Computational engineering design for micro-scale combustors

At present, optimisation is an enabling technology in innovation. Multi-objective and multi-disciplinary design tools are essential in the engineering design process, and have been applied successfully in aerospace and turbomachinery applications extensively. These approaches give insight into the design space and identify the trade-offs between the competing performance measures satisfying a number of constraints at the same time. It is anticipated here that the same benefits can be obtained for the design of micro-scale combustors. In this paper, a multi-disciplinary automated design optimisation system was developed for this purpose, which comprises a commercial computational fluid dynamics package and a multi-objective variant of the Tabu Search optimisation algorithm. The main objectives that are considered in this study are to optimise the main micro-scale combustor design characteristics and to satisfy manufacturability considerations from the very beginning of the whole design operation. Hydrogen-air combustion as well as 14 geometrical and 2 operational parameters are used to describe and model the design problem. Two illustrative test cases will be presented, in which the most important device operational requirements are optimised, and the efficiency of the developed optimisation system is demonstrated. The identification, assessment and suitability of the optimum design configurations are discussed in detail. Copyright © 2012 by ASME.

Identification of RNA editing in the human exome and development of DARNED DatabaseSF

RNA editing is a biological phenomena that alters nascent RNA transcripts by insertion, deletion and/or substitution of one or a few nucleotides. It is ubiquitous in all kingdoms of life and in viruses. The predominant editing event in organisms with a developed central nervous system is Adenosine to Inosine deamination. Inosine is recognized as Guanosine by the translational machinery and reverse-transcriptase. In primates, RNA editing occurs frequently in transcripts from repetitive regions of the genome. In humans, more than 500,000 editing instances have been identified, by applying computational pipelines on available ESTs and high-throughput sequencing data, and by using chemical methods. However, the functions of only a small number of cases have been studied thoroughly. RNA editing instances have been found to have roles in peptide variants synthesis by non-synonymous codon substitutions, transcript variants by alterations in splicing sites and gene silencing by miRNAs sequence modifications. We established the Database of RNA EDiting (DARNED) to accommo-date the reference genomic coordinates of substitution editing in human, mouse and fly transcripts from published literatures, with additional information on edited genomic coordinates collected from various databases e.g. UCSC, NCBI. DARNED contains mostly Adenosine to Inosine editing and allows searches based on genomic region, gene ID, and user provided sequence. The Database is accessible at http://darned.ucc.ie RNA editing instances in coding region are likely to result in recoding in protein synthesis. This encouraged me to focus my research on the occurrences of RNA editing specific CDS and non-Alu exonic regions. By applying various filters on discrepancies between available ESTs and their corresponding reference genomic sequences, putative RNA editing candidates were identified. High-throughput sequencing was used to validate these candidates. All predicted coordinates appeared to be either SNPs or unedited.

Identification of microRNAs expressed in two mosquito vectors, Aedes albopictus and Culex quinquefasciatus.

BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression in a variety of organisms, including insects, vertebrates, and plants. miRNAs play important roles in cell development and differentiation as well as in the cellular response to stress and infection. To date, there are limited reports of miRNA identification in mosquitoes, insects that act as essential vectors for the transmission of many human pathogens, including flaviviruses. West Nile virus (WNV) and dengue virus, members of the Flaviviridae family, are primarily transmitted by Aedes and Culex mosquitoes. Using high-throughput deep sequencing, we examined the miRNA repertoire in Ae. albopictus cells and Cx. quinquefasciatus mosquitoes. RESULTS: We identified a total of 65 miRNAs in the Ae. albopictus C7/10 cell line and 77 miRNAs in Cx. quinquefasciatus mosquitoes, the majority of which are conserved in other insects such as Drosophila melanogaster and Anopheles gambiae. The most highly expressed miRNA in both mosquito species was miR-184, a miRNA conserved from insects to vertebrates. Several previously reported Anopheles miRNAs, including miR-1890 and miR-1891, were also found in Culex and Aedes, and appear to be restricted to mosquitoes. We identified seven novel miRNAs, arising from nine different precursors, in C7/10 cells and Cx. quinquefasciatus mosquitoes, two of which have predicted orthologs in An. gambiae. Several of these novel miRNAs reside within a ~350 nt long cluster present in both Aedes and Culex. miRNA expression was confirmed by primer extension analysis. To determine whether flavivirus infection affects miRNA expression, we infected female Culex mosquitoes with WNV. Two miRNAs, miR-92 and miR-989, showed significant changes in expression levels following WNV infection. CONCLUSIONS: Aedes and Culex mosquitoes are important flavivirus vectors. Recent advances in both mosquito genomics and high-throughput sequencing technologies enabled us to interrogate the miRNA profile in these two species. Here, we provide evidence for over 60 conserved and seven novel mosquito miRNAs, expanding upon our current understanding of insect miRNAs. Undoubtedly, some of the miRNAs identified will have roles not only in mosquito development, but also in mediating viral infection in the mosquito host.

Evidence-ranked motif identification.

cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences, it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets; it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets, and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Solid phase synthesis of Smac/DIABLO-derived peptides using a 'Safety-Catch' resin:Identification of potent XIAP BIR3 antagonists

The N-terminal sequence of the Smac/DIABLO protein is known to be involved in binding to the BIR3 domain of the anti-apoptotic proteins IAPs, antagonizing their action. Short peptides and peptide mimetics based on the first 4-residues of Smac/DIABLO have been demonstrated to re-sensitize resistant cancer cells, over-expressing IAPs, to apoptosis. Based on the well-defined structural basis for this interaction, a small focused library of C-terminal capped Smac/DIABLO-derived peptides was designed in silico using docking to the XIAP BIR3 domain. The top-ranked computational hits were conveniently synthesized employing Solid Phase Synthesis (SPS) on an alkane sulfonamide 'Safety-Catch' resin. This novel approach afforded the rapid synthesis of the target peptide library with high flexibility for the introduction of various C-terminal amide-capping groups. The library members were obtained in high yield (>65%) and purity (>85%), upon nucleophilic release from the activated resin by treatment with various amine nucleophiles. In vitro caspase-9 activity reconstitution assays of the peptides in the presence of the recombinant BIR3-domain of human XIAP (500nM) revealed N-methylalanyl-tertiarybutylglycinyl-4-(R)-phenoxyprolyl-N-biphenylmethyl carboxamide (11a) to be the most potent XIAP BIR3 antagonist of the series synthesized inducing 93% recovery of caspase-9 activity, when used at 1µM concentration. Compound (11a) also demonstrated moderate cytotoxicity against the breast cancer cell lines MDA-MB-231 and MCF-7, compared to the Smac/DIABLO-derived wild-type peptide sequences that were totally inactive in the same cell lines.