Protocols for sampling viral sequences to study epidemic dynamics

With more emphasis being put on global infectious disease monitoring, viral genetic data are being collected at an astounding rate, both within and without the context of a long-term disease surveillance plan. Concurrent with this increase have come improvements to the sophisticated and generalized statistical techniques used for extracting population-level information from genetic sequence data. However, little research has been done on how the collection of these viral sequence data can or does affect the efficacy of the phylogenetic algorithms used to analyse and interpret them. In this study, we use epidemic simulations to consider how the collection of viral sequence data clarifies or distorts the picture, provided by the phylogenetic algorithms, of the underlying population dynamics of the simulated viral infection over many epidemic cycles. We find that sampling protocols purposefully designed to capture sequences at specific points in the epidemic cycle, such as is done for seasonal influenza surveillance, lead to a significantly better view of the underlying population dynamics than do less-focused collection protocols. Our results suggest that the temporal distribution of samples can have a significant effect on what can be inferred from genetic data, and thus highlight the importance of considering this distribution when designing or evaluating protocols and analysing the data collected thereunder.     

A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem

Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic algorithm. Computational results and comparisons show that the proposed algorithms can improve the results obtained by existing algorithms developed for MMUL/BS.     

Genetic polymorphisms and the risk of progressive renal failure in elderly Hungarian patients

The relationship between renal disease progression and genetic polymorphism of enzymes influencing endothelial function remains incompletely understood. We genotyped three cohorts of elderly Hungarian patients: 245 patients with end‐stage renal disease (ESRD) on chronic hemodialysis (HD), 88 patients with mild chronic kidney disease (CKD), and 200 healthy controls. The underlying diagnoses of renal diseases were primary glomerulonephritis, interstitial nephritis, hypertension, diabetic nephropathy, and hereditary diseases. We examined genetic polymorphisms of eight candidate genes associated with endothelial function: endothelial constitutive nitric oxide synthase (ecNOS) T‐786C, endothelin‐1 G5727T, methylenetetrahydrofolate reductase (MTHFR) C677T, paraoxonase‐1 Q192R and M55L, angiotensinogen M235T, angiotensin‐converting enzyme (ACE) I/D and angiotensin II type 1 receptor A1166C gene. Six gene polymorphisms were detected by real‐time polymerase chain reaction with melting‐point analysis, and two via allele‐specific amplification and gel electrophoresis. Control group patients were in Hardy‐Weinberg equilibrium for all tested genotypes. In ESRD patients attributed to hypertension, the endothelin gene G5727T GG genotype occurred significantly less but GT genotype more frequently (P < 0.01 for both). In ESRD patients attributed to primary glomerulonephritis, more ACE DD and less ID genotypes were found (P < 0.02 for both) than in the controls. The underlying diagnosis may modify the association of genetic polymorphism and dialysis‐dependent ESRD.     

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to find the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum.     

Visual data association for real‐time video tracking using genetic and estimation of distribution algorithms

In this article, an efficient and novel approach for video data association is developed. This new method is formulated as a search across the hypotheses space defined by the possible association among tracks and detections, carried out for each frame of a video sequence. The full data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, color, etc. To guarantee real‐time performance, a time limit is set for the search process explore alternative solutions. This time limit defines the upper bound of the number of evaluations depending on search algorithm efficiency. Estimation distribution algorithms are proposed as an efficient evolutionary computation technique to search in this hypothesis space. Finally, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 208–220, 2009     

Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity

Current studies of phenotype diversity by genome-wide association studies (GWAS) are mainly focused on identifying genetic variants that influence level changes of individual traits without considering additional alterations at the system-level. However, in addition to level alterations of single phenotypes, differences in association between phenotype levels are observed across different physiological states. Such differences in molecular correlations between states can potentially reveal information about the system state beyond that reported by changes in mean levels alone. In this study, we describe a novel methodological approach, which we refer to as genome metabolome integrated network analysis (GEMINi) consisting of a combination of correlation network analysis and genome-wide correlation study. The proposed methodology exploits differences in molecular associations to uncover genetic variants involved in phenotype variation. We test the performance of the GEMINi approach in a simulation study and illustrate its use in the context of obesity and detailed quantitative metabolomics data on systemic metabolism. Application of GEMINi revealed a set of metabolic associations which differ between normal and obese individuals. While no significant associations were found between genetic variants and body mass index using a standard GWAS approach, further investigation of the identified differences in metabolic association revealed a number of loci, several of which have been previously implicated with obesity-related processes. This study highlights the advantage of using molecular associations as an alternative phenotype when studying the genetic basis of complex traits and diseases.     

A shrinkage method for causal network detection of brain regions

We present a computationally as well as statistically efficient method of inferring causal networks for the brain regions. It is based on James‐Stein‐type shrinkage estimation of covariance matrix, suggested by (Opgen‐Rhein and Strimmer, BMC Syst Biol 1 ( 2007 ), 37‐40), among different brain regions of interest of the functional magnetic resonance imaging (fMRI) experiment, that enhance the accuracy of vector autoregressive (VAR) model coefficient estimates. We have shown that this approach is well suited for the small number of samples in time and large number of brain regions encountered in real fMRI experiments of seventeen healthy individuals. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 140–146, 2013     

Effects of size distribution on the combustion efficiency of a liquid propellant rocket motor

Abstract

A new algorithm is presented to generate the parameters of the Schwarz canonical form. This new algorithm is simpler than the Routh algorithm in evaluating the Schwarz canonical form. The similarity transformation from a state‐space model in the phase‐variable form to that in the Schwarz canonical form is naturally established.     

Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images.     

几种优化算法在浅海匹配场反演中的性能比较

在分析模拟退火算法、遗传算法、差异进化算法、下山单纯形差异进化算法的优化机理的基础上,定量比较了上述算法在浅海匹配场反演中的效率差异。模拟退火算法与遗传算法只使用目标函数值信息在参数空间搜索全局最优值,效率低且易受参数间耦合的影响。差异进化算法使用种群中个体间的距离与方位信息在参数空间中搜索全局最优值,优化效率随着优化过程的进行而下降。下山单纯形差异进化算法将下山单纯形算法融入差异进化算法,增强了差异进化算法的寻优能力,混合算法对目标函数梯度信息敏感的特性使得这一算法具有较强的解耦能力。浅海匹配场反演仿真算例从最优参数反演结果、最终目标函数值、反演时间等方面检验了上述算法的反演效率。     

ISAR motion compensation using the burst derivative measure as a focal quality indicator

Inverse synthetic aperture radar (ISAR) is an imaging technique that shows great promise in classifying airborne targets in real-time under all weather conditions. The success of classifying targets using ISAR is predicated upon forming highly focused radar images of these target. Efforts to develop highly focused radar imaging computer software have been challenging, mainly because the imaging depends on and is affected by the motion of the target. Computationally intensive motion compensation algorithms have been developed to remove the unwanted degrading effects of target motion. Those particular motion compensation algorithms which require the use of a space domain focal quality indicator (e.g., entropy) to determine image sharpness as processing proceeds pay a severe computational penalty due to the large number of two-dimensional fast Fourier transforms (2D-FFTs) which must be computed. This is due to the fact that the actual processing of ISAR data is done primarily in the spatial frequency domain and not in the space domain where the final ISAR image is displayed. If a focal quality indicator could be developed to measure image sharpness in the spatial frequency domain, then the computational burden introduced by the numerous 2D-FFTs could be greatly relaxed. This article describes the use of a new focal quality indicator called the burst derivative measure for determining ISAR image sharpness in the spatial frequency domain. Tests have been performed on simulated as well as actual ISAR data using both the burst derivative measure and the entropy measure. Results indicate that the burst derivative measure, when used in conjunction with the entropy measure, can greatly reduce the number of 2D-FFTS presently required in these motion compensation algorithms.©1993 John Wiley & Sons Inc     

Through Scalp and Skull NIR‐II Photothermal Therapy of Deep Orthotopic Brain Tumors with Precise Photoacoustic Imaging Guidance

Brain tumor is one of the most lethal cancers owing to the existence of blood–brain barrier and blood–brain tumor barrier as well as the lack of highly effective brain tumor treatment paradigms. Herein, cyclo(Arg‐Gly‐Asp‐D‐Phe‐Lys(mpa)) decorated biocompatible and photostable conjugated polymer nanoparticles with strong absorption in the second near‐infrared (NIR‐II) window are developed for precise photoacoustic imaging and spatiotemporal photothermal therapy of brain tumor through scalp and skull. Evidenced by the higher efficiency to penetrate scalp and skull for 1064 nm laser as compared to common 808 nm laser, NIR‐II brain‐tumor photothermal therapy is highly effective. In addition, via a real‐time photoacoustic imaging system, the nanoparticles assist clear pinpointing of glioma at a depth of almost 3 mm through scalp and skull with an ultrahigh signal‐to‐background ratio of 90. After spatiotemporal photothermal treatment, the tumor progression is effectively inhibited and the survival spans of mice are significantly extended. This study demonstrates that NIR‐II conjugated polymer nanoparticles are promising for precise imaging and treatment of brain tumors.     

MRI brain segmentation in combination of clustering methods with Markov random field

Medical image segmentation is a preliminary stage of inclusion in identification tools. The correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an accurate detection of the disease diagnosis. Due to in‐homogeneity, low distinction and noise the segmentation of the brain MRI images is treated as the most challenging task. In this article, we proposed hybrid segmentation, by combining the clustering methods with Hidden Markov Random Field (HMRF) technique. This aims to decrease the computational load and improves the runtime of segmentation method, as MRF methodology is used in post‐processing the images. Its evaluation has performed on real imaging data, resulting in the classification of brain tissues with dice similarity metric. These results indicate the improvement in performance of the proposed method with various noise levels, compared with existing algorithms. In implementation, selection of clustering method provides better results in the segmentation of MRI brain images.     

An extended non‐local means algorithm: Application to brain MRI

Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient.     

A genetic algorithm approach to probing the evolution of self-organized nanostructured systems

We present a new methodology, based on a combination of genetic algorithms and image morphometry, for matching the outcome of a Monte Carlo simulation to experimental observations of a far-from-equilibrium nanosystem. The Monte Carlo model used simulates a colloidal solution of nanoparticles drying on a solid substrate and has previously been shown to produce patterns very similar to those observed experimentally. Our approach enables the broad parameter space associated with simulated nanoparticle self-organization to be searched effectively for a given experimental target morphology.     

Global parameter optimization for maximizing radioisotope detection probabilities at fixed false alarm rates

Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one‐to‐one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real‐valued and 8 Boolean. A simulated dataset was used for the optimization study; the “threat” set of spectra contained 540 SNM and industrial signatures, and the “benign” set of spectra contained 240 NORM and medical signatures. As compared to a random parameter search, the statistical optimization was able to able to find parameters that yield significantly higher probabilities of detection for all probabilities of false alarm from 0 to 0.1 (and equal to for probabilities of false alarm greater than 0.1), in a relatively small number of iterations. The number of iterations used, 1000, is also many fewer than would be required for a reasonable systematic search of the parameter space.     

Spectrum of genetic diversity and networks of clonal organisms.

Clonal reproduction characterizes a wide range of species including clonal plants in terrestrial and aquatic ecosystems, and clonal microbes such as bacteria and parasitic protozoa, with a key role in human health and ecosystem processes. Clonal organisms present a particular challenge in population genetics because, in addition to the possible existence of replicates of the same genotype in a given sample, some of the hypotheses and concepts underlying classical population genetics models are irreconcilable with clonality. The genetic structure and diversity of clonal populations were examined using a combination of new tools to analyse microsatellite data in the marine angiosperm Posidonia oceanica. These tools were based on examination of the frequency distribution of the genetic distance among ramets, termed the spectrum of genetic diversity (GDS), and of networks built on the basis of pairwise genetic distances among genets. Clonal growth and outcrossing are apparently dominant processes, whereas selfing and somatic mutations appear to be marginal, and the contribution of immigration seems to play a small role in adding genetic diversity to populations. The properties and topology of networks based on genetic distances showed a 'small-world' topology, characterized by a high degree of connectivity among nodes, and a substantial amount of substructure, revealing organization in subfamilies of closely related individuals. The combination of GDS and network tools proposed here helped in dissecting the influence of various evolutionary processes in shaping the intra-population genetic structure of the clonal organism investigated; these therefore represent promising analytical tools in population genetics.     

Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

In modern industries, advanced imaging technology has been more and more invested to cope with the ever‐increasing complexity of systems, to improve the visibility of information and enhance operational quality and integrity. As a result, large amounts of imaging data are readily available. This presents great challenges on the state‐of‐the‐art practices in process monitoring and quality control. Conventional statistical process control (SPC) focuses on key characteristics of the product or process and is rather limited to handle complex structures of high‐dimensional imaging data. New SPC methods and tools are urgently needed to extract useful information from in situ image profiles for process monitoring and quality control. In this study, we developed a novel dynamic network scheme to represent, model, and control time‐varying image profiles. Potts model Hamiltonian approach is introduced to characterize community patterns and organizational behaviors in the dynamic network. Further, new statistics are extracted from network communities to characterize and quantify dynamic structures of image profiles. Finally, we design and develop a new control chart, namely, network‐generalized likelihood ratio chart, to detect the change point of the underlying dynamics of complex processes. The proposed methodology is implemented and evaluated for real‐world applications in ultraprecision machining and biomanufacturing processes. Experimental results show that the proposed approach effectively characterize and monitor the variations in complex structures of time‐varying image data. The new dynamic network SPC method is shown to have strong potentials for general applications in a diverse set of domains with in situ imaging data.     

Wavelet power spectrum-based autofocusing algorithm for time delayed and integration charge coupled device space camera

A novel autofocusing algorithm using the directional wavelet power spectrum is proposed for time delayed and integration charge coupled device (TDI CCD) space cameras, which overcomes the difficulty of focus measure for the real-time change of imaging scenes. Using the multiresolution and band-pass characteristics of wavelet transform to improve the power spectrum based on fast Fourier transform (FFT), the wavelet power spectrum is less sensitive to the variance of scenes. Moreover, the new focus measure can effectively eliminate the impact of image motion mismatching by the directional selection. We test the proposed method's performance on synthetic images as well as a real ground experiment for one TDI CCD prototype camera, and compare it with the focus measure based on the existing FFT spectrum. The simulation results show that the new focus measure can effectively express the defocused states for the real remote sensing images. The error ratio is only 0.112, while the prevalent algorithm based on the FFT spectrum is as high as 0.4. Compared with the FFT-based method, the proposed algorithm performs at a high reliability in the real imaging experiments, where it reduces the instability from 0.600 to 0.161. Two experimental results demonstrate that the proposed algorithm has the characteristics of good monotonicity, high sensitivity, and accuracy. The new algorithm can satisfy the autofocusing requirements for TDI CCD space cameras.     

Identification of the dynamical properties of structures using free vibration data and distributed genetic algorithms

The dynamical properties of structures, such as natural frequencies, damping ratios and mode shapes, can be obtained by several identification methods. Some are based on the direct signal processing in a time domain; others transform response data to the frequency domain. The development of these techniques is useful in the production of more accurate structural models; they can be also used to test the level of damage in structures (or verify their strength to support new load actions) by using experimental data. There are situations where frequency domain algorithms and conventional system identification techniques fail, do not allow adequate solution of the identification problems or become trapped in a local optimum. It is in these cases where evolutionary optimization techniques are important tools for evaluating the dynamical properties of structural systems in practical applications. This article presents a methodology to determine the dynamic properties of structures knowing their response in terms of displacement, velocities or accelerations in the time domain when they are subjected to a free vibration excitation. In order to do that, a specialized evolutionary algorithm capable of adapting its parameters to the different types of registers obtained from the dynamic time response of a structure is implemented in a robust way, making this approach useful in practical situations. A distributed real genetic algorithm (DRGA) based on an island model of different subpopulations is used to adjust a simulated response signal of a building to the real response signal. Initially, computer-generated synthetic response signals are used but, in future, the approach will be validated with signals obtained from free vibration experimental tests and will be extended to other types of dynamical excitation signals. Finally, the method will be tested with data obtained from earthquake events.