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Background: Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. Results: We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into 'resistant' and 'susceptible', highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. Conclusions: We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR. [ABSTRACT FROM AUTHOR]

While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2–6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates. • High collinearity of Monod model fitting results complicates parameter estimation. • Microcosm triplicates are often inadequate for order-of-magnitude estimation. • Microcosm sets with varied initial concentrations yield better parameter estimates. • Regression analysis confidence intervals frequently do not contain true parameters. • Markov chain Monte Carlo is computationally expensive but more reliable. [ABSTRACT FROM AUTHOR]

Natural transformation in theGram-positive pathogen Streptococcus pneumoniae occurswhen cells become "competent," a state that is induced in response to high extracellular concentrations of a secreted peptide signal called competence stimulating peptide (CSP) encoded by the comC locus. Two main CSP signal types (pherotypes) are known to dominate the pherotype diversity across strains. Using 4,089 fully sequenced pneumococcal genomes, we confirm that pneumococcal populations are highly genetically structured and that there is significant variation among diverged populations in pherotype frequencies; most carry only a single pherotype.Moreover, we find that the relative frequencies of the two dominant pherotypes significantly vary within a small range across geographical sites. It has been variously proposed that pherotypes either promote genetic exchange among cells expressing the same pherotype, or conversely that they promote recombination between strains bearing different pherotypes.We attempt to distinguish these hypotheses using a bioinformatics approach by estimating recombination frequencies within and between pherotypes across 4,089 full genomes. Despite underlying population structure, we observe extensive recombination between populations; additionally, we found significantly higher (although marginal) rates of genetic exchange between strains expressing different pherotypes than among isolates carrying the same pherotype. Our results indicate that pherotypes do not restrict, and may even slightly facilitate, recombination between strains; however, these marginal effects suggest the more likely possibility that the cause of CSP polymorphism lies outside of its effects on transformation. Our results suggest that the CSP balanced polymorphism does not causally underlie population differentiation. Therefore,when strains carrying different pherotypes encounter one another during cocolonization, genetic exchange can occur without restriction. [ABSTRACT FROM AUTHOR]

The expansion and intensification of livestock production is predicted to promote the emergence of pathogens. As pathogens sometimes jump between species, this can affect the health of humans as well as livestock. Here, we investigate how livestock microbiota can act as a source of these emerging pathogens through analysis of Streptococcus suis, a ubiquitous component of the respiratory microbiota of pigs that is also a major cause of disease on pig farms and an important zoonotic pathogen. Combining molecular dating, phylogeography, and comparative genomic analyses of a large collection of isolates, we find that several pathogenic lineages of S. suis emerged in the 19th and 20th centuries, during an early period of growth in pig farming. These lineages have since spread between countries and continents, mirroring trade in live pigs. They are distinguished by the presence of three genomic islands with putative roles in metabolism and cell adhesion, and an ongoing reduction in genome size, which may reflect their recent shift to a more pathogenic ecology. Reconstructions of the evolutionary histories of these islands reveal constraints on pathogen emergence that could inform control strategies, with pathogenic lineages consistently emerging from one subpopulation of S. suis and acquiring genes through horizontal transfer from other pathogenic lineages. These results shed light on the capacity of the microbiota to rapidly evolve to exploit changes in their host population and suggest that the impact of changes in farming on the pathogenicity and zoonotic potential of S. suis is yet to be fully realized. [ABSTRACT FROM AUTHOR]

Spores from wild yeast isolates often show great variation in the size of colonies they produce, for largely unknown reasons. Here we measure the colonies produced from single spores from six different wild Saccharomyces paradoxus strains. We found remarkable variation in spore colony sizes, even among spores that were genetically identical. Different strains had different amounts of variation in spore colony sizes, and variation was not affected by the number of preceding meioses, or by spore maturation time. We used time-lapse photography to show that wild strains also have high variation in spore germination timing, providing a likely mechanism for the variation in spore colony sizes. When some spores from a laboratory strain make small colonies, or no colonies, it usually indicates a genetic or meiotic fault. Here, we demonstrate that in wild strains spore colony size variation is normal. We discuss and assess potential adaptive and non-adaptive explanations for this variation. [ABSTRACT FROM AUTHOR]

The opportunistic pathogen Streptococcus pneumoniae is commonly carried asymptomatically in the human nasopharynx. Due to high rates of cocolonization with other pneumococcus strains, intraspecific competitive interactions partly determine the carriage duration of strains and thereby their potential to cause disease. These interactions may be mediated by bacteriocins, such as the type IIb bacteriocins encoded by the blp (bacteriocin-like peptide) locus. To understand blp diversity and evolution, we undertook a bioinformatic analysis of 4,418 pneumococcal genomes, including 168 newly sequenced genomes. We describe immense variation at all levels of genomic organization: Gene presence/absence, gene order, and allelic diversity. If we make the extreme and naive hypothesis that assumes all genes in this operon can assort randomly, this variation could lead to 1015 distinct bacteriocin-related phenotypes, each potentially representing a unique ecological strategy; however, we provide several explanations for why this extreme is not realized. Although rarefaction analysis indicates that the number of unique strategies is not saturated, even after sampling thousands of genomes, we show that the variation is neither unbounded nor random. We delimit three bacteriocin groups, which contain group-specific bacteriocins, immunity genes, and blp operon gene order, and argue that this organization places a constraint on realized ecological strategies. We additionally show that ecological strategy diversity is significantly constrained by pneumococcal phylogeny and clonal structure. By examining patterns of association between alleles within the blp operon, we show that bacteriocin genes, which were believed to function in pairs, can be found with a broad diversity of partner alleles and immunity genes; this overall lack of allelic fidelity likely contributes to the fluid structure of this operon. Our results clarify the diversity of antagonistic ecological strategies in the global pneumococcal population and highlight the potential role of blp bacteriocins in competition within the nasopharynx. [ABSTRACT FROM AUTHOR]

Gene combinations conferring local fitness may be destroyed by mating with individuals that are adapted to a different environment. This form of outbreeding depression provides an evolutionary incentive for self-fertilization. We show that the yeast Saccharomyces paradoxus tends to self-fertilize when it is well adapted to its local environment but tends to outcross when it is poorly adapted. This behavior could preserve combinations of genes when they are beneficial and break them up when they are not, thereby helping adaptation. Haploid spores must germinate before mating, and we found that fitter spores had higher rates of germination across a 24-hour period, increasing the probability that they mate with germinated spores from the same meiotic tetrad. The ability of yeast spores to detect local conditions before germinating and mating suggests the novel possibility that these gametes directly sense their own adaptation and plastically adjust their breeding strategy accordingly. [ABSTRACT FROM AUTHOR]

While recent years have seen considerable progress in image denoising, the leading techniques have been developed for digital photographs or other images that can have very different characteristics than those encountered in X-ray applications. In particular here we examine X-ray backscatter (XBS) images collected by airport security systems, where images are piecewise smooth and edge information is typically more correlated with objects while texture is dominated by statistical noise in the detected signal. In this paper, we show how multiple estimates for a denoised XBS image can be combined using a variational approach, giving a solution that enhances edge contrast by trading off gradient penalties against data fidelity terms. We demonstrate the approach by combining several estimates made using the non-local means (NLM) algorithm, a widely used patch-based denoising method. The resulting improvements hold the potential for improving automated analysis of low-SNR X-ray imagery and can be applied in other applications where edge information is of interest. [ABSTRACT FROM AUTHOR]

Patch-based methods have attracted significant attention in recent years within the field of image processing for a variety of problems including denoising, inpainting, and super-resolution interpolation. Despite their prevalence for processing 2-D signals, they have received little attention in the 1-D signal processing literature. In this letter, we explore application of one such method, the nonlocal means (NLM) approach, to the denoising of biomedical signals. Using ECG as an example, we demonstrate that a straightforward NLM-based denoising scheme provides signal-to-noise ratio improvements very similar to state of the art wavelet-based methods, while giving \sim3\times or greater reduction in metrics measuring distortion of the denoised waveform. [ABSTRACT FROM PUBLISHER]

Dual-energy computerized tomography has gained great interest because of its ability to characterize the chemical composition of a material rather than simply providing relative attenuation images as in conventional tomography. The purpose of this paper is to introduce a novel polychromatic dual-energy processing algorithm, with an emphasis on detection and characterization of piecewise constant objects embedded in an unknown cluttered background. Physical properties of the objects, particularly the Compton scattering and photoelectric absorption coefficients, are assumed to be known with some level of uncertainty. Our approach is based on a level-set representation of the characteristic function of the object and encompasses a number of regularization techniques for addressing both the prior information we have concerning the physical properties of the object and the fundamental physics-based limitations associated with our ability to jointly recover the Compton scattering and photoelectric absorption properties of the scene. In the absence of an object with appropriate physical properties, our approach returns a null characteristic function and, thus, can be viewed as simultaneously solving the detection and characterization problems. Unlike the vast majority of methods that define the level-set function nonparametrically, i.e., as a dense set of pixel values, we define our level set parametrically via radial basis functions and employ a Gauss–Newton-type algorithm for cost minimization. Numerical results show that the algorithm successfully detects objects of interest, finds their shape and location, and gives an adequate reconstruction of the background. [ABSTRACT FROM PUBLISHER]

We propose a new shape-based inversion algorithm to identify an anomaly embedded in an inhomogeneous layered geometry. We apply our approach to microwave breast imaging where the geometry consists of several inhomogeneous layers and the potential tumor is embedded in the innermost layer. In addition to the tumor identification, we estimate the irregular transition layer between the breast inner layers. Our inversion algorithm is based on a low-dimensional parametric form of the relevant geometries, and uses multiple-frequency multi-source data to estimate the unknowns. Several numerical examples are provided to evaluate the effectiveness of our approach and demonstrate its robustness both to the initial choice of parameter values and uncertainty in the complex dielectric properties of the media even with the complex geometry and the simplified inverse model. [ABSTRACT FROM AUTHOR]

Intracranial aneurysms are localized, abnormal arterial dilatations with a variable risk of rupture, leading to medical conditions associated with high morbidity and mortality. Predicting their risk of rupture, especially for incidental asymptomatic aneurysms, is a challenging task. The size of the aneurysm sac is traditionally used to assess the risk, but shape analysis has emerged as a promising differentiator of rupture likelihood. The centroid–radii model (CRM) is introduced here to describe both the size and the shape of the aneurysms, and determine rupture status. The entropy of CRM is proposed as an aneurysm descriptor which is easy to compute, robust to noise and segmentation, and accurate in rupture status discrimination. Analysis is performed on 154 patient-derived saccular aneurysms. The aneurysms are further classified as sidewall and bifurcation, and the shape analysis is performed separately on the two subtypes. Using the entropy of CRM resulted in 80.3% and 70.5% classification accuracy of status rupture in sidewall and bifurcation aneurysms, respectively. When compared to the accuracy of some commonly used size and shape indexes, the entropy of the CRM proved to be a more accurate single index associated with rupture in intracranial aneurysms, for both sidewall and bifurcation subtypes. [ABSTRACT FROM AUTHOR]

We consider the estimation of a Gaussian random vector x observed through a linear transformation H and corrupted by additive Gaussian noise with a known covariance matrix, where the covariance matrix of x is known to lie in a given region of uncertainty that is described using bounds on the eigenvalues and on the elements of the covariance matrix. Recently, two criteria for minimax estimation called difference regret (DR) and ratio regret (RR) were proposed and their closed form solutions were presented assuming that the eigenvalues of the covariance matrix of x are known to lie in a given region of uncertainty, and assuming that the matrices HT Cw-1 and Cx are jointly diagonalizable, where Cw and Cx denote the covariance matrices of the additive noise and of x respectively. In this work we present a new criterion for the minimax estimation problem which we call the generalized difference regret (GDR), and derive a new minimax estimator which is based on the GDR criterion where the region of uncertainty is defined not only using upper and lower bounds on the eigenvalues of the parameter's covariance matrix, but also using upper and lower bounds on the individual elements of the covariance matrix itself. Furthermore, the new estimator does not require the assumption of joint diagonalizability and it can be obtained efficiently using semidefinite programming. We also show that when the joint diagonalizability assumption holds and when there are only eigenvalue uncertainties, then the new estimator is identical to the difference regret estimator. The experimental results show that we can obtain improved mean squared error (MSE) results compared to the MMSE, DR, and RR estimators. [ABSTRACT FROM AUTHOR]

Fluorescence molecular tomography (FMT) allows in vivo localization and quantification of fluorescence biodistributions in whole animals. The ill-posed nature of the tomographic reconstruction problem, however, limits the attainable resolution. Improvements in resolution and overall imaging performance can be achieved by forming image priors from geometric information obtained by a secondary anatomical or functional high-resolution imaging modality such as X-ray computed tomography or magnetic resonance imaging. A particular challenge in using image priors is to avoid the use of assumptions that may bias the solution and reduced the accuracy of the inverse problem. This is particularly relevant in FMT inversions where there is not an evident link between secondary geometric information and the underlying fluorescence biodistribution. We present here a new, two step approach to incorporating structural priors into the FMT inverse problem. By using the anatomic information to define a low dimensional inverse problem, we obtain a solution which we then use to determine the parameters defining a spatially varying regularization matrix for the full resolution problem. The regularization term is thus customized for each data set and is guided by the data rather than depending only on user defined a priori assumptions. Results are presented for both simulated and experimental data sets, and show significant improvements in image quality as compared to traditional regularization techniques. [ABSTRACT FROM AUTHOR]

A fast method for computing the acoustic field of ultrasound transducers is presented with application to rectangular elements that are cylindrically focused. No closed-form solutions exist for this case but several numerical techniques have been described in the ultrasound imaging literature. Our motivation is the rapid calculation of imaging kernels for physics-based diagnostic imaging for which current methods are too computationally intensive. Here, the surface integral defining the acoustic field from a baffled piston is converted to a 3-D spatial coavolution of the element surface and the Green's function. A 3-D version of the overlap-save method from digital signal processing is employed to obtain a fast computational algorithm based on spatial Fourier transforms. Further efficiency is gained by using a separable approximation to the Green's function through singular value decomposition and increasing the effective sampling rate by polyphase filtering. The tradeoff between accuracy and spatial sampling rate is explored to determine appropriate parameters for a specific transducer. Comparisons with standard tools such as Field II are presented, where nearly 2 orders of magnitude improvement in computation speed is observed for similar accuracy. [ABSTRACT FROM AUTHOR]

Abstract-We propose a new approach to approximating the Chapman-Kolmogorov equation (CKE) for particle-based nonlinear filtering algorithms, using a new proposal distribution and the improved fast Gauss transform (IFGT) with tighter accuracy bounds. The new proposal distribution, used to obtain a Monte Carlo (MC) approximation of the CKE, is based on the proposal distribution found in the auxiliary marginal particle filter (AMPF). By using MC integration to approximate the integrals of the AMPF proposal distribution as well as the CKE, we demonstrate significant improvement in terms of both error and computation time. We consider the additive state noise case where the evaluation of the CKE is equivalent to performing kernel density estimation (KDE), thus fast methods such as the IFGT can be used. In practice, the IFGT demonstrates performance far better than that which is predicted by current error analysis, therefore the existing bounds are not useful for determining the IFGT parameters which in practice have to be chosen experimentally in order to obtain satisfactory compromise between accuracy and speed of the filtering algorithm. We provide in this paper much improved performance bounds for the IFGT, and which unlike the previous bound, are consistent with the expectation that the error decreases as the truncation order of the IFGT increases. The new bounds lead to a new definition of the IFGT parameters and give important insight into the effect that these parameters have on the error, therefore facilitating the choice of parameters in practice. The experimental results show that we can obtain similar error to the sequential importance sampling (SIS) particle filter, while using fewer particles. Furthermore the choice of the new LFGT parameters remains roughly the same for all the examples that we give. [ABSTRACT FROM AUTHOR]

Voxel-based reconstructions in diffuse optical tomography (DOT) using a quadratic regularization functional tend to produce very smooth images due to the attenuation of high spatial frequencies. This then causes difficulty in estimating the spatial extent and contrast of anomalous regions such as tumors. Given an assumption that the target image is piecewise constant, we can employ a parametric model to estimate the boundaries and contrast of an inhomogeneity directly. In this paper, we describe a method to directly reconstruct such a shape boundary from diffuse optical measurements. We parameterized the object boundary using a spherical harmonic basis, and derived a method to compute sensitivities of measurements with respect to shape parameters. We introduced a centroid constraint to ensure uniqueness of the combined shape/center parameter estimate, and a projected Newton method was utilized to optimize the object center position and shape parameters simultaneously. Using the shape Jacobian, we also computed the Cramér-Rao lower bound on the theoretical estimator accuracy given a particular measurement configuration, object shape, and level of measurement noise. Knowledge of the shape sensitivity matrix and of the measurement noise variance allows us to visualize the shape uncertainty region in three dimensions, giving a confidence region for our shape estimate. We have implemented our shape reconstruction method, using a finite-difference-based forward model to compute the forward and adjoint fields. Reconstruction results are shown for a number of simulated target shapes, and we investigate the problem of model order selection using realistic levels of measurement noise. Assuming a signal-to-noise ratio in the amplitude measurements of 40 dB and a standard deviation in the phase measurements of 0.1°, we are able to estimate an object represented with an eighth-order spherical harmonic model having an absorption contrast of 0.15 cm-1 and a volume of 4.82 cm³ with errors of less than 10% in object volume find absorption contrast. We also investigate the robustness of our shape-based reconstruction approach to a violation of the assumption that the medium is purely piecewise constant. [ABSTRACT FROM AUTHOR]

A shaped-based ultrasound tomography method is proposed to reconstruct ellipsoidal objects using a linearized scattering model. The method is motivated by the desire to detect the presence of lesions created by high intensity focused ultrasound (HIFU) in applications of cancer therapy. The computational size and limited view nature of the relevant three-dimensional inverse problem renders impractical the use of traditional pixel-based reconstruction methods. However, by employing a shape-based parametrization it is only necessary to estimate a small number of unknowns describing the geometry of the lesion, in this paper assumed to be ellipsoidal. The details of the shape-based nonlinear inversion method are provided. Results obtained from a commercial ultrasound scanner and a tissue phantom containing a HIFU-like lesion demonstrate the feasibility of the approach where a 20 mm×5 mm×6 mm ellipsoidal inclusion was detected with an accuracy of around 5%. [ABSTRACT FROM AUTHOR]

A new parameter-free graph-morphology-based segmentation algorithm is proposed to address the problem of partitioning a 3D triangular mesh into disjoint submeshes that correspond to the physical parts of the underlying object. Curvedness, which is a rotation and translation invariant shape descriptor, is computed at every vertex in the input triangulation. Iterative graph dilation and morphological filtering of the outlier curvedness values result in multiple disjoint maximally connected submeshes such that each submesh contains a set of vertices with similarcurvedness values, and vertices in disjoint submeshes have significantly different curvedness values. Experimental evaluations using the triangulations of a number of complex objects demonstrate the robustness and the efficiency of the proposed algorithm and the results prove that it compares well with a number of state-of-the-art mesh segmentation algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In the context of photoelectrochemical water-splitting, tungsten oxide films have been fabricated at low processing temperatures ) by reactive sputtering from tungsten targets in an argon/oxygen ambient. The films have a dense, compact morphology and show columnar growth. Amorphous and highly polycrystalline films can be produced depending on the deposition conditions; polycrystalline phases appear only at higher temperatures and under certain sputter target conditions. Large crystallites proved beneficial to photoelectrochemical performance. A maximum photocurrent of (at 1.6V vs SCE) was observed in under AM 1.5 Global illumination, exceeding published results for material fabricated at higher temperatures (in the range). Doping of sputtered tungsten oxide films with nitrogen results in a red-shifted absorption edge, but so far not in increased photocurrents. The maximum photocurrent of a nitrogen-doped sample was measured at (at 1.6V vs SCE). A multi-junction photoanode based on the best available sputtered film and an amorphous silicon photovoltaic device is projected to operate at 2.2% solar-to-hydrogen efficiency. [Copyright &y& Elsevier]

We consider the problem of classifying buried objects using electromagnetic induction data collected in a setting where there are errors in sensor positioning. Using a series of decay constants (or equivalently, Laplace plane poles) as features for classification, our algorithm seeks to estimate these poles and, subsequently, to determine the type of object in the sensor field of view. In many practical scenarios, a set of data is often accompanied by domain knowledge that the location of the transmitters and/or receivers is only known to within some degree of accuracy (e.g., 10 cm in the along-track direction and 5-cm cross-track). Here, we develop an approach to the extraction of information from such data sets in which the quantitative positional bound information is used in the context of a mm-max optimization strategy. Specifically, we look for the parameters of interest that minimize the maximum data residual, where the maximum error is computed over ellipsoids or polyhedra of possible sensor locations defined by the bound information. Our formulation admits data collection with independent or dependent positional uncertainty values at successive nominal collection locations. Our algorithms for solving this optimization problem are validated using simulated and measured data. [ABSTRACT FROM AUTHOR]

Conventional ultrasound transducers used for medical diagnosis generally consist of linearly aligned rectangular apertures with elements that are focused in one plane. While traditional beamforming is easily accomplished with such transducers, the development of quantitative, physics-based imaging methods, such as tomography, requires an accurate, and computationally efficient, model of the field radiated by the transducer. The field can be expressed in terms of the Helmholtz-Kirchhoff integral; however, its direct numerical evaluation is a computationally intensive task. Here, a fast semi- analytical method based on Stepanishen’s spatial impulse response formulation [J. Acoust. Soc. Am. 49, 1627–1638 (1971)] is developed to compute the acoustic field of a rectangular element of cylindrically concave transducers in a homogeneous medium. The pressure field, for lossless and attenuating media, is expressed as a superposition of Bessel functions, which can be evaluated rapidly. In particular, the coefficients of the Bessel series are frequency independent and need only be evaluated once for a given transducer. A speed up of two orders of magnitude is obtained compared to an optimized direct numerical integration. The numerical results are compared with Field II and the Fresnel approximation. [ABSTRACT FROM AUTHOR]

This paper proposes a new inverse method for microwave-based subsurface sensing of lossy dielectric objects embedded in a dispersive lossy ground with an unknown rough surface. An iterative inversion algorithm is employed to reconstruct the geometry and dielectric properties of the half-space ground as well as that of the buried object. B-splines are used to model the shape of the object as well as the height of the rough surface. In both cases, the control points for the spline function represent the unknowns to be recovered. A single-pole rational transfer function is used to capture the dispersive nature of the background. Here, the coefficients in the numerator and denominator are the unknowns. The approach presented in this paper is based on the state-of-the-art semianalytic mode matching forward model, which is a fast and efficient algorithm to determine the scattered electromagnetic fields. Numerical experiments involving two- dimensional geometries and TM incident plane waves demonstrate the accuracy and reliability of this inverse method. [ABSTRACT FROM AUTHOR]

The article discusses a study which investigates the repeatability and reproducibility of determination of the nitrogen content of fishmeal using the combustion or Dumas method. Results show that the Dumas method gives values of nitrogen for fishmeals that are 1.1 percent greater and have better repeatability and reproducibility than the Kjeldahl method. The study concludes that the Dumas method gives a reliable measure of organic nitrogen and should be the method of choice.

A reconstruction algorithm to simultaneously estimate the shape and location of three-dimensional breast cancer tumor is presented and its utility is analyzed. The approach is based on a spherical harmonic decomposition to capture the shape of the tumor. We combine a gradient descent optimization method with a direct electromagnetic solver to determine the coefficients in the harmonic expansion as well as the coordinates of the center of the tumor. The results demonstrate the potential advantage of collecting data using a multiple-view/tomographic-type strategy. We show how the order of the harmonic expansion must be increased to capture increasingly ‘irregularly’ shaped tumors and explore the resulting increase in the central processing unit (CPU) time required by the algorithm. Our approach shows accurate reconstruction of the tumor image regardless of the source polarization. This work demonstrates the promise of the algorithm when used on data corrupted with Gaussian noise and when perfect knowledge of the tumor electrical properties is not available. [ABSTRACT FROM AUTHOR]

Abstract: The multijunction hybrid photoelectrode for hydrogen production developed at UH incorporates a metal-oxide photoelectrochemical top junction which is deposited onto an underlying solid state junction which generates additional voltage bias for efficient water splitting. Initial attempts to fabricate hybrid photoelectrodes using nano-structured iron-oxide films deposited by spray pyrolysis were largely unsuccessful because the pyrolysis temperatures of approximately 400°C were high enough to cause significant damage to the underlying amorphous-silicon-based solid state junctions. This paper describes the development of low temperature (<200°C) reactively sputtered metal oxide films with properties specifically optimized for use in hybrid photoelectrode applications. The primary materials investigated include iron oxide, for use in alkaline photoelectrolysis, and tungsten trioxide, for use in acid photoelectrolysis. To date, the sputtered tungsten trioxide materials have demonstrated higher levels of photo-generated current compared with sputtered iron-oxide films. Initial hybrid photoelectrode devices fabricated using tungsten-trioxide films sputter deposited onto tandem amorphous-silicon junctions are described which exhibited stable solar-to-hydrogen conversion in acidic media at efficiencies up to 1% in outdoor tests. Plans to enhance efficiency by further oxide film optimizations, for example using surface texturing and possible doping, are also discussed. [Copyright &y& Elsevier]

The development of a low-temperature reactive-sputtering process for producing high-quality hematite films was explored. Iron oxide thin films were sputter-deposited under different conditions of oxygen partial pressure and at substrate temperatures up to 200 °C, and characterized for comparison. Although all sample films exhibited structural and compositional characteristics consistent with polycrystalline rhombohedral Fe2O3 (hematite), it was found that the detailed grain features and morphology, as well as the optical and electrical properties, were significantly altered by process parameter variations. Average grain size increased with deposition temperature and decreased with oxygen partial pressure. Measured film conductivities ranged from 10-9 S/cm in 100 °C samples up to 5×10-4 S/cm in the 200 °C samples. Highest conductivities were found in the sample films exhibiting the largest grains, which is attributed to the effects of mobility enhancement through reduced inter-grain scattering. [Copyright &y& Elsevier]

A multijunction photoelectrode for hydrogen production is being developed at the University of Hawaii (UH) which incorporates an Fe2O3/electrolyte photoelectrochemical top junction with two underlying amorphous silicon/germanium (a-Si:Ge) solid-state junctions. The device structure is fabricated onto stainless-steel foil, which is coated on the back surface with a thin film of nickel–molybdenum hydrogen catalyst, also developed at UH. The Fe2O3 films for this photoelectrode were initially based on a pyrolytically deposited material developed at Duquesne University which has demonstrated rectifying behavior in alkaline electrolyte with a broad absorption spectra in the 300–500 nm range, and photo-generated currents exceeding 8 mA/cm2 for 1 sun illumination. An investigation into the use of reactively sputtered films, which can be synthesized at lower temperatures than the spray-pyrolytic material, has also been initiated. The a-Si:Ge tandem “nipnip” structure for the initial photoelectrode design is based on the bottom and middle layers of the high-efficiency triple junction photovoltaic devices developed by the University of Toledo. These monolithically stacked solid-state structures, which absorb strongly in the 500–700 and 600–900 nm ranges provide the supplementary voltage bias needed to sustain the hydrogen and oxygen evolution reactions, respectively, occurring simultaneously at the catalyzed back surface and the Fe2O3 front surface, respectively. Performance and stability results from initial tests of fabricated prototype photoelectrodes in potassium hydroxide under simulated AM1.5 illumination are presented. Also, efforts to optimize the device design using bandgap and thickness tailoring for improved optics and current matching are described. [Copyright &y& Elsevier]

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters. [ABSTRACT FROM AUTHOR]

Focuses on the combination of minimum entropy image restoration with the frequency-wavenumber (F-K) migration approach to accurately localize subsurface objects using ground-penetrating radar (GPR). Analysis of the GPR operation and the standard F-K migration.

In this paper, we examine the problem of locating an object in an image when size and rotation are unknown. Previous work has shown that with known geometric parameters, an image restoration method can be useful by estimating a delta function at the object location. When the geometric parameters are unknown, this method becomes impractical because the likelihood surface to be minimized across size and rotation has numerous local minima and areas of zero gradient. In this paper, we propose a new approach where a smooth approximation of the template is used to minimize a well-behaved likelihood surface. A coarse-to-fine approximation of the original template using a diffusion-like equation is used to create a library of templates. Using this library, we can successively perform minimizations which are locally well-behaved. As detail is added to the template, the likelihood surface gains local minima, but previous estimates place us within a wellbehaved "bowl" around the global minimum, leading to an accurate estimate. Numerical experiments are shown which verify the value of this approach for a wide range of values of the geometric parameters. [ABSTRACT FROM AUTHOR]

Dynamic magnetic resonance imaging (MRI) refers to the acquisition of a sequence of MRI images to monitor temporal changes in tissue structure. In this paper we present a method for the estimation of dynamic MRI sequences based on two complimentary strategies: an adaptive framework for the estimation of the MRI images themselves, and an adaptive method to tailor the MRI system excitations for each data acquisition. We refer to this method as the doubly adaptive temporal update method (DATUM) for dynamic MRI. Analysis of the adaptive image estimate framework shows that calculating the optimal system excitations for each new image requires complete knowledge of the next image in the sequence. Since this is not realizable, we introduce a linear predictor to aid in determining appropriate excitations. Simulated examples using real MRI data are included to illustrate that the doubly adaptive strategy can provide estimates with lower steady state error than previously proposed methods and also the ability to recover from dramatic changes in the image sequence. [ABSTRACT FROM AUTHOR]

Introduces a combination of high-dimensional analysis of variance and sequential probability ratio test to detect buried objects from an array ground-penetrating radar. Examples of using the method in different situations; Field data from different radar configurations and test sites; Information on cross-track processing.

Presents information on a study which developed an approach for the problem of reduced-order magnetic resonance imaging (MRI) acquisition. Novel applications of MRI technology; Models; Examples; Summary.

Presents an algorithm for the detection and localization of an unknown number of objects buried in a halfspace and present in the near field of a linear sensor array. Background on the multi, bistatic measurement scheme; Development of a subarray processing method for the detection; Examples; Conclusion.

Describes a low-order polynomial expansion method used in addressing object localization and characterization problems from scattered electric field data. Challenges associated with inverse scattering problems; Formulation of the scattering problem; Algorithms used to solve the inverse scattering problems; Effectiveness of algorithms used through numerical examples.

Presents information on a study focused on an algorithm for detection, localization and characterization of anomalous structures from observations of scattered electromagnetic fields. Overview of the detection methods; Description of the detection algorithm; Examples; Conclusions.

Considers the use of high resolution array processing methods for detecting and localizing near-field extended targets for measurement geometries. Matched field processing for object localization; Description of multiple object localization; Comparison of analytical Cramer-Rao bound with Monte-Carlo simulations for single object geometry.

Presents information on a study which presented an efficient, wavelet domain algorithm for computing the error variances associated with a wide class of linear inverse problems posed in a maximum a posteriori (MAP) estimation framework. Problem formulation; Variance extraction of the study; Conclusion.

Presents a T-matrix algorithm for the calculation of the scattered field from a heterogenous collection of spatially separated objects. Basis for the algorithm method; Objects where the algorithm is beneficial to use; Development of a near-field calculation approach.

We present a new application of the recursive T-matrix algorithm to calculate the scattered field from a single or multiple metallic cylinders of arbitrary shapes. Using the equivalence theorem, each metallic object is replaced with small metallic cylinders along its perimeter; then scattered fields are calculated using the recursive T-matrix algorithm. Results are verified by those in the literature and by analytical calculations. © 1997 John Wiley & Sons, Inc. Microwave Opt Technol Lett 15: 360–363, 1997. [ABSTRACT FROM AUTHOR]

Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective. [ABSTRACT FROM AUTHOR]

Consumer perception of poor hygiene of fresh milk products is a major barrier to promotion of milk consumption as an intervention to alleviate the burden of malnutrition in Ghana. Fresh milk is retailed raw, boiled, or processed into unfermented cheese and spontaneously fermented products in unlicensed outlets. In this study, we have determined microbiological quality of informally retailed fresh milk products and characterized the genomic diversity and antimicrobial resistance (AMR) patterns of non-typhoidal Salmonella (NTS) in implicated products. A total of 159 common dairy products were purchased from five traditional milk markets in Accra. Samples were analysed for concentrations of aerobic bacteria, total and fecal coliforms, Escherichia coli , staphylococci, lactic acid bacteria and yeast and moulds. The presence of Salmonella , E . coli O157:H7, Listeria monocytogenes and Staphylococcus aureus were determined. AMR of Salmonella against 18 antibiotics was experimentally determined. Genome sequencing of 19 Salmonella isolates allowed determination of serovars, antigenic profiles, prediction of AMR genes in silico and inference of phylogenetic relatedness between strains. Raw and heat-treated milk did not differ significantly in overall bacterial quality ( P  = 0.851). E . coli O157:H7 and Staphylococcus aureus were present in 34.3% and 12.9% of dairy products respectively. Multidrug resistant (MDR) Salmonella enterica serovars Muenster and Legon were identified in 11.8% and 5.9% of unfermented cheese samples respectively. Pan genome analysis revealed a total of 3712 core genes. All Salmonella strains were resistant to Trimethoprim/Sulfamethoxazole, Cefoxitin, Cefuroxime Axetil and Cefuroxime. Resistance to Chloramphenicol (18%) and Ciprofloxacin (100%), which are first line antibiotics used in treatment of NTS bacteremia in Ghana, was evident. AMR was attributed to presence and/or mutations in the following genes: golS , sdiA for cephalosporins, aac(6′)-Iy , ant(9) for aminoglycosides, mdtK , gyrA , gyrB , parC , parE for quinolones and cat1 , cat4 for phenicols. Phylogenetic analysis based on accessory genes clustered S . Legon strains separately from the S . Muenster strains. These strains were from different markets suggesting local circulation of related strains. Our study justifies consumer resistance to consumption of unripened soft cheese without further lethal heat treatment, and provides evidence that supports the Ghana Health Service recommendation for use of 3rd generation cephalosporins for the treatment of MDR NTS infections. [ABSTRACT FROM AUTHOR]

Lists some web sites maintained by Native American nations. Types of information offered by the web sites; Web site addresses; Includes Apache; Blackfeet; Cherokee; Brothertown Indians.

Review: 43 refs. [ABSTRACT FROM AUTHOR]

In this paper, we develop machine learning approaches for estimating quantitative features (or metrics) characterizing subsurface zones of chemical contamination, focusing on problems involving dense nonaqueous-phase liquid (DNAPL). Source zone characterization, a necessary first step in the development of a remediation strategy, is challenging due to practical constraints associated with the data available for processing. Our methods focus on the use of manifold regression techniques for estimating source zone metrics related to the distribution of contaminant mass in highly saturated pool regions, as well as more diffuse ganglia regions, based on downgradient measurements of dissolved contaminant concentration at a defined time. We use manifold methods for jointly representing labeled training data composed of known source zone metrics, as well as features derived from the corresponding dissolved concentration data sets. We then propose a new integrated approach to the problems of 1) robustly embedding test data (downgradient dissolved concentration) into the manifold when the source zone metrics are not available and 2) constructing a regression function operating directly in the manifold space for source zone metric estimation. The utility of the approach is enhanced by the explicit incorporation of physical constraints associated with the metrics into the problem formulation. Results based upon simulated data demonstrate the potential effectiveness of the manifold regression approaches, as well as significant improvement in performance relative to the case where the algorithmic components are designed serially. [ABSTRACT FROM AUTHOR]

The image reconstruction of chromophore concentrations using diffuse optical tomography (DOT) data can be described mathematically as an ill-posed inverse problem. Recent work has shown that the use of hyperspectral DOT data, as opposed to data sets comprising a single or, at most, a dozen wavelengths, has the potential for improving the quality of the reconstructions. The use of hyperspectral diffuse optical data in the formulation and solution of the inverse problem poses a significant computational burden. The forward operator is, in actuality, nonlinear. However, under certain assumptions, a linear approximation, called the Born approximation, provides a suitable surrogate for the forward operator, and we assume this to be true in the present work. Computation of the Born matrix requires the solution of thousands of large scale discrete PDEs and the reconstruction problem requires matrix-vector products with the (dense) Born matrix. In this paper, we address both of these difficulties, thus making the Born approach a computational viable approach for hyperspectral DOT reconstruction. In this paper, we assume that the images we wish to reconstruct are anomalies of unknown shape and constant value, described using a parametric level set approach [A. Aghasi, M. Kilmer, and E. L. Miller, SIAM J. Imaging Sci., 4 (2011), pp. 618-650] on a constant background. Specifically, to address the issue of the PDE solves, we develop a novel recycling-based Krylov subspace approach that leverages certain system similarities across wavelengths. To address the expense of using the Born operator in the inversion, we present a fast algorithm for compressing the Born operator that locally compresses across wavelengths for a given source-detector set and then recursively combines the low-rank factors to provide a global low-rank approximation. This low-rank approximation can be used implicitly to speed up the recovery of the shape parameters and the chromophore concentrations. We provide a detailed analysis of the accuracy and computational costs of the resulting algorithms and demonstrate the validity of our approach by detailed numerical experiments on a realistic geometry for breast imaging. [ABSTRACT FROM AUTHOR]

This paper presents a new joint inversion approach to shape-based inverse problems. Given two sets of data from distinct physical models, the main objective is to obtain a unified characterization of inclusions within the spatial domain of the physical properties to be reconstructed. Although our proposed method generally applies to many types of inverse problems, the main motivation here is to characterize subsurface contaminant source zones by processing down-gradient hydrological data and cross-gradient electrical resistance tomography observations. Inspired by Newton's method for multi-objective optimization, we present an iterative inversion scheme in which descent steps are chosen to simultaneously reduce both data-model misfit terms. Such an approach, however, requires solving a non-smooth convex problem at every iteration, which is computationally expensive for a pixel-based inversion over the whole domain. Instead, we employ a parametric level set technique that substantially reduces the number of underlying parameters, making the inversion computationally tractable. The performance of the technique is examined and discussed through the reconstruction of source zone architectures that are representative of dense non-aqueous phase liquid (DNAPL) contaminant release in a statistically homogenous sandy aquifer. In these examples, the geometric configuration of the DNAPL mass is considered along with additional information about its spatial variability within the contaminated zone, such as the identification of low and high saturation regions. Comparison of the reconstructions with the true DNAPL architectures highlights the superior performance of the model-based technique and joint inversion scheme. [ABSTRACT FROM AUTHOR]

Abstract: Thin CuGaSe2 films were deposited by vacuum co-evaporation and characterized for their structure, properties and performance as hydrogen-evolving photoelectrodes. The 0.9μm thick films were nearly stoichiometric with very slight copper deficiency and showed polycrystalline structure with grain sizes of tens of nanometers. An electrode based on such a film was demonstrated operating with outdoor 1-sun photocurrent of up to 13mA/cm2. Spectral response data show significant incident-photon-to-current efficiency throughout the visible spectrum, peaking at 63% at 640nm. Photocurrent output under simulated 1-sun Air Mass 1.5 light was stable over 4h. Unassisted water-splitting is not possible due to high band edge positions, but operation in tandem configuration with a suitable bottom junction is feasible. [Copyright &y& Elsevier]