Estimation of Heat Losses from Modified Cavity Mono-tube Boiler Receiver of Solar Parabolic Dish for Steam Generation

AbstractIn this article, a 3-D numerical investigation is carried out to estimate heat losses from solar parabolic dish with modified cavity receiver used for three different steam generation conditions viz. sub-cooled, saturated, superheated steam. The effect of inclination of the receiver, operating temperature, emissivity of the cavity cover, insulation thickness on the total heat loss from the modified cavity receiver has been investigated. The variable wall temperature boundary conditions and insulation thicknesses are applied to match the actual conditions. The results show that the convection heat losses are higher at 0° inclination and found to be 400 to 500W for superheated steam generation; 300 to 425W for saturated steam generation and 50 to 125W for sub-cooled steam. The radiation heat losses remain constant for all inclinations and vary with the temperature. Nusselt number correlations have been proposed based on the numerical analysis. The present model can be used to predict total heat losses from the modified cavity receiver under all conditions more accurately

Heat Transfer Modeling and Analysis of Solar Thermo-chemical Reactor for Hydrogen Production from Water

AbstractA solar thermo-chemical reactor is modeled and analyzed for the solar thermal dissociation of zinc oxide into zinc and oxygen involved in the thermo-chemical cycle for hydrogen production. The reactor consists of a cavity surrounded by a rotating insulation layer made of alumina. The granular zinc oxide particles are fed into the cavity and are directly exposed to the solar radiation entering the cavity through a quartz window. A three dimensional numerical model coupling the multiphase particle dynamics in gravitational field, multiphase heat transfer, k-ɛ turbulence model, discrete ordinates radiation model, Arrhenius reaction rate model is developed. The cavity temperature and oxygen molar flow rate at the outlet of the reactor which is the indicator of the reaction rate has been validated with a 10kW reactor prototype. An energy balance study of thermal performance parameters including the various losses occurring from the reactor and efficiency is also done. The major losses were contributed by re-radiation (46%) and sensible heating of reactor components (35.5%), while the minor losses were contributed by convection by argon (1%) and conduction through insulation (2%).The thermal efficiency of the reactor is calculated to be 15.5%

Complex-based analysis of dysregulated cellular processes in cancer

Background: Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions. Therefore, complexes aggregate the effect of individual genes (proteins) and can be used to gain a better understanding of cancer mechanisms. Here, we observe that complexes show considerable changes in their expression, in turn directed by the concerted action of transcription factors (TFs), across cancer conditions. We seek to gain novel insights into cancer mechanisms through a systematic analysis of complexes and their transcriptional regulation. Results: We integrated large-scale protein-interaction (PPI) and gene-expression datasets to identify complexes that exhibit significant changes in their expression across different conditions in cancer. We devised a log-linear model to relate these changes to the differential regulation of complexes by TFs. The application of our model on two case studies involving pancreatic and familial breast tumour conditions revealed: (i) complexes in core cellular processes, especially those responsible for maintaining genome stability and cell proliferation (e.g. DNA damage repair and cell cycle) show considerable changes in expression; (ii) these changes include decrease and countering increase for different sets of complexes indicative of compensatory mechanisms coming into play in tumours; and (iii) TFs work in cooperative and counteractive ways to regulate these mechanisms. Such aberrant complexes and their regulating TFs play vital roles in the initiation and progression of cancer.Comment: 22 pages, BMC Systems Biolog

Myosin isoenzymes in human hypertrophic hearts. Shift in atrial myosin heavy chains and in ventricular myosin light chains

The myosin light chain complement and proteolytic peptide patterns of myosin heavy chains were studied by two-dimensional and one-dimensional electrophoretic techniques respectively, in a total of 57 samples from ventricular and atrial tissues of normal and hypertrophied human hearts. Hypertrophies were classified haemodynamically as due to pressure-overload and volume-overload. In addition to the occurrence of ventricular light chains in hypertrophied atria we also observed the atrial light chain-1 (ALC-1) in hypertrophied ventricular tissues. On average over 6% of total light-chain-1 comprised ALC-1 in pressure-overloaded ventricles and around 3% in volume-overloaded ventricles. In single cases of pressure-overload ALC-1 amounted up to over 20% of total light chain-1. With regard to the myosin heavy chains limited digestion by two different proteinases produced over 200 clearly resoluble peptides. The absence of any detectable differences in the peptide patterns between myosin heavy chains from normal and hypertrophic tissues of left or right ventricle is in line with the findings of J. J. Schier and R. S. Adelstein (J Clin Invest 1982; 69: 816-825). In atrial tissues however, reproducible qualitative differences in the peptide patterns indicated that during hypertrophy a different type of myosin heavy chains becomes expressed. No differences were seen between the myosin heavy chains from normal left and right atri

Ant species richness at selected localities of Bangalore

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Hybrid Multicarrier Random Space Vector PWM for the Mitigation the Acoustic Noise

The pulse width modulation (PWM) inverter is obvious for any industrial and power sector application. Particularly industrial drives are very keen on the industrial standards. Many modulations approached such a drives objects of DC-link consumption, harmonics suppression in lower and higher order spectrum and noise reduction. The still random PWM is a best candidate for reducing the noises on the PWM operated AC drives. There are various Random PWM (RPWM) methods has been developed and investigated for the PWM inverter fed drive noise reductions, still the shortcomings are existence on these method items of their less randomness and complex digital circuitry. These PWM dealt the spreading harmonics there by decreasing harmonic effects on the system. However, these techniques overlook the effect of acoustic noise and DC -link utilizations Therefore, in this paper mainly deals with to combined RPWM principle in space vector PWM (SVPWM) to generate random PWM generation using asymmetric frequency multi carrier called multicarrier random space vector PWM (MCRSVPWM). The SVM agreements with multicarrier (different fixed frequencies as carrier waves) which are chosen with the aid of a random binary bit generator. The proposed RSVM generated pulses with a randomized triangular carrier (4 ± 1.5 kHz), while the conventional RPWM method contains of the random pulse position with a fixed frequency triangular carrier. The simulation study is performed through MATLAB/Simulink for 3 HP asynchronous induction motor drive. The Experimental validation of proposed MCRSVPWM is tested with 2kW six switch (Power MOSFET – SCH2080KE) inverter power module fed induction motor drive.publishedVersio

Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach

Copy number alterations (CNAs) are thought to account for 85% of the variation in gene expression observed among breast tumours. The expression of cis-associated genes is impacted by CNAs occurring at proximal loci of these genes, whereas the expression of trans-associated genes is impacted by CNAs occurring at distal loci. While a majority of these CNA-driven genes responsible for breast tumourigenesis are cis-associated, trans-associated genes are thought to further abet the development of cancer and influence disease outcomes in patients. Here we present a network-based approach that integrates copy-number and expression profiles to identify putative cis- and trans-associated genes in breast cancer pathogenesis. We validate these cis- and trans-associated genes by employing them to subtype a large cohort of breast tumours obtained from the METABRIC consortium, and demonstrate that these genes accurately reconstruct the ten subtypes of breast cancer. We observe that individual breast cancer subtypes are driven by distinct sets of cis- and trans-associated genes. Among the cis-associated genes, we recover several known drivers of breast cancer (e.g. CCND1, ERRB2, MDM2 and ZNF703) and some novel putative drivers (e.g. BRF2 and SF3B3). siRNA-mediated knockdown of BRF2 across a panel of breast cancer cell lines showed significant reduction specifically in cell proliferation in HER2+ lines, thereby indicating that BRF2 could be a context-dependent oncogene and potentially targetable in these lines. Among the trans-associated genes, we identify modules of immune-response (CD2, CD19, CD38 and CD79B), mitotic/cell-cycle kinases (e.g. AURKB, MELK, PLK1 and TTK), and DNA-damage response genes (e.g. RFC4 and FEN1).Comment: 23 pages, 2 tables, 7 figure

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

Dynamic modeling and simulation of leukocyte integrin activation through an electronic design automation framework

Model development and analysis of biological systems is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulations of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Several characteristics and issues of biological system modeling are common to the electronics system modeling, such as concurrency, reactivity, abstraction levels, as well as state space explosion during verification. This paper proposes a modeling and simulation framework for discrete event-based execution of biochemical systems based on SystemC. SystemC is the reference language in the electronic design automation (EDA) field for modeling and verifying complex systems at different abstraction levels. SystemC-based verification is the de-facto an alternative to model checking when such a formal verification technique cannot deal with the state space complexity of the model. The paper presents how the framework has been applied to model the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues, by handling the solution space complexity through different levels of simulation accuracy