managing creativity on the margins : a comparative study of Beijing’s Songzhuang Art District and Hangzhou’s White Horse Lake Creative Eco-City

In the past decade the ‘creative cluster’ has become a driver of urban renewal in China. Many cluster developments attract human capital and investment to post-industrial spaces. This paper looks at two developments which are more post-agricultural than post-industrial: the first is Songzhuang, a large scale contemporary art community situated on the eastern fringe of Beijing, the second is Hangzhou’s White Horse Lake Creative Eco-City, a ‘mixed variety’ cluster model which integrates elements of art, fashion, design and animation. The common factor in both cases is how they came into existence. In both districts urban creative workers moved into a rural environment. Drawing on interviews with planners, officials, and residents we investigate the challenges of sustaining such fringe clusters.

Key derivation function based on stream ciphers

A key derivation function (KDF) is a function that transforms secret non-uniformly random source material together with some public strings into one or more cryptographic keys. These cryptographic keys are used with a cryptographic algorithm for protecting electronic data during both transmission over insecure channels and storage. In this thesis, we propose a new method for constructing a generic stream cipher based key derivation function. We show that our proposed key derivation function based on stream ciphers is secure if the under-lying stream cipher is secure. We simulate instances of this stream cipher based key derivation function using three eStream nalist: Trivium, Sosemanuk and Rabbit. The simulation results show these stream cipher based key derivation functions offer efficiency advantages over the more commonly used key derivation functions based on block ciphers and hash functions.

Robust speed tracking control of synchronous motors using immersion and invariance

This paper presents a novel control strategy for velocity tracking of Permanent Magnet Synchronous Machines (PMSM). The model of the machine is considered within the port-Hamiltonian framework and a control is designed using concepts of immersion and invariance (I&I) recently developed in the literature. The proposed controller ensures internal stability and output regulation, and it forces integral action on non-passive outputs.

A mass spectrometry study of XCO+, X = Si, Ge : Is SiCO+ a main group carbonyl? Comments on the bonding in ground state SiCO and the Si,C,O (+) potential energy surface

The cation\[Si,C,O](+) has been generated by 1) the electron ionisation (EI) of tetramethoxysilane and 2) chemical ionisation (CI) of a mixture of silane and carbon monoxide. Collisional activation (CA) experiments performed for mass-selected \[Si,C,O](+), generated by using both methods, indicate that the structure is not inserted OSiC+; however, a definitive structural assignment as Si+-CO, Si+-OC or some cyclic variant is impossible based on these results alone. Neutralisation-reionisation (+NR+) experiments for EI-generated \[Si,C,O](+) reveal a small peak corresponding to SiC+, but no detectable SiO+ signal, and thus establishes the existence of the Si+-CO isomer. CCSD(T)//B3LYP calculations employing a triple-zeta basis set have been used to explore the doublet and quartet potential-energy surfaces of the cation, as well as some important neutral states The results suggest that both Si+-CO and Si+ - OC isomers are feasible; however, the global minimum is (2)Pi SiCO+. Isomeric (2)Pi SiOC+ is 12.1 kcal mol(-1) less stable than (2)Pi SiCO+, and all quartet isomers are much higher in energy. The corresponding neutrals Si-CO and Si-OC are also feasible, but the lowest energy Si - OC isomer ((3)A") is bound by only 1.5 kcal mol(-1). We attribute most, if nor all, of the recovery signal in the +NR' experiment to SiCO+ survivor ions. The nature of the bonding in the lowest energy isomers of Si+ -(CO,OC) is interpreted with the aid of natural bond order analyses, and the ground stale bonding of SiCO+ is discussed in relation to classical analogues such as metal carbonyls and ketenes.

Electromechanical wave in power systems : theory and applications

The continuum model is a key paradigm describing the behavior of electromechanical transients in power systems. In the past two decades, much research work has been done on applying the continuum model to analyze the electromechanical wave in power systems. In this work, the uniform and non-uniform continuum models are first briefly described, and some explanations borrowing concepts and tools from other fields are given. Then, the existing approaches of investigating the resulting wave equations are summarized. An application named the zero reflection controller based on the idea of the wave equations is next presented.

Pressure state response-based method for evaluating social benefits from smart grid development

In this paper, the inherent mechanism of social benefits associated with smart grid development is examined based on the pressure state response (PSR) model from resource economics. The emerging types of technology brought up by smart grid development are regarded as pressures. The improvements of the performance and efficiency of power system operation, such as the enhanced capability of accommodating renewable energy generation, are regarded as states. The effects of smart grid development on society are regarded as responses. Then, a novel method for evaluating social benefits from smart grid development is presented. Finally, the social benefits from smart grid development in a province in northwest China are carried out by using the developed evaluation system, and reasonable evaluation results are attained.

Classifying natural aerial scenery for autonomous aircraft emergency landing

In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.

Transmembrane/cytoplasmic domain-mediated membrane type 1-matrix metalloprotease docking to invadopodia is required for cellinvasion

The invasion of human malignant melanoma cells into the extracellular matrix (ECM) involves the accumulation of proteases at sites of ECM degradation where activation of matrix metalloproteases (MMP) occurs. Here, we show that when membrane type 1 MMP (MT-MMP) was overexpressed in RPMI7951 human melanoma cells, the cells made contact with the ECM, activated soluble and ECM-bound MMP-2, and degraded and invaded the ECM. Further experiments demonstrated the importance of localization of the MT-MMP to invadopodia. Overexpression of MT-MMP without invadopodial localization caused activation of soluble MMP-2, but did not facilitate ECM degradation or cell invasiveness. Up-regulation of endogenous MT-MMP with concanavalin A caused activation of MMP-2. However, concanavalin A treatment prevented invadopodial localization of MT-MMP and ECM degradation. Neither a truncated MT-MMP mutant lacking transmembrane (TM) and cytoplasmic domains (ΔTM(MT-MMP)), nor a chimeric MT-MMP containing the interleukin 2 receptor α chain (IL-2R) TM and cytoplasmic domains (ΔTM(MT-MMP)/TM(IL-2R)) were localized to invadopodia or exhibited ECM degradation. Furthermore, a chimera of the TM/cytoplasmic domain of MT-MMP (TM(MT-MMP)) with tissue inhibitor of MMP 1 (TIMP-1/TM(MT- MMP)) directed the TIMP-1 molecule to invadopodia. Thus, the MT-MMP TM/cytoplasmic domain mediates the spatial organization of MT-MMP into invadopodia and subsequent degradation of the ECM.

Designing atmospheric-pressure plasma sources for surface engineering of nanomaterials

Atmospheric-pressure plasma processing techniques emerge as efficient and convenient tools to engineer a variety of nanomaterials for advanced applications in nanoscience and nanotechnology. This work presents different methods, including using a quasi-sinusoidal high-voltage generator, a radio-frequency power supply, and a uni-polar pulse generator, to generate atmospheric-pressure plasmas in the jet or dielectric barrier discharge configurations. The applicability of the atmospheric-pressure plasma is exemplified by the surface modification of nanoparticles for polymeric nanocomposites. Dielectric measurements reveal that representative nanocomposites with plasma modified nanoparticles exhibit notably higher dielectric breakdown strength and a significantly extended lifetime.

Lower partial moment and information entropy-based dynamic electricity purchasing strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.