Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems

One of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper proposes a data-driven projection-based model reduction technique to reduce this computational cost. The proposed technique has two distinctive features. First, the model reduction strategy is tailored to inverse problems: the snapshots used to construct the reduced-order model are computed adaptively from the posterior distribution. Posterior exploration and model reduction are thus pursued simultaneously. Second, to avoid repeated evaluations of the full-scale numerical model as in a standard MCMC method, we couple the full-scale model and the reduced-order model together in the MCMC algorithm. This maintains accurate inference while reducing its overall computational cost. In numerical experiments considering steady-state flow in a porous medium, the data-driven reduced-order model achieves better accuracy than a reduced-order model constructed using the classical approach. It also improves posterior sampling efficiency by several orders of magnitude compared to a standard MCMC method

A comparative analysis of the cephalic microbiome: The ocular, aural, nasal/nasopharyngeal, oral and facial dermal niches

The human face/head supports a highly diverse population of microorganisms across a diverse range of microhabitats. This biogeographical diversity has given rise to selection pressure resulting in the formation of distinct bacterial communities between sites. This review investigates the similarity and differences of microbiomes across the different biogeographies of the human face and discusses a potential pathway for microbial circulation within individuals and within a population to maintain microbiome niches and diversity

The Relationship between Ciprofloxacin Resistance and Genotypic Changes in S. aureus Ocular Isolates

Staphylococcus aureus (S. aureus) is a frequent cause of eye infections with some isolates exhibiting increased antimicrobial resistance to commonly prescribed antibiotics. The increasing resistance of ocular S. aureus to ciprofloxacin is a serious concern as it is a commonly used as a first line antibiotic to treat S. aureus keratitis. This study aimed to analyse genetic mutations in the genomes of 25 S. aureus isolates from infections or non-infectious ocular conditions from the USA and Australia and their relationship to ciprofloxacin resistance. Overall, 14/25 isolates were phenotypically resistant to ciprofloxacin. All isolates were analyzed for mutations in their quinolone resistance-determining regions (QRDRs) and efflux pump genes. Of the fourteen resistant isolates, 9/14 had ciprofloxacin resistance mutations within their QRDRs, at codons 80 or 84 within the parC subunit and codon 84 within the gyrA subunit of DNA gyrase. The highest resistance (MIC = 2560 μg/mL) was associated with two SNPs in both gyrA and parC. Other resistant isolates (3/14) had mutations within norB. Mutations in genes of other efflux pumps and their regulator (norA, norC, mepA, mdeA, sepA, sdrM, mepR, arlR, and arlS) or the DNA mismatch repair (MMR) system (mutL and mutS) were not associated with increased resistance to ciprofloxacin. The functional mutations associated with ciprofloxacin resistance in QRDRs (gyrA and parC) and norB suggests that these are the most common reasons for ciprofloxacin resistance in ocular isolates. Novel SNPs of gyrA Glu-88-Leu, Asn-860-Thr and Thr-845-Ala and IIe-855-Met, identified in this study, need further gene knock out/in studies to better understand their effect on ciprofloxacin resistance

Virulence Genes of Staphylococcus aureus Associated With Keratitis, Conjunctivitis, and Contact Lens–Associated Inflammation

Purpose: Staphylococcus aureus, cause a range of ocular diseases in humans, including noninfectious corneal infiltrative events (niCIE), infectious conjunctivitis and sight threatening microbial keratitis (MK). This study aimed to determine the possession of known virulence genes of S. aureus associated with MK and conjunctivitis, in strains isolated from these conditions and niCIE. Methods: Sixty-three S. aureus strains—23 from MK, 26 from conjunctivitis, and 14 from niCIE—were evaluated for possession of genes. Polymerase chain reaction was used for the detection of mecA and 10 known virulence genes involved in MK (clfA, fnbpA, eap, coa, scpA, sspB, sspA, hla, hld, and hlg), 2 associated with conjunctivitis (pvl and seb). Results: mecA was present in 35% of infections and 7% of niCIE strains (P = 0.05). It was not seen in infection strains from Australia. Adhesion genes were found in all strains except clfA, which was found in 75% of infection and 93% of niCIE strains. Invasion genes were found in higher frequency in infections strains—hlg (100% vs. 85%; P = 0.04) and hld (94% vs. 50%; P = 0.005)—compared with niCIE strains. Evasion genes were common in infection strains except scpA, which was found at a significantly higher frequency in niCIE strains (86%) compared with infection strains (45%; P = 0.001). Conclusions: The higher rates of hlg and hld in strains isolated from infections than niCIE may have a role in pathogenesis, whereas scpA may be an important virulence factor during niCIEs. Translational Relevance: This study has identified virulence factors involved in the ocular pathogenesis of S. aureus infections and niCIE

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction

Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting-both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We present and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a h eterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.United States. Department of Energy. Office of Advanced Scientific Computing Research (Grant DE-SC0009297