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The power spectrum of the non-linearly evolved large-scale mass distribution recovers only a minority of the information available on the mass fluctuation amplitude. We investigate the recovery of this information in 2D ‘slabs’ of the mass distribution averaged over ≈100 h−1 Mpc along the line of sight, as might be obtained from photometric redshift surveys. We demonstrate a Hamiltonian Monte Carlo method to reconstruct the non-Gaussian mass distribution in slabs, under the assumption that the projected field is a point-transformed Gaussian random field, Poisson-sampled by galaxies. When applied to the Quijote N-body suite at z = 0.5 and at a transverse resolution of 2 h−1 Mpc, the method recovers ∼30 times more information than the 2D power spectrum in the well-sampled limit, recovering the Gaussian limit on information. At a more realistic galaxy sampling density of 0.01 h3 Mpc−3, shot noise reduces the information gain to a factor of 5 improvement over the power spectrum at resolutions of 4 h−1 Mpc or smaller.

 

Conjugative plasmids often encode antibiotic resistance genes that provide selective advantages to their bacterial hosts during antibiotic treatment. Previous studies have predominantly considered these established genes as the primary benefit of antibiotic-mediated plasmid dissemination. However, many genes involved in cellular metabolic processes may also protect against antibiotic treatment and provide selective advantages. Despite the diversity of such metabolic genes and their potential ecological impact, their plasmid-borne prevalence, co-occurrence with canonical antibiotic resistance genes, and phenotypic effects remain widely understudied. To address this gap, we focused onEscherichia coli, which can often act as a pathogen, and is known to spread antibiotic resistance genes via conjugation. We characterized the presence of metabolic genes on 1,775 transferrable plasmids and compared their distribution to that of known antibiotic resistance genes. We found high abundance of genes involved in cellular metabolism and stress response. Several of these genes demonstrated statistically significant associations or disassociations with known antibiotic resistance genes at the strain level, indicating that each gene type may impact the spread of the other across hosts. Indeed, in vitro characterization of 13 statistically relevant metabolic genes confirmed that their phenotypic impact on antibiotic susceptibility was largely consistent with in situ relationships. These results emphasize the ecological importance of metabolic genes on conjugal plasmids, and that selection dynamics ofE. colipathogens arises as a complex consequence of both canonical mechanisms and their interactions with metabolic pathways.

 

Optimizing the performance of complex systems modeled by stochastic computer simulations is a challenging task, partly because of the lack of structural properties (e.g., convexity). This challenge is magnified by the presence of random error whereby an adaptive algorithm searching for better designs can at times mistakenly accept an inferior design. In contrast to performing multiple simulations at a design point to estimate the performance of the design, we propose a framework for adaptive search algorithms that executes a single simulation for each design point encountered. Here the estimation errors are reduced by averaging the performances from previously evaluated designs drawn from a shrinking ball around the current design point. We show under mild regularity conditions for continuous design spaces that the accumulated errors, although dependent, form a martingale process, and hence, by the strong law of large numbers for martingales, the average errors converge to zero as the algorithm proceeds. This class of algorithms is shown to converge to a global optimum with probability one. By employing a shrinking ball approach with single observations, an adaptive search algorithm can simultaneously improve the estimates of performance while exploring new and potentially better design points. Numerical experiments offer empirical support for this paradigm of single observation simulation optimization. 

Surgical site infections (SSIs) are a leading cause of morbidity and mortality and a significant expense to the healthcare system and hospitals. The majority of these infections are preventable; however, increasing bacterial resistance, biofilm persistence, and human error contribute to the occurrence of these healthcare-associated infections. We present a flexible antimicrobial blue-light emitting bandage designed for use on postoperative incisions and wounds. The photonic device is designed to inactivate bacteria present on the skin and prevent bacterial colonization of the site, thus reducing the occurrence of SSIs. This antimicrobial light emitting bandage uses blue light’s proven abilities to inactivate a wide range of clinical pathogens regardless of their resistance to antibiotics, inactivate bacteria without harming mammalian cells, improve wound healing, and inactivate bacteria in biofilms. The antimicrobial bandage consists of a thin 2”x2” silicone sheet with an array of 77 LEDs embedded in multiple layers of the material for thermal management. The 405 nm center wavelength LED array is designed to be a wearable device that integrates with standard hospital infection prevention protocols. The device was characterized for irradiance of 44.5 mW/cm2. Methicillin-resistant Staphylococcus aureus seeded in a petri dish was used to evaluate bacterial inactivation in vitro. Starting with a concentration of 2.16 x 107 colony forming units (CFU)/mL, 45% of the bacteria was inactivated within 15 minutes, 65% had been inactivated by 30 minutes, 99% was inactivated by 60 minutes, and a 7 log reduction and complete sterilization was achieved within 120 minutes.