Simple method for detection of MYH11 DNA rearrangements in patients with inv(16)(p13q22) and acute myeloid leukemia.

The pericentric inversion on chromosome 16 [inv(16)(p13q22)] and related t(16;16)(p13;q22) are recurrent aberrations associated with acute myeloid leukemia (AML) M4 Eo. Both abberations result in a fusion of the core binding factor beta (CBFB) and smooth muscle myosin heavy chain gene (MYH11). A selected genomic 6.9-kb BamHl probe detects MYH11 DNA rearrangements in 18 of 19 inv(16)/t(16;16) patients tested using HindIII digested DNA. The rearranged fragments were not detectable after remission in two cases tested, while they were present after relapse in one of these two cases tested.

Urban Detection From Hyperspectral Images Using Dimension-Reduction Model and Fusion of Multiple Segmentations Based on Stuctural and Textural Features

Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales.

Assessment of thought–shape fusion: initial validation of a short version of the trait thought–shape fusion scale

Objective: Thought–shape fusion (TSF) is a cognitive distortion that has been linked to eating pathology. Two studies were conducted to further explore this phenomenon and to establish the psychometric properties of a French short version of the TSF scale. Method: In Study 1, students (n 5 284) completed questionnaires assessing TSF and related psychopathology. In Study 2, the responses of women with eating disorders (n 5 22) and women with no history of an eating disorder (n 5 23) were compared. Results: The French short version of the TSF scale has a unifactorial structure, with convergent validity with measures of eating pathology, and good internal consistency. Depression, eating pathology, body dissatisfaction, and thought-action fusion emerged as predictors of TSF. Individuals with eating disorders have higher TSF, and more clinically relevant food-related thoughts than do women with no history of an eating disorder. Discussion: This research suggests that the shortened TSF scale can suitably measure this construct, and provides support for the notion that TSF is associated with eating pathology.

An ASIFT-based local registration method for satellite imagery

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points.

TAF15 and the leukemia-associated fusion protein TAF15-CIZ/NMP4 are cleaved by caspases-3 and-7

Caspases are central players in proteolytic pathways that regulate cellular processes Such as apoptosis and differentiation. To accelerate the discovery of novel caspase substrates we developed a method combining in silico screening and in vitro validation. With this approach, we identified TAH15 as a novel caspase Substrate in a trial Study. We find that TAF15 was specifically cleaved by caspases-3 and -7. Site-directed mutagenesis revealed the consensus sequence (106)DQPD/Y(110) as the only site recognized by these caspases. Surprisingly, TAF15 was cleaved at more than one site in staurosporine-treated Jurkat cells. In addition, we generated two oncogenic TAF15-CIZ/NMP4-fused proteins which have been found in acute myeloid leukemia and demonstrate that caspases-3 and -7 cleave the fusion proteins at one single site. Broad application of this combination approach should expedite identification of novel caspase-interacting proteins and provide new insights into the regulation of caspase pathways leading to cell death in normal and cancer cells. (C) 2009 Elsevier Inc. All rights reserved.

X-ray spectrometry: a powerful tool for the measurement of complete fusion of weakly bound nuclei

We describe how the method of detection of delayed K x-rays produced by the electron capture decay of the residual nuclei can be a powerful tool in the investigation of the effect of the breakup process on the complete fusion (CF) cross-section of weakly bound nuclei at energies close to the Coulomb barrier. This is presently one of the most interesting subjects under investigation in the field of low-energy nuclear reactions, and the difficult experimental task of separating CF from the incomplete fusion (ICF) of one of the breakup fragments can be achieved by the x-ray spectrometry method. We present results for the fusion of the (9)Be + (144)Sm system. Copyright (c) 2008 John Wiley & Sons, Ltd.

Feasibility of using in situ fusion for the determination of Co, Cr and Mn in Portland cement by direct solid sampling graphite furnace atomic absorption spectrometry

In situ fusion on the boat-type graphite platform has been used as a sample pretreatment for the direct determination of Co, Cr and Mn in Portland cement by solid sampling graphite furnace atomic absorption spectrometry (SS-GF AAS). The 3-field Zeeman technique was adopted for background correction to decrease the sensitivity during measurements. This strategy allowed working with up to 200 mu g of sample. The in situ fusion was accomplished using 10 mu L of a flux mixture 4.0% m/v Na(2)CO(3) + 4.0% m/v ZnO + 0.1% m/v Triton (R) X-100 added over the cement sample and heated at 800 degrees C for 20 s. The resulting mould was completely dissolved with 10 mu L of 0.1% m/v HNO(3). Limits of detection were 0.11 mu g g(-1) for Co, 1.1 mu g g(-1) for Cr and 1.9 mu g g(-1) for Mn. The accuracy of the proposed method has been evaluated by the analysis of certified reference materials. The values found presented no statistically significant differences compared to the certified values (Student`s t-test, p<0.05). In general, the relative standard deviation was lower than 12% (n = 5). (C) 2009 Elsevier B.V. All rights reserved.

A quadtree driven image fusion quality assessment

In this paper a new method to compute saliency of source images is presented. This work is an extension to universal quality index founded by Wang and Bovik and improved by Piella. It defines the saliency according to the change of topology of quadratic tree decomposition between source images and the fused image. The saliency function provides higher weight for the tree nodes that differs more in the fused image in terms topology. Quadratic tree decomposition provides an easy and systematic way to add a saliency factor based on the segmented regions in the images.

Feature-based image fusion quality metrics

Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.

Color map-based image fusion

An image fusion system accepts two source images and produces a 'better' fused image. The term 'better' differs from one context to another. In some contexts, it means holding more information. In other contexts, it means getting more accurate results or readings. In general, images hold more than just the color values. Histogram distribution, dynamic range of colors, and color maps are all as valuable as the color values presenting the pictorial information of the image. This paper studies the problems of fusing images from different domains. It proposes a method to extend the fusion algorithms to fuse image properties that define the interpretation of captured images as well.

Minimizing the limitations of GL analyser of fusion based email classification

In the last decade, the Internet email has become one of the primary method of communication used by everyone for the exchange of ideas and information. However, in recent years, along with the rapid growth of the Internet and email, there has been a dramatic growth in spam. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs. This problem is mainly caused by the dynamic nature of spam content, spam delivery strategies, as well as the diversification of the classification algorithms. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyser as further refinements of our previous multi-classifier based email classification [10]. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique with two different variations which will analyze only the product of GL emails. Our empirical evidence proofs the improvements of this approach, in terms of complexity and cost, compared to existing GL analyser. This approach also overcomes the limitation of human interaction of existing analyzing technique.

Fusion of GRNN and FA for online noisy data regression

A new online neural-network-based regression model for noisy data is proposed in this paper. It is a hybrid system combining the Fuzzy ART (FA) and General Regression Neural Network (GRNN) models. Both the FA and GRNN models are fast incremental learning systems. The proposed hybrid model, denoted as GRNNFA-online, retains the online learning properties of both models. The kernel centers of the GRNN are obtained by compressing the training samples using the FA model. The width of each kernel is then estimated by the K-nearest-neighbors (kNN) method. A heuristic is proposed to tune the value of Kof the kNN dynamically based on the concept of gradient-descent. The performance of the GRNNFA-online model was evaluated using two benchmark datasets, i.e., OZONE and Friedman#1. The experimental results demonstrated the convergence of the prediction errors. Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.

Multi-sensory face biometric fusion (for personal identification)

The objective of this work is to recognize faces using sets of images in visual and thermal spectra. This is challenging because the former is greatly affected by illumination changes, while the latter frequently contains occlusions due to eye-wear and is inherently less discriminative. Our method is based on a fusion of the two modalities. Specifically: we examine (i) the effects of preprocessing of data in each domain, (ii) the fusion of holistic and local facial appearance, and (iii) propose an algorithm for combining the similarity scores in visual and thermal spectra in the presence of prescription glasses and significant pose variations, using a small number of training images (5-7). Our system achieved a high correct identification rate of 97% on a freely available test set of 29 individuals and extreme illumination changes.

Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion

Statistical time series methods have proven to be a promising technique in structural health monitoring, since it provides a direct form of data analysis and eliminates the requirement for domain transformation. Latest research in structural health monitoring presents a number of statistical models that have been successfully used to construct quantified models of vibration response signals. Although a majority of these studies present viable results, the aspects of practical implementation, statistical model construction and decision-making procedures are often vaguely defined or omitted from presented work. In this article, a comprehensive methodology is developed, which essentially utilizes an auto-regressive moving average with exogenous input model to create quantified model estimates of experimentally acquired response signals. An iterative self-fitting algorithm is proposed to construct and fit the auto-regressive moving average with exogenous input model, which is capable of integrally finding an optimum set of auto-regressive moving average with exogenous input model parameters. After creating a dataset of quantified response signals, an unlabelled response signal can be identified according to a 'closest-fit' available in the dataset. A unique averaging method is proposed and implemented for multi-sensor data fusion to decrease the margin of error with sensors, thus increasing the reliability of global damage identification. To demonstrate the effectiveness of the developed methodology, a steel frame structure subjected to various bolt-connection damage scenarios is tested. Damage identification results from the experimental study suggest that the proposed methodology can be employed as an efficient and functional damage identification tool. © The Author(s) 2014.

An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

  Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body’s co-ordination system and the sensor’s co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77◦ for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.