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Serious infections caused by bacteria that are resistant to commonly used antibiotics have become a major global healthcare problem in the 21st century. Multidrug-resistant bacteria causing severe infections mainly grow in complex bacterial communities known as biofilms, in which bacterial resistance to antibacterial agents and to the host immune system is strengthened. As drug resistance is becoming a threatening problem, it is necessary to develop new antimicrobial agents with novel mechanisms of action. Here, we designed and synthesized a small library of N -substituted hydroxylamine (N-HA) compounds with antibacterial activity. These compounds, acting as radical scavengers, inhibit the bacterial ribonucleotide reductase (RNR) enzyme. RNR enzyme is essential for bacterial proliferation during infection, as it provides the building blocks for DNA synthesis and repair. We demonstrate the broad antimicrobial effect of several drug candidates against a variety of Gram-positive and Gram-negative bacteria, together with low toxicity toward eukaryotic cells. Furthermore, the most promising compounds can reduce the biomass of an established biofilm on Pseudomonas aeruginosa , Staphylococcus aureus , and Escherichia coli . This study settles the starting point to develop new N -hydroxylamine compounds as potential effective antibacterial agents to fight against drug-resistant pathogenic bacteria., Competing Interests: The authors declare no competing financial interest.

Chronic lung infections by the ubiquitous and extremely adaptable opportunistic pathogen Pseudomonas aeruginosa correlate with the formation of a biofilm, where bacteria grow in association with an extracellular matrix and display a wide range of changes in gene expression and metabolism. This leads to increased resistance to physical stress and antibiotic therapies, while enhancing cell-to-cell communication. Oxygen diffusion through the complex biofilm structure generates an oxygen concentration gradient, leading to the appearance of anaerobic microenvironments. Ribonucleotide reductases (RNRs) are a family of highly sophisticated enzymes responsible for the synthesis of the deoxyribonucleotides, and they constitute the only de novo pathway for the formation of the building blocks needed for DNA synthesis and repair. P. aeruginosa is one of the few bacteria encoding all three known RNR classes (Ia, II, and III). Class Ia RNRs are oxygen dependent, class II are oxygen independent, and class III are oxygen sensitive. A tight control of RNR activity is essential for anaerobic growth and therefore for biofilm development. In this work we explored the role of the different RNR classes in biofilm formation under aerobic and anaerobic initial conditions and using static and continuous-flow biofilm models. We demonstrated the importance of class II and III RNR for proper cell division in biofilm development and maturation. We also determined that these classes are transcriptionally induced during biofilm formation and under anaerobic conditions. The molecular mechanism of their anaerobic regulation was also studied, finding that the Anr/Dnr system is responsible for class II RNR induction. These data can be integrated with previous knowledge about biofilms in a model where these structures are understood as a set of layers determined by oxygen concentration and contain cells with different RNR expression profiles, bringing us a step closer to the understanding of this complex growth pattern, essential for P. aeruginosa chronic infections.

Infections caused by biofilm-forming bacteria are a major threat to hospitalized patients and the main cause of chronic obstructive pulmonary disease and cystic fibrosis. There is an urgent necessity for novel therapeutic approaches, since current antibiotic delivery fails to eliminate biofilm-protected bacteria. In this study, ciprofloxacin-loaded poly(lactic-co-glycolic acid) nanoparticles, which were functionalized with DNase I, were fabricated using a green-solvent based method and their antibiofilm activity was assessed against Pseudomonas aeruginosa biofilms. Such nanoparticles constitute a paradigm shift in biofilm treatment, since, besides releasing ciprofloxacin in a controlled fashion, they are able to target and disassemble the biofilm by degrading the extracellular DNA that stabilize the biofilm matrix. These carriers were compared with free-soluble ciprofloxacin, and ciprofloxacin encapsulated in untreated and poly(lysine)-coated nanoparticles. DNase I-activated nanoparticles were not only able to prevent biofilm formation from planktonic bacteria, but they also successfully reduced established biofilm mass, size and living cell density, as observed in a dynamic environment in a flow cell biofilm assay. Moreover, repeated administration over three days of DNase I-coated nanoparticles encapsulating ciprofloxacin was able to reduce by 95% and then eradicate more than 99.8% of established biofilm, outperforming all the other nanoparticle formulations and the free-drug tested in this study. These promising results, together with minimal cytotoxicity as tested on J774 macrophages, allow obtaining novel antimicrobial nanoparticles, as well as provide clues to design the next generation of drug delivery devices to treat persistent bacterial infections., (Copyright © 2015 Elsevier B.V. All rights reserved.)

This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression.

Paul Dan Cristea, professor of Electrical Engineering and Computer Science at 'Politehnica' University of Bucharest died on 17 April 2013, following several years of bravely battling a perfidious illness.

Ptychography is a lensless coherent diffractive imaging that uses intensity measurements of multiple diffraction patterns collected with a localized illumination probe from overlapping regions of an object. An iterative algorithm is proposed that is targeted on optimal processing noisy measurements. The noise suppression is enabled by two instruments: first, the maximum-likelihood technique formulated for Poissonian (photon-counting) measurements, and, second, sparse approximation of the phase and magnitude of the object and probe. It is shown that the maximum-likelihood estimate of the wavefield at the sensor plane for noisy measurements is essentially different from the famous Gerchberg-Saxton-Fienup solution, where the magnitude of the estimate is replaced by the square root of the intensity measurement. The simulation experiments demonstrate the state-of-the-art performance of the proposed algorithm both numerically and visually.

The computational ghost imaging with a phase spatial light modulator (SLM) for wave field coding is considered. A transmission-mask amplitude object is reconstructed from multiple intensity observations. Compressive techniques are used in order to gain a successful image reconstruction with a number of observations (measurement experiments), which is smaller than the image size. Maximum likelihood style algorithms are developed, respectively, for Poissonian and approximate Gaussian modeling of random observations. A sparse and overcomplete modeling of the object enables the advanced high accuracy and sharp imaging. Numerical experiments demonstrate that an approximative Gaussian distribution with an invariant variance results in the algorithm that is efficient for Poissonian observations.

We apply a nonlocal adaptive spectral transform for sparse modeling of phase and amplitude of a coherent wave field. The reconstruction of this wave field from complex-valued Gaussian noisy observations is considered. The problem is formulated as a multiobjective constrained optimization. The developed iterative algorithm decouples the inversion of the forward propagation operator and the filtering of phase and amplitude of the wave field. It is demonstrated by simulations that the performance of the algorithm, both visually and numerically, is the current state of the art.

The 4f optical setup is considered with a wave field modulation by a spatial light modulator located in the focal plane of the first lens. Phase as well as amplitude of the wave field are reconstructed from noisy multiple-intensity observations. The reconstruction is optimal due to a constrained maximum likelihood formulation of the problem. The proposed algorithm is iterative with decoupling of the inverse of the forward propagation of the wave field and the filtering of phase and amplitude. The sparse modeling of phase and amplitude enables the advanced high-accuracy filtering and sharp imaging of the complex-valued wave field. Artifacts typical for the conventional algorithms (wiggles, ringing, waves, etc.) and attributed to optical diffraction can be suppressed by the proposed algorithm.

A complex-valued wave field is reconstructed from intensity-only measurements given at multiple observation planes parallel to the object plane. The phase-retrieval algorithm is obtained from the constrained maximum likelihood approach provided that the additive noise is gaussian. The forward propagation from the object plane to the measurement plane is treated as a constraint in the proposed variational setting of reconstruction. The developed iterative algorithm is based on an augmented lagrangian technique. An advanced performance of the algorithm is demonstrated by numerical simulations.

We consider reconstruction of a wave field distribution in an input/object plane from data in an output/diffraction (sensor) plane. We provide digital modeling both for the forward and backward wave field propagation. A novel algebraic matrix form of the discrete diffraction transform (DDT) originated in Katkovnik et al. [Appl. Opt. 47, 3481 (2008)] is proposed for the forward modeling that is aliasing free and precise for pixelwise invariant object and sensor plane distributions. This "matrix DDT" is a base for formalization of the object wave field reconstruction (backward propagation) as an inverse problem. The transfer matrices of the matrix DDT are used for calculations as well as for the analysis of conditions when the perfect reconstruction of the object wave field distribution is possible. We show by simulation that the developed inverse propagation algorithm demonstrates an improved accuracy as compared with the standard convolutional and discrete Fresnel transform algorithms.

Mycolic acids, the hallmark of mycobacteria and related bacteria, are major and specific components of their cell envelope and essential for the mycobacterial survival. Mycobacteria contain structurally related long-chain lipids, but the metabolic relationships between these various classes of compounds remain obscure. To address this question a series of C(35) to C(54) nonhydroxylated fatty acids (mycobacteric acids), ketones, and alcohols structurally related to the C(70-80) dicyclopropanated or diethylenic mycolic acids were characterized in three mycobacterial strains and their structures compared. The relationships between these long-chain acids and mycolic acids were established by following the in vivo traffic of (14)C labeled alpha-mycolic acids purified from the same mycobacterial species. The labeling was exclusively found in mycobacteric acids. The mechanism of this degradation was established by incorporation of (18)O(2) into long-chain lipids and shown to consist in the rupture of mycolic acids between carbon 3 and 4 by a Baeyer-Villiger-like reaction. We also demonstrated that mycobacteric acids occur exclusively in the triacylglycerol (TAG) fraction where one molecule of these acids esterifies one of the three hydroxyl groups of glycerol. Altogether, these data suggest that these compounds represent a pathway of metabolic energy that would be used by mycobacteria in particular circumstances.

Background: Gene expression microarray technologies are widely used across most areas of biological and medical research. Comparing and integrating microarray data from different experiments would be very useful, but is currently very challenging due to the experimental and hybridization conditions, as well as data preprocessing and normalization methods. Furthermore, even in the case of the widely-used, industry-standard Affymetrix oligonucleotide microarrays, the various array generations have different probe sets representing different genes, hindering the data integration., Results: In this study our objective is to find systematic approaches to normalize the data emerging from different Affymetrix array generations and from different laboratories. We compare and assess the accuracy of five normalization methods for Affymetrix gene expression data using 6,926 Affymetrix experiments from five array generations. The methods that we compare include 1) standardization, 2) housekeeping gene based normalization, 3) equalized quantile normalization, 4) Weibull distribution based normalization and 5) array generation based gene centering. Our results indicate that the best results are achieved when the data is normalized first within a sample and then between-samples with Array Generation based gene Centering (AGC) normalization., Conclusion: We conclude that with the AGC method integrating different Affymetrix datasets results in values that are significantly more comparable across the array generations than in the cases where no array generation based normalization is used. The AGC method was found to be the best method for normalizing the data from several different array generations, and achieve comparable gene values across thousands of samples.

Background: Gene copy number and gene expression values play important roles in cancer initiation and progression. Both can be measured with high-throughput microarrays and some methodologies to integrate and analyze these data exist. However, varying gene sets within different gene expression and copy number microarrays present significant challenges., Results: We report an advanced version of earlier published CGH-Plotter that rapidly can identify amplified and deleted areas using gene copy number data. With CGH-Plotter v2, the copy number values can be filtered based on the genomic location in basepair units. After filtering, the values for the missing genes can be interpolated. Moreover, the effect of non-informative areas in the genome can be systematically removed by smoothing and interpolating. Further, we developed a tool (ECN) to illustrate the CGH-data values annotated based on the gene expression. The ECN-tool is a MATLAB toolbox enabling straightforward illustration of copy numbers annotated based on the gene expression levels., Conclusion: CGH-Plotter v2 provides two methods for analyzing copy number data; dynamic programming and genomic location based smoothing. With ECN-tool the data analyzed with CGH-Plotter v2 can easily be illustrated along the chromosomes individually or along the whole genome. ECN-tool plots the copy number data annotated based on the gene expression data, and it is easy to find the genes that are both over-expressed and amplified or under-expressed and deleted in the samples. From the resulting figures it is straightforward to select interesting genes.

The paper attacks absolute phase estimation with a two-step approach: the first step applies an adaptive local denoising scheme to the modulo-2 pi noisy phase; the second step applies a robust phase unwrapping algorithm to the denoised modulo-2 pi phase obtained in the first step. The adaptive local modulo-2 pi phase denoising is a new algorithm based on local polynomial approximations. The zero-order and the first-order approximations of the phase are calculated in sliding windows of varying size. The zero-order approximation is used for pointwise adaptive window size selection, whereas the first-order approximation is used to filter the phase in the obtained windows. For phase unwrapping, we apply the recently introduced robust (in the sense of discontinuity preserving) PUMA unwrapping algorithm [IEEE Trans. Image Process.16, 698 (2007)] to the denoised wrapped phase. Simulations give evidence that the proposed algorithm yields state-of-the-art performance, enabling strong noise attenuation while preserving image details., ((c) 2008 Optical Society of America)

A discrete diffraction transform (DDT) is a novel discrete wavefield propagation model that is aliasing free for a pixelwise invariant object distribution. For this class of distribution, the model is precise and has no typical discretization effects because it corresponds to accurate calculation of the diffraction integral. A spatial light modulator (SLM) is a good example of a system where a pixelwise invariant distribution appears. Frequency domain regularized inverse algorithms are developed for reconstruction of the object wavefield distribution from the distribution given in the sensor plane. The efficiency of developed frequency domain algorithms is demonstrated by simulation.

The local polynomial approximation (LPA) is a nonparametric regression technique with pointwise estimation in a sliding window. We apply the LPA of the argument of cos and sin in order to estimate the absolute phase from noisy wrapped phase data. Using the intersection of confidence interval (HCI) algorithm, the window size is selected as adaptive pointwise varying. This adaptation gives the phase estimate with the accuracy close to optimal in the mean squared sense. For calculations, we use a Gauss-Newton recursive procedure initiated by the phase estimates obtained for the neighboring points. It enables tracking properties of the algorithm and its ability to go beyond the principal interval [-pi, pi] and to reconstruct the absolute phase from wrapped phase observations even when the magnitude of the phase difference takes quite large values. The algorithm demonstrates a very good accuracy of the phase reconstruction which on many occasion overcomes the accuracy of the state-of-the-art algorithms developed for noisy phase unwrap. The theoretical analysis produced for the accuracy of the pointwise estimates is used for justification of the HCI adaptation algorithm.

The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks remains a difficult task. The minimum description length (MDL) principle has already been used for inferring genetic regulatory networks from time-series expression data and has proven useful for recovering the directed connections in Boolean networks. However, the existing method uses an ad hoc measure of description length that necessitates a tuning parameter for artificially balancing the model and error costs and, as a result, directly conflicts with the MDL principle's implied universality. In order to surpass this difficulty, we propose a novel MDL-based method in which the description length is a theoretical measure derived from a universal normalized maximum likelihood model. The search space is reduced by applying an implementable analogue of Kolmogorov's structure function. The performance of the proposed method is demonstrated on random synthetic networks, for which it is shown to improve upon previously published network inference algorithms with respect to both speed and accuracy. Finally, it is applied to time-series Drosophila gene expression measurements.

Our knowledge on tissue- and disease-specific functions of human genes is rather limited and highly context-specific. Here, we have developed a method for the comparison of mRNA expression levels of most human genes across 9,783 Affymetrix gene expression array experiments representing 43 normal human tissue types, 68 cancer types, and 64 other diseases. This database of gene expression patterns in normal human tissues and pathological conditions covers 113 million datapoints and is available from the GeneSapiens website.

With the growing surge of biological measurements, the problem of integrating and analyzing different types of genomic measurements has become an immediate challenge for elucidating events at the molecular level. In order to address the problem of integrating different data types, we present a framework that locates variation patterns in two biological inputs based on the generalized singular value decomposition (GSVD). In this work, we jointly examine gene expression and copy number data and iteratively project the data on different decomposition directions defined by the projection angle theta in the GSVD. With the proper choice of theta, we locate similar and dissimilar patterns of variation between both data types. We discuss the properties of our algorithm using simulated data and conduct a case study with biologically verified results. Ultimately, we demonstrate the efficacy of our method on two genome-wide breast cancer studies to identify genes with large variation in expression and copy number across numerous cell line and tumor samples. Our method identifies genes that are statistically significant in both input measurements. The proposed method is useful for a wide variety of joint copy number and expression-based studies. Supplementary information is available online, including software implementations and experimental data.

We propose a novel nonparametric regression metthod for deblurring noisy images. The method is based on the local polynomial approximation (LPA) of the image and the paradigm of intersecting confidence intervals (ICI) that is applied to define the adaptive varying scales (window sizes) of the LPA estimators. The LPA-ICI algorithm is nonlinear and spatially adaptive with respect to smoothness and irregularities of the image corrupted by additive noise. Multiresolution wavelet algorithms produce estimates which are combined from different scale projections. In contrast to them, the proposed ICI algorithm gives a varying scale adaptive estimate defining a single best scale for each pixel. In the new algorithm, the actual filtering is performed in signal domain while frequency domain Fourier transform operations are applied only for calculation of convolutions. The regularized inverse and Wiener inverse filters serve as deblurring operators used jointly with the LPA-design directional kernel filters. Experiments demonstrate the state-of-art performance of the new estimators which visually and quantitatively outperform some of the best existing methods.

Background & Aims: Although approximately 50% of Dukes' C colorectal cancer patients are surgically cured, it is currently not possible to distinguish these patients from those at high risk of recurrence. The recent advent of routine adjuvant chemotherapy for these patients has greatly complicated the identification of new markers predicting the response to surgery, which is now reliant on archived materials. Microarray analysis allows fine tumor classification but cannot be used with paraffin-embedded archival samples., Methods: We used microarray analysis of a unique set of fresh-frozen tumor samples from Dukes' C patients who had surgery as the only form of treatment to identify molecular signatures that characterize tumors from patients with good and bad prognosis., Results: Unsupervised hierarchical clustering and a K-nearest neighbors-based classifier identified groups of patients with significantly different survival (P = .019 and P = .0001). Expression profiling outperformed previously reported genetic markers of prognosis such as TP53 and K-RAS mutational status and allelic imbalance in chromosome 18q, which were of limited prognostic power in this study. Functional categories significantly enriched in gene-expression differences included protein transport and folding. The prognostic potential of the RAS homologue RHOA, one of the most differentially expressed genes, was further investigated using immunohistochemistry and a tissue microarray containing 137 independent Dukes' C tumor samples. Reduced RHOA expression was associated with significantly shorter survival (P = .01)., Conclusions: This study shows that gene-expression profiling of surgical tumor samples can predict recurrence in Dukes' C patients. Therefore, this approach could be used to guide decisions concerning the clinical management of these patients.

Distributed arithmetic (DA)-based implementation of linear filters relies on the linear nature of this operation and has been suggested as a multiplication free solution. In this work, we introduce a nonlinear extension of linear filters optimizing under mean-square error criterion the memory function [(MF) multivariate Boolean function with not only binary output] which is in the core of DA based implementation. Such an extension will improve the filtering of noise which may contain non-Gaussian components without increasing the complexity of implementation. Experiments on real images have shown the superiority of the proposed filters over the optimal linear filters. Different versions of these filters are also considered for an impulsive noise removal, faster processing, and filtering using large input data windows.

Background: Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail., Results and Discussion: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies., Conclusions: Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critical parameters in the LOWESS technique is likely to produce data that optimally meets assumptions made in the data preprocessing step and thereby makes studies utilizing the LOWESS method unambiguous and easier to repeat.

Herceptin is a humanized monoclonal antibody targeted against the extracellular domain of the HER2 oncogene, which is amplified and overexpressed in 10-34% of breast cancers. Herceptin therapy provides effective treatment in HER2-positive metastatic breast cancer, although a favorable treatment response is not achieved in all cases. Here, we show that Herceptin treatment induces a dose-dependent growth reduction in breast cancer cell lines with HER2 amplification, whereas nonamplified cell lines are practically resistant. Time-course analysis of global gene expression patterns in amplified and nonamplified cell lines indicated a major change in transcript levels between 24 and 48 h of Herceptin treatment. A step-wise gene selection algorithm revealed a set of 439 genes whose temporal expression profiles differed most between the amplified and nonamplified cell lines. The discriminatory power of these genes was confirmed by both hierarchical clustering and self-organizing map analyses. In the amplified cell lines, the Herceptin treatment induced the expression of several genes involved in RNA processing and DNA repair, while cell adhesion mediators and known oncogenes, such as c-FOS and c-KIT, were downregulated. These results provide additional clues to the downstream effects of blocking the HER2 pathway in breast cancer and may provide new targets for more effective treatment.

Grouping of genes into clusters according to their expression levels is important for deriving biological information, e.g., on gene functions based on microarray and other related analyses. The paper introduces the selection of the number of clusters based on the minimum description length (MDL) principle for the selection of the number of clusters in gene expression data. The main feature of the new method is the ability to evaluate in a fast way the number of clusters according to the sound MDL principle, without exhaustive evaluations over all possible partitions of the gene set. The estimation method can be used in conjunction with various clustering algorithms. A recent clustering algorithm using principal component analysis, the "gene shaving" (GS) procedure, can be modified to make use of the new MDL estimation method, replacing the Gap statistics originally used in GS algorithm. The resulting clustering algorithm is shown to perform better than GS-Gap and CEM (classification expectation maximization), in the simulations using artificial data. The proposed method is applied to B-cell differentiation data, and the resulting clusters are compared with those found by self-organizing maps (SOM).

Motivation: High-throughput microarray technologies enable measurements of the expression levels of thousands of genes in parallel. However, microarray printing, hybridization and washing may create substantial variability in the quality of the data. As erroneous measurements may have a drastic impact on the results by disturbing the normalization schemes and by introducing expression patterns that lead to incorrect conclusions, it is crucial to discard low quality observations in the early phases of a microarray experiment. A typical microarray experiment consists of tens of thousands of spots on a microarray, making manual extraction of poor quality spots impossible. Thus, there is a need for a reliable and general microarray spot quality control strategy., Results: We suggest a novel strategy for spot quality control by using Bayesian networks, which contain many appealing properties in the spot quality control context. We illustrate how a non-linear least squares based Gaussian fitting procedure can be used in order to extract features for a spot on a microarray. The features we used in this study are: spot intensity, size of the spot, roundness of the spot, alignment error, background intensity, background noise, and bleeding. We conclude that Bayesian networks are a reliable and useful model for microarray spot quality assessment., Supplementary Information: http://sigwww.cs.tut.fi/TICSP/SpotQuality/.

A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks.

CGH-Plotter is a MATLAB toolbox with a graphical user interface for the analysis of comparative genomic hybridization (CGH) microarray data. CGH-Plotter provides a tool for rapid visualization of CGH-data according to the locations of the genes along the genome. In addition, the CGH-Plotter identifies regions of amplifications and deletions, using k-means clustering and dynamic programming. The application offers a convenient way to analyze CGH-data and can also be applied for the analysis of cDNA microarray expression data. CGH-Plotter toolbox is platform independent and requires MATLAB 6.1 or higher to operate.

Microarray or DNA chip technology is revolutionizing biology by empowering researchers in the collection of broad-scope gene information. It is well known that microarray-based measurements exhibit a substantial amount of variability due to a number of possible sources, ranging from hybridization conditions to image capture and analysis. In order to make reliable inferences and carry out quantitative analysis with microarray data, it is generally advisable to have more than one measurement of each gene. The availability of both between-array and within-array replicate measurements is essential for this purpose. Although statistical considerations call for increasing the number of replicates of both types, the latter is particularly challenging in practice due to a number of limiting factors, especially for in-house spotting facilities. We propose a novel approach to design so-called composite microarrays, which allow more replicates to be obtained without increasing the number of printed spots.

The mycolic acids of several strains of Mycobacterium gordonae were examined by chromatographic and spectroscopic techniques. Both HPLC and TLC revealed two patterns of mycolates among the M. gordonae strains studied. As determined by TLC, one pattern was composed of alpha-, methoxy- and keto-mycolates; the other was composed of these mycolates plus an additional component, which was identified as dicarboxy-mycolates. The dicarboxy-mycolates were only found in those M. gordonae strains that displayed a so-called HPLC-double-cluster pattern. Detailed structural analyses of the dicarboxy-mycolates indicated that these compounds contained predominantly 61-65 carbon atoms (C(63) was the major component) and a trans-1,2-disubstituted cyclopropane ring. Thus, the dicarboxy-mycolate content of strains of M. gordonae determines their HPLC pattern. In spite of the differences in their HPLC patterns, and although they belonged to different PCR-restriction length polymorphism clusters, all of the M. gordonae strains examined in this study were closely related on the basis of the structural features of their alpha-, keto- and methoxy-mycolates; the predominant alpha-mycolates contained two cis-1,2-disubstituted cyclopropane rings, the major keto-mycolates contained a trans-1,2-disubstituted cyclopropane ring and the methoxy-mycolates contained one cis- or one trans-1,2-disubstituted cyclopropane ring. It is noteworthy that the strains containing dicarboxy-mycolates also displayed significant amounts of alpha-mycolates that contained one cis-1,2-disubstituted cyclopropane ring and one cis double bond. The results obtained in this study demonstrate heterogeneity among M. gordonae strains.

The aim of the study was to determine the contents of mineral elements (Ca, K, Mg, Na, P, Cu, Fe, Mn, Cd, Pb, and Se), vitamins (B(1), B(2), B(12), C, D, folates, and niacin), and certain phenolic compounds (flavonoids, lignans, and phenolic acids) in the cultivated mushrooms Agaricus bisporus/white, Agaricus bisporus/brown, Lentinus edodes, and Pleurotus ostreatus. Selenium, toxic heavy metals (Cd, Pb), and other mineral elements were analyzed by ETAAS, ICP-MS, and ICP methods, respectively; vitamins were detected by microbiological methods (folates, niacin, and vitamin B(12)) or HPLC methods (other vitamins), and phenolic compounds were analyzed by HPLC (flavonoids) or GC--MS methods (lignans and phenolic acids). Cultivated mushrooms were found to be good sources of vitamin B(2), niacin, and folates, with contents varying in the ranges 1.8--5.1, 31--65, and 0.30--0.64 mg/100 g dry weight (dw), respectively. Compared with vegetables, mushrooms proved to be a good source of many mineral elements, e.g., the contents of K, P, Zn, and Cu varied in the ranges 26.7--47.3 g/kg, 8.7--13.9 g/kg, 47--92 mg/kg, and 5.2--35 mg/kg dw, respectively. A. bisporus/brown contained large amounts of Se (3.2 mg/kg dw) and the levels of Cd were quite high in L. edodes (1.2 mg/kg dw). No flavonoids or lignans were found in the mushrooms analyzed. In addition, the phenolic acid contents were very low.

The complexity of the consistency problem for several important classes of Boolean functions is analyzed. The classes of functions under investigation are those which are closed under function composition or superposition. Several of these so-called Post classes are considered within the context of machine learning with an application to breast cancer diagnosis. The considered Post classes furnish a user-selectable measure of reliability. It is shown that for realistic situations which may arise in practice, the consistency problem for these classes of functions is polynomial-time solvable.

A high-performance liquid chromatographic (HPLC) method with in-line connected diode-array (DAD) and electro-array (EC) detection to identify and quantify 17 flavonoids in plant-derived foods is described. Catechins were extracted from the samples using ethyl acetate, and quantification of these compounds was performed with the EC detector. Other flavonoids were quantified with DAD after acid hydrolysis. The methods developed were effective for the determination of catechins and other flavonoids in plant-derived foods. Responses of the detection systems were linear within the range evaluated, 20-200 ng/injection (DAD) and 20-100 ng/injection (EC), with correlation coefficients exceeding 0.999. Coefficient of variation was under 10.5%, and recoveries of flavonoids ranged from 70 to 124%. Purity of the flavonoid peaks was confirmed by combining the spectral and voltammetric data.

In this paper, nonlinear multivariate image filtering techniques are proposed to handle color images corrupted by noise. First, we briefly review the principle of reduced ordering (R-ordering) and then define three R-orderings by selecting different central locations. Considering noise attenuation, edge preservation, and detail retention, R-ordering based multivariate filters are designed by combining the R-ordering schemes. To implement color image filtering more effectively, we develop them into a locally adaptive version. The output of the adaptive filter is the closest sample to a central location that is a weighted linear combination of the mean, the marginal median, and the center sample. As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the identity filter. The performance of the two adaptive filtering techniques is compared with that of some nonadaptive ones. The examples of color image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement.