Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

MicroRNA-155 Is Required for Mycobacterium bovis BCG-Mediated Apoptosis of Macrophages

Pathogenic rnycobacteria, including Mycobacterium tuberculosis and Mycobacterium bovis, cause significant morbidity and mortality worldwide. However, the vaccine strain Mycobacterium bovis BCG, unlike virulent strains, triggers extensive apoptosis of infected macrophages, a step necessary for the elicitation of robust protective immunity. We here demonstrate that M. bovis BCG triggers Toll-like receptor 2 (TLR2)-dependent microRNA-155 (miR-155) expression, which involves signaling cross talk among phosphatidylinositol 3-kinase (PI3K), protein kinase C delta (PKC delta), and mitogen-activated protein kinases (MAPKs) and recruitment of NF-kappa B and c-ETS to miR-155 promoter. Genetic and signaling perturbations presented the evidence that miR-155 regulates PKA signaling by directly targeting a negative regulator of PKA, protein kinase inhibitor alpha (PKI-alpha). Enhanced activation of PKA signaling resulted in the generation of PKA C-alpha; phosphorylation of MSK1, cyclic AMP response element binding protein (CREB), and histone H3; and recruitment of phospho-CREB to the apoptotic gene promoters. The miR-155-triggered activation of caspase-3, BAK1, and cytochrome c translocation involved signaling integration of MAPKs and epigenetic or posttranslational modification of histones or CREB. Importantly, M. bovis BCG infection-induced apoptosis was severely compromised in macrophages derived from miR-155 knockout mice. Gain-of-function and loss-of-function studies validated the requirement of miR-155 for M. bovis BCG's ability to trigger apoptosis. Overall, M. bovis BCG-driven miR-155 dictates cell fate decisions of infected macrophages, strongly implicating a novel role for miR-155 in orchestrating cellular reprogramming during immune responses to mycobacterial infection.

Cascaded walks in protein sequence space: use of artificial sequences in remote homology detection between natural proteins

Over the past two decades, many ingenious efforts have been made in protein remote homology detection. Because homologous proteins often diversify extensively in sequence, it is challenging to demonstrate such relatedness through entirely sequence-driven searches. Here, we describe a computational method for the generation of `protein-like' sequences that serves to bridge gaps in protein sequence space. Sequence profile information, as embodied in a position-specific scoring matrix of multiply aligned sequences of bona fide family members, serves as the starting point in this algorithm. The observed amino acid propensity and the selection of a random number dictate the selection of a residue for each position in the sequence. In a systematic manner, and by applying a `roulette-wheel' selection approach at each position, we generate parent family-like sequences and thus facilitate an enlargement of sequence space around the family. When generated for a large number of families, we demonstrate that they expand the utility of natural intermediately related sequences in linking distant proteins. In 91% of the assessed examples, inclusion of designed sequences improved fold coverage by 5-10% over searches made in their absence. Furthermore, with several examples from proteins adopting folds such as TIM, globin, lipocalin and others, we demonstrate that the success of including designed sequences in a database positively sensitized methods such as PSI-BLAST and Cascade PSI-BLAST and is a promising opportunity for enormously improved remote homology recognition using sequence information alone.

Stabilized SPH-based simulations of impact dynamics using acceleration-corrected artificial viscosity

Artificial viscosity in SPH-based computations of impact dynamics is a numerical artifice that helps stabilize spurious oscillations near the shock fronts and requires certain user-defined parameters. Improper choice of these parameters may lead to spurious entropy generation within the discretized system and make it over-dissipative. This is of particular concern in impact mechanics problems wherein the transient structural response may depend sensitively on the transfer of momentum and kinetic energy due to impact. In order to address this difficulty, an acceleration correction algorithm was proposed in Shaw and Reid (''Heuristic acceleration correction algorithm for use in SPH computations in impact mechanics'', Comput. Methods Appl. Mech. Engrg., 198, 3962-3974) and further rationalized in Shaw et al. (An Optimally Corrected Form of Acceleration Correction Algorithm within SPH-based Simulations of Solid Mechanics, submitted to Comput. Methods Appl. Mech. Engrg). It was shown that the acceleration correction algorithm removes spurious high frequency oscillations in the computed response whilst retaining the stabilizing characteristics of the artificial viscosity in the presence of shocks and layers with sharp gradients. In this paper, we aim at gathering further insights into the acceleration correction algorithm by further exploring its application to problems related to impact dynamics. The numerical evidence in this work thus establishes that, together with the acceleration correction algorithm, SPH can be used as an accurate and efficient tool in dynamic, inelastic structural mechanics. (C) 2011 Elsevier Ltd. All rights reserved.

Oncogenic MicroRNA-155 Down-regulates Tumor Suppressor CDC73 and Promotes Oral Squamous Cell Carcinoma Cell Proliferation IMPLICATIONS FOR CANCER THERAPEUTICS

The CDC73 gene is mutationally inactivated in hereditary and sporadic parathyroid tumors. It negatively regulates beta-catenin, cyclin D1, and c-MYC. Down-regulation of CDC73 has been reported in breast, renal, and gastric carcinomas. However, the reports regarding the role of CDC73 in oral squamous cell carcinoma (OSCC) are lacking. In this study we show that CDC73 is down-regulated in a majority of OSCC samples. We further show that oncogenic microRNA-155 (miR-155) negatively regulates CDC73 expression. Our experiments show that the dramatic up-regulation of miR-155 is an exclusive mechanism for down-regulation of CDC73 in a panel of human cell lines and a subset of OSCC patient samples in the absence of loss of heterozygosity, mutations, and promoter methylation. Ectopic expression of miR-155 in HEK293 cells dramatically reduced CDC73 levels, enhanced cell viability, and decreased apoptosis. Conversely, the delivery of a miR-155 antagonist (antagomir-155) to KB cells overexpressing miR-155 resulted in increased CDC73 levels, decreased cell viability, increased apoptosis, and marked regression of xenografts in nude mice. Cotransfection of miR-155 with CDC73 in HEK293 cells abrogated its pro-oncogenic effect. Reduced cell proliferation and increased apoptosis of KB cells were dependent on the presence or absence of the 3'-UTR in CDC73. In summary, knockdown of CDC73 expression due to overexpression of miR-155 not only adds a novelty to the list of mechanisms responsible for its down-regulation in different tumors, but the restoration of CDC73 levels by the use of antagomir-155 may also have an important role in therapeutic intervention of cancers, including OSCC.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

An Optimal Fuzzy Logic Controller tuned with Artificial Immune System

In this paper, a method for the tuning the membership functions of a Mamdani type Fuzzy Logic Controller (FLC) using the Clonal Selection Algorithm(CSA) a model of the Artificial Immune System(AIS) paradigm is examined. FLC's are designed for two problems, firstly the linear cart centering problem and secondly the highly nonlinear inverted pendulum problem. The FLC tuned by AIS is compared with FLC tuned by GA. In order to check the robustness of the designed PLC's white noise was added to the system, further, the masses of the cart and the length and mass of the pendulum are changed. The PLC's were also tested in the presence of faulty rules. Finally, Kruskal Wallis test was performed to compare the performance of the GA and AIS. An insight into the algorithms are also given by studying the effect of the important parameters of GA and AIS.

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Sonic hedgehog-Dependent Induction of MicroRNA 31 and MicroRNA 150 Regulates Mycobacterium bovis BCG-Driven Toll-Like Receptor 2 Signaling

Hedgehog (HH) signaling is a significant regulator of cell fate decisions during embryogenesis, development, and perpetuation of various disease conditions. Testing whether pathogen-specific HH signaling promotes unique innate recognition of intracellular bacteria, we demonstrate that among diverse Gram-positive or Gram-negative microbes, Mycobacterium bovis BCG, a vaccine strain, elicits a robust activation of Sonic HH (SHH) signaling in macrophages. Interestingly, sustained tumor necrosis factor alpha (TNF-alpha) secretion by macrophages was essential for robust SHH activation, as TNF-alpha(-/-) macrophages exhibited compromised ability to activate SHH signaling. Neutralization of TNF-alpha or blockade of TNF-alpha receptor signaling significantly reduced the infection-induced SHH signaling activation both in vitro and in vivo. Intriguingly, activated SHH signaling downregulated M. bovis BCG-mediated Toll-like receptor 2 (TLR2) signaling events to regulate a battery of genes associated with divergent functions of M1/M2 macrophages. Genome-wide expression profiling as well as conventional gain-of-function or loss-of-function analysis showed that SHH signaling-responsive microRNA 31 (miR-31) and miR-150 target MyD88, an adaptor protein of TLR2 signaling, thus leading to suppression of TLR2 responses. SHH signaling signatures could be detected in vivo in tuberculosis patients and M. bovis BCG-challenged mice. Collectively, these investigations identify SHH signaling to be what we believe is one of the significant regulators of host-pathogen interactions.

NOD2-Nitric Oxide-responsive MicroRNA-146a Activates Sonic Hedgehog Signaling to Orchestrate Inflammatory Responses in Murine Model of Inflammatory Bowel Disease

Background: Genetic variants of NOD2 are linked to inflammatory bowel disease (IBD) etiology. Results: DSS model of colitis in wild-type and inducible nitric-oxide synthase (iNOS) null mice revealed that NOD2-iNOS/NO-responsive microRNA-146a targets NUMB gene facilitating Sonic hedgehog (SHH) signaling. Conclusion: miR-146a-mediated NOD2-SHH signaling regulates gut inflammation. Significance: Identification of novel regulators of IBD provides new insights into pathophysiology and development of new therapy concepts. Inflammatory bowel disease (IBD) is a debilitating chronic inflammatory disorder of the intestine. The interactions between enteric bacteria and genetic susceptibilities are major contributors of IBD etiology. Although genetic variants with loss or gain of NOD2 functions have been linked to IBD susceptibility, the mechanisms coordinating NOD2 downstream signaling, especially in macrophages, during IBD pathogenesis are not precisely identified. Here, studies utilizing the murine dextran sodium sulfate model of colitis revealed the crucial roles for inducible nitric-oxide synthase (iNOS) in regulating pathophysiology of IBDs. Importantly, stimulation of NOD2 failed to activate Sonic hedgehog (SHH) signaling in iNOS null macrophages, implicating NO mediated cross-talk between NOD2 and SHH signaling. NOD2 signaling up-regulated the expression of a NO-responsive microRNA, miR-146a, that targeted NUMB gene and alleviated the suppression of SHH signaling. In vivo and ex vivo studies confirmed the important roles for miR-146a in amplifying inflammatory responses. Collectively, we have identified new roles for miR-146a that established novel cross-talk between NOD2-SHH signaling during gut inflammation. Potential implications of these observations in therapeutics could increase the possibility of defining and developing better regimes to treat IBD pathophysiology.

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like ``linker'' sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be ``plugged-into'' routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold. (C) 2013 Elsevier Ltd. All rights reserved.

Artificial photosynthesis and the splitting of water to generate hydrogen

It is no exaggeration to state that the energy crisis is the most serious challenge that we face today. Among the strategies to gain access to reliable, renewable energy, the use of solar energy has clearly emerged as the most viable option. A promising direction in this context is artificial photosynthesis. In this article, we briefly describe the essential features of artificial photosynthesis in comparison with natural photosynthesis and point out the modest success that we have had in splitting water to produce oxygen and hydrogen, specially the latter.

MicroRNA-125a Reduces Proliferation and Invasion of Oral Squamous Cell Carcinoma Cells by Targeting Estrogen-related Receptor alpha IMPLICATIONS FOR CANCER THERAPEUTICS

Estrogen-related receptor (ESRRA) functions as a transcription factor and regulates the expression of several genes, such as WNT11 and OPN. Up-regulation of ESRRA has been reported in several cancers. However, the mechanism underlying its up-regulation is unclear. Furthermore, the reports regarding the role and regulation of ESRRA in oral squamous cell carcinoma (OSCC) are completely lacking. Here, we show that tumor suppressor miR-125a directly binds to the 3UTR of ESRRA and represses its expression. Overexpression of miR-125a in OSCC cells drastically reduced the level of ESRRA, decreased cell proliferation, and increased apoptosis. Conversely, the delivery of an miR-125a inhibitor to these cells drastically increased the level of ESRRA, increased cell proliferation, and decreased apoptosis. miR-125a-mediated down-regulation of ESRRA impaired anchorage-independent colony formation and invasion of OSCC cells. Reduced cell proliferation and increased apoptosis of OSCC cells were dependent on the presence of the 3UTR in ESRRA. The delivery of an miR-125a mimic to OSCC cells resulted in marked regression of xenografts in nude mice, whereas the delivery of an miR-125a inhibitor to OSCC cells resulted in a significant increase of xenografts and abrogated the tumor suppressor function of miR-125a. We observed an inverse correlation between the expression levels of miR-125a and ESRRA in OSCC samples. In summary, up-regulation of ESRRA due to down-regulation of miR-125a is not only a novel mechanism for its up-regulation in OSCC, but decreasing the level of ESRRA by using a synthetic miR-125a mimic may have an important role in therapeutic intervention of OSCC and other cancers.