Flow resistance network analysis of the back-pressure of automotive mufflers

Complexity of mufflers generally introduces considerable pressure drop, which affects the engine performance adversely. Not much literature is available for pressure drop across perforates. In this paper, the stagnation pressure drop across perforated muffler elements has been measured experimentally and generalized expressions have been developed for the pressure loss across cross-flow expansion and cross-flow contraction elements. A flow resistance model available in the literature has been made use of to analytically determine the flow distribution and thereby the pressure drop of mufflers. A generalized expression has been derived here for evaluation of the equivalent flow resistance for parallel flow paths. Expressions for flow resistance across perforated elements, derived by means of flow experiments, have been implemented in the flow resistance network. The results have been validated with experimental data. Thus, the newly developed integrated flow resistance networks would enable us to determine the normalized stagnation pressure drop of commercial automotive mufflers, thus enabling an efficient flow-acoustic design of silencing systems.

Effect of choice of basis functions in neural network for capturing unknown function for dynamic inversion control

The basic requirement for an autopilot is fast response and minimum steady state error for better guidance performance. The highly nonlinear nature of the missile dynamics due to the severe kinematic and inertial coupling of the missile airframe as well as the aerodynamics has been a challenge for an autopilot that is required to have satisfactory performance for all flight conditions in probable engagements. Dynamic inversion is very popular nonlinear controller for this kind of scenario. But the drawback of this controller is that it is sensitive to parameter perturbation. To overcome this problem, neural network has been used to capture the parameter uncertainty on line. The choice of basis function plays the major role in capturing the unknown dynamics. Here in this paper, many basis function has been studied for approximation of unknown dynamics. Cosine basis function has yield the best response compared to any other basis function for capturing the unknown dynamics. Neural network with Cosine basis function has improved the autopilot performance as well as robustness compared to Dynamic inversion without Neural network.

Inflammation and cancer stem cells

Cancer stem cells are becoming recognised as being responsible for metastasis and treatment resistance. The complex cellular and molecular network that regulates cancer stem cells and the role that inflammation plays in cancer progression are slowly being elucidated. Cytokines, secreted by tumour associated immune cells, activate the necessary pathways required by cancer stem cells to facilitate cancer stem cells progressing through the epithelial-mesenchymal transition and migrating to distant sites. Once in situ, these cancer stem cells can secrete their own attractants, thus providing an environment whereby these cells can continue to propagate the tumour in a secondary niche. (C) 2013 Elsevier Ireland Ltd. All rights reserved.

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of similar to 58% and similar to 40% for localization and functions respectively of proteins were determined at a threshold of similar to 30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k nearest neighbor classifier confirmed that our results compared favorably. Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.

Iron(III) benzhydroxamates of dipicolylamines for photocytotoxicity in red light and cellular imaging

Benzhydroxamate (BHA) iron(III) complexes Fe(BHA)(L)ClICI (I, 2)], where L is (phenyl)dipicolylamine (phdpa in I) and (pyrenyl)dipicolylamine (pydpa in 2), were prepared and their photocytotoxicity in visible (400-700 nm) and red (600-720 nm) light was studied. Complex 1 was structurally characterized by X-ray crystallography. The complexes have high-spin iron(III) centers. Complex 2, with a pyrenyl fluorophore, was used for cellular imaging, showing both mitochondrial and nuclear localization in the fluorescence microscopic study. The complex exhibited photocytotoxicity in red light in HeLa cancer cells, giving IC50 value of 24.4(+/- 0.4) pM, but remained essentially non-toxic in the dark. The involvement of reactive oxygen species and an apoptotic nature of cell death were observed from the cellular studies. (C) 2014 Elsevier Ltd. All rights reserved.

The Presence of Multiple Cellular Defects Associated with a Novel G50E Iron-Sulfur Cluster Scaffold Protein ( ISCU) Mutation Leads to Development of Mitochondrial Myopathy

Background: Muscle-specific deficiency of iron-sulfur (Fe-S) cluster scaffold protein (ISCU) leads to myopathy. Results: Cells carrying the myopathy-associated G50E ISCU mutation demonstrate impaired Fe-S cluster biogenesis and mitochondrial dysfunction. Conclusion: Reduced mitochondrial respiration as a result of diminished Fe-S cluster synthesis results in muscle weakness in myopathy patients. Significance: The molecular mechanism behind disease progression should provide invaluable information to combat ISCU myopathy. Iron-sulfur (Fe-S) clusters are versatile cofactors involved in regulating multiple physiological activities, including energy generation through cellular respiration. Initially, the Fe-S clusters are assembled on a conserved scaffold protein, iron-sulfur cluster scaffold protein (ISCU), in coordination with iron and sulfur donor proteins in human mitochondria. Loss of ISCU function leads to myopathy, characterized by muscle wasting and cardiac hypertrophy. In addition to the homozygous ISCU mutation (g.7044GC), compound heterozygous patients with severe myopathy have been identified to carry the c.149GA missense mutation converting the glycine 50 residue to glutamate. However, the physiological defects and molecular mechanism associated with G50E mutation have not been elucidated. In this report, we uncover mechanistic insights concerning how the G50E ISCU mutation in humans leads to the development of severe ISCU myopathy, using a human cell line and yeast as the model systems. The biochemical results highlight that the G50E mutation results in compromised interaction with the sulfur donor NFS1 and the J-protein HSCB, thus impairing the rate of Fe-S cluster synthesis. As a result, electron transport chain complexes show significant reduction in their redox properties, leading to loss of cellular respiration. Furthermore, the G50E mutant mitochondria display enhancement in iron level and reactive oxygen species, thereby causing oxidative stress leading to impairment in the mitochondrial functions. Thus, our findings provide compelling evidence that the respiration defect due to impaired biogenesis of Fe-S clusters in myopathy patients leads to manifestation of complex clinical symptoms.

Fair Scheduling in Cellular Systems in the Presence of Noncooperative Mobiles

We consider the problem of ``fair'' scheduling the resources to one of the many mobile stations by a centrally controlled base station (BS). The BS is the only entity taking decisions in this framework based on truthful information from the mobiles on their radio channel. We study the well-known family of parametric alpha-fair scheduling problems from a game-theoretic perspective in which some of the mobiles may be noncooperative. We first show that if the BS is unaware of the noncooperative behavior from the mobiles, the noncooperative mobiles become successful in snatching the resources from the other cooperative mobiles, resulting in unfair allocations. If the BS is aware of the noncooperative mobiles, a new game arises with BS as an additional player. It can then do better by neglecting the signals from the noncooperative mobiles. The BS, however, becomes successful in eliciting the truthful signals from the mobiles only when it uses additional information (signal statistics). This new policy along with the truthful signals from mobiles forms a Nash equilibrium (NE) that we call a Truth Revealing Equilibrium. Finally, we propose new iterative algorithms to implement fair scheduling policies that robustify the otherwise nonrobust (in presence of noncooperation) alpha-fair scheduling algorithms.

Emergent properties of the interferon-signalling network may underlie the success of hepatitis C treatment

Current interferon alpha-based treatment of hepatitis C virus (HCV) infection fails to cure a sizeable fraction of patients treated. The cause of this treatment failure remains unknown. Here using mathematical modelling, we predict treatment failure to be a consequence of the emergent properties of the interferon-signalling network. HCV induces bistability in the network, creating a new steady state where it can persist. Cells that admit the new steady state alone are refractory to interferon. Using a model of viral kinetics, we show that when the fraction of cells refractory to interferon in a patient exceeds a critical value, treatment fails. Direct-acting antivirals that suppress HCV replication can eliminate the new steady state, restoring interferon sensitivity and improving treatment response. Our study thus presents a new conceptual basis of HCV persistence and treatment response, elucidates the origin of the synergy between interferon and direct-acting antivirals, and facilitates rational treatment optimization.

Dense network of O-H ... O and C-H ... O interactions in the solid state structure of n-pentyl-2-chloro-2-deoxy-alpha-D-manno-sept 3-uloside

Single crystal X-ray structural analysis of a septanoside, namely, n-pentyl-2-chloro-2-deoxy sept-3-uloside (1) provides many finer details of the molecular structure, in addition to its preferred twist-chair conformation, namely, (TC3,4)-T-5,6 conformation. Structural analysis reveals a dense network of O-H...O, C-H...O and van der Waals interactions that stabilize interdigitized, planar bi-layer structure of the crystal lattice. (C) 2014 Elsevier Ltd. All rights reserved.

Ising model on a random network with annealed or quenched disorder

We study the equilibrium properties of an Ising model on a disordered random network where the disorder can be quenched or annealed. The network consists of fourfold coordinated sites connected via variable length one-dimensional chains. Our emphasis is on nonuniversal properties and we consider the transition temperature and other equilibrium thermodynamic properties, including those associated with one-dimensional fluctuations arising from the chains. We use analytic methods in the annealed case, and a Monte Carlo simulation for the quenched disorder. Our objective is to study the difference between quenched and annealed results with a broad random distribution of interaction parameters. The former represents a situation where the time scale associated with the randomness is very long and the corresponding degrees of freedom can be viewed as frozen, while the annealed case models the situation where this is not so. We find that the transition temperature and the entropy associated with one-dimensional fluctuations are always higher for quenched disorder than in the annealed case. These differences increase with the strength of the disorder up to a saturating value. We discuss our results in connection to physical systems where a broad distribution of interaction strengths is present.

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

A shortest path tree based algorithm for relay placement in a wireless sensor network and its performance analysis

In this paper, we study a problem of designing a multi-hop wireless network for interconnecting sensors (hereafter called source nodes) to a Base Station (BS), by deploying a minimum number of relay nodes at a subset of given potential locations, while meeting a quality of service (QoS) objective specified as a hop count bound for paths from the sources to the BS. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard. For this problem, we propose a polynomial time approximation algorithm based on iteratively constructing shortest path trees and heuristically pruning away the relay nodes used until the hop count bound is violated. Results show that the algorithm performs efficiently in various randomly generated network scenarios; in over 90% of the tested scenarios, it gave solutions that were either optimal or were worse than optimal by just one relay. We then use random graph techniques to obtain, under a certain stochastic setting, an upper bound on the average case approximation ratio of a class of algorithms (including the proposed algorithm) for this problem as a function of the number of source nodes, and the hop count bound. To the best of our knowledge, the average case analysis is the first of its kind in the relay placement literature. Since the design is based on a light traffic model, we also provide simulation results (using models for the IEEE 802.15.4 physical layer and medium access control) to assess the traffic levels up to which the QoS objectives continue to be met. (C) 2014 Elsevier B.V. All rights reserved.

Oxovanadium(IV) complexes of curcumin for cellular imaging and mitochondria targeted photocytotoxicity

Oxovanadium(IV) complexes VO(R-tpy)(cur)](ClO4) (1, 2) of curcumin (Hcur) and terpyridine ligands (R-tpy) where R is phenyl (phtpy in 1) or p-triphenylphosphonium methylphenyl bromide (C6H4CH2PPh3Br) (TPP-phtpy in 2) were prepared and characterized and their DNA photocleavage activity, photocytotoxicity and cellular localization in cancer cells (HeLa and MCF-7) were studied. Acetylacetonate (acac) complexes VO(R-tpy)(acac)](ClO4) of phtpy (3) and TPP-phtpy (4) were prepared and used as the control species. These complexes showed efficient cleavage of pUC19 DNA in visible light of 454 nm and near-IR light of 705 rim. Complexes 1 and 2 showed significant photocytotoxicity in visible light of 400-700 nm. FACS analysis showed sub-G1/G0 phase cell-cycle arrest in cancer cells when treated with 1 and 2 in visible light in comparison with the dark controls. Fluorescence microscopic studies revealed specific localization of the p-triphenylphosphonium complex 2 in the mitochondria of MCF-7 cancer cells whereas no such specificity was observed for complex 1.

Inferring biochemical routes from biochemical networks

Metabolism is a defining feature of life, and its study is important to understand how a cell works, alterations that lead to disease and for applications in drug discovery. From a systems perspective, metabolism can be represented as a network that captures all the metabolites as nodes and the inter-conversions among pairs of them as edges. Such an abstraction enables the networks to be studied by applying graph theory, particularly, to infer the flow of chemical information in the networks by identifying relevant metabolic pathways. In this study, different weighting schemes are used to illustrate that appropriately weighted networks can capture the quantitative cellular dynamics quite accurately. Thus, the networks now combine the elegance and simplicity of representation of the system and ease of analysing metabolic graphs. Metabolic routes or paths determined by this therefore are likely to be more biologically meaningful. The usefulness of the approach is demonstrated with two examples, first for understanding bacterial stress response and second for studying metabolic alterations that occurs in cancer cells.