This paper proposes a general boundary delineation method for 2D serial US images by modeling the spatial-temporal dynamics and generating the object boundary in each slice under the cell-based MAP scheme. The modeling of the spatial-temporal dynamics can serve as a prior to guide the cell-based MAP process and potentially maintain the contextual coherence. Experiments have been conducted on 8 sets of compression breast series and 5 sets of freehand breast acquisitions. The computer-generated results by our algorithm are compared to manual delineations prepared by experts. The experimental results suggest that the boundaries of the proposed method are not significantly different to manual outlines and are quite stable in terms of reproducibility.

Most alignment algorithms find an initial equivalent residue pair followed by an iterative optimization process to explore better near-optimal alignments in the surrounding solution space of the initial alignment. It plays a decisive role in determining the alignment quality since a poor initial alignment may make the final alignment trapped in an undesirable local optimum even with an iterative optimization. We proposed a vector-based alignment algorithm with a new initial alignment approach accounting for local structure features called MIRAGE-align. The new idea is to enhance the quality of the initial alignment based on encoded local structural alphabets to identify the protein structure pair whose sequence identity falls in or below twilight zone. The statistical analysis of alignment quality based on Match Index (MI) and computation time demonstrated that MIRAGE-align algorithm outperformed four previously published algorithms, i.e., the residue-based algorithm (CE), the vector-based algorithm (SSM), TM-align, and Fr-TM-align. MIRAGE-align yields a better estimate of initial solution to enhance the quality of initial alignment and enable the employment of a non-iterative optimization process to achieve a better alignment.

As a potential biomarker for women's cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide more objective measurement for vascular structures with calcium depositions in mammography, a new computerized method is introduced in this paper to delineate the calcified vessels. Specifically, we leverage two underlying cues, namely calcification and vesselness, into a multiple seeded tracking with uncertainty scheme. This new vessel-tracking scheme generates plenty of sampling paths to describe the complicated topology of the vascular structures with calcium depositions. A compiling and linking process is further carried out to organize the sampling paths together to be the vessel segments that likely belong to the same vessel tract. The proposed method has been evaluated on 63 mammograms, by comparison with manual delineations from two experts using various assessment metrics. The experiment results confirm the efficacy and stability of the proposed method, and also indicate that the proposed method can be potentially used as a convenient BAC measurement tool in replacement of the trivial and tedious manual delineation tasks.

BACKGROUND: To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. RESULTS: AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. CONCLUSION: AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.

A fuzzy logic approach, called FuzzyTRN, to infer transcriptional regulatory networks (TRN) inSaccharomycescerevisiaeis proposed. FuzzyTRN predicts potential regulators and their target genes using sequences analysis on transcription factor binding sites (TFBS) of transcriptional factors (TF) and promoter region of target genes. Those potential regulators and target genes are used to form vertices in the TRN. Furthermore, multiple sets of microarray gene expression data (MGED) are used by FuzzyTRN to predict links in the TRN. FuzzyTRN predicts transcriptional interactions by recognizing expression patterns of genes. In this study, a number of confirmed genetic interactions are utilized to train FuzzyTRN. 112 indirect genetic interactions that were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) experiments, and 259 and 86 direct genetic interactions that were collected by TRANSFAC database and literature surveying, were used as training set in this work. A simulation that encompasses 170 TFs and 40 target genes has been conducted and checked against YEASTRACT database to evaluate the performance of the proposed algorithm.

MOTIVATION: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass. RESULTS: PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73% (77%), 46% (51%), and 52% (59%), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10% (10 and 14%), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted. AVAILABILITY: Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/~gshieh/pare.htm.

BACKGROUND: With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality. RESULTS: Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted. CONCLUSION: SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.

In this paper, we study and analyze the computational complexity of the deblocking filter in H.264/AVC baseline decoder based on SimpleScalar/ARM simulator. The simulation result shows that the memory reference, content activity check operations, and filter operations are known to be very time consuming in the decoder of this new video coding standard. In order to improve overall system performance, we propose a configurable, extensible, and synthesizable window-based processing architecture which simultaneously processes the horizontal filtering of vertical edge and vertical filtering of horizontal edge. As a result, the memory performance of the proposed architecture is improved by four times when compared to previous designs. Moreover, the system performance of our window-based architecture significantly outperforms the previous designs from 7 times to 20 times.

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE data set. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

An efficient new method, termed as the cross-reference weighted least square estimate (WLSE) [CRWLSE], is proposed to integrate the incomplete local smoothness information to improve the reconstruction of positron emission tomography (PET) images in the presence of accidental coincidence events and attenuation. The algebraic reconstruction technique (ART) is applied to this new estimate and the convergence is proved. This numerical technique is based on row operations. The computational complexity is only linear in the sizes of pixels and detector tubes. Hence, it is efficient in storage and computation for a large and sparse system. Moreover, the easy incorporation of range limits and spatially variant penalty will not deprive the efficiency. All this makes the new method practically applicable. An automatically data-driven selection method for this new estimate based on the generalized cross validation is also studied. The Monte Carlo studies demonstrate the advantages of this new method.

Hot spots are notorious for degrading the performance of a parallel algorithm. We attempt to minimize the hot-spot access time for a class of problems, namely, Large-Scale Data-Parallel (LSDP) algorithms, on a 2D mesh. An LSDP algorithm has rich data parallelism but without exclusive task and data partitioning. Our approach is to allocate the hot spots at the optimal locations such that the hot-spot access time is minimized. Also, we have designed scheduling algorithms which control hot spot access sequences to achieve the minimal access time. Both uniform and nonuniform hot spots have been considered in this study. We have analytically derived the optimal allocations for wrapped-around and non-wrapped-around square meshes. The theoretical results have been verified by parallelizing the EM algorithm for 3D PET image reconstruction on the Intel iPSC/860.>

The expectation maximization (EM) algorithm is one of the most suitable iterative methods for positron emission tomography (PET) image reconstruction; however, it requires a long computation time and an enormous amount of memory space. To overcome these problems, we present two classes of highly efficient parallelization schemes: homogeneous and inhomogeneous partitionings. The essential difference between these two classes is that the inhomogeneous partitioning schemes may partially overlap the communication with computation by deliberate exploitation of the inherent data access pattern with a multiple-ring communication pattern. In theory, the inhomogeneous partitioning schemes may outperform the homogeneous partitioning schemes. However, the latter require a simpler communication pattern. In an attempt to estimate the achievable performance and to analyze the performance degradation factors without actual implementation, we have derived efficiency prediction formulas for closely estimating the performance for the proposed parallelization schemes. We propose new integration and broadcasting algorithms for hypercube, ring, and n-D mesh topologies, which are more efficient than the conventional algorithms when the link setup time is relatively negligible. The concept of the proposed task and data partitioning schemes, the integration and broadcasting algorithms, and the efficiency estimation methods can be applied to many other problems that are rich in data parallelism, but without balanced exclusive partitioning.>