The emergence and diversification of a zoonotic pathogen from within the microbiota of intensively farmed pigs.

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.

A marine-derived fatty acid targets the cell membrane of Gram-positive bacteria.

IMPORTANCE: With the lack of new antibiotics in the drug discovery pipeline, coupled with accelerated evolution of antibiotic resistance, new sources of antibiotics that target pathogens of clinical importance are paramount. Here, we use bacterial cytological profiling to identify the mechanism of action of the monounsaturated fatty acid (Z)-13-methyltetra-4-decenoic acid isolated from the marine bacterium Olleya marilimosa with antibacterial effects against Gram-positive bacteria. The fatty acid antibiotic was found to rapidly destabilize the cell membrane by pore formation and membrane aggregation in Bacillus subtilis, suggesting that this fatty acid may be a promising adjuvant used in combination to enhance antibiotic sensitivity.

Genome Reduction Is Associated with Bacterial Pathogenicity across Different Scales of Temporal and Ecological Divergence.

Emerging bacterial pathogens threaten global health and food security, and so it is important to ask whether these transitions to pathogenicity have any common features. We present a systematic study of the claim that pathogenicity is associated with genome reduction and gene loss. We compare broad-scale patterns across all bacteria, with detailed analyses of Streptococcus suis, an emerging zoonotic pathogen of pigs, which has undergone multiple transitions between disease and carriage forms. We find that pathogenicity is consistently associated with reduced genome size across three scales of divergence (between species within genera, and between and within genetic clusters of S. suis). Although genome reduction is also found in mutualist and commensal bacterial endosymbionts, genome reduction in pathogens cannot be solely attributed to the features of their ecology that they share with these species, that is, host restriction or intracellularity. Moreover, other typical correlates of genome reduction in endosymbionts (reduced metabolic capacity, reduced GC content, and the transient expansion of nonfunctional elements) are not consistently observed in pathogens. Together, our results indicate that genome reduction is a consistent correlate of pathogenicity in bacteria.

Large-scale genomic analysis of antimicrobial resistance in the zoonotic pathogen Streptococcus suis.

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.

Compressed sensing two-dimensional Bragg scatter imaging.

Here we introduce a new reconstruction technique for two-dimensional Bragg scattering tomography (BST), based on the Radon transform models of Webber and Miller [Inverse Probl. Imaging15, 683 (2021).10.3934/ipi.2021010]. Our method uses a combination of ideas from multibang control and microlocal analysis to construct an objective function which can regularize the BST artifacts; specifically the boundary artifacts due to sharp cutoff in sinogram space (as observed in [arXiv preprint, arXiv:2007.00208 (2020)]), and artifacts arising from approximations made in constructing the model used for inversion. We then test our algorithm in a variety of Monte Carlo (MC) simulated examples of practical interest in airport baggage screening and threat detection. The data used in our studies is generated with a novel Monte-Carlo code presented here. The model, which is available from the authors upon request, captures both the Bragg scatter effects described by BST as well as beam attenuation and Compton scatter.

Closed-loop control of targeted ultrasound drug delivery across the blood-brain/tumor barriers in a rat glioma model.

Cavitation-facilitated microbubble-mediated focused ultrasound therapy is a promising method of drug delivery across the blood-brain barrier (BBB) for treating many neurological disorders. Unlike ultrasound thermal therapies, during which magnetic resonance thermometry can serve as a reliable treatment control modality, real-time control of modulated BBB disruption with undetectable vascular damage remains a challenge. Here a closed-loop cavitation controlling paradigm that sustains stable cavitation while suppressing inertial cavitation behavior was designed and validated using a dual-transducer system operating at the clinically relevant ultrasound frequency of 274.3 kHz. Tests in the normal brain and in the F98 glioma model in vivo demonstrated that this controller enables reliable and damage-free delivery of a predetermined amount of the chemotherapeutic drug (liposomal doxorubicin) into the brain. The maximum concentration level of delivered doxorubicin exceeded levels previously shown (using uncontrolled sonication) to induce tumor regression and improve survival in rat glioma. These results confirmed the ability of the controller to modulate the drug delivery dosage within a therapeutically effective range, while improving safety control. It can be readily implemented clinically and potentially applied to other cavitation-enhanced ultrasound therapies.

Diminishing Returns on Intragenic Repeat Number Expansion in the Production of Signaling Peptides.

Signaling peptides enable communication between cells, both within and between individuals, and are therefore key to the control of complex physiological and behavioral responses. Since their small sizes prevent direct transmission to secretory pathways, these peptides are often produced as part of a larger polyprotein comprising precursors for multiple related or identical peptides; the physiological and behavioral consequences of this unusual gene structure are not understood. Here, we show that the number of mature-pheromone-encoding repeats in the yeast α-mating-factor gene MFα1 varies considerably between closely related isolates of both Saccharomyces cerevisiae and its sister species Saccharomyces paradoxus. Variation in repeat number has important phenotypic consequences: Increasing repeat number caused higher pheromone production and greater competitive mating success. However, the magnitude of the improvement decreased with increasing repeat number such that repeat amplification beyond that observed in natural isolates failed to generate more pheromone, and could actually reduce sexual fitness. We investigate multiple explanations for this pattern of diminishing returns and find that our results are most consistent with a translational trade-off: Increasing the number of encoded repeats results in more mature pheromone per translation event, but also generates longer transcripts thereby reducing the rate of translation-a phenomenon known as length-dependent translation. Length-dependent translation may be a powerful constraint on the evolution of genes encoding repetitive or modular proteins, with important physiological and behavioral consequences across eukaryotes.

Socially mediated induction and suppression of antibiosis during bacterial coexistence.

Despite their importance for humans, there is little consensus on the function of antibiotics in nature for the bacteria that produce them. Classical explanations suggest that bacteria use antibiotics as weapons to kill or inhibit competitors, whereas a recent alternative hypothesis states that antibiotics are signals that coordinate cooperative social interactions between coexisting bacteria. Here we distinguish these hypotheses in the prolific antibiotic-producing genus Streptomyces and provide strong evidence that antibiotics are weapons whose expression is significantly influenced by social and competitive interactions between competing strains. We show that cells induce facultative responses to cues produced by competitors by (i) increasing their own antibiotic production, thereby decreasing costs associated with constitutive synthesis of these expensive products, and (ii) by suppressing antibiotic production in competitors, thereby reducing direct threats to themselves. These results thus show that although antibiotic production is profoundly social, it is emphatically not cooperative. Using computer simulations, we next show that these facultative strategies can facilitate the maintenance of biodiversity in a community context by converting lethal interactions between neighboring colonies to neutral interactions where neither strain excludes the other. Thus, just as bacteriocins can lead to increased diversity via rock-paper-scissors dynamics, so too can antibiotics via elicitation and suppression. Our results reveal that social interactions are crucial for understanding antibiosis and bacterial community dynamics, and highlight the potential of interbacterial interactions for novel drug discovery by eliciting pathways that mediate interference competition.

Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images.

To simultaneously overcome the challenges imposed by the nature of optical imaging characterized by a range of artifacts including space-varying signal to noise ratio (SNR), scattered light, and non-uniform illumination, we developed a novel method that segments the 3-D vasculature directly from original fluorescence microscopy images eliminating the need for employing pre- and post-processing steps such as noise removal and segmentation refinement as used with the majority of segmentation techniques. Our method comprises two initialization and constrained recovery and enhancement stages. The initialization approach is fully automated using features derived from bi-scale statistical measures and produces seed points robust to non-uniform illumination, low SNR, and local structural variations. This algorithm achieves the goal of segmentation via design of an iterative approach that extracts the structure through voting of feature vectors formed by distance, local intensity gradient, and median measures. Qualitative and quantitative analysis of the experimental results obtained from synthetic and real data prove the effcacy of this method in comparison to the state-of-the-art enhancing-segmenting methods. The algorithmic simplicity, freedom from having a priori probabilistic information about the noise, and structural definition gives this algorithm a wide potential range of applications where i.e. structural complexity significantly complicates the segmentation problem.

Asynchronous spore germination in isogenic natural isolates of Saccharomyces paradoxus.

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.

Spore germination determines yeast inbreeding according to fitness in the local environment.

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.

Locally optimized non-local means denoising for low-dose X-ray backscatter imagery.

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.

On the reliable estimation of sequential Monod kinetic parameters.

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.

An updated catalogue of diverse type II polyketide synthase biosynthetic gene clusters captured from large-scale nucleotide databases.

Nature serves as a rich source of molecules with immense chemical diversity. Aptly named, these 'natural products' boast a wide variety of environmental, medicinal and industrial applications. Type II polyketides, in particular, confer substantial medicinal benefits, including antibacterial, antifungal, anticancer and anti-inflammatory properties. These molecules are produced by enzyme assemblies known as type II polyketide synthases (PKSs), which use domains such as the ketosynthase chain-length factor and acyl carrier protein to produce polyketides with varying lengths, cyclization patterns and oxidation states. In this work, we use a novel bioinformatic workflow to identify biosynthetic gene clusters (BGCs) that code for the core type II PKS enzymes. This method does not rely on annotation and thus was able to unearth previously 'hidden' type II PKS BGCs. This work led us to identify over 6000 putative type II PKS BGCs spanning a diverse set of microbial phyla, nearly double those found in most recent studies. Notably, many of these newly identified BGCs were found in non-actinobacteria, which are relatively underexplored as sources of type II polyketides. Results from this work lay an important foundation for future bioprospecting and engineering efforts that will enable sustainable access to diverse and structurally complex molecules with medicinally relevant properties.

Combined optical and X-ray tomosynthesis breast imaging.

PURPOSE: To explore the optical and physiologic properties of normal and lesion-bearing breasts by using a combined optical and digital breast tomosynthesis (DBT) imaging system. MATERIALS AND METHODS: Institutional review board approval and patient informed consent were obtained for this HIPAA-compliant study. Combined optical and tomosynthesis imaging analysis was performed in 189 breasts from 125 subjects (mean age, 56 years ± 13 [standard deviation]), including 138 breasts with negative findings and 51 breasts with lesions. Three-dimensional (3D) maps of total hemoglobin concentration (Hb(T)), oxygen saturation (So(2)), and tissue reduced scattering coefficients were interpreted by using the coregistered DBT images. Paired and unpaired t tests were performed between various tissue types to identify significant differences. RESULTS: The estimated average bulk Hb(T) from 138 normal breasts was 19.2 µmol/L. The corresponding mean So(2) was 0.73, within the range of values in the literature. A linear correlation (R = 0.57, P < .0001) was found between Hb(T) and the fibroglandular volume fraction derived from the 3D DBT scans. Optical reconstructions of normal breasts revealed structures corresponding to chest-wall muscle, fibroglandular, and adipose tissues in the Hb(T), So(2), and scattering images. In 26 malignant tumors of 0.6-2.5 cm in size, Hb(T) was significantly greater than that in the fibroglandular tissue of the same breast (P = .0062). Solid benign lesions (n = 17) and cysts (n = 8) had significantly lower Hb(T) contrast than did the malignant lesions (P = .025 and P = .0033, respectively). CONCLUSION: The optical and DBT images were structurally consistent. The malignant tumors and benign lesions demonstrated different Hb(T) and scattering contrasts, which can potentially be exploited to reduce the false-positive rate of conventional mammography and unnecessary biopsies.

Head motion classification using thread-based sensor and machine learning algorithm.

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.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach.

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

The ascent of the abundant: how mutational networks constrain evolution.

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance--the number of genotypes producing a particular phenotype--varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an "ascent of the abundant" hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit.

Limited-View X-Ray Tomography Combining Attenuation and Compton Scatter Data: Approach and Experimental Results.

X-ray inspection systems are critical in medical, non-destructive testing, and security applications, with systems typically measuring attenuation along straight-line paths connecting sources and detectors. Computed tomography (CT) systems can provide higher-quality images than single- or dual-view systems, but the need to measure many projections leads to greater system cost and complexity. Typically, off-angle Compton scattered photons are treated as noise during tomographic inversion. We seek to maximize the image quality of limited-view systems by combining attenuation data with measurements of Compton-scattered photons, exploiting the fact that the broken-ray paths followed by scattered photons provide additional geometric sampling of the scene. We describe a single-scatter forward model for Compton-scatter data measured with energy-resolving detectors, and demonstrate a reconstruction algorithm for density that combines both attenuation and scatter measurements. The experimental results suggest that including Compton-scattered data in the reconstruction process can improve image quality for density reconstruction using limited-view systems.

Shape-based ultrasound tomography using a Born model with application to high intensity focused ultrasound therapy.

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 x 5 mm x 6 mm ellipsoidal inclusion was detected with an accuracy of around 5%.