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.

Modal Extraction for Wind Turbines using Moving Window Subspace Identification

This paper proposes a method for wind turbine mode identification using the multivariable output error statespace (MOESP) identification algorithm. The paper incorporates a fast moving window QR decomposition and propagator method from array signal processing, yielding a moving window subspace identification algorithm. The algorithm assumes that the system order is known as a priori and remains constant during identification. For the purpose of extracting modal information for turbines modelled as a linear parameter varying (LPV) system, the algorithm is applicable since a nonlinear system can be approximated as a piecewise time invariant system in consecutive data windows. The algorithm is exemplified using numerical simulations which show that the moving window algorithm can track the modal information. The paper also demonstrates that the low computational burden of the algorithm, compared to conventional batch subspace identification, has significant implications for online implementation.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.

Musicologist-driven writer identification in early music manuscripts

Recent renewed interest in computational writer identification has resulted in an increased number of publications. In relation to historical musicology its application has so far been limited. One of the obstacles seems to be that the clarity of the images from the scans available for computational analysis is often not sufficient. In this paper, the use of the Hinge feature is proposed to avoid segmentation and staff-line removal for effective feature extraction from low quality scans. The use of an auto encoder in Hinge feature space is suggested as an alternative to staff-line removal by image processing, and their performance is compared. The result of the experiment shows an accuracy of 87 % for the dataset containing 84 writers’ samples, and superiority of our segmentation and staff-line removal free approach. Practical analysis on Bach’s autograph manuscript of the Well-Tempered Clavier II (Additional MS. 35021 in the British Library, London) is also presented and the extensive applicability of our approach is demonstrated.

Unsupervised Solution Post Identification from Discussion Forums

Discussion forums have evolved into a dependablesource of knowledge to solvecommon problems. However, only a minorityof the posts in discussion forumsare solution posts. Identifying solutionposts from discussion forums, hence, is animportant research problem. In this paper,we present a technique for unsupervisedsolution post identification leveraginga so far unexplored textual feature, thatof lexical correlations between problemsand solutions. We use translation modelsand language models to exploit lexicalcorrelations and solution post characterrespectively. Our technique is designedto not rely much on structural featuressuch as post metadata since suchfeatures are often not uniformly availableacross forums. Our clustering-based iterativesolution identification approach basedon the EM-formulation performs favorablyin an empirical evaluation, beatingthe only unsupervised solution identificationtechnique from literature by a verylarge margin. We also show that our unsupervisedtechnique is competitive againstmethods that require supervision, outperformingone such technique comfortably.

Development of numerical methodologies for parameter identification and shape optimization in metal forming simulations

Por parte da indústria de estampagem tem-se verificado um interesse crescente em simulações numéricas de processos de conformação de chapa, incluindo também métodos de engenharia inversa. Este facto ocorre principalmente porque as técnicas de tentativa-erro, muito usadas no passado, não são mais competitivas a nível económico. O uso de códigos de simulação é, atualmente, uma prática corrente em ambiente industrial, pois os resultados tipicamente obtidos através de códigos com base no Método dos Elementos Finitos (MEF) são bem aceites pelas comunidades industriais e científicas Na tentativa de obter campos de tensão e de deformação precisos, uma análise eficiente com o MEF necessita de dados de entrada corretos, como geometrias, malhas, leis de comportamento não-lineares, carregamentos, leis de atrito, etc.. Com o objetivo de ultrapassar estas dificuldades podem ser considerados os problemas inversos. No trabalho apresentado, os seguintes problemas inversos, em Mecânica computacional, são apresentados e analisados: (i) problemas de identificação de parâmetros, que se referem à determinação de parâmetros de entrada que serão posteriormente usados em modelos constitutivos nas simulações numéricas e (ii) problemas de definição geométrica inicial de chapas e ferramentas, nos quais o objetivo é determinar a forma inicial de uma chapa ou de uma ferramenta tendo em vista a obtenção de uma determinada geometria após um processo de conformação. São introduzidas e implementadas novas estratégias de otimização, as quais conduzem a parâmetros de modelos constitutivos mais precisos. O objetivo destas estratégias é tirar vantagem das potencialidades de cada algoritmo e melhorar a eficiência geral dos métodos clássicos de otimização, os quais são baseados em processos de apenas um estágio. Algoritmos determinísticos, algoritmos inspirados em processos evolucionários ou mesmo a combinação destes dois são usados nas estratégias propostas. Estratégias de cascata, paralelas e híbridas são apresentadas em detalhe, sendo que as estratégias híbridas consistem na combinação de estratégias em cascata e paralelas. São apresentados e analisados dois métodos distintos para a avaliação da função objetivo em processos de identificação de parâmetros. Os métodos considerados são uma análise com um ponto único ou uma análise com elementos finitos. A avaliação com base num único ponto caracteriza uma quantidade infinitesimal de material sujeito a uma determinada história de deformação. Por outro lado, na análise através de elementos finitos, o modelo constitutivo é implementado e considerado para cada ponto de integração. Problemas inversos são apresentados e descritos, como por exemplo, a definição geométrica de chapas e ferramentas. Considerando o caso da otimização da forma inicial de uma chapa metálica a definição da forma inicial de uma chapa para a conformação de um elemento de cárter é considerado como problema em estudo. Ainda neste âmbito, um estudo sobre a influência da definição geométrica inicial da chapa no processo de otimização é efetuado. Este estudo é realizado considerando a formulação de NURBS na definição da face superior da chapa metálica, face cuja geometria será alterada durante o processo de conformação plástica. No caso dos processos de otimização de ferramentas, um processo de forjamento a dois estágios é apresentado. Com o objetivo de obter um cilindro perfeito após o forjamento, dois métodos distintos são considerados. No primeiro, a forma inicial do cilindro é otimizada e no outro a forma da ferramenta do primeiro estágio de conformação é otimizada. Para parametrizar a superfície livre do cilindro são utilizados diferentes métodos. Para a definição da ferramenta são também utilizados diferentes parametrizações. As estratégias de otimização propostas neste trabalho resolvem eficientemente problemas de otimização para a indústria de conformação metálica.

An overview of nonlinear identification and control with neural networks.

The aim of this chapter is to introduce background concepts in nonlinear systems identification and control with artificial neural networks. As this chapter is just an overview, with a limited page space, only the basic ideas will be explained here. The reader is encouraged, for a more detailed explanation of a specific topic of interest, to consult the references given throughout the text. Additionally, as general books in the field of neural networks, the books by Haykin [1] and Principe et al. [2] are suggested. Regarding nonlinear systems identification, covering both classical and neural and neuro-fuzzy methodologies, Reference 3 is recommended. References 4 and 5 should be used in the context of B-spline networks.

Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.

Searching for novel gene functions in yeast : identification of thousands of novel molecular interactions by protein-fragment complementation assay followed by automated gene function prediction and high-throughput lipidomics

La compréhension de processus biologiques complexes requiert des approches expérimentales et informatiques sophistiquées. Les récents progrès dans le domaine des stratégies génomiques fonctionnelles mettent dorénavant à notre disposition de puissants outils de collecte de données sur l’interconnectivité des gènes, des protéines et des petites molécules, dans le but d’étudier les principes organisationnels de leurs réseaux cellulaires. L’intégration de ces connaissances au sein d’un cadre de référence en biologie systémique permettrait la prédiction de nouvelles fonctions de gènes qui demeurent non caractérisées à ce jour. Afin de réaliser de telles prédictions à l’échelle génomique chez la levure Saccharomyces cerevisiae, nous avons développé une stratégie innovatrice qui combine le criblage interactomique à haut débit des interactions protéines-protéines, la prédiction de la fonction des gènes in silico ainsi que la validation de ces prédictions avec la lipidomique à haut débit. D’abord, nous avons exécuté un dépistage à grande échelle des interactions protéines-protéines à l’aide de la complémentation de fragments protéiques. Cette méthode a permis de déceler des interactions in vivo entre les protéines exprimées par leurs promoteurs naturels. De plus, aucun biais lié aux interactions des membranes n’a pu être mis en évidence avec cette méthode, comparativement aux autres techniques existantes qui décèlent les interactions protéines-protéines. Conséquemment, nous avons découvert plusieurs nouvelles interactions et nous avons augmenté la couverture d’un interactome d’homéostasie lipidique dont la compréhension demeure encore incomplète à ce jour. Par la suite, nous avons appliqué un algorithme d’apprentissage afin d’identifier huit gènes non caractérisés ayant un rôle potentiel dans le métabolisme des lipides. Finalement, nous avons étudié si ces gènes et un groupe de régulateurs transcriptionnels distincts, non préalablement impliqués avec les lipides, avaient un rôle dans l’homéostasie des lipides. Dans ce but, nous avons analysé les lipidomes des délétions mutantes de gènes sélectionnés. Afin d’examiner une grande quantité de souches, nous avons développé une plateforme à haut débit pour le criblage lipidomique à contenu élevé des bibliothèques de levures mutantes. Cette plateforme consiste en la spectrométrie de masse à haute resolution Orbitrap et en un cadre de traitement des données dédié et supportant le phénotypage des lipides de centaines de mutations de Saccharomyces cerevisiae. Les méthodes expérimentales en lipidomiques ont confirmé les prédictions fonctionnelles en démontrant certaines différences au sein des phénotypes métaboliques lipidiques des délétions mutantes ayant une absence des gènes YBR141C et YJR015W, connus pour leur implication dans le métabolisme des lipides. Une altération du phénotype lipidique a également été observé pour une délétion mutante du facteur de transcription KAR4 qui n’avait pas été auparavant lié au métabolisme lipidique. Tous ces résultats démontrent qu’un processus qui intègre l’acquisition de nouvelles interactions moléculaires, la prédiction informatique des fonctions des gènes et une plateforme lipidomique innovatrice à haut débit , constitue un ajout important aux méthodologies existantes en biologie systémique. Les développements en méthodologies génomiques fonctionnelles et en technologies lipidomiques fournissent donc de nouveaux moyens pour étudier les réseaux biologiques des eucaryotes supérieurs, incluant les mammifères. Par conséquent, le stratégie présenté ici détient un potentiel d’application au sein d’organismes plus complexes.

Identification of spectral lines of elements using artificial neural networks

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. This paper describes how an ANN can be used to identify the spectral lines of elements. The spectral lines of Cadmium (Cd), Calcium (Ca), Iron (Fe), Lithium (Li), Mercury (Hg), Potassium (K) and Strontium (Sr) in the visible range are chosen for the investigation. One of the unique features of this technique is that it uses the whole spectrum in the visible range instead of individual spectral lines. The spectrum of a sample taken with a spectrometer contains both original peaks and spurious peaks. It is a tedious task to identify these peaks to determine the elements present in the sample. ANNs capability of retrieving original data from noisy spectrum is also explored in this paper. The importance of the need of sufficient data for training ANNs to get accurate results is also emphasized. Two networks are examined: one trained in all spectral lines and other with the persistent lines only. The network trained in all spectral lines is found to be superior in analyzing the spectrum even in a noisy environment.

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

Parameter identification of structural dynamic models by inverse statistical analysis

Auf dem Gebiet der Strukturdynamik sind computergestützte Modellvalidierungstechniken inzwischen weit verbreitet. Dabei werden experimentelle Modaldaten, um ein numerisches Modell für weitere Analysen zu korrigieren. Gleichwohl repräsentiert das validierte Modell nur das dynamische Verhalten der getesteten Struktur. In der Realität gibt es wiederum viele Faktoren, die zwangsläufig zu variierenden Ergebnissen von Modaltests führen werden: Sich verändernde Umgebungsbedingungen während eines Tests, leicht unterschiedliche Testaufbauten, ein Test an einer nominell gleichen aber anderen Struktur (z.B. aus der Serienfertigung), etc. Damit eine stochastische Simulation durchgeführt werden kann, muss eine Reihe von Annahmen für die verwendeten Zufallsvariablengetroffen werden. Folglich bedarf es einer inversen Methode, die es ermöglicht ein stochastisches Modell aus experimentellen Modaldaten zu identifizieren. Die Arbeit beschreibt die Entwicklung eines parameter-basierten Ansatzes, um stochastische Simulationsmodelle auf dem Gebiet der Strukturdynamik zu identifizieren. Die entwickelte Methode beruht auf Sensitivitäten erster Ordnung, mit denen Parametermittelwerte und Kovarianzen des numerischen Modells aus stochastischen experimentellen Modaldaten bestimmt werden können.

Computational Models of Object Recognition in Cortex: A Review

Understanding how biological visual systems perform object recognition is one of the ultimate goals in computational neuroscience. Among the biological models of recognition the main distinctions are between feedforward and feedback and between object-centered and view-centered. From a computational viewpoint the different recognition tasks - for instance categorization and identification - are very similar, representing different trade-offs between specificity and invariance. Thus the different tasks do not strictly require different classes of models. The focus of the review is on feedforward, view-based models that are supported by psychophysical and physiological data.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.