Non-uniform sparsity in rapid compressive sensing MRI

Magnetic Resonance Imaging (MRI) is one of the prominent medical imaging techniques. This process is time-consuming and can take several minutes to acquire one image. The aim of this research is to reduce the imaging process time of MRI. This issue is addressed by reducing the number of acquired measurements using theory of Compressive Sensing (CS). Compressive Sensing exploits sparsity in MR images. Randomly under sampled k-space generates incoherent noise which can be handled using a nonlinear image reconstruction method. In this paper, a new framework is presented based on the idea to exploit non-uniform nature of sparsity in MR images, where local sparsity constrains were used instead of traditional global constraint, to further reduce the sample set. Experimental results and comparison with CS using global constraint are demonstrated.

Defining sub-regions in locally sparsified compressive sensing MRI

Magnetic Resonance Imaging (MRI) is an important imaging technique. However, it is a time consuming process. The aim of this study is to make the imaging process ef?cient. MR images are sparse in the sensing domain and Compressive Sensing exploits this sparsity. Locally sparsi?ed Compressed Sensing is a specialized case of CS which sub-divides the image and sparsi?es each region separately; later samples are taken based on sparsity level in that region. In this paper, a new structured approach is presented for de?ning the size and locality of sub-regions in image. Experiments were done on the regions de?ned by proposed framework and local sparsity constraints were used to achieve high sparsity level and to reduce the sample set. Experimental results and their comparison with global CS is presented in the paper.

Locally sparsified compressive sensing for improved MR image qualtity

The fact that medical images have redundant information is exploited by researchers for faster image acquisition. Sample set or number of measurements were reduced in order to achieve rapid imaging. However, due to inadequate sampling, noise artefacts are inevitable in Compressive Sensing (CS) MRI. CS utilizes the transform sparsity of MR images to regenerate images from under sampled data. Locally sparsified Compressed Sensing is an extension of simple CS. It localises sparsity constraints for sub-regions rather than using a global constraint. This paper, presents a framework to use local CS for improving image quality without increasing sampling rate or without making the acquisition process any slower. This was achieved by exploiting local constraints. Localising image into independent sub-regions allows different sampling rates within image. Energy distribution of MR images is not even and most of noise occurs due to under-sampling in high energy regions. By sampling sub-regions based on energy distribution, noise artefacts can be minimized. Experiments were done using the proposed technique. Results were compared with global CS and summarized in this paper.

A novel aggregate gene selection method for microarray data classification

This paper introduces a novel method for gene selection based on a modification of analytic hierarchy process (AHP). The modified AHP (MAHP) is able to deal with quantitative factors that are statistics of five individual gene ranking methods: two-sample t-test, entropy test, receiver operating characteristic curve, Wilcoxon test, and signal to noise ratio. The most prominent discriminant genes serve as inputs to a range of classifiers including linear discriminant analysis, k-nearest neighbors, probabilistic neural network, support vector machine, and multilayer perceptron. Gene subsets selected by MAHP are compared with those of four competing approaches: information gain, symmetrical uncertainty, Bhattacharyya distance and ReliefF. Four benchmark microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, prostate and colon are utilized for experiments. As the number of samples in microarray data datasets are limited, the leave one out cross validation strategy is applied rather than the traditional cross validation. Experimental results demonstrate the significant dominance of the proposed MAHP against the competing methods in terms of both accuracy and stability. With a benefit of inexpensive computational cost, MAHP is useful for cancer diagnosis using DNA gene expression profiles in the real clinical practice.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

An acetylcholinesterase inhibition biosensor based on a reduced graphene oxide/silver nanocluster/chitosan nanocomposite for detection of organophosphorus pesticides

A sensitive electrochemical acetylcholinesterase (AChE) biosensor based on a reduced graphene oxide (rGO) and silver nanocluster (AgNC) modified glassy carbon electrode (GCE) was developed. rGO and AgNC nanomaterials with excellent conductivity, catalytic activity and biocompatibility offered an extremely hydrophilic surface, which facilitated the immobilization of AChE to fabricate the organophosphorus pesticide biosensor. Carboxylic chitosan (CChit) was used as a cross-linker to immobilize AChE on a rGO and AgNC modified GCE. The AChE biosensor showed favorable affinity to acetylthiocholine chloride (ATCl) and could catalyze the hydrolysis of ATCl. Based on the inhibition effect of organophosphorus pesticides on the AChE activity, using phoxim as a model compound, the inhibition effect of phoxim was proportional to its concentration ranging from 0.2 to 250 nM with a detection limit of 81 pM estimated at a signal-to-noise ratio of 3. The developed biosensor exhibited good sensitivity, stability and reproducibility, thus providing a promising tool for analysis of enzyme inhibitors and direct analysis of practical samples.

Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines.

Comportamento magnético de compósitos de matriz polimérica com adição de ferritas

Polymer matrix composites offer advantages for many applications due their combination of properties, which includes low density, high specific strength and modulus of elasticity and corrosion resistance. However, the application of non-destructive techniques using magnetic sensors for the evaluation these materials is not possible since the materials are non-magnetizable. Ferrites are materials with excellent magnetic properties, chemical stability and corrosion resistance. Due to these properties, these materials are promising for the development of polymer composites with magnetic properties. In this work, glass fiber / epoxy circular plates were produced with 10 wt% of cobalt or barium ferrite particles. The cobalt ferrite was synthesized by the Pechini method. The commercial barium ferrite was subjected to a milling process to study the effect of particle size on the magnetic properties of the material. The characterization of the ferrites was carried out by x-ray diffraction (XRD), field emission gun scanning electron microscopy (FEG-SEM) and vibrating sample magnetometry (VSM). Circular notches of 1, 5 and 10 mm diameter were introduced in the composite plates using a drill bit for the non-destructive evaluation by the technique of magnetic flux leakage (MFL). The results indicated that the magnetic signals measured in plates with barium ferrite without milling and cobalt ferrite showed good correlation with the presence of notches. The milling process for 12 h and 20 h did not contribute to improve the identification of smaller size notches (1 mm). However, the smaller particle size produced smoother magnetic curves, with fewer discontinuities and improved signal-to-noise ratio. In summary, the results suggest that the proposed approach has great potential for the detection of damage in polymer composites structures

An electroanalytical application of 2-aminothiazole-modified silica gel after adsorption and separation of Hg(II) from heavy metals in aqueous solution

2-Aminothiazole covalently attached to a silica gel surface was prepared in order to obtain an adsorbent for Hg(II) ions having the following characteristics: good sorption capacity, chemical stability under conditions of use, and, especially, high selectivity. The accumulation voltammetry of mercury(II) was investigated at a carbon paste electrode chemically modified with silica gel functionalized with 2-aminothiazole (SIAMT-CPE). The repetitive cyclic voltammogram of mercury(II) solution in the potential range -0.2 to + 0.6 V versus Ag/AgCl (0.02 mol L-1 KNO3; V = 20 mV s(-1)) show two peaks one at about 0.1 V and other at 0.205 V. The anodic wave peak at 0.205 V is well defined and does not change during the cycles and it was therefore further investigated for analytical purposes using differential pulse anodic stripping voltammetry in differents supporting electrolytes. The mercury response was evaluated with respect to pH, electrode composition, preconcentration time, mercury concentration, cleaning solution, possible interferences and other variables. The precision for six determinations (n = 6) of 0.02 and 0.20 mg L-1 Hg(II) was 4.1 and 3.5% (relative standard deviation), respectively. The detection limit was estimated as 0.10 mu g L-1 mercury(II) by means of 3:1 current-to-noise ratio in connection with the optimization of the various parameters involved and using the highest-possible analyser sensitivity. (c) 2006 Elsevier Ltd. All rights reserved.

A novel system for large-scale gene expression analysis: bacterial colonies array

In the present work, we report the use of bacterial colonies to optimize macroarray technique. The devised system is significantly cheaper than other methods available to detect large-scale differential gene expression. Recombinant Escherichia coli clones containing plasmid-encoded copies of 4,608 individual expressed sequence tag (ESTs) were robotically spotted onto nylon membranes that were incubated for 6 and 12 h to allow the bacteria to grow and, consequently, amplify the cloned ESTs. The membranes were then hybridized with a beta-lactamase gene specific probe from the recombinant plasmid and, subsequently, phosphorimaged to quantify the microbial cells. Variance analysis demonstrated that the spot hybridization signal intensity was similar for 3,954 ESTs (85.8%) after 6 h of bacterial growth. Membranes spotted with bacteria colonies grown for 12 h had 4,017 ESTs (87.2%) with comparable signal intensity but the signal to noise ratio was fivefold higher. Taken together, the results of this study indicate that it is possible to investigate large-scale gene expression using macroarrays based on bacterial colonies grown for 6 h onto membranes.

Algoritmo SOM com estrutura hierárquica e dinâmica aplicado a compressão de imagens

ln this work the implementation of the SOM (Self Organizing Maps) algorithm or Kohonen neural network is presented in the form of hierarchical structures, applied to the compression of images. The main objective of this approach is to develop an Hierarchical SOM algorithm with static structure and another one with dynamic structure to generate codebooks (books of codes) in the process of the image Vector Quantization (VQ), reducing the time of processing and obtaining a good rate of compression of images with a minimum degradation of the quality in relation to the original image. Both self-organizing neural networks developed here, were denominated HSOM, for static case, and DHSOM, for the dynamic case. ln the first form, the hierarchical structure is previously defined and in the later this structure grows in an automatic way in agreement with heuristic rules that explore the data of the training group without use of external parameters. For the network, the heuristic mIes determine the dynamics of growth, the pruning of ramifications criteria, the flexibility and the size of children maps. The LBO (Linde-Buzo-Oray) algorithm or K-means, one ofthe more used algorithms to develop codebook for Vector Quantization, was used together with the algorithm of Kohonen in its basic form, that is, not hierarchical, as a reference to compare the performance of the algorithms here proposed. A performance analysis between the two hierarchical structures is also accomplished in this work. The efficiency of the proposed processing is verified by the reduction in the complexity computational compared to the traditional algorithms, as well as, through the quantitative analysis of the images reconstructed in function of the parameters: (PSNR) peak signal-to-noise ratio and (MSE) medium squared error

Arquiteturas eficientes para sensoriamento espectral e classificação automática de modulações usando características cicloestacionárias

The increasing demand for high performance wireless communication systems has shown the inefficiency of the current model of fixed allocation of the radio spectrum. In this context, cognitive radio appears as a more efficient alternative, by providing opportunistic spectrum access, with the maximum bandwidth possible. To ensure these requirements, it is necessary that the transmitter identify opportunities for transmission and the receiver recognizes the parameters defined for the communication signal. The techniques that use cyclostationary analysis can be applied to problems in either spectrum sensing and modulation classification, even in low signal-to-noise ratio (SNR) environments. However, despite the robustness, one of the main disadvantages of cyclostationarity is the high computational cost for calculating its functions. This work proposes efficient architectures for obtaining cyclostationary features to be employed in either spectrum sensing and automatic modulation classification (AMC). In the context of spectrum sensing, a parallelized algorithm for extracting cyclostationary features of communication signals is presented. The performance of this features extractor parallelization is evaluated by speedup and parallel eficiency metrics. The architecture for spectrum sensing is analyzed for several configuration of false alarm probability, SNR levels and observation time for BPSK and QPSK modulations. In the context of AMC, the reduced alpha-profile is proposed as as a cyclostationary signature calculated for a reduced cyclic frequencies set. This signature is validated by a modulation classification architecture based on pattern matching. The architecture for AMC is investigated for correct classification rates of AM, BPSK, QPSK, MSK and FSK modulations, considering several scenarios of observation length and SNR levels. The numerical results of performance obtained in this work show the eficiency of the proposed architectures

Análise das curvas de luz do CoroT usando diferentes processos comparativos: estimando períodos de rotação estelar

One of the main goals of CoRoT Natal Team is the determination of rotation period for thousand of stars, a fundamental parameter for the study of stellar evolutionary histories. In order to estimate the rotation period of stars and to understand the associated uncertainties resulting, for example, from discontinuities in the curves and (or) low signal-to-noise ratio, we have compared three different methods for light curves treatment. These methods were applied to many light curves with different characteristics. First, a Visual Analysis was undertaken for each light curve, giving a general perspective on the different phenomena reflected in the curves. The results obtained by this method regarding the rotation period of the star, the presence of spots, or the star nature (binary system or other) were then compared with those obtained by two accurate methods: the CLEANest method, based on the DCDFT (Date Compensated Discrete Fourier Transform), and the Wavelet method, based on the Wavelet Transform. Our results show that all three methods have similar levels of accuracy and can complement each other. Nevertheless, the Wavelet method gives more information about the star, from the wavelet map, showing the variations of frequencies over time in the signal. Finally, we discuss the limitations of these methods, the efficiency to give us informations about the star and the development of tools to integrate different methods into a single analysis

Low voltage and variable-pressure scanning electron microscopy of fractured composites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)